Abstract
In order to classify the Telangana air pollution monitoring stations based on air pollution levels, this study aims to demonstrate the efficacy of hierarchical agglomerative cluster analysis (HACA). A multivariate technique called cluster analysis attempts to group similar items into one group by using a set of measured factors to categorise them. Monthly averages of PM10 and NOx were collected from Telangana State Pollution Control Board (TSPCB) website ranging from January 2016 to December 2019. HACA was successful in classifying monitoring stations into Low Pollution Source (LPS) region, Moderate Pollution Source (MPS) region and High Pollution Source (HPS) region based on the major pollutants PM10 and NOx. Out of 22 monitoring stations located at various districts of Telangana, 10 stations belong to HPS region with the average value of the AQI is 115 during the year, 8 stations belong to MPS region with the average value of the AQI is 82 during the year and 4 stations belong to LPS region with the average value of the AQI is 65 during the year. From the study, it can be stipulated that the application of HACA can disclose meaningful information on the spatial variability of a large and complex air quality data. The findings can aid the authorities concerned in applying different methods at various study sites such as implementing pollution control systems in industries, conducting awareness campaigns, staggered office and educational institutions operating hours.
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Acknowledgements
The authors would like to thank the management of Vasavi College of Engineering, Hyderabad, India. The authors acknowledge the state government of Telangana for providing online data in the website of TSPCB in the public domain.
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Vasudha, N., Rao, P.V. (2023). Telangana Air Pollution Stations Classification Using HACA. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_9
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DOI: https://doi.org/10.1007/978-981-99-2746-3_9
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