Comparison of multiple PM_2.5 exposure products for estimating health benefits of emission controls over New York State, USA

Abstract

Ambient exposure to fine particulate matter (PM 2.5 ) is one of the top global health concerns. We estimate the PM 2.5 -related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM 2.5 products that include information from groundbased observations, remote sensing and chemical transport models. While these PM 2.5 products differ in spatial patterns, they show consistent decreases in PM 2.5 by 28%-37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM 2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM 2.5 -related mortality burden by applying an integrated exposure-response function to the different PM 2.5 products. The multi-product mean PM 2.5 -related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570-12 400) deaths in 2002 to 2750 (CI: 700-5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM 2.5 mortality burden due to the choice of PM 2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

“…The Dalhousie V4.NA.02 data for 2011 shown in Figure 1c capture the spatial gradients of the county-level monitoring data in Figure 1d. The Dalhousie data seem to show the best agreement with the AQS+IMPROVE data in the remote areas of the western U.S. and New England, which is consistent with another comparison study (Jin et al, 2019b). This feature indicates that incorporating satellite data can be valuable for improving estimation of PM 2.5 concentrations in remote areas where monitor coverage is sparse.…”

“…Because ground-based monitors, satellites, and models are often combined to estimated surface PM 2.5 , there are few independent data sources for validation. A recent study over New York State uses independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program and the Saint Regis Mohawk Tribe Air Quality Program to evaluate seven PM 2.5 products (Jin et al, 2019b). Jin et al suggest inclusion of satellite remote sensing improves the estimate of surface PM 2.5 in the remote area, but little gains over urban area.…”

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State

“…2015 ; Wu et al. 2019 ) given that the model we used has been shown to perform well even at rural locations in NYS ( Jin et al. 2019 ) and, thus, any error is likely nondifferential.…”

“…Although the prediction model has excellent predictive accuracy (van Donkelaar et al 2019) and is highly spatially resolved to capture population-averaged county-level exposures, some exposure measurement error is still expected. Any resulting bias, however, is expected to be toward the null (Kioumourtzoglou et al 2014;Hart et al 2015;Wu et al 2019) given that the model we used has been shown to perform well even at rural locations in NYS (Jin et al 2019) and, thus, any error is likely nondifferential. First hospitalization data are likely to miss a number of cases because patients may not be hospitalized even as disease symptoms worsen.…”

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

“…All PM 2.5 datasets used in this study provided daily PM 2.5 concentrations. More details regarding the PM 2.5 products, including validation and comparative statistics, can be found in an existing publication [ 25 ].…”