Leveraging machine learning to analyze and forecast air quality trends in Kota City, India

Aug 4, 2026

Air quality serves as a decisive indicator of environmental health, shaping human well-being and ecological stability. Rapid urbanization and industrialization have intensified air pollution, requiring dependable monitoring and forecasting systems. This study analyzes air quality trends in Kota city, Rajasthan, using 2017–2023 data and machine learning models (LR, RF, DT, SVR, KNN) to predict AQI from key pollutants and meteorological variables, including humidity, wind speed/direction, and barometric pressure. Decision tree regressor shows near-perfect fit with R² 0.9999 (train) and 0.9991 (test), though it risks overfitting. Random forest regressor balances accuracy and robustness with R² 0.9831, supporting reliable predictions. Model evaluation and pollutant contribution analysis support policy-oriented decisions for air quality management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

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Article info

Abstract

Keywords

Introduction

Policy context and city background

Motivation for ML/AI in air quality forecasting

Related machine learning and deep learning approaches

published by EQA-International Journal of Environmental Quality ISSN 2281-4485-Vol. 71 (2026): 19-28 Journal homepage: [https://eqa.unibo.it/](https://eqa.unibo.it/) | Leveraging machine learning to analyze and forecast air quality trends in Kota City, India
Monika Sharma, Mahendra Pratap Choudhary*, Anil K. Mathur
Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan), India
*[Corresponding author E.mail: ](Corresponding author E.mail: mpchoudhary@rtu.ac.in)[mpchoudhary@rtu.ac.in](Corresponding author E.mail: mpchoudhary@rtu.ac.in) | | --- | | Article info
Received 25/5/2025; received in revised form 21/8/2025; accepted 10/9/2025
DOI: 10.60923/issn.2281-4485/21975
© 2026The Authors.
Abstract
Air quality is a critical indicator of environmental health, directly impacting human well-being and ecological stability. Rapid urbanization and industrialization have recently exacerbated air pollution, necessitating robust monitoring and predictive frameworks. This study investigates air quality trends in Kota city of Rajasthan, India and using data from 2017 to 2023 . Machine learning models, including linear regression (LR), random forest (RF), decision tree (DT), support vector regressor (SVR), and K-nearest neighbors (KNN), were employed to analyze predict air quality index (AQI) values based on key pollutants such as PM2.5, PM10, NO, NO2, NOx, NH3, SO2, CO, Ozone, Benzene, Ethyl-Benzene, m & p-Xylene considering the effects of meteorological factors like relative humidity (RH), wind speed (WS), wind directions (WD), and barometric pressure (BP) . Among these, the decision tree regressor shows almost perfect fit on the training set (R2 score 0.9999) and excellent test performance (R2 score 0.9991), suggesting a very accurate prediction model. However, it exhibits potential overfitting, limiting its generalization capabilities. On the other hand, the random forest regressor provides a balance of accuracy and robustness, achieving an R² score of 0.9831, making it the preferred model for reliable predictions. The study delves into pollutant contributions, evaluates model performances, and explores actionable insights for policymakers. By leveraging machine learning approaches, the study aims to provide a comprehensive framework for analyzing air quality trends and supporting decision-making processes.
Keywords: Air quality index, Machine learning models, Exploratory data analysis, NCAP | Introduction Air pollution remains one of the most pressing global challenges, with profound implications for human health, ecosystems, and the climate. According to the World Health Organization, ambient air pollution is responsible for approximately 4.2 million premature deaths annually (WHO, 2018) . This stark statistic underscores the urgent need for effective air quality management strategies across the globe. In India, the introduction of the National Clean Air Programme (NCAP) in 2019 marked a significant step toward tackling urban air pollution, setting ambitious goals to reduce particulate matter (PM10 and PM2.5) concentrations by 20-30% by 2024 compared to 2017 levels (NCAP, 2019; Sharma et al., 2024) . The city of Kota is included in this program, where rapid urbanization, industrial expansion, and vehicular emissions have led to concerning air quality trends. Kota, a growing industrial and educational hub in Rajasthan, epitomizes the challenges faced by rapidly urbanizing cities in India. Industrial activities, construction projects, and a surge in vehicular traffic have contributed to elevated levels of pollutants such as PM10, PM2.5, and nitrogen dioxide (NO2) . As the city grapples with these challenges, leveraging advanced technologies, particularly machine learning (ML) and artificial intelligence (AI), offers promising solutions for monitoring and mitigating air pollution. Recent studies have highlighted the transformative potential of ML and AI in air quality monitorin

FAQs

Which machine learning models are used to analyze and forecast air quality in Kota City?

The study employs linear regression, random forest, decision tree, support vector regressor, and K-nearest neighbors, using pollutants and meteorological variables to predict AQI values.

How does the decision tree model perform compared with the random forest model?

The decision tree regressor achieves R² of 0.9999 on training data and 0.9991 on test data, indicating very accurate prediction but potential overfitting. The random forest regressor provides a robustness balance with an R² of 0.9831, making it the preferred model.

What pollutants and meteorological factors drive the AQI predictions?

Predictions consider pollutants including PM2.5, PM10, NO, NO2, NOx, NH3, SO2, CO, ozone, and selected VOCs, alongside meteorological factors such as relative humidity, wind speed, wind direction, and barometric pressure.

Aug 4, 2026

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