Spatiotemporal Prediction of PM2.5 in Taiwan Using iTransformer and Large-Scale Atmospheric Pressure Features

Air pollution has emerged as one of the most critical environmental health hazards globally. According to statistics from the World Health Organization (WHO) and the Global Burden of Disease Study, approximately 7 million premature deaths occur annually due to air pollution. Fine particulate matter (PM2.5), capable of penetrating deep into the lungs and entering the bloodstream, has been confirmed to be highly correlated with ischemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), and lung cancer. Given its serious threat to public health, establishing high-precision PM2.5 prediction models is critical for early warning systems and health protection.

Addressing the common issue of missing values in environmental monitoring data, this study proposes a data preprocessing framework that combines Principal Component Analysis (PCA) for feature dimensionality reduction with the GRU-D model for time-series imputation. Testing confirms that this method effectively reconstructs data features without causing excessive smoothing. In terms of predictive modeling, this study incorporates East Asian-scale atmospheric pressure field data as a key environmental variable to capture the impact of large-scale weather systems on local air pollution. The performance of three advanced deep learning models—LSTM+CNN, PatchTST, and iTransformer—is evaluated and compared.

The results indicate that, when considering multivariate factors and long- and short-term dependencies, the iTransformer model demonstrates superior predictive performance with an R² of 0.91, exhibiting exceptional non-linear feature extraction capabilities. In comparison, both the LSTM+CNN and PatchTST models achieved an R² of approximately 0.86. Based on the iTransformer's advantages in handling large-scale meteorological features and high-dimensional time-series data, this study employs it as the core model to further extend PM2.5 concentration predictions across Taiwan, aiming to provide a valuable scientific reference for regional air quality management.