CTDI: CNN-Transformer-Based Spatial-Temporal Missing Air Pollution Data Imputation

Abstract

Accurate and comprehensive air pollution data is essential for understanding and addressing environmental challenges. Missing data can impair accurate analysis and decision-making. This study presents a novel approach, named CNN-Transformer-based Spatial-Temporal Data Imputation (CTDI), for imputing missing air pollution data. Data pre-processing incorporates observed air pollution data and related urban data to produce 24-hour period tensors as input samples. 1-by-1 CNN layers capture the interaction between different types of input data. Deep learning transformer architecture is employed in a spatial-temporal (S-T) transformer module to capture long-range dependencies and extract complex relationships in both spatial and temporal dimensions. Hong Kong air pollution data is statistically analyzed and used to evaluate CTDI in its recovery of generated and actual patterns of missing data. Experimental results show that CTDI consistently outperforms existing imputation methods across all evaluated scenarios, including cases with higher rates of missing data, thereby demonstrating its robustness and effectiveness in enhancing air quality monitoring. Additionally, ablation experiments reveal that each component significantly contributes to the model's performance, with the temporal transformer proving particularly crucial under varying rates of missing data.