Vansh Mathur, Shalavya Agrawal, Naina Agrawal, Udit Pandey, Parth Kedawat | International Journal of Distributed Computing and Technology | Vol 11, Issue 01 | pp. 31-42 | ISSN: 2455-7307
Abstract
This paper presents a comparative study of Air Quality Index (AQI) levels in five eastern Indian states–Arunachal Pradesh, Assam, Meghalaya, Nagaland, and Tripura. Using historical data, the study explores the concentration of key pollutants, including PM2.5, PM10, NO2, SO2, CO, and Ozone, highlighting regional variations in air quality. Assam, with its higher levels of urbanization and industrial activity, shows elevated AQI levels, especially for pollutants like PM10 and NO2. In contrast, Arunachal Pradesh and Nagaland, with their rural and forested landscapes, exhibit lower AQI levels, indicating better air quality. The study utilizes AdaBoost (Adaptive Boosting) and XGBoost (Extreme Gradient Boosting) models to predict future AQI trends, providing valuable insights for policymakers to anticipate air quality changes. Monthly and yearly comparisons of pollutant levels reveal significant temporal variations, with some states experiencing seasonal spikes in pollutants, particularly in winter. Through detailed graphs and AdaBoost/XGBoost-based predictions, the study emphasizes the impact of urbanization, industrialization, and geographical factors on air quality, offering crucial data for environmental policy and public health initiatives.
Keywords: Air Quality Index (AQI), AdaBoost, CO, eastern states of India, graphical abstract, machine learning, NO2, Ozone, PM2.5, PM10, pollution, SO2, XGBoost
Keywords
Machine learning, Pollution, XGBoost, AdaBoost, Air Quality Index (AQI), CO, Ozone, graphical abstract, eastern states of India, PM2.5, SO2, NO2, PM10
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- Alrashed S. Key performance indicators for Smart Campus and Microgrid. Sustain Cities Soc. 2020 Sep 1;60:102264.
- Iskandaryan D, Ramos F, Trilles S. Air quality prediction in smart cities using machine learning technologies based on sensor data: A review. Appl Sci. 2020 Apr 1;10(7):2401.
- Zhang Y, Xue W, Long R, Yang H, Wei W. Acetochlor affects zebrafish ovarian development by producing estrogen effects and inducing oxidative stress. Environ Sci Pollut Res. 2020 Aug;27:27688–96.
- Chowdhury S, Dey S. Cause-specific premature death from ambient PM2. 5 exposure in India: Estimate adjusted for baseline mortality. Environ Int. 2016 May1;91:283–90.
- Guttikunda SK, Jawahar P. Atmospheric emissions and pollution from the coal-fired thermal power plants in India. Atmos Environ. 2014 Aug 1;92:449–60.
- Mahato S, Pal S, Ghosh KG. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci Total Environ. 2020 Aug 15;730:139086.
- Ramsey NR, Klein PM, Moore III B. The impact of meteorological parameters on urban air quality. Atmos Environ. 2014 Apr 1;86:58–67.
- Dawar I, Singal M, Singh V, Lamba S, Jain S. Air Quality Prediction Using Machine Learning Models: A Predictive Study in the Himalayan City of Rishikesh. SN Comput Sci. 2024 Dec;5(8):1–6.
- Sothea K, Oanh NT. Characterization of emissions from diesel backup generators in Cambodia. Atmos Pollut Res. 2019 Mar 1;10(2):345–54.
- Baklanov A, Zhang Y. Advances in air quality modeling and forecasting. Global Transitions. 2020 Jan 1;2:261–70.
- Hardini M, Sunarjo RA, Asfi M, Chakim MH, Sanjaya YP. Predicting air quality index using ensemble machine learning. ADI J Recent Innov. 2023Aug 22;5(1Sp):78–86.
- Ren L, Song C, Wu W, Guo M, Zhou X. Reservoir effects on the variations of the water temperature in the upper Yellow River, China, using principal component analysis. J Environ Manage. 2020 May 15;262:110339.
- Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, et al. Top 10 algorithms in data mining. Knowl Inf Syst. 2008Jan;14:1–37.
- Zheng Y, Liu F, Hsieh HP. U-air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD Int Conf Knowl Discov Data Min. 2013 Aug 11:1436–1444.
- Li C, Li Y, Bao Y. Research on air quality prediction based on machine learning. In 2021 2nd Int Conf Intell Comput Hum-Comput Interact. 2021 Nov 17:77–81.
- Breiman L. Random forests. Machine Learning. 2001;45:5–32.
- Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm Sigkdd Int Conf Knowl Discov Data Min. 2016 Aug 13:785–794.
- Decroos T, Van Haaren J, Davis J. Automatic discovery of tactics in spatio-temporal soccer match data. In Proceedings of the 24th acm Sigkdd Int Conf Knowl Discov & Data Min. 2018 Jul 19:223–232.
- Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997Aug1;55(1):119–39.
- Solomos S, Bougiatioti A, Soupiona O, Papayannis A, Mylonaki M, Papanikolaou C, et al. Effects of regional and local atmospheric dynamics on the aerosol and CCN load over Athens. Atmos Environ. 2019 Jan 15;197:53–65.
How to cite this article
@article{MathurV2024,
author = {Vansh Mathur and Shalavya Agrawal and Naina Agrawal and Udit Pandey and Parth Kedawat},
title = {Comparative Analysis of Air Quality Indices Across Five Eastern States of India Using ML Models and Pollutant Based Insights},
journal = {International Journal of Distributed Computing and Technology},
year = {2024},
volume = {11},
number = {01},
pages = {31--42},
issn = {2455-7307},
url = {https://journalspub.com/publication/ijdct/article=13141}
}