Machine learning-based prediction of hazards fine PM2.5 concentrations: a case study of Delhi, India

Abstract

Abstract The air quality of many geographical locations has been deteriorating in the last decades. This deterioration in air quality has affected a very large number of people, and they have been diagnosed with asthma and other respiratory problems. Among various pollutants, PM2.5 is the major cause of numerous health-related problems. Predicting PM2.5 concentration levels using ML models of these dissolved particles might help residents and the government prepare a better prevention and safety plan that can eventually lower the risk factor. The present study is based on predicting the PM2.5 concentration of Delhi by applying various ML models based on meteorological features like wind speed, temperature, humidity, visibility, etc. For prediction of PM2.5, linear regression, decision tree regression, RF regression, KNN regression, and Lasso regression methods were employed in the present study. The model performance was assessed using various parameters, including MAE, MSE, RMSE, and R2 Score. In a comparative study of all regression models, linear regression demonstrated the most favorable outcomes. The model exhibited a superior fit to the data, evidenced by its lowest RMSE value (52.19), outperforming the random forest regression (RMSE = 94.75), K Nearest Neighbor regression (RMSE = 83.93), each of which yielded higher RMSE scores compared to linear regression. Lasso regression (RMSE = 65.20) and decision tree (RMSE = 68.22) also exhibited improved performance following linear regression. The findings of this study advocate for implementing strategies to enforce stringent emission regulations for both industrial operations and vehicular activities. Such measures are imperative for mitigating air pollution levels and subsequently curtailing its adverse impacts on public health within the region. Additionally, this study underscores the necessity for further research endeavours to explore future avenues, with the aim of garnering global attention towards addressing this pressing issue.

Keywords

• PM2.5
• Machine learning
• Prediction
• Multivariate analysis
• Delhi