Regional-scale air quality models and observations at routine air quality monitoring sites are used to determine attainment/non-attainment of the ozone air quality standard in the United States. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Because of the stochastic nature of the atmosphere, the same meteorological conditions would not prevail in the future year. Therefore, we use multi-decadal observations to develop a new method for estimating the confidence bounds for the future ozone design value (based on the 4th highest value in the daily maximum 8-hr ozone concentration time series, DM8HR) for each emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at all monitoring sites in the contiguous United States. To this end, we spectrally decompose the observed DM8HR ozone time series covering the period from 1981 to 2014 using the Kolmogorov-Zurbenko (KZ) filter and examine the variability in the relative strengths of the short-term variations (induced by synoptic-scale weather fluctuations; referred to as synoptic component, SY) and the long-term component (dictated by changes in emissions, seasonality and other slow-changing processes such as climate change; referred to as baseline component, BL). Results indicate that combining the projected change in the ozone baseline level with the adjusted synoptic forcing in historical ozone observations enables us to provide a probabilistic assessment of the efficacy of a selected emissions control strategy in complying with the ozone standard in future years. In addition, attainment demonstration is illustrated with a real-world application of the proposed methodology by using air quality model simulations, thereby helping build confidence in the use of regional-scale air quality models for supporting regulatory policies.

中文翻译：

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一种评估排放控制策略有效性的新方法

区域尺度的空气质量模型和常规空气质量监测站点的观测用于确定美国是否达到臭氧空气质量标准。在当前的监管应用中，区域尺度的空气质量模型应用于基准年和未来一年，使用与基准年相同的气象条件减少排放。由于大气的随机性，未来一年不会出现同样的气象条件。因此，我们使用多年代际观测来开发一种新方法来估计未来臭氧设计值的置信界限（基于每日最大 8 小时臭氧浓度时间序列中的第 4 个最高值，DM8HR) 以及在美国毗邻的所有监测点设计值超过给定臭氧阈值浓度的概率。为此，我们使用 Kolmogorov-Zurbenko (KZ) 滤波器对 1981 年至 2014 年期间观测到的 DM8HR 臭氧时间序列进行光谱分解，并检查短期变化的相对强度的可变性（由天气尺度天气引起）波动；称为天气成分，SY）和长期成分（由排放变化、季节性和其他缓慢变化的过程（如气候变化）决定；称为基线成分，BL）。结果表明，将臭氧基线水平的预计变化与历史臭氧观测中调整后的天气强迫相结合，使我们能够对选定的排放控制策略在未来几年符合臭氧标准的有效性进行概率评估。此外，通过使用空气质量模型模拟，通过所提议方法的实际应用来说明实现证明，从而帮助建立对使用区域尺度空气质量模型支持监管政策的信心。