key: cord-0871354-9xop49se
authors: Huang, Yuankai; Wang, Xingyu; Xiang, Wenjun; Wang, Tianbao; Otis, Clifford; Sarge, Logan; Lei, Yu; Li, Baikun
title: Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review
date: 2022-04-20
journal: Environ Sci Technol
DOI: 10.1021/acs.est.1c07857
sha: 79078e20d8762c4414919b728e87c5584de6f7bc
doc_id: 871354
cord_uid: 9xop49se

[Image: see text] Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Pursuing sustainable solutions for water scarcity, ensuring water quality and availability, protecting human and ecosystem health, and producing renewable energy have become top priorities for water and wastewater treatment facilities. 1−3 Stakeholders from academia, industry and government have put forth concerted efforts to develop innovative technologies (e.g., anaerobic digestion (AD), 4 membrane bioreactor (MBR), 5 and reverse osmosis (RO) 6 ) with the aims of maintaining high water quality and recovering value-added sources (e.g., carbon, 7 nitrogen, 8 metals, 9 biogas, 10 and clean water 11 ). However, the current understanding of process dynamics (e.g., microbial activities, parameter interactions, and reaction kinetics) is still limited, posing hindrances for implementation of state-of-the-art technologies. To address these challenges, thorough knowledge of dynamic water systems is acquired to predict and diagnose acute shocks and/or chronic fluctuations, advance system resilience, augment treatment efficiency, reduce carbon footprint, and maximize resource values. Fulfilling this initiative heavily relies on trustable water sensors to monitor multiplex physicochemical and biochemical reactions occurring in water and wastewater streams, a spatiotemporal data processing capability to promptly process numerous types of data collected at varying time frames, and a hyperspectral data application to interpret and apply sensor data into diverse water systems. 12−14 Water monitoring approaches can be divided into four temporal scales based on the sensing capability, monitoring media, and target analyte, namely, short-term discrete monitoring (STDM; Sensors are deployed in water solutions at the time scale of minutes or hours, in which sensor data are obtained at low temporal resolution (time interval >1 h).), short-term continuous monitoring (STCM; Sensors are deployed at the same time scale as STDM, but sensor data are obtained at high temporal resolution (time interval <1 h).), long-term discrete monitoring (LTDM; Sensors are deployed intermittently into the water solution at the time scale of days, weeks or even months, during which data are obtained at low temporal resolution (time interval >1 h) and human power is needed to deploy and replace sensors over time.), and longterm continuous monitoring (LTCM; Sensors are deployed at the same time scale as LTDM, but with minimal requirement of maintenance, displacement or replacement. Sensor data are obtained at high temporal resolution (time interval <1 h or even in minutes)) ( Table 1) . Among these four monitoring approaches, LTCM possesses distinct advantages by providing real-time, in situ and high-fidelity information essential for tracking transient variations of water parameters (compared to STDM and STCM), elucidating panoramic water dynamics with minimal requirement of manual intervene (compared to LTDM), and implementing spatiotemporal data sets to address data sparsity or deviation through fault detection and selfcalibration.

The last two decades have witnessed numerous applications of water sensors in natural resource water (NRW), drinking water (DW), and wastewater (WW), as demonstrated by an expeditious growth in the number of publications (4157) on water quality monitoring between 2000 and 2020 ( Figure 1a) . However, the publications related with long-term monitoring (216) , continuous monitoring (257) , and LTCM (113, among which 79 papers were limited to lab tests) in water systems/ processes are fairly inadequate, compared with 3941 papers on short-term monitoring (Figure 1b) . Furthermore, existing reviews related with sensing technology and water quality engineering fail to consider the impacts of LTCM applications in water systems. Specifically, the reviews of sensor materials/ data processing innovations have mainly focused on STDM and STCM, 15−19 due to the underperformance of water sensors in long-term accuracy and durability caused by various reasons such as biofouling, 20 aqueous layer formation, 21 sensor material depletion, 22 interference particles/ion, 23 inadequate data collection/interpretation, 24 and labor-intensive and technical-demanding maintenance. 25 Additionally, the reviews on water pollutants (e.g., nutrients such as nitrogen and phosphorus, 26−31 heavy metals such as lead and chromium, 32−36 and emerging contaminants (ECs) such as micro-plastics (MPs) 37−48 and pesticides 49−57 ) , water systems (e.g., AD, 58−61 bioelectrochemical systems (BES), 62−66 and DW distribution networks 67−70 ) , and control strategies (e.g., proportional-integral-derivative (PID) control 71−73 ) have been limited to environmental applications based on either short-term or discrete sensing data without recognizing the new viewpoint that LTCM can bring. To date, the critical roadblocks of LTCM in water systems have not been effectively addressed.

We contend there is a critical need to conceptualize LTCM and its implementations in water systems. This review focuses on bridging the gap between the fundamental knowledge of sensors and the application of LTCM techniques by illustrating the roles and importance of LTCM at three layers (sensor data, water parameters, and system level) and explicating the longitudinal route of LTCM consisting of sensor data generation, data processing, and data application ( Figure 2a) . Specifically, we first explore the current state and challenges of LTCM in three major water systems including NRW, DW, and WW, as they are the principal aquatic environments for the fate/transfer of contaminants through complicated physiochemical and biochemical reactions, and have high impacts on numerous key element cycles, human-nature interactions, and climate change. Afterward, we demonstrate the state-of-the-art water sensing technologies for data acquisition of LTCM and elaborate the capability and limitation of each type of sensing technology. We then present the LTCM data collection and data processing through sensor networks and machine learning (ML) algorithms. Subsequently, we interpret the LTCM data application with water system modeling and process control as two distinct examples. Finally, we unveil the outlook of LTCM in four major domains: water, energy, sensors, and data science. Based on the review findings, we elucidate the complete route of LTCM from initial data acquisition to final data application, differentiate LTCM from traditional monitoring strategies (STDM, STCM, and LTDM), and present future perspectives such as deep learning (DL) sensor fusion, digital water infrastructure, and data-driven carbon footprint modeling.

In this section we demonstrate the critical needs and challenges of LTCM in NRW, DW, and WW systems. Because of the difficulties of LTCM deployment (e.g., seasonal variations in NRW, stringent sensor safety requirements in DW, and high solid contents and microbial activities in WW), current monitoring strategies still depend on conventional monitoring methods including STDM, STCM, and LTDM to predict parameter fluctuation and formulate control strategies. We identify the appropriate LTCM approaches through a three-layer hierarchy: sensor data, water parameters, and system level as well as explore the possibility of incorporating the data obtained from different time-framed regimes (e.g., short-term/long-term and discrete/continuous patterns) to enrich the information output without sacrificing the accuracy. 2.1. LTCM in NRW: Critical Demands and Challenges. Enactment of stringent water quality regulations (e.g., arsenic: 10 ppb in wells 74 ) of NRW becomes imperative due to the deterioration of the water environment from natural changes (e.g., heavy metal desorption from soil to groundwater 75 ), human impacts (e.g., discharge of ECs with long-distance transport pathways 76 ) , and their combinations (e.g., landfill leaching 77 ). In situ treatment techniques have been deployed to remove contaminants in NRW. For example, 45% of polyfluoroalkyl substances (PFASs) were degraded by Phanaerochete chrysosporium within 35 days in groundwater. 78 However, existing conceptual and empirically based hydrological models such as Soil and Water Assessment Tool models 79 are validated only with the data obtained from shortterm and/or long-term discrete monitoring (e.g., temperature sensing interval: one point/day 80 ), posing difficulties for innovating treatment processes in NRW systems featured with varying temporal and spatial scales. 81 At the data level, LTCM in NRW presents long-lasting and affluent patterns of water quality states and fluxes that aids data For example, LTCM can provide year-around information for determining carbon/nitrogen dynamics and concentration gradients in different types of NRW (e.g., groundwater, rivers, and lakes), and developing data-driven models to characterize the carbon footprint. 82 Moreover, the high-dimensional data sets obtained from LTCM, LTDM, STCM, and STDM can be combined through data fusion, in which a metadata schema can be generated to profile the physicochemical and biochemical status and illustrate the uncertainties caused by human−nature interactions in NRW systems. 83 For example, a lab-on-chip analyzer was used to illustrate phosphate variations at a hourly sensing interval and achieve a linear quantification for 60 days in coastal water. 84 In addition, state-of-the art models built from personalized data sets generated from LTCM can be adapted to water dynamics for better prediction of water parameters ( Figure 2a ). ML models based on 30-year remote sensing data have been applied to predict the dissolved oxygen (DO) concentration in the Lake Huron (Michigan, U.S.) with the R 2 value of 0.91. 85 However, the long-term accuracy of the sensors deployed in NRW is severely undermined by seasonal changes of water characteristics (e.g., temperature fluctuations affect the potentiometric sensor readings 86 ), and the continuous accuracy of these sensors is impaired by unpredictable incidences in NRW such as alterations of salinity/water flux under weather variation and human activity associated disruptions. 87,88 2.2. LTCM in DW: Critical Demands and Challenges. Water quality in DW distribution systems (DWDSs) can be deteriorated by pathogen regrowth (e.g., Campylobacter spp. 89 and SARS-CoV-2 90 ), formation of extracellular polymeric substances (EPS) on the pipeline, and pipeline corrosion. In addition, DW quality also suffers from EC pollution (e.g., bisphenol A 91 ) via migration of contaminated groundwater plumes from point sources 92 and/or air emission followed by deposition and soil/liquid desorption. 93 State-of -the-art treatment processes such as nanofiltration (NF) (98% removal efficiency of PFASs 94 ) and metallic microfiltration (MF) (99% removal efficiency of Cryptosporidium parvum 95 ) have been used in DW treatment plants (DWTPs) to remove low concentrated ECs (e.g., perfluorooctanoic acid (PFOA) < 1 μg/L 96 ). Recently, water microgrids capable of minimizing environmental impacts by controlling water distribution in DWDSs have started to gain a great deal of attention for improving the DW quality. 97 However, developing water microgrid models (e.g., mixed integer nonlinear programming model) and visualizing DWDSs require a robust monitoring capability for data communication between operation nodes and command centers, which is limited by current short-term and discrete monitoring techniques. 98 LTCM can capture erratic contaminant shocks occurring in DW pipelines and treatment units by presenting high temporal resolution data. Consequently, DWTPs can expediate implementing appropriate strategies and effective responses in a timely manner at the water parameter level (e.g., dosage control based on the sensor data), and finally improve water treatment strategies and system control ( Figure 2a ). For example, dynamic biofilm-based stainless steel electrochemical sensors have been applied to guide the disinfectant dosage in DWTPs on a real-time basis. 99 Other than discrete data at a low spatiotemporal resolution, LTCM using Supervisory Control and Data Acquisition (SCADA) for DWDSs can provide high-dimensional data-driven detection of cyberattacks to improve resilience and safety in water microgrids. 100 However, the leaching of toxic chemicals (e.g., plasticizers in some types of potentiometric sensors 101 ) to the DW can deteriorate the water quality, leading to the strict limitation of the DW sensors to the reagent-free or reagent release-free (e.g., using a paper-based sensor embedded in a 3D printing device 102 ) to prevent potential contamination caused by these sensors during LTCM. Another challenge for continuous monitoring comes from the difficulties of optimizing sensor deployment locations to promptly capture the episodic and nonperiodic occurrences (e.g., pipeline corrosion, biofilm formation, and transient shocks) in the intricate DW networks.

2.3. LTCM in WW: Critical Demands and Challenges. WW contains abundant substances such as nitrogen and carbon that make it a valuable renewable resource. Ensuring WW effluent quality while achieving resource recovery have been energy intensive due to the limited understanding of multiplex physiochemical and biochemical reactions, dynamic flow patterns, and complicated interactions between contaminants and microorganisms in WW treatment processes. Innovations in controller algorithms are promising to formulate state-of-the-art treatment technologies in a costeffective and energy-efficient way. For example, phosphorus recovery was increased from 13.7% to 29.6% in biological nutrient removal (BNR) systems by using a fuzzy logic control. 103 However, numerous parameters (e.g., DO, inorganic/organic contaminants, suspend solid, and pathogens) are associated with each other, creating intertwined relationships that could not be deciphered using existing shortterm and discrete temporal data.

Implementation of LTCM could provide effective solutions for energy-intensive WW treatment processes by monitoring variations of water parameters in a real time in situ mode and tracking their long-term patterns from the data level to the water parameter level. Additionally, at the system level LTCM can establish high-fidelity parameter inputs and outputs for control models and achieve system visualization and virtualization toward enhanced resilience ( Figure 2a ). For example, LTCM of EPS using cation exchange resin sensors could enhance resource recovery and water reuse in a zerowaste discharge aerobic granular sludge reactor by controlling the organic loading rate. 104 Furthermore, using electrochemical sensors as the LTCM and LTDM devices to quantify nitrogen and DO can accurately profile the water quality variations and reaction kinetics in BNR reactors such as photosequencing batch reactors 105 and integrated vertical membrane bioreactors. 106 However, the main challenge for long-term monitoring in WW is the rapid deterioration of sensor performance, 107 while continuous monitoring is impaired by numerous interference factors in complex WW mediums (e.g., high biomass content, vigorous mixing, and pH fluctuation 4,108 )

DEVELOPMENT FOR DATA ACQUISITION IN LTCM Data acquisition using appropriate water sensors is the fundamental step to achieve LTCM. In this section, we provide an overview of the state-of-the-art sensor development, including electrochemical sensors, optical sensors, and biosensors along with their possible applications. Especially, we discuss the challenges of ECs (e.g., PFCs and MPs) Environmental Science & Technology pubs.acs.org/est Critical Review monitoring in water systems. We differentiate the sensors for STDM, STCM, LTDM, and LTCM as well as identify the distinct features of each type of sensors in various monitoring scenarios.

Electrochemical sensors measure the concentration of target analytes in a water solution by quantifying electrical signals (e.g., current, potential, or charge). 109 According to sensing mechanisms, electrochemical principles, and electronic outputs, electrochemical sensors discussed here are divided into three categories: voltammetric/amperometric sensors, potentiometric sensors, and conductometric sensors. 110 Voltammetric/Amperometric Sensors. Voltammetric/amperometric sensors quantify the analytes by measuring the current generated from reduction/oxidation reactions on the electrode surface under varying (voltammetry) or constant (amperometry) potentials. 111, 112 The current observed at different electron transfer rates is directly associated with the analyte concentration ( Figure 3a ). With proper storage procedures (e.g., two-month storage in darkness for indoaniline-derivative type pH voltammetric sensors 113 ), voltametric/ amperometric sensors can detect chemical oxygen demand (COD), NO 2 − and ClO − with low detection limits (ppm and ppb levels) for 6 to 90 days in water systems (Table 2) . Additionally, amperometric sensors can quantify viable microbes by continuously monitoring dissolved oxygen coupled with principal component analysis. 114 These sensors can also be modified using nanoporous Au, 115 a Si-glass structured sensor chip, 116 and a nanostructured ion imprinted polymer 117 to enhance the responding capability for the oxidation/reduction processes occurring on the electrode surface ( Figure 3b ). One critical challenge of voltammetric/ amperometric sensors for long-term application is the formation of passive compounds on the working electrode surface (e.g., platinum oxide on Pt-based electrodes) that causes measurement drift and finally shortens the lifespan. 118, 119 Moreover, the current value (mA) obtained by voltammetry/amperometry techniques can be drifted by ion interference 120 and temperature variations, 121 which inhibits their applications for continuous monitoring in WW containing high concentration of interfering ions (e.g., chloride >0.5 M 122 ) and featuring frequent temperature fluctuations. Future solutions for voltammetric/amperometric sensors as the LTCM devices can be depositing antipassivation materials (e.g., reduced graphene oxide-molybdenum disulfide nanohybrid 123 ) onto the sensor surface to expand the lifetime, and modifying the sensor morphology with high conductive materials (e.g., metal organic frameworks (MOFs), nanorods, and graphene nanowalls) to expedite the electron transfer rate.

Potentiometric Sensors. Potentiometric sensors quantify target analytes by measuring the difference of open-circuit potential (OCP) between the ion-selective membrane (ISM) (Figure 3c ). Potentiometric sensors have been widely used for long-term (>1 week) and continuous (sensing interval <30 s) monitoring of ions such as protons (pH), nitrogen species (e.g., NH 4 + , NO 3 − , and NO 2 − ), and heavy metals (e.g., Pb 2+ ) based on the specific affinity of target ions to the ISM matrix 125 (Table 2) . Even though potentiometric sensors are the top candidate for LTCM, the main limitation is that the sensing scope is only applicable to ions. Also, the morphology change of the sensor ISM matrix (e.g., aqueous layer formation inside the ISM matrix, 126 physical damage by the debris and particles attached, 127 biofouling, 128 and ISM component leaching 129 ) deteriorates ion affinity and ion diffusion in the ISM matrix and causes measurement errors, and finally declines the longterm accuracy of sensors. Furthermore, because contaminant concentrations are quantified using the Nernst equation

, the signal readings of potentiometric sensors are temperature-dependent, and logarithmic response can result in drastic reading drift. Additionally, data acquisition using potentiostats suffers from acute variation of the initial OCP readings among different types of sensors and thus imposes the need for frequent recalibration over time 130 (Figure 3c ). Future solutions can focus on sensor material innovation (e.g., antifouling coating with zwitterionic copolymers and silver nanoparticles) to prevent ISM from deterioration ( Figure 3d ) and development of novel data processing programs (e.g., Coulometric Signal Transduction, 131 Bayesian Source Separation, 132 and Observations Data Model 133 ) to enhance the sensor accuracy and sensitivity.

Conductometric Sensors. Conductometric sensors quantify the analyte concentration by measuring the resistance/ conductivity variation on the conducting layer of the working electrode ( Figure 3e ). 134 As impedance measurement devices, conductometric sensors are suitable for monitoring certain types of water contaminants (e.g., PFCs 135 and Escherichia coli 136 ) that are unable to trigger electrochemical reactions or adsorption/transfer processes. 137, 138 For example, conductometric sensors monitored PFCs in groundwater for 6 days with a detection limit of 0.05 ng/L (Figure 3f, Table 2 ). In addition, electrical resistance sensors performed real-time continuous online monitoring of water pipeline corrosion for more than 10 days with a sensing interval of 1 h. 139 However, conductometric methods are fundamentally nonselective unless conducting layers (e.g., zeolites and hydrogel) are employed for a specific target analyte 140, 141 (Figure 3e) . Additionally, several possible issues (e.g., temperature fluctuation, the conducting layer peeling off from the electrode surface, and sensor conductance variation 142 ) can cause long-term instability (i.e., measurement drift) of conductometric sensors. Future solutions for conductometric sensors include strengthening sensitivity/selectivity and accelerating the transfer rate of ions and electrons on the sensor surface by using nanoscale and layer structured materials such as 2D carbon nanotubes.

3.2. Optical Sensors for LTCM. Optical sensors measure the target analyte by establishing the relationship between water parameters and optical signals (e.g., absorbance and fluorescence) 16, 143, 144 (Figure 3g ). Three groups of optical sensors reviewed here are fluorescence-based optical sensors, absorbance-based optical sensors, and other types of optical sensors (e.g., colorimetric sensors, surface plasmon resonance (SPR) sensors). Fluorescence-Based Optical Sensors. Fluorescence-based optical sensors are the largest group of optical sensors with the mechanism that fluorophores are excited by a given analyte and emit light radiation at specific wavelengths unique to this analyte. The measurement is performed based on the fluorescent response to the indicator immobilized in a polymeric support. 145 Current studies on fluorescence-based optical sensors include expanding the measurement capacity (e.g., Fe 3+ ion detection based on a quantum dot-doped hydrogel 146 ), and applying online textural image analysis for monitoring water processes (e.g., coagulation and flocculation). 147 Attributed to the long response time (usually >1 min) caused by slow mass transport to reach the required equilibrium concentration on the sensor surface, 148 fluorescence-based optical sensors are not suitable for real-time monitoring and require frequent maintenance (Figure 3h ,i, Table 2 ). Long-term performance of fluorescence-based optical sensors has been hindered by the decay of fluorescence indicators resulted from crystallization in the polymetric supports. 149 Another roadblock is the swelling of polymetric supports in water (e.g., poly(methacrylic acid) starts swelling at the pH of 4 150 ), which weakens the covalently immobilizing ability with indicators. 151 The selectivity of fluorescence-based optical sensors can also be impaired by the similar fluorescence spectra from certain analytes (e.g., dinitrotoluene and trinitrotoluene 152 ) in the complex medium such as WW. MOFs (e.g., Zr(IV) MOF (UiO-67-bpy)) are a promising candidate to ameliorate the immobilizing ability, since the confined space provided by MOFs enhances the supramolecular interactions inside the supporting matrix. 153 UV Absorbance-Based Optical Sensors. UV absorbancebased optical sensors are a subcategory of optical sensors capable of quantifying the analyte using the absorbance of radiation in the UV−vis range (usually from 100 to 400 nm). 154 UV absorbance-based optical sensors have been widely used for measuring nutrients (e.g., nitrate 155 ) and organic compounds (mainly chromophores that contain valence electrons of low excitation energy 156 ) in water ( Table 2) . The major challenges for the long-term deployment of UV absorbance-based optical sensors are the low selectivity caused by interference absorbance in the WW, 157 measurement drift, and poor detection limit from the noises such as temperature fluctuation and water flow during the onsite monitoring. 158 Additionally, standard UV light sources (e.g., a mercury lamp) suffer from a short lifespan and high power consumption. 159 To elevate the LTCM application of UV absorbance-based optical sensors, future studies can focus on combining data processing with the analysis of multiple wavelengths results to enhance the sensing selectivity (e.g., multiple linear regression models have been used to quantitatively analyze the total organic carbon (TOC) concentrations in DW and NRW 156 ) and applying light-emitting diodes (LEDs) light source equipped with capillary column detectors to prolong the sensor lifetime and reduce the noise level. 158 Other Optical Sensors. Other optical sensors including nephelometric, colorimetric, and surface plasmon resonance (SPR) sensors have been employed in water systems ( Table  2) . Nephelometry sensors measure water parameters (e.g., turbidity) by detecting the light energy scattered or reflected toward a detector, and can continuously monitor suspended particulate matters for more than 20 days in rivers 160, 161 (Table 2 ). However, nephelometric sensors are deficient at measuring the particles smaller than 200−300 nm in diameter due to their low scattering cross sections, 162 and thus failing to monitor soluble contaminants in WW. 163 Colorimetric sensors utilize the color change from the interaction (e.g., oxidation/ reduction reactions) between the target analyte and the nanoparticles/nanozymes on the sensor nodes under light illumination. 164 By connecting the sensor nodes with a smartphone app capable of color differentiation, colorimetric sensors can quantify the phosphate concentration in a river for 7 days with the upper detection limit of 20 mg/L 165 (Table 2) . However, colorimetric sensors are unable to discriminate a specific component in a mixture media (e.g., Hg 2+ in WW 166 ) since the color change can be interfered by numerous factors (e.g., pH, suspended solid, and organic matters 167 ). SPR sensors and ion chromatography have been used for LTDM (monitoring interval: ∼3 min) of nitrogen and heavy metals at the ppb and even ppt level for 1 week ( Table 2 ), but the main issue for extending the monitoring duration is the high cost incurred by the instrumentation itself and the complicated sitespecific calibration procedures. 168 Reducing the light source power consumption/cost and developing novel light response materials to shorten the response time are critically demanded for future LTCM application of optical sensors.

Biosensors measure the target analyte qualitatively or quantitatively by converting the observed responses into the measurable electrical or optical signals, which is carried out by the biological recognition elements such as enzymes and microorganisms 169 (Figure 3j ). Biosensors have gained enormous attention in the past decade, especially for monitoring nutrients and toxic analytes such as biological oxygen demand (BOD), 170 microbes, 171 nitrogen, 172 pathogens, 173 and heavy metals 174 with the response time of seconds ( Figure 3k , Table 2 ). Microbial fuel cell-based and biofilm-based sensors were developed for monitoring contaminants in WW and demonstrated distinct features such as self-sustained operational mode, small reagent volume (<30 mL 175, 176 ), low cost (intrinsic microorganisms as recognition elements), 177−179 and clear correlations between potential (mV) output and contaminant concentration. Biosensors are also superior candidates for biological material detection in water systems. For example, biosensors utilizing mediated electron transfer (MET)-type bioelectrocatalysis can be used for microbe activity detection based on the electron transfer between microbes and electrode. 171 However, these sensors may not be suitable for continuous monitoring as they are typically nonselective and many toxic contaminants (e.g., Hg 2+ and Cr 6+ ) can inactivate the microorganisms in these sensors and generate uninterpretable signals. 177, 180, 181 Additionally, enzyme-based and microorganism-based electrochemical/ optical biosensors suffer from low signal outputs in the water with low conductivity (e.g., river and drinking water), 182 and require a large sensor surface area and additional analyte solution to attain usable response magnitudes. 183 Moreover, the decline of metabolic activities and the inactivation of microbial cells result in rapid deterioration of signal intensity and short lifetime of bioreceptors for LTCM. To address this challenge, recombinant enzymes or genetically modified "super bacteria" that can survive in WW could be developed as the recognition elements in the construction of highly stable biosensors. 184 DW influent is normally monitored in a STDM mode, since it is relatively stable and does not represent a risk to public health under normal circumstances. Also, it can be correlated with bacterial counting (conducted in STDM) and total suspended solid (TSS) measurement to avoid misinterpretation. 203 The temperature in NRW (e.g., groundwater and surface water) only needs LTDM due to its slow variation throughout a season. 204 By contrast, ECs are ubiquitous in both natural water environments and engineering infrastructures, and have complicated transport pathways and long-lasting impacts, 205, 206 making the LTCM of ECs critical to execute efficient treatment/control methodologies and alleviate public concerns (Figure 2b ). ECs in water systems are comprised of an extensive spectrum of compounds including organics with complex functional groups (e.g., pharmaceutical and personal care products), long chain carbon backbones (e.g., PFCs), polymers (e.g., MPs), and microbial related substances (e.g., antibiotic resistant gene (ARG), antibiotic resistant bacteria (ARB)) in a trace level (ppb or even ppt). 207 Advanced treatment processes such as AOPs, NF, and RO have been studied at the lab scale to remove ECs. For example, AOPs with the ozone dose ranging from 0.82 to 2.55 mg O 3 /mg DOC (dissolved organic carbon) removed 99% of ECs including antibiotics, steroid hormones, and antineoplastics. 208 However, these processes pose high energy demands and generate toxic byproducts (e.g., trihalomethanes (THMs) 209 ). Existing monitoring devices are unable to monitor the fate and transport (e.g., diffusion, sorption, and degradation) of ECs in a long-term and continuous mode ( Table 2 ). Optical analysis methods using Fourier-transform-infrared (FTIR) imaging 210, 211 and enhanced Raman scattering 212, 213 have been applied to quantify ECs such as microbeads, since they can identify all the molecular and functional groups present in plastic polymers. However, complicated pretreatment, tedious measurement protocols, and bulky equipment settings hinder their LTCM ability. As for electrochemical sensors, the sensor readings for ECs at ppb or ppt level (e.g., <0.44 ng/L PFOS in drinking water, 214 <10 −4 −10 −3 ARGs/16S rRNA gene in drinking water 215 ) are easily interfered by contaminants (e.g., ammonium and chloride) present at much higher concentrations (e.g., 1−40 mg/L) in water. Future studies for LTCM of ECs can focus on the combination of multiple spectroscopic methods (e.g., transmittance/laser-based devices) to construct a comprehensive measurement matrix with a low detection limit and high stability. Moreover, facile portable data logging devices easy to be carried and handled for quantitative on-site optical analysis should be developed as the alternative platforms for ECs monitoring. 216 For example, a portable colorimetry sensor using a smartphone to quantify fluoride in the drinking water has shown a potential for LTCM of ECs in a user-friendly mode. 217 For electrochemical sensors, morphology modifications (e.g., nanocomposites with high electrocatalytic activity, 218 MOFs with specific microporous frameworks 219 ) could be a future solution to improve the sensing detection limit and selectivity during the ECs LTCM.

After data acquisition, the next step for LTCM is to deploy data processing methodologies to ensure the accuracy and stability of the sensor data collected. Establishing a platform capable of processing real-time, high-fidelity, and full-scale data is vital to achieve a data-enabled system. In this section, we demonstrate the current state of data collection and data processing for LTCM. Currently, short-term and discrete monitoring coupled with ex situ lab-based measurement are still the mainstream in water quality measurement. Although Environmental Science & Technology pubs.acs.org/est Critical Review the data sets of those parameters can be combined with other long-term continuous counterparts to form a higher dimension data matrix, the vacant values create large sparsity and compromise the reliability of data processing and water system snapshot. Limited by discrete data collection, feedback adjustments of water parameters are neither timely nor precise from STDM and LTDM data, which makes operations or emergency responses deviate from the actual circumstance in water systems in terms of time and extent. This puts forward a high requirement of data collection and processing algorithms for precise depiction of water systems. 4.1. Data Collection for LTCM through the Sensor Networks. High quality data processing, system modeling, and feedback control require collection of vast amounts of data with wide spatial and temporal variability. In a recent study, 22 water quality parameters (e.g., pH, electrical conductivity, and chloride) from 197 sample locations in a DW system were systematically evaluated. 220 Similarly, another study assessed groundwater quality by incorporating 23 parameters from over 40 regional DW/WW companies with fecal coliform as the pathogen indicator. 221 With data accumulating over time, its collection and storage could lead to an unexpected burden for data transmission and processing. 222 However, a study exploring benchmark sensors for on-site monitoring in a WW treatment plant reveals that proper operation setups (e.g., solid retention time and aeration rate) can maintain and even improve the sensor accuracy, and integration of a sensor surveillance network and control can enhance the robustness of remote monitoring systems. 223 In conventional centralized water systems, water network distribution depends on a centralized cloud model that has a potential risk of exposure to cyberattack with the growing number of network nodes. 224 From the standpoint of security and privacy, each end-user may act as a bottleneck or a failure point to disrupt the entire water network. 225 LTCM can break centralized water systems into smaller units based on regions and functions, within which water parameters become more specific and data throughputs will be more tolerable. 223, 226 Furthermore, the dynamics of multiple water systems (e.g., decentralized water treatment units and water networks) can be associated digitally through data flow (Figure 4a ). As elementary water distribution units become smaller, LTCM sensor networks can be operated compatibly with the water system configuration, which enables real-time monitoring and adjustment of water quality parameters and water facility operations, expands the security capability of decentralized water systems, and protects the water sensor networks from cyberattack. 227−230 4.2. Data Processing to Enhance the Accuracy and Interpretability of LTCM. Appropriate data processing and analysis algorithmic tools such as statistical analysis, 231 regressions, 232 Fourier transform, 233−235 and machine learning 236−238 are required to calibrate sensor readings and attain trustworthy data. On-site testing data are usually discrete within a short period of time (e.g., STDM and STCM), resulting in a limiting temporal resolution. In contrast, LTCM can resolve this hurdle with transitory and flexible data sampling interval at the minute or even second level of precision. However, fully utilizing LTCM to achieve a prediction forward mode in water systems requires the data processing algorithms being redesigned to handle high data throughput, including identifying abnormalities from raw sensor data and evaluating water quality parameters over a long-term period.

Supervised learning algorithms (e.g., support vector machine, neural networks, and random forest) have high efficiency in the preventive maintenance toward data distortion and water system snapshot. 239, 240 A study showed that with discrete training data alone, the noise from interference ions (e.g., K + , Na + , Ca 2+ , and Mg 2+ , all governed by the Nicolskii− Eisenman equation) has been rectified through a backpropagation neural network armed with genetic independent component analysis. 241 As for the evaluation of the environmental impact in complex scenarios, continuous data obtained in LTCM are required to fine-tune water parameters and acquire periodic variation information in water systems. In a recent work of wastewater quality prediction with wavelet denoising techniques, Radial Basis Function Neural Network and Adaptive Neuro-Fuzzy Inference System were used to augment existing algorithms and determine significance of input variables for 12 water parameters including conductivity, turbidity, NO 3 − , and E. coli. 242 The ML structure is divergent from assembled knowledge blocks in human natural logic, 243−246 thereby more information beyond the human analytical capability can be captured in the high dimensional sparse data matrices with both discrete data and continuous data. As time evolves, the integration effect of variation in each layer can be well handled by ML. Even for trace contaminants (e.g., ECs) with more abnormalities (as they are exceptionally close to detection limits), their distribution can be predicted by evaluating periodic correlation with other water indexes in a long period of time to ensure reliability. A recent major endeavor is the development of multilayer perceptron neural networks to predict the translocation of ECs including benzo[a]anthracene, chrysene, perfluorobutanesulfonate (PFBS), and PFOA, in which fuzzy logic was used to determine the physiochemical cutoffs and eliminate the partition effect. 247 With key water parameters being digitalized and visualized through data acquired from LTCM, a modern water system can be decomposed into different dimensional layers connected through data flow (Figure 4b ).

4.3. Current Challenges and Future Perspectives of Reliable Data Processing. Challenges for water system data analysis may arise from the data availability, and the design, operation, and maintenance of algorithms on the top of data. To improve data accessibility, digital and smart sensing systems can be established for acquiring LTCM data for different types of water (e.g., DW, WW, and NRW) on a large scale and construct a comprehensive measurement matrix with a low detection limit and high stability. As for accurate data processing and analysis, integrating different monitoring strategies (LTCM, LTDM, STCM, and STDM) can generate data-driven models to predict pollution events and guide contaminant removal processes. Moreover, multisensor fusion techniques based on extended machine learning algorithms (e.g., neural network) can be applied to integrate multiple water parameters (e.g., flow rate, pH, and inlet/outlet contaminant concentrations) at different spatiotemporal resolutions and perform automated fault detection and diagnosis for contamination prevention and decision-making in water facilities. 248 

APPLICATION FOR LTCM After LTCM data are acquired and processed, they will be applied to implement efficient operational strategies in water Environmental Science & Technology pubs.acs.org/est Critical Review systems. In this section, we use water system modeling and process control as two distinct examples to illustrate LTCM data application and expound their status and challenge. Most of the models applied in water systems (e.g., anaerobic digestors, sequencing batch reactors (SBR), and DWDSs) and treatment processes (e.g., disinfection and BNR) still rely on the water quality data obtained from short-term and discrete monitoring and/or historical records. For example, Anaerobic Digestion Model No. 1 (ADM1) mainly depends on discrete monitoring data (e.g., flow rate, pH, and biogas) with one data point collected per day or even per week. 249 Proportionalintegral-derivative (PID) control has also been widely used for various process control systems. 250 However, most of the PID control methodologies are developed based on STDM or STCM data with the fixed parameter inputs that are unable to predict the time-varying fluctuation and heterogeneity in water systems. For this reason, innovation in system modeling and controller algorithms combined with metadata schemes collected from multiple dimensions is essential to execute efficient operational strategies and ensure system stability and robustness ( Figure 5 ).

Modeling. Achieving energy-efficient DW and WW treatment processes relies on the development of kinetic models (e.g., concentration addition (CA) model for disinfection byproducts 251 and activated sludge model (ASM) 252 ) and system/process visualization and virtualization (e.g., Visual 3D dissolution model 253 ). However, these models have been established using data obtained from STCM and LTDM, such as bacteria count with duplicate samples for CA models 251 and Cu 2+ concentration with a sampling interval of several hours in ASM. 254 Recently, a net-zero-energy (NZE) model based on biomass energy recycling (a sampling interval of 1 day) has been deployed to simulate energy-efficient WW treatment processes, resulting in 79.5% offset of electricity and sludge disposal cost compared to conventional treatment processes. 255 A computational fluid dynamics (CFD) model with fixed water quality parameter inputs (e.g., total organic carbon (TOC), temperature, Br − , and pH) obtained from STDM has been applied to predict the disinfection efficiency by simulating chlorine decay, pathogen inactivation, and byproducts formation in contact tanks. 256 However, existing STDM and STCM approaches fail to adapt to vigorous water quality fluctuations throughout heterogeneous DW/WW treatment units and can only provide fixed inputs and outputs for model implementation. Although LTDM is capable of collecting real-time high-fidelity data, it cannot perform automatically and requires frequent manual maintenance and recalibration, posing an imminent challenge for system visualization and virtualization 257 ( Figure 5 ).

LTCM in conjunction with multivariate data analysis can procure both quantitative and qualitative information essential to combat the data sparsity and model insensitivity. LTCM is capable of providing data at multiple time scales to decode the relationships between water parameters (e.g., pH and oxidation reduction potential (ORP) for biogas production in AD reactors 258 ) and promoting energy-efficient operation through system visualization. For example, a long-term relationship between anaerobic reaction time, denitrifying phosphorus removal rate and microbial community dynamics in the enhanced biological phosphorus removal systems were established through 2-month LTCM of NO 2 − and NO 3 − . 259 Moreover, the open-access data stream consisting of highdimensional data from different monitoring strategies including STDM, STCM, LTDM, and LTCM will enable a platform for evaluating the state-of-the-art multiscale water models (e.g., deep learning sensor fusion, artificial neural network (ANN)) to improve system resilience ( Figure 5) . For example, a multisensor fusion method based on Dempster-evidence theory was deployed to process simulated data as well as real-time long-term and short-term monitored data, demonstrating the capability to capture the occurrence of water contamination in DWDSs with an accuracy 1.2 times high than those only using long-term monitoring regime. 260 In another Environmental Science & Technology pubs.acs.org/est Critical Review study, different types of operational data (e.g., discrete data for biogas, continuous data for pH and nitrogen species) collected over four years were interpreted using XGBoost and random forest algorithms, which were subsequently applied to visualize anaerobic codigestors and predict daily methane production with a reasonable R 2 value of 0.80−0.88. 261 

Water systems require precise control strategies coupled with advanced sensing techniques to detect/correct operational failures and execute swift responses under varying conditions. Existing PID control is only tuned at the beginning of installation, resulting in low adaptability to dynamic variations. 262 By contrast, the model predictive control (MPC) methodology delivers highly accurate control with moderate complexity while allowing for performance improvement via sensor data as the external inputs. 263, 264 Until now, most of the MPC methods depend on classical physical-based unit operation models (e.g., ASM1 and sedimentation tank model 265, 266 ) that normally function under discrete monitoring data (e.g., one data per day or even one data per week) to regulate the process variables. Such control strategies are limited to one-dimensional prediction (i.e., predict one water parameter from sensor outputs) with a low linear regression capability due to data sparsity and low temporal resolution. For example, the fusion-based genetic programming model built on four-year discrete data (one data/day) has been applied to estimate TOC concentrations in the Harsha Lake (Ohio, USA) to optimize treatment operation, but the calibration and validation plots exhibited low R 2 values (0.56 and 0.87, respectively 267 ).

LTCM can provide high temporal scale and resolution analysis to assist fault detection and operation adjustment under transient shocks and promote the innovation of control strategies through continuous and synergistic cross-validation of model sensitivity and sensor data streams. Moreover, personalized data provided by integrating LTCM with other monitoring strategies (e.g., STDM, STCM, and LTDM) could facilitate data-driven models that access a complete set of information being collected across water systems, and finally achieve data dimension reduction through uniform metadata schema (e.g., Internet of Things (IoT) stack) toward programmable water infrastructure ( Figure 5 ). For example, by incorporating STCM data (e.g., pH and ORP) and LTCM data (e.g., NH 4 + concentration), a real-time control strategy was developed and validated throughout a 220-day period to control the duration of nitrification−denitrification phases in an SBR, accomplishing 98% oxidation of ammonium and 20% reduction of reaction time. 268 This unique control strategy consists of high temporal resolution sensing data, data fusion/ data-driven models, and deterministic global dynamic optimization, and offers superior performance over traditional control approaches.

5.3. Current Challenges and Future Perspectives of LTCM Applications. ML algorithms coupled with LTCM has demonstrated the capability of advancing predictability of water system modeling and control through learning patterns. However, those algorithms require large volumes of representative training data relied upon capable sensors that are severely under developed. 12 A critical step is developing reliable sensors and data processing algorithms to present realtime LTCM sensor data for variable inputs and complex dynamics.

Future applications of LTCM in water systems profiling/ modeling could be utilizing the high temporal resolution data obtained by sensors to develop a digital twin toward smart water system management. With the provision of the highfidelity LTCM profiles along with models development, a digital twin can form a central repository to advance predictive analytics, system optimization, and personnel training. 269 Additionally, data-driven analysis based on LTCM data can offer diverse temporal resolutions for fault detection, variable prediction, and automated control in water processes, 248 6. CONCLUSIONS, FUTURE OPPORTUNITIES, AND OUTLOOK OF LTCM In this review, we elaborate a forward-looking roadmap of LTCM through a three-hierarchy perspective: sensor data, water parameters, and system level. We evaluate and analyze the entire route of LTCM consisting of data acquisition (sensor development), data processing (sensor network and algorithms), and data application (modeling and process control). We identify the roadblocks of applying LTCM in natural and engineering water systems and pinpoint the target areas supporting innovations in LTCM technologies. In this section, we lay out future opportunities of LTCM applications in four key domains, water, energy, sensing, and data, covering emerging topics ranging from fundamental scientific exploration to knowledge generalization in broad communities.

6.1. Water Safety and Public Health. LTCM generates comprehensive data sets capable of capturing the heterogeneous status of water system dynamics under both normal operation and transient shocks. A reagent-free electrochemical sensing device recently developed is capable of detecting the presence of SARS-CoV-2 in water within 5 min and lasting for 9 months, 270 indicating that LTCM can closely track the chemical fingerprint of target analytes to alleviate the public concern. Moreover, LTCM provides adequate data across a broad time scale essential for building the finest water dynamics profiles that can be used to develop digital networks connecting water grids with smart-city infrastructure and promote fully integrated water utilities. For example, mass deployment of various water sensors (e.g., humidity, temperature, and pressure) in DWTPs and DWDSs has been executed to detect the water leakage in an IoT-based smart water microgrid. 271 For the future studies, we recommend building IoT-based smart water quality monitoring systems comprising multiple types of sensor nodes to generate LTCM data and extend the sensing coverage range. These large digital water networks possess a high execution speed and the capability to protect the water ecosystems. One notable success is the longterm chlorine surveillance from 2015 to 2019, during which data were collected using digital colorimeters and color wheels monitored by citizen scientists in Flint (Michigan, USA), offering a unique methodology to assuage public health concern after the Flint Water Crisis (2014−2015). 272 6.2. Energy-Positive Treatment Process and Resource Recovery. Implementation of cost-effective and energyefficient treatment processes requires accurate quantification of water constituents as well as formulating multiobjective programmable models for adaptive outputs and process control. By providing real-time in situ information of water systems, LTCM can facilitate stakeholders and decision makers to foresee imminent water-quality problems and execute swift responses. For example, metal concentrations (e.g., Li + ) can be continuously monitored in a capacitive deionization (CDI) Environmental Science & Technology pubs.acs.org/est Critical Review reactor, based on which energy consumption and metal recovery can be balanced. 273 Moreover, multiple time-scale data sets in AD systems can be generated by integrating LTCM data with short-term and discrete data using sensor fusion, which can build the long/short-term memory networks to illuminate complex interactions among various parameters, advance system resilience, and maintain stable biogas production under system fluctuations and shocks. 258, 274, 275 One future application of LTCM is to provide individualized data with high computational efficiency to accomplish energy neutrality of water−energy microgrids and determine the overall impacts of carbon footprint on climate change (e.g., CH 4 and CO 2 emission from AD systems) by providing closely monitoring the critical parameters with unprecedented spatiotemporal resolution, which cannot be achieved based on current practices. 6.3. Sensing Capability Innovations. LTCM data enable us to quantify the impacts of sensor modifications on the overall sensor performance, which can structurally guide future sensor development and material optimization. Developing innovative sensor materials is a vital strategy toward LTCM, such as anticorrosion materials (i.e., superhydrophobic electrically conductive paper 276 ), antimicrobial/antiadhesive materials (i.e., silver nanoparticles 277 and zwitterionic copolymers 278, 279 ) , outer layer protection (e.g., silicon rubber 280 ), and high viscosity materials (e.g., poly(methyl methacrylate) 281 ). In addition, sensor data processing algorithms can be coupled with sensor material development to further elevate sensor performance. For example, a denoising data processing algorithm has been employed to quantify the impacts of polytetrafluoroethylene on potentiometric ammonium sensors in terms of accuracy and lifespan during a 20 day test in WW. 282 Furthermore, the data generated from LTCM delivers months/years monitoring scopes and minute-or even secondtemporal resolutions, which can maximize the computing ability, predict sensor reading variation, and eliminate sensor data drift. 283 Finally, we also suggest applying sensor arrays consisting of different types of miniature sensors for multiplexed detection of a broad spectrum of water parameters and using data fusion/ML algorithms to achieve high-resolution profiling in water systems.

6.4. Data Sharing and Workforce Development. Another step forward should be establishing an open-access metadata platform and advancing data accessibility for general end-users. The unified open-source metadata scheme constructed by the LTCM data library can develop new generation coproduction applications (e.g., real-time DW quality mobile apps 284 ) that address the critical data management need for broad communities. In the meantime, feedback of LTCM implementations can be promptly collected by sharing a vast amount of water quality data sets, which will in turn accelerate the development of LTCM technologies. For example, an Observations Data Model database built by sharing the data stored from a Hydrologic Information System was executed to identify the anomalous performance (e.g., sensor reading drift, calibration error, and sensor fouling) of the pH sensors deployed in the Little Bear River (Utah, USA) for a month with a sensing interval of 30 min, through which sensor readings were corrected and adjusted to achieve high accuracy and reproductivity. 133 To take advantage of the data library, we advocate providing automated digital water networks (e.g., IoT, digital twin, and SCADA) in small-size water facilities and/or decentralized systems for evolving process monitoring and control strategies and acquiring and sharing multiscale LTCM data among underdeveloped communities and groups to promote water data equity and diversity.

With the development of materials science, novel sensing technologies, and machine learning algorithms in recent years, we envision that more reliable LTCM results will be available through data acquisition, processing and application to achieve high-efficiency data-enabled utility operation through a threelayer hierarchy (sensor data, water parameter, and system level), thus allowing the stakeholders and decision makers to probe emerging problems swiftly and executing competent control methodologies in a timely manner. 

A Critical Review of the Risks to Water Resources from Unconventional Shale Gas Development and Hydraulic Fracturing in the United States

From Water-Use to Water-Scarcity Footprinting in Environmentally Extended Input−Output Analysis

Mercury as a Global Pollutant: Sources, Pathways, and Effects

Enhancing Anaerobic Fermentation Performance through Eccentrically Stirred Mixing: Experimental and Modeling Methodology

Anaerobic Fluidized Bed Membrane Bioreactor for Wastewater Treatment

Degradation of Polyamide Nanofiltration and Reverse Osmosis Membranes by Hypochlorite

Carbon Recovery from Wastewater through Bioconversion into Biodegradable Polymers

Comparing Ion Exchange Adsorbents for Nitrogen Recovery from Source-Separated Urine

Performance Recovery in Degraded Carbon-Based Electrodes for Capacitive Deionization

Production of Nitrous Oxide From Anaerobic Digester Centrate and Its Use as a Co-Oxidant of Biogas to Enhance Energy Recovery

Energy-Positive Wastewater Treatment and Desalination in an Integrated Microbial Desalination Cell (MDC)-Microbial Electrolysis Cell (MEC)

Enabling Wastewater Treatment Process Automation: Leveraging Innovations in Real-Time Sensing, Data Analysis, and Online Controls

Water Quality Monitoring Strategies  A Review and Future Perspectives

Precise Control of Water and Wastewater Treatment Systems with Non-Ideal Heterogeneous Mixing Models and High-Fidelity Sensing

Detection of Contaminants in Water Supply: A Review on State-of-the-Art Monitoring Technologies and Their Applications

Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detection Methods of Nitrate in Water: A

Real-Time Water Quality Monitoring Using Internet of Things in SCADA

Sensor Technologies for the Energy-Water Nexus − A Review

Situ Potentiometry and Ellipsometry: A Promising Tool to Study Biofouling of Potentiometric Sensors

Electropolymerized Hydrophobic Polyazulene as Solid-Contacts in Potassium-Selective Electrodes

Electrochemical Sensors and Biosensors Based on Nanomaterials and Nanostructures

Tools and Tactics for the Optical Detection of Mercuric Ion

Studies of Sensor Data Interpretation for

A Proposed Scalable Design and Simulation of Wireless Sensor Network-Based Long-Distance Water Pipeline Leakage Monitoring System

The Relevance of Phosphorus and Iron Chemistry to the Recovery of Phosphorus from Wastewater: A Review

From Removal to Recovery: An Evaluation of Nitrogen Recovery Techniques from Wastewater

Assessing the Feasibility of N and P Recovery by Struvite Precipitation from Nutrient-Rich Wastewater: A Review

Technologies for Recovering Nutrients from Wastewater: A Critical Review

New Directions in Biological Nitrogen Removal and Recovery from Wastewater

Platforms for Energy and Nutrient Recovery from Domestic Wastewater: A Review

The Application of Graphene-Based Materials for the Removal of Heavy Metals and Radionuclides from Water and Wastewater

Adsorption of Heavy Metals on Conventional and Nanostructured Materials for Wastewater Treatment Purposes: A Review

Heavy Metal Removal from Water/Wastewater by Nanosized Metal Oxides: A Review

Comprehensive Review on Phytotechnology: Heavy Metals Removal by Diverse Aquatic Plants Species from Wastewater

A Review of Microplastics in Table Salt, Drinking Water, and Air: Direct Human Exposure

A Review of the Removal of Microplastics in Global Wastewater Treatment Plants: Characteristics and Mechanisms

Distribution and Importance of Microplastics in the Marine Environment: A Review of the Sources, Fate, Effects, and Potential Solutions

Effects of Microplastics on Wastewater and Sewage Sludge Treatment and Their Removal: A Review

Interaction of Toxic Chemicals with Microplastics: A Critical Review

Methods for Sampling and Detection of Microplastics in Water and Sediment: A Critical Review

Microplastics in Freshwater Systems: A Review on Occurrence, Environmental Effects, and Methods for Microplastics Detection

Microplastics in Freshwaters and Drinking Water: Critical Review and Assessment of Data Quality

Removal of Microplastics from the Environment. A Review

Review on the Occurrence and Fate of Microplastics in Sewage Treatment Plants

The Chemical Behaviors of Microplastics in Marine Environment: A Review

A Review on Removal of Organophosphorus Pesticides in Constructed Wetland: Performance, Mechanism and Influencing Factors

Current Status of Persistent Organic Pesticides Residues in Air, Water, and Soil, and Their Possible Effect on Neighboring Countries: A Comprehensive Review of India

Electrochemically Assisted Remediation of Pesticides in Soils and Water: A Review

Impacts and General Aspects of Pesticides in Surface Water: A Review

Recent Strategies for Removal and Degradation of Persistent & Toxic Organochlorine Pesticides Using Nanoparticles: A Review

Removal of Pesticides from Water and Wastewater by Different Adsorbents: A Review

Removal of Pesticides from Water by NF and RO Membranes  A Review

Sample Preparation and Extraction Methods for Pesticides in Aquatic Environments: A Review

The Use of Constructed Wetlands for Removal of Pesticides from Agricultural Runoff and Drainage: A Review

Optimizing the Operation of a Two-Phase Anaerobic Digestion System Digesting Grass Silage

Solid-State Anaerobic Digestion of Mixed Organic Waste: The Synergistic Effect of Food Waste Addition on the Destruction of Paper and Cardboard

Thermophilic Anaerobic Digestion to Increase the Net Energy Balance of Corn Grain Ethanol

Enhancing Anaerobic Digestion and Methane Production of Tetracycline Wastewater in EGSB Reactor with GAC/NZVI Mediator

Nutrients Removal and Recovery in Bioelectrochemical Systems: A Review

Metals Removal and Recovery in Bioelectrochemical Systems: A Review

Optimal Set Anode Potentials Vary in Bioelectrochemical Systems

Design and Fabrication of Bioelectrodes for Microbial Bioelectrochemical Systems

Water Quality Modeling in the Dead End Sections of Drinking Water Distribution Networks

Review of Sensor Placement Strategies for Contamination Warning Systems in Drinking Water Distribution Systems

Potential Impacts of Changing Supply-Water Quality on Drinking Water Distribution: A Review

Sensor Placement Methods for Contamination Detection in Water Distribution Networks: A Review

Event-Triggered PID Control for Wastewater Treatment Plants

Real Time Control of Water Distribution Networks: A State-of-the-Art Review

Arsenic in Groundwater in Private Wells in Rural North Dakota and South Dakota: Water Quality Assessment for an Intervention Trial

Transfer of Heavy Metals from Compost to Red Soil and Groundwater under Simulated Rainfall Conditions

Review of Risk from Potential Emerging Contaminants in UK Groundwater

Identification of Groundwater Pollution Sources in a Landfill Site Using Artificial Sweeteners, Multivariate Analysis and Transport Modeling

Feasibility of Biodegradation of Polyfluoroalkyl and Perfluoroalkyl Substances

A Continental-Scale Hydrology and Water Environmental Science & Technology pubs

Quality Model for Europe: Calibration and Uncertainty of a High-Resolution Large-Scale SWAT Model

Assessing the Capability of the SWAT Model to Simulate Snow, Snow Melt and Streamflow Dynamics over an Alpine Watershed

An Overview of Current Applications, Challenges, and Future Trends in Distributed Process-Based Models in Hydrology

Campíns-Falcó, P. Rapid Evaluation of Ammonium in Different Rain Events Minimizing Needed Volume by a Cost-Effective and Sustainable PDMS Supported Solid Sensor

A Lab-On-Chip Phosphate Analyzer for Long-Term In Situ Monitoring at Fixed Observatories: Optimization and Performance Evaluation in Estuarine and Oligotrophic Coastal Waters. Front

A Generalized Machine Learning Approach for Dissolved Oxygen Estimation at Multiple Spatiotemporal Scales Using Remote Sensing

Real-Time in Situ Monitoring of Nitrogen Dynamics in Wastewater Treatment Processes Using Wireless, Solid-State, and Ion-Selective Membrane Sensors

Multi-Directional Flow Dynamics Shape Groundwater Quality in Sloping Bedrock Strata

Influence of Variable Salinity Conditions in a Tidal Creek on Riparian Groundwater Flow and Salinity Dynamics

Biofilms in Drinking Water and Their Role as Reservoir for Pathogens

Novel Coronavirus (SARS-CoV-2): What Is Its Fate in Urban Water Cycle and How Can the Water Research Community Respond?

Monitoring Emerging Contaminants in the Drinking Water of Milan and Assessment of the Human Risk

Fate and Redistribution of Perfluoroalkyl Acids through AFFF-Impacted Groundwater

Perfluorooctanoic Acid (PFOA), an Emerging Drinking Water Contaminant: A Critical Review of Recent Literature

The Price of Really Clean Water: Combining Nanofiltration with Granular Activated Carbon and Anion Exchange Resins for the Removal of Per-And Polyfluoralkyl Substances (PFASs) in Drinking Water Production

Effective Removal of Fluorescent Microparticles as Cryptosporidium Parvum Surrogates in Drinking Water Treatment by Metallic Membrane

Guideline Levels for PFOA and PFOS in Drinking Water: The Role of Scientific Uncertainty, Risk Assessment Decisions, and Social Factors

The Future of Water Infrastructure Resilience. Procedia Eng

The Future of Water Infrastructure Resilience. Procedia Eng

Assessing Graphite and Stainless-Steel for Electrochemical Sensing of Biofilm Growth in Chlorinated Drinking Water Systems

Integration of Open Source Hardware Arduino Platform in Automation Systems Applied to Smart Grids/Micro-Grids

Mercury Ion-Selective Electrode With Self-Plasticizing Poly(n−Buthylacrylate

Membrane Based On 1,2-Bis-(N′−Benzoylthioureido)Cyclohexane As Ionophore

A Reagent-Free Paper-Based Sensor Embedded in a 3D Printing Device for Cholinesterase Activity Measurement in Serum

Implementation of a Global P-Recovery System in Urban Wastewater Treatment Plants

Resource Recovery from an Aerobic Granular Sludge Process Treating Domestic Wastewater

A Novel Shortcut Nitrogen Removal Process Using an Algal-Bacterial Consortium in a Photo-Sequencing Batch Reactor (PSBR)

A Novel Integrated Vertical Membrane Bioreactor (IVMBR) for Removal of Nitrogen from Synthetic Wastewater/Domestic Sewage

Ultra-Sensitive Phenol Sensor Based on Overcoming Surface Fouling of Reduced Graphene Oxide-Zinc Oxide Composite Electrode

Towards Engineering Application: Potential Mechanism for Enhancing Anaerobic Digestion of Complex Organic Waste with Different Types of Conductive Materials

Electrochemical Sensors for Environmental Monitoring: Design, Development and Applications

ReviewRecent Advances in Microfabrication, Design and Applications of Amperometric Sensors and Biosensors

Voltammetric Determination of Metal Ions beyond Mercury Electrodes

A Voltammetric PH Sensor for Food and Biological Matrices

Detection and Identification of Bacteria Using Antibiotic Susceptibility and a Multi-Array Electrochemical Sensor with Pattern Recognition

Correlating Surface Growth of Nanoporous Gold with Electrodeposition Parameters to Optimize Amperometric Sensing of Nitrite

Batch Microfabrication of Highly Integrated Silicon-Based Electrochemical Sensor and Performance Evaluation via Nitrite Water Contaminant Determination

An Extraordinarily Sensitive Voltammetric Sensor with Picomolar Detection Limit for Pb2+ Determination Based on Carbon Paste Electrode Impregnated with Nano-Sized Imprinted Polymer and Multi-Walled Carbon Nanotubes

Continuous Detection of Hypochlorous Acid/ Hypochlorite for Water Quality Monitoring and Control. Electroanalysis

Recent Advances in Applications of Voltammetric Sensors Modified with Ferrocene and Its Derivatives

Interesting Interference Evidences of Electrochemical Detection of Zn(II), Cd(II) and Pb(II) on Three Different Morphologies of MnO2 Nanocrystals

Review: Highlights in Recent Applications of Electronic Tongues in Food Analysis

Electrochemical Degradation of Reactive Blue 19 in Chloride Medium for the Treatment of Textile Dyeing Wastewater with Identification of Intermediate Compounds. Dyes Pigments

Anti-Poisoning Electrode for Real-Time in-Situ Monitoring of Hydrogen Sulfide Release

Robust Solid-Contact Ion Selective Electrodes for High-Resolution in Situ Measurements in Fresh Water Systems

Carrier-Based Ion-Selective Electrodes and Bulk Optodes. 1. General Characteristics

Design of a Stable Solid-Contact Ion-Selective Electrode Based on Polyaniline Nanoparticles as Ion-to-Electron Transducer for Application in Process Analytical Technology as a Real-Time Analyzer

Diagnostic of Functionality of Polymer Membrane -Based Ion Selective Electrodes by Impedance Spectroscopy

Polymeric Membrane Ion-Selective Electrodes with Anti-Biofouling Properties by Surface Modification of Silver Nanoparticles

Effect of Lipophilic Ion-Exchanger Leaching on the Detection Limit of Carrier-Based Ion-Selective Electrodes

Rational Design of All-Solid-State Ion-Selective Electrodes and Reference Electrodes

Improving the Sensitivity of Solid-Contact Ion-Selective Electrodes by Using Coulometric Signal Transduction

Nonlinear Source Separation Method for Smart Ion-Selective Electrode Arrays

Open Source Software for Visualization and Quality Control of Continuous Hydrologic and Water Quality Sensor Data

Conductometric Gas Sensors Based on Metal Oxides Modified with Gold Nanoparticles: A Review

Metal−Organic Framework-Based Microfluidic Impedance Sensor Platform for Ultrasensitive Detection of Perfluorooctanesulfonate

Conductometric Sensor for Viable Escherichia Coli and Staphylococcus Aureus Based on Magnetic Analyte Separation via Aptamer

Towards High Resolution Monitoring of Water Flow Velocity Using Flat Flexible Thin Mm-Sized Resistance-Typed Sensor Film (MRSF)

Method for Equivalent Circuit Determination for Electrochemical Impedance Spectroscopy Data of Protein Adsorption on Solid Surfaces

Experimental Study on Pipeline Internal Corrosion Based on a New Kind of Electrical Resistance Sensor

Preparation of Cu2O/CNTs Composite and Its Application as Sensing Platform for Detecting Nitrite in Water Environment

A Novel Highly Sensitive Zeolite-Based Conductometric Microsensor for Ammonium Determination

Early Detection of Cardiac Ischemia Using a Conductometric PCO2sensor: Real-Time Drift Correction and Parameterization

Nanomaterial-Based Optical Chemical Sensors for the Detection of Heavy Metals in Water: Recent Advances and Challenges

Review of Optical Sensors for Pesticides

Materials for Fluorescence-Based Optical Chemical Sensors

Ratiometric Fluorescence Sensor for Fe3+ Ions Detection Based on Quantum Dot-Doped Hydrogel Optical Fiber

Floc Sensor Prototype Tested in the Municipal Wastewater Treatment Plant

Klimant, I. Fluorescence Sensors for Trace Monitoring of Dissolved Ammonia

Enhanced Fluorescence Sensing Using Sol-Gel Materials

Aggregation Induced Emission Based Fluorescence PH and Temperature Sensors: Probing Polymer Interactions in Poly

Methacrylic Acid) Interpenetrating Polymer Networks

Fluorescence Sensor for Water in Organic Solvents Prepared from Covalent Immobilization of 4-Morpholinyl-1, 8-Naphthalimide

Diffusion-Controlled Detection of Trinitrotoluene: Interior Nanoporous Structure and Low Highest Occupied Molecular Orbital Level of Building Blocks Enhance Selectivity and Sensitivity

Space-Confined Indicator Displacement Assay inside a Metal−Organic Framework for Fluorescence Turn-on Sensing

Absorbance Based Light Emitting Diode Optical Sensors and Sensing Devices

Simultaneous Determination of Bromide and Nitrate in Contaminated Waters by Ion Chromatography Using Amperometry and Absorbance Detectors

Detection of Organic Compounds in Water by an Optical Absorbance Method

Hydrothermal Treatment of Grass: A Low-Cost, Green Route to Nitrogen-Doped, Carbon-Rich, Photoluminescent Polymer Nanodots as an Effective Fluorescent Sensing Platform for Label-Free Detection of Cu(II) Ions

LED-Based UV Absorption Detector with Low Detection Limits for Capillary Liquid Chromatography

High-Power 365 Nm UV LED Mercury Arc Lamp Replacement for Photochemistry and Chemical Photolithography

Development of a Cost-Effective Optical Sensor for Continuous Monitoring of Turbidity and Suspended Particulate Matter in Marine Environment

A Low-Cost Continuous Turbidity Monitor

Personal Exposure Estimates via Portable and Wireless Sensing and Reporting of Particulate Pollution

On the Use of Light Scattering Selectivity for the Indication of the Variability in the Distributions of the Large and Small Particles Suspended in the Water

Recent Advances in the Design of Colorimetric Sensors for Environmental Monitoring

Autonomous Microfluidic System for Phosphate Detection

Ultratrace Naked-Eye Colorimetric Detection of Hg2+ in Wastewater and Serum Utilizing Mercury-Stimulated Peroxidase Mimetic Activity of Reduced Graphene Oxide-PEI-Pd Nanohybrids

Evolution of Electrospun Nanofibers Fluorescent and Colorimetric Sensors for Environmental Toxicants, PH, Temperature, and Cancer Cells − A Review with Insights on Applications

Applications of Raman Spectroscopy in Detection of Water Quality

Biosensor Technology for PesticidesA Review

Novel BOD Optical Fiber Biosensor Based on Co-Immobilized Microorganisms in Ormosils Matrix

A Silk Derived Carbon Fiber Mat Modified with Au@Pt Urchilike Nanoparticles: A New Platform as Electrochemical Microbial Biosensor

Purification and Characterization of Alanine Dehydrogenase from Streptomyces Anulatus for Its Application as a Bioreceptor in Biosensor

Electrochemical Biosensors for Pathogen Detection

On-Line Monitoring of Heavy Metals-Related Toxicity with a Microbial Fuel Cell Biosensor

Microbial Fuel Cell Sensors for Water Quality Early Warning Systems: Fundamentals, Signal Resolution, Optimization and Future Challenges

Flat Microliter Membrane-Based Microbial Fuel Cell as "on-Line Sticker Sensor" for Self-Supported in Situ Monitoring of Wastewater Shocks

Microbial Fuel Cell-Based Biosensors for Environmental Monitoring: A Review

Microsystems for Biofilm Characterization and Sensing − A Review

Microbial Fuel Cell-Based Biosensor for Online Monitoring Wastewater Quality: A Critical Review

Disposable Self-Support Paper-Based Multi-Anode Microbial Fuel Cell (PMMFC) Integrated with Power Management System (PMS) as the Real Time

A Batch-Mode Cube Microbial Fuel Cell Based "Shock" Biosensor for Wastewater Quality Monitoring

A Comparison of Microbial Fuel Cell and Microbial Electrolysis Cell Biosensors for Real-Time Environmental Monitoring

Biosensors for Wastewater Monitoring: A Review

A Novel Enzyme-Free Electrochemical Biosensor for Rapid Detection of Pseudomonas Aeruginosa Based on High Catalytic Cu-ZrMOF and Conductive Super P

Super-Silent FRET Sensor Enables Live Cell Imaging Environmental Science & Technology pubs

Performance of an Electrochemical COD (Chemical Oxygen Demand) Sensor with an Electrode-Surface Grinding Unit

A Novel Voltammetric Sensor for Amoxicillin Based on Nickel−Curcumin Complex Modified Carbon Paste Electrode

Continuous-Flow System for On-Line Water Monitoring Using Back-Side Contact ISFET-Based Sensors

Toward Long-Term Accurate and Continuous Monitoring of Nitrate in Wastewater Using Poly (Tetrafluoroethylene) (PTFE)−Solid-State Ion-Selective Electrodes (S-ISEs)

Long-Term Continuous and Real-Time in Situ Monitoring of Pb(II) Toxic Contaminants in Wastewater Using Solid-State Ion Selective Membrane (S-ISM) Pb and PH Auto-Correction Assembly

IoT-Based Sensing System for Phosphate Detection Using Graphite/ PDMS Sensors

A Fluorescent Sensor for Low PH Values Based on a Covalently Immobilized Rhodamine−Napthalimide Conjugate

Measurement of PH and Dissolved Oxygen within Cell Culture Media Using a Hydrogel Microarray Sensor

Fully Automated, Low-Cost Ion Chromatography System for in-Situ Analysis of Nitrite and Nitrate in Natural Waters

A Lab-On-Chip Phosphate Analyzer for Long-Term In Situ Monitoring at Fixed Observatories: Optimization and Performance Evaluation in Estuarine and Oligotrophic Coastal Waters. Front

Detection of Heavy Metal Ions in Drinking Water Using a High-Resolution Differential Surface Plasmon Resonance Sensor

Optical, on-Line Bacteria Sensor for Monitoring Drinking Water Quality

NanoMIP Based Optical Sensor for Pharmaceuticals Monitoring

Microbial Potentiometric Sensor: A New Approach to Longstanding Challenges

Real-Time Monitoring and Prediction of Water Quality Parameters and Algae Concentrations Using Microbial Potentiometric Sensor Signals and Machine Learning Tools

Microbial Fuel Cell Type Biosensor for Specific Volatile Fatty Acids Using Acclimated Bacterial Communities

Impedimetric Paper-Based Biosensor for the Detection of Bacterial Contamination in Water

Water Quality and Health-Review of Turbidity: Information for Regulators and Water Suppliers; World Health Organization

Groundwater Temperature and Electrical Conductivity as Tools to Characterize Flow Patterns in Carbonate Aquifers: The Sierra de Las Nieves Karst Aquifer

Screening of Lake Sediments for Emerging Contaminants by Liquid Chromatography Atmospheric Pressure Photoionization and Electrospray Ionization Coupled to High Resolution Mass Spectrometry

Combining Monitoring Data and Modeling Identifies PAHs as Emerging Contaminants in the Arctic

A Review of the Effects of Emerging Contaminants in Wastewater and Options for Their Removal

Advanced Oxidation Process-Mediated Removal of Pharmaceuticals from Water: A Review

Feasibility Studies: UV/ Chlorine Advanced Oxidation Treatment for the Removal of Emerging Contaminants

Quantification of Microplastic Mass and Removal Rates at Wastewater Treatment Plants Applying Focal Plane Array (FPA)-Based Fourier Transform Infrared (FT-IR) Imaging

Identification of Microplastic in Effluents of Waste Water Treatment Plants Using Focal Plane Array-Based Micro-Fourier-Transform Infrared Imaging

Digital Protocol for Chemical Analysis at Ultralow Concentrations by Surface-Enhanced Raman Scattering

Recent Progress in Surface-Enhanced Raman Scattering for the Detection of Chemical Contaminants in Water

Levels of Perfluorochemicals in Water Samples from Catalonia, Spain: Is Drinking Water a Significant Contribution to Human Exposure?

Risk Assessment of Antibiotic Resistance Genes in the Drinking Water System

Recent Progress in the Detection of Emerging Contaminants PFASs

Monitoring of Fluoride in Water Samples Using a Smartphone

Label-Free Electrochemical Immunosensor Based on Flower-like Ag/MoS2/RGO Nanocomposites for Ultrasensitive Detection of Carcinoembryonic Antigen

A Microporous Anionic Metal−Organic Framework for a Highly Environmental Science & Technology pubs

Selective and Sensitive Electrochemical Sensor of Cu 2+ Ions

Evaluation of Water Quality Parameters in Drinking Water of District Bannu

Assessment of Water Quality in Groundwater Resources of Iran Using a Modified Drinking Water Quality Index (DWQI)

Optimal Sampling of Water Distribution Network Dynamics Using Graph Fourier Transform

Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants

Blockchain's Roles in Strengthening Cybersecurity and Protecting Privacy

Benefits and Challenges -IEEE

Outlier Detection in Wireless Sensor Networks Using Bayesian Belief Networks

A Review of Cybersecurity Incidents in the Water Sector

A Systematic Review of the State of Cyber-Security in Water Systems

Cyber Security of Water SCADA SystemsPart I: Analysis and Experimentation of Stealthy Deception Attacks

Water Networks and Cyber Security

Statistical Monitoring of Wastewater Treatment Plants Using Variational Bayesian PCA

Statistical Monitoring and Dynamic Simulation of a Wastewater Treatment Plant: A Combined Approach to Achieve Model Predictive Control

Optimal Sampling of Water Distribution Network Dynamics Using Graph Fourier Transform

Data Processing and Temperature-Emissivity Separation for Tower-Based Imaging Fourier Transform Spectrometer Data

Sensor Data Classification Using Machine Learning Algorithm

How Can Big Data and Machine Learning Benefit Environment and Water Management: A Survey of Methods, Applications, and Future Directions

Wastewater Quality Monitoring System Using Sensor Fusion and Machine Learning Techniques

Drift Reduction of Gas Sensor by Wavelet and Principal Component Analysis

A Feature Extraction Method for Chemical Sensors in Electronic Noses

Application of Neural Networks with Novel Independent Component Analysis Methodologies to a Prussian Blue Modified Glassy Carbon Electrode Array

Application of Mathematical Models and Genetic Algorithm to Simulate the Response Characteristics of an Ion Selective Electrode Array for System Recalibration

The FastICA Algorithm with Spatial Constraints

Effects of Learning Parameters on Learning Procedure and Performance of a BPNN

Sequential Injection System with Higher Dimensional Electrochemical Sensor Signals: Part 2. Potentiometric e-Tongue for the Determination of Alkaline Ions

Examining Plant Uptake and Translocation of Emerging Contaminants Using Machine Learning: Implications to Food Security

Centralized and Decentralized Process and Sensor Fault Monitoring Using Data Fusion Based on Adaptive Extended Kalman Filter Algorithm

Application of the IWA ADM1Model to Simulate Anaerobic Co-Digestion of Organic Waste with Waste Activated Sludge in Mesophilic Condition

Feeding Control of Anaerobic Co-Digestion of Waste Activated Sludge and Corn Silage Performed by Rule-Based PID Control with ADM1

Mixture Effects of Drinking Water Disinfection By-Products: Implications for Risk Assessment

Removal of Antibiotics in Biological Wastewater Treatment SystemsA Critical Assessment Using the Activated Sludge Modeling Framework for Xenobiotics (ASM-X)

Selective Extraction of Monophenols from Pyrolysis Bio-Oil Based on a Novel Three-Dimensional Visualization Model

Adsorption of Cu(II) Ions onto Crosslinked Chitosan/Waste Active Sludge Char (WASC) Beads: Kinetic, Equilibrium, and Thermodynamic Study

Net-Zero-Energy Model for Sustainable Wastewater Treatment

Predicting the Disinfection Efficiency Range in Chlorine Contact Tanks through a CFD-Based Approach

Biofouling in Spiral Wound Membrane Systems: Three-Dimensional CFD Model Based Evaluation of Experimental Data

Relationship between PH, Oxidation Reduction Potential (ORP) and Biogas Production in Mesophilic Screw Anaerobic Digester

Long-Term Impact of Anaerobic Reaction Time on the Performance and Granular Characteristics of Granular Denitrifying Biological Phosphorus Removal Systems

Detection of Water-Quality Contamination Events Based on Multi-Sensor Fusion Using an Extented Dempster−Shafer Method

Interpretable Machine Learning for Predicting Biomethane Production in Industrial-Scale Anaerobic Co-Digestion

A Novel Auto-Tuning PID Control Mechanism for Nonlinear Systems

Model Predictive Control of an Activated Sludge Process: A Case Study

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Simultaneous Design and MPC-Based Control for Dynamic Systems under Uncertainty: A Stochastic Approach

Cost Reduction of the Wastewater Treatment Plant Operation by MPC Based on Modified ASM1 with Two-Step Nitrification/Denitrification Model

Integrated Data Fusion and Mining Techniques for Monitoring Total Organic Carbon Concentrations in a Lake

Actuators Monitoring System for Real-Time Control of Nitrification−Denitrification via Nitrite on Long Term Operation

Water and Wastewater Digital Surveillance for Monitoring and Early Detection of the COVID-19 Hotspot: Industry 4.0

Detection of SARS-CoV-2 Viral Particles Using Direct, Reagent-Free Electrochemical Sensing

An IoT-Based Smart Water Microgrid and Smart Water Tank Management System

Advances in Intelligent Systems and Computing

Citizen Science Chlorine Surveillance during the Flint, Michigan Federal Water Emergency

Energy Consumption Analysis of Constant Voltage and Constant Current Operations in Capacitive Deionization

Situ Volatile Fatty Acids Influence Biogas Generation from Kitchen Wastes by Anaerobic Digestion

Relationship between Anaerobic Digestion Characteristics and Biogas Production under Composting Pretreatment

Superhydrophobic Electrically Conductive Paper for Ultrasensitive Strain Sensor with Excellent Anticorrosion and Self-Cleaning Property

Polymeric Membrane Ion-Selective Electrodes with Anti-Biofouling Properties by Surface Modification of Silver Nanoparticles

Antifouling Thin-Film Composite Membranes by Controlled Architecture of Zwitterionic Polymer Brush Layer

Investigation of the Hydration of Nonfouling Material Poly(Ethylene Glycol) by Low-Field Nuclear Magnetic Resonance

PVC-Based Ion-Selective Electrodes with a Silicone Rubber Outer Coating with Improved Analytical Performance

Marine Antifouling Property of PMMA Nanocomposite Films: Results of Laboratory and Field Assessment

Solving Sensor Reading Drifting Using Denoising Data Processing Algorithm (DDPA) for Long-Term Continuous and Accurate Monitoring of Ammonium in Wastewater

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

Real Time Internet of Things (IoT) Based Water Quality Management System. Procedia CIRP 2020