The general aim of Master’s thesis was to analyze a water supply network in the village Klobouky u Brna using the WaterRisk software through the simple and complex methodology. It was performed a measurement of turbidity on water supply network as practical support of undesirable states. Also it was accomplished detailed exploration of the village Klobouky u Brna. Added value of Master’s thesis was a consultation related to a water supply and a water treatment system with technician working for the Vodovody a kanalizace Hodonín company wherefrom water is bought for the village Klobouky u Brna.

<p>The World Health Organization reports 9% of the world’s population lack access to an improved drinking water source. Safe drinking water is a major global challenge, especially in rural areas where according to UNICEF 80% of those without access to improved water systems reside. While water, sanitation, and hygiene (WASH) related diseases and deaths are common outcomes of unsafe water, there is also an economic burden associated with unsafe water. These burdens are most prominent in rural areas in less developed nations. Slow sand filters (SSFs), or biological sand filters (BSF), are ideal water treatment solutions for these low resource regions. SSFs are the oldest municipal drinking water treatment system and improve water quality by removing suspended particles, dissolved organic chemicals, and other contaminants, effectively reducing turbidity and associated taste and odor problems. Removal of turbidity from the water enables the use of low-cost disinfection methods such as chlorination. While the working principles of slow sand filtration remained the same, the design, sizes and application of slow sand filters have been customized over the years. The first chapter of thesis reviews these adaptations and their performance on contaminant removal, and specifically addresses engineering aspects of slow sand filters that are not widely understood, even by those that implement SSFs in the field.</p> <p>The second and third chapters detail an SSF-based water treatment and monitoring system that seeks to provide portable water to rural schools and communities. Piping drinking water to remote rural areas from centralized treatment facilities requires huge capital investments. On the other hand, delivering drinking water by the less expensive point‐of‐use technologies often results in improper operation, and lack of proper documentation on water quality and usage.</p> <p><br></p> <p>The strategy documented in this research for addressing this problem is to produce drinking water at the point-of-use, and then establish and document drinking water quality through cellphone-based monitoring of this water. By doing both (point-of-use treatment and cellphone-based monitoring), we are effectively using to advantage the best of both worlds. Decentralized (point-of-use) water treatment systems can be deployed in rural communities to produce potable water. Integrating a cellphone-enabled colorimeter-turbidity meter (CT meter), developed as part of this research, into the water treatment system will provides water quality data to ensure public health safety. The integrated water system included slow sand filtration, chlorination, and phone-based monitoring (i.e., the CT meter). To establish larger-scale (thousands of schools) feasibility, pilot treatment systems were established in 3 rural schools in Kenya. This pilot network was established through the collaborative efforts of: (i) The research team at Purdue, (ii) MaJi Safi International (MSI), a Purdue related startup based in Eldoret, Kenya, and (iii) several western Kenya Schools.</p> <p><br></p> <p>The second chapter of details the design and testing of the CT meter at Purdue. The third chapter evaluates, through pilot field tests in Kenyan schools, the integrated water treatment and monitoring system for economic and technical viability. The CT meter performance was successful both in the lab and in the field. The water systems that were installed, used daily, and monitored with the CT meter, consistently produced portable water that met the local regulatory drinking water standards.</p>

<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>