全氟辛烷磺酸在114种特性良好的热带和温带土壤中的吸附：多元人工神经网络分析的应用,Environmental Science & Technology

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全氟辛烷磺酸在114种特性良好的热带和温带土壤中的吸附：多元人工神经网络分析的应用
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2021-01-15 , DOI: 10.1021/acs.est.0c07202
Anthony C. Umeh _{1,

2} , Ravi Naidu _{1,

2} , Sonia Shilpi ₁ , Emmanuel B. Boateng _{3,

4} , Aminur Rahman ₁ , Ian T. Cousins ₅ , Sreenivasulu Chadalavada ₂ , Dane Lamb ₁ , Mark Bowman ₆

Affiliation

尚未完全了解土壤性质对全氟辛烷磺酸吸附的影响，尤其是对于可变电荷土壤。对来自澳大利亚和斐济的114个温带和热带土壤进行了全氟辛烷磺酸分批吸附等温线，这些土壤的土壤特性，包括总有机碳（TOC），阴离子交换容量和表面电荷，具有很好的特征。在大多数土壤中，PFOS的吸附等温线是非线性的。PFOS的吸附分布系数（K _d）为5至229 mL / g（中位数：28 mL / g），其中63％的斐济土壤和35％的澳大利亚土壤的K _d值均超过观察到的中位数K _d。多元线性回归表明，土壤中PFOS K _d的TOC，无定形铝和铁的氧化物含量，阴离子交换容量，pH和淤泥含量共同解释了约53％的PFOS K _d变化。具有净正表面电荷，中度至升高的TOC含量的可变电荷土壤，与以TOC为唯一吸附相的温带或热带土壤相比，通常表现出更高的PFOS吸附能力，尤其是在酸性pH范围内。首次开发了两个人工神经网络来预测土壤中测得的PFOS K _d（R ² = 0.80）。总体而言，土壤的TOC和表面电荷特征对于描述PFOS吸附都很重要。

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Sorption of PFOS in 114 Well-Characterized Tropical and Temperate Soils: Application of Multivariate and Artificial Neural Network Analyses

The influence of soil properties on PFOS sorption are not fully understood, particularly for variable charge soils. PFOS batch sorption isotherms were conducted for 114 temperate and tropical soils from Australia and Fiji, that were well-characterized for their soil properties, including total organic carbon (TOC), anion exchange capacity, and surface charge. In most soils, PFOS sorption isotherms were nonlinear. PFOS sorption distribution coefficients (K_d) ranged from 5 to 229 mL/g (median: 28 mL/g), with 63% of the Fijian soils and 35% of the Australian soils showing K_d values that exceeded the observed median K_d. Multiple linear regression showed that TOC, amorphous aluminum and iron oxides contents, anion exchange capacity, pH, and silt content, jointly explained about 53% of the variance in PFOS K_d in soils. Variable charge soils with net positive surface charges, and moderate to elevated TOC content, generally displayed enhanced PFOS sorption than in temperate or tropical soils with TOC as the only sorbent phase, especially at acidic pH ranges. For the first time, two artificial neural networks were developed to predict the measured PFOS K_d (R² = 0.80) in the soils. Overall, both TOC and surface charge characteristics of soils are important for describing PFOS sorption.

更新日期：2021-02-02

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