Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH), organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN) comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN) to train and validate the data derived from Geographic Information System (GIS) spatial distribution grids. The neural network architecture with (6-20-1) pattern was able to predict the arsenic concentration with reasonable accuracy

Virginia Institute of Marine Science Programs and Services

Programs and faculty, education and Institute support resources are described

Water Resources Management and Modeling

Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

York River Colloquy

Th e 1997 York River Colloquy is intended to provide an overview of some of the ongoing activities con ducted by VIMS scientists in the York River system. This collection of project summaries in not exhaustive, but it does provide a means to identify the interests of various investigators

Patterns, Processes, And Scale: An Evaluation Of Ecological And Biogeochemical Functions Across An Arctic Stream Network

Ecosystems are highly variable in space and time. Understanding how spatial and temporal scales influence the patterns and processes occurring across watersheds presents a fundamental challenge to aquatic ecologists. The goal of this research was to elucidate the importance of spatial scale on stream structure and function within the Oksrukuyik Creek, an Arctic watershed located on the North Slope of Alaska (68°36’N, 149°12’W). The studies that comprise this dissertation address issues of scale that affect our ability to assess ecosystem function, such as: methodologies used to scale ecosystem measurements, multiple interacting scales, translation between scales, and scale-dependencies. The first methodological study examined approaches used to evaluate chlorophyll a in ethanol extracts of aquatic biofilms. Quantification of chlorophyll a is essential to the study of aquatic ecosystems, yet differences in methodology may introduce significant errors to its determination that can lead to issues of comparability between studies. A refined analytical procedure for the determination of chlorophyll a was developed under common acidification concentrations at multiple common reaction times. The refined procedure was used to develop a series of predictive equations that could be used to correct and normalize previously evaluated chlorophyll a data. The predictive equations were validated using benthic periphyton samples from northern Alaska and northwestern Vermont, U.S.A. The second study examined interaction and translation between scales by examining how normalization approaches affect measurements of metabolism and nutrient uptake in stream sediment biofilms. The effect of particle size and heterogeneity on rates of biofilm metabolism and nutrient uptake was evaluated in colonized and native sediments normalized using two different scaling approaches. Functional rates were normalized by projected surface area and sediment surface area scaling approaches, which account for the surface area in plan view (looking top-down) and the total surface area of all sediment particles, respectively. Findings from this study indicated that rates of biogeochemical function in heterogeneous habitats were directly related to the total sediment surface area available for biofilm colonization. The significant interactions between sediment surface area and rates of respiration and nutrient uptake suggest that information about the size and distribution of sediment particles could substantially improve our ability to predict and scale measurements of important biogeochemical functions in streams. The final study examined how stream nutrient dynamics are influenced by the presence or absence of lakes across a variety of discharge conditions and how catchment characteristics can be used to predict stream nutrients. Concentrations of dissolved organic carbon (DOC) and other inorganic nutrients were significantly greater in streams without lakes than in streams in with lakes and DOC, total dissolved nitrogen (TDN), and soluble reactive phosphorus concentrations increased as a function of discharge. Catchment characteristic models explained between 20% and 76% of the variance of the nutrients measured. Organic nutrient models were driven by antecedent precipitation and watershed vegetation cover type while inorganic nutrients were driven by antecedent precipitation, landscape characteristics and reach vegetation cover types. The developed models contribute to existing and future understanding of the changing Arctic and lend new confidence to the prediction of nutrient dynamics in streams where lakes are present

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification