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The use of fluoroquinolones for the treatment of infections in humans and animals has increased in Argentina, and they can be found in large amounts in water bodies. The present study investigated the occurrence and associated ecological risk of 5 fluoroquinolones in rivers and farm wastewaters of San Luis, Santa Fe, Córdoba, Entre Ríos, and Buenos Aires provinces of Argentina by high‐performance liquid chromatography coupled to fast‐scanning fluorescence detection and ultra–high‐performance liquid chromatography coupled to triple quadrupole mass spectrometry detection. The maximum concentrations of ciprofloxacin, enrofloxacin, ofloxacin, enoxacin, and difloxacin found in wastewater were 1.14, 11.9, 1.78, 22.1, and 14.2 μg L–1, respectively. In the case of river samples, only enrofloxacin was found, at a concentration of 0.97 μg L–1. The individual risk of aquatic organisms associated with water pollution due to fluoroquinolones was higher in bacteria, cyanobacteria, algae, plants, and anurans than in crustaceae and fish, with, in some cases, risk quotients >1. The proportion of samples classified as high risk was 87.5% for ofloxacin, 63.5% for enrofloxacin, 57.1% for ciprofloxacin, and 25% for enoxacin. Our results suggest that the prevalence of fluoroquinolones in water could be potentially risky for the aquatic ecosystem, and harmful to biodiversity. Environ Toxicol Chem 2019;38:2305–2313. © 2019 SETAC. [ABSTRACT FROM AUTHOR]

In this study, 110 tea samples from South American countries (Argentina, Brazil, and Paraguay) and Asian countries (India and China) were analyzed using near-infrared spectroscopy (NIRS) together with a two-step chemometric authentication strategy (class modeling techniques and discriminant analysis) to authenticate commercial teas from Argentina. In the first step, one-class models were built and validated to authenticate South American teas using preprocessed NIRS data. For this purpose, data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS) were used. The DD-SIMCA model gave the best results, with a sensitivity of 93.10%, specificity of 100%, and efficiency of 95.00%. In the second step, a support vector machine (SVM) was used to build and validate a multiclass model to discriminate between tea samples from Argentina and neighboring countries of South America. The best model was the combination of nine variables selected by the fast correlation-based filter (FCBF) method, with an accuracy of 98.30%. Therefore, we conclude that the combination of NIRS and two-step chemometric tools can be used to authenticate the geographical origin of samples with high inter-class similarity. • Food and beverages authentication is a growing demand. • NIRS associated to chemometric allowed development of analytical methods. • The developed models are fast and accurate methods to Argentinian tea authentication. [ABSTRACT FROM AUTHOR]

Retinoids are known to regulate important processes such as differentiation, development, and embryogenesis of vertebrates: Alteration in endogenous retinoids concentration is linked with teratogenic effects. Retinol (ROH), retinoid acid (RA), and isoform 13-Cis-retinoic acid (13-Cis-RA), in plasma of a native adults frog, Leptodactylus chaquensis from a rice field (RF) and a forest (reference site; RS) were measured. ROH did not vary between treatment sites. RA and 13-Cis-RA activities were higher (93.7 ± 8.6 μg mL −1 and 131.7 ± 11.4 μg mL −1 , respectively) in individuals collected from RF than in those from RS (65.5 ± 8.6 μg mL −1 and 92.2 ± 10.2 μg mL −1 , respectively). The ratios retinoic acid-retinol (RA/ROH) and 13-Cis-RA/ROH revealed significantly higher values in RF than in RS. RA and 13-Cis-RA concentrations in plasma on wild amphibian’s species such as L . chaquensis would be suitable biomarkers of pesticide exposure in field monitoring. Finally, the mechanism of alteration in retinoid metabolites alteration should be further explored both in larvae and adult, considering that the potential exposition and uptake contaminants vary between the double lives of these vertebrates. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an analytical method to determine directly and simultaneously five phenolic compounds (4-nitrophenol, 2-nitrophenol, phenol, 2,4,6-trichlorophenol and 4-chlorophenol) in sea water (Ria de Bahía Blanca, Argentine). The advantages of this method is that only requires spectrophotometric measurements (separation steps and derivatization reagents are avoided) and chemometric modelling (PLS and MLR–SPA). The statistical comparison between PLS — a well established multivariate method — and MLR–SPA — a recently presented chemometric modelling — demonstrated better analytical performance for the later one. This fact is indicative of the potentiality of MLR–SPA for solving complex analytical problems. [Copyright &y& Elsevier]

• Evaluation of surface water quality of northern Argentina rivers. • Systematic evaluation of physicochemical and biological parameters. • MA-PCA to model spatiotemporal variations of water quality parameters. • Evaluation of pollutant discharge, upstream and downstream areas. • Classification model to predict river self-depuration. In Argentina, both surface and ground water are used for a diverse priority purposes, such as drinking and basic hygiene, but they are also utilized as receivers of different types of industrial and urban and suburban effluents that affect their natural composition. This activity accompanied by the increase of the population and climate changes have activated the alarms of organism water management forced to implement strict quality controls previous to its use. In this work, a systematic evaluation of a set of physicochemical and biological parameters measured in 19 sampling sites during the period 2017–2019 is presented. Principal component analysis (PCA) and matrix augmentation-PCA (MA-PCA) were applied as exploratory analysis tools to visualize and interpret the information contained in the dataset. Both studies allowed to detect the relevant variables and to differentiate the samples based on pollution areas. These models led to similar conclusions; nonetheless, MA-PCA provided a more straightforward overview of the spatiotemporal variation of the samples in comparison to classical PCA. Finally, a significant and sensitive discriminant model (93% non-error rate) was developed to analyze and predict the self-depuration of the rivers. The excellent predictive ability achieved by this model makes its application suitable for the monitoring of the water quality. [ABSTRACT FROM AUTHOR]