We report an electrochemical method for the highly sensitive detection of the widely used chlorinated aromatic herbicide 2,4‐dichlorophenoxyacetic acid (2,4‐D) using a bare screen‐printed carbon electrode. Experimental conditions were optimized, yielding a pulse amplitude of 150 mV, a sample of pH of 2.0, and an electrolyte concentration of 0.5 M. Under these conditions, the method exhibited a linear response from 2 to 100 µM (R2 = 0.9963), with an LOQ of 2.1 µM and an LOD of 0.7 µM. The sensitivity of the sensor is 0.8055 µA µM−1, and the sensitivity normalized for the active surface area of the electrode is 6.2 µA µM−1 cm−2. The overall performance of the proposed method is better or similar, compared to some of the modified electrodes used in recent studies reported in the literature for 2,4‐D sensing. The method showed good electrode‐to‐electrode reproducibility of the signal and reusability of the sensor for the detection of 2,4‐D in different solutions using the same electrode. The developed method also showed acceptable percentage recoveries (ranging from 80.1% to 112.0 %) when used for the detection of 2,4‐D spiked in real water samples from different sources.

Lead contamination in residential water infrastructure poses a persistent public health risk, yet current assessment approaches rely on sparse homeowner sampling and tabular models that do not explicitly capture spatial dependencies in shared urban systems. Here we present a self-supervised graph attention network (SSGAT) that models lead contamination risk using property-level graph structure and reconstruction-based pretraining. Using leave-one-ward-out validation across nine wards in Flint, Michigan, followed by cross-city transfer to Andover, Massachusetts, SSGAT achieved a macro-recall of 0.66 ± 0.03 and an accuracy of 0.81± 0.02 in the transfer setting. Performance was statistically comparable to a fully supervised graph model (macro-recall 0.69 ± 0.04, p = 0.30) and improved contaminated-property detection relative to tabular baselines. Results were evaluated under multiple concentration thresholds, including the current EPA action level of 10 ppb (Parts per billion). These findings indicate that spatial graph representations can support cross-city generalization under class-imbalanced conditions, offering a structured approach for contamination screening in data-limited jurisdictions.

Lead contamination in urban water systems remains a prevalent public health threat, affecting millions of American households and disproportionately endangering vulnerable population groups. Current municipal risk assessment and inspection strategies are overwhelmingly based on random sampling and complaint-driven protocols that overlook spatial complexity, reinforce inequities, and squander limited resources, leaving critical exposure areas unidentified. This paper presents a lead contamination risk prediction framework from socio-demographic housing features analytics, first of its kind, by drawing on partially anonymized residential testing data as ground truth and applying graph neural networks alongside gradient-boosted ensembles. Specifically, our method integrates spatial Deep Graph Attention Networks classifiers to capture inter-neighborhood contamination dependencies, fuse demographic and spatial evidence, and produce interpretable risk scores. Those scores are actionable by municipal water authorities at the intra-neighborhood level. Through extensive experiments on newly constructed Chicago block-group level datasets, our framework achieves a balanced accuracy of 84.8% and reduces false positive lead contamination by up to 44% versus spatial-only baselines and 21% over current practice, without sacrificing recall on contaminated blocks. Our approach not only extends technical boundaries in spatial-ensemble learning and privacy-preserving urban health modeling, but also provides policymakers and public health officials with a means to assess and address contamination risks, supporting efforts to protect community health and safety.

This study introduces hydrodynamic cavitation-integrated preozonation (HCPO), a novel physicochemical strategy that synergistically combines hydrodynamic cavitation (HC) with preozonation to eliminate intracellular taste-and-odor compounds, like 2-methylisoborneol (2-MIB) and geosmin (GSM), from filamentous cyanobacteria. Unlike conventional peroxone preozonation, which fails to degrade cell-bound odorants and requires unstable hydrogen peroxide handling, HCPO employs a Venturi-based HC unit to physically enhance the disruption of cyanobacterial cells while upgrading ozone dispersion and radical generation. Rigorous experimentation demonstrated the superiority of HCPO, achieving 86% and 77% removal of intracellular 2-MIB and GSM within 30 minutes, far exceeding those of standalone methods (p < 0.001). Mechanistically, cavitation amplified •OH production by 141% and extended ozone residence time, enabling near-complete oxidation of released odorants. Multidimensional validation via Scanning Electron Microscope (SEM), Excitation-Emission Matrix (EEM) fluorescence spectroscopy, and radical quantification confirmed irreversible cell lysis and enhanced degradation kinetics. Critically, HCPO minimized bromate formation (<10 ppb) through optimized oxidant utilization and reduced ozone dosage (30-50% lower), yielding operational costs of 0.17-0.25 kWh/m3. This innovation resolves a significant limitation in drinking water treatment by concurrently addressing dissolved and cell-bound contaminants, offering a scalable, safety-enhanced alternative that aligns with sustainable water purification frameworks.

Due to its natural presence and extensive application in water treatment, aluminum ion (Al3+) contamination in water has become a pressing environmental challenge, necessitating the development of a facile yet sensitive analytical method. The current application of voltammetric detection of Al3+ has been limited to cathodic adsorptive stripping voltammetry, requiring a chelating agent for complexation. In this work, we proposed a zinc-assisted graphite carbon nitride-modified screen-printed carbon electrode method (Zn-assisted g-C3N4/SPCE) to carry out the anodic stripping voltammetry (ASV) detection of Al3+. By using zinc ions as a co-deposition species, we shifted the reductive potential of Al3+ to a less negative voltage (i.e., from -1.90 to -1.571 V), preventing severe hydrogen evolution during electrodeposition. Additionally, the g-C3N4 coating increases sensitivity by offering a substantial increase in Al3+ ion adsorption sites and regulating a more alleviated Al3+ nucleation process. The developed method has a detection limit of 8.9 ppb, excellent reproducibility, and strong resistance to interference. Field tests showed high consistency with the conventional atomic absorption spectroscopy method. The proposed method has excellent sensitivity and practicality and can be adapted to other metals that have the same technological issues when using ASV for quantitative detection.

Introduction: Detecting water contamination in community housing is crucial for protecting public health. Early detection enables timely action to prevent waterborne diseases and ensures equitable access to safe drinking water. Traditional methods recommended by the Environmental Protection Agency (EPA) rely on collecting water samples and conducting lab tests, which can be both time-consuming and costly.Methods: To address these limitations, this study introduces a Graph Attention Network (GAT) to predict lead contamination in drinking water. The GAT model leverages publicly available municipal records and housing information to model interactions between homes and identify contamination patterns. Each house is represented as a node, and relationships between nodes are analyzed to provide a clearer understanding of contamination risks within the community.Results: Using data from Flint, Michigan, the model demonstrated higher performance compared to traditional methods. Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. This approach offers a practical tool for policymakers and public health officials to assess and mitigate contamination risks, ultimately improving community health and safety.

As climate change intensifies, leading to more potent storms that transport contaminants and affect water quality, pinpointing groundwater pollution, especially heavy metals, becomes crucial. Current analytical techniques rely on laboratory-based instruments, leading to lengthy wait times. This project developed a handheld sensor system for onsite detection of dissolved heavy metals using disposable screen-printed electrodes, modified with novel nanomaterial coatings, including gold nanostars. Advanced voltammetric techniques were developed and were evaluated by testing groundwater samples collected from known contaminated sites. Inductively coupled plasma mass spectrometry identified arsenic ( III) at 515 ppb, mercury (II) at 420 ppb, cadmium (II) at 19.0 ppb, and chromium (VI) at 3.05 ppm. These voltammetric methods reliably measured concentrations, achieving accuracies of 98.1% for arsenic, 96.7% for mercury, 86.8% for cadmium, and 96.1% for chromium. This novel portable sensor offers a fast, effective solution for onsite heavy metal detection in groundwater.

Lead contamination in residential water supplies constitutes a nationwide public‑health emergency, burdening communities with chronic neurological, cardiovascular, and infrastructural costs. Current Environmental Protection Agency (EPA) Lead and Copper Rule protocols, which hinge on homeowner sampling and laboratory analyses, are prohibitively expensive, slow, and too sparsely deployed to flag emerging contamination clusters in time for preventive action. To bridge this gap, we propose a scalable graph machine learning-aided high‑resolution lead risk assessment framework using publicly available housing datasets (parcel, infrastructure) and historical lead testing data archives. The heart of the approach is a Self‑Supervised Graph Attention Network (SSGAT) that employs graph attention layers to model spatial dependencies between properties, coupled with self-supervised pretraining to enhance generalizability. An adaptive human‑in‑the‑loop module refines these attention weights through rapid expert review, ensuring locality‑specific nuances are captured without retraining from scratch. We pre‑trained the model on the publicly available Flint, Michigan dataset and fine‑tuned it using IRB‑approved parcel‑linked samples and stakeholder feedback collected in Andover, Massachusetts. The resulting system attains 90\% classification accuracy and an AUC of 83.6\%, surpassing state-of-the-art models by as much as 12\% while cutting per‑parcel screening costs by a factor of five. By uniting self‑supervised graph learning, transferability, and participatory validation, this work elevates computational methodology and environmental‑engineering practice toward scalable, proactive surveillance of spatially correlated drinking‑water hazards.

Substantial evidence suggests that all types of water, such as drinking water, wastewater, surface water, and groundwater, can be potential sources of Helicobacter pylori (H. pylori) infection. Thus, it is critical to thoroughly investigate all possible preconditioning methods to enhance the recovery of H. pylori, improve the reproducibility of subsequent detection, and optimize the suitability for various water types and different detection purposes. In this study, we proposed and evaluated five distinct preconditioning methods for treating water samples collected from multiple urban water environments, aiming to maximize the quantitative qPCR readouts and achieve effective selective cultivation. According to the experimental results, when using the qPCR technique to examine WWTP influent, effluent, septic tank, and wetland water samples, the significance of having a preliminary cleaning step becomes more evident as it can profoundly influence qPCR detection results. In contrast, the simple, straightforward membrane filtration method could perform best when isolating and culturing H. pylori from all water samples. Upon examining the cultivation and qPCR results obtained from groundwater samples, the presence of infectious H. pylori (potentially other pathogens) in aquifers must represent a pressing environmental emergency demanding immediate attention. Furthermore, we believe groundwater can be used as a medium to reflect the H. pylori prevalence in a highly populated community due to its straightforward analytical matrix, consistent detection performance, and minimal interferences from human activities, temperature, precipitation, and other environmental fluctuations.

The widely accepted method for detecting trace odor compounds such as 2-methylisoborneol (2-MIB) requires high-cost solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry (SPME-GC/ MS). As for now, cases that directly used SPME-GC/MS in the field are unseen, and most detection activities are still carried out in rigorous laboratory conditions. However, lengthy logistics and storage issues can compromise sample conditions, underscoring the significance of on-site detection methods. Here, we reported molecularly imprinted polymers-enabled headspace extracting and electrochemical sensing (MHEES) with the core feature of using the same piece of MIP for both headspace extracting and electrochemical sensing, allowing rapid on-site detection of trace 2-MIB in natural water bodies with a total cost of less than $2.10 per test. MHEES demonstrated exceptional sensitivity during extensively repeated laboratory experiments, achieving a detection limit of 3.7 ng center dot L- 1 and a standard deviation of 4.58 %. The practicality of MHEES was confirmed by conducting two rounds of on-site analyses on different reservoirs with fluctuating seasonal concentrations of 2-MIB. The developing flow of MHEES, from molecules docking to on-site application, was rigorous and could be crossreferenced to related research. The proposed MHEES design can potentially revolutionize the field of analytical chemistry by providing a versatile and affordable platform for on-site volatile compounds detection. It can readily adapt to various organic compounds and effectively address real-world challenges such as monitoring emerging pollutants, tracing disease biomarkers in body fluids, inspecting food safety, identifying chemical reaction intermediates, and supporting healthcare applications.

For the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed fragility curve, as the value increases value decreases. A fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment.

Due to the almost-complete dependence on clinical diagnosis, current Helicobacter pylori (H. pylori) epidemiological statistics suffer from fragmented geographical coverage and lagged data updating worldwide. Acquiring information based on the quantitative detection of biomarkers from domestic wastewater provides a promising way for the future H. pylori epidemiological study. Here, we report wastewater-adapted electrochemical competitive immunosensor (WECI). This method focuses explicitly on detecting H. pylori cytoplasmic ureB in wastewater and contains a series of designs to overcome the challenges brought by the complicated analytical matrix. The developed WECI achieved a practical detection range from 10 pg mL(-1) to 5.0 ng mL(-1) (R-2 = 0.996) and showed the corresponding limit of detection (LoD) and limit of quantification (LoQ) of 0.37 and 1.4 pg mL(-1), respectively. It can be prepared within 6 h and kept highly sensitive for at least six weeks. When testing real wastewater samples, WECI resulted in an average biomarkers recovery rate of 70.9%, which was remarkably higher than the average of 47.0% achieved using qPCR, indicating the excellent applicability toward actual wastewater detection. Our reported WECI is the first study to quantitatively detect H. pylori ureB from wastewater, opening up new vistas for H. pylori epidemiological study.

Though the bitter global pandemic posed a severe public health threat, it set an unprecedented stage for different research teams to present various technologies for detecting SARS-CoV-2, providing a rare and hard-won lesson for one to comprehensively survey the core experimental aspects in developing pathogens electrochemical biosensors. Apart from collecting all the published biosensor studies, we focused on the effects and consequences of using different receptors, such as antibodies, aptamers, ACE 2, and MIPs, which are one of the core topics of developing a pathogen biosensor. In addition, we tried to find an appropriate and distinctive application scenario (e.g., wastewater-based epidemiology) to maximize the advantages of using electrochemical biosensors to detect pathogens. Based on the enormous amount of information from those published studies, features that fit and favor wastewater pathogen detection can be picked up and integrated into a specific strategy to perform quantitative measurements in wastewater samples.

Developing anti-biofouling and anti-biofilm techniques is of great importance for protecting water-contact surfaces. In this study, we developed a novel double-layer system consisting of a bottom immobilized TiO2 nanoflower arrays (TNFs) unit and an upper superhydrophobic (SHB) coating along with the assistance of nanobubbles (NBs), which can significantly elevate the interfacial oxygen level by establishing the long-range hydrophobic force between NBs and SHB and effectively maximize the photocatalytic reaction brought by the bottom TNFs. The developed NBs-SHB/TNFs system demonstrated the highest bulk chemical oxygen demand (COD) reduction efficiency at approximately 80% and achieved significant E. coli and Chlorella sp. inhibition efficiencies of 5.38 and 1.99 logs. Meanwhile, the system showed a sevenfold higher resistance to biofilm formation when testing in a wastewater matrix using a wildly collected biofilm seeding solution. These findings provide insights for implementing nanobubble-integrated techniques for submerged surface protection.

efficient on-site detection of pesticides such as methyl parathion (MP) is crucial to ensure public health. This study introduces the first disposable electrochemically reduced graphene oxide (ErGO) modified electrode for rapid MP detection. A square wave anodic stripping voltammetry method for the detection of MP was developed using these ErGO-modified carbon screen-printed electrodes. Based on this study, graphene's high electric conductivity and unique structure enhanced the sensitivity of the electrode for MP detection. Following optimization of the pH, equilibrium period, deposition potential, and period, the methodology demonstrated high sensitivity and reproducibility. Using the enhanced experiment conditions, calibration experiments were performed with a concentration range of 0 to 150 mu g L-1 of MP. A consistent oxidation peak was observed at-0.180 V. The calibration data showed the increase in the peak height was linearly correlated to the increase in MP concentration, with a correlation coefficient of 0.9854. The sensitivity of the developed methodology was 0.0887 mu A (mu g L-1)-1, and the limit of detection was 9.06 mu g L-1. The methodology was successfully applied to multiple water samples, specifically river water, groundwater, and General Test Water, with recovery rates of 106.01% (standard deviation = 1.46%), 109.51% (standard deviation=0.44%), and 97.69% (standard deviation=1.49%), respectively.

Perfluorooctance sulfonate (PFOS) is one of the most studied Per-and-polyfluoroalkyl substances (PFAS) due to its relatively high environmental ubiquity and toxicity. This study presented an ultra-sensitive voltammetric sensor for the detection of PFOS in tap water based on a glassy carbon electrode (GCE) modified with a thin coating of gold nanostar (AuNS) and electropolymerized molecularly imprinted polymer (MIP). The AuNS coating helps to enhance the voltammetric response of blank signal intensity regarding the Fe2+ oxidation of the selected ferrocenecarboxylic acid (FcCOOH) redox probe; meanwhile, the statistical optimization of the MIP layer offers a higher peak change. Analytical results indicated the sensor could detect PFOS with an LoD and LoQ of 0.015 and 0.041 nM (i.e., 7.5 and 20.5 ppt), respectively. Moreover, it showed comparable analytical performance in tap water with the U.S. Environmental Protection Agency (EPA) method (i.e., Method 537.1). Interfering effects of approximately 10% underestimation were observed when samples contained equimolar perfluorobutanoic Acid (PFBA) or perfluorobutanesulfonic acid (PFBS). Additionally, common interferents in drinking water sources, such as humic acid and chloride ion, showed minor influence in the voltammetric response in terms of accuracy and reproducibility.

This paper describes a voltammetric method and data analysis program developed for the detection of arsenic (III) in commercial apple juice. Arsenic(III) was detected using square wave stripping voltammetry with gold nanoparticle modified screen printed electrodes. The only sample pretreatment performed was the addition of a 100 mM phosphate buffer with a pH of 7. To compensate for interference from high ascorbic acid concentrations, a data analysis program was developed in MATLAB to fit a non-linear baseline, allowing for accurate peak height measurement. With this data analysis program, the developed methodology had a sensitivity of 0.1007 ?A (?g L-1)-1 and a limit of detection of 16.73 ?g L-1. A comparison between the voltammetric method and graphite furnace atomic absorption spectroscopy showed no bias in the voltammetric results and a good correlation between the two sets of predicted concentrations, with an R2 of 0.939.

Rapid detection of trace mercury (II) in water is very challenging. A novel sensor for the facile detection of mercury (II) was developed using electrochemically co-deposited gold nanofilm modified screen-printed carbon electrodes (EcoD-AuNF/SPCE). Because a monolayer of gold nanofilm is freshly co-deposited during the mercury (II) preconcentration period, no additional modification on an electrode is required prior to a test. Central composite design (CCD) and response surface methodology (RSM) were used to evaluate the effects of the critical testing parameters simultaneously (i.e., the co-deposited gold concentration of 350 μg L -1 , deposition potential of -0.75 V, and testing pH of 3.50). The response of square-wave anodic stripping voltammetry (SWASV) showed a linear relationship with the mercury (II) concentration over a range of 1 to 40 μg/L (ppb), with an LoD of 0.29 μg/L and sensitivity of 0.37 μA/ppb. Significant influences on the mercury (II) stripping peak potential, peak height, and peak shape were observed in the presence of chloride. Therefore, an optimized amount of chloride (i.e., 600 mg/L) was preemptively added to minimize the potential effect of naturally occurring chloride and to improve further the detection sensitivity (i.e., LoD of 0.16 μg/L and sensitivity of 0.55 μA/ppb). Validation testing using real-world water samples indicated reliable prediction of mercury (II) concentration could be obtained in complex media. In summary, this newly developed voltammetric approach has excellent detection performance and practical significance for potential on-site voltammetric determination of trace mercury (II) in water.

Groundwater contamination by toxic heavy metals pose serious health hazards to humans and the environment. The remediation of contaminated groundwater requires effective characterization of the concentrations and spatial distribution of heavy metals. Groundwater samples from long screening monitoring wells do not provide an accurate representation of the distribution of heavy metals with depth, as the data obtained from the well are an average along the screen length. This study reports the development and application of two new technologies, a direct push 1.75 groundwater profiler (GWP) that obtains groundwater samples from multiple depths in a single push, and a portable field screening device that uses novel disposable sensors to facilitate rapid, cost-effective, on-site detection of heavy metals in groundwater, surface water, and sediment pore water. Extensive fieldwork was performed at a chromium(VI)-contaminated site to evaluate and validate these two technologies for on-site contamination profiling of Cr(VI) in groundwater. A preliminary investigation was performed with an OIHPT logging tool to determine formation lithology and relative permeability for identifying potential groundwater sampling intervals and contaminant migration pathways in the subsurface. Groundwater samples from selected depth intervals were collected and analyzed by an outside laboratory analysis for total chromium and hexavalent chromium as per EPA standards 6020 (SW) and 7196, respectively. The excellent comparison of the field results with the laboratory test results confirm the potential of these technologies in obtaining high resolution site characterization data to improve the management of heavy metal-contaminated hazardous sites.

One of the challenges preventing rapid, onsite voltammetric detection of arsenic(III) is the overlapping oxidation peak of copper(II). This paper describes a novel methodology for the voltammetric detection of trace levels of arsenic(III) in the presence of high copper(II) concentrations (up to the action level of 1.3 mg L-1 set by the US EPA for drinking water). Square wave stripping voltammetry tests were performed using disposable carbon screen printed electrodes modified with gold nanostars on samples buffered with Britton-Robinson buffer. The optimized parameters for accurate codetection of arsenic(III) and copper(II) were a buffer pH of 9.5, a loading of gold nanostars of 2.39(*)10(-5) nmol per electrode, a deposition voltage of -0.8 V, and a deposition time of 180 s. Based on calibration testing, the limits of detection for arsenic(III) and copper(II) were determined to be 2.9 mu g L-1 and 42.5 mu g L-1, respectively. Furthermore, the linear ranges for arsenic and copper were 0-100 mu g L-1 and 0-250 mu g L-1 with sensitivities of 0.101 mu A (mu g L-1)(-1) and 0.121 mu A (mu g L-1)(-1), respectively. Interference testing was performed with several common ionic species, sodium bicarbonate, sodium chloride, tannic acid, iron(iii) chloride, magnesium chloride, calcium nitrate, and sodium sulfate, with only sodium bicarbonate significantly affecting the response. Validation testing in real-world samples was performed by comparison with graphite furnace atomic absorption spectroscopy. The validation testing demonstrated good accuracy and precision, expressed as percent recovery and relative standard deviation (RSD), respectively, in river water and tap water, with mean percent recoveries of 87.7% (RSD = 4.20%) and 83.2% (RSD = 10.02%), respectively. (C) 2020 Elsevier B.V. All rights reserved.