Projection pursuit cluster model and its application in water quality assessment

One of the difficulties frequently encountered in water quality assessment is that there are many factors and they cannot be assessed according to one factor, all the effect factors associated with water quality must be used. In order to overcome this issues the projection pursuit principle is introduced into water quality assessment, and projection pursuit cluster(PPC) model is developed in this study. The PPC model makes the transition from high dimension to one-dimension. In other words, based on the PPC model, multifactor problem can be converted to one factor problem. The application of PPC model can be divided into four parts: (1) to estimate projection index function Q(); (2) to find the right projection direction ; (3) to calculate projection characteristic value of the i th sample z-i, and (4) to draw comprehensive analysis on the basis of z-i. On the other hand, the empirical formula of cutoff radius R is developed, which is benefit for the model to be used in practice. Finally, a case study of water quality assessment is proposed in this paper. The results showed that the PPC model is reasonable, and it is more objective and less subjective in water quality assessment. It is a new method for multivariate problem comprehensive analysis.

Development and application of a two-dimensional water quality model for the Daqinghe River Mouth of the Dianchi Lake

Water quality models are important tools to support the optimization of aquatic ecosystem rehabilitation programs and assess their efficiency. Basing on the flow conditions of the Daqinghe River Mouth of the Dianchi Lake, China, a two-dimensional water quality model was developed in the research. The hydrodynamics module was numerically solved by the alternating direction iteration （ADI） method. The parameters of the water quality module were obtained through the in situ experiments and the laboratory analyses that were conducted from 2006 to 2007. The model was calibrated and verified by the observation data in 2007. Among the four modelled key variables, i.e., water level, COD （in CODcr）, NH4＋-N and PO43-P the minimum value of the coefficient of determination （COD） was 0.69, indicating the model performed reasonably well. The developed model was then applied to simulate the water quality changes at a downstream cross-section assuming that the designed restoration programs were implemented. According to the simulated results, the restoration programs could cut down the loads of COD and PO43-P about 15%. Such a load reduction, unfortunately, would have very little effect on the NH4^＋-N removal. Moreover, the water quality at the outlet cross-section would be still in class V （3838-02）, indicating more measures should be taken to further reduce the loads. The study demonstrated the capability of water quality models to support aquatic ecosystem restorations.

Three-Dimensional Tidal Model and Its Application to Numerical Simulation of Water Quality in Coastal Waters

The turbulence mechanism plays an important part in the mixing process and momentum transfer of turbulence. A three-dimensional Prandtl mixing length tidal model has been developed to simulate tidal flows and water quality. The eddy viscosities and diffusivities are computed from the Prandtl mixing length model. In order to model the water quality of an estuary or coastal area many interdependent processes need to be simulated. These may be conveniently separated into three main groups: transport and mixing processes, biochemical interaction of water quality variables and the utilization and re-cycling of nutrients by living matter. The model simulates full oxygen and nutrient balance, primary productivity and the transport, reaction mechanism and fate of pollutants over tidal time-scales. The model is applied to numerical simulation of tidal flows and water quality in Dalian Bay. The model has been calibrated against a limited data set of historical water quality observations and in general demonstrates excellent agreement with all available data.

Assessment of Point and Nonpoint Sources Pollution in Songhua River Basin,Northeast China by Using Revised Water Quality Model

Individual participation of pollutants in the pollution load should be estimated even if roughly for the appropriate environmental management of a river basin.It is difficult to identify the sources and to quantify the load, especially in modeling nonpoint source.In this study a revised model was established by integrating point and nonpoint sources into one-dimensional Streeter-Phelps(S-P) model on the basis of real-time hydrologic data and surface water quality monitoring data in the Jilin Reach of the Songhua River Basin.Chemical oxygen demand(COD) and ammonia nitrogen(NH 3-N) loads were estimated.Results showed that COD loads of point source and nonpoint source were 134 958 t/yr and 86 209 t/yr, accounting for 61.02% and 38.98% of total loads, respectively.NH 3-N loads of point source and nonpoint source were 16 739 t/yr and 14 272 t/yr, accounting for 53.98% and 46.02%, respectively.Point source pollution was stronger than nonpoint source pollution in the study area at present.The water quality of upstream was better than that of downstream of the rivers and cities.It is indispensable to treat industrial wastewater and municipal sewage out of point sources, to adopt the best management practices to control diffuse pollutants from agricultural land and urban surface runoff in improving water quality of the Songhua River Basin.The revised S-P model can be successfully used to identify pollution source and quantify point source and nonpoint source loads by calibrating and validating.

Artificial neural network modeling of water quality of the Yangtze River system:a case study in reaches crossing the city of Chongqing

An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.

Water Quality Model Establishment for Middle and Lower Reaches of Hanshui River,China

With the development of industry and agriculture,nitrogen,phosphorus and other nutrients in the Hanshui River greatly increase and eutrophication has become an important threat to the water quality of the Hanshui River,especially in the middle and lower reaches.The primary objective of this study was to establish the water quality model for the middle and lower reaches of the Hanshui River based on the model of MIKE 11.The main pollutants migration and transformation process could be simulated using the water quality model.The rainfall-runoff model,hy-drodynamic model and water quality model were established using MIKE 11.The pollutants,such as chemical oxygen demand(COD),biochemical oxygen demand(BOD),ammonia nitrogen,nitrate nitrogen,phosphorus,dissolved oxy-gen(DO),were simulated and predicted using the above three models.A set of methods computing non-point source pollution load of the Hanshui River Basin was proposed in this study.The simulated and observed values of COD,BOD5,ammonia,nitrate,DO,and total phosphorus were compared after the parameter calibration of the water quality model.The simulated and observed results match better,thus the model can be used to predict water quality in the fu-ture for the Hanshui River.The pollution trend could be predicted using the water quality model according pollution load generation.It is helpful for government to take effective measures to prevent the water bloom and protect water quality in the river.

An attribute recognition model based on entropy weight for evaluating the quality of groundwater sources

In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.

Application of a water quality model for determining instream aeration station location and operational rules:A case study

Instream aeration has been used as a supplement to secondary treatment or a substitute for tertiary treatment for meeting dissolved oxygen （DO） standards in rivers. Many studies have used water quality models to determine the number, location, and capacity of instream aeration stations （IASs） needed to meet DO standards in combination with other pollution control measures. DO concentrations have been improved in the North Shore Channel and North Branch Chicago River by the Devon Avenue IAS for more than 35 years. A study was initiated to determine whether it was better to rehabilitate or relocate this station and to determine appropriate operational guidance for the IAS at the selected location. A water quality model capable of simulating DO concentrations during unsteady flow was used to evaluate the proper location for an IAS and operational guidance for this IAS. Three test years, a dry year, a wet year, and an extreme year, were considered in the evaluation. The study found that the Devon Avenue IAS should be rehabilitated as this location performed as well as or better than any of 10 alternative locations. According to the new operational guidance for this IAS, the amount of time with blowers operating could be substantially reduced compared to traditional operations while at the same time the attainment of the DO standards could be increased. This study shows that a carefully designed modeling study is key to effective selection, location, and operation of IASs such that attainment of DO standards can be maximized while operation hours of blowers can be minimized.

Water Quality Evaluation in Tidal River Reaches of Liaohe River Estuary, China Using a Revised QUAL2K Model

Rivers in the Liaohe River Estuary area have been seriously polluted by discharges of wastewater containing petroleum pol- lutants and nutrients. In this paper, The Enhanced Stream Water Quality Model （QUAL2K） and its revised model as well as One-dimensional Tide Mean Model （1D model） were applied to predict and assess the water quality of the tidal fiver reach of the Liaohe River Estuary. Dissolved oxygen （DO）, biochemical oxygen demand （BODs）, ammonia nitrogen （NH3-N） and total phosphorus （TP） were chosen as water quality indices in the two model simulations. The modelled results show that the major reasons for degraded rivers remain petroleum and non-point source pollution. Tidal water also has a critical effect on the variation of water quality. The sensitivity analysis identifies that flow rate, point load and diffuse load are the most sensitive parameters for the four water quality indices in the revised QUAL2K simulation. Uncertainty analysis based on a Monte Carlo simulation gives the probability distribution of the four wa- ter quality indices at two locations （6.50 km and 44.84 km from the river mouth）. The statistical outcomes indicate that the observed data fall within the 90% confidence intervals at all sites measured, and show that the revised QUAL2K gives better results in simulating the water quality of a tidal fiver.

A Three-Dimensional Water Quality Model and Its Application to Jiaozhou Bay, China

A three-dimensional coupled physical and water quality model was developed and applied to the Jiaozhou Bay to study water quality involving nutrients, biochemical oxygen demand, dissolved oxygen, and phytoplankton that are closely related to eutrophication process. The physical model is a modified ECOM-si version with inclusion of flooding/draining processes over the intertidal zone. The water quality model is based on WASP5 which quantifies processes governing internal nutrients cycling, dissolved oxygen balance and phytoplankton growth. The model was used to simulate the spatial distribution and the temporal variation of water quality in the Jiaozhou Bay for the period of May 2005 to May 2006. In addition, the effect of reduction of riverine nutrients load was simulated and evaluated. The simulated results show that under the influence of nutrients discharged from river, the concentrations of nutrients and phytoplankton were higher in the northwest and northeast of the bay, and decreased from the inner bay to the outer. Affected by strong tidal mixing, the concentrations of all state variables were vertically homogeneous except in the deeper regions where a small gradient was found. Obvious seasonal variation of phytoplankton biomass was found, which exhibited two peaks in March and July, respectively. The variation of riverine waste loads had remarkable impact on nutrients concentration in coastal areas, but slightly altered the distribution in the center of the bay.

Semi-supervised Support Vector Regression Model for Remote Sensing Water Quality Retrieving

This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was used for retrieving water quality variables from SPOT 5 remote sensing data. The model consisted of two support vector regressors (SVRs). Nonlinear relationship between water quality variables and SPOT 5 spectrum was described by the two SVRs, and semi-supervised co-training algorithm for the SVRs was es-tablished. The model was used for retrieving concentrations of four representative pollution indicators―permangan- ate index (CODmn), ammonia nitrogen (NH3-N), chemical oxygen demand (COD) and dissolved oxygen (DO) of the Weihe River in Shaanxi Province, China. The spatial distribution map for those variables over a part of the Weihe River was also produced. SVR can be used to implement any nonlinear mapping readily, and semi-supervis- ed learning can make use of both labeled and unlabeled samples. By integrating the two SVRs and using semi-supervised learning, we provide an operational method when paired samples are limited. The results show that it is much better than the multiple statistical regression method, and can provide the whole water pollution condi-tions for management fast and can be extended to hyperspectral remote sensing applications.

A study of groundwater irrigation water quality in south-central Bangladesh: a geo-statistical model approach using GIS and multivariate statistics

Southern Bangladesh＇s irrigation and drinking water is threatened by saline intrusion. This study aimed to establish an irrigation water quality index （IWQI） using a geostatistical model and multivariate indices in Gopalganj district, south-central Bangladesh. Groundwater samples were taken randomly （different depths） in two seasons （wet-monsoon and dry-monsoon）. Hydrochemical analysis revealed groundwater in this area was neutral to slightly alkaline and dominating cations were Na^＋, Mg^2＋, and Ca^2＋ along with major anions Cl^- and HCO3^-. Principal component analysis and Gibbs plot helped explain possible geochemical processes in the aquifer. The irrigation water evaluation indices showed： electrical conductivity （EC） 〉750 μS/cm, moderate to extreme saline; sodium adsorption ratio （SAR）, excellent to doubtful; total hardness （TH）, moderate to very hard; residual sodium bicarbonate, safe to marginal; Kelly＇s ratio 〉1; soluble sodium percentage （SSP）, fair to poor; magnesium adsorption ratio, harmful for soil; and IWQI, moderate to suitable. In addition, the best fitted semivariogram for IWQI, EC, SAR, SSP, and TH confirmed that most parameters had strong spatial dependence and others had moderate to weak spatial dependence. This variation might be due to the different origin/sources of major contributing ions along with the influence of variable river flow and small anthropogenic contributions. Furthermore, the spatial distribution maps for IWQI, EC, SSP, and TH during both seasons confirmed the influence of salinity from the sea; low-flow in the major river system was the driving factor of overall groundwater quality in the study area. These findings may contribute to management of irrigation and/or drinking water in regions with similar groundwater problems.

Hyper-spectrum models for monitoring water quality in Dianshan Lake,China

The correlation between water quality parameters and hyper-spectral reflectance is studied with models established for each parameter and applied in Dianshan Lake, in the upstream of the Huangpu River running through Shanghai, China. Models are for dissolved oxygen (DO in mg/L): R720/R680 = 20.362×(R720/R680)2?31.438×(R720/R680)+19.156; for turbidity (NTU): R*714.5 = 206.07× (R*714.5)2?582.5×R*714.5 + 423.24; and for total phosphorus (TP in mg/L): R*509.5 = 16.661× (R*509.5)2?32.646×R*509.5+16.116. The R2 values are 0.770 8, 0.660 4 and 0.738 7, respectively, showing strong positive relationships. The models were then applied to assess water quality of different times. Results are quite satisfactory for some samples.

Parameter selection and model research on remote sensing evaluation for nearshore water quality

Using remote sensing technology for water quality evaluation is an inevitable trend in marine environmental monitoring. However, fewer categories of water quality parameters can be monitored by remote sensing technology than the 35 specified in GB3097-1997 Marine Water Quality Standard. Therefore, we considered which parameters must be selected by remote sensing and how to model for water quality evaluation using the finite parameters. In this paper, focused on Leizhou Peninsula nearshore waters, we found N, P, COD, PH and DO to be the dominant parameters of water quality by analyzing measured data. Then, mathematical statistics was used to determine that the relationship among the five parameters was COD〉DO〉P〉N〉pH. Finally, five-parameter, fourparameter and three-parameter water quality evaluation models were established and compared. The results showed that COD, DO, P and N were the necessary parameters for remote sensing evaluation of the Leizhou Peninsula nearshore water quality, and the optimal comprehensive water quality evaluation model was the four- parameter model. This work may serve as a reference for monitoring the quality of other marine waters by remote sensing.

Inversion of dispersion coeffcient in water quality model using optimal perturbation algorithm

As a primary parameter in the water quality model for shallow bays, the dispersion coefficient is traditionally determined with a trial-and-error method, which is time-consuming and requires much experience. In this paper, based on the measured data of chemical oxygen demand （COD）, the dispersion coefficient is calculated using an inversion method. In the process, the regularization method is applied to treat the ill-posedness, and an operator identity perturbation method is used to obtain the solu- tion. Using the model with an inverted dispersion coefficient, the distributions of COD, inorganic nitrogen （IN）, and inorganic phosphorus （IP） in Bohai Bay are predicted and compared with the measured data. The results indicate that the method is feasible and the inverted dispersion coefficient can be used to predict other pollutant distribution. This method may also be further extended to the inversion of other parameters in the water quality model.

An integrated measurement and modeling methodology for estuarine water quality management

This paper describes research undertaken by the authors to develop an integrated measurement and modeling methodology for water quality management of estuaries. The approach developed utilizes modeling and measurement results in a synergistic manner. Modeling results were initially used to inform the field campaign of appropriate sampling locations and times, and field data were used to develop accurate models. Remote sensing techniques were used to capture data for both model development and model validation. Field surveys were undertaken to provide model initial conditions through data assimilation and determine nutrient fluxes into the model domain. From field data, salinity re- lationships were developed with various water quality parameters, and relationships between chlorophyll a concentrations, transparency, and light attenuation were also developed. These relationships proved to be invaluable in model development, particularly in modeling the growth and decay of chlorophyll a. Cork Harbour, an estuary that regularly experiences summer algal blooms due to anthropogenic sources of nutrients, was used as a case study to develop the methodology. The integration of remote sensing, conventional fieldwork, and modeling is one of the novel aspects of this research and the approach developed has widespread applicability.

Discharge Water Quality Models of Storm Runoff in a Catchment

The relationships between the water qualities of nitrogen and phosphorous contents in the discharge water and the discharge of storm runoff of an experimental catchment including terraced paddy field are analyzed based on experiment results of the catchment. By summarizing the currently related research on water quality models, the water quality models of different components of storm runoff of the catchment are presented and verified with the experiment data of water quality analyses and the corresponding discharge of the storm runoffs during 3 storms. Through estimating the specific discharge of storm runoff, the specific load of different components of nitrogen and phosphorus in the discharge water of the catchment can be forecasted by the models. It is found that the mathematical methods of linear regression are very useful for analysis of the relationship between the concentrations of nitrogen and phosphorus and the water discharge of storm runoff. It is also found that the most content of the nitrogen (75%) in the discharge water is organic, while half of the content (49%) of phosphorus in the discharge water is inorganic.

Sensitivity Analysis of Parameters in Water Quality Models and Water Environment Management

The impacts of changes of various parameters and stochastic factors on water quality models were studied. The impact of deviation of the degradation coefficient on the model results was investigated. The degradation coefficient was decomposed into the exact part and the deviation part, and the relationship between the errors of the water quality model results and the deviation of the degradation coefficient was derived. The impact of changes in the initial concentration on the model results was discussed. A linear relationship between the initial concentration changes and errors in the model results was obtained, and relevant recommendations to the water quality management were made based on the results. The impacts of stochastic factors in the water environment on the water quality model were analyzed. A variety of random factors which may affect the water quality conditions were attributed to one stochastic factor and it was further assumed to be the white noise. The solutions to the water quality model including the stochastic process were obtained by solving the stochastic differential equation. Simulation results showed that the decay trend of the concentration of the solute would not be changed, and that the results would fluctuate around the expectation centered at each corresponding displacement

Application of Water Quality Model QUAL2K to Model the Dispersion of Pollutants in River Ndarugu, Kenya

Ndarugu River, Kenya, during its course through the different agricultural and industrial areas of Gatundu, Gachororo and Juja farms, receives untreated industrial, domestic and agricultural waste of point source discharges from coffee and tea factories. During wet season the water is also polluted by non-point (diffuse) sources created by runoff carrying soil, fertilizer and pesticide residues from the catchment area. This study involved the calibration of water quality model QUAL2K to predict the water quality of this segment of the river. The model was calibrated and validated for flow discharge (Q), temperature (T&deg), flow velocity (V), biochemical oxygen demand (BOD5), dissolved oxygen (DO) and nitrate (NO3-N), using data collected and analyzed during field and laboratory measurements done in July and November-December 2013. The model was then used in simulation and its performance was evaluated using statistical criteria based on correlation coefficient (R2) and standard errors (SE) between the observed and simulated data. The model reflected the field data quite well with minor exceptions. In spite of these minor differences between the measured and simulated data set at some points, the calibration and validation results are acceptable especially for developing countries where the financial resources for frequent monitoring works and higher accuracy data analysis are very limited. The water is being polluted by the human activities in the catchment. There is need for proper control of wastewater by various techniques, and preliminary treatment of waste discharges prior to effluent disposal. Management of the watershed is necessary so as to protect the river from the adverse impacts of agricultural activities and save it from further deterioration.

Modeling Water Quality Impacts of Growing Corn, Switchgrass, and <i>Miscanthus</i>on Marginal Soils

The goal of the study was to model water quality impacts of growing perennial grasses on marginal soils. The GLEAMS-NAPRA and RUSLE models were used to simulate long-term surface runoff, percolation, erosion, total phosphorus (TP), and nitrate (NO3-N) losses associated with the production of corn-based bioenergy systems (i.e. conventional tillage corn and corn grain plus stover removal), switchgrass and Miscanthus on three marginal quality soils and one good quality soil in Indiana. Simulations showed that switchgrass and Miscanthus had no effect on annual runoff, but decreased percolation by at least 17%. Results also suggested a potential for reduction in erosion for Miscanthus across the soil types examined when compared to corn-based bioenergy production. The production of switchgrass and Miscanthus did not have significant effects on the simulated TP and NO3-N losses in runoff compared to corn production systems. Nitrates leached from fertilized Miscanthus production were approximately 90% lower than NO3-N leached from the production of fertilized switchgrass and corn systems. Additional studies are needed to better understand the hydrology, erosion and nutrient responses of Miscanthus and switchgrass production to meet bioenergy demands.