Search

12 pages, 8 figures, supplementary data https://doi.org/10.1016/j.scitotenv.2023.162969.-- Data availability: Data will be made available on request, Sea spray aerosol (SSA) particles strongly influence clouds and climate but the potential impact of ocean microbiota on SSA fluxes is still a matter of active research. Here–by means of in situ ship-borne measurements–we explore simultaneously molecular-level chemical properties of organic matter (OM) in oceans, sea ice, and the ambient PM2.5 aerosols along a transect of 15,000 km from the western Pacific Ocean (36°13′N) to the Southern Ocean (75°15′S). By means of orbitrap mass spectrometry and optical characteristics, lignin-like material (24 ± 5 %) and humic material (57 ± 8 %) were found to dominate the pelagic Pacific Ocean surface, while intermediate conditions were observed in the Pacific-Southern Ocean waters. In the marine atmosphere, we found a gradient of features in the aerosol: lignin-like material (31 ± 9 %) dominating coastal areas and the pelagic Pacific Ocean, whereas lipid-like (23 ± 16 %) and protein-like (11 ± 10 %) OM controlled the sympagic Southern Ocean (sea ice-influence). The results of this study showed that the OM composition in the ocean, which changes with latitude, affects the OM in aerosol compositions in the atmosphere. This study highlights the importance of the global-scale OM monitoring of the close interaction between the ocean, sea ice, and the atmosphere. Sympagic primary marine aerosols in polar regions must be treated differently from other pelagic-type oceans, This research was supported by KOPRI projects (PE23030) and partly supported by grants from the Korean Government (MSIT) (NRF-2021M1A5A1065425, KOPRI-PN23011, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R

The organic fouling characteristics of hollow fiber ultrafiltration (HFUF) and multibore ultrafiltration (MBUF) membranes from long-term ultrafiltration (UF) membrane systems were systemically investigated in this study. The objective was to obtain insights into the fouling behavior of dissolved organic matter (DOM) in a pilot-scale ultra-high-recovery membrane filtration system (p-UHMS) used for surface water treatment. The pilot system consisted of a series of two different UF membranes (1st stage: polyvinylidene fluoride (PVDF) HFUF and 2nd stage: polyethersulfone (PES) MBUF). It was designed to feed the HFUF concentrate to the MBUF membranes to achieve ≥99.5 % total water recovery for surface water treatment, as these advances might enhance the production efficiencies of drinking water. The experimental results confirmed that hydrophobic DOM controlled the formation of HFUF membrane organic fouling, whereas hydrophilic DOM, including polysaccharide-like and protein-like matter, promoted MBUF membrane fouling. These opposing trends were attributed to the hydrophilic characteristics of the MBUF membrane surfaces (contact angle: PVDF = 90-130° and PES ≤ 80°), which reduced the hydrophobic interactions between the UF membrane surfaces and foulants. The performance declines of the MBUF membrane due to fouling layer formation was considerably severer than those of the HFUF membrane, decreasing total permeate water in the p-UHMS. Moreover, the quantity of the desorbed MBUF membrane foulants via 0.1 N NaOH was roughly 7.2 times larger than that of the desorbed HFUF membrane foulants through 0.1 N NaOH, indicating that alkaline-based cleaning agent could much more efficiently recover the performance of the fouled MBUF membranes. Hence, adequate cleaning strategies using alkaline-based agent for the MBUF membrane appeared to be essential for preventing the performance deterioration of the p-UHMS.

Water pollution generated from intensive anthropogenic activities has emerged as a critical issue concerning ecosystem balance and livelihoods worldwide. Although optimizing wastewater treatment efficiency is widely regarded as the foremost step to minimize pollutants released into the environment, this widespread application has encountered two major problems: firstly, the significant variation of influent wastewater constituents; secondly, complex treatment processes within wastewater treatment plants (WWTPs). Based on the data collected hourly using real-time sensors in three different full-scale WWTPs (24 h × 365 days × 3 WWTPs × 10 wastewater parameters), this work introduced the potential application of Machine Learning (ML) to predict wastewater quality. In this work, six different ML algorithms were examined and compared, varying from shallow to deep learning architectures including Seasonal Autoregressive Integrated Moving Average (SARIMAX), Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Long Short-Term Memory (LSTM). These models were developed to detect total phosphorus in the outlet (Outlet-TP), which served as an output variable due to the rising concerns about the eutrophication problem. Irrespective of WWTPs, SARIMAX consistently demonstrated the best performance for regression estimation as evidenced by the lowest values of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the highest coefficient of determination (R

9 pages, 6 figures, supplementary data https://doi.org/10.1016/j.scitotenv.2021.150866, The impact of inorganic salts and organic matter (OM) on the production of primary marine aerosols is still under debate. To constrain their impact, we investigated primary aerosols generated by a sea-spray generator chamber using surface water samples from rivers, estuaries, and seas that were collected along salinity gradients in two temperate Korean coastal systems and one Arctic coastal system. Salinity values showed an increasing trend along the river–estuary–coastal water transition, indicating the lowest amount of inorganic salts in the river but the highest amount in the sea. In river samples, the lowest number concentration of primary aerosol particles (1.01 × 103 cm−3) was observed at the highest OM content, suggesting that low salinity controls aerosol production. Moreover, the number concentration of primary aerosols increased drastically in estuarine (1.13 × 104 cm−3) and seawater (1.35 × 104 cm−3) samples as the OM content decreased. Our results indicate that inorganic salts associated with increasing salinity play a much larger role than OM in aerosol production in river-dominated coastal systems. Laboratory studies using NaCl solution supported the conclusion that inorganic salt is a critical factor in modulating the particles produced from river water and seawater. Accordingly, this study highlights that inorganic salts are a critical factor in modulating the production of primary marine aerosols, This work was supported by grants from the Korean Government (MSIT) (NRF-2021M1A5A1065425) (KOPRI-PN21011) and from the Korean Government (MSIT) (NRF-2016R1A2B3015388) (KOPRI-PN19100), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

This study focuses on the potential capability of numerous machine learning models, namely CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree, Bagging, light gradient boosting machine (LGBM), GaussianProcess, artificial neural network (ANN), and light long short-term memory (LightLSTM). These models were investigated to predict the photocatalytic degradation of malachite green from wastewater using various NM-BiFeO

Capacitive deionization (CDI) is an alternative desalination technology that uses electrochemical ion separation. Although several attempts have been made to maximize the energy efficiency and productivity of CDI with conventional control methods, it is difficult to optimize the CDI processes because of the complex correlation between the operational conditions and the composition of feed water. To address these challenges, we applied deep reinforcement learning (DRL) to automatically control the membrane capacitive deionization (MCDI) process, which is one of the representative CDI processes, to accomplish high energy efficiency while desalinating water. In the DRL model, the numerical model is combined as the environment that provides states according to the actions. The feed water conditions, that is, the input state of the DRL, were assumed to have a random salt concentration and constant foulant concentration. The model was constructed to minimize energy consumption and maximize desalted water volume per cycle. After training of 1,000 episodes, the DRL model achieved a 22.07% reduction in specific energy consumption (from 0.054 to 0.042 kWh m

The presence of micropollutants (MPs), including pharmaceutical, industrial, and pesticidal compounds, threatens both human health and the aquatic ecosystem. The development and extensive use of new chemicals have also inevitably led to the accumulation of MPs in aquatic environments. Recreational beaches are especially vulnerable to contamination, affecting humans and aquatic animals via the absorption of MPs in water during marine activities (e.g., swimming, sailing, and windsurfing). Additionally, marine outfalls in an urbanized coastal city can cause serious chemical and microbial pollution on recreational beaches, leading to an increase in adverse effects on public health and the ecological system. Therefore, the aim of this study was to, with the use of network and decision tree analyses, identify the features and factors that influence the change in MP concentrations in a marine outfall. These analyses were conducted to inspect the relationship between each MP and its hierarchical structure as well as hydrometeorological variables. Additionally, a risk analysis was conducted in this study in which the MPs were prioritized based on their optimized risk quotient values. During our monitoring of MP concentrations over time at the marine outfall, high concentrations of pharmaceutical and industrial compounds were detected when the tide level was low after rainfall. Furthermore, results of the risk analysis and the prioritization revealed that a total of 18 substances identified in our study posed a risk to the ecosystem; these include major ecotoxicologically hazardous substances such as telmisartan, mevinphos, and methiocarb. Results of the network analysis demonstrated distinct trends for pharmaceutical and industrial substances, whilst those for pesticide compounds were irregular. Additionally, the hierarchical structures for most MPs consisted of rainfall, tide level, and antecedent dry hours; this implies that these factors influence MP dynamics. These findings will be helpful for establishing chemical contamination management plans for recreational beaches in the future.

Many countries have attempted to monitor and predict harmful algal blooms to mitigate related problems and establish management practices. The current alert system-based sampling of cell density is used to intimate the bloom status and to inform rapid and adequate response from water-associated organizations. The objective of this study was to develop an early warning system for cyanobacterial blooms to allow for efficient decision making prior to the occurrence of algal blooms and to guide preemptive actions regarding management practices. In this study, two machine learning models: artificial neural network (ANN) and support vector machine (SVM), were constructed for the timely prediction of alert levels of algal bloom using eight years’ worth of meteorological, hydrodynamic, and water quality data in a reservoir where harmful cyanobacterial blooms frequently occur during summer. However, the proportion imbalance on all alert level data as the output variable leads to biased training of the data-driven model and degradation of model prediction performance. Therefore, the synthetic data generated by an adaptive synthetic (ADASYN) sampling method were used to resolve the imbalance of minority class data in the original data and to improve the prediction performance of the models. The results showed that the overall prediction performance yielded by the caution level (L1) and warning level (L2) in the models constructed using a combination of original and synthetic data was higher than the models constructed using original data only. In particular, the optimal ANN and SVM constructed using a combination of original and synthetic data during both training (including validation) and test generated distinctively improved recall and precision values of L1, which is a very critical alert level as it indicates a transition status from normalcy to bloom formation. In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L -1 and L -2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving the imbalance of observed data. Reliable prediction by the improved models can be used to aid the design of management practices to mitigate algal blooms within a reservoir.

Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, and remote sensing images. Deep learning attention models can process these intricate sources to forecast cyanobacteria by identifying important variables in the data sources. However, deep learning attention models for predicting cyanobacteria have rarely been studied using an assemblage of various datasets. Thus, in this study, a convolutional neural network (CNN) model with a convolutional block attention module (CNNan) was developed to predict cyanobacterial cell concentrations by using the observed cell data from field monitoring, chlorophyll-a distribution map from hyperspectral image sensing, and simulated water quality outputs from a hydrodynamic model. Then, the prediction performance of the CNNan model was compared to an environmental fluid dynamics code (EFDC) simulation and a CNN model without an attention network. The seasonal variations of the predicted cyanobacteria that was obtained from CNNan showed the best agreement with the observed variations with Nash–Sutcliffe efficiency values higher than 0.76 when compared to the EFDC and CNN predictions. The daily hydrodynamic outputs allowed the prediction of cyanobacteria cells, while the rich information of the chlorophyll-a map contributed to the improvement of the prediction performance at certain periods. Moreover, the attention network visualized the importance of the additional chlorophyll-a map and improved the CNNan model prediction performance by refining the input features. Therefore, this study demonstrated that a deep learning model with data assemblage is practically feasible for predicting the presence of harmful algae in inland water.

Fecal coliform bacteria (FCB) contamination of natural waters is a serious public health issue. Therefore, understanding and anticipating the fate and transport of FCB are important for reducing the risk of contracting diseases. The objective of this study was to analyze the impacts of climate change on the fate and transport of FCB. We modified both the soil and the in-stream bacteria modules in the soil and water assessment tool (SWAT) model and verified the prediction accuracy of seasonal variability of FCB loads using observations. Forty bias-correcting GCM-RCM projections were applied in the modified SWAT model to examine various future climate conditions at the end of this century (2076–2100). Lastly, we also compared the variability of FCB loads under current and future weather conditions using multi-model ensemble simulations (MMES). The modified SWAT model yielded a satisfactory performance with regard to the seasonal variability of FCB amounts in the soil and FCB loading to water bodies. The modified SWAT model presented substantial proliferation of FCB in the soil (30.1%–147.5%) due to an increase in temperature (25.1%). Also, increase in precipitation (53.3%) led to an increase in FCB loads (96.0%–115.5%) from the soil to water body. In the in-stream environment, resuspension from the stream bed was the dominant process affecting the amount of FCB in stream. Therefore, the final FCB loads increased by 71.2% because of the growing peak channel velocity and volume of water used due to an increase in precipitation. Based on the results of MMES, we concluded that the level of FCB would increase simultaneously in the soil as well as in stream by the end of this century. This study will aid in understanding the future variability of FCB loads as well as in preparing an effective management plan for FCB levels in natural waters.

Isolating dissolved organic matter (DOM) is a preliminary step that improves the accuracy of its characterization. In this study, DOM in brackish water was clearly separated and evaluated by multiple characterization analyses. The sample was divided into three fractions by preparative high-performance liquid chromatography (preparative HPLC) according to molecular size. The homogeneity of each fraction was estimated by analytical size exclusion chromatography (SEC) and fluorescence excitation-emission matrix (FEEM). Pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) and liquid chromatography-organic carbon detection (LC-OCD) were used to characterize the physicochemical properties of each fraction. Py-GC/MS revealed that Fraction 1 consisted of evenly distributed organic matter in order polysaccharides, proteins, polyhydroxy aromatics, lignins, and lipids. However, Fraction 2 was primarily composed of dominant lipids and low portion of proteins, and Fraction 3 was composed predominantly of lignins and lipids. The LC-OCD results showed that Fractions 1 and 2 had similar organic carbon (OC) compositions: a humic substance (ca. 37%), building blocks (ca. 10%), and neutrals (ca. 37%), whereas Fraction 3 contained a high proportion of neutrals (62%). In the fouling experiments, the distinct DOM characteristics in each fraction resulted in different declining flux behaviors, ranked as: Fraction 2 > Fraction 1 > Fraction 3.

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R

Resistance-in-series models have been applied to investigate fouling behavior. However, it is difficult to model the influence of morphology on fouling behavior because resistance is indirectly calculated from the water flux and transmembrane pressure. In this study, optical coherence tomography (OCT) was applied to evaluate the resistance of the fouling layer based on fouling morphology. Sodium alginate, humic acid, and bovine serum albumin (BSA) with high salts concentrations (conductivity: 23 mS/cm) were used as model foulants. At the same total fouling resistance, BSA showed the highest cake layer thickness (BSA (114.5 μm) > humic acid (53.5 μm) > sodium alginate (20.0 μm)). However, a different order was found for the cake layer resistance (BSA > sodium alginate > humic acid). This indicates that fouling thickness is not correlated with cake layer resistance. According to the Carman–Kozeny equation, fouling layer porosity decreased in the following order: humic acid (0.30) > BSA (0.21) > sodium alginate (0.20). In addition, we provided a specific value that was calculated using the ratio between the fouling thickness and cake layer resistance. The results show that alginic acid induced a stronger cake layer resistance, despite its thin fouling layer, whereas BSA showed a relatively low potential for inducing cake layer resistance. The results obtained in this study could be used for estimating and predicting fouling behavior.

This study investigated the abundance and diversity of antibiotic resistance genes (ARGs) and the composition of bacterial communities along a transect covering the western Pacific Ocean (36°N) to the Southern Ocean (74°S) using the Korean icebreaker R/V Araon (total cruise distance: 14,942 km). The relative abundances of ARGs and bacteria were assessed with quantitative PCR and next generation sequencing, respectively. The absolute abundance of ARGs was 3.0 × 10

Hydrodynamic and water quality modeling have provided valuable simulation results that have enhanced the understanding of the spatial and temporal distribution of algal blooms. Typical model simulations are performed with point-based observational data that are used to configure initial and boundary conditions, and for parameter calibration. However, the application of such conventional modeling approaches is limited due to cost, labor, and time constraints that preclude the retrieval of high-resolution spatial data. Thus, the present study applied fine-resolution algal data to configure the initial conditions of a hydrodynamic and water quality model and compared the accuracy of short-term algal simulations with the results simulated using conventional point-based initial conditions. The environmental fluid dynamics code (EFDC) model was calibrated to simulate Chlorophyll-a (Chl-a) concentrations. Hyperspectral images were used to generate Chl-a maps based on a two-band ratio algorithm for configuring the initial condition of the EFDC model. The model simulation with hyperspectral-based initial conditions returned relatively accurate results for Chl-a, compared to the simulation based on point-based initial conditions. The simulations exhibited percent bias values of 9.93 and 14.23, respectively. Therefore, the results of this study demonstrate how hyperspectral-based initial conditions could improve the reliability of short-term algal bloom simulations in a hydrodynamic model.

Micropollutants (MPs) released into aquatic ecosystems have adverse effects on public health. Hence, monitoring and managing MPs in aquatic systems are imperative. MPs can be quantified by high-resolution mass spectrometry (HRMS) with stable isotope-labeled (SIL) standards. However, high cost of SIL solutions is a significant issue. This study aims to develop a rapid and cost-effective analytical approach to estimate MP concentrations in aquatic systems based on deep learning (DL) and multi-objective optimization. We hypothesized that internal standards could quantify the MP concentrations other than the target substance. Our approach considered the precision of intra-/inter-day repeatability and natural organic matter information to reduce instrumental error and matrix effect. We selected standard solutions to estimate the concentrations of 18 MPs. Among the optimal DL models, DarkNet-53 using nine standard solutions yielded the highest performance, while ResNet-50 yielded the lowest. Overall, this study demonstrated the capability of DL models for estimating MP concentrations.

Both algae and bacteria are essential inhabitants of surface waters. Their presence is of ecological significance and sometimes of public health concern triggering various control actions. Interactions of microalgae, macroalgae, submerged aquatic vegetation, and bacteria appear to be important phenomena necessitating a deeper understanding by those involved in research and management of microbial water quality. Given the long-standing reliance on Escherichia coli as an indicator of the potential presence of pathogens in natural waters, understanding its biology in aquatic systems is necessary. The major effects of algae and aquatic vegetation on E. coli growth and survival, including changes in the nutrient supply, modification of water properties and constituents, impact on sunlight radiation penetration, survival as related to substrate attachment, algal mediation of secondary habitats, and survival inhibition due to the release of toxic substances and antibiotics, are discussed in this review. An examination of horizontal gene transfer and antibiotic resistance potential, strain-specific interactions, effects on the microbial, microalgae, and grazer community structure, and hydrodynamic controls is given. Outlooks due to existing and expected consequences of climate change and advances in observation technologies via high-resolution satellite imaging, unmanned aerial vehicles (drones), and mathematical modeling are additionally covered. The multiplicity of interactions among bacteria, algae, and aquatic vegetation as well as multifaceted impacts of these interactions, create a wide spectrum of research opportunities and technology developments.

Understanding the dynamics of harmful algal blooms is important to protect the aquatic ecosystem in regulated rivers and secure human health. In this study, artificial neural network (ANN) and support vector machine (SVM) models were used to predict algae alert levels for the early warning of blooms in a freshwater reservoir. Intensive water-quality, hydrodynamic, and meteorological data were used to train and validate both ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the models, respectively. The results indicated that the two models well reproduced the algae alert level based on the time-lag input and output data. In particular, the ANN model showed a better performance than the SVM model, displaying a higher performance value in both training and validation steps. Furthermore, a sampling frequency of 6- and 7-day were determined as efficient early-warning intervals for the freshwater reservoir. Therefore, this study presents an effective early-warning prediction method for algae alert level, which can improve the eutrophication management schemes for freshwater reservoirs.

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil conditions and adversely affects the health of living organisms. Visible and near-infrared spectroscopy (VNIRS) shows a potential feasibility for estimating heavy metal elements in soil. Moreover, deep learning models have been shown to successfully deal with complex multi-dimensional and multivariate nonlinear data. Thus, this study implemented a deep learning method on reflectance spectra of soil samples to estimate heavy metal concentrations. A convolutional neural network (CNN) was adopted to estimate arsenic (As), copper (Cu), and lead (Pb) concentrations using measured soil reflectance. In addition, a convolutional autoencoder was utilized as a joint method with the CNN for dimensionality reduction of the reflectance spectra. Furthermore, artificial neural network (ANN) and random forest regression (RFR) models were built for heavy metal estimation. Principal component analysis was utilized for dimensionality reduction of the ANN and RFR models. Among these models, the CNN model with convolutional autoencoder showed the highest accuracies for As, Cu, and Pb estimates, having R2 values of 0.86, 0.74, and 0.82, respectively. The convolutional autoencoder disentangled the relevant features of multiple heavy metal elements and delivered robust features to the CNN model, thereby generating relatively accurate estimates.

The fate of antibiotic resistance genes (ARGs) in aquatic environments, especially in rivers and reservoirs, is receiving growing attention in South Korea because reservoirs are an important source of drinking water in this country. Seasonal changes in the abundance of 11 ARGs and a mobile genetic element () in two reservoirs in South Korea, located near drinking water treatment plants in Cheonan and Cheongju cities, were monitored for 6 mo. In these drinking water sources, total ARG concentrations reached 2.5 × 10 copies mL, which is one order of magnitude higher than in influents of some wastewater treatment plants in South Korea. During the sampling periods in August, October, and November 2016 and January 2017, sulfonamides (), β-lactam antibiotics (), and tetracycline () resistance genes were the most abundant genes at the two sites. The ARG abundance consistently increased in January relative to 16S ribosomal ribonucleic acid (rRNA) counts. General stress responses to oxidative stress and other environmental factors associated with the cold season could be significant drivers of ARG horizontal gene transfer in the environment. Accordingly, removal of ARGs as a key step in water treatment warrants more attention.

Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and ) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations ( < 0.01), whereas solar radiation was negatively correlated ( < 0.01). The performance of the ANN model for predicting ENT and at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset ( < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use.

A marine outfall can be a wastewater management system that discharges sewage and stormwater into the sea; hence, it is a source of microbial pollution on recreational beaches, including antibiotic resistant genes (ARGs), which lead to an increase in untreatable diseases. In this regard, a marine outfall must be efficiently located to mitigate these risks. This study aimed to 1) investigate the spatiotemporal variability of Escherichia coli (E. coli) and ARGs on a recreational beach and 2) design marine outfalls to reduce microbial risks. For this purpose, E. coli and ARGs with influential environmental variables were intensively monitored on Gwangalli beach, South Korea in this study. Environmental fluid dynamic code (EFDC) was used and calibrated using the monitoring data, and 12 outfall extension scenarios were explored (6 locations at 2 depths). The results revealed that repositioning the marine outfall can significantly reduce the concentrations of E. coli and ARGs on the beach by 46–99%. Offshore extended outfalls at the bottom of the sea reduced concentrations of E. coli and ARGs on the beach more effectively than onshore outfalls at the sea surface. These findings could be helpful in establishing microbial pollution management plans at recreational beaches in the future.

Harmful algal blooms (HABs) of Cochlodinium (aka Margalefidinium) polykrikoides cause huge economic and ecological damages and thus are considered environmental problems. Previous studies uncovered that the formation and collapse of phytoplankton blooms could be closely related to their associated microbes although their roles in C. polykrikoides bloom have not been elucidated yet. To explore the potential interactions between C. polykrikoides and other microbes (archaea, bacteria, and phytoplankton), we collected water samples in the free-living (FL) (0.22 to 3 μm), nanoparticle-associated (NP) (3 to 20 μm), and microparticle-associated (MP) (>20 μm) fractions when C. polykrikoides blooms occurred from July to August in 2016, 2017, and 2018 in the South Sea of Korea. The microbial composition of the C. polykrikoides-associated microbial cluster (Module I) significantly differed from those of other modules associated with Alexandrium, Chaetoceros or Chattonella. Over half of the interspecies interactions in Module I occurred within the module. That is, specific microbial clusters were associated with the C. polykrikoides bloom. Structural equation modeling (SEM) further confirmed the stronger effects of Module I on C. polykrikoides blooms compared to environmental factors. Among the operational taxonomic units (OTUs) directly correlated with C. polykrikoides, Marine Group I was presumed to supply vitamin B12, the essential element for C. polykrikoides growth, while the potential fish pathogens (Micrococcaceae and Piscirickettsiaceae) could contribute to the massive fish death together with C. polykrikoides itself. In addition, the zoospores of Syndiniales, a parasitoid to dinoflagellates, might be related to the sudden collapse of C. polykrikoides blooms. These microbial groups also contributed to significant alterations of the local microbial community structures. Collectively, network analysis and SEM revealed that the C. polykrikoides bloom is concomitant with distinct microbial communities, contributing to the rise and fall of the bloom, and finally determining the local microbial community structures.

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance.

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R2 value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.

Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value

Despite a growing awareness of the problems associated with cyanobacterial blooms in rivers, and particularly in regulated rivers, the drivers of bloom formation and abundance in rivers are not well understood. We developed a Bayesian hierarchical model to assess the relative importance of predictors of summer cyanobacteria abundance, and to test whether the relative importance of each predictor varies by site, using monitoring data from 16 sites in the four major rivers of South Korea. The results suggested that temperature and residence time, but not nutrient levels, are important predictors of summer cyanobacteria abundance in rivers. Although the two predictors were of similar significance across the sites, the residence time was marginally better in accounting for the variation in cyanobacteria abundance. The model with spatial hierarchy demonstrated that temperature played a consistently significant role at all sites, and showed no effect from site-specific factors. In contrast, the importance of residence time varied significantly from site to site. This variation was shown to depend on the trophic state, indicated by the chlorophyll-a and total phosphorus levels. Our results also suggested that the magnitude of weir inflow is a key factor determining the cyanobacteria abundance under baseline conditions.

Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model for biogas prediction is developed using deep learning (DL). We propose a hybrid DL architecture, i.e., DA-LSTM-VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs). To enhance the model predictability, we perform hyperparameter optimization. The model accuracy is validated using long-term AcoD monitoring data measured over two years of municipal wastewater treatment plant operation and then compared with those of two other DL-based models (i.e., DA-LSTM and the standard LSTM). In addition, the feature importance (FI) is analyzed to investigate the relative contribution of input variables to biogas production prediction. Finally, we demonstrate the successful application of the validated DL model to the AcoD process optimization. Results show that the model accuracy improved significantly by incorporating DA into LSTM, i.e., the coefficient of determination (R2) increased from 0.38 to 0.68; however, the R2 can be further increased to 0.76 by combining DA-LSTM with a VSN. For the biogas prediction of the AcoD model, the VSN contributes significantly by employing the discontinuous time series of measurement data on biodegradable organic-associated variables during AcoD. In addition, the VSN allows the AcoD model to be interpretable via FI analysis using its weighted input features. The FI results show that the relative importance is vital to variables associated with food waste leachate, whereas it is marginal for those associated with the primary and chemically assisted sedimentation sludges. In conclusion, the AcoD model proposed herein can be utilized in practical applications as a robust tool because it can provide the optimal sludge conditions to improve biogas production. This is because it facilitates the time-series biogas prediction at the full scale using unprocessed datasets with either missing value imputation or outlier removal.

We investigated the occurrence and distribution of antibiotic-resistance genes (ARGs) and the composition of a bacterial community under conditions of rainfall on a recreational beach in Korea. Seawater samples, collected every 1‒5 hours in June 2018 and May 2019, were analyzed using quantitative real-time polymerase chain reaction and next-generation sequencing. We found a substantial influence of rainfall and tidal levels on the relative abundance of total ARGs and bacterial operational taxonomic units (OTUs), which showed 1.9 × 103 and 1.1 × 101 fold increases, respectively. In particular, the elevated levels of ARGs were maintained for up to 32 hours after rainfall. An increased abundance of sewage-related ARGs and bacterial OTUs suggested that combined sewer overflow (CSO) may be the major factor contributing to the increase in the number and diversity of ARGs and related bacterial communities. Network analysis of ARGs and OTUs indicated that, at the genus level, Acinetobacter, Pseudomonas, and Prevotella were the main potential pathogens carrying the observed ARGs in the recreational seawater. Overall, these findings highlight the potential threat to public health on beaches, and indicate the requirement for more adequate monitoring, with greater efforts to mitigate the propagation of ARGs arising from CSOs.

Similar to the worldwide proliferation of urbanization, micropollutants have been involved in aquatic and ecological environmental systems. These pollutants have the propensity to wreak havoc on human health and the ecological system; hence, it is important to persistently monitor micropollutants in the environment. Micropollutants are commonly quantified via target analysis using high resolution mass spectrometry and the stable isotope labeled (SIL) standard. However, the cost-intensiveness of this standard presents a major obstacle in measuring micropollutants. This study resolved this problem by developing data-driven models, including deep learning (DL) and machine learning (ML), to estimate the concentration of micropollutants without resorting to the SIL standard. Our study hypothesized that natural organic matter (NOM) could replace internal standards if there was a specific mass spectrum (MS) subset, including NOM information, which correlated with an SIL standard peak. Therefore, we analyzed the MS to find the specific MS subsets for replacing the SIL standard peak. Thirty-five alternative MS subsets were determined for applying DL and ML as input data. Thereafter, we trained four different DL models, namely, ResNet101, GoogLeNet, VGG16, and Inception v3, as well as three different ML models, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN). A total of 680 MS data were used for the model training to estimate five different micropollutants, namely Sulpiride, Metformin, and Benzotriazole. Among the DL models, ResNet 101 exhibited the highest model performance, showing that the average validation R2 and MSE were 0.84 and 0.26 ng/L, respectively, while RF was the best in the ML models, manifesting R2 and MSE values of 0.69 and 0.58 ng/L. The trained models showed accurate training and validation results for the estimation of the five micropollutant concentrations. Therefore, this study demonstrates that the suggested analysis has a potential for alternative micropollutant measurement that has rapid and economic vantages.

Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not fully understood. Furthermore, the sources of large variability in fecal coliform counts, e.g., temporal or spatial sources, remain unexplained, especially at large scales. This study proposes the use of Bayesian overdispersed Poisson models, whereby the combined effects of temperature, rainfall, and land-use characteristics on fecal coliform concentration are quantified with predictive uncertainty, and the sources of variability in fecal coliform concentration are assessed. The models were developed using 8-year weekly observations of fecal coliforms obtained from the Wachusett Reservoir watershed in Massachusetts, USA. The results highlight the importance of interactions among meteorological and land-use characteristics in controlling the instream fecal coliform concentration; the increase in fecal coliform concentration with temperature increase was more drastic when rainfall occurred. Also, the responses of fecal coliforms to temperature increases were more pronounced in forest-dominated than in urban-dominated areas. In contrast, the fecal coliform concentration increased more rapidly with rainfall increases in urban-dominated than in forest-dominated areas. The models also demonstrate that among the sources of variability, the monthly component made the most significant contribution to the variability in fecal coliform concentrations. Our results suggest that seasonally dependent processes, including surface runoff, are critical factors that regulate fecal coliform concentration in streams.

In this study, novel activation of peroxymonosulfate (PMS) by biochar derived from rice husk (generally considered useless agricultural wastes in Vietnam) toward organic pollutants from wastewater was investigated. The basic properties of biochar were characterized through field-emission scanning electron microscopy (FE-SEM), elemental analysis (EA) and gas adsorption analysis (BET). Operating parameters including PMS concentration, dose of biochar and initial concentration of target pollutants (tetracycline and bisphenol A) were systematically studied. The results showed that biochar derived from rice husk effectively activated of PMS, leading to high degradation of organic pollutants in wastewater. The degradation efficiency of organic pollutants increased with increasing PMS concentration and amount of biochar. The reuse of rice husk biochar and the possible mechanism for PMS activation were proposed accordingly. In addition, the evaluation of potential available rice husk biomass in Vietnam was discussed. These findings suggest a novel rice husk biochar for activation of PMS toward toxic organic pollutants from wastewater.

Land use change from annual crops to commercial tree plantations can modify flow and transport processes at the watershed scale, including the fate and transport of fecal indicator bacteria (FIB), such as Escherichia coli. The Soil and Water Assessment Tool (SWAT) is a useful means for integrating watershed characteristics and simulating water and contaminants. The objective of this study was to provide a comprehensive assessment of the impact of land use change on microbial transfer from soils to streams using the SWAT model. This study was conducted for the Houay Pano watershed located in northern Lao People's Democratic Republic. Under the observed weather conditions, the SWAT model predicted a decrease from 2011 to 2012 and an increase from 2012 to 2013 in surface runoff, suspended solids, and E. coli transferred from the soil surface to streams. The amount of precipitation was important in simulating surface runoff, and it subsequently affected the fate and transport of suspended solids and bacteria. In simulations of identical weather conditions and different land uses, E. coli fate and transport was more sensitive to the initial number of E. coli than to its drivers (i.e., surface runoff and suspended solids), and leaf area index was a significant factor influencing the determination of the initial number of E. coli on the soil surface. On the basis of these findings, this study identifies several limitations of the SWAT fertilizer and bacteria modules and suggests measures to improve our understanding of the impacts of land use change on FIB in tropical watersheds.

There is a growing interest in the removal of pharmaceuticals from wastewater because pharmaceuticals have potential ecotoxicological effects. Among several removal mechanisms, the sorption of pharmaceuticals to sediment organic matter is an important mechanism related to the mobility of pharmaceuticals. This study investigated the sorption of pharmaceuticals to soil organic matter (SOM) by electrostatic interactions. SOM located on the surface of soil/sediment generally has a negative charge because of the functional groups present (i.e., carboxylic and phenolic groups). Thus, the electrical characteristics of SOM can induce electrical attraction with positively charged chemical compounds. In this study, SOM was extracted from soils under different aquatic plants (Acorus and Typha) in a constructed wetland in Korea. Experiments were carried out with the following three pharmaceuticals with different electrical characteristics at pH 7: atenolol (positive charge; pKa 9.5), carbamazepine (neutral; no pKa), and ibuprofen (negative charge; pKa 4.9). The SOM in the Acorus pond had a higher hydrophobicity and electrical charge density than that in the Typha pond. Regarding the sorption efficiency between SOM and charged pharmaceuticals, atenolol showed highest sorption efficiency (~60%), followed by carbamazepine (~40%) and ibuprofen (~30%). In addition, the removal efficiency of the targeted pharmaceuticals in the constructed wetland was estimated by comparing the concentrations of the pharmaceuticals at sampling points with flowing water. The results showed that the removal efficiency of atenolol and carbamazepine was almost 50%, whereas that of ibuprofen was only ~10%. A comparison of the results of lab-scale and field experiments showed that electrostatic interaction is one of the major pharmaceutical removal mechanisms in a constructed wetland.

Microbial water quality lies in the nexus of human, animal, and environmental health. Multidisciplinary efforts are under way to understand how microbial water quality can be monitored, predicted, and managed. This special collection of papers in the Journal of Environmental Quality was inspired by the idea of creating a special section containing the panoramic view of advances and challenges in the arena of microbial water quality research. It addresses various facets of health-related microorganism release, transport, and survival in the environment. The papers analyze the spatiotemporal variability of microbial water quality, selection of predictors of the spatiotemporal variations, the role of bottom sediments and biofilms, correlations between concentrations of indicator and pathogenic organisms and the role for risk assessment techniques, use of molecular markers, subsurface microbial transport as related to microbial water quality, antibiotic resistance, real-time monitoring and nowcasting, watershed scale modeling, and monitoring design. Both authors and editors represent international experience in the field. The findings underscore the challenges of observing and understanding microbial water quality; they also suggest promising research directions for improving the knowledge base needed to protect and improve our water sources.

Given that around eight million commuters use the Seoul Metropolitan Subway (SMS) each day, the indoor air quality (IAQ) of its stations has attracted much public attention. We have monitored the concentration of particulate matters (PMx) (i.e., PM10, PM2.5, and PM1) in six major transfer stations per minute for three weeks during the summer, autumn, and winter in 2014 and 2015. The data were analyzed to investigate the relationship between PMx concentration and multivariate environmental factors using statistical methods. The average PM concentration observed was approximately two or three times higher than outdoor PM10 concentration, showing similar temporal patterns at concourses and platforms. This implies that outdoor PM10 is the most significant factor in controlling indoor PM concentration. In addition, the station depth and number of trains passing through stations were found to be additional influences on PMx. Principal component analysis (PCA) and self-organizing map (SOM) were employed, through which we found that the number of trains influences PM concentration in the vicinity of platforms only, and PMx hotspots were determined. This study identifies the external and internal factors affecting PMx characteristics in six SMS stations, which can assist in the development of effective IAQ management plans to improve public health.

The goal of this study was to improve biological nitrogen removal (BNR) efficiency by investigating links between environmental factors and the dynamics of denitrifying and nitrifying bacteria in a combined anoxic–oxic activated sludge system. Principal component analysis (PCA) supported a positive correlation of narG, nirS and nosZ genes of denitrifying bacteria with SCN− and CN−, constitute one part of total nitrogen (TN). Supplemental ammonia produced from the biodegradation of those cyanide compounds increased NO 2 − and NO 3 − by nitrification, increasing concentrations of the denitrifying bacteria by increasing their N substrates. On the other hand, concentrations of both influent COD and phenol adversely affected the dominant nitrite oxidizing bacteria (NOB), Nitrobacter spp., exhibiting an inverse relationship both with the complete nitrification performance (end products, NO 3 − ) as well as with an accumulation of NO 2 − . In this BNR system, therefore, creation of favorable conditions by pretreatment of influent phenol and organic matter may allow achievement of complete nitrification and an increase in quantities of nitrifying bacteria.

A combined three-stage system, (1) coagulation (2) zeocarbon filtration and (3) membrane filtration, a combination of microfiltration (MF) and reverse osmosis (RO), was investigated for reclamation of tunnel construction wastewater having a salinity of 10.8-12.9‰ and a concentration of suspended solids (SS) in the range of 264-1084 mg/L. The initial stages - coagulation, zeocarbon filtration and MF - served as a precursor to RO membrane filtration to successfully reduce water contaminants to less than 0.2 nephelometric turbidity units (NTU) of turbidity, thereby minimizing the potential for fouling. The RO system subsequently removed over 99% of remaining pollutants including ionic substances, resulting in less than 0.02 NTU turbidity, less than 0.04 mg/L total nitrogen (TN) and less than 0.01 mg/L total phosphorus (TP). Also, addition of an RO system markedly reduced high salt concentrations (high chloride (Cl(-)) concentrations) in the wastewater, exceeding 99% salt elimination. Thus, reclaimed water from our combined system met and exceeded currently regulatory quality standards for wastewater reuse (turbidity ≤ 2.0 NTU; TN ≤ 10 mg/L; TP ≤ 0.5 mg/L; Cl(-) ≤ 250 mg/L).

Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vector machine (SVM) were used to predict Chl-a concentration for the early warning in the Juam Reservoir and Yeongsan Reservoir, which are located in an upstream region (freshwater reservoir) and downstream region (estuarine reservoir), respectively. Weekly water quality data and meteorological data for a 7-year period were used to train and validate both the ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the two models, respectively. Results revealed that the two models well-reproduced the temporal variation of Chl-a based on the weekly input variables. In particular, the SVM model showed better performance than the ANN model, displaying a higher prediction accuracy in the validation step. The Williams-Kloot test and sensitivity analysis demonstrated that the SVM model was superior for predicting Chl-a in terms of prediction accuracy and description of the cause-and-effect relationship between Chl-a concentration and environmental variables in both the Juam Reservoir and Yeongsan Reservoir. Furthermore, a 7-day interval was determined as an efficient early warning interval in the two reservoirs. As such, this study suggested an effective early-warning prediction method for Chl-a concentration and improved the eutrophication management scheme for reservoirs.

A pilot-scale combination of a microfiltration (MF) and a reverse osmosis (RO) membrane system was applied on-site to treat tunnel construction wastewater. The MF membrane system initially removed contaminants (turbidity of less than 0.3 NTU) in the form of particulate materials in the feed water, thereby allowing the combined MF-RO system to efficiently remove more than 99% of known organic and inorganic contaminants and qualify the reclaimed water for reuse. The MF membrane autopsy analysis using X-ray fluorescence (XRF) and inductively coupled plasma-mass spectrometry (ICP-MS) revealed that the dominant foulants were inorganic deposits involving Si, Al and Fe, comprising the main components of cement materials, as well as deposits involving heavy metals such as Mn, Cu and Zn in the form of particles. Thus, thick cake contaminants shown by field emission scanning electron microscope (FE-SEM) images might be induced via suspended solids consisting of cement and clay materials and metals.

The occurrence of pathogen bacteria in surface waters is a threat to public health worldwide. In particular, inadequate sanitation resulting in high contamination of surface water with pathogens of fecal origin is a serious issue in developing countries such as Lao P.D.R. Despite the health implications of the consumption of contaminated surface water, the environmental fate and transport of pathogens of fecal origin and their indicators (Fecal Indicator Bacteria or FIB) are still poorly known in tropical areas. In this study, we used measurements of flow rates, suspended sediments and of the FIB Escherichia coli (E. coli) in a 60-ha catchment in Northern Laos to explore the ability of the Soil and Water Assessment Tool (SWAT) to simulate watershed-scale FIB fate and transport. We assessed the influences of 3 in-stream processes, namely bacteria deposition and resuspension, bacterial regrowth, and hyporheic exchange (i.e. transient storage) on predicted FIB numbers. We showed that the SWAT model in its original version does not correctly simulate small E. coli numbers during the dry season. We showed that model's performance could be improved when considering the release of E. coli together with sediment resuspension. We demonstrated that the hyporheic exchange of bacteria across the Sediment-Water Interface (SWI) should be considered when simulating FIB concentration not only during wet weather, but also during the dry season, or baseflow period. In contrast, the implementation of the regrowth process did not improve the model during the dry season without inducing an overestimation during the wet season. This work thus underlines the importance of taking into account in-stream processes, such as deposition and resuspension, regrowth and hyporheic exchange, when using SWAT to simulate FIB dynamics in surface waters.

The goal of this study was to investigate the bacterial community associated with a pilot-scale thermophilic aerobic digestion (TAD) reactor treating sewage sludge. The digestion system removed up to approximately 30% of volatile solids (VS); VS and total solids (TS) decreased from 38.4 to 26.9 g/L and from 51.2 to 43.8 g/L, respectively, and the ratio of VS/TS declined from 75.4% to 62.8%. Coincident with the thermal reduction of sludge, 16S rRNA clone library data showed that a significant shift occurred in the bacterial community composition with the selection of the Firmicutes and Deinococcus-Thermus families as well as a marked decrease in the relative abundance of a large part of bacterial taxa such as Proteobacteria. A unique bacterial community in the TAD reactor developed resulting from specific environmental pressures such as thermal stress. A predominance of thermophilic bacteria belonging to Bacillus sp., Geobacillus sp., Thermaerobacter sp. and Thermus thermophilus sp. suggest their putative roles for potentially high thermophilic degradation of sewage sludge.

Blocking the natural bi-directional flow in an estuarine system using an artificial dyke has commonly caused serious water quality problems. In the southwestern part of South Korea, a parallel triple-reservoir system was constructed by blocking the mouth of three different rivers (Yeongsan, Okcheon, and Kumja), which were then interconnected using two open channels. This system has experienced a deterioration in water quality due to pollutants accumulated from the upper watershed, and has continually discharged pollutant loads to the outer ocean. Therefore, the objective of this study is to establish an effective dam operation plan for reducing nutrient loads released from the integrated reservoir. In this study, the CE-QUAL-W2 model, which is a 2-dimentional hydrodynamic and water quality model, was applied to predict the pollutant load released from each reservoir in response to different flow scenarios for the interconnecting channel. The model was calibrated using two novel methods: a sensitivity analysis to determine meaningful model parameters, and a pattern search to optimize the parameters. From the scenario analysis using flow control, it was determined that the total nitrogen (TN) and total phosphorus (TP) loadings could be reduced by 27.2% and 6.6%, respectively, under the optimal channel flow scenario by regulating the chlorophyll-a concentration in the reservoir. The results confirm that effective dam operation could contribute to a decrease in pollutant loads in the receiving seawater body. As such, this study suggests operational strategies for a multi-reservoir system that can be used to reduce the nutrient load being discharged from reservoirs.

Over a 3 year period, interactions between nitrifying bacterial communities and the operational parameters of a full-scale wastewater treatment plant were analyzed to assess their impact on nitrification performance. Throughout the study period, nitrification fluctuated while Nitrosomonas europaea and Nitrosomonas nitrosa, the two major ammonia oxidizing bacteria (AOB) communities, showed resistance to changes in operational and environmental conditions. Nitrobacter populations mostly exceeded those of Nitrospira within nitrite oxidizing bacteria (NOB). Meanwhile, principal component analysis (PCA) results revealed that a close association between Nitrobacter and nitrite concentration as well as a direct correlation between the quantity of AOB and influent SCN- concentration. The serial shifts of data points over time showed that the nitrification of a full-scale treatment plant has been gradually suppressed by the influence of influent COD and phenol concentrations.

PAHs are potentially carcinogenic substances that are persistent in the environment. Increasing concentrations of PAHs were observed due to rapid urbanization, thus; monitoring PAHs concentrations is necessary. However, it is expensive to conduct intensive monitoring activities of a large number of PAHs. This study addressed this issue by developing a multimedia model coupled with a hydrological model (i.e., Soil and Water Assessment Tool (SWAT)) for Taehwa River (TR) watershed in Ulsan, the industrial capital of South Korea. The hydrologic module of the SWAT was calibrated, and further used to simulate the fate and transport of PAHs in soil and waterbody. The model demonstrated that the temporal or seasonal variation of PAHs in soil and waterbody can be well reproduced. Meanwhile, the spatial distribution of PAHs showed that urban areas in TR watershed have the highest PAH loadings compared to rural areas. Sensitivity analyses of the PAH soil and PAH water parameters were also able to determine the critical processes in TR watershed: degradation, deposition, volatilization, and wash off mechanism. We hope that this model will be able to aid the stakeholders in: regulating PAH concentrations emitted by various sources; and also apply the model to other Persistent Organic Pollutants (POPs).

This study reveals land-use factors that explain stream water quality during wet and dry weather conditions in a large river basin using two different linear models-multiple linear regression (MLR) models and constrained least squares (CLS) models. Six land-use types and three topographical parameters (size, slope, and permeability) of the watershed were incorporated into the models as explanatory variables. The suggested models were then demonstrated using a digitized elevation map in conjunction with the land-use and the measured concentration data for Escherichia coli (EC), Enterococci bacteria (ENT), and six heavy metal species collected monthly during 2007-2008 at 50 monitoring sites in the Yeongsan Watershed, Korea. The results showed that the MLR models can be a powerful tool for predicting the average concentrations of pollutants in stream water (the Nash-Sutcliffe (NS) model efficiency coefficients ranged from 0.67 to 0.95). On the other hand, the CLS models, with moderately good prediction performance (the NS coefficients ranged 0.28-0.85), were more suitable for quantifying contributions of respective land-uses to the stream water quality. The CLS models suggested that industrial and urban land-uses are major contributors to the stream concentrations of EC and ENT, whereas agricultural, industrial, and mining areas were significant sources of many heavy metal species. In addition, the slope, size, and permeability of the watershed were found to be important factors determining the extent of the contribution from each land-use type to the stream water quality. The models proposed in this paper can be considered useful tools for developing land cover guidelines and for prioritizing locations for implementing management practices to maintain stream water quality standard in a large river basin.