Estimating ground-level particulate matter concentrations using satellite-based data: a review

Figures & data

Table 1. Currently available satellite sensors for retrieving aerosol information.

Satellite^a	Orbit^b	Year	Instrument^c	Products^d	Wavelength range^e
Terra	PS	1999-	MODIS	AOD, AE, FMF	VIS, IR
			MISR	AOD, AE	VIS
Aqua	PS	2002-	MODIS	AOD, AE, FMF	VIS, IR
Terra & Aqua	PS	2002-	MODIS	MAIAC AOD	VIS, IR
Aura	PS	2003-	OMI	AOD, SSA	VIS, UV
CALIPSO	PS	2006-	CALIOP	Aerosol profiles	532 nm, 1064 nm (LiDAR)
COMS	GEO	2010-	GOCI	AOD, AE, FMF, SSA	VIS, IR
Suomi NPP	PS	2011-	VIIRS	AOD, AE	VIS, IR
Himawari-8	GEO	2014-	AHI	AOD, AE, FMF	VIS, IR
GOES-16	GEO	2016-	ABI	AOD, AE	VIS, IR

^aSatellite: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Communication, Ocean and Meteorological Satellite (COMS), Suomi National Polar-orbiting Partnership (Suomi NPP), Geostationary Operational Environmental Satellite-16 (GOES-16).

^bOrbit: Polar sun-synchronous orbit (PS), Geostationary orbit (GEO).

^cInstrument: Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), Advanced Himawari Imager (AHI), Advanced Baseline Imager (ABI).

^dProducts: Aerosol Optical Depth (AOD), Ångström Exponent (AE), Fine-Mode Fraction (FMF), Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC), Single Scattering Albedo (SSA).

^eWavelength range: Visible light (VIS), Infrared (IR), Ultraviolet (UV), Light Detection and Ranging (LiDAR).

Figure 1. Approaches for estimating ground-level PM concentrations using satellite-derived aerosol products.

Table 2. Summary of advantages and disadvantages of commonly used approaches for estimating PM concentrations.

Method	Advantages	Disadvantages
Empirical -statistical models	Relatively lighter computation load than CTMs Can estimate empirically without knowledge of complex chemical and physical interactions Users can select approach input variables to configure a model for a specific situation	Rely heavily on ground measurements Relatively high possibility of overfitting samples used for model training Difficult to reflect a physicochemical correlation between input variables
CTM-based approach	Less influenced by ground stations characteristics Provide forecast data Relatively accurate estimates in areas with small variations in PM concentrations	Very complex Highly dependent on initial and boundary conditions High uncertainty, lack of information in emission inventories and complicated physicochemical atmospheric state
Other approaches (e.g. vertical correction method)	Can increase correlation with ground PM by correcting total column AOD according to near-surface conditions	Difficult to consider factors such as other surrounding meteorological variables, land use, and emission sources

Figure 2. Frequency distribution of the top 7 most commonly used methods in reviewed studies that predicted ground PM concentrations using satellite AOD as the main input predictor during the past 10 years.