West Europe from space stock photo

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

Supplementary material to "Do GEMS geostationary satellite observations of tropospheric NO2 always improve NOx emission estimates and related air quality modelling?". 2026-04-01

Substantial reduction of solar photovoltaic potential in China by an extreme dust event. 2026

Supplementary material to "Using Geostationary-Derived Sub-Daily FRP Variability vs. Prescribed Diurnal Cycles: Impact of African Fires on Tropospheric Ozone". 2025-07-03

Using Geostationary-Derived Sub-Daily FRP Variability vs. Prescribed Diurnal Cycles: Impact of African Fires on Tropospheric Ozone. 2025-07-03

Estimating Hourly Nitrogen Oxide Emissions Across Asia Using Data from the GEMS Geostationary Satellite. 2025-01-20

Impacts of Air Pollutant Emissions on Solar Energy Generation. 2025-01-20

Attribution of Solar Energy Yield Gaps due to Transboundary Particulate Matter Pollution Associated with Trade across Northeast Asia. 2025

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

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

An integrated framework for jointly assessing spatiotemporal dynamics of surface urban heat island intensity and footprint: China, 2003–2020. 2024

Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia. 2024

Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia. 2023-08-04

Source Sector Mitigation of Solar Energy Generation Losses Attributable to Particulate Matter Pollution. 2022-06-21

Source-receptor relationship of transboundary particulate matter pollution between China, South Korea and Japan: Approaches, current understanding and limitations. 2022

A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD. 2021-03

Seasonal distribution and drivers of surface fine particulate matterand organic aerosol over the Indo-Gangetic Plain. 2021-02-04

Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. 2021

Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain. 2021

Comments on the manuscript acp-2020-1004. 2020-10-11

Characterizing two distinct biomass burning regimes over Southeast Asia and their impacts on regional air quality. 2020-03-23

Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?. 2020

Who are more exposed to PM2.5 pollution: A mobile phone data approach. 2020

A spatially structured adaptive two-stage model for retrieving ground-level PM 2.5 concentrations from VIIRS AOD in China. 2019

Estimating daily PM2.5 concentrations in Beijing using 750-M VIIRS IP AOD retrievals and a nested spatiotemporal statistical model. 2019

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

A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China. 2018

Saturation Correction for Nighttime Lights Data Based on the Relative NDVI. 2017

VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model. 2016

Contact details

University of Edinburgh