| 重庆市PM2.5浓度空间分异模拟及影响因子 |
| 摘要点击 4033 全文点击 1898 投稿时间：2014-09-01 修订日期：2014-10-15 |
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| 中文关键词 PM2.5 土地利用回归模型 回归映射 空间分布 相关性 GIS |
| 英文关键词 PM2.5 land use regression model regression mapping spatial distribution correlation GIS |
| 作者 | 单位 | E-mail | | 吴健生 | 北京大学深圳研究生院, 城市人居环境科学与技术重点实验室, 深圳 518055
北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京 100871 | wujs@pkusz.edu.cn | | 廖星 | 北京大学深圳研究生院, 城市人居环境科学与技术重点实验室, 深圳 518055
北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京 100871 | 1301213531@sz.pku.edu.cn | | 彭建 | 北京大学深圳研究生院, 城市人居环境科学与技术重点实验室, 深圳 518055
北京大学城市与环境学院, 地表过程分析与模拟教育部重点实验室, 北京 100871 | | | 黄秀兰 | 北京大学深圳研究生院, 城市人居环境科学与技术重点实验室, 深圳 518055 | |
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| 中文摘要 |
| 基于Arcgis平台,利用土地利用回归模型模拟重庆市PM2.5浓度分布,获取了高分辨率结果图. 从重庆市环保局网上获取了17 个空气质量监测站点的PM2.5数据,利用16个监测点数据,结合土地利用数据、路网数据、DEM数据和人口数据建立土地利用回归模型,利用剩余的1个监测点数据来对回归映射结果进行检验. 按照模型设置的变量生成方法,对监测点建立多种尺度的缓冲区,提取变量数据,最终生成了56个变量. 按照土地利用回归模型的设置,56个自变量最终有3个变量进入PM2.5的回归方程,模型的R2逐步增大,且最终R2为0.84,模型拟合程度非常好. 回归方程中,与研究区PM2.5浓度空间分布相关性最大的因素是空气质量监测站点500 m范围内的农用地面积,然后依次是DEM和1000 m范围内一级公路总长度,它们与PM2.5的皮尔森相关系数依次是: 0.695、-0.599和0.394. 回归映射检验结果显示,检验点的误差率为2.7%,误差可以接受. 回归映射结果显示,PM2.5浓度以高值分布于主城区,沿一级公路分布趋势明显,与高层紧密相关,模拟结果与实际情况相符. |
| 英文摘要 |
| Land use regression model (LUR model) was used to simulate the spatial distribution of PM2.5 concentrations in Chongqing with the software of ArcGIS. This research was conducted with a total of 17 PM2.5 concentrations of monitoring points from 17 air quality monitoring stations recorded in the official website of Chongqing Environmental Protection Bureau. Among them, 16 were chosen as the dependent variables, and the last one was chosen for land use regression model validation test. At each site location, we constructed circular buffers with ArcGIS and captured information on roads, population, land use and DEM. Based on the buffer information, 56 potential geographic predictors were built. Finally 3 variables: cropland area within 500 m of the air quality monitoring sites, the site locations' DEM and primary road length within 1000 m of the 56 predictors were left for predicting 84% of the variation of PM2.5 concentrations and the Pearson coefficients between the 3 variables and PM2.5 concentrations were 0.695, -0.599 and 0.394, respectively. The validation test result showed that the spatial distribution map of PM2.5 predicted extremely well with an error rate of only 0.027. And the return map results showed: ① PM2.5 concentrations were high in the center of the main city; ② PM2.5 concentrations were high along the road and ③ the distribution was closely correlated to the DEM of sampling locations. |
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