Spatial Patterns of Satellite-Retrieved PM2.5 and Long-Term Exposure Assessment of China from 1998 to 2016

Abstract

Previous studies have shown that particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM2.5) is tightly associated with adverse effects on human health, i.e., morbidity and mortality. Based on long-term satellite-derived PM2.5 datasets, this study analyzed the spatial patterns and temporal trends of PM2.5 concentrations in China from 1998 to 2016 using standard deviational ellipse and statistical analyses. A long-term assessment of exposure and health impacts due to PM2.5 was undertaken by the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that concentrations of PM2.5 increased nonlinearly in most areas of China from 1998 to 2016. Higher concentrations were found in eastern China and western Tarim Basin, and most areas exceeded the World Health Organization’s (WHO) annual PM2.5 standards. The median center of average PM2.5 concentration of the country shifted to the southeast and then returned during the examined time period. The proportion of the population exposed to equal PM2.5 concentrations increased at first, then trended downward. The proportion of the population exposed to PM2.5 over WHO Interim Target-1 (35 µg/m3) increased 20.6%, which was the largest growth compared with other WHO standard levels. The extent of health risk in China increased and expanded from 1998 to 2016, especially in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta, which are China’s top three urban areas. The implementation of the Air Pollution Prevention and Control Action Plan has gradually paid off. If the government can achieve long-term adherence to its plan, great economic and health benefits will be gotten through the BenMAP-CE model analysis.

Using Satellite Data on Remote Transportation of Air Pollutants for PM2.5 Prediction in Northern Taiwan

“…Satellite-based AOD measurements have been used to estimate and predict PM2.5 levels due to their high correlation with PM2.5 and their large spatial coverage area [2], [19]- [22]. Lee et al predict daily PM2.5 concentrations in the southern and eastern parts of the United States of America (US) at 1km x 1km resolution using AOD data processed by MAIAC, which yields better performance than other algorithms with 10km x 10km resolution [2].…”

Spatial Characteristic of Coal Production-Based Carbon Emissions in Chinese Mining Cities

“…The size of the ellipse can reflect the centralized degree of the spatial element, and the semimajor axis represents the main direction of the spatial pattern. The standard deviational ellipse reveals the spatial characteristics of geographic features not only from the perspective of the gravity center but also from the areal extent, directions and shapes that can be applied, well and quickly, to explore the spatial characteristics of climate and environmental effects [30,31].…”

Temporal and spatial variation in the environmental impacts of China’s resource extraction at the provincial scale

“…Studies using spatial analytical methods can enhance our understanding of past and present spatial patterns (Hao et al, 2020;Song et al, 2020), aid the identification of factors that induce changes in distribution and even facilitate changes in spatial patterns based on the advantages and disadvantages of protecting the environment. Some researchers have used relevant spatial analyses to study PM2.5 emissions (Chen, T. et al, 2018;Peng et al, 2016;Shi et al, 2018), carbon emissions (Cui et al, 2020;Kai Fang, 2019;Liu et al, 2018a) and resource use (Liu et al, 2018b;Zhang et al, 2015;Zhang et al, 2016). To date, however, few spatial analysis has been conducted on multiple indicators of the environmental impacts caused by resource extraction on a regional scale within China.…”