Following large rain events, extraneous freshwater contributions known as inflow and infiltration (I/I) bypass the storm sewer and enter the sanitary sewer system. In areas with a high water table, like Pinellas County and the surrounding Tampa Bay area, a majority of the wastewater infrastructure is submerged year round exacerbating the rate of groundwater infiltration. This excess flow overloads the existing wastewater infrastructure leading to sanitary sewer overflows (SSOs). These SSOs result in serious problems for municipalities and utilities across the country. This study was performed in order to assist Pinellas County Utilities in rehabilitating their southern sewer system. To do this, 59 sub-basins across 8 sewer zones were monitored through Pinellas County’s Phase 1 Flow Monitoring Program accounting for over 150 miles of gravity pipe. For each sub-basin, a flow meter was utilized to measure the flow from May to October, 2017. This data was analyzed to separately quantify the amount of infiltration and inflow in each sub-basin, respectively. Once quantified, a Severity Index (SI) was developed in order to give each sub-basin a score from 1-100 as it relates to the condition of the gravity mains in the sub-basin. The SI was a function of locational features available with the use of a Geographic Information System (GIS), such as the distance to water bodies and the soil hydrologic group (SHG), as well as intrinsic pipe properties including the type of pipe material and the age of pipe. Once validated with additional flow monitoring data, the developed SI framework can serve as an additional tool utilized by Pinellas County Utilities to identify areas in need of sanitary sewer rehabilitation. Being that the model only requires easily attainable information, this approach is less time consuming and is inexpensive as compared to traditional flow monitoring efforts. The study also examined the required monetary investment by Pinellas County Utilities in order to abate the 17 sub-basins observed in the study with an infiltration rate greater than the marginal threshold put forth by the Environmental Protection Agency (EPA). The study indicated that gravity pipe rehabilitation does not make a significant impact on groundwater infiltration until at least 30% of the gravity pipes in the sub-basin are lined. This is due to the groundwater table submerging a majority of the wastewater infrastructure. Once this threshold is met, lining was observed to abate groundwater infiltration linearly. The results found that $4.4 million will be required to rehabilitate the affected sub-basins to a marginal rate of infiltration and reduce the flow to South Cross Bayou Water Reclamation Facility (SCBWRF) by an average of 0.72 mgd (million gallons per day). On an annual basis, this reduction in flow will result in approximately $650,000 in treatment costs savings.

Simulation of a wall jet in an enclosure performed to predict the effect of pulsation flow on improving the performance of mixing ventilation systems which are routine practices in industry. Comparing two flows with equal amount for constant and pulsation modes, it was found out that the same global airflow pattern exists for both of the cases but with generation of more eddies and local periodically velocity variations for pulsation mode. This periodic generation of turbulence at pulsatile ventilation flows happen despite the relatively low Reynolds numbers of such flows.Bigger size of boundary layer and higher turbulent kinetic energy for pulsation mode in comparisonwith the same flow rate in constant velocity mode could result in more ventilation capacity with no need to increase the use of energy. It was seen that while a higher constant velocity rate could produce the same acceptable results in terms of higher efficiency in ventilation, a lower pulsated flow could yields it without the risk of draught. Regarding the thesis procedure, the computational solution started with a grid independency study. 2-Dimensional simulation failed to simulate the results similar to the experimental data. No URANS model was able to yield good outcome in 2D mode. The study was continued with 3D SST-kω which yielded good prediction of velocity profiles near the wall regions. For predicting turbulence parameters in the center of the domain SST-URANS was not helpful so, simulation switched to SAS which was successful to some extent to get close to reality.

Studies in the conflict-climate field usually aim to examine how environmental scarcities canbe linked to conflict, but positive outcomes are generally overlooked. Lately, attention isbeing drawn to the relationship between water scarcity and migration flows. Some researchersargue that efficiency in managing resources is likely to be an imminent issue in migrantreceiving areas and competition over resources are common, especially when they are scarce.As is evident, there is a rule rather than exception that climate related events happensimultaneously, yet few studies do focus on the coupled effect of such climatic events. Torealistically estimate responses to climate change, this study will aim to examine weatherwater scarcity, by itself and in combination with migration-inflow, can encouragecooperation. By using disaggregated data, a sub-analysis was conducted throughout countriesbordering the Mediterranean Sea, covering the years 1997-2009. The results of this studyshow that water scarcity increases the likelihood of non-state cooperation. Overall, thissuggests that water management plays an important role in human interaction and should beconsidered in peacebuilding processes.

Surfactants belong to surface-active compounds that have ability to restrain the surface tension; this ability is exploited to eliminate impurities. This study is focused on determination of surfactants in waste water to which these compounds can be transported from various cleaning and washing articles. In theoretical part are listed basic classifications of surfactants, their properties and requests of Czech legislative for their content in waste water. Furthermore here are described chosen analytical procedures for determination of anion-active, cation-active and non-ionic surfactants in waste water. The conclusion of the work is evaluation of measured results of surfactants concentration in inflow and outflow of waste water from waste-treatment plant in University of Veterinary and Pharmaceutical Sciences Brno and waste water from neutralizating station in FCH BUT. To determination of anionic surfactants were used two methods, arbitration method with usage of methylene blue and mobile analytics method which is based on chemical reaction between target compound and chemical agent. This reaction leads to formation of coloured compound able to spectrofotometric determination.

Using a hybrid of the Heckscher–Ohlin model and specific factor model of trade, this article considers the phenomenon of FDI inflows only in the exportable sector of developing economies. We investigate the impact of such capital flow on factor prices and the real exchange rate (RER) in the host country. Our results indicate that the exportable production expands while both the non-traded good production and the return to the factor specific to the non-traded good decrease, consequent upon an inflow of capital specific to the exportable sector. The effect of such inflow of foreign capital on the RER is unambiguous and it increases. JEL Codes: F1, F21, F31

Sewer pipelines are deteriorating due to aging and sub optimal material selections, leading to the infiltration of clean ground and rainfall water into the pipes. It is estimated that a significant portion (up to 40-50%) of the water entering wastewater treatment plants is actually clean infiltrated water. This infiltration not only contributes to unnecessary energy consumption but also poses the risk of flooding the sewer network and treatment plants. Finding these broken pipes is utmost importance but is not straight forward due to the pipes being located a few meters below ground. There exist methods of pinpointing where these leaks occur, but they are often time consuming and expensive. This thesis seeks to address the following question; Can the estimation of infiltration be accomplished solely through the temperature data obtained from discrete pump stations, or is the inclusion of precipitation data essential for achieving accurate results? Two machine learning algorithms are investigated to solve the regression problem of estimating the amount of rainfall derived infiltration. The first model is a classical linear regression model. The second model is a Convolutional neural network (CNN). Both of these models are trained on the same data set. The temperatures recorded at the stations are reliable and can be trusted. However, the data labeling process involves utilizing calculated flows to the stations during both dry and wet weather periods. This means that the labels of the data cannot be trusted to be the actual ground truth, and there exists an uncertainty in the data set. Both models manage to capture large temperature drops which indicates infiltration has occurred. The linear regression model seems to be too sensitive towards small temperature drops and predicts infiltration when there is none. The CNN model on the other hand seems to be able to capture only large temperature drops when infiltration occurs. However, both models are trained with data from only one station, this means that the models are biased towards the average temperature of that particular station, other stations may have a higher or lower average temperature. When testing the models on a different station with lower average temperature the models predict infiltration when there is none.

Inflow forecasting is important when planning the use of water in a hydropower plant. The process of making forecasts is characterized by using knowledge from previous events and occurrences to make predictions about the future. Traditionally, inflow is predicted using hydrological models. The model developed by the Hydrologiska Byråns Vattenbalansavdelning (HBV model) is one of the most widely used hydrological models around the world. Machine learning is emerging as a potential alternative to the current HBV models but needs to be evaluated. This thesis investigates machine learning for inflow forecasting as a mixed qualitative and quantitative case study. Interviews with experts in various backgrounds within hydropower illustrated the key issues and opportunities for inflow forecasting accuracy and laid the foundation for the machine learning model created. The thesis found that the noise in the realised inflow data was one of the main factors which affected the quality of the machine learning inflow forecasts. Other notable factors were the precipitation data from the three closest weather stations. The interviews suggested that the noise in the realised inflow data could be due to faulty measurements. The interviews also provided examples of additional data such as snow quantity measurements and ground moisture levels which could be included in a machine learning model to improve inflow forecast performance. One proposed application for the machine learning model was as a complementary tool to the current HBV model to assist in making manual adjustments to the forecasts when considered necessary. The machine learning model achieved an average Mean Absolute Error (MAE) of 1.39 compared to 1.73 for a baseline forecast for inflow to the Lake Kymmen river system 1-7 days ahead over the period 2015-2019. For inflow to the Lake Kymmen river system 8-14 days ahead the machine learning model achieved an average MAE of 1.68 compared to 2.45 for a baseline forecast. The current HBV model in place had a lower average MAE than the machine learning model over the available comparison period of January 2018. / Tillrinningsprognostisering är viktig vid planeringen av vattenanvändningen i ett vattenkraftverk. Prognostiseringsprocessen går ut på att använda tidigare kunskap för att kunna göra prediktioner om framtiden. Traditionellt sett har tillrinningsprognostisering gjorts med hjälp av hydrologiska modeller. Hydrologiska Byråns Vattenbalansavdelning-modellen (HBV-modellen) är en av de mest använda hydrologiska modellerna och används världen över. Maskininlärning växer för tillfället fram som ett potentiellt alternativ till de nuvarande HBV-modellerna men behöver utvärderas. Det här examensarbetet använder en blandad kvalitativ och kvantitativ metod för att utforska maskininlärning för tillrinningsprognostisering i en fallstudie. Intervjuer med experter med olika bakgrund inom vattenkraft påtalade nyckelfrågor och möjligheter för precisering av tillrinningsprognostisering och lade grunden för den maskininlärningsmodell som skapades. Den här studien fann att brus i realiserade tillrinningsdata var en av huvudfaktorerna som påverkade kvaliteten i tillrinningsprognoserna av maskininlärningsmodellen. Andra nämnvärda faktorer var nederbördsdata från de tre närmaste väderstationerna. Intervjuerna antydde att bruset i realiserade tillrinningsdatana kan bero på felaktiga mätvärden. Intervjuerna bidrog också med exempel på ytterligare data som kan inkluderas i en maskininlärningsmodell för att förbättra tillrinningsprognoserna, såsom mätningar av snömängd och markvattennivåer. En föreslagen användning för maskininlärningsmodellen var som ett kompletterande verktyg till den nuvarande HBV-modellen för att underlätta manuella justeringar av prognoserna när det bedöms nödvändigt. Maskininlärningsmodellen åstadkom ett genomsnittligt Mean Absolute Error (MAE) på 1,39 jämfört med 1,73 för en referensprognos för tillrinningen till Kymmens sjösystem 1–7 dagar fram i tiden under perioden 2015–2019. För tillrinningen till Kymmens sjösystem 8–14 dagar fram i tiden åstadkom maskininlärningsmodellen ett genomsnittligt MAE på 1,68 jämfört med 2,45 för en referensprognos. Den nuvarande HBV-modellen hade ett lägre genomsnittligt MAE jämfört med maskininlärningsmodellen under den tillgängliga jämförelseperioden januari 2018.

The interaction between rotor blade tip leakage vortex and inflow disturbances, such as encountered in shrouded marine propulsors, was simulated in the Virginia Tech Linear Cascade Wind Tunnel equipped with a moving endwall system. Upstream of the blade row, idealized periodic inflow unsteadiness was generated using vortex generator pairs attached to the endwall at the same spacing as the blade spacing. At three tip gap settings, 1.7%c, 3.3%c and 5.7%c, the flow near the lower endwall of the center blade passage was investigated through three-component mean velocity and turbulence distributions measured by four-sensor hotwires. Besides time-averaged data, the measurements were processed for phase-locked analysis, with respect to pitchwise locations of the vortex generators relative to the blade passage. Moreover, surface pressure distributions at the blade tip were acquired at eight tip gaps from 0.87%c to 12.9%c. Measurements of pressure-velocity correlation were also performed with wall motion but without inflow disturbances. Achieved in this study is an understanding of the characteristics and structures of the tip leakage vortex at its initial formation. The mechanism of the tip leakage vortex formation seems to be independent of the tip gap setting. The tip leakage vortex consists of a vortical structure and a region of low streamwise-momentum fluid next to the endwall. The vortical structure is initially attached to the blade tip that creates it. This structure picks up circulation shed from that blade tip, as well as those from the endwall boundary layer, and becomes stronger with downstream distance. Partially induced by the mirror images in the endwall, the vortical structure starts to move across the passage resulting in a reduction in its rotational strength as the cross sectional area of the vortex increases but little circulation is added. The larger the tip gap, the longer the vortical structure stays attached to the blade tip, and the stronger the structure when it reaches downstream of the passage. Phased-averaged data show that the inflow disturbances cause small-scale responses and large-scale responses upstream and downstream of the vortex shedding location, respectively. This difference in scale is possibly dictated by a variation in the shedding location since the amount of circulation in the vortex is dependent on this location. The inflow disturbances possibly cause a variation in the shedding location by manipulating the separation of the tip leakage flow from the endwall and consequently the flow's roll-up process. Even though this manipulation only perturbs the leakage flow in a small scale, the shedding mechanism of the tip leakage vortex amplifies the outcome. / Ph. D.

A rural-urban divide in health status and healthcare infrastructure has been well-documented in the U.S., where populations residing in census regions classified as rural often exhibit more negative health outcomes, adverse health behaviors, and have reduced access to affordable and proximal health services, compared to their urban and peri-urban counterparts. However, it is important to note that such disparities vary based on specific rural regions and individual circumstances. Rural areas may face elevated risk factors for infectious diseases such as increased proximity to wildlife and livestock and disproportionately high reliance on private, non-federally regulated, primary drinking water sources. Chronic conditions prevalent in rural communities such as diabetes and hypertension are frequently linked with longer duration and higher severity of symptoms than in urban areas; this association suggests that the risk of exposure to infectious diseases and the likelihood of progression to serious illness and hospitalization may be elevated, although this is not universally the case across all rural settings. Alongside documented urban-rural health disparities, there also exist disparities in the nature and quality of data on health-related behaviors, outcomes, and service provision in rural areas compared to urban and peri-urban regions. In this dissertation, two key environmental matrices –drinking water and wastewater– were highlighted as vectors of information to better estimate levels of contaminant exposures and health outcomes in rural communities. First, baseline data on drinking water contaminant levels and associated health outcome data were highlighted as crucial for refining holistic exposure estimates as well as understanding drinking water related health burdens in rural communities where a larger proportion of households use private drinking water sources, such as well water, that are not federally regulated. Second, systematic sampling and testing of pathogen biomarkers in wastewater to non-invasively measure population-level health status, also known as wastewater based surveillance (WBS) and, depending on the context, wastewater based epidemiology (WBE) is not constrained by disadvantages of clinical testing, e.g., limited health-care access, long travel times to testing facilities, delay between symptom-onset and testing. Thus, expanded implementation of WBS in rural communities is proposed here as a strategy to address data disparities in clinical testing for infectious diseases. Collectively, this dissertation advances knowledge on estimated drinking water contaminant levels, exposures, and associated public health outcomes and corresponding research gaps in rural Appalachian U.S., and elucidates pathways toward best practices and considerations for public-health focused wastewater testing adoption in rural communities. For the latter, the question of whether WBS challenges unique to rural wastewater systems hinder application of WBS in small, rural communities was explored, as well as methods to advance best-practices for rural WBS. To summarize existing publicly available peer-reviewed literature on drinking water contaminants in rural Appalachian U.S., in Chapter 2, a systematic review and meta-analysis of microbial and chemical drinking water contaminants was performed. Key contaminants were identified as being elevated beyond regulatory, health-based, maximum contaminant levels in our meta-analyses from rural drinking water sources in Appalachia, including E coli, lead, arsenic, uranium. Overall, we found data on drinking water source quality under baseline conditions (i.e., rather than post anomalous contamination events such as chemical spills) in rural Appalachian U.S. was sparse relative to widespread media coverage on the issue. Epidemiologic-based research studies that collected both drinking water exposure data and paired health outcome data were also limited. As a result, although some instances of anomalously high levels of drinking water contaminants were identified in rural Appalachia from the published literature, we could not obtain a clear picture of baseline exposures to drinking water contaminants in most rural Appalachian communities, highlight need to address these knowledge gaps. In Chapter 3, to evaluate whether wastewater could serve as a reliable metric for estimating community circulation of viruses and antimicrobial resistance (AMR) markers, even when sourced from aging and low-resource sewer collection networks, a 12-month wastewater monitoring study was conducted in a small, rural sewer conveyance system with pronounced infrastructural challenges. Specifically, the field site under study was compromised with heavy inflow and infiltration (IandI). Detection rates and concentrations of viral, AMR, and human fecal markers were grouped by levels of IandI impact across the sewershed, and location-, date-, and sample- specific variables were assessed for their relative influence on viral, AMR, and human fecal marker signal using generalized linear models (GLMs). We found that while IandI likely adversely impacted the magnitude of wastewater biomarker signal to some extent throughout the sewershed, especially up-sewer at sites with more pronounced IandI, substantial diminishment of wastewater signal at WWTP influent was not observed in response to precipitation events. Thus, our data indicated that WWTP influent sampling alone can still be used to assess and track community circulation of pathogens in heavily IandI impacted systems, particularly for ubiquitously circulating viruses less prone to dilution induced decay. Delineations were also made for what circumstances up-sewer sampling may be necessary to better inform population shedding of pathogens, especially where IandI is prevalent. Various normalization strategies have been proposed to account for sources of variability for deriving population-level pathogen shedding from wastewater, including those introduced by IandI-driven dilution. Thus, in Chapter 4, we evaluated the temporal and spatial variability of viral and AMR marker signal in wastewater at different levels of IandI, both unnormalized and with the adoption of several normalization strategies. We found that normalization using physicochemical-based wastewater strength metrics (chemical oxygen demand, total suspended solids, phosphate, and ammonia) resulted in higher temporal and site-specific variability of SARS-CoV-2 and human fecal biomarker signal compared to unnormalized data, especially for viral and AMR marker signal measured in wastewater from sites with pronounced IandI. Viral wastewater signal normalized to physicochemical wastewater strength metrics and flow data also closely mirrored precipitation trends, suggesting such normalization approaches may more closely scale wastewater trends towards precipitation patterns rather than per capita signal in an IandI compromised system. We also found that in most cases, normalization did not significantly alter the relationship between wastewater trends and clinical infection trends. These findings suggest a degree of caution is warranted for some normalization approaches, especially where precipitation driven IandI is heightened. However, data and findings largely supported the utility of using human fecal markers such as crAssphage for normalizing wastewater signal to address site-specific differences in dilution levels, since viral signal scaled to this metric did not result in strong correlations between precipitation and wastewater trends, higher spatial and temporal variation was not observed, and strong correlations were observed between viral signal and viral infection trends. Finally, in chapter 5, we assessed the relationship between monthly Norovirus GII, Rotavirus, and SARS-CoV-2 wastewater trends with seasonal infection trends for each of the viruses to ascertain whether WBE could be used in a rural sewershed of this size with substantial IandI impacts to track and potentially predict population level infection trends. Though up-sewer, or near-source sampling, at sites with permanent IandI impacts did not exhibit a clear relationship with seasonal infection trends for Rotavirus, SARS-CoV-2, and Norovirus GII, WWTP influent signal and consensus signals aggregated from multiple up-sewer sites largely mirrored expected seasonal trends. Findings also suggested that for more ubiquitous viral targets, such as SARS-CoV-2, viral trends measured at WWTP influent in a small IandI impacted system may still provide a sufficiently useful measure of infection trends to inform the use of WBE (assuming appropriate normalization to sewershed population). These findings elucidate the potential utility and relative robustness of wastewater testing to ascertain community-level circulation of pathogens in small, rural sewersheds even those compromised by extensive IandI inputs. Overall, this dissertation examined drinking water and wastewater as critical metrics for assessing contaminant exposures and infectious disease trends in rural communities, particularly in the context of small, rural communities which tend to have more limited health infrastructure and lower-resource wastewater systems. Overall, findings underscore the need for baseline data on drinking water quality by identifying gaps in current knowledge and calling for further research to better understand drinking water contaminant exposure levels in rural areas. Wastewater as a non-invasive, population-level health metric was evaluated in the context of a small, rural sewer system overall, and by varying observed levels of IandI, as well as associated tradeoffs for normalization adoption. By evaluating these environmental surveillance metrics using both desk-based and field-based research study designs, findings from this dissertation offer valuable insights and practical recommendations for improving baseline drinking water quality monitoring and wastewater pathogen testing, all with the overarching goal of supporting more targeted public health interventions in rural settings. / Doctor of Philosophy / In the United States, there is a significant health and healthcare gap between rural and urban areas. Rural communities often face worse health outcomes, poorer health behaviors, and have less access to affordable and nearby healthcare services compared to their urban and peri-urban counterparts. Additionally, rural areas are exposed to higher risks for infectious diseases due to closer proximity to wildlife and livestock and proportionately lower access to regulated drinking water sources. Chronic conditions like diabetes and hypertension, which are more common in rural populations, can exacerbate the severity and duration of symptoms for infectious diseases, potentially leading to more serious illness and hospitalizations. Despite these heightened risks, data on health behaviors, outcomes, and healthcare services in rural areas is often lacking and less comprehensive compared to urban regions. This dissertation investigates two promising avenues of improving monitoring to provide information needed to better understand and address contaminant exposures and health trends in rural communities: drinking water and wastewater. Firstly, this dissertation underscores the importance of establishing baseline data on drinking water quality. This is essential for accurately estimating exposure levels and understanding the health impacts associated with elevated levels of drinking water contaminants, particularly in rural areas where a higher share of primary drinking water sources is unregulated by the federal government compared to urban areas. This study reveals significant gaps in current knowledge and highlights the need for more research to provide a clearer picture of drinking water quality in these communities. Secondly, this dissertation explores the use of wastewater as a non-invasive tool for assessing community health. This method, known as wastewater-based surveillance (WBS) or wastewater-based epidemiology (WBE), offers a way to measure population-level health trends without relying on clinical testing, which can be limited by factors such as access to healthcare and delays in testing. The dissertation evaluates how effective wastewater monitoring can be in small, rural sewer systems, even when these systems face challenges like aging infrastructure and significant inflow and infiltration (IandI) from groundwater and surface water. It examines how different normalization strategies for wastewater data can influence the reliability of this method and how wastewater testing can be adapted to account for varying levels of IandI. Overall, the dissertation provides valuable insights into the effectiveness of using drinking water and wastewater as environmental metrics for informing public health intervention strategies in rural settings. It offers justifications for improving drinking water quality monitoring and wastewater testing practices, aiming to support more targeted and effective public health interventions in rural communities. By addressing the challenges and limitations associated with these environmental monitoring strategies this research contributes to a better understanding of how to reduce health data disparities in rural areas.

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2005. / The two-dimensional flow-field through the stationary blade row of a radial inflow turbine in a torque converter was analysed by means of a potential flow model and a viscous flow model. The purpose was to compare the accuracy with which the two flow models predict the flow field through the static turbine blade row. The freestream turbulence level necessary to optimise the accuracy of the viscous flow model was also investigated. A first order source-vortex panel method with flat panels was used to apply the potential flow model. A radial inflow freestream was used. It was found that the stator blade row directly upstream of the turbine had to be included in the analysis to direct the flow at the turbine inlet. Even then the panel method did not satisfactorily predict the pressure distribution on a typical blade of the static 2nd turbine blade row. A two-dimensional viscous flow model gave excellent results. Furthermore, the two-dimensional viscous flow model was simple to set up due to the fact that symmetry boundary conditions could be used. This facilitated useful predictions of the salient features of the two-dimensional flow through the middle of the radial turbine blade row.