Prenatal Exposure to Fine Particulate Matter and Birth Weight

Yale University, School of Forestry and Environmental Studies, New Haven, CT 06511, USA.
Epidemiology (Cambridge, Mass.) (Impact Factor: 6.2). 11/2010; 21(6):884-91. DOI: 10.1097/EDE.0b013e3181f2f405
Source: PubMed


Exposure to fine particles (PM2.5) during pregnancy has been linked to lower birth weight; however, the chemical composition of PM2.5 varies widely. The health effects of PM2.5 constituents are unknown.
We investigated whether PM2.5 mass, constituents, and sources are associated with birth weight for term births. PM2.5 filters collected in 3 Connecticut counties and 1 Massachusetts county from August 2000 through February 2004 were analyzed for more than 50 elements. Source apportionment was used to estimate daily contributions of PM2.5 sources, including traffic, road dust/crustal, oil combustion, salt, and regional (sulfur) sources. Gestational and trimester exposure to PM2.5 mass, constituents, and source contributions were examined in relation to birth weight and risk of small-at-term birth (term birth <2500 g) for 76,788 infants.
Road dust and related constituents such as silicon and aluminum were associated with lower birth weight, as were the motor-vehicle-related species such as elemental carbon and zinc, and the oil-combustion-associated elements vanadium and nickel. An interquartile range increase in exposure was associated with low birthweight for zinc (12% increase in risk), elemental carbon (13%), silicon (10%), aluminum (11%), vanadium (8%), and nickel (11%). Analysis by trimester showed effects of third-trimester exposure to elemental carbon, nickel, vanadium, and oil-combustion PM2.5.
Exposures of pregnant women to higher levels of certain PM2.5 chemical constituents originating from specific sources are associated with lower birth weight.

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    • "Prenatal and perinatal exposures to air pollutants, such as carbon monoxide, nitrogen dioxide, and particulate matter, have been shown to adversely affect birth outcomes (Bell et al., 2010; Calderon-Garciduenas et al., 2011; Ezziane, 2013; Freire et al., 2010; Lakshmi et al., 2013; Munroe and Gauvain, 2012; Padula et al., 2013; Tang et al., 2014). Associated complications include developmental delay (Tang et al., 2014), congenital heart defects (Padula et al., 2013), low birth weight (Bell et al., 2010; Ezziane, 2013), cognitive deficits (Calderon-Garciduenas et al., 2011; Freire et al., 2010; Munroe and Gauvain, 2012), and mortality (Ezziane, 2013; Lakshmi et al., 2013). Prior research has shown residential proximity to point source pollution to be positively associated with congenital malformations, including chromosomal anomalies (Brender et al., 2008) and neural tube defects (Suarez et al., 2007), increased allergen-specific immunoglobulin-E in children (Patel et al., 2011), adverse birth outcomes (i.e. "
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    ABSTRACT: Prenatal and perinatal exposures to air pollutants have been shown to adversely affect birth outcomes in offspring and may contribute to prevalence of autism spectrum disorder (ASD). For this ecologic study, we evaluated the association between ASD prevalence, at the census tract level, and proximity of tract centroids to the closest industrial facilities releasing arsenic, lead or mercury during the 1990s. We used 2000 to 2008 surveillance data from five sites of the Autism and Developmental Disabilities Monitoring (ADDM) network and 2000 census data to estimate prevalence. Multi-level negative binomial regression models were used to test associations between ASD prevalence and proximity to industrial facilities in existence from 1991 to 1999 according to the US Environmental Protection Agency Toxics Release Inventory (USEPA-TRI). Data for 2489 census tracts showed that after adjustment for demographic and socio-economic area-based characteristics, ASD prevalence was higher in census tracts located in the closest 10th percentile compared of distance to those in the furthest 50th percentile (adjusted RR=1.27, 95% CI: (1.00, 1.61), P=0.049). The findings observed in this study are suggestive of the association between urban residential proximity to industrial facilities emitting air pollutants and higher ASD prevalence. Copyright © 2015 Elsevier B.V. All rights reserved.
    Science of The Total Environment 07/2015; 536:245-251. DOI:10.1016/j.scitotenv.2015.07.024 · 4.10 Impact Factor
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    • "They can also predict a larger suite of pollutants than the standard monitor networks, a fact that dovetails with the recent push toward a multi-pollutant approach to air pollution epidemiology (Mauderly et al. (2010)). The models' characterization of a variety of PM components is noteworthy given the evidence for differential toxicity between species (Bell et al. (2009)), particularly in terms of gestational health end-points (Bell et al. (2010); Brauer and Tamburic (2009); see Dadvand et al. (2013) for a meta-analysis). Indeed, a number of recent studies have used CMAQ or similar models to estimate speciated PM exposure in gestational health studies. "
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    ABSTRACT: In recent years environmental epidemiologists have begun utilizing regional-scale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to data from fixed-site ambient monitors. The advantages of using such models include better spatio-temporal coverage and the capability to predict concentrations of unmonitored pollutants. However, there are also drawbacks, chief among them being that these models can exhibit systematic spatial and temporal biases. In order to use these models in epidemiological investigations it is very important to bias-correct the model surfaces. We present a novel statistical method of spatio-temporal bias correction for the Community Multi-scale Air Quality (CMAQ) model that allows simultaneous bias adjustment of PM2.5 mass and its major constituent species using publically available speciated data from ambient monitors. The method uses mass conservation and the more widespread unspeciated PM2.5 mass observations to constrain the sum of the PM2.5 species’ concentrations in locations without speciated monitors. We develop the model in the context of an epidemiological study investigating the association between PM2.5 species’ ambient concentrations and birth outcomes throughout the state of New Jersey. Since our exposures of interest are multi-month averages we focus specifically on modeling seasonal bias trends rather than daily biases. Using a cross-validation study we find that our bias-corrected CMAQ results are more accurate than either the original CMAQ output or a spline fit without CMAQ. More interestingly, we find that our model clearly performs better when mass conservation is enforced, and furthermore that our model is competitive with kriging in a comparison in which the latter has the advantage.
    Atmospheric Environment 12/2014; 95. DOI:10.1016/j.atmosenv.2014.06.024 · 3.28 Impact Factor
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    • "Births with missing information for gestational age (n ¼62,724), or implausible combinations of birth weight and gestational age (Alexander et al., 1996) were also excluded (n ¼4995). Further, infants born before 260 or after 308 estimated days of gestation (n ¼141,485 and n¼ 22,839 respectively ) were excluded (Bell et al., 2010). Several exclusion criteria overlapped for certain births, leaving 960,945 births for analyses. "
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    ABSTRACT: Background: Low birth weight (LBW, <2500 g) has been associated with exposure to air pollution, but it is still unclear which sources or components of air pollution might be in play. The association between ultrafine particles and LBW has never been studied. Objectives: To study the relationships between LBW in term born infants and exposure to particles by size fraction, source and chemical composition, and complementary components of air pollution in Los Angeles County (California, USA) over the period 2001-2008. Methods: Birth certificates (n=960,945) were geocoded to maternal residence. Primary particulate matter (PM) concentrations by source and composition were modeled. Measured fine PM, nitrogen dioxide and ozone concentrations were interpolated using empirical Bayesian kriging. Traffic indices were estimated. Associations between LBW and air pollution metrics were examined using generalized additive models, adjusting for maternal age, parity, race/ethnicity, education, neighborhood income, gestational age and infant sex. Results: Increased LBW risks were associated with the mass of primary fine and ultrafine PM, with several major sources (especially gasoline, wood burning and commercial meat cooking) of primary PM, and chemical species in primary PM (elemental and organic carbon, potassium, iron, chromium, nickel, and titanium but not lead or arsenic). Increased LBW risks were also associated with total fine PM mass, nitrogen dioxide and local traffic indices (especially within 50 m from home), but not with ozone. Stronger associations were observed in infants born to women with low socioeconomic status, chronic hypertension, diabetes and a high body mass index. Conclusions: This study supports previously reported associations between traffic-related pollutants and LBW and suggests other pollution sources and components, including ultrafine particles, as possible risk factors.
    Environmental Research 07/2014; 134. DOI:10.1016/j.envres.2014.05.003 · 4.37 Impact Factor
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