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Title: MODELING AND VISUALIZATION OF ENVIRONMENTAL DATA IN SPACE AND TIME USING GIS
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MODELING AND VISUALIZATION OF ENVIRONMENTAL DATA IN SPACE AND TIME USING GIS
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1314-2704
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English
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20
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2.1
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The article deals with the development of geoinformatics procedures accelerating and
simplifying the application of the scattering model SYMOS?97 (Gaussian model), for selected pollutants in the open air to an area with a large number of sources and reference points. They are compared here with real measured concentrations of these persistent organic pollutants. One of the chapters illustrates the spatial and temporal variability of the ratios of the contributions of individual resource sectors to the total air pollution by polycyclic aromatic hydrocarbons. The interdisciplinarity of cartography and geoinformatics also lies in a wide range of scientific disciplines, for which it can be a valuable contribution in terms of effective data processing and presentation of achieved results. The next chapter article describes how to apply cartographic knowledge and procedures in the field of environmental chemistry. Thus, its focus is not only on the comparison of data obtained by measuring air pollution and calculated by the SYMOS?97 dispersion model. The main focus is on the demanding data processing for the mentioned model so that these procedures are as simple as possible, automated, and refined using geographic information systems. Because it is hardly possible to imagine manually preparing a larger amount of input data for this software without the use of GIS, automation of this process is obvious. Thanks to the automation of the preparation of the necessary documents, it is possible to achieve results that will not be affected by the subjective perception of the user, as well as a lower probability of entering errors into the processed data. The ideal conclusion of this work would, of course, be a complete agreement of the results obtained by the application of the scattering model and air sampling. However, the relative failure of this comparison is also beneficial if the main causes of this output can be identified. This means the possibility of identifying areas or types of relief in which there are significant discrepancies between the compared data. Finally, there is a warning in areas where SYMOS?97 gives worse results and a warning that in these areas there may be sources that are not part of the emission source database and could not be included in the model calculations. |
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conference
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20th International Multidisciplinary Scientific GeoConference SGEM 2020
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20th International Multidisciplinary Scientific GeoConference SGEM 2020, 18 - 24 August, 2020
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; C
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523-530
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18 - 24 August, 2020
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website
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cdrom
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7028
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Air pollution; Dispersion model; GIS; Polycyclic aromatic hydrocarbons;
Spatial interpolation |
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