Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

AbstractIn this study, an Artificial Neural Network (ANN) was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA) was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10MJha−1, 0.902 and 0.06kgMJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%). Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

Trace element contamination in the arms of the Danube Delta (Romania/Ukraine): Current state of knowledge and future needs

This paper provides the first critical synopsis of contamination by selected trace elements in the whole Danube Delta (Romania/Ukraine) to: identify general patterns of contamination by trace elements across the Delta, provide recommendations to refine existing monitoring networks and discuss the potential toxicity of trace elements in the whole Delta. Sediment samples were collected between 2004 and 2007 in the three main branches of the Delta (Chilia, Sulina and Sfantu Gheorghe) and in the secondary delta of the Chilia branch. Samples were analyzed for trace elements (Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) and TiO2, Fe2O3, MnO, CaCO3 and total organic carbon. Cluster analysis (CA) and Principal Component Analysis (PCA) showed that levels of Cd, Cu, Pb, and Zn were influenced by anthropogenic activities. At the opposite, concentrations of Cr and Ni largely originated from the weathering of rocks located in the Romanian part of the Danube catchment and naturally rich in these elements. Data analysis using Self- Organizing Maps confirmed the conclusions of CA/PCA and further detected that the contamination tended to be higher in the Chilia and Sulina arms than in the Sfantu Gheorghe arm. The potential ecological risks due to trace element contamination in the Danube Delta could be identified as moderate and localized, provided that the presence of the natural sources of Cr and Ni was properly considered. The available results suggest that monitoring sediment quality at the mouths of Sulina and Sfantu Gheorghe arms is probably enough to get a picture of the sediment quality along their entire lengths. However, a larger network of monitoring points is necessary in the Chilia and secondary Chilia delta to account for the presence of local point sources and for the more complex hydrodynamic of this part of the Danube Delta

On the development of decision-making systems based on fuzzy models to assess water quality in rivers

There are many situations where a linguistic description of complex phenomena allows better assessments. It is well known that the assessment of water quality continues depending heavily upon subjective judgments and interpretation, despite the huge datasets available nowadays. In that sense, the aim of this study has been to introduce intelligent linguistic operations to analyze databases, and produce self interpretable water quality indicators, which tolerate both imprecision and linguistic uncertainty. Such imprecision typically reflects the ambiguity of human thinking when perceptions need to be expressed. Environmental management concepts such as: "water quality", "level of risk", or "ecological status" are ideally dealt with linguistic variables. In the present Thesis, the flexibility of computing with words offered by fuzzy logic has been considered in these management issues. Firstly, a multipurpose hierarchical water quality index has been designed with fuzzy reasoning. It integrates a wide set of indicators including: organic pollution, nutrients, pathogens, physicochemical macro-variables, and priority micro-contaminants. Likewise, the relative importance of the water quality indicators has been dealt with the analytic hierarchy process, a decision-aiding method. Secondly, a methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters according to the Water Framework Directive. This methodology has allowed dealing efficiently with the non-linearity and subjective nature of variables involved in this classification problem. The complexity of inference systems, the appropriate choice of linguistic rules, and the influence of the functions that transform numerical variables into linguistic variables have been studied. Thirdly, a concurrent neuro-fuzzy model based on screening ecological risk assessment has been developed. It has considered the presence of hazardous substances in rivers, and incorporates an innovative ranking and scoring system, based on a self-organizing map, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater ecosystems. Hazard factors are combined with environmental concentrations within fuzzy inference systems to compute ecological risk potentials under linguistic uncertainty. The estimation of ecological risk potentials allows identifying those substances requiring stricter controls and further rigorous risk assessment. Likewise, the aggregation of ecological risk potentials, by means of empirical cumulative distribution functions, has allowed estimating changes in water quality over time. The neuro-fuzzy approach has been validated by comparison with biological monitoring. Finally, a hierarchical fuzzy inference system to deal with sediment based ecological risk assessment has been designed. The study was centered in sediments, since they produce complementary findings to water quality analysis, especially when temporal trends are required. Results from chemical and eco-toxicological analyses have been used as inputs to two parallel inference systems which assess levels of contamination and toxicity, respectively. Results from both inference engines are then treated in a third inference engine which provides a final risk characterization, where the risk is provided in linguistic terms, with their respective degrees of certitude. Inputs to the risk system have been the levels of potentially toxic substances, mainly metals and chlorinated organic compounds, and the toxicity measured with a screening test which uses the photo-luminescent bacteria Vibrio fischeri. The Ebro river basin has been selected as case study, although the methodologies here explained can easily be applied to other rivers. In conclusion, this study has broadly demonstrated that the design of water quality indexes, based on fuzzy logic, emerges as suitable and alternative tool to support decision makers involved in effective sustainable river basin management plans.Existen diversas situaciones en las cuales la descripción en términos lingüísticos de fenómenos complejos permite mejores resultados. A pesar de los volúmenes de información cuantitativa que se manejan actualmente, es bien sabido que la gestión de la calidad del agua todavía obedece a juicios subjetivos y de interpretación de los expertos. Por tanto, el reto en este trabajo ha sido la introducción de operaciones lógicas que computen con palabras durante el análisis de los datos, para la elaboración de indicadores auto-interpretables de calidad del agua, que toleren la imprecisión e incertidumbre lingüística. Esta imprecisión típicamente refleja la ambigüedad del pensamiento humano para expresar percepciones. De allí que las variables lingüísticas se presenten como muy atractivas para el manejo de conceptos de la gestión medioambiental, como es el caso de la "calidad del agua", el "nivel de riesgo" o el "estado ecológico". Por tanto, en la presente Tesis, la flexibilidad de la lógica difusa para computar con palabras se ha adaptado a diversos tópicos en la gestión de la calidad del agua. Primero, se desarrolló un índice jerárquico multipropósito de calidad del agua que se obtuvo mediante razonamiento difuso. El índice integra un extenso grupo de indicadores que incluyen: contaminación orgánica, nutrientes, patógenos, variables macroscópicas, así como sustancias prioritarias micro-contaminantes. La importancia relativa de los indicadores al interior del sistema de inferencia se estimó con un método de análisis de decisiones, llamado proceso jerárquico analítico. En una segunda fase, se utilizó una metodología híbrida que combina los sistemas de inferencia difusos y las redes neuronales artificiales, conocida como neuro-fuzzy, para el estudio de la clasificación del estado ecológico de los ríos, de acuerdo con los lineamientos de la Directiva Marco de Aguas. Esta metodología permitió un manejo adecuado de la no-linealidad y naturaleza subjetiva de las variables involucradas en este problema clasificatorio. Con ella, se estudió la complejidad de los sistemas de inferencia, la selección apropiada de reglas lingüísticas y la influencia de las funciones que transforman las variables numéricas en lingüísticas. En una tercera fase, se desarrolló un modelo conceptual neuro-fuzzy concurrente basado en la metodología de evaluación de riesgo ecológico preliminar. Este modelo consideró la presencia de sustancias peligrosas en los ríos, e incorporó un mapa auto-organizativo para clasificar las sustancias químicas, en términos de su peligrosidad hacia los ecosistemas acuáticos. Con este modelo se estimaron potenciales de riesgo ecológico por combinación de factores de peligrosidad y de concentraciones de las sustancias químicas en el agua. Debido a la alta imprecisión e incertidumbre lingüística, estos potenciales se obtuvieron mediante sistemas de inferencia difusos, y se integraron por medio de distribuciones empíricas acumuladas, con las cuales se pueden analizar cambios espacio-temporales en la calidad del agua. Finalmente, se diseñó un sistema jerárquico de inferencia difuso para la evaluación del riesgo ecológico en sedimentos de ribera. Este sistema estima los grados de contaminación, toxicidad y riesgo en los sedimentos en términos lingüísticos, con sus respectivos niveles de certeza. El sistema se alimenta con información proveniente de análisis químicos, que detectan la presencia de sustancias micro-contaminantes, y de ensayos eco-toxicológicos tipo "screening" que usan la bacteria Vibrio fischeri. Como caso de estudio se seleccionó la cuenca del río Ebro, aunque las metodologías aquí desarrolladas pueden aplicarse fácilmente a otros ríos. En conclusión, este trabajo demuestra ampliamente que el diseño y aplicación de indicadores de calidad de las aguas, basados en la metodología de la lógica difusa, constituyen una herramienta sencilla y útil para los tomadores de decisiones encargados de la gestión sostenible de las cuencas hidrográficas

Emprego de processos oxidativos avançados na degradação de uma mistura de corantes têxteis: avaliação de parâmetros de reação, estudo cinético, toxicidade e modelagem por redes neurais artificiais

Organic contaminants in industrial effluents threaten the quality of water resources, especially due to their resistance to natural degradation. The textile industry gain relevance, considering that it generates large volumes. This work aimed to evaluate the efficiency of different advanced oxidation processes (AOP) for the degradation of the mixture textile dyes in solution. After optimization of the main parameters involved in the applied processes and systems, the AOP with greater efficiency in the degradation of the compounds was the photo-Fenton/UV-C (92%) after 360 min of treatment. The experimental data showed a better fit to the Chan and Chu kinetic model and trough an evaluation using artificial neural networks it was possible to predict the maximum degradation achievable by the dye mixture. The toxicity assays, using multiple species of seeds indicated a treated solution with no toxic effects and that the applied methodology can be used without affecting the water resources.Contaminantes orgânicos em efluentes industriais ameaçam a qualidade dos recursos hídricos por apresentarem resistência à degradação natural. Neste sentido, a indústria têxtil ganha relevância, pois é responsável pela geração de grandes volumes de efluentes líquidos. O presente trabalho avaliou a eficiência de diferentes processos oxidativos avançados (POA) para degradação de uma mistura de corantes têxteis em solução. Após testes de eficiência e otimização dos principais parâmetros envolvidos nos processos e sistemas aplicados, o POA que apresentou maior eficiência na degradação dos compostos foi o foto-Fenton/UV-C (92%) de degradação após 360 min. Os dados experimentais do POA foto-Fenton/UV-C apresentaram melhor ajuste ao modelo de Chan e Chu e através da avaliação matemática usando rede neural artificial foi possível prever a degradação máxima da mistura de corantes. O estudo de toxicidade com sementes indicou que a solução tratada não possui efeitos tóxicos, podendo o tratamento proposto ser utilizado sem afetar recursos hídricos