Fei Yao

Fei Yao
The University of Edinburgh | UoE · School of GeoSciences

Doctor of Philosophy
Atmospheric Methane Detection Using Satellite Observations.

About

14
Publications
5,050
Reads
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253
Citations
Education
September 2018 - August 2021
The University of Edinburgh
Field of study
  • Atmospheric and Environmental Sciences
August 2015 - July 2018
Peking University
Field of study
  • Geography: Urban Planning and Design
September 2011 - July 2015
East China Normal University
Field of study
  • Geographical Information System

Publications

Publications (14)
Article
Full-text available
Particulate matter (PM) in the atmosphere and deposited on solar photovoltaic (PV) panels reduce PV energy generation. Reducing anthropogenic PM sources will therefore increase carbon-free energy generation and as a cobenefit will improve surface air quality. However, we lack a global understanding of the sectors that would be the most effective at...
Article
While the aerosol optical depth (AOD) product from the Visible Infrared Imaging Suite (VIIRS) instrument has proven effective for estimating regional ground-level particle concentrations with aerodynamic diameters less than 2.5 μm (PM 2.5), its performance at larger spatial scales remains unclear. Despite the wide application of statistical models...
Article
Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter <2.5μm (PM2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiome...
Article
We present a new method to infer ground-level fine particulate matter (PM2.5) from satellite remote sensing observations of aerosol optical depth (AOD). The conventional method generally uses a range of modelling approaches to determine PM2.5:AOD relationships that are subsequently used to infer ground-level PM2.5 concentrations from satellite-retr...
Article
Full-text available
Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied...
Article
Transboundary particulate matter (PM) pollution in Northeast Asia has raised tremendous concerns in China, South Korea, and Japan, leading to a proliferation of publications in recent years. This article summarizes the existing knowledge on the source-receptor relationship (SRR) of transboundary PM pollution between China, South Korea, and Japan wi...
Article
Full-text available
The Indo-Gangetic Plain (IGP) is home to 9 % of the global population and is responsible for a large fraction of agricultural crop production in Pakistan, India, and Bangladesh. Levels of fine particulate matter (mean diameter < 2.5 µm, PM 2.5) across the IGP often exceed human health recommendations, making cities across the IGP among the most pol...
Article
Full-text available
Particulate matter with an aerodynamic equivalent dimeter less than 2.5 μm (PM2.5) and ozone (O3) are major air pollutants, with coupled and complex relationships. The control of both PM2.5 and O3 pollution requires the identification of their common influencing factors, which has rarely been attempted. In this study, land use regression (LUR) mode...
Preprint
Full-text available
The Indo-Gangetic Plain (IGP) is home to 6 % of the global population and is responsible for a large fraction of agricultural crop production in Pakistan, India, and Bangladesh. Levels of fine particulate matter (mean diameter
Article
Full-text available
Background Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. Objectives This work aims to examine the associations between individual- and neighbourhood-level economic...
Article
Background Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic group...
Article
Full-text available
Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study ar...
Article
Full-text available
DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method to correct the saturation. With China as the study area, the threshold value...
Article
Satellite-based remote sensing data have been widely used in estimating ground-level PM2.5 concentrations as it can provide spatially detailed information. Most modern satellite-based PM2.5 estimates use statistical models that demand dense PM2.5 monitoring networks. As the national PM2.5 monitoring networks in China were not finished until the end...

Questions

Question (1)
Question
NOAA Class website (https://www.class.ncdc.noaa.gov/saa/products/welcome) has provided VIIRS IP AOT products (IVAOT) and corresponding geolocation files (GMTCO). However, the GMTCO files available from CLASS have four granules aggregated into one file. How could I require NOAA Class de-aggregate the geolocation files and deliver me four individual files, one for each VIIRS granule? I have not seen any option of this requirement on the NOAA Class data download website.

Projects

Project (1)
Project
Develop novel retrieval methods to estimate concentrations of atmospheric pollutants (e.g., PM2.5) from satellite remote sensing data.