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Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory. The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors. At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors). The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.

Abstract Throughout the world, ambient fine particulate matter (PM2.5) is the environmental factor that poses the greatest risk to health and most European citizens continue to be exposed to PM2.5 levels well above World Health Organization guidelines. Here we present a comprehensive PM2.5 modelling-based source allocation assessment in 708 urban areas in Europe. The results show that urban cores, together with their commuting zones, contribute an average of 22% to urban PM2.5 concentrations levels. The residential sector is the highest source sector in 56% of cities. Its average contribution to PM2.5 formation is 27%, with a cluster of cities in Northern Italy and Eastern Europe contributing to more than 50%. Industry, agriculture and road transport show average contributions of 18%, 17% and 14%, respectively. Most emissions from residential sectors are anthropogenic primary PM2.5 which includes a condensable fraction. Furthermore, anthropogenic primary PM2.5 represents the precursor with the highest contribution in most cities (72%), contributing an average of 35% to urban PM2.5 levels. Emissions of anthropogenic primary PM2.5 by the residential sector are almost entirely (with exceptions of few countries) due to biomass burning. These results suggest that the residential sector should be a key target of any policy to improve air quality and that climate policies promoting biomass as a climate-neutral fuel could have a detrimental effect on air quality. A more integrated approach to climate and air quality policy design is desirable.

Despite the application of an increasingly strict EU air quality legislation, air quality remains problematic in large parts of Europe. To support the abatement of these remaining problems, a better understanding of the potential impacts of emission abatement measures on air quality is required, and air chemistry transport models (CTMs) are the main instrument to perform emission reduction scenarios. In this study, we study the robustness of the model responses to emission reductions when emission input is changed. We investigate how inconsistencies in emissions impact the modelling responses in the case of emission reduction scenarios. Based on EMEP simulations over Europe fed by four emission inventories – EDGAR 5.0, EMEP-GNFR, CAMS 2.2.1 and CAMS version 4.2 (including condensables) – we reduce anthropogenic emissions in six cities (Brussels, Madrid, Rome, Bucharest, Berlin and Stockholm) and two regions (Po Valley in Italy and Malopolska in Poland) and study the variability in the concentration reductions obtained with these four emission inventories. Our study reveals that the impact of reducing aerosol precursors on PM10 concentrations result in different potentials and potencies, differences that are mainly explained by differences in emission quantities, differences in their spatial distributions as well as in their sector allocation. In general, the variability among models is larger for concentration changes (potentials) than for absolute concentrations. Similar total precursor emissions can, however, hide large variations in sectorial allocation that can lead to large impacts on potency given their different vertical distribution. Primary particulate matter (PPM) appears to be the precursor leading to the major differences in terms of potentials. From an emission inventory viewpoint, this work indicates that the most efficient actions to improve the robustness of the modelling responses to emission changes would be to better assess the sectorial share and total quantities of PPM emissions. From a modelling point of view, NOx responses are the more challenging and require caution because of their non-linearity. For O3, we find that the relationship between emission reduction and O3 concentration change shows the largest non-linearity for NOx (concentration increase) and a quasi-linear behaviour for volatile organic compounds (concentration decrease). We also emphasise the importance of accurate ratios of emitted precursors since these lead to changes in chemical regimes, directly affecting the responses of O3 or PM10 concentrations to emission reductions.

A standardized methodology for the validation of short-term air quality forecast applications was developed in the framework of the Forum for Air quality Modeling (FAIRMODE) activities. The proposed approach, focusing on specific features to be checked when evaluating a forecasting application, investigates the model's capability to detect sudden changes in pollutant concentration levels, predict threshold exceedances and reproduce air quality indices. The proposed formulation relies on the definition of specific forecast modelling quality objectives and performance criteria, defining the minimum level of quality to be achieved by a forecasting application when it is used for policy purposes. The persistence model, which uses the most recent observed value as the predicted value, is used as a benchmark for the forecast evaluation. The validation protocol has been applied to several forecasting applications across Europe, using different modelling paradigms and covering a range of geographical contexts and spatial scales. The method is successful, with room for improvement, in highlighting shortcomings and strengths of forecasting applications. This provides a useful basis for using short-term air quality forecasts as a supporting tool for providing correct information to citizens and regulators.

Some studies show that significant uncertainties affect emission inventories, which may impeach conclusions based on air-quality model results. These uncertainties result from the need to compile a wide variety of information to estimate an emission inventory. In this work, we propose and discuss a screening method to compare two emission inventories, with the overall goal of improving the quality of emission inventories by feeding back the results of the screening to inventory compilers who can check the inconsistencies found and, where applicable, resolve errors. The method targets three different aspects: (1) the total emissions assigned to a series of large geographical areas, countries in our application; (2) the way these country total emissions are shared in terms of sector of activity; and (3) the way inventories spatially distribute emissions from countries to smaller areas, cities in our application. The first step of the screening approach consists of sorting the data and keeping only emission contributions that are relevant enough. In a second step, the method identifies, among those significant differences, the most important ones that provide evidence of methodological divergence and/or errors that can be found and resolved in at least one of the inventories. The approach has been used to compare two versions of the CAMS-REG European-scale inventory over 150 cities in Europe for selected activity sectors. Among the 4500 screened pollutant sectors, about 450 were kept as relevant, among which 46 showed inconsistencies. The analysis indicated that these inconsistencies arose almost equally from large-scale reporting and spatial distribution differences. They mostly affect SO2 and PM coarse emissions from the industrial and residential sectors. The screening approach is general and can be used for other types of applications related to emission inventories.

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.

While the burden caused by air pollution in urban areas is well documented, the origin of this pollution and therefore the responsibility of the urban areas in generating this pollution are still a subject of scientific discussion. Source apportionment represents a useful technique to quantify the city's responsibility, but the approaches and applications are not harmonized and therefore not comparable, resulting in confusing and sometimes contradicting interpretations. In this work, we analyse how different source apportionment approaches apply to the urban scale and how their building elements and parameters are defined and set. We discuss in particular the options available in terms of indicator, receptor, source, and methodology. We show that different choices for these options lead to very large differences in terms of outcome. For the 150 large EU cities selected in our study, different choices made for the indicator, the receptor, and the source each lead to an average difference of a factor of 2 in terms of city contribution. We also show that temporal- and spatial-averaging processes applied to the air quality indicator, especially when diverging source apportionments are aggregated into a single number, lead to the favouring of strategies that target background sources while occulting actions that would be efficient in the city centre. We stress that methodological choices and assumptions most often lead to a systematic and important underestimation of the city's responsibility, with important implications. Indeed, if cities are seen as a minor actor, plans will target the background as a priority at the expense of potentially effective local actions.

In this paper we compute and analyse the ‘cities contribution to their own pollution’, based on the ‘Source Contribution to EU cities’ policy product from the ‘Copernicus Atmosphere Monitoring Service’. The analysis focuses on 38 EU cities, analyzing how the ‘cities contribution’ changes from a temporal and spatial perspective. A comparison with state-of-the-art similar analysis is also provided. Results show that city contributions (for yearly aggregated values) largely change spatially (that is to say city by city) with values ranging from 5% to 54% for PM2.5 and from 24% to 81% for NO2. We also highlight how local contributions are affected by meteorology. From a temporal point of view, the variability is even higher between seasonal and/or daily city contributions. All these results stress the complexity of finding the right balance between local and national/international emission reduction policies and the challenge of applying measures at the right time, to increase their efficiency.

Air pollution is one of the main causes of damages to human health in Europe, with an estimate of about 380 000 premature deaths per year in the EU28, as the result of exposure to fine particulate matter (PM2.5) only. In this work, we focus on one specific region in Europe, the Po basin, a region where chemical regimes are the most complex, showing important non-linear processes, especially those related to interactions between NOx and NH3. We analyse the sensitivity of PM2.5 concentration to NOx and NH3 emissions by means of a set of EMEP model simulations performed with different levels of emission reductions, from 25 % up to a total switch-off of those emissions. Both single and combined precursor reduction scenarios are applied to determine the most efficient emission reduction strategies and quantify the interactions between NOx and NH3 emission reductions. The results confirmed the peculiarity of secondary PM2.5 formation in the Po basin, characterised by contrasting chemical regimes within distances of a few (hundred) kilometres, as well as non-linear responses to emission reductions during wintertime. One of the striking results is the slight increase in the PM2.5 concentration levels when NOx emission reductions are applied in NOx-rich areas, such as the surroundings of Bergamo. The increased oxidative capacity of the atmosphere is the cause of the increase in PM2.5 induced by a reduction in NOx emission. This process could have contributed to the absence of a significant PM2.5 concentration decrease during the COVID-19 lockdowns in many European cities. It is important to account for this process when designing air quality plans, since it could well lead to transitionary increases in PM2.5 at some locations in winter as NOx emission reduction measures are gradually implemented. While PM2.5 chemical regimes, determined by the relative importance of the NOx vs. NH3 responses to emission reductions, show large variations seasonally and spatially, they are not very sensitive to moderate (up to 50 %–60 %) emission reductions. Beyond 25 % emission reduction strength, responses of PM2.5 concentrations to NOx emission reductions become non-linear in certain areas of the Po basin mainly during wintertime.

To take decisions on how to improve air quality, it is useful to perform a source allocation study that identifies the main sources of pollution for the area of interest. Often source allocation is performed with a chemical transport model (CTM) but unfortunately, even if accurate, this technique is time consuming and complex. Comparing the results of different CTMs to assess the uncertainty of source allocation results is even more difficult. In this work, we compare the source allocation (for PM2.5 yearly averages) in 150 major cities in Europe, based on the results of two CTMs (CHIMERE and EMEP), approximated with the SHERPA (Screening for High Emission Reduction Potential on Air) approach. Although contradictory results occur in some cities, the source allocation results obtained with the two SHERPA simplified models lead to similar results in most cases, even though the two CTMs use different input data and configurations.

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 this paper we run the EMEP air quality model with 3 state-of-the-art emission inventories for Europe, that is to say with CAMS-REG-AP, EMEP emissions, and EDGAR. The main aim of the work is to evaluate if the EDGAR inventory (despite its global coverage and independent approach to emission evaluation) is accurate enough, to be used for air quality modelling in Europe. To do so, the EMEP model has been run for the entire year 2015 using the 3 aforementioned emission inventories, and the resulting concentrations have been compared with ‘background’ monitoring stations. Results show that the air quality model, run with the 3 emission inventories, provide similar results despite important differences in emissions. EDGAR emissions provide slightly better validation results for PM2.5, while the EMEP emissions lead to slightly better results for yearly average NO2. The main differences among the model applications arise in the Eastern part of Europe, where the deviations between the officially estimated emissions and those independently evaluated by EDGAR are more important. Results suggest that EDGAR, despite being a methodology aimed at global coverage, with independent sources for activity level, technologies and emission factors and generic gridding practices, can be effectively used for air quality modelling in Europe. The EDGAR dataset (v5.0) used in this paper is available at: https://doi.pangaea.de/10.1594/PANGAEA.921922.

In this manuscript, we evaluated different emission scenarios for light-duty road transport to evaluate their impact on air quality in the EU, with a focus on a number of cities by means of the EMEP (European Monitoring and Evaluation Programme) modelling system. In addition to the reference case scenario, where exhaust emission factors from COPERT (Computer Programme to calculate Emissions from Road Transport) corresponding to the existing fleet were used, we also tested future potential scenarios considering: (a) all passenger cars and light commercial vehicles meet the EURO 6 emissions standard and EDGAR (Emission Database for Global Atmospheric research) EURO 6 emission factors; (b) all passenger cars and light commercial vehicles meet the EURO 6 emissions standard and real-world emission factors derived from actual Euro 6d-TEMP/6d vehicles. Results show how the replacement of old vehicles by newer ones with better emission control technologies can help improve air quality in the EU in terms of reductions in NO2 and PM2.5 concentrations. However, reduced NOx emissions in cities (as foreseen in the two scenarios analysed) will cause tropospheric O3 to increase.

Airborne particulate matter (PM) is a pollutant of concern not only because of its adverse effects on human health but also on visibility and the radiative budget of the atmosphere. PM can be considered as a sum of solid/liquid species covering a wide range of particle sizes with diverse chemical composition. Organic aerosols may be emitted (primary organic aerosols, POA), or formed in the atmosphere following reaction of volatile organic compounds (secondary organic aerosols, SOA), but some of these compounds may partition between the gas and aerosol phases depending upon ambient conditions. This review focuses on carbonaceous PM and gaseous precursors emitted by road traffic, including ultrafine particles (UFP) and polycyclic aromatic hydrocarbons (PAHs) that are clearly linked to the evolution and formation of carbonaceous species. Clearly, the solid fraction of PM has been reduced during the last two decades, with the implementation of after-treatment systems abating approximately 99% of primary solid particle mass concentrations. However, the role of brown carbon and its radiative effect on climate and the generation of ultrafine particles by nucleation of organic vapour during the dilution of the exhaust remain unclear phenomena and will need further investigation. The increasing role of gasoline vehicles on carbonaceous particle emissions and formation is also highlighted, particularly through the chemical and thermodynamic evolution of organic gases and their propensity to produce particles. The remaining carbon-containing particles from brakes, tyres and road wear will still be a problem even in a future of full electrification of the vehicle fleet. Some key conclusions and recommendations are also proposed to support the decision makers in view of the next regulations on vehicle emissions worldwide.

This study investigates the dispersion of atmospheric pollutants over a coastal region of north-western Italy by means of modelling techniques. A series of annual air quality model simulations corresponding to different emission reduction scenarios has been performed with a three-dimensional chemical transport modelling chain running at 3 km resolution. The emission reduction scenarios were used to develop bottom-up (locally produced) source-receptor relationships to perform a local source allocation analysis of the main atmospheric pollutants in a few polluted cities within the domain of interest. Results were compared with default top-down (EU-wide) source-receptor relationships, at roughly 7 km resolution. The results show the benefit of using the two sources of information in an integrated way. The analysis of the impacts of local emission reductions on concentrations and of the source allocation results reveals that nitrogen oxides concentrations are mostly affected by local emission sources, especially road transport and harbour related activities while the contribution of non-local sources is important for particulate matter (especially from industry and agriculture sources). For PM, larger scale modelling approaches (top-down) are necessary. Ideally, both a bottom-up approach for the characterisation of the local emission sources and a top-down larger scale approach to capture the impact of non-local sources would be necessary to perform an accurate source allocation, and provide support to the design of local air quality plans.

The SHERPA tool was used to assess the major pollution sources and the geographical areas impacting on the PM2.5 of the main cities in the Danube and Western Balkans regions. The activity sectors influencing most the PM2.5 levels in the study area are energy production (22%), agriculture (19%), residential combustion (16%) and road transport (7%). The energy production in inefficient coal-fuelled power plants was identified as one of main source of PM2.5 in the Western Balkans. As for the geographical origin of PM2.5, the transboundary pollution is confirmed as the main origin of PM2.5 (44%) in the investigated cities, while the city own emissions and the national sources outside the concerned city impact on average 22% and 15%, respectively. An association was observed between the long-range transport and the impact of agriculture and energy production, while both local urban emissions and long-range transport were associated with the residential sector. A special attention is given in this study to biomass, a renewable source, which use is often promoted in the frame of climate and energy policies. Nevertheless, the combustion of biomass in inefficient small appliances has considerable particulate matter emissions and therefore this type of practice impacts negatively on air quality. Considering that biomass is traditionally used in South-East Europe as fuel for residential heating, the interpretation of the model results was supported with the estimation of biomass burning contributions to PM2.5 obtained with receptor models and data on biomass fuel consumption from the literature. The analysis of the contributions from biomass burning derived from receptor models suggests that biomass burning is the dominant source within the residential heating sector in the studied area and that the emissions from this source are likely underestimated. This study concludes that more effort is needed to improve the estimations of biomass burning emissions and that policies to improve air quality in the cities should involve a geographic context wider than the city level.

Source-receptor relationships (SRRs) are simplified air quality models. They are usually used to replace fully-fledged Chemical Transport models (CTMs) when simulating a huge number of emission reduction scenarios, in policy-related contexts. Even if SRRs do not contain the same richness of information as CTMs (i.e. in terms of spatial/temporal resolution) and are merely a statistical approximation of the original models, their application is usually deemed to be sufficient in the policy arena, when a condensed (i.e. yearly average concentrations instead of hourly detailed ones) representation of the real scenarios is required; and when “simulation time” is a constraint (as i.e. optimization processes). In this paper we identify and validate SRRs, based on the EMEP MSC-W Chemical Transport Model. The proposed statistical SRR approach is implemented using a limited number of CTM simulations. Also, it allows for a flexible selection of the emission reduction scenarios to be simulated, both from a geographical and a sectoral point of view. The validation results, performed on various domains and sectors, demonstrate that the proposed methodology can be used in a policy context, even if certain limitations on its use needs to be recognized. Keywords: Air quality model, Emission reduction scenarios, Source receptor relationships, Integrated assessment modelling

This work reviews the existing methodologies for source apportionment and sensitivity analysis to identify key differences and stress their implicit limitations. The emphasis is laid on the differences between source impacts (sensitivity analysis) and contributions (source apportionment) obtained by using four different methodologies: brute-force top-down, brute-force bottom-up, tagged species and decoupled direct method (DDM). A simple theoretical example to compare these approaches is used highlighting differences and potential implications for policy. When the relationships between concentration and emissions are linear, impacts and contributions are equivalent concepts. In this case, source apportionment and sensitivity analysis may be used indifferently for both air quality planning purposes and quantifying source contributions. However, this study demonstrates that when the relationship between emissions and concentrations is nonlinear, sensitivity approaches are not suitable to retrieve source contributions and source apportionment methods are not appropriate to evaluate the impact of abatement strategies. A quantification of the potential nonlinearities should therefore be the first step prior to source apportionment or planning applications, to prevent any limitations in their use. When nonlinearity is mild, these limitations may, however, be acceptable in the context of the other uncertainties inherent to complex models. Moreover, when using sensitivity analysis for planning, it is important to note that, under nonlinear circumstances, the calculated impacts will only provide information for the exact conditions (e.g. emission reduction share) that are simulated.

The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m−3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

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. Keywords: Urban air pollution, Source allocation, Source apportionment, Increment, Contribution, Impact, Air quality planning, Brute force, Mitigation strategy