Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp.

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Modelling the air quality benefits of EU climate mitigation policies using two different PM2.5-related health impact methodologies.

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.

Inequality in exposure to air pollutants: A new perspective.

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.

Impact of SOx, NOx and NH3 emission reductions on PM2.5 concentrations across Europe: Hints for future measure development.

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.

SHERPA-city: A web application to assess the impact of traffic measures on NO2 pollution in cities.

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.

Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches.

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.

Supporting the improvement of air quality management practices: The "FAIRMODE pilot" activity.

This paper presents the first outcomes of the "FAIRMODE pilot" activity, aiming at improving the way in which air quality models are used in the frame of the European "Air Quality Directive". Member States may use modelling, combined with measurements, to "assess" current levels of air quality and estimate future air quality under different scenarios. In case of current and potential exceedances of the Directive limit values, it is also requested that they "plan" and implement emission reductions measures to avoid future exceedances. In both "assessment" and "planning", air quality models can and should be used; but to do so, the used modelling chain has to be fit-for-purpose and properly checked and verified. FAIRMODE has developed in the recent years a suite of methodologies and tools to check if emission inventories, model performance, source apportionment techniques and planning activities are fit-for-purpose. Within the "FAIRMODE pilot", these tools are used and tested by regional/local authorities, with the two-fold objective of improving management practices at regional/local scale, and providing valuable feedback to the FAIRMODE community. Results and lessons learnt from this activity are presented in this paper, as a showcase that can potentially benefit other authorities in charge of air quality assessment and planning.

Evaluating the impact of "Sustainable Urban Mobility Plans" on urban background air quality.

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.

Adding spatial flexibility to source-receptor relationships for air quality modeling.

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.

Analyzing the efficiency of short-term air quality plans in European cities, using the CHIMERE air quality model.

Regional and local authorities have the obligation to design air quality plans and assess their impacts when concentration levels exceed the limit values. Because these limit values cover both short- (day) and long-term (year) effects, air quality plans also follow these two formats. In this work, we propose a methodology to analyze modeled air quality forecast results, looking at emission reduction for different sectors (residential, transport, agriculture, etc.) with the aim of supporting policy makers in assessing the impact of short-term action plans. Regarding PM10, results highlight the diversity of responses across European cities, in terms of magnitude and type that raises the necessity of designing area-specific air quality plans. Action plans extended from 1 to 3 days (i.e., emissions reductions applied for 24 and 72 h, respectively) point to the added value of trans-city coordinated actions. The largest benefits are seen in central Europe (Vienna, Prague) while major cities (e.g., Paris) already solve a large part of the problem on their own. Eastern Europe would particularly benefit from plans based on emission reduction in the residential sectors; while in northern cities, agriculture seems to be the key sector on which to focus attention. Transport is playing a key role in most cities whereas the impact of industry is limited to a few cities in south-eastern Europe. For NO2, short-term action plans focusing on traffic emission reductions are efficient in all cities. This is due to the local character of this type of pollution. It is important, however, to stress that these results remain dependent on the selected months available for this study.

On the design and assessment of regional air quality plans: The SHERPA approach.

Although significant progress has been made in Europe regarding air quality, problems still remain acute for some pollutants, notably NO2 and Particulate Matter (fine and coarse fractions) in specific regions/cities. One issue regarding air quality management is governance, i.e. the selection of appropriate and cost effective strategies over the area controlled by policy makers. In this work we present a new approach to integrated assessment modelling focusing on regional and urban aspects. One of the key added values is spatial flexibility, namely the possibility to assess the contributions from different regions to air quality at any given location. The SHERPA tool is shown to be particularly helpful in addressing the following tasks: source allocation, governance and the assessment of scenario impacts. Application of the methodology over the London area for yearly averaged PM2.5 concentrations demonstrates these features. Given that it is possible to use the SHERPA interface with other types of data, SHERPA can also be seen as a means to foster harmonization in the field of model evaluation.

A novel approach to screen and compare emission inventories.

A methodology is proposed to support the evaluation and comparison of different types of emission inventories. The strengths and weaknesses of the methodology are presented and discussed based on an example. The approach results in a "diamond" diagram useful to flag out anomalous behaviors in the emission inventories and to get insight in possible explanations. In particular, the "diamond" diagram is shown to provide meaningful information in terms of: discrepancies between the total emissions reported by macro-sector and pollutant, contribution of each macro-sector to the total amount of emissions released by pollutant, and the identification and quantification of the different factors causing the discrepancies between total emissions. A practical example in Barcelona is used for testing and to provide relevant information for the analyzed emission datasets. The tests show the capability of the proposed methodology to flag inconsistencies in the existing inventories. The proposed methodology system may be useful for regional and urban inventory developers as an initial evaluation of the consistency of their inventories.