Modeling of solute transport and retention in Upper Amite River

Streams play an important role in a solute transport through a stream network. Transient storage is one of the important processes that control mass exchange between a main stream and transient storage zones because of increase in the residence time of solute. In order to take account of the effect of transient storage on solute transport in streams, a variable residence time (VART) model was developed. The model is characterized by several features: (1) actual varying residence time was used, (2) no-user specified residence time distributions (RTD) were required, (3) less calibration parameters were involved as compared to existing models, and (4) production of various types of RTDs such as power-law, lognormal, and exponential distributions for an instantaneous release of solute. Comparisons between the VART model and some existing solute transport models using tracer concentration data measured in 33 streams show that the VART model is capable of reproducing exponential, power-law, and lognormal RTDs observed in streams with the accuracy higher than or at least comparable to existing solute transport models. In addition, the results show that stream channel size affects the type of breakthrough curves (BTCs). The BTCs switch from the upwardly curving VART - +U and VART - 0U to a straight line (VART - -P) and further to the downwardly curving VART - -L distributions with increasing flow depth. Large rivers generally exhibit VART - -L distributions. Small streams commonly display either the upwardly curving (VART - +U and VART - 0U) distributions or VART - -P distributions. Moderate rivers may exhibit any types of VART series distributions. The application of VART model to the Upper Amite River shows that the VART model provides a simple yet effective tool for predicting solute dispersion and transport in natural streams and rivers. The VART model also provides a potential tool for estimating nutrient retention in the Amite River and other natural streams

Development of hydrograph-based approach to modeling fate and transport of sediment-borne bacteria in lowland rivers

Fecal pollution is one of the major factors responsible for water quality impairments of rivers and streams, particularly organic-rich fine-grained lowland streams. While predicting fecal pollution is generally required in the development of water quality restoration plans like Total Maximum Daily Loads, no single model has been widely recognized as an efficient and effective tool for estimating fecal pollution. This dissertation develops a simple yet effective modeling approach, called Hydrograph-based Approach, to bacterial fate and transport modeling in lowland rivers. The new hydrograph-based approach is simple and efficient in terms of its less data requirements as compared with other models. The new approach utilizes widely available hydrographs as the primary model input data. The new hydrograph-based approach is effective in terms of its capability in predicting bacterial concentrations for a wide range of flow conditions from low flow without sediment to flood events carrying high concentrations of sediment. The development of this new approach is based on the following major works: 1) a hydrograph-based method for determining bed shear velocity and other flow parameters was developed and tested using measured experimental data as well as simulated results from HEC-RAS for two river flood events; 2) a relatively simple hydrograph-based method for estimating sediment transport during unsteady flows was developed and tested using sediment concentration data collected during several flood events in two US rivers; 3) the solute transport process in rivers, in particular, the effect of channel size on residence time distribution, was investigated using a variable residence time model; and 4) a hydrograph-based approach for modeling bacterial fate and transport was developed, utilizing the variable residence time model for mass transport and hydrograph-based methods for flow and sediment transport, and tested through case studies using data observed in three rivers with distinct flow and sediment transport characteristics. This hydrograph-based approach includes most of the important bacterial transport and fate processes such as advection, dispersion, transient storage exchange, resuspension/deposition, and bacterial growth/decay. The modeling results using this approach appear to be better or at least comparable with the results from other more complicated models

Modeling temporal and spatial variations in dissolved oxygen in Amite River

A watershed-based modeling framework is developed in this dissertation for simulating temporal and spatial variations in DO in lowland rivers with organic-rich fine-grained sediment. The modeling framework is based on three major contributions/new models, including (1)VART-DO model for improved estimation of reaeration coefficient (K2) in natural streams, (2)VART-DOS model for simulation of temporal variations in DO in response to sediment resuspension, and (3)VART DO-3L model for simulation of spatial variations in DO. A major advantage of VART-DO model is the capability of simulating DO exchange across the water-sediment interface through the hyporheic exchange mechanism in addition to the air-water exchange. Simulation results from VART-DO model revealed that hyporheic exchange can reduce K2 by 30% while longitudinal dispersion increases K2 by 50%. VART-DOS model is developed for simulation of temporal variations in DO particularly due to sediment resuspension effect during high flow. Application results of VART-DOS model to the Amite River in Louisiana showed that 83% of DO consumption in water column in July 1990 was because of sediment resuspension. A novel feature of VART DO-3L model is that a fine-grained stream with the flocculent layer can be vertically modeled with three layers: overlying water column, an advection-dominated storage zone, and a diffusion-dominated storage zone in relatively consolidated stream bed-sediment. While the importance of flocculent layer to instream DO has been widely reported, VART-DO-3L model is the first modeling tool that incorporates the flocculent layer into DO modeling. This is a unique feature of VART-DO-3L model, making it possible for determining both longitudinal and vertical profiles of DO in streams. Results of VART-DO-3L for the Amite River indicated that the DO level decreases longitudinally from 7.9mg/L at the Denham Springs station to 2.89mg/L at the Port Vincent station. Vertically, DO level drops rapidly from overlying water column to the advection-dominated storage zone and further to the diffusive layer. The DO level in the advective layer is about 40% of that in water column. The thickness of the diffusive layer varies between 0-10mm, depending on effective diffusion coefficient. Developed models in this dissertation are also applicable to sandy/gravel rivers

Modeling of first-flush reactor for stormwater treatment

Stormwater runoff is one of the most common sources of non-point source water pollution to lakes, rivers and estuaries. Nitrate-nitrogen in stormwater runoff is an important limiting factor to the eutrophication phenomenon. While most pollutants and nutrients, including nitrate-nitrogen, in stormwater are discharged into receiving waters during the first-flush period, no existing Best Management Practices (BMPs) are specifically designed to capture and treat the first-flush portion of urban stormwater runoff. In addition, nitrate-nitrogen removal rates of most existing BMPs are relatively low. This thesis presents results from both laboratory experiments and numerical modeling of nitrate-nitrogen removal in a designed first-flush reactor. A new numerical tool, called VART-DN model, for simulation of denitrification process in the designed first-flush reactor was developed using the Variable Residence Time (VART) model. The new model is capable of simulating various processes and mechanisms responsible for denitrification in the first-flush reactor, including (1) dispersion and transport, (2) mass exchange, (3) oxygen variation, (4) bacterial growth, and (5) nitrate-nitrogen consumption. The VART-DN model is intended to investigate the influence of oxygen, biomass, dissolved carbon, and temperature on denitrification process. The data used in the development of the VART-DN model were from laboratory experiments conducted using both highway stormwater and secondary wastewater. Based on sensitivity analysis results of model parameters, the dispersion coefficient, maximum nitrate utilization rate in mobile phase, biomass concentration, oxygen inhibition constant, biomass inhibition constant, temperature and temperature coefficient for denitrification have significant influence on the denitrification process, with percent change in root mean square error (RMSE) being 16.9%, 15.8%, -13.1%, -11.5%, 14.5%, -9.2% and -29.7%, respectively, when values of the parameters increase by 10%. The average removal rate of nitrate-nitrogen in natural stormwater was 92.05%. The average influent and effluent concentrations in the column experiment with wastewater were 1.189 mg/L and 0.260 mg/L, respectively, with a removal rate of 78.1% for nitrate-nitrogen. The VART-DN model results for the denitrification process of natural stormwater showed good agreements with observed data; the simulation error was lower than 9.0%. The RMSE for simulating denitrification process of wastewater was 0.8157, demonstrating the efficacy of the VART-DN model

Development of watershed-based modeling approach to pollution source identification

Identification of unknown pollution sources is essential to environmental protection and emergency response. A review of recent publications in source identification revealed that there are very limited numbers of research in modeling methods for rivers. What’s more, the majority of these attempts were to find the source strength and release time, while only a few of them discussed how to identify source locations. Comparisons of these works indicated that a combination of biological, mathematical and geographical method could effectively identify unknown source area(s), which was a more practical trial in a watershed. This thesis presents a watershed-based modeling approach to identification of critical source area. The new approach involves (1) identification of pollution source in rivers using a moment-based method and (2) identification of critical source area in a watershed using a hydrograph-based method and high-resolution radar rainfall data. In terms of the moment-based method, the first two moment equations are derived through the Laplace transform of the Variable Residence Time (VART) model. The first moment is used to determine the source location, while the second moment can be employed to estimate the total mass of released pollutant. The two moment equations are tested using conservative tracer injection data collected from 23 reaches of five rivers in Louisiana, USA, ranging from about 3km to 300 km. Results showed that the first moment equation is able to predict the pollution source location with a percent error of less than 18% in general. The predicted total mass has a larger percent error, but a correction could be added to reduce the error significantly. Additionally, the moment-based method can be applied to identify the source location of reactive pollutants, provided that the special and temporal concentrations are recorded in downstream stations. In terms of the hydrograph-based method, observed hydrographs corresponding to pollution events can be utilized to identify the critical source area in a watershed. The time of concentration could provide a unique fingerprint for each subbasin in the watershed. The observation of abnormally high bacterial levels along with high resolution radar rainfall data can be used to match the most possible storm events and thus the critical source area

Quantifying Subsurface Hydrology Effects on Chemical Transport in Agriculture Drainage Ditches Using a 20 Meter Flume

Agriculture drainage ditches serve as the veins of the Midwestern agricultural landscapes. The transport of chemical fertilizers and pesticides in these ditches affects both local and downstream ecosystems. Although much research has already been conducted on chemical transport in streams and drainage ditches, as well as through drainage tiles, there has not been sufficient research on the effects of subsurface hydrology on nutrient storage and interactions between the stream water and the hyporheic zone. In this study, a 20-meter flume was filled with ditch sediment from Marshall Ditch at Purdue University\u27s Agronomy Center for Research and Education in West Lafayette, IN to serve as an artificial drainage ditch. A water table control was built such that the stream bed can be set to either drainage, saturated or seepage conditions. A series of short term injection studies, using phosphorus and bromide, were performed under three subsurface hydrologic conditions representing a losing stream (water table set below the sediment bed), saturation of the sediment bed and a seepage condition (water table set higher than sediment bed). Five treatments were compared: two seepage rates, two drainage rates and one saturation treatment. Surface water quality samples were collected by automatic samplers located at 5, 10, 15, and 20 meters on one minute time intervals for the designated experimental timeframe. Drainage water quality samples were also collected every 2.5 meters during the drainage treatment. It was found that under drainage conditions, there was no influence from the sediments on phosphorus mass transport to the surface water; i.e. surface water that entered the subsurface sediments did not return to the surface water column. The removal rate of total mass phosphorus during drainage conditions was directly related to the hydraulic flux of the treatment. During seepage and saturation conditions, the bromide and phosphorus analysis indicated possible storage in dead pool zones of the flume that were immeasurable, indicating that dead pool zones could be more influential during certain hydrologic conditions

Transition of educational paradigms

This work proposes a complete online learning environment which combines several aspects: categorization of students based on their abilities, learning styles and preferences, adaptability of the presented contents, recommendation of appropriate learning materials. Besides offering series of conventional services, abundancy of information available, message delivery, to effectively meet the needs, preferences, and different knowledge backgrounds the system offers the possibility of selecting the most adequate learning materials: text, images, video, audio and links to external websites. Tag-based collaborative filtering is used for recommendation of learning materials to the student. Within the process of finding appropriate materials to be recommended to the student, the system determines the degree of similarity of the tags most often used by the student with the words in the title, abstract and keywords of the papers. Categorization of students is based on the learning style (VART model) and all activities related to the interaction with the system, such as visited pages, teaching materials and external publications, tags and notes entered in the process of learning, ratings set to the teaching materials, etc. Important aspect of this learning system is generating and recommending adequate teaching materials as well as appropriate tags and ratings. The list of recommended contents is generated by finding similar profiles and learning materials. Information retrieval algorithms are used to determine the similarity between student profiles and teaching materials used. The advantage of this learning system in comparison with the traditional ones lies in the possibility to use knowledge about the domain and the teaching strategies to support individualized learning