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Quantification of (1) the total mass of airborne pollutants deposited around pollution emitters, (2) the contaminated radius around these point sources, and (3) the proportion of emissions available for long-range transport remains challenging for environmental scientists. Here, we present a simple method for comparing yearly airborne emission figures from any point source with observed deposition estimates from the surrounding area. First, the depositiondistance function D(x) around the smokestack must be defined. This requires the empirical determination of total (water soluble + particulate) annual deposition at several locations away from the source and of the extent of the smokestack "shadow" zone. Secondly, the loading function L(x) is obtained by integrating D(x) around the point-source. This allows estimation of the yearly mass of a contaminant deposited within an area corresponding to that of a circle of radius x around the point source. Thus, emission, deposition, and (long-range) transport of airborne contaminants can be assessed quantitatively. Preliminary results from the Kola Peninsula, Russia, suggest that deposition of trace metals overwhelmingly occurs within 200 km from the source.

Snowpack and rainwater samples were used to calculate total yearly deposition rates for Al, As, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mo, Ni, Pb, S, Sb, Sr and V in eight catchments located within the Barents region, in Finland, Norway Ž . Ž . and Russia. Water-soluble -0.45 m deposition and particulate ) 0.45 m deposition were determined separately on snowpack samples representative of winter 1993r1994. Filtered rainwater sampled during the summer months of 1994 was used to obtain monthly water-soluble deposition data. The snow data were used to calculate the characteristic parameters of dispersion of the pollutants, which are constant for any given industrial source. To obtain these parameters, the element ratio of water-soluble to particulate deposition was expressed as a function of Ž . distance from pollution source. This function can then be used to calculate total water-soluble q particulate Ž 2 . summer deposition data from the water-soluble deposition data collected. Yearly deposition estimates in kgrkm obtained for the area range from 0.05 to 0.4 for Cd, 0.02᎐60 for Co, 0.14᎐5.2 for Cr, 0.5᎐494 for Cu, 0.2᎐845 for Ni Ž . and 125᎐1500 for S, depending on location remote vs. industrial . ᮊ 0048-9697r98r$ -see front matter ᮊ 1998 Elsevier Science B.V. All rights reserved.

The Central Kola Expedition and the Geological Surveys of Norway and Finland have undertaken a multi-media investigation of eight catchments situated in Russia (Zapoljarniy, Monchegorsk, Kirovsk, Kurka), Norway (Skjellbekken) and Finland (Kirakka, Naruska, Pallas) to determine the environmental impact of local industrial pollution. Results of analysis of snow samples collected in March/April 1994 are presented for both filtered meltwater and filter residues. Many heavy metals (As, Cd, Co, Cr, Cu, Mo, Ni, Pb, V and Zn), as well as S, Al and Fe, are unusually enriched in both phases in the Russian catchments, primarily in Monchegorsk and/or Zapoljamiy. Typically, some elements are found mostly in the meltwater phase (e.g. Na, S, Sr, Zn), others in the filter residues (e.g. Cr, Fe, Mo, Ni). Cu and Ni are well correlated, both in meltwater and in filter residue, and the ratio of Cu to Ni can be useful in identifying pollution sources. Snow is a well-suited medium for fingerprinting environmental pollution sources. Estimates of deposition from snow meltwater only, will be seriously underestimated for many elements.

Levels and regional distribution of NO, concentrations in rain, stream water, ground water and soil collected in eight small (12-30 km') catchments, located in severely polluted (near NikeVZapoljarnij and Monchegorsk, Kola Peninsula, Russia) to nearly pristine (Northern Finland) parts of the European Arctic are discussed. Yearly atmospheric NO, emissions on the Kola Peninsula exceed 24,000 t. Still wet deposition of NO,-N is very low compared to Central Europe. The atmospheric NO, emissions have no detectable regional influence on the NO, level in rain. The NO,-N pool present in the soil profiles exceeds the annual input from precipitation by a factor of 4-100. Variations of the nitrate content in soil are higher within catchments than between catchments, indicating that NO,-concentrations in soil are rather governed by local site characteristics (water retention capacity and drainage conditions as some of the most important factors) than distance to major emitters. The nitrate concentration in stream and ground water is very low. The disruption of the bio-productive processes and mineralisation of plant debris and soil humus in the most damaged ecosystem result in a significant increase of NO, concentrations in soils, stream and ground water.

1] Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil's Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. The aims of this paper are (1) to measure the abundance of hematite and goethite in Australian soils from their vis-NIR spectra, (2) to compare these results to measurements of soil color, and to describe the spatial variability of hematite, goethite, and soil color and map their distribution across Australia. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The work presented here was validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils.