Detection Of Pollutants In Water Research Articles

Microfluidic devices to monitor water pollution

Microfluidic devices offer a promising future for monitoring water pollution caused by heavy metals, especially as the world continues to develop and the dangers of pollutants increases. This highlights the importance of developing these devices. These devices operate within the dynamics of fluids and quantify pollutants with numerous advantages, such as high sensitivity and specificity. They can also be integrated with mini sensors alongside analytical techniques. This study provides a brief overview of the types of microfluidic devices, such as polydimethylsiloxane (PDMS) and microfluidic paper-based (µPADs), and their application in pollutant detection. Microfluidic devices are associated with analytical methods such as spectrometric, colorimetric, and electrochemical techniques. Their importance lies in their simple manufacturing, rapid detection capabilities, and portability. Additionally, these devices can be updated to meet current needs in water pollution detection by integrating various analytical methods and enhancing these methods with programs that provide on-site results. There for microfluidics are currently of great importance due to their ease of manufacturing and applicability to various analytical methods, particularly for detecting pollutants in water. Many studies highlight the extraordinary potential of paper-based devices, which are the easiest to manufacture among all microfluidic devices and are not subject to stringent engineering and physical constraints. Most importantly, they can utilize colorimetric detection methods, providing instant results visible to the naked eye. This study demonstrates these advantages and suggests the potential for expanding their applications in medical, environmental, and biological fields.

Stable Europium(III) Metal-Organic Framework Fluorescence Probe for Intelligent Visualization Detection of Gossypol and Nitrofuran Antibiotics in Real Samples.

Gossypol (Gsp) and antibiotics present in water bodies become organic pollutants that are harmful to human health and the ecological environment. Accurate and effective detection of these pollutants has far-reaching significance in many fields. A new three-dimensional metal-organic framework (MOF), {[Eu3(L)2(HCOO-)(H2O)3]·2H2O·2DMF}n (Eu-MOF), was synthesized from 3,5-bis(2,4-dicarboxylphenyl)nitrobenzene (H4L) ligand and Eu3+ via the solvothermal method in this paper. The Eu-MOF demonstrates strong red fluorescence and can remain stable in different pH solutions. The MOF fluorescence probe could detect organic pollutants through the "shut-off" effect, with a fast response speed and a low detection limit [Gsp, nitrofurantoin (NFT), and nitrofurazone (NFZ) for 0.43, 0.38, and 0.41 μM, respectively]. During the testing process, Eu-MOF exhibited good selectivity and recoverability. Furthermore, the mechanism of fluorescence quenching was investigated, and the recoveries were also good in real samples. This paper introduced a deep learning model to recognize the fluorescence images, a portable intelligent logic detector designed for real-time detection of Gsp by logic gate strategy, and an anticounterfeiting mark prepared based on inkjet printing. Importantly, this work provides a new way of thinking for the detection of organic pollutants in water with high sensitivity and practicality by combining the fluorescence probe with machine learning and logical judgment.

WATER QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUE

The quality of water is a critical parameter that affects human health, aquatic ecosystems, and environmental sustainability. The prediction of water quality using machine learning techniques has emerged as a promising solution for early detection and management of water pollution. This project focuses on developing a predictive model that leverages historical water quality data to forecast future water quality indices. Various machine learning algorithms, including regression and classification techniques, will be employed to analyze parameters such as pH, turbidity, dissolved oxygen, and contaminant levels. By training the model on a comprehensive dataset, the system aims to provide accurate and timely predictions, enabling proactive measures to be taken to ensure safe water supplies. The implementation of this model can significantly aid regulatory bodies and water management authorities in monitoring and maintaining water quality standards, ultimately contributing to public health and environmental conservation.

Detection of Zn Water Pollution by a Biosensor Based on Alkaloids Derived from Iraqi Catharanthus Roseus

Detection of Zn Water Pollution by a Biosensor Based on Alkaloids Derived from Iraqi Catharanthus Roseus

Research on COD parameter in water based on the combination of UV absorption photometry and laser spectroscopy

Chemical Oxygen Demand (COD) is widely utilized as a representative and required measurement indicator of water pollution as well as water quality monitoring. The spectral technology is an important development direction in water quality detection. In this paper, a novel COD monitoring instrument is designed based on ultraviolet (UV) absorption photometry and laser spectroscopy. The instrument consists of CODcr and CODmn detection modules. The CODcr module is based on the Lambert-Beer law to measure UV absorption of organic matter in water, which can be used to measure the COD parameter in high concentration organic pollution in water. The CODmn module is based on the ratio method of fluorescence and Raman to analyze the spectral signals generated by the laser-excited water body, which can determine the COD parameter in low contaminated water. By analyzing the CODcr and CODmn parameters together, the comprehensive evaluation of organic pollutants in water can be realized. In the experiment, the CODcr and CODmn modules are respectively calibrated with the potassium hydrogen phthalate (KHP) solution as well as the mixed solution of humic acid sodium and glucose. Then the stabilities of the two detection modules are tested, and the detected result drifts are stable within six days. In addition, to verify the reliability of measuring results in complex environments, we test the instruments at 10 monitoring sites of the Sow River in Weihai city. The results of on-site monitoring are highly consistent with those of national standard method. The study result shows that the instrument developed in this paper can measure both CODcr and CODmn simultaneously, which can expand the COD detection range, and provide a new method for the comprehensive detection of organic pollutants in water.

Multicolored Carbon Quantum Dots-Based Expanded Fluorescence Strategy for High-Throughput Detection of Various Water Pollutants

Multicolored Carbon Quantum Dots-Based Expanded Fluorescence Strategy for High-Throughput Detection of Various Water Pollutants

Water pollution classification and detection by hyperspectral imaging

This study utilizes spectral analysis to quantify water pollutants by analyzing the images of biological oxygen demand (BOD). In this study, a total of 2545 images depicting water quality pollution were generated due to the absence of a standardized water pollution detection method. A novel snap-shot hyperspectral imaging (HSI) conversion algorithm has been developed to conduct spectral analysis on traditional RGB images. In order to demonstrate the effectiveness of the developed HSI algorithm, two distinct three-dimensional convolution neural networks (3D-CNN) are employed to train two separate datasets. One dataset is based on the HSI conversion algorithm (HSI-3DCNN), while the other dataset is the traditional RGB dataset (RGB-3DCNN). The images depicting water quality pollution were categorized into three distinct groups: Good, Normal, and Severe, based on the extent of pollution severity. A comparison was conducted between the HSI and RGB models, focusing on precision, recall, F1-score, and accuracy. The water pollution model's accuracy improved from 76% to 80% when the RGB-3DCNN was substituted with the HSI-3DCNN. The results suggest that the HSI has the capacity to enhance the effectiveness of water pollution detection compared to the RGB model.

Detection of water pollutants using super-hydrophobic porous silicon-based SERS substrates.

Super hydrophobic porous silicon surface is prepared using a wet chemical synthesis route. Scanning electron microscopic investigation confirms a correlation between pore size and reaction time. SERS substrates are prepared by silver nanoparticle deposition on porous silicon surface. They exhibit excellent characteristics in terms of sensitivity, reproducibility, stability, and uniformity. They could detect rhodamine 6G in femtomolar range with SERS enhancement factor of ~ 6.1 × 1012, which is best ever reported for these substrates. Molecule-specific sensing of water pollutants such as methylene blue, glyphosate, andchlorpyrifos, is demonstrated for concentrations well below their permissible limits along with excellent enhancement factors. Porous silicon substrate functionalized with Ag nanoparticles demonstrates to be a promising candidate for low-cost, long-life, reliable sensors for environmental conservation applications.

A "2-in-1" Bioanalytical System Based on Nanocomposite Conductive Polymers for Early Detection of Surface Water Pollution.

This work proposes an approach to the formation of receptor elements for the rapid diagnosis of the state of surface waters according to two indicators: the biochemical oxygen demand (BOD) index and toxicity. Associations among microorganisms based on the bacteria P. yeei and yeast S. cerevisiae, as well as associations of the yeasts O. polymorpha and B. adeninivorans, were formed to evaluate these indicators, respectively. The use of nanocomposite electrically conductive materials based on carbon nanotubes, biocompatible natural polymers-chitosan and bovine serum albumin cross-linked with ferrocenecarboxaldehyde, neutral red, safranin, and phenosafranin-has made it possible to expand the analytical capabilities of receptor systems. Redox polymers were studied by IR spectroscopy and Raman spectroscopy, the contents of electroactive components were determined by atomic absorption spectroscopy, and electrochemical properties were studied by electrochemical impedance and cyclic voltammetry methods. Based on the proposed kinetic approach to modeling individual stages of bioelectrochemical processes, the chitosan-neutral red/CNT composite was chosen to immobilize the yeast association between O. polymorpha (ks = 370 ± 20 L/g × s) and B. adeninivorans (320 ± 30 L/g × s), and a bovine serum albumin (BSA)-neutral composite was chosen to immobilize the association between the yeast S. cerevisiae (ks = 130 ± 10 L/g × s) and the bacteria P. yeei red/CNT (170 ± 30 L/g × s). After optimizing the composition of the receptor systems, it was shown that the use of nanocomposite materials together with associations among microorganisms makes it possible to determine BOD with high sensitivity (with a lower limit of 0.6 mg/dm3) and detect the presence of a wide range of toxicants of both organic and inorganic origin. Both receptor elements were tested on water samples, showing a high correlation between the results of biosensor analysis of BOD and toxicity and the results of standard analytical methods. The results obtained show broad prospects for creating sensitive and portable bioelectrochemical sensors for the early warning of environmentally hazardous situations based on associations among microorganisms and nanocomposite materials.

Design of magnetic photonic crystal microdroplet for sensitive detection of cationic organic pollutants by the coupled resonance effect

Photonic crystals (PCs), periodically arranged nanoparticles, have emerged with extraordinary optical properties for light manipulation owing to their photonic band gaps (PBGs). Here, a novel strategy and method was developed for efficient enrichment and sensitive detection of cationic organic pollutants in water. Size-controlled Fe3O4@poly (4-styrenesulfonic acid-co-maleic acid) (Fe3O4@PSSMA) was prepared, and high surface charge were formed with the coating of PSSMA layer on the surface of Fe3O4, which could be used for adsorption and removal of cationic organic pollutants. The Fe3O4@PSSMA after adsorbing cationic organic pollutant were assembled to magnetic photonic crystal microdroplet (MPCM) structure in an external magnetic field, which was used as surface-enhanced Raman scattering (SERS) substrate. By coupling the magnetically tuned PBGs with Raman laser wavelength, the light utilization efficiency can be improved and the coupled resonance effect was greatly enhanced. The enhancement factor (EF) of MB was more than 800 attributing to the dual function of enrichment and coupled resonance effect of MPCM. The developed analytical strategy is the first time to use MPCM as a SERS substrate to realize the sensitive detection of 10 nmol L−1 MB in real water, which greatly improves the application of MPCM in the field of contaminant analysis and detection in water.

SISTEM CERDAS PENGURAS AIR AKUARIUM SECARA OTOMATIS BERBASIS INTERNET oF THINGS (IoT) MENGGUNAKAN ALGORITMA C4.5 pada PLATFORM BLYNK

In general, fishkeeping has been a widely cherished activity among communities since ancient times, owing to its ease of maintenance and care, which encourages many individuals to engage in it. However, due to busy schedules or unforeseen circumstances, checking the water conditions in aquariums often poses a challenge. Therefore, there is a need for a tool capable of automatically controlling and draining aquarium water based on IoT technology using the C4.5 algorithm on the Blynk platform. This system can drain water if the aquarium water becomes cloudy, raise the temperature if it drops below optimal levels, and limit water filling. The ESP8266 and ESP32-S modules are utilized to receive, process, and transmit data to the aquarium owner via Wi-Fi. Relays serve as electrical power distributors for supporting components such as water pumps, water heaters, and solenoid valves. Testing results indicate that the water condition control component effectively operates in line with the system's functions and objectives. The existence of this tool can assist fishkeepers in maintaining real-time aquarium water quality, detecting water pollution, and monitoring the health of fish within the aquarium.

Detection of metallic pollutants in waste water using bio sensors and its remediation

AbstractWater pollution has emerged as the biggest threat to the sustenance of human civilization and the environment. With the increase in rapid industrialization and urbanization, there is a considerable increase in the influx of complex and diverse pollutants in water bodies. Hence continuous and efficient monitoring is required to detect the pollutants in the aquatic environment. Either preventing or detecting and subsequent elimination of these pollutants is of utmost importance for the health of the aquatic ecosystem. Biosensors have proved to be efficient in detecting these pollutants in a better way as compared to other conventional methods. Sensors are devices that have the ability to detect changes in the physical, chemical, or biological domains and produce a signal that can be measured. The distinguisher element in it minimizes noise caused by other components in the analyte mixture by being able to detect a specific analyte or set of analytes. This current review is focused on the role of biosensors in detecting metallic pollutants in water bodies. It also discusses the process of remediation of metallic pollutants in the aquatic environment.

Package-Less Liquid Phase Sensing Using Surface Acoustic Waves on Lithium Tantalate Oxide.

Surface acoustic wave (SAW) transducers propagating shear waves are compatible with sensing chemical compounds in a liquid phase. However, if the liquid surrounding the sensor possesses a higher permittivity than the piezoelectric substrate, then the interdigitated electrodes for converting the incoming electromagnetic wave to acoustic waves are susceptible to capacitive short-circuiting, leading to excessive insertion losses. By using high-permittivity lithium tantalate oxide (LTO), we demonstrate chemical sensing in water without the need for dedicated microfluidic packaging. Nevertheless, the gravimetric sensitivity of these package-less transmission Love-mode delay lines remains comparable to that of low-permittivity quartz when appropriately tuning the guiding layer of thin film to confine energy to the surface in a Love mode. We extend the transmission line gravimetric sensitivity measurement to a reflective delay line geometry for passive transducers that can be wirelessly probed. For instance, ground-penetrating radar (GPR) can be used for subsurface sensing, here targeting water pollution detection, operating in the 100-500-MHz range. This center frequency was selected as a tradeoff between penetration depth (lower frequency) and antenna size (smaller at higher frequency). Nonspecific binding of proteins detection is shown in the context of biosensing applications.

Fluorescence response of metalo-hydrogen-bonded organic framework (MHOF) with room temperature phosphorescence to organic pollutants

For health, environmental and safety reasons, it is necessary to conduct highly sensitive detection of organic contaminant antibiotics (sulfathiazole) and nitro explosive drugs (o-nitroaniline) in water. In this work, metal-hydrogen bonded organic framework (MHOF), {[Co(Ad)2(H2O)4]·(H2L)·2.5H2O}n (1), was synthesized by a one-pot hydrothermal method with 2,6-bis(3,5-dicarboxyphenoxy) pyridine (H4L) and adenine (Ad). This MHOF shows a 4-fold interpenetrated three-dimensional (3D) network structure. Importantly, 1 has excellent fluorescence properties and can be used as fluorescent chemical sensor to detect sulfathiazole (STZ) and 2-nitroanilin (2-NA) with limit of detection (LOD) 203 ppb and 6.8 ppb, respectively. By UV-vis absorption/emission spectroscopic measurements and DFT calculations, it is found that photoinduced electron transfer (PET) and fluorescence resonance energy transfer (FRET) are two potential quenching processes. This is the first report of metal-hydrogen bond organic frameworks (MHOFs) as a promising new fluorescent chemical sensor for the detection of organic pollutants in water.

Advantages of the low-temperature secondary emission mass spectrometry in analysis of metal ions in water samples revisited

A problem of necessity of concentrating trace admixtures of metal ions required for ecological water analysis can be overcome by harnessing a physical phenomenon of phase separation in aqueous solutions during their freezing. It is shown that the accumulation of metal-containing solutes in the channels between ice crystallites in the frozen solids is sufficient for their successful detection by means of low-temperature secondary emission mass spectrometry. Sufficiency of microliters volumes of water is an advantage of such an approach. Observation of various types of metal ions in frozen water samples is demonstrated on the examples of tap water, sea water, snow and a medicinal preparation. Revisiting and summation of physical basics of mass spectrometric examining of frozen water-inorganic salt solutions and estimates of advancement of mass spectrometric instrumentation permit us to propose a workflow for accelerated and simplified mass spectrometric detection of metal pollutants in water.

Comprehensive Analysis of Phylogenetic Relationship and Optimal Codons in Mitochondrial Genomes of the Genus Pseudogastromyzon

As indicator organisms for water pollution detection, Pseudogasteromyzon species play a vital role in aquatic environment monitoring. We have successfully sequenced the mitogenomes of P. fasciatus jiulongjiangensis and P. myersi and downloaded the mitogenomes of nine other Pseudogastromyzon fish on GenBank to conduct a detailed comparative analysis of their phylogenetic relationships and evolutionary history. The findings revealed a conservation in both gene composition and gene order. Except for the trnS1 gene lacking dihydrouracil arms, the other 21 tRNAs showed the typical clover-leaf secondary structure. According to the ΔRSCU method, we identified the seven most abundant optimal codons: CUA, GUA, CCA, CAA, GAA, AGC, and GGC. The construction of maximum parsimony, maximum likelihood, and Bayes trees yielded congruent topologies, and the 11 Pseudogastromyzon species were clustered into two major clusters. Among them, one of which was composed of P. fangi, P. changtingensis changtingensis, and P. changtingensis tungpeiensis, while the remaining eight species formed another cluster, further subdivided into five smaller clusters. Distinct clusters formed between P. fasciatus jiulongjiangensis and P. meihuashanensis, P. cheni and P. peristictus, and P. laticeps and P. lianjiangensis, and the remaining two species were clustered separately, thereby enhancing our understanding of them. Furthermore, our analysis results of divergence times revealed that these 11 Pseudogasteromyzon species underwent rapid differentiation in the Pleistocene epochs. Overall, our study sheds light on the phylogenetic relationship and evolutionary history of Pseudogasteromyzon species, providing a necessary knowledge foundation for further understanding the intricacies of an ecosystem health assessment.

A deep learning-based biomonitoring system for detecting water pollution using Caenorhabditis elegans swimming behaviors

Caenorhabditis elegans is a representative organism whose DNA structure has been fully elucidated. It has been used as a model organism for various analyses, including genetic functional analysis, individual behavioral analysis, and group behavioral analysis. Recently, it has also been studied as an important bioindicator of water pollution. In previous studies, traditional machine learning methods, such as the Hidden Markov Model (HMM), were used to determine water pollution and identify pollutants based on the differences in the swimming behavior of C. elegans before and after exposure to chemicals. However, these traditional machine learning models have low accuracy and a relatively high false-negative rate. This study proposes a method for detecting water pollution and identifying the types of pollutants using the Long Short-Term Memory (LSTM) model, a deep learning model suitable for time-series data analysis. The swimming activities of C. elegans in each of the image frames are characterized by the Branch Length Similarity (BLS) entropy profile. These BLS entropy profiles are converted into input vectors through additional preprocessing using two clustering methods. We conduct experiments using formaldehyde and benzene at 0.1 mg/L each, with observation time intervals varying from 30 to 180 s. The performance of the proposed method is compared with that of the previously proposed HMM approach and variants of LSTM models, such as Gated Recurrent Unit (GRU) and Bidirectional LSTM (BiLSTM).

Surface functionalization of a chalcogenide IR photonic sensor by means of a polymer membrane for water pollution remediation.

Rapid, simultaneous detection of organic chemical pollutants in water is an important issue to solve for protecting human health. This study investigated the possibility of developing an in situ reusable optical sensor capable of selective measurements utilizing a chalcogenide transducer supplemented by a hydrophobic polymer membrane with detection based on evanescent waves in the mid-infrared spectrum. In order to optimise a polyisobutylene hydrophobic film deposited on a chalcogenide waveguide, a zinc selenide prism was utilized as a testbed for performing attenuated total reflection with Fourier-transform infrared spectroscopy. To comply with the levels mentioned in health guidelines, the target detection range in this study was kept rather low, with the concentration range extended from 50 ppb to 100 ppm to cover accidental pollution problems, while targeted hydrocarbons (benzene, toluene, and xylene) were still detected at a concentration of 100 ppb. Infrared measurements in the selected range showed a linear behaviour, with the exception of two constantly reproducible plateau phases around 25 and 80 ppm, which were observable for two polymer film thicknesses of 5 and 10 μm. The polymer was also found to be reusable by regenerating it with water between individual measurements by increasing the water temperature and flow to facilitate reverse exchange kinetics. Given the good conformability of the hydrophobic polymer when coated on chalcogenide photonic circuits and its demonstrated ability to detect organic pollutants in water and to be regenerated afterwards, a microfluidic channel utilising water flow over an evanescent wave optical transducer based on a chalcogenide waveguide and a polyisobutylene (PIB) hydrophobic layer deposited on its surface was successfully fabricated from polydimethylsiloxane by filling a mold prepared via CAD and 3D printing techniques.

Ultrasensitive detection of aromatic water pollutants through protein immobilization driven organic electrochemical transistors.

Xenobiotic aromatic water pollutants pose an extreme threat to environmental sustainability. Due to the lack of detectable functional groups in these compounds and scarcity of selective bio-recognition scaffolds, easy-to-use sensing strategies capable of on-site detection remain unavailable. Herein, to address this lacune, we entail a strategy that combines biosensor scaffolds with organic electronics to create a compact device for environmental aromatic pollution monitoring. As proof of principle, a sensor module capable of rapid, economic, reliable, and ultrasensitive detection of phenol down to 2 ppb (0.02 μM) was designed wherein biosensing protein MopR was coupled with an organic electrochemical transistor (OECT). For effective interfacing of the sensing scaffold MopR, graphene oxide (GO) nanosheets were optimized as a host immobilization matrix. The MopR-GO immobilized sensor module was subsequently substituted as the gate electrode with PEDOT:PSS serving as an organic semiconductor material. The resulting OECT sensor provided a favourable microenvironment for protein activity, maintaining high specificity. Exclusive phenol detection with minimal loss of sensitivity (<5% error) could be achieved in both complex pollutant mixtures and real environmental samples. This fabrication strategy that amalgamates biological biosensors with organic electronics harnesses the potential to achieve detection of a host of emerging pollutants.

Carbohydrate polymer-supported metal and metal oxide nanoparticles for constructing electrochemical sensors

Metal- and metal oxide-carbohydrate polymers are considered a promising tool for electrochemical detection of biological analytes and water pollutants.