Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter

Abstract

Abstract. The negative impacts of fine particulate matter (PM 2.5 ) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the data sets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM 2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and data sets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. Using the Burnett et al. (2014) integrated exposure response function, we estimate that in the United States, exposure to PM 2.5 accounts for approximately 2 % of total deaths compared to 14 % in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 14 % for the US and 2 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM 2.5 burden from satellite observations, which we quantify to be on the order of 20 % due to uncertainties in the AOD-to-surface-PM 2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure.

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

“…On average, uncertainty in exposure-response function causes 130% uncertainty (equation (S10)) in the estimated mortality burden, which is more than a factor of 4 larger than the uncertainty due to the choice of PM 2.5 products (δ PM =28%). Previous studies similarly suggest uncertainties in exposure-response functions have larger impacts than uncertainty in exposure estimates (Silva et al 2013, Ford andHeald 2016). The increasing availability of observations (both in situ and space-based) is expected to better constrain the exposure estimate, thus to further reduce uncertainty in PM 2.5 estimates.…”

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia

“…The IER has been used in a number of recent studies9525354555657 and allows for age-dependent calculation of relative risk for five different diseases (RR d ) associated with PM 2.5 exposure: lower respiratory infections, chronic obstructive pulmonary disease, lung cancer, ischemic heart disease and stroke.…”

Exploring sources of uncertainty in premature mortality estimates from fine particulate matter: the case of China

“…Mortality estimates have also been proposed based on a combination of ground and/or satellite observations (Zhou et al 2015, Jerrett et al 2017, despite being mostly focused on geographically limited areas. A few attempts to combine observations-either from monitoring stations or satellites-and CTM results were made to overcome limitations of prior studies, both worldwide (Ford and Heald 2016, Van Donkelaar et al 2016, Shaddick et al 2018 and in China , Liang et al 2017, Bai et al 2019, Zou et al 2019. For instance, multiple studies used different geostatistical techniques to integrate PM 2.5 ground measurements, CTMs output, satellite observations of AOD and other land use and meteorological factors to estimate PM 2.5 exposure at different spatial scales, ranging from the city-level (Li et al 2016b, Tao et al 2020 to regional and national scales (Liang et al 2017, Bai et al 2019, Wei et al 2019.…”