Air Quality, Atmosphere & Health

, Volume 6, Issue 2, pp 455–463

Prenatal ambient air pollution exposure and small for gestational age birth in the Puget Sound Air Basin

Authors

    • Department of PediatricsUniversity of Washington
    • Center for Child Health, Behavior and DevelopmentSeattle Children’s Research Institute
    • Department of Environmental and Occupational Health SciencesUniversity of Washington
  • Chuan Zhou
    • Center for Child Health, Behavior and DevelopmentSeattle Children’s Research Institute
  • Carole B. Rudra
    • Independent Health
  • Tim Gould
    • Civil and Environmental EngineeringUniversity of Washington
  • Tim Larson
    • Civil and Environmental EngineeringUniversity of Washington
    • Department of Environmental and Occupational Health SciencesUniversity of Washington
  • Jane Koenig
    • Department of Environmental and Occupational Health SciencesUniversity of Washington
  • Catherine J. Karr
    • Department of PediatricsUniversity of Washington
    • Department of Environmental and Occupational Health SciencesUniversity of Washington
Article

DOI: 10.1007/s11869-012-0182-7

Cite this article as:
Sathyanarayana, S., Zhou, C., Rudra, C.B. et al. Air Qual Atmos Health (2013) 6: 455. doi:10.1007/s11869-012-0182-7

Abstract

Several studies have identified high concentrations of air pollution as harmful to the developing fetus, but few studies of traffic-derived air pollution and birth outcomes have been conducted in areas of low to moderate air pollution. We identified singleton live births between 1997 and 2005 (N = 367,046 births) in the Puget Sound Air Basin of Washington State. We estimated nitrogen dioxide (NO2) exposure using a land use regression model of traffic, PM2.5 exposure from the nearest community monitor, and proximity to highways/roadways for the residential location of all subjects. Logistic regression estimates of odds ratios (OR) of small for gestational age (SGA) and low birth weight (<2,500 g) among term births were calculated. We observed a modest association between SGA births with increasing quartile of first trimester NO2 exposure: second (OR = 1.01, 95 % confidence interval (CI) 0.97, 1.04), third (OR = 1.06, 95 % CI 1.03, 1.10), and fourth (OR = 1.08, 95 % CI 1.04, 1.12) (p trend <0.001). We did not observe an association between PM2.5 and SGA or low birth weight among term births. Our findings suggest that prenatal exposure to traffic-derived air pollutants has a modest effect on fetal growth in a region with low overall air pollutant concentrations. Given the modest associations, future studies in similar settings that maximize the opportunity to address potential residual confounding are needed.

Keywords

Air pollutionSmall for gestational ageLow birth weightTraffic

Abbreviations

BMI

Body mass index

CI

Confidence interval

g

Gram

LUR

Land use regression

m

Meters

μg/m3

Micrograms per cubic meter

NO

Nitric oxide

NO2

Nitrogen dioxide

PM2.5

Particulate matter ≤2.5 μm in aerodynamic diameter

OR

Odds ratio

ppb

Parts per billion

SD

Standard deviation

SGA

Small for gestational age

US

United States

Introduction

An accumulating evidence base links higher exposure to traffic-derived air pollutants with adverse birth outcomes, particularly increased risk of low birth weight (Bobak 2000; Glinianaia et al. 2004; Sram et al. 2005; Slama et al. 2007; Brauer et al. 2008; Aguilera et al. 2009; Woodruff et al. 2009; Wilhelm et al. 2011). Most studies have been conducted in settings with relatively high concentrations of ambient air pollutants, including major urban areas around the world and in North America (Dejmek et al. 1999; Ritz et al. 2000; Wilhelm and Ritz 2003; Bell et al. 2007; Slama et al. 2007; Aguilera et al. 2008).

In Washington State, singleton low birth weight rates have increased by 1.2 % between 1993 and 2004 with unidentified etiology (Washington State Department of Health 2007). However, Washington State and its most urbanized regions in the Puget Sound area have relatively low concentrations of ambient air pollutants. Asthma has been associated with exposure to air pollutants in the area (Sheppard et al. 1999; Mar and Koenig 2009). In addition, an increased risk of small for gestational age (SGA) birth in relation to low to moderate level air pollutant exposures has been observed in the Vancouver, Canada, air basin (Brauer et al. 2008).

Traffic exposures are ubiquitous in smaller as well as larger, dense urban areas of North America. If traffic exposures impact birth outcomes even in regions with relatively lower concentrations than major urban cores, the resulting economic and public health burden on child and maternal health could be significant. A key challenge in studies of traffic to date is addressing the combination of both the spatial and temporal components of exposure variability for studies of air pollution and pregnancy outcome (Woodruff et al. 2009). Recently, studies have employed knowledge of both the mother's residential locations and the timing of relevant periods of pregnancy in the predictions using land use regression modeling (LUR) for individual exposure assessment to begin to address some of these questions (Slama et al. 2007; Brauer et al. 2008; Aguilera et al. 2009; Wilhelm et al. 2011).

We sought to explore the role of traffic-derived air pollution in an airshed with relatively low pollution concentrations using the LUR approach to model individual-level NO2 exposures as well as straightforward measures of proximity to roadways to represent traffic exposure and proximal PM2.5 community monitoring results to assign fine particulate matter exposure.

Material and methods

Study subjects

The study population consisted of all singleton live births between 1997 and 2005 in the four-county central Puget Sound Region (King, Kitsap, Snohomish, Pierce) which includes the large metropolitan areas of Seattle and Tacoma in Washington State. Birth record data were obtained from the Washington State Department of Health Birth Records Database which records data from the birth as well as demographic data from the mother and father at the time of birth (N = 371,451 births). We excluded births with missing (N = 4,198) or implausible gestational age (<23 and >45 weeks, N = 183) or implausible birth weight (<450 g, N = 24). We identified multiple singleton births to the same mother (N = 281,104 clusters). The final study population consisted of 367,046 births. We defined SGA as those births below the tenth percentile by gender and week of gestation. Low birth weight was defined as <2,500 g, and term births were defined as greater than or equal to 37 weeks of gestation. We obtained data on potential confounding variables from the birth records database: maternal age, urban/rural residence (based on US Census Bureau classification of urban as areas that have a population density of at least 1,000 people/square mile), duration of residence, year of birth, season of birth, maternal race/ethnicity, parity (number of live births a mother had previous to the current birth), income (median family income per census tract of residence), alcohol use, prenatal care utilization, and smoking. This study was approved by the Washington State Department of Health Human Subjects Board.

Exposure characterization

We estimated NO2 exposures based on a LUR model of NO2 ambient concentrations which we developed for the region. The LUR model followed the form of a model initially developed for Vancouver, Canada, by Henderson et al. in which regression methods were used to model pollutant concentrations measured at specific sites based on variables that characterize surrounding land use, population density, and traffic patterns (Henderson et al. 2007). An NO2 dataset collected in Seattle to refine a field sampling protocol was used to derive our LUR model for the Seattle region (Poplawski et al. 2009). The model was calibrated based on 2-week integrated field measurements of ambient NO2 using passive Ogawa samplers conducted at 26 sites in Seattle, Washington, and an agency continuous analyzer for the period of July 2004 through June 2005.

Values of the predictor variables were generated from data obtained from the Puget Sound Regional Council, county road networks, the US Environmental Protection Agency, the US Census for all of the birth residence sites, and a corresponding annual NO2 concentration estimated from the Seattle LUR model (Archive, Bureau. July 1, 2005).

The land use variables and associated effect distances, or buffers, were selected according to the strength of their correlation with NO2 and a stepwise regression analysis. We started with a full complement of 47 potentially predictive variables related to density of major roadways, land use types, population density, and elevation. The resulting final model form for annual average NO2 was:
$$ {\text{N}}{{\text{O}}_2}\left( {\text{ppb}} \right) = {1}0.{56} + {13}.0{5} \times {\text{Rd1}}.{1}00 + {1}.{91} \times {\text{Rd2}}.{3}00 + 0.{117} \times {\text{Trans}}.{75}0 + 0.0{955} \times {\text{Pop}}.{25}00,{{\text{R}}^2} = 0.{72}.;{\text{root\ mean\ squared\ error}} = {1}.{7}\ {\text{ppb}}. $$
(Trans. 750 is area in hectares of transportation, communications, and utilities land use within 750 m; Rd1.100 is length in kilometers of expressways and highways within 100 m; Rd2.300 is length in kilometers of major roads and arterials within 300 m; and Pop.2500 is population density per hectare within 2,500 m).

The NO2 field measurements were adjusted to reflect the annual averages through comparison of annual and 2-week NO2 measured by continuous analyzer at the Washington Department of Ecology Beacon Hill monitor site where one of the passive samplers was collocated. This approach assumes the yearly variation of 2-week averages follows the same pattern at all sites and has been employed in most previous LUR models.

To derive monthly concentrations of NO2 exposure for our subjects, we derived a monthly NO2 adjustment factor for each month from 1997 through 2005. This was computed as the ratio of the monthly NO2 average at the central Beacon Hill site to the annual average for the period of July 2004 through June 2005. Monthly variation of NO2 followed a reasonably consistent pattern throughout all 9 years of the study: mean annual NO2 exposure ranged from 12.9 to 14.3 ppb. Residential addresses at the time of delivery were geocoded by the Department of Health and Human Services.

We assigned daily exposure concentrations for PM2.5 using Puget Sound Clean Air Agency regulatory data for the years 1997–2005. The mother's geocoded address was linked to the nearest monitor within 20 km. For most monitors during this study period, daily average PM2.5 measurements were conducted at least every third day. The number of ambient PM2.5 monitors in the region was 4, 9, 16, and 17 within the years 1997–2000, respectively, and 18 in subsequent study years. Annual mean PM2.5 ranged from 9.3 to 12.6 μg/m3 across all years of study.

For all births, monthly pollutant averages for potentially specific “toxicity windows” of pregnancy were calculated: first month, first 3 months, last month, last 3 months, and entire pregnancy based on delivery date and date of conception using the last menstrual period. If last menstrual period data were not available, the recorded gestational age in weeks was used. We excluded all 1997 births from the PM2.5 analysis due to a large quantity of missing PM2.5 exposure data for the pregnancies conceived in 1996 (10,531 births in 1997 were missing PM2.5 exposure data). In addition, we excluded births with any period of missing PM2.5 data (35,698 births with some missing exposure data). The final PM2.5 dataset for analysis included 323,899 births.

We also classified maternal residence according to proximity within 50 or 150 m of roads categorized as either expressways or highways such as state interstates (Rd1, Standard Metropolitan Statistical Area classification codes A10, A11, A15, A20, A21, and A23) or major roads and arterials (Rd2, Standard Metropolitan Statistical Area classification codes A30, A31, A32, and A35). The same network of roads based on 2000 data was applied for 1997–2005.

Statistical analysis

We first examined the distribution of exposures during all pregnancy windows and categorized exposures per quartile. We also examined proximity to a major roadway (within 50 and 150 m of expressways or highways and within 50 and 150 m of major roads and arterials) as binary predictor variables. In order to account for correlations arising from using more than or equal to one birth per mother, we used general estimating equations to fit logistic regression models with exchangeable correlation matrices, binomial outcome distribution, and a logit link to assess the relationship between exposures and odds of SGA births. We also examined the relationship between NO2, PM2.5 exposures, and road proximity and risk of low birth weight (<2,500 g) restricted to term births only given that toxicological impact may differ for term versus preterm infants. We chose to examine early and late pregnancy to explore the impact of exposures on different developmental periods during pregnancy. Since preterm infants may not have exposures in later gestational stages, analysis restricted to terms also allowed evaluation of the effects of late term exposures that occur at similar fetal developmental timeframes in the population. Because NO2 and PM2.5 exposures were uncorrelated, we fit logistic regression models with both NO2 and PM2.5 as variables.

Several confounding variables were considered in the SGA analysis. A priori, we decided to use maternal age, smoking during pregnancy, race/ethnicity, season of birth, and income as covariates in all analyses given their well established relationship to the outcomes of interest. We examined the other possible confounding variables to determine their influence on point estimates or improved model precision.

Results

Of the 367,046 births in our population, approximately 51 % (188,306) were male (Table 1). Mothers were predominantly aged 25–34 (54 %) and 68 % white and 13 % Asian/Pacific Islander. Approximately one third of women reported living at their current residence for 1–2.9 years (120,627), and 30 % (111,570) reported living at their current residence for at least 3 years. Mean birth weight for the cohort was 3,432 g with a standard deviation of 554 g. The rate of low birth weight among term births (<2,500 g) was 1.4 %, and the rate of small for gestational age birth was 10 %.
Table 1

Characteristics of the study population (N = 367,046) within the Puget Sound Air Basin

Characteristic

Number

Percent

Maternal age (years)

 15–19

27,224

7.4

 20–24

77,534

21.1

 25–34

199,467

54.3

 35–39

51,705

14.1

 40–46

11,116

3.0

Maternal race/ethnicity

 White

250,649

68.3

 Black

23,426

6.4

 Native American

6,028

1.6

 Asian/Pacific Islander

45,790

12.5

 Hispanic

26,971

7.4

 Other

245

0.1

Parity

 0

155,990

42.5

 1

117,457

32.0

 ≥2

81,509

22.2

Census tract median income (US $)

 9,284–39,211

92,187

25.1

 39,212–49,860

91,457

24.9

 49,861–60,828

91,737

25.0

 60,829–133,756

91,510

24.9

Maternal education (years)

 <12

43,814

11.9

 12

90,913

24.8

 >12–16

162,603

44.3

 >16

41,461

11.3

Smoking during pregnancy

 No

324,848

88.5

 Yes

34,515

9.4

Alcohol use during pregnancy

 No

165,894

45.2

 Yes

2,905

0.8

Prenatal care

 Inadequate

9,546

2.6

 Intermediate/adequate

99,280

27.1

 Adequate plus

182,247

49.7

Duration lived at current residence (years)

 <1

119,357

32.5

 1.0–2.9

120,627

32.9

 ≥3

111,570

30.4

Urban or rural

 Rural

49,541

13.5

 Urban

317,195

86.4

Infant sex

 Male

188,306

51.3

 Female

178,740

48.7

Season of birth

 December–February

86,669

23.6

 March–May

94,126

25.6

 June–August

95,950

26.2

 September–November

90,301

24.6

Small for gestational age

 No

329,478

89.8

 Yes

37,568

10.2

Low birth weight among term births

 No

336,975

91.8

 Yes

5,320

1.4

The minimum NO2 exposure for all exposure windows was 6.3 ppb and the maximum was 36.8 ppb with a normal distribution (Table 2). There was little variability in mean exposure concentrations across each trimester of pregnancy (13.6–13.8 ppb) with a mean of 13.7 ppb for the entire pregnancy (Table 2). The minimum PM2.5 exposure for all exposure windows was 1.9 μg/m3, and maximum, 30.4 μg/m3 with a normal distribution (Table 3). There was again little variability in mean exposure concentrations between each trimester of pregnancy (10.1–10.5 μg/m3) with a mean of 10.3 for the entire pregnancy (Table 3). Correlation coefficients between exposure concentrations for both NO2 and PM2.5 during first and last 3 months of pregnancy were just above 0.4 while those between the first month and first trimester and between the last month and last 3 months were above 0.7 (Tables 4 and 5). Therefore, we present associations between the first and last 3 months of pregnancy averaged NO2 and PM2.5 exposures and SGA separately. We did not observe correlations between NO2 and PM2.5 across pregnancy time periods with the exception of NO2 concentrations during all of pregnancy and the majority of PM2.5 categorized concentrations (Table 6).
Table 2

Distribution of NO2 exposures (by month of gestation) in parts per billion (N = 367,046)

 

Mean (SD)

Min

5 %

25 %

50 %

75 %

95 %

Max

First

13.8 (2.6)

8.3

9.9

12.0

13.6

15.4

18.4

36.8

First 3

13.8 (2.3)

8.7

10.5

12.2

13.6

15.2

17.8

34.7

Last

13.6 (2.6)

8.3

9.8

11.7

13.4

15.2

18.2

36.8

Last 3

13.6 (2.3)

6.3

10.3

12.0

13.4

15.0

17.7

35.7

All

13.7 (1.9)

9.8

11.2

12.4

13.5

14.8

17.2

33.7

Table 3

Distribution of PM2.5 exposures (by month of gestation) in micrograms per cubic meter (N = 323,899)

 

Mean (SD)

Min

5 %

25 %

50 %

75 %

95 %

Max

First

10.5 (3.9)

1.9

5.9

7.5

9.5

12.6

17.8

30.4

First 3

10.5 (3.2)

2.3

6.5

7.9

9.9

12.3

16.6

25.5

Last

10.1 (3.9)

1.9

5.7

7.2

9.0

12.2

17.7

30.4

Last 3

10.1 (3.2)

2.3

6.2

7.6

9.4

12.0

16.2

25.5

All

10.3 (1.9)

3.3

7.4

9.0

10.2

11.6

13.4

21.6

Table 4

Correlation matrix of NO2 exposures (N = 367,046)

 

First

First 3

Last

Last 3

All

First

1

    

First 3

0.85

1

   

Last

0.34

0.39

1

  

Last 3

0.38

0.44

0.85

1

 

All

0.66

0.81

0.67

0.82

1

Table 5

Correlation matrix of PM2.5 exposures (N = 323,899)

 

First

First 3

Last

Last 3

All

First

1

    

First 3

0.78

1

   

Last

−0.19

−0.35

1

  

Last 3

−0.35

−0.43

0.79

1

 

All

0.18

0.45

0.20

0.46

1

Table 6

Correlation matrix of NO2 and PM2.5 exposures (N = 323,878)

 

PM 2.5

First

First 3

Last

Last 3

All

NO2

First

0.40

0.28

−0.08

−0.23

−0.11

First 3

0.33

0.39

−0.22

−0.33

−0.02

Last

−0.23

−0.33

0.41

0.38

0.01

Last 3

−0.32

−0.33

0.29

0.38

0.13

All

0.66

0.81

0.67

0.82

0.16

Table 7 presents the odds ratios (OR) for SGA birth in relation to quartile of NO2 exposure. We observed positive associations between SGA birth with each increasing quartile of the first and last 3 months of NO2 exposure (trend test p value = 0.001 and 0.033 for first and last 3 months of exposures, respectively). The OR of low birth weight among term births per quartile increase in NO2 varied between 0.94 and 1.01 for adjusted analyses (with most confidence intervals spanning 1.0) (results not shown).
Table 7

Associations (OR, 95 % CI) between ambient nitrogen dioxide (NO2) exposures during the first and last 3 months of pregnancy and SGA birth within the Puget Sound Air Basin

 

No. casesa

Total no.a

Unadjusted (N = 367,046)

Adjustedb (N = 337,372)

Exposure

OR

95 % CI

OR*

95 % CI

Average NO2 during first 3 months of pregnancy (ppb)

 Per quartile

 8.7–12.2

8,604

91,761

1.00

Referent

1.00

Referent

 12.2–13.6

8,873

91,775

1.02

[1.00, 1.06]

1.01

[0.97, 1.04]

 13.6–15.2

9,710

91,753

1.14

[1.11, 1.17]

1.06

[1.03, 1.10]

 15.2–34.7

10,381

91,757

1.23

[1.19, 1.26]

1.08

[1.04, 1.12]

Average NO2 during last 3 months of pregnancy (ppb)

 Per quartile

 6.3–12.0

8,300

91,644

1.00

Referent

1.00

Referent

 12.0–13.4

9,073

91,803

1.10

[1.01, 1.13]

1.04

[1.00, 1.07]

 13.4–15.0

9,633

91,798

1.17

[1.14, 1.21]

1.05

[1.02, 1.09]

 15.0–35.7

10,562

91,801

1.30

[1.26, 1.34]

1.07

[1.03, 1.11]

*p trend value = 0.001 for the first and 0.033 for the last 3 months of pregnancy, respectively

aNumbers included in the unadjusted model

bAdjusted for categorized age, race/ethnicity (white, black, Asian, Hispanic, other), parity, categorized median census tract income, smoking during pregnancy, season

The OR for SGA birth in relation to quartile of PM2.5 exposure varied between 1.00 and 1.04 for adjusted analyses (with most confidence intervals spanning 1.0), and we did not observe a trend (p value, 0.395 and 0.581, for the first and last 3 months of exposures, respectively) in relation to increasing quartile of exposure (Table 8). The OR for low birth weight with increasing quartiles of exposure among term births varied between 1.00 and 1.12 for adjusted analyses (with most confidence intervals spanning 1.0) (results not shown in Table 8).
Table 8

Associations (OR, 95 % CI) between ambient fine particulate matter (PM2.5) exposures during the first and last 3 months of pregnancy and SGA birth within the Puget Sound Air Basin

Exposure

No. of casesa

Total no.a

Unadjusted (N = 323,899)

Adjustedb (N = 298,835)

OR

95 % CI

OR*

95 % CI

Average PM2.5 during the first 3 months of pregnancy (μg/m3)

 Per quartile

 2.3–7.6

7,732

68,263

1.00

Referent

1.00

Referent

 7.6–9.8

8,802

78,214

1.00

[0.97, 1.03]

1.02

[0.98, 1.05]

 9.8–12.5

8,595

76,421

0.99

[0.96, 1.03]

1.04

[1.00, 1.08]

 12.5–25.5

7,680

68,192

0.99

[0.96, 1.03]

1.02

[0.97, 1.06]

Average PM2.5 during the last 3 months of pregnancy (μg/m3)

 Per quartile

 2.3–7.6

8,304

77,307

1.00

Referent

1.00

Referent

 7.6–9.4

8,165

73,340

1.04

[1.01, 1.07]

1.03

[1.00, 1.07]

 9.4–12.1

8,336

72,890

1.06

[1.03, 1.10]

1.02

[0.98, 1.06]

 12.1–25.5

8,004

67,553

1.10

[1.07, 1.14]

1.01

[0.97, 1.06]

*p trend value = 0.395 for the first and 0.581 for the last 3 months of pregnancy, respectively

aNumbers included in the unadjusted model

bAdjusted for categorized age, race/ethnicity (white, black, Asian, Hispanic, other), parity, categorized median census tract income, smoking during pregnancy, season

Women whose residences were within 50 m of a freeway or highway had an 11 % increased chance of having a SGA delivery (OR = 1.11, 95 % CI 1.00, 1.23) compared to those who did not live within 50 m of a freeway or highway (Table 9). We observed a more modest point estimate for women whose residences were within 150 m of a freeway or highway (OR = 1.05, 95 % CI 1.00, 1.10). We did not observe an association for having an SGA delivery for women who lived within 50 m of a major road or arterial. We did not observe an association between having a low birth weight infant among term births in relation to proximity to freeway/highway and major road/arterial.
Table 9

Associations (OR, 95 % CI) between proximity to major roadways and SGA birth within the Puget Sound Air Basin

Exposure

No. of casesa

Total no.a

Unadjusted (N = 364,757)

Adjustedb (N = 335,275)

OR

95 % CI

OR

95 % CI

Residence within 50 m of a freeway or highway

 No

36,797

360,767

1.00

Referent

1.00

Referent

 Yes

500

3,990

1.24

[1.13, 1.36]

1.11

[1.00, 1.23]

Residence within 150 m of a freeway or highway

 No

34,952

344,716

1.00

Referent

1.00

Referent

 Yes

2,345

20,041

1.17

[1.12, 1.22]

1.05

[1.00, 1.10]

Residence within 50 m of a major road or arterial

 No

30,638

305,374

1.00

Referent

1.00

Referent

 Yes

6,659

59,383

1.12

[1.09, 1.16]

1.01

[0.98, 1.04]

Residence within 150 m of a major road or arterial

 No

21,735

222,742

1.00

Referent

1.00

Referent

 Yes

15,562

142,015

1.13

[1.11, 1.15]

1.02

[0.99, 1.04]

aNumbers included in the unadjusted model

bAdjusted for categorized age, race/ethnicity (white, black, Asian, Hispanic, other), parity, categorized median census tract income, smoking during pregnancy, season

Discussion

We explored the relationship between several air pollutant measures and SGA in a very large cohort of births in the Puget Sound Air Basin, an area of low air pollution exposures. Our results suggest a relationship between two measures of traffic: prenatal NO2 exposure and proximity to freeway/highway, and SGA birth in the Puget Sound Air Basin.

These results are somewhat consistent with those observed by Brauer et al. in the Vancouver Air Basin (Brauer et al. 2008). In their analysis, pregnancy average NO2 exposure assessed based on the monitoring network yielded the greatest magnitude of effect estimate for SGA among the pollutants examined. However, their analysis of NO2 based on a similarly derived LUR also did not identify an association nor were their assessments of pregnancy averaged PM2.5 exposure (LUR and monitor based) significantly associated with SGA. Brauer et al. found moderate concordance between LUR and ambient network data for NO2 (r = 0.37). This lack of strong correlation may suggest that LUR represents a different measure of air pollution that reflects spatial heterogeneity more precisely (Marshall et al. 2008; Wilhelm et al. 2011). The most recent emission inventory from the Puget Sound Clean Air Agency reported that gasoline and diesel motor vehicles were the largest contributors to NO2 emissions in the region (PSCA 2008). Our model takes into account roadways, land use types, population density, and elevation. Because results of our LUR NO2 analysis and road proximity analyses were not in complete concordance, we believe that our LUR NO2 may also reflect the atmospheric ozone/NOx equilibrium that adds to spatial heterogeneity as suggested by Keuken et al. (2009). We were unable to validate or contrast the results of the Puget Sound NO2 LUR exposure models with monitor data due to lack of extensive NO2 monitor data in the Puget Sound region. Ambient monitors provide numerous direct exposure measurements and include more precise temporal information that the LUR model is not designed to capture. Therefore, there is a considerable amount of uncertainty in our LUR exposure estimates.

Our mean NO2 exposure estimates (13.7 ppb) were lower than annual averages derived from monitor data in the Puget Sound region for this period (approximate mean, 20 ppb from 1997 to 2005) (Puget Sound Clean Air Agency 2006) and slightly lower than those in the neighboring Vancouver Air Basin analysis (16.8 ppb) (Brauer et al. 2008). The former result is not surprising given that the single Environmental Protection Agency NO2 monitor in Seattle was initially sited in a relatively high area of NO2 (Norris and Larson 1999). In contrast, our annual mean PM2.5 exposure estimates (10.3 μg/m3) by monitor data were higher than those in the Vancouver Air Basin study (mean, 5.3 μg/m3) but still lower than the current Environmental Protection Agency annual standard of 15 μg/m3 (Puget Sound Clean Air Agency 2006).

We observed an association between SGA birth (OR = 1.11) associated with living within 50 m of a freeway/highway but not with living in close proximity (50 or 150 m) to major roadways/arterials. In the Vancouver region study, an increased risk of small for gestational age birth in relation to proximity to major roadways/arterials was observed, and Wilhelm et al. also observed increased risk for low birth weight for those living in close proximity to major roadways/arterials in Los Angeles County, an area of high air pollution (Wilhelm and Ritz 2003; Brauer et al. 2008). Taken together, these studies along with ours reported here suggest that road proximity may be an independent predictor of birth outcomes. Proximity may be a more direct measure of exposure to the harmful components of air pollutants than a single pollutant LUR model.

Physiologic mechanisms for air pollutant effects on fetal growth remain largely unknown, but hypotheses include disturbances to placental blood flow, increased risk for premature contractions/premature rupture of membranes through an inflammatory prostaglandin pathway, and increased risk for maternal infections (Wilhelm and Ritz 2005; Leem et al. 2006). Timing of air pollutant exposures and potential windows of susceptibility for fetal growth are inconsistent within the literature, and our analysis explored exposures in the first and last 3 months of pregnancy which may reflect different gestational developmental timeframes for infants born preterm compared to those at term. Known risk factors for prematurity and SGA birth include anemia, drug use, infections, low socioeconomic status, smoking, stress, and previous history of pregnancy complication

Limitations of the current study include the accuracy and availability of potentially important confounder data reported on birth certificates. Smoking and socioeconomic status are recognized as among the most important confounders to address in studies of air pollution. We adjusted for maternal smoking during pregnancy although the limits of self-reporting maternal smoking during pregnancy are appreciated. Some of the potential residual confounding may be captured by the adjustments for socioeconomic status. The majority of our study population was not at a low socioeconomic status as defined by median census tract income, but we attempted to address potential confounding with individual-level data on maternal age and education and income data which were only available at the census tract level. In addition, we were unable to determine how long each woman spent at a particular residence and therefore could not weight our exposure estimates. Lack of exposure variability decreased our ability to discern between time-specific exposures during pregnancy and outcomes. This was specifically true for NO2 exposures and, to a milder extent, PM 2.5. Because categorized NO2 and PM2.5 exposures were relatively uncorrelated (Table 6), we fit logistic regression models with both NO2 and PM2.5 as variables, and results were almost identical to those reported in Tables 7 and 8, suggesting that NO2 and PM2.5 may affect fetal growth with independent pathways and mechanisms of action.

The inability to account for occupational or other sites that constitute the mother's exposure is an exposure assessment limitation. We performed a sensitivity analysis of mothers who lived at their residence for less than 1 year compared to 1–3 years and >3 years and did not observe a significant change in estimates which may reflect that mothers stayed in close proximity to their recorded residence in the birth record or may move to settings with equivalent levels of exposure. We were not able to address personal time-activity or time spent at home versus other settings. Overall, this may have produced nondifferential misclassification of exposure and biased our findings somewhat towards the null hypothesis.

The preterm birth rate of 6.7 % in our primarily white and relatively higher educated population is considerably lower than that of the non-Hispanic white US population in 2005 (12 %). The rate of low birth weight in our entire cohort was 5 % which is similar to that of the corresponding US population (5 %) according to the Centers for Disease Control and Prevention National Vital Statistics Report (Hoyert et al. 2006). Information regarding gestational age and birth weight on birth certificates has been shown to be in high concordance with that abstracted from hospital records (95–99 %) in Washington state; thus, the potential for our outcome definition to be biased is small (DiGiuseppe et al. 2002).

Increasingly, air pollution epidemiological studies are discerning health effects in populations at relatively low concentrations, often below regulatory thresholds (Puget Sound Clean Air Agency 2006). Our findings suggest that maternal first and last 3 months of traffic-derived NO2 exposures as well as road proximity to freeways/highways are associated with SGA birth although we cannot rule out residual confounding. If these findings are causal, they have important implications for public health given that the rate of SGA birth has been increasing in the Puget Sound region without known etiology and the pollutant concentrations observed are within regulatory limits and comparable to levels experienced by the majority of women in US metropolitan areas.

Acknowledgments

We acknowledge the Ambulatory Pediatric Association/AHRQ Young Investigator Grant Program which provided funds for this analysis and the Health Canada Border Air Quality Study—Western Pilot Initiative that provided funds for creating the LUR models. British Columbia Centres for Disease Control Agreement. Grant No. GEH0404.

Conflicts of interest

Each author has no conflicts of interest to report.

Copyright information

© Springer Science+Business Media B.V. 2012