East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST‐AIRCPC)

Z Li, Y Wang, J Guo, C Zhao, MC Cribb… - Journal of …, 2019 - Wiley Online Library
Aerosols have significant and complex impacts on regional climate in East Asia. Cloud‐
aerosol‐precipitation interactions (CAPI) remain most challenging in climate studies. The …

Reconstructing 1-km-resolution high-quality PM2. 5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications

J Wei, Z Li, A Lyapustin, L Sun, Y Peng, W Xue… - Remote Sensing of …, 2021 - Elsevier
Exposure to fine particulate matter (PM 2.5) can significantly harm human health and
increase the risk of death. Satellite remote sensing allows for generating spatially …

Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals.

D Stratoulias, N Nuthammachot… - Remote …, 2024 - search.ebscohost.com
Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs
and a cross-cutting component of certain United Nations Sustainable Development Goals …

Stacking machine learning model for estimating hourly PM2. 5 in China based on Himawari 8 aerosol optical depth data

J Chen, J Yin, L Zang, T Zhang, M Zhao - Science of The Total Environment, 2019 - Elsevier
Aerosol optical depth (AOD) from polar orbit satellites and meteorological factors have been
widely used to estimate concentrations of surface particulate matter with an aerodynamic …

[HTML][HTML] Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Y Kang, M Kim, E Kang, D Cho, J Im - ISPRS Journal of Photogrammetry …, 2022 - Elsevier
Abstract Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important
information for air quality research. Both are mainly obtained from satellite data based on a …

A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2. 5

X Yan, Z Zang, Y Jiang, W Shi, Y Guo, D Li, C Zhao… - Environmental …, 2021 - Elsevier
Being able to monitor PM 2.5 across a range of scales is incredibly important for our ability to
understand and counteract air pollution. Remote monitoring PM 2.5 using satellite-based …

[HTML][HTML] New interpretable deep learning model to monitor real-time PM2. 5 concentrations from satellite data

X Yan, Z Zang, N Luo, Y Jiang, Z Li - Environment International, 2020 - Elsevier
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm
(PM 2.5) is a key air quality parameter. A real-time knowledge of PM 2.5 is highly valuable …

The polarization crossfire (PCF) sensor suite focusing on satellite remote sensing of fine particulate matter PM2. 5 from space

Z Li, W Hou, J Hong, C Fan, Y Wei, Z Liu, X Lei… - Journal of Quantitative …, 2022 - Elsevier
Focusing on satellite remote sensing of fine particulate matter PM 2.5 from space, the
polarization crossfire (PCF) strategy has been developed, which includes the PCF satellite …

How magnitude of PM2. 5 exposure disparities have evolved across Chinese urban-rural population during 2010–2019

M Liu, Y Wang, R Liu, C Ding, G Zhou, L Han - Journal of Cleaner …, 2023 - Elsevier
Rapid industrialization and urbanization in China not only facilitate economic progress, but
also aggravate environmental pollution and disparity. Particulate matter (especially PM 2.5) …

Evaluation of four meteorological reanalysis datasets for satellite-based PM2. 5 retrieval over China

C Zuo, J Chen, Y Zhang, Y Jiang, M Liu, H Liu… - Atmospheric …, 2023 - Elsevier
Meteorological reanalysis data is widely used for satellite-based retrieval of fine particulate
matter (PM 2.5); however, selecting appropriate data for specific regional applications …