<div>International development projects are a massive business, with billions invested annually in the Global South. However, such projects have an unacceptably long record of high failure rates. The problem perpetuates, in part, due to the success factors by which international development projects are judged. Often, projects are assessed on the basis of donor-identified priorities that are not aligned with local impacts. One such international development project involves the construction of small-scale water harvesting structures known as sand dams. Non-governmental organizations (NGOs) continue to raise sufficient funds to build thousands of sand dams across sub-Saharan Africa, and yet 50% of sand dams are estimated to be non-functioning.</div><div><br></div><div>Sand dams are small, reinforced concrete dams built across an impermeable stream-bed. Over time, sand settles behind the dam, creating an upstream sand reservoir that fills with rainwater and surface runoff. The sand helps filter the water, protects it from evapotranspiration, and can provide water to the local community for domestic and agricultural use during the dry season. Sand dams often fail due to poor construction, inadequate siting, and siltation.</div><div><br></div><div>This dissertation explores methodologies for studying the regional and local impacts of sand dams and investigates the feasibility of developing model-based site selection guidelines for sand dams. Three objectives of this study are: (1) to develop a methodology to assess the ability of sand dams in improving the overall water availability in the region; (2) to examine claims made by non-scientific bodies about sand dam impacts by investigating how diverse sand dams influence macroinvertebrate habitat, vegetation, erosion, and local water availability; and (3) to create guidelines for siting new sand dams based on a fully integrated surface and groundwater flow model.</div><div><br></div><div>For the first objective, two multiple regression models are developed to compare (1) water storage and (2) vegetation in an area with a high density of sand dams, termed the sand dam counties (SDC), to those in a control area. The models analyze remotely sensed datasets to assess whether evidence exists of significantly increased storage in the SDC relative to the control area. The results show that the remotely sensed water storage data is unable to consistently detect higher levels of water storage in the SDC. This is likely due to the low resolution of the dataset combined with the small magnitude of sand dams' impact on regional water storage. The results of the vegetation model show that the sand dams have a consistent, positive impact on vegetation within the SDC relative to the control area. Because vegetation health and cover is often correlated with groundwater levels, these results likely indicate that the sand dams are also increasing local groundwater levels. Overall, this study shows that remotely sensed dataset can provide a useful basis to assess the impact of international development projects, particularly those that involve the natural environment. </div><div><br></div><div>For the second objective, data relating to macroinvertebrates, vegetation, erosion, and water table elevations at three sand dams were collected and analyzed during a year-long field study in Tanzania. These study subjects were specifically selected to test an NGO claim that sand dams revitalize the entire ecosystem. The results of this study show that sand dams are not a suitable habitat for macroinvertebrates due to their homogeneity. The impact of sand dams on vegetation cover can be significant, but may be limited by the slope of the surrounding land. Functioning sand dams likely have little impact on streambank erosion, but non-functioning sand dams may contribute to the erosion of streambanks in unstable reaches. Lastly, the water table is locally raised by recharge from sand dams, however, the spatial and temporal extent of the impact is more limited than conveyed by NGOs and previous studies. This study adds to the limited body of knowledge on the environmental responses to sand dams and demonstrates the importance of examining the local impacts of individual international development projects. </div><div><br></div><div>For the third objective, results from four different simulations of a watershed-based model with three cascading sand dams are analyzed to identify overland features that improve vadose zone storage and groundwater recharge and reduce evapotranspiration. Results from this study show that sand dams constructed in a low-lying area that collects surface runoff from adjacent steep slopes, such as in a U-shaped valley, will likely collect and store sufficient water for use by a local community. Watersheds with relatively more area cultivated with low-water-need crops will similarly be beneficial to sand dam performance. In addition, the analysis revealed that the volume of water a sand dam receives during a rainy season is less important for water storage than the duration of dry seasons. Lastly, the simulations showed that sand dams constructed in an area with sandier soils will perform better than those in an area with loamy soils. This study produced a set of guidelines that can be used to identify locations where sand dams are likely to capture and store sufficient water for community use during the dry season.</div>

<p>  </p> <p>Floods all over the world are one of the most common and devastating natural disasters for human society, and the flood risk is increasing recently due to more and more extreme climatic events. In the United States, one of the key resources that provide the flood risk information to the public is the Flood Insurance Rate Map (FIRM) administrated by the Federal Emergency Management Agency (FEMA) and the digitalized FIRMs have covered over 90% of the United States population so far. However, the uncertainty in the modeling process of FIRMs is rarely investigated. In this study, we use two of the widely used multi-model methods, the Bayesian Model Averaging (BMA) and the generalized likelihood uncertainty estimation (GLUE), to evaluate and reduce the impacts of various uncertainties with respect to modeling settings, evaluation metrics, and algorithm parameters on the flood modeling of FIRMs. Accordingly, three objectives of this study are to: (1) quantify the uncertainty in FEMA FIRMs by using BMA and Hierarchical BMA approaches; (2) investigate the inherent limitations and uncertainty in existing evaluation metrics of flood models; and (3) estimate the BMA parameters (weights and variances) using the Metropolis-Hastings (M-H) algorithm with multiple Markov Chains Monte Carlo (MCMC).</p> <p><br></p> <p>In the first objective, both the BMA and hierarchical BMA (HBMA) approaches are employed to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the State of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady flow model configurations that incorporate four uncertainty sources including bridges, channel roughness, floodplain roughness, and upstream flow input. Given the ensemble predictions and the observed water stage data in the training period, the BMA weight and the variance for each model member are obtained, and then the BMA prediction ability is validated for the observed data from the later period. The results indicate that the BMA prediction is more robust than both the original FEMA model and the ensemble mean. Furthermore, the HBMA framework explicitly shows the propagation of various uncertainty sources, and both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges. Hence, it provides insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process. The results show that the probabilistic flood maps developed based on the BMA analysis could provide more reliable predictions than the deterministic FIRMs.</p> <p><br></p> <p>In the second objective, the inherent limitations and sampling uncertainty in several commonly used model evaluation metrics, namely, the Nash Sutcliffe efficiency (<em>NSE</em>), the Kling Gupta efficiency (<em>KGE</em>), and the coefficient of determination (<em>R</em>2), are investigated systematically, and hence the overall performance of flood models can be evaluated in a comprehensive way. These evaluation metrics are then applied to the 1D HEC-RAS models of six reaches located in the states of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of these evaluation metrics are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics should be treated as random statistical variables given both aleatory and epistemic uncertainties in the modeling process. Additionally, the white-noise error in observations has the least impact on the evaluation metrics.</p> <p><br></p> <p>In the third objective, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the MCMC method, is proposed to estimate the BMA parameters (weights and variances), since the reliability of BMA parameters determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Both numerical experiments and two practical 1D HEC-RAS models in the states of Indiana and Texas of the United States are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance of water stage predictions is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.</p>

<p dir="ltr">Reservoirs play a vital role in water resource management, serving essential functions such as flood mitigation, water supply, power generation, and environmental conservation. In the U.S., many of these structures were constructed in the 1900s, and were primarily designed as single purpose facilities for flood risk reduction. Facing increasing threats of water shortages and groundwater depletion, the transition of these reservoirs to multi-purpose operations has never been more imperative. Operational modifications and optimizations emerge as a promising solution, offering cost-effectiveness, swift implementation, and minimal ecological disruption.</p><p dir="ltr">This dissertation advances the theory and framework of modification and optimization of reservoir operations to facilitate their transition from single to multi-purpose use. This dissertation begins with targeted optimization of static operations and progressively advances to dynamic strategies across complex multi-reservoir-river systems. This dissertation sets three primary objectives: (1) To develop a comprehensive framework for assessing the conversion potential of single-purpose reservoirs and optimizing static operation strategies for enhanced multi-purpose functionality. (2) To devise dynamic control strategies that bolster reservoir performance during extreme events through the implementation of inflow-based pre-release operations. (3) To employ a Multi-Objective Simulation-Optimization (MOSO) framework that integrates large-scale datasets and advanced optimization algorithms, optimizing multi-purpose, multi-reservoir operations in complex systems and enhancing decision-making through Multi-Criteria Decision-Making (MCDM) methods.</p><p dir="ltr">In the first objective, a robust framework is developed to evaluate and facilitate the conversion of single-purpose reservoirs into multi-purpose systems. Leveraging historical data, the proposed framework establishes Maximum Safe Water Levels (MSWLs) to optimize flood control while enhancing water supply capabilities. The methodology incorporates numerical reservoir simulation models alongside historical inflow data analysis of 15 reservoirs operated by the U.S. Army Corps of Engineers, Louisville District, all originally designed exclusively for single-purpose flood control. The findings reveal opportunities for some reservoirs to significantly increase their water supply without compromising flood management efficiency.</p><p dir="ltr">The second objective delves into dynamic control strategies for reservoir operation, with a focus on pre-release mechanisms. This objective utilizes inflow-based forecasting models to assess the impacts of different pre-release timings on flood mitigation. This study focuses on 11 of the reservoirs identified in the first objective as having potential for transition to multi-purpose use, exploring dynamic operational adjustments necessary for enhanced performance. The results show that a 72-hour pre-release lead time markedly enhanced flood control effectiveness, whereas a 24-hour lead time provides a practical compromise, achieving substantial flood mitigation with minimal adverse impacts.</p><p dir="ltr">The third objective involves developing an advanced framework utilizing the Multi-Objective Simulation-Optimization (MOSO) model and extensive datasets to optimize pre-release operations in multi-purpose reservoirs. Implementing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Criteria Decision-Making (MCDM) methods, the framework integrates reservoir simulation models and flow routing to refine operations based on projected flood forecasts. Applied to the Green River watershed in Kentucky, this method produces Pareto-optimal solutions, elucidating the trade-offs between flood control, water supply reliability, and downstream channel performance. The results underscore the framework’s potential to significantly refine operational strategies, bolstered by sensitivity analyses that explore the effects of varying storage levels and inflow conditions, thus fostering adaptive, data-driven management for sustainable water resource optimization.</p><p dir="ltr">This dissertation contributes to the field of water resource management by demonstrating and developing innovative strategies and frameworks for the transition of single purpose reservoirs to multi-purpose systems, modifying flood control and enhancing water supply capabilities. This dissertation provides practical solutions with available data, simulation models, and optimization tools, which enable effective decision-making and operational adjustments under varying conditions. Overall, this dissertation presents a foundation for more resilient, reliable, and adaptive water management practices for reservoirs, that can meet diverse demands in a changing environmental landscape.</p>

<p>Ecohydrological models are extensively used to evaluate land use, land management and climate change impacts on hydrology and in-stream water quality conditions. The scale at which these models operate influences the complexity of processes incorporated within the models. For instance, a large scale hydrological model such as Soil and Water Assessment Tool (SWAT) that runs on a daily scale may ignore the sub-daily scale in-stream processes. The key processes affecting in-stream solute transport such as advection, dispersion and transient storage (dead zone) exchange can have considerable effect on the predicted stream solute concentrations, especially for localized studies. To represent realistic field conditions, it is therefore required to modify the in-stream water quality algorithms of SWAT by including these additional processes. Existing reach-scale solute transport models like OTIS (One-dimensional Transport with Inflow and Storage) considers these processes but excludes the actual biochemical reactions occurring in the stream and models nutrient uptake using an empirical first-order decay equation. Alternatively, comprehensive stream water quality models like QUAL2E (The Enhanced Stream Water Quality Model) incorporates actual biochemical reactions but neglects the transient storage exchange component which is crucial is predicting the peak and timing of solute concentrations. In this study, these two popular models (OTIS and QUAL2E) are merged to integrate all essential solute transport processes into a single in-stream water quality model known as ‘Enhanced OTIS model’. A generalized model with an improved graphical user interface was developed on MATLAB platform that performed reasonably well for both experimental data and previously published data (R²=0.76). To incorporate this model into large-scale hydrological models, it was necessary to find an alternative to estimate transient storage parameters, which are otherwise derived through calibration using experimental tracer tests. Through a meta-analysis approach, simple regression models were therefore developed for dispersion coefficient (D), storage zone area (A_s) and storage exchange coefficient (α) by relating them to easily obtainable hydraulic characteristics such as discharge, velocity, flow width and flow depth. For experimental data from two study sites, breakthrough curves and storage potential of conservative tracers were predicted with good accuracy (R²>0.5) by using the new regression equations. These equations were hence recommended as a tool for obtaining preliminary and approximate estimates of D, A_s and α when reach-specific calibration is unfeasible. </p> <p> </p> <p>The existing water quality module in SWAT was replaced with the newly developed ‘Enhanced OTIS model’ along with the regression equations for storage parameters. Water quality predictions using the modified SWAT model (Mir-SWAT) for a study catchment in Germany showed that the improvements in process representation yields better results for dissolved oxygen (DO), phosphate and Chlorophyll-a. While the existing model simulated extreme low values of DO, Mir-SWAT improved these values with a 0.11 increase in R² value between modeled and measured values. No major improvement was observed for nitrate loads but modeled phosphate peak loads were reduced to be much closer to measured values with Mir-SWAT model. A qualitative analysis on Chl-<i>a</i> concentrations also indicated that average and maximum monthly Chl-<i>a</i> values were better predicted with Mir-SWAT when compared to SWAT model, especially for winter months. The newly developed in-stream water quality model is expected to act as a stand alone model or coupled with larger models to improve the representation of solute transport processes and nutrient uptake in these models. The improvements made to SWAT model will increase the model confidence and widen its extent of applicability to short-term and localized studies that require understanding of fine-scale solute transport dynamics. </p>

This thesis details the hydrodynamic research conducted at Delta Marsh as part of the Restoring the Tradition marsh rehabilitation project. Research has indicated that the hydraulic and hydrologic controls on the marsh can have considerable impacts on its ecological function, but the impacts of these controls had not previously been studied. Field hydrography and two-dimensional numerical modelling (using MIKE 21) provided insight into many aspects of the physical behaviour of Delta Marsh. Eighty five percent of the inflow to Delta Marsh from Lake Manitoba passes through Clandeboye Channel, and these discharge signals propagate as far west as Cadham Bay. Inflow to the marsh disperses quickly, and accounts for a small fraction of the water that exits the marsh during subsequent outflow. Thus, Portage Diversion water that enters the marsh through the lake can remain there even if there is a net loss in marsh volume over the season. Wind friction across Lake Manitoba has the greatest impact on short-term fluctuations in marsh volume and on the composition of marsh water, followed by the Portage Diversion and the natural inflows to Lake Manitoba. Expansions to flood diversion infrastructure will considerably impact the composition of Delta Marsh waters. Three methods of wetland discharge estimation were developed and tested. The most promising method was the regressed slope Manning method (RSMM), which estimates two-directional channel discharge as a function of the water surface elevations at both ends of a channel. When used in conjunction with the velocity index method, the RSMM can multiply the amount of reliable discharge data collected per research dollar. Thanks to its simple formulation, the RSMM is likely applicable outside of wetland settings, as well. / February 2016

This research is part of continued efforts to correlate the hydrology of East Fork Poplar Creek (EFPC) and Bear Creek (BC) with the long term distribution of mercury within the overland, subsurface, and river sub-domains. The main objective of this study was to add a sedimentation module (ECO Lab) capable of simulating the reactive transport mercury exchange mechanisms within sediments and porewater throughout the watershed. The enhanced model was then applied to a Total Maximum Daily Load (TMDL) mercury analysis for EFPC. That application used historical precipitation, groundwater levels, river discharges, and mercury concentrations data that were retrieved from government databases and input to the model. The model was executed to reduce computational time, predict flow discharges, total mercury concentration, flow duration and mercury mass rate curves at key monitoring stations under various hydrological and environmental conditions and scenarios. The computational results provided insight on the relationship between discharges and mercury mass rate curves at various stations throughout EFPC, which is important to best understand and support the management mercury contamination and remediation efforts within EFPC.

<p> Inclement challenges of a changing climate and humanity's desire to explore extraterrestrial environments both necessitate efficient methods to obtain freshwater. To accommodate next generation water technology, there is a need for understanding and defining the energy efficiency for unconventional water sources over a broad range of environments. Exergy analysis provides a common description for efficiency that may be used to evaluate technologies and water sources for energy feasibility. This work uses robust thermodynamic theory coupled with atmospheric and planetary data to define water capture efficiency, explore its variation across climate conditions, and identify technological niches and development needs.  </p> <p><br></p> <p> We find that desalinating saline liquid brines, even when highly saline, could be the most energetically favorable option for obtaining water outside of Earth. The energy required to access water vapor may be four to ten times higher than accessing ice deposits, however it offers the capacity for decentralized systems. Considering atmospheric water vapor harvesting on Earth, we find that the thermodynamic minimum is anywhere from 0x (RH≥ 100%) to upwards of 250x (RH<10\%) the minimum energy requirement of seawater desalination. Sorbents, modelled as metal organic frameworks (MOFs), have a particular niche in arid and semi-arid regions (20-30%). Membrane-systems are best at low relative humidity and the region of applicability is strongly affected by the vacuum pumping efficiency. Dew harvesting is best at higher humidity and fog harvesting is optimal when super-saturated conditions exist. Component (e.g., pump, chiller, etc.) inefficiencies are the largest barrier in increasing process-level efficiency and strongly impact the regions optimal technology deployment. The analysis elucidates a fundamental basis for comparing water systems energy efficiency for outer space applications and provides the first thermodynamics-based comparison of classes of atmospheric water harvesting technologies on Earth.</p>

<p>Pumped municipal solid waste landfill leachate samples (7 cells from a site in Nebraska, 4 cells from a site in Illinois) have been analyzed for 62 elements using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). A procedure for complete dissolution of solids in the leachate was developed. Complete dissolution aims to reduce material loss in filtration by eliminating the need for filtration, and frees materials entrapped in undissolved solids. The procedure uses centrifugation to separate solid phase matter from the raw sample to maximize the effect of acid, and uses Chloric, Nitric, Fluoric, and Boric acids with microwave digestion to achieve full dissolution. The dissolved solid fraction precipitates yttrium fluoride and some other metals due to over-solubility concentrations; the precipitate is recovered and redissolved for analysis. Platinum, (Post-) Transition, and Lanthanide group metals were positively detected in the landfill leachate. Individual metals from these groups were detected in either/both aqueous or/and solid phases: solid phase metals are usually at least one magnitude of concentration greater than liquid phase metals, unless the solid phase produced no detection of the metal where the liquid phase did. Noteworthy results are: in the solid phase; Al was quantified from 10 to 103 𝜇g/g of solid mass; Sc, Cr, Ti, and Cu were quantified in the solid phase from 1 to 50 𝜇g/g of solid mass; Zr and Eu were quantified from .5 to ~8 𝜇g/g of solid mass. In the liquid phase: Ti, Cr, Li, Cu, As, and Zr were quantified mostly between 10-2 to 10-1 𝜇g/g of liquid mass, but occasionally reach out of those bounds; Al, Sc, Pt, Co, and V were quantified mostly from 10-3 to 10-2 𝜇g/g of solid mass. Solid phase metals were positively detected with a minimum Limit of Detection (LOD) usually around 10-1 𝜇g/g of solid mass, including: In, Ge, Pb, Ru, Sb, Ta, Hf, Bi, Yb, La, Ti, Pd, Lu, Dy, and Tb. Liquid phase metals were positively detected with a minimum LOD usually around 10-5 𝜇g/g of liquid mass, including: Tm, Ge, Au, Pb, Sb, Ta, Hf, Sm, Nb, Ho, Ga, Bi, Yb, Pd, Er, and Cd.</p>

<p dir="ltr">The recent surge in water levels within the Great Lakes has laid bare the vulnerability of the surrounding coastal areas. Over the past few years, communities along the Great Lakes coast have struggled with widespread coastal transformations, witnessing phenomena such as shoreline retreat, alterations in habitat, significant recession of bluffs and dunes, infrastructure and property damage, coastal flooding, and the failure of coastal protection structures. Unlike the ocean coasts, the Great Lakes coastal regions experience a unique confluence of large interannual water level fluctuations, coastal storms, and ice cover dynamics, which complicates the ongoing coastal management endeavors. To address this multifaceted challenge, the interplay between all these factors and their impact on coastal changes should be understood and applied to improve the resilience of Great Lakes coastal areas.</p><p><br></p><p dir="ltr">In this dissertation, several steps were taken to improve knowledge of coastal processes in the Great Lakes, spanning from the initial use of remote sensing for quantifying coastal changes to the subsequent stages of modeling and predicting shoreline changes as well as leveraging machine learning techniques to simulate and forecast influential factors like waves and ice cover. First, a fully automated shoreline detection algorithm was developed to quantify the shoreline changes in Lake Michigan, detecting the most vulnerable areas, and determining the main factors responsible for the spatial variability in the shoreline changes. Additionally, a reduced complexity model was designed to simulate the shoreline changes in Lake Michigan by considering both waves and water level fluctuations, which significantly improved the shoreline changes modeling and forecasting for Lake Michigan. Furthermore, new deep learning-based frameworks based on the Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural Network (CNN) were introduced to model and extend the current records of wave heights and ice cover datasets, adding 70% and 50% data to the existing waves and ice time series respectively. Finally, the extended waves and ice time series were used to study the long-term trends and the correlation between the interannual water level and waves changes, revealing a statically significant decreasing trend in the ice cover over Lake Michigan of 0.6 days/year, and an increasing trend in the waves interannual variability at Chicago area.</p>

<p>  </p> <p>The pandemic prompted buildings globally to transition to low or no occupancy as social distancing to reduce the spread of Coronavirus Disease (COVID-19). This consequence prompted concerns about the chemical and microbiological safety of building drinking water due to stagnation. At the same time, microplastic (MP) pollution received increasing global attention due to their presence in the environment and recent discoveries within water distribution systems and at building faucets. MP sources have primarily been targeted as originating within the drinking water sources, but plastic plumbing components are less discussed and known to deteriorate into fragments and smaller pieces that reach faucets. Literature at the time of this work as sparse on stagnation impacts to drinking water quality and the fate of MPs in plumbing. In particular, health officials and building owners issued and received many differed guidance documents telling building owners do different things and no standard guideline was available to reduce the health risks caused by stagnant building drinking water. This dissertation  examined three different types of buildings during closed to low water use conditions and conducted bench-scale testing to explore the phenomena observed in the field. Chapter 1 describes water quality impacts during a 7 year old ‘green’ middle school as it transitioned from Summer (low water use) to Fall (normal use). Field experiments revealed that more than half of first draw water samples exceeded the copper (acute) health-based action limit during low water use. Copper concentration within the school increased as distance from building entry point increased. Chapter 2 and 3 describe report on chemical and microbiological water quality in buildings at a university buildings (Chapter 2), and elementary school (Chapter 3). Chapters 2 and 3 revealed that stagnation negatively impacted chemical and microbiological building water quality (cold and hot) but flushing was effective at remediating high concentration of heavy metals and <em>Legionella pneumophila</em> at most locations. But in large buildings, where building plumbing system was more complicated, flushing did not always result in improved water quality. Also discovered was that water quality again deteriorated even after whole building water system was flushed. It is important to understand own building systems to maintain water quality as each building complexity requires specific knowledge and solutions. Chapter 4 describes current knowledge associated with MPs in drinking water and results of bench scale experiments on MP fate and transport in building plumbing. This work identified that while MPs have been reported at building faucets, sampling details lacking from available studies often resulted in study results not being comparable across others. Based on the review of the issue, it was found that MPs have likely reached building faucets for decades but have received no characterization until recently. Bench-scale testing using two MPs, of different density, in copper and crosslinked polyethylene (PEX) pipes revealed size influenced the amount of MPs retained in a pipe. Research needs were identified to determine the fundamental factors that control MP fate in plumbing and their presence at building faucets. </p>