Abstract
Identifying spatiotemporal characteristics of daily fine particulate matter (PM2.5) concentrations is essential for assessing air quality and understanding the environmental consequences of urbanization. More importantly, exposure analysis can provide basic information for appropriate decision making. This paper demonstrates an integrated method incorporating satellite-based aerosol optical depth, machine learning models and multi-time meteorological parameters to analyze spatiotemporal dynamics and exposure levels of daily PM2.5 in the economically developed Yangtze River Delta (YRD) from 2016 to 2018. Ten-fold cross validation (CV) was implemented to evaluate the model performance. Compared to the models with daily means of meteorological fields, the models with multi-time meteorological parameters had higher CV coefficient of determination (R2) and lower CV root mean square error (RMSE) values. The model with the best performance achieved sample- (site-) based CV R2 values of 0.88 (0.88) and RMSE values of 10.33 (10.35) μg/m3. The YRD region was seriously polluted (exceeding the World Health Organization Interim Targets-1 standard of 35 μg/m3) during our study period, especially in Jiangsu Province, but with an improving trend. The residents in Zhejiang Province suffered the least from exposure, with 39 days (4% of the total days) characterized as over polluted (daily average > 75 μg/m3) in our study period. Air pollution in Shanghai Municipality mitigated the most from 2016 to 2018. With the advantages of high-accuracy and high-resolution (daily and 0.01 ° × 0.01 ° resolutions), the proposed method can guide for environmental policy planning.
Highlights
Fine particulate matter with aerodynamic diameters less than 2.5 μm, known as PM2.5, threatens human health by increasing the risk of many diseases including acute lower respiratory illness, cerebrovascular disease, ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke [1,2,3,4,5,6], and causes economic loss [7]
With the advantages of high-accuracy and high-resolution, the proposed method can help explore the effect of air pollution to human health spatiotemporally and guide for environmental policy planning
aerosol optical depth (AOD) represents the integral of the atmospheric extinction coefficient from the surface to space and can be retrieved by various sensors, such as the Seaviewing Wide Field-of-view Sensor [17], the Multi-angle Imaging SpectroRadiometer [18], the Advanced Himawari-8 Imager [19], and the Moderate Resolution Imaging Spectroradiometer (MODIS) [20], The newly released MODIS AOD product, MCD19A2, has a better spatial resolution of 1 km compared to the traditional AOD products [21]
Summary
Fine particulate matter with aerodynamic diameters less than 2.5 μm, known as PM2.5, threatens human health by increasing the risk of many diseases including acute lower respiratory illness, cerebrovascular disease, ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke [1,2,3,4,5,6], and causes economic loss [7]. AOD represents the integral of the atmospheric extinction coefficient from the surface to space and can be retrieved by various sensors, such as the Seaviewing Wide Field-of-view Sensor [17], the Multi-angle Imaging SpectroRadiometer [18], the Advanced Himawari-8 Imager [19], and the Moderate Resolution Imaging Spectroradiometer (MODIS) [20], The newly released MODIS AOD product, MCD19A2, has a better spatial resolution of 1 km compared to the traditional AOD products [21]. Identifying spatiotemporal characteristics of daily fine particulate matter (PM2.5) concentrations is essential for assessing air quality. This study aimed to estimate daily PM2.5 concentrations and analyze the resident exposure level in the economically developed Yangtze River Delta (YRD) from 2016–2018
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