Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIA...

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Published in:Remote Sensing
Main Authors: Yu Wang, Md. Arfan Ali, Muhammad Bilal, Zhongfeng Qiu, Song Ke, Mansour Almazroui, Md. Monirul Islam, Yuanzhi Zhang
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
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Online Access:https://doi.org/10.3390/rs13142842
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author Yu Wang
Md. Arfan Ali
Muhammad Bilal
Zhongfeng Qiu
Song Ke
Mansour Almazroui
Md. Monirul Islam
Yuanzhi Zhang
author_facet Yu Wang
Md. Arfan Ali
Muhammad Bilal
Zhongfeng Qiu
Song Ke
Mansour Almazroui
Md. Monirul Islam
Yuanzhi Zhang
author_sort Yu Wang
collection MDPI Open Access Publishing
container_issue 14
container_start_page 2842
container_title Remote Sensing
container_volume 13
description Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface ...
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spelling ftmdpi:oai:mdpi.com:/2072-4292/13/14/2842/ 2025-05-18T13:52:20+00:00 Identification of Aerosol Pollution Hotspots in Jiangsu Province of China Yu Wang Md. Arfan Ali Muhammad Bilal Zhongfeng Qiu Song Ke Mansour Almazroui Md. Monirul Islam Yuanzhi Zhang agris 2021-07-20 application/pdf https://doi.org/10.3390/rs13142842 eng eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13142842 https://creativecommons.org/licenses/by/4.0/ Remote Sensing Volume 13 Issue 14 Pages: 2842 aerosol AERONET MODIS MAIAC AOD trend Text 2021 ftmdpi https://doi.org/10.3390/rs13142842 2025-04-22T00:41:02Z Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface ... Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 13 14 2842
spellingShingle aerosol
AERONET
MODIS
MAIAC
AOD
trend
Yu Wang
Md. Arfan Ali
Muhammad Bilal
Zhongfeng Qiu
Song Ke
Mansour Almazroui
Md. Monirul Islam
Yuanzhi Zhang
Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title_full Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title_fullStr Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title_full_unstemmed Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title_short Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
title_sort identification of aerosol pollution hotspots in jiangsu province of china
topic aerosol
AERONET
MODIS
MAIAC
AOD
trend
topic_facet aerosol
AERONET
MODIS
MAIAC
AOD
trend
url https://doi.org/10.3390/rs13142842