Detection of multi-class pesticide residues with surface-enhanced Raman spectroscopy

The excessive use of pesticides disturbs the natural balance in the environment, creates resistance to pesticides and leads to water and food contamination. Therefore, the implementation of fast, robust and cost effective techniques for the monitoring of pesticides is required. In this work surface-enhanced Raman spectroscopy (SERS) was used for the detection of four common pesticides: atrazine, simazin, irgarol, and diuron. SERS is nowadays considered an effective technique for detection of various analytes in low concentration. Sensitivity of the SERS method depends on the type of substrate that can be either a colloidal solution of metal nanoparticles (NPs) or a metal surface with a suitable nanostructured topology. Here, we have investigated the application of silver nanospheres and silver nanoprisms as SERS substrates in pesticides detection. Colloids with spherical NPs were produced by chemical reduction while Ag nanoprisms were prepared by reducing silver nitrate with borohydride (with citrate as a stabilizing agent) and stirring under a UV lamp for 4 and 10 h. The SERS results have shown that, in the presence of synthesized NPs, it was possible to detect millimolar concentrations of aforementioned pesticides with the exception of diuron.

Toxicity of binary mixtures of pesticides and pharmaceuticals toward Vibrio fischeri: Assessment by quantitative structure-activity relationships

Pollutants in real aquatic systems commonly occur as chemical mixtures. Yet, the corresponding risk assessment is still mostly based on information on single-pollutant toxicity, accepting the assumption that pollutant mixtures exhibit additive toxicity effect which is often not the case. Therefore, it is still better to use the experimental approach. Unfortunately, experimental determination of toxicity for each mixture is practically unfeasible. In this study, quantitative structure-activity relationship (QSAR) models for the prediction of toxicity of binary mixtures towards bioluminescent bacteria Vibrio fischeri were developed at three toxicity levels (EC₁₀, EC₃₀ and EC₅₀). For model development, experimentally determined toxicity values of 14 pollutants (pharmaceuticals and pesticides) were correlated with their structural features, applying multiple linear regression together with genetic algorithm. Statistical analysis, internal validation and external validation of the models were carried out. The toxicity is accurately predicted by all three models. EC₃₀ and EC₅₀ values are mostly influenced by geometrical distances between nitrogen and sulfur atoms. Furthermore, the simultaneous presence of oxygen and chlorine atoms in mixture can induce the increase in toxicity. At lower effect levels (EC₁₀), nitrogen atom bonded to different groups has the highest impact on mixture toxicity. Thus, the analysis of the descriptors involved in the developed models can give insight into toxic mechanisms of the binary systems.

Combined toxicities of binary mixtures of alachlor, chlorfenvinphos, diuron and isoproturon

Pesticides are the chemicals of increased concern regarding their adverse environmental effects. In particular, the reports on their joint toxicity effects are scarce in the literature. Therefore, this paper describes the experiments on toxicities of four pesticides: alachlor, chlorfenvinphos, diuron, and isoproturon, toward Vibrio fischeri. In particular, the joint toxicity effects for all possible binary combinations of the pesticides were analyzed. The analysis included the application of concentration addition and independent action models at two toxicity levels: EC₁₀ and EC₅₀. The analysis revealed additive behavior between all pesticide pairs. The only exception was isoproturon and chlorfenvinphos whose combination resulted in synergistic toxic activity. The original form of the logistic function was given preference over the linearized form in describing the response-dose relationships of investigated pesticides.

Toxicity of pharmaceuticals in binary mixtures: Assessment by additive and non-additive toxicity models

Current risk assessment in many countries, including European Union, is still placing focus on single substances rather than their mixtures, although mixtures are commonly found in the environment. To overcome this problem and gain new insights, six pharmaceuticals, namely: azithromycin (AZM), erythromycin (ERM), carbamazepine (CBA), oxytetracycline (OTC), dexamethasone (DXM), and diclofenac (DCF), were selected in order to analyze their combined toxicity in binary mixtures. Overall, 45 binary mixtures were analyzed. Single component toxicities were determined as well, for modelling purpose. Two most common mathematical models for the description of mixture toxicities were applied: concentration addition (CA) and independent action (IA) model. Comparison of the predicted and experimentally obtained toxicities provided information about the modes of toxicity action in the mixtures. OTC-DCF binary mixture indicated synergism with respect to additive behavior (CA model). All other binary combinations containing OTC or DCF were acting very similarly: the synergism with respect to additive behavior was observed for OTC-CBA and DCF-CBA combinations, while OTC-AZM, OTC-ERM, DCF-AZM and DCF-ERM exhibited antagonistic behavior with respect to CA model. All the remaining binary mixtures indicated additive behavior. The applicability of IA model as a proof of independent toxic action of the components was confirmed in cases of DCF-AZM, DCF-ERM, and OTC-AZM mixtures.