engleski

Long-Term Monitoring of Area under Artificial Snow Covering Based on Spectrometric Analysis Data

INTRODUCTION Chemical composition of snow is important information in research studies of environmental impact of atmospheric and climate changes, and moreover, in the estimation of anthropogenic influences of urbanization on high altitude area [1]. The production of ski-race tracks in areas with little or no natural snow covering promotes the extensive use of artificial snow. Technology of artificial snow production is generally based on dispersion of water with chemical additives such as alkaline and earth- alkaline salts, urea (which is forbidden by legislation acts in Europe) or bacterial proteins. Additives are also widely used in conditioning of ski-tracks, because they affect the flowability, moisture and hardness of snow. The effects of artificial snow covering on ecosystems, particularly on vegetation, natural water aquifers and soil properties, have already been described in several research studies [2, 3]. However, the scientific interpretation of artificial snow impacts are still very rare and insufficient. In general, the consequences of different chemical composition of artificial snow compared to natural snow deposits become more pronounced during longer period of frequent use [4]. MATERIALS AND METHODS In this work, the chemical composition of snow and water samples, which were collected from Medvednica Nature Park, was studied from 2005 to 2016. Analytical methods included standard classical and spectrometric determinations of selected species. Metal content determination was performed by inductively coupled plasma atomic emission spectrometry (ICP-AES) and classical chemical procedures were used for determination of ammonia, dissolved organic matter, chlorides content and water hardness. RESULTS AND DISCUSSION Quantitative data that were obtained through period of eleven years were collected and processed by statistical analysis. The software package Statistica 12.7. was used to perform univariate (box-whisker) and multivariate statistical analysis such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) (Fig 1.). CONCLUSION Metal content determination along with chloride and nitrate analysis of snow and water samples from Nature Park Medvednica were performed in 10 years period. Long-term monitoring data were processed by statistical tools, which allows classification and discrimination of samples. Applied chemometric approach allows us the better observation of changes in chemical composition of water systems and could serve as a basis for prediction of future environmental impacts.

Snow ; ICP-AES ; Classification

nije evidentirano