Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, New South Wales 2308, Australia.
Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, New South Wales 2308, Australia.
Environ Sci Technol. 2021 Feb 2;55(3):1779-1789. doi: 10.1021/acs.est.0c07202. Epub 2021 Jan 15.
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 () ranged from 5 to 229 mL/g (median: 28 mL/g), with 63% of the Fijian soils and 35% of the Australian soils showing values that exceeded the observed median . 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 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 ( = 0.80) in the soils. Overall, both TOC and surface charge characteristics of soils are important for describing PFOS sorption.
土壤性质对全氟辛烷磺酸(PFOS)吸附的影响尚不完全清楚,特别是对于可变电荷土壤。对来自澳大利亚和斐济的 114 种温带和热带土壤进行了 PFOS 批量吸附等温线实验,这些土壤的性质得到了很好的描述,包括总有机碳(TOC)、阴离子交换容量和表面电荷。在大多数土壤中,PFOS 吸附等温线是非线性的。PFOS 吸附分配系数(Kd)范围为 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 Kd 值超过了观察到的中位数。多元线性回归表明,TOC、无定形铝和铁氧化物含量、阴离子交换容量、pH 值和粉土含量共同解释了 PFOS 在土壤中的 53%的变异性。带净正表面电荷且 TOC 含量适中或较高的可变电荷土壤通常比仅含 TOC 作为唯一吸附相的温带或热带土壤表现出更强的 PFOS 吸附能力,尤其是在酸性 pH 范围内。首次利用两种人工神经网络来预测土壤中实测的 PFOS Kd 值(r2 = 0.80)。总的来说,土壤的 TOC 和表面电荷特征对于描述 PFOS 的吸附都很重要。