Principal component-based predictive modelling of particulate matters using air pollutants and meteorological variables in Bangkok, Thailand

Urban air pollution remains a critical global issue, particularly fine particulate matter (PM), which has direct and significant impacts on human health. Understanding PM patterns is challenging due to the large number of associated variables, many of which are highly correlated and must lead to multicollinearity issues in predictive modeling. To address this problem, this study applies Principal Component Analysis (PCA) as a dimensionality reduction technique and develops forecasting models for PM2.5 and PM10 concentrations. The used air pollution data were collected from 4 monitoring stations in Bangkok, Thailand. Prior to analysis, the datasets were preprocessed through imputation, transformation, and standardization to ensure consistency across variables. PCA results identified 4 principal components for each station based on cumulative variance and eigenvalue criteria. Among these, relative humidity (RH) and zonal wind direction (ZWD) consistently contributed most to the principal components. The selected components were then used as predictors in 4 machine learning models, and their performance was compared with models using all original variables. Model evaluation was conducted using mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE). The results indicate that models based on reduced predictors consistently outperform those using the full set of variables across all stations. These findings offer an alternate effective approach for addressing multicollinearity and improving model performance in urban air pollution forecasting.