An Improved Global Land Anthropogenic Aerosol Product Based on Satellite Retrievals From 2008 to 2016

Significant levels of aerosols originate from anthropogenic activities, markedly influencing regional air quality and, consequently, human health. Generally, fine-mode aerosol optical depth (fAOD) data are used to evaluate the concentration of anthropogenic aerosols. Although the moderate resolution...

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Vydané v:IEEE geoscience and remote sensing letters Ročník 18; číslo 6; s. 944 - 948
Hlavní autori: Liang, Chen, Zang, Zhou, Li, Zhanqing, Yan, Xing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Additives
Aerosol concentrations
Aerosol optical depth
Aerosol Robotic Network
Aerosols
Air quality
Algorithms
Anthropogenic aerosols
Anthropogenic factors
Data
Distribution functions
Errors
fine-mode aerosol optical depth (fAOD)
Global aerosols
Graphical models
lookup table-spectral deconvolution algorithm (LUT-SDA)
Lookup tables
moderate resolution imaging spectroradiometer (MODIS)
MODIS
Optical analysis
Optical imaging
Optical sensors
Optical thickness
Satellites
Spectroradiometers
ISSN:1545-598X, 1558-0571
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Abstract Significant levels of aerosols originate from anthropogenic activities, markedly influencing regional air quality and, consequently, human health. Generally, fine-mode aerosol optical depth (fAOD) data are used to evaluate the concentration of anthropogenic aerosols. Although the moderate resolution imaging spectroradiometer (MODIS) provides fine-mode fraction (FMF) data that can be used to produce fAOD products, these data remain highly uncertain over land, in terms of global validation, relative to Aerosol Robotic Network (AERONET) measurements. To overcome this limitation, we developed an improved global land-scale fAOD product combining the lookup table-spectral deconvolution algorithm (LUT-SDA), generalized additive model (GAM), and MODIS Collection 6.1 aerosol products. Validation of the improved product revealed that over 63% of the fAOD values are within an expected error (EE) envelope of ±(0.05 + 15%), with strong positive correlations (<inline-formula> <tex-math notation="LaTeX">R^{2}= 0.65 </tex-math></inline-formula>) and low bias (root-mean-square error = 0.185; mean absolute error = 0.104) compared to AERONET-observed fAOD values. Furthermore, the fAOD developed eliminates the multiple zeroes in the MODIS FMF-based fAODs. In the improved fAOD product, eastern China and northern India exhibit the highest 9-year-mean fAOD loading, with values generally exceeding 0.6. The improved global land fAOD product provides a new avenue with which to obtain data on anthropogenic aerosols and can also be useful in aerosol-climate interaction research.
AbstractList Significant levels of aerosols originate from anthropogenic activities, markedly influencing regional air quality and, consequently, human health. Generally, fine-mode aerosol optical depth (fAOD) data are used to evaluate the concentration of anthropogenic aerosols. Although the moderate resolution imaging spectroradiometer (MODIS) provides fine-mode fraction (FMF) data that can be used to produce fAOD products, these data remain highly uncertain over land, in terms of global validation, relative to Aerosol Robotic Network (AERONET) measurements. To overcome this limitation, we developed an improved global land-scale fAOD product combining the lookup table-spectral deconvolution algorithm (LUT-SDA), generalized additive model (GAM), and MODIS Collection 6.1 aerosol products. Validation of the improved product revealed that over 63% of the fAOD values are within an expected error (EE) envelope of ±(0.05 + 15%), with strong positive correlations ([Formula Omitted]) and low bias (root-mean-square error = 0.185; mean absolute error = 0.104) compared to AERONET-observed fAOD values. Furthermore, the fAOD developed eliminates the multiple zeroes in the MODIS FMF-based fAODs. In the improved fAOD product, eastern China and northern India exhibit the highest 9-year-mean fAOD loading, with values generally exceeding 0.6. The improved global land fAOD product provides a new avenue with which to obtain data on anthropogenic aerosols and can also be useful in aerosol–climate interaction research.
Significant levels of aerosols originate from anthropogenic activities, markedly influencing regional air quality and, consequently, human health. Generally, fine-mode aerosol optical depth (fAOD) data are used to evaluate the concentration of anthropogenic aerosols. Although the moderate resolution imaging spectroradiometer (MODIS) provides fine-mode fraction (FMF) data that can be used to produce fAOD products, these data remain highly uncertain over land, in terms of global validation, relative to Aerosol Robotic Network (AERONET) measurements. To overcome this limitation, we developed an improved global land-scale fAOD product combining the lookup table-spectral deconvolution algorithm (LUT-SDA), generalized additive model (GAM), and MODIS Collection 6.1 aerosol products. Validation of the improved product revealed that over 63% of the fAOD values are within an expected error (EE) envelope of ±(0.05 + 15%), with strong positive correlations (<inline-formula> <tex-math notation="LaTeX">R^{2}= 0.65 </tex-math></inline-formula>) and low bias (root-mean-square error = 0.185; mean absolute error = 0.104) compared to AERONET-observed fAOD values. Furthermore, the fAOD developed eliminates the multiple zeroes in the MODIS FMF-based fAODs. In the improved fAOD product, eastern China and northern India exhibit the highest 9-year-mean fAOD loading, with values generally exceeding 0.6. The improved global land fAOD product provides a new avenue with which to obtain data on anthropogenic aerosols and can also be useful in aerosol-climate interaction research.
Author Liang, Chen
Zang, Zhou
Li, Zhanqing
Yan, Xing
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SubjectTerms Additives
Aerosol concentrations
Aerosol optical depth
Aerosol Robotic Network
Aerosols
Air quality
Algorithms
Anthropogenic aerosols
Anthropogenic factors
Data
Distribution functions
Errors
fine-mode aerosol optical depth (fAOD)
Global aerosols
Graphical models
lookup table-spectral deconvolution algorithm (LUT-SDA)
Lookup tables
moderate resolution imaging spectroradiometer (MODIS)
MODIS
Optical analysis
Optical imaging
Optical sensors
Optical thickness
Satellites
Spectroradiometers
Title An Improved Global Land Anthropogenic Aerosol Product Based on Satellite Retrievals From 2008 to 2016
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