Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment

Abstract

Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004–2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m3), secondary inorganic aerosol (11.1 ± 5.0 μg/m3), and mineral dust (11.1 ± 7.9 μg/m3). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m3 over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 μg/m3) could be almost as large as from fossil fuel combustion sources (17 μg/m3). These estimates offer information about global population exposure to the chemical components and sources of PM2.5.

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

“…Prior global analyses have also found evidence for the importance of residential biomass fuel use in India (e.g. Verma et al 2008;Philip et al, 2014b;Lelieveld et al 2015;Silva et al, 2016;Lacey et al, 2017). The dominance of residential biomass fuel emissions is an important underlying cause for the regional nature of air pollution in India, because of the widely dispersed and distributed nature of this uncontrolled source.…”

Estimation of Satellite-Based SO42− and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model

“…Our approach for estimating satellite-retrieved surface PM 2.5 concentrations using the relationship between model-simulated PM 2.5 concentrations and chemical components of PM 2.5 is based on the method developed by Sajeev et al [15], who concluded that the correlation between satellite-modeled PM 2.5 components in North American and in situ observations reached 0.45 for organic matter and 0.95 for sulfate. That approach was further developed by Geng et al, who found that the chemical compositions over four polluted regions showed good consistency with ground measurements, with r values in the range of 0.65-0.75 for different species.…”

“…To date, a variety of mature studies have been conducted to establish the relationship between satellite-derived products and fine particulate matter near the ground using scale factors [9,10], physical semi-empirical methods [11,12], and statistical models [13,14]. Moreover, satellite remote sensing technology is an effective way of obtaining fine particulate matter compositions, providing researchers the chance to understand the distribution characteristics of each composition [15].…”

“…Considering emission inventories and precursor gases as input, CTMs have also been capable of simulating various chemical components of PM 2.5 . For example, inspired by AOD assimilation methods [20], Sajeev et al [15] combined satellite-derived AOD data with global modeling of the coincident aerosol vertical profile and composition to produce a global long-term mean ambient outdoor satellite-model PM 2.5 composition dataset at a spatial resolution of 0.1 × 0.1 • . Following [15], Geng et al estimated the chemical composition of PM 2.5 over China.…”

“…For example, inspired by AOD assimilation methods [20], Sajeev et al [15] combined satellite-derived AOD data with global modeling of the coincident aerosol vertical profile and composition to produce a global long-term mean ambient outdoor satellite-model PM 2.5 composition dataset at a spatial resolution of 0.1 × 0.1 • . Following [15], Geng et al estimated the chemical composition of PM 2.5 over China. Their results demonstrated that it was feasible to estimate PM 2.5 composition by integrating CTMs and satellite-derived PM 2.5 data [21].…”