Ambient Metals, Elemental Carbon, and Wheeze and Cough in New York City Children through 24 Months of Age

Ambient Metals, Elemental Carbon, and Wheeze and Cough in New York City Children

through Age 24 Months

Molini M. Patel1,2

, Lori Hoepner2,3

, Robin Garfinkel2,3

, Steven Chillrud2,4

, Andria Reyes2,3

,

James W. Quinn5, Frederica Perera

2,3, Rachel L. Miller

1,2,3

1Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine

College of Physicians and Surgeons, Columbia University

2Columbia Center for Children’s Environmental Health, Columbia University

3Department of Environmental Health Sciences, Mailman School of Public Health, Columbia

University

4Lamont-Doherty Earth Observatory, Columbia University

5The Institute for Social and Economic Research and Policy, Columbia University

Correspondence and request for reprints should be addressed to Rachel L. Miller M.D.,

PH8E, Columbia University College of Physicians and Surgeons, 630 W. 168th

St, New York,

NY 10032. Phone: 212-305-7759, fax: 212-305-2277, Email: [email protected].

This manuscript has an Online Data Supplement, which is accessible in this issue’s table of

content online www.atsjournals.org

Sources of funding: Funding for the study is provided by the National Institute of

Environmental Health Sciences (grants R01 ES013163, P50ES015905, P01 ES009600, P30 ES

009089, and R01 ES008977), U.S. Environmental Protection Agency (grants R827027, RD-

832141), Irving General Clinical Research Center (grant RR00645), Educational Foundation of

Page 1 of 47 AJRCCM Articles in Press. Published on September 10, 2009 as doi:10.1164/rccm.200901-0122OC

Copyright (C) 2009 by the American Thoracic Society.

1

America, Gladys & Roland Harriman Foundation, The New York Community Trust, and

Trustees of the Blanchette Hooker Rockefeller Fund.

Running Head: Ambient Metals and Asthma in Children

Descriptor Number: 101 – Asthma in children or 57 – Asthma: epidemiology

Word Count: 4319

AT A GLANCE COMMENTARY

Scientific Knowledge on the Subject

Associations between PM2.5 and asthma development and acute asthma exacerbations are well-

documented. However, health effects of exposure to specific airborne components from traffic

and heating oil combustion, including metals and elemental carbon, have not been fully

characterized.

What This Study Adds to the Field

This paper presents new evidence that implicates exposures to ambient nickel, vanadium, and

elemental carbon as possible risk factors for respiratory symptoms in a young inner city cohort.

The report provides evidence that exposures to PM2.5-associated metals and elemental carbon

from sources such as heating oil combustion and traffic may be important health-relevant PM2.5

fractions associated with asthma morbidity in urban children as young as age 2 years.

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Abstract

Rationale: The effects of exposure to specific components of ambient fine particulate matter

(PM2.5), including metals and elemental carbon (EC), have not been fully characterized in young

children.

Objectives: To compare temporal associations among PM2.5; individual metal constituents of

ambient PM2.5, including nickel (Ni), vanadium (V), and zinc (Zn); and EC and longitudinal

reports of respiratory symptoms through age 24 months.

Methods: Study participants were selected from the Columbia Center for Children’s

Environmental Health (CCCEH) birth cohort recruited in New York City between 1998 and

2006. Respiratory symptom data were collected by questionnaire every 3 months, through age 24

months. Ambient pollutant data were obtained from state-operated stationary monitoring sites

located within the study area. For each subject, 3-month average inverse-distance weighted

concentrations of Ni, V, Zn, EC, and PM2.5 were calculated for each symptom reporting period

based on the questionnaire date and the preceding 3 months. Associations between pollutants and

symptoms were characterized using generalized additive mixed effects models, adjusting for sex,

ethnicity, environmental tobacco smoke exposure, and calendar time.

Measurements and Main Results: Increases in ambient Ni and V concentrations were associated

significantly with increased probability of wheeze. Increases in EC also were associated

significantly with cough during “cold/flu season”. Total PM2.5 was not associated with either

wheeze or cough.

Conclusions: These results suggest that exposure to ambient metals and elemental carbon from

heating oil and/or traffic at levels characteristic of urban environments may be associated with

respiratory symptoms among very young children.

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247 words

Keywords: traffic, heating oil combustion, metals, asthma

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Introduction

Epidemiologic evidence links increases in ambient levels of fine particulate matter

(PM2.5) to asthma exacerbations, lung function decrements, and greater utilization of medical

services for asthma [1-3]. Because of geographic and seasonal differences in PM2.5 composition

and PM2.5-associated health effects [4-7] current mass-based standards for ambient PM2.5 may

not adequately target specific components that are causally associated with adverse health

effects. Diesel exhaust particles (DEP) are a significant driver of local urban PM2.5 levels and are

a dominant source of atmospheric elemental carbon (EC) [8]. Traffic is an important source of

ambient metals from tailpipe emissions, brake and tire abrasion, and resuspended roadway dust

[9, 10]. In New York City (NYC), residual oil combustion for heating contributes to ambient

nickel (Ni) and vanadium (V) concentrations that exceed levels in most other US cities [6, 11].

Given the large contributions of traffic and heating oil combustion to urban ambient PM2.5 levels,

there is a need to characterize the contributions of specific components such as metals and EC to

adverse health effects.

Studies have demonstrated that communities with higher EC concentrations have higher

prevalence of asthma and chronic respiratory symptoms [12, 13]. More recently, proximity to

major roadways has been associated with chronic respiratory symptoms, asthma, and allergic

sensitization [14, 15]. One key longitudinal study in Southern California observed that long-term

exposure to EC, PM2.5, nitrogen dioxide, and acid vapors, derived primarily from motor vehicle

emissions, were associated with deficits in lung function growth between ages 10-18 [16].

Relatively fewer studies have examined respiratory health effects associated with ambient

metals exposures. In a national-scale study, PM2.5-associated risks of respiratory and

cardiovascular hospital admissions were higher in communities with higher levels of PM2.5-

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related Ni, V, and EC [5]. Recently, increases in ambient zinc (Zn) were associated with

increases in asthma emergency department visits and hospital admissions among children living

in Baltimore [17]. Mechanistic support is provided by observations of greater release of

proinflammatory cytokines from airway cells in response to metals exposure [18, 19]. Studies are

needed that elucidate the potential health effects of ambient metal exposures in young children

living in urban communities with high asthma morbidity [20] and examine the differential health

effects associated with exposures to ambient metals, EC, and PM2.5.

We hypothesized that exposure to ambient metals and EC would be associated with

wheeze and cough among young urban children. In a longitudinal design, associations between

local measurements of ambient metals, EC, and PM2.5 and concurrent respiratory symptoms

among children through age 24 months were characterized. The findings provide evidence of a

link between the disproportionately high burden of ambient metals and diesel emission sources

and disproportionately high asthma morbidity among young residents of NYC and possibly other

cities. Some results have previously been reported in the form of abstracts [21, 22].

METHODS

Study cohort data

Detailed methods are provided in the online supplement. Children living in Northern

Manhattan and the South Bronx were enrolled between 1998 and 2006 into a prospective birth

cohort study conducted by the Columbia Center for Children’s Environmental Health (CCCEH)

[23-25]. Briefly, 725 fully enrolled pregnant women, recruited from prenatal clinics associated

with New York Presbyterian Medical Center or Harlem Hospital, were followed throughout

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pregnancy and provided maternal and/or cord blood at delivery. Informed consent was obtained

in accordance with the Columbia University Institutional Review Board. Data on subject

characteristics, residence, environmental tobacco smoke exposure (ETS), and respiratory

symptoms were collected by questionnaires administered to mothers in person or by telephone

every 3 months, between child ages 3 and 24 months.

Stationary site monitoring

Twenty-four hour average ambient concentrations of PM2.5 and PM2.5 fractions of Ni, V,

Zn, and EC were measured every third day by the New York State Department of Environmental

Conservation between 1999 and 2007. Datasets were downloaded (http://www.dec.ny.gov/) for

two sites in the Bronx that were located in the study area: New York Botanical Gardens (NYBG)

and Intermediate School 52 (IS52). Data were aggregated by week and site, as described [26].

Statistical analysis

Associations between metals, EC, and PM2.5 and presence of wheeze and cough were

analyzed using generalized additive mixed effects models (GAMM) using the mgcv library in R

version 2.9.0 (R Foundation for Statistical Computing, Vienna, Austria). Nitrogen dioxide (NO2)

was evaluated as a gaseous indicator of traffic emissions. Single pollutant models were

constructed in which each pollutant was analyzed as a parametric continuous variable. For each

subject, 3-month moving average concentrations of Ni, V, Zn, EC, and PM2.5 were calculated for

each symptom reporting period based on the follow-up questionnaire date and the preceding 3

months. Exposures were assigned to subjects by calculating inverse-distance weighted

concentrations using pollutant measurements from IS52 and NYBG. Address data were collected

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only at prenatal, 6, 12, and 24 month questionnaires, and addresses at interim time points were

assigned using data on moves since last questionnaire and previous addresses. A first order

autoregressive correlation structure was specified to account for correlation among the repeated

observations collected over a 2-year period from each subject. Other covariates included

parametric terms for sex, ethnicity, postnatal ETS, and a nonparametric smoothed term for

calendar time using natural cubic splines (4.7 degrees of freedom per calendar year).

The robustness of results was evaluated using the following methods: models that

included gaseous and particulate copollutants related to traffic, models that excluded the highest

5% of pollutant concentrations, and analyses stratified by season. For stratified analyses, season

was defined as a dichotomous variable: “cold/flu season” (September 1 to March 31) or “non-

cold/flu season” (April 1 to August 31).

In descriptive summaries of symptom prevalence and pollutant levels, season was defined

by calendar year as follows: winter = December 21-March 20, spring = March 21-June 20,

summer = June 21-September 20, and fall = September 21-December 20. Except for GAMM,

statistical tests and modeling were performed using SAS 9.1.3 (Cary, NC, release 2005), and

results with p<0.05 were considered statistically significant.

RESULTS

Cohort characteristics

Among 687 subjects who reached their 2nd

birthday by October 31, 2007, 653 (90% of

the fully enrolled) provided any follow-up data, and thus were included in this study. Seventy

five percent of participants completed at least 5 of the 8 follow-up questionnaires, and 20%

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completed all 8 questionnaires. Thirty-four subjects (5%) were lost to follow-up between ages 3

and 24 months. Characteristics of the study population are shown in Table 1. Approximately

64% of mothers identified themselves as Dominican, and 36% identified themselves as African

Americans. A majority of mothers had at least a twelfth-grade education, and greater than 90%

reported receiving Medicaid at enrollment. At age 24 months, 30% of children were told by a

doctor that they have or may have asthma. There were no significant differences in any of the

displayed demographics between the full cohort at age 24 months, and the subgroup that had

completed one or more follow-up questionnaires.

Spatial and temporal trends in ambient metals, EC, and PM2.5

Across all time points, 72-78% of subjects lived closer to IS52, and 22-28% of subjects

lived closer to NYBG. The range of mean distance was 3.9-4.2 km for IS52 and 5.8-5.9 km for

NYBG. Between 2000 and 2007, mean concentrations of Ni, EC, and NO2 varied significantly

between IS52 and NYBG (Tables E1-E4 of online supplement). Mean concentrations of

pollutants also varied significantly by season (Table E5 of online supplement). Concentrations of

metals in fall and winter often were double the levels in spring and summer, whereas EC

concentrations were higher in winter and fall by approximately 27%. PM2.5 concentrations were

significantly higher in winter and summer by 24%. NO2 concentrations were significantly higher

in winter and spring, however, by less than 10%.

Prevalence of wheeze and cough

Forty seven percent of subjects reported wheeze during at least 1 follow-up period

through age 24 months, whereas 89% reported cough. The overall prevalence of wheeze and

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cough did not change over the study period between 1998 and 2007, but did display consistent

seasonal patterns, with maxima in winter and fall months and minima in spring and summer

months (Figure 1). The proportions of subjects reporting wheeze in the fall and winter were

similar and averaged approximately 20% and 19%, respectively. The proportions of subjects

reporting wheeze in the spring and summer were similar and averaged approximately 14% each.

The proportion of subjects reporting cough was highest in the fall at 56.2%. The proportion of

subjects reporting cough in the winter, summer, and spring and summer were 53.3%, 40.3%, and

37.0%, respectively.

Association between ambient metals, EC, PM2.5 and wheeze and cough

Significant positive associations were observed between metals and wheeze but not

cough. Among all pollutants evaluated, the largest effect estimates were observed in association

with Ni exposure. In models that adjusted for sex, ethnicity, postnatal ETS exposure, and

calendar time, an increase in interquartile range (IQR) concentration of ambient Ni (0.014 µg/m3)

was associated significantly with 28% increased probability of wheeze (p = 0.0006) (Table 2).

These findings were robust to the inclusion of the copollutants EC, NO2, copper (Cu), and iron

(Fe), with an 11% decrease in the magnitude of effect.

Vanadium and wheeze were not significantly associated in the singlepollutant model

(Table 2). An IQR (0.003 µg/m3) increase in 3-month average concentrations of V was

associated with a 10% increased probability of wheeze (p = 0.13). However, after adjusting for

EC, NO2, Cu, and Fe, there was suggestion of association between V and wheeze (β = 0.14 per

IQR increase in V, p = 0.08). Zinc was not associated with wheeze in either single or

multipollutant models.

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Elemental carbon was not significantly associated with wheeze or cough in either single

or multipollutant models that included NO2 and Ni (Table 2). Additionally, total PM2.5 was not

associated significantly with wheeze or cough in single pollutant models. PM2.5 was negatively

associated with wheeze in a multipollutant model that included NO2 and Ni (β = -0.13 per IQR

increase in PM2.5, p = 0.03). Adjustment for Ni but not NO2 resulted in the change of the PM2.5

effect estimate from positive and nonsignificant to negative and significant. Ni was strongly and

positively associated with wheeze and was more positively correlated with PM2.5 in cold/flu

season (r = 0.41) than in non-cold/flu season (r = -0.21), which may explain the apparent

protective effects of PM2.5 on wheeze. An IQR (0.004 ppb) increase in NO2 was significantly

associated with 26% increased probability of wheeze (p = 0.002) (Table 2). However, in the

multipollutant model that included EC and Ni, the effect estimate decreased to 0.13 per IQR

increase in NO2, and the association became nonsignificant (p = 0.27). The association between

NO2 and cough was not significant in the single pollutant model, but there was suggestion of an

association in a model that adjusted for EC and Ni (β = 0.14 per IQR increase in NO2, p = 0.08).

Effects of cold/flu season

To examine differences in effects by season, multipollutant analyses were performed after

stratifying by cold/flu season. Despite the 50% smaller sample sizes in these models, significant

relationships were observed between several pollutants and symptoms, mostly during the cold/flu

season (Table 3). For example, Ni and V remained significantly associated with wheeze in the

model that included only observations during cold/flu season, and the effect estimates were

larger than those estimated in the all-season models. EC was significantly associated with cough

in analyses restricted to observations during cold/flu season, and the association between NO2

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and cough was borderline significant (p = 0.05). In analyses restricted to non-cold/flu season

(April 1-August 31), NO2 was significantly associated with wheeze (Table 3). Significant

negative associations also were observed during cold/flu season between Zn and cough and

PM2.5 and wheeze in multipollutant models. For PM2.5 and wheeze, adjustment for Ni resulted in

a negative effect estimate for PM2.5. For Zn and cough, adjustment for Fe produced a negative

effect estimate for Zn. Fe was significantly associated with cough in the multipollutant model

and also was more highly correlated with Zn during cold/flu season (r = 0.29) than during non-

cold/flu season (r = 0.52), which may provide explanation for the apparent protective effect of

Zn on cough in cold/flu season.

Sensitivity and exploratory analyses

To examine whether the observed findings were driven by extreme pollutant

measurements, relationships with symptoms were examined after excluding the highest 5%

pollutant concentrations. Extreme measurements were clustered by season and year. Timing of

peak measurements also varied among pollutants. For example, peak concentrations of both Ni

and V were measured between December 2000 and February 2001. High concentrations of EC

and Zn were measured between January and February 2006. Peak NO2 concentrations were

predominately clustered between February and May 2000. After excluding peak Ni

measurements, associations with wheeze remained significant in both single and multipollutant

models (Table E6 of online supplement). After excluding extreme V concentrations, associations

with wheeze were no longer significant in the multipollutant model. There was suggestion of

negative association between Zn and cough, and the association between PM2.5 and wheeze was

significantly negative only in multipollutant models. Similar to the full model, NO2 was

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significantly associated with wheeze after excluding the highest 5% concentrations, however, the

association was not robust to the inclusion of the copollutants Ni and EC.

Exploratory cross-sectional analyses were conducted to examine the effects of prenatal

pollutant exposures, implicated in asthma pathogenesis [24, 27], on wheeze and cough at later

ages (data not shown). Ambient metals, EC, and PM2.5 concentrations from the 3 months prior to

birth were not associated with symptoms at age 9 months, and exposures between 3 and 6

months before birth were not associated with symptoms at age 12 months. Models that included

prenatal ETS as a covariate did not produce results that differed from models that included

postnatal ETS as a covariate (Table E7 of online supplement).

DISCUSSION

Our objective was to characterize the differential relationships between exposure to

ambient PM2.5 and its specific components, including metals and EC, and respiratory symptoms

in a cohort of very young children living in high-density NYC neighborhoods. We found that Ni

and V were associated significantly with wheeze in this cohort during the first 24 months of life,

after adjusting for sex, ethnicity, ETS, seasonal trends, and copollutants. Additionally, EC was

associated significantly with cough only during cold/flu season. This study provides new

evidence using an individual-level longitudinal study design that specific components of PM2.5

related to residual oil combustion and/or traffic are associated with adverse respiratory health

effects in children during the first two years of life. PM2.5, a heterogeneous mix of particles of

various chemical constituents from multiple sources, was not associated significantly with

wheeze or cough. This latter result suggests that mass-based standards for total PM2.5 may not

adequately protect against adverse health effects from exposures to the individual toxic metals

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and EC components which are believed to represent approximately only 4% and 3% of the mass,

respectively [28].

Children participating in this study reside in NYC communities with very high pediatric

asthma prevalence and hospitalization rates [20] and that contain major trucking thoroughfares,

bus depots, and waste transfer stations that emit multiple air pollutants [29]. Traffic emissions,

particularly from diesel vehicles, are a dominant source of EC in the atmosphere. Traffic also

contributes to ambient metals from direct tailpipe emissions, brake and tire abrasion, and

resuspension of roadway dust [9, 10]. Residual oil fuel, which is the major source of ambient Ni

and V in NYC, continues to be used for space heating in older residential and commercial

buildings that are common in the study area [11]. Concentrations of EC, Ni, V, and Zn are higher

at the Bronx monitoring sites in our study area, compared to an average of 87 US counties [7],

and Ni concentrations at the Bronx sites are higher than those at other NYC monitoring sites

[11]. Hence, these results suggest that metals and EC from heating oil combustion and diesel

traffic and may be important ambient pollutants that contribute to asthma-related symptoms in

these communities.

The largest effect size and most consistent associations were observed between Ni and

wheeze. The effects of Ni on wheeze were robust to the inclusion of indicators of traffic

emissions such as EC and NO2. Although NO2 was significantly associated with wheeze in a

single pollutant model, associations became nonsignificant when Ni and EC were included in the

model. Therefore, residual oil combustion, an important non-traffic source of ambient Ni in the

study area, could be responsible for many asthma-related symptoms among young residents of

these communities. Recent studies support a role for Ni in increasing risk of asthma -related

outcomes. In a national scale study, county- and season-specific PM2.5 risk estimates for

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respiratory and cardiovascular admissions were higher in counties and seasons with a PM2.5-Ni

fraction in the 75th

compared to the 25th

percentile [5]. Additionally, in reanalyses of the National

Mortality and Morbidity Air Pollution Study data, PM10-mortality risk estimates were higher for

communities with higher long-term averages of ambient Ni and V [4, 6], and this effect

modification was driven by strong associations observed in NYC [4]. Although these previous

studies included adult populations, their findings support even further the premise that Ni and V

may be important airborne pollutants that contribute to adverse respiratory health effects in

NYC.

In analyses stratified by cold/flu season, larger effect estimates for Ni and V on wheeze

and significant effects of EC on cough were observed in models containing observations from

only cold/flu season (September 1-March 31) (Table 3). Concentrations of metals and EC are

higher in the winter due to emissions from heating sources such as roof-top furnaces and due to

lower mixing height in the atmosphere, resulting in diminished dispersion of emitted pollutants

[7, 11]. Respiratory symptoms and asthma exacerbations show peaks in the fall and winter, as

well, and are related to viral infections [30]. In models that excluded the highest 5% of pollutant

concentrations, V and EC were no longer associated with wheeze and cough, respectively,

suggesting that extreme concentrations occurring primarily during winter may be highly

influential in terms of their effects on respiratory symptoms. Nickel remained significantly

associated with wheeze after removing the highest 5% measurements. In a study of human

airway cells, coexposure to human rhinovirus and nitrogen dioxide (NO2) or ozone (O3)

stimulated greater production of the pro-inflammatory cytokine IL-8 than did exposure to

rhinovirus or either pollutant alone [31]. Therefore, significant associations between Ni and V

and wheeze and EC and cough during cold/flu season may occur as a consequence of synergistic

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effects on airway inflammation induced by exposures to viral infections and airborne Ni. NO2

was significantly associated with wheeze during the non-cold/flu season, after adjusting for Ni

and EC. NO2 concentrations did not display strong seasonal variation (Table E5 of online

supplement). Hence, the effects of NO2 on wheeze may have been masked by the larger effects

of Ni and/or viral infections exposures during the cold/flu season and became apparent in the

absence of exposures to high Ni concentrations and/or viral infections during non-cold/flu

season. Unexpected significant negative associations were observed between PM2.5 and wheeze

and Zn and cough that were driven by effects in cold/flu season. Because these apparent

protective effects were observed only in multipollutant models, they are likely explained by

inclusion of copollutants such as Ni and Fe that were found to have strong positive effects on

symptoms and by higher correlations between pollutants observed in cold/flu season.

Ambient levels of Ni, V, or EC may be serving as surrogates of pollutant mixtures or

other individual components from residual oil combustion and/or traffic that are causally

associated with respiratory symptoms. Many PM2.5 species evaluated in our models displayed

high correlation between sites (Table E1 and E4 of online supplement) and also were highly

correlated with other trace elements within sites (Tables E2 and E3 of online supplement),

making it difficult to distinguish among the effects of pollutants from common sources. For

example, due to high correlation among Ni and V, we did not include them in the same model to

evaluate as potential confounders. Copper (Cu) and iron (Fe) were moderately correlated with

Ni, V, and Zn and have been associated with increased mortality [32, 33] and stimulation of

airway inflammation [34] in the literature. In the current study, however, neither Cu nor Fe was

associated with wheeze or cough in single pollutant models (data not shown), and neither altered

the observed associations between Ni or V and wheeze. EC and NO2, both indicators of traffic

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tailpipe emissions, were moderately correlated at NYBG but not significantly correlated at IS52.

EC was significantly associated with cough during the cold/flu season after adjusting for NO2

and Ni, and NO2 was significantly associated with wheeze during the non-cold/flu season after

adjusting for Ni and EC. Thus, although traffic emissions appear to contribute to respiratory

morbidity, it is difficult to distinguish between the effects of particulate and gaseous pollutants.

We acknowledge several limitations to this study. The study population was comprised of

only Dominican and African American children living in Northern Manhattan and the South

Bronx, and populations that differ in ethnic composition also may differ with respect to the

relative strength of association between particular outcomes and exposures. Furthermore, this

cohort may differ from the overall population in several other factors including asthma

prevalence, the distribution of traffic- and oil combustion-related pollutants, genetic

polymorphisms, and cultural differences that may influence symptom reporting and behaviors

relevant to dose of environmental exposures.

To characterize associations between EC, metals, and PM2.5 and respiratory symptoms,

exposures were assigned using data from two monitoring sites located in the study area: IS52 and

NYBG. Previously, personal exposures of NYC adolescents to PM2.5-associated Ni, Zn, and BC

were observed to display the greatest spatial variability, whereas exposures to V showed the least

spatial variability [35]. In the current study, significant differences were observed in mean

concentrations of Ni and EC between sites, and EC was weakly correlated between sites. Small-

scale differences in EC, occurring mostly in winter and spring periods, have been attributed to

local stack emissions of EC that cause random spikes in ambient concentrations (Dr. Oliver

Rattigan, NYSDEC, personal communication). Using data from existing monitoring stations to

represent individual exposures to pollutants with high spatial variability may not represent true

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exposure as accurately as personal or residential measurements. To incorporate spatial

heterogeneity in ambient concentrations of PM2.5 components, exposure estimates were assigned

to subjects using inverse-distance weighted pollution measurements from the two stationary

monitoring sites. Furthermore, in these longitudinal analyses, relationships between pollutants

and symptoms were examined within subjects over time, and previous studies have shown that

central site measurements of PM2.5, EC, and metals are correlated temporally with personal

exposures within subjects. Thus, in the current analyses, central site measurements may provide

reasonable estimates of exposure [36]. Given the significant spatial differences observed in

ambient Ni and EC concentrations, exposure misclassification may be higher for these pollutants.

However, such measurement error is likely to be random and would tend to underestimate the

effects of Ni and EC on respiratory symptoms.

Symptom data covered a three month period and were compared with concurrent 3-

month averages of metals, EC, and PM2.5. Much of the previous evidence regarding effects of

particulate matter or its components pertains to acute (daily) exposures to metals or EC in time-

series analyses or to long-term exposures (yearly or multi-year) in cohort studies. For example,

deficits in lung function growth have been observed in children in association with community-

level pollution exposures between ages 10 and 18 years [16]. In a recent population-level time

series study of children ages 0-17 living in Baltimore, Maryland, high ambient Zn levels,

measured at a central monitoring site, were associated with increases in asthma emergency

department visits and hospitalizations on the following day [17]. From its onset, CCCEH chose

the 3-month time interval as the shortest duration in which structured, high quality questionnaires

could be administered to hundreds of women as part of the parent cohort design. The intent was

to capture recent chronic (i.e. subacute) exposures and related symptoms. For the purpose of this

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specific study, our hypothesis testing was based on determining the effects of subacute

environmental exposures on respiratory symptoms, in part to ascertain a signal that goes beyond

those related to hourly or daily changes in activities. Given the longitudinal design of our study,

collecting data about symptoms on shorter lags, for example, the previous 7 days, may have

improved our characterization of the associations between metals and EC exposures and

respiratory symptoms. However, our findings provide evidence that subacute (i.e. 3 month)

exposures of very young urban children may be associated with increased probability of

respiratory symptoms in addition to acute and long-term exposures to metals and BC or EC that

are documented in the literature.

In conclusion, the associations between increases in ambient concentrations of Ni, V, and

EC, but not total PM2.5, and increased probability of respiratory symptoms, after adjusting for

copollutants, suggest that specific PM2.5 components related to residual oil combustion and/or

traffic may be health-relevant PM2.5 fractions associated with increased respiratory morbidity in

children through age 24 months. While it has been previously demonstrated that exposures to

traffic-related air pollution early in life may be important risk factors for later development of

asthma [24, 37], the current results improve our understanding of the potential deleterious

consequences of exposure to specific metals for children in inner cities. Given that metal and EC

components of ambient PM2.5 are only indirectly regulated as part of the PM2.5 mass-based

standard, improved regulatory action directed at specific sources such as traffic and residential

boilers or at ambient concentrations of individual components such as EC and metals, may be

needed to help protect young children living in urban areas.

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Acknowledgements: The authors acknowledge the contribution of Oliver Rattigan, David

Wheeler, Paul Sierzenga, Dirk Felton, and Patrick Lavin from the Bureau of Air Quality

Surveillance of the New York State Department of Environmental Conservation. The authors

thank Dr. Shuang Wang for her statistical consultation and also thank the women and children

participating in the study.

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composition of fine particle air pollution. Am J Respir Crit Care Med 2009;179:1115-

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in ambient air. Environ Health Perspect 2006;114:1662-1669.

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11. Peltier RE, Hsu S-I, Lall R, Lippmann M, Residual oil combustion: a major source of

airborne nickel in New York City. J Expos Sci Environ Epidemiol 2008 Oct 8. [Epub ahead

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Gauderman J, Avol E, Thomas D, Peters J. Traffic, susceptibility, and childhood asthma.

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pollution near busy roads: The East Bay children's respiratory health study. Am J Respir

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14. Ryan PH, Lemasters GK, Biswas P, Levin L, Lindsey M, Bernstein DI, Lockey J,

Villareal M, K. KHG, Grinshpun SA. A comparison of proximity and land use regression

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15. Morgenstern V, Zutavern A, Cyrys J, Brockow I, Koletzko S, Kramer U, Behrendt H,

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Kuenzli N, Lurmann F, Rappaport E, Margolis H, Bates D, Peters J. The effect of air

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pollution on lung development from 10 to 18 years of age. N Engl J Med 2004;351:

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cells. Toxicol Appl Pharmacol 2004;196:258-265.

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Perera FP, Miller RL. Effects of ambient elemental carbon and metals on wheeze and

cough during early childhood. Epidemiology 2008;19:S159.

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Immunology, 2008. 122(5): p. 914-920.

26. Narvaez RF, Hoepner L, Chillrud SN, Yan B, Garfinkel R, Whyatt RM, Camann D,

Perera FP, Kinney PL, Miller RL. Spatial and temporal trends of polycyclic aromatic

hydrocarbons and other traffic-related airborne pollutants in New York City. Environ Sci

Technol 2008;42:7330-7335.

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pulmonary function in asthmatic children: Effects of prenatal and lifetime exposures.

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29. Tonne CC, Whyatt RM, Camann DE, Perera FP, Kinney PL. Predictors of personal

polycyclic aromatic hydrocarbon exposures among pregnant minority women in New

York City. Environ Health Perspect 2004;112:754-759.

30. Lin RY, Pitt TJ, Lou WWY, Qilong Y. Asthma hospitalization patterns in young children

relating to admission age, infection presence, sex, and race. Ann Allergy Asthma Immunol

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31. Spannhake WE, Reddy SPM, Jacoby DB, Yu XY, Saatian B, Tan J. Synergism between

rhinovirus infection and oxidant pollutant exposure enhances airway epithelial cell

cytokine production. Environ Health Perspect 2002;110:665-670.

32. Burnett RT, Brook J, Dann T, Delocla C, Philips O, Cakmak S, Vincent R, Goldberg MS,

Krewski D. Association between particulate- and gas-phase components of urban air

pollution and daily mortality in eight canadian cities. Inhal Toxicol 2000;12:15-39.

33. Ostro B, Feng W-Y, Broadwin R, Green S, Lipsett M. The effects of components of fine

particulate air pollution on mortality in California: Results from CALFINE. Environ

Health Perspect 2007;115:13-19.

34. Gavett SH, Haykal-Coates N, Copeland LB, Heinrich J, Gilmour IM. Metal composition

of ambient PM2.5 influences severity of allergic airways disease in mice. Environ Health

Perspect 2003;111:1471-1477.

35. Kinney PL, Chillrud SN, Sax S, Ross JM, Pederson DC, Johnson D, Aggarwal M,

Spengler JD. Toxic exposure assessment: A Columbia-Harvard (TEACH) study (The

New York City report). Mickey Leland National Urban Air Toxics Research Center

Research Report No. 3. 2005.

36. Janssen, NAH, Lanki T, Hoek G, Vallius M, de Hartog JJ, Van Grieken R, Pekkanen J,

Brunekreef B. Associations between ambient, personal, and indoor exposure to fine

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37. Salam, MT, Gauderman WJ, McConnell R, Lin PC, Gilliland FD. Transforming growth

factor- 1 C-509T polymorphism, oxidant stress, and early-onset childhood asthma. Am J

Respir Crit Care Med 2007;176:1192-1199.

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Figure Legends

Figure 1. Seasonal trends in wheeze and cough. Prevalence calculated as proportion of subjects

reporting presence of wheeze or cough each season. Wheeze prevalence was higher in the fall

(September 21-December 20) and winter (December 21-March 20) (p<0.0001), compared with

spring (March 21-June 20). Similar proportions of subjects reported wheeze in the spring and

summer (June 21-September 20). Prevalence of cough was higher in fall, winter, and summer

(p<0.0001 for all 3 seasons), compared with spring. Su = summer, F = fall, W = winter, Sp =

spring.

Page 26 of 47

26

Tables Table 1. Selected cohort characteristics

Cohort at

age 24

months

(n = 687)

Children with

follow- up

data

(n = 653)

Male 48% 49% Child’s sex

Female 52% 51%

Dominican 65% 64% Mother’s

Ethnicity African American 35% 36%

Mother with at least 12th grade education 64% 64%

Child age 6 months 13% 13%

Child age 12 months 12% 12%

Child age 24 months 11% 11%

Maternal history

of smoking

Any time child age 0-24 months 11% 12%

Child age 6 months 25% 25%

Child age 12 months 23% 23%

Child age 24 months 19% 19%

Smoker in

household

Any time child age 0-24 months 25% 27%

Maternal history of asthma 23% 22%

Mother receiving Medicaid at enrollment 91% 90%

Child with asthma/possible asthma* 30% 30%

*Doctor says child has or might have asthma at time of 24 month questionnaire

Page 27 of 47

27

Table 2. Effect estimates of presence of wheeze or cough associated with 3-month average

ambient pollutant concentrations (β-coefficient*, p)

Pollutant

(IQR)

Symptom

Single pollutant model Multipollutant model

nll 636 (3085) 636 (3075)

Wheeze 0.28 (0.0006) 0.25 (0.0006)

Ni†

(0.014 µg/m3)

Cough -0.05 (0.51) -0.14 (0.10)

n 636 (3085) 636 (3075)

Wheeze 0.10 (0.13) 0.14 (0.08)

V†

(0.003 µg/m3)

Cough 0.04 (0.49) -0.04 (0.59)

n 636 (3085) 636 (3075)

Wheeze 0.04 (0.66) 0.01 (0.94)

Zn†

(0.018 µg/m3)

Cough 0.03 (0.75) -0.17 (0.15)

n 636 (3075) 636 (3075)

Wheeze 0.04 (0.43) 0.02 (0.66)

EC‡

(0.29 µg/m3)

Cough 0.04 (0.34) 0.05 (0.25)

n 638 (3131) 636 (3075)

Wheeze -0.0009(0.89) -0.13 (0.03)

PM2.5‡

(2.1 µg/m3)

Cough -0.03 (0.62) -0.06 (0.36)

n 650 (3553) 636 (3075)

Wheeze 0.26 (0.002) 0.13 (0.27)

NO2§

(0.004 ppm)

Cough 0.05 (0.44) 0.14 (0.08)

Page 28 of 47

28

* Beta coefficient estimates change in probability of outcome per interquartile range (IQR)

increase in pollutant concentration adjusted for sex, ethnicity, smoking by mother or other

smoker in the home, calendar week (df = 4.72).

†Copollutants include EC, NO2, copper, and iron.

‡Copollutants include NO2 and Ni.

§Copollutants include EC and Ni.

llTotal number of subjects included in model (number of subjects*number of observations per

subject).

Values in boldface are statistically significant (p<0.05).

Page 29 of 47

29

Table 3. Effects estimates of presence of wheeze or cough associated with 3-month average

ambient pollutant concentrations stratified by season (β-coefficient*, p)

Pollutant (IQR) Symptom Cold/Flu Season†

n††

= 580 (1661)

Non-Cold/Flu Season‡

n = 606 (1414)

IQR 0.012 µg/m3 0.009 µg/m

3

Wheeze 0.31 (0.003) 0.46 (0.07)

Ni

Cough -0.14 (0.10) -0.20 (0.30)

IQR 0.0033 µg/m3 0.0029 µg/m

3

Wheeze 0.31 (0.0003) 0.17 (0.39)

V

Cough -0.15 (0.13) 0.12 (0.46)

IQR 0.011 µg/m3 0.007 µg/m

3

Wheeze -0.13 (0.46) 0.34 (0.25)

Zn

Cough -0.31 (0.04) 0.20 (0.44)

IQR 0.319 µg/m3 0.232 µg/m

3

Wheeze 0.07 (0.32) -0.02 (0.80)

EC

Cough 0.11 (0.04) -0.001 (0.99)

IQR 2.1 µg/m3 1.9 µg/m

3

Wheeze -0.30 (0.008) 0.02 (0.85)

PM2.5

Cough -0.06 (0.36) -0.13 (0.18)

IQR 0.0040 ppm 0.0038 ppm

Wheeze -0.08 (0.47) 0.38 (0.02)

NO2

Cough 0.22 (0.05) 0.12 (0.33)

Page 30 of 47

30

* Beta coefficient estimates change in probability of outcome per increase in interquartile range

(IQR) of pollutant concentration, adjusted for sex, ethnicity, smoking by mother or other smoker

in the home, and calendar week, and copollutants as described in Table 2.

†Includes observations between September 1 and March 31.

‡Includes observations between April 1 and August 31.

††Total number of observations included in model (number of subjects*number of observations

per subject).

Values in boldface are statistically significant (p<0.05).

Page 31 of 47

Figures Figure 1.

0

10

20

30

40

50

60

70

Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su F W Sp Su

Season

Pro

po

rtio

n o

f su

bje

cts

(%)

Wheeze

Cough

2007 1998

Page 32 of 47

0

Ambient Metals, Elemental Carbon, and Wheeze and Cough in New York City Children

through Age 24 Months

Molini M. Patel, Lori Hoepner, Robin Garfinkel, Steven Chillrud, Andria Reyes, James W.

Quinn, Frederica Perera, Rachel L. Miller

ONLINE DATA SUPPLEMENT

Page 33 of 47

1

METHODS

Study cohort recruitment

Pregnant women who identified themselves as Dominican or African-American and who

were residents of Washington Heights, Central Harlem, or the South Bronx were screened and

recruited from prenatal clinics associated with New York Presbyterian

Medical Center or Harlem

Hospital between 1998 and 2006 as part of the Columbia Center for Children’s Environmental

Health (CCCEH) cohort. Eligibility was limited to nonsmoking women aged 18 to 35 years who

had obtained their first prenatal visit by the twentieth week of pregnancy. Additional exclusion

criteria included pre-existing conditions such as diabetes, hypertension, and known HIV as well

as self-reported illegal drug use. Of the 2835 women screened, 1917 were eligible, and 841

expressed interest in the study and completed prenatal questionnaires. Seven hundred twenty five

women were followed throughout pregnancy and provided maternal and/or cord blood sample at

delivery and thus, were considered fully enrolled. Informed consent was obtained in accordance

with the Columbia University Institutional Review Board.

Questionnaire data

Data on subject characteristics, residence, environmental tobacco smoke (ETS) exposure

and respiratory symptoms were collected by questionnaires administered to the mothers by

telephone or in person prenatally and every 3 months postnatally, between 3 and 24 months of

age for a maximum of 8 follow-up questionnaires. Data were collected only about the previous

3-month period, regardless of the actual time elapsed since the last follow-up questionnaire.

Thirty four subjects (5%) were lost to follow-up between birth and age 24 months and were

Page 34 of 47

2

excluded from analyses. There were no significant differences in subject characteristics between

study participants and those lost to follow-up.

Stationary site monitoring for metals, elemental carbon, and PM2.5

Twenty-four hour average ambient concentrations of PM2.5 and concentrations of EC, Ni,

V, and Zn contained within the PM2.5 fraction of particles were measured every third day by the

New York State Department of Environmental Conservation (NYSDEC) between 1999 and 2007

as part of the US Environmental Protection Agency’s Speciation Trends Network (STN).

Publicly available datasets were downloaded (http://www.dec.ny.gov/) from the two sites

in the Bronx which were located in the study area: New York Botanical Gardens (NYBG) and

Intermediate School 52 (IS52). At NYBG, data on PM2.5 were available between January 1999

and December 2005. EC and metals data were available between April 2000 and December

2005. At IS52, PM2.5 data were available between January 1999 and May 2007, and EC and

metals data were available between December 2000 and May 2007. Because daily data were not

available for both sites, and data were not always available on the same day between sites,

pollutant measurements were aggregated by week and site, as described [25]. Analysis was

conducted by the Research Triangle Institute (Research Triangle Park, NC), and protocols were

standardized across the years queried. Flagged observations indicating deviations from protocol

(most commonly, shipping temperature of samples outside of specifications), equipment

malfunction, and atypical incidents in the local environment (e.g., sandblasting, unusual traffic

congestion) were excluded from calculations of 1-week average concentrations.

Statistical analysis

Page 35 of 47

3

Distributions of continuous variables were examined, and data were log transformed

when necessary to reduce variance and fulfill the distribution requirements of the statistical test

being used. To assess potential selection bias with respect to subjects included in these analyses,

Student’s t-test (continuous variables) and Χ2 (discrete variables) were used to compare

demographic characteristics of the fully enrolled CCCEH cohort at age 24 months with those of

the subgroup that had completed follow-up questionnaires.

Data from NYSDEC’s web sites were integrated into CCCEH's database, which is

maintained in Scientific Information Retrieval 2002. Initially, descriptive statistics were

compared using the complete DEC datasets versus the verified, non-flagged DEC dataset. Some

significant differences were identified, therefore, analyses continued with only verified data

points. Because of non-normal distribution of metals, EC, and PM2.5 concentrations, data are

presented as geometric means (GM) and standard deviation (GSD). Descriptive statistics were

performed on 1-week average pollutant concentrations. Mean pollutant levels between sites were

compared using non-parametric Mann Whitney U-test. Correlations among pollutants within

sites and between the same pollutant measured at the 2 sites were examined using Spearman

correlations. For quality control, PM2.5 duplicate samples from the IS52 site were verified as

highly correlated (r = 0.810, p < 0.001). Seasonal differences in pollutant concentrations were

analyzed using Kruskal Wallis test. In descriptive summaries of symptom prevalence and

pollutant levels, season was defined by calendar year as follows: winter = December 21-March

20, spring = March 21-June 20, summer = June 21-September 20, and fall = September 21-

December 20.

Generalized linear models were used to examine whether the prevalence of wheeze or

cough varied by season. Prevalence was calculated as the proportion of subjects reporting

Page 36 of 47

4

presence of symptoms each season. Associations between metals, EC, and PM2.5 and presence of

wheeze and cough were analyzed using generalized additive mixed effects models (GAMM)

using the mgcv library in R version 2.9.0 (R Foundation for Statistical Computing, Vienna,

Austria). A first order autoregressive correlation structure was specified to account for

correlation among the repeated observations collected over a 2-year period from each subject

since it is expected that correlations between observations diminish over time. Other covariates

included parametric terms for sex, ethnicity, postnatal ETS, and a nonparametric smoothed term

for calendar time using natural cubic splines (4.7 degrees of freedom per calendar year). Data on

ETS were available only on questionnaires administered prenatally, and at 6, 12, and 24 months

of age. In models, exposure to ETS was analyzed as a dichotomous variable and was defined as

smoking by mother or presence of smoker in the home at any time prenatally to 24 months of

age.

For each subject, 3-month moving average concentrations of Ni, V, Zn, EC, and PM2.5

were calculated for each symptom reporting period based on the follow-up questionnaire date

and the preceding 3 months. As a comparison to traffic-associated particles, nitrogen dioxide

(NO2) was evaluated as a gaseous indicator of traffic emissions. Single pollutant models were

constructed in which each pollutant was analyzed as a parametric continuous variable.

Exposures were assigned to subjects by calculating inverse-distance weighted concentrations

using pollutant measurements from IS52 and NYBG. Address data were collected only at

prenatal, 6, 12, and 24 month questionnaires, and addresses during interim time points were

assigned using data on moved since last questionnaire and previous addresses.

The robustness of results was evaluated using the following methods: models that

included gaseous and particulate copollutants related to traffic, models that excluded the highest

Page 37 of 47

5

5% of pollutant concentrations, and analyses stratified by season. Season was defined as a

dichotomous variable: “cold/flu season” (September 1 to March 31) or “non-cold/flu season”

(April 1 to August 31). Separate models included observations only from cold/flu season and

only from non-cold/flu season.

Except for GAMM, statistical tests and modeling were performed using SAS 9.1.3 (Cary,

NC, release 2005), and results with p<0.05 were considered statistically significant.

Page 38 of 47

6

TABLE E1. SUMMARY STATISTICS FOR 1-WEEK AVERAGE POLLUTANT

CONCENTRATIONS*, 1999-2007

Pollutant Site Geometric

mean (GSD) Min Median Max

Spearman’s

correlation

(r)

IS52 (n=210) 0.016 (0.015) 0.003 0.017 0.120 Ni

†

NYBG (n=190) 0.021 (0.014) 0.003 0.024 0.100

0.75‡

IS52 (n=210) 0.006 (0.005) 0.0003 0.006 0.027 V

NYBG (n=190) 0.006 (0.004) 0.0002 0.007 0.024 0.73

‡

IS52 (n=210) 0.032 (0.022) 0.002 0.032 0.192 Zn

NYBG (n=190) 0.031 (0.019) 0.002 0.032 0.115 0.73

‡

IS52 (n=205) 1.1 (0.7) 0.0002 1.1 4.0 EC

†

NYBG (n=189) 1.3 (0.6) 0.40 1.3 4.9 0.38

‡

IS52 (n=393) 13.0 (5.1) 3.4 13.4 37.5 PM2.5

NYBG (n=413) 12.3 (6.3) 3.2 12.3 38.4 0.77

‡

IS52 (n=360) 0.029 (0.006) 0.017 0.029 0.053 NO2

NYBG (n=416) 0.027 (0.005) 0.019 0.027 0.045 0.79

‡

* All concentrations are expressed as µg/m3, except for NO2, which is in parts per

million.

p<0.05 for difference in mean concentrations between sites, Mann-Whitney U-test.

‡p<0.0001 for Spearman’s correlation.

Page 39 of 47

8

TABLE E2. SPEARMAN’S CORRELATION COEFFICIENTS FOR 3-MONTH MOVING AVERAGES OF POLLUTANTS

WITHIN THE IS52 SITE*

IS52

NO2

IS52

EC

IS52

PM2.5

IS52

Br

IS52

Ca

IS52

Cl

IS52

Cu

IS52

Fe

IS52

Mn

IS52

Ni

IS52

Pb

IS52

V

IS52 EC -0.32

IS52 PM2.5 0.38 -0.15

IS52 Br 0.22 0.32 -0.02

IS52 Ca -0.22 0.56 -0.40 0.52

IS52 Cl 0.32 0.25 -0.05 0.63 0.37

IS52 Cu -0.21 0.51 -0.09 0.43 0.74 0.22

IS52 Fe -0.11 0.56 0.15 0.53 0.42 0.23 0.52

IS52 Mn 0.32 0.61 -0.31 0.05 0.84 0.27 0.71 0.51

IS52 Ni 0.47 0.07 0.11 0.67 0.19 0.76 0.09 0.31 0.06

IS52 Pb 0.03 0.33 -0.13 0.64 0.49 0.49 0.47 0.43 0.43 0.59

IS52 V 0.27 0.36 0.09 0.81 0.57 0.67 0.56 0.58 0.41 0.76 0.64

IS52 Zn 0.14 0.37 -0.17 0.75 0.63 0.71 0.45 0.48 0.52 0.78 0.68 0.82

*p>0.05 for shaded correlation coefficients

Page 40 of 47

9

TABLE E3. SPEARMAN’S CORRELATION COEFFICIENTS FOR 3-MONTH MOVING AVERAGES OF POLLUTANTS

WITHIN THE NYBG SITE*

NYBG

NO2

NYBG

EC

NYBG

PM2.5

NYBG

Br

NYBG

Ca

NYBG

Cl

NYBG

Cu

NYBG

Fe

NYBG

Mn

NYBG

Ni

NYBG

Pb

NYBG

V

NYBG EC 0.49

NYBG PM2.5 0.19 0.02

NYBG Br 0.35 0.47 0.24

NYBG Ca 0.29 0.52 -0.14 0.49

NYBG Cl -0.03 0.11 -0.26 0.24 0.17

NYBG Cu 0.03 0.32 0.16 0.47 0.39 0.06

NYBG Fe 0.04 0.71 0.14 0.41 0.58 -0.25 0.42

NYBG Mn -0.07 0.51 0.04 0.50 0.55 -0.05 0.65 0.69

NYBG Ni 0.59 0.69 -0.04 0.35 0.23 0.28 -0.06 -0.06 -0.03

NYBG Pb 0.58 0.53 0.16 0.33 0.11 0.03 0.07 0.24 0.04 0.62

NYBG V 0.30 0.68 0.02 0.69 0.44 0.40 0.32 0.25 0.32 0.62 0.48

NYBG Zn 0.33 0.82 -0.28 0.61 0.61 0.41 0.23 0.21 0.36 0.64 0.40 0.78

*p>0.05 for shaded correlation coefficients.

Page 41 of 47

10

TABLE E4. SPEARMAN’S CORRELATION COEFFICIENTS FOR 3-MONTH MOVING AVERAGES OF METALS, EC,

AND PM2.5 BETWEEN THE IS52 and NYBG SITES*

IS52

EC

IS52

PM2.5

IS52

Br

IS52

Ca

IS52

Cl

IS52

Cu

IS52

Fe

IS52

Mn

IS52

Ni

IS52

Pb

IS52

V

IS52

Zn

IS52

NO2

NYBG EC 0.22 -0.09 0.67 0.44 0.76 0.43 0.30 0.40 0.80 0.64 0.69 0.73 0.14

NYBG PM2.5 0.19 0.77 0.12 -0.10 0.00 0.22 0.36 0.02 0.07 0.03 0.21 0.00 0.21

NYBG Br 0.47 0.07 0.71 0.46 0.47 0.54 0.56 0.58 0.46 0.54 0.55 0.55 -0.07

NYBG Ca 0.52 -0.25 0.57 0.90 0.31 0.63 0.50 0.71 0.32 0.47 0.55 0.66 -0.07

NYBG Cl 0.11 -0.23 0.17 0.14 0.66 0.03 0.02 0.13 0.41 0.03 0.21 0.40 0.20

NYBG Cu 0.32 0.16 0.18 0.25 -0.04 0.54 0.36 0.43 -0.07 0.20 0.15 0.10 -0.15

NYBG Fe 0.71 -0.06 0.24 0.55 -0.04 0.50 0.68 0.70 -0.04 0.28 0.28 0.28 -0.38

NYBG Mn 0.51 -0.11 0.18 0.49 -0.06 0.50 0.48 0.70 -0.13 0.22 0.14 0.20 -0.32

NYBG Ni 0.05 0.02 0.46 0.16 0.76 0.03 0.14 -0.04 0.84 0.64 0.59 0.53 0.28

NYBG Pb 0.24 0.12 0.33 0.12 0.45 0.14 0.38 0.12 0.64 0.75 0.49 0.47 0.09

NYBG V 0.24 -0.03 0.69 0.44 0.69 0.48 0.50 0.41 0.74 0.64 0.85 0.76 0.02

NYBG Zn 0.19 -0.33 0.64 0.57 0.72 0.39 0.30 0.45 0.75 0.61 0.67 0.81 0.04

NYBG NO2 0.09 0.29 0.56 0.13 0.46 0.15 0.33 0.06 0.72 0.61 0.58 0.52 0.56

*p>0.05 for shaded correlation coefficients

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TABLE E5. SEASONAL VARIATIONS IN POLLUTANT CONCENTRATIONS*

Ni† V† Zn† EC† PM2.5† NO2†

IS52 NYBG IS52 NYBG IS52 NYBG IS52 NYBG IS52 NYBG IS52 NYBG

GM (GSD) 0.024

(0.014)

0.033

(0.011)

0.007

(0.006)

0.009

(0.004)

0.037

(0.020)

0.043

(0.017)

1.0

(0.76)

1.6

(0.68)

15.2

(5.2)

14.0

(5.4)

0.031

(0.005)

0.029

(0.004)

Min 0.003 0.010 0.001 0.002 0.002 0.021 0.28 0.70 8.6 5.9 0.021 0.022

Median 0.025 0.034 0.008 0.009 0.039 0.043 1.1 1.6 14.6 14.1 0.031 0.029

Winter

Max 0.075 0.067 0.026 0.020 0.111 0.115 3.7 4.9 37.5 34.1 0.046 0.045

GM (GSD) 0.011

(0.008)

0.014

(0.014)

0.004

(0.005)

0.003

(0.003)

0.025

(0.016)

0.019

(0.014)

0.96

(0.68)

0.96

(0.46)

11.7

(4.2)

10.7

(5.7)

0.029

(0.006)

0.027

(0.005)

Min 0.003 0.004 0.001 0.001 0.009 0.002 0.06 0.37 4.7 4.0 0.016 0.015

Median 0.011 0.015 0.005 0.004 0.023 0.021 0.98 0.92 11.6 10.7 0.030 0.027

Spring

Max 0.036 0.100 0.014 0.012 0.090 0.072 3.5 2.5 25.8 30.7 0.043 0.043

GM (GSD) 0.007

(0.005)

0.011

(0.003)

0.005

(0.003)

0.005

(0.002)

0.024

(0.009)

0.020

(0.007)

1.3

(0.57)

1.2

(0.21)

14.9

(5.3)

13.9

(7.1)

0.027

(0.006)

0.024

(0.005)

Min 0.003 0.006 0.001 0.002 0.010 0.011 0.52 0.93 5.8 4.1 0.017 0.015

Median 0.007 0.012 0.006 0.005 0.025 0.020 1.3 1.2 14.8 14.3 0.027 0.025

Summer

Max 0.021 0.015 0.013 0.010 0.044 0.035 2.1 1.6 30.9 38.4 0.053 0.034

GM (GSD) 0.016

(0.019)

0.023

(0.013)

0.005

(0.006)

0.006

(0.005)

0.037

(0.029)

0.038

(0.020)

1.2

(0.78)

1.3

(0.58)

11.8

(4.7)

10.9

(6.1)

0.028

(0.006)

0.027

(0.005)

Min 0.003 0.003 0.001 0.001 0.007 0.007 0.44 0.45 3.4 3.2 0.010 0.011

Median 0.017 0.025 0.005 0.006 0.039 0.041 1.1 1.3 9.4 10.6 0.029 0.027

Fall

Max 0.120 0.058 0.027 0.024 0.192 0.109 4.0 3.2 30.8 35.2 0.045 0.044

All concentrations are expressed as µg/m3, except for NO2, which is in parts per million.

† p<0.05, for differences in means across seasons, evaluated by Kruskal-Wallis Test, except for IS52 EC.

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11

Table E6. Effect estimates of presence of wheeze or cough associated with 3-

month average ambient pollutant concentrations, excluding highest 5% pollutant

concentrations (β-coefficient*, p)

Pollutant (IQR) Symptom Single pollutant model Multipollutant model

nll 632 (2933) 632 (2923)

Wheeze 0.39 (<0.0001) 0.31 (0.0004)

Ni†

(0.014 µg/m3)

Cough 0.03 (0.74) -0.08 (0.51)

n 633 (2924) 632 (2914)

Wheeze 0.05 (0.52) 0.07 (0.49)

V†

(0.003 µg/m3)

Cough 0.07 (0.27) -0.03 (0.74)

n 636 (2919) 636 (2909)

Wheeze 0.12 (0.31) 0.06 (0.67)

Zn†

(0.018 µg/m3)

Cough 0.008 (0.94) -0.25 (0.08)

n 636 (2914) 636 (2910)

Wheeze 0.10 (0.10) 0.09 (0.15)

EC‡

(0.27 µg/m3)

Cough 0.05 (0.28) 0.06 (0.21)

n 635 (2982) 633 (2934)

Wheeze -0.03 (0.65) -0.18 (0.003)

PM2.5‡

(1.9 µg/m3)

Cough -0.06 (0.25) -0.09 (0.14)

n 648 (3382) 633 (2924)

Wheeze 0.21 (0.02) 0.12 (0.23)

NO2§

(0.0038 ppm)

Cough 0.07 (0.31) 0.12 (0.16)

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12

* Beta coefficient estimates change in probability of outcome per interquartile range

(IQR) increase in pollutant concentration adjusted for sex, ethnicity, smoking by mother

or other smoker in the home, calendar week.

†Copollutants include EC, NO2, copper, and iron.

‡Copollutants include NO2 and Ni.

§Copollutants include EC and Ni.

llTotal number of subjects included in model (number of subjects*number of

observations per subject).

Values in boldface are statistically significant (p<0.05).

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13

TABLE E7. Associations of wheeze or cough with 3-month average ambient

pollutant concentrations, adjusting for postnatal versus prenatal ETS exposure

(β-coefficient*, p-value)

Pollutant

(IQR)

Symptom Controlling for postnatal

ETS exposure

nll = 636 (3075)

Controlling for prenatal

ETS exposure

nll = 630 (3047)

Wheeze 0.25 (0.0006) 0.25 (0.005) Ni

(0.014 µg/m3) Cough -0.14 (0.10) -0.15 (0.07)

Wheeze 0.14 (0.08) 0.16 (0.05) V

(0.003 µg/m3) Cough -0.04 (0.59) -0.05 (0.50)

Wheeze 0.01 (0.94) 0.03 (0.83) Zn

(0.018 µg/m3) Cough -0.17 (0.15) -0.18 (0.14)

Wheeze 0.02 (0.66) 0.02 (0.71) EC

(0.29 µg/m3) Cough 0.05 (0.25) 0.06 (0.19)

Wheeze -0.13 (0.03) -0.14 (0.02) PM2.5

(2.1 µg/m3) Cough -0.06 (0.36) -0.06 (0.35)

Wheeze 0.13 (0.27) 0.12 (0.19) NO2

(0.004 ppm) Cough 0.14 (0.08) 0.14 (0.08)

ETS = environmental tobacco smoke

* Beta coefficient estimates change in probability of outcome per interquartile range

(IQR) increase in pollutant concentration adjusted for sex, ethnicity, ETS (either prenatal

or postnatal, calendar week (df = 4.72), and copollutants as described in Table 2.

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14

llTotal number of subjects included in model (number of subjects*number of

observations per subject).

Values in boldface are statistically significant (p<0.05).

Page 47 of 47