Evaluating and optimizing PM2.5 stations in Yangtze River Delta from a spatial representativeness perspective. (May 2023)

• Record Type: Journal Article
• Authors: Bai, Heming; Gao, Wenkang; Seong, Myeongsu; Yan, Rusha; Wei, Jing; Liu, Chong
• Is Part Of: Applied geography. Volume 154(2023)
• Publication Date: 2023-05
• Subjects: Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2023.102949
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: The spatial representativeness (SR) of air quality monitoring stations is an important parameter when using site observations for air quality evaluation and health assessment. In this study, by using daily 1-km-resolution PM2.5 concentrations from China High Air Pollutants dataset from 2016 to 2020, we adopted a Concentration Similarity Frequency method to estimate SR of the current PM2.5 stations in 25 cities over Yangtze River Delta (YRD) in Eastern China. These stations were further adjusted based on our proposed optimization scheme. For the current stations, SR areas cover 68.53% of urban area and 79.63% of urban population in YRD, but only cover 25.82% of rural area and 40.50% of rural population. Additionally, annual population-weighted mean (PWM) PM2.5 based on SR is more accurate for urban regions than rural regions. Compared to full coverage PWM PM2.5, the attributable deaths using SR-based PWM PM2.5 for urban and rural regions of YRD were overestimated by 1.04% and 4.09%. These overestimations were only 0.10% and 2.26% when using the optimized stations. Applying the optimization scheme also led to a 25.71% reduction in the number of stations. Our findings would provide a valuable reference for deploying new stations in YRD, especially in rural regions. Graphical abstract: Image 1 Highlights: Spatial representativeness (SR) of PM2.5 stations in YRD was analyzed. SR area of the current stations only covers 41% of rural population. The number of stations decreases by 26% after optimizing the current stations. SR area of the optimized stations covers 95% of urban and rural population. Applying the optimized stations obviously improves SR-based health assessment.