Recent advances in algal bloom detection and prediction technology using machine learning.

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

Applying microelectrodes to investigate aged ductile iron and copper coupon reactivity during free chlorine application.

In drinking water distribution systems, including premise plumbing, dissolved oxygen (DO) and free chlorine (FC) are common oxidants and ductile iron (DI) and copper (Cu) are commonly used pipe materials. Microelectrodes as a tool have been applied in previous corrosion research and were used in this study to collect quantifiable data and understand DO and FC reactivity and pH changes at the water-metal interface. Using microelectrodes, pH, DO, and FC profiles from the bulk water to near and at the surface of aged DI (154-190 d) and Cu (2 d and 86-156 d) coupons were investigated during periods of flow and stagnation (30 min). Using the measured microelectrode profiles, oxidant fluxes and apparent surface reaction rate constants were calculated to elucidate differences between DO and FC reactivity with the coupons. Microelectrodes were successfully applied to measure pH, DO, and FC profiles from the bulk water to near aged DI and Cu coupon surfaces; Cu coupons aged quickly and exhibited less reactivity at 2 d with DO and FC than aged DI coupons did after 154-190 d; and for the aged DI coupon experiments, orthophosphate presence stabilized pH profiles where without orthophosphate pH fluctuations of greater than 2 pH units occurred from the bulk water to the DI coupon surface.

Photodegradation of MC-LR using a novel Au-decorated Ni metal-organic framework (Au/Ni-MOF).

Microcystins (MCs) are toxins produced by cyanobacteria commonly found in harmful algal blooms (HAB) occurring in many surface waters. Conventional methods for removing MC-LR such as membrane filtration and activated carbon are only phase change removal methods and are often expensive in operation and maintenance. It is urgent to develop a rapid, easy-to-use, and cost-effective method for the degradation of MC-LR. In this study, a novel Au-decorated Ni-metal-organic framework (Au/Ni-MOF) was newly developed on a hydrophilic carbon fiber paper (2 cm × 2 cm) using an air spraying method. The Au/Ni-MOF was then applied for the photodegradation of MC-LR in water under UV-Vis. The addition of Au onto the surface of the Ni-MOF resulted in a nearly fivefold enhancement in the reaction rate coefficient (k), reaching a value of 0.0599 min-1 for the photodegradation of MC-LR (initial concentration of 20 ppb). It was found that 94.2% of MC-LR removal was attributed to photodegradation, with the remaining 5.8% from adsorption. The rate coefficient of 20 ppb of MC-LR in the surface water sample (pH 6.0) was 0.06 min-1 likely due to the presence of other contaminates including scavenger agents within the sample which inhibits the degradation reaction of the MC-LR. Overall, this study demonstrated the potential for the novel Au/Ni-MOF to effectively reduce the concentration of the MC-LR toxin in the contaminated water.

Microelectrode evaluation of in situ oxidant reactivity and pH variability at new ductile iron and copper coupon surfaces.

Thirty-two short term (∼7.5 h) abiotic experiments were conducted with new ductile iron and copper coupons exposed to various water qualities, including pH (7 or 9), dissolved inorganic carbon (DIC, 10 or 50 mg C L-1) and phosphate (0 or 3 mg P L-1) concentrations and 4 mg Cl2 L-1 free chlorine or monochloramine. To quantify oxidant reactivity with the new metal coupons, microelectrodes were used to obtain oxidant (free chlorine or monochloramine and dissolved oxygen (DO)) concentration and pH microprofiles from the bulk water to near the metal coupon surface. From the microprofiles, apparent surface reaction rate constants (k) were determined for each oxidant. An ANOVA analysis evaluated if the five variables (Material, Oxidant, Phosphate, DIC, and pH) significantly affected estimates of k, finding that the Material and Oxidant variables and their interaction were statistically significant (p<0.05), but the effect of variables of Phosphate, DIC, and pH on k values were not significant in this study. In general, both ductile iron and copper coupons showed significant surface reactivity towards free chlorine and monochloramine. For ductile iron, DO consumption was greater than for copper, which showed minimal DO reactivity, and DO was less reactive towards the copper surface than either free chlorine or monochloramine. Furthermore, pH microprofiles provided insight into the complexity that might exist near corroding metal surfaces where the bulk water pH may be substantially different from that measured near metal surfaces which is significant as pH is a controlling variable in terms of scale formation and metal solubility. This study represents an important first step towards using microelectrodes to (1) understand and provide direct measurement of oxidant microprofiles from the bulk water to the metal surface; (2) determine pipe wall reactivity using the directly measured concentrations profiles versus estimated pipe wall reactivity from bulk water measurements, and (3) understand how variables measured by bulk water samples (e.g., pH) may be drastically different from what is occurring at and near the metal surface. Together, these insights will assist in understanding disinfectant residual maintenance, corrosion, and metal release.

Cost-effective screen-printed carbon electrode biosensors for rapid detection of microcystin-LR in surface waters for early warning of harmful algal blooms.

Microcystins (MCs) are toxins produced by cyanobacteria commonly found in harmful algal blooms (HABs). Due to their toxicity to humans and other organisms, the World Health Organization (WHO) sets a guideline of 1 µg L-1 for microcystin-leucine-arginine (MC-LR) in drinking water. However, current analytical techniques for the detection of MC-LR such as liquid chromatography-mass spectrometry (LC-MS) and ELISA are costly, bulky, time-consuming, and mostly conducted in a laboratory, requiring highly trained personnel. An analytical method that can be used in the field for rapid determination is essential. In this study, an anti-MC-LR/MC-LR/cysteamine-coated screen-printed carbon electrode (SPCE) biosensor was newly developed to detect MC-LR, bioelectrochemically, in water. The functionalization of the electrode surface was confirmed with surface characterization methods. The sensor performance was evaluated by electrochemical impedance spectroscopy (EIS), obtaining a linear working range of MC-LR concentrations between 0.1 and 100 µg L-1 with a limit of detection (LOD) of 0.69 ng L-1. Natural water samples experiencing HABs were collected and analyzed using the developed biosensor, demonstrating the excellent performance of the biosensor with a relative standard deviation (RSD) of 0.65%. The interference tests showed minimal error and RSD values against other common MCs and possible coexisting ions found in water. The biosensor showed acceptable functionality with a shelf life of up to 12 weeks. Overall, the anti-MC-LR/MC-LR/cysteamine/SPCE biosensors can be an innovative solution with characteristics that allow for in situ, low-cost, and easy-to-use capabilities which are essential for developing an overarching and integrated "smart" environmental management system.

Predicting bilgewater emulsion stability by oil separation using image processing and machine learning.

Bilgewater is a shipboard multi-component oily wastewater, combining numerous wastewater sources. A better understanding of bilgewater emulsions is required for proper wastewater management to meet discharge regulations. In this study, we developed 360 emulsion samples based on commonly used Navy cleaner data and previous bilgewater composition studies. Oil value (OV) was obtained from image analysis of oil/creaming layer and validated by oil separation (OS) which was experimentally determined using a gravimetric method. OV (%) showed good agreement with OS (%), indicating that a simple image-based parameter can be used for emulsion stability prediction model development. An ANOVA analysis was conducted of the five variables (Cleaner, Salinity, Suspended Solids [SS], pH, and Temperature) that significantly impacted estimates of OV, finding that the Cleaner, Salinity, and SS variables were statistically significant (p < 0.05), while pH and Temperature were not. In general, most cleaners showed improved oil separation with salt additions. Novel machine learning (ML)-based predictive models of both classification and regression for bilgewater emulsion stability were then developed using OV. For classification, the random forest (RF) classifiers achieved the most accurate prediction with F1-score of 0.8224, while in regression-based models the decision tree (DT) regressor showed the highest prediction of emulsion stability with the average mean absolute error (MAE) of 0.1611. Turbidity also showed a good emulsion prediction with RF regressor (MAE of 0.0559) and RF classifier (F1-score of 0.9338). One predictor variable removal test showed that Salinity, SS, and Temperature are the most impactful variables in the developed models. This is the first study to use image processing and machine learning for the prediction of oil separation for the application of bilgewater assessment within the marine sector.

Recycling urine for bioelectrochemical hydrogen production using a MoS2 nano carbon coated electrode in a microbial electrolysis cell.

In this study, a novel molybdenum disulfide (MoS2) nano-carbon (NC) coated cathode was developed for hydrogen production in a microbial electrolysis cell (MEC), while treating simulated urine with 2-6 times dilution (conductivity <20 mS cm-1). MoS2 nanoparticles were electrodeposited on the NC coated cathodes at -100, -150 and -200 µA cm-2 and their performances were evaluated in the MEC. The chronopotentiometry (CP) tests showed the improved catalytic activity of MoS2-NC cathodes with much lower cathode overpotential than non-MoS2 coated electrodes. The MoS2-NC200 cathode, electrodeposited at -200 µA cm-2, showed the maximum hydrogen production rate of 0.152 ± 0.002 m3 H2 m-2 d-1 at 0.9V of Eap, which is comparable to the previously reported Pt electrodes. It was found that high solution conductivity over 20 mS cm-1 (>600 mg L-1 NH3-N) can adversely affect the biofilm architecture and the bacterial activity at the anode of the MEC. Exoelectrogenic bacteria for this system at the anode were identified as Tissierella (Clostridia) and Bacteroidetes taxa. Maximum ammonia-nitrogen (NH3-N) and phosphorus (PO4 3--P) removal were 68.7 and 98.6%, respectively. This study showed that the newly fabricated MoS2-NC cathode can be a cost-effective alternative to the Pt cathode for renewable bioelectrochemical hydrogen production from urine.

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence.

Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.

Nanoparticle-embedded hydrogel synthesized electrodes for electrochemical oxidation of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS).

In this study, sliver (Ag) and gold (Au) nanoparticles (NPs) were embedded on poly (acrylic acid) (PAA)/poly (allylamine) hydrochloride (PAH) hydrogel fibers for improved electrochemical oxidation (EO) of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) removal. The NPs-loaded PAA/PAHs shows the better charge transport compared to the ceramic nanofiber membranes (CNM) electrodes. At 10 mA cm-2 of current density, the Ag-PAA/PAH electrodes showed a faster removal of PFAS compared to the Ag-CNM electrode probably due to large surface area-volume ratio and high porosity from the hydrogel. Among NPs-loaded PAA/PAH electrodes, the Ag/Au-PAA/PAH electrodes showed the highest removal of PFOA (72%) and PFOS (91%) in 2 h with the maximum removal rate of PFOA (0.0046 min-1) and PFOS (0.0093 min-1). The rapid PFOS removal is possibly due to the high activity of electron transfer with a higher redox potential of SO4•- than •OH. The highly stable F- generation was obtained from each electrode during reproducibility (n = 3). The net energy consumption from Ag/Au-PAA/PAH electrode was 164.9 kWh m-3 for 72% PFOA removal and 90 kWh m-3 for 91% PFOS removal, respectively. The developed Au-PAA/PAH electrodes were applied to lake water samples and showed acceptable PFOS removal (65%) with relative standard deviations (RSD) of 10.2% (n = 3) at 10 mA cm-2 of current density. Overall, the NP-embedded hydrogel nanofibers were proven to be a promising sustainable catalyst for the electrochemical PFAS oxidation in water.

Direct Mercury Detection in Landfill Leachate Using a Novel AuNP-Biopolymer Carbon Screen-Printed Electrode Sensor.

A novel Au nanoparticle (AuNP)-biopolymer coated carbon screen-printed electrode (SPE) sensor was developed through the co-electrodeposition of Au and chitosan for mercury (Hg) ion detection. This new sensor showed successful Hg2+ detection in landfill leachate using square wave anodic stripping voltammetry (SWASV) with an optimized condition: a deposition potential of -0.6 V, deposition time of 200 s, amplitude of 25 mV, frequency of 60 Hz, and square wave step voltage of 4 mV. A noticeable peak was observed at +0.58 V associated with the stripping current of the Hg ion. The sensor exhibited a good sensitivity of ~0.09 µA/µg (~0.02 µA/nM) and a linear response over the concentration range of 10 to 100 ppb (50-500 nM). The limit of detection (LOD) was 1.69 ppb, which is significantly lower than the safety limit defined by the United States Environmental Protection Agency (USEPA). The sensor had an excellent selective response to Hg2+ in landfill leachate against other interfering cations (e.g., Zn2+, Pb2+, Cd2+, and Cu2+). Fifteen successive measurements with a stable peak current and a lower relative standard deviation (RSD = 5.1%) were recorded continuously using the AuNP-biopolymer-coated carbon SPE sensor, which showed excellent stability, sensitivity and reproducibility and consistent performance in detecting the Hg2+ ion. It also exhibited a good reliability and performance in measuring heavy metals in landfill leachate.

Impact of Endocrine-Disrupting Chemicals in Breast Milk on Postpartum Depression in Korean Mothers.

Previous human and animal studies have reported an association between endocrine-disrupting chemicals (EDCs) and anxiety/depression. This study aimed to determine how the concentrations of phthalate metabolites, bisphenol A, triclosan, and parabens in breast milk are associated with the risk of developing postpartum depression (PPD) in Korean mothers. We recruited 221 mothers who were receiving lactation coaching at breastfeeding clinics between July and September 2018. The breast milk samples were collected along with responses to the Edinburgh Postnatal Depression Scale. The multivariable logistic regression results revealed that the phthalate, bisphenol A, parabens, and triclosan levels in the breast milk were not significantly associated with the risk of PPD. This study was the first attempt to analyze the association between the levels of EDCs in breast milk and the risk of PPD. Considering that PPD is a condition that affects not only the women diagnosed with it, but also their children and families, the results of this study may have great relevance to populations in environmentally sensitive periods.

A strategy for power generation from bilgewater using a photosynthetic microalgal fuel cell (MAFC).

Microbial fuel cells (MFCs) have recently been applied to generate electricity from oily wastewater. Although MFCs that utilize microalgae to provide a self-supporting oxygen (O2) supply at the cathode have been well discussed, those with microalgae at the anode as an active biomass for treating wastewater and producing electrons are still poorly studied and understood. Here, we demonstrated a bilgewater treatment using single- and double-chamber microalgal fuel cells (SMAFC and DMAFC) capable of generating energy with a novel microalgal strain (Chlorella sorokiniana) that was initially isolated from oily wastewater. Compared to previous MFC studies using green algae, relatively high voltage output (151.3-160.1 mV, 71.3-83.4 mV m-2 of power density) was observed in the SMAFC under O2 controlled systems (i.e., acetate addition or light/dark cycle). It was assumed that, under the O2 depletion, alternative electron acceptors such as bicarbonate may be utilized for power generation. A DMAFC showed better power density (up to 23.9%) compared to the SMAFC due to the separated cathode chamber which fully utilizes O2 as an electron acceptor. Both SMAFC and DMAFC removed 67.2-77.4% of soluble chemical oxygen demands (SCOD) from the synthetic bilgewater. This study demonstrates that the application of algae-based MFCs is a feasible strategy to treat oil-in-water emulsion while generating electricity.

Recent advances in biosensors for detecting viruses in water and wastewater.

As there is a considerable number of virus particles in wastewater which cause numerous infectious diseases, it is necessary to eliminate viruses from domestic wastewater before it is released in the environment. In addition, on-site detection of viruses in wastewater can provide information on possible virus exposures in the community of a given wastewater catchment. For this purpose, the pre-detection of different strains of viruses in wastewaters is an essential environmental step. Epidemiological studies illustrate that viruses are the most challenging pathogens to be detected in water samples because of their nano sizes, discrete distribution, and low infective doses. Over the past decades, several methods have been applied for the detection of waterborne viruses which include polymerase chain reaction-based methods (PCR), enzyme-linked immunosorbent assay (ELISA), and nucleic acid sequence-based amplification (NASBA). Although they have shown acceptable performance in virus measurements, their drawbacks such as complicated and time-consuming procedures, low sensitivity, and high analytical cost call for alternatives. Although biosensors are still in an early stage for practical applications, they have shown great potential to become an alternative means for virus detection in water and wastewater. This comprehensive review addresses the different types of viruses found in water and the recent development of biosensors for detecting waterborne viruses.

Enhanced Fouling Resistance and Antimicrobial Property of Ultrafiltration Membranes Via Polyelectrolyte-Assisted Silver Phosphate Nanoparticle Immobilization.

Ultrafiltration (UF) is a low-pressure membrane that yields higher permeate flux and saves significant operating costs compared to high-pressure membranes; however, studies addressing the combined improvement of anti-organic and biofouling properties of UF membranes are lacking. This study investigated the fouling resistance and antimicrobial property of a UF membrane via silver phosphate nanoparticle (AgPNP) embedded polyelectrolyte (PE) functionalization. Negatively charged polyacrylic acid (PAA) and positively charged polyallylamine hydrochloride (PAH) were deposited on the membrane using a fluidic layer-by-layer assembly technique. AgPNPs were immobilized within the crosslinked "bilayers" (BL) of PAH/PAA. The effectiveness of AgPNP immobilization was confirmed by microprofile measurements on membrane surfaces using a solid contact Ag micro-ion-selective electrode. Upon stable and uniform BL formation on the membrane surface, the permeate flux was governed by a combined effect of PAH/PAA-derived hydrophilicity and surface/pore coverage by the BLs "tightening" of the membrane. When fouled by a model organic foulant (humic acid), the functionalized membrane exhibited a lower flux decline and a greater flux recovery due to the electrostatic repulsion imparted by PAA when compared to the unmodified membrane. The functionalization rendered antimicrobial property, as indicated by fewer attachments of bacteria that initiate the formation of biofilms leading to biofouling.

Recent Developments of PFAS-Detecting Sensors and Future Direction: A Review.

Per- and poly-fluoroalkyl substances (PFASs) have recently been labeled as toxic constituents that exist in many aqueous environments. However, traditional methods used to determine the level of PFASs are often not appropriate for continuous environmental monitoring and management. Based on the current state of research, PFAS-detecting sensors have surfaced as a promising method of determination. These sensors are an innovative solution with characteristics that allow for in situ, low-cost, and easy-to-use capabilities. This paper presents a comprehensive review of the recent developments in PFAS-detecting sensors, and why the literature on determination methods has shifted in this direction compared to the traditional methods used. PFAS-detecting sensors discussed herein are primarily categorized in terms of the detection mechanism used. The topics covered also include the current limitations, as well as insight on the future direction of PFAS analyses. This paper is expected to be useful for the smart sensing technology development of PFAS detection methods and the associated environmental management best practices in smart cities of the future.

Microelectrode Investigation on the Corrosion Initiation at Lead-Brass Galvanic Interfaces in Chlorinated Drinking Water.

In this study, the effects of pH, dissolved inorganic carbon (DIC), and flow on changes in surface chemistry (pH, dissolved oxygen, and free chlorine) of lead-brass joints at initial stages of corrosion were investigated using microelectrodes. Surface measurements showed that the water chemistry at the metal surfaces was highly heterogeneous. At pH 7 and during water stagnation, local pH difference between anodic (leaded-solder) and cathodic (brass) regions differed by as much as 7.5 pH units. High DIC water under the water flowing condition showed minimal pH changes on the surface, whereas in low DIC water, a pH range of 7.6-5.4 (ΔpH 2.2) was observed over the surface. Free chlorine consumption near the lead-brass surface was greater under stagnation, regardless of bulk pH. It was also found that flow can move the low pH plume that originated at the anode. Overall, this study provides direct evidence for highly localized galvanic corrosion in a chlorinated drinking water environment.

A Novel Bismuth-Chitosan Nanocomposite Sensor for Simultaneous Detection of Pb(II), Cd(II) and Zn(II) in Wastewater.

A novel bismuth (Bi)-biopolymer (chitosan) nanocomposite screen-printed carbon electrode was developed using a Bi and chitosan co-electrodepositing technique for detecting multiple heavy metal ions. The developed sensor was fabricated with environmentally benign materials and processes. In real wastewater, heavy metal detection was evaluated by the developed sensor using square wave anodic stripping voltammetry (SWASV). The nanocomposite sensor showed the detection limit of 0.1 ppb Zn2+, 0.1 ppb Cd2+ and 0.2 ppb Pb2+ in stock solutions. The improved sensitivity of the Bi-chitosan nanocomposite sensor over previously reported Bi nanocomposite sensors was attributed to the role of chitosan. When used for real wastewater samples collected from a mining site and soil leachate, similar detection limit values with 0.4 ppb Cd2+ and 0.3 ppb Pb2+ were obtained with relative standard deviations (RSD) ranging from 1.3% to 5.6% (n = 8). Temperature changes (4 and 23 °C) showed no significant impact on sensor performance. Although Zn2+ in stock solutions was well measured by the sensor, the interference observed while detecting Zn2+ in the presence of Cu2+ was possibly due to the presence of Cu-Zn intermetallic species in mining wastewater. Overall, the developed sensor has the capability of monitoring multiple heavy metals in contaminated water samples without the need for complicated sample preparation or transportation of samples to a laboratory.

Identification and characterization of bilgewater emulsions.

Literature on bilgewater focuses on empirically determined treatment methods and lacks specific information on emulsion characteristics. Therefore, this review discusses potential emulsion stabilization mechanisms that occur in bilgewater and evaluates common approaches to study their behavior. Current knowledge on emulsion formation, stabilization, and destabilization is outlined to provide researchers and bilgewater treatment operators with the knowledge needed to determine emulsion prevention and treatment strategies. Furthermore, a broad assessment of bilgewater emulsion characterization techniques, from general water quality analysis to advanced droplet stability characterization methods are discussed in detail. Lastly, a survey of typical bilgewater characteristics and information on standard synthetic bilgewater mixtures used in the testing of oil pollution abatement equipment are presented. Overall, the goal of this article is to provide a better understanding of physical and thermodynamic properties of emulsions to help improve bilgewater treatment and management.

Improving Electrochemical Pb2+ Detection Using a Vertically Aligned 2D MoS2 Nanofilm.

Recent advancements in MoS2 nanofilms have aided in the development for important water-related environmental applications. However, a MoS2 nanofilm-coated sensor has yet to have been applied for heavy metal detection in water-related environmental samples. In this study, a novel vertically aligned two-dimensional (2D) MoS2 (edge exposed) nanofilm was applied for in situ lead ion (Pb2+) detection. The developed sensor showed an excellent linear relationship toward Pb2+ between 0 and 20 ppb at -0.45 V vs Ag/AgCl using square wave anodic stripping voltammetry (SWASV) with the improved limit of detection (LOD) of 0.3 ppb in a tap water environment. The vertically aligned 2D MoS2 sensor exhibited improved detection sensitivity (2.8 folds greater than a previous metallic [Bi] composite electrode) with lower relative standard deviation for repetitive measurements (n = 11), indicating enhanced reproducibility for Pb2+ detection. The vertically aligned 2D MoS2 layers exhibited 2.6 times higher sensitivity than horizontally aligned 2D MoS2 (basal plane exposed). Density functional theory calculations demonstrated that adsorption energy of Pb on the MoS2 side edge was much higher (4.11 eV) than those on the basal plane (0.36 and 0.07 eV). In addition, the band gap center of vertical MoS2 was found to be higher than the Pb2+ → Pb reduction potential level and capable of reducing Pb2+. Overall, the newly developed vertically aligned 2D MoS2 sensor showed excellent performance for detecting Pb2+ in a real drinking water environment with good reliability.

Variation of efficiencies and limits of ultrasonication for practical algal bloom control in fields.

Algal blooms are an increasing issue in managing water resources for drinking water production and recreational activities in many countries. Among various techniques, ultrasonication is known as a cost-effective method for control of harmful algal blooms (HABs) in relatively large area of water bodies. Most of engineering parameters for operating ultrasonication have been empirically determined based on laboratory scale tests, however, field or pilot tests in real environments are still rare. For field application, duration of ultrasonication is often on a monthly basis which is impractical for stream where there is flow and thus retention time is short. More realistic experimental approaches are required for practical applications of ultrasound. In this study, relatively low frequencies (36-175 kHz) of ultrasonication with low power intensity, less than 650 W, were tested for algal control in various pilot (100-750 L) and field (4 m3) tests in a short duration (<20 min). Generally, rapid decline of sound pressure (Pa) of ultrasonication was observed with distance (80% decrease even with 0.5 m difference). In a pilot test (100 L), the highest algae reduction was achieved at 36 kHz with 0.003 W mL-1 of power density within 10 min duration, but there was a noticeable increase in microcystin due to damaged algal cells by the low frequency of ultrasound. In a short-term operation without flow, distance from the ultrasound system was an important parameter for effective algae reduction, while longer exposure time ensured sufficient algae reduction. In a circulation pond (4 m3) with flow, 108 kHz-450 W showed the greatest efficiency in algal control and approximately 50-90% algal cells reduction was observed at 36-175 kHz with less than 650 W power and 60 min duration.