Association between airborne PM_2.5chemical constituents and birth weight—implication of buffer exposure assignment

Abstract

Several papers reported associations between airborne fine particulate matter (PM2.5) and birth weight, though findings are inconsistent across studies. Conflicting results might be due to (1) different PM2.5 chemical structure across locations, and (2) various exposure assignment methods across studies even among the studies that use ambient monitors to assess exposure. We investigated associations between birth weight and PM2.5 chemical constituents, considering issues arising from choice of buffer size (i.e. distance between residence and pollution monitor). We estimated the association between each pollutant and term birth weight applying buffers of 5 to 30km in Connecticut (2000–2006), in the New England region of the U.S. We also investigated the implication of the choice of buffer size in relation to population characteristics, such as socioeconomic status. Results indicate that some PM2.5 chemical constituents, such as nitrate, are associated with lower birth weight and appear more harmful than other constituents. However, associations vary with buffer size and the implications of different buffer sizes may differ by pollutant. A homogeneous pollutant level within a certain distance is a common assumption in many environmental epidemiology studies, but the validity of this assumption may vary by pollutant. Furthermore, we found that areas close to monitors reflect more minority and lower socio-economic populations, which implies that different exposure approaches may result in different types of study populations. Our findings demonstrate that choosing an exposure method involves key tradeoffs of the impacts of exposure misclassification, sample size, and population characteristics.

Exposure to coarse particulate matter during gestation and birth weight in the U.S.

“…Although the spatial heterogeneity of PM 10-2.5 is not fully understood due to the lack of a large ambient monitoring network, this study only assigned PM 10-2.5 exposure estimates to counties with population weighted centroid ≤ 35 km from the co-located monitor to minimize exposure misclassification. Using other buffer sizes (20 and 50 km) showed similar results, though results should be interpreted with caution since population characteristics could differ by different buffer sizes (25). Nevertheless, some exposure misclassification may exist due to the spatial heterogeneity of PM 10-2.5 , measurement error, and relying on to ambient monitors, which do not account for individual behavior patterns.…”

“…The spatial heterogeneity of pollutants can vary by pollutant (e.g., PM 10 , PM 2.5 , and PM 10-2.5 ) (19, 25, 40). Supplementary Figure 2 presents correlations of PM 2.5 or PM 10 monitor pairs against distances between monitors.…”

“…Furthermore, births were excluded from analysis if gestational age was <37 or >44 weeks, plural deliveries, or impossible gestational age and birth weight combinations (23). These selection criteria were used in previous studies, and further description is available elsewhere (24, 25). …”

Particulate air pollution, fetal growth and gestational length: The influence of residential mobility in pregnancy

“…We observed that such misclassification had little influence on the observed estimates of the effect of PM 10 exposure on restricted fetal growth and gestational length in this study population. This provides greater credibility to past perinatal studies when the particulate matter exposure contrast under investigation is largely due to city-wide spatial comparisons and daily temporal comparisons 12–15, 23, 24 . For fetal growth endpoints (SGA and term LBW), choice of method of exposure assessment (closest monitor vs IDW) and buffer size had greater influence on effect estimates and their precision than the extent of ascertainment of residential mobility in pregnancy.…”

Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women

“…By using a larger buffer, we obtained similar exposure estimates to those from the 10 km buffer but a 2–3 times increased sample sizes. A bigger buffer distance also enabled us to keep a diverse population, because areas close to monitors where sources are usually located tend to be low socioeconomic communities (Ebisu et al 2014). Furthermore, we tracked the changes of participants’ home addresses, which maximized accuracy of the exposure estimation.…”