Understanding Microbial Agents and Exposures through the Collection and Production of Urine-Derived Fertilizers

Source-separated urine is rich in nutrients and provides numerous benefits, including: offsetting energy requirements at wastewater treatment plants; offsetting energy required to produce nitrogen and phosphorus fertilizers; reducing the environmental impact of fertilizer production; and providing an alternative source of fertilizer. Source-separated urine can contain chemical and biological contaminants that need to be managed prior to its use as a fertilizer. Bacteria, viruses, and extracellular nucleic acids, if present in fertilizer, all have the potential to impact the environment being fertilized and consumers of fertilized products. Thus, it is important to understand their behavior and fate in urine and urine-derived products. Information about the fate of chemical and biological contaminants can help inform appropriate treatment technologies that transform urine into useful products while mitigating public and environmental health exposures. This dissertation is focused on microbiological contaminants that may impact public and environmental health. The presence of polyomavirus, a urinary tract virus, was evaluated in stored urine in which urea had been hydrolyzed and the solution pH was around 9.0. Polyomavirus infectivity measured through tissue culture assays was compared to its genome integrity measured through qPCR assays. The virus infectivity was also compared to two surrogate viruses, the bacteriophages MS2 and T3. The infectivity of polyomavirus decreased rapidly in stored urine within 1.1 - 11 hours, compared to surrogate virus infectivity, which remained stable for 3 - 5 weeks. In contrast, polyomavirus genomes were stable for more than 3 weeks despite this loss of infectivity. This led us to look at the fate of extracellular DNA, which may carry antibiotic resistance genes, in hydrolyzed urine. DNA transformation, integrity, and conformation were evaluated using transformation assays, qPCR assays, and gel electrophoresis. Based on filtered and pasteurization experiments, the loss in transformation efficiency correlated to plasmid linearization and appeared to be microbially driven, likely from organisms smaller than 0.22 μm or enzymes larger than 100 kDa. Collectively, these results indicate that the microbial activity of hydrolyzed urine reduced viral infectivity and the transformation of extracellular DNA, decreasing both the risk of exposure to infectious polyomavirus and spread of plasmid- associated antibiotic resistance genes. Finally, urine-diverting toilets, which are used to collect urine separate from other wastes, were compared to conventional flush toilets in terms of virus exposure. Virus-laden droplets were detected at a higher frequency outside the conventional institutional high- flush toilet compared to a urine- diverting toilet, indicating an added benefit of urine- diverting toilets. We conclude that the conditions of hydrolyzed urine reduce the potential risk of polyomaviruses and plasmid- associated antibiotic resistance gene transfer, and that using urine- diverting toilets can reduce one’s exposure to viruses from flushing events. Because storage is a common pretreatment before other fertilizer conversion technologies, this work demonstrates that microbial risks may be low and further advances the possibility of recovering urine for beneficial reuse.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147522/1/hgoetsch_1.pd

Geometric Approaches to Big Data Modeling and Performance Prediction

Big Data frameworks (e.g., Spark) have many configuration parameters, such as memory size, CPU allocation, and the number of nodes (parallelism). Regular users and even expert administrators struggle to understand the relationship between different parameter configurations and the overall performance of the system. In this work, we address this challenge by proposing a performance prediction framework to build performance models with varied configurable parameters on Spark. We take inspiration from the field of Computational Geometry to construct a d-dimensional mesh using Delaunay Triangulation over a selected set of features. From this mesh, we predict execution time for unknown feature configurations. To minimize the time and resources spent in building a model, we propose an adaptive sampling technique to allow us to collect as few training points as required. Our evaluation on a cluster of computers using several workloads shows that our prediction error is lower than the state-of-art methods while having fewer samples to train