2021-2022 Cohort II Projects

Most children and adults spend 8 hours or more per day in commercial and institutional (C&I) buildings (e.g., offices, schools), and even longer in elder care facilities or large apartment complexes. Architects and building managers understand how to manage energy use and air conditioning within buildings. However, over the past 5 years there is greater visibility of high-profile incidents wherein water quality within buildings puts people at risk from lead, copper, waterborne pathogens, or unaesthetic water. 

Due to a regulatory grey area where buildings can be classified as a consecutive (and therefore regulated) drinking water treatment system if additional disinfectant is added to water at the building level, facilities managers have relied on limited risk management tools of water flushing, changing the water heater temperature set point, or managing the water softener regeneration schedule to reduce the likelihood that conditions are present such as stagnant conditions in water where bacterial pathogens such as Legionella grow 1,2. As a result, new CDC and state regulations for Legionella are forcing institutional buildings to spend $25k to >$75K per year for each building in preventative building operations to meet recommendations for the development of water safety plans 3,4. 

A major cause for increased risk is the design or replacement of low-water flow devices within buildings. This conserves water quantity but leads to longer water age in buildings which is associated with loss of disinfectant residuals, corrosion and conditions that favor microbial growth. As a result, water conservation can have a tradeoff for maintaining high water quality within buildings. Water quality problems in buildings are typically evaluated using occasional sampling at a few points in buildings, which does not provide adequate data for decision-making. There is a need to be able to fingerprint water quality within buildings so that managers can be proactive (rather than reactive) in their approach to conserving water while avoiding undesirable health outcomes. 

This project will use a multi-pronged approach to scale-up and validate a mechanistic model of water quality in a building to a full-building. High-resolution sensors for water quality parameters and occupancy installed in a building testbed will be used to gather data alongside microbiological measurements of water quality in the building. Using these data, different mechanistic and data-driven modeling approaches of building water quality will be compared to assess the best approach for informing a water management plan software. Following development of a predictive model at the building scale, water management interventions (flushing water to remove stagnant water in pipes, changing the water softener operation, and adjusting water heater set point temperatures) will be tested to evaluate the impact on water quality parameters and to test the efficacy of different predictive modeling approaches under real building operations. This work will reduce costs and human health issues from water quality problems in the built environment, reduce administrative and logistical burdens on facilities managers, increase trust in sustainable technologies for green buildings, and provide a road map for the expansion of decision support tools in the built environment.

References

(1)     NASEM. Management of Legionella in Water Systems | The National Academies Press; National Academies of Sceinces, Engineering and Medicine: Washington, DC, 2019.

(2)     Rhoads, W. J.; Ji, P.; Pruden, A.; Edwards, M. A. Water Heater Temperature Set Point and Water Use Patterns Influence Legionella Pneumophila and Associated Microorganisms at the Tap. Microbiome 2015, 3, 67. https://doi.org/10.1186/s40168-015-0134-1.

(3)     Centers for Disease Control and Prevention. Developing a Water Management Program to Reduce Legionella Growth & Spread in Buildings: A Practical Guide to Implementing Industry Standards; US Department of Health and Human Services: Atlanta, GA, USA, 2017.

(4)     Centers for Medicare & Medicaid Services (CMS). Requirement to Reduce Legionella Risk in Healthcare Facility Water Systems to Prevent Cases and Outbreaks of Legionnaires’ Disease (LD) (Revised 06.09.2017); 2018.

IoT Sensors for Pedestrian and Traffic Safety Applications in Smart Cities

Project Team:

Dr. David King (PI), ASU School of Geographical Sciences and Urban Planning
Dr. Hongbin Yu, Director, ASU NSF Center for Efficient Vehicles and Sustainable Transportation Systems
Darryl Keeton, President, Sensagrate
Mailen Pankiewicz, Pedestrian Safety Coordinator, City of Phoenix
Simon Ramos, Traffic Management and Operations Manager, City of Phoenix

PROJECT SUMMARY

On a per capita basis, the City of Phoenix is the most dangerous city in the U.S. for people walking. In 2018, 112 people were killed by cars, a 200 percent increase from 10 years prior (Suerth 2019), and over 1,000 people are injured each year by motorists. In 2018, Arizona had 3.3 pedestrian deaths per 100,000 people (Vandell 2020), while in Phoenix the per capita rate is double that. The map at left shows the locations of serious and fatal hotspots in the city, yet these data are not adequate to understand what causes these places to be so dangerous. The data shown on the map only describe where people were killed and injured, but do not help understand why these places are so deadly. For the City of Phoenix, the pedestrian safety crisis is a pressing public safety and quality of life challenge.

A key obstacle to improving safety is a lack of data for pedestrian and driver behavior. Phoenix does not have data that describes when and where pedestrians’ cross streets, at what frequency, and the cause of collisions. Moreover, cities do not have data detailing near-miss between people and drivers. These types of data have traditionally been extremely difficult to collect, if even possible, but new lidar technologies combined with software that detects and analyzes people and vehicles, including their speed, paths and locations, offer hope for granular analyses of behavioral aspects to safety and can inform safety interventions.

This research project will provide Phoenix with a data-driven approach to identify potentially life-saving improvements, and will be applied within the first year of this project from new city planning, roadway design changes, community awareness, and education.

In a collaborative partnership, ASU researchers, Sensagrate, and the City of Phoenix will deploy SensaVision, a real-time computer vision AI software for intelligent transportation system (ITS) solutions that provides traffic and safety data and analytics. The project team includes faculty and researchers from ASU, City of Phoenix staff, and the Sensagrate team. The City of Phoenix’s Pedestrian Safety Coordinator will assist in the planning, design integration and implementation of this project. The Sensagrate team brings the technology solution with technology updates for SensaVision, and has already been approved by Phoenix to implement it as a Proof of Concept Project. Sensagrate has eight employees who have expertise in computer vision, IoT, machine learning, AI, data analytics, smart city smart infrastructure, and ITS traffic monitoring and safety technology. This dynamic partnership between ASU researchers, a technology start-up a part of the ASU entrepreneur ecosystem, and the public sector leverages the capacities of each to succeed in improving pedestrian safety and assessing long-term and scalable solutions to deploy city- and region-wide.

The deployment of photovoltaic (PV) systems has soared over the recent years. However, addition of PV modules to any environment shifts the thermal balance by increasing the daytime net available radiation—mostly due to a reduction in albedo—and decreasing the nocturnal heat release—by shielding radiative emission from the surface beneath the modules. Field studies have shown that, in Arizona, the ambient temperature within PV power production facilities can increase by up to 2 °C over built environments such as parking lots. In urban environment, this additional heat contributes to the urban heat island (UHI) effect. In these UHIs, the economic and human costs associated with each additional Celsius degree is well documented and can be particularly severe in regions affected by increasing heatwaves, such as the Southwestern United States. 

The thermal effect of PV modules on their environment is not a surprise when considering the balance of energy within them. In a standard crystalline-silicon-based module, only about 20% of the incoming solar irradiance is converted into electric power. The bulk of the remaining 80% is lost for electricity production, but still absorbed by the PV module and released to the surrounding environment as heat. As a result, the temperature of PV modules in the field is usually 20–30 °C above the ambient.

Some of these “waste-heat-generating” losses are inherent to the physics of solar cells and cannot be avoided without deeply altering the architecture of the module. Conversely, long-wavelength below-bandgap photons—corresponding to photons that are too-low energy to be absorbed by the solar cell—cannot be converted to electricity but they do not need to be absorbed (and converted into waste heat) either. The solar cell absorber is generally transparent to these longer-wavelength photons, and their absorption and thermalization is due to other components of the module. Ideally, these photons would be reflected towards the sky and would not contribute to the thermal balance of the module and its surroundings. However, this parameter is rarely considered in the optimization of PV products.

In this project, we propose to investigate and demonstrate how reducing the absorption of longer-wavelength photons (>1200 nm) through optical management (increased transparency and reflectance) can decrease the thermal effect of PV systems on their surroundings and reduce the associated UHI effect. We aim to achieve a reflectance above 90% for long-wavelength photons. This would increase the solar-irradiance-weighted albedo of PV modules from less than 8% today to above 20%. Consequently, accounting for the electric power production from the module, the fraction of the solar irradiance absorbed and converted to waste heat by the PV module would decrease from 70–75% to 55–60%, better than most natural and urban artificial surfaces. When compared with current unoptimized commercial PV modules, we estimate that our smart PV module technology would result in a temperature decrease of 1 °C or more in the surroundings of the PV system, thus reducing the contributions of the PV system to the UHI effect. 

The worldwide market for water softening (i.e. removal of Ca2+ and Mg2+ hardness ions from water) is growing at ~7% and will exceed $10 Billion by 2024. Point-of-use (POU) water treatment is  widespread due to concerns and aesthetics regarding the quality of municipal drinking water. Additionally, industrial water needs by major sectors (beverage, semiconductor, energy production, hospitality, etc.) requires water softening to prevent corrosion, scaling and product purity. However, an unintended consequence of POU water softening is the issue of salt accumulation in sewer systems, which influence city’s discharge regulations and water reuse goals. For example in the year 2000, annual salt loadings to Phoenix wastewater from water ion exchange softening systems were ~45,500 tons, representing 39% of society’s salt contribution to wastewaters. Furthermore, a significant negative health impact associated with current softening systems (ion exchange-IX) involves the addition of salt in order to remove hardness. The EPA guidance level for sodium in drinking water is 20 mg/L. This value was developed for those sensitive individuals restricted to a total sodium intake of 500 mg/day. After conventional IX treatment, Arizona drinking water contains ~160 mg/L of sodium, which exceeds this guidance level by a factor of eight. 

SoftLight technology will provide chemical-free (i.e., no  salt or other chemicals) treatment that selectively removes hardness ions from water with associated benefits that include (1) broader health benefits associated with reduced sodium release in drinking water by conventional ion exchange systems, and (2) environmental benefits associated with reduced sodium and salinity discharge to the environment. The key innovation of the SoftLight system is the integration on optical fibers of photo-responsive polymers, which can undergo conformational changes between isomer structures – with the two isomers having orders of magnitude different complexation capacity for hardness ions in water. When light from a light emitting diode (LED) is launched into the optical fibers, a portion of the light is refracted through the core, but a portion of the light also is absorbed by the photo-responsive polymers. Photons of light can actuate the photo-responsive polymer structures, transforming the structure (e.g., trans- or cis- isomers). Upon changes in UV-light irradiation the open form of photo-responsive polymers/copolymers bind Ca2+ and Mg2+. Subsequently, by switching to visible light results in a high concentration of the closed polymer form, thereby releasing the hardness ions.  

Beyond manufacturing or in-home drinking water uses, other device-specific needs exist. For example, in the biomedical space the global dialysis market exceeds $100B annually, growing at 4.5% with higher growth of home dialysis applications that require softened water to avoid dialysis device operational or human health problems. Baxter Healthcare is a leader in home dialysis and identified the need for a chemical free, low energy use, compact size technology to soften water; they added additional funding on SoftLight after the initial Zimin Foundation award. We seek to develop a transformative, scalable softening technology capable of addressing multiple markets – but will focus initially on meeting stringent requirements of the home dialysis device, after which other POU or industrial applications could be explored.