Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

Abstract

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.

Transient climate and ambient health impacts due to national solid fuel cookstove emissions

“…We consider a range of emissions factors for BC to OC, but our estimates do not explicitly consider uncertainties in total carbonaceous emissions (although marginal impacts, to first order, are less sensitive to this type of uncertainty). We attempt to account for this when estimating health impacts by considering a range of satellitederived PM2.5 datasets (24,28), but additional assessment of cookstove emissions inventories and resulting PM2.5 concentrations would be valuable (33). Our work only crudely treats SOA formation and is sensitive to uncertainties in emission factors of nonmethane organic compounds (34,35).…”

“…Uncertainties in the health response are calculated following Lee et al (13), in which the model was run with perturbed IER responses and baseline mortality rates corresponding to ±1 SD for each impact. These are combined with a comparison of results obtained using different satellite-derived PM 2.5 surfaces (24,28) to estimate the range of annual premature deaths attributed to ambient PM 2.5 exposure from solid fuel cookstove use; our central estimate uses the Global Burden of Disease 2013 exposure dataset (24) for consistency with other health impacts studies.…”

Short-term effects of fine particulate matter pollution on daily health events in Latin America: a systematic review and meta-analysis

“…In our review, all Brazilian studies included estimated population exposure through satellite estimations. Sophisticated satellite-based estimates are a promising tool to overcome the scarcity of exposure data, given that correlation with ground-level PM 2.5 mass might reach 81% (Van Donkelaar et al 2016) and The Global Burden of Disease Study is already using satellite-based PM 2.5 estimates (Brauer et al 2012).…”

Source influence on emission pathways and ambient PM<sub>2.5</sub> pollution over India (2015–2050)

“…The GEOS-Chem model has been previously applied to study PM2.5 over India (e.g., Boys et al 2014;Kharol et al 2013;Philip et al 2014a;Li et al 2017) including relating satellite observations of aerosol optical depth to ground-level PM2.5 for the GBD assessment (Brauer et al 2012van Donkelaar et al 2010van Donkelaar et al , 2015van Donkelaar et al , 2016. The simulations undertaken in this work represent one of the finest resolution efforts to date to both represent India, and global scale processes.…”