| 摘要: |
| 基于2018年常州市14个自动监测点位ρ（PM2.5），采用变异函数法和正交经验分解法（EOF）对其ρ（PM2.5）的空间变异性和逐日质量浓度序列的时空分区特征进行了研究。结果表明，常州市PM2.5高值均发生在11和1月，其次为2，4和5月，18：00后至次日上午时段PM2.5易出现峰值；ρ（PM2.5）具有较大的空间差异性，其在东西方向上的空间异质性程度要大于南北方向，随着站点之间空间距离的增加，各个站点局地污染分布因素的差异性逐渐增大；受市区重点污染源分布和气象条件影响，ρ（PM2.5）总体呈现沿东北向西南区域依次递减的分区特征，高值区位于常州市区中心偏北、偏东地区，低值区位于市区西南部区域，且具有明显的季节变化特征。 |
| 关键词: 细颗粒物 空间分布 正交经验分解 变异函数 常州市 |
| DOI： |
| 分类号:X513 |
| 文献标识码:B |
| 基金项目:常州市科技计划基金资助项目（CJ20180039） |
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| Spatio temporal Distribution Characteristics of PM2.5in Changzhou City Based on EOF Method |
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YANG Wei-fen,WANG Zhen,LI Chun-yu,WU Jing-lu1,2
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1. Jiangsu Changzhou Environmental Monitoring Center, Changzhou, Jiangsu 213001, China;2.Changzhou Meteorological Office, Changzhou, Jiangsu 213022, China
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| Abstract: |
| This paper summarized the characteristics of spatio temporal distribution and spatial variability of fine particulate matter (PM2.5) based on PM2.5 concentration of 14 auto monitoring sites in Changzhou city in 2018. Meanwhile, the temporal and spatial regionalization of daily PM2.5 concentration were further studied and discussed by EOF method. The results indicated that the high concentration PM2.5 occurred in November and January, followed by February, April and May, and the peak value of PM2.5 was easy to appear from 18:00 to the morning of the next day. There were significant spatial variation of PM2.5 in Changzhou city, the spatial heterogeneity of PM2.5 in East West directions were greater than that in North South directions and the differences of local pollution distribution factors at each station increased with the increase of spatial distance among sites. According to the modes of EOF analysis, the regionalization of PM2.5 correlated well with the main pollution source of Changzhou, and the weather conditions also impacted the regionalization results. The concentration of PM2.5 showed a decreasing trend along the northeast to southwest regions, the north and east of the center of Changzhou City had relatively high PM2.5 concentrations while the southwest of the city had relatively low PM2.5concentrations, and it has obvious seasonal variation characteristics. |
| Key words: PM2.5 Spatial distribution EOF Variation function Changzhou |
