Abstract
Air pollution is becoming increasingly serious with rapid economic development in China, and the primary pollutant has converted from PM10 to PM2.5, which is associated with more adverse impacts on human health. Satellite remote sensing, with help of its quantitative observations over large spatial and temporal extent, has become a significant supplement to the in situ measurements. This study exploits the aerosol optical depth (AOD) product retrieved from the Medium Resolution Spectrum Imager (MERSI) onboard Fengyun-3C satellite to estimate PM2.5 estimation over the major urban areas of Chongqing, a metropolitan city in the Southwestern China. A semi-empirical model and the linear mixed effect (LME) model are combined based on in situ observations from local air quality and meteorological networks from May 2014 to May 2015. This combined model is able to explain about 90% of the variation of the estimated PM2.5, and performs better than LME model by achieving higher correlation and smaller deviations between the satellites estimated PM2.5 and in situ measurements. Benefiting from the high resolution of 1 km × 1 km, MERSI AOD achieves much more detailed spatial distribution of ground-level PM2.5 over the major urban areas of Chongqing, compared to most of concurrent satellite products, such as the MODIS L2 AOD. According to the estimation, PM2.5 concentration is higher in cold seasons than in warm seasons in Chongqing. Peak levels of PM2.5 is found in Yuzhong District, the center of Chongqing urban area, while the concentration gradually decreases in surrounding areas, indicating that air pollution in Chongqing is highly contributed by local anthropogenic emissions.
Highlights
Epidemiologic studies have indicated a strong connection between the concentration of PM2.5(particulate matter of which the aerodynamic diameter is smaller than 2.5 μm) and public respiratory and cardiovascular diseases [1,2,3]
Many researchers found that meteorological parameters e.g., temperature, relative humidity, and wind influence the size, composition, and mixing of particles, which were added into the model improved the accuracy of PM2.5 estimation
The 1 km aerosol optical depth (AOD) retrieved by Medium Resolution Spectrum Imager (MERSI) onboard FY-3C satellite was employed to estimate the ground-level PM2.5 over the main urban area in Chongqing, one of the significant metropolitan city in China
Summary
Epidemiologic studies have indicated a strong connection between the concentration of PM2.5. Several methods are widely used to estimate PM2.5 from satellite-derived AOD, including proportional factor models, semi-empirical formula models, and statistical models. Hu et al developed the Geographically Weighted Regression model (GWR) to estimate PM2.5 in North American with MODIS AOD, meteorological parameters, and land use information. Linear regression Model (GLM) and Generalized Addition and Model (GAM) to build the relationship between PM2.5 and AOD, respectively They found that several factors influenced the association between AOD and PM2.5 , including the boundary layer height, relative humidity, season, geographical region, monitoring site location, and distance from coast, but the model estimations were unbiased estimation of observations when PM2.5 concentrations were more than 40 μg/m3 [15,16]. This study utilizes MERSI AOD to depict the air quality over the major most polluted cities in China.
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