Development and application of an advanced algorithm for environmental risk assessment and safety management of pesticide residues in agricultural soils: Monitoring of currently used pesticide in upland soils.

This study conducted the development of an advanced risk assessment algorithm system and safety management strategies using pesticide residue monitoring data from soils. To understand the status of pesticide residues in agricultural soils, monitoring was performed on 116 types of pesticides currently in use across 300 soil sites. The analysis of the monitoring results, alongside the physicochemical properties of the pesticides, led to the selection of soil half-life as a critical component in residue analysis. The use of Toxicity Exposure Ratio (TER) and Risk Quotient (RQ) for environmental risk assessment, based on monitoring data, presents limitations due to its single-component, conservative approach, which does not align with actual field conditions. Therefore, there is a necessity for a risk assessment process applicable in real-world scenarios. In this research, an efficient and accurate risk assessment algorithm system, along with a safety management model, was developed. Using the physicochemical properties of pesticides (such as soil half-life), monitoring results, and toxicity data, cluster analysis and Principal Component Analysis (PCA) validation identified four pesticides: boscalid, difenoconazole, fluquinconazole, and tebuconazole. The k-mean cluster analysis selected three priority management sites where the contribution of these four pesticides to the RQ was between 94-99 %, showing similar results to the RQ calculated for all pesticides. Predictions made with the developed model for the time required for soil half-life based RQ to drop below 1 at these priority sites showed only a 1-9 day difference between the four pesticides of concern and all pesticides, indicating comparable outcomes. The scenario of replacing high-risk pesticides with those of lower risk demonstrated that the RQ could be consistently maintained at about 50 % level. The results of this study suggest that through monitoring, evaluation, and management, effective and accurate environmental safety management of pesticides in soil can be achieved.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Optimization of the Design Configuration and Operation Strategy of Single-Pass Seawater Reverse Osmosis.

The numerical study was conducted to compare process performance depending on the pump type and process configuration. The daily monitoring data of seawater temperature and salinity offshore from Daesan, Republic of Korea was used to reflect the site-specific seawater conditions. An algorithm for reverse osmosis in constant permeate mode was developed to simulate the process in time-variant conditions. Two types of pumps with different maximum leachable efficiencies were employed to organize pump-train configuration: separated feed lines and common pressure center design. The results showed pump type and design configuration did not have a significant effect on process performance. The annual means of specific energy consumption (SEC) for every design configuration were under 2 kWh/m3, except for a worst-case. The worst-case was decided when the pump was operated out of the best operation range. The two operation strategies were evaluated to determine the optimal configuration. The permeate flow rate was reduced to 80% of the designed permeate flow rate with two approaches: feed flow rate reduction in every train and pump shutdown in a specific train. The operation mode with feed flow rate reduction was more efficient than the other. The operating pressure reduction led to a decrease in SEC.

Liquid Phase Plasma Synthesis of Iron Oxide Nanoparticles on Nitrogen-Doped Activated Carbon Resulting in Nanocomposite for Supercapacitor Applications.

Iron oxide nanoparticles supported on nitrogen-doped activated carbon powder were synthesized using an innovative plasma-in-liquid method, called the liquid phase plasma (LPP) method. Nitrogen-doped carbon (NC) was prepared by a primary LPP reaction using an ammonium chloride reactant solution, and an iron oxide/NC composite (IONCC) was prepared by a secondary LPP reaction using an iron chloride reactant solution. The nitrogen component at 3.77 at. % formed uniformly over the activated carbon (AC) surface after a 1 h LPP reaction. Iron oxide nanoparticles, 40~100 nm in size, were impregnated homogeneously over the NC surface after the LPP reaction, and were identified as Fe3O4 by X-ray photoelectron spectroscopy and X-ray diffraction. NC and IONCCs exhibited pseudo-capacitive characteristics, and their specific capacitance and cycling stability were superior to those of bare AC. The nitrogen content on the NC surface increased the compatibility and charge transfer rate, and the composites containing iron oxide exhibited a lower equivalent series resistance.

Assembling a supercapacitor electrode with dual metal oxides and activated carbon using a liquid phase plasma.

Developing supercapacitor electrodes at an affordable cost while improving their energy and/or power density values is still a challenging task. This study introduced a recipe which assembled a novel electrode composite using a liquid phase plasma that was applied to a reactant solution containing an activated carbon (AC) powder with dual metal precursors of iron and manganese. A comparison was made between the composites doped with single and dual metal components as well as among those synthesized under different precursor concentrations and plasma durations. The results showed that increasing the precursor concentration and plasma duration raised the content of both metal oxides in the composites, whereas the deposition conditions were more favorable to iron oxide than manganese oxide, due to its higher standard potential. The composite treated with the longest plasma duration and highest manganese concentration was superior to the others in terms of cyclic stability and equivalent series resistance. In addition, the new composite selected out of them showed better electrochemical performance than the raw AC material only and even two types of single metal-based composites, owing largely to the synergistic effect of the two metal oxides. Therefore, the proposed methodology can be used to modify existing and future composite electrodes to improve their performance with relatively cheap host and guest materials.

Pesticide sorption and leaching potential on three Hawaiian soils.

On the Hawaiian Islands, groundwater is the principal source of potable water and contamination of this key resource by pesticides is of great concern. To evaluate the leaching potential of four weak acid herbicides [aminocyclopyrachlor, picloram, metsulfuron-methyl, biologically active diketonitrile degradate of isoxaflutole (DKN)] and two neutral non-ionizable herbicides [oxyfluorfen, alachlor], their sorption coefficients were determined on three prevalent soils from the island of Oahu. Metsulfuron-methyl, aminocylcopyrachlor, picloram, and DKN were relatively low sorbing herbicides (K(oc) = 3-53 mL g(-1)), alachlor was intermediate (K(oc) = 120-150 mL g(-1)), and oxyfluorfen sorbed very strongly to the three soils (K(oc) > 12,000 mL g(-1)). Following determination of K(oc) values, the groundwater ubiquity score (GUS) indices for these compounds were calculated to predicted their behavior with the Comprehensive Leaching Risk Assessment System (CLEARS; Tier-1 methodology for Hawaii). Metsulfuron-methyl, aminocyclopyrachlor, picloram, and DKN would be categorized as likely leachers in all three Hawaiian soils, indicating a high risk of groundwater contamination across the island of Oahu. In contrast, oxyfluorfen, regardless of the degradation rate, would possess a low and acceptable leaching risk due to its high sorption on all three soils. The leaching potential of alachlor was more difficult to classify, with a GUS value between 1.8 and 2.8. In addition, four different biochar amendments to these soils did not significantly alter their sorption capacities for aminocyclopyrachlor, indicating a relatively low impact of black carbon additions from geologic volcanic inputs of black carbon. Due to the fact that pesticide environmental risks are chiefly dependent on local soil characteristics, this work has demonstrated that once soil specific sorption parameters are known one can assess the potential pesticide leaching risks.

Applying a statewide geospatial leaching tool for assessing soil vulnerability ratings for agrochemicals across the contiguous United States.

A large-scale leaching assessment tool not only illustrates soil (or groundwater) vulnerability in unmonitored areas, but also can identify areas of potential concern for agrochemical contamination. This study describes the methodology of how the statewide leaching tool in Hawaii modified recently for use with pesticides and volatile organic compounds can be extended to the national assessment of soil vulnerability ratings. For this study, the tool was updated by extending the soil and recharge maps to cover the lower 48 states in the United States (US). In addition, digital maps of annual pesticide use (at a national scale) as well as detailed soil properties and monthly recharge rates (at high spatial and temporal resolutions) were used to examine variations in the leaching (loads) of pesticides for the upper soil horizons. Results showed that the extended tool successfully delineated areas of high to low vulnerability to selected pesticides. The leaching potential was high for picloram, medium for simazine, and low to negligible for 2,4-D and glyphosate. The mass loadings of picloram moving below 0.5 m depth increased greatly in northwestern and central US that recorded its extensive use in agricultural crops. However, in addition to the amount of pesticide used, annual leaching load of atrazine was also affected by other factors that determined the intrinsic aquifer vulnerability such as soil and recharge properties. Spatial and temporal resolutions of digital maps had a great effect on the leaching potential of pesticides, requiring a trade-off between data availability and accuracy. Potential applications of this tool include the rapid, large-scale vulnerability assessments for emerging contaminants which are hard to quantify directly through vadose zone models due to lack of full environmental data.

Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.

Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined searches for the best locations of BMPs at the sub-basin level.

Advancing assessment and design of stormwater monitoring programs using a self-organizing map: characterization of trace metal concentration profiles in stormwater runoff.

Stormwater runoff poses a great challenge to the scientific assessment of the effects of diffuse pollution sources on receiving waters. In this study, a self-organizing map (SOM), a research tool for analyzing specific patterns in a large array of data, was applied to the monitoring data obtained from a stormwater monitoring survey to acquire new insights into stream water quality profiles under different rainfall conditions. The components of the input data vectors used by the SOM included concentrations of 10 metal elements, river discharge, and rainfall amount which were collected at the inlet and endpoint of an urban segment of the Yeongsan River, Korea. From the study, it was found that the SOM displayed significant variability in trace metal concentrations for different monitoring sites and rainfall events, with a greater impact of stormwater runoff on stream water quality at the upstream site than at the downstream site, except under low rainfall conditions (≤ 4 mm). In addition, the SOM clearly determined the water quality characteristics for "non-storm" and "storm" data, where the parameters nickel and arsenic and the parameters chromium, cadmium, and lead played an important role in reflecting the spatial and temporal water quality, respectively. When the SOM was used to examine the efficacy of stormwater quality monitoring programs, between 34 and 64% of the sample size in the current data set was shown to be sufficient for estimating the stormwater pollutant loads. The observed errors were small, generally being below 10, 6, and 20% for load estimation, map resolution, and clustering accuracy, respectively. Thus, the method recommended may be used to minimize monitoring costs if both the efficiency and accuracy are further determined by examining a large existing data set.

Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin.

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.

Factors dominating stratification cycle and seasonal water quality variation in a Korean estuarine reservoir.

A comprehensive monitoring program was conducted during 2005-2007 to investigate seasonal variations of hydrologic stability and water quality in the Yeongsan Reservoir (YSR), located at the downstream end of the Yeongsan River, Korea. A principal component analysis (PCA) was performed to identify factors dominating the seasonal water quality variation from a large suite of measured data--11 physico-chemical parameters from 48 sampling sites. The results showed that three principal components explained approximately 62% of spatio-seasonal water quality variation, which are related to stratifications, pollutant loadings and resultant eutrophication, and the advective mixing process during the episodic rainfall-runoff events. A comparison was then made between YSR and an upstream freshwater reservoir (Damyang Reservoir, DYR) in the same river basin during an autumn season. It was found that the saline stratification and pollutant input from the upstream contributed to greater concentrations of nutrients and organic matter in YSR compared to DYR. In YSR, saline stratification in combination with thermal stratification was a dominant cause of the longer period (for two consecutive seasons) of hypoxic conditions at the reservoir bottom. The results presented here will help better understand the season- and geography-dependent characteristics of reservoir water quality in Asian Monsoon climate regions such as Korea.

Factors affecting metal exchange between sediment and water in an estuarine reservoir: a spatial and seasonal observation.

Water quality response in a reservoir has often been assessed using relatively restricted datasets that cannot provide sufficient information, thereby giving rise to a dramatic over- or underestimate of actual figures. In this paper we discuss how the levels of metallic elements between the sediment and overlying water in an estuarine reservoir can be influenced by aquatic parameters in response to spatial and seasonal conditions. To better elucidate the interfacial exchange between sediment and water, statistical analyses are employed to intensive data sets collected from the Yeongsan Reservoir (YSR), Korea, which has undergone widespread deterioration in water quality due to the continuous growth of anthropogenic sources. During three seasonal sampling campaigns, we found that oxygen deficiency at the bottom water layer promotes Fe and Ni accumulation in sediment, likely due to the formation of sulfide and oxide complexes under anoxic and suboxic environments, respectively. In addition, salinity levels as high as 11 per thousand in the bottom water layer during autumn substantially increase the release of Mn, restricting the use of YSR as a primary source of agricultural irrigation water. Although most dissolved metals are at acceptable levels for sustaining aquatic life, it is recommended that for long-term planning the elevated Fe and Mn levels in sediment should be controlled with oxygen deficiency during dry weather to ensure a sustainable water supply or, at a minimum, better coordinated operation of YSR.

Evaluation of pollutants removal efficiency to achieve successful urban river restoration.

Greater efforts to provide alternative scenarios are key to successful urban stream restoration planning. In this study, we discuss two different aspects of water quality management schemes, biodegradation and human health, which are incorporated in the restoration project of original, pristine condition of urban stream at the Gwangju (GJ) Stream, Korea. For this study, monthly monitoring of biochemical oxygen demand (BOD(5)) and fecal indicator bacteria (FIB) data were obtained from 2003 to 2008 and for 2008, respectively, and these were evaluated to explore pollutant magnitude and variation with respect to space and time window. Ideal scenarios to reduce target pollutants were determined based on their seasonal characteristics and correlations between the concentrations at a water intake and discharge point, where we suggested an increase of environmental flow and wetland as pollutants reduction drawing for BOD(5) and FIB, respectively. The scenarios were separately examined by the Qual2E model and hypothetically (but planned) constructed wetland, respectively. The results revealed that while controlling of the water quality at the intake point guaranteed the lower pollution level of BOD(5) in the GJ Stream, a wetland constructed at the discharge point may be a promising strategy to mitigate mass loads of FIB. Overall, this study suggests that a combination of the two can be plausible scenarios not only to support sustainable urban water resources management, but to enhance a quality of urban stream restoration assignment.

An analytical model for non-conservative pollutants mixing in the surf zone.

Accurate simulation of the surf zone is a prerequisite to improve beach management as well as to understand the fundamentals of fate and transport of contaminants. In the present study, a diagnostic model modified from a classic solute model is provided to illuminate non-conservative pollutants behavior in the surf zone. To readily understand controlling processes in the surf zone, a new dimensionless quantity is employed with index of kappa number (K, a ratio of inactivation rate to transport rate of microbial pollutant in the surf zone), which was then evaluated under different environmental frames during a week simulation period. The sensitivity analysis showed that hydrodynamics and concentration gradients in the surf zone mostly depend on n (number of rip currents), indicating that n should be carefully adjusted in the model. The simulation results reveal, furthermore, that large deviation typically occurs in the daytime, signifying inactivation of fecal indicator bacteria is the main process to control surf zone water quality during the day. Overall, the analytical model shows a good agreement between predicted and synthetic data (R(2) = 0.51 and 0.67 for FC and ENT, respectively) for the simulated period, amplifying its potential use in the surf zone modelling. It is recommended that when the dimensionless index is much larger than 0.5, the present modified model can predict better than the conventional model, but if index is smaller than 0.5, the conventional model is more efficient with respect to time and cost.

Statistical assessment for spatio-temporal water quality in Angkor, Cambodia.

Comprehensive water quality monitoring was conducted to assess the water quality conditions and to determine the impact of urban infrastructure on ambient water quality in Angkor, Cambodia. During this study, surface water, groundwater, and sediment samples were collected for two distinctive seasons in 2006-2007 at 58 monitoring sites along and near the Siem Reap River, in Tole Sap Lake (TSL), and West Baray, the primary water resources in this region. To assess the seasonal and spatial variability of 27 water quality parameters, multivariate analysis of variance, hierarchical cluster analysis, and the Kruskal-Wallis test were conducted using the obtained data. Differences and relationships between the surface water and groundwater were also investigated using t-test and correlation analysis, respectively. The results of these tests showed that the bacterial indicators need special attention as the urban infrastructure of the downtown area caused increased levels of these bacterial indicators in both surface water and groundwater. However, for most parameters, though surface water showed strong seasonal variations, groundwater presented relatively stable conditions between seasons (p > 0.05) with site-specific geochemical conditions. Sediment quality illustrated that pollution levels of 10 trace metals were the highest in TSL because of its unique characteristic (river with backward flow), but did not reflect any potential enrichment from urban development. Overall, the results reveal that while the urban infrastructure in this region has not significantly affected most of the water quality parameters, bacteria and coliphages are still a main concern due to their contributions in widespread waterborne diseases. Thus, careful mitigation plans for reducing each pollutant source are needed in the Angkor area.

Interpretation of seasonal water quality variation in the Yeongsan Reservoir, Korea using multivariate statistical analyses.

The Yeongsan (YS) Reservoir is an estuarine reservoir which provides surrounding areas with public goods, such as water supply for agricultural and industrial areas and flood control. Beneficial uses of the YS Reservoir, however, are recently threatened by enriched non-point and point source inputs. A series of multivariate statistical approaches including principal component analysis (PCA) were applied to extract significant characteristics contained in a large suite of water quality data (18 variables monthly recorded for 5 years); thereby to provide the important phenomenal information for establishing effective water resource management plans for the YS Reservoir. The PCA results identified the most important five principal components (PCs), explaining 71% of total variance of the original data set. The five PCs were interpreted as hydro-meteorological effect, nitrogen loading, phosphorus loading, primary production of phytoplankton, and fecal indicator bacteria (FIB) loading. Furthermore, hydro-meteorological effect and nitrogen loading could be characterized by a yearly periodicity whereas FIB loading showed an increasing trend with respect to time. The study results presented here might be useful to establish preliminary strategies for abating water quality degradation in the YS Reservoir.

Effect of environmental flow management on river water quality: a case study at Yeongsan River, Korea.

This paper describes a management scheme to control river water quality using additional water discharges from upstream dams, which results in an increase environmental flow (EF) followed by an enhancement of water quality in a target river. To suggest a creditable management plan among a suite of ideal scenarios, the monthly averaged water quality monitoring data from 2001 to 2006 at the Yeongsan (YS) River, Korea were investigated with respect to seasonal variation and spatial distribution. From the analysis, it was found that while biochemical oxygen demand (BOD(5)) level in the YS River was extremely high during the dry/drought season (April, May, and June; AMJ), the level was subsequently decreased during the monsoon season (July, August, and September; JAS) due mainly to the dilution effect of rainfall. To improve the water quality in AMJ, we here suggested a scenario of increasing EF using surplus water discharges from upstream dams, which was examined by one dimensional riverine water quality model, QUAL2E model. Simulation result showed that additional discharge from the upstream dams could lead, on average, to a 36% of water quality improvement in mainstream with respect to BOD(5). Model coefficients were validated by comparing the six year monitoring data to minimize a sum of squares error, and showed a good agreement with the observed data. Overall, the methodology developed in this paper appears to be quite clear and straightforward, and thus, can be applied to a wide range of the flow managements or water quality controls in a stream with artificial structures.

Characteristics of wet and dry weather heavy metal discharges in the Yeongsan Watershed, Korea.

A comprehensive water quality monitoring program was conducted in the Yeongsan (YS) River, Korea from 2005 to present to investigate wet and dry weather pollutant discharge in an attempt to establish point and non-point pollution management strategies. As part of this monitoring program, 11 heavy metal species were measured during dry and wet weather conditions in the YS River, where Gwangju City (GJ), a subcatchment of the YS River, was further monitored to clarify the responsibility of different metal species discharged into the mainstream. Monthly grab water samples showed that greater amounts of metals along the YS River were discharged during the wet summer months due largely to storm runoff. In addition, further monitoring results revealed that GJ, a highly urbanized area, was a significant contributor of the heavy metals being discharged into the YS River during both wet and dry weather. The most abundant metal species discharged from GJ were manganese, aluminum and iron with different contributions of wet and dry weather flows to the total discharge load. Wet weather flow was a significant contributor to the annual dissolved metal loads, accounting for 44-93% of the annual load depending on the metal species, with the exception of chromium and cadmium (9% and 27%, respectively). Mostly, metal loads during wet weather were shown to be proportional to the rainfall depth and antecedent dry period. A substantial fraction of metals were also associated with solids, suggesting that sedimentation might be an appropriate management practice for reducing the metal load generated in GJ. Overall, although dissolved metal concentrations in YS River were at an acceptable level for aquatic community protection, continual metal discharge throughout the year was considered to be a potential problem in the long-term due to gradual water quality degradation as well as continuous metal accumulation in the system.

Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan Reservoir, Korea.

Statistical regression models involve linear equations, which often lead to significant prediction errors due to poor statistical stability and accuracy. This concern arises from multicollinearity in the models, which may drastically affect model performance in terms of a trade-off scenario for effective water resource management logistics. In this paper, we propose a new methodology for improving the statistical stability and accuracy of regression models, and then show how to cope with pitfalls in the models and determine optimal parameters with a decreased number of predictive variables. Here, a comparison of the predictive performance was made using four types of multiple linear regression (MLR) and principal component regression (PCR) models in the prediction of chlorophyll-a (chl-a) concentration in the Yeongsan (YS) Reservoir, Korea, an estuarine reservoir that historically suffers from high levels of nutrient input. During a 3-year water quality monitoring period, results showed that PCRs could be a compact solution for improving the accuracy of the models, as in each case MLR could not accurately produce reliable predictions due to a persistent collinearity problem. Furthermore, based on R(2) (goodness of fit) and F-overall number (confidence of regression), and the number of explanatory variables (R-F-N) curve, it was revealed that PCR-F(7) was the best model among the four regression models in predicting chl-a, having the fewest explanatory variables (seven) and the lowest uncertainty. Seven PCs were identified as significant variables, related to eight water quality parameters: pH, 5-day biochemical oxygen demand, total coliform, fecal indicator bacteria, chemical oxygen demand, ammonia-nitrogen, total nitrogen, and dissolved oxygen. Overall, the results not only demonstrated that the models employed successfully simulated chl-a in a reservoir in both the test and validation periods, but also suggested that the optimal parameters should cautiously be considered in the design of regression models.

Mass load-based pollution management of the Han River and its tributaries, Korea.

Spatio-temporal variations of biochemical oxygen demand (BOD) and total coliform (TC) in the Han River, Korea, were investigated in terms of concentration-based and mass loading-based approaches. Considering the river water quality criteria regulated by the Ministry of Environment in Korea, the tributaries linked to the mainstream of the Han River were found to be highly contaminated with respect to both BOD and TC and, in fact, most of the tributaries exceeded the maximum water quality criteria. To evaluate the pollution impact of tributaries on the mainstream, the monthly water quality monitoring data for six years (from 1995 to 2000) were collected from the Han River basin, and statistically analyzed using Pearson's correlation coefficient. The results revealed that mass loading-based approach was superior to the concentration-based approach for effective Han River watershed management. Overall results supported that the mass loading-based approach associated with total maximum daily loads (TMDL) management would be a useful and suitable protocol in watershed management for improving the water quality of the Han River and protecting public health. Therefore, this study supporting TMDL management can be applicable to a wide array of contaminants and watershed settings in Korea.