Found 9883 publications. Showing page 45 of 396:
FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking. Version 3.3.
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
2022
Information on the origin of pollution is an essential element of air quality management that helps identifying measures to control air pollution. In this document, we review the most widely used source-apportionment (SA) methods for air quality management. The focus is on particulate matter but examples are provided for NO2 as well. Using simple theoretical examples, we explain the differences between these methods and the circumstances where they give different results and thus possibly different conclusions for air quality management. These differences are a consequence of the assumptions that underpin each methodology and determine/limit their range of applicability. We show that ignoring these underlying assumptions is a risk for efficient/successful air quality management when the methods are used outside their scope or range of applicability.
Publications Office for the European Union
2022
Best practices for local and regional air quality management. Version 1.
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
2022
2022
This report aims to support the on-going revision of the Ambient Air Quality Directives by providing a series of recommendations on the reciprocal exchange of information and reporting of ambient air quality (e-reporting) following the Commission Implementing Decision (2011/850/EU). It builds on the experience and understanding from the EEA and technical experts at its European Topic Centre for Human Health and the Environment (ETC HE) working with implementing provisions for reporting (IPR) and identifies areas for further efficiency gains in e-reporting, in particular concerning the H-K dataflows.
ETC/HE
2022
Environmental contaminants in freshwater food webs, 2021
This report presents monitoring data from freshwater food webs and abiotic samples from Lake Mjøsa and Femunden within the
Milfersk programme. Studies and monitoring of legacy and emerging contaminants have been carried out through this programme
for several years, focusing on the pelagic food web. This is the first report in the monitoring program focusing on a benthic food
chain (Chironomids, ruffe, roach and perch) in addition to inputs to Lake Mjøsa by analysis of lake sediments, surface waters,
stormwater, effluent and sludge from a wastewater treatment plant (WWTP). The analytical programme includes the determination
of a total of ̴ 260 single components.
Norsk institutt for vannforskning
2022
Atmospheric Supply of Nitrogen, Copper, HCB, BDE-99, SCCP and PFOS to the Baltic Sea in 2020.
Norwegian Meteorological Institute
2022
Microplastics in Norwegian coastal areas, rivers, lakes and air (MIKRONOR1)
Norsk institutt for vannforskning
2022
State of the Climate in 2021: The Arctic
American Meteorological Society
2022
Frontiers Media S.A.
2022
Microplastics in Norwegian coastal areas, rivers, lakes and air (MIKRONOR1)
Norsk institutt for vannforskning
2022
Forskerne fant flere varianter av det syntetiske stoffet PFAS i dyr enn forventet
Norges forskningsråd
2022
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