Detection Of Pollutants In Water Research Articles

Algae-functionalized hydrogel-gated organic field-effect transistor. Application to the detection of herbicides

In this work, a first demonstration of an hydrogel-gated organic field-effect transistor (HGOFET) for the detection of water pollutants is described. Photosynthetic cyanobacteria were entrapped in a hydrogel grafted onto the platinum gate of the transistor. Photosynthesis was continuously monitored by electroreduction of the oxygen produced or consumed by the cyanobacteria on the gate electrode, under illumination or in the dark, respectively. The presence of herbicides such as diuron and glyphosate was found to strongly affect the cyanobacteria's photosynthetic activity, which was transduced into a significant drop of the device's gate current, strongly amplified onto the drain. This study demonstrates the suitability of HGOFETs for environmental monitoring.

Water Pollution Control and Treatment Based on Quantum Dot Chemical and Biological High‐Sensitivity Sensing

Inorganic pollutants in water can have an important impact on ecosystems and human health, so the development of rapid and sensitive detection methods for typical inorganic pollutants in water samples is important for understanding the pollution status of the water environment, as well as water pollution prevention and protection of drinking water safety. Fluorescence sensing technology has the advantages of fast response, high sensitivity, simple operation, and low cost but still has the problems of low quantum yield, cumbersome construction process, and limited practical applications. Based on the excellent fluorescence properties, a series of fluorescence sensing was constructed for the rapid, highly sensitive, and selective detection of various typical inorganic pollutants in water. And the related fluorescence sensing mechanism was investigated in this paper. In this paper, nitrogen/sulfur codoped carbon quantum dots (N, S‐CQDs) were prepared for the sensitive and selective detection of sulfide and ferric ion. The blue fluorescent N, S‐CQDs were prepared by a one‐step hydrothermal method using ammonium citrate and L‐cysteine as raw materials, which have excitation wavelength dependence and fluorescence quantum yield of 16.1% for the selective detection of sulfides with a detection limit (S/N = 3) of 11.0 nM (about 0.35 μg/L). CQDs with significantly higher fluorescence quantum yields (69%) and no excitation dependence were prepared when citric acid was used instead of ammonium citrate and were used for the selective detection of ferric ion with a detection limit of 14.0 nM (~0.8 μg/L). The method has been successfully applied to the determination of total phosphorus in surface water and human urine, and the fluorescence color change of the dual‐emission sensing can be used for the naked‐eye identification and semiquantitative detection of phosphate.

Molecular investigation of hormonal alterations in Oreochromis niloticus as a bio-marker for long-term exposure to zinc oxide nanoparticles

This study aimed to investigate the potential effects of long-term zinc oxide nanoparticles’ (Zn-ONPs) exposure to the hormonal profile of O. niloticus and their application as a bio-marker for fresh water pollution by Zn-ONPs. Two hundred-forty female O. niloticus were used. Growth hormone (GH), thyroid stimulating hormone (TSH), triiodothyronine (T3), tetraiodothyronine (T4), follicular stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), glucagon, insulin, Adrenocorticosteroid hormone (ACTH) serum concentrations and the expression levels of GH, insulin-like growth factor gene (IGF-I), insulin and insulin receptor A (IRA) genes were determined. The levels of GH, TSH, FSH, LH, T3, T4, Estradiol, E2, insulin and GH, IGF-I, insulin and IRA genes expression decreased significantly in Zn-ONPs’ exposed fish in a concentration-dependent manner; meanwhile, serum ACTH and cortisol increased. The alterations in hormone levels and their gene expressions are considered as good bio-markers for the detection of fresh water pollution with Zn-ONPs.

Portable Real-Time Detection of Pb(II) Using a CMOS MEMS-Based Nanomechanical Sensing Array Modified with PEDOT:PSS.

Detecting the concentration of Pb2+ ions is important for monitoring the quality of water due to it can become a health threat as being in certain level. In this study, we report a nanomechanical Pb2+ sensor by employing the complementary metal-oxide-semiconductor microelectromechanical system (CMOS MEMS)-based piezoresistive microcantilevers coated with PEDOT:PSS sensing layers. Upon reaction with Pb2+, the PEDOT:PSS layer was oxidized which induced the surface stress change resulted in a subsequent bending of the microcantilever with the signal response of relative resistance change. This sensing platform has the advantages of being mass-produced, miniaturized, and portable. The sensor exhibited its sensitivity to Pb2+ concentrations in a linear range of 0.01–1000 ppm, and the limit of detection was 5 ppb. Moreover, the sensor showed the specificity to Pb2+, required a small sample volume and was easy to operate. Therefore, the proposed analytical method described here may be a sensitive, cost-effective and portable sensing tool for on-site water quality measurement and pollution detection.

Development of a low-cost colorimeter and its application for determination of environmental pollutants

Herein, a novel colorimeter based on the Beer-Lambert law was designed for detection of environmental pollutants in water with a high precision, simple, and miniaturized device using a tetracycline-Eu3+ complex, cadmium reduction, diazotization, 1,10-phenanthroline, and periodate oxidation. The newly developed colorimeter could detect many environmental pollutants including tetracycline, nitrate, nitrite, Fe, and Mn, which were used to evaluate its performance. Simultaneously, a modified algorithm was applied to extend the linear response range. The colorimeter was comprised of an Red Green Blue Light Emitting Diode (RGB LED) light, focusing len, 3D printed stand for the cuvette, and light-sensitive photodiode detector. Microcontroller Arduino Uno processing technology was used to form a stable integrated structure. With the use of a novel algorithm, the device exhibited a wide linear response, ranging from 0–20, 0–17, 0–0.3, 0–1.75, and 0–15 mg/L for tetracycline, N-NO3-, N-NO2-, Fe, and Mn, respectively, and low limits of detection (0.88, 0.34, 0.031, 0.08, and 0.47 mg/L for tetracycline, N-NO3-, N-NO2-, Fe, and Mn, respectively). The advantages of high precision and low cost allow the novel design to be used for the detection of environmental pollutants.

Water Quality Detection Based on FCN and Embedded System

Yan, Y.; Han, Y.; Zhang, P.H., and Wang, H.F., 2020. Water quality detection based on FCN and embedded system. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 72–76. Coconut Creek (Florida), ISSN 0749-0208.In this paper, the convolutional neural network (CNN) based on deep learning is applied to embedded systems. After summarizing the shortcomings of traditional algorithms, the image processing technology of full convolutional neural network (FCN) is adopted to study a method for online water quality measurement, which solves the key problem of image recognition. In view of the complex nonlinear characteristics of water quality related data, the full convolutional neural network is used to train water quality data, determine the mapping relationship between input and output, and input the obtained relationship into the embedded system to form a water quality detection system, so as to quickly and efficiently detect water pollution. The experimental results show that the accuracy of the predicted grade and the measured grade of water quality in the test area can be as high as 90%.

Toward Smart Selective Sensors Exploiting a Novel Approach to Connect Optical Fiber Biosensors in Internet

The selective detection of pollutants in water in a laboratory scenario has been presented by authors exploiting low-cost optical biosensors based on plastic optical fibers (POFs) and biological or biomimetic receptors. So, for instance, the detection in water of naphthalene, perfluoroalkyl substances (PFAs) and polyfluoroalkyl substances have been investigated with interesting detection limits when compared to those obtained by using different expensive traditional approaches (e.g., liquid chromatography-mass spectrometry with high performances). In this article, we have developed and tested a novel approach used in a smart measuring system to use POF sensors in situ for the remote measures of pollutants in water for smart cities applications. More specifically, we have used different water–glycerin solutions to test the novel sensor system based on a Raspberry Pi connected to the Internet and to a spectrometer, a light source, a POF sensor, and two computers connected to Internet used as client and server.

SPATIO-TEMPORAL SALINITY MONITORING OF THE GHAGHARA RIVER USING LANDSAT TIME-SERIES IMAGERY AND MULTIPLE REGRESSION ANALYSIS

Abstract. Nowadays, water has become one of the most important environmental issues for our ecosystem and is facing major challenges today. During the COVID-19 pandemic, the world has understood the need for good quality of water for sanitation and hygiene. Earth observing satellites plays a critical role in near-real-time detection and monitoring of land and water change and quality. This research presents a methodology for modeling and mapping water salinity in high spatial resolution. Data for modeling were measured on the five monitoring stations (Ayodhya, Basti, Birdghat, Paliakalan, and Turtipar) along the Ghagraha River Basin in India, during the period of 28 years (1985–2013). In this research, Electrical Conductivity (EC) as water salinity parameter modeled by means of Landsat 5 satellite imagery. All available Landsat 5 imagery were acquired on the same date as the ground measurement data was utilized for the modeling. Modeling was done based on linear, 2nd and 3rd polynomial multiple regression analysis. All statistical parameters for accuracy assessment show that 3rd degree polynomial performs better EC prediction capability than 2nd degree polynomial and linear regression. The 3rd degree polynomial multiple regression model RMSE, R2, MAE, p-value were 8.682, 0.993, 6.493, 0.008, respectively. The developed algorithm provides new knowledge that can be widely applied in various environmental research mapping and monitoring like water salinity. Also, this method allows rapid detection of water pollution, which has an important impact on human health, agriculture, and the environment.

Simultaneous measurement of the BOD concentration and temperature based on a tapered microfiber for water pollution monitoring.

An optical sensor that simultaneously measures the concentration of the biochemical oxygen demand (BOD) and temperature in water based on a tapered microfiber is proposed for environmental monitoring. The sensor is characterized by a strong evanescent field, which is more sensitive to liquids with a low refractive index and a low transmission loss. The results show that as the BOD concentration increases, the interference spectrum shifts toward longer wavelengths, the spectral loss decreases, and the sensitivities of the BOD are 12.17 nm/mg/mL and -2.387dB/mg/mL in the range of 0.25-1 mg/mL, which indicates the extent of the water pollution. The detection limit for the BOD concentration is as low as 0.0016 mg/mL. As the ambient temperature increases, the interference spectrum shifts toward shorter wavelengths, the spectral loss decreases, and the temperature sensitivities are -0.339nm/∘C and -0.031dB/∘C in the range of 30°C-60°C. The matrix method can be used to achieve the simultaneous measurement of the BOD concentration and environmental temperature because the spectral interference peaks have different responses to these two parameters. The sensor can not only be used for detecting water pollution in rivers, drinking water, and groundwater but can also be utilized for other types of environmental monitoring. This sensor has great potential to act as a basic sensing unit in fiber-optic sensor networks for multiparameter measurements and intelligent monitoring.

Nanostructured Raman substrates for the sensitive detection of submicrometer-sized plastic pollutants in water

We prepared novel Raman substrates for the sensitive detection of submicron-sized plastic spheres in water. Anisotropic nanostar dimer-embedded nanopore substrates were prepared for the efficient identification of submicron-sized plastic spheres by providing internal hot spots of electromagnetic field enhancements at the tips of nanoparticles. Silver-coated gold nanostars (AuNSs@Ag) were inserted into anodized aluminum oxide (AAO) nanopores for enhanced microplastic (MP) detection. We found that surface-enhanced Raman scattering (SERS) substrates of AuNSs@Ag@AAO yielded stronger signals at the same weight percentages for polystyrene MP particles with diameters as small as 0.4 μm, whereas such behaviors could not be observed for larger MPs (diameters of 0.8 μm, 2.3 μm, and 4.8 μm). The detection limit of the submicrometer-sized 0.4 μm in our Raman measurements were estimated to be 0.005% (∼0.05 mg/g =50 ppm) along with a fast detection time of only a few min without any sample pretreatments. Our nano-sized dimensional matching substrates may provide a useful tool for the application of SERS substrates for submicrometer MP pollutants in water.

A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources

Water is a natural resource for agricultural irrigation. Recycling use of water is important in terms of resource conservation and is good for sustainable development of the ecological environment. The wastewater from daily living and industrial production contains various chemicals that are supposed as pollutants leading to the decline of water quality. For the demand of water protection and recycling, the assessment of water pollution level should be evaluated. An effective scientific technique is required for rapid detection of water pollution. Near-infrared (NIR) spectroscopy is a modern technology suitable for rapid detection of agricultural targets. For monitoring the agricultural water resource, the NIR modeling methods are required to be smart and artificially controlled to solve the issues when we confront a considerable number of data or a dynamic situation. In this study, an improved convolutional neural network (CNN) architecture was designed for a deep calibration on the NIR data. The architecture is shallow, simply constructed with one convolution layer and one pooling layer. The decision tree algorithm was employed in the pooling layer for extracting the informative features in a data driven manner. The CNN architecture was trained by combined tuning of multiple parameters in different layers. The convolution filters, the decision tree branches and the hidden neurons in the fully connected layer were automatically adaptive with fidelity to the measured data. A CNN calibration model for NIR quantitatively determination of water pollution level was then established and optimized in deep learning mode, and eventually improved the NIR prediction accuracy. Prospectively, the designed shallow CNN architecture is feasible to be used for establishing intelligent spectroscopic models for evaluating the level of water pollution, and is expected to provide smart technical support in dealing with the issues of water recycling and conservation for agricultural cultivation.

Synthesis and Electrochemical Application of MxFe3-XO4 (M=Cu,Co) for the Detection and Removal of Metal Pollutants

Over the last two decades, nanomaterials (metallic nanoparticles, carbonaceous nanostructures) have triggered great interest thanks to their specific physic-chemical properties. The improvements they have made in the field of electro-analysis are numerous. Thus, a wide range of physic-chemical technologies based on the use of inorganic materials for the detection and removal of water pollutants has become a promising voice. In this context, we were interested in the design of electrodes modified by nanomaterials of the spinel type CxFe3-xO4 for the detection and quantification of metallic micro pollutants in water. The phases of the spinel system were synthesized by the sol-gel method, and physic-chemical characterization was performed by ATD-TG, DRX and MEB. The methods used for the electrochemical test are cyclic voltammetry (CV), square wave voltammetry (SWV) and impedance spectroscopy (EIS).The results of the physic-chemical analysis of the oxides showed that the phase is obtained at a calcination temperature of 600°C, and the electrochemical tests of the elaborate electrode revealed a very high sensitivity to ions (Mn2+, cd2+, pb2+, Hg2+). Keywords: nanoparticles, spinals, metal pollutants, modified electrode, electrochemical tests.

Portable Sensors for Rapid Assessment of Phosphate Level in Eutrophic Waters

Portable, easy-to use sensors for in situ detection of water pollutants is of great importance. This presentation will describe the development of a paper-based sensor to determine phosphate levels in eutrophic water bodies. Although phosphorus (P)–based fertilizers are essential for sustaining agricultural food production, the oversupply of P leads to water eutrophication which has severe consequences on the environment and human health. To achieve the objective to develop effective management tools for assessing water eutrophication, we develop sensors that can be used to screen the affected areas and quantify the levels of phosphate in situ. The sensor incorporates nanomaterials with P-specific binding properties deposited on cellulosic paper. This presentation will discuss assembly of the sensing materials on cellulosic paper, the recognition mechanism and the performance characteristics of the system for phosphate detection. The developed sensor is easy to use, portable and can provide rapid and selective onsite detection for monitoring nutrient content in eutrophic water

Nitrogen Surplus—A Unified Indicator for Water Pollution in Europe?

Pollution of ground-and surface waters with nitrates from agricultural sources poses a risk to drinking water quality and has negative impacts on the environment. At the national scale, the gross nitrogen budget (GNB) is accepted as an indicator of pollution caused by nitrates. There is, however, little common EU-wide knowledge on the budget application and its comparability at the farm level for the detection of ground-and surface water pollution caused by nitrates and the monitoring of mitigation measures. Therefore, a survey was carried out among experts of various European countries in order to assess the practice and application of fertilization planning and nitrogen budgeting at the farm level and the differences between countries within Europe. While fertilization planning is practiced in all of the fourteen countries analyzed in this paper, according to current legislation, nitrogen budgets have to be calculated only in Switzerland, Germany and Romania. The survey revealed that methods of fertilization planning and nitrogen budgeting at the farm level are not unified throughout Europe. In most of the cases where budgets are used regularly (Germany, Romania, Switzerland), standard values for the chemical composition of feed, organic fertilizers, animal and plant products are used. The example of the Dutch Annual Nutrient Cycling Assessment (ANCA) tool (and partly of the Suisse Balance) shows that it is only by using farm-specific “real” data that budgeting can be successfully applied to optimize nutrient flows and increase N efficiencies at the farm level. However, this approach is more elaborate and requires centralized data processing under consideration of data protection concerns. This paper concludes that there is no unified indicator for nutrient management and water quality at the farm level. A comparison of regionally calculated nitrogen budgets across European countries needs to be interpreted carefully, as methods as well as data and emission factors vary across countries. For the implementation of EU nitrogen-related policies—notably, the Nitrates Directive—nutrient budgeting is currently ruled out as an entry point for legal requirements. In contrast, nutrient budgets are highlighted as an environment indicator by the OECD and EU institutions.

Conformable surface acoustic wave biosensor for E-coli fabricated on PEN plastic film

Over the last decades, great effort has gone into developing new biosensor technologies for applications in different fields such as disease diagnosis and detection of pollutants in water and food. Global developments in robotic, IoT technologies and in healthcare sensors require new flexible sensor technologies that are low cost and built from sustainable and reusable or recyclable materials. One of the most promising technologies is based on the development of surface acoustic wave (SAW) flexible biosensors, which are highly reproducible, reliable and wirelessly controllable. This work presents for the first time a novel aluminum nitride (AlN)-based conformable SAW immunosensor fabricated on recyclable polyethylene naphthalate. We apply it to the detection of E.Coli using a faster and innovative functionalization method that exploit Protein-A/antibody affinity. A higher sensitivity (Limit of detection-LoD, 6.54*105 CFU/ml) of the Lamb wave traveling on the polymeric device has been obtained in comparison with SAWs traveling on AlN on silicon substrate (LoD, 1.04*106 CFU/ml). Implementation of a finite element method allowed for the estimation of the single E.Coli mass of approximately 9*10−13 g. This work demonstrates the high biosensing potential of flexible polymeric SAW devices for bacteria contamination control in food chain, water and smart packaging.

Hybrid U-Shaped-Microbend SMF Evanescent Wave Sensor for River Water Quality Assessment: A Preliminary Study

A hybrid U-shaped-microbend fiber optic evanescent wave sensor was developed by combining two types of bending structures on the optical communication single mode optical fiber (SMF28). To study the effect optical microbending on the output power, corrugated plates consisted of cylindrical structured surface with various distance between the glass rods of 6 cm, 12 cm and 18 cm were constructed. The macrobending effect was introduced by bending the SMF into two shapes, namely U-shaped and Sshaped. The bare SMF with various bending designs were immersed into numerous water sources from Sg. Simin, Sg. Batang Benar and Sg. Klang. The output demonstrated that Sg. Simin was the most polluted river, followed by Sg. Klang and Sg. Batang Benar using U-shaped microbend SMF with distance between glass rod of 6cm and 1310 nm laser source. This result showed an excellent agreement with water quality index (WQI) data released by the Department of Environment (DOE), Malaysia. Maximum optical output power was obtained by using Sg. Simin’s water sample due to better light absorption from the evanescent waves by the pollutant particles, that avoided light leakage in comparison with less polluted water sources. The optimum sensing performance was successfully resulted by using U-shaped SMF due to its durability and uniform evanescent waves radiated from the cladding. In conclusion, the hybrid U-shaped-microbend SMF sensor based on evanescent waves propagation portrays an excellent potential to detect water pollution by monitoring the presence of pollutants around the fiber.

Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy

Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management.

Nanoparticle-based 'turn-on' scattering and post-sample fluorescence for ultrasensitive detection of water pollution in wider window.

Ultrasensitive detection of heavy metal ions in available water around us is a great challenge for scientists since long time. We developed an optical technique that combines Rayleigh scattering of UV light (365 nm) and post-sample fluorescence detection from colloidal silver (Ag) nanoparticles (NPs) having a surface plasmon resonance (SPR) band at 420 nm. The efficacy of the technique is tested by the detection of several model toxic ions, including mercury, lead, and methylmercury in aqueous media. The light scattering from the Hg-included/inflated Ag NPs at 395 nm was observed to saturate the light sensor even with ppm-order concentrations of Hg ions in the water sample. However, the pollutant is not detected at lower concentrations at this wavelength. Instead, the fluorescence of a high-pass filter (cut-off at 400 nm) at 520 nm is applied to detect pollutant concentrations of up to several hundreds of ppm in the water sample. We also detected lead and methylmercury as model pollutants in aqueous media and validated the efficacy of our strategy. Finally, we report the development of a working prototype based on the strategy developed for efficient detection of pollutants in drinking/agricultural water.

Detection of humic acid in water using flat-sheet and folded-rod viscous alkaline glucose syrups.

The utility of a low-cost biocompatible material for the detection of pollutants in water is highly essential to ensure safety and economic efficiency. In this paper, solutions of two viscous alkaline glucose syrups (AGS@22-sheet and AGS@60-rod), obtained under two different temperature conditions (22 °C and 60 °C) were used to detect low levels of humic acid (HA), a carcinogen pro-molecule and metal-complexing agent in an aqueous solution. The AGS materials were characterized using ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy and scanning electron microscopy (SEM). By evaluation, a detection limit (LOD) as low as 4.6 × 10-5 mg L-1 was obtained. The sensing capability of the new technology was further extended to the detection of HA in a real water sample (tap water) using the standard addition method with 98 and 100.05% recoveries. The sensing was improved in the presence of sodium acetate and sodium citrate and was found to follow a pseudo-first order reaction. These findings show that the as-synthesized glucose syrups have the potential to detect humic acid in water and thus may be employed for the quantification of HA in water treatment plants or textile industry.

Luminescent metal–organic frameworks (LMOFs) as potential probes for the recognition of cationic water pollutants

This review aims to provide an overview regarding the development of luminescent metal–organic frameworks (LMOFs) based sensory materials for the detection of cationic inorganic and organic water pollutants.