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

通讯作者：Wang, J[1];Wang, J[2]

机构：[1]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China;[2]Beijing Union Univ, Lab Urban Cultural Sensing & Comp, Beijing 100191, Peoples R China;[3]Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China;[4]Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;[5]Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China;[6]Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China

第一机构：北京联合大学应用文理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China;[2]corresponding author), Beijing Union Univ, Lab Urban Cultural Sensing & Comp, Beijing 100191, Peoples R China.|[11417]北京联合大学;[114172]北京联合大学应用文理学院;

年份：2022

卷号：14

期号：16

外文期刊名：REMOTE SENSING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000845373100001)】；

基金：Supported by the Hundred Talents Program Youth Project (Category B) of the Chinese Academy of Sciences (E2Z10501), the R&D Program of Beijing Municipal Education Commission (KM202211417015), the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (CIT&TCD201904070), and the Academic Research Projects of Beijing Union University (Grant Nos. ZK40202001, RB202101).

语种：英文

外文关键词：social media Weibo data; PM2.5-related health; GWR; Beijing; spatiotemporal heterogeneity

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.