Rapport

FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking. Version 3.3.

Janssen, S.; Thunis, P.; Adani, M.; Piersanti, A.; Carnevale, C.; Cuvelier, C.; Durka, P.; Georgieva, E.; Guerreiro, Cristina; Malherbe, L.; Maiheu, B.; Meleux, F.; Monteiro, A.; Miranda, A.; Olesen, H.; Pfafflin, F.; Stocker, J.; Santos, Gabriela Sousa; Stidworthy, A.; Stortini, M.; Trimpeneers, E.; Viaene, P.; Vitali, L.; Vincent, K.; Wesseling, J.

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, chaired by the JRC. A 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 summarise 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 harmonised 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), normalised 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 criterion 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. This aim of this document is to support modelling groups, local, regional and national authorities in their modelling application, in the context of air quality policy.

Publications Office for the European Union

Best practices for local and regional air quality management. Version 1.

Pisoni, E.; Guerreiro, Cristina; Namdeo, A.; Ortiz, A. G.; Thunis, P.; Janssen, S.; Ketzel, M; Wackenier, L.; Eisold, A.; Volta, M.; Nagl, C.; Monteiro, A.; Eneroth, K.; Fameli, K. M.; Real, E.; Assimakopoulos, V; Pommier, M; Conlan, B.

FAIRMODE is the Forum for Air Quality Modeling created for exchanging experience and results from air quality modeling in the context of the Air Quality Directives (AQD) and for promoting the use of modeling for air quality assessment and management. FAIRMODE is organized in different activities and task, called cross-cutting tasks, to which representative of Member States and experts participate. Among the different activities, one is devoted to Air Quality management practices, called cross-cutting task 5 (CT5). This report is indeed based on the last activities of the FAIRMODE Cross Cutting Task 5 (CT5), focusing, in particular, on elaborating recommendations to support local, regional and national authorities in the use of modelling for the development of air quality plans, defining on how to quantify emission changes associated to a set of measures, and quantifying their impacts in terms of concentration (using an ‘impact pathway approach’ from ‘abatement measure’ to ‘emissions’ to ‘concentrations’). This is done on one side taking advantage of the results already produced by previous FAIRMODE working groups and in coordination with existing activities under other FAIRMODE CTs. On the other side, examples of best practice policies are presented, focusing on Low emission zones: with an example on Antwerp and Copenhagen, Measures on non-exhaust traffic to reduce PM, with an application on Stockholm. How to reduce ozone concentrations, with a focus on local to global contributions. How to build an air quality plan in an integrated way, with an application on Italy. How to evaluate the socio-economic impact of measures, focusing on a case study on UK. The results show how different pollutants should be tackled differently, the importance of integration among different sectoral plans (on emissions, greenhouse gases mitigation, …) and also how other dimensions of the problem (i.e. social aspects) should be considered when building air quality plans.

Publications Office for the European Union

Monitoring of environmental contaminants in air and precipitation. Annual report 2019.

Bohlin-Nizzetto, Pernilla; Aas, Wenche; Nikiforov, Vladimir

This report presents environmental monitoring data from 2019 and time-trends for the Norwegian programme for Long-range atmospheric transported contaminants. The results cover 200 organic compounds (regulated and non-regulated), 11 heavy metals, and organic chemicals of potential Arctic concern.

NILU

Drivers and sector disaggregation of projections and trajectories. ETC technical paper.

Akkermans, Sander; Lopez, Pepa; Chornet, Javier; Petrides, Yannis Robles; Vella, Annabel; Dauwe, Tom; Ebrahimi, Babak; Bouman, Evert; Moran, Daniel

Member States are required to report on the country’s greenhouse gas emission projections and national integrated climate and energy policies and measures under the Governance Regulation of the Energy Union and Climate Action (EU) 2018/1999 every two years. This data is quality-checked by the ETC CM and subsequently used in several analysis and reports. GHG projections are an important information source to assess if countries are on track to achieve their mitigation targets. In this study, we delve deeper into the reporting to identify the primary drivers of GHG emissions at the most detailed disaggregation level possible. We aim to assess their impact on projections and evaluate the consistency between policies and projections, with the ultimate objective of improving the quality control activities of the ETC CM.

ETC Climate change mitigation

Ozone measurements 2018

Hjellbrekke, Anne-Gunn; Solberg, Sverre

NILU

Microplastics in Norwegian coastal areas, rivers, lakes and air (MIKRONOR1)

van Bavel, Bert; Lusher, Amy Lorraine; Consolaro, Chiara; Hjelset, Sverre; Singdahl-Larsen, Cecilie; Buenaventura, Nina Tuscano; Röhler, Laura; Pakhomova, Svetlana; Lund, Espen; Eidsvoll, David; Herzke, Dorte; Bråte, Inger Lise Nerland

Norsk institutt for vannforskning