Chemical transport models (CTMs) are widely used in scientific studies and air quality management. A particular application is to assess how pollutant concentrations, particularly ozone and PM, respond to emission controls. As part of a dynamic evaluation, two widely used CTMs, CMAQ and CAMx, were evaluated for how well they captured trends in observed surface ozone, as well as the changes in associated concentrations, between 2001 to 2011 and 2011 to 2016. Those periods were chosen as additional effort was used to improve emissions estimates for all three years. Prior studies found that both emissions inventories and observations indicate reductions in both ozone and NO₂ during these periods, but studies have found that NO₂ observations have not agreed as well with estimated emissions. In general, given the efforts to harmonize model inputs, the two models performed very similarly and captured the ozone declines for both the 2001–2011 and 2011–2016 periods at the national level, though moderate biases were found in some locations. At the national scale, ozone trends for the 2011–2016 period were better captured by the models than those for the 2001–2011 period. Both models overestimated the observed decrease in ground level NO₂ at the higher end of the concentration spectrum. This is linked to, and can cause, the detected tendency of the models to have larger than observed increases in ozone at the lower end over time. At the metropolitan statistical area (MSA) level, the performance of capturing the ozone trends varied. For some MSA's, the models estimated the wrong direction in the trends; e.g., in Denver, where maximum 8-hr average ozone levels increased from 2001 to 2011, the models predicted a decrease. The US EPA recommends location specific Relative Response Factors (RRFs) to adjust results when applying the models for air quality management purposes. At the regional level, the simulated ozone RRFs were up to 8% larger than the observed RRFs for the 2001–2011 period, suggesting that the models underestimated the ozone decrease. For the 2011–2016 period, with a couple of exceptions, the simulated RRFs were up to 9% smaller than observed RRFs, indicating a negative bias in the simulated trend. For individual MSAs, the ratio of the simulated to observed RRFs ranged between 0.86 (Knoxville for 2001–2011) and 1.40 (Los Angeles for 2001–2011). This is possibly linked to potential biases in the estimated emissions trends. Such high biases could lead to similar biases in the estimated emissions controls required for an area to attain the air quality standards. The results indicate that there are both spatial and temporal biases in how well the two models have captured the observed ozone and NO₂ trends. Air quality planning agencies should aim to diagnose and reduce such biases before using model results for air quality management.

化学传输模型 (CTM) 广泛用于科学研究和空气质量管理。一个特定的应用是评估污染物浓度，特别是臭氧和 PM，如何响应排放控制。作为动态评估的一部分，评估了两种广泛使用的 CTM，CMAQ 和 CAMx，它们在 2001 年至 2011 年和 2011 年至 2016 年期间捕捉观察到的地表臭氧趋势以及相关浓度变化的能力。这些时期是被选为额外努力用于改进所有三年的排放估计。先前的研究发现，排放清单和观察结果都表明在这些时期臭氧和 NO ₂都减少了，但研究发现 NO ₂观察结果与估计的排放量也不一致。总的来说，考虑到协调模型输入的努力，这两个模型的表现非常相似，并捕捉到了 2001-2011 年和 2011-2016 年国家层面的臭氧下降情况，尽管在某些地方发现了适度的偏差。在国家范围内，与 2001-2011 年期间相比，模型更好地捕捉了 2011-2016 年期间的臭氧趋势。两个模型都高估了观测到的地平面 NO _{2 的}下降在浓度谱的高端。这与模型检测到的趋势有关，并且可能导致检测到的臭氧在低端随时间的增加大于观察到的增加。在大都市统计区 (MSA) 级别，捕捉臭氧趋势的性能各不相同。对于一些 MSA，模型估计了错误的趋势方向；例如，在丹佛，最大 8 小时平均臭氧水平从 2001 年到 2011 年增加，模型预测会下降。美国环保署建议在将模型应用于空气质量管理目的时，使用特定于位置的相对响应因子 (RRF) 来调整结果。在区域层面，模拟的臭氧 RRF 比 2001 年至 2011 年期间观测到的 RRF 大 8%，表明模型低估了臭氧的减少。在 2011-2016 年期间，除了几个例外，模拟 RRF 比观察到的 RRF 小 9%，表明模拟趋势存在负偏差。对于单个 MSA，模拟 RRF 与观察到的 RRF 之比介于 0.86（诺克斯维尔 2001-2011）和 1.40（洛杉矶 2001-2011）之间。这可能与估计排放趋势的潜在偏差有关。如此高的偏差可能会导致一个地区达到空气质量标准所需的估计排放控制出现类似偏差。结果表明，两个模型在捕捉观测到的臭氧和 NOx 方面存在空间和时间偏差 模拟与观察到的 RRF 之比介于 0.86（诺克斯维尔 2001-2011）和 1.40（洛杉矶 2001-2011）之间。这可能与估计排放趋势的潜在偏差有关。如此高的偏差可能会导致一个地区达到空气质量标准所需的估计排放控制出现类似偏差。结果表明，两个模型在捕捉观测到的臭氧和 NOx 方面存在空间和时间偏差 模拟与观察到的 RRF 之比介于 0.86（诺克斯维尔 2001-2011）和 1.40（洛杉矶 2001-2011）之间。这可能与估计排放趋势的潜在偏差有关。如此高的偏差可能会导致一个地区达到空气质量标准所需的估计排放控制出现类似偏差。结果表明，两个模型在捕捉观测到的臭氧和 NOx 方面存在空间和时间偏差₂趋势。在使用模型结果进行空气质量管理之前，空气质量规划机构应致力于诊断和减少此类偏差。