
Our People
Fei Yao

Research Scientist
Research interests
I am interested in atmospheric composition modelling and remote sensing. I work on the GEOS-Chem Adjoint model, which provides an efficient approach to interpreting geostationary satellite data through a process known as inverse modelling.
Recent publications
Do GEMS geostationary satellite observations of tropospheric NO2 always improve NOx emission estimates and related air quality modelling?. 2026-04-01
DOI: https://doi.org/10.5194/egusphere-2026-1499
Supplementary material to "Do GEMS geostationary satellite observations of tropospheric NO2 always improve NOx emission estimates and related air quality modelling?". 2026-04-01
DOI: https://doi.org/10.5194/egusphere-2026-1499-supplement
Substantial reduction of solar photovoltaic potential in China by an extreme dust event. 2026
https://www.scopus.com/inward/record.url?eid=2-s2.0-105027542331&partnerID=MN8TOARS
Supplementary material to "Using Geostationary-Derived Sub-Daily FRP Variability vs. Prescribed Diurnal Cycles: Impact of African Fires on Tropospheric Ozone". 2025-07-03
DOI: https://doi.org/10.5194/egusphere-2025-2594-supplement
Using Geostationary-Derived Sub-Daily FRP Variability vs. Prescribed Diurnal Cycles: Impact of African Fires on Tropospheric Ozone. 2025-07-03
DOI: https://doi.org/10.5194/egusphere-2025-2594
Estimating Hourly Nitrogen Oxide Emissions Across Asia Using Data from the GEMS Geostationary Satellite. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-3198
Impacts of Air Pollutant Emissions on Solar Energy Generation. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-4715
Attribution of Solar Energy Yield Gaps due to Transboundary Particulate Matter Pollution Associated with Trade across Northeast Asia. 2025
https://www.scopus.com/inward/record.url?eid=2-s2.0-105012779819&partnerID=MN8TOARS
Quantifying the Source–Receptor Relationships of PM2:5 Pollution and Associated Health Impacts among China, South Korea, and Japan: A Dual Perspective and an Interdisciplinary Approach. 2025
https://www.scopus.com/inward/record.url?eid=2-s2.0-105004227468&partnerID=MN8TOARS
Using geostationary-satellite-derived sub-daily fire radiative power variability versus prescribed diurnal cycles to assess the impact of African fires on tropospheric ozone. 2025
https://www.scopus.com/inward/record.url?eid=2-s2.0-105024096452&partnerID=MN8TOARS
An integrated framework for jointly assessing spatiotemporal dynamics of surface urban heat island intensity and footprint: China, 2003–2020. 2024
https://www.scopus.com/inward/record.url?eid=2-s2.0-85198031971&partnerID=MN8TOARS
Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia. 2024
https://www.scopus.com/inward/record.url?eid=2-s2.0-85188839431&partnerID=MN8TOARS
Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia. 2023-08-04
DOI: https://doi.org/10.5194/egusphere-2023-1232
Source Sector Mitigation of Solar Energy Generation Losses Attributable to Particulate Matter Pollution. 2022-06-21
DOI: https://doi.org/10.1021/acs.est.2c01175 ISSN: https://portal.issn.org/resource/ISSN/0013-936X ISSN: https://portal.issn.org/resource/ISSN/1520-5851
Source-receptor relationship of transboundary particulate matter pollution between China, South Korea and Japan: Approaches, current understanding and limitations. 2022
https://www.scopus.com/inward/record.url?eid=2-s2.0-85113551968&partnerID=MN8TOARS
A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD. 2021-03
DOI: https://doi.org/10.1016/j.atmosenv.2021.118217 ISSN: https://portal.issn.org/resource/ISSN/1352-2310
Seasonal distribution and drivers of surface fine particulate matterand organic aerosol over the Indo-Gangetic Plain. 2021-02-04
DOI: https://doi.org/10.5194/acp-2021-69
Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. 2021
https://www.scopus.com/inward/record.url?eid=2-s2.0-85105267488&partnerID=MN8TOARS
Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain. 2021
https://www.scopus.com/inward/record.url?eid=2-s2.0-85111083987&partnerID=MN8TOARS
Comments on the manuscript acp-2020-1004. 2020-10-11
DOI: https://doi.org/10.5194/acp-2020-1004-SC1
Characterizing two distinct biomass burning regimes over Southeast Asia and their impacts on regional air quality. 2020-03-23
DOI: https://doi.org/10.5194/egusphere-egu2020-7653
Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?. 2020
https://www.scopus.com/inward/record.url?eid=2-s2.0-85088741111&partnerID=MN8TOARS
Who are more exposed to PM2.5 pollution: A mobile phone data approach. 2020
https://www.scopus.com/inward/record.url?eid=2-s2.0-85088130801&partnerID=MN8TOARS
A spatially structured adaptive two-stage model for retrieving ground-level PM 2.5 concentrations from VIIRS AOD in China. 2019
https://www.scopus.com/inward/record.url?eid=2-s2.0-85063492637&partnerID=MN8TOARS
Estimating daily PM2.5 concentrations in Beijing using 750-M VIIRS IP AOD retrievals and a nested spatiotemporal statistical model. 2019
https://www.scopus.com/inward/record.url?eid=2-s2.0-85069887334&partnerID=MN8TOARS
Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China. 2019
https://www.scopus.com/inward/record.url?eid=2-s2.0-85073434281&partnerID=MN8TOARS
A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. 2018
https://www.scopus.com/inward/record.url?eid=2-s2.0-85033581741&partnerID=MN8TOARS
Saturation Correction for Nighttime Lights Data Based on the Relative NDVI. 2017
https://www.scopus.com/inward/record.url?eid=2-s2.0-85081999813&partnerID=MN8TOARS
VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model. 2016
https://www.scopus.com/inward/record.url?eid=2-s2.0-84978924159&partnerID=MN8TOARS



