Comparing multiple machine learning models to investigate the relationship between urban morphology and PM2.5 based on mobile monitoring

Abstract

The development of compact cities has deteriorated the air quality and threatened public health. To improve air quality, it is necessary to systematically analyze the complex relationship between urban morphology and air pollution to propose spatial optimization strategies. This study selected Nanshan District in Shenzhen, China as a case study, and used the street-level exposure to PM2.5 obtained from mobile monitoring as the dependent variable. Nine urban morphology parameters extracted from 3D building dataset and street view images, as well as other environmental parameters, were used as independent variables. A multiple linear regression model and six machine learning models were constructed for investigating the optimal one. For the study area, the results indicated that the RF and the XGBoost were the best-fit models with the R2 of 0.95 and 0.96 respectively. The frontal area index at 20 m height, the standard deviation of volume obstruction ratio, the building coverage ratio at 20 m height and the standard deviation of building height are the key urban form factors influencing PM2.5. In urban renewal and design practices, it is recommended to maintain the frontal area index within the range of 0.10–0.35, the standard deviation of volume obstruction ratio within the range of 4 × 10−5–8 × 10−5, and the standard deviation of building height below 48 m or above 64 m within the 1000 m buffer zone. These findings provide a generalizable analytical framework for understanding PM2.5 dispersion, and the differentiated applicability of models and parameters is recommended to be considered for different cities.