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张淑平,韩立建,周伟奇,郑晓欣.冬季PM2.5的气象影响因素解析.生态学报,2016,36(24):7897~7907 本文二维码信息
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冬季PM2.5的气象影响因素解析
Relationships between fine particulate matter (PM2.5) and meteorological factors in winter at typical Chinese cities
投稿时间:2015-05-02  最后修改时间:2016-10-15
DOI: 10.5846/stxb201505020901
关键词细颗粒物PM2.5  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
Key Wordsfine particulate (PM2.5)  temperature  maximum temperature  minimum temperature  humidity  wind speed  maximum sustained wind speed  precipitation
基金项目国家自然科学基金青年基金(41301199);中国科学院生态环境研究中心"一三五"重点培育方向项目(YSW2013B04);国家科技支撑计划子课题(2012BAC13B01)
作者单位E-mail
张淑平 中国科学院生态环境研究中心, 城市与区域生态国家重点实验室, 北京 100085;中国科学院大学, 北京 100049  
韩立建 中国科学院生态环境研究中心, 城市与区域生态国家重点实验室, 北京 100085  
周伟奇 中国科学院生态环境研究中心, 城市与区域生态国家重点实验室, 北京 100085 wzhou@rcees.ac.cn 
郑晓欣 中国科学院生态环境研究中心, 城市与区域生态国家重点实验室, 北京 100085  
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摘要:
气象因素能够显著影响PM2.5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PM2.5浓度的关系,进而研究气象因素对PM2.5浓度变异解释度的差异,以及气象因子对PM2.5浓度影响的相对重要性,进一步对比分析气象因素对PM2.5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PM2.5日浓度显著相关,且在不同污染程度的城市与PM2.5浓度相关的气象因子不同。与石家庄冬季PM2.5浓度相关的气象因素为相对湿度、平均风速;与西安PM2.5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PM2.5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PM2.5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PM2.5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)PM2.5浓度越高的地区,气象因素能够解释的PM2.5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R2为0.27,重污染区的西安气象因素多元回归分析R2为0.29,中污染区的北京气象因素多元回归分析R2为0.46,污染地区的太原气象因素多元回归分析R2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PM2.5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。
Abstract:
Meteorological conditions may have a great impact on PM2.5 pollution during the heavy haze winter in Chinese cities. Here, we examined the effects of meteorological factors on PM2.5 concentrations from December 2013 to February 2014, and from December 2014 to February 2015, in five cities—Shijiazhuang, Xi'an, Beijing, Taiyuan, and Guangzhou exhibiting different levels of PM2.5. We found (1) meteorological factors affected daily PM2.5 concentrations, and variables differed in cities with varied pollution levels. Significant positive correlations were obtained between humidity and PM2.5 concentrations in Shijiazhuang, and a significant negative correlation was also obtained for wind speed. Significant positive correlations were obtained between humidity and PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentrations in Guangzhou, and a significant negative correlation was found for wind speed. (2) Meteorological factors can explain the smaller variability in PM2.5 concentration in cities that have heavier PM2.5 pollution. Shijiazhuang, which represented a severely polluted area, showed meteorological factors of 0.27 after multiple regression analysis (R2). Xi'an, which represents a heavy polluted area, showed meteorological factors of 0.29 (R2). Beijing, which represents a moderately polluted area, showed meteorological factors of 0.46 (R2). Taiyuan, which represents a polluted area, showed meteorological factors of 0.67 (R2). 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 PM2.5 in different cities provides a theoretical reference for air pollution control and improvement of prediction accuracy.
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