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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:京津冀地区细颗粒物对大气污染物排放的非线性响应模型研究

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摘要

The Beijing–Tianjin–Hebei (BTH) region has been suffering fromthe most severe fine-particle (PM) pollution in China, which causesserious health damage and economic loss. Quantifying the source contributionsto PM concentrations has been a challenging task because of thecomplicated nonlinear relationships between PM concentrations andemissions of multiple pollutants from multiple spatial regions and economicsectors. In this study, we use the extended response surface modeling (ERSM)technique to investigate the nonlinear response of PM concentrationsto emissions of multiple pollutants from different regions and sectors overthe BTH region, based on over 1000 simulations by a chemical transport model(CTM). The ERSM-predicted PM concentrations agree well withindependent CTM simulations, with correlation coefficients larger than 0.99and mean normalized errors less than 1 %. Using the ERSM technique, wefind that, among all air pollutants, primary inorganic PM makes thelargest contribution (24–36 %) to PM concentrations. Thecontribution of primary inorganic PM emissions is especially high inheavily polluted winter and is dominated by the industry as well asresidential and commercial sectors, which should be prioritized in PMcontrol strategies. The total contributions of all precursors (nitrogenoxides, NO; sulfur dioxides, SO; ammonia, NH; non-methanevolatile organic compounds, NMVOCs; intermediate-volatility organiccompounds, IVOCs; primary organic aerosol, POA) to PM concentrationsrange between 31 and 48 %. Among these precursors, PMconcentrations are primarily sensitive to the emissions of NH,NMVOC + IVOC, and POA. The sensitivities increase substantially forNH and NO and decrease slightly for POA and NMVOC + IVOC withthe increase in the emission reduction ratio, which illustrates the nonlinearrelationships between precursor emissions and PM concentrations. Thecontributions of primary inorganic PM emissions to PMconcentrations are dominated by local emission sources, which account forover 75 % of the total primary inorganic PM contributions. Forprecursors, however, emissions from other regions could play similar roles tolocal emission sources in the summer and over the northern part of BTH. Thesource contribution features for various types of heavy-pollution episodesare distinctly different from each other and from the monthly mean results,illustrating that control strategies should be differentiated based on themajor contributing sources during different types of episodes.
机译:京津冀地区一直遭受着中国最严重的细颗粒物污染,造成严重的健康损害和经济损失。由于PM浓度与来自多个空间区域和经济部门的多种污染物的排放之间存在复杂的非线性关系,因此量化源对PM浓度的贡献一直是一项艰巨的任务。在这项研究中,我们基于化学传递模型(CTM)进行的1000多次模拟,使用扩展响应表面建模(ERSM)技术来研究PM浓度对BTH区域不同区域和部门的多种污染物排放的非线性响应。 ERSM预测的PM浓度在独立的CTM模拟中非常吻合,相关系数大于0.99,平均归一化误差小于1%。使用ERSM技术,我们发现,在所有空气污染物中,主要的无机PM对PM浓度的贡献最大(24–36%)。在严重污染严重的冬季中,主要的无机PM排放的贡献特别高,并且受工业以及居民和商业部门的支配,在PM控制策略中应优先考虑。所有前体(氮氧化物,NO,二氧化硫,SO,氨,NH,非甲烷挥发性有机化合物,NMVOC,中等挥发性有机化合物,IVOC,初级有机气溶胶,POA)对PM浓度的总贡献在31%至48%之间。在这些前体中,PM浓度主要对NH,NMVOC ++ IVOC和POA的排放敏感。随着排放减少率的增加,对NH和NO的敏感性显着增加,而对POA和NMVOC ++ IVOC的敏感性则略有降低,这说明前体排放与PM浓度之间存在非线性关系。初级无机PM排放对PM浓度的贡献主要由当地排放源决定,占本地无机PM贡献总量的75%以上。然而,作为前体,在夏季和BTH北部,其他地区的排放可能与本地排放源起类似的作用。各种类型的重污染事件的源贡献特征彼此不同,并且与月平均结果也存在明显差异,这说明在不同类型的事件中应根据主要的贡献源来区分控制策略。

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