Zhao-Yue Chen is a predoctoral researcher at ISGlobal. The primary focus of his current research is to analyze the link between air pollution and health impacts. To achieve it, he aims to use the remote sensing technique to estimate ground-level air pollutant exposure, and it can be used for further health assessment.
He is also handling spatio-temporal datasets at different timescale, from days to seasons, or spatial scale, from communities to countries. Furthermore, Zhao-Yue Chen hopes to investigate the interaction between air pollution and adaptation to climate changes. His ultimate goal is to improve the well-being of societies by providing some clues to policymakers or other adaption studies.
- Chen Z Y, Zhang R, Zhang T H, et al. A kriging-calibrated machine learning method for estimating daily ground-level NO2 in mainland China[J]. Science of The Total Environment, 2019, 690: 556-564. DOI: http://doi.org/10.1016/j.scitotenv.2019.06.349.
- Chen Z-Y, Jin J-Q, Zhang R, Zhang T-H, Chen J-J, Yang J, Ou C-Q, Guo Y. Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM2.5 Levels. Remote Sensing. 2020; 12(18):3008. https://doi.org/10.3390/rs12183008.
- Chen ZY, Zhang TH, Zhang R, Zhu ZM, Yang J, Chen PY, Ou CQ*, Guo Y. Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China. Atmospheric Environment. 2019, 202: 180~189 DOI: http://doi.org/10.1016/j.atmosenv.2019.01.027.
- Chen Z Y, Zhang T H, Zhang R, et al. Estimating PM2.5 concentrations based on nonlinear exposure-lag-response associations with aerosol optical depth and meteorological measures[J]. Atmospheric Environment, 2018, 173: 30-37. DOI: http://doi.org/10.1016/j.atmosenv.2017.10.055 .
- Chen J, Yang J, Zhou M, Yin P, Wang B, Liu J, Chen Z, Song X, Ou CQ, Liu Q. Cold spell and mortality in 31 Chinese capital cities: Definitions, vulnerability and implications. Environmental International. 2019,128:271-278. DOI: http://doi.org/10.1016/j.envint.2019.04.049