2014—2019年冬季京津冀地区PM_(2.5)质量浓度时空分布特征

Spatial-temporal distribution characteristics of PM_(2.5) concentrations in Beijing-Tianjin-Hebei region in winter from 2014 to 2019

基于2014—2019年冬季京津冀地区101个空气质量监测站的小时PM_(2.5)质量浓度数据,探讨了冬季京津冀地区PM_(2.5)浓度的时空分布特征。利用MATLAB编程提取监测站点小时PM_(2.5)浓度数据并合成月、季均值数据,分析PM_(2.5)浓度小时、月、季节尺度的时间变化特征。对比两种插值方法,选用普通克里金插值法对区内PM_(2.5)浓度进行插值,分析各尺度PM_(2.5)浓度空间分布特征,借助全局莫兰指数和LISA图分析PM_(2.5)浓度的空间自相关性。研究结果表明:2014年冬季比2019年冬季的PM_(2.5)浓度下降了28.4μg/m^(3),除2018年外,小时浓度均呈现“双峰双谷型”分布;PM_(2.5)浓度在29.1~193.3μg/m^(3)波动,均值为92.1μg/m^(3),总体呈现出“北部山区低,南部平原高”的空间分布格局;冬季PM_(2.5)浓度具有显著的空间聚集性。全局莫兰指数在0.29~0.66,呈现全局正相关,南部呈现“高—高”聚集模式,北部呈现“低—低”聚集模式。

In this paper,hourly data of PM_(2.5) concentration from 101 monitoring stations from 2014 to 2019 were used to discuss the spatial and temporal distribution characteristics of PM_(2.5) concentration in the Beijing-Tianjin-Hebei region in winter.MATLAB programming could extract hourly PM_(2.5) concentration data of each monitoring station,by combining the monthly mean and seasonal mean to analyze the time variation characteristics of different scales.Compared the two different interpolation methods,the ordinary Kriging interpolation method generated continuous PM_(2.5) concentration in the area.The spatial distribution characteristics of PM_(2.5) concentration at various scales were analyzed.The global Moran index and LISA chart analyzed the spatial autocorrelation of PM_(2.5) concentration.(1)Compared with the winter of 2014,the PM_(2.5) concentrations in the winter of 2019 decreased by 28.4μg/m^(3).Except for 2018,there was“two peaks and two valleys”distribution.(2)The PM_(2.5) concentrations fluctuated between 29.1μg/m^(3) and 193.3μg/m^(3) with a mean value of 92.1μg/m^(3).The spatial distribution pattern presented a“lower in the northern mountains and higher in the southern plains”.(3)The PM_(2.5) concentrations in the Beijing-Tianjin-Hebei region provide significant spatial aggregation.The global Moran index fluctuates between 0.29 and 0.66,showing a global positive correlation.The southern plain showed a high-high aggregation pattern,while the northern mountainous part showed a low-low aggregation pattern.