Abstract Typical air pollution events involving ozone (O3) and PM2.5 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 O3 and PM2.5 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 O3 and PM2.5 concentrations than the LUR model. The results also showed that O3 concentrations were mainly affected by meteorological factors while PM2.5 concentrations were more heavily determined by background level and residential area. Both O3 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 O3. With the superiority to the discrete observation, the decision tree model based concentration surfaces clearly revealed the heterogeneity of O3 and PM2.5 distributions. This study not only preliminarily identifies the impacts of land-use type and meteorological factors on the spatial patterns of O3 and PM2.5, but also provides a possible alternative method for assessing the neighborhood air pollution in the future.

中文翻译：

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使用决策树方法评估臭氧和 PM2.5 浓度的邻域变化

摘要 典型的臭氧（O3）和PM2.5大气污染事件在我国频繁发生，而细尺度污染变化，尤其是邻域层面（2km*2km）的污染变化复杂且尚不明确。为评估城市形态和气象对邻里空气污染分布的影响，本研究以上海市闵行区为实验案例，利用移动测量对O3和PM2.5进行邻域尺度调查。土地利用回归模型和决策树模型都被用来检验空气污染物浓度与影响变量之间的关系。由于决策树模型捕捉了变量之间的线性和非线性关系，证明比 LUR 模型可以解释更多的 O3 和 PM2.5 浓度变化。结果还表明，O3 浓度主要受气象因素的影响，而 PM2.5 浓度更多地受本底水平和居民区的影响。O3 和 PM2.5 均与气温、交通量、建筑高度和绿地面积显着相关。有趣的是，绿地与 PM2.5 的变化呈负相关，这几乎与 O3 的变化相反。与离散观测相比，基于浓度面的决策树模型清楚地揭示了 O3 和 PM2.5 分布的异质性。本研究不仅初步确定了土地利用类型和气象因素对O3和PM2.5空间格局的影响，