Short-Term PM2.5 Concentration Changes Prediction: A Comparison of Meteorological and Historical Data

Abstract

Machine learning is being extensively employed in the prediction of PM2.5 concentrations. This study aims to compare the prediction accuracy of machine learning models for short-term PM2.5 concentration changes and to find a universal and robust model for both hourly and daily time scales. Five commonly used machine learning models were constructed, along with a stacking model consisting of Multivariable Linear Regression (MLR) as the meta-learner and the ensemble of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as the base learner models. The meteorological datasets and historical PM2.5 concentration data with meteorological datasets were preprocessed and used to evaluate the model’s accuracy and stability across different time scales, including hourly and daily, using the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE). The results show that historical PM2.5 concentration data are crucial for the prediction precision of the machine learning models. Specifically, on the meteorological datasets, the stacking model, XGboost, and RF had better performance for hourly prediction, and the stacking model, XGboost and LightGBM had better performance for daily prediction. On the historical PM2.5 concentration data with meteorological datasets, the stacking model, LightGBM, and XGboost had better performance for hourly and daily datasets. Consequently, the stacking model outperformed individual models, with the XGBoost model being the best individual model to predict the PM2.5 concentration based on meteorological data, and the LightGBM model being the best individual model to predict the PM2.5 concentration using historical PM2.5 data with meteorological datasets.