The Estimation of Total Gaseous Mercury Concentration (TGM) Using Exploratory and Stochastic Methods
Grzegorz Majewski1, Piotr O. Czechowski2, Artur Badyda3, Wioletta Rogula-Kozłowska4
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1Division of Meteorology and Climatology, Warsaw University of Life Sciences,
Nowoursynowska 166, 02-776 Warszawa, Poland
2Information Systems Department, Gdynia Maritime University, Morska 83, 81-225 Gdynia, Poland
3Environmental Engineering Faculty, Warsaw University of Technology,
Nowowiejska 20, 00-653 Warszawa, Poland
4Institute of Environmental Engineering of the Polish Academy of Sciences, Department of Air Protection,
M. Skłodowskiej-Curie 34, 41-819 Zabrze, Poland
Pol. J. Environ. Stud. 2013;22(3):759–771
Our paper presents the results of the first one-year measurement series of total gaseous mercury collected at an automatic air quality monitoring station in the village of Granica (Granica-KPN). The measurement series of mercury concentrations was used to estimate the model that identifies the influence of selected measurement results, both imission and meteorological ones, on the concentration of gaseous mercury in the air. Such a model can be a useful tool for the estimation of gaseous mercury concentration over a certain area, and for the estimation of the mercury deposition rate, as well as for the reduction of costs of expensive measuring devices used for recording concentration of that air pollutant.
The advantage of the presented method for mercury concentration identification is the relatively low cost of acquiring precise results, when meteorological conditions are known and the measurements of imission are significantly connected with mercury.
Such a low cost is related, first of all, to the computation time and the software, assuming that the considered analytical system is fully functional. The disadvantages include the need to have measurement series without gaps in data. It is a practical problem for which the solution is stochastic interpolations as proposed in this paper. In order to obtain precise resultant estimations of a variable we need to have high-quality input data - in a sense it is a truism that is often not sufficiently implemented in practice. For this reason, a detailed diagnosis of measurement data is required, including stochastic- exploratory tools, which were presented in this paper in their most effective implementation. It is essential not to include in calculations those data that contain errors, e.g. having an influential or atypical character in relation to other distributions or measurements.
These errors in the data will be transferred onto results unless they are identified in the initial phase of modeling.
Mean annual TGM concentration was equal to 1.52 ng·m-3 and was considerably lower than in other parts of Poland. Seasonal variability of TGM concentration was observed, and the TGM concentration was higher in the winter period than in summer. Mean concentration of TGM in the winter period (heating season)was equal to 1.65 ng·m-3, but in the summer it reached 1.40 ng·m-3. The TGM concentration at the Granica- KPN station was influenced mostly by local emission sources, in both warm and cold periods of the year. The analysis of fluxes of total gaseous mercury proved that the state of air pollution with mercury in the surroundings of the station also was influenced by the sources of a high emission rate in the winter period, located in WSW, W, and WNW sectors.