| 基于LUR模型的中国PM2.5时空变化分析 |
| 摘要点击 3142 全文点击 881 投稿时间：2018-05-04 修订日期：2018-06-07 |
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| 中文关键词 PM2.5 土地利用回归(LUR) 地理加权 时空分布 大气污染 |
| 英文关键词 PM2.5 land use regression(LUR) geographically weighted temporal and spatial distribution air pollution |
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| 中文摘要 |
| 土地利用回归（LUR）模型是模拟大气污染物浓度时空分异最主要、最体系化的方法之一，为了探索LUR模型在中国国家尺度空气污染物模拟的适应性，挖掘中国2015年空气细颗粒物（PM2.5）的时空变化特征及其与不同地理要素相关关系，以2015年国家控制监测站点PM2.5数据为因变量，土地利用类型、地形地貌、人口、道路交通与气象要素等影响因素为自变量，构建基于地理加权的LUR模型，通过模型回归制图得到2015年全国月均与年均PM2.5浓度分布图，以胡焕庸线为参考线分析中国2015年PM2.5浓度的时空变化特征.结果表明，引入地理加权算法的LUR模型残差Moran's Ⅰ显著降低，残差空间自相关性明显减弱，判别系数R2明显提高，更好地揭示出PM2.5空间分布和各影响因子间的复杂关系；耕地、林地、草地和城镇居民工矿用地以及气象要素、主干道路对PM2.5浓度的影响比较显著.不同地理要素的不同空间分布对PM2.5影响作用不同；胡焕庸线两侧PM2.5表现出明显的时空差异，人口规模大、工业化水平高的发达城市PM2.5浓度较高；PM2.5浓度在冬季月份较高，秋季、春季、夏季月份污染情况逐渐减弱. |
| 英文摘要 |
| The land use regression (LUR) model is one of the most important systematic methods to simulate the temporal and spatial differentiation of the atmospheric pollutant concentration. To explore the adaptability of the LUR model to the simulation of air pollutants at the national scale in China and the temporal and spatial variation characteristics of fine air particulate matter (PM2.5) in China in 2015 and its correlation with different geographical elements, we built a LUR model. The LUR model is based on a geographically weighted algorithm using PM2.5 data acquired from the national control monitoring site in 2015 as the dependent variable and applying factors such as the type of land use, altitude, population, road traffic, and meteorological elements as independent variables. Based on model regression mapping, we obtained the distributions of monthly and annual PM2.5 concentrations nationwide in 2015 and analyzed the temporal and spatial variation characteristics of PM2.5 concentrations using the Hu line as a reference line. The results indicate that introducing the geographically weighted algorithm can significantly reduce the residual Moran's Ⅰ of the LUR model, weaken the spatial autocorrelation of residuals, and improve the coefficient of determination R2, which is better to reveal the complex relationship between the spatial distribution and impact factors of PM2.5. Cropland, forest, grass and urban industrial and residential land, and meteorological elements and major roads noticeably impact the PM2.5 concentration. Different spatial distributions of different geographical elements have distinct effects on PM2.5. The PM2.5 shows distinct temporal and spatial differences on both sides of the Hu line. The PM2.5 concentration is relatively high in developed cities with a large population and high industrialization levels. The concentration of PM2.5 is higher in winter and gradually decreases in autumn, spring, and summer. |
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