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.
•Energy profiles are numerically modeled at city scale.•They integrate the urban heat island complexity and building internal heat gains.•Based on degree-days a fast energy management tool for urban ...planner is then framed.•Reliable estimates of urban-scale building energy loads are obtained.•Degree-day daily temperature definition causes the largest results’ discrepancies.
Efficient strategies are required to reduce space heating energy demands in buildings at city scale. Models taking into account the dynamic of the Urban Heat Island (UHI) phenomenon may be useful tools to help urban planners in this task. In this paper, we propose a new methodology to assess the energy demands for space heating in buildings at city scale: a degree-day method is applied, coupled with the use of a dynamic urban meteorological model that computes a building energy budget. First, it is shown that the total building space heating energy demand at city scale, as simulated by the meteorological model, is quasi- linearly dependent on the daily mean city scale air temperature. The developed city-scale degree-day method applied to assess the space heating energy demands in Strasbourg Eurometropolis (France) is shown to be consistent with the estimates issued by local official energy sources. A sensitivity analysis highlights the fact that while the heating energy demands are dependent on the building insulation performance and thermostat heating temperatures, scenarios in which building energy properties are changed do not significantly affect the UHI.
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 quality models are often used to simulate how emission scenarios influence the concentration of primary as well as secondary pollutants in the atmosphere. In some cases, it is necessary to ...replace these air quality models with source–receptor relationships, to mimic in a faster way the link between emissions and concentrations. Source–receptor relationships are therefore also used in Integrated Assessment Models, when scenario responses need to be known in very short time. The objective of this work is to present a novel approach to design a source–receptor relationship for air quality modeling. Overall the proposed approach is shown to significantly reduce the number of simulations required for the training step and to bring flexibility in terms of emission source definition. A regional domain application is also presented, to test the performances of the proposed approach.
•A novel approach to design source–receptor relationships for air quality is proposed.•It needs a small number of simulations to be implemented.•It also brings flexibility in terms of application in Integrated Assessment Models.•A case study on a regional domain is presented.
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.
•The SHERPA tool is proposed to support the design and assessment of air quality plans.•SHERPA is flexible and allows assessing impacts in any given region in Europe.•SHERPA delivers information regarding source allocation, governance and scenario impact assessment.•A case study over the London region highlights how SHERPA can provide support to policy makers.
Air quality models which are nowadays used for a wide range of scopes (i.e. assessment, forecast, planning) see their intrinsic complexity progressively increasing as better knowledge of the ...atmospheric chemistry processes is gained. As a result of this increased complexity potential non-linearities are implicitly and/or explicitly incorporated in the system. These non-linearities represent a key and challenging aspect of air quality modeling, especially to assess the robustness of the model responses. In this work the importance of non-linear effects in air quality modeling is quantified, especially as a function of time averaging. A methodology is proposed to decompose the concentration change resulting from an emission reduction over a given domain into its linear and non-linear contributions for each precursor as well as in the contribution resulting from the interactions among precursors. Simulations with the LOTOS-EUROS model have been performed by TNO over three regional geographical areas in Europe for this analysis. In all three regions the non-linear effects for PM10 and PM2.5 are shown to be relatively minor for yearly and monthly averages whereas they become significant for daily average values. For Ozone non-linearities become important already for monthly averages in some regions. An approach which explicitly deals with monthly variations seems therefore more appropriate for O3. In general non-linearities are more important at locations where concentrations are the lowest, i.e. at urban locations for O3 and at rural locations for PM10 and PM2.5. Finally the impact of spatial resolution (tested by comparing coarse and fine resolution simulations) on the degree of non-linearity has been shown to be minor as well. The conclusions developed here are model dependent and runs should be repeated with the particular model of interest but the proposed methodology allows with a limited number of runs to identify where efforts should be focused in order to include the relevant terms into a simplified surrogate model for integrated assessment purposes.
•Methodology to quantify non-linearities in air quality models responses.•The non-linearity quantification methodology is applied to daily, monthly and yearly averaged concentrations.•Seasonal dependencies are analysed for both PM10 and O3 compounds.•Quantification of non-linearity's is useful for policy-maker to ensure robust strategies.
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.
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•Source-receptor relationships (SRRs) needed for fast air quality simulations.•SRRs are also a key component in Integrated Assessment Models.•We present updated SRRs, applied to a state-of-the-art air quality model.•Results show the simplicity and flexibility of the resulting SRRs.•Limitations in terms of the developed SRRs are also stressed.
Chemistry-transport models are increasingly used in Europe for estimating air quality or forecasting changes in pollution levels. But with this increased use of modeling arises the need of ...harmonizing the methodologies to determine the quality of air quality model applications. This is complex for planning applications, i.e. when models are used to assess the impact of realistic or virtual emission scenarios. In this work, the methodology based on the calculation of potencies proposed by Thunis and Clappier (2014) to analyze the model responses to emission reductions is applied on three different domains in Europe (Po valley, Southern Poland and Flanders). This methodology is further elaborated to facilitate the inter-comparison process and bring in a single diagram the possibility of differentiating long-term from short-term effects. This methodology is designed for model users to interpret their model results but also for policy-makers to help them defining intervention priorities. The methodology is applied to both daily PM10 and 8 h daily maximum ozone.
•Air quality model responses to emission reduction scenarios are presented.•Maximum potential for local emission abatement is identified.•Relative importance of the various precursor emissions is assessed.•Degree of non-linearity of the model responses is estimated.•Three case studies in Europe are considered.
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