A pharmacological study of transmission in a sympathetic ganglion

This thesis presents an account of three related investigations on synaptic transmission in the sympathetic ganglion of the frog. Part I is concerned with the mode of action of the transmitter, its identity and the modifications in transmission caused by various drugs. Parts II and III are concerned with the spontaneous and the evoked release of the transmitter from, the presynaptic nerve

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

The need to allocate the existing water in a sustainable manner, even with the projected population growth, has made to assess the consumptive use or evapotranspiration (ET), which determines the irrigation demand. As underscored in the literature, Penman-Monteith method which is a combination of aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the application of Penman-Monteith relies on many climate parameters such as relative humidity, solar radiation, temperature, and wind speed. Therefore, there exists a need to determine the parameters that are most sensitive and correlated with dependent variable (i.e., ET), to strengthen the knowledge base. However, the sensitivity of ET using Penman-Monteith is oftentimes estimated using meteorological data from climate stations. Such estimation of sensitivity may vary spatially and thus there exists a need to estimate sensitivity of ET spatially. Thus, in this paper, based on One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically encompass all the best available climate datasets to produce ET and its sensitivity at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010. To summarize the outcome of the sensitivity analysis using OAT method, sensitivity indices are developed for each raster cell. The demonstration of the tool shows that, among the considered parameters, the computed ET using Penman-Monteith is highly sensitive to solar radiation followed by temperature for the state of Nebraska, as depicted by the sensitivity index. The computed sensitivity index of wind speed and the relative humidity are not that significant compared to the sensitivity index of solar radiation and temperature

A Spatial Evapotranspiration Tool at Grid Scale

The drastic decline in groundwater table and many other detrimental effects in meeting irrigation demand, and the projected population growth have force to evaluate consumptive use or evapotranspiration (ET), the rate of liquid water transformation to vapor from open water, bare soil, and vegetation, which determines the irrigation demand. As underscored in the literature, Penman- Monteith method which is based on aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the estimation of ET is oftentimes carried out using meteorological data from climate stations. Therefore, such estimation of ET may vary spatially and thus there exists a need to estimate ET spatially at different spatial or grid scales/resolutions. Thus, in this paper, a spatial tool that can geographically encompass all the best available climate datasets to produce ET at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010

The Assessment of Groundwater Vulnerability Due to Leaching of Chemicals: The Review of Attenuation Factor

To assess the groundwater vulnerability due to leaching of chemicals, the groundwater system in the unsaturated zone is characterized by conceptual models that are further extended and refined with more detailed mathematical models to understand the governing physical processes in detail. However, due to lack of data and uncertainty level, an intermediate transition through index based models is researched. The attenuation factor (AF) approach, which works under the assumption that the chemicals degrade following a first-order kinetics and determines the fraction of the chemicals that goes to groundwater table, is one of the index based models that has been widely used due to its simplicity. Therefore, the objective of this paper is to review the research works done using the AF approach, to outline the future research needs. Furthermore, the mathematical implementation of the AF approach and the associated uncertainty levels is explained through an example and MATLAB source code

A Spatial Evapotranspiration Tool at Grid Scale

The drastic decline in groundwater table and many other detrimental effects in meeting irrigation demand, and the projected population growth have force to evaluate consumptive use or evapotranspiration (ET), the rate of liquid water transformation to vapor from open water, bare soil, and vegetation, which determines the irrigation demand. As underscored in the literature, Penman- Monteith method which is based on aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the estimation of ET is oftentimes carried out using meteorological data from climate stations. Therefore, such estimation of ET may vary spatially and thus there exists a need to estimate ET spatially at different spatial or grid scales/resolutions. Thus, in this paper, a spatial tool that can geographically encompass all the best available climate datasets to produce ET at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010

Risk and Cost Assessment of Nitrate Contamination in Domestic Wells

This study combines empirical predictive and economics models to estimate the cost of remediation for domestic wells exceeding suggested treatment thresholds for nitrates. A multiple logistic regression model predicted the probability of well contamination by nitrate, and a life cycle costing methodology was used to estimate costs of nitrate contamination in groundwater in two areas of Nebraska. In south-central Nebraska, 37% of wells were estimated to be at risk of exceeding a threshold of 7.5 mg/L as N, and 17% were at risk of exceeding 10 mg/L as N, the legal limit for human consumption in the United States. In an area in northeastern Nebraska, 82% of wells were at risk of exceeding the 10 mg/L as N legal threshold. Reverse osmosis Point-of-Use (POU) treatment was the option with the lowest costs for a household (3–4 individuals), with an average of $4–$164 total regional cost per household per year depending on the threshold for treatment. Ion exchange and distillation were the next most cost-effective options. At the community level (~10,000 individuals), a reverse osmosis Point-of-Entry (POE) treatment system was the most expensive option for a community due to high initial costs and ongoing operation and maintenance costs, whereas the biological denitrification system was least expensive due to economies of scale. This study demonstrates integrated modeling methods to assess water treatment costs over time associated with groundwater nitrate contamination, including quantification of at-risk wells, and identifies suitable options for treatment systems for rural households and communities based on their cost

Microgenetic algorithms and artificial neural networks to assess minimum data requirements for prediction of pesticide concentrations in shallow groundwater on a regional scale

Artificial neural networks (ANNs) have been extensively used for forecasting problems involving water quantity and quality. In most cases, the geometry and model parameters of the ANN are set using a trial-and-error approach to achieve better network generalization ability, whereby the available data are divided arbitrarily into training, testing, and validation subsets. It has been shown that using the arbitrary sample selection method to assign samples into the training subset commonly results in the inclusion of samples from densely clustered regions and omission of samples from sparsely represented regions. This paper presents a systematic approach using the self-organizing map (SOM) clustering technique that identifies which samples and determines how many samples should be included in each of the three subsets required by ANN for optimum predictive performance efficiency. In addition, this paper presents the microgenetic algorithms (mGA) that optimize ANN’s geometry and model parameters in terms of the correlation coefficient (R). In the sensitivity analysis, mGA model parameters are found to be least sensitive to the optimum R value, while ANN’s predictive performance is significantly affected by (1) the poor selection of its geometry and model parameters and (2) the arbitrary selection of samples for the three subsets of data used. It is demonstrated that the mGA-ANN model using the SOM technique for data division outperforms the mGA-ANN model using arbitrary data division. For the training subset, the model using the SOM technique identifies samples that are representative of the region, requiring only 20% of the total samples, whereas the arbitrary sample selection method requires 50–90%. Because resampling on a regional scale is expensive and time consuming, substantial cost and time could be saved if resampling could be done only on the 20% representative drinking water wells

Riverbank Filtration Impacts on Post Disinfection Water Quality in Small Systems—A Case Study from Auburn and Nebraska City, Nebraska

Small water systems can experience a fluctuating quality of water in the distribution system after disinfection. As chlorine is the most common disinfectant for small systems, the occurrence of disinfection byproducts (DBPs) represents a common problem for these systems. Riverbank filtration (RBF) can be a valuable solution for small communities located on riverbanks. The objectives of this study were to evaluate (i) the improvements in water quality at two selected RBF systems, and (ii) the potential lower concentrations of DBPs, in particular, trihalomethanes (THMs), in small systems that use RBF. Two small communities in Nebraska, Auburn and Nebraska City, using RBF were selected. Results from this study highlight the ability of RBF systems to consistently improve the quality of the source water and reduce the occurrence of THMs in the distribution water. However, the relative removal of THMs was directly impacted by the dissolved organic carbon (DOC) removal. Different THM concentrations and different DOC removals were observed at the two RBF sites due to the different travel distances between the river and the extractions wells

Irrigation Water Quality—A Contemporary Perspective

In the race to enhance agricultural productivity, irrigation will become more dependent on poorly characterized and virtually unmonitored sources of water. Increased use of irrigation water has led to impaired water and soil quality in many areas. Historically, soil salinization and reduced crop productivity have been the primary focus of irrigation water quality. Recently, there is increasing evidence for the occurrence of geogenic contaminants in water. The appearance of trace elements and an increase in the use of wastewater has highlighted the vulnerability and complexities of the composition of irrigation water and its role in ensuring proper crop growth, and long-term food quality. Analytical capabilities of measuring vanishingly small concentrations of biologically-active organic contaminants, including steroid hormones, plasticizers, pharmaceuticals, and personal care products, in a variety of irrigation water sources provide the means to evaluate uptake and occurrence in crops but do not resolve questions related to food safety or human health effects. Natural and synthetic nanoparticles are now known to occur in many water sources, potentially altering plant growth and food standard. The rapidly changing quality of irrigation water urgently needs closer attention to understand and predict long-term effects on soils and food crops in an increasingly fresh-water stressed world

Beach Sand Filtration as Pre-Treatment for RO Desalination

Membrane fouling has a strong negative impact on the efficiency of reverse osmosis membranes in seawater desalination. Although reports indicate that water abstracted by beach sand filtration systems on the Mediterranean and Red Seas leads to less membrane fouling compared to direct seawater intakes, only limited information can be found on the efficiency of such systems in removing biodegradable dissolved organic carbon (BDOC), an important fouling agent. This article describes different designs of beach sand filtration systems. In order to investigate the reduction during beach sand filtration of parameters relevant to membrane fouling, such as total organic carbon (TOC), turbidity and total nitrogen, column experiments have been carried out using natural and wastewater spiked seawater with coral beach sand from Hawaii, USA at low and high infiltration rates. Additionally, operational results from existing beach sand filtration sites were collected and supplemented with data from a field site visit of the Dahab beach well desalination plant, Egypt. Preliminary results show good reduction of the targeted parameters and indicate that beach sand filtration would be a valuable pre-filtration step in RO-based drinking water production systems