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Impact of mobile source emission inventory adjustment on air pollution photochemical model performance
Urban Climate ( IF 6.4 ) Pub Date : 2020-04-07 , DOI: 10.1016/j.uclim.2020.100618
Hossein Shahbazi , Vahid Hosseini

Coupled weather forecasting and chemical transport models are useful tools to evaluate air pollution episodes in big cities for the purpose of forecasting and air pollution abatement measures' evaluation. However, large set of accurate data of various sources and modeling calibrations are needed for such complex modeling system to be reliable. The problem becomes more obvious when the model is operated over a domain in which there is a general lack of accurate input data such as emission inventory data. The current study investigates the possibility of model tuning for more accurate prediction of air pollutant concentrations in the city of Tehran in an air pollution episode as a case study. In the last several years, Tehran has frequently experienced long episodes of air pollution, often resulted in forced holidays in the city. WRF/CAMx modeling system were used to simulate the spatial distribution of pollutants concentrations over the city of Tehran in a highly polluted episode during December 2017. In order to improve model performance, a methodology was developed and used to adjust the emission inventory of the system. Due to lack of national emission inventory and background data, effects of introducing a top-down national emission inventory to the modeling system was investigated. This emission inventory can also be used to prepare more accurate boundary concentrations for countries located near Iran. In addition, emission rates of the most dominant emission sources of the city of Tehran – mobile sources – were adjusted using the results from chassis dynamometer tests. Also, the hourly variations of emission were improved using measured ambient NO2 concentrations. Comparison of modeled and measured concentrations at air quality monitoring stations for various pollutant showed that emission adjustment was the most effective method which resulted in considerable model performance improvement over Tehran. Results showed that due to better and more realistic emission factors used in the model, bias error for CO, NO2, O3 and PM2.5 reduced by 0.36 ppm, 3.4 ppb, 0.19 ppb and 2.14 μg/m3, respectively and correlation coefficient for CO, O3 and PM2.5 increased by 0.13, 0.02 and 0.6. This proves the possibility of using such method to improve model performance when there is a general lack of accurate input data.



中文翻译:

移动源排放清单调整对空气污染光化学模型性能的影响

天气预报和化学物运输模型相结合是评估大城市空气污染事件的有用工具,目的是进行预测和空气污染减轻措施的评估。但是,要使这种复杂的建模系统可靠,就需要大量大量的各种来源的准确数据和建模校准。当模型在通常缺少准确的输入数据(例如排放清单数据)的域上运行时,问题变得更加明显。当前的研究以案例研究的方式研究了模型调整的可能性,以更准确地预测德黑兰市空气污染事件中的空气污染物浓度。在过去的几年中,德黑兰经常经历长时间的空气污染,常常导致该城市被迫休假。WRF / CAMx建模系统用于模拟德黑兰市2017年12月高污染事件中污染物浓度的空间分布。为了提高模型性能,开发了一种方法并用于调整系统的排放清单。由于缺乏国家排放清单和背景数据,因此研究了将自上而下的国家排放清单引入建模系统的效果。此排放清单还可用于为伊朗附近的国家准备更准确的边界浓度。此外,德黑兰市最主要的排放源(移动源)的排放率使用底盘测功机测试的结果进行了调整。而且,使用测量的环境NO2浓度可以改善排放的每小时变化。在空气质量监测站对各种污染物的建模浓度和实测浓度的比较表明,排放调节是最有效的方法,与德黑兰相比,其模型性能得到了显着改善。结果表明,由于在模型中使用了更好,更实际的排放因子,CO,NO2,O3和PM2.5的偏差分别降低了0.36 ppm,3.4 ppb,0.19 ppb和2.14μg/ m3,CO的相关系数,O3和PM2.5分别增加0.13、0.02和0.6。当普遍缺乏准确的输入数据时,这证明了使用这种方法改善模型性能的可能性。

更新日期:2020-04-07
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