Linking chemical surface water monitoring and pesticide regulation in selected European countries.

The progress in chemical analytics and understanding of pesticide dynamics in surface waters allows establishing robust data on compounds with frequent exceedances of quality standards. The current chemical, temporal, and spatial coverage of the pesticide monitoring campaigns differs strongly between European countries. A questionnaire revealed differences in monitoring strategies in seven selected European countries; Nordic countries prioritize temporal coverage, while others focus on spatial coverage. Chemical coverage has increased, especially for non-polar classes like synthetic pyrethroids. Sweden combines monitoring data with agricultural practices for derived quantities, while the Netherlands emphasizes spatial coverage to trace contamination sources. None of the EU member states currently has established a process for linking chemical surface water monitoring data with regulatory risk assessment, while Switzerland has recently established a legally defined feedback loop. Due to their design and objectives, most strategies do not capture concentration peaks, especially 2-week composite samples, but also grab samples. Nevertheless, for substances that appear problematic in many data sets, the need for action is evident even without harmonization of monitoring programs. Harmonization would be beneficial, however, for cross-national assessment including risk reduction measures.

Risk mitigation measures for pesticide runoff: How effective are they?

BACKGROUND: One of the most important sources of pesticide pollution of surface waters is runoff and erosion from agricultural fields after rainfall. This study analyses the efficacy of different risk mitigation measures to reduce pesticide runoff and erosion inputs into surface waters from arable land excluding rice fields. RESULTS: Three groups of risk mitigation measures were quantitatively analyzed: vegetative filter strips, micro-dams in row crops and soil conservation measures. Their effectiveness was evaluated based on a meta-analysis of available experimental data using statistical methods such as classification and regression trees, and exploratory data analysis. Results confirmed the effectiveness of vegetative filter strips and micro-dams. Contrary to common assumption, the width of vegetative filter strips alone is not sufficient to predict their effectiveness. The effectiveness of soil conservation measures (especially mulch-tillage) varied widely. This was in part due to the heterogeneity of the available experimental data, probably resulting from the inconsistent implementation and the inadequate definitions of these measures. CONCLUSION: Both vegetative filter strips and micro-dams are effective and suitable, and can therefore be recommended for quantitative assessment of environmental pesticide exposure in surface waters. However, the processes of infiltration and sedimentation in vegetative filter strips should be simulated with a mechanistic model like Vegetative Filter Strip Modeling System, VFSMOD. The reduction effect of micro-dams can be modelled by reducing the runoff curve number, e.g., in the pesticide root zone model, PRZM. Soil conservation measures are in principle promising, but further well-documented data are needed to determine under which conditions they are effective. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pesticide Risks in Small Streams-How to Get as Close as Possible to the Stress Imposed on Aquatic Organisms.

The risks associated with pesticides in small streams remain poorly characterized. The challenges reside in understanding the complexities of (1) the highly dynamic concentration profiles of (2) several hundred active substances with (3) differing seasonality. The present study addressed these three challenges simultaneously. Five small streams in catchments under intensive agricultural land use were sampled using half-day composite samples from March to August 2015. Of 213 active substances quantified using liquid chromatography-high resolution mass spectrometry, a total of 128 was detected at least at one of the sites. Ecotoxicological acute and/or chronic quality criteria were exceeded for a total of 32 different active substances. The evaluation of risks over time revealed the necessity to evaluate the sequences of different active substances that are imposed on aquatic organisms. In contrast, a substance-specific perspective provides only a very limited assessment. Scenarios for reduction of either temporal resolution, number of substances or seasonal coverage were defined. It could be shown that risks can be underestimated by more than a factor of 10 in vulnerable catchments and that an increased temporal resolution is essential to cover acute risks but that a focused selection of substances is a possibility to reduce expenditures.

Comparison of different models predicting the phospholipid-membrane water partition coefficients of charged compounds.

A large fraction of commercially used chemicals is ionizable. This results in the need for mechanistic models to describe the physicochemical properties of ions, like the membrane-water partition coefficient (K(mw)), which is related to toxicity and bioaccumulation. In this work we compare 3 different and already existing modelling approaches to describe the liposome-water partition coefficient (K(lipw)) of organic ions, including 36 cations, 56 anions, 2 divalent cations and 2 zwitterions (plus 207 neutral compounds for ensuring model consistency). 1) The empirical correlation with the octanol-water partition coefficient of the corresponding neutral species yielded better results for the prediction of anions (RMSE = 0.79) than for cations (RMSE = 1.14). Though describing most anions reasonably well, the lack of mechanistic basis and the poor performance for cations constrain the usage of this model. 2) The polyparameter linear free energy relationship (pp-LFER) model performs worse (RMSE = 1.26/1.12 for anions/cations). The different physicochemical environments, due to different sorption depths into the membrane of the different species, cannot be described with a single pp-LFER model. 3) COSMOmic is based on quantum chemistry and fluid phase thermodynamics and has the widest applicability domain. It was the only model applicable for multiply charged ions and gave the best results for anions (RMSE = 0.66) and cations (RMSE = 0.71). We expect COSMOmic to contribute to a better estimation of the environmental risk of ionizable emerging pollutants.

Prediction of Phospholipid-Water Partition Coefficients of Ionic Organic Chemicals Using the Mechanistic Model COSMOmic.

The partition coefficient of chemicals from water to phospholipid membrane, K(lipw), is of central importance for various fields. For neutral organic molecules, log K(lipw) correlates with the log of bulk solvent-water partition coefficients such as the octanol-water partition coefficient. However, this is not the case for charged compounds, for which a mechanistic modeling approach is highly necessary. In this work, we extend the model COSMOmic, which adapts the COSMO-RS theory for anisotropic phases and has been shown to reliably predict K(lipw) for neutral compounds, to the use of ionic compounds. To make the COSMOmic model applicable for ionic solutes, we implemented the internal membrane dipole potential in COSMOmic. We empirically optimized the potential with experimental K(lipw) data of 161 neutral and 75 ionic compounds, yielding potential shapes that agree well with experimentally determined potentials from the literature. This model refinement has no negative effect on the prediction accuracy of neutral compounds (root-mean-square error, RMSE = 0.62 log units), while it highly improves the prediction of ions (RMSE = 0.70 log units). The refined COSMOmic is, to our knowledge, the first mechanistic model that predicts K(lipw) of both ionic and neutral species with accuracies better than 1 log unit.

The chiral herbicide beflubutamid (I): Isolation of pure enantiomers by HPLC, herbicidal activity of enantiomers, and analysis by enantioselective GC-MS.

For many chiral pesticides, little information is available on the properties and fate of individual stereoisomers. A basic data set would, first of all, include stereoisomer-specific analytical methods and data on the biological activity of stereoisomers. The herbicide beflubutamid, which acts as an inhibitor of carotenoid biosynthesis, is currently marketed as racemate against dicotyledonous weeds in cereals. Here, we present analytical methods for enantiomer separation of beflubutamid and two metabolites based on chiral HPLC. These methods were used to assign the optical rotation and to prepare milligram quantities of the pure enantiomers for further characterization with respect to herbicidal activity. In addition, sensitive analytical methods were developed for enantiomer separation and quantification of beflubutamid and its metabolites at trace level, using chiral GC-MS. In miniaturized biotests with garden cress, (-)-beflubutamid showed at least 1000× higher herbicidal activity (EC50, 0.50 µM) than (+)-beflubutamid, as determined by analysis of chlorophyll a in 5-day-old leaves. The agricultural use of enantiopure (-)-beflubutamid rather than the racemic compound may therefore be advantageous from an environmental perspective. In further biotests, the (+)-enantiomer of the phenoxybutanoic acid metabolite showed effects on root growth, possibly via an auxin-type mode of action, but at 100× higher concentrations than the structurally related herbicide (+)-mecoprop.

COSMOmic: a mechanistic approach to the calculation of membrane-water partition coefficients and internal distributions within membranes and micelles.

A new approach for the modeling of molecules in micellar systems and especially in biomembranes, COSMOmic, is presented, and its performance is validated on the example of the partitioning of molecules between water and biological membranes. Starting from quantum chemical calculations of the surfactant, solvent, and solute molecules, and being based on the COSMO-RS method for fluid-phase thermodynamic properties, COSMOmic is essentially free of additional adjustable parameters. The inclusion of an elastic energy correction into the COSMOmic model did not turn out to yield any significant improvement. The novel COSMOmic method allows for the efficient prediction of the distribution of molecules in micellar systems.

Toward a class-independent quantitative structure--activity relationship model for uncouplers of oxidative phosphorylation.

A mechanistically based quantitative structure-activity relationship (QSAR) for the uncoupling activity of weak organic acids has been derived. The analysis of earlier experimental studies suggested that the limiting step in the uncoupling process is the rate with which anions can cross the membrane and that this rate is determined by the height of the energy barrier encountered in the hydrophobic membrane core. We use this mechanistic understanding to develop a predictive model for uncoupling. The translocation rate constants of anions correlate well with the free energy difference between the energy well and the energy barrier, Delta G well-barrier,A (-) , in the membrane calculated by a novel approach to describe internal partitioning in the membrane. An existing data set of 21 phenols measured in an in vitro test system specific for uncouplers was extended by 14 highly diverse compounds. A simple regression model based on the experimental membrane-water partition coefficient and Delta G well-barrier,A (-) showed good predictive power and had meaningful regression coefficients. To establish uncoupler QSARs independent of chemical class, it is necessary to calculate the descriptors for the charged species, as the analogous descriptors of the neutral species showed almost no correlation with the translocation rate constants of anions. The substitution of experimental with calculated partition coefficients resulted in a decrease of the model fit. A particular strength of the current model is the accurate calculation of excess toxicity, which makes it a suitable tool for database screening. The applicability domain, limitations of the model, and ideas for future research are critically discussed.

A quantitative structure--activity relationship model for the intrinsic activity of uncouplers of oxidative phosphorylation.

A quantitative structure-activity relationship (QSAR) has been derived for the prediction of the activity of phenols in uncoupling oxidative and photophosphorylation. Twenty-one compounds with experimental data for uncoupling activity as well as for the acid dissociation constant, pKa, and for partitioning constants of the neutral and the charged species into model membranes were analyzed. From these measured data, the effective concentration in the membrane was derived, which allowed the study of the intrinsic activity of uncouplers within the membrane. A linear regression model for the intrinsic activity could be established using the following three descriptors: solvation free energies of the anions, an estimate for heterodimer formation describing transport processes, and pKa values describing the speciation of the phenols. In a next step, the aqueous effect concentrations were modeled by combining the model for the intrinsic uncoupling activity with descriptors accounting for the uptake into membranes. Results obtained with experimental membrane-water partitioning data were compared with the results obtained with experimental octanol-water partition coefficients, log Kow, and with calculated log Kow values. The properties of these different measures of lipophilicity were critically discussed.

Use of structure descriptors to discriminate between modes of toxic action of phenols.

Two classification models were developed based on a data set of 220 phenols with four associated Modes of Toxic Action (MOA). Counter-propagation neural networks (CPG NN) and multinomial logistic regression (multinom) were used as classification methods. The combination of topological autocorrelation of empirical pi-charge and sigma-electronegativity and of surface autocorrelation of hydrogen-bonding potential resulted in a 21-dimensional model that successfully discriminated between the four MOAs. Its overall predictive power was estimated to 92% using 5-fold cross-validation. Subsequently, a simple score for the distance to the training data was used to determine the prediction space of the model and used in an exploratory study on the phenols contained in the open NCI database. The use of a prediction space metric proved indispensable for the screening of such a diverse database. The prediction space covered by the proposed model is still of rather local nature which is either caused by the limited diversity and size of the training set or by the high dimensionality of the descriptors.