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In order to evaluate the impact of intensive horticulture on the water resources of the Fucino Plain, one of the most important agricultural settlements of Central Italy, the mobility and persistence in the soil of five commonly used pesticides was investigated by means of multi-lysimeter experiments. The fate of simazine, carbaryl, dicloran, linuron and procymidone was evaluated in the laboratory under experimental conditions simulating as closely as possible both pesticide application and irrigation practices required by the local crops. An efficient extraction procedure followed by chromatographic analysis, allowing the simultaneous determination of the applied chemicals, was used to monitor the pesticide residues in the soil columns as a function of time from application and depth. The experiment, carried out for about 60 days, revealed that soil contamination apparently involves only the surface layer since none of the investigated pesticides was detected at depths greater than 20-30 cm. However, the five pollutants exhibit a quite different behaviour that can be related to their physico-chemical properties.

A quantitative structure-retention relationship (QSRR) analysis based on multilinear regression (MLR) and artificial neural networks (ANNs) is carried out to model the combined effect of solute structure and eluent composition on the retention behaviour of pesticides in isocratic reversed-phase high-performance liquid chromatography (RP-HPLC). The octanol-water partition coefficient and four quantum chemical descriptors (the total dipole moment, the mean polarizability, the anisotropy of the polarizability and a descriptor of hydrogen-bonding based on the atomic charges on acidic and basic chemical functionalities) are considered as solute descriptors. In order to identify suitable mobile phase descriptors, encoding composition-dependent properties of both methanol- and acetonitrile-containing mobile phases, the Kamlet-Taft solvatochromic parameters (polarity-dipolarity, hydrogen-bond acidity and hydrogen-bond basicity, pi*, alpha and beta, respectively) and the 14N hyperfine-splitting constant (aN) of a spin-probe dissolved in the eluent are examined. A satisfactory description of mobile phase properties influencing the solute retention is provided by aN and beta or alternatively pi* and beta. The two seven-parameter models resulting from combination of aN and beta, or pi* and beta, with the solute descriptors were tested on a set of 26 pesticides representative of 10 different chemical classes in a wide range of mobile phase composition (30-60% (v/v) water-methanol and 30-70% (v/v) water-acetonitrile). Within the explored experimental range, the acidity of the eluent, as quantified by alpha, is almost constant, and this parameter is in fact irrelevant. The results reveal that aN and pi*, that can be considered as interchangeable mobile phase descriptors, are the most influent variables in the respective models. The predictive ability of the proposed models, as tested on an external data set, is quite good (Q2 close to 0.94) when a MLR approach is used, but the modelling capability can be further improved using an artificial neural network.

In this paper, a quantitative structure-retention relationships (QSRR) method is employed to predict the retention behaviour of pesticides in reversed-phase high-performance liquid chromatography (HPLC). A six-parameter nonlinear model is developed by means of a feed-forward artificial neural network (ANN) with back-propagation learning rule. Accurate description of the retention factors of 26 compounds including commonly used insecticides, herbicides and fungicides and some metabolites is successfully achieved. In addition to the acetonitrile content, included to describe composition of the water-acetonitrile mobile phase, the octanol-water partition coefficient (from literature) and four quantum chemical descriptors are considered to account for the effect of solute structure on the retention. These are: the total dipole moment, the mean polarizability, the anisotropy of polarizability and a descriptor of hydrogen bonding ability based on the atomic charges on hydrogen bond donor and acceptor chemical functionalities. The proposed nonlinear QSRR model exhibits a high degree of correlation between observed and computed retention factors and a good predictive performance in wide range of mobile phase composition (40-65%, v/v acetonitrile) that supports its application for the prediction of the chromatographic behaviour of unknown pesticides. A multilinear regression model based on the same six descriptors shows a significantly worse predictive capability.

In the present work are reported the results of a monitoring on a vast scale, carried out through evaluation of opportune chemical parameters, of the pollution state of the agricultural-industrial settlement of the Fucino Plain. The parameters took into consideration have been the presence of wide consumption pesticides and of ionic species as Cl-, NO2-, NO3-, NH4+ the quantification of the dissolved oxygen, of the temperature, of the conductivity, of pH and eH. Collected data are used for chemometric elaboration. The water systems examined, by means of drawing campaigns carried out at regular intervals in winter, spring, summer, and autumnal seasons, are represented by superficial waters constituting a network of irrigation canals fed by stratum and meteoric waters. In this work are reported the results relative to the drawing campaigns. [ABSTRACT FROM AUTHOR]

Abstract: Previously, retention of 26 pesticides in the reversed-phase column Gemini (Phenomenex) and water–acetonitrile mobile phase was modelled using a feed-forward artificial neural network (ANN) learned by error back-propagation, accounting for both the effect of solute structure and mobile phase composition. To this end, log K ow of solutes and four quantum chemical molecular descriptors (the dipole moment, the mean polarizability, the anisotropy of the polarizability and an hydrogen-bonding descriptor based on the atomic charges located on the acid and basic functional groups) and acetonitrile % (v/v) in the eluent (%ACN) were used as ANN inputs. The above ANN-based approach is here tested on further five similar octadecylsiloxane-bonded columns in water–acetonitrile mobile phase within the %ACN range 30–70%. A quite good predictive performance evaluated on three external solutes in the whole %ACN range is observed, prediction errors being lower than ±0.1log k units or slightly higher although still within ±0.15logk units. On the other hand, multilinear regression used in place of ANN provides a more diffuse and non-uniform residual distribution for all the investigated columns. ANN multiple-column retention prediction is attempted by adding to the above variables a column descriptor defined as the average retention of calibration solutes extrapolated to 100% water. This more general model is built using 16 solutes and five 5-μm columns in calibration, while its predictive performance is tested on the remaining 10 compounds. Under these conditions, prediction errors are generally within ±0.2log k units regardless of the kind of column. The possibility of cross-column prediction is evaluated by column leave-one-out cross-validation within the five 5-μm stationary phases and on a 4-μm external column. This analysis reveals that accuracy of retention prediction for unknown solutes in unknown columns is acceptable provided that the external column is not very dissimilar to those used in calibration. [Copyright &y& Elsevier]

Abstract: The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the retention of pesticides in reversed-phase high-performance liquid chromatography (RP-HPLC) is investigated. To this end, two retention data sets previously collected in water–acetonitrile mobile phase are re-examined. The first data set (data-set-1) consists of retention data of 26 neutral compounds belonging to widely used pesticide classes, collected within the mobile phase composition range 40–65% (v/v) acetonitrile. The second data set (data-set-2) describes retention of phenoxy acid herbicides and structurally related compounds (benzoic acid and phenylacetic acid derivatives), as a whole covering the pK a range 2.3–4.3, as a function of mobile phase composition, ranging between 30 and 70% (v/v) acetonitrile, and pH, ranging between 2 and 5. For each data set, the mobile phase attributes are combined with WHIM or GETAWAY descriptors into “mixed” predictive models in order to attempt retention modelling within the whole mobile phase composition range of analytical interest. Six- or seven-dimensional multilinear models, preliminarily selected using a genetic algorithm, were improved using a multi-layer artificial neural network (ANN) learned by back propagation. ANN performance was tested on three molecules not used in the learning stage and by leave-more-out cross validation. The results reveal that while WHIM descriptors seem not adequate to model retention of solutes of data-set-1, GETAWAY descriptors provide a satisfactory retention model. On the other hand WHIM and GETAWAY descriptors applied to data-set-2 provide a similar performance, even if slightly worse as compared with the above case. Accuracy of retention modelling in these cases is comparable or slightly poorer as compared with the results previously obtained by combining quantum chemical descriptors or usual physico-chemical solute properties (log k ow and pK a) and mobile phase attributes. [Copyright &y& Elsevier]

Abstract: A procedure based on solid-phase extraction (SPE) followed by high-performance liquid chromatography (HPLC) with diode array detection has been developed for the simultaneous analysis of 16 widely used pesticides in groundwater samples. The compounds analysed were: aldicarb, atrazine, desethylatrazine, desysopropylatrazine, carbofuran, 2,4-D, dicloran, fenitrothion, iprodione, linuron, metalaxyl, metazachlor, phenmedipham, procymidone, simazine and vinclozolin. Five different SPE sorbents, C18 bonded silica (Isolute SPE C18 (EC)), graphitised carbon black (Superclean Envi-Carb), highly cross-linked polystyrene–divinylbenzene (Lichrolut EN), divinylbenzene-N-vinylpyrrolidone (Oasis HLB) and surface modified styrene–divinylbenzene (Strata X), were compared. HPLC separation and quantification of the selected pesticides was carried out under isocratic conditions by means of a new reversed-phase column (Gemini from Phenomenex) based on C18 bonded to organic–silica particles. Oasis HLB and Strata X provided the best results in the preconcentration of 1-l samples, yielding average recoveries higher than 70%, except for phenmedipham that rapidly degrades in groundwater. Detection limits of the target pesticides provided by the proposed SPE-HPLC procedure were between 0.003 and 0.04μgl−1. [Copyright &y& Elsevier]

Abstract: A method based on solid-phase extraction (SPE) and high-performance liquid chromatography (HPLC) was developed for the simultaneous determination of 3-(3,5-diclorophenyl)-5-ethenyl-5-methyl-2,4-oxazolidinedione (vinclozolin) and 3-(3,5-diclorophenyl)-N-(1-methylethyl)-2,4-dioxo-1-imidazolidinecarboxamide (iprodione) in human urine. Urine samples containing vinclozolin and iprodione were collected by solid phase extraction using C18 cartridges. The chromatographic separation was achieved on a Spherisorb ODS2 (250mm×4.6mm, 5μm) column with an isocratic mobile phase of acetonitrile–water (60:40, v/v). Detection was UV absorbance at 220nm. The calibration graphs were linear from 30 to 1000ng/mL for the two fungicides. Intra- and inter-day R.S.D. did not exceed 2.9%. The quantitation limit was 50ng/mL for vinclozolin and 30ng/mL for iprodione, respectively. [Copyright &y& Elsevier]