Perfluorinated compounds in environmental matrices of Lake Victoria Gulf

ABSTRACT One of the major ecological challenges on Lake Victoria resources is the existence of “hot spots”, caused by human waste, urban runoff, and industrial effluents. The lake is tending towards eutrophication which is attributed to the increasing human population in its watershed. A report of the levels of perfluorooctane sulfonate and perfluorooctanoic acid in environmental matrices of Lake Victoria is presented, and the management implication of perfluorinated compounds and similar potential organic pollutants examined. Two widely consumed and economically important fish species namely Lates niloticus (Nile perch) and Oreochromis niloticus (Nile tilapia) were obtained from Winam gulf of Lake Victoria, Kenya, and analysed for perfluorooctane sulfonate and perfluorooctanoic acid in muscles and liver using liquid chromatography coupled with mass spectroscopy. Variability in the concentrations of perfluorooctanoic acid or perfluorooctane sulfonate in river waters (range perfluorooctanoic acid 0.4 – 96.4 ng/L and perfluorooctane sulfonate < 0.4 – 13.2 ng/L) was higher than for Lake waters (range perfluorooctanoic acid 0.4 – 11.7 ng/L and perfluorooctane sulfonate < 0.4 – 2.5 ng/L respectively). Significant correlations were tested between perfluorinated compounds levels in sediments, fish and water. Wastewater treatment plants and other anthropogenic sources have been identified as significant sources or pathways for the introduction of perfluoroalkyl compounds into Lake Victoria ecosystem. In this study, elevated concentrations of perfluorooctanoic acid and perfluorooctane sulfonate was found in two wastewater treatment plants (WWTPs) in Kisumu, City of Kenya. An alternative analytical method to liquid chromatography/ mass spectroscopy for analysis of perfluorocarboxylic acids in abiotic and biotic matrices where high concentrations are expected is also presented. Derivatisation of the acid group to form a suitable alkyl ester provided a suitable compound for mass spectroscopy detection coupled to gas chromatography instrumental analysis. The acid is esterified by an alkyl halide i.e benzyl bromide as the alkylating agent for Perfluorocarboxylic acids quantification. The study also involved degradability measurements of emerging perfluorinated surfactants substitutes. The stability of the substitutes of perfluorinated surfactants was tested by employing advanced oxidation processes, followed by conventional tests, among them an automated method based on the manometric respirometry test and standardized fix bed bioreactor [FBBR] on perfluorobutane sulfonate (PFBS), a fluoroethylene polymer, fluorosurfactant (Zonyl), two fluoraliphaticesters (NOVEC ™ FC4430 and NOVEC ™ FC4432) and 10-(trifluoromethoxy) decane-sulfonate. Most of these emmerging surfactants are well-established in the market and have been used in several applications as alternatives to PFOS and PFOA based surfactants. The results of this study can be used as pioneer information for further studies on the sources, behaviour and fate of PFOA and PFOS and other related compounds in both abiotic and biota compartments of Lake Victoria and other lakes. Further an overview in degradation of emerging perfluorinated compounds substitutes is presented. Contribution in method development especially for acid group based fluorosurfactants is presented. The data obtained in this study can particularly be considered when formulating policies and management measures for preservation and sustainability of Lake Victoria resources.

Visualization and validation of (Q)SAR models

Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.