Spatial patterns and temporal changes of heavy metal distributions in river sediments in a region with multiple pollution sources

Purpose

The main purpose of this study was to evaluate temporal and regional variability of contamination by heavy metals (HMs) in river sediments using their enrichment factors (EFs) and benchmarking according to sediment quality guidelines (SQGs). The Zlin region in the Czech Republic (Morava and Drevnice River basins) represents a model area where several regionally specific ecological risk assessment studies have recently been conducted with a focus on organic pollution, eco-toxicity, geological, and geochemical characteristics.

Materials and methods

Four consecutive sediment sampling campaigns were undertaken in spring and autumn 2005–2006. Aqua-regia leachable content of Cd, Co, Cr, Cu, Ni, Pb, Sb, V, and Zn in surface sediments from 14 sites was analyzed using ICP-MS, and Hg content was analyzed using AMA-254 analyzer. EFs were calculated to identify the human impact on pollution in the area. Comparisons to SGQs were conducted to identify the areas and HMs of greatest risk.

Results and discussion

Calculation of EFs contributed to the effective clustering of HMs. Median EFs of Co, Ni, and V ranged from 0.9 to 1.4 at all sites indicating concentrations very close to natural geological background levels. There was greater enrichment at locally polluted sites, the highest in the cases of Cd, Sb, Hg, and Cr. Widespread influence of diffuse HM sources (traffic, agriculture, and urban wastes) was apparent from elevated concentrations of Pb, Cu, and Zn at all sites. EF values also helped to identify the greatest temporal changes and shifts in HMs contamination between adjacent sites caused by 50-year recurrence interval floods in early spring 2006. The impact was most apparent in downstream sites; namely directly below the confluence of the two major rivers.

Conclusions

The overall contamination of HMs in the region was classified as low-to-moderate with significantly contaminated sub-areas. The study showed relatively stable spatial distributions of HMs, indicating potential sources of pollution. Cu was identified as the HM of greatest risk. The study emphasizes the necessity of considering both environmental circumstances and background HM occurrence to prevent misinterpretation of the pollution situation. The use of EFs which include grain size proxy normalization and HM background levels, along with the comparison of the detected concentrations to SQGs, proved an efficient way to identify hazardous contamination from anthropogenic sources.