Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China
文献类型：期刊论文
| 作者 | Su, Ling2; Gao, Chanchan2; Ren, Xiaoli3,4; Zhang, Fengying1; Cao, Shanshan2; Zhang, Shiqing2; Chen, Tida2; Liu, Mengqing2; Ni, Bingchuan2; Liu, Min2,5 |
| 刊名 | GEOSCIENCE FRONTIERS
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 13期号:3页码:9 |
| ISSN号 | 1674-9871 |
| 关键词 | PM2.5 Euclidean distance Spatial representativeness China |
| DOI | 10.1016/j.gsf.2022.101370 |
| 通讯作者 | Liu, Min(mliu@re.ecnu.edu.cn) |
| 英文摘要 | Air pollution has seriously endangered human health and the natural ecosystem during the last decades. Air quality monitoring stations (AQMS) have played a critical role in providing valuable data sets for recording regional air pollutants. The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution. In this paper, we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based P-2.5 observation in the mainland of China. Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network. In addition, the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS. The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China. The monitoring stations could well represent the PM2.5 spatial distribution of the entire region, and the effectively represented area (i.e. the area where the Euclidean distance between the pixels and the stations was lower than the average value) accounted for 67.32% of the total area and covered 93.12% of the population. Forty additional stations were identified in the Northwest, North China, and Northeast regions, which could improve the spatial representativeness by 14.31%. (C) 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. |
| WOS关键词 | TIBETAN PLATEAU ; STATIONS ; TRANSPORT ; AEROSOL ; REGION |
| 资助项目 | National Natural Science Founda-tion of China[41977399] ; National Key Research and Development Program[2017YFC0505800] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| 出版者 | CHINA UNIV GEOSCIENCES, BEIJING |
| WOS记录号 | WOS:000788107300004 |
| 资助机构 | National Natural Science Founda-tion of China ; National Key Research and Development Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/175294]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Min |
| 作者单位 | 1.China Natl Environm Monitoring Ctr, Beijing 100012, Peoples R China 2.East China Normal Univ, Sch Ecol & Environm Sci, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200241, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Grad Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Inst Ecochongming, Shanghai 200241, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Ling,Gao, Chanchan,Ren, Xiaoli,et al. Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China[J]. GEOSCIENCE FRONTIERS,2022,13(3):9. |
| APA | Su, Ling.,Gao, Chanchan.,Ren, Xiaoli.,Zhang, Fengying.,Cao, Shanshan.,...&Liu, Min.(2022).Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China.GEOSCIENCE FRONTIERS,13(3),9. |
| MLA | Su, Ling,et al."Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China".GEOSCIENCE FRONTIERS 13.3(2022):9. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。