Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach

Herbicide detection: A review of enzyme- and cell-based biosensors

Herbicides are the most widely used class of pesticides in the world. Their intensive use raises the question of their harmfulness to the environment and human health. These pollutants need to be detected at low concentrations, especially in water samples. Commonly accepted analytical techniques (HPLC-MS, GC-MS, ELISA tests) are available, but these highly sensitive and time-consuming techniques suffer from high cost and from the need for bulky equipment, user training and sample pre-treatment. Biosensors can be used as complementary early-warning systems that are less sensitive and less selective. On the other hand, they are rapid, inexpensive, easy-to-handle and allow direct detection of the sample, on-site, without any further step other than dilution. This review focuses on enzyme- and cell- (or subcellular elements) based biosensors. Different enzymes (such as tyrosinase or peroxidase) whose activity is inhibited by herbicides are presented. Photosynthetic cells such as algae or cyanobacteria are also reported, as well as subcellular elements (thylakoids, chloroplasts). Atrazine, diuron, 2,4-D and glyphosate appear as the most frequently detected herbicides, using amperometry or optical transduction (mainly based on chlorophyll fluorescence). The recent new WSSA/HRAC classification of herbicides is also included in the review.

Recent advances on rapid detection and remediation of environmental pollutants utilizing nanomaterials-based (bio)sensors

Environmental safety has become a significant issue for the safety of living species, humans, and the ecosystem as a consequence of the harmful and detrimental consequences of various pollutants such as pesticides, heavy metals, dyes, etc., emitted into the surroundings. To resolve this issue, various efforts, legal acts, scientific and technological perspectives have been embraced, but still remain a global concern. Furthermore, due to non-portability, complex detection, and inappropriate on-site recognition of sophisticated laboratory tools, the real-time analysis of these environmental contaminants has been limited. As a result of innovative nano bioconjugation and nanofabrication techniques, nanotechnology enables enhanced nanomaterials (NMs) based (bio)sensors demonstrating ultra-sensitivity and a short detection time in real-time analysis, as well as superior sensitivity, reliability, and selectivity have been developed. Several researchers have demonstrated the potent detection of pollutants such as Hg²⁺ ion by the usage of AgNP-MD in electronic and optoelectronic methods with a detection limit of 5-45 μM which is quite significant. Taking into consideration of such tremendous research, herein, the authors have highlighted 21st-century strategies towards NMs based biosensor technology for pollutants detection, including nano biosensors, enzyme-based biosensors, electrochemical-based biosensors, carbon-based biosensors and optical biosensors for on-site identification and detection of target analytes. This article will provide a brief overview of the significance of utilizing NMs-based biosensors for the detection of a diverse array of hazardous pollutants, and a thorough understanding of the detection processes of NMs-based biosensors, as well as the limit of quantification (LOQ) and limit of detection (LOD) values, rendering researchers to focus on the world's need for a sustainable earth.

A cumulative-risk assessment method based on an artificial neural network model for the water environment

To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10^-6. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.

A Review of Nanocomposite-Modified Electrochemical Sensors for Water Quality Monitoring

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors' overall performance, especially concerning real-sample performance and the capability for actual field application.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

Analysis of bioavailable toluene by using recombinant luminescent bacterial biosensors with different promoters

In this study, we constructed recombinant luminescent Escherichia coli with T7, T3, and SP6 promoters inserted between tol and lux genes as toluene biosensors and evaluated their sensitivity, selectivity, and specificity for measuring bioavailable toluene in groundwater and river water. The luminescence intensity of each biosensor depended on temperature, incubation time, ionic strength, and concentrations of toluene and coexisting organic compounds. Toluene induced the highest luminescence intensity in recombinant lux-expressing E. coli with the T7 promoter [T7-lux-E. coli, limit of detection (LOD) = 0.05 μM], followed by that in E. coli with the T3 promoter (T3-lux-E. coli, LOD = 0.2 μM) and SP6 promoter (SP6-lux-E. coli, LOD = 0.5 μM). Luminescence may have been synergistically or antagonistically affected by coexisting organic compounds other than toluene; nevertheless, low concentrations of benzoate and toluene analogs had no such effect. In reproducibility experiments, the biosensors had low relative standard deviation (4.3-5.8%). SP6-lux-E. coli demonstrated high adaptability to environmental interference. T7-lux-E. coli biosensor-with low LOD, wide measurement range (0.05-500 μM), and acceptable deviation (- 14.3 to 9.1%)-is an efficient toluene biosensor. This is the first study evaluating recombinant lux E. coli with different promoters for their potential application in toluene measurement in actual water bodies.