Identification of Sources Causing Air Pollution in Indian Cities Using Hierarchical Agglomerative Cluster Analysis

Nannaparaju Vasudha, Polisetty Venkateswara Rao
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Abstract

The distressing levels of air pollution in India is becoming health hazard to the inhabitants. It's important to note that due to the nation's continued urbanisation and its heavy reliance on coal for electricity generation, air pollution is expected to get worse in some areas of India over the next few decades. Present study aims to address the issue by identifying the sources causing air pollution using Hierarchical Agglomerative Cluster Analysis [HACA]. Two years daily data (2018 and 2019), downloaded from publicly available source Kaggle.com of sixteen selected air pollution monitoring stations was used for the study. The stations were selected based upon diversified environmental conditions and local sources. HACA was successful in grouping the monitoring stations into four clusters based on their average Air Quality Index (AQI) level. These four clusters are named as Low Pollution, Moderate Pollution, High Pollution and Very High Pollution Region [LPR, MPR, HPR and VHPR] with average AQI 96; 135; 173 and 227 respectively. Discriminant Analysis (DA) confirmed the resulting clusters with 100% accuracy. It was found that stations with similar environmental factors, regional sources, and pollution amounts were clustered together. Despite numerous actions taken by the authorities to reduce air pollution, it was noticed that topographical conditions play an essential role in the rise of pollution. This study helps to implement different strategies by the authorities’ concern based on local sources and topographical conditions.
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用层次聚类分析识别印度城市空气污染源
印度令人痛心的空气污染程度正在对居民的健康造成危害。值得注意的是,由于印度持续的城市化和对煤炭发电的严重依赖,预计未来几十年印度一些地区的空气污染将变得更严重。本研究旨在通过使用层次聚集聚类分析[HACA]确定空气污染的来源来解决这个问题。该研究使用了从公开来源Kaggle.com下载的16个选定空气污染监测站的两年每日数据(2018年和2019年)。这些站点是根据不同的环境条件和当地资源选择的。HACA成功地根据空气质量指数的平均水平将监测站分为四组。这四个区域被命名为低污染、中度污染、高污染和极高污染区域[LPR、MPR、HPR和VHPR],平均AQI为96;135;分别是173和227。判别分析(DA)以100%的准确率确认了所得聚类。结果表明,具有相似环境因子、区域来源和污染量的站点聚集在一起。尽管当局采取了许多措施来减少空气污染,但人们注意到,地形条件在污染加剧中起着至关重要的作用。这项研究有助于当局根据当地资源和地形条件实施不同的策略。
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