The objective of this paper is to present a probabilistic method of analyzing the combinations of snow and wind loads using meteorological data and to determine their combination factors. Calculations are based on data measured at twelve Polish meteorological stations operated by the Institute for Meteorology and Water Management. Data provided are from the years 1966 - 2010. Five combinations of snow load and 10-minute mean wind velocity pressure have been considered. Gumbel probability distribution has been used to fit the empirical distributions of the data. As a result, the interdependence between wind velocity pressure and snow load on the ground for a return period of 50 years has been provided, and the values of the combination factors for snow loads and wind actions are proposed.
Trace metal composition of snowpack, snow-melt filter residues and top-soils were determined along transects through industrial towns in the Usa River Basin: Inta, Usinsk and Vorkuta. Elevated concentrations of deposition elements and pH in snow and soils associated with alkaline coal ash within 25-40 km of Vorkuta and Inta were found. Atmospheric deposition in the vicinity of Vorkuta and Inta, added significantly to the soil contaminant loading as a result of ash fallout. The element concentrations in soils within 20-30 km of Vorkuta do not reflect current deposition rates, but instead, reflect an historical pollution legacy, when coal mining activity peaked in the 1960s. There is little evidence of anthropogenic metal deposition around the gas and oil town of Usinsk.
The paper deals with application of the Gumbel model to evaluation of the environmental loads. According to recommendations of Eurocodes, the conventional method of determining return period and characteristic values of loads utilizes the theory of extremes and implicitly assumes that the cumulative distribution function of the annual or other basic period extremes is the Gumbel distribution. However, the extreme value theory shows that the distribution of extremes asymptotically approaches the Gumbel distribution when the number of independent observations in each observation period from which the maximum is abstracted increases to infinity. Results of calculations based on simulation show that in practice the rate of convergence is very slow and significantly depends on the type of parent results distribution, values of coefficient of variation, and number of observation periods. In this connection, a straightforward purely empirical method based on fitting a curve to the observed extremes is suggested.