Using multiple normalization procedures and contamination indices in the assessment of trace metal(loid) contents of the surface sediments of Lake Xingyun, southwestern China

Trace metal(loid) (TM) contamination, especially of aquatic ecosystems, is a global ongoing environmental problem. Fully and accurately determining their anthropogenic sources is a key requirement for formulating remediation and management strategies. Herein, we developed a multiple normalization procedure, combined with principal compo-nent analysis (PCA) to assess the influence of data-treatment and environmental factors on the traceability of TMs in surface sediments of Lake Xingyun, China. Multiple contamination indices, i.e., Enrichment factor (EF), Pollution Load Index (PLI), Pollution Contribution Rate (PCR) and Exceeded multiple discharge standard limits (BSTEL) suggest that contamination is dominated by Pb with the average EF exceed 3, especially within the estuary aeras with the PCR >40 %. The analysis demonstrates that the mathematical normalization of data, which adjusts it for various geochem-ical influences, has a significant effect on analysis outputs and interpretation. Routine (Log) and extreme (outlier re-moving) transformations may mask and skew important information contained within the original (raw) data, which create biased or meaningless principal components. Granulometric and geochemical normalization procedures can obviously identify the influence of grain size and other environmental impact on TM contents in principal compo-nents, but incorrectly explains the potential sources and contamination on different sites. Reducing the influence of or-ganic matter by normalization allowed the mineralogy, bio-degradation, salinity, and anthropogenic sources associated with local sewage and anthropogenic smelting to be identified and interpreted more clearly. Moreover, the co-occurrence network analysis also confirms that the influence of grain size, salinity, and organic matter content are the primary factors controlling the spatial variability in the type and concentrations of TMs.

Automated summary

This study developed a multiple normalization procedure combined with principal component analysis to assess the influence of data treatment and environmental factors on the traceability of trace metals in surface sediments of Lake Xingyun, China. The results showed that mathematical normalization of data had a significant effect on analysis outputs and interpretation, and granulometric and geochemical normalization procedures could identify the influence of grain size and other environmental impact on trace metal contents. Co-occurrence network analysis confirmed that grain size, salinity, and organic matter content were the primary factors controlling the spatial variability in the type and concentrations of trace metals.