Monitoring viruses and beta-lactam resistance genes through wastewater surveillance during a COVID-19 surge in Suwon, South Korea.

The present study reports data on a long-term campaign for monitoring SARS-CoV-2, norovirus, hepatitis A virus, and beta-lactam resistance genes in wastewater samples from a wastewater treatment plant during COVID-19 surge in Suwon, South Korea. Real-time digital PCR (RT-dPCR) assays indicated 100 % occurrence of all but hepatitis A virus and blaNDM gene in influent wastewater samples. CDC-N1 assay detected SARS-CoV-2 in all influent samples with an average log-transformed concentration of 5.1 ± 0.39 and the highest level at 6.02 gene copies/L. All samples were also positive for norovirus throughout the study with a mean concentration 5.67 ± 0.65 log10 gene copies/L. On the contrary, all treated wastewater (effluent) tested negative for both viruses' genetic materials. Furthermore, plasmid-mediated AmpC ß-lactamases (PABLs) genes blaDHA, blaACC, and blaFOX, extended-spectrum ß-lactamases (ESBLs) genes blaTEM and blaCTX, and Klebsiella pneumoniae carbapenemase (blaKPC) gene were measured at average concentrations of 7.05 ± 0.26, 5.60 ± 0.35, 7.82 ± 0.43, 8.38 ± 0.20, 7.64 ± 0.29, and 7.62 ± 0.41 log10 gene copies/L wastewater, respectively. Beta-lactam resistance genes showed strong correlations (r), the highest being 0.86 for blaKPC - blaFOX, followed by 0.82 for blaTEM - blaCTX and 0.79 for blaTEM - blaDHA. SARS-CoV-2 RNA occurrence in the wastewater was strongly associated (r = 0.796) with COVID-19 cases in the catchment during the initial study period of six months. A positive association of the SARS-CoV-2 RNA with the prevalence of COVID-19 cases showed a promising role of community-scale monitoring of pathogens to provide considerable early signals of infection dynamics. High concentrations of beta-lactam resistance genes in wastewater indicated a high concern for one of the biggest global health threats in South Korea and the need to find control measures. Moreover, antibiotic-resistance genes in treated wastewater flowing through water bodies and agricultural environments indicate further dissemination of antibiotic resistance traits and increasing microbial antibiotic resistance.

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.

222Radon reduction in small-scale water supply systems using low-technology reduction methods in the Republic of Korea: A field research and mass balance model approach.

In rural areas, low-technology radon reduction methods are essential for safe access to clean groundwater. This study monitored the radon reduction rates in small-scale groundwater-based water supply systems in the Republic of Korea and also presented a mass balance equation using physical environmental conditions from three radon reduction methods. The mass balance results showed that the radon reduction rate would be affected by the groundwater flow rate (m3/day), capacity of the drainage facility (m3), surface area of air-water interface (m2), air-water ratio (dimensionless), and ventilation system. The radon reduction order was as follows: simultaneously powered and non-powered aeration method (free-fall (60.0 %) > aeration (19.6 %) > decay (0.9 %) > diffusion (0.2 %)), low-technology non-powered aeration (free-fall (60.0 %) > decay (3.4 %) > diffusion (0.9 %)), and only storage (free-fall (35.5 %) > decay (4.4 %) > diffusion (1.1 %)). Overall, non-powered aeration using the maximum free-fall effect has the potential for use as a low-technology reduction method and natural decay during water storage is the most important factor underlying seasonal variations in the reduction effect.

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.

Identifying the acute toxicity of contaminated sediments using machine learning models.

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors.

The immediate response to the state disturbances of anaerobic digestion is essential to prevent anaerobic digestion failure. However, frequent monitoring of the state and performance of anaerobic digestion is challenging. Thus, deep learning models were investigated to predict the state and performance variables from online sensor data. The online sensor data, including pH, electric conductivity, and oxidation-reduction potential, were used as the input features to build deep learning models. The state and performance data measured offline were used as the labels. The model performance was compared for several deep learning models of convolutional neural network (CNN), long short-term memory (LSTM), dense layer, and their combinations. The combined model of CNN and bidirectional LSTM was robust and well-generalized in predicting the state and performance variables (R2 = 0.978, root mean square error = 0.031). The combined model is an excellent soft sensor for monitoring the state and performance of anaerobic digestion from electrochemical sensors.

Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal.

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.

Transcription Factor-Based Biosensors for Detecting Pathogens.

Microorganisms are omnipresent and inseparable from our life. Many of them are beneficial to humans, while some are not. Importantly, foods and beverages are susceptible to microbial contamination, with their toxins causing illnesses and even death in some cases. Therefore, monitoring and detecting harmful microorganisms are critical to ensuring human health and safety. For several decades, many methods have been developed to detect and monitor microorganisms and their toxicants. Conventionally, nucleic acid analysis and antibody-based analysis were used to detect pathogens. Additionally, diverse chromatographic methods were employed to detect toxins based on their chemical and structural properties. However, conventional techniques have several disadvantages concerning analysis time, sensitivity, and expense. With the advances in biotechnology, new approaches to detect pathogens and toxins have been reported to compensate for the disadvantages of conventional analysis from different research fields, including electrochemistry, nanotechnology, and molecular biology. Among them, we focused on the recent studies of transcription factor (TF)-based biosensors to detect microorganisms and discuss their perspectives and applications. Additionally, the other biosensors for detecting microorganisms reported in recent studies were also introduced in this review.

Complete Genome and Plasmid Sequences of Raphidiopsis curvispora Strain GIHE-G1, Isolated from a Drinking Water Reservoir in South Korea.

The complete genome and plasmid sequences of Raphidiopsis curvispora strain GIHE-G1, a coiled filamentous heterocyst-forming cyanobacterium isolated from a drinking water reservoir in South Korea, are reported here. The genome information is expected to improve understanding of this species.

Preparation of Nanoscale Zinc Oxide-Laponite Composites by Polyvinyl Alcohol Polymerization and Usability for Removal of Trichloroethylene in Water.

Innovative nanoscale ZnO-laponite-polyvinyl alcohol composites (NZLPc) were developed as a valid alternative to nanoscale photocatalysts for mineralization of chlorinated hydrocarbons without difficulties in recovery of nanoscale photocatalyst particles. NZLPc were synthesized by mixing nanoscale ZnO particles with laponite in PVA solution, and by crosslinking PVA solution in the presence of boric acid (≥1.6 M). Different mixing ratios of the raw materials were investigated to develop the stable NZLPc, and X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and transmission electron microscopy characterizations were performed. From the results, a 3:1:1:10 ratio of ZnO, laponite, PVA, and deionized water by weight was appropriate to form spherical NZLPc with high porosities and enhanced mechanical strengths. Also, the degradation efficiencies of trichloroethylene were significantly improved with both NZLPc and UV irradiation, indicating that ZnO-mediated heterogeneous photocatalytic degradation occurred. Thus, the proposed synthesis of NZLPc paves a way for the economical and effective photocatalytic approach to remove the recalcitrant organic compounds in water through the multiple reaction processes (i.e., sorption, photolysis, and photocatalysis).

Phosphoserine Phosphatase Is Required for Serine and One-Carbon Unit Synthesis in Hydrogenobacter thermophilus.

Hydrogenobacter thermophilus is an obligate chemolithoautotrophic bacterium of the phylum Aquificae and is capable of fixing carbon dioxide through the reductive tricarboxylic acid (TCA) cycle. The recent discovery of two novel-type phosphoserine phosphatases (PSPs) in H. thermophilus suggests the presence of a phosphorylated serine biosynthesis pathway; however, the physiological role of these novel-type metal-independent PSPs (iPSPs) in H. thermophilus has not been confirmed. In the present study, a mutant strain with a deletion of pspA, the catalytic subunit of iPSPs, was constructed and characterized. The generated mutant was a serine auxotroph, suggesting that the novel-type PSPs and phosphorylated serine synthesis pathway are essential for serine anabolism in H. thermophilus. As an autotrophic medium supplemented with glycine did not support the growth of the mutant, the reversible enzyme serine hydroxymethyltransferase does not appear to synthesize serine from glycine and may therefore generate glycine and 5,10-CH2-tetrahydrofolate (5,10-CH2-THF) from serine. This speculation is supported by the lack of glycine cleavage activity, which is needed to generate 5,10-CH2-THF, in H. thermophilus Determining the mechanism of 5,10-CH2-THF synthesis is important for understanding the fundamental anabolic pathways of organisms, because 5,10-CH2-THF is a major one-carbon donor that is used for the synthesis of various essential compounds, including nucleic and amino acids. The findings from the present experiments using a pspA deletion mutant have confirmed the physiological role of iPSPs as serine producers and show that serine is a major donor of one-carbon units in H. thermophilusIMPORTANCE Serine biosynthesis and catabolism pathways are intimately related to the metabolism of 5,10-CH2-THF, a one-carbon donor that is utilized for the biosynthesis of various essential compounds. For this reason, determining the mechanism of serine synthesis is important for understanding the fundamental anabolic pathways of microorganisms. In the present study, we experimentally confirmed that a novel phosphoserine phosphatase in the obligate chemolithoautotrophic bacterium Hydrogenobacter thermophilus is essential for serine biosynthesis. This finding indicates that serine is synthesized from an intermediate of gluconeogenesis in H. thermophilus In addition, because glycine cleavage system activity and genes encoding an enzyme capable of producing 5,10-CH2-THF were not detected, serine appears to be the major one-carbon donor to tetrahydrofolate (THF) in H. thermophilus.

Effect of salt type and concentration on the growth and lipid content of Chlorella vulgaris in synthetic saline wastewater for biofuel production.

Microalgae can offer several benefits for wastewater treatment with their ability to produce large amounts of lipids for biofuel production and the high economic value of harvested biomass for biogas and fertilizer. This study found that salt concentration (∼45gL-1) had more of an effect than salt type on metabolisms of Chlorella vulgaris for wastewater treatment and biofuel production. Salinity stress decreased the algal growth rate in wastewater by 0.003day-1permScm-1 and slightly reduced nutrient removal rates. However, salinity stress was shown to increase total lipid content from 11.5% to 16.1% while also increasing the saturated portions of fatty acids in C. vulgaris. In addition, salinity increased the algal settling rate from 0.06 to 0.11mday-1 which could potentially reduce the cost of harvesting for algal biofuel production. Overall, C. vulgaris makes a suitable candidate for high salinity wastewater cultivation and biofuel production.

Recent advances in ultrasonic treatment: Challenges and field applications for controlling harmful algal blooms (HABs).

Algal blooms are a naturally occurring phenomenon which can occur in both freshwater and saltwater. However, due to excess nutrient loading in water bodies (e.g. agricultural runoff and industrial activities), harmful algal blooms (HABs) have become an increasing issue globally, and can even cause health effects in humans due to the release of cyanotoxins. Among currently available treatment methods, sonication has received increasing attention for algal control because of its low impact on ecosystems and the environment. The effects of ultrasound on algal cells are well understood and operating parameter such as frequency, intensity, and duration of exposure has been well studied. However, most studies have been limited to laboratory data interpretation due to complicated environmental conditions in the field. Only a few field and pilot tests in small reservoirs were reported and the applicability of ultrasound for HABs prevention and control is still under question. There is a lack of information on the upscaling of ultrasonication devices for HAB control on larger water bodies, considering field influencing factors such as rainfall, light intensity/duration, temperature, water flow, nutrients loading, and turbidity. In this review article, we address the challenges and field considerations of ultrasonic applications for controlling algal blooms. An extensive literature survey, from the fundamentals of ultrasound techniques to recent ultrasound laboratory and field studies, has been thoroughly conducted and summarized to identify future technical expectations for field applications. Case studies investigating spatial distribution of frequency and pressure during sonication are highlighted with future implications.