Fine Simulation and Analysis of Temporal and Spatial Characteristics of PM2.5 Concentration Distribution in Different Urban Scenarios based on Mobile Monitoring Data

The distribution of PM2.5 concentration has obvious spatial heterogeneity in the inner city. However, traditional analysis methods based on remote sensing data or monitoring station data are difficult to reveal the distribution characteristics of PM2.5 concentration in the inner city at high spatial-temporal resolution, and there is also a lack of analysis on the complex nonlinear effect of urban scenes (e.g., roads, industrial areas, residential areas, etc.) on PM2.5 concentration. In this study, we installed the mobile monitoring sensor on the express van to collect PM2.5 concentration in different urban scenes in the south of Fuzhou main urban area. The PM2.5 simulation and scene analysis model based GWR-GBDT method was proposed by fusing Geographical Weighted Regression (GWR) and Gradient Boosting Decision Tree (GBDT). The model can fit the nonlinear relationship between meteorological factors, scene factors, and PM2.5 concentration, and enhance the fine-scale monitoring ability of PM2.5 pollution in city. Combined with partial dependency plot, the nonlinear effect of different urban scenes on PM2.5 concentration in different periods was analyzed to provide support for urban PM2.5 pollution control. The results show that: (1) Based on the mobile PM2.5 concentration monitoring data, the GWR-GBDT model can well simulate the nonlinear relationship between urban scene factors, meteorological factors, and PM2.5 concentration, and simulate the fine spatial distribution of PM2.5 concentration. The results of cross-validation R2 was between 0.52 and 0.94; (2) The heterogeneity of the response of the same scene to PM2.5 concentration in different time periods was analyzed by the partial dependence plots, and we found that the effect of various scenes on PM2.5 concentration was different; (3) By analyzing the interaction of human activities and urban scenes on PM2.5 concentration in different periods, we found that the effect of urban scenes on PM2.5 concentration was related to human commuting between schools, hospital, and residential areas. As the high pollution scene, construction site can effectively reduce PM2.5 pollution in several hours after taking watering measures. In the park and sports service area, PM2.5 concentration was low in most periods. For industrial area and roads, PM2.5 concentration was high in most periods; (4) For the spatial distribution of PM2.5 concentration, PM2.5 concentration in the south of Fuzhou main urban area presented a general trend of high pollution in the southeast and low pollution in the northwest. The proportion of slightly polluted areas in construction sites, roads, and industrial areas was significantly higher than that in other scenes. The overall PM2.5 concentration in the park scene was low, however, the park in mountains was affected by the surrounding industrial areas at nightfall, resulting in increased PM2.5 concentration. The urban outer waters have mitigation effect on PM2.5 concentration around them. This study can provide support for fine-scale PM2.5 pollution treatment, urban planning, and PM2.5 pollution exposure risk prevention of high-risk groups such as the elderly and children in different scenarios. © 2022, Science Press. All right reserved.