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题名: 冬季PM_(2.5)的气象影响因素解析
作者: 张淑平; 韩立建; 周伟奇; 郑晓欣
刊名: 生态学报
出版日期: 2016
卷号: 36, 期号:24, 页码:7897-7907
关键词: 细颗粒物PM_(2.5) ; 日均温 ; 最高温 ; 最低温 ; 相对湿度 ; 平均风速 ; 最大持续风速 ; 降雨量
其他题名: Relationships between fine particulate matter (PM_(2.5)) and meteorological factors in winter at typical Chinese cities
中文摘要: 气象因素能够显著影响PM_(2.5)浓度,可减轻或加剧城市空气污染,尤其是在雾霾严重的冬季。同时由于城市间污染物排放强度和扩散条件的差异,雾霾的发生往往具有较强的区域性。选择了石家庄、西安、北京、太原、广州5个不同污染区域的典型城市,首先分析多个气象因子与PM_(2.5)浓度的关系,进而研究气象因素对PM_(2.5)浓度变异解释度的差异,以及气象因子对PM_(2.5)浓度影响的相对重要性,进一步对比分析气象因素对PM_(2.5)浓度影响在不同污染程度的城市之间的差异,解析了不同城市的主要气象影响因素和气象因素的综合影响程度。研究结果表明: (1)气象条件与PM_(2.5)日浓度显著相关,且在不同污染程度的城市与PM_(2.5)浓度相关的气象因子不同。与石家庄冬季PM_(2.5)浓度相关的气象因素为相对湿度、平均风速;与西安PM_(2.5)浓度相关的主要气象因素为相对湿度、平均风速和最大持续风速;与北京PM_(2.5)浓度相关的主要气象因素相对湿度、日均温度、平均风速、最大持续风速和最低温;与太原PM_(2.5)浓度相关的主要气象因素为日均温、相对湿度、平均风速、最高温、最低温和最大持续风速;与广州PM_(2.5)浓度相关的主要气象因素为相对湿度、平均风速、最高温和降雨量。(2) PM_(2.5)浓度越高的地区,气象因素能够解释的PM_(2.5)浓度变异越小。严重污染区的石家庄气象因素多元回归分析的R~2为0.27,重污染区的西安气象因素多元回归分析R~2为0.29,中污染区的北京气象因素多元回归分析R~2为0.46,污染地区的太原气象因素多元回归分析R~2为0.67。研究结果揭示了不同城市的主要气象影响因素及其综合影响程度,可为城市PM_(2.5)控制和预测精度提高提供理论参考,并为区域生态环境规划和城市协调发展提供科学依据。
部门归属: 城市与区域生态国家重点实验室
英文摘要: Meteorological conditions may have a great impact on PM_(2.5) pollution during the heavy haze winter in Chinese cities. Here,we examined the effects of meteorological factors on PM_(2.5) concentrations from December 2013 to February 2014,and from December 2014 to February 2015,in five citiesShijiazhuang,Xi'an,Beijing,Taiyuan,and Guangzhou exhibiting different levels of PM_(2.5). We found (1) meteorological factors affected daily PM_(2.5) concentrations,and variables differed in cities with varied pollution levels. Significant positive correlations were obtained between humidity and PM_(2.5) concentrations in Shijiazhuang,and a significant negative correlation was also obtained for wind speed. Significant positive correlations were obtained between humidity and PM_(2.5) concentrations in Xi'an,and a significant negative correlation was found for wind speed. Significant positive correlations were obtained between humidity /temperature /minimum temperature and PM_(2.5) concentrations in Beijing,and a significant negative correlation was found for wind speed. Significant positive correlations were obtained between humidity /temperature /minimum temperature /maximum temperature and PM_(2.5) concentrations in Taiyuan,and a negative correlation was found for the maximum sustained wind speed. Significant positive correlations were obtained between humidity /maximum temperature /precipitation and PM_(2.5) concentrations in Guangzhou, and a significant negative correlation was found for wind speed. (2) Meteorological factors can explain the smaller variability in PM_(2.5) concentration in cities that have heavier PM_(2.5) pollution. Shijiazhuang,which represented a severely polluted area,showed meteorological factors of 0.27 after multiple regression analysis (R~2). Xi' an,which represents a heavy polluted area,showed meteorological factors of 0.29 (R~2). Beijing,which represents a moderately polluted area, showed meteorological factors of 0.46 (R~2). Taiyuan,which represents a polluted area,showed meteorological factors of 0.67 (R~2). The results provide a scientific basis for regional ecological environment planning and coordinated urban development. Therefore,understanding the main meteorological factors and their impact on urban PM_(2.5) in different cities provides a theoretical reference for air pollution control and improvement of prediction accuracy.
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内容类型: 期刊论文
URI标识: http://ir.rcees.ac.cn/handle/311016/36473
Appears in Collections:城市与区域生态国家重点实验室_期刊论文

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Recommended Citation:
张淑平,韩立建,周伟奇,等. 冬季PM_(2.5)的气象影响因素解析[J]. 生态学报,2016,36(24):7897-7907.
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