Deena Hannoun’s research while affiliated with Southern Nevada Water Authority and other places

The Las Vegas Wash (the Wash) provides a mechanism for delivering recycled indoor water used in the Las Vegas Valley, NV to the most downstream basin of Lake Mead. The Wash introduces different water quality to Lake Mead, including higher nitrogen and phosphorus concentrations, and it may contain constituents common to urban runoff including microbial organisms and trace contaminants. A strong link has been established between the residential population of the Las Vegas Valley and the mean annual flowrate in the Wash; however, with the onset of the SARS‐CoV‐2 pandemic in 2020, significantly reduced tourism led to reduced flow in the Wash. This work expands previous modeling efforts which project Wash flowrates based on population projections by incorporating tourism numbers, using the individuals employed in hospitality as a surrogate. The resulting model suggests that mean yearly Wash flowrates could increase 20.6%–23.2% (between 1.95 and 2.19 by 2060, compared to 2022 levels. Numerical simulations of Lake Mead show that these increased Wash flowrates are not expected to have a significant thermal influence on either the drinking water intake (Intake) or Hoover Dam outflows. The Wash delivers about MW of heat into Lake Mead, while heat transfer at the Intake and Hoover Dam water columns was two orders of magnitude less. Wash water concentration and salinity increased in the simulations at the Intake and Hoover Dam outflows, respectively, by at most 0.91% and 1.3%.

This review identified 30 studies from 1992–2024 that performed quantitative microbial risk assessments on potable reuse and compared individual assumptions, summarized influential parameters, and analyzed results.

Wastewater samples spanning three years were analyzed for human enteric pathogens and fecal indicators, highlighting seasonal, geographic, and pandemic-related variations of interest for water reuse, microbial risk assessment, and source tracking.

The goal of this study was to assess health of male Common Carp (carp, Cyprinus carpio) at four sites with a wide range in environmental organic contaminant (EOC) concentrations and water temperatures in Lake Mead National Recreation Area NV/AZ, US, and the potential influence of regional drought. Histological and reproductive biomarkers were measured in 17–30 carp at four sites and 130 EOCs in water per site were analyzed using passive samplers in 2010. Wide ranges among sites were noted in total EOC concentrations (>10Xs) and water temperature/degree days (10Xs). In 2007/08, total polychlorinated biphenyls (tPCBs) in fish whole bodies from Willow Beach (WB) in the free-flowing Colorado River below Hoover Dam were clearly higher than at the other sites. This was most likely due to longer exposures in colder water (12–14 °C) and fish there having the longest lifespan (up to 54 years) for carp reported in the Colorado River Basin. Calculated estrogenicity in water exceeded long-term, environmentally safe criteria of 0.1–0.4 ng/L by one to three orders of magnitude at all sites except the reference site. Low ecological screening values for four contaminants of emerging concern (CEC) in water were exceeded for one CEC in the reference site, two in WB and Las Vegas Bay and three in the most contaminated site LVW. Fish health biomarkers in WB carp had 25% lower liver glycogen, 10Xs higher testicular pigmented cell aggregates and higher sperm abnormalities than the reference site. Sperm from LVW fish also had significantly higher fragmentation of DNA, lower motility and testis had lower percent of spermatozoa, all of which can impair reproduction. Projections from a 3D water quality model performed for WB showed that EOC concentrations due to prolonged regional drought and reduced water levels could increase as high as 135%. Water temperatures by late 21st century are predicted to rise between 0.7 and 2.1 °C that could increase eutrophication, algal blooms, spread disease and decrease dissolved oxygen over 5%.

Water age in drinking water systems is often used as a proxy for water quality but is rarely used as a direct input in assessing microbial risk. This study directly linked water ages in a premise plumbing system to concentrations of Legionella pneumophila via a growth model. In turn, the L. pneumophila concentrations were used for a quantitative microbial risk assessment to calculate the associated probabilities of infection (Pinf) and clinically severe illness (Pcsi) due to showering. Risk reductions achieved by purging devices, which reduce water age, were also quantified. The median annual Pinf exceeded the commonly used 1 in 10,000 (10–4) risk benchmark in all scenarios, but the median annual Pcsi was always 1–3 orders of magnitude below 10–4. The median annual Pcsi was lower in homes with two occupants (4.7 × 10–7) than with one occupant (7.5 × 10–7) due to more frequent use of water fixtures, which reduced water ages. The median annual Pcsi for homes with one occupant was reduced by 39–43% with scheduled purging 1–2 times per day. Smart purging devices, which purge only after a certain period of nonuse, maintained these lower annual Pcsi values while reducing additional water consumption by 45–62%.

Within this paper, a machine learning algorithm is used to investigate the importance of certain setpoints and parameters in the filtration processes of a large-scale water treatment facility. Previously, a model for the filtration process based on Run-to-Run Control was proposed and tested against sample data from the treatment plant, but it was quickly found that such a model was incompatible for successfully computing setpoints of operation which minimize the energy cost of running the filtration systems. The machine learning model described herein is an attempt to elucidate the importance of the available data on the filtration systems and to identify the most important variables that influence the filtration run time.