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.

Active mobility versus motorized transport? User choices and benefits for the society.

Measures promoting active mobility - walking or cycling - are often seen as an effective strategy to meet multiple urban objectives. The advantages of such behavioural changes cover multiple dimensions at public and individual level, including positive impacts on health, safety, climate, economy, environment and air quality. However, there is still a considerable potential for increasing the uptake of active mobility in urban areas. This paper explores the determinants of active mobility choice and compares the demographic, socio-economic and cultural factors that influence it. The methodology combines extensive survey data, an EU-wide transport model and detailed indicators of external costs of transport with a Gradient Boosting Machine Learning approach. The model based scenarios quantify the benefit in terms of external costs savings from increasing active mobility shares. Such savings - at EU level, can reach the amount of 15 billion euro per year for a shift of 10% of trips to active mobility modes.

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.

Comment to the paper "Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality", by Ogen, 2020.

In this paper we critically review the work "Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality" (Ogen, 2020), stressing the fact that we think there are flaws in the published methodology. We do this as we think it is important, given the current deluge of 'COVID-19 related' publications, to clearly define what can be stated and what on the contrary, cannot be stated, due to limitations in terms of data quality and/or methodology.

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.

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.

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.

Integrating Saharan dust forecasts into a regional chemical transport model: a case study over Northern Italy.

The Po Valley in Northern Italy is frequently affected by high PM10 concentrations, where both natural and anthropogenic sources play a significant role. To improve air pollution modeling, 3D dust fields, produced by means of the DREAM dust forecasts, were integrated as boundary conditions into the mesoscale 3D deterministic Transport Chemical Aerosol Model (TCAM). A case study of the TCAM and DREAM integration was implemented over Northern Italy for the period May 15-June 30, 2007. First, the Saharan dust impact on PM10 concentration was analyzed for eleven remote PM10 sites with the lowest level of air pollution. These remote sites are the most sensitive to Saharan dust intrusions into Northern Italy, because of the absence of intensive industrial pollution. At these remote sites, the observed maxima in PM10 concentration during dust events is evidence of dust aerosol near the surface in Northern Italy. Comparisons between modeled PM10 concentrations and measurements at 230 PM10 sites in Northern Italy, showed that the integrated TCAM-DREAM model more accurately reproduced PM10 concentration than the base TCAM model, both in terms of correlation and mean error. Specifically, the correlation median increased from 0.40 to 0.65, while the normalized mean absolute error median dropped from 0.5 to 0.4.

MODIS and OMI satellite observations supporting air quality monitoring.

Within the framework of air quality monitoring, measurements by Earth-observing satellite sensors are combined here with regional meteorological and chemical transport models. Two satellite-derived products developed within the QUITSAT project, regarding significant pollutants including PM(2.5) and NO(2), are presented. Estimates of PM(2.5) concentrations at ground level were obtained using moderate resolution imaging spectroradiometer (Terra-Aqua/NASA) aerosol optical properties. The semi-empirical approach adopted takes into account PM(2.5) sampling and meteorological descriptions of the area studied, as simulated by MM5, to infer aerosol optical properties to PM projection coefficients. Daily maps of satellite-based PM(2.5) concentrations over northern Italy are derived. Monthly average values were compared with in situ PM(2.5) samplings showing good agreement. Ozone monitoring instrument (OMI) (Aura/NASA) NO(2) tropospheric contents are merged using the GAMES chemical model simulations. The method employs a weighted rescaling of the model column in the troposphere according to the OMI observations. The weightings take into account measurement errors and model column variances within the satellite ground pixel. The obtained ground-level concentrations of NO(2) show good agreement with the environmental agencies' in situ.