北京市PM2.5时空分布特征及其与PM10关系的时空变异特征

PM_(2.5)时空分布特征及其与其它污染物的相关关系是PM_(2.5)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PM_(2.5)浓度的聚集分布特征;同时,常用的地理加权回归在对PM_(2.5)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PM_(2.5)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PM_(2.5)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PM_(2.5)与PM_(10)季节平均浓度间关系进行建模,依据回归结果分析PM_(2.5)-PM_(10)间关系的时空变异特征.实验结果表明,春夏季节PM_(2.5)污染程度及空间变异程度均低于秋冬季节,各季节PM_(2.5)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PM_(2.5)-PM_(10)相关性低于秋冬季PM_(2.5)-PM_(10)相关性;各季节均表现为西北部PM_(2.5)-PM_(10)的相关性高于东南部PM_(2.5)-PM_(10)的相关性.

Spatio-temporal Distribution Characteristics of PM2.5 and Spatio-temporal Variation Characteristics of the Relationship Between PM2.5 and PM10 in Beijing

Spatio-temporal distribution of PM_2.5 and variations in the relationship between PM_2.5 and other pollutants are the main components of PM_2.5spatio-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 PM_2.5 concentrations. Geographically weighted regression, commonly used to model the relationships between PM_2.5 and other pollutants, cannot accurately describe the spatio-temporal variability of dependency. In this study, the clustering structure of PM_2.5 concentrations in Beijing was identified using the spatial clustering algorithm and the seasonal distribution characteristics of PM_2.5 were analyzed based on the clustering results. The relationship between PM_2.5 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 PM_2.5 pollution levels and spatial variability were lower in spring and summer than those in autumn and winter and the concentration of PM_2.5 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 PM_2.5 and PM₁₀ in spring and summer are weaker than those in autumn and winter and the correlation between PM_2.5 and PM₁₀ in the northwest is stronger than that in the southeast in each season.