Environmental Research Institute, Ajou University, Suwon, Republic of Korea.
Department of Environmental Engineering, Ajou University, Suwon, Republic of Korea.
Environ Int. 2023 Aug;178:108069. doi: 10.1016/j.envint.2023.108069. Epub 2023 Jun 27.
In this study, we developed a practical approach to augment elemental carbon (EC) emissions to improve the reproducibility of the most recent air quality with photochemical grid modeling in support of source-receptor relationship analysis. We demonstrated the usefulness of this approach with a series of simulations for EC concentrations over Northeast Asia during the 2016 Korea-United States Air Quality study. Considering the difficulty of acquiring EC observational data in foreign countries, our approach takes two steps: (1) augmenting upwind EC emissions based on simulated upwind contributions and observational data at a downwind EC monitor considered as the most representative monitor for upwind influences and (2) adjusting downwind EC emissions based on simulated downwind contributions, including the effects of updated upwind emissions from the first step and observational data at the downwind EC monitors. The emission adjustment approach resulted in EC emissions 2.5 times higher than the original emissions in the modeling domain. The EC concentration in the downwind area was observed to be 1.0 μg m during the study period, while the simulated EC concentration was 0.5 μg m before the emission adjustment. After the adjustment, the normalized mean error of the daily mean EC concentration decreased from 48 % to 22 % at ground monitor locations. We found that the EC simulation results were improved at high altitudes, and the contribution of the upwind areas was greater than that of the downwind areas for EC concentrations downwind with or without emission adjustment. This implies that collaborating with upwind regions is essential to alleviate high EC concentrations in downwind areas. The developed emission adjustment approach can be used for any upwind or downwind area when transboundary air pollution mitigation is needed because it provides better reproducibility of the most recent air quality through modeling with improved emission data.
在这项研究中，我们开发了一种实用的方法来增强元素碳 (EC) 排放，以提高光化学网格模型在支持源-受体关系分析方面的最新空气质量的可重复性。我们通过一系列针对 2016 年韩美空气质量研究期间东北亚地区 EC 浓度的模拟演示了这种方法的有用性。考虑到在国外获取 EC 观测数据的困难，我们的方法分为两步：(1) 根据下风 EC 监测器的模拟上风贡献和观测数据来增强上风 EC 排放，该监测器被认为是最能代表上风影响的监测器；(2) 根据模拟下风贡献调整下风 EC 排放，包括第一步中更新的上风排放和下风 EC 监测器的观测数据的影响。排放调整方法使建模域中的 EC 排放增加了 2.5 倍。在研究期间，下风地区的 EC 浓度观察到为 1.0μg/m，而在排放调整之前，模拟的 EC 浓度为 0.5μg/m。调整后，地面监测点的每日平均 EC 浓度的归一化平均误差从 48%降至 22%。我们发现，EC 模拟结果在高海拔地区得到了改善，并且无论是否进行排放调整，上风区对 EC 浓度下风的贡献都大于下风区。这意味着与上风区域合作对于减轻下风区域的高 EC 浓度至关重要。当需要减轻跨境空气污染时，可以将开发的排放调整方法用于任何上风或下风区域，因为它通过使用改进的排放数据进行建模，提供了对最新空气质量的更好可重复性。
