Predicting Concentrations of PM2.5, PM10, CO, VOC, and NOx on the Urban Scale Using Machine Learning-Based Surrogate Models

This work addresses the issue of estimating air pollution maps for urban areas. Spatially dense maps of air pollution can be calculated using physical models, such as ADMS-Urban; however, due to the high computational cost of such models, maps are verified with low temporal resolution (such as monthly or yearly averages). We investigate the feasibility of using machine learning models to predict air pollution maps based on historical data and current measurements from a limited number of monitoring stations. The models are trained on spatially dense pollution maps generated by physical models, along with corresponding measurements from monitoring stations and selected meteorological data. We evaluate the performance of the models using real-world data from a central district in Wrocław, Poland, considering various pollutants such as PM2.5, PM10, CO, VOC, and NOx, presented on spatially dense pollution maps with ca. 2×105 points with a 10 × 10 m grid. The results demonstrate that the proposed method can effectively predict air pollution maps with high spatial resolution and a fast inference time, making it suitable for generating pollution maps with significantly higher temporal resolution (e.g., hourly) compared to physical models. We also experimentally demonstrated that PM10, CO, and VOC pollution models can be built based on measurements from PM2.5 monitoring stations only with similar, and in the case of CO, higher, accuracy than using measurements from PM10, CO, and VOC monitoring stations, respectively.