Air quality analysis and PM_2.5 modelling using machine learning techniques: A study of Hyderabad city in India

Abstract

The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM_2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM_2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM_2.5 forecasting have received a lot of attention. In this work, a spatio-temporal analysis of air quality was first performed for Hyderabad, indicating that average PM_2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM_2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R² value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m³ and a Root Mean Square Error (RMSE) of 7.647 μg/m³, further confirming its accuracy in predicting PM_2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM_2.5 modelling models by having the lowest error and the highest R² value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models.

Keywords:

• air pollution
• seasonal variation
• forecasting
• PM2.5 modelling
• environment
• HGBoost regression

Acknowledgments

The authors express their gratitude to the editor and anonymous reviewers for their valuable and insightful feedback, which significantly enhanced the quality of this paper. Furthermore, the authors extend their thanks to the CPCB (Central Pollution Control Board) for providing the air pollution data via its online CAAQMS website, enabling the research and analysis presented in this study. We are also thankful to the editors and potential reviewers.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

On behalf of all authors, the corresponding author states that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions statement

Conceptualization and supervision: AM writing review and editing: AM, GPR, PRS, AKS and HGA; data curation and formal analysis: GPR, AM, AKS and PRS; evidence collection, review, and editing: GPR, AM, PRS, HGA, HA and AAA.

Availability of data and material

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.