UMR LISAH, Université Montpellier, INRAE, IRD, Institut Agro, AgroParisTech, Montpellier 34060, France.
UMR ECOSYS, Université Paris-Saclay, INRAE, AgroParisTech, Palaiseau 91120, France.
Environ Pollut. 2024 Mar 15;345:123566. doi: 10.1016/j.envpol.2024.123566. Epub 2024 Feb 13.
The cocktail of pesticides sprayed to protect crops generates a miscellaneous and generalized contamination of water bodies. Sorption, especially on soils, regulates the spreading and persistence of these contaminants. Fine resolution sorption data and knowledge of its drivers are needed to manage this contamination. The aim of this study is to investigate the potential of Mid-Infrared spectroscopy (MIR) to predict and specify the adsorption and desorption of a diversity of pesticides. We constituted a set of 37 soils from French mainland and West Indies covering large ranges of texture, organic carbon, minerals and pH. We measured the adsorption and desorption coefficients of glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D) and difenoconazole and acquired MIR Lab spectra for these soils. We developed Partial Least Square Regression (PLSR) models for the prediction of the sorption coefficients from the MIR spectra. We further identified the most influencing spectral bands and related these to putative organic and mineral functional groups. The prediction performance of the PLSR models was generally high for the adsorption coefficients Kd (0.4 < R < 0.9 & RPIQ >1.8). It was contrasted for the desorption coefficients and related to the magnitude of the desorption hysteresis. The most significant spectral bands in the PLSR differ according to the pesticides indicating contrasted interactions with mineral and organic functional groups. Glyphosate interacts primarily with polar mineral groups (OH) and difenoconazole with hydrophobic organic groups (CH, CC, COO, C-O, C-O-C). 2,4-D has both positive and negative interactions with these groups. Finally, this work suggests that MIR combined with PLSR is a promising and cost-effective tool. It allows both the prediction of adsorption and desorption parameters and the specification of these mechanisms for a diversity of pesticides including polar active ingredients.
用于保护作物的农药混合物会对水体造成混杂和普遍的污染。吸附作用，特别是在土壤上，会调节这些污染物的扩散和持久性。需要精细分辨率的吸附数据和了解其驱动因素来管理这种污染。本研究的目的是研究中红外光谱（MIR）预测和具体指定多种农药的吸附和解吸的潜力。我们从法国大陆和西印度群岛组成了 37 种土壤，涵盖了广泛的质地、有机碳、矿物质和 pH 值范围。我们测量了草甘膦、2,4-二氯苯氧基乙酸（2,4-D）和三唑酮的吸附和解吸系数，并为这些土壤获取了 MIR Lab 光谱。我们为从 MIR 光谱预测吸附系数开发了偏最小二乘回归（PLSR）模型。我们进一步确定了最具影响力的光谱带，并将其与假定的有机和矿物官能团相关联。PLSR 模型对吸附系数 Kd（0.4 < R < 0.9 & RPIQ > 1.8）的预测性能通常较高。与解吸系数相比，解吸系数存在差异，并与解吸滞后的幅度有关。PLSR 中最重要的光谱带因农药而异，表明与矿物和有机官能团的相互作用不同。草甘膦主要与极性矿物基团（OH）相互作用，三唑酮与疏水性有机基团（CH、CC、COO、C-O、C-O-C）相互作用。2,4-D 与这些基团既有正相互作用，也有负相互作用。最后，这项工作表明 MIR 与 PLSR 相结合是一种很有前途且具有成本效益的工具。它不仅可以预测吸附和解吸参数，还可以指定这些机制对于包括极性有效成分在内的多种农药的特异性。
