A fuzzy logic-classification of sediments based on data from in vitro biotests

Background, aim, and scope Ecotoxicological risk assessment of sediments is usually based on a multitude of data obtained from tests with different endpoints. In the present study, a fuzzy logic-based model was developed in order to reduce the complexity of these data sets and to classify sediments on the basis of results from a battery of in vitro biotests. Materials and methods The membership functions were adapted to fit the specific sensitivity and variability of each biotest. For this end, data sets were categorized into three toxicity levels using the box plot and empirical methods. The variability of each biotest was determined to calculate the range of the gradual membership. In addition, the biotests selected were ranked according to the biological organisation level in order to consider the ecological relevance of the endpoints measured by selected over- or underestimation of the toxicity levels. In the next step of the fuzzy logic model, a rule-base was implemented using if...and...then decisions to arrive at a system of five quality classes. Results The results of the classification of sediments from the Rhine and Danube Rivers showed the highest correlation between the biotest results and the fuzzy logic alternative based on the empirical method (i.e. the classification of the data sets into toxicity levels). Discussion Many different classification systems based on biological test systems are depending on respective data sets; therefore, they are difficult to compare with other locations. Furthermore, they don't consider the inherent variability of biotests and the ecological relevance of these test systems as well. In order to create a comprehensive risk assessment for sediments, mathematical models should be used which take uncertainties of biotest systems into account, since they are of particular importance for a reliable assessment. In the present investigation, the variability and ecological relevance of biotests were incorporated into a classification system based on fuzzy logic. Furthermore, since data from different sites and investigations were used to create membership functions of the fuzzy logic, this classification system has the potential to be independent of locations. Conclusions In conclusion, the present fuzzy logic classification model provides an opportunity to integrate expert knowledge as well as acute and mechanism-specific effects for the classification of sediments for an ecotoxicological risk assessment. Recommendations and perspectives In order to achieve a more comprehensive classification, further investigation is needed to incorporate results of chemical analyses and in situ parameters. Furthermore, more discussions are necessary with respect to the relative weight attributed to different ecological and chemical parameters in order to obtain a more precise assessment of sediments.