Water Quality Monitoring Applications Research Articles

Programmable multi-stimulus-responsive whirligig beetle inspired soft robot with multifunctionality based on composite materials

In nature, surfaces are evolutionarily designed to allow species to adapt to their environments. Nowadays, more and more researchers have paid attention to soft robots based on superhydrophobic surfaces. Inspired by the swimming characteristics of whirligig beetles on the surface of water, we propose in this study a multi-stimulus-responsive soft robot that can be driven by light, magnetism and chemistry to achieve fast straight-line motions, turns and rotations on the surface of water. The soft robot is made of a composite material based on graphene oxide (GO) and polydimethylsiloxane (PDMS), which has high hydrophobicity and excellent photothermal properties. With a multi-stimulus-responsive driving mechanism, the soft robot can be used in applications such as oil recovery, cargo transportation, obstacle avoidance, and material dispersion. It is envisioned that this study can guide the development of multi-stimulus-responsive soft robots and offer some useful reference for their applications in water quality monitoring and oil recovery.

A Ratiometric Fluorescent Detection Platform Using G‐CDs@[Ru(bpy)3]2+ for the Specific Detection of Hypochlorite and Live Cell Imaging

AbstractHypochlorite (ClO−), an essential reactive oxygen species (ROS) in physiological processes, is identified to be closely connected with oxidative stress and related diseases. Meanwhile, ClO− is a commonly‐used disinfector for water treatment, and in public places, under acidic conditions, it's easily decomposed into hypertoxic chlorine gas. Since the strong oxidizing property of ClO−, many oxidizing agents may disturb the ClO− detection. Specific and accurate detection of ClO− with superior sensitivity is a challenge. In this work, a sensing platform for rapid, sensitive, and specific ClO− detection is constructed using green fluorescent carbon dots (G‐CDs), with a linear detection range of 0.5–11 µm and a detection limit of 0.233 µm. Moreover, introducing a red fluorescent tripyridinium ruthenium ([Ru(bpy)3]2+) as a reference, a ratiometric fluorescence nanoprobe G‐CDs@[Ru(bpy)3]2+ is prepared and shows favorable intracellular imaging of exogenous and endogenous ClO−. With G‐CDs@[Ru(bpy)3]2+‐based test paper microarrays and a color recognition APP, a smartphone‐based sensing system for point‐of‐care testing of ClO− is also fabricated. In summary, this work proposed a versatile and economical smartphone‐based sensing system that featured reliability and simplicity, and suggested its potential applications in environmental water quality monitoring and live cell imaging.

Photoacoustic Spectroscopy Using a Quantum Cascade Laser for Analysis of Ammonia in Water Solutions.

Ammonia (NH3) toxicity, stemming from nitrification, can adversely affect aquatic life and influence the taste and odor of drinking water. This underscores the necessity for highly responsive and accurate sensors to continuously monitor NH3 levels in water, especially in complex environments, where reliable sensors have been lacking until this point. Herein, we detail the development of a sensor comprising a compact and selective analyzer with low gas consumption and a timely response based on photoacoustic spectroscopy. This, combined with an automated liquid sampling system, enables the precise detection of ammonia traces in water. The sensor system incorporates a state-of-the art quantum cascade laser as the excitation source emitting at 9 μm in resonance with the absorption line of NH3 located at 1103.46 cm-1. Our instrument demonstrated detection sensitivity at a low ppm level for the ammonia molecule with response times of less than 60 s. For the sampling system, an ammonia stripping solution was designed, resulting in a prompt full measurement cycle (6.35 min). A further evaluation of the sensor within a pilot study showed good reliability and agreement with the reference method for real water samples, confirming the potential of our NH3 analyzer for water quality monitoring applications.

Smart nanocomposites: Harnessing magnetically recoverable MWCNT-CF for efficient organic dyes reduction in water quality monitoring applications

The accelerating use of organic dyes in various industries has led to a surge in water pollution, especially from non-biodegradable dye effluents discharged into water resources. This study addresses the critical issue of catalyzing the reduction of two prevalent dyes, methylene blue (MB) and rhodamine-B (RhB), using a multiwalled carbon nanotube-cobalt ferrite (MWCNT-CF) nanocomposite. The synthesized nanocomposite demonstrates exceptional catalytic activity, stability, and recyclability. Conventional methods for treating dye-containing wastewater often prove expensive. This study explores the efficacy of catalytic reduction, a relatively fast process facilitated by semiconductor nanoparticles. Structural analyses using X-ray diffraction and high-resolution transmission electron microscopy (HRTEM) confirm the formation of the nanocomposite, revealing unsaturated surface bonds and chains conducive to adsorption. The nanocomposite exhibits a remarkable reduction in both dyes, with easy recyclability for multiple cycles. Magnetization studies confirm the ferrimagnetic nature of the nanocomposite, facilitating its efficient separation from the reaction mixture using a magnet. The study delves into the kinetics of the catalytic reduction following pseudo-first-order kinetics. The surface modifications of the nanocomposite, as revealed by TEM, contribute to enhanced adsorption and catalytic efficiency. Notably, the MWCNT-CF nanocomposite demonstrates negligible loss of catalytic activity during recycling, highlighting its potential for cost-effective and sustainable applications in dye reduction across various industries.

A2DWQPE: Adaptive and automated data-driven water quality parameter estimation

Accurate remote sensing estimation of inland water quality parameters (WQPs) plays a crucial role in guiding water resource management. To achieve this, researchers have explored various data-driven approaches utilizing machine learning (ML) techniques. However, there are two major challenges in WQPs estimation for inland waters. Firstly, current data-driven approaches focus on building a unified estimation model for an entire study area, which underestimates the complex dynamics of water constituents and optical properties. Secondly, ML models, particularly neural networks, require extensive hyperparameter tuning and are not user-friendly for researchers lacking relevant background and experience. In this paper, we propose an innovative method called adaptive and automated data-driven water quality parameter estimation (A2DWQPE) to address both challenges. Our method operates under the assumption that water bodies with similar spectral characteristics should share the same WQP estimation model. A2DWQPE is composed of three phases. Firstly, water types are automatedly classified by unsupervised hierarchical clustering according to spectral similarity. Then, optimal Deep Neural Network (DNN) models for estimating WQPs from multi-spectral satellite images are customized for each water type utilizing Bayesian optimization (BO). Finally, the target WQP is estimated based on the type-specific estimates and degree of membership of each water type. To evaluate the effectiveness of A2DWQPE, we applied it to estimate Secchi disk depth (SDD) in Lake Erie with in situ measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results demonstrate that A2DWQPE outperforms the traditional approaches of developing a unified model for the entire study area. A2DWQPE achieved high accuracy with coefficient of determination (R2) over 0.72 and root mean square error (RMSE) below 1.4 m. Our method also outperforms the methods that applied Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) instead of BO, and several traditional ML algorithms. We firmly believe that A2DWQPE holds great potential for accurate inland water quality estimation and will contribute significantly to various applications in water quality monitoring and pollution prevention.

Microfluidic Sensor Based on Cell-Imprinted Polymer-Coated Microwires for Conductometric Detection of Bacteria in Water.

The rapid, inexpensive, and on-site detection of bacterial contaminants using highly sensitive and specific microfluidic sensors is attracting substantial attention in water quality monitoring applications. Cell-imprinted polymers (CIPs) have emerged as robust, cost-effective, and versatile recognition materials with selective binding sites for capturing whole bacteria. However, electrochemical transduction of the binding event to a measurable signal within a microfluidic device to develop easy-to-use, compact, portable, durable, and affordable sensors remains a challenge. For this paper, we employed CIP-functionalized microwires (CIP-MWs) with an affinity towards E. coli and integrated them into a low-cost microfluidic sensor to measure the conductometric transduction of CIP-bacteria binding events. The sensor comprised two CIP-MWs suspended perpendicularly to a PDMS microchannel. The inter-wire electrical resistance of the microchannel was measured before, during, and after exposure of CIP-MWs to bacteria. A decline in the inter-wire resistance of the sensor after 30 min of incubation with bacteria was detected. Resistance change normalization and the subsequent analysis of the sensor's dose-response curve between 0 to 109 CFU/mL bacteria revealed the limits of detection and quantification of 2.1 × 105 CFU/mL and 7.3 × 105 CFU/mL, respectively. The dynamic range of the sensor was 104 to 107 CFU/mL where the bacteria counts were statistically distinguishable from each other. A linear fit in this range resulted in a sensitivity of 7.35 μS per CFU/mL. Experiments using competing Sarcina or Listeria cells showed specificity of the sensor towards the imprinted E. coli cells. The reported CIP-MW-based conductometric microfluidic sensor can provide a cost-effective, durable, portable, and real-time solution for the detection of pathogens in water.

A Review on Applications of Artificial Intelligence in Wastewater Treatment

In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

Chiral Quasi-Bound States in the Continuum of a Dielectric Metasurface for Optical Monitoring and Temperature Sensing

Chiral BIC can reach ultrahigh quality factors (Q-factor) based on its asymmetry, with broken mirror symmetries and in-plane inversion. Only by in-plane structural perturbation can chiral quasi-BIC (q-BIC) appear, so it is much more realizable and reasonable for the manufacturers in practical productions and fabrications considering the technology and means that are available. In this paper, we design a new dielectric metasurface employing H-shaped silica meta-atoms in the lattice, which is symmetrical in structure, obtaining chiral BIC with ultrahigh Q-factor (exceeding 105). In this process, we change the length of the limbs of the structure to observe the specific BICs. Previous scholars have focused on near-infrared-wavelength bands, while we concentrate on the terahertz wavelength band (0.8–1 THz). We found that there is more than one BIC, thus realizing multiple BICs in the same structure; all of them exhibit excellent circular dichroism (CD) (the maximum value of CD is up to 0.8127) for reflectance and transmittance, which provides significant and unique guidance for the design of multi-sensors. Meanwhile, we performed temperature sensing with chiral BIC; the sensitivity for temperature sensing can reach 13.5 nm/°C, which exhibits high accuracy in measuring temperature. As a consequence, the result proposed in this study will make some contributions to advanced optical imaging, chiral sensors with high frequency and spectral resolution, optical monitoring of environmental water quality, multiple sensors, temperature sensing, biosensing, substance inspection and ambient monitoring and other relevant optical applications.

Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions

Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.

AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water

Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water. A deep learning model was used to identify and quantify target bacteria based on their microscopic patterns generated by specific interactions with bacteriophages. The model was trained on augmented datasets to maximize data efficiency, using input images of selected bacterial species, and then fine-tuned on a mixed culture. Model inference was performed on real-world water samples containing environmental noises unseen during model training. Overall, our AI model trained solely on lab-cultured bacteria achieved rapid (< 5.5 h) prediction with 80–100% accuracy on the real-world water samples, demonstrating its ability to generalize to unseen data. Our study highlights the potential applications in microbial water quality monitoring during food and agricultural processes.

Triple-mode hybridized generator for efficient water flow energy harvesting and water quality monitoring applications

Water quality monitoring is essential for river ecological environment. Harvesting water flow energy for river environmental monitoring is a promising solution for self-powered sensing technology. In this work, based on the triboelectric–electromagnetic (TENG-EMG) working principle, a comprehensive strategy appropriately hybridizes the solid-solid TENG (SS-TENG), solid-liquid TENG (SL-TENG) and EMG based on water wheel structure for harvesting water flow energy. The triple-mode hybridized generator (TMHG) in a limited space is combined effectively through ingenious structural design, which greatly improves the space utilization. An introduction of the combination of TMHG and buoy structure provides stable operating conditions for power supply. The basic TMHG in terms of structural parameters, response characteristics, power supply capacity and sensing characteristics is systematically discussed. Specially, after optimizing the structural parameters, the SS-TENG, SL-TENG and EMG deliver an instantaneous power of 1.44, 0.15 and 15.9 mW, and 480, 120 and 122 LEDs array can be simultaneously illuminated at the rotate speed of 90 rpm, respectively. The output of SL-TENG has strong correlation with flowing velocity and water PH, further demonstrating a potential application of TMHG on self-driven sensing of river state. A triple-channel hybridized power management (TcHPM) strategy consisting of energy collection module, storage module and mechanical switch is developed to co-manage the electrical outputs of three generating modules in TMHG. Furthermore, a wireless self-powered monitoring system using TMHG as power supply is further developed and demonstrated for water quality monitoring in terms of water temperature and total dissolved solids. This paper not only provides a potential strategy towards effective water flow energy harvesting, but also demonstrates the potential applications of the TMHG in water quality monitoring.

Deep Learning-Based Water Quality Retrieval in an Impounded Lake Using Landsat 8 Imagery: An Application in Dongping Lake

Attempts have been made to incorporate remote sensing techniques and in situ observations for enhanced water quality assessments. Estimations of nonoptical indicators sensitive to water environment changes, however, have not been fully studied, mainly due to complex nonlinear relationships between the observed values and surface reflectance. In this study, we applied a novel deep learning approach driven by a range of spectral properties to retrieve 6-year changes in water quality variables, i.e., Chl-a, BOD, TN, CODMn, NH3-N, and TP, on a monthly basis between 2013 and 2018 at Dongping Lake, an impounded lake located in the Yellow River in China. Band arithmetic was used to compute 26 predictors from Landsat 8 OLI imagery for model inputs. The results showed generally strong agreement between in situ and ConvLSTM-derived lake variables, generating R2 of 0.92, 0.88, 0.84, 0.80, 0.83, and 0.77 for TN, NH3-N, CODMn, Chl-a, TP, and BOD, which suggest good performance of the developed model. We then used statistical analysis to identify the spatial and temporal heterogeneity. The framework established in this study has applications in effective water quality monitoring and serves as an alarming tool for water-environment management in the complex inland lake waters.

Electrochemical and physicochemical degradability evaluation of printed flexible carbon electrodes in seawater

• Sustainable printed electrodes using a graphite paste (G-PE) were fabricated. • Study of the G-PEs against commercial silver and carbon SPEs (Ag- and C-SPEs). • Enhanced electrochemical stability in seawater by using carbon-based electrodes. • Excellent degradability and dispersibility of G-PEs against C- and Ag-SPEs. • The G-PEs are a promising sustainable alternative for disposable electrodes. The environmental impact of metals and non-degradable plastics in printed electrodes has highlighted the need of employing sustainable materials in environmental monitoring. In this work, we developed a new sustainable graphite-based paste (G-PE) printed on a bioderived and biodegradable polyhydroxybutyrate polyhydroxyvalerate substrate for tap water and seawater monitoring. We compared its performance against two standard screen-printed electrodes (SPEs) fabricated using commercial silver and carbon pastes (Ag-SPEs and C-SPEs) printed on a polyvinyl chloride substrate. The Ag-SPEs exhibited the lowest sheet resistance ( R S = 0.053 Ω/sq), however they were also less reliable as silver oxidised and reacted with ions present in seawater. Meanwhile, the C-SPEs and G-PE presented similar R S (26.9 and 30.1 Ω/sq, respectively), were inert in different media and showed relatively stable response during cyclic bending studies (less than 2.3% relative resistance variation for the C-SPE). Physical and chemical degradation studies of the sustainable G-PE in seawater demonstrated relatively quick ultrasound induced dissolution (less than5 min) while the SPEs did not dissolve even after 30 min of sonication, highlighting the suitability of this new, eco-friendly G-PE for single use or short-term water quality monitoring applications.

Recent Advancements in Electrochemical Biosensors for Monitoring the Water Quality.

The release of chemicals and microorganisms from various sources, such as industry, agriculture, animal farming, wastewater treatment plants, and flooding, into water systems have caused water pollution in several parts of our world, endangering aquatic ecosystems and individual health. World Health Organization (WHO) has introduced strict standards for the maximum concentration limits for nutrients and chemicals in drinking water, surface water, and groundwater. It is crucial to have rapid, sensitive, and reliable analytical detection systems to monitor the pollution level regularly and meet the standard limit. Electrochemical biosensors are advantageous analytical devices or tools that convert a bio-signal by biorecognition elements into a significant electrical response. Thanks to the micro/nano fabrication techniques, electrochemical biosensors for sensitive, continuous, and real-time detection have attracted increasing attention among researchers and users worldwide. These devices take advantage of easy operation, portability, and rapid response. They can also be miniaturized, have a long-life span and a quick response time, and possess high sensitivity and selectivity and can be considered as portable biosensing assays. They are of special importance due to their great advantages such as affordability, simplicity, portability, and ability to detect at on-site. This review paper is concerned with the basic concepts of electrochemical biosensors and their applications in various water quality monitoring, such as inorganic chemicals, nutrients, microorganisms’ pollution, and organic pollutants, especially for developing real-time/online detection systems. The basic concepts of electrochemical biosensors, different surface modification techniques, bio-recognition elements (BRE), detection methods, and specific real-time water quality monitoring applications are reviewed thoroughly in this article.

Long range multi-step water quality forecasting using iterative ensembling

Real-life water quality monitoring applications such as aquaculture domains and water resource management need long range multi-step prediction for disaster control. However, prediction accuracy usually degrades gradually as the prediction target timepoint is further away from the current timepoint. To address this, recent water quality forecasting methods mostly rely on complex deep learning models. In this paper, we propose a simple time-variant iterative ensembling method that strives to significantly improve the performance of a given arbitrary long range multi-step time series predictor for water quality data with minimal increase in computational cost. With the given predictor, our proposed method iteratively uses ensembles of predicted values for preceding steps to improve the prediction accuracy for the succeeding steps. The iterative ensembling operation is performed on the trained model and only at the inference stage, and so does not need any further computing-intensive training for the performance improvement. We experimentally show that the proposed method is effective with 7 predictors and 9 water quality datasets of various types, and it outperforms the state-of-the-art results in those datasets by around 2%–29% in mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) metrics. Similar improvement has also been found in two other metrics such as normalized Nash–Sutcliffe model efficiency coefficient (NNSE) metric and Taylor diagram plot. Overall, the proposed iterative ensembling is a promising approach for multi-step long range water quality prediction for high-frequency water quality monitoring systems.

A new zinc-organic framework with 1D channel for constructing a ratiometric Al3+-selective sensor and four inputs INHIBIT logic gate

To develop Al3+ fluorescent sensor is significant because the abnormal levels of Al3+ in environment may pose great threat to human body. Herein, a novel metal–organic framework {Zn(Dpada)(Imdba)·H2O}n (Dpada = 3, 6-di(1H-imidazol-1-yl) pyridazine and Imdba = 2, 2′-iminodibenzoic acid), named Zn-MOF, has been architected with one-dimensional channel under hydrothermal conditions. Zn-MOF exhibits good thermal and solvent stability and can also keep structural integrity over the pH range of 5.0 – 9.0. Fluorescent experiments show that Zn-MOF has high selectivity and sensitivity towards Al3+ via ratiometric fluorescence signal changes (F470 nm/F390 nm) and the detection limit is evaluated to be 0.69 μM. In addition, Zn-MOF performs good recyclability in sensing of Al3+ with at least 5 cycles. Besides, an INHIBIT logic gate has been constructed with chemical ions (Al3+, Cr3+, Fe3+ and Hg2+) as input signals and emission ratio (F470 nm/F390 nm) as output signal. Significantly, Zn-MOF can be applied to tracing Al3+ using real water samples, presenting great potential in water quality monitoring application.

Low complexity and accurate Machine learning model for waterborne pathogen classification using only three handcrafted features from optofluidic images

Pathogenic microorganism can cause human diseases and even death, so waterborne pathogen detection is essential for water quality monitoring. Conventional microorganism detection/classification through biochemical approaches is expensive, time-consuming, labour-intensive and leaves chemical pollutions. Recently, many image processing algorithms are developed for cheaper, faster, more automatic, and more environmentally friendly microorganism classification. However, most existing classification algorithms are designed for microscopic images and focus only on the classification performance, not the computational cost. Alternatively, the optofluidic systems provide a smaller and cheaper way to capture microorganism images than conventional microscopes. Thus, in this paper, an image classification algorithm based on optofluidic images is proposed for portable, real-time water quality monitoring applications. With a comprehensive investigation on different categories of features and careful analysis on our optofluidic images, 2 morphological features were selected to classify the waterborne pathogens with a proposed feature, EHHOG (entropy of histogram of HOG) (HOG: histogram of oriented gradients). The proposed algorithm can achieve higher or similar classification accuracy as existing methods, while the computational cost is much lower with only 3 features.

A Bibliometric Analysis and Review of Resource Management in Internet of Water Things: The Use of Game Theory

To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.

Low-Cost Resistive Microfluidic Salinity Sensor for High-Precision Detection of Drinking Water Salt Levels.

Rapid, inexpensive, and precise water salinity testing remains indispensable in water quality monitoring applications. Despite many sensors and commercialized devices to monitor seawater salinity, salt detection and quantification at very low levels of drinking water (below 120 ppm) have been overlooked. In this paper, we report on optimization of a low-cost microfluidic sensor to measure water salinity in the range of 1–120 ppm. The proposed design employs two copper microbridge wires suspended orthogonally in a PDMS microchannel to measure salinity based on the electrical resistance between the wires. The preliminary design of the sensor microchannel with a rectangular cross-section width (w) of 900 μm and height (h) of 500 μm could measure the water salinity in the range of 1–20 ppm in less than 1 min with detection sensitivity, limit of detection (LOD), and limit of quantification (LOQ) of 17.1 ohm/ohm·cm, 0.31 ppm, and 0.37 ppm, respectively. Data from the preliminary design was used for developing and validating a numerical model which was subsequently used for parametric studies and optimization to improve the sensor’s performance. The optimized design demonstrated an order of magnitude increase in sensitivity (385 ohm/ohm·cm), a 6-fold wider detection range (1–120 ppm), and a 15-fold enhancement in miniaturization of the microfluidic channel (w = 200 μm and h = 150 μm) with LOD and LOQ of 0.39 and 0.44 ppm, respectively. In the future, the sensor can be integrated into a hand-held device to remove present impediments for low-cost and ubiquitous salinity surveillance of drinking water.

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.