Phy-APMR: A physics-informed air pollution map reconstruction approach with mobile crowd-sensing for fine-grained measurement

Abstract

Fine-grained air pollution map reconstruction is critical for urban pollution management and healthy building construction. Recent advancements in air pollution measurement, such as mobile sensing platforms that equip sensors on urban vehicles (e.g., taxis, buses), have greatly enhanced data collection for pollution analysis. However, two major challenges remain: (1) the uncontrolled mobility of vehicles leads to data sparsity in certain areas and times, reducing the effectiveness of data-driven reconstruction methods, and (2) training these methods is often time-consuming, hindering frequent updates needed for improved accuracy. In this paper, we propose Phy-APMR, i.e., a fine-grained physics-informed air pollution map reconstruction approach, which combines the advantages of a physical air pollution propagation model and a deep-learning model to mitigate the data sparsity issue. In addition, to make our method feasible for a high-frequency updating strategy to further improve the reconstruction accuracy, we propose ASUS (adaptive short-time update sampling), a novel collocation point sampling algorithm, speeding up the convergence of the training for Phy-APMR. Experiments are conducted in three cities, showing that Phy-APMR surpasses the state-of-the-art by 15% in reconstruction accuracy and 84% in convergence efficiency.