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

被引:5
作者
Bai, Heming [1 ]
Gao, Wenkang [2 ]
Seong, Myeongsu [1 ]
Yan, Rusha [3 ]
Wei, Jing [4 ]
Liu, Chong [5 ,6 ]
机构
[1] Nantong Univ, Res Ctr Intelligent Informat Technol, Nantong, Peoples R China
[2] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Layer Phys & Atmo, Beijing, Peoples R China
[3] Shanghai Acad Environm Sci, State Environm Protect Key Lab Format & Prevent Ur, Shanghai, Peoples R China
[4] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, Dept Atmospher & Ocean Sci, College Pk, MD USA
[5] China Meteorol Adm, CMA Earth Syst Modeling & Predict Ctr, Beijing, Peoples R China
[6] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
QUALITY MONITORING STATIONS; AIR-POLLUTION; SEPARATING CONTRIBUTIONS; ANTHROPOGENIC EMISSIONS; CHINA; POLLUTANTS; MODEL;
D O I
10.1016/j.apgeog.2023.102949
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
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.
引用
收藏
页数:10
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