Author(s): Piotr Holnicki | Zbigniew Nahorski
Journal: Journal of Theoretical and Applied Computer Science
ISSN 2299-2634
Volume: 7;
Issue: 1;
Start page: 56;
Date: 2013;
VIEW PDF
DOWNLOAD PDF 
Original page
Keywords: air quality model | urban-scale emission inventory | uncertainty analysis
ABSTRACT
Decision support of air quality management needs to connect several categories of the input data with the analytical process of air pollution dispersion. The aim of the respective model of air pollution is to provide a quantitative assessment of environmental impact of emission sources in a form of spatial/temporal maps of pollutants’ concentration or deposition in the domain. These results are in turn used in assessment of environmental risk and supporting respective planning actions. However, due to the complexity of the forecasting system and the required input data, such environmental prognosis and related decisions contain many potential sources of imprecision and uncertainty. The main sources of uncertainty are commonly considered meteorological and emission input data. This paper addresses the problem of emission uncertainty, and impact of this uncertainty on the forecasted air pollution concentrations and adverse health effects. The computational experiment implemented for Warsaw Metropolitan Area, Poland, encompasses one-year forecast with the year 2005 meteorological dataset. The annual mean concentrations of the main urban pollutants are computed. The impact of uncertainty in emission field inventory is also considered. Uncertainty assessment is based on the Monte Carlo technique where the regional scale CALPUFF model is the main forecasting tool used in air quality analysis.
Journal: Journal of Theoretical and Applied Computer Science
ISSN 2299-2634
Volume: 7;
Issue: 1;
Start page: 56;
Date: 2013;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: air quality model | urban-scale emission inventory | uncertainty analysis
ABSTRACT
Decision support of air quality management needs to connect several categories of the input data with the analytical process of air pollution dispersion. The aim of the respective model of air pollution is to provide a quantitative assessment of environmental impact of emission sources in a form of spatial/temporal maps of pollutants’ concentration or deposition in the domain. These results are in turn used in assessment of environmental risk and supporting respective planning actions. However, due to the complexity of the forecasting system and the required input data, such environmental prognosis and related decisions contain many potential sources of imprecision and uncertainty. The main sources of uncertainty are commonly considered meteorological and emission input data. This paper addresses the problem of emission uncertainty, and impact of this uncertainty on the forecasted air pollution concentrations and adverse health effects. The computational experiment implemented for Warsaw Metropolitan Area, Poland, encompasses one-year forecast with the year 2005 meteorological dataset. The annual mean concentrations of the main urban pollutants are computed. The impact of uncertainty in emission field inventory is also considered. Uncertainty assessment is based on the Monte Carlo technique where the regional scale CALPUFF model is the main forecasting tool used in air quality analysis.
