Threats to critical infrastructure are increasing, especially within the water industry. The purpose of this study was to evaluate the history of water attacks within the United States, determine the vulnerabilities, examine current and future methods of attack, and identify potential trends. Risk assessments of the water supply systems were evaluated based on threats, vulnerabilities, and consequences according to the 4 attack methods: physical, cyber, biological, and chemical. Each attack method was given a comprehensive background in the context of historical evidence, vulnerabilities, prevention, and response to a potential attack. In analyzing the risk assessment of water supply systems, it was determined that a majority of the vulnerabilities are due to outdated equipment or structure failure. Additionally, the increase in cyber-attacks worldwide signals a threat the U.S. is unprepared for. Therefore, physical, cyber, biological, and chemical attacks can be best mitigated through prevention and detection. Some prevention methods include establishing emergency protocols, training employees in risk management, and running emergency simulations.

Drinking water quality violations are widespread in the United States and elsewhere in the world. More than half of Americans are not confident in the safety of their tap water, especially after the 2014 Flint, Michigan water crisis. Other than accidental contamination events, stagnation is a major cause of water quality degradation. Thus, there is a pressing need to build a real-time control system that can make control decisions quickly and proactively so that the quality of water can be maintained at all times. However, towards this end, modeling the dynamics of water distribution systems are very challenging due to the complex fluid dynamics and chemical reactions in the system. This challenge needs to be addressed before moving on to modeling the optimal control problem. The research in this dissertation leverages statistical machine learning approaches in approximating the complex water system dynamics and then develops different optimization models for proactive and real-time water quality control. This research focuses on two effective ways to maintain water quality, flushing of taps and injection of chlorine or other disinfectants; both of these actions decrease the equivalent “water age”, a useful proxy for water quality related to bacteria growth. This research first develops linear predictive models for water quality and subsequently linear programming optimization models for proactive water age control via flushing. The second part of the research considers both flushing and disinfectant injections in the control problem and develops mixed integer quadratically constrained optimization models for controlling water age. Different control strategies for disinfectant injections are also evaluated: binary on-off injections and continuous injections. In the third part of the research, water demand is assumed to be uncertain and stochastic. The developed approach to control the system relates to learning the optimal real-time flushing decisions by combing reinforced temporal-difference learning approaches with linear value function approximation for solving approximately the underlying Markov decision processes. Computational results on widely used simulation models demonstrates the developed control systems were indeed effective for water quality control with known demands as well as when demands are uncertain and stochastic.

Chlorine is a typical disinfectant that is used in water treatment. There are different forms and types of chlorine that are used as disinfectants, City of Tempe uses free chlorine. Regulations require that a residual is detected to ensure drinking water is “safe.” Large commercial buildings can undergo difficulties with maintaining a chlorine residual on every floor due to issues with underusage or lack of occupancy. Monitoring systems can activate an automatic flow of fresh water throughout the building or in a specific location based on set levels of chlorine within the monitoring system. There are different approaches to monitoring chlorine residual. Chlorine sensors are the typical process, but can be very expensive due to replacement materials required to promote further use of the sensor and acquire accurate measurements. Also, the chlorine system may require continual maintenance due to membrane replacement and management of the pressurized flow, which is required for accurate measurements. Oxidation reduction potential (ORP) is an efficient alternative from a cost perspective and accurate if the relationship between chlorine and ORP is understood.

Humans are exposed up to thousands of per- and polyfluoroalkyl substances (PFAS) in the environment, but most of the research and action has been directed towards only two PFAS compounds. These two compounds are part of a subcategory of PFAS called perfluoroalkyl acids (PFAAs). It has been a challenge for the environmental community to mitigate risks caused by PFAAs due to their high persistence and lack of effective measures to remove them from the environment, especially in heavily impacted areas like fire-training sites. The goal of this work was to further answer some questions regarding the removal of PFAAs in the environment by looking at anion exchange resin characteristics and presence of a competing compound, natural organic matter (NOM), in the adsorption of environmentally relevant PFAS compounds including the two often monitored 8-carbon chain PFAAs. Two different resins were tested with two forms of counterions, in both groundwater and NOM impacted groundwater. Resin polymer matrix was the most important property in the adsorption of PFAAs, the two resins used A520E and A860 had similar properties except for their matrices polystyrene (PS) and polyacrylic (PA), respectively. The PS base is most effective at PFAAs adsorption, while the PA is most effective at NOM adsorption. The change in the counterion did not negatively affect the adsorption of PFAAs and is, therefore, a viable alternative for future studies that include regeneration and destruction of PFAAs. The presence of NOM also did not significantly affect the adsorption of PFAAs in the PS resin A520E, although for some PFAAs compounds it did affect adsorption for the PA resin. Ultimately, PS macroporous resins with a strong Type I or Type II base work best in PFAAs removal.

Zero-Valent Metals (ZVM) are highly reactive materials and have been proved to be effective in contaminant reduction in soils and groundwater remediation. In fact, zero-Valent Iron (ZVI) has proven to be very effective in removing, particularly chlorinated organics, heavy metals, and odorous sulfides. Addition of ZVI has also been proved in enhancing the methane gas generation in anaerobic digestion of activated sludge. However, no studies have been conducted regarding the effect of ZVM stimulation to Municipal Solid Waste (MSW) degradation. Therefore, a collaborative study was developed to manipulate microbial activity in the landfill bioreactors to favor methane production by adding ZVMs. This study focuses on evaluating the effects of added ZVM on the leachate generated from replicated lab scale landfill bioreactors. The specific objective was to investigate the effects of ZVMs addition on the organic and inorganic pollutants in leachate. The hypothesis here evaluated was that adding ZVM including ZVI and Zero Valent Manganese (ZVMn) will enhance the removal rates of the organic pollutants present in the leachate, likely by a putative higher rate of microbial metabolism. Test with six (4.23 gallons) bioreactors assembled with MSW collected from the Salt River Landfill and Southwest Regional Landfill showed that under 5 grams /liter of ZVI and 0.625 grams/liter of ZVMn additions, no significant difference was observed in the pH and temperature data of the leachate generated from these reactors. The conductivity data suggested the steady rise across all reactors over the period of time. The removal efficiency of sCOD was highest (27.112 mg/lit/day) for the reactors added with ZVMn at the end of 150 days for bottom layer, however the removal rate was highest (16.955 mg/lit/day) for ZVI after the end of 150 days of the middle layer. Similar trends in the results was observed in TC analysis. HPLC study indicated the dominance of the concentration of heptanoate and isovalerate were leachate generated from the bottom layer across all reactors. Heptanoate continued to dominate in the ZVMn added leachate even after middle layer injection. IC analysis concluded the chloride was dominant in the leachate generated from all the reactors and there was a steady increase in the chloride content over the period of time. Along with chloride, fluoride, bromide, nitrate, nitrite, phosphate and sulfate were also detected in considerable concentrations. In the summary, the addition of the zero valent metals has proved to be efficient in removal of the organics present in the leachate.

Nitrate contamination to groundwater and surface water is a serious problem in areas with high agricultural production due to over application of fertilizers. There is a need for alternative technologies to reduce nutrient runoff without compromising yield. Carbon nanoparticles have adsorptive properties and have shown to improve germination and yield of a variety of crops. Graphite nanoparticles (CNP) were studied under a variety of different fertilizer conditions to grow lettuce for the three seasons of summer, fall, and winter. The aim of this thesis was to quantify the effect of CNPs on nitrate leaching and lettuce growth. This was accomplished by measuring the lettuce leaf yield, formulating a nutrient balance using the leachate, plant tissue, and soil data, and changing the hydraulic conductivity of the soil to assess the effect on nutrient mobility. summer and fall experiments used Arizona soil with different amounts of nitrogen, phosphorus, and potassium (NPK) fertilizer being applied to the soil with and without CNPs. The winter experiments used three different soil blends of Arizona soil, Arizona soil blended with 30% sand, and Arizona soil blended with 70% sand with a constant fertilizer treatment of 30% NPK with and without CNPs. The results showed that the 70% NPK with CNP treatment was best at reducing the amount of nitrate leached while having little to no compromise in yield. The winter experiments showed that the effectiveness of CNPs in reducing nitrate leaching and enhancing yield, improved with the higher the hydraulic conductivity of the soil.

As we already know, fresh water is essential to human life as it sustains and replenishes our bodies. Water sustainability is clearly an important issue that need to be addressed in our world of growing demand and shrinking resources. The ASU Future H2O program seeks to make a difference in the development of water sustainability programs by performing experiments that convert urine into reusable water. The goal is to make reusable water processes become inexpensive and easily accessible to local businesses. This promises a significant environmental impact. In order to make the process of development more efficient we can combine engineering technology with scientific experimentation. As an engineering student and an advocate of water sustainability, I have a chance to design the front-end platform that will use IoT to make the experimental process more accessible and effective. In this paper, I will document the entire process involved in the designing process and what I have learned.