LUR模型模拟的南昌市PM2.5浓度与土地利用类型的关系

城市土地利用对城市大气污染具有重要影响,探究两者间的关系对于促进城市大气污染治理、保障人体健康具有重要意义。该研究首先利用土地利用回归(land use regression,LUR)模型模拟南昌市中心城区PM_(2.5)浓度空间分布。其次,根据土地利用主导方式的不同,在南昌市中心城区选择商业区、工业区、居住区、教育区和对照区各5个,作为样本功能区,分春、夏、秋、冬四季统计各样本功能区PM_(2.5)浓度,运用方差分析与多重比较法定量研究不同类型功能区四季PM_(2.5)浓度差异。研究结果表明:1)四季LUR模型调整R2分别为0.713、0.741、0.898、0.964,检验样本平均绝对误差率为12.03%,说明构建的四季LUR模型拟合情况好,可以有效地对监测点以外区域PM_(2.5)浓度进行估计;2)功能区类型对PM_(2.5)浓度影响显著,城市土地利用方式显著影响PM_(2.5)浓度,且这种影响与季节无关;3)各类功能区之间PM_(2.5)浓度差异显著水平不一致,工业区与商业区、居住区与教育区均无显著差异,工业区、商业区均与教育区和居住区有显著差异,对照区与其他4类功能区均有显著差异。该研究探索了城市土地利用与大气污染耦合的新思路,研究结果为优化城市土地利用,缓解大气污染提供参考。

Relationship of PM2.5 concentration and land use type in Nanchang City based on LUR simulation

Urban land use can greatly influence the urban atmospheric pollution conditions. Obtaining a deeper understanding of the relationship between urban land use and atmospheric pollution has an important practical significance in preventing atmospheric pollution and protecting human health. However, the relationship between urban land use and atmospheric pollution has rarely been investigated and the consensus about the exact nature of the relationship has not been reached, which is yet to be fully explored. The purpose of this paper was to study the relationship through coupling land use and atmospheric pollution at city scale. PM2.5, consisting of particles with aerodynamic diameters no greater than 2.5μm, can absorb various toxic substances and easily enter the lungs, resulting in respiratory and cardiovascular diseases. Now, PM2.5 has already become one of the major air pollutants in many Chinese cities. Therefore, PM2.5 was chosen as the typical atmospheric pollutant in our paper. However, getting sufficient PM2.5 data is a big challenge due to the sparsely distributed air quality monitoring sites. Then LUR (land use regression) models, in which atmospheric pollutant concentrations are as the dependent variables and surrounding geographical data as the independent variables, were applied to PM2.5 concentrations simulation to strengthen insufficient PM2.5 data. Central area of Nanchang City was selected as the study area in this paper. According to the dominated land use type, 5 groups of sample function districts including commercial function districts, industrial function districts, residential function districts, educational function districts and control function districts were selected in the study area. The PM2.5 concentrations of four seasons in these sample function districts were calculated. Methods of variance analysis and multiple comparisons were employed to quantitatively study the seasonal PM2.5 concentration differences among different function districts. The results showed that: 1) The best fitting LUR models for four seasons were established and the adjusted R2 values were 0.713, 0.741, 0.898 and 0.964 respectively. The mean absolute percentage error of 24 test samples was 12.03%. These results illustrated that the fitting degree of the 4 LUR models were good and the estimation of PM2.5 concentrations in the study area could be effectively achieved through LUR models. 2) The PM2.5 concentration differences among sample function districts were significant, indicating that urban land use had an obvious impact on PM2.5 concentrations. And the impact would not change as the seasons changed. 3) The significance levels of PM2.5 concentration differences among different function districts were not all the same. The PM2.5 concentration differences between industrial function districts and commercial function districts, residential function districts and educational function districts were insignificant. The PM2.5 concentrations in industrial function districts or commercial function districts were significantly different from those in residential function districts or educational function districts. The PM2.5 concentration differences between control function districts and the other 4 categories of function districts were all significant. The results demonstrated that the layout of function districts could impact the spatial distribution characteristics of PM2.5 concentration. This research explores a new approach to couple urban land use and atmospheric pollution. The results can provide valuable references for urban land-use optimization and atmospheric pollution control in future.