Production of volatile fatty acids from wastewater screenings using a leach-bed reactor.

Screenings recovered from the inlet works of wastewater treatment plants were digested without pre-treatment or dilution using a lab-scale, leach-bed reactor. Variations in recirculation ratio of the leachate of 4 and 8 l/lreactor/day and pH values of 5 and 6 were evaluated in order to determine the optimal operating conditions for maximum total volatile fatty acids (VFA) production. By increasing the recirculation ratio of the leachate from 4 to 8 l/lreactor/day it was possible to increase VFA production (11%) and soluble COD (17%) and thus generate up to 264 g VFA/kg-dry screenings. These VFA were predominantly acetic acid with some propionic and butyric acid. The optimum pH for VFA production was 6.0, when the methanogenic phase was inhibited. Below pH 5.0, acid-producing fermentation was inhibited and some alcohols were produced. Ammonia release during the hydrolysis of screenings provided adequate alkalinity; consequently, a digestion process without pH adjustment could be recommended. The leach-bed reactor was able to achieve rapid rates of screenings degradation with the production of valuable end-products that will reduce the carbon footprint associated with current screenings disposal techniques.

Mathematical modelling of hydrogen sulphide emission and removal in aerobic biofilters comprising chemical oxidation.

Four different empirical expressions have been compared for estimating the removal of hydrogen sulphide (H(2)S) from wastewater by chemical oxidation during its treatment in an aerated biofilter. The relative importance of this removal process is considered in a mass balance proposed by an emission model. Two of the four models investigated were able to predict the mean H(2)S removed fraction within a confidence interval of 95% and they demonstrated good agreement with experimental data. Biodegradation and oxidation were the two main removal mechanisms in the biofilter whereas stripping and volatilization made only minor contributions. However they can be of significance when the emission rates are calculated.

Genetic programming for the induction of decision trees to model ecotoxicity data.

Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.

Prediction of noninteractive mixture toxicity of organic compounds based on a fuzzy set method.

Current methods for the prediction of mixture toxicity have shown to be valid for mixtures that conform to some assumptions that were ideally formulated for mixtures comprising constituents exhibiting either completely similar or dissimilar mechanisms of action. Approaches are needed that predict the toxicity of mixtures representative of real environmental occurrences i.e., those comprising constituents of mixed similar and dissimilar compounds and therefore are more complex. In this paper such a methodology is proposed which uses molecular descriptors and fuzzy set theory to characterize the degree of similarity and dissimilarity of mixture constituents, integrates the concentration addition and independent action models, and therefore is called INFCIM (INtegrated Fuzzy Concentration addition--Independent action Model). INFCIM is tested in two case studies using toxicity data of four mixtures, and its performance is compared against those of both concentration addition and independent action models. Mixture 1 consists of 18 s-triazines acting on green freshwater algae scenedemus vacuolatus. Mixture 2 comprises 16 acting constituents tested on scenedemus vacuolatus. Both mixtures inhibit reproduction in the biological assays. There are 10 quinolone compounds in mixture 3 and 16 phenol derivative compounds in mixture 4 all causing long-term inhibition of bioluminescence in the marine bacterium Vibrio fischeri. It was shown that INFCIM performed comparably or better than the best performing existing model in the original studies for all the mixtures tested.