Abstract
Often spatiotemporal resolution/scale of environmental and health data do not align. Therefore, researchers compute exposure by interpolation or by aggregating data to coarse spatiotemporal scales. The latter is often preferred because of sparse geographic coverage of environmental monitoring, as interpolation method cannot reliably compute exposure using the small sample of sparse data points. This paper presents a methodology of diagnosing the levels of uncertainty in exposure at a given distance and time interval, and examines the effects of particulate matter (PM) ≤ 2.5 μm and ≤ 10 μm in diameter (PM2.5and PM10, respectively) on birth weight (BW) and low birth weight (LBW), i.e., birth weight <2500 g in Chicago (IL, USA), accounting for exposure uncertainty. Two important findings emerge from this paper. First, uncertainty in PM exposure increases significantly with the increase in distance from the monitoring stations, e.g., 50.6% and 38.5% uncertainty in PM10and PM2.5exposure respectively for 0.058° (~6.4 km) distance from the monitoring stations. Second, BW was inversely associated with PM2.5exposure, and PM2.5exposure during the first trimester and entire gestation period showed a stronger association with BW than the exposure during the second and third trimesters. But PM10did not show any significant association with BW and LBW. These findings suggest that distance and time intervals need to be chosen with care to compute exposure, and account for the uncertainty to reliably assess the adverse health risks of exposure.
Original language | English (US) |
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Article number | 906 |
Journal | International Journal of Environmental Research and Public Health |
Volume | 13 |
Issue number | 9 |
DOIs | |
State | Published - Sep 13 2016 |
Keywords
- Air pollution epidemiology
- Chicago
- Coarse and fine particulates
- Exposure uncertainty
- Semivariance
- Spatiotemporal autocorrelation
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis