The fine-scale variation in pollution, especially at the street level, is complex and requires further exploration. Here, the influence of urban form on the street-level air pollution distribution was comprehensively assessed according to the urban factors, high-resolution meteorological data, PM2.5 and CO concentration data collected via mobile monitoring along roads. Furthermore, potential urban factors, including the land-use and urban form characteristics, were obtained from geographic information system. Both linear regression and gradient boosting decision tree (GBDT) models were developed to explore the relationship between the observed concentrations and the predictor variables. The modeling results demonstrate that the GBDT model, which captured the non-linear relationship, helps to better explain more of the variations in the pollutant concentrations than the linear model. This study provides insights into machine learning models for pollution prediction and demonstrate the important relationship between urban form and street-level pollutants. Thus, quantitatively demonstrating the impact of urban form on the PM2.5 and CO concentrations can help decision-makers with urban planning and management.
References can be traced here. https://www.sciencedirect.com/science/article/abs/pii/S036013232100665X?via%3Dihub