Analysis and assessment of heavy metal contamination in the vicinity of Lake Atamanskoe (Rostov region, Russia) using multivariate statistical methods.

Assessment of spatial patterns of potentially toxic metals is one of the most urgent tasks in soil chemistry. In this study, descriptive statistics and three methods of multivariate statistical analysis, such as the hierarchical cluster analysis (HCA), correlation analysis, and conditional inference tree (CIT), were used to identify patterns and potential sources of heavy metals (Co, Ni, Cu, Cr, Pb, MnO, and Zn). The investigation was carried out on 81 sample points, using 20 testing parameters.

Spatial distribution of heavy metals in soils of the flood plain of the Seversky Donets River (Russia) based on geostatistical methods.

Soil contamination by heavy metals (HM) is a worldwide problem for human health. To reduce risk to human health from exposure to toxic chemicals associated with soil contamination, it is necessary to monitor and assess HM concentrations in the soil for places where the concentration exceeds the acceptable levels. Spatial patterning is a necessary tool for assessment of the exposure risk of HM contamination.

Geochemical assessment and spatial analysis of heavy metals pollution around coal-fired power station.

Coal-fired power stations are significant sources of soil contamination with heavy metals and a source of hazard to human health. The soil samples (n = 25) selected in the area around Novocherkassk Power Station (Rostov Region, Russia) within a radius of up to 20 km revealed the enrichment with Pb, Cu and Zn. The heavy metals (HM) content in soil is reduced in the following sequence: Mn > Cr > Zn > Ni > Cu > Pb > Co.