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A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges

Asia-Pacific Journal of Atmospheric Sciences volume 57, pages 679–699 (2021)Cite this article

Abstract

Detailed, reliable, and continuous monitoring of aerosol optical depth (AOD) is essential for air quality management and protection of human health. The satellite-based AOD datasets have been typically used in many studies for the estimation of particulate matter (PM2.5 and PM10) concentration in the tropospheric region. The prime focus of this study is to review the past studies to analyze the performance of various satellite-based AOD datasets and models used for PM estimation. The review results suggest that every satellite sensors data have some specific capabilities as well as some drawbacks. Multi-angle imaging spectroradiometer (MISR) and visible infrared imaging radiometer suite (VIIRS) datasets showed better consistency in AOD and PM estimation in comparison to the moderate resolution imaging spectroradiometer (MODIS) datasets. In the context of PM estimation models’ accuracy, the mixed-effect model (MEM) has been extensively used and found to be more consistent in general, whereas, geographically weighted regression (GWR) model outperforms other statistical regression models in regional scale. Incorporation of land use parameters along with meteorological parameters improves the PM estimation accuracy at various spatial scale. The review suggests that in the near future, high resolution (spatial and temporal) satellite data with the improved algorithms will be highly appreciable for accurate estimation of AOD and PM.

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Notes

  1. World Health Organization (WHO) (https://www.who.int/airpollution/en/).

  2. MAIA (Multi-Angle Imager for Aerosols) (https://maia.jpl.nasa.gov/).

Abbreviations

AATSR:

Advanced along-track scanning radiometer

ABI:

Advanced baseline imager

AERONET:

AERosol RObotic NETwork

AHI:

Advanced Himawari Imager

ANN:

Artificial neural networks

AOD:

Aerosol optical depth

AVHRR:

Advanced very high-resolution radiometer

CALIOP:

Cloud-aerosol lidar with orthogonal polarization

CPCB:

Central pollution control board

CTM:

Chemical transport model

GAM:

Generalized additive model

GEOS:

Geostationary operational environment satellite

GOCI:

Geostationary ocean colour imager

GTWR:

Geographically and temporally weighted regression

GWR:

Geographically weighted regression

IR:

Infrared

LR:

Linear regression model

LUR:

Land use regression

MAIAC:

Multi-angle implementation of atmospheric correction

MEM:

Mixed-effect model

MISR:

Multi-angle imaging spectroradiometer

MLR:

Multiple linear regression

MODIS:

Moderate resolution imaging spectroradiometer

OLS:

Ordinary least squares

OMI:

Ozone monitoring instrument

PBLH:

Planetary boundary layer height

PM:

Particulate matter

POLDER:

Polarization of earth’s reflectance and directionality

RH:

Relative humidity

RS:

Remote sensing

SeaWiFS:

Sea-viewing wide field-of-view sensor

SVR:

Support vector regression

TSM:

Two-stage model

UV:

Ultraviolet

VIS:

Visible light

VIIRS:

Visible infrared imaging radiometer suite

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Acknowledgements

Authors sincerely thanks to all the anonymous reviewers and responsible editor for their insightful review and giving constructive comments which have certainly enhanced the overall quality of the manuscript. Authors also sincerely acknowledge the articles which have been used in this review paper.

Funding

This research was supported by the Council of Scientific and Industrial Research (CSIR), New Delhi, grant no. 24(0352)/18/EMR-II.

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Authors and Affiliations

  1. Department of Mining Engineering, National Institute of Technology, Rourkela, 769008, India

    Avinash Kumar Ranjan & A. K. Gorai

  2. Department of Mining Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    Aditya Kumar Patra

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  1. Avinash Kumar Ranjan
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  2. Aditya Kumar Patra
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Correspondence to A. K. Gorai.

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Responsible Editor: Chang-Keun Song.

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Ranjan, A.K., Patra, A. & Gorai, A.K. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges. Asia-Pacific J Atmos Sci 57, 679–699 (2021). https://doi.org/10.1007/s13143-020-00215-0

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  • DOI: https://doi.org/10.1007/s13143-020-00215-0

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