Detection Of Contaminants In Water Research Articles

A photosynthetic toxicity biosensor for water

Effective detection of contaminants in water is the basis of safe water provision to communities. Traditional analytical methods are, however, expensive, time-consuming and cannot be easily adapted to emerging pollutants (i.e. herbicides, pharmaceuticals and their metabolites). Microalgae have been shown to be an ideal sensing probe for bioactive compounds, with great sensitivity and limit of detections at very low concentration levels (nM range). In this study, we explore the use of microalgae in microbial fuel cells (MFCs) as a means to generate a sensitive, portable and cost-effective bioelectrochemical sensor for onsite monitoring of pollutants in water. In particular, we report an innovative miniature single chamber photosynthetic MFC (photoMFC) and demonstrate its ability to detect formaldehyde, a highly toxic compound that can arise in drinking water from the oxidation of natural organic matter during ozonation and chlorination. The photoMFC, inoculated with a mixed microalgae culture from a wastewater treatment algal pond, generated a peak power and current density of 0.18 mW m−2 and 7.2 mA m−2 respectively, when exposed to light. A current response to formaldehyde proportional to its concentration was produced in less than 1 h, with a sensitivity of 69.2 ± 16.7%−1 cm−2. As such, this work provides the first miniature photosynthetic MFC device water shock sensor, with the great benefit of simple operation (with light being the sole energy source) and rapid onsite biosensing capability.

Berichtigung: Spontaneously Self‐Assembled Naphthalimide Nanosheets: Aggregation‐Induced Emission and Unveiling a‐PET for Sensitive Detection of Organic Volatile Contaminants in Water

Berichtigung: Spontaneously Self‐Assembled Naphthalimide Nanosheets: Aggregation‐Induced Emission and Unveiling a‐PET for Sensitive Detection of Organic Volatile Contaminants in Water

Realization and characterization of a protective coating for plastic scintillators used as sensors of radioactive contaminants in water

A new sensor for alpha particles based on plastic scintillators immersed in water was demonstrated to provide a viable solution for real-time detection of radioactive contaminants in water. It is obtained by stacking an array of plastic modules composed by a thin layer of Ag-doped zinc sulphide, ZnS(Ag), deposited on a plastic scintillator foil. Alpha particles are detected in the ZnS(Ag) layer, whereas beta are detected inside the plastic substrate. One still unexplored aspect of this novel detection technology is how the long-term contact with water affects the detector performance. To protect its surface from water-induced damages, we identified a coating protocol based on a poly(methyl-methacrylate) film deposited by spraying on the ZnS(Ag) active layer of the scintillator. The ageing process of the scintillator was carried out by using suitable aqueous solutions and the effect on the sensing layer was monitored by measuring its wettability and surface morphology. The performance of the coated scintillator was tested by measuring the efficiency with alpha radioactive sources.

Spontaneously Self-Assembled Naphthalimide Nanosheets: Aggregation-Induced Emission and Unveiling a-PET for Sensitive Detection of Organic Volatile Contaminants in Water.

A simple design strategy of long alkyl chain substitution was formulated to block the detrimental π-π interaction that potentially transforms the aggregation-caused quenching (ACQ) chromophores into aggregation-induced emission (AIE) active smart nanomaterials. The long octadecyl pendant chain substituted naphthalimide (NI) derivatives self-assembled into fluorescent nanosheets (NS)-like structures that spontaneously have surfaces coated with NI cores in water. The fluorescent NS were subsequently used to recognize the organic volatile contaminants (OVCs) at ppb levels via an acceptor-excited photoinduced electron transfer (a-PET) mechanism, unveiled as the first representative example. A new design strategy is thereby provided to detect toxic xylene derivatives in water using smart nanomaterials.

Spontaneously Self‐Assembled Naphthalimide Nanosheets: Aggregation‐Induced Emission and Unveiling a‐PET for Sensitive Detection of Organic Volatile Contaminants in Water

AbstractA simple design strategy of long alkyl chain substitution was formulated to block the detrimental π–π interaction that potentially transforms the aggregation‐caused quenching (ACQ) chromophores into aggregation‐induced emission (AIE) active smart nanomaterials. The long octadecyl pendant chain substituted naphthalimide (NI) derivatives self‐assembled into fluorescent nanosheets (NS)‐like structures that spontaneously have surfaces coated with NI cores in water. The fluorescent NS were subsequently used to recognize the organic volatile contaminants (OVCs) at ppb levels via an acceptor‐excited photoinduced electron transfer (a‐PET) mechanism, unveiled as the first representative example. A new design strategy is thereby provided to detect toxic xylene derivatives in water using smart nanomaterials.

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters.

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.

Ultra-sensitive electrocatalytic detection of bromate in drinking water based on Nafion/Ti3C2Tx (MXene) modified glassy carbon electrode

A versatile and ultra-sensitive sensor platform based on lamellar Ti3C2Tx (MXene)-modified glassy carbon electrode has been developed for the detection of bromate (BrO3−) in water. MXene showed unique electrocatalytic properties towards effective BrO3− reduction and the developed sensor displayed a linear response for the BrO3− concentration from 50 nM to 5 μM with a detection limit of 41 nM. The catalytic reduction of BrO3− by MXene was assumed by an increase of the peak current and a shift to lower potential. Surface changes to MXene after electrocatalytic reduction of BrO3− was investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The formation of trace TiO2 crystals at the surface of Ti3C2Tx during the electrochemical reduction of BrO3− have confirmed the redox reaction between BrO3− and MXene. Also the developed sensor showed excellent selectivity for BrO3− among other interfering ions. This work provides an evidence for the application of MXene as an effective platform for detection of water contaminants especially when its redox functionality can be utilized.

Research on ultraviolet-visible absorption spectrum preprocessing for water quality contamination detection

The contaminant detection in water is important to secure public health against potentially harmful substances. As a noninvasive detection technique, ultraviolet-visible (UV-Vis) spectroscopy is studied and widely applied in detecting water contamination. However, current methods for contamination detection reveal the defects of noise sensitivity and data redundancy. In this study, de-noising and dimensionality reduction of UV-Vis absorption spectrum are focused on. With regards to de-noising, the influence of various discrete wavelet transforms families, decomposition levels and threshold methods on UV-Vis absorption spectrum de-noising are analyzed and discussed in detail. Concerning dimensionality reduction, the principal component analysis (PCA) is utilized. The performance of de-noising and dimensionality reduction is assessed by signal-to-noise ratio (SNR) and normalized reconstruction error (εnorm), respectively. Results from experiments and analysis reveal the decomposition level and wavelet transforms families are the most significant parameters influencing the de-noising algorithm efficiency. Moreover, using sym5 at five level decomposition with hard threshold method had the best effect for UV-Vis absorption spectrum de-noising. Besides, PCA can project UV-Vis absorption spectrum to a much lower dimension but representative data space.

Impedimetric paper-based biosensor for the detection of bacterial contamination in water

According to the World Health Organisation, worldwide waterborne diseases are responsible for nearly two million human deaths annually. Rapid and at-site screen of pathogenic microorganisms in drinking water can help to markedly reduce this number. Here we report an innovative, simple and low-cost, paper-based probe for detection of bacteria in water, fabricated by screen printing carbon electrodes onto hydrophobic paper. Electrochemical characterization of the printed electrodes confirmed fast-electron transfer, with an estimated electroactive surface area of 0.25 cm2. The electrode surface was functionalised with carboxyl groups, prior to covalent immobilization of the lectin Concanavalin A (Con A), used as the biorecognition element. The system was then tested as an impedimetric sensor for bacteria in water. A linear increase in the probe charge transfer resistance was observed for bacterial concentrations ranging from 103 to 106 CFU mL−1, with an estimated lower detection limit of 1.9 × 103 CFU mL−1. Considering its remarkable simplicity, cost-effectiveness and biodegradability, the sensor here reported could be an attractive solution for portable testing kits that address the challenges of traditional time-consuming and expensive lab-based analyses.

A stable electron-deficient metal–organic framework for colorimetric and luminescence sensing of phenols and anilines

A Eu3+ MOF with ideally spaced electron-deficient viologen moieties is capable of highly sensitive colorimetric/luminescence detection of electron-rich aromatic contaminants in water through charge transfer and resonance energy transfer.

Communication—Electrochemical Sensor Concept for the Detection of Lead Contamination in Water Utilizing Lead Underpotential Deposition

A novel electrochemical sensor concept, which enables detection of ppb-levels of Pb2+ in water, is outlined. The sensor works on the principle of underpotential deposition of Pb onto a Cu electrode followed by measurement of the hydrogen evolution reaction (HER) current on the Pbupd-modified electrode surface. The degree of suppression of the HER current is correlated to the Pbupd coverage, which in turn depends on the Pb2+ concentration in solution. Feasibility of achieving a detection limit of 10 ppb Pb2+ in aerated DI water and tap water is demonstrated and the sensing response time is analyzed using diffusion-reaction calculations.

Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains

Drinking Water Distribution Systems facilitate to carry portable water from water resources such as reservoirs, river, and water tanks to industrial, commercial and residential consumers through complex buried pipe networks. Determining the consequences of a water contamination event is an important concern in the field of water systems security and in drinking water distribution systems. The proposed work is based on the development of low cost fuzzy based water quality monitoring system using wireless sensor networks which is capable of measuring physiochemical parameters of water quality such as pH, temperature, conductivity, oxidation reduction potential and turbidity. Based on selected parameters a sensing unit is developed along with several microsystems for analog signal conditioning, data aggregation, sensor data analysis and logging, and remote representation of data to the consumers. Finally, algorithms for fusing the real time data and decision making using fuzzy logic at local level are developed to assess the water contamination risk. Based on the water contamination level in the distribution pipeline the drinking water quality is classified as acceptable/reject/desirable. When the contamination is detected, the sensing unit with ZigBee sends signals to close the solenoid valve inside the pipeline to prevent the flow of contaminated water supply and it intimates the consumers about drinking water quality through mobile app. Experimental results indicate that this low cost real time water quality monitoring system acts as an ideal early warning system with best detection accuracy. The derived solution can also be applied to different IoT (Internet of Things) scenario such as smart cities, the city transport system etc.

Detection of contaminants in water supply: A review on state-of-the-art monitoring technologies and their applications.

Water monitoring technologies are widely used for contaminants detection in wide variety of water ecology applications such as water treatment plant and water distribution system. A tremendous amount of research has been conducted over the past decades to develop robust and efficient techniques of contaminants detection with minimum operating cost and energy. Recent developments in spectroscopic techniques and biosensor approach have improved the detection sensitivities, quantitatively and qualitatively. The availability of in-situ measurements and multiple detection analyses has expanded the water monitoring applications in various advanced techniques including successful establishment in hand-held sensing devices which improves portability in real-time basis for the detection of contaminant, such as microorganisms, pesticides, heavy metal ions, inorganic and organic components. This paper intends to review the developments in water quality monitoring technologies for the detection of biological and chemical contaminants in accordance with instrumental limitations. Particularly, this review focuses on the most recently developed techniques for water contaminant detection applications. Several recommendations and prospective views on the developments in water quality assessments will also be included.

DNA as an environmental sensor: detection and identification of pesticide contaminants in water with fluorescent nucleobases.

Environmental contaminants pose a substantial health risk in many areas of the world. One of these risks is contamination of water with toxic organic species, such as herbicides and insecticides. Here we describe the discovery and properties of a set of fluorescent chemosensors that respond to micromolar concentrations of a broad range of common organic pesticides. The chemosensors are short DNA-like oligomers with fluorophores replacing DNA bases that are assembled via a DNA synthesizer. We screened a library of 1296 tetrameric compounds on polystyrene microbeads, and identified a set of chemosensor sequences that respond strongly to a set of structurally varied pesticide analytes. We show that ten chemosensors on beads can be used to detect and identify 14 different common pesticides at 100 μM, using the pattern of fluorescence intensity and wavelength changes. Limits of detection for two analytes were as low as 2 μM. The chemosensors are shown to function successfully in a practical setting, correctly identifying unknown pesticide contaminants in water from Felt Lake, California. The results establish a simple, low cost strategy for sensing environmental spills of toxic organics.

Rapid detection of mercury contamination in water by surface enhanced Raman spectroscopy

Mercury (Hg) is a potent neurotoxin in fish, wildlife, and humans.

Two-dimensional nanomaterial-based field-effect transistors for chemical and biological sensing.

Meeting the increasing demand for sensors with high sensitivity, high selectivity, and rapid detection presents many challenges. In the last decade, electronic sensors based on field-effect transistors (FETs) have been widely studied due to their high sensitivity, rapid detection, and simple test procedure. Among these sensors, two-dimensional (2D) nanomaterial-based FET sensors have been demonstrated with tremendous potential for the detection of a wide range of analytes which is attributed to the unique structural and electronic properties of 2D nanomaterials. This comprehensive review discusses the recent progress in graphene-, 2D transition metal dichalcogenide-, and 2D black phosphorus-based FET sensors, with an emphasis on rapid and low-concentration detection of gases, biomolecules, and water contaminants.

Analysis of organophosphates in borehole water of Mailinne and Sosianni, Uasin Gishu county, Kenya.

The noxiousness of pesticides as well as their harmful environmental effects have generated interests in the identification and quantification of organophosphate in borehole water. These contaminants can move from agricultural land or poorly managed sewerage systems and get into drinking water by surface runoff or by leaching into groundwater. The study sought to analyze the organophosphates contaminants of borehole water of Maili-nne a residential area and Sosiani an agricultural area. This was through detection of organic contaminants in borehole water; quantification of the detected organochemicals of borehole water of Maili-nne and Sosiani areas. Fifteen boreholes, five at Maili-nne (MB1-MB5) and ten at Sosiani (SB6-SB15) were systematically selected and water sampled during the dry and the wet seasons. Organic contaminants were analysed by detection and quantification of organic pesticides based on retention times by High Performance Liquid Chromatography (HPLC) and Gas Chromatography–Mass Spectroscopy (GC-MS) in correlation to computed element libraries for quantification. Diazinon was detected in boreholes SB9, SB12 and SB13, with highest level of 2.21 mg/L during the dry season and 1.52 mg/L in the wet season. Fenitrothion was detected in MB1 (0.145 mg/L and 0.191 mg/L) during the dry and the wet season, respectively. Malathion was found in SB6 and SB15 at 0.207 mg/L and 0.940 mg/L, respectively only during the dry season. Chloropyrifos was present in both Mailli-nne and Sosiani areas in borehole number MB1, SB9 and SB13 only during the dry season. Sulfotep was only detected during dry season in water samples obtained from SB9 and SB13. Dimethoate was only detected in Sosiani (SB7) during the wet season. It can therefore be concluded that residues of some organophosphate contaminants were above the WHO standards leading to decrease of water pH during the dry season beyond WHO limits (pH 6.5). The study also established that there was a positive significant correlation between presence of organic contaminants and deviation of physico-chemical properties (p=0.016, rho=0.68) in Maili-nne and Sosiani. In this regard the borehole water in the study areas should be treated and awareness be made on the impacts of pesticides use.

A sensitive biomarker for the detection of aquatic contamination based on behavioral assays using zebrafish larvae

An effective biological early warning system for the detection of water contamination should employ undemanding species that rapidly react to the presence of contaminants in their environment. The demonstrated reaction should be comprehensible and unambiguously evidential of the contamination event. This study utilized 96h post fertilization zebrafish larvae and tested their behavioral response to acute exposure to low concentrations of cadmium chloride (CdCl2) (5.0, 2.5, 1.25, 0.625mg/L) and permethrin (0.05, 0.029, 0.017, 0.01μg/L). We hypothesize that the number of larvae that show advanced trajectories in a group corresponds with water contamination, as the latter triggers avoidance behavior in the organisms. The proportion of advanced trajectories in the control and treated groups during the first minute of darkness was designated as a segregation parameter. It was parametrized and a threshold value was set using one CdCl2 trial and then applied to the remaining CdCl2 and permethrin replicates. For all cases, the method allowed distinguishing between the control and treated groups within two cycles of light: dark. The calculated parameter was statistically significantly different between the treated and control groups, except for the lowest CdCl2 concentration (0.625mg/L) in one replicate. This proof-of-concept study shows the potential of the proposed methodology for utilization as part of a multispecies biomonitoring system.

Monitoring of β-d-Galactosidase Activity as a Surrogate Parameter for Rapid Detection of Sewage Contamination in Urban Recreational Water

Simple, automated methods are required for rapid detection of wastewater contamination in urban recreational water. The activity of the enzyme β-d-galactosidase (GAL) can rapidly (<2 h) be measured by field instruments, or a fully automated instrument, and was evaluated as a potential surrogate parameter for estimating the level of fecal contamination in urban waters. The GAL-activity in rivers, affected by combined sewer overflows, increased significantly during heavy rainfall, and the increase in GAL-activity correlated well with the increase in fecal indicator bacteria. The GAL activity in human feces (n = 14) was high (mean activity 7 × 107 ppb MU/hour) and stable (1 LOG10 variation), while the numbers of Escherichia coli and intestinal enterococci varied by >5 LOG10. Furthermore, the GAL-activity per gram feces from birds, sheep and cattle was 2–3 LOG10 lower than the activity from human feces, indicating that high GAL-activity in water may reflect human fecal pollution more than the total fecal pollution. The rapid method can only be used to quantify high levels of human fecal pollution, corresponding to about 0.1 mg human feces/liter (or 103 E. coli/100 mL), since below this limit GAL-activity from non-fecal environmental sources may interfere.

Towards Monitoring the Water Quality Using Hierarchal Routing Protocol for Wireless Sensor Networks

Water is a limited resource and essential for agriculture, industry and creatures existence on earth including human beings. Water quality monitoring is essential to control physical, chemical and biological characteristics of water. In addition, water quality monitoring can help with water pollution detection and discharge of toxic chemicals and contamination in water. A large number of inexpensive sensor nodes using hierarchical communication structure can be deployed to cover a large monitoring area with enough density. The paper provides a hierarchical routing protocol to reduce the communication overhead and increase the life time of wireless sensor network (WSN) suitable for river/lake water quality monitoring. The proposed routing protocol is compared with its predecessor LEACH. Experiments conducted using our custom simulator shows that the proposed routing protocol, power by genetic algorithm, create an energy balance in the network, save the node energy and thus increase the lifetime of the network by 25% in comparison to LEACH.