Water Pollution Monitoring Research Articles

A novel hybrid deep learning model for real-time monitoring of water pollution using sensor data

This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNN-LSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration.

Diversity and structure of microalgae and their potential use in monitoring water pollution and eutrophication in the Loumbila reservoir (Burkina Faso)

Diversity and structure of microalgae and their potential use in monitoring water pollution and eutrophication in the Loumbila reservoir (Burkina Faso)

Water Quality Monitoring using ONLIMO Automatic Water Monitoring Device in Water Pollution Monitoring Efforts

Water Quality Monitoring using ONLIMO Automatic Water Monitoring Device in Water Pollution Monitoring Efforts

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.

Integrating vision-based AI and large language models for real-time water pollution surveillance.

Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man-made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface-level water pollution in local water bodies, an informative real-time vision-based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi-model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run-offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi-model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention. PRACTITIONER POINTS: Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information. Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste. Supports various modules and environments, including drones and mobile apps for broad monitoring. Educates on environmental healthand alerts authorities about waterpollution.

Validation of the method for determining priority phthalates by GC-MS at trace concentrations in surface water with a background pollution level

A methodology for determining priority phthalates (PP) at trace concentrations in surface waters with a background pollution level has been validated. Lake Baikal, the largest natural reservoir that retains up to 20% of the world’s fresh surface waters, was chosen as a natural model for the study. Baikal is characterized by a minimal content of suspended organic matter, a low degree of mineralization and a background level of organic pollutants. Four priority phthalates were found in Baikal water: dimethyl phthalate, diethyl phthalate di-n-butyl phthalate and di-(2-ethylhexyl) phthalate present in the concentration range from 0.01 to 0.66 μg/liter. The method for phthalate determination includes a single liquid-liquid extraction of phthalates (Vsample = 1 liter) and direct analysis of the extracts by GC-MS. Considering the minimum content of suspended particles in water and the use of high sensitivity of the mass spectrometry, the stages of sample filtration and concentration of extracts are excluded from the procedure. Deuterated phthalates are used as surrogate internal standards for the quantitative determination of priority phthalates. The laboratory background of phthalates was assessed using reagent-blank methods, exhaustive multiple extraction and subsequent exclusion of the resulting value from the determination result as a systematic error. The limits of phthalate determination (0.01 – 0.17 μg/liter) and the error of determination (±δ from 12 to 38%) were assessed in the concentration range from 0.01 to 0.66 μg/liter. The method was validated during monitoring of persistent organic pollutants in water of Lake Baikal for the period 2015 – 2023.

The Missing Piece of the Puzzle: How Different Water-related Public Concerns Contribute to Water Governance in China.

Public participation is crucial to improving the performance of water governance systems, especially in a governance model dominated by a top-down hierarchical structure. Public concerns, as a vital form of public participation, have been acknowledged as an essential component in contributing to water governance. However, few studies explore the varying effects of diverse public concerns in leading to different water governance outcomes. This study addresses this gap by exploring the direct and indirect effects of water-related public concerns on water pollution control and governments' pollution mitigating actions, using citizens' water-related posts crawled from China's social media. Results show that public water-related concerns contribute to water governance both through its direct effects on reducing water pollution and indirect effects by promoting policy actions. Specifically, the concerns related to water pollution hazards, water pollution monitoring, prevention products, and water pollution control measures have more positive impacts on water pollution reduction compared to other types of concern. Meanwhile, public concerns demonstrate stronger effects in triggering economic-related and infrastructure-related water pollution mitigation actions. This study provides nuanced insights to understand the role of public participation in improving water governance, the findings of which are insightful to enhance accountability of water governance systems through a bottom-up approach.

Highly sensitive water pollution monitoring using colloid-processed organic photodetectors

Highly sensitive water pollution monitoring using colloid-processed organic photodetectors

Time-series variation in the locomotor behavior and vocal traits of Japanese medaka (Oryzias latipes) acutely exposed to organophosphorus pesticide chlorpyrifos

Organophosphorus pesticides (OPs), such as chlorpyrifos (CPF), are the most commonly used pesticides worldwide. Considering that OPs will eventually enter aquatic ecosystems due to runoff from agricultural lands, accidental leakage, and other unforeseen emergencies, monitoring water pollution of those substances is crucial for environmental protection and public health. In this study, Japanese medaka (Oryzias latipes) were exposed to CPF (0.03, 0.06, and 0.12 mg/L) for 6 h, and the time-series variations in their locomotor behavior and vocal traits were investigated. Compared with that measured before exposure, significantly changed locomotor behavior and vocal traits in Japanese medaka exposed to CPF could be observed at 4 h after exposure and thereafter, and the pattern of behavioral changes depends on the CPF concentrations. Exposure to CPF also changed the frequency-sound pressure level curve of Japanese medaka at 6 h after exposure, especially at 0.12 mg/L. Moreover, CPF exposure could significantly inhibit the acetylcholinesterase (AChE) activity in the brains and eyes of medaka, which exhibited significant correlations with the variation of locomotor behavioral and vocal traits. Considering that inhibiting the AChE activity is the primary mechanism underlying the neurobehavioral toxicity of all OPs, our finding suggested that simultaneously monitoring changes in the locomotor behavioral and vocal traits has a high potential to reflect the pollution of organophosphorus substances.

Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm2; ∼500 nm/pixel) and could simultaneously capture the projection image of >3-μm microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the number and length of >10-μm linear and branched MPFs derived from melamine cleaning sponges in each image (∼0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available.

Application of big data analysis in water pollution monitoring

Contemporarily, the quick development of an abundant amount of big data analysis technologies has brought great convenience to individuals' everyday existence. In terms of environmental protection, especially water pollution monitoring, this technological progress is particularly critical. As the global demand for clean water resources grows and global industrialization intensifies pollution of the water environment, the adoption of advanced data analysis technologies has become critical. Among the vast array of machine learning architectures, three particularly stand out due to their significance and widespread adoption: the artificial neural network (ANN), which serves as a foundational pillar in understanding complex data patterns; the multi-layer perceptron neural network (MLPNN), a sophisticated evolution that allows for deeper computations and learning; and the adaptive neuro-fuzzy inference system (ANFIS), which brilliantly combines neural and fuzzy logic principles for intricate problem solving. These models not only have high accuracy due to their wide application, but they still have their own limitations. This article aims to introduce the methods, basic principles, and application scenarios of these models. In addition, this article also compares the advantages and limitations of these machine learning models, thereby providing some new ideas for future improvements and innovations in model algorithms, application scenarios, and integration.

On-line biological oxygen demand (BOD) monitoring using a microbial membrane reactor

An on-line biological oxygen demand (BOD) monitoring instrument, utilizing an in-situ microbial membrane reactor, automates membrane cultivation, calibration, and rapid BOD detection, eliminating reagent pollution is presented. The core instrument used was an STM32F103RCT6 microcontroller embedded in the FreeRTOS real-time operating system. A series of practical application tests were carried out by the instrument. The results demonstrate that the instrument provides good stability and accuracy in detecting multiple concentrations of glucose and glutamic acid (GGA) standards, with a relative error range and relative standard deviation range from −3.01 to 11.88% and 4.19 to 6.69%, respectively. The tested water samples had a relative error range from −4.93 to 13.37%. The on-line monitoring instrument for BOD has been tested for almost three months and has shown stable performance, high accuracy, and real-time, in-situ, and on-line BOD detection. In the past three months of application testing, the on-line biological oxygen demand monitoring instrument has shown stable performance, good accuracy, and real-time, in-situ, and on-line BOD measurements. This device has significant environmental benefits for continuous water quality monitoring and the timely monitoring of water pollution.

Smart system for water quality monitoring utilizing long-range-based Internet of Things

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service.

Enhanced dual function of Ag nanoparticles decorated one-dimensional polymorphic TiO2 composites for sustainable environmental applications

The hydrothermally synthesized rutile-TiO2 nanorods were spin-coated with anatase-TiO2 film and decorated with Ag nanoparticles via sputtering to construct Ag nanoparticles decorated heterophase TiO2 composites. By extending the sputtering duration to altering the Ag content in the TiO2 composition, we aim to investigate the correlation between the microstructures and the dual function of photoelectrochemical photocatalytic and gas-sensing performances for environmental applications. The Ag nanoparticles on the anatase/rutile-TiO2 composites facilitate photon absorption through surface plasmon resonance. Also, Ag nanoparticles contribute the hot electron to enhance the synergy effect in the anatase-TiO2/rutile-TiO2 to prevent recombination of photoinduced carriers under irradiation, thereby strengthening the photocatalytic characteristics. Furthermore, the gas sensing performance is improved due to the electronic and chemical sensitization effects caused by the Ag nanoparticles on the anatase-TiO2/rutile-TiO2 composite. This study demonstrates that tuning Ag nanoparticle content in the anatase/rutile-TiO2 composites is a promising material design strategy for use in highly efficient photoexcited and gas-sensing devices. The proposed Ag nanoparticles decorated polymorphic TiO2 composite nanorods are suitable for eliminating organic pollutants in water and ethanol pollution monitoring.

Smart polarization and spectroscopic holography for real-time microplastics identification

Optical microscopy technologies as prominent imaging methods can offer rapid, non-destructive, non-invasive detection, quantification, and characterization of tiny particles. However, optical systems generally incorporate spectroscopy and chromatography for precise material determination, which are usually time-consuming and labor-intensive. Here, we design a polarization and spectroscopic holography to automatically analyze the molecular structure and composition, namely smart polarization and spectroscopic holography (SPLASH). This smart approach improves the evaluation performance by integrating multi-dimensional features, thereby enabling highly accurate and efficient identification. It simultaneously captures the polarization states-related, holographic, and texture features as spectroscopy, without the physical implementation of a spectroscopic system. By leveraging a Stokes polarization mask (SPM), SPLASH achieves simultaneous imaging of four polarization states. Its effectiveness has been demonstrated in the application of microplastics (MP) identification. With machine learning methods, such as ensemble subspace discriminant classifier, k-nearest neighbors classifier, and support vector machine, SPLASH depicts MPs with anisotropy, interference fringes, refractive index, and morphological characteristics and performs explicit discrimination with over 0.8 in value of area under the curve and less than 0.05 variance. This technique is a promising tool for addressing the increasing public concerning issues in MP pollution assessment, MP source identification, and long-term water pollution monitoring.

A Three-Dimensional Hydrophobic Surface-Enhanced Raman Scattering Sensor via a Silver-Coated Polytetrafluoroethylene Membrane for the Direct Trace Detection of Molecules in Water.

We report a three-dimensional (3D) SERS substrate consisting of a silver nanoparticle (AgNP) coating on the skeleton-fiber surfaces of a polytetrafluoroethylene (PTFE) membrane. Simple thermal evaporation was employed to deposit Ag onto the PTFE membrane to produce grape-shaped AgNPs. The 3D-distributed AgNPs exhibit not only strong localized surface plasmon resonance (LSPR) but also strong hydrophobic performance. High-density hotspots via silver nano-grape structures and nanogaps, the large 3D interaction volume, and the large total surface area, in combination with the hydrophobic enrichment of the specimen, facilitate high-sensitivity sensing performance of such a SERS substrate for the direct detection of low-concentration molecules in water. An enhancement factor of up to 1.97 × 1010 was achieved in the direct detection of R6G molecules in water with a concentration of 10-13 mol/L. The lowest detection limit of 100 ppt was reached in the detection of melamine in water. Such a SERS sensor may have potential applications in food-safety control, environmental water pollution monitoring, and biomedical analysis.

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

Rapid determination of chromium species in environmental waters using a diol-bonded polymer-stationary column under water-rich conditions coupled with ICPMS.

Chromium speciation analysis in environmental water is of great significance for the monitoring of water pollution and assessing its influences on human health. This study proposes a rapid analytical approach for the simultaneous determination of Cr(VI) and Cr(III) in environmental waters by hydrophilic interaction chromatography (HILIC) coupled with ICPMS under a water-rich condition. 2,6-Pyridinedicarboxylic acid (PDCA) was used to unify Cr(III) species in various chemical forms into a stable Cr(III)-PDCA anion complex and then separated from Cr(VI) oxyanion on a diol-bonded polymer-based HILIC column. An aqueous mobile phase containing 50mmol L-1 ammonium acetate (pH 7.0), 2mmol L-1 PDCA, and 4% acetonitrile successfully separates chromium species as well as chloride ions. In addition, our method elutes Cr(VI) preferentially over Cr(III)-PDCA, enabling rapid determination of Cr(VI), and both chromium species were analyzed within 6.2min. The detection limits of 0.19μg L-1 for Cr(VI) and 0.35μg L-1 for Cr(III) at m/z 52 under He collision mode are comparable to or better than the conventional ion exchange chromatography-ICPMS methods, and quantitative recovery was obtained from spike-recovery tests on river water samples containing various levels of matrix. Optimization experiments of the HPLC conditions indicate that the retentions of Cr(VI) and Cr(III)-PDCA are characterized by electrostatic and nonpolar interactions, respectively. The retention behavior of inorganic anions and cations in water-rich conditions observed in this study will provide new insights into the separation mechanism in polymer-based HILIC columns, which has been poorly understood.

The Application of a Mobile Unmanned Device for Monitoring Water and Sediment Pollution in the Port of Gdynia

Pollution in the Port of Gdynia can encompass various types of substances and contaminants that affect the quality of water and sediment in this region. Ships entering and leaving the port can release pollutants such as oil, fuel, waste, and chemicals into the water. Controlling and monitoring these pollutants is a crucial part of environmental stewardship. In recent years, uncrewed units have been increasingly in use for in situ water and sediment sampling. Boat-based water sampling crews face significant safety risks at sea. Eliminating the need for a boat-based sampling crew, uncrewed units allow sampling from locations that are difficult to access by traditional sampling methods. To validate the HydroDron-1 method, water samples and bottom sediments were collected from five basins in the Port of Gdynia using the HydroDron-1 method and accredited sampling methods. The values of measured parameters were similar for both methods. Using the HydroDron-1 method at the Port of Gdynia has the potential to improve safety while reducing sampling costs and increasing data collection. The project was implemented as part of the POLNOR 2019 Call program, announced by the National Center for Research and Development (NCRD): “Marine port surveillance and observation system using mobile unmanned research units” NOR/POLNOR/MPSS/0037/2019.

Study on the Distribution Patterns and Treatment Effectiveness of Water Body Pollution Monitoring by Remote Sensing Technology

Abstract With the continuous development of science and technology level, remote sensing technology has a wide range of application prospects in water pollution monitoring. The study combines the Grab-Cut image segmentation algorithm, atmospheric radiation correction, and water pollution identification to construct a remote sensing technology-based method for monitoring water pollution. The research is focused on analyzing the water quality distribution pattern of the upper and lower lakes in the sample lake area using the constructed water pollution monitoring method. On this basis, from the four dimensions of water pollution prevention and control, recycled water recycling, ecological restoration and protection, and environmental monitoring, the comprehensive management measures for water pollution are proposed. The pollutant contents of the upper and lower lakes before and after the management are compared to explore the effect of the comprehensive management of water pollution. The results show that COD contributes 86.77% to the water pollution in the sample lake area. The water quality of the upper and lower lakes is more distributed by class V and class III, which account for 36.67% and 43.33%, respectively. The proposed water pollution monitoring method is able to accurately identify and classify the pollution. After the comprehensive treatment, the COD and ammonia nitrogen content of the upper lake decreased by 30.51% and 37.43%, and that of the lower lake decreased by 35.90% and 39.06%. The effect of water pollution treatment was remarkable.