Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

TLDR

Air pollution is a major environmental issue, and forecasting air quality is crucial for warning and controlling pollution. The study aims to build SVR and RFR regression models to predict Beijing’s AQI and an Italian city’s NOX concentration using publicly available datasets. Model performance was assessed using RMSE, correlation coefficient, and coefficient of determination. SVR achieved superior AQI prediction (RMSE = 7.666, R² = 0.9776, r = 0.9887), while RFR yielded better NOX prediction (RMSE = 83.6716, R² = 0.8401, r = 0.9180), demonstrating that machine‑learning approaches effectively address air‑quality prediction challenges.

Abstract

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.