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.
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.
Sci Total Environ. 2021 Feb 10;755(Pt 1):142437. doi: 10.1016/j.scitotenv.2020.142437. Epub 2020 Sep 21.
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.
作为评估土壤或沉积物中重金属 (HM) 的最广泛应用方法，富集因子 (EF) 容易受到数据不确定性、缺乏本地背景值以及无法评估多种 HM 综合污染等因素的影响，从而导致评估结果产生偏差甚至产生误导。在这里，我们开发了一种改进的 EF 模型，该模型集成了随机数学方法和地球化学基线 (GBs)。首先，使用相对累积频率分布方法获得 GBs。然后，根据最大熵法推导出的概率密度函数，量化每个 HM 属于每个富集程度的概率。此外，我们定义了一个综合指数，以揭示考虑每个 HM 权重时多种 HM 综合富集程度的概率。最后，根据一阶矩原理确定每个 HM 和多种 HM 的富集类别。改进的 EF 模型成功应用于评估和预测中国最大的淡水湖鄱阳湖沉积物中的 HM 污染。发现鄱阳湖沉积物存在轻微的多种 HM 富集（1.88），其特征是向“中度富集”类别恶化的概率明显（0.35）。在不同的 HM 中，考虑到 Cd 对综合污染的主导贡献（0.51）及其恶化的高概率（0.65），需要更多关注。否则，采用改进的 EF 模型进行评估的结果与基于空间自相关分析和 Pb 同位素特征和主成分分析的源分配的 HM 浓度空间模式一致。与传统的 EF 方法相比，改进的 EF 模型的评估结果更准确、全面和可靠。总之，改进的 EF 模型在评估和预测沉积物中 HM 富集方面具有更好的能力，有助于优化 HM 污染控制措施。
