Author Contributions: Mr Martens and Dr Nawrot had full access to all of the data in the study and tak e responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Martens, Janssen, Nawrot.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Martens, Cox, Nawrot.
Critical revision of the manuscript for important intellectual content: Martens, Janssen, Clemente, Gasparrini, Vanpoucke, Lefebvre, Roels, Plusquin, Nawrot.
Statistical analysis: Martens, Cox, Gasparrini, Nawrot.
Obtained funding: Nawrot.
Administrative, technical, or material support: Martens, Janssen, Clemente, Lefebvre, Plusquin, Nawrot.
Study supervision: Nawrot.
Conflict of Interest Disclosures: None reported.
Additional Contributions: We thank Benoit Nemery , MD, PhD (full professor of occupational & environmental medicine at the Department of Public Health and Primary care, Leuven University, Belgium), for his critical revision of the manuscript and Immaculata De Vivo, MPH, PhD (associate professor in the Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts), for providing help by the telomere assay. They did not receive any compensation for their contribution to this work.
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Author Man
uscript
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Published in final edited form as:
JAMA Pediatr. 2017 December 01; 171(12): 1160–1167. doi:10.1001/jamapediatrics.2017.3024.
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Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium; Department of Public Health & Primar
y Care, Leuven University, Leuven, Belgium
Abstract
Importance—Telomere length is a mark er of biological aging that may provide a cellular memory of exposures to oxidative stress and inflammation. Telomere length at birth has been related to life expectancy. An association between prenatal air pollution exposure and telomere length at birth could provide new insights in the environmental influence on molecular longevity.
Objective—To assess the association of prenatal e
xposure to particulate matter (PM) with
newborn telomere length as reflected by cord blood and placental telomere length.
Design, Setting, and Participants—In a prospectiv
e birth cohort (ENVIRONAGE
[Environmental Influence on Ageing in Early Life]), a total of 730 mother-newborn pairs were recruited in Flanders, Belgium between February 2010 and December 2014, all with a singleton full-term birth (≥37 weeks of gestation). For statistical analysis, participants with full data on both cord blood and placental telomere lengths were included, resulting in a final study sample size of 641.
Exposures—Maternal residential PM2.5 (particles with an aerodynamic diameter ≤2.5 μm) exposure during pre
gnancy.
Main Outcomes and Measures—In the newborns, cord blood and placental tissue relati ve telomere length were measured. Maternal residential PM2.5 exposure during pregnancy was estimated using a high-resolution spatial-temporal interpolation method. In distributed lag models, both cord blood and placental telomere length were associated with a verage weekly exposures to
PM2.5 during pregnancy, allowing the identification of critical sensitive exposure windows.
Results—In 641 newborns, cord blood and placental telomere length were signif icantly and inversely associated with PM2.5 exposure during midgestation (weeks 12-25 for cord blood and weeks 15-27 for placenta). A 5-μg/m3 increment in PM2.5 exposure during the entire pregnancy was associated with 8.8% (95% CI, −14.1% to −3.1%) shorter cord blood leuk ocyte telomeres and
13.2% (95% CI, −19.3% to −6.7%) shorter placental telomere length. These associations were controlled for date of delivery, gestational age, maternal body mass index, maternal age, paternal age, newborn sex, newborn ethnicity, season of delivery, parity, maternal smoking status, maternal educational level, pregnancy complications, and ambient temperature.
Conclusions and Relevance—Mothers who were exposed to higher le vels of PM2.5 gave
birth to newborns with shorter telomere length. The observ ed telomere loss in newborns by
prenatal air pollution exposure indicates less buffer for postnatal influences of factors decreasing telomere length during life. Therefore, improvements in air quality may promote molecular longevity from birth onward.
Telomeres are nucleoprotein structures that cap the end of chromosomes. They can consist of sev
eral thousands of tandem-repeated TTAGGG sequences.1,2 With each cellular division, telomeres shorten. T
elomere length (TL) has been associated with age-related diseases and mortality and is considered a marker of biological aging.3–8 Telomere length is also associated with environmental and lifestyle f
actors that influence the oxidative stress
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and inflammatory status in humans, such as smoking,9 obesity,10 Mediterranean diet,11 exposure to violence,12 and life stress,13 which underscores the vulnerability of telomeres to reactiv
e oxygen species (ROS). In a 2015 update of the Global Burden of Disease study, ambient particulate matter (PM) was ranked as the sixth most leading risk factor influencing public health worldwide.14 Increased oxidative stress and inflammation are proposed as underlying mechanisms through which PM may influence human health.15,16 Exposure to air pollution during in utero life may hav
e adverse effects on the fetus and neonate.17–19
The ability of PM air pollution to generate ROS has led to the hypothesis that telomere attrition is influenced by air pollution exposure.
20 Occupational and population-based
studies hav
e described both positive and negative associations between different types of air pollution and TL in adults.21–27 In general, long-term exposure to PM25 and black carbon (BC)24,26 are associated with shorter TL in adults. Because TL is highly variable at birth, 28,29 potential determinants, such as newborn sex,28 paternal age,28 maternal prepregnancy body mass index (BMI),
20 maternal stress,30 maternal educational level,31 maternal smoking during pregnanc
y,32 and maternal residential proximity to a major road,33 have recently been explored for explaining this phenomenon. In this regard, we hypothesized that exposure to PM air pollution during pregnancy may contribute to telomere setting at birth.
Because in utero life is believed to be a critical time window in the early programming of diseases later in life,
34 unravelling the connection between prenatal air pollution exposure and TL at birth may help to gain new insights in the causes of diseases linked with telomere biological characteristics. In this study, we assessed the association between prenatal exposure to PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) and TL at birth.
Methods
Study Population
From the ongoing population-based prospective Environmental Influence on Ageing in Early Life (ENVIR
ONAGE) birth cohort study, 730 mothers with a singleton full-term birth (≥37 weeks of gestation) were selected.35 These mother-newborn pairs were recruited between February 2010 and December 2014 from noon on Fridays to 7 AM Mondays. The study protocol was appro
ved by the ethical committees of Hasselt University and East-Limburg Hospital in Genk, Belgium, and has been carried out according to the Helsinki declaration. 36 The mother’s ability to fill out questionnaires in Dutch was a criterion for selection.
Owing to missing exposure data (n = 10), the unavailability of DNA or bad DNA quality for cord blood (n = 12) and placenta (n = 51), and too high a variability of TL measurement between triplicates of cord blood (n = 10) or placental tissue (n = 9), we obtained cord blood TL samples from 698 mother-newborn pairs and placental TL samples from 660. For statistical analysis, we used participants with full data on both cord blood and placental TLs, resulting in a final study sample size of 641.
Mean Relative TL Measurement
DNA was extracted from cord blood buffy coat and placental tissue (details on sample collection are provided in eMethods 1 in the Supplement). Mean relati ve TL was determined
in triplicate with a previously37 described modified quantitative, real-time polymerase chain Martens et al. Page 3
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reaction (qPCR) protocol.38 For cord blood, the triplicates of the telomere runs showed a coeff
icient of variation (CV) of 0.68%, those of single-copy gene runs a CV of 0.41%, and those of T/S ratios a CV of 6.4%. For placental telomeres, the triplicates of the telomere runs showed a CV of 0.70%, those of the single-copy gene runs a CV of 0.45%, and those of T/S ratios a CV of 6.9%.
Exposure Assessment
Based on the mother's residential address, daily mean PM2.5 concentrations (in micrograms per cubic meter) were estimated using a high-resolution spatial-temporal interpolation method (kriging)39 in combination with a dispersion model.40,41 This interpolation method uses hourly measured PM2.5 pollution data collected at the official fixed-site monitoring stations (n = 34) and land-cov
er data obtained from satellite images.42 The model chain provides daily PM2.5 v
alues on a dense, irregular receptor grid by using data both from the Belgian telemetric air-quality netw
ork and emissions from point sources and line sources. In the Flemish region of Belgium, more than 80% (R2 = 0.8) of the temporal and spatial variability w
as explained by this interpolation tool.43 For each week of pregnancy from the date of conception onward, a mean PM2.5 concentration w as calculated using daily mean
PM2.5 concentrations at the mother’s residence. In case the mother had a gestation of less than 40 weeks, exposure w
as set at zero for the weeks after giving birth. The number of mothers who changed address during pregnancy (69 of 641 [10.7%]) was taken into account when calculating the weekly exposures. Weekly mean ambient temperatures were calculated based on the daily mean temperatures in degrees Celsius provided by the Royal Meteorological Institute, Brussels, Belgium.
General Study Procedures
At the first antenatal visit (weeks 7-9 of pregnancy), maternal BMI was determined by calculating weight in kilograms divided by height in meters squared. The date of conception w
as estimated on the basis of the first day of the mother’s last menstrual period combined with the first ultrasonographic examination. After delivery, mothers provided written informed consent and completed the study questionnaires in the post-delivery ward. We collected detailed information about the mothers, fathers, and newborns from questionnaires and medical records. Parity was categorized in mothers having their first newborn, having their second newborn, or having their third or more newborn. Maternal educational level was coded “low” when mothers did not obtain any diploma, “middle” when they obtained a high school diploma, and “high” when they obtained a college or university degree. Maternal smoking status was categorized as “never smoker,” “former smoker” when the mother had quit smoking before pregnancy, and “smoker” when smoking continued during pregnancy.
Newborns were classified as white European when 2 or more grandparents were Europeans, and non-European when at least 3 grandparents were of non-European origin. The presence of pregnancy complications was defined as the experience by the mother of 1 or more of the following conditions during pregnancy: gestational diabetes, hypertension, infectious disease, preeclampsia, vaginal bleeding, and hyperthyroidism or hypothyroidism. Perinatal measures, such as birth date, newborn sex, birth weight, and Apgar score were obtained after birth.
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Statistical Analysis
We used distributed lag models (DLMs) to model the association between log10-transformed TL and mean weekly PM2.5 exposures during gestational weeks 1 to 40. A distributed lag (nonlinear) model (DLNM) is defined through a “cross-basis” function, which allo ws the
simultaneous estimation of an (nonlinear) exposure-response association and nonlinear effects across lags, the latter termed lag-response association.44The exposure-response function was assumed to be linear and the lag structure was modeled using a natural cubic spline with 5
df, setting the knots at equally spaced values in the original lag scale (1-40 weeks). The number of knots was chosen based on the Akaike information criterion (AIC). 45 We also included a cross-basis for weekly mean temperature in the model. We used a natural cubic spline with 4 df for the temperature-TL function and a natural cubic spline with 5 df for the lag structure (with knots at equally spaced values in the original lag scale).
In addition, we accounted for a priori selected covariates that include known determinants of newborn or adult TL and variables with a potential link with PM2.5 and TL, such as date of deliv
ery, gestational age, maternal BMI, maternal age, paternal age, newborn's sex, ethnicity, season of delivery, parity, maternal smoking status, maternal educational level, and pregnancy complications. We calculated cumulative effect estimates for the 3 trimesters of pregnancy (weeks 1-13, weeks 14- 26, and weeks 27-40) and the overall (40-week) cumulative estimate. Final estimates are presented as percentage change (with 95% CI) in TL for a 5-μg/m3 increment in PM2.5. Details on secondary analyses (ie, average exposure models, effect modif
ication by sex, and nonlinear dose-response models) and sensitivity analyses are provided in eMethods 2 in the Supplement. All analyses were performed with the statistical software R, version 3.3.2 (R Project for Statistical Computing) using the dlnm package.
46 Results
Characteristics of the Study Population
Table 1 describes the general characteristics of the study population (n = 641). The newborns, among them 318 girls (49.6%), had a mean (SD) gestational age of 39.4 (1.0) weeks and a mean (SD) birth weight of 3451 (428) g. Most (n = 567, 88.5%) of the ne
wborns were Europeans of white ethnicity. The mean relative TL of newborns ranged from 0.51 to 1.75 in cord blood and from 0.52 to 1.89 for placental tissue. Associations of covariates with newborn TL are reported in eTable 1 in the Supplement. Mean (SD) maternal age was 29.1 (4.6) years, and mean (SD) maternal BMI was 24.3 (4.5). Among the mothers, 351 (54.8%) were primiparous and 224 (34.9%) secundiparous. Mean weekly PM2.5 exposure was 13.4 μg/m3 (5th-95th percentile, 4.3-32.5 μg/m3). eFigure 1 in the Supplement shows the contours of the annual PM2.5 e
xposure in the recruitment area and the
places of residence of the mothers during pregnanc
y.
Association Between Prenatal PM2.5 Exposure and Newborn TL Scatterplots showing newborn TL in association with average PM2.5 exposure during the entire pregnanc
y are shown in eFigure 2 in the Supplement. Lag-specific (weekly) DLM estimates of the association between PM2.5 exposure during pregnancy and TL at birth are presented in the Figure. Cord blood as well as placental TL were inv ersely associated with
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PM2.5 exposure during midgestation: significant estimates were observed for weeks 12 to 25 in cord blood (Figure, A), with the largest ne
gative association in week 19 and for weeks 15
to 27 in placenta (Figure, B), with the largest negative association in week 21. In contrast, a positive association between PM2.5 and cord blood TL was observed for exposure in weeks 32 to 34. The estimated ov
erall (weeks 1-40) change in TL for a 5-μg/m3 increment in PM2.5 exposure was −8.8% (95% CI, −14.1 to −3.1%) for cord blood and −13.2% (95% CI, −19.3% to −6.7%) for placenta (Table 2). T
rimester-specific cumulative estimates were only
significant for the second trimester: −9.4% (95% CI, −13.1 to −5.6%) for cord blood and −7.1% (95% CI, −11.6% to −2.4%) for placental TL.
The existence of vulnerable exposure windows (ie, the hypothesis that exposure during some weeks of pregnanc
y is more critical than during others) was tested by comparing the main DLM model with a DLM model assuming a constant risk during the different weeks of pregnancy (likelihood ratio test on 4
df). The main DLM model provided a better fit than the
constant-risk model for cord blood (AIC, -1418.4 vs -1407.0, P = .001) but not for placental telomeres (AIC, -1173.2 vs -1177.4, P = .46). The DLM estimates are corroborated by results from the average exposure models (Table 2). For instance, the change in TL for a 5- μg/m3 increment in mean PM2.5 over the entire pregnancy was -8.4% (95% CI, -13.5% to -2.9%) in cord blood and -12.5% (95% CI, -18.4% to -6.2%) in placenta. We did not observ e
a significant modification in the association by newborn sex for cord and placental TL, and we observed a nonlinear dose-response correlation (eTables 2 and 3 in the Supplement, respectively). Assuming constant associations within the strata of lags 1 to 10, 11 to 20, 21 to 30, and 31 to 40 (eFigure 3 in the Supplement), evidence for the positive association between cord blood TL and PM2.5 toward the end of pregnancy is less evident. Cumulative estimates from sensitivity analyses were similar to those from the main model (T able 3).
Discussion
To our knowledge, this is the first study reporting an association between PM2.5 exposure during in utero life and newborn TL. After adjustment for se veral covariates and potential
confounders, maternal exposure to PM2.5 during pregnancy was associated with 8.8% shorter newborn cord blood and 13.2% shorter placental telomeres. By applying distrib uted
lag models based on weekly mean PM2.5 exposures, we identified specific vulnerable periods during pregnanc
y. Both cord blood and placental TLs were negatively associated with PM2.5 exposure during the second trimester of pregnancy. The finding that early-life TL might forecast life span, as observed in an animal-based study of zebra f inches,47 underlines
the importance of the identification of early-life TL determinants. In this re gard, our results
may have important health consequences later in life because a shorter TL at birth indicates less buffer capacity for postnatal influence of insults (eg, inflammation on TL). Particulate air pollution may generate ROS in a direct manner via the Fenton reaction operating at the particle surface,48 or in an indirect manner via altered mitochondrial and nicotinamide adenine dinucleotide phosphate-oxidase functions or via inflammatory cell activ ation.49
Telomeres contain a great amount of guanine bases, which are vulnerable to ROS. Reactive oxygen species can induce DNA breakage, leading to increased telomere shortening in addition to cellular replication.50 The major route for airborne particles to enter the maternal org
anism is via inhalation. Ultrafine particles (UFPs) with a diameter less than 0.1 μm (<100 Martens et al. Page 6
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nm) are able to cross the airway-blood barrier, may enter the bloodstream, and are transported to different body compartments.51–53 Whether particles can cross the placental barrier is still debated because of the rather limited evidence. Nevertheless, it has been shown recently that nanoparticles up to 240 nm can cross the human placental barrier and nanoparticles up to 500 nm may enter the fetal circulation in mice.54,55 An elevated oxidativ
e stress status in both mother and newborn may be a potential explanation for our findings concerning PM2.5 exposure observed in the present study. Earlier studies present positiv
e associations between air pollution exposure and placental tissue nitrosative stress56 and mitochondrial oxidative stress.57 We identified the mid-pregnancy period as a critical time window for the association of PM2.5 e
xposure with newborn TL. This finding is in
agreement with the dev
elopment of the placental barrier and maternal-fetal circulation. In the first months of pregnancy, the placental barrier remains thick with no perfusion but becomes thinner during the course of pregnancy concomitantly with increased fetal capillary development until week 10, which from then onward will facilitate an enhanced maternal- fetal exchange of nutrients and waste products.58 Exposure to PM2.5 late in pregnancy (from weeks 32-34) is associated with longer telomeres in cord blood, although evidence is not conclusi
ve. This finding may suggest the presence of a potential compensatory or overcompensatory mechanism in response to air pollution exposure in cord that is absent in placenta. Positive associations between leukocyte TL and recent exposure to air pollution have been observed previously in adults.21,22,25 An increase of telomerase activity in lymphocytes and a clonal e
xpansion of subpopulations of lymphocytes with longer
telomeres following acute exposure have been suggested as potential underlying mechanisms.21,59,60 Telomere length as assessed in the present study is a result of the combination between shortening and elongating processes prior to the telomere assessment time point. Ho
wever, owing to the cumulative burden of oxidative stress during the entire pregnancy, overall newborn TL was inversely associated with prenatal exposure to air pollution. The stronger overall influence of PM on placental telomeres compared with cord blood may be due to this potential compensatory mechanism in cord blood, as we observed both positive and negative estimates for PM2.5 exposure and cord blood TL during pregnanc
y, whereas all estimates were negative for placental TL.
Strengths and Limitations
Our study has several strengths. First, we have a large sample size of newborns with matching cord blood and placental tissue to study TL in relation to PM2.5 air pollution exposure. Second, we used high-resolution e
xposure estimates based on the home addresses
of the mothers, and we integrated daily concentrations to estimate weekly mean exposure during pregnancy. Compared with the more conventional approach of averaging exposures over relatively large time windows (trimesters or the entire pregnancy), the use of DLM allowed a more detailed investigation of prenatal exposure windows and enabled the identification of midpregnancy as a critical period for the association of PM2.5 with TL in cord blood as well as placenta. Third, our findings are generalizable because our study population is representati
ve of the gestational segment of the population at large.35 However owing to spatial variations in PM2.5 concentrations, differences in exposure may exist, as our population w
as recruited in a relatively small area. Our study should also be Martens et al. Page 7
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interpreted within the context of its potential limitations. Our results are based on exposure at the maternal residence, and potential misclassification may be present because we could not account for other e
xposure sources that contribute to personal exposure, such as exposure during a commute, at work, and elsewhere. However, proxies of exposure, such as residential proximity to major roads, have been shown recently to be associated with internal exposure to nanosized particles, reflecting exposure to black carbon.61 The assessment of TL at birth represents a specific snapshot in the gestational period. W e were not able to
evaluate telomere dynamics throughout the entire pregnancy period, and, in view of our results, the role of telomerase needs further evaluation. Parental TL may be a determinant of the initial telomere length setting of the next generation.28 Because parents exposed to PM2.5 may have shorter telomeres, the association between PM2.5 exposure and newborn TL may be mediated by parental TLs. Unfortunately, this mediation could not be e valuated in
the ENVIRONAGE study because data on parental TLs were not available.
Conclusions
To our knowledge, this study is the first to report an association between prenatal exposure to PM2.5 air pollution and TL at birth, both in cord blood and placental tissue. We theorize that biological aging is associated with PM2.5 air pollution exposure, even before birth, which may underlie potential adverse health consequences later in life. This study adds to the gro
wing body of evidence that even relatively low levels of prenatal exposure to air pollution contributes to fetal programming at the molecular level and more precisely at the level of telomere biological features. Adequate reduction of environmental fine-particle air pollution levels may promote longevity as from birth onwards and may enhance overall quality of life. Prospective follow-up studies are needed to further elucidate the outcome of PM2.5-linked telomere shortening at birth in relation to pediatric and adult health and disease later in life.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Funding/Support
The ENVIR
ONAGE birth cohort is supported by grant ERC-StG310898 from the EU Program "Ideas" and by the grant FWO, G073315N from the Flemish Scientific Fund. Dr Cox is a postdoctoral fellow of the Flemish Scientific Fund supported by grant 12Q0517N. Dr Gasparrini's contribution was supported by grant MR/M022625/1 from the Medical Research Council UK.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, revie w, or approval of the manuscript; and
decision to submit the manuscript for publication.
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61. Saenen ND, Bové H, Steuwe C, et al. Children's Urinary Environmental Carbon Load: A Novel Marker Reflecting Residential Ambient Air Pollution Exposure? Am J Respir Crit Care Med. 2017; [published online July 7, 2017]. doi: 10.1164/rccm.201704-0797OC Martens et al. Page 11
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Key Points
Question
Is telomere length at birth (a marker of biological aging) influenced by exposure to particulate matter air pollution during in utero life?
Findings
In this birth cohort study of 641 mother-newborn pairs, mothers with higher residential exposure to PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm air pollution) ga
ve birth to newborns with significantly lower telomere length that could not be explained by other factors including socioeconomic class. For a 5-μg/m3 increase in residential PM2.5 exposure during pregnancy, cord blood telomeres were 9% shorter and placental telomeres 13% shorter.
Meaning
Impro
ved air quality may promote molecular longevity from birth onward.
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Figure. Cord Blood and Placental Telomere Length (TL) in Association With Week-Specific Prenatal PM2.5. Exposur
e During Pregnancy
Week-specific estimates provided as a percentage change in mean relative TL (with 95% CI) for a 5-μg/m3 increment of PM2.5 air pollution exposure. Models were adjusted for date of deliv
ery, gestational age, maternal body mass index, maternal age, paternal age, newborn sex, newborn ethnicity, season of delivery, parity, maternal smoking status, maternal educational level, pregnancy complications, and ambient temperature. PM2.5 indicates particulate matter with an aerodynamic diameter of 2.5 μm or less.
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T
able 1
Characteristics of 641 Mother-Newborn Pairs
Characteristic No. (%)
Newborns
Birth weight, mean (SD), g 3451 (428)
Females 318 (49.6)
White European 567 (88.5)
Gestational age, wk
37 33 (5.1)
38 87 (13.6)
39 196 (30.6)
40 242 (37.8)
41 83 (12.9)
Season of birth
Winter 163 (25.4)
Spring 140 (21.8)
Summer 153 (23.9)
Autumn 185 (28.9)
Apgar score 5 min after birth
7 11 (1.7)
8 45 (7.0)
9 187 (29.2)
10 398 (62.1)
Mothers
Age, mean (SD), y 29.1 (4.6)
BMI, mean (SD) 24.3 (4.5)
Educational level
a
Low 71 (11.1)
Middle 245 (38.2)
High 325 (50.7)
Smoking statusb
Never smoker 401 (62.6)
Former smoker 159 (24.8)
Current smoker 81 (12.6)
Pregnancy complicationsc 73 (11.4)
Cesarean delivery 24 (3.7)
Parity
1 351 (54.8)
2 224 (34.9)
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Characteristic No. (%)
≥3 66 (10.3)
W
eekly mean PM2.5 exposure, μg/m3d 13.4 (4.3 to 32.5) Weekly mean temperature, °Cd 10.7 (−0.2 to 19.4)
Fathers
Age, mean (SD), y 31.7 (5.3)
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); PM2.5, particulate matter with an aerodynamic diameter of 2.5 μm or less.
aMaternal educational level was coded "low" when mothers did not obtain any diploma, "middle" when they obtained a high school diploma, and "high" when they obtained a colle
ge or university degree.
bMaternal smoking status was categorized as "never smoker," "former smoker" when quitted smoking before pregnancy, and "smoker" when smoking continued during pregnanc
y.
cPregnancy complications was defined as the experience by the mother of one or more of the following conditions during pregnancy: gestational diabetes, hypertension, infectious disease, pre-eclampsia, v aginal bleeding, and hyperthyroidism or hypothyroidism. dPresented as mean (5th to 95th percentile) and for the actual pregnancy duration.
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T
able 2
Association Between Newborn Telomere Length and Prenatal PM2.5 Exposurea Percentage Change (95% CI)
Exposure Window Distributed Lag Model Average Exposure Model Cord Blood (n = 641)
Overall (1-40 wk)b −8.8 (−14.1 to −3.1) −8.4 (−13.5 to −2.9) Trimester 1 −2.3 (−6.1 to 1.7) −0.8 (−4.7 to 3.2)
Trimester 2 −9.4 (−13.1 to −5.6) −9.8 (−13.3 to −6.2) Trimester 3 3.1 (−1.8 to 8.3) 2.6 (−1.4 to 6.8) Placental Tissue (n = 641)
Overall (1-40 wk)b −13.2 (−19.3 to −6.7) −12.5 (−18.4 to −6.2) Trimester 1 −1.4 (−6.0 to 3.5) −0.8 (−5.5 to 4.1)
Trimester 2 −7.1 (−11.6 to −2.4) −7.4 (−11.7 to −2.9) Trimester 3 −5.3 (−10.8 to 0.5) −4.5 (−9.0 to 0.2)
Abbreviations: BMI, body mass index; PM2.5, particulate matter with an aerodynamic diameter of 2.5 μm or less. aEstimates provided as a percentage change in mean relative telomere length for a 5-μg/m3 increment in PM2.5, estimated by distributed lag models using weekly mean exposures, and by mean PM2.5 during specif ic exposure windows (average exposure model).
bIn the average exposure models, the overall exposure window of 1 to 40 weeks is the actual pregnancy duration (ranging from 37-41 weeks).
Models were adjusted for date of deliv
ery, gestational age, maternal BMI, maternal age, paternal age, newborn sex, newborn ethnicity, season of delivery, parity, maternal smoking status, maternal educational level, pregnancy complications, and ambient temperature.
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able 3
Sensitivity Analysesa
Percentage Change (95% CI)
Model No. Cord Blood Telomere Length Placental Telomere Length Main model 641 −8.8 (−14.1 to −3.1) −13.2 (−19.3 to −6.7) Unconstrained lag structure 641 −8.4 (−14.0 to −2.5) −14.0 (−20.2 to −7.3) Additional adjustment for month of delivery 641 −9.2 (−14.6 to −3.4) −12.3 (−18.6 to −5.6) Excluding non-European mothers 567 −8.7 (−14.4 to −2.6) −11.9 (−18.6 to −4.8) Excluding mothers with low educational level 570 −10.0 (−15.4 to −4.1) −13.4 (−19.8 to −6.4) Excluding current and former smokers 401 −12.1 (−18.8 to −4.9) −16.2 (−23.5 to −8.2) Excluding mothers with pregnancy complications 568 −9.1 (−14.7 to −3.2) −14.3 (−20.7 to −7.4) Excluding cesarean deliveries 617 −8.7 (−14.1 to −2.9) −12.8 (−19.0 to −6.1) Excluding all of the above 281 −14.0 (−21.9 to −5.2) −13.3 (−22.3 to −3.2) Abbreviations: BMI, body mass index; PM2.5, particulate matter with an aerodynamic diameter of 2.5 μm or less. aEstimates provided as a cumulative (weeks 1-40) percentage change in mean relative telomere length for a 5-μg/m3 increment in PM2.5. Models were adjusted for date of deliv
ery, gestational age, maternal BMI, maternal age, paternal age, newborn sex, newborn ethnicity, season of delivery, parity, maternal smoking status, maternal educational level, pregnancy complications, and ambient temperature. The season of delivery was removed from the model adjusting for month of delivery.
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EXHIBIT 11
UNCORRECTED PROOF
ARTICLE INFO
Article history:
Received
17 June 2016
Received in revised form 5 September
2016
Accepted 12 September 2016
Available online xxx
Keywords:
Aerosols
Highway
Size profiles
Lateral transects
Ultra fine metals
ABSTRACT
As one com
ponent of a study investigating the impact of vehicle emissions on near-road air quality, human exposures, and potential health effects, particles were measured from September 21 to October 30, 2010 on both sides of a major roadway (Interstate-96) in Detroit. Traffic moved freely on this 12 lane freeway with a mean velocity of 69 mi/hr. with little braking and acceleration. The UC Davis DELTA Group rotating drum (DRUM) impactors were used to collect parti- cles in 8 size ranges at sites nominally 100 m south, 10 m north, 100 m north, and 300 m north of the highway. Ultra-fine particles were continuously collected at the 10 m north and 100 m north sites. Samples were analyzed every 3 h for mass (soft beta ray transmission), 42 elements (synchrotron-induced x-ray fluorescence) and optical attenuation (350–800 nm spectroscopy). A three day period of steady southerly winds along the array allowed direct measurement of freeway emis- sion rates for coarse (10 > Dp > 1.0 μm), PM2.5, very fine (0.26 > Dp > 0.09 μm), and ultra-fine (Dp < 0.09 μm) particles.
The PM2.5 mass concentrations were modeled using literature emission rates during the south to north wind periods, and averaged 1.6 ± 0.5 μg/m3, versus the measured value of 2.0 ± 0.7 μg/m3. Using European freeway emission rates from 2010, and modeling them at the I-96 site, we would predict roughly 3.1 μg/m3 of PM2.5 particles, corrected from the 4.9 PM10 value by their measured road dust contributions. Using California car and truck emission rates of 1973, this value would have been about 16 μg/m3, corrected down from the 19 μg/m3 PM5.0 using measured roadway dust contributions.
This would have included 2.7 μg/m3 of lead, versus the 0.0033 μg/m3 measured. Very fine particles were distributed across the array with a relatively weak falloff versus distance. For the ultra-fine particles, emissions of soot and metals seen in vehicular braking studies correlated with traffic at the 10 m site, but only the soot was statistically significant at the 100 m north site. Otherwise, the 10 m north and 100 m north sites were essentially identical in mean concentration and highly correlated in time for most of the 5 week study. This result supports earlier publications showing the ability of very fine and ultra-fine particles to transport to sites well removed from the freeway sources. The concentrations of very fine and ultra-fine metals from brake wear and zinc in motor oil observed in Detroit have the potential of being a significant component in statistically established PM2.5 mortality rates. © 2016 Published by Elsevier Ltd.
Atmospheric Environment xxx (2016) xxx-xxx
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com
Transition metals in
coarse, fine, very fine and ultra-fine particles from an interstate highway transect near Detroit
Thomas A. Cahill a, ∗, David E. Barnes a, Jonathan A. Lawton a, Roger Miller a, Nicholas Spada b, Robert D. Willis c, Sue Kimbrough d
a DELTA Group, University of California, Davis, CA 95616, USA b University of Houston, Houston, TX, USA
c US Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC 27711, USA d US Environmental Protection Agency, National Risk Management Research Laboratory, Research Triangle Park, NC 27711, USA
1. Introduction
Enormous advances have been made in the past 40 years in reduc- ing particulate pollutants from highway vehicles, including the elim- ination of lead from gasoline. Additionally, important advances have been made in reducing emissions from diesel trucks by improved en- gine design, low-sulfur fuels, and especially diesel particulate filters.
However, studies show that highways continue to be a source of both coarse and fine particulate matter (PM) including known toxics such as diesel exhaust (Zhu et al., 2002; Karner et al., 2010; Cahill and Cahill, 2013). In addition, particles associated from vehicular wear such as from brake pads and drums are currently roughly equal to tailpipe emissions and are on track to exceed tail pipe emissions in the near future (Denier Van der Gon et al., 2013). Only limited data ∗ ∗Corresponding author.
Email address: [email protected] (T.A. Cahill)
are available on wear particles by size and composition, but a major source is known to be ultra-fine metals from abrasion of brake pads and drums (Cahill et al., 2014).
In vitro laboratory studies of nanoparticles raise concerns about the health impacts of highway emissions, especially very fine and ul- tra-fine metals due to their propensity for penetrating to the deepest portions of the lung and diffusing into the circulatory system (Lewis et al., 2005; Chen and Lippmann, 2009; Lippmann, 2009; Oberdorster et al., 2007; Denier Van der Gon et al., 2013). Additionally, epidemi- ological health studies continue to show that highway emissions are damaging to both pulmonary and cardiovascular systems of people living near highways (Cahill et al., 2011), with reduced lung function in children (Peters et al., 1999a,b); Gauderman et al., 2000; HEI, 2009; Lin and Peng, 2010; Karner et al., 2010). These data have been com- bined with data on the health impacts of roadway pollutants, including potential cancer impacts largely from diesel exhaust, and used to gen- erate estimated health impacts in models such as Emfac2007 (ARB, 2007).
http://dx.doi.org/10.1016/j.atmosenv.2016.09.023
1352-2310/© 2016 Published by Elsevier Ltd.
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A recent European survey noted that wear particles were approach- ing and would soon surpass exhaust particles near roadways, with un- certain health impacts. Their consensus statement concludes, “In light of the continuous increase of the relative contribution of non-exhaust emission to ambient PM, where it is becoming the dominant emission process for urban transport, it is more than timely to devote greater efforts to properly quantifying non-exhaust emissions and assessing health relevance.” (Denier Van der Gon et al., 2013).
The U.S. Environmental Protection Agency (EPA) and the Uni- versity of Michigan conducted the Near-road Exposures to Urban Air Pollutants Study (NEXUS) – a research project to study the impact of vehicle emissions on near-road air quality, human exposures, and po- tential health effects. Components of NEXUS include but are not lim- ited to human exposures in near-road residences (Vette et al., 2013), studies of near roadway allergic impacts in mice (McGee et al., 2015).
The NEXUS aerosol component reported in this publication was de- signed and conducted to update our information on highway emissions and their potential human impact with a component on vehicular wear emissions.
1.1. Experimental methodologies
1.1.1. Sampling array
Particles were measured from September 21 to October 30, 2010 on both sides of the Interstate 96 freeway 9 miles west of downtown Detroit. The I-96 monitoring location was established as part of a col- laborative research study conducted by the U.S. Environmental Pro- tection Agency (EPA) and the U.S. Department of Transportation Fed- eral Highway Administration (FHWA) (US EPA, 2013). The test sec- tion was a 12-lane section of I-96 with an at-grade roadway config- uration aligned east to west. Obstructions from vegetation between the test array of four sites, which extended from 100 m south of the highway to 300 m north of the highway, were negligible, as seen in Fig. 1. As part of NEXUS, additional PM monitoring was conducted at these sites.
Particles were collected in 8 size ranges at sites 100 m south, 10 m north, 100 m north, and 300 m north of the highway, while ultra-fine particles were continuously collected at the 10 m north and 100 m north sites. (Table 1)All sampling sites were on the top of trailers or one-story buildings with minimal obstructions (approximately 4 m from ground to inlets). The exception was the 100 m south site that had heavy trees south of the site, which did not obstruct the air flow from the highway.
1.1.2. Weather
Wind direction and wind speed were measured continuously at the 100 m north site (Fig. 2). During most of the study winds were weak, and extensive periods of calms occurred, especially at night. Wind di- rections were most frequent from the northwest (315°) through north northeast (35°). Given the southeast to northwest orientation of the sampling array, an upwind/downwind analysis of I-96 traffic emis- sions is best conducted when winds are from the southeast to south- west sector.
Thus, in order to address the prime goal of the study, a small frac- tion of the entire five-week period was chosen for which there was a clean upwind-downwind profile along the array. Since the goal was to quantify aerosol transport downwind of the freeway, results were limited to periods when the upwind-downwind trajectory was within ±67° of the north-south axis across the east-west freeway with wind speeds >1 km/hr. Periods of extreme wind or rain events were ex- cluded as well as wind trajectories pointing back to strong upwind sources such as the Monroe coal-fired power plant.
In terms of duration, while any 3 h period provides a valid signa- ture in time, the ideal is several days in a row. The reasons include re- ducing the inherent 1½ hr uncertainty in the elemental data set by the width of the x-ray analysis microprobe to roughly 30 min, which al Fig. 1. Moni
toring site transect array across I-96 west of Detroit established for the EPA/FHWA National Near Road Study and used as a component of the NEXUS study. I-96 runs east-west and Telegraph Road is the multi lane road running north-south to the west of the sampling sites.
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Fig. 2. a) Wind
direction and rainfall, and b) wind speed, with the dotted box showing the periods with the wind from the south, 180° ± 67°, measured at the 100 m north site. Data below the dashed line are calm periods with wind speed <1.0 km/hr. lows better accord with meteorology and traffic patterns. It also allows better sensitivity by summing the periods for averaging actions such as size profiles. Limiting the intensive to week days avoided the com- plexity of highly variable weekend conditions, especially in terms of the truck traffic.
1.2. Experimental techniques
1.2.1. Sample collection
The primary aerosol collection instrument was the UC Davis DELTA Group rotating drum (DRUM) impactor (Cahill et al., 1985; Raabe et al., 1988; ) delivering aerosol samples in 8 size ranges: 10 to 5.0, 5.0 to 2.5, 2.5 to 1.15, 1.15 to 0.75, 0.75 to 0.56, 0.56 to 0.34,
0.34 to 0.26 and 0.29 to 0.09μm aerodynamic diameter. Samples were impacted at the rate of 4 mm/day onto 168 mm long, lightly greased Mylar foils, which were then transferred to plastic frames for analy- ses. Excellent agreement (r 2 = 0.99, slope = 0.99 ± 0.011, and inter- cept = 14 ng/m3) was seen in a side by side comparison of the DRUM mass (summed over all stages including ultra-fines) versus a Califor- nia Air Resources Board PM 2.5 Federal Reference Monitor (Cahill et al., 2014; Nichols, 2009, Supplemental Materials A).
For this study, a newly developed continuous ultra-fine stage was added at the 10 m north and 100 m north sites. These allow continu- ous collection of ultra-fine (<0.09 μm) particles directly after the last stage of the DRUM and keyed to deliver a stretched Teflon filter strip exactly matching in time the rotating drum stages (Cahill and Barnes, 2009).
1.2.2. Sample analysis
Details of the accuracy and precision of the mass, optical, and elemental analyses of the DRUM are included in Supplemental Materials. The measurements were made in 3 h increments using 500 μm wide excitation sources, and included:
• Mass (soft beta ray transmission, Ni63 source, MDL = 0.7 μg/m3). • Elements sodium through molybdenum, plus lead (Synchrotron-in- duced X-Ray Fluorescence (S-XRF), MDLs typically ∼ 0.1 ng/m3). • Diesel soot (optical attenuation, 380–820 nm, 50 nm bites, Ocean Optics spectrophotometer).
Cahill -- 00000326
AR002333
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4 Atmospheric Environment xxx (2016) xxx-xxx
1.3. Black carbon data
Measurements of black carbon were also collected at the I-96 sites based on optical absorption (880 nm) using a Magee Scientific rack mount aethalometer (US EPA, 2013) and were compared to the DRUM optical attenuation.
1.4. Traffic data
Traffic volume during the study period showed a highly repro- ducible weekly pattern (Fig. 3). Weekday traffic averaged 175,000 cars and light trucks/day along with 12,000 heavy-duty trucks/day, or 6.6% trucks. Weekend traffic averaged 110,000 cars and light trucks/day, and about 3% trucks. Traffic data were collected as part of the EPA/FHWA study in 5 min increments individually for each of the 12 traffic lanes and for 6 classes of vehicle by length: 10 ft. (3 m), 30 ft. (9 m), 40 ft. (12 m), 50 ft. (15 m), 60 ft. (18 m), and 70 ft. (21 m) to 80 ft. (24.2 m), the maximum length allowed (US EPA, 2013). Traffic moved freely on the test section during the study, with a mean speed of 69 ± 3 miles/hr (110 ± 5 km/hr), with little braking activity. Below we plot the data reduced to one hour averages over all 12 lanes, using the 10 ft. (3 m) and 30 ft. (9 m) categories for cars and pickup trucks (light-duty vehicles or LDVs, mostly gasoline powered) and 40 ft. (12 m) to 70 ft. (24.2 m) for heavy trucks (mostly diesel).
The very strong morning and afternoon peaks seen in the light duty vehicle (LDV) data is much less pronounced in the truck data, which tends to be more uniform all day. Traffic data were not available be- fore Sep. 29.
1.5. Results of the 5-week study
The meteorology encountered during most of the study resulted in aerosol concentrations that, even at the close-in 10 m north site, displayed little or no correlation with the local traffic volume (Fig. 3). In Figs. 4–10 below we show four examples of typically regional particles and three examples of particles likely to have local road- way influence. Regional particles include PM 10 and PM2.5 mass, sil- icon (soil), sulfur (sulfates) and fine potassium (wood smoke). Par- ticles with known roadway sources include zinc in coarse mode Fig. 3. Traffic
measured on I-96 west of Detroit on the EPA/FHWA National Near Road Study site. Heavy diesel truck numbers are scaled by a factor of 15 to facilitate comparison.
Weekends occurred on Sept. 25–26, Oct. 2–3, Oct. 9–10, Oct. 16–17, Oct. 23–24, and Oct. 30–31.
Fig. 4. PM10 and PM2.5 mass
(without ultra-fines < 0.09 μm), at the 10 m north site. Each data point represents a 3 h measurement. The notation “tm” denotes a 6 mm long integral blank for timing and background subtraction validation.
Cahill -- 00000327
AR002334
20 ~----------------------------~
~
::,
0 .c
~
"' C..:;
Cl> C
~ Heavy diesels> 40 ft (x 15) - Cars and light trucks < 40 15 t------,-,----------++....--:----r--.-r+-11------ --!T-t-,------, § ~ 10 f--llH .... II-- -H----.im-lls-tl- - --...llHIIHll ~ - D-IIHl-1-111 - ~ -IHll-a..._ -----, ~~
<.>
If=
~ 5
30 25
l~ 2D
16 10
6 0
29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1
October 2010
- FMIOnuss · - • - F'llll2.6 nuss
~ ~ ~ 25 ~ ~ 2 4 6 8 ~ fl ~ ~ ff ~ ~ ~ 25 ~ ~
~ Z 25 ~ ~ 1 3 6 7 9 ~ ~ ~ ~ ~ 2D ~ ~ 25 ~ ~
S::fAe,, let O:tdJer
UNCORRECTED PROOF
Atmospheric Environment xxx (2016) xxx-xxx 5
Fig. 5. Sili
con from 10 to 0.09 μm derived from soil. The notation “tm” denotes a 6 mm long integral blank for timing and background.
Fig. 6. Sulfur
(for sulfate, x 3.0) at 10 m north.
(5.0–1.15 μm) from tire wear, zinc in the very fine mode (0.26 > Dp > 0.09 μm) from motor oil combustion, and black carbon (diesel soot) (Malm et al., 1994; Supplemental Materials 1).
Zinc is seen routinely both in transects from the roadway and in the ambient atmosphere. Zinc at the 10 m north site was highly variable in concentration, with highest concentrations typically occurring when the site was upwind of I-96. Note that unlike the potassium in Fig. 7, there are strong sources of coarse zinc not associated with the very fine zinc. Since there were no known sources close to the NEXUS site, the calculated rate of settling favors regional industrial processes with stack emissions.
Optical absorption was measured by two very different techniques, namely light transmission through the DRUM Mylar foils using a multi-wavelength optical spectrometer (Fig. 9), and direct measure- ment of optical absorption (b abs) from an aethalometer (Fig. 10; data provided by the EPA). The average b abs in units of ng/m 3 of Black Carbon (BC ≅ soot) for the entire period was 648 ng/m 3 at 100 m south, 1014 ng/m3 at 10 m north, 638 ng/m3 at 100 m north, 438 ng/m3 at 300 m north, showing the impact of soot from the freeway.
1.6. Results of the 3-day intensive
The conclusion from the aethalometer data is that even close to the freeway, regional sources are a major factor compared to roadway par- ticles for much of the five week study. Thus, for the remainder of this report, we will focus on those periods during which the criteria for a clean upwind-downwind profile were achieved. The results of the transects can then be compared to the regional particles seen during the entire study, isolating roadway impacts.
In order to identify those periods that met these criteria, nonpara- metric trajectory analysis (NTA) was performed using the meteoro- logical data collected at the 100 m north site (Henry et al., 2011).
Cahill -- 00000328
AR002335
1000
900
C"l 800
-E 700
II)
E 600
lU 500 c,
0 400
C:
300 ttl
z 200
100
0 800
700
C"l 600 -E
II)
500 E
ttl 400 C)
0 300 C
ttl 200 z
100
0 Detroit transect 1 0 m north
Silicon (for soil, - x 4)
- - 10 to 5 .0 - 2.5 to 1.15 - 0.75 to 0.56 - 0.34 to 0.26 - 5 .0 to 2.5 - 1.15 to 0.75 - 0.56 to 0.34 - 0.26 to 0.09 22 24 26 28 30 2 4 6 8 10 tm 12 14 16 18 20 22 24 26 28 30 23 25 27 29 1 3 5 7 9 11 tm 13 15 17 19 21 23 25 27 29 31 September October
Detroit transect 10 m north
Sulfur(for ammonium sulfate x 4.125)
- -10 to 5.0 - 2.5 to 1.15 - 0.75 to 0 .56 - 0.34 to 0.26 - 5.0to2 .5 - 1.15to0 .75 - 0.56to0 .34 - 0.26to0 .09
23 25 27 29 1 3 5 7 9 11 11 13 15 17 19 21 23 25 27 29
22 24 26 28 30 2 4 6 8 10 tm 12 14 16 18 20 22 24 26 28 30 September October
UNCORRECTED PROOF
6 Atmospheric Environment xxx (2016) xxx-xxx
Fig. 7. Potassium
at 10 m north, with coarse modes derived from soil and fine modes largely from wood smoke and industrial combustion processes.
Fig. 8. Zinc from
10 μm to 0.09 μm at 10 m north.
Hourly averaged met data were aggregated into 3 h averages to bet- ter match the 3 h aerosol data. Fig. 11 shows the fraction of time in each 3 h period when the wind was blowing across I-96 from the south to the north. Upon early analysis of the data, an additional criterion was added, namely avoidance of sulfur (MDL = 5 ng/m 3) and sele- nium-rich (Se and As, MDL = 0.1 ng/m3) particles transported by SSE winds that passed over the Ford Rouge River complex, other indus- trial sources, and the large Monroe coal fired power plant 34 miles (54 km) south (185°) of the array (Fig. 6). This eliminated the po- tential October 25 day, while October 23 and 24 were both weekend days and impacted by upwind aerosol sources. October 30 was also eliminated as a weekend day. Although October 8 and 20 met the trajectory criteria, both had extended periods of calm winds making clean upwind-downwind analysis uncertain. A final criterion for the upwind-downwind study was that the 10 m north site should be mini- mally impacted by Telegraph Road, a multi lane road west of the I-96 sites (Fig. 1). Fig. 12 shows the subset of trajectories for which the 10 m north site was downwind of I-96 and without significant influ- ence from Telegraph Road.
Although there was some influence of Telegraph Road traffic on Oct. 27 and 28 at the 10 m north site, the distance to Telegraph road was much farther than at the 100 m north site and a belt of trees in a residential neighborhood interfered with wind and aerosol transport. (Fig. 1). Also, Telegraph Road traffic volume was a tiny fraction of I-96 traffic volume. For these reasons, the impact should be small, confirmed by the low aethalometer soot readings (Fig. 13 below). The 300 m north site, on the other hand, is much closer to Telegraph Road and was probably significantly impacted by Telegraph Road traffic for much of the study period. The longest period during the NEXUS cam- paign that met all criteria for a north-south transect occurred from Oct 26 through Oct. 28, 24–3 h periods, shown in the dashed box in Fig.
11. Cahill -- 00000329
AR002336
150
C')
_g 100
"' E
!!!
0)
0 C: 50 RS
z
40 (')
-E 30
(I)
E
~ 20
C)
0 C:
~ 10
0 0
Detroit transect 10 m north
Potassium
- - 10 to 5.0 - 2.5 to 1.15 - 0 .75 to 0 .56 - 0.34 to 0 .26 - 5 .0 to 2 .5 - 1.15 to 0 .75 - 0 .56 to 0 .34 - 0 .26 to 0 .09 22 24 26 28 30 2 4 6 8 10 tm 12 14 16 18 20 22 24 26 28 30 23 25 27 29 1 3 5 7 9 11 tm 13 15 17 19 21 23 25 27 29 31 September October
Detroit transect 10 m north
Zinc
- -10 to 5.0 - 2.5 to 1.15 - 0.75 to 0.56 - 0.34 to 0.26 - 5.0 to 2.5 - 1.15 to 0.75 - 0.56 to 0.34 - 0.26 to 0.09 21 23 25 27 29 1 3 5 7 9 11 12 14 16 18 20 22 24 26 28 30 22 24 26 28 30 2 4 6 8 10 tm 13 15 17 19 21 23 25 27 29 31 September October
UNCORRECTED PROOF
Atmospheric Environment xxx (2016) xxx-xxx 7
Fig. 9. Optical
extinction at two wavelengths for very fine particles at the 10 m north site.
Fig. 10. Optical
absorption (babs) from an aethalometer at both 10 m north and 100 m south.
The wind speed for the 3-day intensive averaged 10.7 ± 3.0 km/ hr, and the average direction was SSW, 208± 43°. However, the 10 m north site was in the turbulent mixed zone of the northernmost lane of the freeway, as evidenced by the winds from passing trucks. Thus, this site was impacted by one lane of the freeway under most wind condi- tions, representing only a small fraction of all traffic volume on the 12 lane freeway.
1. Validation of the meteorological analysis
Validation of the Oct. 26–28 choice for the upwind-downwind study was obtained by correlation of soot and typical roadway parti- cles with the daily daytime traffic maxima as shown in Figs. 13 and
14. The days chosen for the intensive are in the dotted box area. Data are from the 10 m north site.
During the intensive period, the calcium (roadbed abrasion) and iron (vehicle “wear”) particles (Fig. 14a and b) are correlated with traffic volume (Fig. 14d), with daytime peaks and nighttime low val- ues, unlike during most of the prior sampling period. Both elements are strongly enhanced over Earth crustal average, calcium by a factor of 5, and iron by a factor of 10, suggesting resuspension of roadbed concrete erosion and vehicle “wear” particles by wind and vehicular turbulence. There was almost no braking of vehicles during the study, so concentrations of the very fine Fe, Ni, Cu and Zn particles charac- teristic of braking (Cahill et al., 2014) are limited.
Highway zinc coarse particles (Fig. 14c) are enhanced by the zinc in tires and brake wear, while fine and ultra-fine zinc are observed in both brake wear and the burning of the zinc thiophosphate stabilizer used in many motor and lubricating oils, including diesels (Zielinska et al., 2003).
2. Size profiles of the elements
Fig. 15a, b and c plot particle size distributions during the intensive for crustal species (a), transition metals and lead (b) and sulfur (c). The mean size distributions for crustal elements peak in the supra-micron particles, indicating a mechanical source, such as mechanical wear and resuspension of contaminated soils. Iron is very interesting, as it peaks in the 2.5–5.0 μm mode but in concentrations far in excess of what would be expected in soil.
Cahill -- 00000330
AR002337
2 8 1.5
u
C:
~
o'i c.,
a
0 0.5
0 Very fine aerosols 0.26 > Dp > 0.09 micrometers
m Far red , 780 nm - Far blue, 380 nm
21 23 25 27 29 1 3 5 7 9 11 tm 14 16 18 20 22 24 26 28 30 1 22 24 26 28 30 2 4 6 8 10 Im 13 15 17 19 21 23 25 27 29 31 2 September October
Aethalometer Data
- 10 m north D 100 m south
6.-------------------------------,
5 1--------- -+------------------ -,----,
~4---------~~--------------------, </I C:
~~ 3----------~---------~----------, J:l 8
~2---•~----~--~---,--~---~~-----~---,
28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3
27 29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2
September October November
UNCORRECTED PROOF
8 Atmospheric Environment xxx (2016) xxx-xxx
Fig. 11. Trajec
tory analysis for the 100 m north site isolating those periods when the northern sites were approximately downwind of I-96 and the 100 m south site was a clean upwind background site. The period chosen for the intensive is shown in the dashed box, October 26 through October 28.
Fig. 12. Trajec
tory analysis isolating those periods when the 100 m south site was a clean upwind background for 100 m north. The 10 m north site had less Telegraph Road interfer- ence.
Size distributions for transition metals (Fig. 15b) also peak in the supra-micron range. Iron (brake drums and rust), copper (brake pads), zinc (stabilizing agent in lube oil, tire wear), and lead (“legacy lead ” from past emissions) are known to be associated with traffic (Cahill et al., 2011). Note that in addition to a coarse mode (re-suspended road dust), zinc, lead and copper also exhibit an ultra-fine mode. Very fine and ultra-fine zinc is most likely associated with lubricating oil.
A very different profile is shown for sulfur (Fig. 15c). Typically the accumulation mode around 0.5 μm represents regional ammonium sulfate, while very fine and ultra-fine sulfate has local combustion ori- gins, including from diesel combustion in trucks and cars.
3. Transects in the three day intensive: Coarse (5.0 > D p > 2.5) parti- cles – experimental
For the time-resolved coarse particles, we used only part of the distribution since the time resolution of the coarsest DRUM stage, 10 to 5.0 μm, was large enough to blur the day/night differences. (Supplemental Materials B).
Results of the highway transect (Fig. 16) show highest concentra- tions at the near freeway site at 10 m north, supporting traffic-derived sources. Concentrations fall off from the roadway to 100 m north, but concentrations of the coarsest particles increase again at 300 m north, especially for crustal species. The presence of the heavily traveled Telegraph Road approximately 380 m west of the 300 m site (Fig. 1) as well as local exposed soils near the site might be contributory fac- tors. In addition, the sampler inlet cut point may have been raised by an estimated 10% due to a non-standard, slightly wider inlet slot. This effect disappeared for the PM2.5 particles which are not influenced by coarse local soils.
4. Transects in the three day intensive: Coarse
(10 μm > Dp > 1.15 μm) particles – theoretical
I-96 traffic during the intensive was typical of the entire 5 week period (Fig. 2). For modeling purposes, traffic data for the intensive was broken into two periods: daytime (6 a.m. through 9 p.m.), and nighttime (9 p.m. through 6 a.m.). Daytime traffic averaged 10,479 Cahill -- 00000331
AR002338
1 "C
C
"j
0.8 ~ 0
"C
0.6 a,
:§
0 0.4
C
0 ~ 0.2
~
IL
0 1 ,::,
C
"j 0.8 C
~ 0 ,::,
0.6 a,
~
0 0.4
C
.Q
ti
~
0 .2
LL
0 D Trnjectories downwind of 1-96 and Telegraph Road
I
- f-- f--
rvr fl )_ f ~-
I
- f-- - I 1" - -I
I
~- -- ~ - I 11- -~ I
I
I
~
-- - ~ I r
n-
I
j I
I
29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2
30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3
-
-
-
-
September October November
D Trajectories downwind of 1-96 but not downwind of Telegraph Road I
I
I
I
I
I
I
I
I
I
t
I
~
I _,_
A
I
~ I I\ J
I
I 1 /,,,.
29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2
30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3
-
September October November
UNCORRECTED PROOF
Atmospheric Environment xxx (2016) xxx-xxx 9
Fig. 13. Aethalometer
data on optical absorption (∼soot) from the EPA/FHWA National Near Road Study. The days chosen for the intensive are in the shaded area. October 25 and earlier days were rejected because of substantial upwind aerosol pollution seen in Figs. 5–7. cars and light trucks/hr, and 824 heavy trucks/hr, while the numbers at night were 3874 and 187 vehicles/hr respectively.
For the theoretical analysis of the data, all coarse particles from 10 to 1.15 μm were used so as to allow comparisons to other work.
We used a Sliding Box Model Estimate of the I-96 10 to 1.15 μm mass emission rate for the 3-day intensive. The “sliding box model ” (Cahill and Feeney, 1973, Feeney et al., 1975; Courtney et al., 1978) is a well-tested way to measure roadway emissions when one has ac- cess to both upwind and downwind information on pollutants, a flat terrain, and a lateral wind across the roadway. The dimensions of the box are set by the roadway width, including lateral turbulence, and the top of the mixed zone, set by vehicle height and velocity. The lat- ter is the greatest uncertainty, but extensive data on the height of the mixed zone were taken in Los Angles in 1973 and used in several stud- ies. The length of the box is arbitrary, and we use 1.6 km, which was needed since the project accepted winds at ± 45° from normal and still had to meet the line source assumption even 300 m downwind.
The box so defined represents a volume into which pollutants are uni- formly mixed by vehicular turbulence. The box then slides laterally across the freeway while vehicles are emitting into the box. A new box takes its place upwind in a smooth transition and the process repeats.
The model was validated with data taken in 80 two-hr up- wind-downwind periods of lead transport from five freeway sites in 1972 (Cahill and Feeney, 1973; Feeney et al., 1975). Since accurate traffic data and meteorology were available and emissions of auto- motive lead were precisely known from the literature (Habibi, 1973), the sliding box model gave a roadway edge prediction at the at-grade freeway section of 4.0 ± 0.4 μg/m3 for 5000 vehicles/hr. The three day average measured at the at-grade site was in excellent agreement,
4.0 ± 0.15 μg/m3. The sliding box model was also successfully ap- plied in General Motors test facility studies of sulfur from catalytic converters (Courtney et al., 1978)
Sliding box model calculations were run separately for daytime, 6 a.m. to 9 p.m., and nighttime, 9 p.m. to 6 a.m., keyed to the traffic flow. Traffic flow, wind velocities were also separated in the analy- sis. It was assumed that trucks had 10 times the emission rates as cars (Gertler et al., 2003). Table 2 below shows the analysis for cars and trucks for calcium, and then the net result for calcium, iron, and zinc.
Coarse calcium and iron are enhanced by enrichment factors 5 and 10 times Earth crustal averages respectively (Fig. 14a and b), likely a consequence of the erosion of the concrete highway and iron-rich debris from vehicles. The transition and heavy metals, vanadium, copper, zinc, and lead, are enormously enhanced compared to Earth crustal averages and represent vehicular debris, including brake pads, brake drums and tire wear.
In this analysis, the unknown emission rates were calculated by matching the predicted concentrations to the measured concentrations at the 10 m north site after correcting for concentrations at the 100 m south site.
In Table 2, the emission rates in mg/km were fit to the NEXUS background corrected concentrations (μg/m 3) at 10 m north (Fig. 14a–c). Matching the upwind-corrected data at the 10 m north site for freeway impact to the predicted concentrations, emission rates of 1.5 mg/km for calcium, 2.8 mg/km for iron, and 0.09 mg/km for zinc are calculated, with truck emission rates 10 times these values.
Direct comparisons are possible to similar analyses in Los An- geles in 1972 from an at-grade concrete freeway. The NEXUS free- way-sourced calcium level in the mixed zone, 0.24 μg/m3, is about 1/ 5 of that seen in 1973, 1.13 μg/m3. Note that the Los Angeles freeway had not seen rain in over 2 months, while rain fell about once/week in the NEXUS study. For iron, which has both a concrete component and one associated with vehicle wear, the NEXUS values are 0.52 μg/m3, versus 1.43 μg/m3 in Los Angeles. For zinc, the NEXUS level of 0.017 μg/m3 is far less than the 0.22 μg/m3 seen in Los Angeles in 1973.
The California data (Cahill et al., 2003) for 15 > D p > 5.0 μm parti- cles from tire wear were run through the sliding box model of Table 2 and yielded 120 ng/m 3 of zinc, about 10 times what was observed in NEXUS for < 5.0 μm particles (Fig. 14c). However, the upper cut point for the Los Angeles particles was ∼15 μm, which increased the numbers, while better high mileage tires and aerosol size differences reduce the NEXUS value.
A very similar protocol (upwind-downwind samplers, rotating drum impactors, x-ray analysis) was utilized in Switzerland (Bukowiecki et al., 2010) for an extensive series of measurements to separate vehicular wear from exhaust emissions. The Swiss results showed most vehicular wear particles in the 10 to 1 μm range. The vehicular wear results were 50 ± 13 mg/km per vehicle for light duty Cahill -- 00000332
AR002339
4 3
"' ~
~ lJl 2
"' :J .0 0 .c
I-
24 Aethalometer Data
10 m north - - - 100 m north ~ 100 m south
25 26 27
October
28 29 30
UNCORRECTED PROOF
10 Atmospheric Environment xxx (2016) xxx-xxx
Fig. 14. a, b,
and c. Typical roadway particles during the Oct. 26 through Oct 28 intensive (in shaded box). d. Traffic from Fig. 3 on the same time scales (trucks x 15). vehicles, 288 ± 72 mg/km for heavy duty vehicles, with 3% brake wear, 56% suspended/abraded road dust, and no evident tire wear.
This gives roughly 3.2 mg/km brake wear, assuming 6% trucks in their mix. If the NEXUS iron and zinc were all from brake wear, it would amount to roughly 5.2 mg/km, similar to the Swiss results.
This is an overestimate, since the NEXUS iron value should be re Cahill -- 00000333
AR002340
..,
900
800
700
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5 500
~ 400
0 fi 300
z
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0 fe :z
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0 C:
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200
100
0 500
400
300
200
100
0 14
12 10
e
6 4
2 0
15 "'10 -0
<=
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0 I= 5
Calcium
-- 5.0 to 2 .5 - - 2.5 to 1 .15 -- 1.15 to 0.75 micrometers ~------------------~--------------------~------,
23 23
...
24 24
26 Iron
-- 5.0 to 2 .5 · - - 2.5 to 1 .15
27 28
1 .15 to 0. 75 micrometers
I ✓
.l...l.lww 1..lal..l...l.L~ ' ' .I..J..,.I,,,~---- -~~~~~~ 25 27 29
26 28
Zinc
-- 5.0 to 2.5 - - 2.5 to 1.15 -- 1.15 to 0.75 micrometers ~------------------~--------------------~------~
~
23 24 25 26 27 28 29
October
c::J Truck traffic (scaled x 15) -- Car Traffic
24 25 26 27 28 29 30
Octo ber
UNCORRECTED PROOF
Atmospheric Environment xxx (2016) xxx-xxx 11
Fig. 15. Size dis
tributions of a) crustal species; b) transition metals and lead; and c) sulfur at the 10 m north site during the NEXUS intensive. duced due to iron from vehicle debris and roadbed wear.
5. Transects in the three day intensive: Fine PM 2.5 particles – experi- mental
Fig. 17a and b shows transects for fine crustal species and fine transition metals and lead, respectively, during the Oct. 26–28 inten- sive. Fine crustal species, like coarse, show a near-freeway enhance- ment, especially for iron, enhanced by a factor of 20 over typical soils. The fall-off versus distance is slower than with coarse species, with aluminum and silicon being essentially the same at the 10 m north and 100 m north sites The 300 m north site is almost identical to the 100 m south site, consistent with a regional background.
The data clearly show the impact of highway derived particles.
First, the aluminum and silicon have about the correct ratio for Earth's crustal averages, and therefore represent crustal materials either in the roadway aggregate or the surrounding soils. The parking Cahill -- 00000334
AR002341
300
('") 250
,€ 200 VJ
E
~ 150 o
0 100 c;
~
z 50
0 10
..,
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E 6
~ ...
Cl 4 0 c;
~
2 z
0 100
90 § 80
VJ 70
E 60
:! 50
g, 40
ij 30
z 20
10 0
10 m north, south winds Oct. 26 - Oct. 28
--- Silicon • .. Calcium · ... Iron -- Potassium
-.. - •""
< 0.09 0.25 to 0.34 0.56 to 0.75 1.15 to 2.5 5.0 to 10
0.09 to 0.26 0.34 to 0.56 0.75 to 1.15 2.5 to 5.0
Particle aerodynamic diameter (micrometers )
10 m north, south winds Oct. 26 - Oct. 28
- Copper -,...... Nickel ..... · Chromium ·- • Lead
Zinc -- Vanadium
/---
/
'
◊
/ '- .,, .
/
" / - _. - v \
.._ ..., ,.... _, v· '-~' - .,,,,,,,,,,., _ a, ,, _ ...... _ \ ............. '- -- - - - .o- - ... ..,,v- - ~ __,,~ ~
- ....... •
~
< 0.09 0.25 to 0.34 0.56 to 0.75 1.15 to 2.5 5.0 to 1 O
0.09 to 0.26 0.34 to 0.56 0.75 to 1.15 2.5 to 5.0
Particle aerodynamic diameter (micrometers )
10 m north, south winds Oct. 26 - Oct. 28
_._ Sulfur (for ammonium sulfate, x 4.125)
..
" I
I
' I•
' .,, ., ..... "'\
I T
' ,
\ -T
' ., ' '
'
.,
' _.,
----__ .,.,.
~ ~
< 0.09 0.25 to 0.34 0.56 to 0.75 1.15 to 2.5 5.0 to 1 O
0.09 to 0.26 0.34 to 0.56 0.75 to 1.15 2.5 to 5.0
Particle aerodynamic diameter (micrometers)
UNCORRECTED PROOF
12 Atmospheric Environment xxx (2016)
xxx-xxx
Fig. 16. Coarse aerosol
transect during the Oct. 26–28 intensive: a) crustal species; b) transition metals and lead.
Table 1
Distance
of samplers from roadway median and nearest active traffic lane of the 10 lane freeway. We also include the relative roadway impacts on cancer from the widely used model Emfac2007 (ARB, 2007).
Site
Distance from
median
Distance from closest traffic
lane
Relative cancer
impact
100 m
south
92 m south 71 m south 0.13 upwind
10 m
north
26 m north 10 m north 1.00 downwind
100 m
north
142 m north 100 m north 0.25 downwind
300 m
north
332 m north 270 m north 0.10 downwind
area just upwind
of the 100 m north site may also be a source of such
materials. Calcium, however, is enhanced by a factor of about 3 over Earth's crustal average, and is consistent with roadway abrasion from the concrete highway. Iron is enhanced by a factor of about 10, which is consistent with brake wear and resuspended rust in a region where road salting is a common source of vehicle degradation. Fine cop- per (Fig. 17b) also shows enhancement, possibly from brake pads. In summary, fine concentrations of these resuspended soil and vehicular wear particles are about ½ of the observed mass, similar to European estimates that “wear” particles are about equal to tailpipe emissions (Denier Van der Gon et al., 2013).
All the non-crustal elements as well as zinc show a near freeway enhancement during the Oct. 26–28 intensive. All are strongly en- hanced over what would be expected from soil, suggesting roadway pollution. The iron is about 16×the copper, while the iron/copper ratio from braking is only about 3. Thus, most of the iron is not from brake drums and pads, but probably represents re-suspended rust particles from degraded cars and trucks. Recall that little vehicular braking oc- curred in this freeway section. The 300 m north site is almost identical to the 100 m south site, establishing a small regional background. The fall-off versus distance for zinc is much slower, and the upwind value elevated, showing regional fine zinc aerosol sources. The enhanced zinc at 10 m north may be due to the zinc thiophosphate oil additive.
6. Transects in the three day intensive: Fine PM 2.5 particles – theoret- ical
In the sliding box model, we assumed an I-96 PM 2.5 mass emis- sion rate (row labeled “Source PM2.5 mass” in Table 3) based on re- cently measured car and truck emission rates for PM 2.5 in the eastern US (Gertler et al., 2003). The sliding box model was then used to cal- culate PM2.5 values downwind of the freeway which were compared to measured PM2.5 concentrations.
The results shown in Table 3 can be compared to the PM concen- trations measured at the 10 m north site which was just on the edge of the lateral turbulence zone. The 24 h average PM 2.5 estimated for the 3 day intensive was 1.6 ± 0.5 μg/m3, versus the measured 2.0 ± 0.7 Cahill -- 00000335
AR002342
5.0 to 2.5 micron aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
100 m S 10m N 100 m N 300 mN
Transect south to north
5.0 to 2.5 micron aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
14 ~------------------~
M 12 1-------- -------------- --1
E vi 10 t------- - - -"c-------------1
E S f----- ~ ~~.----4,-----------~ (II ,_
~ 6 1----- - - --~--_,,_ ________ ___.
0 ~ 4 f--- r'Cf-- -:7' ....... .c-- ~ ~ ------=,._.-=----~ z 2
0 100 m S 10 m N 100 m N 300 m N
Transect south to north
Aluminum
-Silicon
-Calcium
-Iron
-
Zinc
-Copper
Lead
-Vanadium
-lron/30
·-. -
UNCORRECTED PROOF
Atmospheric Environment xxx (2016)
xxx-xxx 13
Table 2
Sliding
box model of coarse (10 > Dp > 1.15 μm) particles for daytime periods during the NEXUS 3 day intensive. LDV = Light duty vehicles, cars and light trucks (Class 1, 2); HDV = Heavy duty vehicles, trucks (Class 3–6).
Site I-96 Detroit I-96 Detroit I-96 Detroit I-96 Detroit I-96 Detroit Date October 26–28, 2010 Calcium Calcium Calcium Iron Zinc Time 15 h blocks Days 6–21 Days 6 –21 Days 6 –21 Days 6–31 Days 6–21 Vehicle type Classes LDV HDV Total cars and trucks Total cars and trucks Total cars and trucks Fit to data mg/km 1.5 15.0 1.5, 15 2.8, 28.3 0.09, 0.90 Box dimensions height m 5 5 5 5 5
width m 54 54 54 54 54
length m 1000 1000 1000 1000 1000
Box volume x 1000 m3 270 270 270 270 270
Traffic vehicles/hr 10,479 824 11,303 11,303 11,303
Speed mi/hr 69 69 69 69 69
Speed km/hr 110.4 110.4 110.4 110.4 110.4
vehicles in box # vehicles 95 7 102 102 102
Emissions mg/min 142 112 254 480 15.3
Concentration in box per minute μg/m3 0.53 0.41 0.94 1.78 0.057 Wind velocity m/second 3.1 3.1 3.1 3.1 3.1
Sliding box translation Seconds 17.4 17.4 17.4 17.42 17.4 Sliding box translation Fraction of minute 0.29 0.29 0.29 0.29 0.29 Calculated concentration at 10 m north μ μg/m3 0.153 0.120 0.273 0.517 0.0164 Measured concentrations 10 m north μ μg/m3 0.242 ± ±0.050 0.516 ± ±0.05 0.0169 ± ±0.008 Los Angeles 1972
5.0–15 μm mg/mi 6.2, 62 8, 80 1.2, 12
Concentration in mixed zone μ μg/m3 1.13 1.43 0.22
Fig. 17. Fine aerosol
transect during the Oct. 26–28 intensive: a) crustal species; b) transition metals and lead.
Cahill -- 00000336
AR002343
PM2.5 ( 2.5 to 0.09) aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
M 250 t--------.,...--------------,
_§
r,i 200 t------- - - --"<-------------,
E r: 150 t------------~---- ..,.._-------------1
Cl
0 ij 100 1---- ~ ___,,,.,,,.==-----="""'--= c-'ll l.:?' "--.::---------i z
100 m S 10 m N 100 m N 300m N
Transect south to north
PM 2.5 (2.5 to 0.09) aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
100 m S 10 m N 100 m N 300 m N
Transect south to north
Aluminum
~
Silicon
-Calcium
Iron
-
Zinc
-Copper
Lead
-Vanadium
-lron/30
- . -
UNCORRECTED PROOF
14 Atmospheric Environment xxx
(2016) xxx-xxx
Table 3
Slid
ing box model of the three day intensive, when meteorological and traffic conditions were suitable. Also shown are the Swiss data (2010) and Los Angeles data (1973) estimated from measured values.
Emission rate estimates Gertler et al., 2003
eastern US
Gertler et al., 2003,
eastern US
Gertler et al., 2003,
eastern US
Bukowiecki et al., 2010,
Swiss freeways
Cahill and Feeney, 1973,
LA freeways
Time 15 h
increment
Daytime 6 to 21 Daytime 6 to 21 24 h average 24 h average 24 h average Vehicle type Classes LDV HDV All vehicles All vehicles All vehicles Source PM2.5 mass mg/km 14 135 14/135 50/288 1056
Box dimensions height m 5 5 5 5 5
width m 54 54 54 54 54
length m 1000 1000 1000 1000 1000
Box volume x 1000 m3 270 270 270 270 270
Traffic vehicles/hr 10,479 824 7682 7682 7682
Speed km/hr 110 110 110 110 110
vehicles in box # vehicles 95 7 70 70 70
Emissions mg/min 1329 1008 1528 4538 17,636
Concentration in box per minute μg/m3 4.9 3.7 5.7 16.9 65.3 Wind velocity m/second 3.1 3.1 3.1 3.1 3.1
Sliding box translation Seconds 17.4 17.4 17.4 17.4 17.4 Sliding box translation Fraction of
minute
0.29 0.29 0.29 0.29 0.29
Calculated PM2.5 Concentration μg/m3 1.43, 1.08 0.53, 0.25 1.6 ± ±0.5 4.9 ± ±1.2 19.0 ± ±3 Size ranges μg/m3 PM2.5 PM2.5 PM2.5 PM10 PM5.0
Measured PM2.5 Concentrations
(some estimated)
μg/m3 2.0 ± ±0.7 3.1 ± ±1(est) 16 ± ±2 (est)
μg/
m3. Note that the Gertler et al., 2003 emission values were for Cal- ifornia cars and fuels.
Table 3 also includes estimates of the 10 m north concentrations based on the Swiss (Bukowiecki et al., 2010) and Los Angeles (Habibi, 1973; Cahill and Feeney, 1973) freeway emission rates. The Swiss study gave PM 10 emission rates of 50 ± 13 mg/km for light duty vehicles, and 288 ± 72 mg/km for heavy duty vehicles. Apply- ing these emission rates to the NEXUS configuration, one would pre- dict about 4.9 μg/m3 PM10 mass at the freeway edge, slightly less than the 10 μg/m3 to 19 μg/m3 PM2.5 values reported as the traffic-im- pacted Central European average (Denier Van der Gon et al., 2013).
The Swiss data included about ½ roadway crustal particles, which would likely reduce their 4.9 μg/m3 PM10 values to around 3.1 μg/m3 of PM2.5.
The right hand column of Table 3 is the predicted 24 h PM5.0 mass at the 10 m north site using the emission rates from the 1973 Los An- geles study (Habibi, 1973). The mean estimated PM 2.5 mass concen- tration of 19 μg/m3, would also have included 2.7 μg/m3 of lead. Mak- ing the correction for presumed coarse roadway wear particles, the 1973 PM 5.0 data would be reduced to roughly 16 μg/m3 equivalent PM2.5 in the NEXUS configuration.
7. Transects in the three day intensive: Very fine
(0.26 μm > Dp > 0.09 μm) particles
Very fine particles (Fig. 18) were less impacted by distance from the freeway compared to fine and coarse particles. These results rein- force the behavior seen in the Los Angeles I-710 studies (Fig. 19, Zhu et al., 2002) in which very fine and ultra-fine components extended both upwind and downwind from the array with little change in con- centration with distance from the freeway once one was farther away than about 150 m (see Fig.19 ).
1.7. Ultra-fine particles
The development of a means to measure ultra-fine particles as a function of time allows for the first time the ability to match com- positionally-resolved ultra-fine particles to potential sources and me- teorology. Two such units were built and tested to run behind the DELTA 8 DRUM, which has a validated lower cut point of 0.09 μm.
Thus, for this study, ultra-fine particles are particles having aerody- namic diameters <0.09 μm. These two units were placed at the 10 m north site and the main site at 100 m north, and ran for 5 weeks to match the 8 DRUMs.
The ultra-fine data did not have an upwind site, but the agreement between the 10 m north and 100 m north sites is confirmation of a re- gional distribution. The major exception to this pattern was optical at- tenuation (Fig. 13), which in the 0.26 > Dp > 0.09 μm size mode is al- most entirely soot, largely from diesel trucks. We did not measure soot in the DRUM ultra-fine mode.
An examination of the 3 day intensive period at 10 m north showed that, of the four elements known to be present in the ultra-fine size ranges from traffic, zinc and iron show some modest correlation with daytime traffic peaks (Fig. 21). Recall, however, that relatively little braking occurs on this stretch of I-96.
Ultra-fine particles at the 100 m north site showed essentially no correlation with daytime traffic, in accord with the factor of ∼10 re- duction seen in previous studies (Cahill and Feeney, 1973; Zhu et al., 2002; Emfac2007). For diesel-generated ultra-fine sulfur, (Zielinska et al., 2004), the freeway contribution is largely lost in high levels of ul- tra-fine sulfur associated with upwind sources possibly including the Monroe coal-fired power plant.
2. Discussion
The particles in the NEXUS study ranged from coarse concrete erosion and resuspended roadway particles contaminated with tire and brake drum debris, to very fine and ultra-fine particles generated by traffic and diverse regional sources.
The behavior of the particles during the three day transect inten- sive was roughly as expected, with coarser particle concentrations de- clining rapidly downwind while fine and especially very fine particle concentrations dropped more slowly, as shown in Figs. 16–18.
The agreement between the predictions of the sliding box model and the observed near-roadway PM 2.5 concentrations (Table 3) gives confidence that literature emission rates used in the model must be close to reality. The results also highlight the enormous improvement in vehicle tailpipe emissions in the past 40 years.
Cahill -- 00000337
AR002344
UNCORRECTED PROOF
Atmospheric Environment xxx (2016) xxx-xxx 15
Fig. 18. Very fine
aerosol transect during the Oct. 26–28 intensive. a) crustal species; b) transition metals and lead.
Fig. 19. Transect
of Interstate 710 in Los Angeles from Zhu et al. (2002). Superimposed are lead data from 1973 (Cahill and Feeney, 1973; Feeney et al., 1975). The arrow marks the distance of the 100 m north site from the I-96 median in the present study.
The situation with the ultra-fine particles, on the other hand, in- dicates a dominance of regional concentrations little affected by the nearby freeway traffic with the striking exception of optical absorp- tion (diesel soot) as seen in both optical spectroscopy from the DRUM finest stages and the aethalometer data (Fig. 13). The ul- tra-fine particles have elemental signatures consistent with brake wear and zinc in motor oil, but the concentrations are small, consistent with little braking on that section of I-96 during the 3 day intensive.
Cahill -- 00000338
AR002345
C")
_§
(/J
E ro ...
Cl
0 C: ro z
(")
E
-(/J
E ('ll
I..
Cl
0 C:
('ll
z
"' <I)
:,
cij
>
<I)
>
~
<ii
Cl'.'.
Very fine (0.26 to 0.09) aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
100 m S 10 m N 100 m N 300m N
Transect south to north
Very fine (0.26 to 0.09) aerosol transect during NEXUS
South winds Oct. 26 through Oct. 28
5 4
3 2
0 ... 'Ill -
... ,,. - ... .. ,.... ... .. ... - -----...
----
~
---
......_
=:::::-.
100 m S 10 m N 100 m N 300 m N
Transect south to north
Aluminum
~
Silicon
-Calcium
-Iron
-
Zinc
-Copper
-Lead
-Vanadium
-lron/30
-•-
- CO - Black Carbon - Number - Lead (1974)
1000
900
800 1----------- •1--,,..._ __ _. Location of 100 111 north 700 site from I-96 rnedian
600
500
400
300
· - .... ■ - ■ - • ■ 11111 ■ •
.. ,. .
200
100
0 -240 -160 -80 0 80 160 240 320 400
-200 -120 -40 40 120 200 280 360
Upwind Distance from Freeway 710 (m) Downwind
UNCORRECTED PROOF
16 Atmospheric Environment xxx (2016) xxx-xxx
Fig. 20. Ultra
-fine sulfur, potassium, and zinc at the 10 m N and 100 m N sites. The large ultra-fine sulfur peaks on October 24 through 25 were likely from the Monroe coal fired power plant. The Sept. 30 and Oct. 12 plumes were from a source to the north. Black lines above the dates in the zinc plot are weekends.
These three days were selected when there was minimal impact from Telegraph Avenue which had extensive stop and go periods. The ul- tra-fine data are a more extreme case of the behavior seen in the very fine particles, which had large regional sources and fell off slowly downwind.
The potential exposure and human health implications of these par- ticles can be estimated by comparison with recent work tying road- way-derived ultra-fine particles to ischemic heart disease in the Cal- ifornia Central Valley (Cahill et al., 2011). An increase in IHD mor- tality of ∼30% at Bakersfield, CA, was associated with elevated lev- els of very fine and ultra-fine roadway-derived transition metal parti- cles, mostly from brake drums and pads (Table 4 below). However, the concentrations in Detroit were generally far lower than those mea- sured in Bakersfield.
As shown in Table 4, ultra-fine concentrations measured at the 10 m north and 100 m north sites are similar to those at Watt Avenue (Spring) in Sacramento, CA measured 20 m downwind of a 65,000 ve- hicles/day secondary street, at a stop light, during non-inversion con- ditions (Cahill et al., 2014). In both cases, little influence was seen from the nearby roadway, and the ultra-fine particles were regionally distributed. However, there was only very limited braking and accel- eration during the NEXUS intensive, thus limiting concentrations of brake wear particles. Nevertheless, if the observed ultra-fine metallic particles see in the NEXUS experiment extend over large numbers of people, it could potentially enhance the ischemic heart disease death rate in the same manner seen in Bakersfield, CA (Cahill et al., 2011).
3. Conclusions
Important differences in the behavior of particles of different sizes were observed during this study. For coarse species, the fall-off in concentration versus distance was rapid, roughly in accord with cur Cahill -- 00000339
AR002346
(")
E <ii
Ii c,
0 ~ z
400
300
200
100
NEXUS ultra fines
Sulfur 100 m N ~ Sulfur 1 0 m N
24 26 28 3 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 23 25 27 29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 -- Potassium 100 m N C:=J Potassium 10 m N
22 24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 23 25 27 29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 12 10
-- Zinc 100 m N c:J Zinc 10 m N
24 26 28 30 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1
23 25 27 29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 September October
UNCORRECTED PROOF
Atmospheric Environment xxx (2016)
xxx-xxx 17
Fig. 21. Ultra
-fine nickel, copper, iron and zinc at 10 m north during the 3 day intensive.
Table 4
Compar
ison of ultra-fine (D p < 0.09 μm) traffic-derived particles measured in NEXUS versus Watt Ave. in suburban Sacramento, CA during spring non-inversion conditions (Cahill et al., 2014). NEXUS concentrations are 5-week averages. na = not available.
For comparison, winter values which are impacted by strong surfaced-based inversions in the California Central Valley are included. The sources of the high nickel values seen at Watt Avenue in winter were totally absent 500 m upwind, so they must be associated with Watt Avenue traffic. Such levels, however, have never been seen in our other stud- ies.
Ultra-fine elements (ng/m3) S P Fe (non-soil) Ni Cu Zn Pb Non-inversion conditions
Detroit
10 m n 68.1 10.5 3.4 0.5 1.9 3.2 2.9
100 m n 61.4 9.5 3.4 0.5 1.8 2.6 3.3
Watt Ave (Spring) 129 na 4.8 0.3 0.4 2.0 1.4
Strong inversions conditions
Watt Ave (Winter) 42.8 na 29.3 12.1 9.3 13.7 1.6
Sacramento (Winter) 52 4.0 14.3 0.6 na 14.3 2.5
Bakersfield (Winter) 242 10.5 27.7 1.6 na 12.3 2.6
rent models
such as Emfac2007. Concentrations were strongly af-
fected by meteorology such as wind velocity and rain fall. For the PM2.5 particles, the fall off versus distance was still close to the models, reaching a 90% reduction in freeway-derived particles in the 200–250 m range. Very fine particles, 0.26 > D p > 0.09 μm diameter, persisted all the way to the 300 m downwind site, with only a modest reduction in concentrations. Ultra-fine particles were only measured at two sites, 10 m north and 100 m north, and the concentrations at both sites were almost identical indicating a very slow fall off and/or strong upwind sources. The persistence of ultra-fine particles in urban areas away from freeways was also observed in the Los Angeles data (Zhu et al., 2002).
A 3-day period of southerly winds provided an opportunity to conduct an upwind-downwind highway transect to assess the impact of roadway emissions. For PM 2.5, the transect yielded a measured near-roadway PM2.5 concentration of 2.0 ± 0.7 μg/m3 versus a value of 1.6 ± 0.5 μg/m3 predicted from recent California vehicle emis- sion rates and ∼3 μg/m3 using current European freeway data. Thus, measured PM 2.5 concentrations at the near-road site showed excel- lent agreement with predicted PM 2.5 concentrations assuming recent published emission rates. By contrast, using 1973 emission rates one would predict roughly 16 μg/m3 of PM 2.5, thus illustrating the dra matic progress that
has been made in reducing roadway emissions in
the US during the past four decades.
The NEXUS and European estimates of the contribution of brake drums were also similar, although numerous assumptions are needed to make the comparison. The European value is roughly 3.2 mg/km brake wear, assuming 6% trucks in their mix. The NEXUS iron val- ues were strongly enhanced over soil concentrations, indicating road- way sources including brake wear. The fine and very fine zinc parti- cles were too fine in size to be from tire erosion. With these assump- tions, the expected emission rate from brake wear is roughly 5.2 mg/ km, similar to the European values especially after some iron reduc- tion from vehicle debris and roadbed wear.
For ultra-fine particles, there was a dramatic increase in diesel soot, closely tied to traffic volumes, but only a modest increase in traf- fic-correlated zinc and other transition metals during the 3 day in- tensive. For the rest of the 5 week study, mean elemental concentra- tions were essentially identical at the 10 m and 100 m north sites and highly correlated in time. Further, this correlation existed independent of wind direction, including having both the 10 m north and 100 m north sites upwind of the freeway. From these results, we conclude that most of the time the freeway was a negligible enhancement of a regional ultra-fine background of transition metals.
The regional ultra-fine species closely resembled those seen in a recent study of stop-and-go traffic on a heavily traveled secondary road with heavy braking (Cahill et al., 2014). These particles are sim- ilar in composition to debris from brake pads, drums, and the zinc ad- ditive in motor oil (Cahill et al., 2011). These results reinforce the be- havior seen in the Los Angeles I-710 studies (Zhu et al., 2002) for ultra-fine particle number and black carbon, e.g., these components extended well upwind of the freeway and were soon reestablished at the same concentrations downwind beyond 150 m, thus showing a re- gional distribution of ultra-fines well away from the local freeway.
The NEXUS data also support the conclusions of Zhu et al. (2002) that little coagulation or particle size growth was seen in ultra-fines from freeways, as both Zhu's ultra-fine soot and particle number data were essentially identical versus distance with non-reactive CO.
The conclusion from this study is that for traffic on a freely flow- ing, high speed freeway with little braking and acceleration, almost all ultra-fine particles except for diesel soot, and most of the very fine particles, are generated from diverse vehicular and industrial sources Cahill -- 00000340
AR002347
Ultra fine aerosols < 0.09 mi crome ters
Nickel - Copper -- Zinc - lron/2
12 ,--------------- ------------------------------,
10 ..., 8 E .;
E
e 6
"' 0
C
"' z 4
2 0
23 noon 24 noon 25 noon 26 noon 27 noon 28 noon 29 noon October , 2010
UNCORRECTED PROOF
18 Atmospheric Environment xxx (2016) xxx-xxx
in the region and are not directly associated with local freeway traffic.
Uncited reference
Acknowledgements
The authors gratefully acknowledge the assistance in meteorolog- ical models of Ron Henry, as well as the detailed aethalometer, me- teorological and traffic data of the US EPA/FHWA study (US EPA, 2013). The United States Environmental Protection Agency through its Office of Research and Development funded and collaborated in the research described here under contract EP-12-D-000219 to the University of California, Davis. It has been subjected to Agency re- view and approved for publication.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.atmosenv.2016.09.023.
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Zielinska, B., Sagebiel, J., McDonald, J.D., Whitney, K., Lawson, D.R., 2004. Emis- sion rates and comparative Chemical composition from selected in-use diesel and gasoline fueled vehicles. J. Air Waste Manag. Assoc. 54, 1138–1150.
Zhu, Y., Hinds, W.C., Kim, S., Shen, S., Sioutas, C., 2002. Study of ultra-fine particles near a major highway with heavy duty diesel traffic. Atmos. Envi- ron. 36, 4323–4335.
Cahill -- 00000341
AR002348
EXHIBIT 12
1 TRAINING FOR HEALTH CARE PROVIDERSTRAINING FOR HEALTH CARE PROVIDERS [Date [ Date …… Place Place …… EventEvent…… SponsorSponsor…… Organizer]Organizer]
CHILDREN AND NOISECHILDREN AND NOISE
Children's Health and the Environment
WHO Training Package for the Health Sector
W
orld Health Organization
www.who.int/ceh
< < N O T E T O U S E R : P l e a se a d d d e t a i l s o f t h e d a t e , t i m e, p l a ce a n d< < N O T E T O U S E R : P l e a se a d d d e t a i l s o f t h e d a t e , t i m e, p l a ce a n d< < N O T E T O U S E R : P l e a se a d d d e t a i l s o f t h e d a t e , t i m e, p l a ce a n d< < N O T E T O U S E R : P l e a se a d d d e t a i l s o f t h e d a t e , t i m e, p l a ce a n d sp o n s o r sh i p o f t h e m e e t i n g f o r w h i c h y o u sp o n s o r sh i p o f t h e m e e t i n g f o r w h i c h y o u sp o n s o r sh i p o f t h e m e e t i n g f o r w h i c h y o u sp o n s o r sh i p o f t h e m e e t i n g f o r w h i c h y o u a r e u si n g t h i s p r e se n t a t i o n i n t h e s p a ce i n d i ca t e d . > >a r e u si n g t h i s p r e se n t a t i o n i n t h e s p a ce i n d i ca t e d . > >a r e u si n g t h i s p r e se n t a t i o n i n t h e s p a ce i n d i ca t e d . > >a r e u si n g t h i s p r e se n t a t i o n i n t h e s p a ce i n d i ca t e d . > > This pre s en tati on on Ch ildr en and No ise i s par t of a compreh ensiv e s e t of trai nin g materi al s for h ealt h care provider s o n chil dren, the en v i ronment and hea lth. < < N O T E T O U S E R : T h i s i s a l a r g e se t o f sl i d e s f r o m w h i ch t h e p r< < N O T E T O U S E R : T h i s i s a l a r g e se t o f sl i d e s f r o m w h i ch t h e p r< < N O T E T O U S E R : T h i s i s a l a r g e se t o f sl i d e s f r o m w h i ch t h e p r< < N O T E T O U S E R : T h i s i s a l a r g e se t o f sl i d e s f r o m w h i ch t h e p re se n t e r sh o u l d s e l e ct t h e m o st r e l e v a n t o n e s e se n t e r sh o u l d s e l e ct t h e m o st r e l e v a n t o n e s e se n t e r sh o u l d s e l e ct t h e m o st r e l e v a n t o n e s e se n t e r sh o u l d s e l e ct t h e m o st r e l e v a n t o n e s t o u se i n a sp e ci f i c p r e se n t a t i o n . T h e se sl i d e s co v e r m a n y fa ce tt o u se i n a sp e ci f i c p r e se n t a t i o n . T h e se sl i d e s co v e r m a n y fa ce tt o u se i n a sp e ci f i c p r e se n t a t i o n . T h e se sl i d e s co v e r m a n y fa ce tt o u se i n a sp e ci f i c p r e se n t a t i o n . T h e se sl i d e s co v e r m a n y fa ce ts o f t h e p r o b l e m . P r e se n t o n l y t h o se sl i d e s t h a t s o f t h e p r o b l e m . P r e se n t o n l y t h o se sl i d e s t h a t s o f t h e p r o b l e m . P r e se n t o n l y t h o se sl i d e s t h a t s o f t h e p r o b l e m . P r e se n t o n l y t h o se sl i d e s t h a t a p p l y m o s t d i r e ct l y t o t h e l o c a l si t u a t i o n i n t h e r e g i o n . I t i s a p p l y m o s t d i r e ct l y t o t h e l o c a l si t u a t i o n i n t h e r e g i o n . I t i s a p p l y m o s t d i r e ct l y t o t h e l o c a l si t u a t i o n i n t h e r e g i o n . I t i s a p p l y m o s t d i r e ct l y t o t h e l o c a l si t u a t i o n i n t h e r e g i o n . I t i s a l s o ve r y u se f u l i f y o u p r e s e n t r e g i o n a l / l o c al e xa m p l e s a l s o ve r y u se f u l i f y o u p r e s e n t r e g i o n a l / l o c a l e xa m p l e s a l s o ve r y u se f u l i f y o u p r e s e n t r e g i o n a l / l o c a l e xa m p l e s a l s o ve r y u se f u l i f y o u p r e s e n t r e g i o n a l / l o c a l e xa m p l e s o f n o i s e p r e ve n t i o n p r o g r a m s, i f a va i l a b l e , a n d ch o o s e l o ca l r e lo f n o i s e p r e ve n t i o n p r o g r a m s, i f a va i l a b l e , a n d ch o o s e l o ca l r e lo f n o i s e p r e ve n t i o n p r o g r a m s, i f a va i l a b l e , a n d ch o o s e l o ca l r e lo f n o i s e p r e ve n t i o n p r o g r a m s, i f a va i l a b l e , a n d ch o o s e l o ca l r e l e va n t p i ct u r e s. > > e va n t p i ct u r e s. > >e va n t p i ct u r e s. > >e va n t p i ct u r e s. > > Children and noise Children and noise
2 1.1. IntroductionIntroduction
2.2 . Vulnerability of childrenVulnerability of children
3.3 . Adverse health effectsAdverse health effects
4.4 . Effects by ageEffects by age--group group
5.5 . Taking actionTaking action
6.6 . DiscussionDiscussion
CONTENTSCONTENTS
Children and noise Children and noise
3 To understand, recognize and know
1. Definition and characteristics of sound and noise
2 . Sources and settings of noise exposure
3. Adverse effects of noise exposure
− O n physical health
− On psychological health
− On cognition
4. W eight of the evidence of the harm to children
– S pecial vulnerability of children
– Various noise exposure scenarios in settings where ch ildren
develop
5. Interventions and preventive strategies
LEARNING OBJECTIVESL EARNING OBJECTIVES
These ar e th e le arni ng obj ec t i v es for t hi s modu le. Af ter t he p res e ntat ion, the au die nc e s h o uld under s ta nd, r ecogn iz e an d kn ow
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
4 1. I ntroduction
2. Vulnerability of children
3 . Adverse health effects
4. Effects by age-group
5. Taking action
6. Discussion
CONTENTS CONTENTS
Children and noise Children and noise
5 DEFINITION: SOUND AND NOISEDEFINITION: SOUND AND NOISE So
und is characterized by:
/xrhombusV ibration
• F requency ( Hz)
• I
ntensity (Pa or dB)
• D ecibel scale logarithmic
• Begins at threshold of hearing
/xrhombusP eriodicity
/xrhombusDuration
“Noise is an unwanted or objectionable sound”
NASA
Wha t i s so un d?Wha t i s so un d?Wha t i s so un d?Wha t i s so un d? Sound is a mec hanic v ibration prop ag ate d by elas tic media (as air and water) whic h alters the pres s ure dis plac ing the partic les , and c an be rec ogniz ed by a pers on or an in s trument.
Vibration an d n ois e c an nev er be s eparate d b ut v ibration c an ex is t without au dible n ois e.
Soun d is c harac teri z ed by it s intr ins i c c harac terist ics :
• V i b r a t i on : V i b r a t i on : V i b r a t i on : V i b r a t i on : Sound is a mec hanic v i bration, ex pres s ed as a c ombination of pr es s ure (Pas c al s , Pa) and f requ enc y (Hertz , Hz ) • F r e qu en cyF r e qu en cy F r e qu en cyF r e qu en cy or pi tc h i s the number of c yc le s per s ec ond (Hertz , H z or k i lo Hertz , KH z ). • I n t en si t y I n t en si t y I n t en si t y I n t en si t y or loudn es s i s the “lev el of s onoro us pres s ure” and is meas ured in Pas c als (Pa) or dec ibels ( dB). The audi ble s pec trum of the human e a r is between 0.00 00 2 Pa (c orres p onds to 0 dB) a nd 2 0 Pa (c orres p onds to 1 20 dB). The intens ity of human s peec h is approx imately 50 dB. Dec ibels are us ed for c onv enienc e to ex p res s s ound on a c ompres s ed, logarithmic sc al e in the h uman a udible s pec trum. • P e r i od i c i t y: P e r i od i c i t y: P e r i od i c i t y: P e r i od i c i t y: desc ribes the pattern of repetition of a s ou nd within a p eriod of time: s hort s ounds that ar e rep eate d. • D u ra t i o nD u ra t i o n D u ra t i o nD u ra t i o n : is the ac ous tic s ens e dev eloped by the c ontinuity of a s ound in a p eriod of time, for ex ample music, v oi c e or mac hinery .
Wha t i s n o i se ? Wha t i s n o i se ? Wha t i s n o i se ? Wha t i s n o i se ? Nois e i s an unwan ted or o bjec tionable s o un d. Gen erally , the ac ous tic signals that prod uc e a pleas a nt sens e (music , bells ) are rec ogniz ed as “s oun d” a nd th e un pleas a nt s oun ds as “nois e” (for ex ample: produc ed by a mac hine or airplan e). It c an be a polluta nt an d env ironmental s tress or, and the meaning of s ound is important in determining re ac tion of different indiv iduals to the s ame soun d. On e pers o n’s mus i c is another’s nois e.
The huma n ear is an ins trument that detec ts vibration within a s et range of frequ enc ies . Air, liquid or solid prop ag ates v ibration; without them, s ound do e s not ex i s t. Sound do es not ex is t i n the v ac uum. The high er the lev e l of pres s ure of the s onoro us wav e, the s horter the p e riod of time neede d to b e perc eiv e d by the ear.
Wh y a r e no t a l l vi b r a t i o n s a u d i b le ?Wh y a r e no t a l l vi b r a t i o n s a u d i b le ?Wh y a r e no t a l l vi b r a t i o n s a u d i b le ?Wh y a r e no t a l l vi b r a t i o n s a u d i b le ? The ear is a frequ enc y analy z er. T he e ardrum s epar ates tone a nd c ond uc tion in two different way s : by the nerv ous s ys te m and by the bo nes . The n erv ous sys tem c onnec ts the coc hlea to the temporal regio n of b oth h emis pheres of the brain. The c oc hlea perc eiv es vibration trans mit ted direc tl y from the bon es of the h ea d.
Picture:
• NASA
Children and noise Children and noise
Frequency (KHz)
6 THRESHOLDS OF HUMAN HEARINGTHRESHOLDS OF HUMAN HEARING Sound level (dB)
EPA
Wh y i s n o i se so m e t i m e s i n au d i b l e ?Wh y i s n o i se so m e t i m e s i n au d i b l e ?Wh y i s n o i se so m e t i m e s i n au d i b l e ?Wh y i s n o i se so m e t i m e s i n au d i b l e ? Thres hold of h earin g is defined as the mini mum eff icient s onorous p res s ure (Pa or dB) that c an be h e ard without b ac k groun d n ois e of a pur e ton e at a s pec ific freque nc y (H z or KH z , cyc les per s e c ond ).
The huma n au dible freq ue nc y range is from 20 to 20.0 00 H ertz (H z ) . Frequenc ies out of this range are not detec te d by the huma n ear. The e ar is not equ ally s ens it ive to all the frequ enc ies .* The most audible fre qu enc ies are betwe en 200 0 an d 3 00 0 Hz (range within whic h the leas t pres s ure i s nee de d to pr ov ok e the c ons c ious rec ognition of a s ou nd). This rang e c an be eas ily identified where th e c urv e is at it s mini mum and c orres p on ds to human s pe ak i ng freq ue nc ies .
For this reas on, s ound meters are us ually f itted with a f ilter whos e res pons e to freq ue nc y i s a bit li k e th at of the huma n ear. The mos t widely us ed s ound lev el fil ter is the A sc ale, whic h roughly c orr es pon ds to the inv ers e of the 40 d B (at 1 k H z ) equal-loud nes s c urv e. U s ing this fi lter, the s ound lev el meter i s thus less s ensiti v e to v ery high and v ery low frequenc ie s . Meas urements made o n this s c ale are ex pres s ed as d B Ad B A d B Ad B A .
T he " n o r m a l t h r e sh o l d " o f he a r i ng i s d e f i n e d i n T he " n o r m a l t h r e sh o l d " o f he a r i ng i s d e f i n e d i n T he " n o r m a l t h r e sh o l d " o f he a r i ng i s d e f i n e d i n T he " n o r m a l t h r e sh o l d " o f he a r i ng i s d e f i n e d i n “ “ ““ yo un g p eo p l e w i t h a he a l t h y a ud i t o r y s ys t e myo un g p eo p l e w i t h a he a l t h y a ud i t o r y s ys t e myo un g p eo p l e w i t h a he a l t h y a ud i t o r y s ys t e myo un g p eo p l e w i t h a he a l t h y a ud i t o r y s ys t e m ” ” ”” . . . .
The ““ ““ p a i n t h re sh o l dp a i n t h re sh o l dp a i n t h re sh o l dp a i n t h re sh o l d ” ” ”” is the high le v el (high dB) audible s o un d wher e the lev el of pres s ure of the s ound prod uc es disc omfort or pain.
The pres s ures of the s oun ds are o v er the c urv e: “ultra s ounds ”. Very powerf ul lev els of s ound c an b e p erc eiv ed by the human e ar but c aus e dis c omfort and p ain.
*Pres s ures below the au dible lev el are c alled “infra-s ounds ”: the pre s s ure i s detec ted but o ur he aring mec hanis m i s not ada pted to mak ing the s ound ev ide nt to the h uman ear (u nd er the c urv e in the grap hic ). Thes e frequenc ies (les s than 2 0 Hz , not audi ble for the huma n ear) c an be pr od uc ed by mac hines or “ult ras onic " motors of planes . Out of the li mit s of the human thres h old of he aring ex ists s ound that c an b e p erc eiv ed by s pec ial equipment or animals s uc h as dolphins and b ats that are eq uipp ed to perc eiv e s ound th at huma ns c an not perc eiv e. The human b eing h ears a v ery s hort portion of the ex is ting s oun ds , the v ery weak and the ones a bov e a nd b elow of the thr e s holds are not p erc eiv ed or they are ac c ompanied by pain, and can produce damage to a system and can produce damage to a system and can produce damage to a system and can produce damage to a system that is not prepared to perceive them as the person may not be at hat is not prepared to perceive them as the person may not be athat is not prepared to perceive them as the person may not be athat is not prepared to perceive them as the person may not be able to protect her/himself from this deleterious exposure ble to protect her/himself from this deleterious exposureble to protect her/himself from this deleterious exposureble to protect her/himself from this deleterious exposure . There is i
ndiv idual v ariation within thes e ge neral p arameters .
Reference:
•Nois e effec t s hand bo ok , National Ass oc iation of Nois e Control Offi ci als . Of fice of the Scientific Assista nt, Office of Noise Abatement and Control, U.S. Environmental Protection Agency , 1979, rev is ed 1981 (www.nonois e.org/library /ha nd bo o k /hand bo ok .htm).
Picture :
• EPA (U. S. Environmental Protection Agency)
Children and noise Children and noise
7 MAGNITUDE AND EFFECTS OF SOUNDMAGNITUDE AND EFFECTS OF SOUND COMMON EXAMPLE dBA EFFECT
Breathing 0 -10 Hearing threshold
Conversation at home 50 Quiet
Freeway traffic (15 m), vacuum
cleaner, noisy party
70 Annoying, intrusive, interferes
with phone use
Average factory, train (at 15 m) 80 Possible hearing damage Jet take-off (at 305 m), motorcycle 100 Damage if over 1 minute Thunderclap, textile loom, chain saw,
siren, rock concert
120 Human pain threshold
Toy cap pistol, Jet takeoff (at 25 m),
firecracker
150 Eardrum rupture
This ab breviat ed t abl e correl at es c ommon soun ds wi th e ffe c t s on he arin g.
Addit
ion al e x ampl es for d isc u ss io n are li s te d be low:
-Quiet s ub urb or qu iet c on v er satio n 50 dB A No s i gnif icant effe c t -Conv er s at ion in a b usy pl ace,
ba c k gr oun d music or traf fic 60 dB A Intru s ive -Freewa y traf fic at 15 metre s 70 dB A Anno y in g
-Av era ge f ac t ory , trai n at 15 m etres 80 dB A Pos sibl e h eari ng d amage -Bus y urb an s tree t, d iesel tr uck 90 dB A Chroni c h eari ng d amage if e x p os ur e o v er 8 ho urs -Subway no is e 90 dB A Chroni c h eari ng d amage, s p e ec h int erferi ng -J et t ake-off 300 metres 100 dB A More s e v ere tha n a bove -Stereo hel d clo s e e ar 110 dB A More s e v ere tha n a bove -Live ro c k mu s ic,
jet t ak e off 160 mts 120 dB A As ab ove, h uman p ain thre s ho ld -Earpho ne s at lou d l ev e l 130 dB A More s e v ere tha n a bove -Toy c ap pi s to l,
firec r ack er c lo s e ear 150 dB A Ac ute damag e (e ardrum ru ptu re) d B Ad B A d B Ad B A weig hti ng c urve: re s p onse of a fi lter t hat is app lie d to s o und level met ers t o mimic (r ou ghl y ) th e re s po ns e of human h eari ng. S o a ty p ical h uman eq ual lo udn ess curv e is s omewhat s imi lar t o th e dBA curv e, but in v ert ed.
Reference:
•Childr en's h ealt h a nd t he e nvir onment: A review o f e v id ence. Tamburl ini G et a l., e ds . EEA-WHO, 2002 (www.eea.e urop a.eu /pu bli c at i ons / en v iro nmenta l_ i s sue _ rep o rt_ 20 02 _ 29) Children and noise Children and noise
8 SOURCES OF NOISES OURCES OF NOISE
Ou
tdoor sources
/xrhombusT ransport
• A ircraft
• Road
• Rail
/xrhombusO ccupational
• M achinery
/xrhombusN eighbours
• M achinery
• Loud music
Indoor sources
/xrhombusA
mbient noise outside
/xrhombusBuilding design and location
/xrhombusRoom acoustics
/xrhombusActivities of occupants
• C hildren
Common s our c e s of out door n oise ari s e from tran s p ortat ion ( airc r aft, c ar and truck tr affi c , a nd tra in s ), oc cupa tio ns ( c on s tru c ti on ma chiner y , ass embl y li ne s ), a nd e v en from ne igh bour s (yard equ ipment, loud mus i c ). I ndo or no ise i s aff ec te d b y o utdo or n oise, a nd in door s our c e s such a s TV, r adi o, mus i c and c hi ldre n at pl ay . The le v el is mod ifie d b y b uil din g de s i gn and locati on a s we ll a s ro om a c ou s ti c s .
Children and noise Children and noise
9 Hypothesized lifestyle
noise exposure patterns
SETTINGS OF NOISE EXPOSURE: SETTINGS OF NOISE EXPOSURE: ““ NOISENOISE--SCAPESCAPE”” EPA
Sleep
Eat,
Relax,
Watch
TVSleep Eat, Dress
Noon MidnightMidnight
HOUR OF DAY
The c on c ep t of a “n oise-sc ap e ” c an be useful in thi nk i ng abo ut no ise e x po s ure s . T hat is , ob v io us lou d noi s es are imposed upo n a ba c k gr oun d of no is e s t hat wi ll v a ry a c c or din g to ge nera l lo c at io n (urb an v s . rural), time of da y (d ay v s . ni g ht) an d a c ti v it y (sc h ool v s. pl ay). Thi s imag e i s a s chemati c repre s ent ati on wh ic h il lustrate s th es e dif fere nt a s pe c t s of the “noi s e- s c a pe”.
Reference:
•Nois e eff ec t s h and boo k , Nat io nal A s soc i ati on o f Noi s e Co ntr ol Offi c ia ls . Office of the Scientific Assistant, Office of Noise Abatement and Control, U.S. Environmental Protection Agency , 1 979, rev i s ed 19 81 (www.no noi s e. or g/li brary/ha ndb ook/ha ndb ook. htm).
Picture:
•EPA (U.S. Environmental Protection Agency)
Children and noise Children and noise
NOISE EXPOSURE IN EUN OISE EXPOSURE IN EU
/xrhombus40% of population exposed to Leq > 55 dBA
du
ring the day
/xrhombus20% of population exposed to Leq > 65 dBA
du
ring the day
/xrhombus30% of population exposed to Lmax > 55 dBA
du
ring the night
/xrhombusHazard is increasing
10 Leq: averag e soun d le v e l o v er the peri od o f th e measuremen t, usual ly me as ur ed A-we igh te d Lmax : maximum A-weigh ted n oise le v e l
dBA weig hti ng c urve: re s p onse of a fi lter t hat is app lie d to s o und level met ers t o mimic (r ou ghl y ) th e res p onse of human he arin g. S o a t y pi c a l huma n eq ual lo udn es s c ur v e is s omewh at similar to th e dBA c urve, b ut i nverted .
Reference:
•Berglu nd B et a l., eds. Guid eli nes for Communit y No is e . Ge neva, WHO, 1 999.
Children and noise Children and noise
11 NOISE CONTAMINATIONNOISE CONTAMINATION
/xrhombusN oise exceeding safety threshold is widespread:
• I n neighbourhoods
• Schools, hospitals and care centres
• Urban and suburban areas
• Activities inside the buildings (elevators, water tub s, music in discotheque) • From children themselves (toys, equipment, children p laying or practicing sports in a close yard)
• Traffic: heavy road, railways, highways, subways, air ports • Industrial activities
• Building and road construction, renovation
/xrhombusI ncreased environmental noise levels - more noise sour ces /xrhombusAlso linked to population growth
Nois e c o ntamin ati on or no is e poll uti on i s a c o ncept wh ich im plie s h armful level s of exc e s s noi s e.
Nois e int en s e e nou gh t o cau se harm i s wid el y sprea d. < < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
12 1. Introduction
2. V ulnerability of children
3. Adverse health effects
4 . Effects by age-group
5. Taking action
6. Discussion
CONTENTS CONTENTS
Children and noise Children and noise
13 VULNERABLE GROUPS OF CHILDRENV ULNERABLE GROUPS OF CHILDREN /xrhombusThe fetus and babies
/xrhombusPreterm, low birth weight and small for gestational a ge babies
/xrhombusChildren with dyslexia and hyperactivity
/xrhombusChildren on ototoxic medication
It is lo gical t o con s id er certai n s ub grou ps of chil dren (s i nce concepti on) t o b e par ticular ly at risk for harm from exc e s s noi s e exposure. T hese i nc l ude the fet us , b abie s a nd v er y youn g in fant s b orn preterm, wit h l ow birt h wei ght or s mal l for ge s tat ion al a ge. A l s o, c hi ldre n who ha v e lear nin g disabi lit ies or att enti on diff ic u lti es ma y be more li k el y to de v el op earl y pr oblems with mi ld h eari ng loss c ompare d to chil dren w itho ut t hese chal l enge s , and c h ildr en o n ot oto x i c medi c at ion s may ha v e h igh e r likeli hoo d of de v e lop ing prob lems fr om e x po s ure to exc ess n oi s e.
Reference:
•Carv al ho WB , et al. N oise l ev e l in a p edi atri c in ten s i v e c are u nit. J Pediatr , 2 005, 81: 495- 8.
OBJECTIVES: The purpose of this study was to verify the noise level at a PICU. METHODS: This prospective observational study was performed in a 10 bed PICU at a teaching hospital located in a densely populated district within the city of São Paulo, Brazil. Sound pressure levels (dBA) were measured 24 hours during a 6-day period. Noise recording equipment was placed in the PICU access corridor, nursing station, two open wards with three and five beds, and in isolation rooms. The resulting curves were analyzed. RESULTS: A basal noise level variation between 60 and 70 dBA was identified, with a maximum level of 120 dBA. The most significant noise levels were recorded during the day and were produced by the staff. CONCLUSION: The basal noise level identified exceeds International Noise Council recommendations. Education regarding the effects of noise on human hearing and its relation to stress is the essential basis for the development of a noise reduction program.
Children and noise Children and noise
14 VULNERABILITY OF CHILDRENVULNERABILITY OF CHILDREN
/xrhombusD ifferent perception of dangers of noise
• C an not recognize the dangerous exposures
/xrhombusL ack of ability to control the environment
• A re not able to identify and avoid the source of noxi ous noise • Exposure intra utero
/xrhombusN oise can interfere with communication of danger /xrhombusMay be more exposed due to their behaviour
• E xploratory or risk behaviour (in children and teenag ers) Special v u lner abi lit y of c h ildr e n to noi s e. The k n own i nc re ased ri s k is due to < < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Noise effects in childrenN o ise effects in childrenNoise effects in childrenNoise effects in childrenNoise effects in childrenNo ise effects in childrenNoise effects in childrenNoise effects in children ““ Children may be more prone to the adverse effects of noise becauChildren may be more prone to the adverse effects of noise becau se they may be more frequently se they may be more frequently exposedexposed …… .and they are more susceptible to the impact of noise.and they are more susceptible to the impact of noise ”” . (Tamburlini, 2002). (Tamburlini, 2002) ReferenceReference ::
•Childr en's
h ealt h a nd t he e nvir onment: A review o f e v id ence. Tamburl ini G et a l., e ds . EEA-WHO, 2002 (www.eea. euro pa. eu/p u bli c ati on s /en v ir onment al_iss ue _ rep ort_200 2_ 2 9) Children and noise Children and noise
15 Why might children be more susceptible to noise effects? /xrhombusP
ossible increased risk due to immaturity
Increased cochlear susceptibility?
• I
n utero
• Animal data studies
/xrhombusC ritical periods in relation to learning
/xrhombusLack of developed coping repertoires
/xrhombusVulnerable tasks \ Vulnerable settings (schools, home,
streets)
What might be the implications of noise effects?
/xrhombusLifelong impairment of learning and education
/xrhombusShort-term deficit followed by adaptation
/xrhombusNon intentional lesions
VULNERABILITY OF CHILDRENV ULNERABILITY OF CHILDREN
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Ex po s ure to exc ess i v e noi s e a nd vibra tio n dur ing pre gna ncy may result in hig h fre que ncy h earin g l oss in th e n ewborn , may be a s soci ated w ith prematur ity and gro wth retar dat ion, alt hou gh t he sc ie ntif ic ev i den c e r emain s in c o nc l usiv e .
The ro le of th e amni oti c fl uid is not y et def ine d, n or whe n an d which no ises or vibrat ion s c an damage the f etal de v el opment of t he a udit ory s ys tem (e. g. c ochle a). Conc er n a bout sy ner gis m b et ween ex p os ur e to no is e an d ot oto x ic drug s remai ns in c omple tel y d efin ed. T here are s tu die s o n fe tal a udi tio n dati ng fr om 193 2 th at e x pl ore t he re ac t ion of t he f etu s to ex t e rnal noi s e s bu t e v en tod ay t hi s remain s incomplet ely c h ara c teri z e d.
References:
•Childr en's h ealt h a nd t he e nvir onment: A review o f e v id ence, Ed. Tamb urli ni G. e t al , EEA-WHO, 2002 (www.eea. euro pa. eu/p u bli c ati on s /en v ir onment al_iss ue _ rep ort_200 2_ 2 9). •Nation al I ns t itut e of Pub lic He alth D enmark . Heal th Eff ec t s o f Noise o n Chi ldre n an d Per c e ptio n of the Ris k of No is e . Bi s trup ML, ed . Copenhagen, Denmark: National Institute of Public Health Denmark, 2001 , 29 .
Children and noise Children and noise
16 1. Introduction
2 . Vulnerability of children
3. A dverse health effects
4. Effects by age-group
5 . Taking action
6. Discussion
CONTENTS CONTENTS
Children and noise Children and noise
17 ADVERSE EFFECTS A DVERSE EFFECTS
FROM EXCESS NOISE EXPOSUREFROM EXCESS NOISE EXPOSURE
/xrhombusDirect ear damage
• N oise induced hearing loss
• Noise induced threshold shift
/xrhombusI ndirect adverse effects
• P hysiological effects
• Psychological effects
/xrhombusI mpaired cognition
Ch
aracteristics of the sound can modify effect
Adv er s e effe c ts c a n b e di v i ded into dir ec t damag e, i ndir ec t a dv er s e effe c ts an d impa ired c o gnit ion.
Many ef fects of n oise e x po s ur e are mor e th orou ghl y stud ied in a dult s t han in c hi ldre n.
The d egre e of ad v er s e e ffe c t i s modif ied by the s o und c h ara c teri s ti c s. • V i b r a t i o n :V i b r a t i o n : V i b r a t i o n :V i b r a t i o n : c a n b e a c ute or c hro nic , au dib le or in aud ibl e. Vi bra tion c a n be tran s mitte d to al l th e bo dy dire c tl y thr oug h th e sk in or b o nes .
•• F r e q u e n c i e s: F r
e q u e n c i e s: F r e q u e n c i e s: F r e q u e n c i e s: F r e q u e n c i e s: F r e q u e n c i e s: F r e q u e n c i e s: F r e q u e n c i e s: l ower a nd h igh er (ultra an d in fra s ou nds ) c a n a l s o d amage the human hear ing s ys tem, despite be ing impercept ibl e, a nd h av e import ant c on s e que n c es for life (lo s s of h eari ng). T hese c on s eq uen c e s can also b e pre s ent aft er chron ic ex p osure t o low fre que ncy no n a udi ble s o u nds (c hro nic ba c k noi s e exposure). Incubat ors are an e x ampl e of this expo s ure. • I n t e n si t y: I n t e n si t y: I n t e n si t y: I n t e n si t y: Dir ec t bl ows t o th e e ars , v er y lo ud noi s e ( pne umati c hammer or dril l, f ire arms, ro c k e t), a nd s ud den but int ense soun ds c a n de s tro y th e e ardrum a nd d a mage th e ha ir cell s o f th e coc h lea by by p ass in g th e pro tective ref le xes . A c ut e tra uma c a n cau s e a life lon g le s i on. • P e r i o d i c i t y a n d D u r a t i o nP e r i o d i c i t y a n d D u r a t i o nP e r i o d i c i t y a n d D u r a t i o nP e r i o d i c i t y a n d D u r a t i o n : Impul s e n oi s e i s more harmful tha n c ont inu ou s be c a us e it by p ass the natur al pr ote c ti v e r eactio n, th e dampin g-ou t of the oss i c le s media ted by the facial ner v e. Lo ud n oise may result in t emporar y d ec re as e in t he s en s it ivity of h eari n g an d ti nnit us , but rep eate d e xposure may c au s e t he s e t emporar y cond iti ons to b ecome perman ent.
Children and noise Children and noise
18 ORGAN DAMAGEORGAN DAMAGE
NOISE INDUCED HEARING LOSSNOISE INDUCED HEARING LOSS
No
rmal hair cell Noise damaged hair cell
VIMM
DIRECT DAMAGE
VIMM
Normal he alth y “h air c el ls ” tran s form vibrat ion int o n erv e imp ulses s en din g mess ag es to t h e bra in. Tr auma t o th e hair c el ls of t he c ochle a re s ul t s in he arin g lo s s. Prol ong ed e x po s ure to s ou nd s lo uder tha n 85 dBA is pot ent ial ly injur iou s (8 5 dBA is tol erab le f or an occ up ati ona l e x po s ure) . Contin uou s e x p os ur e to ha z ar dou s le v e ls of n oise te nd to aff ec t hi gh fr equ encie s reg i ons of t he c ochle a fir s t. N oise i ndu c es he arin g lo s s gra dua lly, impercepti bl y , a nd o ften pain le s s l y . Oft en, t he prob lem is n ot re c o gni z ed ear ly eno ugh to pr ov i de prote c ti on. Furth er, it may not be rec o gni z ed as a pr obl em, but merely c on s i dere d a normal consequ ence of ord inar y e x po sure, a nd part of th e environmen t an d d ail y l ife.
References:
•Moeller, Environme ntal he alth , Harvard University Press , 19 92 •VIMM (Veterinar ian Institu te o f Molecular Me dicine , Ita ly ):
www.v imm.it/c o c hl ea/ c o c hl eap age s /th eory/hcell s /h c e lls.htm Pictures:
•VIMM (Veterinarian Institute of Molecular Medicine, Italy):
www.vimm.it/cochlea/cochleapages/theory/hcells/hcells.htm - used with copyright permission.
Children and noise Children and noise
19 AUDIOGRAMA UDIOGRAM
OSHA
DIRECT DAMAGE
Noise-induced
hearing loss
< < < < < < < < N O T E T O U S E R N O T E T O U S E RN O T E T O U S E RN O T E T O U S E R : I f p o s si b l e p l a ce a n a u d i o g r a m o f a ch i l d l i vi n g i n y o u r l o c a l : I f p o s si b l e p l a ce a n a u d i o g r a m o f a ch i l d l i vi n g i n yo u r l o c a l: I f p o s si b l e p l a ce a n a u d i o g r a m o f a ch i l d l i vi n g i n yo u r l o c a l: I f p o s si b l e p l a ce a n a u d i o g r a m o f a ch i l d l i vi n g i n yo u r l o c a l e n v i r o n m e n t h e r e t o e n v i r o n m e n t h e r e t o e n v i r o n m e n t h e r e t o e n v i r o n m e n t h e r e t o i l l u st r a t e e i t h e r n o r m a l h e a r i n g , o r h e a r i n g d a m a g e d b y e n vi r o n mi l l u st r a t e e i t h e r n o r m a l h e a r i n g , o r h e a r i n g d a m a g e d b y e n vi r o n mi l l u st r a t e e i t h e r n o r m a l h e a r i n g , o r h e a r i n g d a m a g e d b y e n vi r o n mi l l u st r a t e e i t h e r n o r m a l h e a r i n g , o r h e a r i n g d a m a g e d b y e n vi r o n m e n t a l n o i s e . > > e n t a l n o i s e . > > e n t a l n o i s e . > > e n t a l n o i s e . > > Nois e- ind uced h eari ng loss i s i ns i dio us , but in c rea s e s with ti me, us u all y b egi nni ng in a dol e s c e nt y ear s . A s shown here, it affe c t s th e hi gh fr equ en c ie s fir s t. Th e s p ee c h wi ndow is betwe en 5 00 a nd 4000 Hz , s o it i s n ot surpri s i ng that hig h fre que ncy l oss of lar ge magn itu de c ou ld g o u ndet e c ted for long per iod s of time wit hou t for mal testin g.
Picture:
•OSHA (U.S. Department of Labor Occupational Safety & Health Administration) www.osha.gov/dts/osta/otm/noise/images/sensorineural_loss_audiogram.gif Children and noise Children and noise
20 CHILDREN AND NOISE: SETTINGSCHILDREN AND NOISE: SETTINGS Noise at home 5 0 - 80 dB A
Home appliances 78 - 102 dB A
Noise in incubators 60 - 75 dB A,
peak sounds 120 dB A
Noise in hospitals > 70 dB A
Day-care institutions 75 – 81 dB A
Noise from toys peak sounds 79 - 140 dB A
Background noise in schools 46.5 – 77.3 dB A
DIRECT DAMAGE
These ranges repres ent ex cessive everyday ex posures of children These ranges repres ent ex cessive everyday ex posures o f children to sound.to sound.
References:References:
•• Committee on Environmental Health. Nois e: A Hazard f o r the Committee on Environmental Health. Nois e: A Hazard fo r the FetusFetus and Newborn.and Newborn.
PediatricsPediatrics ,, 1997, 100:7241997, 100:724 -- 2727 .. •• Etzel RA, ed. Etzel RA, ed. PediatricPediatric Env ironmental Health.Env ironmental Health. 2nd ed. American Ac ademy of 2nd ed. American Ac ademy of PediatricsPediatrics Committee on Environmental Health.; Committee on Environmental Health.; Elk Grove Village, IL: American Academy of Elk Grove Village, IL: American Academy of PediatricsPediatrics , , 2003.2003.
Children and noise Children and noise
21 NOISE INDUCED THRESHOLD SHIFT (NITS)NOISE INDUCED THRESHOLD SHIFT (NITS) /xrhombusInitially - a temporary condition
• D ecrease in sensitivity to noise
• Tinnitus
/xrhombusC aused by exposure to loud noises
/xrhombusMay be reversible or irreversible
• S everity and duration of exposure
• Continuous and recurrent exposure
DIRECT DAMAGE
Ex po s ure to lou d no ise may r e s ult in a temp orary de c rea s e i n the s e nsiti v it y of hear ing an d tinn itu s .
This c o ndi tio n, call ed t emporar y no ise-in duced t hre s ho ld s hif t (NITS), la s t s for s e v era l h ours depe ndi ng on t he d egre e of exposure, and ma y b ec ome per manent dep end ing on t he s e verity an d durat ion of n oi s e e x p os ur e. No is e ind uced t hreshol d shift s m ay b e re v er s ib le; howe v er, c on tinu ed ex cess i v e n oi s e e x p os ur e coul d le ad t o pro gre s s i on of NITS t o in c lu de other freq uen c i es an d le ad t o inc r ease severit y an d p erman ent h eari ng loss . T he c on s e qu ences of th ese measured NIT S may be enormou s if the y pr ogre s s to a persis t ent mi nimal s en s or ine u ral h eari ng l oss . I n sc ho ol-a ge d c h ildr en, minimal sen s ori neur al h eari ng loss h as bee n a s s o c i ated with poor s choo l per forman c e a nd s ocial an d emotion al dys fun c ti on.
Children and noise Children and noise
Non-metropolitan
22 PREVALENCE NOISE INDUCED THRESHOLD SHIFTSPREVALENCE NOISE INDUCED THRESHOLD SHIFTS Niskar AS, Pediatrics, 2001, 108(1):40-3
National survey US children (n=5249)
DIRECT DAMAGE
Characteristics % (95% CI)
Ag
e:
Sex:
Urban status:
6-11 years old
12-19 years old
Male
Female
Metropolitan
8.5 15.5
14.8 10.1
11.9 13.0
(6.9-10.0)
(13.3-17.6)
(12.3-17.3)
(8.3-11.8)
(9.8-14.0)
(11.3-14.6)
This is evidence that children are experiencing changes in hearing which are consistent with excess noise exposure. These data s
how the prevalence of Noise Induced Threshold Shift (NITS) in children which increases with age. The prevalence of NITS in one or both ears among children 6-19 year of age in the USA was recently found to be 12.5% (or 5.2 million) children affected.
Most children with NITS have an early phase of NITS in only one ear and involving only a single frequency, however among children with NITS, 4.9% had moderate to profound NITS. This table demonstrates several points. First, older children have a higher prevalence of NITS compared to younger children suggesting that ongoing exposure to excess noise in the environment may be causing cumulative hearing damage. Boys in this survey were more likely to have evidence of excess noise exposure measured as NITS compared to girls, but there was little difference between urban and non-urban status.
Reference:
•Niskar AS. Estimated prevalence of noise-induced hearing threshold shifts among children 6 to 19 years of age: the Third National Health and Nutrition Examination Survey, 1988-1994, United States. Pediatrics, 2001, 108(1):40-3 This analysis estimates the first nationally representative prevalence of noise-induced hearing threshold shifts (NITS) among US children. Historically, NITS has not been considered a common cause of childhood hearing problems. Among children, NITS can be a progressive problem with continued exposure to excessive noise, which can lead to high-frequency sound discrimination difficulties (eg, speech consonants and whistles). The Third National Health and Nutrition Examination Survey (NHANES III) was conducted from 1988 to 1994. NHANES III is a national population-based cross-sectional survey with a household interview, audiometric testing at 0.5 to 8 kHz, and compliance testing. A total of 5249 children aged 6 to 19 years completed audiometry and compliance testing for both ears in NHANES III. The criteria used to assess NITS included audiometry indicating a noise notch in at least 1 ear. RESULTS: Of US children 6 to 19 years old, 12.5% (approximately 5.2 million) are estimated to have NITS in 1 or both ears. In the majority of the children meeting NITS criteria, only 1 ear and only 1 frequency are affected. In this analysis, all children identified with NITS passed compliance testing, which essentially rules out middle ear disorders such as conductive hearing loss. The prevalence estimate of NITS differed by sociodemographics, including age and sex.
CONCLUSIONS: These findings suggest that children are being exposed to excessive amounts of hazardous levels of noise, and children's hearing is vulnerable to these exposures. These data support the need for research on appropriate hearing conservation methods and for NITS screening programs among school-aged children. Public health interventions such as education, training, audiometric testing, exposure assessment, hearing protection, and noise control when feasible are all components of occupational hearing conservation that could be adapted to children's needs with children-specific research.
Children and noise Children and noise
23 INDIRECT ADVERSE EFFECTSI NDIRECT ADVERSE EFFECTS
/xrhombusS tress-related somatic effects
• S tress hormone
• Blood pressure
• Muscle spasm
/xrhombusP sychological effects
• A nnoyance / Isolation
• Sleep disturbance
• Mental health
/xrhombusC
ognitive effects
• R eading, concentration, memory, attention
INDIRECT DAMAGE
The n ex t s e c ti on wi ll r ev i ew th e in dire c t a dvers e eff ec t s of no is e li s ted her e.
Children and noise Children and noise
24 There might be harmful consequences to health during the state of alertness as well as when the body is unaware or asleep.
PHYSIOLOGICAL EFFECTS OF NOISEPHYSIOLOGICAL EFFECTS OF NOISE
INDIRECT DAMAGE
EPA
There are a variet y o f ph y siol o gical e ffe c ts that ha v e bee n do c ument ed or po s tu late d a s a r es u lt of exc e s s noi s e ex p os ur e.
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > References:
S t r e s s r e sp o n se :S t r e s s r e sp o n se :S t r e s s r e sp o n se :S t r e s s r e sp o n se :
•Frankenh aeu s er M. Immedi ate and del ayed e ffe c ts of n oi s e o n perf ormance a nd ar ou s al. Biol Psychol, 19 74, 2:1 27- 33 Increased excretion of adrenaline and noradrenaline demonstrated in humans exposed to noise at 90 dBA for 30 minutes.
•Henk i n RI. Ef fect of s o und on t he h y po tha lamic - pitu itar y -adr e nal ax i s . Am J. Physiol, 196 3, 204: 710- 14 Hypothalamic- pituitary- adrenal axis is sensitive to noise as low as 65 dBA (53% increase in plasma 17 HO corticosteroid levels).
•Ros en berg J . J et s o v er Labr a dor a nd Que be c : n oise ef fects on h uman h ealt h. Can. Med. Assoc. J., 199 1, 144(7) :86 9-75.
Biochemical evidence of the stress response was found in elevated urinary cortisol and hypertension accompanied a 30 minute exposure to 100dBA in 60 children aged 11 to 16 years.
S l e e p d e r i va t i o n :S l e e p d e r i va t i o n :S l e e p d e r i va t i o n :S l e e p d e r i va t i o n :
Noise levels at 40-50 dBA result in 10-20% increase in awakening or EEG changes •Falk SA. Ho s pit al noi s e level s and pote nti al h eal th h azards. Engl. J Med., 19 73, 289( 15): 7 74-81 •Hilton BA. Qua ntit y a nd q ual ity of p atie nt’ s slee p a nd s le ep-d i s turb ing fa c tor s in respira tory i nten s i v e c are uni t, J Adv Nurs, 1 976 , 1(6) :45 3-68
•Thie s sen GJ . Dis t urba nce of s l eep by noi s e . J. Acoustic Soc. Am., 1978 , 64( 1):2 16-2 2 C a r d i o va scu l a r e f f e ct s : C a r d i o va scu l a r e f f e ct s : C a r d i o va scu l a r e f f e ct s : C a r d i o va scu l a r e f f e ct s :
•• Etz el RA, ed . Etz el RA, ed . PediatricPediatric Environmental Health. Environmental Health. 2nd ed. Amer i c an A c a demy o f 2nd ed. Amer i c an A c a demy o f Ped iatr icsPed iatr ics Co mmittee on Co mmittee on Env ir onment al He alth . El k Gro v e Vi lla ge, IL: Ameri c a n Acade my ofEnv ir onment al He alth . El k Gro v e Vi lla ge, IL: Ameri c a n Acade my of Pe diatr icsPe diatr ics ; 2 003 .; 2 003 .
Ex po
sure to noise levels greater than 70 dBA causes increases in vasoconstriction, heart rate and blood pressure Picture:
•EPA (U.S. Environmental Protection Agency)
Children and noise Children and noise
25 STRESS HORMONES S TRESS HORMONES -- CHILDRENCHILDREN + increase with noise, - decrease with noise, 0 no effect
INDIRECT DAMAGE
Adapted from Babisch W, Noise Health, 2003, 5(18):1-11
Evans, 1998
I
sing, 1999
Stansfeld, 2001
Haines, 2001
Evans, 2001
Ising, 2001
AuthorC ortisolNoradrenalineAdrenaline
+ ++
00 0
0 0 +
0 0
+
00 N°
N
oise
exposure
Noise
type (leq)
Aircraft 53, 62 217
4 0
115
56 238
204
56, 70
<50, >60
<57, >66
53, 62
30-54, 55-78
Aircraft
Aircraft
Aircraft
Road, Rail
Road
In e x per imenta l s t udi es w ith h umans c arri ed out in t he l abor atory, un equ ivoc a l fi ndi ng s of noi s e ex
p os ur e o n th e en do c rin e sy stem have b een s omet imes o bserv e d. Howe v er, exposure c on diti ons v ary c o nsider abl y b etwee n e xperiment s . F urth ermore, s ec r e tory patt erns of hormone exc re tion v ar y betwee n i ndi v id ual s . I t i s no t cl ear a s to what ex t ent fin din gs f rom ex p eriment al s tud ie s on e ndo c rin e res p onses of n oi s e re fle c t a po tenti al hea lth hazard. To more c omple tel y chara c ter ize th es e indir ec t advers e eff ec t s of exc e s s noi se, th ere i s a ne ed t o 1) develo p a c on s en s u s o n measureme nt tec h niq ue s , 2) r epl icate r es u lts of a dult s t udi es in c hi ldre n, a n d 3) l in k hormo ne l evels to hea lth impairment. Whe n it is do ne, stress hormo ne re s p onses ma y ide ntif y ri s k grou ps .
Leq: averag e soun d le v e l o v er the peri od o f th e measuremen t, usual ly me as ur ed A-we igh te d N°: number of s u bje c t s
Reference:
•Babi s c h W. Str ess hormo nes i n the resear c h o n cardi ovasc ul ar eff ec t s of no is e . Noise Health, 20 03, 5(18): 1-11
In recent years, the measurement of stress hormones including adrenaline, noradrenaline and cortisol has been widely used to study the possible increase in cardiovascular risk of noise exposed subjects.
Since endocrine changes manifesting in physiological disorders come first in the chain of cause-effect for perceived noise stress, noise effects in stress hormones may therefore be detected in populations after relatively short periods of noise exposure. This makes stress hormones a useful stress indicator, but regarding a risk assessment, the interpretation of endocrine noise effects is often a qualitative one rather than a quantitative one. Stress hormones can be used in noise studies to study mechanisms of physiological reactions to noise and to identify vulnerable groups. A review is given about findings in stress hormones from laboratory, occupational and environmental studies.
Children and noise Children and noise
26 BLOOD PRESSUREB LOOD PRESSURE -- AIRCRAFT NOISEAIRCRAFT NOISE Study Psys (mmHg) Pdia (mmHg) Sound level (Leq)
Karagodina, 1969 abnormalities abnormalities distance from airport Cohen, 1980 3-7 3-4 <70 dBA (indoors)
Cohen, 1981 no effect no effect 70 dBA (indoors)
Evans, 1995 2 0 68 dBA (outdoors)
Evans, 1998 3 3 64 dBA (outdoors)
Morrell, 1998 negative negative ANE I 45 (outdoors)
Morrell, 2000 no effect negative ANE I 45 (outdoors)
/xrhombusInconsistent picture: 3 positive, 4 negative studies /xrhombusProspective studies: 1 positive, 1 negative study /xrhombusMagnitude of effect found in positive studies may be relevant
INDIRECT DAMAGE
Studies on elevated blood pressure and noise exposure (from aircraft) are also inconsistent. Only the cross-sectional study of Cohen shows that aircraft noise exposure (specifically at school) is statistically significantly associated with increases in systolic and diastolic blood pressure.
Leq: average sound level over the period of the measurement, usually measured A-weighted Psys: systolic pressure
Pdia: diastolic pressure
dBA weighting curve: response of a filter that is applied to sound level meters to mimic (roughly) the response of human hearing.
So a typical human equal loudness curve is somewhat similar to the dBA curve, but inverted.
ANEI: Australian Noise Exposure Index.
References:
Aircraft Noise:
•Cohen S. Physiological, motivational and cognitive effects of aircraft noise on children: moving from the laboratory to the field . Am Psychol. , 1980, 35:231-43.
•Cohen S. Aircraft noise and children: longitudinal and cross-sectional evidence on adaptation to noise and the effectiveness of noise abatement. J. Pers Soc Psychol ., 1981, 40:331-45
•Evans G. Chronic noise and psychological stress. Psychological Science, 1995, 6:333-38 •Evans G. Chronic noise exposure and physiological response: a prospective study of children living under environmental stress.
Psychological Science , 1998, 9:75-77
•Karagodina IL. Effect of aircraft noise on the population near airports. Hygiene and Sanitation , 1969, 34:182187 •Morrell S. Cross-sectional relationship between blood pressure of school children and aircraft noise. In N.L. Carter, & R.F.S Job (Eds.), Noise Effects. Proceedings of the 7th International on Noise as a Public Health Problem. Sydney, Australia: Noise Effects Inc , 1998, 275-79. •Morrell S. Cross sectional and longitudinal results of a follow up examination of child blood pressure and aircraft noise. The Inner Sydney Child Blood Pressure Study. Proceedings Internoise, SFA, Nice, France , 2000, 4:2071. •van Kempen E. et al. Noise exposure and children's blood pressure and heart rate: the RANCH project. Occup Environ Med., 2006, 63:632-39
BACKGROUND: Conclusions that can be drawn from earlier studies on noise and children's blood pressure are limited due to inconsistent results, methodological problems, and the focus on school noise exposure. OBJECTIVES: To investigate the effects of aircraft and road traffic noise exposure on children's blood pressure and heart rate. METHODS: Participants were 1283 children (age 9-11 years) attending 62 primary schools around two European airports. Data were pooled and analysed using multilevel modelling. Adjustments were made for a range of socioeconomic and lifestyle factors. RESULTS: After pooling the data, aircraft noise exposure at school was related to a statisticall y non-significant increase in blood pressure and heart rate. Aircraft noise exposure at home was related to a statistically significant increase in blood pressure. Aircraft noise exposure during the night at home was positively and significantly associated with blood pressure. The findings differed between the Dutch and British samples. Negative associations were found between road traffic noise exposure and blood pressure, which cannot be explained. CONCLUSION: On the basis of this study and previous scientific literature, no unequivocal conclusions can be drawn about the relationship between community noise and children's blood pressure.
Traffic Noise:
•Babisch W. Blood pressure of 8-14 year old children in relation to traffic noise at home--results of the German Environmental Survey for Children (GerES IV). The Science of the total environment , 2009, 407(22):5839-43. •Babisch W, Kamp I. Exposure-response relationship of the association between aircraft noise and the risk of hypertension. Noise Health . 2009 Jul-Sep, 11(44):161-8.
•Belojevic G et al. Urban road-traffic noise and blood pressure and heart rate in preschool children. Environ Int. 2008, 34(2):226-31. Epub 2007 Sep 14.
Children and noise Children and noise
HYPERTENSION AND EXPOSURE TO NOISE H YPERTENSION AND EXPOSURE TO NOISE
NEAR AIRPORTS NEAR AIRPORTS
The The HyENAHyENA study study
27 INDIRECT DAMAGE
R
esults
/xrhombusS ignificant exposure-response relationship
/xrhombusNight time aircraft noise exposure: borderline signif icant relationship /xrhombusRisk of myocardial infarction in relation to noise ex posure: analysis ongoing /xrhombusEffects of noise exposure on stress hormone level (co rtisol): statistical analyses and epidemiological ongoing
Conclusion
/xrhombusP revalence of hypertension increased with increasing noise exposure /xrhombusLong-term road traffic noise exposure effects on BP /xrhombusAcute effect on hypertension of night-time aircraft n oise /xrhombusHighly annoyed people are found at aircraft noise lev els An in c rea s i ng n umber o f pe opl e li v e near airp orts with c o ns i d erabl e n oise a nd a ir p oll utio n. The Hy pert ension an d Exposure t o Noise ne ar Air port s (HYENA) proje c t aims t o a s s e s s the imp ac t of airpor t-rel ated no is e expo s ure on b loo d pre s sure (BP) and c a rdio v asc u lar d isease u s in g a cros s - s ectio nal s tu dy de s ig n.
Altho ugh the s tu dy ha s be en made in ad ult s (men a nd wom en b etwee n 45- 70 y ear s o ld), i t might be a good c ar dio v a s cular di s ea s e p redi c tor in c hi ldre n.
Reference :
•J aru p L. H y per ten s io n a nd E xposure t o Noi s e near A irpor ts ( HYENA): Study Design and Noise Ex po s ure Ass ess ment. Environ Health Perspect., 20 05, 113( 11):1 473 – 14 78.
Children and noise Children and noise
28 PSYCHOLOGICAL DAMAGEPSYCHOLOGICAL DAMAGE
/xrhombusE xposure to mo derate level of noise can cause • P sychological stress
• Annoyance, interference with activity, isolation
• Headache, tiredness and irritability; may impair inte llectual function and performance of complex tasks
/xrhombusE xposure to in tense level of noise can
• C ause personality changes and aggressive/violent reac tions • Reduce ability to cope
• Alter work performance and intellectual function
• May cause muscle spasm and also break a bone (when co mbined with strong vibration) •Sleep disturbance
•Changes in mental health.
/xrhombusE xposure to su dden, unexpected noise can cause • S tartle reaction with stress responses
• Cause non intentional injuries
INDIRECT DAMAGE
Ps yc ho log ic a l ef fects c orre lat e with i nte ns i ty ( or lo udn ess ) of Ps yc ho log ic a l ef fects c orre lat e with i nte ns i ty ( or lo udn ess ) of the n oi s e.the n oi s e.
Ex po
s ure to m o d e r a t e l e ve l s o f n o i sem o d e r a t e l e ve l s o f n o i sem o d e r a t e l e ve l s o f n o i sem o d e r a t e l e ve l s o f n o i se c a n cause p s yc ho log ical stress .
Other eff ec t s c an be:
• Anno y an c e ( fear, an ger, f eel i ng b other ed, fee lin gs of b ein g involu ntari ly and un av o ida bl y harmed, and fee lin gs of ha v i ng pr ivacy in v a ded), int erfere nc e with ac t ivity . •Heada c he , tir edn ess an d irr ita bili ty are a lso common rea c tio ns t o no ise. •Pos sibl e impa irment of i ntel le ctual fun c ti on a nd performa nc e of comple x ta s ks . De pen ds on the natur e of s o und and ind iv i dua l to lera nce.
Ex po s ure to i n t e n s e l e ve l o f n o i s ei n t e n s e l e ve l o f n o i s ei n t e n s e l e ve l o f n o i s ei n t e n s e l e ve l o f n o i s e can:
• Cause per s o nal ity c h ang es a nd pr ov o k e aggr ess i v e a nd v io lent r ea c tio ns . • Redu c e a bil ity to c op e.
• Alter wor k p erforman c e and in tell ec t ual fun c ti on. • Cause musc le s p as m an d a lso bre ak a bo ne (wh en c ombi n ed wit h s tr ong v i brat ion). • Cause slee p di s tur ban c e.
• Prov o k e c h ang es in ment al h ealt h.
Ex po s ure to su d d e n , u n e x p e c t e d n o i s esu d d e n , u n e x p e c t e d n o i s esu d d e n , u n e x p e c t e d n o i s esu d d e n , u n e x p e c t e d n o i s e can c au s e:
• Startl e rea c ti on wi th s tre s s r e s po ns e s . •Caus e no n in tent ion al inj urie s .
Stress re s po ns e c o nsis t ing in ac ut e terr or a nd p ani c wa s d e s c ri bed in c h ildr en u pon expo sure to s o ni c bo oms .
References:
•Kam PC. Noise po llu tio n in the ana es t heti c a nd inte nsiv e c ar e en v iro nment. Anaesthesia, 1 994, 49(1 1):9 82-6 •Kujal a T, Br atti c o E . Detrime nt al n oise ef fects on br ain's s p e ec h fun c ti ons. Biol Psychol. 20 09, 8 1(3): 135- 43.
Epub 200 9 Apr 8.
•Ros en berg J . J et s o v er Labr a dor a nd Que be c : n oise ef fects on h uman h ealt h. Can. Med. Assoc. J ., 199 1, 144(7) :86 9-75
Children and noise Children and noise
29 IMPAIRED COGNITIVE FUNCTIONI MPAIRED COGNITIVE FUNCTION /xrhombusC hronic noise exposure impairs cognitive function • R eading comprehension
• Long term memory
/xrhombusD ose-response relationships
• S upported by both laboratory and field studies
/xrhombusS tudy of possible mechanisms and noise reduction inte rventions • T uning out of attention / concentration
• Impairment of auditory discrimination
INDIRECT DAMAGE
The most rob us t are a of s tu dy on n oise a nd e ffe c ts in c hi ldre n c ome s from s tud ie s whi c h e v al uate the effe c t of no is e on lear nin g a nd c og nit iv e fun c ti on; ther e are p os sibl e mechan is ms , in c lu din g noi s e- relat ed c ha nge s i n att ent ion or distractio n an d impa ired au dit ory d isc rimin atio n. < < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
30 ENVIRONMENTAL NOISE AND COGNITIVE E NVIRONMENTAL NOISE AND COGNITIVE
DEVELOPMENT IN PRESCHOOL CHILDRENDEVELOPMENT IN PRESCHOOL CHILDREN /xrhombusC hildren 6 months - 5 years
/xrhombusInverse associations between noise level at home and cognitive development
IMPAIRED COGNITION
Wachs TD. Early Experience and Human Development. New York Plenum, 1982 E
vans GW. Children's Environments,1993,10(1):31-51
Effects of noise on cognitive development have been documented in preschool ages as well. Higher levels of noise at home are associated with decrements in cognitive development for age.
References:
•Evans GW. Non-auditory effects of noise on children: A critical review . Children's Environments, 1993,10(1):31-51.
•Maxwell LE et al. The effects of noise on pre-school children's pre-reading skills. Journal of Environmental Psychology , 2000, 20(1):91-97.
•Wachs TD. Early Experience and Human Development. New York Plenum, 1982. •Yang W, Bradley JS. Effects of room acoustics on the intelligibility of speech in classrooms for young children. J Acoust Soc Am . 2009, 125(2):922-33.
Children and noise Children and noise
31 APARTMENT NOISE AND READING ABILITYA PARTMENT NOISE AND READING ABILITY /xrhombus5 4 children living in apartments above interstate hig hway 32nd floor: 55 dBA,
2 0th floor: 60 dBA,
8th floor: 66 dBA
/xrhombusM easures of auditory discrimination and reading abili ty /xrhombusCorrelations between floor level and auditory discrim ination vary by duration of residence
/xrhombusFloor level correlates with reading-abolished by adju stment for auditory discrimination
/xrhombusReading powerfully associated with mothers’ education
IMPAIRED COGNITION
Cohen S. Journal of Experimental and Social Psychology, 1973, 9:407-22.
This s t udy s h ows t hat s tre et tr affi c no ise measured on diff er ent f loor s of a mult ile v e l ap art ment c orre late s i nvers e ly w ith aud it ory d isc rimin atio n a nd re adi ng abil ity. Th e hi gher flo ors w ere quie ter a nd c hi ldre n sc ore d b etter on r ead i ng a bil ity and au ditor y d isc rimi nati on. Corr ela tio ns v ari ed wi t h dur atio n of re s id ence, a nd wh en r ead in g le v el sc ore s were ad justed f or au ditor y d isc rimin atio n mea s ure s , t he floor le v el effe c t disapp eare d. Readi ng i s a lso pow erful ly assoc i ated with mot her’ s e du c ati o n.
Reference:
•Cohen S . Apar tment n oi s e, au ditor y d isc rimina tio n, a nd re adi ng a bil ity in c hi ldre n. Journal of Experimental and Social Psychology , 19 73, 9:40 7-22 .
Children and noise Children and noise
/xrhombusReading scores compared between classes in same schoo l /xrhombusExposed/not exposed to railway noise
/xrhombusNo selection of children into classes
/xrhombusPoorer performance on achievement test on noisy side /xrhombusMeasuring reading age 3-4 months behind on noisy side 32 RAILWAY NOISE AND READING SCORESR AILWAY NOISE AND READING SCORES
IMPAIRED COGNITION
Bronzaft AL. Environment and Behavior, 1975, 7:517-28
This s t udy c omp ared r ead ing s c or es betwe en c la s s r ooms in the s ame s c h ool tha t were exposed a nd not e x p os e d to rai lway noi s e. Poorer performa nc e was not e d on the noi s y s i de wi th a 3-4 month d ela y c ompare d to the qu ieter s i de. There w as no s el ec t ion of t he c hi ldre n in ea c h c la s s. Th is is s up porti v e ev i den c e t hat noi s e impair ed r eadi ng lear nin g.
Reference:
•Bronzaft AL . Th e eff ec t of ele vated trai n no ise on rea din g ab i lity. Environment and Behavior. 197 5, 7:51 7-28.
Children and noise Children and noise
33 /xrhombusL os Angeles airport study
Cohen S. Am Psychol., 1980, 35:231-43.
/xrhombusN ew York airport city
Evans G. Environment and Behavior, 1997, 29(5):638-656. /xrhombusM unich airport study
Evans G. Psychological Science, 1998, 9:75-77; Psychological Science, 1995,6:333-38 /xrhombusH eathrow studies
Haines MM. Psychological Medicine, 2001a,b,c; J Epidemiol Community Health, 2002, 56(2):139
IMPAIRED COGNITIVE FUNCTIONI MPAIRED COGNITIVE FUNCTION Ov
er 20 studies have reported that
noise adversely affectsadversely affects children’s academic performance
IMPAIRED COGNITION
Many studies have reported that noise can adversely affect children’s academic performance. Transport noise is well-studied. Some of the most important studies are the Los Angeles airport study, the New York airport study, the Munich and Heathrow studies.
References:
•Cohen S. Physiological, motivational and cognitive effects of aircraft noise on children: moving from the laboratory to the field. Am Psychol ., 1980, 35:231-43.
•Cohen S. Aircraft noise and children: longitudinal and cross-sectional evidence on adaptation to noise and the effectiveness of noise abatement. J. Pers Soc Psychol. , 1981, 40:331-45 •Evans G. Chronic noise and psychological stress. Psychological Science, 1995, 6:333-38 •Evans G. Chronic noise exposure and physiological response: a prospective study of children living under environmental stress.
Psychological Science, 1998, 9:75-77
•Evans G. Chronic noise exposure and reading deficits: The mediating effects of language acquisition. Environment and Behavior , 1997, 29(5):638-656.
•Haines MM. Chronic aircraft noise exposure, stress responses, mental health and cognitive performance in school children.
Psychological Medicine , 2001a, 31:265-77.
•Haines MM. The West London Schools Study: the effects of chronic aircraft noise exposure on child health. Psychological Medicine , 2001b, 31:1385-96.
•Haines MM. A follow-up study of effects of chronic noise exposure on child stress responses and cognition. International Journal of Epidemiology , 2001c, 30:839-45.
•Haines MM. Multilevel modelling of aircraft noise on performance tests in schools around Heathrow Airport London. J Epidemiol Community Health , 2002, 56(2):139-44
•Ristovska G. et al. Psychosocial effects of community noise: cross sectional study of school children in urban center of Skopje, Macedonia. Croat Med J. 2004, 45(4):473-6.
AIM: To assess noise exposure in school children in urban center in different residential areas and to examine psychosocial effects of chronic noise exposure in school children, taking into account their socioeconomic status. METHODS: We measured community noise on specific measurement points in residential-administrative-market area and suburban residential area. We determined the average energy-equivalent sound level for 8 hours (LAeq, 8 h) or 16 hours (LAeq, 16 h) and compared measured noise levels with World Health Organization (WHO) guidelines. Psychological effects were examined in two groups of children: children exposed to noise level LAeq, 8 h >55 dBA (n=266) and children exposed to noise level LAeq, 8 h <55 dBA (n=263). The examinees were schoolchildren of 10-11 years of age. We used a self-reported questionnaire for each child - Anxiety test (General Anxiety Scale) and Attention Deficit Disorder Questionnaire intended for teachers to rate children's behavior. We used Mann Whitney U test and multiple regression for identifying the significance of differences between the two study groups. RESULTS: School children who lived and studied in the residential-administrative-market area were exposed to noise levels above WHO guidelines (55 dBA), and school children who lived and studied in the suburban residential area were exposed to noise levels below WHO guidelines.
Children exposed to LAeq, 8 h >55 dBA had significantly decreased attention (Z=-2.16; p=0.031), decreased social adaptability (Z =-2.16; p=0.029), and increased opposing behavior in their relations to other people (Z=-3; p=0.001). We did not find any correlation between socioeconomic characteristics and development of psychosocial effects. CONCLUSION: School children exposed to elevated noise level had significantly decreased attention, and social adaptability, and increased oppos ing behavior in comparison with school children who were not exposed to elevated noise levels. Chronic noise exposure is associated with psychosocial effects in school children and should be taken as an important factor in assessing the psychological welfare of the children. •Stansfeld SA. Aircraft and road traffic noise and children’s cognition and health: a cross-national study. Lancet, 2005, 365: 1942–
49. •van Kempen EE et al. Children's annoyance reactions to aircraft and road traffic noise. J Acoust Soc Am . 2009, 125(2):895-904.
Children and noise Children and noise
34 MUNICH AIRPORT M UNICH AIRPORT
SCHOOL PERFORMANCE
/xrhombusC
losure of old airport, opening of new airport
/xrhombusDeficits in long-term memory and reading
ar
ound old airport
/xrhombusImpairments diminish within 2 years after
ai
rport closed
/xrhombusSame impairments develop in new group
of
children within 2 years of new airport
opening
IMPAIRED COGNITION
Hygge S, Psychol Sci. (2002)13(5):469
US Transportation Security
Administration
When an old airp ort wa s clo s e d down in Mu nich, d efi c it s i n lo ng term memory and rea din g i n c h ildr en ex p os e d to the ol d air port impr ov e d with in 2 years of t he airp ort's c l osure a nd t he ass ociat e d dec r eased noi s e ex p osure. I nt eres t ing ly , the c h ildr en ex p ose d to noi s e fr om the new a irpor t repl ac i ng the o ld beg an t o ha v e the s am e def icits in lon g term memory and r ead ing that were s ee n i n the c hi ldre n e x po s ed to t he old air port—a ls o with in 2 y e ars .
Reference:
•Hy gge S. et al. A pro s p ec t iv e s tud y o f some eff ec t s of air c raf t noi s e on c og nit iv e perf orman c e i n s c h ool c h ildr en, Psychol Sci., 2 002, 13(5) :46 9.
Before the opening of the new Munich International Airport and the termination of the old airport, children near both sites were recruited into aircraft-noise groups (aircraft noise at present or pending) and control groups with no aircraft noise (closely matched for socioeconomic status). A total of 326 children (mean age = 10.4 years) took part in three data-collection waves, one before and two after the switch-over of the airports. After the switch, long-term memory and reading were impaired in the noise group at the new airport. and improved in the formerly noise-exposed group at the old airport. Short- term memory also improved in the latter group after the old airport was closed. At the new airport, speech perception was impaired in the newly noise-exposed group. Mediational analyses suggest that poorer reading was not mediated by speech perception, and that impaired recall was in part mediated by reading.
Picture:
•US Transportation Security Administration
Children and noise Children and noise
35 STRENGTH OF EVIDENCE FOR EFFECTS OF S TRENGTH OF EVIDENCE FOR EFFECTS OF
AIRCRAFT NOISE ON CHILDRENAIRCRAFT NOISE ON CHILDREN
InadequateI mmune effects
InadequateBirth weight
Inadequate / no effectSleep disturbance
Inconclusive / no effectPsychiatric disorder
Limited (weak associations)Hypertension
L
imited / inconclusiveCatecholamine secretion
Sufficient / limitedWellbeing/perceived stress
Sufficient / limitedMotivation
InconclusiveCognitive performance - attention
SufficientCognitive performance - academic performance
SufficientCognitive performance - speech perception
SufficientCognitive performance - auditory discrimination SufficientCognitive performance - memory
SufficientCognitive performance - reading
SufficientHearing loss
SufficientAnnoyance
STRENGTH OF EVIDENCEHEALTH OUTCOME
IMPAIRED COGNITION
Here i s a brief s ummary s l ide ex amin ing the weig ht o f th e e viden c e for h eal th o utcomes in c hi ldre n from airc r aft noi s e. We ar e i nd ebte d to Dr. Step hen Stan s fe l d (Quee n Mary , U niversity of L ondo n) for k in dly le ndi ng u s t his an d man y of the pre v io us s li de s for thi s proj ec t. Thi s s li de h igh lig hts t he clear as sociati on s in c h ildr en betwe en a nno y an c e, he arin g lo s s an d impair ed c og nit iv e perf orma nc e and ex cess n oise. T he l ower c ate g orie s are s t ill in nee d of in v esti gati on. < < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
36 1. Introduction
2 . Vulnerability of children
3. Adverse health effects
4. E ffects by age-group
5. Taking action
6 . Discussion
CONTENTS CONTENTS
Children and noise Children and noise
37 EFFECTS OF NOISE BY AGEE FFECTS OF NOISE BY AGE--GROUPGROUP /xrhombusFetus
/xrhombusInfant
/xrhombusPre-school, school-aged children
/xrhombusTeenager
/xrhombusYouth
Children and noise Children and noise
38 EFFECTS OF NOISE ON THE EFFECTS OF NOISE ON THE FETUSFETUS /xrhombusGrowth retardation
• O ccupational exposure of the mother to noise
• Environmental noise unlikely to cause effects,
bu
t exposure to chronic low-dose noise requires more study /xrhombusH earing impairment
• P ossible effects
There are s e v era l pa edi atri c p opul ati ons whi c h may be at i ncreased ri s k of harm from no ise . The fetu s i s o ne in whic h ther e i s some e v id ence that occ up atio nal expo s ure to a pre gna nt woman ma y re s ul t in gr owth re tard atio n and/ or he arin g impa irment. Litt le i s known abo ut t he e ffe c ts o f non- occ up ati ona l no ise on fet al d ev e lopme nt, a nd further s t udi es are n eed ed.
Reference:
•Americ an Ac a demy of Pa edi at ric s, Committee on E nv ir onme ntal H ealt h. No is e : a hazard t o the f etu s a nd newbor n. Pediatrics . 1 997 , 10 0:72 4-72 7.
Children and noise Children and noise
Pre-term and full-term baby
/xrhombusE xposed to “Neonatal Intensive Care Unit" (NICU) nois e • P re-term babies have immature hearing organs / system s /xrhombusA dverse noise-induced effects on the pre-term baby • H earing impairment: po ssible effect
• Sleep disturbances: awakening, sleep disruption
• Others: crying
39 EFFECTS OF NOISE ON EFFECTS OF NOISE ON INFANTSINFANTS Babie s wh o are bor n pre- term or req uire int ensive care i n h o s pit al are e x p os e d to lar ge am ount s of no is e from incuba tors an d bu s y ho spita l setti ngs. Furt hermore, thi s no ise may be c ont inu ou s , 2 4 hour s /d ay .
They are exposed t o “Neo nat a l Int ensive Care U nit" (NICU) n oise (60 - 90 dBA ma x . 1 20 d BA) and noi s e inside the in c ub ator s (60 – 75 dBA max . 100 dBA). Pr e-term bab ies mus t c op e wit h th eir e nv ir onment with immature org an s ys tems ( audi tory, vis u al and c e ntral ner v ou s sy stem). Th es e la s t s tag es o f maturati on o c cur, i n p art, d urin g the time t he pr e-term chil d i s in a n i nc u bat or or neo nata l i nt ensiv e c ar e unit (NICU).
References :
•Brando n DH. Eff ec t of En v ir on mental Ch ang es on No ise in t he NICU. Advances in Neonatal Care , 20 08, 8(5):S5-S 10
•Milette IH, Carn ev a le FA . I'm try in g to hea l... noi s e level s in a pedi atri c i nten s i v e c are un it. Dynamics, 2003 , 14 :14- 21.
The literature demonstrates clearly that most intensive care units exceed the standard recommendations for noise levels in hospitals, and that high noise levels have negative impacts on patients and staff. The purpose of this study was to evaluate the level of noise in a PICU and compare it to the recommendations of ternational bodies. We outline recommendations to promote the awareness of this problem and suggest strategies to decrease the level of noise in a PICU. The orientations of these strategies are threefold: 1) architectural-acoustic design, 2) equipment design and, most importantly, 3) staff education.
Children and noise Children and noise
40 EFFECTS OF NOISE EFFECTS OF NOISE
IN IN PREPRE--SCHOOLSCHOOL AND AND SCHOOLSCHOOL--AGEDAGED CHILDRENCHILDREN /xrhombusH earing impairment
• I n isolated cases by toys or equipment
/xrhombusS leep disturbances
• E arlier responses than adults (EEG awakenings)
/xrhombusS omatic effects
• B lood pressure and stress hormones
/xrhombusP sycho-social effects
• N o studies on behaviour with high environmental noise levels • Cognitive tasks are impaired, like reading, long term memory, attention and motivation
/xrhombusV ocal nodule
EEG: ele c troe nceph alo gram
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Childr en ra ise th eir v oi c e s an d risk de v e lop ing hoar s e ness a nd vocal n odu le s be c a us e of n oise an d re lati v e ov er c rowd ing. The number of c hi ldre n sc reami ng s o much a nd so l oud ly t hat the ir voices a re damag ed and r equ ire treatment in c rea s e d in Denma rk dur ing the 19 90s. Noi s e in sc ho ols an d da y c are in s tit utio n s re s ult s i n b oys ’ v oi c e s getti ng hoar s e and girl s ’ v o ic e s sque aky . Chi ldre n wit h voc a l nod ule s can be d iffi c u lt to un d ers ta nd and r isk l os i ng t heir v oi c e s al tog ether . Other c h ildr en b ec ome s o tire d of s c r eami ng or of tr y in g to mak e themse lv e s h eard that the y g ive up s aying an y th ing at a ll and, for ex ampl e, d o n ot ra is e the ir h a nds in c la s s. If c hi ldre n g iv e up s pe aking, the ir voi c es do not d evelo p pro perl y a nd lan g uage le arni ng i s n ot re inf orc e d .
References:
•Boman, E. T he effe c ts of noi se an d ge nder on c h ildr en's e pi s od ic and s eman tic memory . Scandinavian Journal of Psychology, 2004, 45:40 7 –41 6.
•Bowen C.Vocal nodules and voice strain in pre-adolescents . 1997 (members .tr ipo d.com/Ca rolin e_ B owen/t een- nodu le s .htm, a c cess ed Nove mber 200 9).
•Clark C et a l. Expo s ure- effe c t relat ion s b etwee n air c raf t an d road tr affi c n oi s e e x p os ur e at s c h ool an d rea din g c ompreh en s io n: th e RANCH p roje c t. Am J Epidemiol . 20 06, 163: 27-3 7.
Transport noise is an increasingly prominent feature of the urban environment, making noise pollution an important environmental public health issue. This paper reports on the 2001-2003 RANCH project, the first cross-national epidemiologic study known to examine exposure-effect relations between aircraft and road traffic noise exposure and reading comprehension. Participants were 2,010 children aged 9-10 years from 89 schools around Amsterdam Schiphol, Madrid Barajas, and London Heathrow airports. Data from The Netherlands, Spain, and the United Kingdom were pooled and analyzed using multilevel modeling. Aircraft noise exposure at school was linearly associated with impaired reading comprehension; the association was maintained after adjustment for socioeconomic variables (beta = -0.008, p = 0.012), aircraft noise annoyance, and other cognitive abilities (episodic memory, working memory, and sustained attention). Aircraft noise exposure at home was highly correlated with aircraft noise exposure at school and demonstrated a similar linear association with impaired reading comprehension. Road traffic noise exposure at school was not associated with reading comprehension in either the absence or the presence of aircraft noise (beta = 0.003, p = 0.509; beta = 0.002, p = 0.540, respectively). Findings were consistent across the three countries, which varied with respect to a range of socioeconomic and environmental variables, thus offering robust evidence of a direct exposure-effect relation between aircraft noise and reading comprehension. •J ess e n B, Ru ge G. S k ol ebørn s k ri ger s ig sy ge [Sc h ool c h ildr e n sc ream u ntil the y g et s ick ]. Berlingske Tidende, 2000 :26.
Children and noise Children and noise
41 EFFECTS OF NOISEEFFECTS OF NOISE…… . .
A WORD APART FOR A WORD APART FOR TEENAGERSTEENAGERS!!!! /xrhombusP otential sources of hearing impairment
• N oisy toys, firecrackers, boom-cars, musical instrumen ts, others /xrhombusD iscotheques and pop concerts
• E xposure similar to occupational exposures
• Use of music headphones
/xrhombusL oss of hearing may go undetected for many years afte r chronic exposure to high levels of noise
/xrhombusIncreased rates of adolescent hearing impairment in l ast 3 decades /xrhombusProtection needed from the start
• B e instructed to use personal hearing protection
• Not only at work but also at technical and polytechni c schools
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Nois e is ass o c ia ted w ith y ou th . Often, tee nag ers ' e x po s ure i s constant . Prol ong ed ex p osur e c a n l ead to a transitor y l oss of 10-3 0 dB for s everal minut es after the noi s e c ea s e s . Fr equ ency o f e x po s u re, per s o nal v ar iab ilit y , and age of e x p os ur e d etermin e the pat tern of t he d amage.
Mus ic occ ur s ou ts i de of th e major fr equ en c ie s of the human v oi c e and over e x po s ure to lou d mus i c cau s es lo s s of disc rimin atio n at low fr equ en c i es wh ic h may n ot b e d ete c ted withou t formal te s tin g for y e ars. “Wal k man” e qui pment is d esign ed f or emiss i ons not high er th an 80 dB , bu t th e co mbinati on of a n immature hear ing s ys tem a nd a prolo nge d u s e ma y cause cumulat iv e damag e. T ec h nol og y can b e modif ied to by p ass fa c to ry -impo s ed limita tio ns and r es u lt i n very lou d music / n oise e x po s ure . Lo s s of c on c e n tratio n b ec a use of the focus o n the mus i c , i n th e presence of a p ote ntia ll y da ng erou s situa tio n, makes a youn g per s on more vuln erab le t o a c c i den ts .
Teen ager s shou ld be i ns tr uc t e d to us e per s on al hear ing prot ec ti on as s oo n a s th ey s tar t be ing ex p osed t o hi gh noi s e l evels, no t on ly at wor k , but a lso at techni c a l an d p olyt ec h nic sc ho ols. If noi s e-a bat e ment measures are not tak e n, g ood he arin g wil l no t be preserved a nd noi s e-i ndu c e d tinn itu s wil l no t be pre v en ted. The e x te nt o f he arin g impairment in t een ager s , c au sed b y o c cupat ion al noi s e ex p o s ure, an d e x po s ure at t echni cal a nd p ol y te c hn ic s c h ool s i s u nknown.
There are insuffi c i ent number s of stud ies on s omat ic , psy cho -s ocial an d be ha v io ural effe c t s of n oise i n te ena gers.
References :
•Ax el s son A. et a l. Ear ly noi s e- i ndu c ed hear ing lo s s in t een ag e bo y s . Scand Audiol, 198 1:10 : 91 – 96. •Baig LA. et al. Heal th a nd s af e ty mea s ure s a v ai lab le f or youn g la bour ers in t he c ott age ind u s trie s o f Kara c h i. J Coll Physicians Surg Pak, 2005, 15:380.
•Fonta na AM. e t al . Bra z il ian y o ung adu lts and no is e : Atti tud e s , h abit s , and aud iol ogi c a l cha rac ter is t ics .
International Journal of Audiology , 20 09, 48(1 0):69 2-69 9 •Plont k e SK et a l. T he i ncide nce of ac o us t ic trauma due to Ne w Year's fir ec ra c kers . Eur Arch Otorhinolaryngol, 2002 , 25 9:2 47-5 2.
•Ry berg J B. A nat ion al proj ec t t o evalu ate and red uc e hi gh s o und press ur e le v e ls fr om mus i c . Noise Health, 2 009 , 11(43) :12 4-8.
•Segal S. et al. Inn er e ar dama ge i n chil dren due to noi s e ex p os ur e from to y c ap pi s tol s a nd firec r ack er s : a retrospe c ti v e re v i ew of 53 c ases . Noise Health, 200 3, 5 :13- 8 . •Vogel I et .al. You ng Pe opl e’ s Ex po s ure to Lou d Mus i c . A S u mmary of th e Li terat ure. Am J Prev Med, 20 07, 33(2): 124- 133.
Children and noise Children and noise
42 1. Introduction
2 . Vulnerability of children
3. Adverse health effects
4. Effects by age-group
5. T
aking action
6. Discussion
C
ONTENTS CONTENTS
Children and noise Children and noise
43 PREVENTIONPREVENTION AND AND INTERVENTIONINTERVENTION /xrhombusMore research needed, especially in vulnerable group s /xrhombusP
reventive action
/square4N oise has to be controlled at the source
/square4Hearing protection devices are a last resort
/xrhombusC hild hearing conservation programs
/xrhombusEducation and dissemination
Future research:Future research:Future research:Future research:
•Effects of noise on cognitive functions.
•Effects of noise on children’s sleep.
•Magnitude/significance of noise annoyance.
•Children’s perception and risk perception.
•Settings: home, schools, hospital, day care centres.
•Teenagers' attention when driving and listening to loud music. •Effect of non-audible noise.
•Identification of more vulnerable groups!
•Intervention programs/best practices for preventing harmful effects.
Preventive actionsPreventive actionsPreventive actionsPreventive actions Noise has to be controlled at the source by:
•Reducing.
•Enclosing the vibrating surfaces.
•Placing sound absorbers and other protections.
Hearing protection devices are a last resort!
Child hearing conservation programChild hearing conservation programChild hearing conservation programChild hearing conservation program •Noise monitoring where children live, study and play.
•Hearing protection programs diffusion for teachers and parents. •Vibration detection and protection.
•Protection of the pregnant woman.
Education and disseminationEducation and disseminationEducation and disseminationEducation and dissemination References:
•Folmer RL, et al. Hearing conservation education programs for children: a review. J Sch Health. 2002;72:51-7.
Prevalence of noise-induced hearing loss (NIHL) among children is increasing. Experts have recommended implementation of hearing conservation education programs in schools. Despite these recommendations made over the past three decades, basic hearing conservation information that could prevent countless cases of NIHL remains absent from most school curricula. This paper reviews existing hearing conservation education programs and materials designed for children or that could be adapted for classroom use. This information will be useful as a resource for educators and school administrators and should encourage further development, implementation, and dissemination of hearing conservation curricula. The overall, and admittedly ambitious, goal of this review is to facilitate implementation of hearing conservation curricula into all US schools on a continuing basis. Ultimately, implementation of such programs should reduce the prevalence of noise-induced hearing loss among children and adults. •Moeller. Environmental Health, Harvard University Press, 1992.
Children and noise Children and noise
44 WHEREW HERE TO INTERVENE?TO INTERVENE?
/xrhombusT echniques for reducing or eliminating noise
• A t the source
• By installing a barrier between the source and the re cipient • At the point of reception / At the human recipient
/xrhombusP otential settings for intervention
• N ICU
• Child care settings
• Primary schools
• Discotheques and rock festivals
/xrhombusA ddress external and internal noise sources
TA
KING ACTION
< < R E A D S L I D E > > < < R E A D S L I D E > > < < R E A D S L I D E > > < < R E A D S L I D E > > I d e n t i f i e d p o t e n t i a l s e t t i n g s f o r i n t e r ve n t i o nI d e n t i f i e d p o t e n t i a l s e t t i n g s f o r i n t e r ve n t i o nI d e n t i f i e d p o t e n t i a l s e t t i n g s f o r i n t e r ve n t i o nI d e n t i f i e d p o t e n t i a l s e t t i n g s f o r i n t e r ve n t i o n
1.NICU
2.Child c ar e setti ngs : more and more c h ildr en s ta y in v ar iou s child c ar e setti ng s . Th ese pl ay an impor tant rol e in the init ial s ta ge s of c h ildr en b egi n ning to es t abl is h the ir b as i c e d uc at ion .
3.Primary sc h ool s : primary s ch ool c hi ldre n of ten s p end lon g p eriod s o f time in o ne c la s s r oo m, and a no isy room c an advers e ly affe c t t he occ up ant s of th at ro om.
4.Dis cothe que s a nd r ock fe s ti v al s : t he n oi s e l evel c an be v er y high in di s c o the que s , o ften resulti ng i n ti nni tus or a temporar y thr es h old s h ift among p atron s . Man y maj or citie s have fe s ti v al s , and ma ny of th e no is i er at tractio ns ine v ita bl y ap pea l to y o ung er p eopl e.
References:
•Bis tru p M.L., K eid ing L., ed. ( 2 002). C hil dren and no is e - pre v ent ion of adverse eff ec t s . Copenhagen, National Institute of Public Health (a ls o av a ila ble at www.ni ph. dk ). •By ers J F, et al. S oun d le v e l e xposure o f hi gh-ri s k inf ant s i n di fferen t en v ir onment al c on diti o ns . Neonatal Netw. 2006 , 25( 1):2 5-32.
PURPOSES: To provide descriptive information about the sound levels to which high-risk infants are exposed in various actual environmental conditions in the NICU, including the impact of physical renovation on sound levels, and to assess the contributions of various types of equipment, alarms, and activities to sound levels in simulated conditions in the NICU. DESIGN: Descriptive and comparative design. SAMPLE: Convenience sample of 134 infants at a southeastern quarternary children's hospital. MAIN OUTCOME VARIABLE: A-weighted decibel (dBA) sound levels under various actual and simulated environmental conditions. RESULTS: The renovated NICU was, on average, 4-6 dBA quieter across all environmental conditions than a comparable nonrenovated room, representing a significant sound level reduction. Sound levels remained above consensus recommendations despite physical redesign and staff training. Respiratory therapy equipment, alarms, staff talking, and infant fussiness contributed to higher sound levels. CONCLUSION: Evidence-based sound-reducing strategies are proposed. Findings were used to plan environment management as part of a developmental, family-centered care, performance improvement program and in new NICU planning.
Children and noise Children and noise
45 HOWH OW TO INTERVENE?TO INTERVENE?
TechnicallyTechnically
/xrhombusP lanning and designing outdoors and indoors “soundsca pes” /xrhombusImproving road surfaces and developing green spaces a nd green barriers /xrhombusDeveloping noise barriers, building sound insulation /xrhombusPlanning internal spaces according to activities (e.g . schools, sports- centres, others that involve noise), strategically using the space & location /xrhombusReducing internal noise (eg. fans, ventilators) /xrhombusUsing sound-absorbent materials
/xrhombusSetting sound limits for concerts
/xrhombusIncreasing public and professional education to recog nize noise pollution and reduction!
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
46 Organizationally and EducationallyO rganizationally and Educationally /xrhombusE ducating children, adults, professionals
/xrhombusTeaching methods/interventions
/xrhombusDisseminating information
/xrhombusInforming the media and decision-makers and health pr
ofessionals!
/xrhombusCreating silent areas (“silence islands”) for resting /xrhombusD
istributing earplugs at work and setting limits for the earphones /xrhombusIdentifying and turning off noise at the source! HOWH OW TO INTERVENE?TO INTERVENE?
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
47 PlanningP lanning
/xrhombusI dentifying noise sources and recognizing noise as a problem /xrhombusRecognizing health effects in children caused by nois e /xrhombusRecognizing and diagnosing adults' health problems or
iginated in childhood exposure
/xrhombusRaising awareness
/xrhombusSetting-up noise control campaigns in hospitals and s chools /xrhombusApplying the “Precautionary Principle”
/xrhombusThinking about noise exposure when planning the setti ngs where children dwell
/xrhombusPromoting sound landscape design
/xrhombusDeveloping noise mapping, action plans, community inv olvement /xrhombusStandardizing noise measurements
HOWH OW TO INTERVENE?TO INTERVENE?
< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > >< < R E A D S L I D E > > Children and noise Children and noise
48 POINTS FOR POINTS FOR DISCUSSIONDISCUSSION
< < N O T E T O U S E R : A d d p o i n t s f o r d i scu ss i o n a c co r d i n g t o t h e n e e d s< < N O T E T O U S E R : A d d p o i n t s f o r d i scu ss i o n a c co r d i n g t o t h e n e e d s< < N O T E T O U S E R : A d d p o i n t s f o r d i scu ss i o n a c co r d i n g t o t h e n e e d s< < N O T E T O U S E R : A d d p o i n t s f o r d i scu ss i o n a c co r d i n g t o t h e n e e d s o f yo u r a u d i e n c e . > > o f yo u r a u d i e n c e . > >o f yo u r a u d i e n c e . > >o f yo u r a u d i e n c e . > > Children and noise Children and noise
49 First draft prepared by Lilian Corra, MD, Argentina
W
ith the advice of the Working Group on Training Package for the Health Sector:
Cristina Alonzo, MD (Uruguay); Yona Amitai, MD, MPH (Israel); Stephan Boese- O’Reilly, MD, MPH (Germany); Stephania Borgo MD (ISDE, Italy); Irena Buka, MD (Canada); Ligia Fruchtengarten, MD (Brazil); Amalia Laborde, MD (Uruguay); Leda Nemer, TO (WHO/EURO); R. Romizzi, MD (ISDE, Italy); Katherine M. Shea, MD, MPH (USA) .
Reviewers: Yoon JungWon (Republic of Korea)
WHO CEH Training Project Coordination: Jenny Pronczuk, MD Medical Consultant: Ruth A. Etzel, MD, PhD
Technical Assistance: Marie-Noel Bruné, MSc
Latest update: December 2009 (C. Espina, PhD)
ACKNOWLEDGEMENTSA
CKNOWLEDGEMENTS
WHO is grateful to the US EPA Office of ChildrenW HO is grateful to the US EPA Office of Children’’s Health Protection for the s Health Protection for the financial support that made this project possible and for the dafinancial support that made this project possible and for the data, graphics and text ta, graphics and text used in preparing these materials.used in preparing these materials.
We are indebted to Dr. Stephen Stansfeld (Queen Mary, University of L
ondon) for kindly lending us slides for this project.
Children and noise Children and noise
50 DISCLAIMER
• T he designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Dotted lines on maps represent approximate border lines for which there may not yet be full agreement. • The mention of specific companies or of certain manuf acturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.
• The opinions and conclusions expressed do not necessa rily represent the official position of the World Health Organization.
• This publication is being distributed without warrant y of any kind, either express or implied. In no event shall the World Health Organization be liable for damages, including any general, special, incidental, or consequential damages, arising out of the use of this publication
• The contents of this training module are based upon r eferences available in the published literature as of June 2004. Users are encouraged to search standard medical databases for updates in the science for issues of particular interest or sensitivity in their regions and areas of specific concern. • If users of this training module should find it neces sary to make any modifications (abridgement, addition or deletion) to the presentation, the adaptor shall be responsible for all modifications made. The World Health Organization disclaims all responsibility for adaptations made by others. All modifications shall be clearly distinguished from the original WHO material.
EXHIBIT 13
Journal of Urban Health: Bulletin of the New York Academy of Medicine, Vol. 89, No. 6 doi:10.1007/s11524-012-9721-7
* 2012 The New York Academy of Medicine
Noise Levels Associated with Urban Land Use
Gavin King, Marek Roland-Mieszkowski, Timothy Jason,
and Daniel G. Rainham
ABSTRACT Recent trends towards the intensification of urban development to increase urban densities and avoid sprawl should be accompanied by research into the potential for related health impacts from environmental exposure. The objective of the current study was to examine the effect of the built environment and land use on levels of environmental noise. Two different study areas were selected using a combination of small area census geography, land use information, air photography, and ground- truthing. The first study area represented residential land use and consisted of two- to three-story single-family homes. The second study area was characteristic of mixed-use urban planning with apartment buildings as well as commercial and institutional development. Study areas were subdivided into six grids, and a location was randomly selected within each grid for noise monitoring. Each location was sampled four times over a 24-h day, resulting in a total of 24 samples for each of the two areas. Results showed significant variability in noise within study areas and significantly higher levels of environmental noise in the mixed-use area. Both study areas exceeded recommended noise limits when evaluated against World Health Organization guidelines and yielded average noise events values in the moderate to serious annoyance range with the potential to obscure normal conversation and cause sleep disturbance.
KEYWORDS Noise, Land use, Urban, Geographic information systems, Sound level meter
INTRODUCTION
The human environment has become increasingly shaped by urbanization and the built environment, which comprises the physical infrastructure arising from urban development as well as managed green space such as urban forests, parks, and sport fields.
1 Indeed, more than half of the global population and over 80 % of North Americans now reside in urban areas. 2 The built environment is now attracting the attention of public and environmental health researchers, as its inherent quality, characteristics, and spatial orientation (i.e., urban sprawl) have been linked both positively (e.g., parks, trails) and negatively (obesity, injuries, stress) to a variety of health outcomes.
3,4 Increasing urbanization has been linked to a rise in the prevalence of health disparities, as well as a growing culture of sedentary living, King is with the Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada; Roland-Mieszkowski is with the Digital Recordings —Advanced Research and Development, Halifax, Nova Scotia, Canada; Jason and Rainham are with the Environmental Science, Faculty of Science, LSC827, Dalhousie University, Halifax, Nova Scotia, Canada.
Correspondence: Daniel G. Rainham, Environmental Science, Faculty of Science, LSC827, Dalhousie University, Halifax, Nova Scotia, Canada. (E-mail: [email protected]) 1017
contributing to the development of several chronic disease outcomes. 5 In efforts to improve urban conditions and enhance human well-being, municipal planning groups have developed and promoted several initiatives, including mixed-use development strategies. A potential consequence of these strategies is an increase in environmental noise levels.
Environmental noise is an increasingly common feature of urban areas that can be described as an unwanted or undesirable sound within non-occupational settings.
Road, rail, and air traf fic sources account for the majority of noise in urban and surrounding areas.
6 Additional sources of noise include industrial/commercial enterprise, construction projects, and such familiar domestic sources as pets and radios/stereos. Municipal planning strategies emphasizing increases in urban development densities, mixed-uses, as well as a continuation of automobile-centered traffic planning policies may lead to an increase in population level exposure to traffic and related urban environmental noise. At present, little is known regarding how noise levels may vary with forms of urban development and affect the health of a population.
Environmental noise has been linked to several non-auditory, biologically relevant health outcomes, including: increased levels of hypertension and high blood pressure,
7 lowered cognitive ability, 8 and an increased prevalence of cardiovascular disease.9 Exposure to environmental noise from traf fic-related sources is reportedly the most annoying of all urban pollution types, 10 interfering with enjoyment of daily activities and largely affecting sleep and rest patterns. 10–12 In a recent Canadian survey, 20 –28 % of urban populations attributed noise from road traf fict o disruptions during sleep, conversation, and communication tasks such as reading and writing. 13 Few studies have conducted field measurements to assess levels of environmental noise in Canadian cities; furthermore, it is still unknown whether recent trends towards the intensi fication of urban development will impact environmental noise levels and in turn population health.
Acceptable noise level guidelines have been developed by several agencies based on levels of annoyance, interference with communications, disturbance to sleep, and the potential to cause hearing impairments. 14,15 For example the US Environmental Protection Agency recommended a maximum indoor noise level of 45 dB(A) * and outdoor noise level of 55 dB to allow for intelligible communication. 16 Typically, values are derived for speci fic settings and time periods. Some agencies also provide guidelines according to land use and population density (e.g., Italian legislation in 1997). Recommended urban residential noise levels generally range from 45 to 55 dB depending on the time of day and location of measurement. For example, Australian Environmental Protection Authority noise guidelines state that noise levels in urban residential neighborhoods should not exceed 55 dB(A) during the day and 47 dB(A) at night (i.e., from 22:00 to 06:00). The maximum recommended noise levels generally increase in relation to the amount of commercial activity, which presents challenges for cities dev eloping policies related to integrated residential and commercial land uses.
As with many urban centers in Canada and abroad, the Halifax Regional Municipality intends to intensify urban development by combining residential and *Sound is measured by comparing the logarithm of a given sound to a reference sound pressure, and is expressed on a logarithmic decibel (dB) scale. The A-weighting [dB (A)] system was devised to adjust results in studies examining the impact of environmental noise on human hearing speci fically.
KING ET AL.1018
commercial land-use types. The objective is to promote mixed-use neighborhoods with focused development in core areas. A number of reasons have been cited for this development strategy including the high costs of municipal services and rising costs of health care (e.g., obesity, transportation injuries) related to sprawl and associated increased automobile use.
17–19 Research into these issues is required not
only to protect the health and well-being of urban inhabitants, but also to ensure that planning decisions are based on evidence that considers the potential health and environmental consequences of development. To date, few studies have examined how noise varies as a function of urban development.
The aim of this study was to assess and compare noise levels in two urban neighborhoods: one completely residential and comprised of mostly single and multi-family dwellings, and the other characteristic of mixed residential and commercial land uses. Ambient environmental noise was recorded, measured, and analyzed within de fined spatial locales in order to determine the potential for cumulative exposure to the local population. This research is timely and potentially informative given current trends in urban development.
METHODS
For the purpose of this study, two neighborhoods were selected: one almost exclusively residential to represent traditional planning strategies and the other comprised of residential and commercial land uses to represent more modern planning strategies that emphasize mixed-use development in urban core areas. The boundaries of each neighborhood matched the smallest statistical boundaries developed for the dissemination of Canadian census data (see Figure 1). Area 1, the representative residential area, mostly contained single-family dwelling units up to 10 m in height with 653 residents and a population density of approximately 3,950 persons per square kilometer. Buildings in this area are generally free standing and constructed of wood, stone, and brick. Area 1 also included seven roads (total length=3,506 m) that either border or are situated within the area. Area 2, representing mixed commercial and residential land uses, was larger in area yet housed a smaller population of 566 residents (1,836.5 persons per square kilometer). This area is bounded by several major roads and is generally oriented east to west. Area 2 contains commercial, institutional, and residential zones, with mostly concrete multi-story buildings. Sixteen roads traversed the area totaling 6,271 m in length.
Sampling Strategy
Study areas 1 and 2 were each divided by a grid into six identical cells. A geographic information system was used to randomly select one sample site location within each cell in the following manner. First, road network polygons were imported and a 4-m buffer polygon was inserted from the edge of the road. Second, a spatial random point generator, constrained to one point per grid cell within the buffer polygon, identified six sampling locations per study area. As a result, one randomly selected sample point per grid square was included in the analysis (Figure 1). Forty-fi ve- minute noise recordings were randomly sampled during each of four distinct time periods from each of the six sampling locations per study area.
Environmental noise sampling methods vary considerably. For example, studies have used a sampling frequency of 15-min measurements every 2 h, 20 while others
have employed continuous assessments. 21 Studies have measured noise levels during NOISE LEVELS ASSOCIATED WITH URBAN LAND USE 1019
the day and at night, 22 while others have only considered measurements during the day.23 In 2007, Ng and Tang adopted a three-period assessment in which a 24- h clock was divided into three periods (day, evening, and night) that differed slightly in their period start times and sample lengths. 24 For the purpose of the current study, we incorporated a modi fied version of the three-period assessment method with certain re finements, as discussed by Ng and Tang, 24 for improving statistical accuracy. Each sample location yielded 3 h of data distributed across four time periods (i.e., 45 min per sampling period for each location). Daytime periods were subdivided into morning (06:00 –12:00 h) and afternoon (12:00 –18:00 h) segments FIGURE 1. Study areas and sampling sites.
KING ET AL.1020
to enhance assessment quality. In addition, hours in the evening period (18:00 – 24:00 h) and the night period (24:00 –06:00 h) were randomly sampled in order to capture the full daily spectrum of environmental noise production.
Data Collection
Noise data were collected using a Centre 322 Logging Sound Level Meter (SLM) and a Marantz PMD-660 Solid State Digital Recorder. The Centre SLM is an ANSI S1.4 Type 2 instrument with a 0.5 ″ electrets condenser microphone, frequency range of 31.5 Hz to 8 KHz, measuring level range of 30 –130 dB, and capacity to weight frequencies to either the A or C scale. The Marantz PMD-660 Solid State Digital Recorder was connected to an external microphone that can record 4 h of data at frequencies of 44.1/48 KHz.
The SLM and sound recorder were mounted on a camera tripod and microphone stand at a height of 1.5 m, a distance of 0.5 m from the curb, and were oriented perpendicularly to the nearest road. The SLM logged noise using an average of 1 s measurements, while the digital sound recorder facilitated continuous recordings to qualitatively identify peak noise events. Recordings commenced at the top of each hour (e.g., 1:00, 2:00 …); in addition, the particular time at which recordings commenced was randomly assigned to sample locations thereby ensuring that the full 6-h time period (i.e., day, afternoon, evening, and night) was sampled. No data collection occurred on days ( n=2) with rain, snow, or high winds, because these elements can both damage equipment and decrease the accuracy of measurements.
Preliminary analysis of noise data from a related and, as of yet, unpublished study found that weather conditions, precipitation and wind in particular, had no influence on noise levels measured at a frequency of one measurement per hour.
This conclusion was derived from comparing statistically noise levels measured during high wind or rain events (or both) with noise levels during times when weatherproofing of instrumentation would not be required.
Data Analysis
The SLM data included the minimum and maximum sound pressure level (SPL) averaged over 1 s, which resulted in 2,700 data points for each sampled time period and 10,800 data points for each grid sample area in a 24-h period. Basic noise descriptors were calculated. In addition, the equivalent continuous sound pressure level ( L
Aeq) and day –evening–night composite whole-day rating level ( LRden) were derived for the sample periods, grid sample areas, and study areas to identify variations in environmental noise over both space and time.
The two study areas were statistically evaluated and compared. First, each study area was examined individually to determine the spatial variation of environmental noise during each 6-h period and the full 24-h period. Noise levels associated with individual sample sites within each study area were compared statistically using a series of Kruskal –Wallis tests for non-parametric data. Then, the two primary study areas were compared statistically using the Mann –Whitney two-sample rank test.
L
Aeq values were compared with environmental noise exposure limits as dictated by Italian legislation (see Piccolo et al. 2005 for the exposure limits). In order to accomplish this, the study data were recalculated to correspond with the standardized time periods adopted by Italian legislation. This approach provided a means to determine levels of noise exposure with comparison to standards developed to prevent potential human health risk.
NOISE LEVELS ASSOCIATED WITH URBAN LAND USE 1021
Calculation
Each study area yielded 18 h of data comprising 3 h per site (four time period samples of 45 min each). The A weighted equivalent continuous sound pressure level (LAeq) was calculated for each sample using the following formula:
LAeq ¼ 10 log 1
T
/C0/C1 R
PA2ðtÞ
po 2 dtðÞ ð1Þ
PA
2 – The A-weighted instantaneous sound pressure at the running time t; po – The standard reference sound 20 μPa
The resultant LAeq values were then adjusted according to the particular sampled time period (+5 dB for evening hours and +10 dB for night-time hours) using the formula indicated below:
L
Reqj;Tn ¼ LAeqj;Tn þ Kj ð2Þ
Kj – Adjustment for the specifi ed sample and time period; LAeqj,Tn – The actual LAeq value at the speci fied time period Using the adjusted LAeq values, the day –evening–night rating levels were derived using the following formula:
LRden ¼ 10 log d
24 /C2 10
LRd
10 þ e
24 /C2 10
LRe
10 þ 24 /C0 d /C0 eðÞ
24 /C2 10
LRn
10 /C20/C21
db ð3Þ
d – The number of daytime hours;
n – The number of night-time hours;
e – The number of evening hours;
LRd – The rating level for daytime hours including adjustments; LRe – The rating level for evening hours including adjustments; LRn – The rating level for night-time hours including adjustments
RESULTS
Area 1
The distribution of sound in area 1 was skewed to the right and somewhat peaked with an overall mean sound level of 48.1 dB(A) (SD=7.6) and substantial variation among individual sites (Table 1). Maximum values for the individual sites ranged from 60.6 dB(A) at site 6 to 93.3 dB(A) at site 3, while minimum values ranged from
20.0 dB(A) at site 3 to 47.0 dB(A) at site 4. Site 3 evidenced the greatest range of sound with night recordings of 20.0 dB(A) to 93.3 dB(A). L A90 values (90th
percentile), representing background noise in the area, ranged from a low of 38.2 dB (A) at site 3 to a high of 50.3 dB(A) at site 4. Site 3 yielded higher than average L A1
values (1st percentile), indicating high levels of road traf fic near the sample points.
Adjusted (Adj) LAeq values ranged from a low of 44.7 dB(A) at site 6 to a high of
76.8 dB(A) at site 3. A comparison of the four sample time periods across sites evidenced maximum SPLs between 71.3 dB(A) and 77.4 dB(A) and mean SPLs from a low of 44.0 dB(A) to a high of 51.5 dB(A) (Table 2). L A90 values for the four time
periods ranged from a low of 41.6 dB(A) to a high of 45.4 dB(A), while Adj LAeq (/C22x ¼ 57:3d BAðÞ ) values ranged from a low of 56.0 dB(A) to a high of 59.1 dB(A).
Table 1 shows site 3 ( /C22x ¼ 68:9d BAðÞ ) and the night period ( /C22x ¼ 59:1d BAðÞ )a s having the highest overall Adj LAeq levels.
KING ET AL.1022
As evident from Table 1, Adj LAeq values peaked at 05:00, 09:00, 14:00, and 23:00 (site 3), as well as at 21:00 and 00:00 (site 4). LAeq values mirrored this trend.
The results suggest that the maximum values associated with these particular sites may have augmented the average noise level of the study area. The composite whole day rating for area 1 equaled 63.8 dB(A).
A signi ficant difference in noise among individual sample sites in area 1 was observed, χ2 (5, N=24)=16.2, p=0.01. Site 6 was associated with the lowest Adj LAeq levels in the area ( /C22x ¼ 51:8) yet produced a comparatively high number of outlier values throughout the day from elevated noise events. Site 3, which contributed the highest levels of environmental noise in area 1 ( /C22x ¼ 68:9), yielded a different data distribution pattern with fewer outlier points all of which occurred in the evening and night-time periods. A similar comparison across time periods failed to yield a signi ficant difference, χ
2 (3, N=24)=0.55, p=0.91.
TABLE 1 Summary statistics for area 1
Site Period Start time Max Min Mean
Percentiles
LAeq Adj LAeqLA1 LA90
1 1 07:00 73.0 40.1 44.2 60.0 41.8 48.5 48.5
2 16:00 73.3 41.4 47.7 65.1 42.8 53.2 53.2
3 18:00 66.6 25.8 43.9 61.8 39.5 49.2 54.2
4 03:00 66.3 41.7 43.9 51.6 42.8 45.0 55.0
2 1 08:00 72.9 43.7 51.3 67.4 46.3 55.4 55.4
2 12:00 75.4 40.9 48.0 63.7 43.3 53.0 53.0
3 22:00 65.2 21 44.2 55.9 41.5 46.6 51.6
4 01:00 66.3 38.8 40.3 49.2 39.4 42.0 52.0
3 1 09:00 90.0 42.3 61.4 80.3 48.0 69.1 69.1
2 14:00 86.6 40.0 57.7 76.3 45.7 66.3 66.3
3 23:00 81.4 37.0 43.1 72.1 38.2 58.6 63.6
4 05:00 93.3 20.0 48.0 77.6 43.3 66.8 76.8
4 1 10:00 79.8 47.0 58.1 67.1 50.3 63.1 63.1
2 15:00 77.5 23.0 49.0 56.7 43.0 54.8 54.8
3 21:00 78.9 43.9 53.8 63.7 46.8 60.0 65.0
4 24:00 77.4 39.9 45.8 55.0 41.3 52.9 62.9
5 1 11:00 72.7 41.6 50.8 66.6 43.9 55.4 55.4
2 13:00 77.5 23.0 49.0 66.7 43.0 54.8 54.8
3 19:00 73.9 37.8 48.4 65.0 40.5 54.2 59.2
4 04:00 63.7 42.2 44.4 53.4 43.0 45.2 55.2
6 1 06:00 67.9 40.5 43.1 50.0 41.9 44.7 44.7
2 17:00 73.8 42.6 49.8 66.5 45.6 54.6 54.6
3 20:00 73.4 41.2 45.5 61.7 43.0 50.3 55.3
4 02:00 60.6 38.3 41.7 51.5 40.2 42.7 52.7
TABLE 2 Statistical values for area 1 by sample time period Max Mean LA1 LA90 LAeq Adj LAeq
Morning 76.0 51.5 66.4 45.4 56.0 56.0
Afternoon 77.4 50.2 67.5 43.9 56.1 56.1
Evening 73.2 46.5 64.6 41.6 53.2 58.2
Night 71.3 44.0 57.8 41.7 49.1 59.1
NOISE LEVELS ASSOCIATED WITH URBAN LAND USE 1023
Area 2
Data from area 2 yielded a similar distribution to area 1 with an overall mean of
56.6 dB(A). However, area 2 evidenced less variation in recorded sound values among individual sites and time periods (Table 3). Peak SPLs ranged from 69.7 dB (A) at site 2 to 90.3 dB(A) at site 6, while LA90 values ranged from a low of 44.0 dB (A) at site 6 to a high of 59.3 dB(A) at site 1. Adj LAeq values across sites ranged from a low of 55.4 dB(A) at site 4 to a high of 72.2 dB(A) at site 6. A comparison of the four sample time periods across sites yielded maximum SPLs between 77.2 dB(A) and 84.9 dB(A). L
A90 values for the four time periods ranged from a low of 47.1 dB (A) to a high of 54.6 dB(A), while Adj LAeq values ranged from 61.8 dB(A) in the afternoon to 66.3 dB(A) at night (Table 4). The results indicate that area 2, the mixed use area, is associated with a more consistent level of environmental noise across sample sites. For example, L
A90 values were highest recording in the
afternoon at 54.6 dB(A), which varied little from the morning value of 53.1 dB (A), and then decreased through the evening to 47.1 dB(A) at night. Site 6 (/C22x ¼ 69:9d BAðÞ ) and the night period ( /C22x ¼ 66:3d BAðÞ ) were associated with the highest overall Adj L
Aeq values (Table 3).
Table 3 displays LAeq and Adj LAeq values for selected sites over a 24-h period. As evident from this table, area 2 yielded Adj LAeq peaks at 01:00 (site 5), 03:00, 07:00, TABLE 3 Summary statistics for area 2
Site Period Start time Max Min Mean
Percentiles
LAeq Adj LAeqLA1 LA90
1 1 09:00 87.0 52.3 63.1 79.1 56.4 68.2 68.2
2 12:00 88.3 55.4 65.1 75.9 59.3 68.1 68.1
3 20:00 77.3 49.1 56.0 69.1 51.4 59.0 64.0
4 02:00 79.4 42.3 50.0 65.3 45.9 55.8 65.8
2 1 08:00 89.0 46.7 58.3 75.1 52.2 65.0 65.0
2 14:00 85.9 46.7 56.0 69.3 51.9 60.8 60.8
3 23:00 77.8 48.9 53.4 67.3 50.2 56.7 61.7
4 04:00 69.7 42.5 47.3 59.9 44.9 49.6 59.6
3 1 10:00 86.8 54.5 60.8 77.0 56.2 66.0 66.0
2 15:00 85.2 54.3 60.3 71.6 56.6 62.7 62.7
3 18:00 83.3 54.1 60.4 72.5 55.9 63.5 68.5
4 05:00 75.1 49.7 54.0 67.5 51.5 56.4 66.4
4 1 11:00 72.7 45.4 52.6 65.9 49.0 55.4 55.4
2 13:00 83.4 47.3 53.7 67.7 50.0 58.5 58.5
3 22:00 75.1 28.6 50.4 66.0 47.2 54.1 59.1
4 24:00 71.9 45.7 49.7 62.9 47.3 52.4 62.4
5 1 06:00 77.3 47.4 54.0 70.1 49.0 58.9 58.9
2 16:00 86.0 23.7 60.9 72.1 55.0 64.0 64.0
3 19:00 77.6 48.5 57.7 72.3 51.7 62.1 67.1
4 01:00 85.7 46.2 53.8 73.5 49.1 61.3 71.3
6 1 07:00 90.3 49.8 65.6 81.3 56.0 71.1 71.1
2 17:00 80.4 49.6 63.1 75.7 54.9 66.7 66.7
3 21:00 83.7 46.7 60.1 74.3 51.8 64.8 69.8
4 03:00 81.4 23.6 51.7 75.6 44.0 62.2 72.2
KING ET AL.1024
and 21:00 (site 6). LAeq values, although de flated, mirrored this trend. The composite whole day rating was calculated and produced a result of 65.0 dB(A).
A signi ficant difference in noise among individual sample sites in area 2 was yielded, χ2 (5, N=24)=14.51, p=0.01. However, a similar comparison across time periods failed to yield a signi ficant difference, χ2 (3, N=24)=1.29, p=0.73. Areas 2 and 1 sample sites exhibited similar patterns of variation among sample sites and time periods; still, area 2 evidenced fewer outlier points due to higher overall levels of environmental noise. Traf fic events characteristic of area 2 were absorbed by ambient background noise and therefore did not produce signi ficant increases in sound. In contrast, sample sites associated with less road traf fic and therefore lower ambient levels of noise produced more outlier points.
Comparison Between Areas 1 and 2
Differences were observed between the two sample areas both in terms of noise distribution and overall level s of environmental noise. First, Adj L Aeq
values among area 1 sites presented greater overall variability than area 2 sites (Figure 2). This difference can be attributed to variations in traf fic volume related to land use, background institutional noise, and pedestrian activity. The noisier sites in area 1 were located near major roads, while sites associated with less noise were located further from the sa me roads. Although area 2 evidenced higher overall levels of environmental noise, sample sites produced fairly consistent and stable noise recordings. The consistency in noise levels across sites in area 2 likely relates to land use and background noise. More speci fically, area 2 produces greater levels of background noise throughout the day from vehicle traf fic in the area, industrial sounds (e.g., ventilation fans), delivery trucks, and high pedestrian traf fic. This is con firmed by the higher L A90 values
(representing background noise) in area 2 in addition to higher Adj LAeq values as ar e s u l to fl a n du s e .
Results indicate that area 1 is more in fluenced by the disturbance effect of noise events. For example, a moving vehicle may generate an increase in sound levels of
10.0–30.0 dB(A), which would certainly lead to residential disturbances in area 1, yet remain unnoticed in the higher background sound levels inherent to area 2. It should be mentioned that the composite full day rating ( L Rden) values for the two
areas evidenced very little difference in daily sound exposure (area 1=63.8 dB(A); area 2=65.0 dB(A)).
Findings from the Kruskal –Wallis tests provide evidence of statistically different levels of environmental noise among sample sites in areas 1 and 2. Using the Mann – Whitney test, a signi ficant difference in Adj LAeq values associated with area 1 (mdn =55.1) and area 2 ( mdn =65.4) was obtained ( U=102, p=0.0001, r=0.56), thus supporting the hypothesis that land use (e.g., built environments) affects levels of environmental noise.
TABLE 4 Statistical values for area 2 by sample time period Max Mean LA1 LA90 LAeq Adj LAeq
Morning 83.9 59.1 74.8 53.1 64.1 64.1
Afternoon 84.9 59.9 72.1 54.6 61.8 61.8
Evening 79.1 56.3 70.3 51.4 60.0 65.0
Night 77.2 51.1 67.5 47.1 56.3 66.3
NOISE LEVELS ASSOCIATED WITH URBAN LAND USE 1025
DISCUSSION
The objective of the current research was to investigate and analyze spatial and temporal variations in environmental noise with respect to land use, speci fically the built urban environment. In the analyses it was important to account for differences between neighborhood types in order to assess how increasing the frequency of mixed-used development land use would impact urban environmental noise levels.
First, we found that noise levels varied signi ficantly between residential and mixed- use neighborhoods. Noise levels in the mixed-use neighborhood were signi ficantly greater than in the residential neighborhood. Second, noise values were analyzed to Area 1
Area 2
40 45
50 55
60 65
70 75
80 06:00-12:00 12:00-18:00 18:00-24:00 24:00-06:00
Site 1
Site 2
Site 3
Site 4
Site 5
Site 6
40 45
50 55
60 65
70 75
80 06:00-12:00 12:00-18:00 18:00-24:00 24:00-06:00
Site 1
Site 2
Site 3
Site 4
Site 5
Site 6
FIGURE 2. Adjusted LAeq values for areas 1 and 2.
KING ET AL.1026
determine the spatial and temporal variability within and between sample sites.
Greater variation in noise levels was found in the residential neighborhood. This reflected the co-location of the sound-level recording with major roads bounding the sample area, as well as speci fic traf fic-related noise sources such as buses, trucks, and street cleaning equipment. Noise variation within the sample areas was much greater in the residential neighborhood.
Analyses revealed statistically significantly higher levels of environmental noise in the mixed-use neighborhood (area 2) compared to the predominantly residential neigh- borhood (area 1). Area 1 generated absolute environmental noise levels within the range of an of fice environment or normal conversation both of which are considered comfortable for human hearing. Area 2, on the other hand, produced higher absolute environmental noise levels considered, according to annoyance scales, intrusive and slightly annoying. Noise values were on average ( L
eq) 8 db(A) greater during the day
and 6 dB(A) greater during night-time hours in the mixed-use neighborhood. The higher overall levels of noise in area 2 likely re flect the continual presence of vehicular and pedestrian traf fic in the area as well as background noise generated by institutional and industrial noise sources such as delivery trucks and ventilation systems. Evaluated against World Health Organization guidelines, both study areas yielded average noise events values in the moderate to serious annoyance range with the potential to obscure normal conversation and cause sleep disturbance. 14 Our results also show signi ficant variability in environmental noise within sample areas. With respect to area 1, environmental noise appeared to vary as a function of traffic patterns. For example, sites nearer to high traf fic roads (e.g., heavy truck or bus traf fic) presented higher levels of environmental noise. Because residential zones such as area 1 are associated with low(er) levels of background (i.e., continuous environmental noise) noise, traf fic events can potentially contribute to high levels of disruption and disturbance. For example, people living close to site 3 in the residential area experienced on average a 10-dB(A) higher noise level during night- time hours compared to residents living elsewhere in the study area (Table 3). Site 3 is closest to two relatively major roads that are preferred routes for commuter, truck, and traf fic from public transit (buses). In contrast, area 2 is associated with higher levels of background noise from steady traf fic flow; consequently, results evidenced less intra-study area variability in noise despite the higher levels of noise associated with sites near high-traf fic roads.
Our sampling approach also included measurement at random points within defined time periods to ensure sufficient noise measurements over a 24-h period. We did notfind signific a n td i f f e r e n c e si na v e r a g en o i s ev a l u e sa c r o s ss t u d ys i t e sw i t h i ne a c hs a m p l ea r e a (Figure 2). Noise levels were somewhat higher during daytime hours, although the differences with evening and night-time measurements were minimized once values were adjusted. The consistency of noise values among day, evening, and night-time periods in urban environments has also been found in other studies. 20,25
Although noise values in both study areas did not vary signi ficantly over time, there was relatively good correspondence in the intensity of average adjusted values between areas for the time periods selected. For example, noise levels increased incrementally from the afternoon, through the evening, and peaked in the overnight hours for both study areas, even though there was an overall difference in absolute noise levels. In both areas, adjusted noise levels were greater in the overnight hours, particularly for the residential study area (area 1). Adjusted noise levels in the residential study area will be affected greatly by unusual noise sources, such as loud motorcycles, automobiles, or even bus traffic, since typical noise values are much lower throughout the day. Normally NOISE LEVELS ASSOCIATED WITH URBAN LAND USE 1027
quiet neighborhoods in urban areas may thus be particularly prone to noise disturbances, especially during evening and night-time periods.
These findings support our initial hypothesis about the potential for variation in noise levels as a function of land use development in an urban environment. Urban planning initiatives developed to intensify urban d evelopment and promote mixed-use develop- ment may consider the potential for increased human exposure to noise and“design with noise in mind”, especially as there is good evidence in support of an association between environmental noise and stress-related health effects.
7,9 When compared to guidelines
designed to protect environmental quality and human health, adjusted noise levels in both areas exceed available recommended values for residential and mixed-use development and are indicative of relatively intensive land use development strategy (Table 5). Although Halifax is not a large city (population in 2006 of 372,675), noise levels in the mixed-use neighborhood are comparable to those measured in much larger urban centers such as Stockholm and Göteborg ( LAeq, 24h =62 dB), 26 San Fransisco (Ldn=65 dB), 12 and V ancouver (LAeq, 5min =61.7 dB). 27 From a public health perspective, noise levels measured in this study are of sufficient intensity to be injurious. For example, a 5-dB(A) increase in noise level between 45 and 65 dB(A) has been associated with a 38 % increased odds for hypertension even after control for several well-known risk factors. 28 The most deleterious health impacts arise from excessive noise exposures resulting in sleep disturbance. Sleep is a process of mental and physiological recovery essential to healthy functioning. It has been estimated that between 50 and 150 noise-induced awakenings per year may occur at outdoor noise levels equivalent to those measured in this study.
29 Subsequent impacts to health and well-being are numerous, including: impairment to cognitive performance, changes in hormone (epinephrine) levels, and changes in heart rate, sleep patterns, and mood. Ultimately, the constellation of noise-induced morbidities can lead to more severe health outcomes at noise levels not much greater than those measured in this study. Several studies have demonstrated an increased prevalence of cardiovascular diseases at noise levels as low as 70 dB(A).
9,30 Given the high prevalence of heart disease in Halifax, when compared to similar size cities in Canada, there is a clear rationale to investigate in more detail the level and distribution of noise for the rest of the city.
Certain study limitations may affect the generalizability of the results. First, noise levels were measured in two neighborhoods and within a limited time period.
Increasing the number of study areas to include additional land-use types would provide a deeper understanding of the relationship between environmental noise, the built environment, and human health risks. Second, an extended sampling campaign could investigate the potential for seasonal variation on noise levels. For example, the source and character of environmental noise may change with weather and road conditions. Third, the collection of full 24-h samples would help to eliminate TABLE 5 Study LAeq valuesa compared to noise exposure limits set by Italian legislation Area 1 (residential) Area 2 (mixed use)
Noise exposure limits LAeq Noise exposure limits LAeq
Day (06:00 –22:00) 55.0 55.4 60.0 63.4
Night (22:00 –06:00) 45.0 50.0 50.0 56.1
aExpressed in dB(A)
KING ET AL.1028
measurement error in the LAeq calculation. Future research should consider the variation of noise with land use in a similar fashion to air quality research to enable prediction of noise levels in locations without direct noise measurement. This approach could be complemented by interviews with neighborhood residents in order to investigate annoyance and the potential for noise-related human health risks.
Despite these limitations, this study provides important evidence concerning the relationship between land use and environmental noise. A planning strategy focused on mixed-use development may result in an increase in noise levels and human exposures to noise at levels with potential health implications. In a 2007 paper on urban growth and population health, the authors recommended the inclusion of urbanicity as a potential determinant of health.
31 Indeed, our findings suggest a
sensitivity of residential areas to noise disruptions from such urban standards as traffic intensi fication. Municipal planning policies and initiatives should consider integrating traf fic restrictions and controls in residential areas and school zones. At present there are no quantitative noise standards on which to compare measured noise levels or evaluate noise exceedances in Halifax, and all excess noise levels are controlled through a complaint driven process based on perceived noise levels.
Municipal representatives should consider the institution of new environmental noise standards and policies in order to protect the health of residents and preserve urban environmental quality. Such policies could include improving the quality of mufflers on buses especially in light of findings that relate potentially harmful noise levels to mass transit systems.
32 Ideally, policy development and regulation should
originate from sound planning and an inclusive multi-sectoral approach, 33 to protect and improve population health in increasingly urbanized living environments.
No financial support was received for the conduct of this research. Geomatics support was provided by the Health Geomatics Laboratory at Dalhousie University (hgl.science.dal.ca).
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EXHIBIT 14
Sustainable Cities and Society 91 (2023) 104470
Available online 15 February 2023
2210-6707/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by- nc-nd/4.0/).
Construction noise effects on human health: Evidence from physiological measures
Mostafa Mir a,*, Farnad Nasirzadeh a, Hannah Bereznicki b, Peter Enticott b, SangHyun Lee c, Anthony Mills a
a School of Architecture & Built Environment, Deakin University, Australia b School of Psychology, Cognitive Neuroscience Unit, Deakin University, Geelong, Australia c Department of Civil and Environmental Engineering, University of Michigan, USA
ARTICLE INFO
Keywords:
Construction noise
Physiological parameters
Environmental impact
Urban environment
Occupational health
ABSTRACT
Noise pollution caused by construction activities in urban areas is a serious problem. Construction noise can affect human health and well-being negatively. This study utilised physiological data including heart rate pa - rameters, Respiratory Rate (RR), and Electrodermal Activity (EDA) to explore the effects of construction noise on human health. Different construction noise types (saw, jackhammer, pile driver, bulldozer), levels (55, 65, 75 and 85 dBA), and exposure durations (30, 60, 120, and 180 s) were examined in a lab-based experiment. The noise conditions were reproduced for twenty-three participants through speakers. To statistically analyse the changes in the physiological measures caused by the examined noise conditions, mixed models were utilised. The results revealed significant effects of the construction noise types on heart rate parameters including heart rate (HR), high frequency (HF), and standard deviation of the normal beat intervals (SDNN). In addition, the results showed that exposure duration significantly affects physiological responses, including RR and SDNN. This study will help to identify the non-auditory health effects of exposure to construction noise. The outputs of this research will provide critical inputs to revisit the current environmental noise regulations to protect the com -
munity against the negative health effects of construction noise more efficiently.
List of acronyms
Acronym Definition
ANS Autonomic Nervous System
DALY Disability-Adjusted Life Year
dBA A-weighted decibel
ECG Electrocardiogram
EDA Electrodermal Activity
EDL Electrodermal Level
EDR Electrodermal Response
HF High Frequency
HR Heart Rate
HRV Heart Rate Variability
LF Low Frequency
MD Mean Difference
PNS Parasympathetic Nervous System
RMSSD Root mean square of successive normal beats
RR Respiratory Rate
SDNN Standard deviation of the normal beat intervals
SNS Sympathetic Nervous System
WHO World Health Organization
1. Introduction
Noise pollution is a serious and emerging problem that threatens the quality of life in urban areas globally (Clifford Rosenberg, 2016; Das, Talukdar, Ziaul, Das, & Pal, 2019). Construction noise is among the most important environmental pollutants in urban areas (Jung et al., 2020; Kang et al., 2021; Mir et al., 2022). Construction noise is characterized by sudden, non-permanent, high intensity, concentrated duration, and difficult control, which have a serious impact on urban residents (Guan, Hu, Liu, & Zhang, 2020). The human population in cities is expected to grow from 55% in 2018 to 68% by 2050 (the United Nations, 2018).
Increasing the population in cities would increase the need for buildings and infrastructure in urban areas. Therefore, having more construction projects in urban areas would expose more people to construction noise. * Corresponding author: 1 Gheringhap St, Geelong, VIC, 3220, Australia.
E-mail address: [email protected] (M. Mir).
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https://doi.org/10.1016/j.scs.2023.104470
Received 13 November 2022; Received in revised form 14 January 2023; Accepted 13 February 2023 Sustainable
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2 Complaints related to noise pollution from construction activities are prevalent in urban areas (Xu, Yiwen, Cheng, Li, & Zhang, 2020). During the last two decades, there have been serious concerns about the nega- tive health effects of exposure to construction noise in urban areas. A report by World Health Organization (WHO) showed that Western Eu - ropean countries lose between 1 and 1.6 million DALYs (dis - ability-adjusted life year) each year due to noise exposure (WHO, 2011).
A significant amount of the lost years could be connected to construction noise as around 90% of noise-related disputes in urban areas are related to construction projects (Kwon, Cho, Lee, Yoon, & Park, 2019).
Noise can directly affect the human nervous system (Erfanian, Mitchell, Kang, & Aletta, 2019; Mir, Nasirzadeh, Lee, Cabrera, & Mills, 2022). Based on stress theory (Selye, 1946), noise affects the homeo - static function of the autonomic nervous system (ANS) which increases the risk of cardiovascular disease (Babisch & others, 2011; Babisch, Beule, Schust, Kersten, & Ising, 2005; Goadsby, 2013; Erfanian, Mitch- ell, Kang, & Aletta, 2019). Homeostasis is the most important function of ANS which regulates different human body systems such as the heart, endocrine, and exocrine glands, and respiration (Ernst, 2017). ANS can be divided into two major components, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) (Ernst, 2017; Pham, Lau, Chen, & Makowski, 2021; Rajendra Acharya et al., 2006).
The SNS is responsible for reaction in times of external threat by phys -
iological changes which mostly associate with “flight or fight” responses to stress such as cardiac output, change in blood flow to the skeletal muscles, myocardium, brain, kidneys, gastrointestinal tract, and skin Diamond and Cribbet (2013). PNS activation is associated with relaxa -
tion (“rest and digest”) such as decreased heart rate and blood pressure (Diamond & Cribbet, 2013; Medvedev et al., 2015). Heart rate param -
eters, respiratory rate, and electrodermal activity (EDA) reflect activa - tion of SNS and PNS (Medvedev et al., 2015; Pham, Lau, Chen, & Makowski, 2021). Therefore, these measurable physiological responses can be used for investigating the effects of external stimuli on ANS.
Now, there is no question that noise exposure affects ANS, and ho -
meostasis which can increase the risk for cardiovascular disease (Babisch & others, 2011; Babisch, Beule, Schust, Kersten, & Ising, 2005).
Several detrimental effects on the cardiovascular system are associated with short and long-term exposure to noise including; hypertension (Bluhm et al., 2007; Dzhambov & Dimitrova, 2018; Petri, Licitra, Vig -
otti, & Fredianelli, 2021; Regecov´a & Kellerov´a, 1995; Zhou et al., 2019); increased risk of cardiovascular disease (Babisch et al., 2013; Gimaeva et al., 2018; Girard et al., 2015; Hansell et al., 2013) and associated risks that lead to myocardial infarction (Babisch, Beule, Schust, Kersten, & Ising, 2005; Pettersson, Olsson, & J¨arvholm, 2020; Poulsen et al., 2019; Sørensen et al., 2012). However, the noise char -
acteristics that increase the risk of cardiovascular issues are unknown (Babisch & others, 2011; Hao et al., 2022; Thacher et al., 2022). Re - searchers have studied responses of ANS and physiological measures in humans to different auditory stimuli and they found various results.
Therefore, ANS and the physiological responses including Heart Rate Variability (HRV), RR, and EDA can react differently to exposure to different auditory stimuli (Bradley & Lang, 2000; Hume & Ahtamad, 2013). Each of these physiological responses can provide useful infor -
mation about the effects of noise on human health by revealing the ef - fects on ANS and homeostasis. Following explains these physiological responses and their reactions to different sounds as external stimuli.
1.1. Effects of noise on heart rate parameters
Heart rate (HR) indicates the number of heartbeats per minute (Hwang et al., 2016). An increase in HR is an indicator of SNS domi -
nance in ANS (Thayer et al., 2012). HR increases in response to traffic noise (Gallasch et al., 2016; Raggam et al., 2007), simple noise (Holand et al., 1999), steel industry noise (Goyal, Gupta & Walia, 2010), envi- ronmental noise (Gomez & Danuser, 2004; Irwin et al., 2011; Kraus et al., 2013), and music (Koelsch & J¨ancke, 2015). However, some studies found a decrease in HR caused by exposure to unpleasant noise (Bradley & Lang, 2000; Martin-Soelch et al., 2006; Medvedev et al., 2015) and environmental noise (Park & Lee, 2017).
Beat-to-beat fluctuations in heart rate can be quantified using HRV which indicates ANS adaptation to environmental stimuli and different psychological states (Han et al., 2021; Munoz et al., 2015). Short-term exposure to external stimuli for a few minutes can affect HRV which indicates a complex interplay between the SNS and PNS (Han et al., 2021). HRV generally presents two categories of measures including time domain and frequency domain measures (Xhyheri et al., 2012).
The most widely used time-domain measures are SDNN and RMSSD (Rajendra Acharya et al., 2006). SDNN is the standard deviation of the normal beat intervals which presents in the unit of milliseconds (Pham, Lau, Chen, & Makowski, 2021). Normal beats (called NN intervals) are achieved by excluding the beats related to technical or physiological artifacts from the total beats (Pham, Lau, Chen, & Makowski, 2021).
RMSSD is the other important HRV measure that presents the root mean square of successive normal beats in milliseconds (Shaffer & Ginsberg, 2017).
SDNN is the indicator for the total power of HRV and reflects both sympathetic and parasympathetic responses of heart rate (Xhyheri et al., 2012). SDNN increase has been induced by exposure to traffic noise (Sim et al., 2015) and environmental noise (Kraus et al., 2013; el Aar -
baoui et al., 2017). However, some studies found a decrease in SDNN during exposure to environmental noise lower than 65 dBA (Kraus et al., 2013) and low-frequency noise (Walker et al., 2016). In addition to exploring the effects on SDNN caused by noise types, the past studies indicate that the effects of sound levels on SDNN can be different. For example, increased levels of heavy metal music decreased SDNN (do Amaral et al., 2016), conversely, SDNN was raised by increased noise levels (el Aarbaoui et al., 2017). RMSSD is more influenced by PNS than SDNN, and it can be considered an indicator of the effects of PNS on HRV (Ciccone et al., 2017; Pham, Lau, Chen, & Makowski, 2021; Shaffer & Ginsberg, 2017; Shaffer, McCraty, & Zerr, 2014). Ferreira et al. (2015) and Veternik et al. (2018) observed an increase in RMSSD during listening to music and noise, however, Kraus et al. (2013) and do Amaral et al. (2016) failed to find significant effects of noise and music on RMSSD. The time-domain measures of HRV are important indicators for the effects of auditory stimuli on SNS and PNS.
In terms of frequency-domain HRV parameters, generally, two measures can be distinguished from short-term recordings of HRV including high-frequency (HF) and low-frequency (LF) components (Xhyheri et al., 2012). HF ranges between 0.15 to 0.4 Hz and LF ranges between 0.04 and 0.15 Hz (Xhyheri et al., 2012). HF is known as an indicator for parasympathetic modulation (Pham, Lau, Chen, & Makowski, 2021) while LF is more difficult to interpret as it reflects both sympathetic and parasympathetic modulation as well as other factors, including baroreceptor activity (Rajendra Acharya et al., 2006). Due to the complexity that exists in LF interpretation, it will not be used in this study. Past research showed an increase in HF in response to noise exposure (Gallasch et al., 2016; Sim et al., 2015). However, Kraus et al. (2013) found a decrease in HF for noise levels higher than 65 dBA and an increase in HF for noise levels lower than 65 dBA. el Aarbaoui et al. (2017) observed an increase in HF with an increase in noise level.
Walker et al. (2016) observed a decrease in HF for low-frequency noise and no change in HF during exposure to high-frequency noise. The reviewed studies on the observed responses from HR and HRV param -
eters to different auditory stimuli reveal the mixed results obtained by the past studies. This shows the necessity of exploring the changes in HR and HRV parameters caused by construction noise.
1.2. Effects of noise on respiratory rate
The respiratory system is the other physiological response that in -
teracts with the cardiovascular system under the influence of ANS (Pham, Lau, Chen, & Makowski, 2021). Some researchers observed an M. Mir et al.
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3 increase in respiratory rate (RR) because of exposure to noise (Hume & Ahtamad, 2013; Park & Lee, 2017; Sim et al., 2015) and music (Koelsch & J¨ancke, 2015). However, some researchers failed to find a relationship between noise and RR (Raggam et al., 2007; Veternik et al., 2018). This might be an indicator for the effects of auditory stimuli characteristics on RR which means different results might be observed for different auditory stimuli.
1.3. Effects of noise on electrodermal activity
EDA is the other valid and reliable physiological indicator for SNS activity (Erfanian, Mitchell, Kang, & Aletta, 2019). EDA measures the electrical flow in the skin in response to sweat secretion (Ahn et al., 2019; Alberdi et al., 2016; Kamiˇsali´c, Fister, Turkanovi´c, & Karakatiˇc, 2018; Sharma & Gedeon, 2012). Sweat glands are exclusively inner - vated by the SNS (Kappeler-Setz et al., 2013). EDA data can be decomposed into two components including tonic (electrodermal level- EDL) and phasic (electrodermal response- EDR) (Boucsein, 2012; Cacioppo et al., 2007; Choi et al., 2019). The tonic component, which shows slow changes in the skin conductance, often indicates SNS activity induced by long-term stressors, while the phasic component, which is the faster-changing component of the skin conductance, is an indicator of immediate response to external stressors (Ahn et al., 2019; Boucsein, 2012; Braithwaite et al., 2013). None of the previous studies on the ef -
fects of auditory stimuli on EDA decomposed the EDA signal into its components; however, they investigated the effects of the noise on the undecomposed EDA level. Past research showed that there was an in - crease in EDA because of exposure to noise (Bradley & Lang, 2000; Martin-Soelch et al., 2006; Medvedev et al., 2015; Park & Lee, 2017).
However, some research failed to find significant effects of noise on EDA response (Gomez & Danuser, 2004).
The review of past studies on the effects of auditory stimuli on ANS showed that no single pattern of the ANS responses and the associated changes in the HR, HRV, RR, and EDA can be applied universally across all auditory stimuli as external stressors (Berntson & Cacioppo, 2004).
Therefore, investigating the effects of construction noise, as one of the most prevalent external stressors in urban areas, on the physiological responses seems necessary.
There are only two studies that have explored the effects of con -
struction noise on physiological responses in terms of HR. Both studies explored the effects of tunnelling noise on heart rate of drivers (Xing et al., 2021; Zhang et al., 2018). Zhang et al. (2018) observed an in - crease in HR and emotional intensity (including anxiety, fear and dis - liking) by an increase in noise level in the range of 92 dBA to 102 dBA.
Xing et al. (2021) proposed a model, by combining RMSSD, HF and LF and brain signals, to identify qualified people to work in construction environments with high levels of noise. However, neither study explored the effects of construction noise on HR, HRV, EDA and RR to identify the ANS responses. In addition, neither study examined the physiological responses to different types and levels of construction noise. Another important parameter that might affect the physiological responses to construction noise is the exposure duration. The analysis of the exposure duration can help us to understand how the ANS habituation process works for different durations of exposure to noise (Erfanian, Mitchell, Kang, & Aletta, 2019). However, there is no study on the effects of different exposure times on the ANS habituation. Therefore, the aim of this study is to explore how different types, levels and exposure duration of construction noise affect the ANS by exploring their effects on HR, HRV, RR and EDA. Construction noise as unpleasant auditory stimuli can cause psychological effects such as stress for the receivers and these psychological effects can dramatically impact the ANS (Berntson & Cacioppo, 2004). In this study, in addition to recording physiological responses, a questionnaire was used to report the level of stress expe -
rienced by the participants during exposure to different noise condi - tions. The reported level of stress can be used as an indicator of the unpleasant feeling that the noise created for the participants (Gidl¨of-Gunnarsson & ¨Ohrstr¨om, 2007). Collecting subjective assess - ments of construction noise as the external stressor in addition to the objective physiological responses would help us to identify the effects of the noise on the participants (Berntson & Cacioppo, 2004).
2. Methods and materials
2.1. Participants
23 voluntary participants (5 female) with the average age of 30.4 years and standard deviation of 6.5 (min 20 years and max 46 years) provided their informed consent for participating in this research. The participants reported no history of hearing impairment or drug con - sumption. The participants were asked to avoid intense physical activity and consuming alcohol within 24 h before the tests. In addition, they were asked to avoid intaking caffeine on the data collection day to prevent its effects on the collected physiological measures. The study was approved by Deakin University Human Research Ethics Committee.
2.2. Stimulus
Different types of construction noise including saw, jackhammer, pile driver, and bulldozer were used in this research to explore their effect on physiological measures. These noise types are among the most prevalent noise types emitting from construction sites and they were rated among the noise types with the highest negative psychological effects (Lee et al., 2015, 2019). The noise was recorded from real con -
struction operations according to recommendations provided by ISO 12913-2. The microphone (Type 4101-A, Brüel & Kjær, Sound Devices 702) was located 10 m away from the noise source and 1.5 m above the ground in an open area. The recordings were conducted with 48 Hz sampling rate and 24 bits depth. Noise levels during measurement for saw, jackhammer, bulldozer and pile driver were 106.1 dBA, 108.3 dBA, 101.2 dBA and 103 dBA, respectively.
For each of the noise types, four noise levels, including 55 dBA, 65 dBA, 75 dBA, and 85 dBA, were selected to be emitted to the partici -
pants. These noise levels were selected as the people in the vicinity of construction sites generally receive construction noise levels in the range of 55 dBA to 75 dBA (Jeon et al., 2010; Lee et al., 2015). In addition, 85 dBA is the highest recommended level in workplaces to avoid hearing loss for workers during 8 working hours (OSHA, 2013; Safe Work, 2019; WHO, 2018). In terms of acoustics characterization of the noise types, pile driver and jackhammer are impulsive noise types and saw and bulldozer are tonal noise types based on the definitions presented by ISO 1996–1. Three minutes of sixteen noise conditions (four types and four levels of noise) were played for the participants through speakers. The noise conditions and the data collection settings are identical to those in a previous study (Mir et al., 2022).
2.3. Data collection design
To avoid fatigue for the participants due to long data collection sessions, the data was collected in three sessions on different days. Two of the sessions contained five noise conditions and one of the sessions contained six noise conditions. The number of noise conditions in each session and the order of the noise conditions in the experiment were randomized for different participants to avoid their effects on the results.
The data collection procedure is shown in Fig. 1. Subjective levels of stress experienced by the participants were collected after each noise condition. The participants selected a number from 1 to 10 which pre -
sented ‘very low’ and ‘extremely high’ stress levels, respectively (Hebert & Lupien, 2009; H´ebert & Lupien, 2007).
2.4. Physiological data collection and pre-processing
This study used EQ02 (Equivital EQ02, Hidalgo, UK) for data M. Mir et al.
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4 collection. The sampling rates for ECG and EDA data were 256 and 4 Hz, respectively. The ECG data were collected using a belt worn by the participants around their chest and the EDA data were collected by connecting the electrodes on the index and middle fingers of the par - ticipants’ left hand. The collected ECG and EDA data were cleaned and pre-processed using the default settings of NeuroKit2 (Makowski et al., 2021), which is an open-source toolbox on Python for physiological data analysis. In addition, this tool was used to calculate HR, HRV measures, respiratory rate, and the values for EDA components. Since this study investigates the effects of short-term construction noise exposure and tonic EDA represents the effects of long-term stressors, this study only considers phasic EDA to explore the effects of construction noise. HR and time-domain HRV measures including SDNN and RMSSD (Rajendra Acharya et al., 2006) as the most widely used measures were extracted.
In terms of frequency-domain HRV, HF was investigated in this study. 30 s recording is enough to obtain reliable values for the above HRV measures included in this study (Melo et al., 2018; Shaffer & Ginsberg, 2017). Therefore, four noise exposure durations including the first 30 s, the first 60 s, the first 120 s and all 180 s were extracted from each of the three minutes of noise exposure (Park et al., 2018).
2.5. Statistical analysis
This study used linear mixed models to explore the effects of the examined noise conditions, and their exposure durations on the psy - chological and physiological responses. Subjective data about the stress level was obtained from the questionnaires. HR, SDNN, RMSSD, HF, and phasic EDA were the dependent variables in the mixed models. The values of the physiological responses were calculated based on the percentage changes compared to the baseline data which was recorded in no noise condition at the start of the sessions (Fig. 1). Mixed models can reflect the primary impacts of the fixed effects as well as the impacts of random sample selection as the random effects on the estimation.
Mixed model analysis was conducted in SPSS Version 27 (SPSS IBM, New York). This study considered the four noise types, the four noise levels, and exposure duration (30 s, 60 s, 120 s, and 180 s) as fixed ef - fects. The participants and the data collection sessions were considered as the random effects in the mixed models. Since it is inappropriate to compare SDNN calculated from recordings with different lengths (Camm et al., 1996; Saul et al., 1988; Shaffer & Ginsberg, 2017), exposure duration was not included in the analysis for SDNN. Assump -
tions for mixed models were checked for linearity, normality of re - siduals, and homoscedasticity. HR, SDNN, and RMSSD violated the assumptions and were transformed using a logarithm with base 10 to comply with the mixed models’ assumptions. Bonferroni correction was used for the pairwise analysis. The developed mixed models for the subjective data did not include the exposure duration as a fixed effect since the participants completed the questionnaire only once at the end Fig. 1. Data collection procedure. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) M. Mir et al.
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5 of the noise exposure.
3. Results
3.1. The results for the effects of noise on the physiological measures The achieved results revealed significant effects on heart rate (HR) caused by noise types (Table 1). The data analysis revealed a significant decrease in HR caused by saw comparing to pile driver (MD = 0.015, p = .026). Fig. 2 shows the percentage of changes observed for HR comparing to the baseline value for different noise types.
Data analysis showed a significant effect on SDNN caused by the noise types (Table 1). The analysis showed a significant decrease in SDNN caused by saw compared to bulldozer (MD = 0.36, p = .012).
Fig. 3 shows the significant increase of SDNN for bulldozer compared to saw noise.
Statistical analysis found a significant effect of the interaction effect of types and levels of noise on RMSSD (Table 1). Further analysis showed that RMSSD decreased significantly by increasing pile driver noise level from 55 to 85 dBA (MD = 0.1659, t = 2.028, p = .043).
The statistical analysis revealed a significant effect of noise types on HF (Table 2). Further analysis revealed a significant increase in HF caused by to saw comparing to bulldozer (MD = 0.093, p = .018) and pile driver (MD = 0.105, p = .002) noise types. In addition, the data revealed significant effects caused by noise levels on HF (Table 2), however, the p-value (0.048) was close to the 0.05 significance threshold. This might be the reason that the post hoc analysis failed to find significant differences for HF between different levels of noise.
The results for respiratory rate (RR) showed a significant difference for RR during different noise exposure duration (Table 3). Post hoc analysis revealed a significant increase in RR in the first 30 s of noise exposure compared to 60 s (MD = 7.286, p < .001), 120 s (MD = 6.916, p < .001), and 180 s (MD = 7.728, p < .001). The analysis found that the interaction between types and levels of noise caused significant effects on RR (F(9, 1128.787)=3.733, p < .001). Mixed models results showed that bulldozer noise at 75 dBA caused significantly higher RR comparing to 85 dBA (Estimate = 18.485, t == 2.394, p = .017). Fig. 4 shows the significant increase for RR in the first 30 s compared to other exposure durations. The same was observed for pile driver noise where RR significantly increased for pile driver at 75 dBA compared to 85 dBA (Estimate = 24.18, t == 3.158, p = .002). Significant effect of the interaction of the types and levels was found on phasic-EDA (Table 4).
3.2. The results for the effects of noise on the stress level The subjective data collected from the participants showed that the noise types caused significantly different stress levels for the subjects (F (3, 237.594)=3.415, p = .018). Post hoc analysis revealed significantly higher stress levels caused by jackhammer noise compared to bulldozer noise (MD = 0.588, p = .035). The statistical analysis also found sig -
nificant effects caused by noise levels on stress (F(3, 245.508)=48.942, p < .001). Post hoc analysis found a significant increase in stress level caused by noise at 65 dBA comparing to 55 dBA (MD = 0.638, p = .016).
Noise caused significant lower stress at 55 dBA comparing to 75 dBA (MD = 1.438, p < .001) and 85 dBA (MD = 2.488, p < .001).
Additionally, there was a significantly lower stress caused by noise as 65 comparing to 75 (MD = 0.746, p = .005) and 85 dBA (MD = 1.79, p < .001). Finally, we found a significant decrease in stress level at 75 comparing to 85 dBA (MD = 1.05, p < .001). Another significant effect found by the mixed model was the interaction of types and levels of noise on the stress levels (F(9, 237.263)=2.294, p = .017). Fig. 5 pre -
sents the participants’ stress caused by the examined noise conditions.
4. Discussion
This study explored the participants’ physiological responses (HR, SDNN, RMSSD, HF, RR, and phasic EDA) to the examined types, levels, and exposure durations of construction noise. The subjective stress re - ported using questionnaires and the findings from the past studies hel - ped to explore the effects of the noise conditions on the physiological responses. The following discusses the observed changes in the physio- logical measures caused by different construction noise types, levels, and exposure duration.
4.1. The effects of different noise types on the physiological responses All noise types caused an increase in SDNN, which is consistent with the findings from past studies (el Aarbaoui et al., 2017; Kraus et al., 2013; Sim et al., 2015). This increase in SDNN was significantly lower for saw noise compared to bulldozer noise. SDNN can be considered as the overall indicator for HRV condition (Xhyheri et al., 2012) and a long-term decrease in SDNN indicates a higher risk of cardiovascular issues (Huikuri & Stein, 2013). Higher SDNN for bulldozer noise compared to other noise types (Fig. 3) might be a sign of lower SNS activity for bulldozer noise comparing to the other examined noise types. This was in line with the outputs of the questionnaires where the lowest stress level was caused by bulldozer noise. Lower SDNN for saw noise compared to other noise types (Fig. 3) might be an indicator of higher SNS activity caused by saw noise comparing to other noise types.
While saw noise caused the lowest level of SDNN compared to other examined noise types, the collected stress results showed that the highest and the second highest stress level was caused by jackhammer and saw, respectively. Jackhammer and saw are both high-frequency noise types; however, jackhammer is a highly impulsive noise type, and this might be the reason for ranking it as the most stressful noise type.
It was expected to see an increase in HR for the noise types with higher stress levels since those noise types trigger SNS and it increases HR (Pham, Lau, Chen, & Makowski, 2021). However, past research found various results in this regard. Some researchers found an increase in HR caused by exposure to noise (Gallasch et al., 2016; Gomez & Danuser, 2004; Goyal, Gupta & Walia, 2010; Holand et al., 1999; Kraus Table 1
The results of the mixed model analysis on HR and the time domain HRV measures.
HR Numerator
df
Denominator
df
F Sig.
Noise type 3 1193.866 3.049 0.028
Noise level 3 1206.190 2.300 0.076
Exposure duration 3 1179.616 0.158 0.925
Noise type * Noise level 9 1193.036 0.895 0.529
Noise type * exposure
duration
9 1179.238 0.337 0.963
Noise level * exposure
duration
9 1179.239 0.808 0.609
Noise type * Noise level *
exposure duration
27 1179.239 0.377 0.999
SDNN Numerator df Denominator df F Sig.
Noise type 3 1252.730 3.326 0.019
Noise level 3 1267.801 2.247 0.081
Noise type * Noise level 9 1251.198 2.620 0.005
RMSSD Numerator
df
Denominator
df
F Sig.
Noise type 3 1199.552 0.595 0.618
Noise level 3 1216.385 1.751 0.155
Exposure duration 3 1180.847 1.983 0.115
Noise type * Noise level 9 1198.579 2.651 0.005
Noise type * exposure
duration
9 1180.294 0.543 0.844
Noise level * exposure
duration
9 1180.295 0.466 0.898
Noise type * Noise level *
exposure duration
27 1180.295 0.526 0.979
M. Mir et al.
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6 Fig. 2. Change for HR for different noise types (* p < .05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 3. Change for SDNN for different noise types (* p < .05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 2
The results of the mixed model analysis on HF.
HF Numerator
df
Denominator
df
F Sig.
Noise type 3 1209.404 6.154 <0.001
Noise level 3 1219.204 2.638 0.048
Exposure duration 3 1181.634 0.904 0.439
Noise type * Noise level 9 1207.369 1.551 0.125
Noise type * exposure
duration
9 1180.746 0.239 0.989
Noise level * exposure
duration
9 1180.748 0.658 0.747
Noise type * Noise level *
exposure duration
27 1180.748 0.258 1.000
Table 3
The results of the mixed model’s analysis on RR.
RR Numerator
df
Denominator
df
F Sig.
Noise type 3 1129.940 1.611 0.185
Noise level 3 1144.638 1.459 0.224
Exposure duration 3 1108.875 11.950 <0.001
Noise type * Noise level 9 1128.787 3.733 <0.001
Noise type * exposure
duration
9 1107.648 .391 0.940
Noise level * exposure
duration
9 1107.694 .579 0.815
Noise type * Noise level *
exposure duration
27 1107.567 1.047 0.399
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7 et al., 2013; Raggam et al., 2007), some found a decrease in HR (Bradley & Lang, 2000; Hume & Ahtamad, 2013; Koelsch & J¨ancke, 2015; Mar- tin-Soelch et al., 2006; Medvedev et al., 2015; Park & Lee, 2017) and some did not find any effect of noise exposure on HR (Irwin et al., 2011).
The inconsistent results from the literature shows that the effects of noise on HR might depend on the examined types, levels, and duration of the noise exposure. Therefore, studies that used different noise exposure conditions might find different results. In addition, there may be other factors that influence the effects of noise on HR, such as the target population and the data collection settings. This study found an increase in HR during exposure to all noise types. This might be an in - dicator of an increase in SNS activity during exposure to all noise types.
The achieved results found a significant increase in HR caused by pile driver comparing to saw. This is consistent with the findings from part of the literature which found lower HR during exposure to more unpleas -
ant noise (Bradley & Lang, 2000; Koelsch & J¨ancke, 2015; Martin-- Soelch et al., 2006).
Most of the past studies observed an increase in HF caused by exposure to noise (el Aarbaoui et al., 2017; Gallasch et al., 2016; Sim et al., 2015). Our findings show that HF was significantly higher for saw comparing to bulldozer and pile driver. Therefore, the findings are consistent with the previous research where higher increase in HF was observed for more stressful noise types (Sim et al., 2015). Although jackhammer and saw were identified as the noise types with higher stress levels via self-reported questionnaires, the physiological data showed significantly higher HR for pile driver and significantly lower HF for bulldozer and pile driver compared to saw noise. Potentially, this indicates that although piledriver and bulldozer were ranked as the noise types with lower stress levels according to the subjective data, these noise types might affect ANS negatively, and eventually they might have negative effects on the cardiovascular system.
As discussed before, the results from SDNN were in line with our expectation that more stressful noise types caused lower SDNN.
Although an increase in average of HR for all noise types were observed, it was expected to see higher HR for noise types which caused higher stress level (higher SNS activity) (Pham, Lau, Chen, & Makowski, 2021).
In addition, we were expecting to see lower HF for noise types which caused higher stress level (higher SNS activity) (Shaffer & Ginsberg, 2017), however, average of HF for saw was significantly higher comparing to pile driver and bulldozer noise. The lower level of HR and SDNN, and the increase in HF caused by more stressful noise types could be explained by the theory explained by Lacey & Lacey, 1970 and Williams (1985). Their theory explains that intake or rejection of stimuli creates particular patterns of physiological response. The rejection happens during attention to an internal act and the need for rejecting the environmental stimuli, which creates emotional arousal. On the other hand, intake happens when the environmental stimuli attract attention and require to be perceived. Intake is characterised by a decrease in emotional expression (Frescura & Lee, 2022; Krantz & Manuck, 1984).
In the case of our study, saw and jackhammer noise as the stressful noise types might activate the intake reaction to the noise. The intake function can be activated by being exposed to more stressful noise types and it increases noticeability which could affect the physiological responses (Park & Lee, 2017). HR decreases by activating intake function (Frescura & Lee, 2022; Krantz & Manuck, 1984). In addition, HF can increase during exposure to stressful sounds (Sim et al., 2015). Therefore, the observed increase in HF and decrease in HR might be an indicator of intake activation during exposure to more stressful noise which showed its effects by decreasing SDNN.
This study selected bulldozer and saw noise as the tonal noise types and jackhammer and pile driver as the impulsive noise types. However, this study did not find any evidence that the tonality or impulsiveness of these noise types affected the physiological responses.
Fig. 4. Mean change for RR for different noise exposure durations (** p < .001). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 4
The results of the mixed model analysis on phasic EDA.
Phasic EDA Numerator
df
Denominator
df
F Sig.
Noise type 3 1055.222 0.829 0.478
Noise level 3 679.273 0.228 0.877
Exposure duration 3 1102.407 2.432 0.064
Noise type * Noise level 9 1018.566 2.020 0.034
Noise type * exposure
duration
9 1101.205 0.511 0.868
Noise level * exposure
duration
9 1101.201 0.987 0.449
Noise type * Noise level *
exposure duration
27 1101.209 0.362 0.999
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8 The findings for HR, HF, and SDNN show that all types of con - struction noise can change the balance in the ANS. Therefore, all noise types might cause negative physiological effects on human health.
However, considering the results from the subjective and the objective data together showed that some noise types might have more negative physiological and psychological effects by increasing SNS activity and activating the intake reaction. Activation of the intake reaction might reduce the attention performance of the human by attracting attention to the noise.
4.2. The effects of different noise levels on the physiological responses The subjective results showed a significant increase in stress by an increase in noise level. It was expected to see significant changes in the physiological responses caused by noise levels (el Aarbaoui et al., 2017; Regecov´a & Kellerov´a, 1995). However, similar to past studies we failed to find significant changes in the physiological measures caused by noise level (Frescura & Lee, 2022). Noise levels affected the physiological measures with their interactions with noise types. This might indicate that exposure to construction noise at different levels, depending on noise type, can affect physiological responses. This is consistent with findings from the past studies where noise at low and high levels may have health consequences (Kraus et al., 2013).
The changes caused by construction noise level on the physiological and psychological measures is important since environmental and occupational noise control regulations provide guidance to protect people against noise by specifying some noise level limits. The result of this research showed significant changes in the psychological measures caused by construction noise levels. Additionally, it was found that the effects of noise levels on physiological responses depend on the noise type.
4.3. The effects of noise exposure duration on physiological responses The results revealed significant effect of exposure duration on RR. RR for the first 30 s of noise exposure was significantly higher than 60, 120, Fig. 5. The subjective results for the stress level for each noise condition (** p < .001, * p < .05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) M. Mir et al.
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9 and 180 s. This is very similar to the results found by past research (Park et al., 2018) where exposure to noise increased RR in the first 30 s, and then decreased sharply. An increase in RR is an indicator of the domi - nance of SNS (Narkiewicz et al., 2006). It can be concluded that noise highly affected participants’ RR in the first 30 s and SNS activity dominated. However, ANS reaches a balance between SNS and PNS activity over time. The significant increase in RR in the first 30 s can be seen in Fig. 4.
We failed to find the effects of noise exposure duration on phasic EDA. This might be explained by the previous studies where Martin- -
Soelch et al. (2006) found strong habituation behaviour for EDA. This habituation behaviour was observed over a period of a few seconds. This might have happened in this study and examining shorter exposure duration might reveal the changes in the EDA caused by construction noise.
5. Limitations
It would be very beneficial to conduct the current study with larger sample size. The current study is the first to examine the effects of types, levels, and exposure durations of construction noise on human physio - logical health. However, the study was conducted during the Covid-19 lockdowns, and recruiting more subjects was very challenging. The sample size in this study can be considered acceptable considering the number of participants recruited by the past lab-based studies on the effects of noise on the physiological responses (Fan, Liang, Cao, Pang, & Zhang, 2022; Guan et al., 2020; Masullo et al., 2022; Park & Lee, 2017; Xing et al., 2021). However, future research can repeat the current research by providing a larger sample size. Future research can also investigate the effects of construction noise on annoyance using the physiological responses. In addition, future research can study the ef -
fects of noise levels higher than 85 dBA on construction workers. Con - struction workers may experience higher levels of construction noise during their working hours than the levels investigated in this study. The workers are also exposed to the noise for a longer time every day. To identify the health effect of construction noise, further research can be conducted by exposing the participants to higher levels and longer time of construction noise. In addition, since most of the noise emitted from construction projects is a combination of different noise types, further research can also be conducted on physiological responses to combined construction noise. There is only one subjective study on the health ef - fects of combinations of construction noise (Lee et al., 2015), however, further objective research is needed.
6. Conclusion
This study found significant effects of different types, levels, and exposure durations of construction noise on the physiological responses.
The noise effects on ANS were explored by analysing physiological re -
sponses including HR, SDNN, RMSSD, HF, RR, and phasic EDA. In addition, subjective responses about the stress caused by the noise conditions were collected from the participants. These subjective re -
sponses would be an indicator of how unpleasant the noise is. Mixed models were used for the statistical analysis. The analysis showed that different noise types affected ANS and homeostasis. In addition, expo -
sure to construction noise significantly affected the RR at the beginning of the exposure, however, the body habituates through time.
Considering the increasing population in the cities, it is expected that more people would be exposed to construction noise in the near future.
This study shed light on the non-auditory health effects of construction noise. The results of this study showed that exposure to construction noise can have negative effects on human health. In addition, it revealed the necessity of conducting more studies on the health effects of con -
struction noise. The findings of this study can be useful for the regulatory bodies in providing more efficient environmental and occupational regulations to protect people against the negative health effects of construction noise.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgment
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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M. Mir et al.
EXHIBIT 15
International Journal of
Environmental Research
and Public Health
Article
DALY-Based Health Risk Assessment of Construction
Noise in Beijing, China
Jun Xiao, Xiaodong Li * and Zhihui Zhang
School of Civil Engineering, Tsinghua University, Beijing 100084, China; [email protected] (J.X.); [email protected] (Z.Z.)
* Correspondence: [email protected]; Tel.: +86-106-278-4957 Academic Editor: Peter Lercher
Received: 20 July 2016; Accepted: 19 October 2016; Published: 26 October 2016 Abstract: Noise produced by construction activities has become the second most serious acoustic polluting element in China. To provide industry practitioners with a better understanding of the health risks of construction noise and to aid in creating environmentally friendly construction plans during early construction stages, we developed a quantitative model to assess the health impairment risks (HIA) associated with construction noise for individuals living adjacent to construction sites.
This model classifies noise-induced health impairments into four categories: cardiovascular disease, cognitive impairment, sleep disturbance, and annoyance, and uses disability-adjusted life years (DALYs) as an indicator of damage. Furthermore, the value of a statistical life (VSL) is used to transform DALYs into a monetary value based on the affected demographic characteristics, thereby offering policy makers a reliable theoretical foundation for establishing reasonable standards to compensate residents suffering from construction noise. A practical earthwork project in Beijing is used as a case study to demonstrate the applicability of the proposed model. The results indicate that construction noise could bring significant health risks to the neighboring resident community, with an estimated 34.51 DALYs of health damage and 20.47 million yuan in social costs. In particular, people aged 45–54 are most vulnerable to construction noise, with the greatest health risks being caused by sleep disturbance.
Keywords: construction noise; disability-adjusted life years; social cost; health risk
1. Introduction
Noise pollution is a negative externality of construction activities, especially earthwork and concrete construction. People respond differently to noise pollution; however, when noise levels reach a certain threshold, people tend to be affected negatively [1]. In China, 42.1% of environmental complaints are associated with acoustic pollution, 25.6% of which are attributed to construction noise, and acoustic pollution is becoming a more serious problem as rapid urbanization occurs [ 2].
Given that the typical projected goals for population urbanization levels increase from 52.6% in 2012 to approximately 60% by 2020, vast construction projects underway to meet the needs of immigrants’ daily lives are creating pervasive environmental pollution and insufferable noise for the neighboring communities. In addition, the impacts of construction noise may be growing more serious because the increasing population density means that more individuals are becoming more exposed to construction noise. Thus, it is critical to take steps to balance the desire for urbanization with sustainable development.
Many measures have been taken to mitigate construction noise pollution. One common method is to reduce noise emissions at the source by using quiet construction technology and equipment and limiting the duration of noisy activities. When source control is unable to reduce noise levels at receptors to below critical levels, the next most common measure is to place obstacles, Int. J. Environ. Res. Public Health 2016, 13, 1045; doi:10.3390/ijerph13111045 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2016, 13, 1045 2 of 18 such as soundproof barriers and enclosures, along the transmission path [ 3,4]. In addition, some novel techniques derived from industry, such as active noise control techniques [ 5] and off-site construction [6], can be introduced as supplemental measures to overcome the limitations of the above-mentioned method. However, these above-mentioned measures have high investment costs.
For noise control to be successfully implemented in practice, governments must not only implement a strict regulatory system but also develop economic, social and environmental criteria that make such investments worthwhile.
As economic leverage for motivating enterprises to take steps to consciously reduce their pollution, a noise pollution discharge and compensation fee rule has been enforced by the Chinese government in the first national law against noise pollution [ 7]. In most cities, the discharge fee is intended to fund activities related to noise abatement. A contractor must pay the noise discharge fees to the environmental protection department if the environmental noise boundary of a construction site exceeds the standard limits [8]. The amount of this fee depends on the degree of the violation, ranging from 350 yuan per month for 1 dB above the limit to 11,200 yuan per month for 16 dB or more above the limit [9]. In Beijing, in addition to the discharge fee, a contractor who violates standard limits [8] must compensate the neighboring community by household because of the noise nuisance [10]. According to the rule [10], the actual amount of compensation depends on a negotiation between the contractor and neighbors and is limited to 30–60 yuan per month. These current rules are easy to follow; however, they have three theoretical and operational drawbacks that make it difficult to provide persuasive judgments in their support. First, it is illogical to use the environmental noise level of a construction site boundary as a criterion to determine whether construction noise is a nuisance to neighboring communities, as the noise level at a receptor is not always consistent with that at a site boundary due to propagation attenuation [8,11]. Second, neither rule fully incorporates the health risks caused by noise pollution. Although the noise disturbance compensation rule [10] admits that construction noise can disturb people’s lives, it does not expound upon the exposure–response relationship or even provide a disturbance-grading reference. It is arbitrary to weigh a noise nuisance based on an approximate noise level at a construction site boundary and negotiation. In addition, these rules were enacted many years ago. As environmental awareness and pollution-prevention costs are increasing, the negative externality of construction noise valued by those rules can neither offset people’s losses nor force enterprises to lower noise emissions. Thus, objective pollution assessment methods, as well as convenient estimation methods, are crucial for the economic leverage function of these rules.
Researchers worldwide have used two methods for studies of construction noise emission estimation: the first examines noise data from equipment [ 3,4,12–15] and the second from construction sites [16]. Taking advantage of such estimation methods, individual exposure levels can be plausibly measured during the planning phase. However, most studies related to the environmental impacts of construction noise pollution still anchor estimates to noise exposure levels and to the number of communities affected [
16–18]. A few studies have targeted the negative externality of construction noise. In a study by Gilchrist [19], three sources of social cost (productivity reduction, property damage, and health cost) associated with construction noise are outlined and valued by current bid evaluation methods. Nevertheless, the impacts of construction noise on these three sources were not presented in detail. Moreover, Hong et al. [ 20] used official standards to calculate the environmental cost of construction noise, but the details of this standard are beyond the scope of this article. Thus, current estimates give no further reference regarding how pollution discharge and compensation fee rules for construction noise are developed and implemented.
Researchers have extended the environmental assessment of transportation noise pollution to social cost valuation using revealed [ 21–25] and stated preference methods [ 26–30]. The most common end points for valuation are typically expressed in the form of the Noise Depreciation Index (NDI) or the willingness to pay (WTP), which represent, respectively, the average decrease in house value caused by a 1-decibel increase in the noise level or the amount of money people are willing to pay (WTP) for a 1-decibel decrease in the noise level. These measurements can be directly Int. J. Environ. Res. Public Health 2016, 13, 1045 3 of 18 applied to negotiate transportation-noise charge schemes. However, the models proposed by these studies are not suitable for the assessment of construction noise for two reasons. First, house prices are not vulnerable to construction noise because construction projects usually last 2 years or less.
Additionally, the house prices of neighbors will go up if the building being constructed has a public service function. Second, it is questionable that people understand the associated health risks and will make corresponding choices when they value environmental goods [31]. Hence, health risks are apparently more appropriate for deriving the social costs of construction noise.
It has long been recognized that noise has harmful impacts on health [32]. Both experimental and epidemiological studies have been conducted over the years, and the results have revealed an increased disease incidence during periods of noise exposure [33]. Disability-adjusted life years (DALYs) are one of the most widely used metrics for assessing the human burden of disease, and this measure has been adopted as an endpoint indicator for health risk assessment of noise [34–38]. The concept of DALYs was developed in the original 1990 Global Burden of Disease (GBD) study to assess disease burden consistently across different diseases, risk factors and regions [39], and the necessary parameters have been further updated by the World Health Organization (WHO) [ 40]. A DALY measures the gap between the affected health status and ideal health status and equates it to the sum of time lived with disability and the time lost due to premature death. Social preference, for example, values the life year and can be incorporated to make the results more applicable for environmental decision making [41]. As such, the DALY method is well suited for application in health risk assessment of construction noise.
This paper aims to establish a health risk assessment model for construction noise in Beijing based on the concept of DALYs. The model is intended to serve as a tool to assist the government in improving noise pollution-charge fee rules and to assist contractors in predicting health risks due to noise in order to optimize construction plans during pre-construction and to promote green construction.
A commercial building in Beijing is used as a case study to demonstrate how the model works.
2. Materials and Methods
2.1. Health Risk Analysis
It is necessary to conduct a health risk analysis of construction noise due to the lack of a convenient reference. This section was created to summarize available information on the exposure–response relationships between construction noise and specific health impairments and to describe the background prevalence of disease and the disability weights (DWs) of the outcomes so that DALYs could be applied to assess the health risks of construction noise. Based on the currently available scientific evidence supporting a causal association, the health impairments considered in this study include cardiovascular disease (CVD), cognitive impairment, sleep disturbance and annoyance [33].
Notably, tinnitus was excluded because there are no empirical data to propose that an adverse effect is associated with noise-induced tinnitus [33].
2.1.1. CVD
Noise is considered a general stressor that leads to CVD via arousal of the autonomic nervous system and the endocrine system, which are associated with changes in physiological functions and metabolism, including blood pressure, heart rate, and other functions [32,33]. Recent epidemiological studies have increased understanding of the linkage between community noise and cardiovascular disease (CVD) [42,43], including hypertension [44,45], myocardial infarction [46–48] and stroke [49].
However, most CVDs, except myocardial infarction, have only been linked with construction noise by a biological reaction model [50–53]. An exposure–response relationship with myocardial infarction was demonstrated in a study by Babisch et al. [46]. In that study, the annoyance induced by construction noise had an odds ratio similar to that induced by road traffic noise with respect to myocardial infarction. Hence, our model only considers myocardial infarction when calculating DALYs due to Int. J. Environ. Res. Public Health 2016, 13, 1045 4 of 18 CVD and assumes that construction noise and road traffic noise have the same odds ratio as myocardial infarction at a different sound level. The detailed data shown in Table 1 was originated from local statistics [54] and meta-analysis research on noise exposure–response relationships [47]. Based on six studies and over 17,000 samples, this referenced meta-analysis [47] provided reliable results, and it has also been cited by a 2011 WHO report [33]. Thereafter, Babisch et al. continued their study including more data from five additional studies [48]. However, three of the studies provided information on the overall exposure–response relationship with respect to CVD [55], ischemic heart disease [56] and coronary heart disease [57], which are more common than myocardial infarction. Hence, the present paper relies on the results of previous research [47] to assess myocardial infarction.
The categorical approach of Babisch’s work [ 47] is noise level-oriented. Relative risks from different studies referring to the same noise category were pooled to derive an exposure–response relationship. The results indicated that exposure to noise increases the incidence of myocardial infarction in people aged 25–74 and has no effects on myocardial infarction incidence in women.
To infer the probability risk of myocardial infarction caused by exposure to construction noise, the background prevalence of myocardial infarction in Beijing is also cited. The details are as follows (Table 1):
Table 1. Probability risk a of myocardial infarction caused by construction noise, in percent.
Age
Sound Level, Ldn, (dB(A))
<60 60–64 65–69 70–74 75 Disability
Weight b, DW
25–34 0.0105 0.0106 0.0112 0.0121 0.013
0.443
35–44 0.0796 0.0808 0.0850 0.0924 0.1036
45–54 0.2059 0.2090 0.2197 0.2391 0.2681
55–64 0.3936 0.3995 0.4200 0.4570 0.5125
65–74 1.3436 1.3637 1.4336 1.5599 1.7493
a R = odds ratio × Pmt, where odds ratio is derived from [47], and Pmt represents the local average mortality level of myocardial infarction [54]; b The DW for myocardial infarction in the WHO WPRO B1 (mainly China) region is 0.433 [58].
2.1.2. Cognitive Impairment
The negative effects of noise on learning and memory in children have been studied for many years. Noise can reduce the clarity of a teacher’s voice [ 59,60], and exposure to noise increases the time required for children to process information [ 61,62]. The mechanism of this effect is similar to that of CVD. Acute and chronic noise increases the workload of cortical and sub-cortical brain structures, interrupting the original biological response [ 63]. Children residing in noisier areas of communities suffer psychological distress, as shown by increased systolic blood pressure, greater heart rate reactivity and higher overnight cortisol levels [64,65].
Evidence from recent, well-controlled epidemiological studies [66–68] with representative samples of children aged 5–19 has made it possible to quantify the magnitude of noise-induced cognitive impairment in children. Based on the results of these studies, the WHO performed a meta-analysis that created an approximate curve (Figure 1) to represent the exposure–response relationship between noise and impairment [33]. This curve assumes that 100% of children exposed to noise at 95 Ldn, dB(A) suffer from cognitive impairment and that no children are affected at an exposure level of 50 L dn, dB(A). The approximate probability of a child developing noise-induced cognitive impairment (NICI) is shown in Table 2.
NICI is not an outcome of a clinical diagnosis [ 33]. In 2002, WHO have suggested DWs for cognitive impairment and contemporaneous cognitive deficit of different diseases (Japanese encephalitis; Ascariasis; Trichuriasis; Hookworm disease; Iron-deficiency anaemia) in the WPRO B1 (mainly China) region [58]. In 2011, WHO [33] gave a conservative DW of 0.006 in estimates of NICI.
Int. J. Environ. Res. Public Health 2016, 13, 1045 5 of 18 Int. J. Environ. Res. Public Health 2016, 13, 1045 5 of 17
Figure 1. Hypothetical exposure–risk curves and estimated percentages of affected people [33].
Table 2. Health risk of noise-induced cognitive impairment (NICI).
Noise Level
Ldn, (dB(A))
Percentage of Children Who Will
Suffer from NICI
Disability Weight,
DW Boundary Condition
<55 0%
0.006 Age: 7–19 years old 55–64 20%
64–75 50%
>75 75%
2.1.3. Sleep Disturbance
It is obvious that exposure to noise over a certain threshold during sleeping may cause a person to awaken. Complete sleep cycles are vital to main taining performance during the day, as well as general good health. Humans perceive, evaluate and react to acoustic information even while asleep.
Acute and chronic noise exposure induces the auditory system to continuously engage in these actions and results in sleep structure changes and an increased heart rate. Thus, sleep is disrupted, and its restorative power is decreased.
Sleep disturbance is the most pervasive complaint in China [2], especially in metropolises such as Beijing, Shanghai and Shenzhen, where the traf fic conditions make cont ractors favor arranging material transportation and work at night. Researchers worldwide [69–71] have investigated the adverse effects of construction noise exposure on sleep. For example, Sun [69] provided direct evidence in support of health risk assessment for estimating construction noise-induced sleep disturbance in China. In his study [69], 98% of respondents confessed that construction noise reduced their sleep quality, 95% of respondents admitted being awakened by construction noise, and 40% of them reported being awakened frequently. By analyzing the results of noise monitoring and the questionnaire survey, the threshold value of constr uction noise that leads to sleep disturbance was found to be 52 dB(A) [69]. However, the study di d not address the exposure–response relationship between construction noise and sleep disturbance. In contrast, road-traffic noise has been well studied. Miedema [72,73] evaluated 43 data sets obtained from 36 field studies; the data were presented as a synthesis curve that can be seen as the exposure–response relationship between road traffic and sleep disturbance. The responses were gathered from people aged 12–98 years through self-reporting, and the questions addressed problems that included waking up or being disturbed by noise. These two analyses yielded very similar curves and included 95% confidence intervals that considered the variations across individuals and studies. Because construction noise at night in China mainly originates from engines of motor vehicles and heavy equipment, its character could be similar to road traffic noise. Hence, this study assumed that construction noise has the same health risk factor as road traffic noise. The risk factor can be calculated by the following Equation (1) [72]:
Figure 1. Hypothetical exposure–risk curves and estimated percentages of affected people [33].
Table 2. Health risk of noise-induced cognitive impairment (NICI).
Noise Level Ldn,
(dB(A))
Percentage of Children Who
Will Suffer from NICI Disability Weight, DW Boundary Condition <55 0%
0.006 Age: 7–19 years old55–64 20%
64–75 50%
>75 75%
2.1.3. Sleep Disturbance
It is obvious that exposure to noise over a certain threshold during sleeping may cause a person to awaken. Complete sleep cycles are vital to maintaining performance during the day, as well as general good health. Humans perceive, evaluate and react to acoustic information even while asleep.
Acute and chronic noise exposure induces the auditory system to continuously engage in these actions and results in sleep structure changes and an increased heart rate. Thus, sleep is disrupted, and its restorative power is decreased.
Sleep disturbance is the most pervasive complaint in China [2], especially in metropolises such as Beijing, Shanghai and Shenzhen, where the traffic conditions make contractors favor arranging material transportation and work at night. Researchers worldwide [ 69–71] have investigated the adverse effects of construction noise exposure on sleep. For example, Sun [69] provided direct evidence in support of health risk assessment for estimating construction noise-induced sleep disturbance in China. In his study [ 69], 98% of respondents confessed that construction noise reduced their sleep quality, 95% of respondents admitted being awakened by construction noise, and 40% of them reported being awakened frequently. By analyzing the results of noise monitoring and the questionnaire survey, the threshold value of construction noise that leads to sleep disturbance was found to be 52 dB(A) [69].
However, the study did not address the exposure–response relationship between construction noise and sleep disturbance. In contrast, road-traffic noise has been well studied. Miedema [72,73] evaluated 43 data sets obtained from 36 field studies; the data were presented as a synthesis curve that can be seen as the exposure–response relationship between road traffic and sleep disturbance. The responses were gathered from people aged 12–98 years through self-reporting, and the questions addressed problems that included waking up or being disturbed by noise. These two analyses yielded very similar curves and included 95% confidence intervals that considered the variations across individuals and studies. Because construction noise at night in China mainly originates from engines of motor vehicles and heavy equipment, its character could be similar to road traffic noise. Hence, this study Int. J. Environ. Res. Public Health 2016, 13, 1045 6 of 18 assumed that construction noise has the same health risk factor as road traffic noise. The risk factor can be calculated by the following Equation (1) [72]:
R = 20.8 − 1.05 × (Lnight) + 0.01486 ×
(
Lnight
)2
(1)
where Lnight represents the sound level from 11 pm to 7 am in dB(A). The threshold value was found to be 52 dB(A) [69].
Thus far, no DW is available for evaluating noise-induced sleep disturbance in the WPRO B1 (mainly China) region. The only suggested interval that has been published is in the Night Noise Guidelines for Europe [74]. In this report, the median of an interval (0.04, 0.1) was chosen as the DW for noise-induced sleep disturbance. With the assumption that the human response to noise is mainly founded on biological, racial, traditional and other subjective mechanisms, the present study uses a DW of 0.07 to calculate the DALY in Beijing.
2.1.4. Annoyance
Annoyance is widely accepted as a basis for evaluating the impact of noise on an exposed population [33]. People who suffer annoyance may experience multiple negative physical and mental reactions, such as tiredness, stomach discomfort, anger, disappointment and anxiety. Because health is defined as a state of not only physical but also mental and social well-being [ 32], it is reasonable to consider noise-induced annoyance as a health risk.
Transportation noise is considered the most common source of environmental noise that leads to annoyance. Based on the results of several high-quality papers [ 75–77], the European Commission has created synthesis curves of dose–response relationships for noise annoyance from aircraft, road traffic and railway noise, with their 95% confidence intervals taking into account the variation among individuals and studies [78].
Several studies have shown that construction noise is also a significant noise source that leads to people feeling annoyed [4,69,70,79–81]. In a study by Sun [69], field noise levels around a construction plant were measured, and self-reported responses of annoyance were collected. Based on these data, the threshold value of annoyance for construction noise was calculated as 60.3 dB(A); however, no reference to an exposure–response relationship was made.
Hence, this study assumes that construction noise and road traffic have the same dose–response relationship with annoyance because the annoyance induced by construction noise is primarily caused by inner-combustion engine machines [46,69], which are also the main noise source for road noise.
Based on Sun’s study [69] and a report made by the European Commission [78], the function used for risk factor be achieved by Equation (2):
Rdaytime = 9.994 × 10−4 (Ldn − 60.3)3 − 1.523 × 10−2 (Ldn − 60.3)2 + 0.538 × (Ldn − 60.3) (2) where the unit of Ldn is dB(A).
In the same vein as sleep disturbance, annoyance has no authentic DW. Fortunately, the report by the WHO [33] recommends a conservative DW of 0.02 for estimating DALYs due to annoyance.
The age boundary is the same as that for sleep disturbance.
2.2. Assessment Model
The health risk of construction noise can be assessed using a model that considers money as a term. First, the DALYs due to construction noise are estimated, using noise level as a dependent variable. Next, the economic value of the DALYs is calculated based on the value of a statistical life (VSL), which is calculated according to WTP .
Int. J. Environ. Res. Public Health 2016, 13, 1045 7 of 18
2.2.1. DALY Estimation Model
In the original method, DALY calculation is performed by statistically analyzing actual data after damage to health has occurred. The following formula is used [33]:
DALY = YLL + YLD (3)
In the Equation (3), YLL represents the number of years of life lost, which is calculated by Equation (4):
YLL = ∑(Nm
i × lm
i + Nf
i × lf
i) (4)
where Nm
i and Nf
i represent the numbers of deaths of males and females in age group i multiplied by the standard life expectancies lm
i and lf
i, of males and females, which is the age at which death occurs.
YLD is the number of “years lived with disability”, and is estimated using Equation (5):
YLD = I × DW × D (5)
where I represents the number of incident cases multiplied by a DW and the average duration D of disability in years. DW is associated with each health condition and lies on a scale between 0 (indicating that the health condition is equivalent to full health) and 1 (indicating that the health condition is equivalent to death).
According to this method, noise health risks can be accounted for with ∫ ∞
0 AD[L, n(L)]dL,
where AD (L) represents the DALY loss of each individual exposed to noise levelL, and n (L) represents the densities of exposed persons at different noise levels. However, this type of statistical formula cannot satisfy the intention of environmental impact assessment [82]. Hence, in this model, DALYs are calculated using a method modified based on the methodology of the population attributable fraction (PAF), in which the key factor is the relative risk (RR). The expected Nm
i , Nf
i and I can then
be derived by n (L) × RR (L, c ), which depends not only on the exposed noise level, L, but also on personal characteristics, c, such as age and gender. In addition, data on the distributions of individuals, noise levels and health risks are often available as both discrete and continuous data sets. The formula used for calculating the environmental impact (EI) of noise is expressed as Equation (6):
HR = ∑
L
V [n (L) × RR (L, c ) × (PDT × l + (1 − PDT) × DW × D) , c ] (6) However, noise around construction sites consists both of background noise and construction noise. Residents could be affected by noise even when there is no construction noise. For example, an environmental noise level above 45 dB(A) could lead to sleep disturbance and annoyance [ 32],
and the standard noise level in most regions of China is above 55 dB(A) [ 11]. Therefore, to assess construction noise objectively, it is very critical to take the background noise into consideration when calculating DALYs due to construction noise. The formula for this calculation is shown as Equation (7):
DALYsc= DALYsd − DALYsb = ∆ [n (L) × R (L, c ) × (l + DW × D)] (7) where DALYsc represents the DALYs caused by construction noise Lc; DALYsb represents the DALYs caused by the background noise level Lb; and DALYsd represents the DALYs caused by the environmental noise level Ld during construction. Hence, the environmental impact of construction noise, EIc, can be expressed as Equation (8):
EIc = ∑
L
∆V [n (L) × R (L, c ) × (l + DW × D) , c] (8)
Int. J. Environ. Res. Public Health 2016, 13, 1045 8 of 18
2.2.2. Monetization of Health Risk
In this model, health risks are estimated by DALYs, which consist of both YLL and YLD.
YLL influences the duration of life, and YLD influences the quality of life. The key to monetizing the DALY is determining the monetary value of human life. Because life is not a commodity that has a selling price, the economic value of life is not the price of actual life but rather the amount of money people or society are willing to pay for health damage prevention. This amount is the concept of the value of a statistical life (VSL) [83]. VSL is widely used for the valuation of human life in assessments of public safety issues, such as environmental problems and transportation safety [83].
Large quantities of basic data are needed for the estimation of VSL. For research efficiency, the current work calculated the VSL of a citizen in Beijing based on the existing research. Visusi [83] and Aldy [84] summarized numerous methods of estimating VSL and published the VSL for Americans according to the hedonic wage model, which is widely accepted in the field. The VSL is calculated by Equation (9):
VSL = rf × w × t × rv (9)
where rf represents the mortality risk factor (0.00172 according to [ 85]), w represents the hourly after-tax wage rate, t represents the work hours per year, and rv represents the units of the mortality risk variable (100,000 according to [86,87]).
Because the hourly after-tax wage rate and work hours per year are not provided by state-generated statistics [88], the present study instead uses the per capita disposable income (PCDI) for VSL calculation. After that, the value of a statistical life year (VSLY) for people living in each district of Beijing is calculated based on the VSL and Equation (10), as described below. The results are shown in Table 3.
VSLY = VSL × r[
1 − (1 + r)−n
] (10)
where r represents the discount rate (4% according to the European Commission Guideline [89]) and n represents the expected years of remaining life (31.81 according to [84,88]).
Hence, the environmental impact of construction noise, EIc, can be expressed as Equation (11):
EIc = DALYsc × VSLY (11)
Table 3. VSLY for Beijing citizens (in RMB).
Location PCDI VSLY
Dong Cheng 45,052 434,770.61
Xi Cheng 47,392 592,962.95
Chao Yang 44,646 558,605.34
Feng Tai 41,334 517,165.99
Shi Jingshan 41,943 524,785.73
Hai Dian 50,088 626,694.98
Men Tougou 38,023 475,739.16
Fang Shan 35,912 449,326.59
Tong Zhou 37,095 464,128.14
Shun Yi 36,428 455,782.72
Chang Ping 35,517 444,384.39
Da Xing 37,131 464,578.57
Huai Rou 35,771 447,562.41
Ping Gu 36,226 453,255.32
Mi Yun 35,499 444,159.18
Yan Qing 33,778 422,626.24
Int. J. Environ. Res. Public Health 2016, 13, 1045 9 of 18 In the next section, an example of estimating noise-induced health risks from an earthwork operation is presented to demonstrate the performance of the proposed assessment model. The results are presented for different types of buildings and ages.
3. Application to Earthwork
This project is situated in the Da Shilan neighborhood, Xuan Wu District of Beijing, China.
This project uses two work platforms, C1 and C2, and the earthwork construction is divided into five parts with a total workload of approximately 400,000 cubic meters. According to the project construction plan, the earthwork construction occurs 24 h a day, is executed by six excavators and eight dump trucks, and lasts 123 days. However, due to changes in site factors and the regulation limits of the Beijing city government, only two excavators are allowed to work, and the earthwork transportation is forbidden from 6:00 am to 11:00 pm, which extends the project schedule to at least 369 days.
Additionally, there are six adjacent buildings, including hotels and residential buildings, within 100 m. According to the “Environmental quality standard for noise” [11], the level of environmental noise in the areas around this project should abide by the limits for the second area, which is 60 dB(A) during the daytime and 50 dB(A) at night.
3.1. Data Processing
In this case, the earthwork occurs from 11:00 pm to 6:00 am, and the excavators work all day.
Because the environmental impact documents from this project have no relevant data regarding noise, its noise pollution data are collected by field measurement and simulation. The field measurements are performed from 11:00 pm to 6:00 am, and other noise sources that may impact the accuracy of monitoring are virtually nonexistent. The types of data collected include the sound levels of construction noise sources (SL
1), the local environmental noise levels around the site during construction (SL2) and the indoor noise levels in the adjacent buildings (SL 3). For objectivity and convenience during analysis, data collection is conducted by field monitoring, following the guidelines of the “Emission standard of environmental noise for boundary of construction site” [8], and analysis is performed using the noise-modeling software package SoundPLANTM Acoustics [90]. Details of the collected data are summarized in Table 4.
Table 4. Description of the data.
Data Type Collection Method Utility
SL1 Field monitoring Forecast the acoustic environment of the adjacent buildings during construction
SL2
Field monitoring and acoustics
simulation software Test the validity of simulation
SL3 Acoustics simulation software Represent the acoustic environment in the adjacent buildings during construction
Based on the site conditions and the national standards [8], noise observation points are set to collect noise data. Four of the noise observation points, which are labeled with numbers, are used to measure the noise level of the site, while the other points are set to collect mechanical noise data.
The orientation of the observation points and the site terrain are shown in Figure 2.
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noise in the areas around this project should abide by the limits for the second area, which is 60 dB(A) during the daytime and 50 dB(A) at night.
3.1. Data Processing
In this case, the earthwork occurs from 11:00 pm to 6:00 am, and the excavators work all day.
Because the environmental impact documents from this project have no relevant data regarding noise, its noise pollution data are collected by field me asurement and simulation. The field measurements are performed from 11:00 pm to 6:00 am, and other noise sources that may impact the accuracy of monitoring are virtually nonexist ent. The types of data collected include the sound levels of construction noise sources (SL 1), the local environmental noise levels around the site during construction (SL 2) and the indoor noise levels in the adjacent buildings (SL 3). For objectivity and convenience during analysis, data collection is conducted by field monitoring, following the guidelines of the “Emission standard of environmental noise for boundary of construction site” [8], and analysis is performed using the noise-modeling software package SoundPLAN TM Acoustics [90].
Details of the collected data are summarized in Table 4.
Table 4. Description of the data.
Data Type Collection Method Utility
SL1 Field monitoring Forecast the acoustic environment of the adjacent buildings during construction
SL2 Field monitoring and acoustics
simulation software Test the validity of simulation
SL3 Acoustics simulation software Represent the acoustic environment in the adjacent buildings during construction
Based on the site conditions and the national st andards [8], noise observation points are set to collect noise data. Four of the noise observation points, which are labeled with numbers, are used to measure the noise level of the site, while the other points are set to collect mechanical noise data. The orientation of the observation points and the site terrain are shown in Figure 2.
Figure 2. Locations of the observation points and adjacent buildings.
The residents living adjacent to the project re fused indoor data monitoring; therefore, the environmental noise level before construction was set to 50 dB(A), as authorized by the local denotes
observation
point
denotes
excavator
Figure 2. Locations of the observation points and adjacent buildings.
The residents living adjacent to the project refused indoor data monitoring; therefore, the environmental noise level before construction was set to 50 dB(A), as authorized by the local government [ 11]. The environmental noise levels in the adjacent buildings during construction were simulated by SoundPLAN noise-modeling software based on the measured sound power level of the excavator (113 dB) and the environmental noise level before construction. The simulation results are shown in Figure 3 and Table 5. This study first simulates noise levels at the observation points and tests the validity of simulation by comparing the measured data and the simulation data.
According to Table 6, the correlation coefficient between the measured data and the simulation data is 1, which indicates a perfect correlation.
Int. J. Environ. Res. Public Health 2016, 13, 1045 10 of 17
government [11]. The environmental noise levels in the adjacent buildings during construction were simulated by SoundPLAN noise-modeling software based on the measured sound power level of the excavator (113 dB) and the environmental noise level before construction. The simulation results are shown in Figure 3 and Table 5. This study first simulates noise levels at the observation points and tests the validity of simulation by comparing the measured data and the simulation data. According to Table 6, the correlation coefficient between the measured data and the simulation data is 1, which indicates a perfect correlation.
Figure 3. Sound map of the construction site and its vicinity.
Table 5. Simulated noise levels in the surrounding buildings (unit: dB(A)).
Building No. Noise Level LAeq
1st Floor 2nd Floor 3rd Floo r 4th Floor 5th Floor 6th Floor 1 80.6 80.0 79.2 78.2 77.2 76.2
2 69.5 68.0 67.4 67.6 67.7 69.2
3 73.0 73.2 72.9 72.6 72.3 72.0
4 75.0 74.8 74.4 74.0 73.6 73.1
5 52.6 52.9 53.0 53.0 52.9 52.8
6 59.3 59.1 59.5 59.6 59.8 59.9
Table 6. Description of the noise data (unit: dB(A))
Point No. Measured Value (L Aeq) Simulation Value (L Aeq) Errors a 1 67.8 66.4 1.4
2 73.0 73.2 −0.2
3 73.9 73.5 0.4
4 68.5 67.0 1.5
Correlation coefficient 1
b
a Errors equals the measured value minus the simulation value. b Correlation is significant at the 0.01 level (2-tailed).
After noise data, population information (e.g., size, age and gender) is important for model application. Because the aim of this case study is not to investigate environmental impacts, but rather to present an application of the assessment model, the population-related data used were based on data collected by the local statistical bureau. According to the latest population census statistics of Beijing municipality [91], the per capita floor space for people living in Xuan Wu is 23.54 square Figure 3. Sound map of the construction site and its vicinity.
Int. J. Environ. Res. Public Health 2016, 13, 1045 11 of 18 Table 5. Simulated noise levels in the surrounding buildings (unit: dB(A)).
Building No.
Noise Level LAeq
1st Floor 2nd Floor 3rd Floor 4th Floor 5th Floor 6th Floor 1 80.6 80.0 79.2 78.2 77.2 76.2
2 69.5 68.0 67.4 67.6 67.7 69.2
3 73.0 73.2 72.9 72.6 72.3 72.0
4 75.0 74.8 74.4 74.0 73.6 73.1
5 52.6 52.9 53.0 53.0 52.9 52.8
6 59.3 59.1 59.5 59.6 59.8 59.9
Table 6. Description of the noise data (unit: dB(A))
Point No. Measured Value (LAeq) Simulation Value (L Aeq) Errors a 1 67.8 66.4 1.4
2 73.0 73.2 −0.2
3 73.9 73.5 0.4
4 68.5 67.0 1.5
Correlation coefficient 1 b
a Errors equals the measured value minus the simulation value; b Correlation is significant at the
0.01 level (2-tailed).
After noise data, population information (e.g., size, age and gender) is important for model application. Because the aim of this case study is not to investigate environmental impacts, but rather to present an application of the assessment model, the population-related data used were based on data collected by the local statistical bureau. According to the latest population census statistics of Beijing municipality [91], the per capita floor space for people living in Xuan Wu is 23.54 square meters.
Combined with the building area, the population size of each building can therefore be inferred.
Then, the expected age and gender distributions for the population in each building can be deduced based on local data and the population size. The Beijing Statistical Association has published census information for the Xi Cheng district that accounts for 10 years and includes the sex ratio [91]. In this case, the distributions of age and gender are assumed to be consistent with that of the Xi Cheng district.
For the benefit of data integration, the age interval was set to 10 years, as shown in Figure 4.
Int. J. Environ. Res. Public Health 2016, 13, 1045 11 of 17
meters. Combined with the building area, the popula tion size of each buil ding can therefore be inferred. Then, the expected age and gender distributions for the population in each building can be deduced based on local data and the population size. The Beijing Statistical Association has published census information for the Xi Cheng district that accounts for 10 years and includes the sex ratio [91].
In this case, the distributions of age and gender are assumed to be consistent with that of the Xi Cheng district. For the benefit of data integration, the age interval was set to 10 years, as shown in Figure 4.
Figure 4. Distribution of age and gender.
3.2. Assessment Results
Based on the above analysis of health risks and VSLY using the presented data on noise levels and population, the DALYs for each building were calculated and are presented in Figures 5 and 6.
In the process of DALY calculation, the disability duration of each health impairment, D, is consistent with the duration of the construction period because the evidence showed that these health impairments would disappear once the noise faded away [33].
Figure 5. Distribution of environmental impact by building.
Figure 4. Distribution of age and gender.
Int. J. Environ. Res. Public Health 2016, 13, 1045 12 of 18
3.2. Assessment Results
Based on the above analysis of health risks and VSLY using the presented data on noise levels and population, the DALYs for each building were calculated and are presented in Figures5 and 6. In the process of DALY calculation, the disability duration of each health impairment, D, is consistent with the duration of the construction period because the evidence showed that these health impairments would disappear once the noise faded away [33].
Int. J. Environ. Res. Public Health 2016, 13, 1045 11 of 17
meters. Combined with the building area, the popula tion size of each buil ding can therefore be inferred. Then, the expected age and gender distributions for the population in each building can be deduced based on local data and the population size. The Beijing Statistical Association has published census information for the Xi Cheng district that accounts for 10 years and includes the sex ratio [91].
In this case, the distributions of age and gender are assumed to be consistent with that of the Xi Cheng district. For the benefit of data integration, the age interval was set to 10 years, as shown in Figure 4.
Figure 4. Distribution of age and gender.
3.2. Assessment Results
Based on the above analysis of health risks and VSLY using the presented data on noise levels and population, the DALYs for each building were calculated and are presented in Figures 5 and 6.
In the process of DALY calculation, the disability duration of each health impairment, D, is consistent with the duration of the construction period because the evidence showed that these health impairments would disappear once the noise faded away [33].
Figure 5. Distribution of environmental impact by building.
Figure 5. Distribution of environmental impact by building.
Int. J. Environ. Res. Public Health 2016, 13, 1045 12 of 17
Figure 6. Environmental impacts on age groups.
The total health risk for the neighboring community is 3.80 × 10 DALYs, and the corresponding expected social cost of construction noise in this case is almost 1.98 × 10 million yuan, of which Building No. 1, Building No. 3 and Building No. 4 are the main contributors. People living in these buildings faced significantly more health risk caused by construction noise for two reasons: (1) they are closer to the noise sources; and (2) there are no barriers to block the noise pathway. People living in Building No. 5 suffered the lowest DALYs because Building No. 4 obstructed the pathway between the noise sources and Building No. 5, where the re sidents were only slightly influenced by sleep disturbance.
4. Discussion
4.1. Impact of Noise on Human Health
The present study clearly shows that noise is an important reason for the environmental impact of construction projects, as revealed by the higher health risk faced by the communities neighboring the studied construction site. Among the health ri sk categories assessed, sleep disturbance formed the largest proportion (more than 55%) and was pe rvasive in each building. This conclusion is consistent with the fact that most complaints asso ciated with construction noise occurred at night.
However, as another widely accepted basis for ev aluating the impact of noise, annoyance was not found to have a strong impact, which is likely because its threshold in China is set at 60 dB(A), which is over 10 units higher than that in Europe. Thus, lowering noise emissions at night, developing low- noise equipment and avoiding performing noisy wo rk during nighttime hours represent the most efficient ways to improve a building’s environmental performance, as well as to meet policy goals.
The second most widespread health risk in this case was cognitive impairment, which is likely because noise exposure easily challenges a juvenile’s attention. Because only people aged between 5 and 19 years have been shown to be sensitive to NICI, this health risk is only responsible for approximately 0.36 DALYs, which is the smallest calculated health risk. However, cognitive impairment has a negative effect on learning, which is very important for children’s futures. Thus, both the government and contractors should pay special attention to this factor. Compared with the aforementioned risks, CVD only occurred in four buildings but caused approximately 13.93 DALYs, which accounted for 40% of the estimated environmen tal impact of construction noise. Due to its lethality, which leads to YLL, and the increase in mortality with age, CVD was identified as the most serious health risk for older individuals.
4.2. Methodological Limitations
In a study reported by Gilchrist and Allouche [19], property damage was assessed to quantify the social cost of construction noise pollution. As a quiet acoustic climate is critical to recreation, the Figure 6. Environmental impacts on age groups.
The total health risk for the neighboring community is 38.0 DALYs, and the corresponding expected social cost of construction noise in this case is almost 19.8 million yuan, of which Building No. 1, Building No. 3 and Building No. 4 are the main contributors. People living in these buildings faced significantly more health risk caused by construction noise for two reasons:
(1) they are closer to the noise sources; and (2) there are no barriers to block the noise pathway.
People living in Building No. 5 suffered the lowest DALYs because Building No. 4 obstructed the pathway between the noise sources and Building No. 5, where the residents were only slightly influenced by sleep disturbance.
Int. J. Environ. Res. Public Health 2016, 13, 1045 13 of 18
4. Discussion
4.1. Impact of Noise on Human Health
The present study clearly shows that noise is an important reason for the environmental impact of construction projects, as revealed by the higher health risk faced by the communities neighboring the studied construction site. Among the health risk categories assessed, sleep disturbance formed the largest proportion (more than 55%) and was pervasive in each building. This conclusion is consistent with the fact that most complaints associated with construction noise occurred at night. However, as another widely accepted basis for evaluating the impact of noise, annoyance was not found to have a strong impact, which is likely because its threshold in China is set at 60 dB(A), which is over 10 units higher than that in Europe. Thus, lowering noise emissions at night, developing low-noise equipment and avoiding performing noisy work during nighttime hours represent the most efficient ways to improve a building’s environmental performance, as well as to meet policy goals. The second most widespread health risk in this case was cognitive impairment, which is likely because noise exposure easily challenges a juvenile’s attention. Because only people aged between 5 and 19 years have been shown to be sensitive to NICI, this health risk is only responsible for approximately 0.36 DALYs, which is the smallest calculated health risk. However, cognitive impairment has a negative effect on learning, which is very important for children’s futures. Thus, both the government and contractors should pay special attention to this factor. Compared with the aforementioned risks, CVD only occurred in four buildings but caused approximately 13.93 DALYs, which accounted for 40% of the estimated environmental impact of construction noise. Due to its lethality, which leads to YLL, and the increase in mortality with age, CVD was identified as the most serious health risk for older individuals.
4.2. Methodological Limitations
In a study reported by Gilchrist and Allouche [19], property damage was assessed to quantify the social cost of construction noise pollution. As a quiet acoustic climate is critical to recreation, the value that non-residents attach to the amenity may also be relevant, especially in areas that attract large numbers of tourists or where employees working close by go out for lunch [21]. In the present research, Building No. 3, a hotel located on Qianmen Street, a tourist hot spot in Beijing, went from 80% occupancy to 20% occupancy in the second month after the construction began. Due to the absence of actual financial data, the economic loss experienced by this hotel was deduced as 665,118 yuan based on room price (218 RMB per day), number of rooms (15) and duration of construction (at least 369 days).
Such costs suffered by businesses cannot be reflected by the model proposed in the present study.
Hence, further studies aiming to quantify the social costs of construction noise should integrate property damage and health risk. Another limitation of our study is that the exposure–response factors used were derived from noise with little variation in roughness. The annoyance induced by concrete breaker noise increases as roughness increases [
1]. Therefore, some of the risk factors used may have
led us to underestimate the severity of the health risk induced by the construction noise produced during the demolition stage. Note, however, that this study focused on applying the DALY concept to quantify the health risks resulting from construction noise. Thus, at best, we can only propose this as a reasonable assessment model. As concern regarding the health impairments caused by construction noise [1,69–71] grows, this limitation is expected to be resolved by future studies.
5. Conclusions
During urbanization, numerous construction projects occur, which benefit city development and have an environmental impact on the surroundings. As a common source of environmental noise, construction noise not only deteriorates the acoustic climate quality but also threatens resident health, leading to many complaints. To prevent construction noise pollution, some municipal governments, such as that in Beijing, have decided to impose noise discharge fees, which incentivize contractors to reduce emissions. However, these rules are not adequate for policy purposes because thorough Int. J. Environ. Res. Public Health 2016, 13, 1045 14 of 18 consideration of factors that are related to noise level, impact mechanisms and individual characteristics is lacking.
Accordingly, the present study proposes a health risk assessment model for construction noise and establishes an evaluation index for construction projects in Beijing, which has been adopted by the local government to revise noise discharge regulations. Impact assessment is performed using the DALY metric. Four well-accepted health impairments related to noise are thoroughly analyzed.
Considering the mechanisms underlying the different health impairments, the related DALYs were assessed by noise level and based on other key factors, such as age, gender and local disease incidence.
VSLY was invoked as a monetary index to quantify the social cost of construction noise. Based on the availability of data, the VSLY of individuals living in each district of Beijing are presented, which adds salience to the current noise discharge regulations. In addition, a case study of a construction project in downtown Beijing was used to demonstrate an application of the model.
The case study demonstrated the applicability of the proposed model for assessing the health risk of construction noise. Based on the construction plan, the health risks associated with construction noise from the earthwork case and the corresponding social cost were evaluated using the proposed model. The results of this case study indicate that the health risks caused by construction noise are limited to individuals living in buildings adjacent to construction sites; however, the social cost of the noise is serious. Using this model, administrative departments and contractors can directly predict the environmental impacts of construction noise and take the social cost of construction activity into consideration to implement environmentally friendly construction that helps avoid impairing the health of the people who live and work near a construction site.
However, additional work remains to be conducted. Measurement and assessment efforts based on the methods proposed in the present study should be applied to additional construction sites to gain sufficient empirical data to attain statistical significance. In addition, economic loss should be further investigated to objectively assess the social cost of construction noise. Note that, in principle, property damages evaluated by rental loss, occupancy loss and other kinds of income loss are determined by people’s perceptions of noise nuisance. Hence, simply combing the value of health risks and economic loss would lead to overestimation. Therefore, future improvements should also emphasize strategies and approaches using integrated methodologies.
Acknowledgments: The authors would like to thank the National Science Foundation of China (No. 51078200, No. 51378297) and the National High Technology Research and Development Program of China (No. 2103AA041306) for providing financial support for this study. In addition, the authors would like to thank the Beijing Municipal Commission of Housing and Urban-Rural Development and the Beijing Municipal Institute of Labor Protection for their data-collection efforts.
Author Contributions: Jun Xiao designed the study with all co-authors and drafted the manuscript.
The manuscript was revised by all authors, especially Xiaodong Li. All authors read and approved the final manuscript.
Conflicts of Interest: The authors declare no conflicts of interest.
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© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
October 28, 2024
SENT VIA EMAIL ([email protected])
Sacramento County, Department of Community Development
Planning and Environmental Review Division
Attention: Environmental Coordinator
827 7th Street, Room 225
Sacramento, CA 95814
RE: Draft Environmental Impact Report for the
Upper Westside Specific Plan
(State Clearinghouse No 2020100069,
County Control No. PLNP2018-00284)
Dear Environmental Coordinator for the Upper Westside Specific Plan Project:
On behalf of Environmental Council of Sacramento (“ECOS”) and Friends of Swainson’s Hawk (“FOSH”), and Natomas resident Brandon Castillo, this letter provides comments regarding the Environmental Impact Report (“DEIR”) for the Upper Westside Specific Plan (“Project”).
After extensive review,1 we conclude that the DEIR is woefully inadequate as an informational document. This letter also transmits expert comments on biological resource issues prepared by Shawn Smallwood, PhD (Exhibit 1), expert comments on transportation impacts by Dan Smith (Exhibit 2), and expert comments on air quality by SWAPE (Exhibit 3), which are all incorporated by reference. The County of Sacramento (“County”) must prepare and recirculate a new DEIR that addresses the many shortcomings identified in this comment letter and other comment letters. The County must also seriously consider how to prepare a new EIR that complies with its legal duty to objectively analyze the Project and project alternatives including the no project alternative.
1 While we have expended extraordinary efforts reviewing the DEIR within the allotted 60 days, the DEIR’s pervasive informational deficiencies in a variety of resource areas prevent us from confidently representing that all such defects have been identified.
We therefore reserve the right to supplement these comments. (Galante Vineyards v.
Monterey Peninsula Water Management Dist. (2007) 60 Cal.App.4th 1109, 1121.) Sacramento County, Department of Community Development, Planning and Environmental Review Division
October 28, 2024