Estimation of surface PM2.5 with MODIS Aerosol optical depth and source identification using trajectory analysis: A case of Hyderabad City, India

Pub Date : 2022-10-07 DOI:10.3126/ije.v11i2.44538
Selvetikar Ashok, M. Sekhar, D. R. B. Reddy
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Abstract

Satellite measurements are important for quantifying the ground observations and atmosphere columnar properties like Aerosol Optical Depth (AOD) especially in developing countries like India. In this study Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval’s AOD product has been used having 3 km and 10 km spatial resolution from terra and aqua satellites, The MODIS AOD data and meteorological parameters from May 2017 to May 2019 were used. The Multiple linear regression method is implemented in this study. The study concluded that there is a good agreement in the prediction of PM2.5 at Zoopark location, whereas in other monitoring locations the agreement between AOD and measured PM2.5 is relatively poor. The particulate matter (PM) concentrations are influenced by the local source regions and the long-range transport of pollutant through the wind, whereas the source regions identified based on the Potential Source Contribution Function (PSCF), Concentration-Weighted Trajectory (CWT) and Cluster analysis indicate the dominant source regions. Results indicate that the Central India and East Indian regions are more dominating source regions at Hyderabad location in the winter season. It was found that the lower altitude layer showed the major source of local regions nearby receptor. The cluster analysis indicates that the high intensity from the East Indian regions. This paper not only demonstrates reasonable prediction accuracy but also provides the model uncertainties which lays foundation for further study.
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基于轨迹分析的MODIS气溶胶光学深度估算和地表PM2.5源识别——以印度海得拉巴市为例
卫星测量对于量化地面观测和大气柱状特性(如气溶胶光学深度(AOD))非常重要,特别是在印度等发展中国家。本研究使用中分辨率成像光谱仪(MODIS)反演的陆地和海洋卫星3 km和10 km空间分辨率的AOD产品,使用2017年5月至2019年5月的MODIS AOD数据和气象参数。本研究采用多元线性回归方法。研究结果表明,在Zoopark地点对PM2.5的预测具有较好的一致性,而在其他监测地点AOD与实测PM2.5的一致性相对较差。颗粒物(PM)浓度受局地源区和污染物长距离风输运的影响,而基于潜在源贡献函数(PSCF)、浓度加权轨迹(CWT)和聚类分析确定的源区是主导源区。结果表明,冬季海得拉巴地区以印度中部和东印度地区为主。发现低空层是局部区域附近受体的主要来源。聚类分析表明,高强度来自东印度地区。本文不仅证明了合理的预测精度,而且提供了模型的不确定性,为进一步的研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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