This research demonstrates technology for detection of pathogens and environmental monitoring using a handheld optofluidic immunosensor and CFD simulation. The current methods such as ELISA and PCR require few hours for identification which means it is unavailable for early-monitoring. The use of a near-real-time, handheld biosensor device in a real animal/human environment is the key to monitoring the spread of dangerous pathogens. A 3-D computational fluid dynamics (CFD) simulation is needed to track the pathogens within an environment.This dissertation has four papers that demonstrate technologies for the detection and monitoring of pathogens and the miniaturization of these detection systems for in field applications with a handheld immunosensor and CFD simulation.In the first paper, an environmental prediction model was developed for optimal ventilation in a mushroom house by using sensible heat balance and 3-D CFD method. It is shown that the models can be used for farmers to predict the environmental conditions over different locations in a mushroom house.In the second paper, a field lab-on-a-chip system was constructed to detect mouse immunoglobulin G and Escherichia coli by using light scattering detection of particle immunoagglutination. Antibody-conjugated particles were able to be stored in a 4°C refrigerator for at least 4 weeks and to be lyophilized as a powder form for the storage in room temperature.In the third paper, rapid monitoring of the spreads of porcine reproductive and respiratory syndrome virus (PRRSV) was attempted using samples collected from nasal swabs of pigs and air samplers within an experimental swine building. An optofluidic device containing liquid-core waveguides was used to detect. It is shown that the developed optofluidic device and 3-D CFD model can serve as a good model for monitoring the spread of airborne viruses within animal and human environments.In the fourth paper, a handheld optofluidic immunosensor was developed for rapid detection of H1N1/2009 virus inside a 1:10 scale mock classroom. Both miniature spectrometer and cell phone camera were used as detector. A 3-D computational fluid dynamics (CFD) model was developed to track the transport/distribution of H1N1/2009 viruses, and corresponded very well with immunosensor readings.

Worldwide, 3.575 million people die each year from water-related diseases. The water and sanitation crisis claims more lives than any warfare and is predicted to be one of the biggest global challenges of this century. The rapid, accurate detection of viral pathogens from environmental samples is an ongoing and pertinent challenge in biological engineering. Currently employed methods are lacking in either efficiency or specificity. Here we explore a novel method for virus detection and concurrently use this method to learn more about the very early stages of the virus infection process. The method combines Fourier transform infrared (FTIR) spectroscopy, a method of visualizing molecules based on changes in vibration of particles, and mammalian cells as the biosensor. This method is used to detect and investigate viruses from the family picornaviridae, chosen due to their public health burden and their widespread presence in environmental samples, especially water sources. This family includes the Polioviruses, echoviruses and Coxsackieviruses, among others, many of which are human pathogens.The research outlined in this dissertation is aimed at developing and implementing a new cell-based biosensor that combines the advantages of FTIR spectroscopy with the ability of buffalo green monkey kidney (BGMK) cells to sense diverse stimuli, including infective enteroviruses. The goal of developing this biosensor is outlined in the first paper. The second paper focuses on the application of advanced statistical methods to analyze the spectra to discriminate different viral infections in BGMK cells. Finally, we designed a non-reactive metal biochamber to use with attenuated total reflectance-FTIR. This allowed near-continuous acquisition of real-time spectral data for the study of biochemical changes in mammalian cells caused by poliovirus (PV1) infection. This system is capable of tracking changes in cell biochemistry in minute intervals for many hours at a time.This work demonstrates the feasibility of FTIR spectroscopy in combination with the broad sensitivity of mammalian cells for potential use in the detection of infective viruses from environmental samples. We envision this method being extended to high throughput, automated systems to screen for viruses or other toxins in drinking water systems and medical applications.

The greenhouse industry is facing significant challenges such as the demand for more efficient use of energy and natural resources and prevention of detrimental environmental impacts. Reducing negative environmental impacts can be achieved by utilizing recycled and environmentally friendly products and by optimizing the use of water and root zone substrates. New and advanced root zone substrates are currently tested as substitute for natural soils in greenhouse agriculture. They can be inert non-organic materials such as rockwool and perlite. These are mined products from the earth, and are difficult to dispose after use. Natural substrates such as peat are being consumed faster than being regenerated. A new potential substrate that consists of recycled foamed glass aggregates is considered an alternative, as it is environmentally friendly, non-toxic and disposable. Experiments with foamed glass aggregates and with foamed glass aggregate/coconut coir mixtures indicated that the yield of greenhouse tomatoes was not statistically significant different (α=0.05) when compared to rockwool. To investigate the potential application of recycled glass as a root zone substrate, physical and hydraulic properties were measured. For comparison, the same measurements were completed for rockwool, coconut coir, perlite, and PET/PE fibers as well as for a mixture of coconut coir and recycled glass. The water characteristics (WC) determined for each substrate exhibited distinct air entry potentials, which provided information for irrigation scheduling, water storage and aeration for optimum plant growth conditions. Coconut coir and rockwool exhibited a unimodal shaped water retention curve, while foamed glass aggregates and perlite exhibited bimodal shaped curves. The obtained substrate properties were used as input paramaters for HYDRUS- 2D/3D model to simulate water mass balance and matric potential distributions within a typical growth container of foamed glass aggregates. The simulated matric potential and water content distributions were compared to tensiometer measurements of matric potential in the foamed glass aggregates. The simulations compared favorably with laboratory experiments measured under controlled environmental conditions.

Climatic conditions inside the dairy barn do not concern dairy farmers until those conditions begin to affect productivity and, consequently, profits. As heat and humidity increase beyond the cow's comfort levels, milk production declines, as does fertility and the welfare of the cow in general. To reinforce the cooling mechanisms currently used, this work proposes an alternative system for reducing the risk of heat stress. This innovative conductive cooling system does not depend on current weather conditions, and it does not require significant modifications when it is installed or during its operation. Also, the system circulates water that can be reused. Given that a review of the literature found very few related studies, it is suggested that each freestall be equipped with a viable prototype in the form of a waterbed able to exchange heat. Such a prototype has been simulated using Computational Fluid Dynamics (CFD) and later verified by a set of experiments designed to confirm its cooling capacity. Furthermore, this investigation sets the foundation for modeling temperature in a water supply system linked to the waterbeds. EPANET, a software program developed by the Environmental Protection Agency, simulates the hydraulic model. Its Water Quality Solver has been modified according to an analogy in the governing equation that compares mass to heat transfer and serves to simulate water temperature as the water is transported from its source to the point of delivery and then as it returns to the same source.

Ultrafine bubbles, often referred to as nanobubbles, have been used in various applications from environmental remediation to medicine. Even though the technology to generate ultrafine bubbles has been around for many years, the full potential of its applications has not been completely studied. This project seeks to study the use of ultrafine bubble technology for water treatment in combination with ozone gas. A factorial design experiment was chosen to test the effects of ultrafine bubbles on the concentration of an indicator organism, E. coli, in water as well as their effects on ozone gas being injected into water. Ozone gas or nitrogen gas was injected into water contaminated with E. coli as either ultrafine bubbles or fine bubbles as treatments for up to 60 minutes. Ultrafine bubbles were found to not have any significant effect on the concentration of E. coli in water. However, ultrafine bubbles did provide benefits when used in conjunction with ozone gas that regular, fine bubbles did not provide. The benefits included allowing the concentration of dissolved ozone in the water to decrease at a slower rate as well as allowing more ozone to dissolve into water at a higher rate than conventional methods of bubbling in ozone. While in this particular set of experiments the concentration of dissolved ozone in water didn't surpass 2 mg/L, which didn't allow for rapid disinfection and treatment of water, it is believed that with a more powerful ozone generator better results can be achieved. This project demonstrates the benefits and potential of injecting ozone gas as ultrafine bubbles into water as a way to effectively and efficiently disinfect and treat water.

Current competitive status among potable and non-potable use makes the water reuse mandatory. Presently, water reuse is common only for reclaimed water coming from municipal or industrial water treatment plants. In those facilities, the treatment includes disinfection. The disinfection methods widely used are chlorination and Ultra Violet (UV) lamps adapted to the conditions of large volume of municipal and industrial systems. This study proposes a disinfection method adequate to the household level to reuse graywater. The method is called solar disinfection (SODIS), which allows the reuse of graywater even though it contains fecal contamination. In this research, natural sun radiation as a free source of heat and UV radiation was utilized. In a first stage, periods of sun exposure, graywater depth, and cell covers as external factors were studied. In later stages, the graywater temperature (GWT) and the UV radiation effects on the reduction of the microbial indicators were observed. Results showed that graywater depth of five cm had a statistical significant reduction rather than ten cm depth (p = 0.0035). Plexiglas and poly-vinyl chloride (PVC), as transparent covers, had a statistical significant reduction (p<0.00001) due to the greenhouse effect increasing the GWT. The black cover had the lowest GWT and reduction of the bacterial and viral indicators. This research found different behavior between bacteria and virus reduction by graywater solar disinfection. In order to reduce the concentration of total coliforms, Escheriquia coli (E. coli) and enterococcus to non-detectable levels (<1.0 most probable number, MPN 100 ml⁻¹), a combination of GWT >45 °C, and UV radiation >24 W m⁻² was required. In contrast, coliphage MS-2, as viral indicator, was resistant to different UV radiation magnitudes (up to 50 W m⁻²), but with a GWT >55 °C non-detectable levels (<1.0 plaques forming units, PFU) were reached.

The original ARID (Algae Raceway Integrated Design) raceway was an effective method to increase temperature toward the optimal growth range. However, the energy input was high and flow mixing was poor. Thus, the ARID-HV (High Velocity Algae Raceway Integrated Design) raceway was developed to reduce energy input requirements and improve flow mixing. This was accomplished by improving pumping efficiency and using a serpentine flow pattern in which the water flows through channels instead of over barriers. A prototype ARID-HV system was installed in Tucson, Arizona, and the constructability, reliability of components, drainage of channels, and flow and energy requirements of the ARID-HV raceway were evaluated. Each of the electrical energy inputs to the raceway (air sparger, air tube blower, canal lift pump, and channel recirculation pump) was quantified, some by direct measurement and others by simulation. An algae growth model was used to determine the algae production rate vs. flow depth and time of year. Then the electrical energy requirement of the most effective flow depth was calculated. Channel hydraulics was evaluated with Manning's equation and the corner head loss equation. In this way, the maximum length of channels for several raceway slopes and mixing velocities were determined. Algae production in the ARID-HV raceway was simulated with a temperature and light growth model. An energy efficient design for the ARID-HV raceway was developed.

Synoptic global remote sensing provides a multitude of land surface state variables. The continuous collection, for more than 30 years, of global observations has contributed to the creation of a unique and long term satellite imagery archive from different sensors. These records have become an invaluable source of data for many environmental and global change related studies. The problem, however, is that they are not readily available for use in research and application environment and require multiple preprocessing. Here, we looked at the daily global data records from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), two of the most widely available and used datasets, with the objective of assessing their quality and suitability to support studies dealing with global trends and changes at the land surface. Findings show that clouds are the major data quality inhibitors, and that the MODIS cloud masking algorithm performs better than the AVHRR. Results show that areas of high ecological importance, like the Amazon, are most prone to lack of data due to cloud cover and aerosols leading to extended periods of time with no useful data, sometimes months. While the standard approach to these challenges has been compositing of daily images to generate a representative map over a preset time periods, our results indicate that preset compositing is not the optimal solution and a hybrid location dependent method that preserves the high frequency of these observations over the areas where clouds are not as prevalent works better. Using this data quality information the Vegetation Index and Phenology (VIP) Laboratory at The University of Arizona produced over 30 years of seamless sensor independent record of vegetation indices and land surface phenology metrics. These data records consist of 0.05-degree resolution global images for daily, 7-days, 15-days and monthly temporal frequency. These sort of remote sensing based products are normally made available through the internet by large data centers, like the Land Processes Distributed Active Archive Center (LP DAAC), however, in this project an online tool, the VIP Data Explorer, was developed to support the visualization, exploration, and distribution of these Earth Science Data Records (ESDRs) keeping it closer to the data generation center which provides a more active data support and distribution model. This web application has made it possible for users to explore and evaluate the products suite before download and use.

An autonomous machine vision guided plant sensing and monitoring system was designed and constructed to continuously monitor plant related features: color (red-green-blue, hue-saturation-luminance, and color brightness), morphology (top projected canopy area), textural (entropy, energy, contrast, and homogeneity), Normalized Difference Vegetative Index (NDVI) (as well as other similar indices from the color and NIR channels), and thermal (plant and canopy temperature). Several experiments with repeated water stress cycles, using the machine vision system, was conducted to evaluate the machine vision system's performance to determine the timeliness of induced plant water stress detection. The study aimed at identifying significant features separating the control and treatment from an induced water stress experiment and also identifying, amongst the plant canopy, the location of the emerging water stress with the found significant features. Plant cell severity had been ranked based on the cell's accumulated feature count and converted to a color coded graphical canopy image for the remote operator to evaluate. The overall feature analysis showed that the morphological feature, Top Projected Canopy Area, was found to be a good marker for the initial growth period while the vegetation indices (ENDVI, NDVIBlue, and NDVIRed) were more capable at capturing the repeated stress occurrences during the various stages of the lettuce crop. Furthermore, the crop's canopy temperature was shown to be a significant and dominant marker to timely detect the water stress occurrences. The graphical display for the remote user showed the severity of summed features to equal the detection of the human vision. Capabilities and limitations of the developed system and stress detection methodology were documented with recommendations for future improvements for the crop monitoring/production system. An example web based decision support platform was created for data collection, storage, analysis, and display of the data/imagery collected for a remote operator.

This dissertation develops biosensors for rapid detection of pathogens for environmental monitoring and food safety applications and utilizes the multidisciplinary and multi-application characteristics of biosensors to develop a lesson plan that can be implemented in secondary education classrooms. The detection methods evolve from particle immunoagglutination assay, PDMS optofluidic lab-on-a-chip, and spectrum analysis to smartphone and image analysis without any reagent; the potential application in secondary education also underlines the extended value of biosensors. In the first paper presented here, an optofluidic lab-on-a-chip system and subsequent sampling procedure were developed for detecting bacteria from soil samples utilizing Mie scattering detection of particle immunoagglutination assay. This system and protocol detected the presence of Escherichia coli K12 from soil particles in near real-time (10 min) with a detection limit down to 1 CFU mL⁻¹ and has the potential to be implemented in the field. We also compared the interaction between E. coli and soil particles to the two-step protein-surface interaction. In the second paper, a smartphone-utilized biosensor consisting of a near-infrared (NIR) LED (wavelength of 880 nm) and a digital camera of a smartphone was developed for detecting microbial spoilage on ground beef, without using any reagents. The method was further improved by programming a smartphone application that allows the user to position the smartphone at an optimum distance and a range of angles utilizing its internal gyro sensor to measure a series of scatter intensities against the detection angle. This handheld device can be used as a preliminary screening tool to monitor microbial contamination on meat products. In the third paper, we designed a lesson plan for secondary education classrooms using biosensors as a core and branching out to different applications and fields of study with the goal of heightening students' interest and motivation toward attaining degrees and careers in STEM fields. Results revealed that the lesson was more effective in affecting younger students than older students, and more effective in teaching about the applications of biosensors than about the techniques of biosensor development.