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Research on the effects of microplastics on water environments is being widely conducted because the discharge of microplastics into the ocean and freshwater systems is rapidly increasing. For the numerical simulation of the transport and diffusion of microplastics introduced into water systems and evaluation of the ecological impacts, the settling velocities of microplastics must be investigated first. The settling behavior of microplastics has been commonly assumed to be similar to that of sediment particles, but this has not been sufficiently verified for various types of microplastic. In this study, a laboratory experiment was performed to measure the settling velocities of spherical microplastic particles in freshwater using a particle tracking technique, and a new calibration coefficient of the settling velocity was proposed based on the experimental data. The experimental results showed a clear linear relationship between the settling velocity and dimensionless particle diameter in the laminar flow region, as suggested by Stokes' law. However, the settling velocities in the transition region had a nonlinear relationship, which was also clearly demonstrated in the relationship between the drag coefficient and the particle Reynolds number. The evaluation of the existing equations for settling velocity showed that previous empirical equations based on experiments on sediment particles produced relative errors of 20% or more. However, the proposed calibration coefficient provided a more accurate prediction of the settling velocities of microplastic particles than the existing empirical equations based on sediment studies.

COVID-19 is unique in that it is spread through everyday contact with other people. Therefore, social protective measures, beyond medical protective measures, such as social distancing, lockdowns, ...

Remote sensing of suspended sediment in shallow waters is challenging because of the increased optical variability of the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment. This experiment was conducted with four sediment types injected into an experimental meandering channel divided into two reaches with submerged vegetation and a natural sand bottom. We obtained high-resolution hyperspectral images using unmanned aerial vehicles (UAVs) and measured the in situ suspended sediment concentration using laser diffraction sensors. Based on optical similarity, we used CMR-OV to divide the hyperspectral dataset into several clusters. Then, we built separate RFR models for each cluster using the corresponding spectral bands that were selected using recursive feature elimination (RFE). Thus, we found that the proposed CMR-OV yielded superior results compared to the conventional RFR model, decreasing the total error score by 10.81%. The optical spectral bands of each cluster were distinguished from each other, indicating that the datasets that were spectrally discriminated from clustering enhanced the performance of the estimator. By comparing the clustered spectral dataset and physical factors, we proved the bottom type was the most critical factor in separating the clusters, even though the variability in the sediment properties also induced substantial spectral changes. Our findings demonstrated that CMR-OV accurately reproduced the spatiotemporal distribution of suspended sediment under optically complex conditions by addressing the heterogeneity of bottom reflectance in shallow water.

Techniques for predicting the contaminant cloud propagation along a stream are necessary for swift action against contaminant spill accidents in fluvial systems. Due to their low computational cost, one-dimensional solute transport models have conventionally been employed, in which the complex channel characteristics are considered using model parameters. However, the determination of such parameters relies predominantly on optimization techniques based on pre-measured tracer data, which are usually unavailable for unexpected accidents. The present paper suggests an alternative method for predicting a breakthrough curve (BTC) variation along an unmeasured stream reach where no flow information is provided. In this study, we investigated the relationship between directly-measured flow properties and BTC characteristics based on field tracer experiments. Using statistical features of the tracer BTCs, we devised a regressive prediction method for estimating the BTC features as a function of travel distance, and validated the method by comparison with simulations using both a one-dimensional advection-dispersion equation (ADE) and transient storage model (TSM), whose parameters were calibrated at upstream reaches. The proposed regressive predictions were relatively accurate than those from parameter-calibrated models, and this advantage was more apparent for long-distance predictions for the unmeasured river reach.

Bathymetric mapping is a prerequisite procedure to conduct assessments of water quality, habitat and environmental flow for riverine ecosystems using hydraulic modelling. This study evaluates the capability of a geographically weighted regression (GWR) model, which can capture a spatially heterogeneous relationship between inputs and an output, to retrieve bathymetry of a shallow stream, of which water depth is less than about 1 m from simple RGB imagery. A field experiment was performed for measuring water depth and simultaneously for acquiring remotely-sensed data with RGB digital numbers (DN) using a digital camera mounted on an unmanned aerial vehicle (UAV). A 2D shallow water model, which was validated by comparison with the field-surveyed data, was used to simulate the water depth of unmeasured regions. Band ratios of ln(DNG/DNR) was selected as an optimal spectral input of bathymetric inversion models through the principal component analysis (PCA). Results showed that global inversion models based on multiple linear regression (MLR) and artificial neural network (ANN) resulted in large discrepancy between estimation and observation due to the spatially varying response of the PCA-selected band ratio to water depth over the experimental channel. In contrast, the GWR model successfully alleviated the biases of the conventional models as R2 increased to 0.85 from 0.60 by accurately modelling the effect of spatial heterogeneity, which arose from variable bottom types attributed to submerged vegetation, on the remote-sensing radiance-water depth relationship.

We evaluated the potential of remote sensing to enhance tracer experiments by providing spatially distributed information on visible dye concentration. During tests in an experimental facility and ...

A new method of unmanned aerial vehicle (UAV)-based tracer tests using RGB (red, green, blue) images was developed in order to acquire the spatio-temporal concentration distribution of tracer clouds in open channel flows. Tracer tests using Rhodamine WT were conducted to collect the RGB images using a commercial digital camera mounted on a UAV, and the concentration of Rhodamine WT using in-situ fluorometric probes. The correlation analysis showed that the in-situ measured concentrations of Rhodamine WT were strongly correlated with the digital number (DN) of the RGB images, even though the response of DN to the concentration was spatially heterogeneous. The empirical relationship between the DN values and the Rhodamine WT concentration data was estimated using artificial neural network (ANN) models. The trained ANN models, which consider the effect of water depth and river bed, accurately retrieved the detailed spatio-temporal concentration distributions of all study areas that had an R2 higher than 0.9. The acquired spatio-temporal concentration distributions by the proposed method based on the UAV images gave general as well as detailed views of the tracer cloud moving dynamically in open channel flows that cannot be easily observed using conventional in-situ measurements.

In order to analyze the shear effect of secondary currents on the flow structures in a meandering channel, this research developed a two-dimensional shallow water model, which included the dispersion stress term accounting for the shear effect in the vertical velocity profile. A new equation for the vertical velocity profile that included nonlinear shear effects was derived from the equation of motion in the meandering channel with sharp curvature. Using the experiment data obtained from large-scale meandering channels, the ratio of the depth over the radius-of-curvature was incorporated into the shear intensity of the secondary flow in the proposed equation. Comparisons with the experimental results by previous research showed that the computed values of the primary velocity distribution by the proposed model showed better fit with the observed data than the simulations with linear models and models without secondary flow consideration. The simulated results in the large-scale meandering channels demonstrated that simulations with the nonlinear secondary flow effect added into modeling gave higher accuracy, reducing the relative error by 19% in reproducing the skewed distributions of the primary flow in meandering channels, particularly in the regions where the effects from spiral motion were strong, due to sharp meanders.

A Transient Storage Model (TSM) which considers the storage exchange process that induces an abnormal mixing phenomenon has been widely used to analyze solute transport in natural rivers. The primary step in applying TSM is calibration of four key parameters: flow zone dispersion coefficient (Kf), main flow zone area (Af), storage zone area (As), and storage exchange rate (&alpha, ), by fitting the measured Breakthrough Curves (BTCs). In this study, to overcome the costly tracer tests that are necessary for parameter calibration, two dimensionless empirical models were derived, to estimate TSM parameters, using multi-gene genetic programming (MGGP) and principal components regression (PCR). A total of 128 datasets with complete variables from 14 published papers were chosen from an extensive meta-analysis and were applied to derivations. The performance comparison revealed that the MGGP-based equations yielded the superior prediction results. According to TSM analysis of field experiment data from Cheongmi Creek, South Korea, although all assessed empirical equations produced acceptable BTCs in monotonous reach, the MGGP model was superior to the other models. The predicted BTCs obtained by the empirical models in some highly complicated reaches were biased due to misprediction of As. Sensitivity analyses of MGGP models showed that the sinuosity is the most influential factor in Kf, As, and &alpha, whlie Af is more sensitive to U/U*. This study proves that the MGGP-based model can be used for economic TSM analysis, thus providing an alternative option to direct calibration and the inverse modeling initial parameters.

This paper asks whether the technological development of a nation reduces inter-regional hierarchy in knowledge flow. We examine two scenarios that are the alternative to each other. The first is what we call, the globalization of regional innovation system/ weakening of inter-regional hierarchy scenario: As many regions develop their niches in the global economy, the national “anchor” regions loses its relative importance as the importer and distributor of new knowledge, rendering the domestic inter-regional hierarchy less significant as a result. The second scenario is the globalization of national innovation system/ persistence of inter-regional hierarchy. The nation’s traditional anchor region becomes even more active in importing technology and distributing it to other regions of the country. To test which scenario is closer to reality, we employ social network metrics to analyse inter-regional technology diffusion networks using Chinese patent licensing data for the 1998– 2013 period. Our findings support the second scenario, showing that the influence of the traditional anchor region persists in the hierarchical network structure as new cities enter the network. We found five anchor regions: the three usual suspects—Beijing, Shanghai, and Shenzhen—plus two that were slightly unexpected—Dongguan and Suzhou.

In this study, data-based classification models were developed for real-time prediction of the exceedance of the safety level on fecal coliform in Daesung-ri site of North Han River. The prediction models were developed using the logistic regression model (LRM) and the tree-based models such as classification and regression model (CART), bagging model (BGM), and random forest model (RFM). For model development, rainfall, water quality, and dam discharge data from 2010 to 2015 were collected from the study site. Clustering methods were applied to reduce the sampling bias of training and test datasets and to improve the prediction accuracy. The developed four models were compared with each other in terms of prediction accuracy and applicability. The test results of developed models showed that the total correct classification rate of the four models ranged from 83.7% to 93.0%. Each classification model showed its own strengths; LRM offered flexibility by tuning cutoff values, while RFM showed the highest accuracy among the four models. The hydro-ecological process on fecal coliform could be explained by analyzing important variables in the prediction models and identifying the impacting factors through the field monitoring. The important factors both in the models and field monitoring were revealed as the rainfall-related variables, dam discharge and total phosphorus, which imply that the fecal pollution in North Han River came mainly from the rainfall events and runoff including nutrients from farmland and livestock farming in the upstream basin of Guwoon Creek and Chungpyung Dam.

It is fundamental task to reveal the mechanism of the dispersion and the diffusion on solute transport in open channel flow. In this study, the dispersion and the diffusion coefficients were observed and compared with each other based on the tracer tests at a prototype channel in the Andong experimental site, South Korea. The channel has sinuosity of 1.2 and can control the flow rate. For each experimental condition, measurements of average velocity by wading and the turbulence intensity were performed with a fixed device. The dispersion and diffusion coefficients were calculated using a dissolved tracer (Rhodamine WT) and a surface particle tracer (GPS floater), respectively. For each channel section, based on the tracer experiments, the transverse dispersion coefficient and transverse diffusion coefficient were calculated. The observed diffusion and dispersion coefficients were laid on the reliable ranges. The relation between dispersion and diffusion was analyzed by comparing the transverse diffusion coefficient, calculated by the diffusion of surface particles, and the transverse dispersion coefficient, determined by the surface flow velocity, which was calculated using the vertical distribution equation. Based on the analysis, the results that the turbulent diffusion coefficient is proportional to the turbulence intensity, and the transverse turbulence intensity is proportional to the transverse flow velocity. Also, the transverse flow velocity is relative to the transverse dispersion coefficient. Thus, the correlation between dispersion and diffusion is confirmed by the experimental results, and a formula that could be converted based on the relation is proposed.

The depth-averaged two-dimensional (2D) model was applied to investigate the spatial variability of harmful algal bloom (HAB) in regulated rivers. This study adopted geographically weighted regression (GWR) to retrieve the spatial distribution of cyanobacterial concentration from satellite imagery data for the calibration of the 2D numerical model in the Nakdong River, South Korea. The GWR model proposed in this study yielded more accurate results compared to a conventional multiple linear regression (MLR) model. The 2D numerical model calibrated with the GWR results well reproduced a high accumulation of cyanobacteria along the shoreline where the cyanobacterial growth was preferred due to longer water residence time (WRT) associated with lower flow velocity. Simulations of HAB reduction scenarios using the calibrated 2D numerical model revealed that the blooming area and maximum concentration of the cyanobacteria in the study reach decreased drastically with an increase in inflow discharge from Gangjeong weir located upstream of the study reach. When the inflow discharge increased up to about 6 times the original level, the blooming area of cyanobacteria was almost eliminated as WRT decreased to about 37% of its original value. Furthermore, the shear velocity enhanced by the fast flow adequately suppressed the localized proliferation of the cyanobacteria along the shallow and slow flow zones in the study reach as it stimulated the lateral dispersion of water quality.

In many eutrophic rivers, the diatom dynamics mainly depends on change of water temperature rather than nutrient concentration because phosphorus and nitrogen loadings are usually sufficient to saturate the diatom growth. In this study, a two-dimensional contaminant transport model was developed in which diatom growth was predicted by incorporating the dynamics of water temperature, dissolved inorganic phosphorus and dissolved inorganic nitrogen as well as the hydrodynamics of river flow. In this study, in order to accurately model the effect of the water temperature on the diatom growth, a new form of the water temperature limitation function, f ( T ) was proposed, using the field data obtained from water quality monitoring stations at the Nakdong River, one of the eutrophic rivers in South Korea. Fitting the water quality data, the skewed Gaussian function was empirically derived for f ( T ) with parameter estimation to calculate the growth rate of the centric diatom, Stephanodiscus hantzschii (S. hantzschii) for which the optimal water temperature was found to be 8.7 °C from winter to spring seasons. The calibration of the two-dimensional contaminant transport model with the proposed f ( T ) showed that the Nash Sutcliffe Efficiency (NSE) of the simulation with the observed data at each monitoring station was 0.63 and 0.85. The validation of the calibrated model also showed that the simulation results were in good agreement with the observed data with NSE larger than 0.74. The results of the parametric sensitivity analysis revealed that the optimal water temperature, T opt most influenced the temporal variation in the diatom abundance. According to results of the scenario simulation using the calibrated and validated model, the thermal effluents introduced from the tributary as well as dispersion coefficients, D L and D T significantly affected the spatial distribution of the diatom concentration in the study site of the Nakdong River.

The non-Fickian particle dispersion model was developed in this study to model two-dimensional pollutant mixing in open channel flows. The proposed model represents shear dispersion using step-by-step arithmetic calculations, which consist of horizontal transport and vertical mixing steps, instead of using Fick's law. In the sequential calculations, the model directly applied the effect of vertical variations of both longitudinal and transverse velocities, whereas the Fickian dispersion model incorporates the effect of shear flow in the dispersion coefficients. Furthermore, in order to avoid the numerical diffusion errors induced by the grid tracking method of previously developed non-Fickian dispersion models, this model adopted the particle tracking technique to trace each particle. The simulation results in the straight channel show that the proposed model reproduced the anomalous mixing, which shows a non-linear increase of variance with time and large skewness coefficient in the initial period. However, in the Taylor period, the variance and skewness of the concentration curves approached the Fickian mixing. The simulation results in the meandering channel reveal that the proposed model adequately reproduced the skewed concentration–time curves of the experimental results whereas the Fickian dispersion model, CTM-2D, generated symmetrical curves. Further comparison between the simulation results and the tracer test results conducted in the Hongcheon River shows that the proposed model properly demonstrated the two-dimensional mixing without adopting Fick's law.

With concerns regarding global climate change increasing, recent studies on adapting to nonstationary climate change recommended a different planning strategy that could spread risks. Uncertainty in global climate change should be considered in any decision-making processes for flood mitigation strategies, especially in areas within a monsoon climate regime. This study applied a novel planning method called real option analysis (ROA) to an important water resources planning practice in Korea. The proposed method can easily be applied to other watersheds that are threatened by flood risk under climate change. ROA offers flexibility for decision-makers to reflect uncertainty at every stage during the project planning period. We successfully implemented ROA using a binomial tree model, including two real options—delay and abandon—to evaluate flood mitigation alternatives for the Yeongsan River Basin in Korea. The priority ranking of the four alternatives between the traditional discount cash flow (DCF) and ROA remained the same; however, two alternatives that were assessed as economically infeasible using DCF, were economically feasible using ROA. The binomial decision trees generated in this study are expected to be informative for decision-makers to conceptualize their adaptive planning procedure.

This study suggests a systematic assessment method that jointly uses the exploratory factor analysis (EFA) and empirical orthogonal function (EOF-patterns) of Principal Component Analysis (PCA) to assess the water quality variation of the monitoring network of Nakdong River, Korea, in which 28 stations measuring 15 water quality parameters are located. The EFA results showed the monitoring stations to be distinguished by two main factors. The representative stations of which the variance was almost explained by the specific factor were selected. We applied PCA to the monitoring data of representative stations, and then analyzed the EOF-patterns that indicate the characteristics of water-quality variation for each factor. With the interpretation of main factors and EOF-patterns causing dominant water quality variations, the monitoring network of Nakdong River could be spatially and seasonally evaluated according to the contribution of each factor.

Existing equations to predict the transverse dispersion coefficient for the two-dimensional model of mixing and transport in natural streams use the channel aspect ratio and roughness factor as well as the sinuosity of the channel as the controlling parameters, and the performance of the equation varies largely according to the range of each parameter that was studied. To this end, this study suggests the criteria to select the proper method to calculate the transverse dispersion coefficient according to the availability and range of each parameter. The strengths and limitations of existing methodologies were compared against observed data acquired from tracer experiments conducted in natural streams. Further, a flow chart was proposed with the criteria to select a suitable method under specific hydraulic and geometric conditions of the stream. The results of the classification flow chart showed that, at the first step, in the cases where secondary currents data were available for natural streams, the theoretical equation by Baek and Seo (2011) could be used to estimate the transverse dispersion coefficient. At the second step, in the case of large value of P , i.e., P > 0.04, the equation by Baek and Seo (2013) was suitable to estimate the transverse dispersion coefficient, while in the case of a small value of P , equations by Yotukura and Sayer (1976) and Baek and Seo (2013), could be used with little differences. At the third step, for the narrow streams with W / h W / h > 50, the results of Jeon et al. (2007) showed much better agreement with the observed values than the others.

The feasibility of using the velocity-based method for calculating the transverse mixing coefficient of the two-dimensional contaminant transport model was studied to substitute the concent...

While extant studies on brokerage address its structural competitiveness and network performance, few studies address the fundamental question of whether all the brokerage types have identical and positive effects on network development. Extending the Gould and Fernandez&rsquo, s brokerage topology, the study measures the contribution of the brokerage roles over to the network evolution. For the purpose, the Chinese technology transfer flow networks were utilized to reveal the brokerage patterns of three anchor regions&mdash, Beijing, Shanghai, and Shenzhen. The analysis outlines that Beijing and Shenzhen, as nation-wide brokerage hubs, transmit the technology across the whole regions, while Shanghai seems to be a more balanced broker region connecting the neighbour regions with the others. The longitudinal simulation analysis demonstrates that a liaison-type brokerage function, connecting the heterogeneous knowledge sources, contributes to the growth of the regional network.

Solute transport models are widely employed to the predict spatio-temporal fate of contaminants in rivers. However, most previous studies have assumed that contaminants spilled are unreactive; they disregard the inherent reactivity of contaminants in water. The Reactive Solute Transport Model (RSTM) (Gooseff et al., 2005) includes lumped decay terms to reflect the decayability of the chemicals, but its applicability was still poor due to dependence on an optimization method. The purpose of this study is to perform reactive transport modelling considering chemicals’ reactivities for sorption, volatilization, and biodegradation. To this end, we manipulated the governing equations of the RSTM and suggested theoretical and empirical methods of estimating the key parameters of the reaction terms. The results showed, for example, that benzene lost 57.7% of its primary mass after being transported 4.54 km downstream due to its high volatility. Also, the arrival time of toluene was delayed by 10.4% due to adsorption.

To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.

In this study, the strengths and weaknesses of existing methods for determining the dispersion coefficient in the two-dimensional river mixing model were assessed based on hydraulic and tracer data sets acquired from experiments conducted on either laboratory channels or natural rivers. From the results of this study, it can be concluded that, when the longitudinal dispersion coefficient as well as the transverse dispersion coefficients must be determined in the transient concentration situation, the two-dimensional routing procedures, 2D RP and 2D STRP, can be employed to calculate dispersion coefficients among the observation methods. For the steady concentration situation, the STRP can be applied to calculate the transverse dispersion coefficient. When the tracer data are not available, either theoretical or empirical equations by the estimation method can be used to calculate the dispersion coefficient using the geometric and hydraulic data sets. Application of the theoretical and empirical equations to the laboratory channel showed that equations by Baek and Seo [[3], 2011] predicted reasonable values while equations by Fischer [23] and Boxwall and Guymer (2003) overestimated by factors of ten to one hundred. Among existing empirical equations, those by Jeon et al. [28] and Baek and Seo [6] gave the agreeable values of the transverse dispersion coefficient for most cases of natural rivers. Further, the theoretical equation by Baek and Seo [5] has the potential to be broadly applied to both laboratory and natural channels.

This paper identifies the structural properties of a technological knowledge network and the role of major organizations in the network, and analyzes actual contents of technological knowledge flows in terms of organization-technology linkage, by targeting the field of organic solar cells (OSC). Network analysis and matrix analysis methods are used to achieve these purposes. The results show the small-world effect exists in the technological knowledge network of OSCs, and the organizations with high betweenness centrality lead the technological knowledge flows. We also find technological knowledge in classes 136 and 313 flows relatively actively in key organizations׳ network of OSCs. This means that technological knowledge regarding photoelectric batteries and electric lamp and discharge devices is mainly circulated between key organizations and indicates that the electronics or display sector will become a major consumer for early commercialization of OSCs. The target of analysis in this study is a patent citation network in the field of OSCs. Since we did not analyze all scientific publications, we cannot conclude that the results represent the entire flow of technological knowledge in that field. However, given that little attention has been paid to empirical studies of technological knowledge flows at the organizational level, this study makes an academic contribution by directly analyzing technological knowledge flows between organizations and presenting new taxonomic method based on centralities. The analytical process and methodology of this study, which include analysis of the structural properties of technological knowledge networks, matrix analysis and taxonomical grouping, and analysis of technological knowledge flows between key organizations, will be usefully applied to the analysis of technological knowledge networks in other fields.

The analysis of two-dimensional (2D) pollutant mixing is essential to implement the effective water quality management in natural streams. In this study, numerical simulations were performed to predict the 2D pollutant behavior, using the FEM-based river analysis software, RAMS. Pollution propagation was estimated by the 2D advection-dispersion model, CTM-2D. Velocity field was computed by the 2D shallow water model, HDM-2D. Dispersion coefficients are key input parameters in CTM-2D. To calibrate these parameters, tracer tests using Rhodamine-WT dye solution were conducted in the Sum River, Korea. The calibrated longitudinal dispersion coefficients from field studies showed values about 6 times larger than the theoretical from the Elder's equation (Fischer, 1967).