Application of mid-infrared spectroscopy to the prediction and specification of pesticide sorption: A promising and cost-effective tool.

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 Kdads (0.4 < R2 < 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 (CH2, 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.

Agronomic characterization of anaerobic digestates with near-infrared spectroscopy.

Anaerobic digestion is an increasingly widespread process for organic waste treatment and renewable energy production due to the methane content of the biogas. This biological process also produces a digestate (i.e., the remaining content of the waste after treatment) with a high fertilizing potential. The digestate composition is highly variable due to the various organic wastes used as feedstock, the different plant configurations, and the post-treatment processes used. In order to optimize digestate spreading on agricultural soils by optimizing the fertilizer dose and, thus, reducing environmental impacts associated to digestate application, the agronomic characterization of digestate is essential. This study investigates the use of near infrared spectroscopy for predicting the most important agronomic parameters from freeze-dried digestates. A data set of 193 digestates was created to calibrate partial least squares regression models predicting organic matter, total organic carbon, organic nitrogen, phosphorus, and potassium contents. The calibration range of the models were between 249.8 and 878.6 gOM.kgDM-1, 171.9 and 499.5 gC.kgDM-1, 5.3 and 74.1 gN.kgDM-1, 2.7 and 44.9 gP.kgDM-1 and between 0.5 and 171.8 gK.kgDM-1, respectively. The calibrated models reliably predicted organic matter, total organic carbon, and phosphorus contents for the whole diversity of digestates with root mean square errors of prediction of 70.51 gOM.kgDM-1, 34.84 gC.kgDM-1 and 4.08 gP.kgDM-1, respectively. On the other hand, the model prediction of the organic nitrogen content had a root mean square error of 7.55 gN.kgDM-1 and was considered as acceptable. Lastly, the results did not demonstrate the feasibility of predicting the potassium content in digestates with near infrared spectroscopy. These results show that near infrared spectroscopy is a very promising analytical method for the characterization of the fertilizing value of digestates, which could provide large benefits in terms of analysis time and cost.