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APFoam 1.0: integrated computational fluid dynamics simulation of O 3 –NO x –volatile organic compound chemistry and pollutant dispersion in a typical street canyon

机译:APFOAM 1.0:O 3的综合计算流体动力学模拟-NO X -Volatile有机化合物化学和污染物分散在典型的街道峡谷中

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Urban air quality issues are closely related to human health and economic development. In order to investigate street-scale flow and air quality, this study developed the atmospheric photolysis calculation framework (APFoam 1.0), an open-source computational fluid dynamics (CFD) code based on OpenFOAM, which can be used to examine microscale reactive pollutant formation and dispersion in an urban area. The chemistry module of APFoam has been modified by adding five new types of reactions, which can implement the atmospheric photochemical mechanism (full O 3 –NO x –volatile organic compound chemistry) coupled with a CFD model. Additionally, the model, including the photochemical mechanism (CS07A), air flow, and pollutant dispersion, has been validated and shows good agreement with SAPRC modeling and wind tunnel experimental data, indicating that APFoam has sufficient ability to study urban turbulence and pollutant dispersion characteristics. By applying APFoam, O 3 –NO x –volatile organic compound (VOC) formation processes and dispersion of the reactive pollutants were analyzed in an example of a typical street canyon (aspect ratio H / W = 1 ). The comparison of chemistry mechanisms shows that O 3 and NO 2 are underestimated, while NO is overestimated if the VOC reactions are not considered in the simulation. Moreover, model sensitivity cases reveal that 82?%–98?% and 75?%–90?% of NO and NO 2 , respectively, are related to the local vehicle emissions, which is verified as the dominant contributor to local reactive pollutant concentration in contrast to background conditions. In addition, a large amount of NO x emissions, especially NO, is beneficial to the reduction of O 3 concentrations since NO consumes O 3 . Background precursors (NO x /VOCs) from boundary conditions only contribute 2?%–16?% and 12?%–24?% of NO and NO 2 concentrations and raise O 3 concentrations by 5?%–9?%. Weaker ventilation conditions could lead to the accumulation of NO x and consequently a higher NO x concentration but lower O 3 concentration due to the stronger NO titration effect, which would consume O 3 . Furthermore, in order to reduce the reactive pollutant concentrations under the odd–even license plate policy (reduce 50?% of the total vehicle emissions), vehicle VOC emissions should be reduced by at least another 30?% to effectively lower O 3 , NO, and NO 2 concentrations at the same time. These results indicate that the examination of the precursors (NO x and VOCs) from both traffic emissions and background boundaries is the key point for understanding O 3 –NO x –VOCs chemistry mechanisms better in street canyons and providing effective guidelines for the control of local street air pollution.
机译:城市空气质量问题与人类健康和经济发展密切相关。为了调查街道尺度流量和空气质量,本研究开发了基于OpenFoam的开源计算流体动力学(APFOAM 1.0),这是一种基于OpenFoam的开源计算流体动力学(CFD)代码,可用于检查MicrossoRME无功污染物形成和城市地区的分散。通过添加五种新型反应来改变APFOAM的化学模块,其可以实现与CFD模型相结合的大气光化学机制(全o 3-no x -volatile有机化合物化学)。此外,该模型包括光化学机制(CS07a),空气流和污染物分散,并验证了与SAPRC建模和风洞实验数据良好的一致性,表明APFOAM具有学习城市湍流和污染物分散特性的能力。 。通过施加APFOAM,在典型的街道峡谷(纵横比H / W = 1)的实例中,分析了O 3-NO X-Volatile有机化合物(VOC)形成方法和反应污染物的分散体。化学机制的比较表明,o 3和否2被低估,而如果在模拟中不考虑VOC反应,则不估计。此外,模型敏感性案例显示,分别82?%-98?%和75?% - 90?%和75?% - 90?%,没有2,没有局部车辆排放,其被验证为局部反应性污染物浓度的主要贡献者与背景条件相反。此外,大量的NO X排放,特别是否,是有益于O 3浓度的减少,因为不消耗O 3。背景技术来自边界条件的前体(无X / VOC)仅贡献2?% - 16?%,24〜24〜24〜24倍,NO 2浓度,并将O 3浓度升高5〜%-9?%。较弱的通风条件可能导致NO X的积累,因此由于不滴定效应较强的滴定效应而导致的NO X浓度较高,而不是X浓度,这将消耗O 3。此外,为了降低奇数甚至车牌政策下的反应性污染物浓度(减少50?%的总车辆排放量),载体VOC排放应至少另外30倍,有效降低O 3,没有,没有2个浓度同时。这些结果表明,来自交通排放和背景边界的前体(NO X和VOC)的检查是在街道峡谷中更好地了解O 3-No X -Vocs化学机制的关键点,并为当地控制提供有效的指导方针街头空气污染。

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