CEE Sponsored Awards

The effective separation of CO2, ammonia, hydrogen sulfide, and siloxanes from bio-gas to produce high purity renewable natural gas (RNG) has been challenged by the need for multiple unit operations and the need to dispose these gases post-capture. One less studied but highly promising approach is to harness earth abundant hydrated magnesium silicates (e.g., serpentine, (Mg, Fe)3Si2O5(OH)4 and olivine, (Mg, Fe)2SiO4) to produce iron oxide for capturing hydrogen sulfide, silica that is functionalized to capture siloxanes and ammonia, and dissolved magnesium that is used to capture CO2 to produce magnesium carbonates. These silicate minerals are abundant in mine tailings and as naturally occurring minerals. Strategies to produce useful by-products from the captured gases enable us to close material cycles. Therefore, the aim is to develop an integrated technology to produce high purity renewable natural gas through the reactive separation of acid gases (e.g., CO2, H2S), ammonia, and siloxanes. This approach will also enable us to produce useful products that be reintegrated into chemical processes. The team will harness the latest advancements in the low cost of renewable energy (e.g., wind, solar) to produce acids and bases from brine to aid integrated reactive separation. The resulting products from reactive separation such as magnesium carbonate and iron sulfide can be reused. The central hypothesis that the effective separation of multi-component gases (e.g., H2S, CO2, ammonia, and siloxane) can be systematically separated using the by-products from magnesium silicate dissolution such as iron oxide, dissolved magnesium, and silica functionalized for ammonia and siloxane capture is investigated. The target level of performance is to capture at least 90% of the waste gases by volume via reactive separation.

Advancing gigaton-scale solutions to capture, convert, store, and remove carbon dioxide (CO2) from gas emissions and air is crucial to limit the detrimental environmental impacts of climate change. Carbon mineralization is a scalable and thermodynamically favorable approach for converting anthropogenic CO2 into calcium or magnesium carbonates using earth-abundant nanoporous silicates. The carbonate products are stable, water-insoluble inorganic compounds, ideal for CO2 storage applications. However, our limited understanding of the pore-scale interfacial mechanisms that influence the carbon mineralization process challenges our ability to predictively control carbonate formation. To address this scientific challenge, this research project will develop architected siliceous nanochannels with ordered porosity to investigate carbonate crystallization mechanisms in confinement. The fundamental knowledge gained from these studies will inform the design of new technologies that harness carbonate crystallization mechanisms for the reactive separation of gases, recovery of high-value metals, and predicting the fate of CO2 injected into geological reservoirs. The research program is closely integrated with educational and outreach activities focused on training the future STEM workforce and co-creating community-level carbon removal solutions by engaging members of underrepresented groups in STEM and members of underserved rural communities. 

This project will investigate the crystallization mechanisms of calcium and magnesium carbonate crystallization in confined fluids within architected siliceous nanochannels with sizes ranging from 2 to 20 nm. Carbonate crystallization mechanisms in confined fluids will be investigated in less reactive silica interfaces and more reactive calcium and magnesium silicate surfaces. Morphological and compositional controls on siliceous pores will be achieved via sol-gel synthesis in anodic alumina membranes with ordered pores. Non-invasive and dynamic characterization of carbonate crystallization will be realized through operando X-ray scattering and advanced spectroscopy measurements. The experimental results will be used to investigate the validity of classical and non-classical mechanisms of carbonate formation and propose new mechanisms if needed. These research activities will serve as an educational and outreach platform for engaging underrepresented K-12 students in science education and communication through illustrative workbooks, mentoring videos in collaboration with PBS, and hands-on experimental modules supported by Expanding Your Horizons and Sciencenter. Undergraduate students will be recruited and mentored through the NSF Louis Stokes Alliances for Minority Participation program using research activities related to carbon transformations as a platform. Topics related to decarbonization in the context of science and engineering for climate, energy, and environmental technologies will be made more accessible through blended learning formats for undergraduate and graduate students. Insights from research activities will be used to co-create carbon removal solutions through mineral weathering with local farmers. The closely integrated research, outreach, and educational activities are designed to foster enthusiasm for and engagement in sustainable climate, environment, and energy solutions.

Microbes in host microbiomes, human infections and the natural environment often live in spatially structured aggregates and interact antagonistically with each other. Toxin-mediated antagonistic interactions are widespread in the gut, skin, and other human microbiomes, and protect these communities against external invasion. Recent results suggest that spatial structure can strongly affect the evolutionary dynamics of microbial populations, and, in turn, microbial interactions can feedback on the formation of spatial structure. For example, we found that mechanical interactions among dividing cells in growing yeast colonies reduce the power of natural selection by reducing the rates at which lower fitness strains go extinct and fitter ones expand in these populations. Despite spatial structure and microbial interactions have a strong impact on the evolutionary dynamics of microbes relevant for human health, most of what we know about microbial evolutionary dynamics comes from experiments with well-mixed liquid cultures with limited interactions among cells. To fill this gap, my group is interested in understanding quantitatively how spatial structure, mechanics and biological interactions impact the adaptive evolutionary dynamics of microbial populations. We approach this question via experimental evolution, synthetic biology, and mathematical modeling. In preliminary experiments, we found that evolving yeast colonies selecting for faster expansion on agar surfaces results in notable changes in cell shape: cells evolved from an ellipsoidally shaped ancestor to being elongated and almost rod-like, changing the way cells interact mechanically when growing and dividing. We hypothesize that an elongated cell shape is advantageous for faster expansion because it reduces cell packing, and that this adaptive change is associated with changes in the way genotypes cluster in space leading to increased genetic drift, the temporal change in allele frequencies due to chance events. Recently, we showed that a toxin-producing microbe can only invade a landscape occupied by a weaker toxin-producer if its inoculum is larger than a critical size, and that adaptive evolution can alter the dynamics of antagonism. We will experimentally investigate the dependence of the critical inoculum size on the strength of the interaction, and we will study how spatial structure controls the fate of mutations that confer resistance to the toxin produced by either the invader or resident strain. Finally, we will investigate how antagonistic interactions among microbes affect the dynamics of invasion in the gut of the nematode Caenorhabditis elegans: these experiments will help us translate results obtained in simple laboratory settings to the more complicated but more realistic dynamics of invasion of a host microbiome

Artificial Intelligence (AI) holds great promise for revolutionizing scientific discovery and enabling the engineering of unprecedented technologies. While existing AI tools have profoundly impacted our day-to-day lives (within the spheres of finance, consumer experience, entertainment, travel, etc.), to realize an AI enabled revolution in science and engineering will require groundbreaking advances that pioneer fundamentally new AI approaches, that are interpretable to humans, and useful within “high regret” contexts.

The aim of this grant is to survey the field and identify a cohort of experts that can form a team of researchers who are committed to the purpose of developing the AI foundations and tools required to revolutionize science and engineering. The envisioned team will consist of the best talent from HBCUs and other, leading universities. A large part of the proposed effort is aimed at identifying and recruiting potential partners for a larger research effort that will commence in FY24. Additionally, the PI will visit other ONR identified collaborative teams, to meet with them, understand their goals/objectives, and study their anticipated organizational structures; in order that the PI may glean a “best practices” understanding for his FY24 effort.

Lithium (Li) is considered a Critical Material by the US Department of Interior due to its significance for meeting future energy demands since the domestic production of Li to facilitate self-reliance is essential to the national interest. The demand for Li is expected to grow up to 0.84 – 105 Mt in 2100 and this projected demand for lithium and aims to transform towards a more sustainable future necessitates the exploration of new domestic resources, and development of new extraction technologies. Further, the contribution of geothermal energy to electricity generation in the US is expected to quadruple by 2040, and these geothermal sources offer a growing potential for contributing to domestic Li supply security. Li recovered from such domestic geothermal brines could potentially accommodate from 4%-8% of domestic demand. The extraction of Li from geothermal brines is attractive because it uses produced brine - after power generation - and can make use of existing facilities to lower production and capital costs. In the US, the Salton Sea field in California is one of the most mineral-rich geothermal brine sources, with the most geothermal plants in the region. Coupling geothermal energy production with mineral recovery is an attractive approach for a sustainable and competitive future energy-mineral landscape. Our team has developed a robust approach utilizing Engineered Lithium-Ion Sieve Technology (E-LIST) to facilitate Li adsorption from brine chemistries via an endothermic (entropically driven) pathway, and thus the adsorption performance, i.e., capacity and selectivity, increases with fluid temperature. Separation of Li through these sieves is size dependent, therefore, higher separation of Li is noted from larger ions (Na+, K+, Ca2+, Mg2+, and Cs+), which are commonly present in brines. Moreover, dehydration enthalpy of Li+ ions is significantly lower than Mg2+ ions, thus using these engineered sieves for separation yield higher Li+/Mg2+ ratios. Using these sieves also avoids the evaporation-based concentration steps, saving weeks’ worth of time and other resources. In our technology, we use Ti-based layered ionic sieves (Li2TiO3), which offer higher adsorption capacity (5-40 mg/g) and are stable in acidic environments representative of Salton Sea brines, offering the advantage of wider operational conditions. After the selective adsorption of Li in sieve materials, the adsorbed Li is recovered using dilute acidic solutions and then passed to an electrodialysis cell to produce battery-grade LiOH without the intermediate production of Li2CO3. We use the electrodialysis approach to produce LiOH since it offers high purity product and avoids any remnant Mg2+ carryover in the final product. Further, during this step, H2 gas and acid are produced as by-products. H2 can be used as an energy carrier for Li extraction, and the acid is recycled back to the elution step. Besides these circular economic benefits, this approach can harness inexpensive industrial residues (e.g., titanium slag) to engineer sieves thereby reducing the sorbent material’s manufacturing costs.

The extensive use of hydrogen is seen as a key strategy to achieve large-scale decarbonization. However, hydrogen (H2 ) is a potent and short-lived indirect greenhouse gas in addition to being highly flammable. H2 in the atmosphere can warm the climate indirectly by extending the lifetime of methane, triggering reactions that form tropospheric ozone, and breaking down into water vapor in the stratosphere. H2 leakage can therefore undermine the benefits of decarbonization, the extent of which depends on the currently unknown leak rates and how much hydrogen we ultimately produce. Currently, there is no data on H 2 emissions from small leaks that are below the flammable levels and limited insights into the robustness of sensors to detect and quantify these emissions. To address this challenge, we propose a Cornell-EDF collaborative effort to bring together sensor and analytics technology to quantify fugitive emissions from the H2 supply chain.

The aim of this CAREER project is to elucidate the organization and dynamics of confined fluids in nanoporous environments at freezing conditions to probe the mechanisms underlying the immobilization of contaminants such as aqueous hydrocarbons (e.g., fuel leaks) and greenhouse gases (e.g., CO2 and CH4). This project is motivated by the need to advance fundamental insights underlying the chemical structure of water and contaminants in confined fluids in cold climate as the basis to inform their reactivity, fate and transport in these environments. This information is crucial for the rational design of solutions to address contaminant remediation in these regions. The scientific outcomes and methods developed through this effort will advance polar science and engineering research at the Cold Regions Research and Engineering Laboratory (CRREL) through the U.S. Army Corps of Engineers.

The project will present opportunities for improved hydropower decision-making using Decision Making under Deep Uncertainity (DMDU) methods to evaluate potential vulnerabilities and adaptive measures to guide strategies that increase hydropower generation and reliability to benefit bulk power system resilience and economic operations. Additionally, the project will ensure wider community benefit and impact by developing a framework for considering deep uncertainties in hydropower futures, which can then be used to serve stakeholder engagement efforts.  In collaboration with NREL, the objective of this work is to adapt and apply DMDU approaches to address deep uncertainties in hydropower generation availability and power system expansion/operations using a novel framework to assess hydropower operations and its role in the future grid.

Background: A variety of per- and polyfluoroalkyl substances (PFAS) are used in semiconductor manufacturing and in photolithography processes in particular. Recent work conducted by PI Helbling has demonstrated that wastewater generated during photolithography contains a complex mixture of known and previously unknown PFAS. Wastewater samples collected along the conveyance path within three semiconductor fabs in the United States revealed relatively high abundances of PFAS in samples that primarily contained aqueous wastewater from the photolithography step, and lower abundances of PFAs at the point-of discharge from the fab, likely resulting from dilution with other site wastewater. Limited to no removal of residual PFAS was observed during on-site biological wastewater treatment in two of the three fabs (the third fab had no on-site treatment), suggesting that the residual PFAS are persistent and mobile. These data confirm that PFAS are present in fab wastewater, although preliminary measurements of total organic fluorine (TOF) in fab wastewater suggest that the sum concentration of individual PFAS that have been measured make up 10% or less of the total abundance of PFAS in fab wastewater. Targeted sampling and measurements are essential to develop mass balances on PFAS that can be used to inform wastewater management practices to better control for the release of PFAS from fabs.

Objectives: The goal of this project is to collect data to help close mass balances on PFAS within three semiconductor fabrication facilities. Process chemicals and wastewater samples will be collected by operators at each of the respective fabs and shipped to PI Helbling’s laboratory at Cornell. Wastewater samples will be analyzed for fluoride, adsorbable organic fluorine (AOF), and for target and nontarget PFAS species. The resulting data will be essential for understanding the origin, fate and transport of PFAS within fab wastewater.

Hydrodynamics often influence fish movement near river infrastructure, yet no technology exists that can measure river-wide hydrodynamics at a detailed scale for the durations that it takes individuals to transit the area. We will evaluate a new technology recently developed at Cornell University that can measure actual, instantaneous hydrodynamics over relatively long durations across the width of a river at centimeter scale. The technology, Infrared Quantitative Image Velocimetry or IR-QIV, uses infrared imagery and provides a near-complete measurement of river-wide hydrodynamics at the water surface (Figures 1 and 2). Our goal is to determine whether IR-QIV can be implemented on the Boardman (Ottaway) River for eventual use at FishPass by involved scientists in their studies. Our pilot project has four main objectives: evaluating the impact of (1) different high-resolution infrared cameras, (2) different camera locations and orientations, and (3) Traverse City environmental factors on IR-QIV performance, and (4) the ability of IR-QIV to register subsurface hydrodynamic manipulations.

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to produce new products for filler materials and in construction materials. The proposed technology enables industries emitting CO2 and producing alkaline industrial residues to capture and convert their emissions into commercially useful and stable products. It will replace the mining of carbonate materials with engineered products, meeting the growing demand for green construction materials.

The proposed project will lead to the development of an innovative low-temperature approach to convert captured CO2 emissions into calcium and magnesium carbonates with regular particle size distributions. Currently there is no process in industry that integrates CO2 capture and regeneration via solid carbonate formation with regular pore size distributions. The proposed technology is an energy-efficient alternative to conventional thermally driven energy-intensive grinding approaches. The project will address challenges associated with the low solubility of CO2 by harnessing regenerable solvents, such as sodium glycinate. As an alternative to thermal regeneration of solvents, the CO2-loaded solvents are chemically regenerated using calcium and magnesium bearing materials that react with CO2 to produce inorganic carbonates and regenerate the solvents. The chemical regeneration of solvents at 50-75 °C via solid carbonate formation is significantly lower compared to conventional thermal regeneration of solvents which occurs above 90 °C. Calcium and magnesium carbonates with well-ordered particle size distributions are realized using turbulent vortices in a Taylor-Couette reactor customized for these studies. The research will advance fundamental aspects of reactor engineering, tuning of multiphase chemical interactions, and practical challenges of scalable implementation.

More than half of the global population live in urban areas, which nontrivially modify the atmosphere through two broad pathways: urban form (i.e., changes in surface properties) and urban function (i.e., anthropogenic activities emitting heat and mass). These two pathways occur via turbulent exchanges of momentum, energy and mass in the atmospheric boundary layer and carry ‘distinct fingerprints’ of a city’s form and function. For increasingly fine-scale climate and numerical weather prediction (NWP) models, it is a persistent challenge to reflect these ‘distinct fingerprints’ of different cities across the world, yet in a manner that is generalizable and computationally tractable. Due to incomplete understanding of multi-scalar transport, whether different scalars of anthropogenic origins obey similarity relations in the urban surface layer remains unclear. This is also one of the key stumbling blocks to generalize urban land-atmosphere exchanges for multiple scalars. In particular, incorporating the effect of urban function on surface-atmosphere exchanges into climate and NWP models is almost completely missing. Therefore, the overarching goal is to improve basic understanding of multi-scalar transport and inform physically realistic, generalizable estimates of the surface-atmosphere exchanges, especially for less explored scalars. The project will lead to findings necessary for the next-generation urban climate modeling tools, which can be implemented to develop more precise (i.e., city or neighborhood-specific) mitigation and adaptation measures with changing climates. 

To achieve the overall project goal, the approach of this project is motivated by a critical comparison between flow and transport in the urban canopy versus vegetated one, which has been extensively studied. Such an approach will generate new insight into the transferability of theories and models between the two, informing development of urban-specific models for surface-atmosphere exchanges based on the existing ones for vegetation counterpart. To advance basic understanding of multi-scalar transport and departure from similarity, the mechanisms responsible for scalar dissimilarity will be separately investigated at the micro- and local scale of heterogeneities in urban form and function. Understanding of multi-scalar transport at both the micro- and local scales will be systematically studied by first deriving a ‘city profile generation’ module to generalize urban form and function (Aim 1). Then, hypotheses regarding multi-scalar transport and their similarity will be tested to advance basic understanding (Aim 2). The new understanding will help improve urban surface-atmosphere exchange modeling and interpretation of observations that rely on scalar similarity theory (Aim 3).

Cornell will lead efforts to extend reserve valuation methods for systems with energy-limited resources and produce a journal article on the developed methods.

We propose to employ machine learning within a revolutionary framework of model discovery that combines clear box and black box modeling aspects in a way that uncovers new engineering theories from mechanistic insight into complex systems. The new mathematical models, that the proposed method finds, will appeal to an engineer’s intuition and be amenable to the application of engineering judgment. Since these models will be expressed in the form of well understood mathematical constructions, they can be employed as reduced order models, suitable for technological and design purposes. It is pointed out that one important goal of the proposed work is to apply the later outlined methods to gain mechanistic insight and understanding regarding the complex phenomenology observed within soft material (e.g. polymer) bond lines. We do this by learning partial differential equations (PDE), as well as associated solution operators (e.g. Green’s functions.) The structure of the PDE and Green’s functions discovered with the proposed method will offer deep and important clues regarding the mechanics of adhesive bond lines. Such mechanistic insight will be of great value and utility to domain experts when they design experiments and make performance predictions.

This Faculty Early Career Development (CAREER) project will address fundamental research questions related to designing and operating transit-centric transportation systems, with the aim of enabling an efficient, sustainable and equitable transportation system for all. While the past decade has seen enormous advances in transportation technologies such as ridesharing and self-driving cars powered by, for example, artificial intelligence, mobile phone adoption and new business models, it remains unclear whether these innovations alone can lead us toward a future that is sustainable and equitable. This project argues that fundamental progress in this regard is best achieved via hybrid transit systems, services that seamlessly integrate the efficiencies of mass transit with agile, demand-responsive modes related to ridesharing. The technical focus will be on algorithms for designing and operating such systems, an area with key research gaps. The research will be conducted through the lens of Algorithm Engineering, which focuses on developing theoretical insights from successful data-driven and heuristic approaches, and heuristics from theory. Collaborations with stakeholders, such as transit agencies, technology providers, community groups, and policy makers will enable an understanding of practical and societal needs, model calibration using real-data, and validation through simulation and deployments. The project aims to broaden the renewed national focus on transit infrastructure to innovations in service modes, and will involve community outreach and education efforts targeting high school students, college students and public agencies.

Samitha Samaranayake Group

Abstract not available.

Professor Linda Nozick

Water systems in the U.S. unable to meet the Stage 1 and 2 Disinfectants and Disinfection By-Products Rules (D/DBPR) through enhanced coagulation or softening, or other lower cost optimization options (e.g., moving the point of chlorine addition, or optimizing pH) may implement additional treatment such as oxidation with chlorine dioxide or ozone, total organic carbon (TOC) adsorption with GAC, or change from free chlorine to chloramines for maintenance of a distribution system residual. However, treatment processes designed to reduce the formation of one DBP class can enhance the production of other DBP classes (Li and Mitch, 2018). The objective of this project is to investigate the impact of GAC with and without pre-chlorination on DBP formation and the resulting toxicity of drinking water using appropriate bioassays.

April Z. Gu Group Website

Abstract not available

Dr. Derek Warner's research group's website: Cornell Fracture Group

Abstract not available

The Reed Research Group

Abstract not available

Richardson Lab

Abstract not available

Dr. Matt Reid's research group website: Ecological Engineering Laboratory

The overall goal of this proposed study is to conduct mechanistic and pilot testing for investigating: “The Kinetic analysis and Acclimation to Low Dissolved Oxygen Conditions For Biological Nutrient Removal.” We will collaborate with HRSD to evaluate how the low-DO operation and control will impact the key design kinetic parameters, and the acclimation of various functionally-relevant populations such as AOB, NOB, PAOs, GAO, et. The outcome will provide a mechanistic understanding of Low DO Operation Develop repeatable kinetic testing techniques to identify parameters for activated sludge model (ASM), help for parameterization of nitrifier (AOB & NOB) populations when acclimated to low DO conditions, and establish best methods for specific parameters. It will provide greater understanding on how AOB and NOB groups acclimate differently using kinetic testing strategies and examine heterotroph adaptation and associated connection to SND rates. In addition, PAO and GAO competition is an important consideration to this work and results will help to bridge gap between existing Low DO and SND research.

April Z. Gu Group Website

We address resource and transmission (R&T) investment planning methods, focusing on four objectives: (1) Determine ways new software tools can facilitate long-term planning while exploring interdependencies within RTO interconnection queues, transmission expansion processes, capacity markets, and interregional transmission considerations; (2) Implement a reliability evaluation tool that coordinates with a long-term expansion planning application, explicitly accounting for utility-scale renewables, storage, and DER. (3) We will test an implementation of our tools using large-scale industry-size models, developing results to illustrate relationships between investment robustness, resource adequacy, and operational flexibility. (4) Develop a new cost allocation approach which incorporates investment robustness in identifying benefit.

Mays Research Group

The overall goal of this proposed study is to fill in knowledge gaps and develop technologies to enable more sustainable nutrient removal and recovery, reduce energy and carbon footprint at DC Water wastewater treatment plants.

April Z. Gu Group Website

The aim of INSIGHT is to develop an integrated technology to co-utilize slag, sludge and dust for producing useful products such as nanoscale calcium carbonate (CaCO3) for reuse as a fluxing agent, functionalized silica particles for heavy metal removal, and iron oxide for reuse in the steel manufacturing process. The project objective is to reduce the landfilling of slag, sludge, and dust materials, and recover value-added materials to enhance the overall efficiency of material use in steel manufacturing facilities. The technological impacts of INSIGHT include (i) creation of novel chemical pathways for synthesizing CaCO3 using recyclable solvents and flue gas generated at steel manufacturing facilities, (ii) recovery of silica and its functionalization for the separation of undesired metals (e.g., Pb, Cu, Ni, Cd, and Zn), and (iii) recovery of iron oxide for reuse in the steel manufacturing process. The goals of INSIGHT are to produce nanoscale CaCO3 with purity exceeding 90% and remove undesired metal constituents (e.g., Pb, Cu, Ni, Cd, Zn) in flue dust and sludge with a separation efficiency of 90% or higher. We will aim to recover > 85% iron oxide from sludge and dust, with the intent of reutilizing this material in steel manufacturing processes.

Gadikota Research Group

Abstract not available

Gao Group

The GRA supported by this project will investigate the chemical and morphological features of the electrochemically synthesized silicate materials, develop carbon mineralization pathways to utilize the waste construction materials, and perform techno-economic and life cycle assessments for the proposed technology. The GRA will disseminate the findings from this project through peer-reviewed journal articles and conference presentations.

Gadikota Research Group

The scope of work is divided into two major activities related to 1) definition of lining product failure mechanisms that will extend the service life of existing gas pipelines by a minimum of 50 years, and 2) large-scale testing of lined or otherwise enhanced natural gas pipelines.

Dr. Thomas O'Rourke's research website: Geotechnical Lifelines Large-Scale Testing Facility

Cornell will use the grant funds to study the performance of a lab-scale ENRe reactor system using dairy anaerobic digestate as feed and conduct a feasibility study of the ENRe system at a dairy farm.

April Z. Gu Group Website

Dr. Reed and his team will be responsible for broadening the use of sensitivity analysis and uncertainty characterization within the IM3 modeling teams through collaborative modeling efforts, training, and the development of guidance documents. 

The Reed Research Group

Abstract not available

Dr. Matt Reid's research group website: Ecological Engineering Laboratory

Urban population in the United States is confronted with degraded air quality and elevated thermal stress, especially under heat waves. The joint human health implications of these two environmental stressors impose new challenges to urban livability. Urban green infrastructure (UGI), as an integral part of the built environment, has been considered as one of the effective mitigation strategies for potentially improving air quality and reducing thermal stress. Past research extensively evaluates the impact of UGI on either heat or air pollution separately but a few recent studies provided mounting evidence of non-trivial interactions between heat and air pollution. Such evidence raises the question whether a win-win situation for heat and air pollution mitigation is always achievable when implementing UGIs. Yet their joint impacts on heat stress and air quality have rarely been assessed. This project therefore aims to elucidate how UGIs impact the interactions between urban thermal environment and air pollution across multiple spatial scales. Secondly, we are motivated to develop a new method for evaluating the design strategies of UGI by considering the human health implications of heat stress and air pollution combined. In particular, we address two scientific questions: 1) How does the occurrence of extreme heat events affect the joint impacts of heat and air quality? 2) What mitigation outcomes are expected given different design strategies of UGI under normal and extreme heat conditions? To answer these questions, we will apply the Weather Research and Forecast model (WRF) coupled with chemistry (Chem) and urban canopy model (UCM) with improved representation of UGI. The model will be evaluated with observational data under normal summer and high temperature conditions (Task 1). The effects of the present UGI in both NYC and Phoenix in mitigating heat stress and air pollution will be quantified by considering the combined statistic Itotal (Task 2) that measures the joint health impacts. Different UGI implementations (spatially undifferentiated vs. targeted) at both the neighborhood and city scales will be evaluated (Task 3).

Dr. Qi Li's Research Group: City-CLImate-People

The Cornell team will participate in the following tasks:

• integrating uncertainty and behavioral considerations to route planner
• service and demand flexibility
• studying scalable and sustainable operations
• exploring integration with fixed-line planning

Samitha Samaranayake Group

The Cornell team will participate in the following tasks:

• integrating uncertainty and behavioral considerations to route planner
• service and demand flexibility
• studying scalable and sustainable operations
• exploring integration with fixed-line planning

Samitha Samaranayake Group

Abstract not available

Mays Research Group

Abstract not available

Helbling Research Group

A recent SRC-funded study conducted by the PI (Task 2818.002) has demonstrated that wastewater generated during photolithography contains complex mixtures of known and previously unknown PFASs that are persistent and mobile. The proposed project builds on this previous study and aims to elucidate the sources of and mechanisms by which PFASs are introduced or generated during photolithography.

Helbling Research Group

Ruth Richardson will:

• work with undergraduate and/or graduate students to train new and in-coming Future Science Leaders (FSL) in microbial testing methods including cutting edge molecular biological techniques like qPCR;
• provide opportunities for mentoring, such as hosting field trips for FSL to at Cornell research labs;
• guide a field trip about microbial life in nearby watersheds;
• provide ongoing feedback and contribute to the Project evaluation​​​​

Richardson Lab

A major challenge in managing hurricane evacuations effectively and efficiently is the substantial uncertainty in population behavior (e.g., how many people will leave, when, where from, and where to). Although a great deal of research has sought to improve understanding of population behavior, gaps remain. Available statistical models of population behavior have limited predictive power. Differences in behavior across subpopulations and events, and the sequence and timing of events unfolding over the duration of a hurricane for different individuals are not well known. These gaps are largely a consequence of the limitations of the traditional types of data that have supported the research to date - surveys, interviews, and focus groups. In this project, we will capitalize on the availability of a new type of data - location information from smartphones - to make a leap forward in understanding and predicting the behavior of the population in hurricanes. Specifically, we will use smartphone location data to: (1) Test hypotheses that have previously been examined in the literature, but using an independent, much larger dataset of actual, observed behavior, and test new hypotheses developed based on the availability of a new type of data; (2) Develop a quantitative model of the sequence and timing of events for each individual during a hurricane; (3) Develop statistical models to predict the probability a person will evacuate at each time period and go to a particular geographic destination as a function of attributes of the individual/household, official events, hurricane, forecast, time markers, and past actions since the hurricane formed; (4) Test the route choice assumptions built into traffic models and determine the effects of road closures on traffic patterns during both evacuation and reentry; and (5) Identify new behaviors and questions for future traditional research using a general inductive approach.

Professor Linda Nozick

The overall goal of this proposed study is to leverage the recently awarded WRF project: “Practice to Enhance Internal Fermentation of Side-stream Secondary Sludge and Mixed Liquor Suspended Solids for Biological Phosphorus Removal” to identify the new PAOs that may play a role in the S2EBPR Process integrated with A-B stage Nitrite-Shunt/Deammonification process that is being piloted at HRSD to enable simultaneous N and P removal.

April Z. Gu Group Website

There is mounting evidence that microplastics (MP) are present in many terrestrial systems. These findings are prompting interest in the dominant mechanisms by which MPs escape from their main source and travel long-distance aerially. Cities are major sources of MPs because of the human activities that produce them. Establishing links between MP source distribution within urban environments, MP deposition rates, escape probability out of urban environments, and long-distance transport remain unexplored. Daunting challenges to be confronted are the complexity of air flow and intermittent releases of various MP types in urban settings, the interaction between urban canopy and deposition/escape of MPs, the MP particulate properties, and mesoscale forcing. To address the overall project goal, 3 inter-related science questions and 5 tasks are proposed: Q1: How MPs associated with urban emission sources get transported out of an urban systems? (Tasks 1-3); Q2: How do different characteristics of urban surface morphology (e.g. high-density urban center versus low-density residential areas) interact with the spatial configurations of MP source/sink distributions (street network) to impact their transport and deposition? (Task 4), and Q3: How to quantify the probability of MPs becoming air-borne in relation to urban surface morphology and mesoscale forcing? (Task 5).

Dr. Qi Li's Research Group: City-CLImate-People

The aqueous alkaline amine looping (A3L) technology utilizes a novel and integrated CO2 capture and conversion approach to produce calcium and magnesium carbonates from industrial and mining residues. The process is designed to operate in a single step CO2 capture and conversion mode, and can be adapted to convert heterogeneous alkaline industrial or mining residues with varying compositions and capture CO2 from very dilute sources (e.g., air) to concentrated emissions (e.g., emissions from ethanol producing facilities). This process is a significant advancement over existing chemically or thermally intensive approaches to convert CO2 to inorganic carbonates starting from heterogeneous alkaline residues and naturally occurring minerals. The A3L process is unique in its adaptive capabilities such that it can be deployed as a modular technology with science that can be easily scaled up based on targets to reduce alkaline residues or CO2 emissions. Our laboratory scale results demonstrate high capture and conversion rates of CO2 to calcium and magnesium carbonates using the A3L technology. Our team is applying to I-Corps to allow us to further explore the market for such a process with a focus on customer discovery and the determination of the best market model.

Gadikota Research Group

Epidemiological models, such as the susceptible-exposed-infectious-recovered (SEIR) model and its extensions, are essential to understand how infectious diseases spread throughout a population. While impacts of travel are indirectly accounted for within the parameters that describe the overall disease transmission rate, standard models do not explicitly represent mobility. As a consequence, mobility-restricting policies can only be coarsely understood. Recent experiences with COVID-19 highlight the significant coupling between personal mobility and the dynamics of disease contagion. This has sparked renewed interest in directly modeling transportation flows through the use of spatial meta-population models. However, these efforts still typically model mobility at a very coarse level and ignore many of the complex, local-scale travel patterns and their network effects. This work aims to bridge that gap and help address questions of the following nature: i) Which communities are most likely to accelerate disease propagation throughout the network? ii) Which recurrent travel patterns are most likely to become disease vectors? iii) What combination of social-distancing and travel restriction measures are needed to safely reactivate a region? iv) Where should preventive screening be administered (when resources are limited) to minimize contagion throughout the network?

Samitha Samaranayake Group

The project will develop a probability-based method for the analysis and design of linear/nonlinear structures subjected to Gaussian/non-Gaussian, stationary/nonstationary wind loads based on climatological data, wind tunnel time histories of pressure coefficients, and structural mechanical/aerodynamic characteristics. To promote and facilitate its use in practice, the project will deliver (1) an end-to-end realistic example, requisite data, and MATLAB functions with adequate documentation such that its output can be reproduced and (2) a computational module for wind design to the NHRI SimCenter. Work will be organized in three technical tasks. Task 1 will develop a new class of finite dimensional (FD) wind load models, i.e., deterministic functions of time depending on finite numbers of random variables. The models will match the sample properties of pressure coefficient time histories, in addition to their mean and correlation functions. Task 2 will develop efficient and accurate methods for calculating wind effect samples in linear/nonliear structures based on novel multi-fidelity models by using the FD wind load models of Task 1. The task will deliver large sets of wind effect samples within a reasonable computational budget. Task 3 will use the output of Task 2 to develop a conceptually simple method which will check ultimate and serviceability limit states simultaneously at all structural members, rather than member-by-member as in current practice. This system reliability approach constitutes a new direction in Wind Engineering.

Professor Mircea Grigoriu

The COVID-19 pandemic is bringing an unexplored dimension to microeconometric modeling of individual decisions under crowded conditions, where high density of people in public spaces poses risk of contagion. Intended work of this project is to collect and analyze time-sensitive choice and attitudinal microdata in the metropolitan area of New York City to better understand and model individual decision-making under threats to health.

Daziano Research Group

Technical services associated with investigating and modeling climate extremes for Colorado River Risk Management using Colorado’s Decision Support System model platform. 

The Reed Research Group