Air Quality in the Danube macro-region: Towards a coordinated science-based approach in support of policy development

A study on selected EU cities of the Danube macro-region concludes that energy production/industry, agriculture, residential heating and transportation are the main pollution sources. Long –range transport of pollutants from within and outside EU-28 has considerable impacts in certain cities while in others local emissions are the key to reduce urban pollution. Measures to be adopted at different policy levels to address the identified issues are analysed and the cross-policy implications are discussed.JRC.C.5-Air and Climat

Air Pollution and Emission Reductions over the Po-Valley: Air Quality Modelling and Integrated Assessment

The Po-valley located in northern Italy at the footstep of the Alps is characterized by a high density of anthropogenic emissions (inhab) and by the frequent occurrence of stagnant meteorological conditions. The area has therefore been identified as one hot spot place where pollutant levels will remain problematic in spite of application of the current European legislation devoted to air pollution control. By 2020, health impact on population and effects on ecosystems by ozone and eutrophication are indeed calculated to be amongst the highest in Europe and anthropogenic fine particulate matter levels are expected to be responsible for a loss of ten months of life expectancy. In general, long-range transported air pollution in the Po-Valley represents only a fraction of 30-40%, stressing the importance of local control measures in the area to efficiently reduce the impact of air pollution. In the frame of a collaboration agreement between the JRC (Joint Research Centre of the European Commission) and the government of the Lombardy region, a Model Inter-comparison exercise over the PO-valley (POMI) is organised to explore the changes in urban air-quality predicted by different air quality models in response to changes in emissions in the Po-Valley. POMI focuses on ambient levels of ozone and PM. Current Legislation (CLE) and Maximum Technically Feasible Reduction (MTFR) Emission scenarios are analysed at different spatial scales together with a set of ¿in-between¿ emission reductions corresponding to the application of regional air-quality plans over the Po-Valley and in particular over the Lombardy region. In parallel to this model inter-comparison exercise, an integrated assessment tool is being developed to design and assess the effectiveness of regional abatement policies. This tool is planed to make use of information available at the local/regional scale (technological costs, emission factors¿) and to allow investigating the efficiency of both technical and non-technical abatement measures. POMI is expected to provide information useful for the development of sectoral regional source relationships and for better accounting of the different sources of model-related uncertainties (emissions, meteorology¿) in the assessment of efficient strategies. In this work, an overview of the structure of the regional integrated assessment tool will be provided and its links with the POMI modelling exercise discussed.JRC.DDG.H.4-Transport and air qualit

Methods for Regional Integrated Assessment: High resolution gridded emission distribution in the LUISA Platform

This report illustrates the progresses made towards the inclusion of air quality related issues in the Land Use-based Integrated Sustainability Assessment (LUISA) platform. It focuses on the description of the methodology to derive high-resolution gridded-emission spatially geo-referenced layers from outputs and datasets integrated in LUISA. In the framework of the integration of the Regional Integrated Assessment Tool (RIAT model) and the Land Use Modelling Integrated Sustainability Assessment (LUISA) platform, we implemented the downscaling of atmospheric emission data from national level to very high spatial resolution (100m). The GAINS model (IIASA) provides the input emission data for different scenarios, up to year 2030, which are disaggregated based on 34 different surrogates. Each surrogate is calculated by means of the integration of several proxies derived by statistical datasets, ancillary models and GIS layers in the framework of the LUISA platform. The preliminary results for NOx, PM10 and NH3 (year 2010) are presented in this report together with their first assessment, based on existing emission maps at 7 and 10 Km resolution. Future steps for further refinements are also discussed.JRC.H.8-Sustainability Assessmen

Comparing air quality model performance for planning applications

The current policy making needs for emission abatement of air pollutants in Europe call for having simple yet robust tools that allow evaluating the effect of measures and sorting those that produce the most significant effects. As a result, the FAIRMODE Planning Working Group (WG4) seeks to develop a consistent framework for streamlining the understanding of models in order to identify more efficiently the relationship between changes in emissions and their effect in ambient concentration through a series of indicators or potencies. The comparison of sector-specific potencies was carried out using the Delta Tool for the AERIS integrated assessment model for the Iberian Peninsula and the SERCA modelling system, on which it is based. Air quality observations from 11 monitoring stations located in Spain and Portugal were used as independent comparison dataset, focusing on a winter and summer month (January and August), as well as on an annual basis. The comparison revealed that the main difference between AERIS and SERCA is the description of the non-linear relationship between changes in emissions and the formation of secondary pollutants (e.g. secondary particles, ground-level ozone). This is a consequence of the linear simplification that was used to construct AERIS, as opposed to the deterministic formulation that is contained in SERCA and is basically composed of the WRF-CMAQ ensemble. The comparison also suggested differences in the ability to reproduce seasonal variations of pollutants, something which is a consequence of the annual character of AERIS. However, AERIS is able to reproduce its parent air quality model (SERCA) and complies with the general modelling performance requirements stipulated under FAIRMODE. Moreover, its simplified approach, as evidenced by the values of the potencies allows identifying the interactions between emissions and concentrations, facilitating choosing mitigation measures depending on the abatement needs. Additionally, the ability of AERIS to reproduce ambient concentrations under a simplified approach makes it a robust alternative to SERCA for informing policy making and planning in Spain

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, and more specifically the comparison of bottom-up versus top-down approaches. 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 on 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. Its main strength as an indicator is to allow investigating the relative contribution of activity and weighted emission factors. 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.JRC.H.2-Air and Climat

FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking

The development of the procedure for air quality model benchmarking in the context of the Air Quality Directive 2008/50/EC (AQD) has been an on-going activity in the context of the FAIRMODE community. Central part of the studies was the definition of proper modelling quality indicators and criteria to be fulfilled in order to allow sufficient level of quality for a given model application under the AQD. The focus initially on applications related to air quality assessment has gradually been expanded to other applications, such as forecasting and planning. The main purpose of this Guidance Document is to explain and summarize the current concepts of the modelling quality objective methodology, elaborated in various papers and documents in the FAIRMODE community, addressing model applications for air quality assessment and forecast. Other goals of the Document are linked to presentation and explanation of templates for harmonized reporting of modelling results. Giving an overview of still open issues in the implementation of the presented methodology, the document aims at triggering further research and discussions. A core set of statistical indicators is defined using pairs of measurement-modelled data. The core set is the basis for the definition of a modelling quality indicator (MQI) and additional modelling performance indicators (MPI), which take into account the measurement uncertainty. The MQI describes the discrepancy between measurements and modelling results (linked to RMSE), normalized by measurement uncertainty and a scaling factor. The modelling quality objective (MQO) requires MQI to be less than or equal to 1. With an arbitrary selection of the scaling factor of 2, the fulfilment of the MQO means that the allowed deviation between modelled and measured concentrations is twice the measurement uncertainty. Expressions for the MQI calculation based on time series and yearly data are introduced. MPI refer to aspects of correlation, bias and standard deviation, applied to both the spatial and temporal dimensions. Similarly to the MQO for the MQI, modelling performance criteria (MPC) are defined for the MPI; they are necessary, but not sufficient criteria to determine whether the MQO is fulfilled. The MQO is required to be fulfilled at 90% of the stations, a criteria which is implicitly taken into account in the derivation of the MQI. The associated modelling uncertainty is formulated, showing that in case of MQO fulfilment the modelling uncertainty must not exceed 1.75 times the measurement one (with the scaling factor fixed to 2). A reporting template is presented and explained for hourly and yearly average data. In both cases there is a diagram and a table with summary statistics. In a separate section open issues are discussed and an overview of related publications and tools is provided. Finally, a chapter on modelling quality objectives for forecast models is introduced. In Annex 1, we discuss the measurement uncertainty which is expressed in terms of concentration and its associated uncertainty. The methodology for estimating the measurement uncertainty is overviewed and the parameters for its calculation for PM, NO2 and O3 are provided. An expression for the associated modelling uncertainty is also given.JRC.C.5 - Air and Climat

The Impact of MM5 and WRF Meteorology over Complex Terrain on CHIMERE Model Calculations

The objective of this study is to evaluate the impact of meteorological input data on calculated gas and aerosol concentrations. We use two different meteorological models (MM5 and WRF) together with the chemistry transport model CHIMERE.We focus on the Po valley area (Italy) for January and June 2005. Firstly we evaluate the meteorological parameters with observations. The analysis shows that the performance of both models in calculating surface parameters is similar, however differences are still observed. Secondly, we analyze the impact of using MM5 and WRF on calculated PM10 and O3 concentrations. In general CHIMERE/MM5 and CHIMERE/WRF underestimate the PM10 concentrations for January. The difference in PM10 concentrations for January between CHIMERE/MM5 and CHIMERE/WRF is around a factor 1.6 (PM10 higher for CHIMERE/MM5). This difference and the larger underestimation in PM10 concentrations by CHIMERE/WRF are related to the differences in heat fluxes and the resulting PBL heights calculated by WRF. In general the PBL height by WRF meteorology is a factor 2.8 higher at noon in January than calculated by MM5. This study showed that the difference in microphysics scheme has an impact on the profile of cloud liquid water (CLW) calculated by the meteorological driver and therefore on the production of SO4 aerosol. A sensitivity analysis shows that changing the Noah Land Surface Model (LSM) in our WRF pre-processing for the 5-layer soil temperature model, calculated monthly mean Correspondence to: A. de Meij ([email protected]) PM10 concentrations increase by 30%, due to the change in the heat fluxes and the resulting PBL heights. For June, PM10 calculated concentrations by CHIMERE/MM5 and CHIMERE/WRF are similar and agree with the observations. Calculated O3 values for June are in general overestimated by a factor 1.3 by CHIMERE/MM5 and CHIMERE/WRF. High temporal correlations are found between modeled and observed O3 concentrations.JRC.H.4 - Transport and air qualit

EURODELTA - Evaluation of a Sectoral Approach to Integrated Assessment Modeling - Second Report

The EURODELTA project is a continuing collaboration between the European Commission Joint Research Centre (JRC) at Ispra (Italy) and five air quality modeling teams at Ineris (France), the Free University of Berlin (Germany), Met.no (Norway), TNO (Netherlands) and SMHI (Sweden). This phase of Eurodelta investigates how different air quality models would represent the effect on pollutant impacts of applying, on a European scale, emission reductions to individual emission sectors. The reason for doing this is to test whether there are important sensitivities not captured by the sound science approach to air quality policy making on a European scale which is based on an integrated assessment (IA) approach and embodied in the IIASA RAINS/GAINS model. This study shows that there are important differences between sectors in the amount of concentration (deposition) reduction obtained by changing a pollutant emission. This difference is not accounted for in the present process used to evaluate future national emissions ceiling reductions for both beneficial effect and cost-effectiveness. This raises the possibility that, when national bodies consider how to implement an emission ceiling taking account of the information used in deriving that ceiling, choices might be made that are less effective than expected.JRC.DDG.H.4-Transport and air qualit

EURODELTA II - Evaluation of a Sectoral Approach to Integrated Assessment Modelling Including the Mediterranean Sea

The EURODELTA II (ED II) project is a continuing collaboration between the European Commission Joint Research Centre (JRC) at Ispra (Italy) and five air quality modelling teams at Ineris (France), the Free University of Berlin (Germany), Met.no (Norway), TNO (Netherlands) and SMHI (Sweden) in which the results from air quality model simulations are brought together in the JRC assessment toolkit and compared with each other and against data. ED I examined the common performance of the models in predicting recent (2000) and future (2020) air quality in Europe using the concept of a model ensemble to measure robustness of predictions. The spread of predictions about the ensemble gave a measure of uncertainty for each predicted value. In a 2020 world the effect of making emission reductions for key pollutants of NOx, SO2, VOC and NH3 independently in France, Germany and Italy, and of NOx and SOx in sea areas, was investigated. Source-receptor relationships used in integrated assessment (IA) modelling were derived for all the models and compared to assess how model choice might affect this key input. ED II builds on this project by taking a closer look at how the different models represent the effect on pollutant impacts on a European scale of applying emission reductions to individual emission sectors. A total of 60 different emission scenario calculations were run using meteorology from 1999. Sectors were defined using the SNAP97 designation and main focus was on Sectors 1,2,3,4,7 and 10 with some scenarios including sectors 6 and 8. Sector definitions are given in the introduction. Although the time-line for the scenarios is 2020 in line with the EU CAFE study and with the NECD review, an extra set of three 2010 scenarios was run for the Mediterranean Sea. These examine the effect of EU legislation requiring the use of a 0.1% S fuel for ships at berth in ports and the use of a maximum 1.5% S fuel for ferries. The main recommendation is that, at minimum, validation calculations are carried out as part of the NEC process to examine if the implied sectoral reductions are able to deliver the intended benefits. If sectoral weights could be incorporated into the integrated assessment itself then this may lead to an overall better recommendation for emission ceilings.JRC.H.4 - Transport and air qualit

Health impacts of air quality measures across sectors and spatial scales

Poor air quality and related health impacts are still an issue in cities and regions which exceed the guidelines set by the World Health Organisation. In order to reduce the impacts of air pollution it is necessary to abate emissions of precursors through measures across sectors and spatial scales. The approach developed in this study aims at quantifying the variability of the health outcomes that can be obtained by applying emissions reductions with priority to specific sectors (e.g. transport) and/or in specific areas (e.g. in urban areas) in comparison to reductions applied per precursor at the national level. Emission reductions are evaluated in terms of the resulting PM2.5 concentrations (and related health impact) through the spatially flexible cell-to-cell source receptor relationships (SRR) integrated in the SHERPA tool - Screening for High Emission Reductions Potentials for Air quality (http://aqm.jrc.ec.europa.eu/sherpa.aspx). The application of a European directive is taken as a conceptual case study to show the variability of the impacts considering different implementation scenarios of emission reduction targets. Results show the importance of considering the areas and the sectors where emissions reductions are applied, taking into account the local specificities, in order to obtain the highest reduction in health risks and design the most effective pollution reduction strategies. The SRR used in this study can therefore be a useful support for national and local authorities to comply with national emissions reductions targets and at the same time address their local air quality issues