Fast and Efficient Sensing of Drugs in Water Using Self-Assembling D-Glucamine-Functionalized Naphthalenediimide and 1,8-Naphthalimide Fluorophores.

Fast and sensitive quantification of drugs as emerging pollutants in water bodies is a pressing need in contemporary society, to prevent serious environmental concerns that could negatively impact on human health. This explains the surge of interest in this field, and the need to identify highly selective sensing systems. Addressing this issue, in this work we synthesized two D-glucamine functionalized fluorophores bearing self-assembling cores, as 1,8-naphthalimide and naphthalene diimide. We studied their self-assembly in water solution, and characterized the aggregated formed by determining their stability constant, their morphology and size by scanning electron microscopy, resonance light scattering and dynamic light scattering, as well their solid-state emission ability. Then, we studied their sensing ability, in water, towards pharmaceutically active compounds such as ciprofloxacin, nalidixic acid, carbamazepine and diclofenac sodium salt, by fluorescence investigation. Data collected show that the self-assembling ability is significantly affected by the fluorophore structure, which in turn also determines sensing ability. In particular, the naphtalene diimide-based probe was the most sensitive, with LOD as low as 0.01 µM in the presence of nalidixic acid, which is in line and competitive with more complex sensing systems, recently reported in the literature.

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation.

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

What, How, When, and Where: Spatiotemporal Water Quality Hazards of Cyanotoxins in Subtropical Eutrophic Reservoirs.

Though toxins produced during harmful blooms of cyanobacteria present diverse risks to public health and the environment, surface water quality surveillance of cyanobacterial toxins is inconsistent, spatiotemporally limited, and routinely relies on ELISA kits to estimate total microcystins (MCs) in surface waters. Here, we employed liquid chromatography tandem mass spectrometry to examine common cyanotoxins, including five microcystins, three anatoxins, nodularin, cylindrospermopsin, and saxitoxin in 20 subtropical reservoirs spatially distributed across a pronounced annual rainfall gradient. Probabilistic environmental hazard analyses identified whether water quality values for cyanotoxins were exceeded and if these exceedances varied spatiotemporally. MC-LR was the most common congener detected, but it was not consistently observed with other toxins, including MC-YR, which was detected at the highest concentrations during spring with many observations above the California human recreation guideline (800 ng/L). Cylindrospermopsin was also quantitated in 40% of eutrophic reservoirs; these detections did not exceed a US Environmental Protection Agency swimming/advisory level (15,000 ng/L). Our observations have implications for routine water quality monitoring practices, which traditionally use ELISA kits to estimate MC levels and often limit collection of surface samples during summer months near reservoir impoundments, and further indicate that spatiotemporal surveillance efforts are necessary to understand cyanotoxins risks when harmful cyanobacteria blooms occur throughout the year.

Year-round monitoring of three water sources in Québec, Canada, reveals site-specific differences in conditions for Cryptosporidium and Giardia contamination.

Cryptosporidium and Giardia are protozoan parasites responsible for gastrointestinal illnesses in humans and in animal species. The main way these parasites are transmitted is by ingestion of their (oo)cysts in drinking water. Monitoring (oo)cysts in water sources is beneficial to evaluate the quality of raw water supplying treatment plants. Currently, the only standardized protocol to enumerate these parasites from water samples is United States Environmental Protection Agency (USEPA) Method 1623.1. With this method, we monitored three major water sources in Quebec over a year to assess temporal and geographical variations of these parasite (oo)cysts. These three water sources have independent watersheds despite being in the same region. We found a general pattern for Giardia, with high concentrations of cysts during cold and transition periods, and significantly lower concentrations during the warm period. Cryptosporidium's concentration was more variable throughout the year. Statistical correlations (Pearson's correlation coefficients) were established between the concentration of each parasite and various environmental parameters. The three study sites each showed unique factors correlating with the presence of both protozoa, supporting the idea that each water source must be seen as a unique entity with its own particular characteristics and therefore, must be monitored independently. Although some environmental parameters could be interesting proxies to the parasitic load, no parameter was strongly correlated throughout the whole sampling year and none of the parameters could be used as a single proxy for all three studies sources.

Risk factors for Cryptosporidium contamination in Minnesota public supply wells.

In a recent monitoring study of Minnesota's public supply wells, Cryptosporidium was commonly detected with 40% of the wells having at least one detection. Risk factors for Cryptosporidium occurrence in drinking water supply wells, beyond surface water influence, remain poorly understood. To address this gap, physical and chemical factors were assessed as potential predictors of Cryptosporidium occurrence in 135 public supply wells in Minnesota. Univariable analysis, regression techniques, and classification trees were used to analyze the data. Many variables were identified as significant risk factors in univariable analysis and several remained significant throughout the succeeding analysis techniques. These factors fell into general categories of well use and construction, aquifer characteristics, and connectedness to the land surface, well capture zones, and land use therein, existence of potential contaminant sources within 200-feet of the well, and variability in the chemical and isotopic parameters measured during the study. These risk categories, and the specific variables and threshold values we have identified, can help guide future research on factors influencing Cryptosporidium contamination of wells and can be used by environmental health programs to develop risk-based sampling plans and design interventions that reduce associated health risks.

Innovative Non-Invasive and Non-Intrusive Precision Thermometry in Stainless-Steel Tanks Using Ultrasound Transducers.

Measuring temperature inside chemical reactors is crucial to ensuring process control and safety. However, conventional methods face a number of limitations, such as the invasiveness and the restricted dynamic range. This paper presents a novel approach using ultrasound transducers to enable accurate temperature measurements. Our experiments, conducted within a temperature range of 28.8 to 83.8 °C, reveal a minimal temperature accuracy of 98.6% within the critical zone spanning between 70.5 and 75 °C, and an accuracy of over 99% outside this critical zone. The experiments focused on a homogeneous environment of distilled water within a stainless-steel tank. This approach will be extended in a future research in order to diversify the experimental media and non-uniform environments, while promising broader applications in chemical process monitoring and control.

Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development.

Comprehensive river water quality monitoring using convolutional neural networks and gated recurrent units: A case study along the Vaigai River.

Effective monitoring of river water quality is required for long-term water resource management. Convolutional Neural Networks and Gated Recurrent Unit-based water quality monitoring (CNGRU-WQM) were used in this investigation to develop a comprehensive monitoring system along the Vaigai River. The system was designed to collect real-time data on several crucial water quality parameters. The collected characteristics encompassed factors like water pollution levels, turbidity, pH readings, temperature, and total dissolved solids, offering a comprehensive view of river water quality. The monitoring system was methodically set up, with sensors strategically positioned at various locations along the river. This ensured that data collection would take place at regular intervals. The CNGRU-WQM model achieved a validation accuracy of 97.86%, surpassing the performance of other state-of-the-art approaches. Particularly noteworthy is the fact that the actual use of this system incorporates real-time warnings, which enable stakeholders to be instantly informed if water quality measurements surpass pre-set criteria. The study's contributions include its efficient river water quality monitoring system, which encompasses a variety of indicators, and its ability to significantly affect environmental conservation efforts by offering a helpful tool for informed decision-making and timely interventions.

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Safe Today, Unsafe Tomorrow: Tanzanian Households Experience Variability in Drinking Water Quality.

Measuring Escherichia coli in a single-grab sample of stored drinking water is often used to characterize drinking water quality. However, if water quality exhibits variability temporally, then one-time measurement schemes may be insufficient to adequately characterize the quality of water that people consume. This study uses longitudinal data collected from 193 households in peri-urban Tanzania to assess variability in stored water quality and to characterize uncertainty with different data collection schemes. Households were visited 5 times over the course of a year. At each visit, information was collected on water management practices, and a sample of stored drinking water was collected for E. coli enumeration. Water quality was poor for households, with 80% having highly contaminated (>100 CFU per 100 mL) water during at least one visit. There was substantial variability of water quality for households, with only 3% of households having the same category (low, medium, or high) of water quality for all five visits. These data suggest a single sample would inaccurately characterize a household's drinking water quality over the course of a year and lead to misestimates of population level access to safe drinking water.

Bridged EGFET Design for the Rapid Screening of Sorbents as Sensitisers in Water-Pollution Sensors.

We further simplify the most 'user-friendly' potentiometric sensor for waterborne analytes, the 'extended-gate field effect transistor' (EGFET). This is accomplished using a 'bridge' design, that links two separate water pools, a 'control gate' (CG) pool and a 'floating gate' (FG) pool, by a bridge filled with agar-agar hydrogel. We show electric communication between electrodes in the pools across the gel bridge to the gate of an LND150 FET. When loading the gel bridge with a sorbent that is known to act as a sensitiser for Cu2+ water pollution, namely, the ion exchanging zeolite 'clinoptilolite', the bridged EGFET acts as a potentiometric sensor to waterborne Cu2+. We then introduce novel sensitisers into the gel bridge, the commercially available resins PurometTM MTS9140 and MTS9200, which are sorbents for the extraction of mercury (Hg2+) pollution from water. We find a response of the bridged EGFET to Hg2+ water pollution, setting a template for the rapid screening of ion exchange resins that are readily available for a wide range of harmful (or precious) metal ions. We fit the potentiometric sensor response vs. pollutant concentration characteristics to the Langmuir-Freundlich (LF) model which is discussed in context with other ion-sensor characteristics.

Toward Optimal Irrigation Management at the Plot Level: Evaluation of Commercial Water Potential Sensors.

Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation-accuracy and variability-of the world's leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa-200 kPa) was determined in two soils of contrasting textures. The variability in the recordings-repeatability and reproducibility-was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0-100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150-200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.

An Evidence Theory Based Embedding Model for the Management of Smart Water Environments.

Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.

In Situ Water Quality Monitoring Using an Optical Multiparameter Sensor Probe.

Optical methods such as ultraviolet/visible (UV/Vis) and fluorescence spectroscopy are well-established analytical techniques for in situ water quality monitoring. A broad range of bio-logical and chemical contaminants in different concentration ranges can be detected using these methods. The availability of results in real time allows a quick response to water quality changes. The measuring devices are configured as portable multi-parameter probes. However, their specification and data processing typically cannot be changed by users, or only with difficulties. Therefore, we developed a submersible sensor probe, which combines UV/Vis and fluorescence spectroscopy together with a flexible data processing platform. Due to its modular design in the hardware and software, the sensing system can be modified to the specific application. The dimension of the waterproof enclosure with a diameter of 100 mm permits also its application in groundwater monitoring wells. As a light source for fluorescence spectroscopy, we constructed an LED array that can be equipped with four different LEDs. A miniaturized deuterium-tungsten light source (200-1100 nm) was used for UV/Vis spectroscopy. A miniaturized spectrometer with a spectral range between 225 and 1000 nm permits the detection of complete spectra for both methods.

Environmental Sensing in High-Altitude Mountain Ecosystems Powered by Sedimentary Microbial Fuel Cells.

The increasing need for fresh water in a climate change scenario requires remote monitoring of water bodies in high-altitude mountain areas. This study aimed to explore the feasibility of SMFC operation in the presence of low dissolved oxygen concentrations for remote, on-site monitoring of physical environmental parameters in high-altitude mountainous areas. The implemented power management system (PMS) uses a reference SMFC (SMFCRef) to implement a quasi-maximum power point tracking (quasi-MPPT) algorithm to harvest energy stably. As a result, while transmitting in a point-to-point wireless sensor network topology, the system achieves an overall efficiency of 59.6%. Furthermore, the control mechanisms prevent energy waste and maintain a stable voltage despite the microbial fuel cell (MFC)'s high impedance, low time response, and low energy production. Moreover, our system enables a fundamental understanding of environmental systems and their resilience of adaptation strategies by being a low-cost, ecological, and environmentally friendly alternative to power-distributed and dynamic environmental sensing networks in high-altitude mountain ecosystems with anoxic environmental conditions.

Linking multivariate statistical methods and water quality indices to evaluate the natural and anthropogenic geochemical processes controlling the water quality of a tropical watershed.

The improvement of water management requires monitoring techniques that accurately evaluate water quality status and detect the effects of land use changes on water chemistry. This study aimed to evaluate how multivariate statistical methods and water quality indices can be applied together to evaluate the processes controlling water chemical composition and the overall water quality status of a tropical watershed. Thirty-four water samples were collected in the Formoso River basin, located on the border of the Amazon Forest. Water parameters were measured in situ using a multiparameter and in the lab using spectroscopic and volumetric techniques. The water quality dataset was interpreted through principal component analysis, multivariate linear regression, and water quality indices. Statistical methods allowed us to identify the sources and geochemical processes controlling water quality chemistry, which were carbonate dissolution, runoff/erosion, nutrient input due to anthropogenic activities, and redox reactions in flooded zones. They were also used to create linear functions to evaluate the effects of land use changes on the geochemical processes controlling water chemistry. Conversely, the water quality indices provide information about the overall condition of the water. The Weight-Arithmetic Quality Index correctly evaluates water suitability for its multiple uses, according to the Brazilian guidelines. Conversely, the Ontario Water Quality Index is not suitable to evaluate the water quality of tropical rivers, since the usual higher water temperature and the low oxygen contents associated with tropical environments result in biased water quality evaluations by this index.

Unique On-Site Spinning Sampling of Highly Water-Soluble Organics Using Functionalized Monolithic Sorbents.

Water utilities encounter unpredictable odor issues that cannot be explained by routine water parameters during spring runoff, even in the summer and fall. Highly water-soluble organics (e.g., amino acids and saccharides) have been reported to form odorous disinfection byproducts during disinfection, but the lack of simple and practical on-site sampling techniques hampers their routine monitoring at trace levels in source water. Therefore, we have created two functionalized nested-in-sponge silica monoliths (NiS-SMs) using a one-pot synthesis method and demonstrated their application for extracting highly soluble organics in water. The NiS-SMs functionalized with the sulfonic group and phenylboronic moiety selectively extracted amino acids and monosaccharides, respectively. We further developed a spinning sampling technique using the composites and evaluated its robust performance under varying water conditions. The spinning sampling coupled to high-performance liquid chromatography tandem mass spectrometry analysis provided limits of detection for amino acids at 0.038-0.092 ng L-1 and monosaccharides at 0.036-0.14 ng L-1. Using the pre-equilibrium sampling-rate calibration, we demonstrated the applicability of the spinning sampling technique for on-site sampling and monitoring of amino acids and monosaccharides in river water. The new composite materials and rapid on-site sampling technique are unique and efficient tools for monitoring highly soluble organics in water sources.

Unravelling the role of membrane pore size in polar organic chemical integrative samplers (POCIS) to broaden the polarity range of sampled analytes.

Polar organic chemical integrative samplers (POCIS) are widely used in their standard configuration for sampling contaminants in water bodies. A wider polyethersulfone (PES) membrane pore size was employed in POCIS exposed in a static calibration experiment to investigate the uptake of 21 emerging contaminants ranging from hydrophilic (perfluoroalkyl compounds, xanthines, an artificial sweetener) to more hydrophobic compounds (pharmaceuticals, oestrogens, UV filters). Compared to standard POCIS with 0.1-µm pore size PES membranes, the POCIS with 5-µm pore size PES membranes did not increase sampling rates for compounds of relatively low and mid-hydrophobicity. However, the uptake of more hydrophobic and anionic compounds, which either poorly diffuse through or are retained within the standard 0.1-µm PES membrane, showed a marked increase. This led to the first ever recorded sampling rates for triclosan (0.249 L day-1) and two UV filters (0.075-0.123 L day-1). Based on these results, more attention should be placed on the choice of the appropriate membrane for each POCIS application. The most suitable configuration depends on the studied compound physico-chemical characteristics-such as the polarity and the compound membrane-to-sorbent partitioning coefficient-but also on the site conditions (deployment time, fouling, flow variations, et.).

A home-made sampling system coupled to hectowatt-MPT mass spectrometry in positive ion mode to confirm target ions of copper and zinc from Poyang Lake, China.

A novel home-made H2SO4-Nafion (HN) tube sampling system coupled to a line ion trap mass spectrometer (LTQ-MS) with a versatile ambient ionization source, hectowatt microwave plasma torch (HMPT), has manifested unique advantages for picking directly metal elements in aqueous samples and acquiring the fully characteristic MPT mass spectra of copper and zinc composite ions. Here, we report the development of a novel HN-HMPT-LTQ-MS for metal elements assay based on environmental water to analyze samples of Poyang Lake, China. Detailed multi-stage tandem mass spectra show that the general structural form of target ions is [M(NO3)x(H2O)y(OH)z]+ for the positive ion mode. Under the optimized conditions, the proposed method provided low limits of detection (LODs) of 0.23 µg.L-1 for 63Cu+ and 1.1 µg.L-1 for 66Zn+, with relative standard deviations (RSDs) of less than 12.7% by MPT-LTQ-MS. This new result has met the requirements of national standards (GB 5750.6-2006) and is only about one magnitude order larger than the LOD of ICP-MS method. A wide linear response range of about 4 orders of magnitude for the method with linear coefficients (R2) of 0.99709 - 0.99962 for copper and zinc tested was in accordance with that of ICP-MS. Except for the recovery of 79% for the third sample and 123.8% for the seventh sample, the present method also provided good recoveries (84 - 119.3%) in spiked 10 batches of drinking water samples. Furthermore, it is envisioned that the developed approach might build a powerful hectowatt-MPT-MS platform for food security detection, drug analysis, and origin traceability.

Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring.

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.