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

https://doi.org/10.1016/j.scitotenv.2020.142437Get rights and content

Highlights

  • We proposed an improved EF model using stochastic methods and geochemical baselines.

  • The improved EF model has a better capability in predicting the HM pollution.

  • Slight enrichment of Cd and multiple HMs was observed in sediments from Poyang Lake.

  • Cd in Poyang Lake requires more attention considering its deteriorative probability.

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.

Introduction

Heavy metals (HMs) have drawn increasingly wide attention globally due to their toxicity, non–biodegradability, accumulation, and persistence in the food chain (Liu et al., 2018; Setia et al., 2020; Shakeri et al., 2020; Wang et al., 2018). Through natural processes (such as volcanism, rock weathering, and dry and wet atmospheric deposition), and anthropogenic activities (such as mining, industrial effluents, agricultural cultivation, and sewage runoff), HMs can be transported to aquatic environments, posing potential risks to aquatic organisms, and eventually human beings (Deng et al., 2020; Kumar et al., 2019; L. Li et al., 2020; Shakeri et al., 2020). Sediment is generally considered to be a natural enrichment pool and adsorbs or precipitates up to 90% of HMs in aquatic ecosystems (Reis et al., 2016; Zhang et al., 2018). In contrast, with physical, chemical, or biological variations in the water environment, sediments accumulating HMs can become potential sources for the secondary endogenesis of HM contamination. In particular, HMs that settle in surficial sediments can be released back into the water column by disturbance, desorption, or dissolution (Yu et al., 2019; Zhang et al., 2020). Consequently, the scientific evaluation and effective prediction of HM pollution in sediments are crucial issues for maintaining aquatic ecosystems.

The enrichment factor (EF) is a widely used tool for assessing HM contamination in sediments (Hu et al., 2011; Wang et al., 2018; X. Wang et al., 2020). Grain size and provenance are two significant factors controlling the HM deposition in aquatic ecosystems (Dendievel et al., 2020; Dutu et al., 2019). Therefore, EF normalizes HM concentrations to the “conservative” or “reference” element to reduce interference from particle size and mineral composition. The reference element should ideally not be impacted by anthropogenic activities. The reference element should also be an important constituent of one or more of the major metal carriers and reflect their granular variability in sediments (Loring, 1990). Previous studies have generally used Al, Fe, Li, Mn, Sc, Ti, and Zr as “conservative” elements (Kumar et al., 2018; Kumar et al., 2020; Reimann and Caritat, 2000). The normalized HM concentrations are then compared with the normalized background values (BVs) to determine the HM contamination degree. However, the conventional EF method ignores three crucial factors: (1) uncertainties and randomness in the water environment due to natural variations and human activities; (2) the difficulty in obtaining locally true BVs for HMs, leading to bias or even unreliable assessment results (Selvaggi et al., 2020; Tian et al., 2017); and (3) the comprehensive pollution attributable to multiple HMs in sediments. Moreover, the conventional EF method cannot predict the deterioration tendency of HM pollution. Although this tool has been applied for half a century, the above weaknesses remain unresolved. Hence, there is a need to improve the EF method.

Considering these issues, we present an attempt at advancing the conventional EF method in terms of mathematical analysis and alternative BVs. Accordingly, we develop improved EF model considering information from stochastic mathematical methods and geochemical baselines (GBs). A stochastic mathematical method based on probability density function (PDF) is used to quantify the randomness attributable to accidental or inevitable errors (such as sampling and calculation errors). Then, the comprehensive pollution vector was further deduced by combining the possibility of each HM belonging to each enrichment category with the weight of the corresponding HM. The GBs consider natural variations containing slight anthropogenic impacts on HM concentrations in regional areas (B. Gao et al., 2018; Han et al., 2019). Compared with BVs, GBs, which are increasingly used as an alternative for assessing HM pollution in the regional area (Gao et al., 2019; Tian et al., 2017), are more reliable.

We verify the practicability and scientificity of the improved EF model by applying it to the evaluation of HM pollution (i.e., Zn, Pb, Ni, Cu, Cr, Cd, and As) in sediments from Poyang Lake, which is the largest freshwater lake in China and one of the most important wetland resources in the world. The main objectives of our study are to establish the improved EF model and test a relatively simple hypothesis: the improved EF model can be adopted as a more effective tool for evaluating and forecasting HM pollution in sediments, compared with the conventional EF method. In further detail, the objectives of this study are as follows: (1) reveal the occurrence characteristics of HMs in sediments; (2) establish GBs for HMs in sediments from Poyang Lake; (3) evaluate the pollution degree of HMs in sediments employing the improved EF model and reveal the advantages by comparing the assessment results using the improved EF model with those of the conventional EF method; and (4) identify the potential sources of HMs in sediments using the Pb isotopic composition and principal component analysis (PCA) to simultaneously test the assessment results by applying the improved EF model.

Section snippets

Study area

As an important part of the Yangtze River, Poyang Lake (3583 km2) is located in Jiangxi province, southeastern China (Fig. 1). The main inflows of Poyang Lake are the Ganjiang, Xinjiang, Xiushui, Fuhe, and Raohe rivers (Dai et al., 2018; D.D. Xu et al., 2020), among which Gangjiang River is the river largest sediment transporter (Liu et al., 2020).

The landforms of the Poyang Lake Basin are dominated by plains in the north (Poyang Lake Plain), mountainous areas in the south, and transition areas

Summary statistics of HM concentrations

Table 1 summarizes the determined HM concentrations in the sediments collected from Poyang Lake. In decreasing order, the mean HM concentrations were Zn > Cr > Pb > Cu > Ni > As > Cd, with the average HM concentrations 1.11–5.69 times the BVs of HMs from stream sediments in China, and the average concentrations of HMs were 0.84–4.10 times the BVs of HMs in sediments from Yangtze River (Chi and Yan, 2007). Compared with other lakes and the Three Gorges Reservoir in China, the average HM

Conclusions

In this study, we introduced the improved EF model, which overcomes the drawbacks of the conventional EF method and unavailability of BVs for HMs. Furthermore, the improved EF model supports the comprehensive assessment and prediction of the deteriorative probability of HM pollution in sediments. Overall, the major contributions of the improved EF model are that it has better prediction ability, comprehensiveness, accuracy, and reliability in pollution assessment of HMs in sediments. The main

CRediT authorship contribution statement

Yanyan Li: Investigation, Data curation, Methodology, Writing - original draft. Huaidong Zhou: Supervision, Formal analysis, Writing - review & editing. Bo Gao: Project administration, Conceptualization, Methodology, Funding acquisition. Dongyu Xu: Investigation, Writing - review & editing, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Research & Development Support Program of China Institute of Water Resources and Hydropower Research (WE0145B662017).

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