ASSESSMENT OF RIVER SEDIMENT QUALITY ACCORDING TO THE EU WATER FRAMEWORK DIRECTIVE IN LOWLAND FLUVIAL CONDITIONS. A CASE STUDY IN THE DRAVA RIVER AREA, DANUBE RIVER BASIN

The EU Water Framework Directive requires the monitoring and evaluation of surface water sediment quality based on the assessment of risk posed by contamination on the biotic receptors. Fluvial sediments are important receptors of hazardous substances (HSs) pollution from the upstream catchment areas in the Danube River Basin (DRB). For the development of systematic sediment quality monitoring and evaluation, the Drava River region on the border of Hungary and Croatia was selected as a test area representative of lowland hydromorphological conditions. Overbank (floodplain) sediments and river bottom sediments (stream sediments) were sampled at two depths at 9 locations in the test area. Eight heavy metal(oid)s were analyzed As, Cd, Cr, Cu, Hg, Ni, and Zn as hazardous substances. The sediment quality assessment was carried out according to the 2013/39/EU Directive and EU Water Framework Directive standards. Most of the analysed HS concentrations in river bottom sediment and overbank (floodplain) sediments fall within the limits of environmental quality standards (EQS). Results show that there is no significant differences in metal(oid) HS concentrations among the various sediment types and between shallow (0-5cm) and deeper (stream sediment: 5-10cm; floodplain sediment: 40-50cm) sediment which suggests that the large lowland Drava River fluvial system is an extensive single fluvial system with homogeneous distribution of sediments and the associated contaminants. Specifically, the studied sediments in the tributaries of the Drava River do not seem to be contaminated with metal(oid) hazardous substances but at certain sites concentrations are elevated above the environmental limit values, especially for As and Zn, and to lesser extent for Cr. The data analysis techniques used enabled the identification of sites with anthropogenic pollution and the recognition of regional pattern in HSs distribution.