Improved enrichment factor model for correcting and predicting the evaluation of heavy metals in sediments

Yanyan Li; Huaidong Zhou; Bo Gao; Dongyu Xu

doi:10.1016/j.scitotenv.2020.142437

Improved enrichment factor model for correcting and predicting the evaluation of heavy metals in sediments

Sci Total Environ. 2021 Feb 10;755(Pt 1):142437. doi: 10.1016/j.scitotenv.2020.142437. Epub 2020 Sep 21.

Authors

Yanyan Li¹, Huaidong Zhou¹, Bo Gao², Dongyu Xu³

Affiliations

¹ State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; Department of Water Ecology and Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
² State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; Department of Water Ecology and Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. Electronic address: gaobo@iwhr.com.
³ Department of Water Ecology and Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.

PMID: 33011598
DOI: 10.1016/j.scitotenv.2020.142437

Abstract

As the most widely used method for evaluating heavy metals (HMs) in soil or sediment, the enrichment factor (EF) is prone to bias and even yields misleading assessment results for HM pollution due to data uncertainties, lack of local background values and a failure to assess the comprehensive pollution of multiple HMs. Here, we developed an improved EF model integrating stochastic mathematical methods and geochemical baselines (GBs). First, GBs were obtained using the relative cumulative frequency distribution method. The probability that each HM belongs to each enrichment degree was then quantified based on the probability density function deduced from the maximum entropy method. Furthermore, we defined a synthetic index to reveal the probability that multiple HMs belongs to comprehensive enrichment degree considering the weight of each HM. Finally, the enrichment category for each HM and multiple HMs were determined following the first-order moment principle. The improved EF model was successfully applied to evaluate and predict the HM pollution in sediments collected from Poyang Lake, the largest freshwater lake in China. Slight enrichment (1.88) of multiple HMs was found in sediments from Poyang Lake, characterized by a pronounced probability (0.35) to deteriorate to the "moderate enrichment" category. Among the different HMs, Cd requires more attention considering its dominant contribution (0.51) to the comprehensive pollution and high probability (0.65) for deterioration. Otherwise, assessment results employing the improved EF model agree with the spatial patterns of HM concentrations based on spatial autocorrelation analysis and source apportionment using Pb isotopic signatures and principal component analysis. Compared with the conventional EF method, the assessment results of the improved EF model were more accurate, comprehensive and reliable. In conclusion, the improved EF model has a better capability of evaluating and predicting HM enrichment in sediments and can be helpful for optimizing control measures for HM pollution.

Keywords: Geochemical baseline; Heavy metals; Improved enrichment factor model; Pb isotopic signature; Pollution assessment; Stochastic method.