A thesis submitted to the School of Graduate Studies in partial fulfillment of the requirements for the degree Master of Science / The effect of changing environmental conditions on microbial population structure can be observed at both the species and community level. Within the Escherichia coli species, null mutations in the RpoS stationary phase regulator are commonly selected by growth on poor carbon sources. In contrast, mutations which restore RpoS function may provide a selective advantage for cells exposed to environmental stress. The loss and subsequent restoration of RpoS form a population-level switch for adaptation within poor carbon and high stress environments. To investigate selection for RpoS reversion, we exposed rpoS-deficient E. coli to high salt concentrations and assessed the phenotype of presumptive mutants. 3-9% of salt-resistant mutants contained reversion mutations within rpoS, while in 91-97% the loss of RpoS function was maintained and mutations at alternative gene loci were identified. These results show that RpoS function can be restored in deficient E. coli under selective pressure. At the community level, the application of next-generation sequencing (NGS) technology to characterize environmental microbial diversity can potentially augment traditional water quality monitoring methods. To investigate the use of NGS in identifying microbial taxa within the Niagara Region, we collected water samples from Lake Erie, Lake Ontario, and nearby areas and examined the metagenome of microbial communities. A QIIME (Quantitative Insights Into Microbial Ecology) analysis of sequence data identified significant differences in relative microbial abundance with respect to sample metadata (e.g. location and subtype), significant correlations between relative abundance and quantitative parameters (e.g. Escherichia coli counts and fecal DNA markers), and detected pathogen-containing taxa at a relative abundance of 0.1-1.5%. These results show that sequence-based analyses can be used in conjunction with traditional identification methods to profile the metagenomic community of environmental samples and predict water quality. Both within-species and community-wide analyses thus offer insight into how microbial populations respond and adapt to environmental fluctuations. / Thesis / Master of Science (MSc) / The effect of changing environmental conditions on microbial population structure can be observed at both the species and community level. Within the Escherichia coli species, we investigated reversion of loss of function mutations in the RpoS protein regulator in high salt conditions and identified RpoS restoration under selective pressure. At the community level, we examined the microbial DNA of water samples from the Niagara Region under select environmental conditions and assessed the viability of next-generation sequencing in augmenting traditional water quality monitoring methods. Both within-species and community-wide analyses offer insight into how microbial populations respond and adapt to environmental fluctuations.

Department of Hydrology and Water Resources / PhDH

<p>A neural network model was developed to predict the E. Coli levels and classes in six (6) select Lake Michigan beaches. Water quality observations at the time of sampling and discharge information from two close tributaries were used as input to predict the E. coli. This research was funded by the Indiana Department of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was developed based on the best model for making future predictions of E. coli classes. This tool will facilitate beach managers to take real-time decisions.</p> <p>The nowcast model was developed based on historical tributary flows and water quality measurements (physical, chemical and biological). The model uses experimentally available information such as total dissolved solids, total suspended solids, pH, electrical conductivity, and water temperature to estimate whether the E. Coli counts would exceed the acceptable standard. For setting up this model, field data collection was carried out during 2019 beachgoer’s season.</p> <p>IDEM recommends posting an advisory at the beach indicating swimming and wading are not recommended when E. coli counts exceed advisory standards. Based on the advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony forming units per 100 milliliters (cfu/100ml). Advisories are removed when bacterial levels fall within the acceptable standard. However, the E. coli results were available after a time lag leading to beach closures from previous day results. Nowcast models allow beach managers to make real-time beach advisory decisions instead of waiting a day or more for laboratory results to become available.</p> <p>Using the historical data, an extensive experiment was carried out, to obtain the suitable input variables and optimal neural network architecture. The best feed-forward neural network model was developed using Bayesian Regularization Neural Network (BRNN) training algorithm. Developed ANN model showed an average prediction accuracy of around 87% in predicting the E. coli classes. </p>

No description available.

This study addresses the challenges faced by industries requiring precise water quality monitoring by developing and evaluating a modular multi-sensor system. Existing solutions often lack scalability and flexibility, necessitating multiple devices for comprehensive analysis. The methodology employed a recursive prototype development approach, integrating various hardware and software components, including microcontrollers and a user-friendly mobile application. The prototype facilitated real-time data acquisition and management through a dedicated server, supporting essential water quality parameters such as pH, temperature, and conductivity. Results indicate that the system significantly enhances measurement accuracy and operational efficiency. However, the implementation of a smart home connectivity standard was unsuccessful, highlighting the complexities associated with integrating new communication protocols. Despite this, the system offers a scalable, cost-effective solution for continuous water quality monitoring, presenting significant improvements over existing technologies in terms of flexibility, user engagement, and data reliability. / Denna studie behandlar de utmaningar som industrier står inför som kräver noggrann övervakning av vattenkvalitet genom att utveckla och utvärdera ett modulärt multisensorsystem. Befintliga lösningar saknar ofta skalbarhet och flexibilitet, vilket kräver flera enheter för omfattande analys. Metodiken använde en rekursiv prototyputvecklingsansats, som integrerade olika hårdvaru- och mjukvarukomponenter, inklusive mikrokontroller och en användarvänlig mobilapplikation. Prototypen underlättade insamling och hantering av realtidsdata genom en dedikerad server, som stödde viktiga vattenkvalitetsparametrar såsom pH, temperatur och konduktivitet. Resultaten indikerar att systemet avsevärt förbättrar mätnoggrannhet och operationell effektivitet. Dock var implementeringen av smarta hem-anslutningsstandard inte framgångsrik, vilket belyser komplexiteten med att integrera nya kommunikationsprotokoll. Trots detta erbjuder systemet en skalbar, kostnadseffektiv lösning för kontinuerlig övervakning av vattenkvalitet, med betydande förbättringar jämfört med befintliga teknologier när det gäller flexibilitet, användarengagemang och datareliabilitet.