1118 VOLUME 115 | NUMBER 7 | July 2007 • Environmental Health Perspectives

Research | Children’s Health

Low birth weight is an important predictor of

children’s health and is associated with higher

risk of infant and childhood mortality

(McCormick 1985), coronary heart disease

(Vos et al. 2006), and other health problems

(Ashdown-Lambert 2005). For example, in a

cohort of 10,803 singleton births, Lawlor et al.

(2005a) found an inverse relationship between

birth weight and coronary heart disease and

stroke. Identified risk factors for low birth

weight include mother’s age (Khoshnood et al.

2005), prenatal care (Shi et al. 2004), maternal

smoking and educational status (Kleinman and

Madans 1985), race (Alexander et al. 2003),

and socioeconomic factors (Valero de Bernabe

et al. 2004). Several studies examined whether

maternal exposure to air pollution adversely

impacts birth outcomes, such as low birth

weight, preterm delivery, intrauterine growth

restriction, and postneonatal infant mortality

(Glinianaia et al. 2004; Maisonet et al. 2004;

Sˇrám et al. 2005; Woodruff et al. 2006). 

Results regarding the relationship between

air pollution and birth weight are inconclusive,

with some studies identifying associations

where others did not, and the suite of adverse

pollutants and exposure periods differing by

study. For instance, higher levels of carbon

monoxide were associated with low birth

weight in southern California; six Northeastern

U.S. cities; São Paulo, Brazil; Sydney, Australia;

and Seoul, South Korea (Gouveia et al. 2004;

Ha et al. 2001; Maisonet et al. 2001; Mannes

et al. 2005; Ritz et al. 2000; Salam et al. 2005;

Wilhelm and Ritz 2003, 2005). However no

association was identified in studies based in

the Czech Republic, Taiwan, Nevada, and

California (Bobak 2000; Chen et al. 2002; Lin

et al. 2004; Parker et al. 2005). Particulate matter (PM) with an aerodynamic diameter < 10

μm (PM10) was linked with low birth weight in

São Paulo, Southern California, Taiwan, the

Czech republic, and Seoul (Dejmek et al. 1999;

Gouveia et al. 2004; Ha et al. 2001; Wilhelm

and Ritz 2005; Yang et al. 2003), with no such

evidence provided by other work in Taiwan

and six U.S. cities (Lin et al. 2004; Maisonet

et al. 2001) and limited evidence in Nova

Scotia, Canada (Dugandzic et al. 2006). 

Three recent reviews summarized scientific evidence regarding the association

between air pollution and birth weight. One

review concluded that the effects of air pollution on low birth weight are not fully apparent and that current scientific knowledge is

limited (Maisonet et al. 2004). Another determined that PM has either a small effect on

fetal growth or no effect, and recommended

further research (Glinianaia et al. 2004). The

most recent review concluded that existing literature supports a causal link between air pollution and birth weight, although additional

research is needed to confirm the effect, investigate the exposure window of importance,

and distinguish which pollutants cause harm

(Sˇrám et al. 2005).

The seemingly conflicting evidence may

result from inadequate control for confounders, variation in populations and pollution characteristics, or differences in study

design such as modeling structure, exposure

time frame, and sample size. Residential

mobility may differ by study population,

resulting in varying levels of exposure misclassification. Effect estimates for PM and mortality and hospital admissions show spatial

and temporal heterogeneity, which may be

related to dissimilar chemical composition. In

particular, the risk of cardiovascular admissions for the elderly from PM with an aerodynamic diameter < 2.5 μm (PM2.5) is higher in

the eastern United States, including the

Northeast region (Dominici et al. 2006), and

mortality effects of PM10 are strongest in the

northeastern United States (Peng et al. 2005).

Variation in PM composition may partially

explain differing results from studies of PM

and low birth weight. 

Many study areas for air pollution and

birth weight research are outside the United

States, such as those areas listed above as well

as Lithuania (Maroziene and Grazuleviciene

2002), Zimbabwe (Mishra et al. 2004),

Canada (Liu et al. 2003), Croatia (Mohorovic

2004), Poland (Jedrychowski et al. 2004), and

China (Wang et al. 1997). Of the U.S.-based

studies, most focused on Southern California

(Basu et al. 2004; Parker et al. 2005; Ritz and

Yu 1999; Salam et al. 2005; Wilhelm and Ritz

2003, 2005). Only one study investigated the

northeastern United States, using births from

six cities over a 3-year period, and found

adverse effects of CO and sulfur dioxide, but

not PM10 (Maisonet et al. 2001). To the best

Address correspondence to M.L. Bell, Yale

University, School of Forestry and Environmental

Studies, 205 Prospect St., New Haven, CT 06511

USA. Telephone: (203) 432-9869. Fax: (203) 432-

3817. E-mail: michelle.bell@yale.edu

Supplemental Material is available online at

http://www.ehponline.org/docs/2007/9759/suppl.pdf

Funding for M.L.B. and K.E. was provided by the

Health Effects Institute through the Walter A.

Rosenblith New Investigator Award (4720-RFA04-

2/04-16. Funding for M.L.B. was also provided by

the National Institute of Environmental Health

Sciences (NIEHS) Outstanding New Environmental

Scientist (ONES) Award (R01 ES015028). Funding

for K.B. was provided by the Health Effects Institute

4720-FRA04 and by NIEHS grants ES015028,

ES07456-07, and ES11013. 

The authors declare they have no competing

financial interests.

Received 24 September 2006; accepted 11 April

2007.

Ambient Air Pollution and Low Birth Weight in Connecticut and Massachusetts

Michelle L. Bell,1 Keita Ebisu,2 and Kathleen Belanger3

1School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA; 2Department of Biostatistics, and 3Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, Connecticut, USA

BACKGROUND: Several studies have examined whether air pollution affects birth weight; however

results vary and many studies were focused on Southern California or were conducted outside of

the United States.

OBJECTIVES: We investigated maternal exposure to particulate matter with aerodynamic diameter

< 10, < 2.5 μm (PM10, PM2.5), sulfur dioxide, nitrogen dioxide, and carbon monoxide and birth

weight for 358,504 births in Massachusetts and Connecticut from 1999 to 2002.

METHODS: Analysis included logistic models for low birth weight (< 2,500 g) and linear models

with birth weight as a continuous variable. Exposure was assigned as the average county-level concentration over gestation and each trimester based on mother’s residence. We adjusted for gestational length, prenatal care, type of delivery, child’s sex, birth order, weather, year, and mother’s

race, education, marital status, age, and tobacco use. 

RESULTS: An interquartile increase in gestational exposure to NO2, CO, PM10, and PM2.5 lowered

birth weight by 8.9 g [95% confidence interval (CI), 7.0–10.8], 16.2 g (95% CI, 12.6–19.7), 8.2 g

(95% CI, 5.3–11.1), and 14.7 g (95% CI, 12.3–17.1), respectively. Lower birth weight was associated with exposure in the third trimester for PM10, the first and third trimesters for CO, the first

trimester for NO2 and SO2, and the second and third trimesters for PM2.5. Effect estimates for

PM2.5 were higher for infants of black mothers than those of white mothers. 

CONCLUSIONS: Results indicate that exposure to air pollution, even at low levels, may increase risk

of low birth weight, particularly for some segments of the population.

KEY WORDS: Air pollution, birth weight, carbon monoxide, nitrogen dioxide, particulate matter,

PM10, PM2.5, pregnancy, sulfur dioxide. Environ Health Perspect 115:1118–1125 (2007).

doi:10.1289/ehp.9759 available via http://dx.doi.org/ [Online 11 April 2007]
of our knowledge, no previous study explored

the impacts of nitrogen dioxide or fine PM

(PM2.5) on birth weight in the northeastern

United States. In this research we investigated

the effects of air pollution on birth weight in

Connecticut and Massachusetts over a 4-year

period for SO2, NO2, CO, PM10, and PM2.5

and explored effects by gestational and

trimester exposure and by race.

Materials and Methods 

Birth data. Birth records data were obtained

from the Division of Vital Statistics,

Reproductive Statistics Branch of the

National Center for Health Statistics (2006)

for all registered births in Connecticut and

Massachusetts from 1 January 1999 to 31

December 2002 (n = 495,260). Each observation contained variables for the residence and

birth location by county; prenatal care;

mother’s characteristics (age, race, marital status, education, and alcohol and tobacco use

during pregnancy); father’s characteristics (age

and race); birth order; gestational age in

weeks; sex of child; and birth weight. 

We excluded births with unspecified

county location (0.8%), plural deliveries

(4.3%), births with gestational period > 44

weeks or < 32 weeks (2.7%), births with

weight < 1,000 g or > 5,500 g (1.2%), and

births with impossible gestational age and birth

weight combinations (0.5%) (Alexander et al.

1996). Also omitted were births for which the

counties of mother’s residence and birth were

not identical or in adjacent states (3.9%).

We used gestational period and last menstrual period (LMP), corrected for conception

at an average of 2 weeks past LMP, to estimate

air pollution exposure for each pregnancy and

pollutant for the total pregnancy and each

trimester. Various definitions of trimesters

have been applied, and some studies have not

reported trimester specifications. We used

trimester divisions of 1–13 weeks, 14–26

weeks, and 27 weeks to birth; similar definitions have been applied elsewhere (e.g., Parker

et al. 2005). Births were omitted if LMP was

missing (10.1%) or the date of birth estimated

through LMP and gestational period was > 30

days from the midday of the birth month

reported on the birth certificate, because this

condition implies inaccurate data (10.4%). 

Pollution and weather data. Monitoring

data from the U.S. Environmental Protection

Agency (U.S. EPA 2006a) was used to estimate

daily county-level concentrations for NO2,

SO2, PM2.5, PM10, and CO. We included air

pollution data for 1998 in addition to the years

in which eligible births occurred, 1999–2002,

to estimate exposure for births in 1999. Each

county’s daily average was based on measurements from a single monitor or average of data

from multiple monitors within the county. We

then used this information to estimate exposure

to each pollutant over the gestational period

and each trimester using the county of mother’s

residence. The frequency of measurement and

number of monitors varied by pollutant and

county. PM2.5 and PM10 were measured an

average of every 2.7 and 5.5 days, respectively.

Other pollutants were measured approximately

every day. Some monitors began or ceased

operation from 1998–2002, so not all counties

had pollutant concentrations for the entire

study period.

We excluded births for which air pollutant data were not available for ≥ 75% of the

weeks in each trimester for that pollutant

(21.5%). This prevents inaccurate exposure

estimates due to data gaps or seasonal measurements. For example, exposure for a gestational period of June to February cannot be

appropriately based on measurements taken

only from July to September. We generated a

weighted average of the concentrations on

days with data available to account for missing data and changes in frequency measurement. For instance, PM10 was measured daily

in Hartford County, Connecticut, from

January 1998 through May 1999 and measured every 6 days thereafter. A simple average of available data for a gestational period of

April to December 1999 would be biased

toward the first trimester of exposure. We first

generated weekly averages and then overall

exposure averages, to avoid this problem. 

Temperature has been associated with

birth weight for all trimesters and explains

much of the seasonal variability (Lawlor et al.

2005b; Murray et al. 2000). Daily countylevel temperature and dew point temperature

data were obtained from the National

Climatic Data Center (2006). Average temperature and dew point temperature exposures were generated based on the same

procedure described for air pollution.

Apparent temperature, also called the temperature–heat index, was calculated for each

trimester to reflect overall temperature discomfort, accounting for temperature and

humidity (Kalkstein and Valimont 1986).

After all exclusion criteria, 358,504 births

remained in the data set, with most exclusions

attributed to lack of sufficient pollution data.

Multiple exclusion criteria may apply to a single observation. Characteristics of excluded

births are summarized in the Supplemental

Material (Table S1; http://www.ehponline.

org/docs/2007/9759/suppl.pdf). 

Analysis included 15 counties: Fairfield,

Hartford, New Haven, New London, and

Windham, Connecticut; and Barnstable,

Berkshire, Bristol, Essex, Hampden, Middlesex,

Norfolk, Plymouth, Suffolk, and Worcester,

Massachusetts; with most observations from

Massachusetts (71.5%). Air pollutant data were

available for 7 counties for CO and for SO2, 10

for NO2 and PM10, and 13 for PM2.5. 

Modeling design. We applied a linear

model with birth weight as a continuous variable and a logistic model comparing low

weight (< 2,500 g) and non-low weight

(≥ 2,500 g) births. These methodologies were

applied in previous air pollution and birth

weight research for the linear model (e.g.,

Basu et al. 2004; Parker et al. 2005;

Woodruff et al. 2003) and logistic version

(e.g., Maisonet et al. 2001; Ritz and Yu 1999;

Wilhelm and Ritz 2003).

We adjusted for mother’s marital status

(married/unmarried), tobacco use during

pregnancy (yes/no), alcohol use during pregnancy (yes/no), and education (< 12 years,

12 years, 13–15 years, > 15 years, unknown).

To incorporate the nonlinear relationship

between mother’s age and birth weight, models contained categorical variables of mother’s

age (< 20, 20–24, 25–29, 30–34, 35–39, > 39

years). Mother’s race was indicated as white,

black, or other. The “other” category incorporates races for which separate analysis was prohibited by sample size (e.g., Chinese, Filipino,

and Vietnamese). Covariates included apparent temperature by trimester, and indicator

variables for child’s sex, type of delivery (primary cesarean section, repeat cesarean section,

vaginal birth), the time frame in which prenatal care began (first, second, or third

trimester; no care; missing data), first in birth

order (yes/no), gestational length (32–34,

35–36, 37–38, 39–40, 41–42, 43–44 weeks),

and year of birth.

Covariates were chosen based on previous

literature identifying potential risk factors for

low birth weight. We first applied linear and

logistic models for gestational exposure

including all potential covariates listed above

and excluding air pollution variables to

explore whether expected associations were

observed (e.g., maternal smoking associated

with lower birth weight). Covariates exhibiting a statistically significant relationship with

birth weight were incorporated into models

investigating air pollution exposure. 

We also applied the linear model using

exposures for each trimester. Because

trimesters’ exposure can be correlated, we performed sensitivity analysis with a model that

used trimester exposures as: a) P i

a,j = exposure

to pollutant j over trimester a for birth i;

b) residuals of E[P i

b,j] = β1 + β2P ia,j , representing exposure to pollutant j over trimester b for

birth i, adjusted for exposure over trimester a;

and c) residuals of the model E[P i

c,j] = β3 +

β4P i

a,j + β5P ib,j , representing exposure to pollutant j over the trimester c for birth i,

adjusted for exposure over trimesters a and b.

The β values are regression coefficients, in

which β1 and β3 represent intercepts, β2 represents the association between exposure in

trimesters a and b (i.e., change in exposure in

trimester b associated with a unit increase in

Air pollution and low birth weight

Environmental Health Perspectives • VOLUME 115 | NUMBER 7 | July 2007 1119
exposure in trimester a), β4 represents the

association between exposure in trimesters a

and c adjusted for exposure in trimester b,

and β5 represents the association between

exposure in trimesters b and c adjusted for

exposure in trimester a. We repeated this

analysis using each trimester as the initial reference trimester (i.e., trimester a). This

approach avoids covariance among variables

representing trimester exposures. 

We conducted sensitivity analysis for the

linear and logistic gestational exposure models

restricting the data set to first births only.

Two pollutant linear models were implemented for gestational exposures to explore

potential confounding by co-pollutants. To

investigate whether air pollution’s effects on

birth weight differ by race, we applied an

interaction model of race and gestational

exposure for each air pollutant.

Results

Pollutant exposure was estimated for 358,504

births, although not all births had data for all

pollutants. Low birth weight (< 2,500 g)

comprised 4.0% of total births, 3.6% of male

births, and 4.5% of female births. Table 1

provides descriptive statistics of the study

population, weather, and pollutant exposures.

Birth weights followed a seasonal pattern with

highest weights in spring (mean, 3431.0 g)

and lowest in winter (3409.4 g). Figure S1 in

the Supplemental Material (http://www.

ehponline.org/docs/2007/9759/suppl.pdf)

shows monthly patterns of pollutant levels

and the percentage of births that were low

birth weight (< 2,500 g).

Parents were predominantly married and

white, with mean mother’s and father’s ages

of 29.5 and 32.3 years, respectively. Father’s

race and age were not included in the analysis,

but were strongly related to mother’s race and

age. Correlations between father’s and

mother’s races were 0.78 to 0.82 for the three

race categories considered (black, white, and

other), and 86.3% of mother–father pairs had

the same race classification. Father’s and

mother’s ages were correlated at 0.75.

Exposure estimates for some pollutants covaried. Gestational exposure to PM2.5 had

0.77 and 0.64 correlations with PM10 and

NO2, respectively. The correlation between

exposures to NO2 and PM10 was 0.55. 

Table 2 shows results for selected variables

for the linear model including all variables

except air pollution. Lower birth weights were

associated with female infants, shorter gestational periods, maternal tobacco use, less

maternal education, prenatal care beginning

later in pregnancy, first in the birth order, and

unmarried, older, or younger mothers. The

logistic model provided similar results. All

covariates other than alcohol exhibited statistically significant associations. Models examining the effects of air pollution were adjusted

for apparent temperature and indicators for

year, as well as all covariates listed in Table 2,

except for maternal alcohol use.

Table 3 shows single-pollutant linear and

logistic model results for the associations

between birth weight and gestational exposure to air pollutants. NO2 and PM2.5 were

linked to lower birth weight in the linear

model and increased odds of low birth weight

in the logistic model. Associations were also

observed for PM10 and CO in the linear

model. No relationship with birth weight was

identified for gestational SO2 exposure. 

Sensitivity analysis using first births only

(n = 129,282) for the linear model provided

similar results to those based on all data (n =

358,504) (Figure 1). For the logistic model,

analysis of first births only did not show statistically significant associations; the increase in

risk of low birth weight per interquartile range

Bell et al.

1120 VOLUME 115 | NUMBER 7 | July 2007 • Environmental Health Perspectives

Table 1. Descriptive statistics of the study population and exposures (n = 358,504).

Variables relating 

to birth and mother Value

Birth weight (g) 3422.7 ± 514.9

Low birth weight [< 2,500 g (%)] 4.01

Child’s sex (%)

Male 51.1

Female 48.9

Type of birth (%)

Primary cesarean section 13.9

Repeat cesarean section 8.4

Vaginal birth 77.4

Unknown 0.3

First child (%)

Yes 36.1

No 63.9

Month prenatal care began (%)

First 3 months of pregnancy 89.4

4th–6th month of pregnancy 8.4

7th month of pregnancy or later 1.8

No care 0.2

Unknown 0.3

Gestational length (weeks) 39.1 ± 1.73

32–34 (%) 1.7

35–36 (%) 5.0

37–38 (%) 23.7

39–40 (%) 51.1

41–42 (%) 16.6

43–44 (%) 1.8

Alcohol use by mother (%)

Yes 1.6

No 97.5

Unknown 0.9

Tobacco use by mother (%)

Yes 8.7

No 90.5

Unknown 0.9

Mother’s education (%) 

< 12 years 12.2

12 years 24.8 

13–15 years 22.4

> 15 years 39.9

Unknown 0.7

Mother’s race (%)

White 83.4

Black 10.7

Other 6.0

Mother’s marital status (%)

Married 72.7

Unmarried 27.3

Mother’s age (years) 29.5 ± 6.0

< 20 (%) 6.7

20–24 (%) 15.3

25–29 (%) 24.3

30–34 (%) 32.7

35–39 (%) 17.5

> 39 (%) 3.5

Season and weather

Season of birth (%)

Winter 23.1

Spring 24.7

Summer 26.6

Fall 25.6

Temperature (°F) 50.5 ± 5.6

Dew point temperature (°F) 40.0 ± 5.8

Gestational pollution exposures (mean ± SD)

NO2 (IQR 4.8 ppb) 17.4 ± 5.0 ppb

CO (IQR 303 ppb) 656 ± 180 ppb

SO2 (IQR 1.6 ppb) 4.7 ± 1.2 ppb 

PM10 (IQR 7.4 μg/m3

) 22.3 ± 5.3 μg/m3

PM2.5 (IQR 2.2 μg/m3) 11.9 ± 1.6 μg/m3

Values are percentages or mean ± SD.

Table 2. Difference in birth weight associated with

selected nonpollution variables (95% confidence

interval).

Variable Difference in birth weight (g)

Child’s sex

Female (reference)

Male 133.1 (130.1 to 136.1)

Mother’s education (years)

12 (reference)

< 12 –32.7 (–38.3 to –27.1)

13–15 22.9 (18.4 to 27.3)

> 15 34.1 (29.7 to 38.4)

Unknown –36.1 (–56.1 to –16.1)

Tobacco use by mother

No (reference)

Yes –176.5 (–182.2 to –170.9)

Alcohol use by mother

No (reference)

Yes –9.4 (–21.5 to 2.6)

Mother’s marital status

Married (reference)

Unmarried –46.5 (–50.9 to –42.1)

Mother’s race

White (reference)

Black –97.8 (–102.9 to –92.7)

Other –176.3 (–182.7 to –169.8)

Month prenatal care began

Month 1–3 (reference)

Month 4–6 –29.2 (–34.8 to –23.6)

Month ≥ 7 –50.2 (–61.7 to –38.7)

No care –102.8 (–140.1 to –65.4)

Mother’s age (years)

30–34 (reference)

< 20 –41.8 (–49.7 to –34.0)

20–24 –39.4 (–44.8 to –34.1)

25–29 –16.4 (–20.6 to –12.3)

35–39 2.9 (–1.6 to 7.4)

> 39 –19.8 (–28.3 to –11.3)

Birth order

Firstborn (reference)

Not firstborn 101.9 (98.4 to 105.3)

Gestational length (weeks)

39–40 (reference)

32–34 –1050.2 (–1061.8 to –1038.5)

35–36 –585.2 (–592.3 to –578.2)

37–38 –227.7 (–231.5 to –224.0)

41–42 122.3 (118.0 to 126.6)

43–44 62.4 (51.0 to 73.8)

p < 0.01 for all associations except alcohol use.
(IQR) increase in pollutant was 2.01% for

NO2 and 3.44% for PM2.5, compared with

2.65% and 5.39% using the full data set.

Two-pollutant linear models were applied

for gestational exposure for pollutants exhibiting significant associations in Table 3, excluding pairs of pollutants that covaried. Figure 2

compares results with and without co-pollutant adjustment. Results for all pollutants

considered in the multivariate analysis were

robust to co-pollutant adjustment, remaining

statistically significant in all cases. 

Exposures among trimesters were often

related. Correlations among trimester exposures

were 0.48, 0.70, and 0.72 for CO; 0.51, 0.76,

and 0.77 for NO2; 0.63, 0.66, and 0.67 for

PM10; 0.10, 0.19, and 0.60 for PM2.5; and

–0.60, 0.08, and 0.20 for SO2. We performed

analysis including all trimesters of exposure in

the linear model, and analysis of adjusted

trimester exposures as described in “Materials

and Methods” (correlation among adjusted

trimester exposures for the same pollutant

< 0.005). Table 4 shows the relationship

between birth weight and trimester exposure for

results that were consistent across all trimester

models. The most important trimester of exposure was the third for PM10, and the first and

third for CO, the first for NO2 and SO2, and

the second and third for PM2.5. 

To investigate whether effect estimates for

pollution and birth weight differ by race, we

applied linear models including an interaction

term for race and gestational exposure

(Table 5). Lower birth weight was associated

with PM2.5 for infants of either black or white

mothers. However, the effects were higher for

infants of black mothers (i.e., the regression

coefficients were statistically different). No

statistical difference was observed between the

effect estimates for infants of black and white

mothers for the other pollutants.

Discussion

Exposure to air pollution has been linked to

low birth weight in various studies, including

exposure at each trimester. Table 6 compares

our results with those of previous U.S.-based

studies. For comparability, results from other

studies were converted to a common metric

based on the IQR for gestational exposure for

our data set (see Table 1). 

A previous study of six northeastern U.S.

cities found a stronger relationship between

CO exposure and low birth weight for AfricanAmerican infants than for whites (Maisonet

et al. 2001). We identified no difference

between the effects of CO by race, although

effect estimates were higher for infants of black

mothers for PM2.5. This indicates that some

populations may face disproportionate health

burdens of air pollution. This may result from

differences in baseline health status, health care

service, or occupational exposure. Effect modification by race may also be due to differential

exposure such as higher traffic pollution relating to residential proximity to highways,

because effects were statistically different by

race for PM2.5, a traffic-related pollutant.

Factors pertaining to occupation and health

care status could be explored in future studies,

such as a cohort study with detailed information on each observation.

Relationships between gestational exposure and birth weight were robust to inclusion

of another pollutant (Figure 2). However,

only pollutant pairs that were uncorrelated

were included in two pollutant models.

Exposure to PM10, PM2.5, and NO2 were

highly correlated in this data set, resulting

from common sources. Thus this research

cannot distinguish between detrimental

effects of various pollutants (e.g., effects of

NO2 versus PM2.5); pollutants identified in

this work could be functioning as a proxy for

other forms of pollution sharing similar

sources. Additional research is needed to better isolate the key pollutants of harm. In particular, differences in chemical composition

may help explain variation in results across

study areas. For instance, PM2.5 in the eastern

United States has a higher contribution of

sulfate whereas PM2.5 in the western United

States has a larger nitrate component (Bell

et al. 2007).

Several studies examined how variation in

air pollution levels can affect epidemiologic

research. In Los Angeles, PM2.5 exhibited

within-city gradients, which produced larger

effect estimates for mortality than models comparing PM2.5 across communities (Jerrett et al.

2005). Residential proximity to traffic was

associated with low birth weight and preterm

delivery in Los Angeles (Wilhelm and Ritz

2003, 2005). A California study found that

various air pollution metrics (county-wide

average, nearest monitor, distance-weighted

average of monitors < 5 miles of mother’s

residence) produced different effect estimates

for the relationship between air pollution and

birth weight (Basu et al. 2004). These studies

suggest that heterogeneity in air pollution concentrations can exist at sub-county scales and

that this variation can affect health effects estimates, which may affect our study based on

countywide estimates. Additionally, we used

Air pollution and low birth weight

Environmental Health Perspectives • VOLUME 115 | NUMBER 7 | July 2007 1121

Table 3. Change in birth weight per IQR increase in pollution for the gestational period (95% confidence

interval).

Pollutant Difference in birth weight (g) Odds ratio for low birth weight (< 2,500 g)

NO2 –8.9 (–10.8 to –7.0)* 1.027 (1.002 to 1.051)**

CO –16.2 (–19.7 to –12.6)* 1.028 (0.983 to 1.074)

SO2 –0.9 (–4.4 to 2.6) 1.003 (0.961 to 1.046)

PM10 –8.2 (–11.1 to –5.3)* 1.027 (0.991 to 1.064)

PM2.5 –14.7 (–17.1 to –12.3)* 1.054 (1.022 to 1.087)**

*p < 0.001; **p < 0.05.

Figure 1. Change in birth weight per IQR increase

in gestational exposure, using the linear model

with all births (solid line, n = 358,504) and first births

only (dashed line, n = 129,282). The point reflects

the central estimate; the vertical line represents

the 95% confidence interval. Change in birth weight (g) per IQR increase in pollutant 10

5

0

–5

–10

–15

–20

–25

NO2 CO SO2 PM10 PM2.5

Figure 2. Change in birth weight per IQR increase

in gestational exposure to pollutant, for single and

two-pollutant linear models. The point reflects the

central estimate; the vertical line represents the

95% confidence interval. 

0

–5

–10

–15

–20

–25

None NO2CO

SO2

CO

None

SO2

NO2

PM10

PM2.5

PM10

None

CO

SO2

PM2.5

None

CO

SO2

Co-pollutant adjustment

Change in birth weight (g)

per IQR increase in pollutant

Table 4. Change in birth weight per IQR increase in

pollutant for trimester exposure.

Range among 

the trimester models for 

change in birth weight (g) 

Pollutant Trimester per IQR increase in pollutant

NO2 1st –9.6 to –8.3

CO 1st –11.3 to –9.9

3rd –14.0 to –16.3

SO2 1st –3.7 to –3.3 

PM10 3rd –6.6 to –4.7

PM2.5 2nd –7.2 to –5.4

3rd –9.0 to –7.0

For comparability, the IQR for gestational exposure was

used.
ambient monitoring as a surrogate for personal

exposure, which does not address indoor/outdoor activity patterns and occupational exposure. Nondifferential misclassification, such as

from subcounty heterogeneity in pollution

concentrations, would drive effect estimates

toward the null, so any effect estimates would

more likely underestimate true effects.

Our adjustment for mother’s educational

status may not fully address potential confounding by socioeconomic status. Actual

socioeconomic position is related not only to

education but to numerous other factors such

as income, social class, and historical socioeconomic status (Krieger et al. 1997). Similarly,

our binary measure of tobacco use during

pregnancy may not have fully captured its

effects, and use of alcohol and tobacco was

based on self-reporting. The misclassification

of a covariate can bias results (Marshall and

Hastrup 1996; Marshall et al. 1999). The use

of more detailed information is preferable,

such as number of cigarettes smoked during

pregnancy rather than a yes/no category

(Greenland 2001). Associations may be biased

if covariate definitions are based on occurrence

anytime during pregnancy (e.g., smoking as a

yes/no variable) and the actual association

between the covariate and birth weight occurs

during a vulnerable period of pregnancy, such

as the third trimester (Hertz-Picciotto et al.

1996).

We estimated the time frame for exposure

on the basis of LMP as documented on birth

certificates; however, LMP is self-reported. As

an example, the 10th, 15th, and 20th days of

the month should each account for about

3.3% of total LMP days, but were reported as

the approximate LMP date more often at

4.4%, 5.6%, and 4.8%, respectively.

Exposure misclassification as a function of

date of birth and gestational period is less

likely to affect exposure estimates for the

entire pregnancy than for individual

trimesters, based on the longer time frame.

For example, if the actual LMP is a week earlier than reported, exposure will be misclassified for about 2.6% of the gestational period

and about 7.7% of a trimester, for a typical

length pregnancy.

Bell et al.

1122 VOLUME 115 | NUMBER 7 | July 2007 • Environmental Health Perspectives

Table 6. Comparison of results from U.S.-based studies.

Study location (time period) No. of births Results Study (confounders considered)

Connecticut and 358,504 NO2: Effect for first trimester, gestational exposure This study 

Massachusetts CO: Effect for first and third trimesters, gestational exposure (1–7,9,11,13,17,19)

(1999–2002) SO2: Effect for first trimester

PM2.5: Effect for second and third trimesters, gestational exposure

PM10: Effect for third trimester and gestational exposure

(see Tables 3 and 4)

Georgia 325 PM10: 94% (–2 to 283%) increased odds of very low birth weight (< 1,500 g) for exposure Rogers and Dunlop (2006)

(April 1986–March 1988) > 15.07 μg/m3 compared with < 1.48 μg/m3 (1,3,4,6–9,11–16,18,23,24)

California (2000) 18,247 PM2.5: 6.4 g (3.6 to 9.3 g) decrease per 2.2 μg/m3. Did not find a particular trimester to be Parker et al. (2005)

most important (1,5–7,9,18)

CO: No effect

California (1975–1987) 3,901 CO: 4.7 g (0.24 to 9.2 g) decrease per 303 ppb in first trimester. No effect for gestational exposure Salam et al. (2005)

PM10: 8.0 g (0.41, 15.6 g) decrease per 7.4 μg/m3 in third trimester. No effect for gestational (2,3,5–7,9,11,18–21,25)

exposure

Los Angeles, California Up to ~ 115,000* CO: 3.5% (1.5 to 5.4%) increased odds of low birth weight per 303 ppb in third trimester Wilhelm and Ritz (2005)

(1994–2000) PM10: 2.2% (–2.2 to 6.6%) increased odds of low birth weight per 7.4 μg/m3 in third trimester. (1–4,6,7,9,18,20,22)

For mothers ≤ 1 mile of monitoring station effect was 15.6% (3.6 to 10.0%)

California (2000) 16,693 PM2.5: 8.9 g (3.0 to 14.8 g) decrease per 2.2 μg/m3 Basu et al. (2004)

(1,5–7,9]

Los Angeles, California ~ 48,000* Lower birth weight associated with distance-weighted traffic density Wilhelm and Ritz (2003)

(1994–1996) (1–4,6,7,9,17,18,20,22)

United States 7,355,696 No effect for small for gestational age based on air pollution index Woodruff et al. (2003)

(1998–1999) (1,5–7,9,25)

Northern Nevada 39,338 PM10: 8.1 g (1.7 to 14.7 g) decrease per 7.4 μg/m3 in third trimester. No effect in logistic model Chen et al. (2002)

(1991–1999) CO: No effect (3–7,9–13,15,17,18,25,27)

Six U.S. Northeastern 101,153 CO: 8.6% (1.8 to 15.7%) increased odds of low birth weight per 303 ppb in third trimester Maisonet et al. (2001)

cities (1994–1996) PM10: No effect (1–7,9,11,13,15,18,28)

SO2: 0.0002% (–0.001 to 0.002%) increased odds of low birth weight per 1.6 ppb in second 

trimester. Statistically significant effects identified when comparing strata of SO2 levels

Georgia 345 TSP and SO2: 188% (16 to 613%) increased odds of very low birth weight for exposure Rogers et al. (2000)

(April 1986–March 1988) > 56.75 μg/m3 compared with < 9.94 μg/m3 (1–4,6–9,11–16,23–26)

Los Angeles, California 125,573 CO: 22% (3 to 44%) increased odds of low birth weight for exposure ≥ 5.5 ppm compared Ritz and Yu (1999)

(1989–1993) with < 2.2 ppm for third trimester (1–4,6,7,9,20,29,30)

Denver, Colorado 2,870 CO: No effect for third trimester Alderman et al. (1987)

(1975–1983) (1–7,9)

No effect indicates the lack of a statistically significant effect. Confounders considered may be addressed by inclusion of a variable in models, restriction, or stratification. Gestational

length is listed if it was included in modeling; gestational age was also addressed in some studies by including only term births. Confounders: 1 = parity, 2 = gestational length, 3 = sex, 4

= prenatal care, 5 = maternal marital status, 6 = maternal age, 7 = maternal socioeconomic status (education, financial stress, or income), 8 = paternal education, 9 = maternal race, 10 =

paternal race, 11 = maternal tobacco use, 12 = maternal drug use, 13 = maternal alcohol use, 14 = maternal exposure to passive smoke, 15 = maternal weight gain, 16 = maternal prepregnancy weight, 17 = year, 18 = season or month of birth, 19 = weather, 20 = time since last live birth, 21 = gestational diabetes, 22 = previous low birth weight delivery, 23 = maternal

working status, 24 = toxemia, 25 = community, 26 = urban or rural area, 27 = risk factors (e.g., anemia, uterine bleeding), 28 = previous terminations, 29 = restricted to no maternal hypertension, uterine bleeding, or diabetes, 30 = commute time, percent walking to work, and percent of women working with children considered as ecologic variables.

*Sample size varies by analysis.

Table 5. Change in birth weight per IQR increase in pollutant for the gestational period, by race (95% confidence interval).

Difference in birth weight (g) per IQR of pollutant

Pollutant Black mother White mother

NO2 –12.7 (–18.0 to –7.5) –8.3 (–10.4 to –6.3)

CO –10.9 (–20.2 to –1.6) –17.5 (–21.3 to –13.7)

SO2 1.2 (–6.5 to 8.8) –1.4 (–5.1 to 2.3)

PM10 –7.9 (–16.0 to 0.2) –9.0 (–12.2 to –5.9)

PM2.5* –22.6 (–29.3 to –15.9) –14.7 (–17.3 to –12.0)

*p < 0.05 for comparison of results from infants with black mothers to infants with white mothers.
The exposure estimates assume that the

county of mother’s residence at the time of

birth is constant throughout gestation. A limited number of studies have investigated residential mobility during pregnancy. Although

they are based on small sample sizes (295–398

births), results indicated that 12–24.8% of

mothers moved during pregnancy, but that

only 4–7.7% moved to a different county (Fell

et al. 2004; Khoury et al. 1988; Shaw and

Malcoe 1992). One study concluded that

exposure based on residence at delivery is a reasonable estimate of exposure during pregnancy

for exposures at county-level resolution (Shaw

and Malcoe 1992). Based on results of these

residential mobility studies, misclassification of

exposure introduced by the mother moving

during pregnancy is unlikely to greatly influence our results. Such misclassification is more

likely to affect results for the first and second

trimesters than the third because they are further from the date at which residence was

obtained (i.e., date of delivery). Future research

incorporating detailed maternal history including residence throughout the pregnancy would

improve pollution exposure estimates as well as

advance adjustment by potential confounders

such as socioeconomic factors.

Factors associated with higher mobility

during pregnancy include lower education

(Fell et al. 2004; Shaw and Malcoe 1992);

mother ≤ 25 years (Fell et al. 2004), ≤ 20

years (Shaw and Malcoe 1992), 20–24 years

(Khoury et al. 1988), or ≥ 35 years of age

(Fell et al. 2004); low family income (Fell

et al. 2004); unmarried mother (Fell et al.

2004); maternal tobacco use (Fell et al.

2004); and white mother (Khoury et al.

1988). All of the above factors linked with

increased mobility, other than white mother,

are also associated with low birth weight,

leading to the potential for more exposure

misclassification for mothers of lower weight

infants. 

The physiological mechanism by which

air pollution could affect birth weight is not

fully understood, although several theories

have been proposed. The pathways could be

similar to those of maternal smoking, which

can increase risk of preterm delivery through

premature rupture of membranes and placental abruption and lower birth weight. Air pollution could affect fetal health either through

direct effects on the fetus by exposure through

the placenta or from effects on the mother’s

health (Glinianaia et al. 2004), and multiple

mechanisms may occur simultaneously.

Maternal pulmonary function has been linked

to altered placental vascular function and

growth retardation in asthmatic pregnancies

(Bracken et al. 2003; Clifton et al. 2001;

Schatz et al. 1990). Other theories include a)

altered cardiac function from changes in heart

rate variability; b) inhalation by the mother of

polycyclic aromatic hydrocarbons (PAHs)

that then relate to placental exposure, potentially disrupting endocrine and nervous systems; c) changes in blood viscosity due to

alveolar inflammation from PM, which in

turn affects placental function; and d) binding

of CO to hemoglobin binding sites, preventing the binding of oxygen and subsequent

function (Glinianaia et al. 2004; Maisonet

et al. 2004; Sˇrám et al. 2005). 

In summary, this research indicates that

maternal exposure to air pollution may

adversely affect risk of low birth weight, even

in areas without high pollution levels. Average

concentrations for all pollutants in the study

were below the U.S. EPA health-based

National Ambient Air Quality Standards

(NAAQS). Further, all counties in the study

are currently in compliance with the NAAQS

for NO2, CO, SO2, and PM10. Only two

counties, New Haven and Fairfield,

Connecticut, are in noncompliance with the

NAAQS for PM2.5 (U.S. EPA 2006b). 

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