Assessing the responsiveness of an air quality model to real-world emission changes for air quality planning.

Publication date: Aug 25, 2026

This paper exploits a real-world scenario of significant emission reductions, such as that provided by the COVID-19 pandemic, to assess the responsiveness of an air quality model to real-world emission changes across Europe. The assessment is performed by evaluating the CHIMERE chemical transport model results against observation-based estimates derived from five-year mean concentrations, a Generalised Additive Model, and, for Spain, Gradient Boosting Machine. The impacts estimated by CHIMERE agree quite well with the observation-based estimates, with larger differences in some parts of northern Italy and northern Spain. All methodologies demonstrate generalized and robustly agreed reductions in NO concentrations. Regarding O, the results from the observation-based methodologies concur with the differing impacts estimated by CHIMERE depending on the metric used. Consistent across all methods, the largest O reductions were found for metrics influenced by periods with higher solar radiation. Conversely, for the annual mean in areas with high NOx emissions, all methodologies consistently estimate O increases, a non-linear response driven by reduced NO-titration that validates the model’s ability to capture complex chemical dynamics. By successfully validating CTM responsiveness under real-world emission shifts, this study indicates that these models can be considered useful tools for evaluating future emission reduction scenarios in air quality planning, although considering the uncertainties in areas where local conditions are challenging to model.

Semantics

Original Article