The sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O₃ and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O₃ and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

空气质量模型对输入数据（例如排放、气象和边界条件）或模型配置修改的敏感性被认为是支持空气质量计划的空气质量建模应用的一个重要问题。在 FAIRMODE（欧洲空气质量建模论坛，https://fairmode.jrc.ec.europa.eu/）的框架内，专门设计了空气质量建模练习来解决这个问题。主要目标是在研究排放情景/预测时，通过评估模型输出响应排放变化的变化来评估空气质量模型响应的幅度和变异性。这项工作基于多个空气质量模型，这些模型用于支持模型用户和开发人员，从而支持政策制定者。我们介绍了 FAIRMODE 演习和参与模型，并分析了减排情景导致的O _{3和 PM 浓度的变化。}与其他做法相比，关键的新颖特征是，本工作中的减排策略是在大量城市的城市规模上使用绝对潜力、相对潜力和绝对效力等新指标进行应用和评估的。结果表明，当应用减排情景时，模型之间的浓度变化比各自的基线绝对浓度有更大的变异性。对于臭氧，绝对基线浓度模型之间的变异性低于 10%，而浓度变化的变异性（当排放受到类似干扰时）在某些情况下在事件期间超过 100% 或更高。_{对于O 3}和PM，组合减排通常比单一前体减排的总和更有效。特别是对于臭氧，模型响应在线性和可加性方面显示出非线性化学过程的明显影响。该分析深入了解了设计空气质量计划时可能考虑的模型敏感性对减排量的影响，并为更深入的分析铺平了道路，以理清排放量在当前和未来空气质量评估的模型制定中的作用。