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A modeling study of the nonlinear response of fine particles to air pollutant emissions in the Beijing-Tianjin-Hebei region

机译：京津冀地区空气污染物排放的非线性响应的建模研究

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摘要

The Beijing-Tianjin-Hebei (BTH) region has been suffering from the most severe fine particle (PM2.5) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM2.5 concentrations has been a challenging task because of the complicated non-linear relationships between PM2.5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the Extended Response Surface Modeling (ERSM) technique to investigate the nonlinear response of PM2.5 and its major chemical components to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM2.5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that primary inorganic PM2.5 is the single pollutant which makes the largest contribution (24-36 %) to PM2.5 concentrations. The contribution of primary inorganic PM2.5 emissions is especially high in heavily polluted winter, and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM2.5 control strategies. The total contributions of all precursors (nitrogen oxides, NOx; sulfur dioxides, SO2; ammonia, NH3; non-methane volatile organic compounds, NMVOC; intermediate-volatility organic compounds, IVOC; primary organic aerosol, POA) to PM2.5 concentrations range between 31 % and 48 %. Among these precursors, PM2.5 concentrations are primarily sensitive to the emissions of NH3, NMVOC + IVOC, and POA. The sensitivities increase substantially for NH3 and NOx, and decrease slightly for POA and NMVOC + IVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM2.5 concentrations. The contributions of primary inorganic PM2.5 emissions to PM2.5 concentrations are dominated by local emission sources, which account for over 75 % of the total primary inorganic PM2.5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.
机译：北京天津 - 河北（BTH）地区一直遭受中国最严重的细颗粒（PM2.5）污染，这导致严重的健康损害和经济损失。由于多个空间区域和经济部门的多种污染物的多种污染物的浓度和排放之间的非线性关系复杂的非线性关系，量化对PM2.5浓度的源贡献一直是一个具有挑战性的任务。在这项研究中，我们使用扩展响应表面建模（ERSM）技术来研究PM2.5的非线性响应及其主要化学成分，基于超过1000模拟的不同地区和地区的多个污染物的排放。化学传输模型（CTM）。 ERSM预测的PM2.5浓度与独立的CTM仿真吻合良好，相关系数大于0.99，并且平均归一化误差小于1％。使用ERSM技术，我们发现主要无机PM2.5是单一的污染物，使PM2.5浓度最大的贡献（24-36％）。主要无机PM2.5排放的贡献在严重污染的冬季尤为较高，并且由该行业以及住宅和商业部门主导，应在PM2.5控制策略中优先考虑。所有前体的总贡献（氮氧化物，NOx;硫化二氧化硫，SO2;氨，NH3;非甲烷挥发性有机化合物，NMVOC;中间挥发性有机化合物，IVOC;初级有机气溶胶，POA）至PM2.5浓度范围31％和48％之间。在这些前体中，PM2.5浓度主要对NH3，NMVOC + IVOC和POA的排放敏感。对于NH 3和NOx而言，敏感性基本上增加，并且POA和NMVOC + IVOC略微降低，随着排放减少率的增加，该抑制率下降，其说明前体排放和PM2.5浓度之间的非线性关系。初级无机PM2.5排放对PM2.5浓度的贡献由当地排放来源主导，占总初级无机PM2.5捐款的75％以上。然而，对于前体，其他地区的排放可以在夏天和BTH北部的局部排放来源发挥类似的作用。各种类型的重污染剧集的源贡献特征彼此明显不同，并且从月平均结果均不同，说明控制策略应基于不同类型的发作期间的主要贡献来源来区分。

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  • 来源
    《Atmospheric chemistry and physics》 |2017年第19期|共20页
  • 作者

    Zhao Bin; Wu Wenjing; Wang Shuxiao; Xing Jia; Chang Xing; Liou Kuo-Nan; Jiang Jonathan H.; Gu Yu; Jang Carey; Fu Joshua S.; Zhu Yun; Wang Jiandong; Lin Yan; Hao Jiming;

  • 作者单位

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Univ Calif Los Angeles Joint Inst Reg Earth Syst Sci &

    Engn Los Angeles CA 90095 USA;

    CALTECH Jet Prop Lab Pasadena CA 91109 USA;

    Univ Calif Los Angeles Joint Inst Reg Earth Syst Sci &

    Engn Los Angeles CA 90095 USA;

    US EPA Res Triangle Pk NC 27711 USA;

    Univ Tennessee Dept Civil &

    Environm Engn Knoxville TN 37996 USA;

    South China Univ Technol Sch Environm Sci &

    Engn Guangzhou 510006 Guangdong Peoples R China;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

    Norwegian Inst Water Res N-0349 Oslo Norway;

    Tsinghua Univ Sch Environm Beijing 100084 Peoples R China;

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  • 正文语种 eng
  • 中图分类 大气科学（气象学）;
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