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A review of machine learning for modeling air quality: Overlooked but important issues
D Tang, Y Zhan, F Yang - Atmospheric Research, 2024 - Elsevier
Abstract Machine learning models based on satellite remote sensing have gained
widespread use in estimating ground-level air pollutant concentrations, which overcome the …
widespread use in estimating ground-level air pollutant concentrations, which overcome the …
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing
Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive)
uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users …
uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users …
The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2
The Aerosol Robotic Network (AERONET) Version 3 (V3) aerosol retrieval algorithm is
described, which is based on the Version 2 (V2) algorithm with numerous updates …
described, which is based on the Version 2 (V2) algorithm with numerous updates …
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia
Abstract In East Asia, air quality has been recognized as an important public health problem.
In particular, the surface concentrations of air pollutants are closely related to human life …
In particular, the surface concentrations of air pollutants are closely related to human life …
New era of air quality monitoring from space: Geostationary Environment Monitoring Spectrometer (GEMS)
Abstract The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for
launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and …
launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and …
[HTML][HTML] Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia
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 …
information for air quality research. Both are mainly obtained from satellite data based on a …
A high-precision aerosol retrieval algorithm (HiPARA) for advanced Himawari imager (AHI) data: Development and verification
Due to the complexity of land cover and aerosol types, the high-precision retrieval of land
aerosol properties is challenging. A land general aerosol (LaGA) algorithm called the High …
aerosol properties is challenging. A land general aerosol (LaGA) algorithm called the High …
Meteorology influencing springtime air quality, pollution transport, and visibility in Korea
In an environment with many local, remote, persistent, and episodic sources of pollution,
meteorology is the primary factor that drives periods of unhealthy air quality and reduced …
meteorology is the primary factor that drives periods of unhealthy air quality and reduced …
Fengyun 4A land aerosol retrieval: Algorithm development, validation, and comparison with other datasets
The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A)
satellite has high spatiotemporal resolution and provides useful spectral information that can …
satellite has high spatiotemporal resolution and provides useful spectral information that can …
Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign
Recently launched multichannel geostationary Earth orbit (GEO) satellite sensors, such as
the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI) …
the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI) …