Molecular-level chemical composition of aerosol and its potential source tracking at Antarctic Peninsula.

Marine organic aerosols play crucial roles in global climatic systems. However, their chemical properties and relationships with various potential organic sources still need clarification. This study employed high-resolution mass spectrometry to investigate the identity, origin, and transportation of organic aerosols in pristine Antarctic environments (King Sejong Station; 62.2°S, 58.8°W), where complex ocean-cryosphere-atmosphere interactions occur. First, we classified the aerosol samples into three clusters based on their air mass transport history. Next, we investigated the relationship between organic aerosols and their potential sources, including organic matter dissolved in the open ocean, coastal waters, and runoff waters. Cluster 1 (C1), in which the aerosols mainly originated from the open ocean area (i.e., pelagic zone-influenced), exhibited a higher abundance of lipid-like and protein-like organic aerosols than cluster 3 (C3), with ratios 1.8- and 1.6-times higher, respectively. In contrast, C3, characterized by longer air mass retention over sea ice and land areas (i.e., inshore-influenced), had higher lignin- and condensed aromatic structures (CAS)-like organic aerosols by 2.2- and 3.4-times compared to C1. Cluster 2 (C2) has intermediate characteristics between C1 and C3 concerning the chemical properties of the aerosols and air mass travel history. Notably, the chemical properties of the aerosols assigned to C1 are closely related to those of phytoplankton-derived organics enriched in the open ocean. In contrast, those of C3 are comparable to those of terrestrial plant-derived organics enriched in coastal and runoff waters. These findings help evaluate the source-dependent properties of organic aerosols in changing Antarctic environment.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach.

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model's performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Prediction of Oxidant Exposures and Micropollutant Abatement during Ozonation Using a Machine Learning Method.

Oxidation of micropollutants (MPs) by ozonation proceeds via the reactions with molecular ozone (O3) and hydroxyl radicals (•OH). To predict MP abatement during ozonation, a model that can accurately predict oxidant exposures (i.e., ∫0t[O3]dt⁢ and⁢ ∫0t[O•H]dt) needs to be developed. This study demonstrates machine learning models based on the random forest (RF) algorithm to output oxidant exposures from water quality parameters (input variables) that include pH, alkalinity, dissolved organic carbon concentration, and fluorescence excitation-emission matrix (FEEM) data (to characterize organic matter). To develop the models, 60 different samples of natural waters and wastewater effluents were collected and characterized, and the oxidant exposures in each sample were determined at a specific O3 dose (2.5 mg/L). Four RF models were developed depending on how FEEM data were utilized (i.e., one model free of FEEM data, and three other models that used FEEM data of different resolutions). The regression performance and Akaike information criterion (AIC) were evaluated for each model. The models using high-resolution FEEM data generally exhibited high prediction accuracy with reasonable AIC values, implying that organic matter characteristics quantified by FEEM can be important factors to improve the accuracy of the prediction model. The developed models can be applied to predict the abatement of MPs in drinking water and wastewater ozonation processes and to optimize the O3 dose for the intended removal of target MPs. The machine learning models using higher-resolution FEEM data offer more accurate prediction by better calculating the complex nonlinear relationship between organic characteristics and oxidant exposures.

Changes in adsorption mechanisms of radioactive barium, cobalt, and strontium ions using spent coffee waste biochars via alkaline chemical activation: Enrichment effects of O-containing functional groups.

The single adsorption of radioactive barium (Ba(II)), cobalt (Co(II)), and strontium (Sr(II)) ions using pristine (SCWB-P) and chemically activated spent coffee waste biochars with NaOH (SCWB-A) were thoroughly explored in order to provide deeper insights into the changes in their adsorption mechanisms through alkaline chemical activation. The greater removal efficiencies of SCWB-A (76.6-97.3%) than SCWB-P (45.6-75.2%) and the consistency between the adsorptive removal patterns (Ba(II) > Sr(II) > Co(II)) and oxygen bond dissociation enthalpies (BaO (562 kJ/mol) > SrO (426 kJ/mol) > CoO (397 kJ/mol)) of radioactive species supported the assumption that the adsorption removal of radioactive species with spent coffee waste biochars highly depended on the abundances of O-containing functional groups. The calculated R2 values of the pseudo-first-order (SCWB-P = 0.998-0.999; SCWB-A = 0.850-0.921) and pseudo-second-order kinetic models (SCWB-P = 0.988-0.998; SCWB-A = 0.935-0.966) are evident that the physisorption mainly controlled the adsorption of radioactive species toward SCWB-P and the chemisorption played a crucial role in their adsorptive removal with SCWB-A. From the calculated intra-particle diffusion, isotherm, thermodynamic parameters, it can be concluded that the intra-particle diffusion and monolayer adsorption primarily governed the adsorption of radioactive species using SCWB-P and SCWB-A, and their adsorption processes occurred spontaneously and endothermically. The dominant adsorption mechanism of spent coffee waste biochars was changed from physisorption (ΔH° of SCWB-P = 21.6-29.8 kJ/mol) to chemisorption (ΔH° of SCWB-A = 42.4-81.3 kJ/mol) through alkaline chemical activation. The distinctive M-OH peak in the O1s XPS spectra of SCWB-A directly corresponding to the decrease in the abundances of O-containing functional groups confirms again that the enrichment of O-containing functional groups markedly facilitated the adsorption removal of radioactive species by chemisorption occurred at the inner and outer surfaces of spent coffee waste biochars.

Discoidin Domain Receptor 2 Mediates Lysophosphatidic Acid-Induced Ovarian Cancer Aggressiveness.

Lysophosphatidic acid (LPA), a bioactive lipid produced extracellularly by autotaxin (ATX), has been known to induce various pathophysiological events, including cancer cell invasion and metastasis. Discoidin domain receptor 2 (DDR2) expression is upregulated in ovarian cancer tissues, and is closely associated with poor clinical outcomes in ovarian cancer patients. In the present study, we determined a critical role and signaling cascade for the expression of DDR2 in LPA-induced ovarian cancer cell invasion. We also found ectopic expression of ATX or stimulation of ovarian cancer cells with LPA-induced DDR2 expression. However, the silencing of DDR2 expression significantly inhibited ATX- and LPA-induced ovarian cancer cell invasion. In addition, treatment of the cells with pharmacological inhibitors of phosphoinositide 3-kinase (PI3K), Akt, and mTOR abrogated LPA-induced DDR2 expression. Moreover, we observed that HIF-1α, located downstream of the mTOR, is implicated in LPA-induced DDR2 expression and ovarian cancer cell invasion. Finally, we provide evidence that LPA-induced HIF-1α expression mediates Twist1 expression to upregulate DDR2 expression. Collectively, the present study demonstrates that ATX, and thereby LPA, induces DDR2 expression through the activation of the PI3K/Akt/mTOR/HIF-1α/Twist1 signaling axes, aggravating ovarian cancer cell invasion.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.

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.

Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models.

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.

Assessment of a green roof practice using the coupled SWMM and HYDRUS models.

Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.

Modeling the Impact of Land Use Change on Basin-scale Transfer of Fecal Indicator Bacteria: SWAT Model Performance.

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 . 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 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, fate and transport was more sensitive to the initial number of 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 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.

Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea.

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.

Seasonal Changes in Antibiotic Resistance Genes in Rivers and Reservoirs in South Korea.

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 (), ß-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.

Fluxes of nutrients and trace metals across the sediment-water interface controlled by sediment-capping agents: bentonite and sand.

The effect of bentonite and sand, as natural capping agents, on the fluxes of nutrients and trace metals across the sediment-water interface was studied through sediment incubation, and the ecotoxicological impact was assessed by using Daphnia magna. Bentonite and sand were layered on the sediment at 15, 75, and 225 mg cm(-2), and the concentration of cations, nutrients, and trace metals was measured. Sediment incubation showed that bentonite reduced the N flux but increased the P flux as a result of dissolution of non-crystalline P from bentonite, while sand slightly decreased the N fluxes but not the P flux. The concentration of Na increased in the overlying water with increasing application rates of bentonite, while that of Ca decreased. However, regardless of the rate of sand application, concentrations of all cation species remained unchanged. The concentration of As and Cr increased with bentonite application rate but decreased with sand. Both capping materials suppressed fluxes of Cd, Cu, Ni, and Zn compared to control, and the extent of suppression was different depending on the trace metal species and capping agents used. During sediment incubation, the survival rate of D. magna significantly decreased in bentonite suspension but began to decrease at the end in sand suspension. Sediment capping of mildly polluted sediments by using bentonite and sand lowered the level of nutrients and trace metals. However, unexpected or undesirable side effects, such as influxes of P and As from bentonite to the overlying water and a possibility of toxic impacts to aquatic ecosystems, were observed, suggesting that capping agents with an adequate assessment of their side effects and toxicity should be predetermined for site-specific sediment management strategies.

Breast cancer metastasis suppressor 1 (BRMS1) attenuates TGF-ß1-induced breast cancer cell aggressiveness through downregulating HIF-1α expression.

BACKGROUND: Cancer metastasis is a multi-step event including epithelial-to-mesenchymal transition (EMT). Breast cancer metastasis suppressor 1 (BRMS1) is a novel metastasis suppressor protein without anti-proliferating activity. However, a detailed underlying mechanism by which BRMS1 attenuates cancer cell EMT and invasion remained to be answered. In the present study, we report an additional mechanism by which BRMS1 attenuates Transforming growth factor-beta1 (TGF-ß1)-induced breast cancer cell EMT and invasion. METHODS: Experimental analysis involving chromosome immunoprecipitation (ChIP) and luciferase reporter assays were used to validate hypoxia inducible factor-1alpha (HIF-1α) as a transcriptional regulator of TWIST1 and Snail. Quantitative RT-PCR was used to analyze transcript expression. Immunoblotting and immunofluorescence were used to analyze protein expression. Matrigel-coated in vitro invasion insert was used to analyze cancer cell invasion. RESULTS: BRMS1 strongly inhibited TGF-ß1-induced breast cancer cell EMT and invasion. Unexpectedly, we observed that BRMS1 downregulates not only TWIST1 but also Snail expression, thereby inhibiting breast cancer cell invasion. In addition, we provide evidence that HIF-1α is required for Snail and TWIST1 expression. Further, BRMS1 reduced TGF-ß1-induced HIF-1α transcript expression through inactivation of nuclear factor kappaB (NF-κB). CONCLUSION: Collectively, the present study demonstrates a mechanical cascade of BRMS1 suppressing cancer cell invasion through downregulating HIF-1α transcript and consequently reducing Snail and TWIST1 expression.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models.

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R2), Nash-Sutcliff efficiency (NSE), relative efficiency criteria (drel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.

A ROS/STAT3/HIF-1α signaling cascade mediates EGF-induced TWIST1 expression and prostate cancer cell invasion.

BACKGROUND: Epidermal growth factor (EGF) has been known to induce epithelial-mesenchymal transition (EMT) and prostate cancer cell progression. However, a detailed underlying mechanism by which EGF induces EMT and prostate cancer cell progression remained to be answered. Hypoxia-inducible factor (HIF)-1α and TWIST1 are transcription factors implicated in EMT and cancer metastasis. The purpose of this study is to determine the underlying mechanism of EGF-induced TWIST1 expression and prostate cancer invasion. METHODS: siRNAs were used to silence genes. Immunoblotting, quantitative RT-PCR and immunofluorescence analysis were used to examine protein or mRNA expression. Modified Boyden chamber and invasion assay kit with Matrigel-coated inserts were used to determine prostate cancer cell migration and invasion, respectively. RESULTS: We observed that EGF induced HIF-1α expression and morphological change of prostate cancer epithelial cells to mesenchymal cells. Silencing HIF-1α expression dramatically reduced EGF-induced TWIST1 expression and prostate cancer cell EMT. Conversely, transfection of the cells with HIF-1α siRNA reversed the reduced E-cadherin expression by EGF. Pretreatment of the cells with pharmacological inhibitors of reactive oxygen species [ROS, N-acetylcysteine (NAC)] and STAT3 (WP1066) but not p38 MAPK (SB203580) significantly reduced EGF-induced HIF-1α mRNA and protein expression. Further, pretreatment of the cells with NAC attenuated EGF-induced STAT3 phosphorylation. In addition, we showed that TWIST1 mediated EGF-induced N-cadherin expression, leading to prostate cancer invasion. CONCLUSIONS: We demonstrate a mechanism by which EGF promotes prostate cancer cell progression through a ROS/STAT3/HIF-1α/TWIST1/N-cadherin signaling cascade, providing novel biomarkers and promising therapeutic targets for prostate cancer cell progression.

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency.

Climate warming is linked to earlier onset and extended duration of cyanobacterial blooms in temperate rivers. This causes an unpredictable extent of harm to the functioning of the ecosystem and public health. We used Microcystis spp. cell density data monitored for seven years (2016-2022) in ten sites across four temperate rivers of the Republic of Korea to define the phenology of cyanobacterial blooms and elucidate the climatic effect on their pattern. The day of year marking the onset, peak, and end of Microcystis growth were estimated using a Weibull function, and linear mixed-effect models were employed to analyze their relationships with environmental variables. These models identified river-specific temperatures at the beginning and end dates of cyanobacterial blooms. Furthermore, the most realistic models were employed to project future Microcystis bloom phenology, considering downscaled and quantile-mapped regional air temperatures from a general circulation model. Daily minimum and daily maximum air temperatures (mintemp and maxtemp) primarily drove the timing of the beginning and end of the bloom, respectively. The models successfully captured the spatiotemporal variations of the beginning and end dates, with mintemp and maxtemp predicted to be 24℃ (R2 = 0.68) and 16℃ (R2 = 0.35), respectively. The beginning and end dates were projected to advance considerably in the future under the Representative Concentration Pathway 2.6, 4.5, and 8.5. The simulations suggested that the largest uncertainty lies in the timing of when the bloom ends, whereas the timing of when blooming begins has less variation. Our study highlights the dependency of cyanobacterial bloom phenology on temperatures and earlier and prolonged bloom development.

Predicting the distribution coefficient of cesium in solid phase groups using machine learning.

The migration and retention of radioactive contaminants such as 137Cesium (137Cs) in various environmental media pose significant long-term storage challenges for nuclear waste. The distribution coefficient (Kd) is a critical parameter for assessing the mobility of radioactive contaminants and is influenced by various environmental conditions. This study presents machine-learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict the Kd values for Cs in solid phase groups. We used three different machine learning models: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The models were trained on 14 input variables from the JAEA-SDB, including factors such as the Cs concentration, solid-phase properties, and solution conditions, which were preprocessed by normalization and log-transformation. The performances of the models were evaluated using the coefficient of determination (R2) and root mean squared error (RMSE). The RF, ANN, and CNN models achieved R2 values greater than 0.97, 0.86, and 0.88, respectively. We also analyzed the variable importance of RF using an out-of-bag (OOB) and a CNN with an attention module. Our results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. Our models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and long-term risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry.

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal.

Photocatalytic reactions with semiconductor-based photocatalysts have been investigated extensively for application to wastewater treatment, especially dye degradation, yet the interactions between different process parameters have rarely been reported due to their complicated reaction mechanisms. Hence, this study aims to discern the impact of each factor, and each interaction between multiple factors on reaction rate constant (k) using a decision tree model. The dyes selected as target pollutants were indigo and malachite green, and 5 different semiconductor-based photocatalysts with 17 different compositions were tested, which generated 34 input features and 1527 data points. The Boruta Shapley Additive exPlanations (SHAP) feature selection for the 34 inputs found that 11 inputs were significantly important. The decision tree model exhibited for 11 input features with an R2 value of 0.94. The SHAP feature importance analysis suggested that photocatalytic experimental conditions, with an importance of 59%, was the most important input category, followed by atomic composition (39%) and physicochemical properties (2%). Additionally, the effects on k of the synergy between the metal cocatalysts and important experimental conditions were confirmed by two feature SHAP dependence plots, regardless of importance order. This work provides insight into the single and multiple factors that affect reaction rate and mechanism.

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models.

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.