Air quality in European cities is still a challenge, with various urban areas frequently exceeding the PM2.5 and NO2 concentration levels allowed by the European Union Air Quality Standards. This is ...a problem both in terms of legislation compliance, but also in terms of health of citizens, as it has been recently estimated that 400 to 450 thousand people die prematurely every year due to poor air quality. Air quality in cities can be improved with a number of interventions, at different sectoral (industry, traffic, residential, etc …) and geographical (international, European, national, local, etc.) levels. In this paper we explore the potential of city level plans to improve mobility and air quality (excluding electro-mobility options, not considered in this study). We applied the “Sustainable Urban Mobility Plans” (SUMPs) framework to 642 cities in Europe and modelled how the measures they include may impact at first on mobility and emissions at urban level, and then on urban background concentrations of PM2.5 and NO2. Results show that annual averages moderately improve for both pollutants, with reductions of urban background concentrations up to 2% for PM2.5 and close to 4% for NO2. The impact on NO2 at street level (that will be higher than on urban background) is not evaluated in this work. The air quality improvement of the simulated SUMP would only partially alleviate air quality problems in urban areas, but such a reduction in the emissions of air pollutants should still be considered as a positive result of SUMPs, given that they correspond to a set of low-cost measures that can be implemented at local level. Furthermore, the introduction of electro-mobility options (not considered here) would increase the impact on air quality. Other types of benefits, such as reduced fuel consumption, greenhouse gas emissions, higher impact at street level or accident rates reduction further add to the overall positive impact.
•SUMPs (Sustainable Urban Mobility Plans) concept have been applied to 642 cities In Europe.•Impact on modelled “urban background concentrations” has been evaluated.•Results show that this impact is limited for “urban background concentrations”.•The impact of SUMPs on street level concentrations, and electro-mobility options, are not evaluated in this paper.•Other benefits of SUMPs (on greenhouse gases, reduced accident rates, etc …) should be also considered.
Information on the origin of pollution constitutes an essential step of air quality management as it helps identifying measures to control air pollution. In this work, we review the most widely used ...source-apportionment methods for air quality management. Using theoretical and real-case datasets we study the differences among these methods and explain why they result in very different conclusions to support air quality planning. These differences are a consequence of the intrinsic assumptions that underpin the different methodologies and determine/limit their range of applicability. We show that ignoring their underlying assumptions is a risk for efficient/successful air quality management as these methods are sometimes used beyond their scope and range of applicability. The simplest approach based on increments (incremental approach) is often not suitable to support air quality planning. Contributions obtained through mass-transfer methods (receptor models or tagging approaches built in air quality models) are appropriate to support planning but only for specific pollutants. Impacts obtained via “brute-force” methods are the best suited but it is important to assess carefully their application range to make sure they reproduce correctly the prevailing chemical regimes.
•Different source-apportionment approaches may lead to different conclusions to support air quality planning.•The incremental approach is generally not suited to support air quality planning.•Receptor models or tagging approaches are appropriate to support planning but only for specific pollutants.•“Brute-force” methods are the best suited but their application range must be assessed.
•For moderate reductions, emission and PM concentration changes are linearly linked.•Reducing SO2 emissions where abundant is always efficient.•Reducing NH3 emissions is more efficient where it is ...less abundant.•Reducing NOx emissions where NOx are abundant can be counter-productive.•Both NOx and NH3 regimes occur in some regions, calling for combined reductions
Given the remaining air quality issues in many European regions, smart air quality strategies are necessary to reduce the burden of poor air quality. While designing effective strategies for non-reactive primary pollutants is straightforward, this is not the case for secondary pollutants for which the relationship between emission changes and the resulting concentration changes can be nonlinear. Under such conditions, strategies targeting the largest emitting sources might not be the most effective. In this work, we provide elements to better understand the role of the main emission precursors (SO2, NOx, NH3) on the formation of secondary inorganic aerosols. By quantifying the PM2.5 sensitivity to emission reductions for each of these three precursors, we define and quantify the intensity of PM2.5 formation chemical regimes across Europe. We find that for emission reductions limited to 25%, the relation between emission and PM concentration changes remain mostly linear, with the exception of the Po Valley where non-linearities reach more than 30% in winter. When emission reductions increase to 50%, non-linearity reaches more than 60% in the Po Valley but stay below 30% in the rest of Europe. In terms of implications on abatement strategies, our findings can be summarized in the following key messages: (1) reducing SO2 emissions where abundant is always efficient (e.g. eastern Europe and Balkans); (2) reducing NH3 emissions is more efficient where it is less abundant (e.g. the Po basin) than where it is abundant, given the limiting role of NH3 in the PM formation; (3) reducing NOx emissions where NOx are abundant can be counter-productive with potential increases of PM due to the increased oxidant capacity of the atmosphere (e.g. Po valley); (4) because regions with both NH3 and NOx sensitive chemical regimes are mixed within countries, both need to be reduced together, as pollution reduction policies need at least to be defined at a country level; (6) while for NH3 the focus is clearly on wintertime, it is the whole year for NOx. The simulations proposed in this work could be used as benchmark for other models as they constitute the type of scenarios required to support air quality strategies. In addition, the straight and systematic emission reductions imposed for the scenarios in this work are well suited for a better understanding of the behavior of the model, in terms of responses to emission reductions.
In research and policy design we mainly use a ‘population weighted average concentrations’ perspective to study changes in air quality, to evaluate if past policies have been effective, or to assess ...the impact of future air quality plans. This angle is useful and informative, but sometimes masks other important patterns.
In this paper we propose to add, to the existing population weighted average point of view, a new indicator that brings to the fore the ‘inequalities’ in exposure. This inequality indicator is based on the Gini coefficient, usually applied in Economics and here considered to evaluate if exposure to air pollutants is equally distributed among population.
A case study for this new indicator is then proposed, to assess the evolution of exposure to air pollutants in Europe from 2000 to 2018, in terms of both average exposure and inequality levels. The results show that using only average exposure metrics can mask other interesting patterns, and confirm the benefits of including this alternative perspective into the analysis.
The EU, seeking to be a global leader in the fight against climate change, is moving ahead with ambitious policies to mitigate greenhouse gases emissions. In this context, the Fit for 55 package ...(FF55) is a set of proposals to revise and update EU legislation, to ensure that policies are in line with the climate goals of cutting emissions by at least 55% by 2030. Whilst these policies are designed for climate purposes, they will have positive side-effects (co-benefits) on air quality. Separately, additional policies are also in place to reduce emissions of related air pollutants and to improve air quality concentrations on EU territory. In this work, through a modelling study, we analyse the benefits of these policies via the health benefits arising from the resulting reductions in yearly average PM2.5 concentrations. Results are analysed by assessing and comparing morbidity and mortality impacts as computed using both the HRAPIE (Health risks of air pollution in Europe, WHO, as implemented in the CaRBonH model) and the GBD (Global Burden of Disease, as implemented in FASST-GBD model) approaches. Even when considering the uncertainty and variability in the results obtained using the two approaches, it is clear that EU policies can bring health and economic benefit in EU, with several Billions of Euro of benefits both in terms of morbidity and mortality indicators.
A set of statistical indicators fit for air quality model evaluation is selected based on experience and literature: The Root Mean Square Error (RMSE), the bias, the Standard Deviation (SD) and the ...correlation coefficient (R). Among these the RMSE is proposed as the key one for the description of the model skill. Model Performance Criteria (MPC) to investigate whether model results are ‘good enough’ for a given application are calculated based on the observation uncertainty (U). The basic concept is to allow for model results a similar margin of tolerance (in terms of uncertainty) as for observations. U is pollutant, concentration level and station dependent, therefore the proposed MPC are normalized by U. Some composite diagrams are adapted or introduced to visualize model performance in terms of the proposed MPC and are illustrated in a real modeling application. The Target diagram, used to visualize the RMSE, is adapted with a new normalization on its axis, while complementary diagrams are proposed. In this first application the dependence of U on concentrations level is ignored, and an assumption on the pollutant dependent relative error is made. The advantages of this new approach are finally described.
► New method to evaluate air quality models based on observation uncertainty. ► The same margin of tolerance is allowed for models as for observations. ► New criteria and diagrams to visualize fulfillment zones are described. ► Various dependencies (pollutant, concentrations level…) can be considered. ► Example diagrams and criteria values from measurements are provided.
Air quality models are useful tools for the assessment and forecast of pollutant concentrations in the atmosphere. Most of the evaluation process relies on the “operational phase” or in other words ...the comparison of model results with available measurements which provides insight on the model capability to reproduce measured concentrations for a given application. But one of the key advantages of air quality models lies in their ability to assess the impact of precursor emission reductions on air quality levels. Models are then used in a dynamic mode (i.e. response to a change in a given model input data) for which evaluation of the model performances becomes a challenge.
The objective of this work is to propose common indicators and diagrams to facilitate the understanding of model responses to emission changes when models are to be used for policy support. These indicators are shown to be useful to retrieve information on the magnitude of the locally produced impacts of emission reductions on concentrations with respect to the “external to the domain” contribution but also to identify, distinguish and quantify impacts arising from different factors (different precursors). In addition information about the robustness of the model results is provided. As such these indicators might reveal useful as first screening methodology to identify the feasibility of a given action as well as to prioritize the factors on which to act for an increased efficiency.
Finally all indicators are made dimensionless to facilitate the comparison of results obtained with different models, different resolutions, or on different geographical areas.
•Proposed indicators to evaluate air quality models for dynamic evaluation.•Proposed diagram to evaluate emission reduction impacts on concentrations.•Assessment of the robustness and non-linearity of model responses.•Diagram and indicators are useful for policy-maker and model developers.
To cope with computing power limitations, air quality models that are used in integrated assessment applications are generally approximated by simpler expressions referred to as “source-receptor ...relationships (SRR)”. In addition to speed, it is desirable for the SRR also to be spatially flexible (application over a wide range of situations) and to require a “light setup” (based on a limited number of full Air Quality Models - AQM simulations). But “speed”, “flexibility” and “light setup” do not naturally come together and a good compromise must be ensured that preserves “accuracy”, i.e. a good comparability between SRR results and AQM.
In this work we further develop a SRR methodology to better capture spatial flexibility. The updated methodology is based on a cell-to-cell relationship, in which a bell-shape function links emissions to concentrations. Maintaining a cell-to-cell relationship is shown to be the key element needed to ensure spatial flexibility, while at the same time the proposed approach to link emissions and concentrations guarantees a “light set-up” phase. Validation has been repeated on different areas and domain sizes (countries, regions, province throughout Europe) for precursors reduced independently or contemporarily. All runs showed a bias around 10% between the full AQM and the SRR.
This methodology allows assessing the impact on air quality of emission scenarios applied over any given area in Europe (regions, set of regions, countries), provided that a limited number of AQM simulations are performed for training.
•Integrated Assessment Modeling applies source receptor relationships (SRR).•SRR need to be fast, flexible, accurate and easy to be set-up.•Existing SRR lack flexibility in terms of spatial emission reductions.•In this work a novel approach to flexible SRR is formalized and validated.•These SRR can simulate emission scenarios applied over any domain in Europe.
Air pollution causes adverse effects on human health as well as ecosystems and crop yield and also has an impact on climate change trough short-lived climate forcers. To design mitigation strategies ...for air pollution, 3D Chemistry Transport Models (CTMs) have been developed to support the decision process. Increases in model resolution may provide more accurate and detailed information, but will cubically increase computational costs and pose additional challenges concerning high resolution input data. The motivation for the present study was therefore to explore the impact of using finer horizontal grid resolution for policy support applications of the European Monitoring and Evaluation Programme (EMEP) model within the Long Range Transboundary Air Pollution (LRTAP) convention. The goal was to determine the “optimum resolution” at which additional computational efforts do not provide increased model performance using presently available input data. Five regional CTMs performed four runs for 2009 over Europe at different horizontal resolutions.
The models’ responses to an increase in resolution are broadly consistent for all models. The largest response was found for NO2 followed by PM10 and O3. Model resolution does not impact model performance for rural background conditions. However, increasing model resolution improves the model performance at stations in and near large conglomerations. The statistical evaluation showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability. This study clearly shows that increasing model resolution is advantageous, and that leaving a resolution of 50 km in favour of a resolution between 10 and 20 km is practical and worthwhile. As about 70% of the model response to grid resolution is determined by the difference in the spatial emission distribution, improved emission allocation procedures at high spatial and temporal resolution are a crucial factor for further model resolution improvements.
•Four European CTMs were used to compare model performance at different resolutions.•CTM resolution increase from ∼50 to ∼14 km is worthwhile and practical.•Model performance improves with resolution for NO2 and PM10.•For further resolution increase, high resolution emission and meteorological data are crucial.
This paper presents SHERPA-City, a web application to assess the potential of traffic measures to abate NO2 air pollution in cities. The application is developed by the Joint Research Centre. It is ...freely available (https://integrated-assessment.jrc.ec.europa.eu) and allows the user to perform a fast screening of possible NO2 abatement measures addressing traffic in European cities. SHERPA-City results depend on the quality of the default input data. It is therefore important to stress that the SHERPA-City default traffic flows, emission factors, fleet composition, road network topology, NO2 pollution from other sources and meteorological data are based on EU-wide datasets that may not always represent perfectly a particular local situation. This is why the SHERPA-City allows the default data to be substituted by local data, to better reflect local features. This tool must be considered as a first step in exploring options to abate NO2 air pollution through transport measures. The final decisions should be based, wherever possible, on full-scale modelling studies incorporating local knowledge.
Display omitted
•A free user-friendly web application to evaluate the impact of traffic measures on NO2 concentrations in European cities.•EU wide default traffic data and emission factors are provided.•Possibility to upload own traffic data and emission factors.•A case study on Madrid Low Emission Zones demonstrates the key features of the tool.
PDF
