| 北京地区冬春PM2.5和PM10污染水平时空分布及其与气象条件的关系 |
| 摘要点击 4897 全文点击 4341 投稿时间：2013-05-24 修订日期：2013-10-17 |
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| 中文关键词 PM2.5 PM10 污染水平 时空分布 气象条件 |
| 英文关键词 PM2.5 PM10 pollution levels temporal and spatial distribution meteorological condition |
| 作者 | 单位 | E-mail | | 赵晨曦 | 北京林业大学水土保持学院, 水土保持与荒漠化防治教育部重点实验室, 北京 100083 | kakaanew@163.com | | 王云琦* | 北京林业大学水土保持学院, 水土保持与荒漠化防治教育部重点实验室, 北京 100083 | wangyunqi@bjfu.edu.cn | | 王玉杰 | 北京林业大学水土保持学院, 水土保持与荒漠化防治教育部重点实验室, 北京 100083 | | | 张会兰 | 北京林业大学水土保持学院, 水土保持与荒漠化防治教育部重点实验室, 北京 100083 | | | 赵冰清 | 北京林业大学水土保持学院, 水土保持与荒漠化防治教育部重点实验室, 北京 100083 | |
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| 中文摘要 |
| 北京2012 ~2013年的冬春多次出现雾霾天气，可吸入颗粒物（PM10）污染严重. 而PM2.5作为PM10中粒径较小的部分，在PM10中所占比重越高，污染越严重. 因此，本研究选取了能够覆盖北京所有区县的30个PM2.5和PM10的质量浓度监测点，对该地区的PM2.5和PM10污染特征进行分析，确定其空间差异特征和时间性变化特征. 普通克里格插值 （Original Kriging）法得到的北京地区冬、春季颗粒物浓度分布图显示，颗粒物浓度从北部山区到南部地区逐渐递增，在中心城区处，西部高于东部，且局部地区存在一定的城乡差异. 颗粒物浓度月变化曲线呈单峰单谷型，1月最高，4月最低；逐日变化反映了PM2.5和PM10浓度具有较好的相关性，且受气象条件影响显著；日变化呈双峰趋势. 本文选取日平均气温（℃）、相对湿度（%）、风速（风级）、降水量（mm）等气象因子，利用Spearman 秩相关分析研究各个气象因子对大气PM2.5和PM10浓度的影响. 北京冬季PM2.5和PM10的质量浓度分别与气温、相对湿度正相关，与风速负相关，风速和相对湿度是影响污染物质量浓度分布的主要因素. |
| 英文摘要 |
| Fogs and hazes broke out many times in winter and spring of 2012-2013 in Beijing, inducing severe pollution of respirable particulate matters (PM10). As a fine particle component in PM10, PM2.5 would cause more severe air pollution if the proportion of PM2.5 to PM10 is high. Based on this, 30 monitoring stations recording the concentration of PM2.5 and PM10 all over Beijing were selected, and the contamination characteristics of particulate matters were analyzed, which further served to determine the characteristics of temporal and spatial pollution variations of PM2.5 and PM10. The distribution of PM2.5 and PM10 mass concentration in winter and spring in Beijing were derived by the Original Kriging interpolation method, and it was depicted from the figure that the concentration of particulate matters gradually increased from the northern mountain area to the southern part of Beijing; in the central urban area, the particulate concentration of the western region was generally higher than that of the eastern region, with certain differences between urban and rural area within some local areas. Monthly variation curve of PM2.5 and PM10 mass concentration showed single peak-valley pattern: the maximum was in January and the minimum was in April; daily variation indicated a good correlation between PM2.5 and PM10, both of which were significantly influenced by meteorological conditions; diurnal variation curve showed a double peak-valley type. Meteorological factors such as daily average temperature (℃), relative humidity (%), wind speed (wind scale), precipitation (mm) were chosen and their individual relationships with concentrations of PM10 and PM2.5 were investigated using Spearman rank correlation analyses. It was demonstrated that the concentrations of PM10 and PM2.5 were positively correlated with temperature and relative humidity, respectively, and strongly negatively correlated with wind speed; wind speed and relative humidity were two key factors affecting the distributions of PM2.5 and PM10 concentration. |
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