Detection Of Contaminants In Water Research Articles

Monitoring of Water Quality using Machine Learning - A Review

Abstract: The incorporation of machine learning (ML) can improve the monitoring of water quality. It improves the sustainability and security of water resources by enabling forecasting and early detection of possible water contamination. A real-time system that collects information on numerous water parameters, including pH level, dissolved oxygen, turbidity, temperature, and conductivity, is made possible by the combination of IoT technology with ML algorithms. The system models complex links between water quality parameters and looks for variations from typical patterns using a mix of supervised and unsupervised learning and anomaly detection methods. Advantage of this method include cost-effectiveness, scalability, remote accessible, and real-time monitoring

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools.

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2µgL-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

Molecularly Imprinted Polymer-Based Electrochemical Sensor for the Detection of Azoxystrobin in Aqueous Media.

This work presents an electrochemical sensor detecting a fungicide-azoxystrobin (AZO) in aqueous environments. This AZO sensor utilizes a thin-film metal electrode (TFME) combined with an AZO-selective molecularly imprinted polymer (AZO-MIP). The AZO-MIP was directly generated on TFME through electrochemical polymerization from the solution containing two functional monomers: aniline (Ani) and m-phenylenediamine (mPD), and the template: AZO, which was afterwards removed to form AZO-selective cavities in the polymer matrix. The AZO-MIP preparation was characterized by electrochemical and ellipsometry measurements. Optimization of the synthesis parameters, including the charge density applied during electrodeposition, the monomer-to-template ratio, was performed to enhance the sensor's performance. The results demonstrated that the AZO sensor achieved a low limit of detection (LOD) of 3.6 nM and a limit of quantification (LOQ) of 11.8 nM in tap water, indicating its sensitivity in a complex aqueous environment. The sensor also exhibited satisfactory selectivity for AZO in both ultrapure and tap-water samples and achieved a good recovery (94-119%) for the target analyte. This study highlights the potential of MIP-based electrochemical sensors for the rapid and accurate detection of fungicide contaminants in water, contributing to the advancement of analytical tools for water-quality monitoring and risk assessment.

Role of Silica, Carbon, and ZnO Nanomaterials in the Fabrication of Electrochemical Sensors for the Detection of Water Contaminants and Food Dye

AbstractSilica‐based nanomaterials have attracted huge attention for maximizing their safety and efficacy due to their nontoxicity, chemical and thermal stability, size tunability, and versatile functionality. Nanosilica with ZnO or carbon in a composite has excellent usage as an electrochemical sensor. Recent technological progression in nanotechnology and nanoscience has seen a number of applications of zinc oxide (ZnO) nanomaterials ranging from electronics, and sensing to environmental, and biomedical applications because of its various applications, multifunction, high specific surface area, stability, biocompatibility, nontoxicity, electrochemical activities, and so on. Carbon also has various advantageous properties like renewability, low ohmic resistance, and very stable response due to which carbon paste electrodes have attracted attention in the fabrication of electrochemical sensors. Electrochemical sensors are inexpensive, portable, and have excellent ability in detecting water contaminants, pesticides, disinfectants, pathogens, and different molecules. Artificial dyes are usually mixed with vegetable sauces, drinks, and other food items, which can cause cancer in human body. Voltametric methods with electrochemical sensors can be used to detect them in food samples. In this review, the present applications of ZnO and carbon nanomaterial‐based chemical sensors are meticulously studied to detect water contaminants and food dyes where nanosilica plays an important role as a sensor modifier.

Metal-organic frame material encapsulated Rhodamine 6G: A highly sensitive fluorescence sensing platform for the detection of picric acid contaminants in water

As a water pollutant with excellent solubility, 2,4,6-trinitrophenol (also known as picric acid, PA) poses a potential threat to the natural environment and human health, so it is crucial important to detect PA in water. In this study, a novel composite material (MIL-53(Al)@R6G) was successfully synthesized by encapsulating Rhodamine 6G into a metal-organic frame material, which was used for fluorescence detection of picric acid (PA) in water. The composite exhibits bright yellow fluorescence emission with a fluorescence quantum yield of 58.23 %. In the process of PA detection, the composite has excellent selectivity and anti-interference performance, and PA can significantly quench the fluorescence intensity of MIL-53(Al)@R6G. MIL-53(Al)@R6G has the advantages of fast detection time (20 s), wide linear range (1–100 µM) and low detection limit (4.8 nM). In addition, MIL-53(Al)@R6G has demonstrated its potential for the detection of PA in environmental water samples with satisfactory results.

A highly sensitive SERS optical fiber probe with PSS modified gold nano-bipyramids for trace detection of water contamination

The combination of surface-enhanced Raman scattering (SERS) and optical fiber has led to the development of SERS optical fiber probe, which demonstrates potential for real-time, in-situ, and flexible detection of water contamination. In this paper, a highly sensitive SERS optical fiber probe of gold nano-bipyramids (Au NBPs) was successfully developed for the first time. The Au NBPs were controllably synthesized by a heat-treated seed growth method. Surface modification of the Au NBPs was carried out by adding poly (sodium 4-styrenesulfonate) (PSS) instead of hexadecyl trimethyl ammonium bromide (CTAB), which changed the Zeta potential of the Au NBPs solution from positive to negative. The SERS properties of the Au NBPs can be significantly enhanced by optimizing the parameters of PSS content. The optical fiber end face was pretreated with silane coupling agent to introduce amino groups and exhibit positive charge. As a result, highly sensitive SERS optical fiber probes were prepared through electrostatic adsorption of the Au NBPs. The performance of the SERS optical fiber probe was evaluated. The crystalline violet (CV) limit concentration of 10−10 mol/L was detected by the optimized SERS optical fiber probe with satisfactory reproducibility and stability. Additionally, a limit concentration of 10−5 mol/L for thiram could also be detected by the SERS optical fiber probe. Finally, the SERS enhancement mechanism was further discussed. The finite element method was utilized to analyze the electromagnetic field distribution of Au NBPs, optical fibers, and SERS optical fiber probes, theoretically explaining the performance advantages of Au NBPs SERS optical fiber probes. Based on our results, the Au NBPs SERS optical fiber probe holds significant value for trace detection of water contamination and biomolecules.

Electrochemical Detection of Single Aqueous Droplets in Organic Solvents via Pitting Collisions.

We report a novel detection method for single aqueous droplets in organic solvents by the collisional contact of the droplet, inducing the partial deformation of the ultramicroelectrode (UME) surface. For various chemical reactions in organic solvents, water impurities affect the catalytic activity, leading to a loss of productivity and selectivity. Therefore, it is necessary to monitor the water content of organic solvents in real time between many chemical production processes, from the laboratory to the industrial scale. Our method enables the detection of water contamination by real-time monitoring of the electrochemical signals or observing morphological changes in the microelectrode. When an aqueous droplet collides with the UME, the contact area of the electrode is electrolyzed, forming pits on the surface where the droplet falls. Current transient analysis shows a unique current spike corresponding to the reaction inside the adsorbed single aqueous droplet, which differs from those detected by the faradaic/nonfaradaic reaction of collision of other particles. Moreover, this analytical method can record the history of collision events from pits on the UME surface, implying that inspecting the UME surface could be a quick screening method for solvent contamination. Based on a comparison of the electrochemical signals and morphological changes of the electrode after each event, the sizes of the pits and droplets are related. A COMSOL simulation is performed to explain the shape of the peak current and pit formation during collision events. This experimental concept elucidates the dynamic behavior of aqueous droplets on a positively biased metal electrode.

Optical Method for Detection and Classification of Heavy Metal Contaminants in Water Using Iso-pathlength Point Characterization.

Water pollution caused by hazardous substances, particularly heavy metal (HM) ions, poses a threat to human health and the environment. Traditional methods for measuring HM in water are expensive and time-consuming and require extensive sample preparation. Therefore, developing robust, simple, and sensitive techniques for the detection and classification of HM is needed. We propose an optical approach that exploits the full scattering profile, meaning the angular intensity distribution, and utilizes the iso-pathlength (IPL) point. This point appears where the intensity is constant for different scattering coefficients, while the absorption coefficient is set. The absorption does not affect the IPL point position, it only reduces its intensity. In this paper, we explore the wavelength influence on the IPL point both in Monte Carlo simulations and experimentally. Next, we present the characterization of ferric chloride (FeCl2) by this phenomenon. Eventually, we exhibit the detection of FeCl2 and intralipid mixed in concentrations of 50-100 and 20-30 ppm, respectively. These findings endorse the idea that the IPL point is an intrinsic parameter of a system serving as an absolute calibration point. The method provides an efficient way of differentiating contamination in water. Its characterization technique is easy, precise, and versatile making it preferable for water monitoring.

Simple microfluidic devices for in situ detection of water contamination: a state-of-art review.

Water security is an important global issue that is pivotal in the pursuit of sustainable resources for future generations. It is a multifaceted concept that combines water availability with the quality of the water's chemical, biological, and physical characteristics to ensure its suitability and safety. Water quality is a focal aspect of water security. Quality index data are determined and provided via laboratory testing using expensive instrumentation with high maintenance costs and expertise. Due to increased practices in this sector that can compromise water quality, innovative technologies such as microfluidics are necessary to accelerate the timeline of test procedures. Microfluidic technology demonstrates sophisticated functionality in various applications due to the chip's miniaturization system that can control the movement of fluids in tiny amounts and be used for onsite testing when integrated with smart applications. This review aims to highlight the basics of microfluidic technology starting from the component system to the properties of the chip's fabricated materials. The published research on developing microfluidic sensor devices for monitoring chemical and biological contaminants in water is summarized to understand the obstacles and challenges and explore future opportunities for advancement in water quality monitoring.

Fabrication of novel Ag3PO4/LiFe5O8 nanocomposites for dual applications in sustainable photocatalytic removal of harmful antibiotics and ratiometric electrochemical detection of hazardous carbendazim insecticide

Herein, for the first time bi-functional magnetic nanocomposites based on Ag3PO4 (SP) and LiFe5O8 (LF) were prepared with different ratio of LF (xLFSP, x = 2.5, 5, 10, 15 and 20 wt% of LF) and synergy between the individual components of the nanocomposite were studied for dual applications. Different analytical techniques were employed to examine the physicochemical features of the synthesized materials. In the presence of H2O2 and visible light, 10LFSP nanocomposite possess superior photocatalytic activity towards levofloxacin (LFX, 98% removal) and tetracycline (TC, 95% removal) drugs. Further, 10LFSP was employed to modify a glassy carbon electrode (GCE) and fabricate a ratiometric electrochemical sensor for the detection of the pernicious insecticide carbendazim (CBM). Under optimized conditions, ratiometric signal is linear to the CBM concentration in the range of 0.05 to 10 μM with a detection limit of 20 nM. Moreover, the proposed sensor 10LFSP/GCE was successfully validated in the detection of CBM in real water samples. Thus, the present work provides a venue for the design of multifunctional nanomaterial that could solve the dual purpose of detection and removal of water contaminants.

Dynamic Multivariate Outlier Detection Algorithm Using Ultraviolet Visible Spectroscopy for Monitoring Surface Water Contamination With Hydrological Fluctuation in Real-Time.

The contamination of surface water is of great harm. Ultraviolet-visible (UV-Vis) spectroscopy is an effective method to detect water contamination. However, surface water quality is influenced by hydrological fluctuation caused by rain, change of flow, etc., leading to changes of spectral characteristics over time. In the process of contamination detection, such changes cause confusion between hydrological fluctuation spectra and contaminated water spectra, thus increasing the false alarm rate. Besides, missing alarms of contaminated water is a common problem when the signal-to-noise ratio is low. In this paper, a dynamic multivariable outlier sampling rate detection (DM-SRD) algorithm is proposed. A dynamic updating strategy is introduced to increase adaptability to hydrological fluctuation. Additionally, multiple outlier variables are adopted as outlying degree indicators, which increases the accuracy of contamination detection. Two experiments were carried out using spectra collected from real surface water sites and hydrological fluctuation was constructed. To verify the effectiveness of the DM-SRD method, a comparison with the static SRD method and spectral match method was conducted. The results show that the accuracy of the DM-SRD method is 97.8%. Compared with the other two detection methods, DM-SRD significantly reduces false alarm rate and avoids missing alarms. Additionally, the results demonstrate that whether the database contained prior information on hydrological fluctuation or not, DM-SRD maintained high detection accuracy, which indicates great adaptability and robustness.

Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing

Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study’s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML’s instrumental role in enhancing water infrastructure’s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation.

From Radio to In-Pipe Acoustic Communication for Smart Water Networks in Urban Environments: Design Challenges and Future Trends

The smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.

Ratiometric probe of PQDs/R6G: Achieving high sensitivity and precision in contaminant detection

This paper discusses the utilization of water stable, biocompatible CsPbBr3 perovskite quantum dots (BPQDs) as donors in combination with Rhodamine 6G (R6G) as acceptors, as ratiometric probe, for detection of pathogens and water contaminants via the fluorescence resonance energy transfer (FRET) mechanism. Biocompatibility of BPQDs was attained through phase engineering using succinic acid (SA) ligand which at appropriate pH provides carboxylic environment on the outer surface, facilitating transfer to aqueous phase. Photoluminescence (PL) studies on ratiometric probe reveal quenching of BPQDs’ emission with a simultaneous enhancement of R6G fluorescence, representing a singlet energy transfer. The probe was successfully validated for ratiometric sensing of a model carcinogen (thioacetamide (TAA)) and a model pathogen (Escherichia coli (E. coli)) and the detection limit achieved was 1.8 CFU/mL for E. coli sensing and 1.5 µM for TAA sensing. Dual emission and its quenching mechanism involved in the detection of aforesaid analytes have been elaborately discussed and elucidated, indicating that quenching mechanisms are different for the two analytes. Our work not only demonstrates an important milestone for the integration of perovskite quantum dot in clinical diagnostics but also as an easy, unique, rapid and accurate E. coli detection technique that is complimentary to the rather complicated high throughput and high-sensitivity approaches that are existing.

Microfluidic Detection of Water Contamination Using THz Antenna with Metamaterials: Analysis of Silver Nanospheres in the THz Spectrum

Microfluidic Detection of Water Contamination Using THz Antenna with Metamaterials: Analysis of Silver Nanospheres in the THz Spectrum

Detection of Water Contaminants by Organic Transistors as Gas Sensors in a Bottom-Gate/Bottom-Contact Cross-Linked Structure.

Detecting volatile organic compounds is a fundamental step in water quality analysis. Methylisoborneol (MIB) provides a lousy odor to water, whereas geosmin (GEO) is responsible for its sour taste. A widely-used technique for their detection is gas-phase chromatography. On the other hand, an electronic nose from organic thin-film transistors is a cheaper and faster alternative. Poly(2,5-bis(3-tetradecyl-thiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT-C14) features semiconducting properties suitable for organic electronics. However, in order to expose the active layer in a bottom-gate transistor structure with photolithographically patterned electrodes, a cross-linked dielectric such as poly(4-vinyl phenol) (PVP) is necessary. In this work, the cross-linking was demonstrated using FTIR and Raman spectroscopies, as well as high-k capacitors with a dielectric constant of 5.3. The presence of enhanced crystallinity with terrace formation in the semiconducting film was confirmed with UV-visible spectrophotometry, atomic force microscopy, and X-ray diffraction. Finally, for the first time, a PBTTT-C14 transistor on cross-linked PVP was shown to respond to isoborneol with a sensitivity of up to 6% change in mobility per ppm. Due to its similarity to MIB, a system comprising these sensors must be investigated in the future as a tool for sanitation companies in real-time water quality monitoring.

Easy-to-prepare graphene-based inkjet-printed electrodes for diclofenac electrochemical sensing

Inkjet-printed electrodes (IPEs) from flexible Kapton® as substrate and easy to prepare graphene oxide (GO)-based inks (K/GO) were prepared using a standard desktop printer. Subsequently, they were submitted to a post-processing thermal treatment (K/GO_TR400) and assessed as disposable working electrodes (WEs) for diclofenac (DCF) indirect electrochemical detection at pH 7.0 (close to physiological pH value). DCF monitoring is attracting much interest, due to its classification as contaminant of emerging concern in water sources. The performance of the printed active sensor material was compared to that of thermally reduced (at 400 °C) graphene oxide (TRGO-400, in a powder form), which has been successfully used as glassy carbon electrode (GCE) modifier for the detection of DCF. This electrode worked linearly within the concentration range between 5 and 25 μM with an LOD value of 2.25 μM. Moreover, the morphology and chemical composition of the obtained IPEs were characterized by means of scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) and compared with GCE/TRGO-400. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) measurements using a miniaturized 3-electrode cell of the post-processed IPEs showed a promising electrochemical performance for DCF sensing. Thus, this study demonstrated the effective partial recovery of the valuable properties of graphene on the IPEs after the thermal treatment and the successful production of flexible and disposable electrodes for target analyte detection. Therefore, this is a promising alternative to conventional modified GCEs, as it offers a facile approach for the fabrication of cutting edge graphene-based electrodes and widens the available portfolio of electrochemical sensors towards detection of water contaminants of emerging concern.

Review on the Use of Magnetic Nanoparticles in the Detection of Environmental Pollutants

Magnetic nanomaterials (MNPs) have been widely used in the detection of pollutants in the environment because of their excellent nano effect and magnetic properties. These intrinsic properties of MNPs have diversified their application in environmental contaminant detection. In this paper, the research status quo of the use of MNPs in detecting organic and inorganic contaminants from wastewater and soil is reviewed. The preparation method and modification technology of magnetic nanoparticles are also described in detail. The application prospect of magnetic nanoparticle composites in the detection of contaminants in water and soil is discussed. Compared with traditional detection methods, MNPs are more accurate and efficient in pollutant enrichment. Moreover, the biological synthesis of MNPs was proven to be eco-friendly and aided in sustainable development. The study shows that MNPs have good application prospects in soil pollution detection, but the mechanism still needs to be investigated to realize their popularization and application.

Ultra-trace level detection of nickel(II) ions in real water samples using surface anchored recognition probes at liquid crystal sensing platform

Rational fabrication approach for ultra-trace level detection of nickel (Ni(II)) ions in water samples has been developed using surface anchored recognition probes at liquid crystal sensing platform. In this system, the optimized volume per cent (5:1, %v/v) of synthesized surface anchored diacetate probes along with DMOAP decorated on glass substrate is able to perfectly align 5-cyanobiphenyl (5-CB) liquid crystal (LC) giving dark textural pattern. On the other hand, the fabricated probe is able to selectively reorient the transitions of the decorated LC molecules in the presence of Ni(II) ions too, leading to bright textural pattern under cross polarizers. The optimized and fabricated system demonstrated high selectivity and sensitivity, detecting Ni(II) ions as low as 0.08 μM (∼4.7 ppb) in distilled water and 0.8 μM (∼47 ppb) in real water with results observable to the naked eye. Moreover, the sensor can detect Ni(II) in drinking water, providing an approach to develop LC-based sensor for detection of natural water contaminants with simplified operating techniques.

Coordination Unsaturation and Basic Site-Immobilized Nanochannel in a Chemorobust MOF for 3-Fold-Increased High-Temperature Selectivity and Fixation of CO2 under Mild Conditions with Nanomolar Recognition of Roxarsone.

A multifaceted metal-organic framework (MOF) with task-specific site-engineered pores can promise high-temperature and moisture-tolerant capture and non-redox fixation of CO2 under mild conditions as well as ultrasensitive detection of carcinogenic contaminants in water. Herein, we report a pillar-bilayered MOF that holds a nanochannel with contrasting functionalities for both these sustainable applications with improved performance characteristics. The twofold entangled robust framework exhibits CO2 adsorption at elevated temperatures with considerable MOF-gas interaction. Interestingly, CO2 selectivity unveils nearly a 3-fold improvement upon the rise of temperature, affording a CO2/N2 value of 820 at 313 K, which outperforms many porous adsorbents. Additionally, breakthrough simulation establishes complete separation and attests the potential of this MOF in the separation of flue gas mixture. Importantly, minor CO2 loss during multiple capture-release cycles and under a relative humidity of 75% promise practical usability of the material. Density functional theory (DFT) not only portrays the atomistic level snapshots of temperature-triggered CO2 inclusion inside this microporous vessel alongside the role of diverse CO2-philic sites but also validates the basis of N2-phobicity of an azo-functionalized linker on such increased selectivity. The guest-free MOF further demonstrates non-redox and recyclable CO2 fixation with wide epoxide tolerance under solvent-free mild conditions and even works at atmospheric pressure and room temperature. The crucial roles of high-density acid-base sites in both adsorption and catalysis are supported by control experiments and by comparing the activity of an unfunctionalized MOF. The hydrolytic stability and strong luminescence signature benefit the framework in aqueous-phase selective and fast responsive detection of detrimental roxarsone (ROX) with high quenching (7.56 × 104 M-1) and very low sensitivity (68 nM). Apart from varying degrees of an energy-transfer mechanism, the fluorosensing of ROX is comprehensively supported by in-depth DFT studies that manifest alteration of MOF energy levels in the presence of organoarsenic compounds and depict MOF-analyte supramolecular interactions.