Assessing neighborhood variations in ozone and PM2.5 concentrations using decision tree method

Typical air pollution events involving ozone (O-3) and PM(2.5 )occurred frequently in China, while the fine-scale pollution variation, especially at a neighborhood level (2 km*2 km), is complex and still not clear. To assess how urban form and meteorology influence neighborhood air pollution distribution, this study took the Minhang district in Shanghai, as experimental cases, and performed a neighborhood-scale investigation on O-3 and PM(2.5 )( )by using mobile measurements. Both land-use regression model and decision tree model were used to examine the relationship between air pollutant concentration and influenced variables. As the decision tree model captured the linear and non-linear relationship between variables, it was demonstrated that explained more variations of O-3 and PM(2.5 )concentrations than the LUR model. The results also showed that O-3 concentrations were mainly affected by meteorological factors while PM2.5 concentrations were more heavily determined by background level and residential area. Both O-3 and PM2.5 showed a significant correlation with air temperature, traffic volume, building height, and green space. Interestingly, green spaces were negatively correlated with the PM2.5 variations, which was almost the opposite to that of O-3. With the superiority to the discrete observation, the decision tree model based concentration surfaces clearly revealed the heterogeneity of O-3 and PM2.5 distributions. This study not only preliminarily identifies the impacts of land-use type and meteorological factors on the spatial patterns of O(3 )and PM2.5 , but also provides a possible alternative method for assessing the neighborhood air pollution in the future.