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基于长时间序列的北京PM2.5浓度日变化及气象条件影响分析
引用本文:苗蕾,廖晓农,王迎春.基于长时间序列的北京PM2.5浓度日变化及气象条件影响分析[J].环境科学,2016,37(8):2836-2846.
作者姓名:苗蕾  廖晓农  王迎春
作者单位:中国气象科学研究院, 北京 100081;中国气象局北京城市气象研究所, 北京 100089,中国气象局北京城市气象研究所, 北京 100089,北京市气象局, 北京 100089
基金项目:国家科技支撑计划项目(2014BAC16B04);国家级雾霾监测与预报团队建设项目
摘    要:基于2005~2014年北京宝联(城区)和上甸子大气成分本底站(郊区)监测结果得到了PM_(2.5)质量浓度日变化特征,并且讨论了气象条件的影响.结果表明,北京城区PM_(2.5)质量浓度10年平均值的日变化呈双峰分布,对应早晚出行高峰.但是,该特征在2007年以后才比较明显.月际(季节)变化表现为单峰与双峰之间、日最大值在早高峰与晚高峰之间的转换.一年中早峰值浓度在5~8月最高,与地面风速小、相对湿度以及水汽压(空气绝对含水量)较高有关,该季节晚高峰期间浓度变化相对较小主要受混合层较厚、地面风速大、降雨天气发生频繁的影响.11~12月和1~2月的16:00以后浓度明显升高,混合层顶高度在14:00~17:00大幅度下降是重要的影响因素.此外,严重和重度污染日09:00以后污染加重,与年平均值和中度污染日不同.边界层偏南风对来自周边地区高架源的污染物输送是其重要的影响机制.PM_(2.5)质量浓度日变化幅度随污染加重而增大.日最大风速和相对湿度日变化对浓度日变化幅度有影响,而且午后地面出现4~6 m·s-1的偏南风也会加大日变化幅度.北京郊区PM_(2.5)质量浓度多年平均值日变化呈单峰分布,日最大值超前于城区.而且,昼间的浓度在5~7月高于冬季.本研究结果将有助于细化不利扩散条件下的污染减排方案.

关 键 词:PM2.5质量浓度  中度以上污染日  日变化  混合层顶高度  水汽压  风速  南风输送
收稿时间:2015/12/27 0:00:00
修稿时间:3/8/2016 12:00:00 AM

Diurnal Variation of PM2.5 Mass Concentration in Beijing and Influence of Meteorological Factors Based on Long Term Date
MIAO Lei,LIAO Xiao-nong and WANG Ying-chun.Diurnal Variation of PM2.5 Mass Concentration in Beijing and Influence of Meteorological Factors Based on Long Term Date[J].Chinese Journal of Environmental Science,2016,37(8):2836-2846.
Authors:MIAO Lei  LIAO Xiao-nong and WANG Ying-chun
Institution:China Academy of Meteorological Science, Beijing 100081, China;Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China,Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China and Beijing Meteorological Bureau, Beijing 100089, China
Abstract:Diurnal variation of PM2.5 mass concentration is analyzed based on data of BAOLIAN (representing urban area) and SAHNDIANZI BAP-Station (representing rural area) from 2005 to 2014. Furthermore, the influence of meteorological factors was also discussed. The results showed that 10-year-average diurnal variation of PM2.5 mass concentration in urban area had a two-peak pattern being coincident with rush-hour. However, it became clear only after 2007. The monthly (including seasonal) variation presented the change from one-peak pattern to two-peak pattern with the max mass concentration appearing in the morning or late afternoon during rush hour. The mass concentration in the morning rush hour reached its maximum between May and August which was to some degree related with weak wind and high relative humidity as well as great water vapor pressure (indicating the absolute water content in the air). But the smaller variety of mass concentration in the late afternoon attributed to the thicker mixing layer, higher wind speed and more showers. By contraries, it went up greatly after 4 pm in Nov., Dec., Jan. and Feb.. One of the reasons was that the height of mixing layer top decreased sharply. Besides, in some severely and seriously polluted days, the PM2.5 mass concentration increased after morning rush hour till afternoon which was different from the mean pattern and that in moderately polluted day. The main mechanism attributed to the aerosol from aerial source around brought by south wind to Beijing. The more severe the pollution was, the greater the daily concentration fluctuated. The range of PM2.5 diurnal variation was determined by the max wind speed and daily change of relative humidity in a day. Besides, it also extended the diurnal variation of concentration when the south wind speed reached 4-6 m·s-1 in the afternoon. In rural area, the mean diurnal variation of PM2.5 mass concentration showed a one-peak pattern. And the time of concentration reaching its maximum was ahead of that of urban area. Moreover, the values in the day time during May and July were higher than that in winter. These results would be helpful to make policy for finer emission control when the atmosphere is in lower diffusivity situation.
Keywords:PM2  5 mass concentration  pollution day in moderate grade or over  diurnal variation  height of mixing layer top  water vapor pressure  wind speed  south wind transportation
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