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Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

Lab of Environmental Remote Sensing (LERS), School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Geological Survey of Jiangsu Province, Nanjing 210018, China
Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Department of Electrical and Electronic Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh
Author to whom correspondence should be addressed.
These authors with equal contributions.
Academic Editor: Maria João Costa
Remote Sens. 2021, 13(14), 2842;
Received: 15 June 2021 / Revised: 12 July 2021 / Accepted: 14 July 2021 / Published: 20 July 2021
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 temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD. View Full-Text
Keywords: aerosol; AERONET; MODIS; MAIAC; AOD; trend aerosol; AERONET; MODIS; MAIAC; AOD; trend
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MDPI and ACS Style

Wang, Y.; Ali, M.A.; Bilal, M.; Qiu, Z.; Ke, S.; Almazroui, M.; Islam, M.M.; Zhang, Y. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China. Remote Sens. 2021, 13, 2842.

AMA Style

Wang Y, Ali MA, Bilal M, Qiu Z, Ke S, Almazroui M, Islam MM, Zhang Y. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China. Remote Sensing. 2021; 13(14):2842.

Chicago/Turabian Style

Wang, Yu, Md. A. Ali, Muhammad Bilal, Zhongfeng Qiu, Song Ke, Mansour Almazroui, Md. M. Islam, and Yuanzhi Zhang. 2021. "Identification of Aerosol Pollution Hotspots in Jiangsu Province of China" Remote Sensing 13, no. 14: 2842.

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