Electronic tongue applications for wastewater and soil analysis

Assessment of water and soil quality is critical for the health, economy, and sustainability of any community. The release of a range of life-threatening pollutants from agriculture, industries, and the residential communities themselves into the different water resources and soil requires of analytical methods intended for their detection. Given the challenge that represents coping with the monitoring of such a diverse and large number of compounds (with over 100,000 chemicals registered, yet in continuous increase), holistic solutions such as electronic tongues (ETs) are emerging as a promising tool for a sustainable, simple, and green monitoring of soil and water resources. In this direction, this review aims to present and critically provide an overview of the basic concepts of ETs, followed by some relevant applications recently reported in the literature in environmental analysis, more specifically, the monitoring of water and wastewater, their quality and the detection of water pollutants as well as soil analysis

Development of a Distributed Artificial Neural Network for Hydrologic Modeling

Hydrological models are used to represent the rainfall-runoff and pollutant transport mechanisms within watersheds. Accurate representation of these dynamic and complex natural processes within a watershed is an important step in managing and protecting a watershed Artificial neural network (ANN) models are often used in hydrologic modeling. Typical ANN models are trained to use lumped data. However, watershed characteristics used as inputs in hydrological modeling are spatially and often temporally dynamic. Therefore, a lumped model does not have the ability to represent changes in spatial dynamics of a watershed. Therefore, the purpose of this study was to develop and test a distributed ANN model for simulating the rainfall-runoff process in the L\u27Anguille River Watershed located in Eastern Arkansas. The watershed was divided into nine sub-basins to account for the spatial dynamics of flow within the watershed Inputs for the model were rainfall, average temperature, antecedent flow and curve number. Output was runoff collected from gage-stations at Colt and Palestine representing two of the sub-basins. Daily SCS curve numbers were developed and adjusted for crop planting and harvesting dates and crop rotation practices in each sub-basin. The model had nine layers with one neuron each to represent the nine sub-basins. The layers were connected so that if one sub-basin spatially flowed into another, its output would be an input for the downstream sub-basin. The model performed well, showing R2 values of 0.93 and 0.98 and Nash-Sutcliffe Efficiency values of 0.92 and 0.97 for the validation and test datasets

Environmental odour management by artificial neural network – A review

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome. Keywords: Electronic nose, Environmental pollution, Human health, Odour emission, Public concer

Disposable E-Tongue For The Assessment Of Water Quality In Fish Tanks [TD370. C456 2008 f rb].

E-lidah pakai buang cetakan skrin yang sesuai untuk pemantauan kualiti air dalam tangki pemeliharaan ikan berdasarkan penderia susun atur dan pengecaman pola diterangkan. Disposable screen-printed e-tongues that are suitable for the monitoring of water quality in fish tanks, based on sensor array and pattern recognition is described

Application of Artificial Barrier as Mitigation of <em>E. coli</em> Which Pass through Riverbank Filtration

Water security in the water treatment plant has been doubted, and the treatment process may have given unreliable and unsafe water to the public. A newspaper reported on November 19, 2011, that laboratory tests on water samples in Kelantan for each year by the Ministry of Health have found harmful bacteria including Escherichia coli (E. coli) in the water samples. More worryingly, it was stated in a study that chlorine in water treated with high chlorine can be harmful to human health. In 2010, Malaysia has begun to approach a natural treatment technique, namely, riverbank filtration (RBF), and firstly used it at the Water Treatment Plant in Jeli, Kelantan, and Kuala Kangsar, Perak. RBF limitation is the invisible groundwater flow that makes it difficult to predict the transport of contaminants. Managing groundwater is important to ensure that water is aligned in compliance with government legislation and environmental protection. Due to that, this study suggests an implementation of an artificial barrier for microorganism in RBF to sustain the good water quality abstracted from the abstraction well. This pretreatment or purifying method is to improve the effectiveness of RBF in removing pollutants during shock loads and reduce the load placed in the water treatment process

Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids

Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L(-1) and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L(-1) and less than 600 NTU, respectively and used rather than using whole dataset (R(2) = 0.93 versus 0.88 for turbidity and R(2) = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R(2) = 0.66) was better than with the MR approach (R(2) = 0.58), as expected due to the nonlinear nature of the transformation model

Degradation of the pharmaceuticals lamivudine and zidovudine using advanced oxidation processes

The existence of pharmaceuticals in nature is a growing environmental problem, turning necessary the use of efficient treatments for the degradation of these substances, as the advanced oxidation processes (AOPs). In this work the AOPs UV/H2O2 and photo-Fenton were applied to degrade the pharmaceuticals lamivudine and zidovudine in an aqueous solution using a bench reactor, composed of three UV-C lamps. It was verified that the UV/H2O2 process presented a degradation of 97.33 ± 0.14% for lamivudine and 93.90 ± 0.33% for zidovudine, after 180 min of treatment and for an initial concentratin of each pharmaceutical of  5 mg.L-1 and [H2O2] of 600 mg.L-1.  A methodology by artificial neural networks (ANNs) was used to model the photocatalytic process, with the MLP 7-23-2 ANN representing it well, and determining the relative importance (%) of each of the input variables for the pharmaceutical’s degradation process. Kinetic studies for the pharmaceutical degradation and the conversion of organic matter showed good adjustments to the pseudo first-order models with R2 raging from 0.9705 to 0.9980. Toxicity assays for the before treatment solution indicated that the seeds Lactuca sativa and Portulaca grandiflora showed growth inhibition whereas the post-treatment solution inhibited only the growth of Lactuca sativa

Degradação dos fármacos nimesulida e ibuprofeno empregando processo foto-Fenton: estudos da toxicidade, modelagem cinética e emprego de redes neurais artificiais

The growth of pollution in aquatic environments increases every day, causing compounds like pharmaceuticas to be detected in surface waters. Thus, tecniques such as advanced oxidation processes (AOP) have been used to degrade this compounds. In this work, the efficiency of AOP in the degradation of nimesulide and ibuprofen pharmaceuticals was evaluated through chromatographic analysis as well as organic matter through the levels of chemical oxygen demand (COD) and total organic carbon (TOC). It was verified that the photo-Fenton process presented the bests results, degrading 89.70% of nimesulide and 93.35% of ibuprofen. This same process managed to reduce COD by 91.60% and mineralize 90.04% of the TOC. The kinetic study showed a good linear fit (R2=0.993) for the clustered kinetic model, as well as a good fit to the mathematical model of artificial neural networks (ANNs), with a value of R2=1.000 for the MLP4-4-1 BFGS 4567 model. Finally, the toxicity of the solution after treatment was verified against the seeds of Lactuta sativa, Cichorium endívia, Ocimum basilicum and American Hard grain. It was found that the seeds that received the solution before treatment had a lower germination amount than the ones where the post AOP treatment solution was added. Then, the root growth was evaluated, in which a relative toxic effect was observed.O crescimento da poluição de ambientes aquáticos tem aumentado todos os dias, fazendo com que compostos como os fármacos sejam verificados em águas superficiais. Desse modo, técnicas como processos oxidativos avançados (POA) tem sido utilizadas. Neste trabalho a eficiência dos POA na degradação dos fármacos nimesulida e ibuprofeno foi avaliada, através de análises cromatográficas, bem como de matéria orgânica através dos níveis de demanda química de oxigênio (DQO) e carbono orgânico total (COT). Verificou-se que o processo foto-Fenton apresentou os melhores resultados degradando 89,70% do nimesulida e 93,35% do ibuprofeno. Esse mesmo processo conseguiu reduzir em 91,60% a DQO e mineralizar 90,04% do COT. O estudo cinético mostrou bom ajuste linear (R2=0,993) para o modelo cinético agrupado, além de uma boa adequação ao modelo matemático de redes neurais artificiais (RNA), com um valor de R2=1,000 para o modelo MLP4-4-1 BFGS4567. Por fim, verificou-se a toxicidade da solução após tratamento, frente às sementes de Lactuta Sativa, Cichorium endívia, Ocimum basilicum e do grão Americano Hard. Verificou-se que as sementes que receberam a solução antes do tratamento apresentaram uma quantidade menor germinação, do que quando foi adicionada a solução pós-tratamento via POA. Em seguida, avaliou-se o crescimento radicular, no qual foi percebido relativo efeito tóxico