Hotspots Identification of Heavy Metals in Sediments and Revelation of the Relationship Between Heavy Metal Contents and Environmental Variables

摘要

The aim of the study was to identify the hotspots of heavy metals in sediments for exploring possible sources and understand the relationship between heavy metals and environmental variables. West Chaohu Lake was selected as the study area, 38 surface sediment samples and 4 river mouth sediment samples were obtained and analysed for three typical metals (i.e. Co, Mn and Pb). Local indicators spatial associate (LISA) analysis detected spatial clusters and spatial outliers of enrichment factor (EF) values of the three metals and found the samples with pollution belong to high-high clusters, low-low clusters, even low-high outliers. Geostatistics and local Moran’s I were combined, and the results indicated that Co is mainly from natural sources, Mn is influenced by upward migration and reprecipitation, and Pb is influenced by anthropogenic sources. Furthermore, Pb was chosen as an example to understand the relationship between heavy metals and environmental variables. Compared to ordinary least squares (OLS) model, spatial autoregressive regression (SAR) model performed better and accounted for the phenomenon of spatial autocorrelation. Grain particle percent, loss on ignition (LOI), distance to Nanfei River mouth has a significant influence on the variation of Pb concentrations in sediments. Hotspots identification and spatial regression analysis can play an important role in understanding the pollution process for pollution management and restoration.