The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa.

Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model's observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R2) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R2 = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.

Detection of Pharmaceutical Residues in Surface Waters of the Eastern Cape Province.

Pharmaceuticals are emerging contaminants in the aquatic environments. Their presence poses toxicological effects in humans and animals even at trace concentrations. This study investigated the presence of antibiotics, anti-epilepsy and anti-inflammatory drugs in river water of selected rivers in the Eastern Cape Province in South Africa. Enzyme-linked immunosorbent assay was used for screening of sulfamethoxazole and fluoroquinolones antibiotics. The samples were collected in upper-stream, middle-stream and lower-stream regions of the rivers and effluent of selected wastewater treatment plants. Pre-concentration of the samples was conducted using lyophilisation and extraction was conducted using solid phase extraction (SPE) on Waters Oasis hydrophilic-lipophilic-balanced cartridge. The percentage recovery after sample clean-up on SPE was 103% ± 6.9%. This was followed by ultra-performance liquid chromatography electrospray ionization tandem mass spectrometry. The detected analytes were sulfamethoxazole, erythromycin, clarithromycin and carbamazepine. Carbamazepine and erythromycin were detected in high concentrations ranging from 81.8 to 36,576.2 ng/L and 11.2 to 11,800 ng/L respectively, while clarithromycin and sulfamethoxazole were detected at moderate concentrations ranging from 4.8 to 3280.4 ng/L and 6.6 to 6968 ng/L, respectively. High concentrations of pharmaceuticals were detected on the lower-stream sites as compared to upper-stream sites.

Occurrence of selected endocrine disrupting compounds in the eastern cape province of South Africa.

Endocrine-disrupting compounds are attracting attention worldwide because of their effects on living things in the environment. Ten endocrine disrupting compounds: 4-nonylphenol, 2,4-dichlorophenol, estrone, 17ß-estradiol, bisphenol A, 4-tert-octylphenol, triclosan, atrazine, imidazole and 1,2,4-triazole were investigated in four rivers and wastewater treatment plants in this study. Rivers were sampled at upstream, midstream and downstream reaches, while the influent and effluent samples of wastewater were collected from treatment plants near the receiving rivers. Sample waters were freeze-dried followed by extraction of the organic content and purification by solid-phase extraction. Concentrations of the compounds in the samples were determined with ultra-high performance liquid chromatography-tandem mass spectrometry. The instrument was operated in the positive electrospray ionization (ESI) mode. The results showed that these compounds are present in the samples with nonylphenol > dichlorophenol > bisphenol A > triclosan > octylphenol > imidazole > atrazine > triazole > estrone > estradiol. Nonylphenol has its highest concentration of 6.72 µg/L in King Williams Town wastewater influent and 2.55 µg/L in midstream Bloukrans River. Dichlorophenol has its highest concentration in Alice wastewater influent with 2.20 µg/L, while it was 0.737 µg/L in midstream Bloukrans River. Uitenhage wastewater effluent has bisphenol A concentration of 1.684 µg/L while it was 0.477 µg/L in the downstream samples of the Bloukrans River. Generally, the upstream samples of the rivers had lesser concentrations of the compounds. The wastewater treatment plants were not able to achieve total removal of the compounds in the wastewater while runoffs and wastes dump from the cities contributed to the concentrations of the compounds in the rivers.

1H-NMR Determination of Organic Compounds in Municipal Wastewaters and the Receiving Surface Waters in Eastern Cape Province of South Africa.

Surface water is the recipient of pollutants from various sources, including improperly treated wastewater. Comprehensive knowledge of the composition of water is necessary to make it reusable in water-scarce environments. In this work, proton nuclear magnetic resonance (1H-NMR) was combined with multivariate analysis to study the metabolites in four rivers and four wastewater treatment plants releasing treated effluents into the rivers. 1H-NMR chemical shifts of the extracts in CDCl were acquired with Bruker 400. Chemical shifts of 1H-NMR in chlorinated alkanes, amino compounds and fluorinated hydrocarbons were common to samples of wastewater and lower reaches or the rivers. 1H-NMR chemical shifts of carbonyl compounds and alkyl phosphates were restricted to wastewater samples. Chemical shifts of phenolic compounds were associated with treated effluent samples. This study showed that the sources of these metabolites in the rivers were not only from improperly treated effluents but also from runoffs. Multivariate analyses showed that some of the freshwater samples were not of better quality than wastewater and treated effluents. Observations show the need for constant monitoring of rivers and effluent for the safety of the aquatic environment.

Association of ß-amyloid peptide fragments with neuronal nitric oxide synthase: Implications in the etiology of Alzheimers disease.

Neuronal nitric oxide synthase (nNOS) was purified on DEAE-Sepharose anion-exchange in a 38% yield, with 3-fold recovery and specific activity of 5 µmol.min(-1).mg(-1). The enzyme was a heterogeneous dimer of molecular mass 225 kDa having a temperature and pH optima of 40°C and 6.5, K(m) and V(max) of 2.6 µM and 996 nmol.min(-1).ml(-1), respectively and was relatively stable at the optimum conditions (t(½) = 3 h). ß-Amyloid peptide fragments Aß(17-28) was the better inhibitor for nNOS (K(i) = 0.81 µM). After extended incubation of nNOS (96 h) with each of the peptide fragments, Congo Red, turbidity and thioflavin-T assays detected the presence of soluble and insoluble fibrils that had formed at a rate of 5 nM.min(-1). A hydrophobic fragment Aß(17-21) [Leu(17) - Val(18) - Phe(19) - Phe(20) - Ala(21)] and glycine zipper motifs within the peptide fragment Aß(17-35) were critical in binding and in fibrillogenesis confirming that nNOS was amyloidogenic catalyst.