Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

• Published in: Chinese geographical science Vol. 33; no. 2; pp. 320 - 332
• Main Authors: Zhang, Zheyuan, Wang, Jia, Xiong, Nina, Liang, Boyi, Wang, Zong
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01-04-2023; Springer Nature B.V; Beijing Forestry University,Institute of GIS,RS&GPS,Beijing100083,China%Beijing Forestry University,Beijing Key Laboratory of Precision Forestry,Beijing100083,China; Beijing Forestry University,Institute of GIS,RS&GPS,Beijing100083,China; Beijing Key Laboratory of Municipal Solid Wastes Testing Analysis and Evaluation,Beijing100028,China; Beijing Forestry University,Beijing Key Laboratory of Precision Forestry,Beijing100083,China; Beijing Municipal Institute of City Management,Beijing100028,China
• Subjects: Air pollution; Air quality; Earth and Environmental Science; Environmental health; Exposure; Geography; Outdoor air quality; Pollution levels; Population density; Population statistics; Public health; Remote sensing; Towns; Beijing China; China; population pollution exposure; Luojia-1; random forest; nighttime light remote sensing; air quality index (AQI)
• ISSN: 1002-0063; 1993-064X
• DOI: 10.1007/s11769-023-1339-z

Air pollution is a problem that directly affects human health, the global environment and the climate. The air quality index (AQI) indicates the degree of air pollution and effect on human health; however, when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored. In the present study, multi-source data were combined to map the distribution of the AQI and population data, and the analyze their pollution population exposure of Beijing in 2018 was analyzed. Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018. Using Luojia-1 nighttime light remote sensing data, population statistics data, the population of Beijing in 2018 and point of interest data, the distribution of the permanent population in Beijing was estimated with a high precision of 200 m × 200 m. Based on the spatialization results of the AQI and population of Beijing, the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level (PWEL) formula. The results show that the southern region of Beijing had a more serious level of air pollution, while the northern region was less polluted. At the same time, the population was found to agglomerate mainly in the central city and the peripheric areas thereof. In the present study, the exposure of different districts and towns in Beijing to pollution was analyzed, based on high resolution population spatialization data, it could take the pollution exposure issue down to each individual town. And we found that towns with higher exposure such as Yongshun Town, Shahe Town and Liyuan Town were all found to have a population of over 200 000 which was much higher than the median population of townships of 51 741 in Beijing. Additionally, the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same, with the peak value being in winter and the lowest value being in summer. The exposure intensity in population clusters was relatively high. To reduce the level and intensity of pollution exposure, relevant departments should strengthen the governance of areas with high AQI, and pay particular attention to population clusters.