The Air Quality Management System AirQUIS

Slørdal, Leiv Håvard; McInnes, Harold; Krognes, Terje

doi:10.1007/978-3-540-71335-7_6

Cited by 15 publications

(20 citation statements)

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“…Fedra and Witner, 2009;Slørdal et al, 2008;Karatzas and Nikolaou, 2009). Specifically, we provide some examples of how CWF models are currently being applied by end users.…”

Section: Mm5-camxmentioning

confidence: 99%

A review of operational, regional-scale, chemical weather forecasting models in Europe

et al. 2012

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Abstract. Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research Published by Copernicus Publications on behalf of the European Geosciences Union. J. Kukkonen et al.: A review of operational, regional-scale, chemical weather forecasting models in Europedirections, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

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“…Fedra and Witner, 2009;Slørdal et al, 2008;Karatzas and Nikolaou, 2009). Specifically, we provide some examples of how CWF models are currently being applied by end users.…”

Section: Mm5-camxmentioning

confidence: 99%

A review of operational, regional-scale, chemical weather forecasting models in Europe

et al. 2012

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“…The City-scale Chemistry (CityChem) model is an extension of the urban dispersion model EPISODE (Slørdal et al, , 2008 with the aim to enable chemistry/transport simulations of multiple reactive pollutants on urban scales. The new model industrial point sources with high spatial resolution for the entire study domain.…”

Section: Discussionmentioning

confidence: 99%

“…The following features, which are not part of the operational EPISODE model (Slørdal et al, , 2008, have been implemented in CityChem-EPISODE:…”

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confidence: 99%

Development of the city-scale chemistry transport model CityChem-EPISODE and its application to the city of Hamburg

Karl

2018

Preprint

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Abstract. This paper describes the City-scale Chemistry (CityChem) extension of the urban dispersion model EPISODE with the aim to enable chemistry/transport simulations of multiple reactive pollutants on urban scales. The new model is called CityChem-EPISODE. The primary focus is on the simulation of urban ozone concentrations. Ozone is produced in photochemical reaction cycles involving nitrogen oxides (NO X ) and volatile organic compounds (VOC) emitted by various anthropogenic activities in the urban area. The performance of the new model was evaluated with a series of synthetic tests 5 and with a first application to the air quality situation in the city of Hamburg, Germany. The model performs fairly well for ozone in terms of temporal correlation and bias at the air quality monitoring stations in Hamburg. In summer afternoons, when photochemical activity is highest, modelled median ozone at an inner-city urban background station was about 30 % lower than the observed median ozone. Inaccuracy of the computed photolysis frequency of nitrogen dioxide (NO 2 ) is the most probable explanation for this. CityChem-EPISODE reproduces the spatial variation of annual mean NO 2 concentrations between urban 10 background, traffic and industrial stations. However, the temporal correlation between modelled and observed hourly NO 2 concentrations is weak for some of the stations. For daily mean PM 10 , the performance of CityChem-EPISODE is moderate due to low temporal correlation. The low correlation is linked to uncertainties in the seasonal cycle of the anthropogenic particulate matter (PM) emissions within the urban area. Missing emissions from domestic heating might be an explanation for the too low modelled PM 10 in winter months. Four areas of need for improvement have been identified: (1) dry and wet deposition 15 fluxes; (2) treatment of photochemistry in the urban atmosphere; (3) formation of secondary inorganic aerosol (SIA); and (4) formation of biogenic and anthropogenic secondary organic aerosol (SOA). The inclusion of secondary aerosol formation will allow for a better sectorial attribution of observed PM levels. Envisaged applications of the CityChem-EPISODE model are urban air quality studies, environmental impact assessment, sensitivity analysis of sector-specific emission and the assessment of local and regional emission abatement policy options.

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“…The dispersion model employed is the AirQUIS-EPISODE model, which is an integral part of the AirQUIS air quality management system (AirQUIS, 2008;Slørdal et al, 2008). It is a Eulerian chemical transport model combined with sub-grid models for line sources, HIWAY-2 (Petersen, 1980), and point sources.…”

Section: Dispersion Modellingmentioning

confidence: 99%

Source apportionment of PM<SUB align="right">2.5 in urban areas using multiple linear regression as an inverse modelling technique

Denby

2011

IJEP

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Abstract:In many countries emissions of particulate matter from urban sources, such as traffic and domestic wood burning, can lead to high episodic concentrations. Though it is important for air quality management and exposure studies to understand the individual source contributions to these concentrations, the complexity of the urban environment does not always allow a clear separation of sources when using conventional monitoring techniques that measure particulate mass only. Chemical analysis of the particulates, combined with receptor modelling, is one method for determining source contributions but these do not provide direct information on emissions. Inverse modelling methods, that make use of both dispersion models and measurements, can in principle be used to determine emissions strengths and distributions. However, the urban environment is generally so complex and the number of observations so limited that most inverse modelling methods cannot be effectively applied. In this paper a straight forward inverse modelling method, using multiple linear regression, is described and applied. The method determines the optimal fit of the calculated source contributions using dispersion modelling, providing scaling factors for the individual source contributions. The method is applied to the urban area of Oslo for PM 2.5 in the winter of 2004 and the results of the inverse modelling are compared to independent receptor modelling. The method shows that the modelled source contribution from suspended road dust is underestimated by a factor of 7 -10. For domestic wood burning the method shows an overestimate of the modelled source contribution by a factor of 2 -3. These results are confirmed using independent analysis by receptor modelling. The methodology cannot distinguish directly between model or emission error and so further assessment of the model itself, and its uncertainty, is required before concrete statements concerning emission strengths can be made.

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The Air Quality Management System AirQUIS

Cited by 15 publications

References 0 publications

A review of operational, regional-scale, chemical weather forecasting models in Europe

A review of operational, regional-scale, chemical weather forecasting models in Europe

Development of the city-scale chemistry transport model CityChem-EPISODE and its application to the city of Hamburg

Source apportionment of PM<SUB align="right">2.5 in urban areas using multiple linear regression as an inverse modelling technique

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