PM2.5 是大气重要污染物之一, 模拟 PM2.5 浓度空间分布对于大气污染防治具有重要意义。将土地利用回归模型 (LUR) 应用到安徽省污染较重的皖北地区, 以监测点为中心, 建立半径分别为 0.5、1、1.5、2、3、4、5 km 的缓冲区, 结合土地利用因子、道路因子、污染源因子、气象因子、高程因子及人口因子共 105 个变量, 建立了该地区四季和年均 LUR 模型, 并通过留一交叉互验, 验证了模型精度。结果表明: 研究区 PM2.5 浓度受草地、湿地、降水量、相关湿度、气压、风速、二级公路、三级公路、废气污染企业、人口数量影响较大。调整 R2 分别为 0.828 (春)、0.731 (夏)、0.831 (秋)、0.775 (冬)、0.892 (年均); 均方根误差 (RMSE) 分别为 6.34 μg·m-3 (春)、7.01 μg·m-3 (夏)、6.28 μg·m-3 (秋)、6.71 μg·m-3 (冬)、5.33 μg·m-3 (年均); 模拟精度 R2 分别为 0.825 (春)、0.730 (夏)、0.834 (秋)、0.772 (冬)、0.897 (年均), 模型表现良好, 解释力强。从模拟的 PM2.5 浓度空间分布可以看出, 不同季节呈现明显不同的空间分布特征, 这与来自北方的大量污染颗粒物、当地的煤矿开采以及秋耕秸秆燃烧等潜在污染源有关。

PM2.5 is one of the important pollutants in the atmosphere, so simulating the spatial distribution of PM2.5 concentration is of great significance to the prevention and control of air pollution. The Land Use Regression (LUR) model was applied to the heavily polluted Northern Anhui region in Anhui Province, China. Taking the monitoring points as the center, the buffer zones with radius of 0.5, 1, 1.5, 2, 3, 4 and 5 km were established respectively. Combined with 105 variables including land use factor, road factor, pollution source factor, meteorological factor, elevation factor and population factor, a four-season and annual average LUR model for this district was established, and the accuracy of the model was verified by leave-one-out cross validation. The results show that the PM2.5 concentration in the study area is greatly affected by grassland, wetland, rainfall, relative humidity, atmospheric pressure, wind speed, secondary roads,tertiary roads, air-polluting enterprise, and population. The adjusted R2 is 0.828 (spring), 0.731 (summer), 0.831 (autumn), 0.775 (winter) and 0.892 (annual average) respectively. The root mean square error (RMSE) is 6.34 μg·m-3 (spring), 7.01 μg·m-3 (summer), 6.28 μg·m-3 (autumn), 6.71 μg·m-3 (winter) and 5.33 μg·m-3 (annual average). The simulation accuracy R2 is 0.825 (spring), 0.730 (summer), 0.834 (autumn), 0.772 (winter) and 0.897 (annual average). The model shows good performance and strong explanatory power. As can be seen from the simulated spatial distribution of PM2.5 concentration, the spatial distribution characteristics in the area are obviously different in different seasons, which is related to a large number of pollution particles from the north, local coal mining, straw burning during autumn tillage and other potential pollution sources.