Application of neural-based modeling in an assessment of pollution with mercury in the middle part of the Warta River.
The level of pollution with various mercury species (organomercury, water- and acid-soluble mercury, mercury bound to humic matter and to sulphides) of the floodplain soils and sediments from middle part of the Warta River has been assessed using self-organizing maps (SOM). Chemometric evaluation allowed identification of moderately (median 173-187 ng g(-1), range 54-375 ng g(-1) in soil and 130 ng g(-1), range 47-310 ng g(-1) in sediment) and heavily polluted samples (662 ng g(-1), range 426-884 ng g(-1)). Heavily polluted were located mainly below and in the area of the Poznań city. Statistical comparison of mercury species distribution in floodplain soils of the Warta River shows different patterns for moderately and heavily polluted samples. In heavily polluted soils the contribution of mobile mercury (sum of organomercury species, water- and acid soluble species) is lower (4.2%) than in moderately polluted soils (6.1%). Higher contribution of mobile mercury was observed in sediments of the Warta River (12%). In case of moderately polluted samples, statistical differences in the contribution of mercury species are relatively low and thus the environmental risk from mercury deposited in aquatic system of the Warta River is relatively low. However, higher water levels and heavy floods may incite remobilisation of some organomercuries (2.2-2.9 ng g(-1) in soil and 10 ng g(-1) in sediment) and acid-soluble species of mercury (2.6-2.9 ng g(-1) in soil and 0.5 ng g(-1) in sediment).
Boszke
Leonard
L
Department of Environmental Protection, Collegium Polonicum, Adam Mickiewicz University, 1 Kościuszki Str., 69-100, Słubice, Poland. boszke@euv-frankfurt-o.de
Astel
Aleksander
A
D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000737:chemistry; D019015:Geologic Sediments / Q000032:analysis*; D006801:Humans; D008628:Mercury / Q000032:analysis*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011044:Poland; D045483:Rivers / Q000737:chemistry*; D012989:Soil Pollutants / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis
D012989:Soil Pollutants; D014874:Water Pollutants, Chemical; D008628:Mercury