| 北京市PM2.5时空分布特征及其与PM10关系的时空变异特征 |
| 摘要点击 3735 全文点击 1064 投稿时间：2017-03-26 修订日期：2017-10-26 |
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| 中文关键词 PM2.5 时空分布 空间聚类分析 模糊C均值聚类(FCM) 地理时空加权回归 |
| 英文关键词 PM2.5 spatio-temporal distribution spatial clustering analysis fuzzy-c-means(FCM) clustering geographically temporally weighted regression |
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| 中文摘要 |
| PM2.5时空分布特征及其与其它污染物的相关关系是PM2.5时空统计分析的主要研究内容.然而，现有的方法直接从监测站点的角度对时空分布特征进行分析，难以有效地揭示PM2.5浓度的聚集分布特征；同时，常用的地理加权回归在对PM2.5与其它污染物间关系进行建模的过程中，缺乏同时考虑时间异质性与空间异质性，从而不能准确地描述依赖关系的时空变异特征.为此，首先借助于空间聚类分析技术，对北京市2014年PM2.5浓度的聚集结构进行探测，在此基础上，通过聚集结构来分析PM2.5季节性时空分布特征.然后，利用地理时空加权回归对北京市PM2.5与PM10季节平均浓度间关系进行建模，依据回归结果分析PM2.5-PM10间关系的时空变异特征.实验结果表明，春夏季节PM2.5污染程度及空间变异程度均低于秋冬季节，各季节PM2.5浓度均表现为北部浓度低、南部浓度高的空间分布特征；地理时空加权回归具有更好的拟合效果，由回归系数进一步可发现，春夏季PM2.5-PM10相关性低于秋冬季PM2.5-PM10相关性；各季节均表现为西北部PM2.5-PM10的相关性高于东南部PM2.5-PM10的相关性. |
| 英文摘要 |
| Spatio-temporal distribution of PM2.5 and variations in the relationship between PM2.5 and other pollutants are the main components of PM2.5spatio-temporal statistical analysis. Existing methods directly analyze spatio-temporal distribution based on monitoring data; thus, it is difficult to effectively reveal the aggregation structure of PM2.5 concentrations. Geographically weighted regression, commonly used to model the relationships between PM2.5 and other pollutants, cannot accurately describe the spatio-temporal variability of dependency. In this study, the clustering structure of PM2.5 concentrations in Beijing was identified using the spatial clustering algorithm and the seasonal distribution characteristics of PM2.5 were analyzed based on the clustering results. The relationship between PM2.5 and PM10 was modeled by geographically and temporally weighted regression and the spatio-temporal variability of dependency was analyzed according to the regression results. The results showed that PM2.5 pollution levels and spatial variability were lower in spring and summer than those in autumn and winter and the concentration of PM2.5 in each season was characterized by low spatial distribution in the north and high spatial distribution in the south. Geographically and temporally weighted regression showed better performance; the correlations between PM2.5 and PM10 in spring and summer are weaker than those in autumn and winter and the correlation between PM2.5 and PM10 in the northwest is stronger than that in the southeast in each season. |
