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- Title
LUR 模型模拟的南昌市 PM<sub>2.5</sub>浓度与土地利用类型的关系.
- Authors
阳海鸥; 陈文波; 梁照凤
- Abstract
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 2.5 μm, 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 average accuracy of 24 test samples was 87.97%. These results illustrated that the fitting degree and accuracy 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.
- Publication
Transactions of the Chinese Society of Agricultural Engineering, 2017, Vol 33, Issue 6, p232
- ISSN
1002-6819
- Publication type
Article
- DOI
10.11975/j.issn.1002-6819.2017.06.030