Assessment of the sensitivity of model responses to urban emission changes in support of emission reduction strategies

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/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.Peer reviewe

Association of air pollution and green space with all-cause general practitioner and emergency room visits:A cross-sectional study of young people and adults living in Belgium

Background: Residing in areas with lower levels of air pollution and higher green space is beneficial to physical and mental health. We investigated associations of PM2.5, tree cover and grass cover with in-hours and out-of-hours GP visits and ER visits, for young people and adults. We estimated potential cost savings of GP visits attributable to high PM2.5. Methods: We linked individual-level health insurance claims data of 315,123 young people (10–24 years) and 885,988 adults (25–64 years) with census tract-level PM2.5, tree cover and grass cover. Deploying negative binomial generalized linear mixed models, we estimated associations between quartile exposures and the three outcome measures. Results: For in-hours and out-of-hours GP visits, among young people as well as adults, statistically significant pairwise differences between quartiles suggested increasing beneficial effects with lower PM2.5. The same outcomes were statistically significantly less frequent in quartiles with highest tree cover (&gt;30.00%) compared to quartiles with lower tree cover, but otherwise pairwise differences were not statistically significant. These associations largely persisted in rural and urban areas. Among adults living in urban areas lower grass cover was associated with increased in-hours GP visits and ER visits. Assuming causality, reducing PM2.5 levels to the lowest quartile (4.91–7.49 μg/m³), among adults, 195,964 in-hours and 74,042 out-of-hours GP visits could be avoided annually. Among young people, 27,457 in-hours and 22,423 out-of-hours GP visits could be avoided annually. Nationally, this amounts to an annual potential cost saving of €43 million (€5.7 million in out-of-pocket payments and €37.2 million in compulsory health insurance). Conclusion: Higher ambient PM2.5 and lower tree cover show associations with higher non-urgent and urgent medical care utilization. These findings confirm the importance of reducing air pollution and fostering green zones, and that such policies may contribute positively to economic growth.</p

Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models

The Forum of Air Quality Modelling in Europe (FAIRMODE) was launched in 2007 to bring together air quality modellers and users in order to promote and support the harmonised use of models by EU Member States, with emphasis on model application under the European Air Quality Directive. In this context, a methodology for evaluating air quality model applications has been developed. This paper presents an analysis of the strengths and weaknesses of the FAIRMODE benchmarking approach, based on users’ feedback. European wide, regional and urban scale model applications, developed by different research groups over Europe, have been taken into account. The analysis is focused on the main pollutants under the Air Quality Directive, namely PM10, NO2 and O3. The different case studies are described and analysed with respect to the methodologies applied for model evaluation and quality assurance. This model evaluation intercomparison demonstrates the potential of a harmonised evaluation and benchmarking methodology. A SWOT analysis of the FAIRMODE benchmarking approach is performed based on feedback from users of the tool. This analysis helps to identify the main advantages and value of this model evaluation benchmarking approach compared with other methodologies, in addition to highlighting requirements for future development.JRC.C.5-Air and Climat