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基于天气背景天津大气污染输送特征分析
引用本文:蔡子颖,杨旭,韩素芹,姚青,刘敬乐.基于天气背景天津大气污染输送特征分析[J].环境科学,2020,41(11):4855-4863.
作者姓名:蔡子颖  杨旭  韩素芹  姚青  刘敬乐
作者单位:天津市环境气象中心,天津300074;中国气象局-南开大学大气环境与健康研究联合实验室,天津300074,天津市环境气象中心,天津300074;中国气象局-南开大学大气环境与健康研究联合实验室,天津300074,天津市环境气象中心,天津300074;中国气象局-南开大学大气环境与健康研究联合实验室,天津300074,天津市环境气象中心,天津300074;中国气象局-南开大学大气环境与健康研究联合实验室,天津300074,天津市气象科学研究所,天津300074
基金项目:天津市自然科学基金项目(19JCQNJC08000);国家自然科学基金项目(4177012485);天津市重大专项(18ZXAQSF00130,18ZXSZSF00160);中国气象局预报员专项(CMAYBY2019-007)
摘    要:区域输送是大气污染防治中需要考虑的重要因素,本文利用大气化学模式定量估算2016年10月~2017年9月区域输送对天津的影响,重点基于天气背景分析区域输送影响和气象条件的关系,为京津冀地区大气污染联防联控提供支撑.结果表明,京津冀地区各城市区域输送贡献百分率平原城市显著高于沿山城市,天津一次PM2.5本地贡献62.9%,区域输送贡献37.1%,主要受沧州、廊坊、河北中南部、北京、唐山和山东等地输送影响,每年4~6月区域输送最显著,7~8月区域输送最弱.区域输送与天气形势、风场和降水等气象条件密切相关,高压后和锋前低压是区域输送占比最高的两种污染天气类型,西南风、西风和南风3个风向下天津大气污染输送影响最为明显,风速2~3 m ·s-1时最有利于PM2.5区域传输,降水超过5 mm以上将降低大气污染物区域传输效率.对于不同污染类型和重污染阶段,轻度污染天气时区域输送贡献最为明显,比均值偏高20.5%,重污染天气虽受静稳气团控制,但由于周边区域高浓度的PM2.5,污染气团迁移对区域内污染聚集传输有显著影响,重污染期间PM2.5输送贡献占比超过均值,约偏高10%~15%.重污染过程中,开始积累阶段和峰值阶段,输送贡献占比高于其它时期,与暴发阶段相比偏高14.5%和19.5%,重污染暴发阶段本地排放贡献更明显,比均值偏高9.9%.

关 键 词:区域输送  数值模拟  标记法  天津  天气背景
收稿时间:2020/4/28 0:00:00
修稿时间:2020/5/20 0:00:00

Transport Characteristics of Air Pollution in Tianjin Based on Weather Background
CAI Zi-ying,YAN Xu,HAN Su-qin,YAO Qing,LIU Jin-le.Transport Characteristics of Air Pollution in Tianjin Based on Weather Background[J].Chinese Journal of Environmental Science,2020,41(11):4855-4863.
Authors:CAI Zi-ying  YAN Xu  HAN Su-qin  YAO Qing  LIU Jin-le
Institution:Tianjin Environmental Meteorological Center, Tianjin 300074, China;CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China; Tianjin Meteorological Institute, Tianjin 300074, China
Abstract:Regional transport is an important factor when considering the prevention and control of air pollution. The aim of this study was to provide support for the joint prevention and control of air pollution in the Beijing-Tianjin-Hebei region. With a focus on an analysis of the relationship between regional transport and meteorological conditions based on the weather background, an atmospheric chemical model was developed to quantitatively estimate the impact of regional transport on Tianjin from October 2016 to September 2017. The results showed that the contribution percentage of regional transport in cities in plains in the Beijing-Tianjin-Hebei region was significantly higher than in cities in mountains. The local contribution of PM2.5 in the Tianjin area was 62.9% and the contribution of regional transport was 37.1%. This was mainly affected by transmissions of Chanzhou, Langfang, central and southern Hebei, Beijing, Tanshan, and Shandong. Regional transport was the most significant from April to June, the weakest from July to August, and the highest contributor to local emissions. Regional transport was closely related to weather situation, wind field, precipitation, and other meteorological conditions. Post-high pressure and pre-frontal low pressure were the two types of pollution weather with the highest proportion in regional transport, and the impact of air pollution transport under the southwest wind, westerly wind and south wind was the most apparent. Wind speed of 2-3 m·s-1 was beneficial to the regional transport of PM2.5, and precipitation above 5 mm will effectively reduce the regional transport of air pollutants. For different pollution types and heavy pollution stages, the contribution of regional transport was the most apparent in light pollution weather, being 20.5% higher than the average. The heavy pollution weather was controlled by static stable air mass, and because of the migration of high PM2.5 concentrations, pollution air mass in the surrounding area had a significant impact on the accumulation of pollution and transport in the region. The contribution ratio of PM2.5 transport in the heavy pollution period was more than the average and was approximately 10% and 15% higher. In the process of heavy pollution, the proportion of transport contribution in the initial accumulation stage and peak stage were higher than in other periods, and 14.5% and 19.5% higher than in the outbreak stage. The contribution of local emissions in the outbreak stage was more significant, being 9.9% higher than average.
Keywords:regional transport  numerical simulation  labeling method  Tianjin  weather background
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