Integrating satellite information, land use regression, and machine learning to estimate the spatiotemporal variation of ionic composition in PM2.5 across Taiwan

Lo, KangKangLoLin, Tang-HuangTang-HuangLinWang, Fu-ChengFu-ChengWangShiu, Yi-ShiangYi-ShiangShiuChen, Chu-ChihChu-ChihChenLin, Yuan-ChienYuan-ChienLinHuang, Chun-ShengChun-ShengHuangLiao, Ho-TangHo-TangLiaoWu, Yee-LinYee-LinWuWu, Chang-FuChang-FuWu2026-01-272026-01-272025-09https://scholars.lib.ntu.edu.tw/handle/123456789/735606[SDGs]SDG13Integrating satellite information, land use regression, and machine learning to estimate the spatiotemporal variation of ionic composition in PM2.5 across Taiwanjournal-article10.1016/j.scitotenv.2025.180050