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Spatiotemporal characteristics of aerosols and their trends over mainland China with the recent Collection 6 MODIS and OMI satellite datasets

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Abstract

With the rapid development of China’s economy and high rate of industrialization, environmental pollution has become a major challenge for the country. The present study is aimed at analyzing spatiotemporal heterogeneities and changes in trends of different aerosol optical properties observed over China. To achieve this, Collection 6 Level 3 data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS; 2002–2016) and Ozone Monitoring Instrument (OMI; 2005–2016) sensors were used to investigate aerosol optical depth (AOD550), Ångstrӧm exponent (AE470–660), and Absorption Aerosol Index (AAI). The spatial distribution of annual mean AOD550 was noticed to be high over economically and industrialized regions of the east, south, and northeast of China, while low aerosol loadings were located over rural and less-developed areas of the west and northeast of China. High AE470–660 (> 1.0) values were characterized by the abundance of fine-mode particles and vice versa, likely attributed to large anthropogenic activities. Similarly, high AOD with corresponding high AE and low AAI was characterized over the urban-industrialized regions of the central, east, and south of China during most of the months, being more pronounced in June and July. On seasonal scale, AOD values were found to be high during spring, followed by the summer and autumn, and low during the winter season. It is also evident that all aerosol parameters showed a single-peak frequency distribution in all seasons over entire China. Further, the annual, monthly, and seasonal spatial trends revealed a decreasing trend in AOD over most regions of China, except in the southwest of China, which showed a positive increasing trend. Significant increasing trends were noted in AAI for all the seasons, particularly during autumn and winter, resulting in a large amount of the absorbing type of aerosols produced from biomass burning and desert dust.

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Acknowledgements

The MODIS data used in this study was produced and provided by the Giovanni online data system and developed and maintained by the NASA. The authors would like to acknowledge Prof. Dr. Gerhard Lammel, the Editor-in-Chief of the journal, and the two anonymous reviewers for their helpful comments and constructive suggestions towards the improvement of an earlier version of the manuscript.

Funding

This work was financially supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20140996), the National Natural Science Foundation of China (Grant No. 91644224), the Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, NUIST (Grant No. KDW1404), and the Central Public-Interest Scientific Institution Basal Research Fund (Grant No. 2015IAE-CMA04).

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Authors and Affiliations

  1. Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disasters, Ministry of Education (KLME), International Joint Laboratory on Climate and Environment Change (ILCEC), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China

    Kang Hu, Kanike Raghavendra Kumar, Na Kang & Richard Boiyo

  2. Department of Physical Sciences, Meru University of Science and Technology, P.O. Box 972-60200, Meru, Kenya

    Richard Boiyo

  3. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, Liaoning, 110016, China

    Jinwen Wu

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  1. Kang Hu
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  2. Kanike Raghavendra Kumar
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Correspondence to Kanike Raghavendra Kumar.

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Responsible editor: Gerhard Lammel

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Hu, K., Kumar, K.R., Kang, N. et al. Spatiotemporal characteristics of aerosols and their trends over mainland China with the recent Collection 6 MODIS and OMI satellite datasets. Environ Sci Pollut Res 25, 6909–6927 (2018). https://doi.org/10.1007/s11356-017-0715-6

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