School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
Sci Total Environ. 2020 Nov 25;745:141130. doi: 10.1016/j.scitotenv.2020.141130. Epub 2020 Jul 21.
Regarding the continuous worsening of tropospheric ozone pollution, the scenario in Shanghai is a microcosm of the entire China. Understanding the ozone formation regimes (OFRs), their variations, and driving factors is a prerequisite for formulating effective ozone control strategies. Traditional OFR estimation by numerical model, which often involves sensitivity analysis on at least tens of scenarios, is labor-intensive and time-consuming; therefore, it is not appropriate to make OFR forecasts to guide ozone contingency control. In this study, by using a localized modeling system consisting of the Weather Research and Forecasting, Sparse Matrix Operator Kernel Emissions, and Community Multiscale Air Quality models and considering the latest emission inventory over the Yangtze River Delta of China, we discovered a strong connection between the variations of large-scale circulation (LSC) and OFRs over Shanghai in July 2017, thereby providing an alternative way to infer OFR. During the northward movement of Western Pacific Subtropical High from South China Sea, the wind field over Shanghai changed from weak westerly to moderate southwesterly and to one without a distinct direction. The local OFR shifted from anthropogenic volatile organic compounds (AVOCs)-limited to NO-limited and ultimately to the transitional regime. Such a variation in OFR is essentially driven by the spatial heterogeneity of NO and AVOC emissions in different directions of Shanghai, brought on by the wind under different LSC patterns. With the existing weather forecasting technology, the LSC patterns can be well-predicted 48-72 h in advance. Hence, we propose the adoption of a dynamic ozone control strategy for Shanghai with the priority control target on AVOC or NO emission sources adjusted according to the LSC pattern and OFR forecasts in a forthcoming O pollution episode. This would serve to maximize the peak ozone reduction under varying pollution conditions.
针对日益恶化的对流层臭氧污染问题，上海的情况是中国整体的一个缩影。了解臭氧形成机制（OFR）、其变化及其驱动因素是制定有效臭氧控制策略的前提。传统的数值模型 OFR 估计通常涉及至少数十个情景的敏感性分析，既耗费人力又耗时，因此不适合进行 OFR 预测以指导臭氧应急控制。在这项研究中，我们使用了一个由天气研究与预报模型、稀疏矩阵算子核排放模型和社区多尺度空气质量模型组成的本地化建模系统，并考虑了中国长江三角洲的最新排放清单，发现 2017 年 7 月上海的大尺度环流（LSC）变化与 OFR 之间存在很强的联系，从而提供了一种推断 OFR 的替代方法。当西太平洋副热带高压从南海向北移动时，上海的风场从弱西风变为中西南风，最后变为无明显方向的风。当地的 OFR 从人为挥发性有机化合物（AVOC）限制转变为氮氧化物（NO）限制，最终转变为过渡状态。这种 OFR 的变化本质上是由上海不同方向的 NO 和 AVOC 排放的空间异质性以及不同 LSC 模式下的风引起的。利用现有的天气预报技术，可以提前 48-72 小时很好地预测 LSC 模式。因此，我们建议为上海采用一种动态臭氧控制策略，根据即将到来的 O 污染事件中的 LSC 模式和 OFR 预测，优先控制目标调整 AVOC 或 NO 排放源，以在不同污染条件下最大限度地减少峰值臭氧。
