文献类型：期刊文献

英文题名：Quantifying Spatiotemporal Heterogeneities in PM2.5-Related Health and Associated Determinants Using Geospatial Big Data: A Case Study in Beijing

作者：Zhu, Yanrong[1,2]; Wang, Juan[1,2]; Meng, Bin[1,2]; Ji, Huimin[1,2]; Wang, Shaohua[3,4,5]; Zhi, Guoqing[1,2]; Liu, Jian[6]; Shi, Changsheng[1,2]

第一作者：Zhu, Yanrong

机构：[1] College of Applied Arts and Sciences, Beijing Union University, Beijing, 100191, China; [2] Laboratory of Urban Cultural Sensing & Computing, Beijing Union University, Beijing, 100191, China; [3] International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China; [4] Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; [5] State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; [6] College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China

第一机构：北京联合大学应用文理学院

通讯机构：[1]College of Applied Arts and Sciences, Beijing Union University, Beijing, 100191, China|[114172]北京联合大学应用文理学院;[11417]北京联合大学;

年份：2022

卷号：14

期号：16

外文期刊名：Remote Sensing

收录：EI(收录号：20223712738022);Scopus(收录号：2-s2.0-85137802222)

语种：英文

外文关键词：Air quality - Big data - Public health - Social networking (online)

摘要：Air pollution has brought about serious challenges to public health. With the limitations of available data, previous studies overlooked spatiotemporal heterogeneities in PM2.5-related health (PM2.5-RH) and multiple associated factors at the subdistrict scale. In this research, social media Weibo data was employed to extract PM2.5-RH based on the Bidirectional Encoder Representations from Transformers (BERT) model, in Beijing, China. Then, the relationship between PM2.5-RH and eight associated factors was qualified based on multi-source geospatial big data using Geographically Weighted Regression (GWR) models. The results indicate that the PM2.5-RH in the study area showed a spatial pattern of agglomeration to the city center and seasonal variation in the spatially non-stationary effects. The impacts of varied factors on PM2.5-RH were also spatiotemporally heterogeneous. Specifically, nighttime light (NTL), population density (PD) and the normalized difference built-up index (NDBI) had outstanding effects on PM2.5-RH in the four seasons, but with spatial disparities. The impact of the normalized difference vegetation index (NDVI) on PM2.5-RH was significant in summer, especially in the central urban areas, while in winter, the contribution of the air quality index (AQI) was increased. This research further demonstrates the feasibility of using social media data to indicate the effect of air pollution on public health and provides new insights into the seasonal impacts of associated driving factors on the health effects of air pollution. ? 2022 by the authors.