It is important for air pollution study and control to understand the relationships between the concentrations of fine particulate matter and the influencing factors. This study builds a PM2.5 concentration estimation model with high accuracy considering major factors including meteorological conditions, traffic conditions, and urban spatial layouts based on Gradient Boosted Regression Tree approach. Based on the proposed model, the relative importance of the explanatory variables is presented. The meteorological variables, traffic condition variables and urban spatial layout variables have the predictive powers of 64.6%, 21.2% and 14.2% respectively. The nonlinear relationships between each variable and urban PM2.5 concentration are visualized by partial dependency plots and discussed in detail. The results show that in order not to exceed the limitation of regional PM2.5 concentration, the local government can lower city's temperature, improve urban ventilation and precipitation, alleviate traffic congestions, balance the road construction and PM2.5 generation, increase urban green area and concentrate Points of Interests.

了解细颗粒物浓度与影响因素之间的关系对于大气污染研究和控制具有重要意义。本研究基于梯度提升回归树方法，建立了考虑气象条件、交通条件和城市空间布局等主要因素的高精度PM2.5浓度估计模型。基于所提出的模型，解释变量的相对重要性被提出。气象变量、交通状况变量和城市空间布局变量的预测能力分别为64.6%、21.2%和14.2%。每个变量与城市 PM2.5 浓度之间的非线性关系通过部分依赖图可视化并详细讨论。