Utah Lake is a really large but shallow lake located in the arid environment of the Western United States. Due to a variety of factors it is listed by the Environmental Protection Agency as an "impaired water body" and must be closely monitored. Because of its large extent and shallow depth the water quality is heterogeneous and can change rapidly. This means that traditional water quality monitoring methods, which require large investments in field personnel, equipment, and water sample analysis, cannot produce a model that is truly representative of the entire water body. This thesis examines the feasibility of using remotely sensed imagery to develop a water quality monitoring system for Utah Lake that is accurate, repeatable and cost-effective. Due to the paucity of in situ water quality information, this is primarily a pilot study using Landsat satellite imagery collected within a 5-day window of existing in situ water samples measuring chlorophyll a. The brightness values of the imagery were regressed against the water samples to produce a model to accurately predict chlorophyll a concentrations across the entire lake. The results of the pilot study conclude that Landsat imagery could be a very useful monitoring tool if sufficient in situ data for calibration were available.

Modern streamflow measuring equipment, water quality sampling techniques and a better understanding of pollutant washoff are continuously being developed as today's society is in critical need of improving water management, minimizing developmental impacts and preventing environmental hazards. In particular, the study of the spatial, temporal and volumetric characteristics of annual pollutant loading caused by variations in precipitation, land use and other anthropogenic factors is of great significance due to their relation to future global water demands. The research presented here falls in three parts. In the first part of the dissertation, an acoustical Doppler velocity profiler installed in a submerged concrete channel is proposed to continually measure the annual fluctuation in streamflow levels down to dry channel conditions. The tailwater influenced, intermittent streamflow conditions for the City of Kissimmee, Florida were selected for the evaluation of this approach under a 3-year study from 2006 to 2008. The performance of these concrete channels were systematically evaluated by comparisons with established field measurement techniques over various stream configurations and flow conditions. The second part of this research investigates the dynamics of flood wave detection with respect to enabling an automatic water quality sampler to start collecting samples. The main focus was on the accurate detection of flood waves in the absence of rainfall and the presence of fluctuating baseflows and stream stages. In the 3-year study, it was shown that a dual parameter trigger, utilizing independent measuring equipment, resulted in accurate flood wave detection with minimal false triggering of the autosampler. In addition, an incremental or percent deviation from a moving average of stage or flow proved to be a more consistent indicator for the presence of a flood wave. In the third part of this work, the frequency of water quality sampling and the associated level of detail for sampling of rainfall events were investigated with respect to accurately depicting annual pollutant loads. It was found that the seasonal variations in baseflow pollutant loads are not accurately represented by current 4-quarter grab sampling. Also, significant pollutant loading within rainfall events may not be captured by only performing grab sampling during baseflow conditions. In addition, although increased pollutant concentrations were observed within the initial 30 minutes of the flood wave, their actual loadings did not represent a significant impact on the annual pollutant loads. A biweekly grab sampling frequency was found to be adequate in many cases to depict the annual pollutant loads, but depending upon the targeted constituent and particular streamflow condition, rainfall event sampling might also be necessary. The results of this research complemented with other studies will promote better understanding of intermittent streamflows, accurate flood wave detection, and assessment of annual pollutant loads to our nation's waterbodies.

The expanding urbanisation, growing population, and industrial development are threatening global surface water quality. With increasing concern about surface-water quality, it is crucial to deeply understand the evolution of surface-water quality problems and comprehensively de-termine its fundamental driving forces. In this Dissertation, systematic work on the mechanisms of water pollution with trace elements has been carried out in three steps: i) to identify the sources contributing to surface water pollution by receptor-based models, ii) to determine the factors dominating the pollution risk transmission from sources to surface water by a source-based model, and iii) to capture the primary driving forces to the spatiotemporal variation in surface water pollution by Bayesian-based approaches. The following specific topics were ad-dressed based on five publications: a) The temporal trends of trace metal pollution in the surface water were characterised by the Mann-Kendall test and the Generalised Additive Model. b) The primary source contributors to the long-term trace metal pollution in a river system were determined by the Self-organised Map, Positive Matrix Factorization receptor model, and Bayesian multivariate receptor model. The distributions of the source contributions to trace metal pollution were estimated. c) The risk transmission of trace pollutants in the surface water was estimated by a source-based dynamic model. The sensitivities of the risk to human activities, characteristics of wastewater treatment plants, and river flow regimes were evaluated. d) The contributions of hydro-chemical factors, climate impact, and sampling methods to water pollution and data uncertainty were analysed by the Wavelet Analysis and Bayesian Net-work. Both the models’ accuracy and robustness were evaluated by statistical analysis. The methods and results provided herein could improve the standard of statistical rigour and support the authorities’ decision-making.

This thesis work aims to develop electrochemical sensors for sub-ppb level detection of inorganic and organic pollutants in drinking water with environmentally benign materials and processes. While traditional laboratory-based methods such as mass spectroscopy, and chromatography have been used to analyze the concentration of contaminants in drinking water, miniaturized electrochemical sensors offer a compelling alternative to those methods, enabling rapid on-site cost-effective detection of low concentrations of pollutants. In this research, a set of three-electrode sensors was designed and fabricated on a flexible substrate using a screen-printing technique. Additionally, an in-situ electrochlorination process was implemented to create the reference electrode. These sensors were utilized to precisely detect lead ions and perfluorooctane sulfonate (PFOS) in drinking water. The first set of sensors was fabricated to measure the concentration of lead ions, a toxic inorganic pollutant, in potable water. The novelty of the proposed research lies in using non-toxic, biodegradable sodium alginate grafted with 2- acrylamido-2-methyl propane sulfonic acid (AMPS) and conductive fillers for trace-level lead ion detection in water. The principle of square wave anodic square wave stripping voltammetry (SWASV) was used to determine the trace level lead ion concentration. Employing a similar approach with a different material, a PFOS sensor was developed. Utilizing chitosan, one of the sustainable and biodegradable biopolymers found in crustacean shells, rapid parts-per-trillion (ppt) level PFOS detection by electrochemical impedance spectroscopy (EIS) was demonstrated. The proposed sensors made low-cost electrochemical detection of contaminants such as lead ions and PFOS possible with eco-friendly materials and processes.

In an electrochemical sensor, the sensing performance is mainly dependent on the mass transport of the analyte towards the working electrode-electrolyte interface and working electrode properties. Carbon nanomaterials like carbon nanotubes are widely employed to modify the working electrode properties for sensitive detection. A simulation model is formulated to investigate the effects of modifying a planar bare electrode with carbon nanotubes on electrochemical detection of fenitrothion (FT, an organophosphate). The model revealed that porous electrodes caused the change in mass transport regime and influenced FT’s electrochemical response. The results aided in understanding the influence of the porous electrode on analyte detection and thus assisted in the fabrication of an ultrasensitive electrochemical sensor. Simulation supported synthesis of a highly sensitive ink to produce highly porous and electrocatalytic electrodes. Activated carbon (AC) possesses high porosity and surface area, but they suffer from lower electrical conductivity. To enhance their conductivity, AC was co-doped with nitrogen and sulfur. Multiwalled carbon nanotubes were incorporated to further improve their porosity and electrocatalytic properties. The synthesized nitrogen-sulfur co-doped activated carbon coated multiwalled carbon nanotube (NS-AC-MWCNT) ink produced highly porous electrocatalytic electrodes. The sensor revealed a 4.9 nM limit of detection (LOD) under optimized conditions. However, it failed to overcome the enzymatic sensors’ performances. The ultrasensitive performance was achieved by incorporating a detecting agent in the ink that instilled analyte capture ability. Metal oxides like ZrO2, MnO2, and MgO possessed affinity towards organophosphate (fenitrothion), heavy-metal ion (lead), and nutrient (nitrite). Metal oxides were modified with 3,4-dihydroxylbenzaldehyde (DHBA) – Chitosan (CHIT) to produce well dispersed and uniformly coated stable electrodes. The ZrO2-DHBA-CHIT/NS-AC-MWCNT sensor achieved a remarkable limit of detection of 1.69 nM for FT. The sensor's performance exceeded the enzymatic-based sensors. The commonly found chemical interferents had negligible interference. The sensor produced reliable and satisfactory performance in lake and tap water. The MnO2-DHBA-CHIT/NS-AC-MWCNT/GCE and MgO-DHBA-CHIT/NS-AC-MWCNT/GCE sensors produced an enormous improvement in the sensor performance compared to unmodified electrodes for lead and nitrite detection. The preliminary results on detecting other pollutants like lead and nitrite showed the importance of the methodology in providing a platform for a new class of metal oxide-based sensors. / Thesis / Doctor of Philosophy (PhD) / The growing population and rapid industrial development are affecting the water quality worldwide. The major water pollutants are organophosphates, heavy metal ions, and nutrients. These water pollutants are harmful, and their bioaccumulation poses a major health concern. In the USA alone, water quality issues are predicted to cost $210 billion annually. Therefore, sensors to detect water pollutants are developed to monitor their environmental footprints. Electrochemical sensors are popularly used to detect water pollutants owing to their low-cost and high sensitivity. The objective of this dissertation was to fabricate highly sensitive and specific electrochemical sensors to detect organophosphate (e.g., fenitrothion, FT), heavy metal ion (e.g., lead), and nutrient (e.g., nitrite). The sensors were fabricated with ink based on nanomaterials like carbon nanotubes and detecting agents like metal oxides. The fabricated sensors achieved very high sensitivity and specificity and can detect water pollutants in lake and tap water.

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