ON IMPROVING THE EFFICIENCY OF ENVIRONMENTAL MONITORING: A STATISTICAL MODEL OF SNOW POLLUTION BY DIFFERENT SOURCES

Air pollution is known to be one of the main environmental factors which can affect human health in large cities. In this regard, the issues of development and improvement of the models and methods for estimation and forecasting of atmospheric pollution remain relevant. The importance of such models development is also largely determined by the need of reduction cost of monitoring studies, which may be achieved by reducing the quantity of observation points on the territory and by using corresponding models to calculate pollution indicators in all necessary points. The similar measures allow improving the overall efficiency of the environmental monitoring at industrial and other objects. Land Use Regression (LUR) is one of the new methods for atmospheric pollution description which is being developed in Europe and the United States since 1993. Current work describes the possibility of applying of the LUR method for description of pollution of snow cover from multiple areal and local sources of different power. Snow is an indicator of atmospheric pollution because it accumulates dust and other substances from the atmosphere and may be used as an object of monitoring in many Russian regions with cold climate. During formation and fall of snow in the processes of dry and wet leaching concentration of pollutants in it is usually in 2-3 orders of magnitude higher than in atmospheric air. Taking samples of snow weighing about 3-5 kg allows determining with sufficient accuracy the content of dust and other substances in it. Sampling can be performed at any given network of observations for the purpose of obtaining pictures of the spatial distribution of atmospheric deposition in the study area. The research showed that Land-Use Regression approach may be used for description of the dust spatial distribution not only from a single powerful emission source, but also in case of presence of several sources of different type.