School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
United States Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States.
Environ Sci Technol. 2021 Apr 20;55(8):4504-4512. doi: 10.1021/acs.est.0c08625. Epub 2021 Mar 16.
US background (US-B) ozone (O) is the O that would be present in the absence of US anthropogenic (US-A) emissions. US-B O varies by location and season and can make up a large, sometimes dominant, portion of total O. Typically, US-B O is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O. We develop and apply a method to fuse observed O with US-B O simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O. Trends in O bias are explored across different simulation years and varying model scales. We found that the CTM US-B O estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O. Across the four estimates, we found annual mean CTM-simulated US-B O ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.
美国背景（US-B）臭氧（O）是指在不存在美国人为排放的情况下存在的臭氧。US-B O 因地点和季节而异，可能占总 O 的很大一部分，有时甚至占主导地位。通常，使用化学输送模型（CTM）来量化 US-B O，但由于模型过程描述和输入中存在潜在误差，结果不确定，并且各种模型对 US-B O 的估计存在显著差异。我们开发并应用了一种方法，将观测到的 O 与区域 CTM（CMAQ）模拟的 US-B O 融合。我们根据空间和时间将模型偏差分配给 US-B 和 US-A O。我们探讨了不同模拟年份和不同模型尺度下 O 偏差的趋势。我们发现，在过去几年（2016-2017 年）和不同的模型尺度中，CTM 模拟的 US-B O 在春季通常偏低，秋季偏高。US-A O 平均偏高，随着分辨率较粗的模拟，偏差增大。通过应用我们的数据融合偏差调整方法，我们估计调整后的 US-B O 的一致性提高了 28%。在这四个估计中，我们发现 CTM 模拟的年度平均 US-B O 范围从 30 到 37 ppb，春季平均值范围从 32 到 39 ppb。应用偏差调整后，我们发现年度平均 US-B O 范围从 32 到 33 ppb，春季平均值范围从 37 到 39 ppb。
