研究论文
赵芸程,李杰,杜惠云,王辉,王自发,杨文夷,吴其重.北京夏季近地面臭氧及其来源的数值模拟研究[J].环境科学学报,2019,39(7):2315-2328
北京夏季近地面臭氧及其来源的数值模拟研究
- Numerical simulation of near-surface ozone and its sources in Beijing in summer
- 基金项目：国家自然基金重大国际合作与交流项目（No.41571130034）；国家自然基金重大研究计划重点项目（No.91744203）
- 作者
- 单位
- 赵芸程
- 1. 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室, 北京 100029;2. 中国科学院大学, 北京 100049
- 李杰
- 1. 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室, 北京 100029;2. 中国科学院城市环境研究所区域大气环境研究卓越创新中心, 厦门 361021
- 杜惠云
- 1. 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室, 北京 100029;2. 中国科学院大学, 北京 100049
- 王辉
- 北京师范大学全球变化与地球系统科学研究院, 北京 100875
- 王自发
- 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室, 北京 100029
- 杨文夷
- 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室, 北京 100029
- 吴其重
- 北京师范大学全球变化与地球系统科学研究院, 北京 100875
- 摘要：高浓度的近地面臭氧一直是北京夏季面临的主要污染问题，本文利用自主发展的空气质量数值模式WRF-NAQPMS（Weather Research and Forecasting Model-Nested Air Quality Prediction Modelling System）以及生物源排放模式MEGAN（Model of Emission of Gases and Aerosols from Nature），数值模拟了2017年6月华北区域臭氧的时空分布，评估了生物源排放可挥发有机物对臭氧的影响，并对北京臭氧的关键源区和形成时间进行量化解析.结果发现：NAQPMS（Nested Air Quality Prediction Modelling System）模式合理再现了北京及其周边臭氧的时空演变规律，特别是生物源的加入有效改善臭氧浓度的模拟效果.生物源对北京6月臭氧浓度月均值的贡献为4%~6%，对最大1小时浓度的贡献最高可达8%以上.源解析结果发现，本地当天排放的臭氧前体物对北京城区浓度影响最大，对最大1小时浓度和8小时移动平均浓度的贡献达到50.2%和45.4%，远高于1~2天前排放污染物的影响.河北对北京的影响主要集中在当天和1天前排放的污染物，对最大1小时浓度的贡献分别为7.9%和6.5%.河南和山东对北京城区最大1小时浓度的贡献较小，分别为2.4%和3.7%，且主要为1~2天前排放的污染物在区域输送过程中的化学反应所贡献.对于北京区域平均来讲，本地的贡献率较城区明显偏小，河北的贡献显著增加，这也说明北京市臭氧来源的空间不均匀性较大.北京地区生成的臭氧沿怀柔区向北输送，到达承德市西侧，对月均值的贡献达到20~30 μg·m-3.
- Abstract：High near-surface ozone has been the main pollution problem in Beijing during the summer. This study employed the self-developed Nested Air Quality Prediction Modeling System (NAQPMS) and the model of Emission of Gases and Aerosols from Nature (MEGAN) to numerically simulate the spatial and temporal distribution of ozone in North China for June 2017. The impact of volatile organic compounds emitted by biological sources on ozone was evaluated, and the key source areas and formation time of Beijing ozone were quantified. The results show that the NAQPMS model reasonably reproduces the temporal and spatial evolution of ozone in Beijing and its surrounding areas. Particularly the added biological sources effectively improved the model skill on ozone concentration. The contribution of biological sources to Beijing's monthly average ozone concentration in June was 4%~6%, and the contribution to the maximum 1-hour concentration is up to 8%. The source apportionment of ozone found that the ozone precursors emitted on current day had the greatest impact on the concentration of ozone in Beijing urban area, and contributed 50.2% and 45.4% to the maximum 1-hour concentration and 8 hour moving average concentration, which was much higher than the influence of emissions before 1~2 days. The impact of Hebei on Beijing mostly came from the chemical transformation of emissions on the current day and one day ago, contributing 7.9% and 6.5% to the maximum 1-hour concentration, respectively. Henan and Shandong contributed less to the maximum 1-hour concentration in the urban area of Beijing, 2.4% and 3.7%, respectively. For the Beijing area on average, the local contribution was significantly smaller than that of the urban area, while the contribution of Hebei significantly increased. This indicates the highly spatial heterogeneity of ozone sources in Beijing. The ozone generated in the Beijing area was able to be transported northward along the Huairou area to the west side of Chengde City, contributing 20~30 μg·m-3 to the monthly mean.
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