Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods

Heavy metals prediction system in groundwater using online sensor and machine learning for water management: the case of typical industrial park

With the expansion of human industrial activities, heavy metal contamination in groundwater environments has become increasingly severe. Environmental management agencies invest significant financial resources into groundwater monitoring, primarily due to its inherent invisibility. Automatic monitoring is a new way to monitor groundwater, the existing sensors often can only achieve simple indicators, and it is difficult to achieve complex indicators such as heavy metals. This study integrated pH and conductivity online monitoring probes with machine learning algorithms to develop a real-time, automated heavy metal prediction system for groundwater. The predictive performance demonstrated that the highest R2 values for chromium (Cr), nickel (Ni), and copper (Cu) were 0.73, 0.78, and 0.87, respectively, with mean absolute errors of 11.9, 0.83, and 1.02 μg/L. While random forest and extreme gradient boosting (XGB) models demonstrate greater robustness. To enhance the practicality and management significance of the prediction system, interval prediction is employed. Uncertainty assessment results indicate that the performance order of prediction intervals across different models is XGB > Random Forest > Multiple Linear Regression (MLR) > Backpropagation neural network (BP). We proposed that Groundwater risk is acceptable when the prediction interval of pollutants falls below regional screening levels. The integration of automated sensors with machine learning algorithms can offer advanced recommendations for long-term environmental monitoring.

CrAssphage distribution analysis in an Amazonian river based on metagenomic sequencing data and georeferencing

Viruses are the most abundant biological entities in all ecosystems of the world. Their ubiquity makes them suitable candidates for indicating fecal contamination in rivers. Recently, a group of Bacteroidetes bacteriophages named CrAssphages, which are highly abundant, sensitive, and specific to human feces, were studied as potential viral biomarkers for human fecal pollution in water bodies. In this study, we evaluated the presence, diversity, and abundance of viruses with a focus on crAssphages via metagenomic analysis in an Amazonian river and conducted correlation analyses on the basis of physicochemical and georeferencing data. Several significant differences in viral alpha diversity indexes were observed among the sample points, suggesting an accumulation of viral organisms in the river mouth, whereas beta diversity analysis revealed a significant divergence between replicates of the most downstream point (IT4) when compared to the rest of the samples, possibly due to increased human impact at this point. In terms of the presence of crAssphage, the analysis identified 61 crAssphage contigs distributed along the Itacaiúnas River. Moreover, our analysis revealed significant correlations between 19 crAssphage contigs and human population density, substantiating the use of these viruses as possible markers for human fecal pollution in the Itacaiúnas River. This study is the first to assess the presence of crAssphages in an Amazonian river, with results suggesting the potential use of these viruses as markers for human fecal pollution in the Amazon.

IMPORTANCE

The Amazon biome is one of the most diverse ecosystems in the world and contains the most vast river network; however, the continuous advance of urban centers toward aquatic bodies exacerbates the discharge of pollutants into these water bodies. Fecal contamination contributes significantly to water pollution, and the application of an improved fecal indicator is essential for evaluating water quality. In this study, we evaluated the presence, diversity, and abundance of crAssphages in an Amazonian river and performed correlation analysis on the basis of physicochemical and georeferencing data to test whether crAssphages are viable fecal pollution markers. Our analysis revealed both the presence of crAssphages and their correlation with physicochemical data and showed significant correlations between the relative abundance of crAssphages and human density. These results suggest the potential use of these viruses as markers for water quality assessment in Amazonian rivers.

Natural and anthropogenic factors controlling hydrogeochemical processes in a fractured granite bedrock aquifer, Korea

Contamination of groundwater has become a critical environmental concern, prompting international inquiries. In this study, the impacts of natural and anthropogenic factors in the granite bedrock groundwater system were identified based on the hydrogeochemical compositions including environmental isotopes (δ18O, δ2H, 222Rn, δ34SSO4, δ18OSO4) using multivariate statistical methods. Hierarchical clustering analysis classified the groundwater samples into three groups for both dry and wet seasons. The first group, observed in both seasons, represents groundwater influenced by water-rock interactions in low flow and also demonstrates anthropogenic contamination near densely populated residential areas. The second group corresponds to higher flow groundwater, where surface water interaction affects with minimal anthropogenic impact. The third group characterizes relatively radon-contaminated groundwater, representing the predominant groundwater type in the study area. The isotope mixing model based on δ34SSO4 and δ18OSO4 identified proportional contributions of precipitation (~ 14%), sewage (~ 22%), soil (~ 78%), and sulfide oxidation (~ 27%) sources. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal influence on sulfur isotope fractionation within the system. By integrating hydrogeochemical analysis, sulfur isotopes, and the MixSIAR model to trace sulfate sources, uncertainties are able be accounted in source contributions. The groundwater system was mainly influenced by natural factors through infiltration, particularly via the unsaturated soil layer during the wet season. This also indicates enhanced mixing of multiple factors during the recharge or discharge processes triggered by rainfall events. In contrast, anthropogenic contributions declined indicating strong seasonal influences, especially from sewage which decreased from 22 to 6% in groundwater most affected by human activity. This highlights the role of rainfall in diluting human-induced contaminants from the groundwater system. To understand the fractured granite groundwater system, a conceptual model was developed, detailing groundwater types and identifying sulfur sources.

Electrochemical reduction for chlorinated hydrocarbons contaminated groundwater remediation: Mechanisms, challenges, and perspectives

Electrochemical reduction technology is a promising method for addressing the persistent contamination of groundwater by chlorinated hydrocarbons. Current research shows that electrochemical reductive dechlorination primarily relies on direct electron transfer (DET) and active hydrogen (H⁎) mediated indirect electron transfer processes, thereby achieving efficient dechlorination and detoxification. This paper explores the influence of the molecular charge structure of chlorinated hydrocarbons, including chlorolefin, chloroalkanes, chlorinated aromatic hydrocarbons, and chloro-carboxylic acid, on reductive dechlorination from the perspective of molecular electrostatic potential and local electron affinity. It reveals the affinity characteristics of chlorinated hydrocarbon pollutants, the active dechlorination sites, and the roles of substituent groups. It also comprehensively discusses the current progress on electrochemical reductive dechlorination using metal, carbon-based, and 3D electrode catalysts, with an emphasis on the design and optimization of electrode materials and the impact of catalyst microstructure regulation on dechlorination performance. It delves into the current application status of coupling electrochemical reduction technology with biodegradation and electrochemical circulating well technology for the remediation of groundwater contaminated by chlorinated hydrocarbons. The paper discusses practical application challenges such as electron transfer, electrode corrosion, water chemistry environment, and aquifer heterogeneity. Finally, considerations are presented from the perspectives of environmental impact and sustainable application, along with a summary and analysis of potential future research directions and technological prospects.

An improved CNN model in image classification application on water turbidity

Water turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNN) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. This study has opened up new applications of CNN in fine-grained image classification, and further demonstrated the effectiveness of convolutional neural networks in water turbidity image classification through experiments.

Physicochemical and isotopic similarity between well water and intruding surface water is not synonymous with similarity in prokaryotic diversity and community composition

Intruding surface water can impact the physicochemical and microbiological quality of groundwater. Understanding these impacts is important because groundwater provides much of the world's potable water, and reduced quality is a potential public health risk. In this study, we monitored six shallow groundwater wells and three surface water bodies in the North Ostrobothnia region of Finland twice monthly for 12 months (October 2021-October 2022) via (i) on-site and off-site measurements of physicochemical water quality parameters, (ii) determination of stable water isotope compositions, and (iii) analysis of microbial communities (via amplicon sequencing of the V3-V4 16S rRNA gene sub-regions). Water from one well showed clear overall physicochemical and isotopic similarity with a nearby pond, as well as temporal fluctuations in water temperature and isotopes that mirrored those of the pond. Isotope mixing analyses suggested that about 80-95 % of the well water comes from the pond. Such large-scale intrusion might be expected to reduce prokaryotic diversity and composition in the aquifer, either by strong influx of surface water taxa or changes to aquifer physicochemistry. Compared to the pond, however, prokaryotic communities from the well showed significantly higher alpha diversity and a composition more similar to a nearby well unaffected by intrusion. The finding that physicochemical and isotopic similarity between well water and intruding surface water is not synonymous with similarity in prokaryotic diversity and community composition makes clear the need for a multi-method approach when studying the impact of surface water intrusion on shallow wells.

Predicting leachate impact on groundwater using electrical conductivity and oxidation-reduction potential measurements: An empirical and theoretical approach

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation-reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models-empirical and theoretical-were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate-groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management.

High-Precision and Real-Time Measurement of Water Isotope Ratios Based on a Mid-Infrared Optical Sensor

A compact spectrometer based on a mid-infrared optical sensor has been developed for high-precision and real-time measurement of water isotope ratios. The instrument uses laser absorption spectroscopy and applies the weighted Kalman filtering method to determine water isotope ratios with high precision and fast time response. The precision of the measurements is 0.41‰ for δ18O and 0.29‰ for δ17O with a 1 s time. This is much faster than the standard running average technique, which takes over 90 s to achieve the same level of precision. The successful development of this compact mid-infrared optical sensor opens up new possibilities for its future applications in atmospheric and breath gas research.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Remote sensing, GIS, and analytic hierarchy process-based delineation and sustainable management of potential groundwater zones: a case study of Jhargram district, West Bengal, India

The present investigation delineates groundwater potential zones (GPZ) in the Jhargram district through an integrated approach employing analytical hierarchical process (AHP), remote sensing, and geographical information systems (GIS). Twelve parameters were utilized for GPZ analysis based on the Groundwater Potential Index, subsequent to multicollinearity testing. Classification of GPZ yielded five distinct categories: very poor, poor, moderate, good, and very good. Validation through receiver operating characteristics (ROC) and cross-validation with borewell yield data affirmed prediction accuracies of 78.4% and 84%, respectively. Spatial distribution analysis revealed that 30.39%, 30.86%, and 13.19% of the surveyed area fell within the poor, moderate, and good potentiality zones, respectively, whereas 15.86% and 9.69% were categorized as very poor and very good GPZs. Sensitivity analysis highlighted the significance of geology, elevation, geomorphology, slope, and lineament density as influencing parameters; elimination of any single parameter engendered significant alterations in the GPZ classification. The investigation culminated in the formulation of a block-wise sustainable groundwater management blueprint designed to inform policy initiatives.

IR-based device to acquire real-time online heart ECG signals of fish (Cyprinus carpio) to evaluate the water quality

Water quality monitoring is a challenging task due to continuous pollution. The rapid development of engineering technologies has paved the way for the development of efficient and convenient computer-based online continuous water-quality assessment techniques. Techniques based on biological-responses are gaining attention, worldwide. Different biosensors have been developed in recent years to monitor real-time biological responses to evaluate water-quality. The survival and function of various organs of the organism depends on the cardiac system. Alterations in the cardiac system could signify the occurrence/initiation of stress in the organism. We developed a real-time online cardiac function assessment system-OCFAS to acquire fish ECG-signals. We obtained P-wave, R-wave, T-wave, PR-intervals, QT-intervals and QRS-complex continuously, which did not affect the normal activities of carp. We exposed Cyprinus carpio to different concentrations (National Environmental Quality Standards) of ammonia for 48 h. Our OCFAS has precisely acquired the required ECG-signals. A real-time dataset reveals sensitivity to ammonia in carp ECG-indexes. Compared with the control group the P-wave, R-wave and T-wave were weaker in ammonia-treated groups. In contrast, the PR-intervals, QT-intervals and QRS-complex were prolonged in the ammonia-treatment groups. The self-organizing map signifies that the PR-intervals, the QRS-complex and the QT-intervals are consistent with environmental stress. Linear regression analysis also quantitatively signifies that the PR interval has the highest R2 value and the lowest SSE-value, followed by the QRS complex and the QT interval. A concentration-related effect was observed in the ammonia treated groups. The integrated biomarker response (IBRv2) index was used to determine the overall stress of ammonia on carp heart ECG-indexes. IBRv2 also supports the real-time response of carp to ammonia stress. Ammonia levels in the aquaculture and water environment require special attention to avoid its adverse effects on the health of aquatic biota. Our study emphasizes the importance of online real-time fish ECG for water-quality assessment.

Bacterial contamination of medical face mask wearing duration and the optimal wearing time

Introduction

Bacterial contamination is a critical parameter for how long a medical mask will be worn.

Methods

In this study, we used the pour plate method to observe the total bacteria counts in used medical face masks. The bacterial community analysis was detected using bio-Mass spectrometry technology and 16SrRNA gene sequencing technology. The wearing time of the mask from 0.5 hours to 5 hours were studied.

Results

These results shown that the total number of bacteria on the inside surface of the mask were higher than the outside. The total number of bacteria on the inner surface of masks worn for 0.5 h, 1 h 2 h, 4 h and 5 h was 69 CFU/m2,91.3 CFU/m2, 159.6 CFU/m2, 219 CFU/m2, and 879 CFU/m2, respectively. The total number of bacteria on the outside surface of masks worn for 0.5 h, 1 h 2 h, 4 h and 5 h was 60 CFU/m2, 82.7 CFU/m2, 119.8 CFU/m2, 200 CFU/m2, and 498 CFU/m2, respectively. The bacterial abundance obtained from bio-Mass spectrometry were consistent with the results of 16SrRNA sequencing. Both the methods discovered the maximum number of Neisseria followed by Corynebacterium species in mask worn 5 hours. The top 100 bacteria isolated from inside and outside surface of mask belong to 11 phyla.

Conclusions

We analyzed bacterial penetration efficiency of the bacteria that were detected both on the inside and outside surface of the masks. In the top 10 bacteria, no bacteria were detected both inside and outside the mask worn for four hours, while 6 bacteria species were detected on the inside and outside of the mask after wearing for five hours. Bacterial penetration rates ranged from 0.74% to 99.66% for masks worn continuously for five hours, and the penetration rate of four strains exceeded 10% in the top 10 colonies. We recommend timely replacement of masks worn for more than four hours.

Three-Dimensional Printed Biomimetic Robotic Fish for Dynamic Monitoring of Water Quality in Aquaculture

The extensive water pollution caused by production activities is a key issue that needs to be addressed in the aquaculture industry. The dynamic monitoring of water quality is essential for understanding water quality and the growth of fish fry. Here, a low-cost, low-noise, real-time monitoring and automatic feedback biomimetic robotic fish was proposed for the dynamic monitoring of multiple water quality parameters in aquaculture. The biomimetic robotic fish achieved a faster swimming speed and more stable posture control at a swing angular velocity of 16 rad/s by using simulation analysis. A fast swimming speed (0.4 m/s) was achieved through the control of double-jointed pectoral and caudal fins, exhibiting various types of movements, such as straight swimming, obstacle avoidance, turning, diving, and surfacing. As a demonstration of application, bionic robotic fish were placed in a lake for on-site water sampling and parameter detection. The relative average deviations in water quality parameters, such as water temperature, acidity and alkalinity, and turbidity, were 1.25%, 0.07%, and 0.94%, respectively, meeting the accuracy requirements for water quality parameter detection. In the future, bionic robotic fish are beneficial for monitoring water quality, fish populations, and behaviors, improving the efficiency and productivity of aquaculture, and also providing interesting tools and technologies for science education and ocean exploration.