Monitoring Water Contamination Research Articles

Are Membrane Bioreactors Really More Efficient in Removing Pharmaceutical Substances?—Variance Component Analysis Of Micropollutant Removal

This study evaluates the influence of micropollutant chemical characteristics on the removal of pharmaceutical substances through three different treatments: membrane bioreactor, full wastewater treatment with final filtration (WWTP), and secondary treatment through a conventional activated sludges system, operated in parallel at realistic sludge retention time (SRT) over three years and four sampling campaigns. Treated wastewater from the WWTP enters the local canal with a low dilution ratio. Therefore, the monitoring of water contamination is of particular interest for a reliable assessment of environmental risk. A total of 39 pharmaceutical substances were screened. While differences among the three types of treatments were found statistically not significant, data analysis performed through a generalized linear model showed that both the influent concentration and the physicochemical characteristics are strong predictors for the removal of micropollutant. SRT had no significance for the three types of treatment of this study. Finally, pharmaceuticals were divided into three major classes based on their influent concentration and removal. A canonical discriminant analysis was used to predict the removals and showed that the pharmaceuticals removal rates are strongly influenced by their hydrophobicity/hydrophilicity and enabled to predict their removal categories with high accuracy (i.e., 65% of correct predictions).

Bifunctional Zirconium Phosphate with Greigite for Electrochemical Detection and Simultaneous Removal of Heavy Metal Ions and Nitro Compounds.

Electrochemical sensing is emerging as a method of choice for the sensing and monitoring of contaminants in water. Various sensing platforms have been designed for sensing heavy metal ions and organic pollutants in water bodies. Herein, we report a new electrochemical platform that can be used for the detection of both heavy metal ions and nitro-based organic contaminants in water bodies. The electrochemical sensor uses a modified electrode based on Fe3S4-impregnated zirconium phosphate (ZrP) nanoparticles synthesized by a simple ultrasonication method. The ZrP@Fe3S4 nanoparticles were thoroughly characterized by power X-ray diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX), and ζ-potential studies. The material exhibits an excellent electrochemical performance for the detection of Pb2+, Hg2+, nitrophenol, nitroaniline, and picric acid with low limits of detection of ca. 0.93, 0.70, 0.98, 1.10, and 1.53 ppm, respectively. Since ZrP@Fe3S4 nanoparticles are magnetically recyclable, their adsorption capacity and recyclability have been thoroughly investigated for the uptake of Pb2+ and Hg2+ ions from contaminated water. We observed that the adsorption of Pb2+ and Hg2+ ions on ZrP@Fe3S4 is best described by the Langmuir isotherm and pseudo-second-order kinetic models, with adsorption capacities of 219.44 and 118.4 mg/g, respectively. Similarly, the removal efficiency of ZrP@Fe3S4 was found to be 91, 57.6, and 31.3% for nitrophenol, nitroaniline, and picric acid, respectively. Furthermore, the theoretical calculations using density functional theory (DFT) were carried out to find the adsorption energy, affinity, and point of adsorption, which are in line with the experimental results. DFT calculations further suggest that the incorporation of Fe3S4 on ZrP improves the surface charge density and promotes efficient electron transfer between the electrode and the analyte. We have shown the real-time analysis of Dal lake water as a proof of concept, and the synthesized composite exhibits good recovery and promising results for metal ion sensing. ZrP@Fe3S4 demonstrated an excellent cycling stability and long-term stability without noticeable degradation for 1 week.

Real Time Water Quality Monitoring of River Pamba (India) using Internet of Things

— All of Earth’s living things depend on food and clean water to survive. River water monitoring is essential for gathering the data required to manage water quality in the present as well as the future. In this research, the water quality of the Pamba River is monitored and analyzed using the Internet of Things (IoT) during the monsoon and summer seasons. The Pamba River is the third-longest river in the Indian state of Kerala and one of the primary sources of drinking water in Kerala’s Pathanamthitta and Alappuzha districts. In conventional systems, the monitoring procedure entails manually collecting water samples from various places, followed by testing and analysis in a lab. This is costly and time-consuming. With developments in sensors, communication, and the Internet of Things (IoT), monitoring water contamination has become more and more crucial in recent years and provides accurate data. The system collects information from IoT sensors to evaluate the quality of the water, including pH, dissolved oxygen, temperature changes, and other parameters. The relevant officials are alerted in the case of any variation, and all of this data is stored in the cloud. The system also ensures real-time water quality monitoring.

E. coli contamination of drinking water sources in rural and urban settings: an analysis of 38 nationally representative household surveys (2014-2021).

The world is not on track to achieve universal access to safely managed water by 2030, and access is substantially lower in rural areas. This Sustainable Development Goal target and many other global indicators rely on the classification of improved water sources for monitoring access. We aimed to investigate contamination in drinking water sources, comparing improved and unimproved sources in urban and rural settings. We used data from Multiple Indicator Cluster Surveys, which tested samples from the household water source and a glass of water for Escherichia coli contamination across 38 countries. Contamination was widespread and alarmingly high in almost all countries, settings, and water sources, with substantial inequalities between and within countries. Water contamination was found in 51.7% of households at the source and 70.8% in the glass of water. Some improved sources (e.g., protected wells and rainwater) were as likely to be contaminated as unimproved sources. Some sources, like piped water, were considerably more likely to be contaminated in rural than urban areas, while no difference was observed for others. Monitoring water contamination along with further investigation in water collection, storage, and source classification is essential and must be expanded to achieve universal access to safely managed water.

Illuminating Progress: The Contribution of Bioluminescence to Sustainable Development Goal 6-Clean Water and Sanitation-Of the United Nations 2030 Agenda.

The United Nations Agenda 2030 Sustainable Development Goal 6 (SDG 6) aims at ensuring the availability and sustainable management of water and sanitation. The routine monitoring of water contaminants requires accurate and rapid analytical techniques. Laboratory analyses and conventional methods of field sampling still require considerable labor and time with highly trained personnel and transport to a central facility with sophisticated equipment, which renders routine monitoring cumbersome, time-consuming, and costly. Moreover, these methods do not provide information about the actual toxicity of water, which is crucial for characterizing complex samples, such as urban wastewater and stormwater runoff. The unique properties of bioluminescence (BL) offer innovative approaches for developing advanced tools and technologies for holistic water monitoring. BL biosensors offer a promising solution by combining the natural BL phenomenon with cutting-edge technologies. This review provides an overview of the recent advances and significant contributions of BL to SDG 6, focusing attention on the potential use of the BL-based sensing platforms for advancing water management practices, protecting ecosystems, and ensuring the well-being of communities.

A review of physicochemical and biological contaminants in drinking water and their impacts on human health

Clean drinking water is one of the United Nations Sustainable Development Goals. Despite significant progress in the water purification technology, many regions still lack access to clean water. This paper provides a review of selected water contaminants and their impacts on human health. The World Health Organization (WHO) guidelines and regional standards for key contaminants were used to characterise water quality in the European Union and UK. The concept of safe drinking water was explained based on the non-observed adverse effect level, threshold concentrations for toxic chemicals, and their total daily intake. Various techniques for monitoring water contaminants and the drinking water standards from five different countries, including the UK, USA, Canada, Pakistan and India, were compared to WHO recommended guidelines. The literature on actual water quality in these regions and its potential health impacts was also discussed. Finally, the role of public water suppliers in identifying and monitoring drinking water contaminants in selected developed countries was presented as a potential guideline for developing countries. This review emphasised the need for a comprehensive understanding of water quality and its impacts on human health to ensure access to clean drinking water worldwide.

Development of polypropylene membranes grafted with nanocellulose to analyze organic pollutants in environmental waters using miniaturized passive samplers based on liquid-phase microextraction

Reinforcing hollow polypropylene fibers with nanocellulose is a viable alternative for implementing miniaturized passive sampling devices (MPSDs) based on liquid-phase microextraction (LPME). This modification improves the extractive properties of microporous membranes, including the sensitivity to fluctuations in the uptake steps. The new material, polypropylene hollow fiber grafted with nanocellulose, was developed and characterized using X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) coupled with Energy Dispersive System (EDS) and Thermogravimetric (TGA) and Differential Thermal (DTA) analysis. The morphology type of the crystallite of cellulose most abundant after chemical regeneration was the CII (Celulose II) allomorphic. Sorption studies were carried out and revealed that the modified membrane could absorb 177% more water than its untreated commercial counterpart, making it suitable for long-term sampling. Miniaturized passive sampling devices were equipped with the new material, and the devices were deployed in situ for passive sampling of organic pollutants in environmental waters of marginal lagoons. Fingerprint analysis of samples was performed by comprehensive two-dimensional gas chromatography coupled with mass spectrometry and identified a variety of pollutants such as pesticides, plasticizers and prescription and illegal drugs and their metabolites. Therefore, these MPSDs can be employed for the sampling and long-term monitoring of water contaminants with a wide range of polarities.

Development of lab-on-chip biosensor for the detection of toxic heavy metals: A review

Recently, a decrease in water availability and quality has been raised due to rapid industrialization, unsustainable agricultural activities and anthropogenic activities. Heavy metals are considered significant pollutants in the water environment, cause environmental hazards and health effects to humans. For monitoring water contaminants utilized different conventional techniques. Still, they have some drawbacks, such as cost expensive, ecological issues, and processing time, requiring technicians and researchers to operate them effectively. Biosensors have become reasonable devices for screening and identifying environmental contaminants because of their different benefits contrasted with other detecting techniques. This review summarizes the toxic effect of heavy metal and their source, occurrence. A detailed discussion is provided on the heavy metal recognition materials for detecting heavy metals in wastewater. Lab on chip (LOC) is an emerging micro-electrical mechanical system (MEMS) device that intakes liquid and makes it move through the micro-channels, to accomplish fast, cost-effective and profoundly sensitive analysis with significant yield. LOC also provided a discussion on numerous laboratory functions on a single platform. This article attempts to discuss the detection of heavy metals using lab on a chip by suitable recognition materials. Further, the design and fabrication mechanism and their recognition abilities of LOC were also reviewed. The review mainly focuses on the application of LOC biosensors, pros, and cons, and suggests a roadmap towards future development to enhance the practical use in pollutant monitoring.

Emerging and Persistent Pollutants in the Aquatic Ecosystems of the Lower Danube Basin and North West Black Sea Region—A Review

The tremendous impact of natural and anthropogenic organic and inorganic substances continuously released into the environment requires a better understanding of the chemical status of aquatic ecosystems. Water contamination monitoring studies were performed for different classes of substances in different regions of the world. Reliable analytical methods and exposure assessment are the basis of a better management of water resources. Our research comprised publications from 2010 regarding the Lower Danube and North West Black Sea region, considering regulated and unregulated persistent and emerging pollutants. The frequently reported ones were: pharmaceuticals (carbamazepine, diclofenac, sulfamethoxazole, and trimethoprim), pesticides (atrazine, carbendazim, and metolachlor), endocrine disruptors—bisphenol A and estrone, polycyclic aromatic hydrocarbons, organochlorinated pesticides, and heavy metals (Cd, Zn, Pb, Hg, Cu, Cr). Seasonal variations were reported for both organic and inorganic contaminants. Microbial pollution was also a subject of the present review.

Allosteric Regulation of DNA Circuits Enables Minimal and Rapid Biosensors of Small Molecules.

Detection of environmental pollutants is crucial to safeguard ecological and public health. Here, we report a modular biosensing approach for the detection of contaminants based on the regulation of a minimal DNA signal amplifier and transducer circuit by allosteric transcription factors and their cognate ligands. We leverage the competition between allosteric proteins and an endonuclease to modulate cascade toehold-mediated strand displacement reactions, which are triggered in the presence of specific effectors and sustained by the endonuclease. We built two optical biosensors for the detection of tetracyclines and macrolides in water using repressors TetR and MphR, respectively. We demonstrate that our minimal, fast, and single-step biosensors can successfully detect antibiotics in nanomolar levels and apply them to report the presence of spiked-in antibiotics in water samples in a matter of minutes, suggesting great potential for monitoring of water contaminants.

Dynamically controlling the electrode potential of a microbial fuel cell-powered biocathode for sensitive quantification of nitrate

Microbial fuel cell (MFC) powered biocathode sensors are promising for self-powered monitoring of water contaminants. However, the extensive electrode potential fluctuation would occur when the MFC powered biocathode were used for detecting different concentrations of target analytes, which in turn limits the sensitivity and accuracy. To address this, for the first time, an electrokinetic model for the nitrate reduction by an MFC powered biocathode was developed. Interestingly, both the theoretical simulation and experimental results disclosed an optimal external resistance, under which the biocathode potential was dynamically controlled in the range that electron transfer from the electrode to the nitrate though biocathodic reduction was kinetically saturated, and the output current was exclusively nitrate concentration depended. With this basis, an MFC powered biocathode sensor was for the first time developed, which showed an extremely low limit of detection (0.11 μM) and wide linear detection range (1–500 μM) in nitrate detection. Moreover, this biosensing system also exhibited good reusability and excellent reliability in complex wastewater environment. This work developed an MFC powered biocathode sensing system with excellent nitrate detection capacity, and demonstrated a novel strategy to accurately quantify the target analytes with dynamically controllable electrode potential.

Mid-IR sensing platform for trace analysis in aqueous solutions based on a germanium-on-silicon waveguide chip with a mesoporous silica coating for analyte enrichment.

A novel platform based on evanescent wave sensing in the 6.5 to 7.5 µm wavelength range is presented with the example of toluene detection in an aqueous solution. The overall sensing platform consists of a germanium-on-silicon waveguide with a functionalized mesoporous silica cladding and integrated microlenses for alignment-tolerant back-side optical interfacing with a tunable laser spectrometer. Hydrophobic functionalization of the mesoporous cladding allows enrichment of apolar analyte molecules and prevents strong interaction of water with the evanescent wave. The sensing performance was evaluated for aqueous toluene standards resulting in a limit of detection of 7 ppm. Recorded adsorption/desorption profiles followed Freundlich adsorption isotherms with rapid equilibration and resulting sensor response times of a few seconds. This indicates that continuous monitoring of contaminants in water is possible. A significant increase in LOD can be expected by likely improvements to the spectrometer noise floor which, expressed as a relative standard deviation of 100% lines, is currently in the range of 10-2A.U.

Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review

Because of its robust autonomous learning and ability to address complex problems, artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges faced in drinking water treatment (DWT). AI technology provides technical support for the management and operation of DWT processes, which is more efficient than relying solely on human operations. AI-based data analysis and evolutionary learning mechanisms are capable of realizing water quality diagnosis, autonomous decision making and operation process optimization and have the potential to establish a universal process analysis and predictive model platform. This review briefly introduces AI technologies that are widely used in DWT. Moreover, this paper reviews in detail the mature applications and latest discoveries of AI and machine learning technologies in the fields of source water quality, coagulation/flocculation, disinfection and membrane filtration, including source water contaminant monitoring and identification, accurate and efficient prediction of coagulation dosage, analysis of the formation of disinfection by-products and advanced control of membrane fouling. Finally, the challenges facing AI technologies and the issues that need further study are discussed; these challenges can be briefly summarized as a) obtaining more effective characterization data to screen and identify targeted contaminants in the complex background with the assistance of AI technologies and b) establishing a macro intelligence model and decision scheme for entire drinking water treatment plants (DWTPs) to support the management of the water supply system.

A web-based spatial decision support system for monitoring the risk of water contamination in private wells

ABSTRACT Long-term exposure to contaminated water can cause health effects, such as cancer. Accurate spatial prediction of inorganic compounds (e.g. arsenic) and pathogens in groundwater is critical for water supply management. Ideally, environmental health agencies would have access to an early warning system to alert well owners of risks of such contamination. The estimation and dissemination of these risks can be facilitated by the combination of Geographic Information Systems and spatial analysis capabilities – i.e., spatial decision support system (SDSS). However, the use of SDSS, especially web-based SDSS, is rare for spatially explicit studies of drinking water quality of private wells. In this study, we introduce the interactive Well Water Risk Estimation(iWWRE), a web-based SDSS to facilitate the monitoring of water contamination in private wells across Gaston County, North Carolina (US). Our system implements geoprocessing web services and generates dynamic spatial analysis results based on a database of private wells. Environmental health scientists using our system can conduct fine-grained spatial interpolation on 1) a particular type of contaminant such as arsenic, 2) on various subsets through a temporal query. Visuals consist of an estimation map, cross validation information, Kriging variance and contour lines that delineate areas with maximum contaminant levels (MCL), as set by the US Environmental Protection Agency (EPA). Our web-based SDSS was developed jointly with environmental health specialists who found it particularly critical for the monitoring of local contamination trends, and a useful tool to reach out to private well users in highly elevated contaminated areas.

Cytochrome P450 1A mRNA in the Gambusia affinis and Response to Several PAHs.

Cytochrome P4501A (CYP1A) has been used as a specific biomarker for monitoring water contamination such as PAHs, PCBs and dioxins. In the present study, the cyp1a gene of Gambusia affinis was cloned and sequenced and their expressions under PAHs exposure were characterized. The newly identified cyp1a encodes a protein with 521 amino acids that shared 96-80% identity with other Cyprinodontiformes fishes. RT-PCR analysis revealed that the basal mRNA level of cyp1a was highly expressed in liver and intestine. The expression level of cyp1a was significantly induced by exposure to 100μg/L 3, 4-Benzopyrene (BaP) for 5days in the muscle, testis, brain, liver and intestine of adult male fish. Except in the testis, the induced mRNA level of cyp1a ultimately decreased after prolonging the exposure time to 25days. As for testis, the induced mRNA level of cyp1a was maintained at a high level during the entire exposure time under 100μg/L BaP exposure. Furthermore, the expression of cyp1a increased with exposure time under a relatively low exposure concentrations 1μg/L. Regarding the effects of other PAHs, D(a,h)A, BbF, and BaA showed a statistically significant effect of induction on mRNA level of cyp1a in the muscle, testis, brain, liver and intestine.

Random Forest Ensemble for River Turbidity Measurement From Space Remote Sensing Data

River turbidity, serving as an important evaluation index for monitoring water contamination and guiding pollution control, is mainly measured based on the data gathered from contacting turbidity sensors or contactless space satellites. Nevertheless, the prevalence of those abovementioned two measurements is strongly limited due to the disadvantages of low-density spatial distribution of sensor data and extremely high price of satellite data. To solve such difficulty, depending on the Google earth engine (GEE) that freely supplies hyperspectral remote sensing data, in this article, we propose a novel river turbidity measurement model based on random forest ensemble. First, by fully taking advantage of each spectral information and their tuned spectral information, a newly proposed full combination subspace is deployed to generate all the possible base random forests. Second, we introduce a novel error-minimization-based pruning algorithm to circularly delete poor base random forests in accordance with the dynamic threshold. Finally, a weighted average method solved by regularized linear regression is used to aggregate the entire remainder base random forests that are preserved after pruning, thereby yielding the final measurement result of river turbidity. Experiments corroborate the superiority of our proposed model over state-of-the-art competitors and its simplified counterparts.

Technological Developments for Lake and River Water Contamination Monitoring Systems Across the Globe

This paper is an outcome of investigation to understand how the technology has been involved in monitoring the quality of water in realtime situation around the world. From the review of many papers it’s evident that water quality monitoring system is very much essentional as the scarcity of potable/good quality water has increased across the world due to various reasons, and its found that new technological methods are used to measure various parameters like Dissolved Oxygen, pH of water, turbidity, temperature etc . Many technologies are used to measure the level of contamination at lakes and other water resources. These technologies are invented in silo for specific measurement. We need an integrated approach to get the measurement of contamination by encompassing in all the angles. With the emergence of technologies like IoT, Cloud, Robotic Process Automation (RPA)., we will be able to develop technology platform that will enable to extrack data which comprehends wide variety of contaminants. This paper specifically focuses on the survey of new emerging technologies that can be integrated for measuring contamination with high precision and accuracy in lakes and rivers.

In Situ Reproductive Bioassay with Caged Gammarus fossarum (Crustacea): Part 2—Evaluating the Relevance of Using a Molt Cycle Temperature‐Dependent Model as a Reference to Assess Toxicity in Freshwater Monitoring

Active biomonitoring approaches are now recognized as relevant for monitoring water contamination and toxicity. Nevertheless, due to the confounding influence of variable and uncontrolled environmental conditions such as temperature, biological markers measured on transplanted individuals to assess water quality are difficult to interpret. The purpose of the present study is to propose a methodology for adapting a laboratory test of chronic sublethal toxicity based on the molting cycle of Gammarus fossarum to in situ assays. To this end, we 1) adapted the molt cycle temperature-dependent model developed in Part 1 (Chaumot et al. 2020, this issue) to the fluctuating temperatures measured in the field; 2) assessed the predictive power of our approach as a "reference value" from gammarids caged in 9 nonimpacted sites at different seasons; and 3) tested the relevance of our tool to interpret in situ reproductive bioassays from 5 upstream/downstream studies and a large-scale deployment in 12 sites. Our approach based on modeling the progress of gammarid molting cycle as a function of temperature appeared to be a relevant and robust tool for interpreting in situ observations in different environmental contexts in time and space. By avoiding using a "reference" or upstream situation as a baseline from which water quality could be assessed, this approach provides a real added value to water quality diagnosis in biomonitoring programs. Environ Toxicol Chem 2020;39:678-691. © 2019 SETAC.

Monitoring Water Contamination in Jet Fuel Using Silica-Based Bragg Gratings

This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with the emulsification of water droplets and Stokes’ settling. The measurements are observed for cooling and heating cycles between ranges of 22 °C and −60 °C. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v, with a resolution of <5 ppm-v.

Contamination of a hospital plumbing system by persister cells of a copper-tolerant high-risk clone of Pseudomonas aeruginosa

BackgroundPseudomonas aeruginosa (PA) is an important opportunistic pathogen that thrives best in the distal elements of plumbing and waste-water systems. Although nosocomial outbreaks of PA have been associated with water sources, the role of the plumbing system of healthcare premises as a reservoir for this pathogen is still unclear. Materials and methodsWe collected water samples from 12 technical areas, distant from any medical activity, in a teaching hospital in France once a week for 11 weeks. We used a method that resuscitates persister cells because of the nutrient-poor conditions and the presence of inhibitors (e.g. chlorine and copper ions). Briefly, water was sampled in sterile bottles containing 100 μM of the copper-ion chelating agent diethyldithiocarbamate (DDTC). A portion of the samples was immediately filtered through 0.45-μm membranes, deposited on R2A agar plates, and incubated seven days at 22 °C (following European recommendations). The remaining water was incubated 14 days at 22 °C and then filtered and cultured on R2A, blood-, or cetrimide-containing agar plates. PA isolates were identified by MS MALDI-TOF, genotyped by PFGE and WGS, and tested for survival in a 150 μg/L copper (II) sulphate solution. ResultsAlthough the 12 water sampling points always tested negative with the recommended method, 67% were positive at least once for PA with the adapted method (i.e. with DDTC). The 14 PA persister isolates found throughout the plumbing system were clonal and belong to the high-risk clone ST308. Their genome harbours a 37-kb genomic island (GI-7) containing 13 genes linked to copper resistance. ST308 survived better in the copper solution than comparators that did not harbour GI-7 (P. aeruginosa strains PAO1, PA14, and ST235). The deletion of GI-7 in ST308 abrogated its tolerance to copper. The GI-7 nucleotide sequence shares 98% and 72% identity with sequences from the environmental species Pseudomonas putida and the phytopathogenic species Pseudomonas syringae, respectively. ConclusionWe report the contamination of the plumbing system of a healthcare premises by persister cells of the high-risk clone P. aeruginosa ST308. New recommendations for the monitoring of water contamination should consider persister cells. The genomic island GI-7, which confers tolerance to copper, probably originates from Pseudomonas species found in copper-contaminated soils and plants. Agricultural practices may have an unexpected consequence, allowing copper-tolerant pathogens to survive in the hospital environment and contaminate fragile patients.