Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Electrochemical Acid-Base Transport Limitation Principle for Low Electroactive Analyte Sensing in Wastewater Monitoring

Electrochemical sensing (ES) is crucial for improving data acquisition in wastewater treatment, but obtaining the signal for a low electroactive analyte is challenging. Here, we propose an electrochemical acid-base transport limitation (eABTL) principle for inertness-based sensing, offering a new insight into generating ES signals from an interfacial transport process rather than electron transfer. This principle enables potential ES application for various weak acids and bases (WABs) without reactions themselves. We established an eABTLP method for detecting orthophosphate in solutions as a proof of concept, demonstrating commendable accuracy and precision, and a wide detection range from 10 μM to over 300 mM. Endogenous interferences were identified using 23 weak acids, indicating no significant endogenous interfering factors in typical wastewaters. Of them, volatile fatty acids are the main interference, but their effect can be eliminated by adjusting pH above 6.0. Exogenous factors like anions, cations, ion strength, temperature, organic load, and dissolved oxygen were examined, and most of their effects can be ignored by maintaining consistent analytical procedures between calibration curve and sample. Furthermore, measurement of wastewater samples confirmed the applicability toward domestic wastewater and demonstrated its wide applicability when combined with digestion pretreatment. Given the merits of inertness-based sensing, the eABTL-based methods have the potential to be a crucial part of ES techniques for environmental and industrial monitoring.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

Ecotoxicological risk assessment on coagulation-flocculation in water/wastewater treatment: a systematic review

It is undeniable that removal efficiency is the main factor in coagulation-flocculation (C-F) process for wastewater treatment. However, as far as environmental safety is concerned, the ecotoxicological aspect of the C-F process needs to be examined further. In this study, a systematic review was performed based on publications related to the toxicity research in C-F technology for wastewater treatment. Through a series of screening steps, available toxicity studies were categorized into four themes, namely acute toxicity, phytotoxicity, cytotoxicity, and genotoxicity, which comprised 48 articles. A compilation of the methodologies executed for each theme was also outlined. The findings show that conventional metallic coagulants (e.g., alum, iron chloride, and iron sulfate) were less toxic when tested on test species such as Daphnia magna (water flea), Lattuca sativa (lettuce), and animal cells compared to synthetic polymers. Natural coagulants such as chitosan or Moringa oleifera were less toxic compared to metallic coagulants; however, inconsistent results were observed. Moreover, an advanced C-F (electrocoagulation) as well as integration between C-F and Fenton, adsorption, and photocatalytic does not significantly change the toxicological profile of the system. It was found that diverse coagulants and flocculants, species sensitivity, complexity in toxicity testing, and dynamic environmental conditions were some key challenges faced in this field. Finally, it was expected that advances in technology, interdisciplinary collaboration, and a growing awareness of environmental sustainability will drive efforts to develop more effective and eco-friendly coagulants and flocculants, improve toxicity testing methodologies, and enhance the overall efficiency and safety of water and wastewater treatment processes.

Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design

Polyvinyl chloride (PVC) membrane-based ion-selective electrode (ISE) sensors are common tools for water assessments, but their development relies on time-consuming and costly experimental investigations. To address this challenge, this study combines machine learning (ML), Morgan fingerprint, and Bayesian optimization technologies with experimental results to develop high-performance PVC-based ISE cation sensors. By using 1745 data sets collected from 20 years of literature, appropriate ML models are trained to enable accurate prediction and a deep understanding of the relationship between ISE components and sensor performance (R 2 = 0.75). Rapid ionophore screening is achieved using the Morgan fingerprint based on atomic groups derived from ML model interpretation. Bayesian optimization is then applied to identify optimal combinations of ISE materials with the potential to deliver desirable ISE sensor performance. Na+, Mg2+, and Al3+ sensors fabricated from Bayesian optimization results exhibit excellent Nernst slopes with less than 8.2% deviation from the ideal value and superb detection limits at 10-7 M level based on experimental validation results. This approach can potentially transform sensor development into a more time-efficient, cost-effective, and rational design process, guided by ML-based techniques.

Water monitoring with an automated smart sensor supported with solar power for real-time and long range detection of ferrous iron

Low-power and smart sensing systems for iron detection are necessary for in situ monitoring of water quality. Here, a potentiometric Fe2+-selective electrode (ISE) was fabricated based on cyanomethyl N-methyl-N-phenyl dithiocarbamate for the first time as an ionophore. Under optimal conditions, the ISE showed a Nernstian slope of 29.76 ± 0.6 mV per decade for Fe2+ ions over a wide concentration range from 1.0 × 10-1 to 1.0 × 10-5 M with a lower detection limit (LOD) of 1.0 × 10-6 M. The ISE interference of various cations on the potentiometric response was also investigated. The ISE had a response time less than 3 s and the lifetime was two months. Also, an automated, long-range (LoRa), wireless enabled sampling microfluidic device powered with a solar panel as an autonomous power source was developed for a continuous sampling and sensing process. The sensing platform was employed in the determination of Fe2+ in acid mine drainage and spiked water samples with an average recovery of 100.7%. This simple, inexpensive (below $350), portable sensing platform will allow for rapid real-time monitoring of ground-, drinking-, and industrial waters contaminated with iron.

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.

MXene-Coated Ion-Selective Electrode Sensors for Highly Stable and Selective Lithium Dynamics Monitoring

Lithium holds immense significance in propelling sustainable energy and environmental systems forward. However, existing sensors used for lithium monitoring encounter issues concerning their selectivity and long-term durability. Addressing these challenges is crucial to ensure accurate and reliable lithium measurements during the lithium recovery processes. In response to these concerns, this study proposes a novel approach involving the use of an MXene composite membrane with incorporated poly(sodium 4-styrenesulfonate) (PSS) as an antibiofouling layer on the Li+ ion selective electrode (ISE) sensors. The resulting MXene-PSS Li+ ISE sensor demonstrates exceptional electrochemical performance, showcasing a superior slope (59.42 mV/dec), lower detection limit (10-7.2 M), quicker response time (∼10 s), higher selectivity to Na+ (-2.37) and K+ (-2.54), and reduced impedance (106.9 kΩ) when compared to conventional Li+ ISE sensors. These improvements are attributed to the unique electronic conductivity and layered structure of the MXene-PSS nanosheet coating layer. In addition, the study exhibits the long-term accuracy and durability of the MXene-PSS Li+ ISE sensor by subjecting it to real wastewater testing for 14 days, resulting in sensor reading errors of less than 10% when compared to laboratory validation results. This research highlights the great potential of MXene nanosheet coatings in advancing sensor technology, particularly in challenging applications, such as detecting emerging contaminants and developing implantable biosensors. The findings offer promising prospects for future advancements in sensor technology, particularly in the context of sustainable energy and environmental monitoring.

Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring

Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.

Real-Time and Online Monitoring of Hazardous Volatile Organic Compounds in Environmental Water by an Unmanned Shipborne Mass Spectrometer System

Hazardous volatile organic compounds (VOCs) are one of the critical concerns in environmental water due to their toxicity to aquatic organisms and drinking water. Therefore, rapid detection of hazardous VOCs in environmental water is highly needed as many analytical methods are limited to on-site monitoring. In this work, we designed a novel unmanned shipborne mass spectrometer (US-MS) system for the real-time and online monitoring of hazardous VOCs in environmental water. The US-MS system consists of a miniaturized mass spectrometer, an automatic sampling device, a robust unmanned ship, and other monitoring and control devices. Along with the navigation route of the US-MS system, environmental water was continuously introduced into the MS system for the online and real-time detection of hazardous VOCs via a liquid/gas exchange membrane. Analytical performances of the US-MS system were investigated by a mixture of 10 VOCs showing low limits of detection (LODs: 0.31-1.26 ng/mL), good reproducibility (RSDs: 2.93-11.03%, n = 7), and excellent quantitative ability (R2 > 0.99). Furthermore, on-site detection and online monitoring of hazardous volatile contaminants such as benzene, chloroprene, and toluene in different aquatic environments such as rivers and lakes were successfully demonstrated, showing excellent field applicability of the US-MS system. Overall, the newly developed US-MS system could perform on-site, online, and real-time monitoring of complex VOCs in environmental water, showing good performances and versatile applications in water analysis.

2D Fluorinated Graphene Oxide (FGO)-Polyethyleneimine (PEI) Based 3D Porous Nanoplatform for Effective Removal of Forever Toxic Chemicals, Pharmaceutical Toxins, and Waterborne Pathogens from Environmental Water Samples

Although water is essential for life, as per the United Nations, around 2 billion people in this world lack access to safely managed drinking water services at home. Herein we report the development of a two-dimensional (2D) fluorinated graphene oxide (FGO) and polyethylenimine (PEI) based three-dimensional (3D) porous nanoplatform for the effective removal of polyfluoroalkyl substances (PFAS), pharmaceutical toxins, and waterborne pathogens from contaminated water. Experimental data show that the FGO-PEI based nanoplatform has an estimated adsorption capacity (qm) of ∼219 mg g-1 for perfluorononanoic acid (PFNA) and can be used for 99% removal of several short- and long-chain PFAS. A comparative PFNA capturing study using different types of nanoplatforms indicates that the qm value is in the order FGO-PEI > FGO > GO-PEI, which indicates that fluorophilic, electrostatic, and hydrophobic interactions play important roles for the removal of PFAS. Reported data show that the FGO-PEI based nanoplatform has a capability for 100% removal of moxifloxacin antibiotics with an estimated qm of ∼299 mg g-1. Furthermore, because the pore size of the nanoplatform is much smaller than the size of pathogens, it has a capability for 100% removal of Salmonella and Escherichia coli from water. Moreover, reported data show around 96% removal of PFAS, pharmaceutical toxins, and pathogens simultaneously from spiked river, lake, and tap water samples using the nanoplatform.

An ion-selective chemiresistive platform as demonstrated for the detection of nitrogen species in water

The use of ion-selective electrodes (ISE) is a well-established technique for the detection of ions in aqueous solutions but requires the use of a reference electrode. Here, we introduce a platform of ion-selective chemiresistors for the detection of nitrogen species in water as an alternative method without the need for reference electrodes. Chemiresistors have a sensitive surface that is prone to damage during operation in aqueous solutions. By applying a layer of ion-selective membrane to the surface of the chemiresistive device, the surface becomes protected and highly selective. We demonstrate both anion-selective (NO3-, NO2-) and cation-selective (NH4+) membranes. The nitrate sensors are able to measure nitrate ions in a range of 2.2-220 ppm with a detection limit of 0.3 ppm. The nitrite sensors respond between 67 ppb and 67 ppm of nitrite ions (64 ppb detection limit). The ammonium sensors can measure ammonium concentrations in a wide range from 10 ppb to 100 ppm (0.5 ppb detection limit). The fast responses to nitrate and nitrite are due to a mechanism involving electrostatic gating repulsion between negative charge carriers of the film and anions while ammonium detection arises from two mechanisms based on electrostatic gating repulsion and adsorption of ammonium ions at the surface of the p-doped chemiresistive film. The adsorption phenomenon slows down the recovery time of the ammonium sensor. This sensor design is a new platform to continuously monitor ions in industrial, domestic, and environmental water resources by robust chemiresistive devices.

A Weak Electricigen-Based Bioelectrochemical Sensor for Real-Time Monitoring of Chemical Pollutants in Water

Electroactive microorganisms are now understood to be abundant across nature, though many are categorized as "weak electricigens" not suitable for reasonable power generation. We report the use of weak electricigens from the natural environment for rapid, real-time water quality monitoring. Using a variety of pesticides as model chemical pollutants, the bioelectrochemical sensor was responsive within minutes at all concentrations tested (0.05-2 ppm) and could be repreatedly used long-term. Due to the prevalence of electroactive microorganisms in the natural environment, such sensors could work in tandem with conventional monitoring methods and may be useful for detecting emerging contaminants.

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.

In situ and self-adaptive BOD bioreaction sensing system based on environmentally domesticated microbial populations

Biochemical oxygen demand (BOD) is a water quality parameter of vital importance. Rapid BOD analysis methods have emerged to simplify the five-day BOD (BOD₅) measurement protocol. However, their universal implementations are restricted by the tricky environmental matrix (including environmental microbes, contaminants, ionic compositions, etc.). Here, an in situ and self-adaptive BOD bioreaction sensing system consisting of a "gut-like" microfluidic coil bioreactor with self-renewed biofilm was proposed for the establishment of a rapid, resilient and reliable BOD determination method. With the spontaneous surface adhesion of environmental microbial populations, the biofilm was colonized in situ on the inner surface of the microfluidic coil bioreactor. Exploiting the environmental domestication during every real sample measurement, the biofilm was capable of self-renewal to adapt to the environmental changes and exhibited representative biodegradation behaviors. The aggregated abundant, adequate and adapted microbial populations in the BOD bioreactor rendered a total organic carbon (TOC) removal rate of 67.7% within a short hydraulic retention time of 99 s. As validated by an online BOD prototype, exceptional analytical performance was achieved in terms of reproducibility (relative standard deviation of 3.7%), survivability (inhibition by pH and metal ion interference of <20%) and accuracy (relative error of -5.9% to 9.7%). This work rediscovered the interactive effects of the environmental matrix on BOD assays and demonstrated an instructive attempt by making use of the environment to develop practical online BOD monitoring devices for water quality assessments.

Smartphone-based spectrometry system as a prescreening assessment of copper and iron for real time control of water pollution

Due to anthropogenic actions, the presence of pollutants in water bodies, such as toxic metals, are increasingly negatively affecting water quality, biodiversity and sustainable goals worldwide. Therefore, decentralization of water pollution monitoring with low-cost devices, such as using smartphones, suggests an innovative green technology for in situ and real-time control. In this study, a Handheld Smartphone Spectrophotometry System (HSSS) was developed to estimate copper and iron concentration water samples. The system mainly comprises a portable commercial spectrometer (GoSpectro) that can measure the spectrum of light in the visible region. The HSSS LOD and LOQ for copper were equal to 0.589 and 1.784 mg/L, respectively, and 0.479 and 1.450 mg/L, respectively for iron. In addition, the results of copper and iron concentrations in samples with unknown concentrations using HSSS were close to the Benchtop Spectrophometer (BS). Finally, HSSS performance showed to be a new green technology for water quality management with potential applications for monitoring water resources and also providing further possibilities to measure other pollutants by the same technique, in addition to metals.