Multi-dimensional distribution prediction of PM2.5 concentration in urban residential areas based on CNN

Air pollution has been one of the major environmental health issues in urban residential areas. Enhancing the air quality of the human environment has become a relevant research topic, but the study of the distribution characteristics of air pollutants under the influence of multi-dimensional indicators of residential building design is rarely considered. Herein, the PM2.5 spatial distribution of residential environment in Hefei, China, was simulated using ENVI-met. This research explored the influence mechanism of different design indexes on PM2.5 in the residential environment. A fast prediction method was proposed for PM2.5 based on the convolutional neural network (CNN). Furthermore, 4183 test points in the residential environment were selected to explore the influence of design indexes on PM2.5. The morphological features around the test points were transform to the images as input datasets for CNN model training. Results indicated that the PM2.5 mass concentration in the residential area decreases with the increase in height, and it is correlated with the sky viewfactor, the average height of the building and the density of the building. The accuracy of the CNN prediction model exceeds that of the ANN prediction model by 14.2 %, and the results can effectively predict the multi-dimensional distribution of the PM2.5 mass concentration in the residential environment, which could provide an effective reference and analytical tool for the creation of a healthy environment in residential areas.