Effect of urban morphology on air pollution distribution in high-density urban blocks based on mobile monitoring and machine learning

It is essential to investigate the morphological factors that contribute to air pollution's spatial distribution using mobile monitoring data, and to regulate them at the urban planning level. However, mobile monitoring data are unstable and more difficult to model under real-world atmospheric circumstances. This work assesses the nonlinear relationship between spatial distribution of air pollutants and building morphological indicators in a high-density city based on mobile monitoring and machine learning. By conducting a vehicle-mounted mobile monitoring experiment, we establish spatial distribution data sets for PM2.5 and PM10 on three typical regions in Huangpu District, Shanghai. 9 indicators of urban morphology are derived, including green view index and sky view factor, using semantic segmentation and deep learning on street-view images. Correlation analysis demonstrates that the difficulty lies in implementing linear modeling methods. The performances of six machine learning algorithms for predicting the spatial variability of pollutants are compared. The result shows that neural networks have the highest performance for repidly predicting pollutant diffusion levels in conceptual designs.

Automated summary

This study investigates the nonlinear relationship between building morphological indicators and the spatial distribution of air pollutants in a high-density city using mobile monitoring data and machine learning. The results show that neural networks perform the best in rapidly predicting pollutant diffusion levels.