Epidemiological studies investigating the human health effects of PM_2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM_2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM_2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM_2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM_2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM_2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM_2.5-AOD relationships was used to predict location-specific PM_2.5 levels. PM_2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM_2.5 concentrations. Furthermore, the estimated PM_2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM_2.5 concentrations within the study domain.

Background: Epidemiological studies have demonstrated associations between short-term exposure to PM2.5 and hospital admissions. The chemical composition of particles varies across locations and time periods. Identifying the most harmful constituents and sources is an important health and regulatory concern. Objectives: We examined pollutant sources for associations with risk of hospital admissions for cardiovascular and respiratory causes. Methods: We obtained PM2.5 filter samples for four counties in Connecticut and Massachusetts and analyzed them for PM2.5 elements. Source apportionment was used to estimate daily PM2.5 contributions from sources (traffic, road dust, oil combustion, and sea salt as well as a regional source representing coal combustion and other sources). Associations between daily PM2.5 constituents and sources and risk of cardiovascular and respiratory hospitalizations for the Medicare population (> 333,000 persons ≥ 65 years of age) were estimated with time-series analyses (August 2000–February 2004). Results: PM2.5 total mass and PM2.5 road dust contribution were associated with cardiovascular hospitalizations, as were the PM2.5 constituents calcium, black carbon, vanadium, and zinc. For respiratory hospitalizations, associations were observed with PM2.5 road dust, and sea salt as well as aluminum, calcium, chlorine, black carbon, nickel, silicon, titanium, and vanadium. Effect estimates were generally robust to adjustment by co-pollutants of other constituents. An interquartile range increase in same-day PM2.5 road dust (1.71 μg/m3) was associated with a 2.11% (95% CI: 1.09, 3.15%) and 3.47% (95% CI: 2.03, 4.94%) increase in cardiovascular and respiratory admissions, respectively. Conclusions: Our results suggest some particle sources and constituents are more harmful than others and that in this Connecticut/Massachusetts region the most harmful particles include black carbon, calcium, and road dust PM2.5. Citation: Bell ML, Ebisu K, Leaderer BP, Gent JF, Lee HJ, Koutrakis P, Wang Y, Dominici F, Peng RD. 2014. Associations of PM2.5 constituents and sources with hospital admissions: analysis of four counties in Connecticut and Massachusetts (USA) for persons ≥ 65 years of age. Environ Health Perspect 122:138–144; http://dx.doi.org/10.1289/ehp.1306656

Background: Exposure to fine particles (PM_2.5) during pregnancy has been linked to lower birth weight; however, the chemical composition of PM_2.5 varies widely. The health effects of PM_2.5 constituents are unknown. Methods: We investigated whether PM_2.5 mass, constituents, and sources are associated with birth weight for term births. PM_2.5 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 PM_2.5 sources, including traffic, road dust/crustal, oil combustion, salt, and regional (sulfur) sources. Gestational and trimester exposure to PM_2.5 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. Results: 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 PM_2.5. Conclusions: Exposures of pregnant women to higher levels of certain PM_2.5 chemical constituents originating from specific sources are associated with lower birth weight.

Background: Air pollution may be related to adverse birth outcomes. Exposure information from land-based monitoring stations often suffers from limited spatial coverage. Satellite data offer an alternative data source for exposure assessment. Methods: We used birth certificate data for births in Connecticut and Massachusetts, United States (2000–2006). Gestational exposure to PM_2.5 was estimated from US Environmental Protection Agency monitoring data and from satellite data. Satellite data were processed and modeled by using two methods—denoted satellite (1) and satellite (2)—before exposure assessment. Regression models related PM_2.5 exposure to birth outcomes while controlling for several confounders. Birth outcomes were mean birth weight at term birth, low birth weight at term (<2500 g), small for gestational age (SGA, <10th percentile for gestational age and sex), and preterm birth (<37 weeks). Results: Overall, the exposure assessment method modified the magnitude of the effect estimates of PM_2.5 on birth outcomes. Change in birth weight per interquartile range (2.41 μg/m³) increase in PM_2.5 was −6 g (95% confidence interval = −8 to −5), −16 g (−21 to −11), and −19 g (−23 to −15), using the monitor, satellite (1), and satellite (2) methods, respectively. Adjusted odds ratios, based on the same three exposure methods, for term low birth weight were 1.01 (0.98–1.04), 1.06 (0.97–1.16), and 1.08 (1.01–1.16); for SGA, 1.03 (1.01–1.04), 1.06 (1.03–1.10), and 1.08 (1.04–1.11); and for preterm birth, 1.00 (0.99–1.02), 0.98 (0.94–1.03), and 0.99 (0.95–1.03). Conclusions: Under exposure assessment methods, we found associations between PM_2.5 exposure and adverse birth outcomes particularly for birth weight among term births and for SGA. These results add to the growing concerns that air pollution adversely affects infant health and suggest that analysis of health consequences based on satellite-based exposure assessment can provide additional useful information.