A new methodology to assess the performance of AOPs in complex samples: Application to the degradation of phenolic compounds by O3 and O3/UV-A-Vis.

A methodology combining experimental design methodology, liquid chromatography, excitation emission matrixes (EEM) and bioassays has been applied to study the performance of O3 and O3/UVA-vis in the treatment of a mixture of eight phenolic pollutants. An experimental design methodology based on Doehlert matrixes was employed to determine the effect of pH (between 3 and 12), ozone dosage (02-1.0 g/h) and initial concentration of the pollutants (1-6 mg/L each). The following conclusions were obtained: a) acidic pH and low O3 dosage resulted in an inefficient process, b) increasing pH and O3 amount produced an enhancement of the reaction, and c) interaction of basic pH and high amounts of ozone decreased the efficiency of the process. The combination of O3/UVA-vis was able to enhance ozonation in those experimental regions were this reagent was less efficient, namely low pH and low ozone dosages. The application of EEM-PARAFAC showed four components, corresponding to the parent pollutants and three different groups of reaction product and its evolution with time. Bioassys indicated important detoxification (from 100% to less than 30% after 1 min of treatment with initial pollutant concentration of 6 mg/L, pH = 9 and ozone dosage of 0.8 g/h) according to the studied methods (D. magna and P. subcapitata). Also estrogenic activity and dioxin-like behavior were significantly decreased.

Correction: Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study.

Correction for 'Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study' by S. García Ballesteros et al., Photochem. Photobiol. Sci., 2017, 16, 38-45.

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.