Poster Session Index

Urban flood modeling is crucial for understanding and mitigating the effects of stormwater in densely populated areas. The study focuses on specific watersheds in Baltimore City that are facing significant challenges, including poverty, and a growing threat of pluvial flooding. The city faces increased flooding due to frequent and intense rainfall, an aging drainage system, low-lying areas, poorly drained neighborhoods, and regions near water bodies. This poses significant risks to the residents and the socio-economic well-being of the people. To address this issue, the study uses the City Catchment Analysis Tool (CityCAT), a 2D hydrodynamic flood model that simulates pluvial flooding in a complex urban environment and provides insights into flood depths and major flow paths. The research integrated high-resolution datasets, including the Digital Terrain Model (DTM). An event-based (June 10, 2021) model was run for Tiffany Run watershed, whereas a 10-year design storm was run for other watersheds such as Cherry Hill and Broadway East. Validation of the model was carried out using crowdsourced data, such as 311 and 911 calls and local newsletters. The study aims to offer insights into pluvial flooding in these watersheds and support the use of advanced modeling software for managing floods.

Decisions regarding power grid expansion and operation often rely on models which minimize the expected financial cost of plan fulfillment, but which ignore associated externalities which may have harmful financial and societal impacts. The first component of this research is concerned with human health damages as a consequence of pollution caused by electrical generation in effort to fulfill demand over a policy’s time horizon. Specifically, we investigate the impact of various long–term energy planning scenarios on pollution-based human health damages and evaluate the relative impact that those damages have on the cost of policy achievement. The second component of this research surrounds expanding our policy decision support tool to include analyses of grid reliability associated with the long–term recommendations given by the generation expansion planning model results of the first component. This augmentation is motivated by ubiquity of temperamental solar and wind power within achievement of green energy policies. This work required the creation of a modeling framework which includes both a long-term strategic model for generation expansion planning and a short-term, day-ahead tactical dispatch model. These projects exist within a continuing scope of work which includes investigations into demand response and the incorporation of machine learning into framework decision-making. 

Single atom catalysts (SACs) demonstrate a bright future in electrochemical carbon dioxide reduction reactions (CO2RR) to address demanding global issues of renewable energy. They are attractive because of their low cost, unique structure and properties, and their high performance. Moreover, their selectivity and activity can be altered by the substrate, active SAC attached, and coordination environment. Recently, a novel N8 polynitrogen (PN) chain was successfully synthesized by a cyclic voltammetry approach and acts as an excellent electron donor, allowing for the enhancement of SACs stabilized on it. In this work, we compute density functional theory (DFT) calculations to investigate the performance of Pd and Ni SACs supported on N8 PN towards the CO2RR to valuable hydrocarbons, such as carbon monoxide, formic acid, methane, and methanol. We concluded that formic acid is the most likely product on both Ni@N8 and Pd@N8, however for Pd@N8 methanol can be a competing product. Furthermore, when excess H2 is ejected into the system, PN undergoes spontaneous reconfiguration to a ring structure, which improves reactivity and proton transfer. We also observed the reactions proceed better under acidic conditions. Overall, a mechanistic knowledge on structure-performance relationships for a novel active catalyst material is established.

Single atom catalysts (SACs) demonstrate a bright future in electrochemical carbon dioxide reduction reactions (CO2RR) to address demanding global issues of renewable energy. They are attractive because of their low cost, unique structure and properties, and their high performance. Moreover, their selectivity and activity can be altered by the substrate, active SAC attached, and coordination environment. Recently, a novel N8 polynitrogen (PN) chain was successfully synthesized by a cyclic voltammetry approach and acts as an excellent electron donor, allowing for the enhancement of SACs stabilized on it. In this work, we compute density functional theory (DFT) calculations to investigate the performance of Pd and Ni SACs supported on N8 PN towards the CO2RR to valuable hydrocarbons, such as carbon monoxide, formic acid, methane, and methanol. We concluded that formic acid is the most likely product on both Ni@N8 and Pd@N8, however for Pd@N8 methanol can be a competing product. Furthermore, when excess H2 is ejected into the system, PN undergoes spontaneous reconfiguration to a ring structure, which improves reactivity and proton transfer. We also observed the reactions proceed better under acidic conditions. Overall, a mechanistic knowledge on structure-performance relationships for a novel active catalyst material is established.

The US Farm Bill ($867×109 authorized in the Agriculture Improvement Act of 2018-2025 is now projected to cost taxpayers approximately $1,300×109) is the largest source of funding for food crops and energy crops in the US. With reappropriation coming in 2025, it is fitting to reexamine the true costs of energy crops on our food and energy security and biodiversity. Energy crops are used to make biofuels, exclusively. They are perceived as green energy solutions, though they pose severe risks to humans and Earth’s ecosystems that are dismissed. Contrary to common belief, they are not mainly sourced from agricultural wastes from food/feed crops. Rather, they compete directly with food/feed crops for land and water, while requiring large energy and fertilizer inputs. They are major sources of deforestation and global carbon emissions. They are not green in any sense of the word. Energy crops receive subsidies disguised as if they were food crops that are hidden from the public. The hidden subsidies on energy crops increase the cost of food and undermine food security, while benefiting politicians who appropriate them. Another falsehood is they are produced from super-efficient energy crops that exceed the poor efficiency of natural photosynthesis. In this poster, I will document the above claims for the common energy crops (corn, sugarcane, palm oil) using data obtained from 5-12 year-long commercial field studies validated by the UN-FAO and USDA, and their carbon intensities taken from 66 harmonized DOE studies.

This study seeks to answer the question: What are the vulnerabilities and adaptation strategies of women migrating to urban slums in Khulna, Bangladesh, following disaster-induced displacements? This question addresses a notable gap in the literature regarding the lived experiences of disaster migrant women and their unique challenges in urban environments. Utilizing qualitative data from nine focus group discussions, three case studies, and ten key informant interviews, this research examines the socio-economic status and adaptation challenges of women migrants in the Rupsha slum. ​The findings reveal that women face significant vulnerabilities related to poverty, tenure insecurity, and inadequate institutional support.​ Among different age cohorts, elderly women exhibit the highest vulnerability, whereas middle-aged women demonstrate greater adaptive capacity due to their experience. These findings suggest that while migration may initially serve as a survival strategy, it often perpetuates vulnerability in urban settings. This research underscores the need for targeted interventions from local and central government institutions, particularly regarding tenure security and support programs for elderly women migrants. Addressing these issues is crucial to enhance the resilience of displaced women and ensure their rights to development, thereby contributing to the broader discourse on climate-induced migration and gender vulnerabilities.

This study investigates heavy precipitation patterns in New Jersey, and potential changes stemming from climate change. I utilized weather data from various sources, consisting of monthly and annual averages, along with records, from historical climate databases, including the Monthly Climate Tables from the Office of the New Jersey State Climatologist, David A. Robinson at Rutgers University. The climate observations for New Jersey date back to 1895, when statewide weather observations began. Various factors regarding the formation and progression of heavy precipitation were analyzed using data, where trends were found. Specifics in respective events were recorded, namely frequency, intensity, and duration. Based on the analysis of data, there is consensus towards an increase in overall precipitation averages, in addition to total rainfall and rainfall intensity in heavy precipitation events. The spatial variability of these changes was also highlighted in the study, indicating vulnerable areas that experience more pronounced impacts. The results of the findings are crucial for local infrastructure planning and disaster preparedness, as New Jersey faces increased risks of flooding and related hazards. This research contributes to the broader understanding of climate change in the state of New Jersey and on a global scale.

This research focuses on the requirements for deploying wave and tidal energy devices, selecting the most environmentally friendly, affordable, and flexible mass production option. As the world shifts towards renewable energy, wave and tidal energy offer promising solutions for generating clean power and reducing fossil fuels dependence. Tidal and wave energy are complementary energy sources and important for power security. Traditional methods of tidal and wave energy resource assessment focused on velocity distribution, leaving a knowledge gap to convert the available resources to final power output. Our study conducts a device-level survey that bridges the gap between resource assessment and utility level power generation.

In addition to resource assessment, we further our study on environmental impact of the technology. We reviewed international regulations and standards crucial for protecting marine biodiversity. We explored costs, ease of mass production, and overall environmental impact on the ocean. The methods used included numerical modeling, time series analysis, and optimization of virtual device deployment.

The research concluded that with proper regulations and as technology progresses, wave and tidal energy could play a crucial role in lowering carbon emissions while protecting marine life. Several locations are identified as feasible venues to harness tidal and wave power.

Climate change-induced environmental disasters disproportionately affect racial minority communities, highlighting the need to address systemic racism’s role in disaster experiences. Harlem Park in Baltimore, Maryland, is located on the west side of Baltimore City. Though the community has a rich history of activism, it has also struggled with systemic discrimination through housing, redlining, and education.This research, focusing on Harlem Park, investigated how residents and stakeholders define “disasters”, “environmental hazards” and “disaster preparedness” through working with nine Harlem Park Community Members and Stakeholders. Rooted in an abolitionist climate justice approach, it sought to understand the interplay between equity, resilience, and preparedness. Methodologically, this research adopts a Community-Based Participatory Action Research (CBPAR) approach, utilizing focus groups and photovoice methods. Data analysis involved qualitative coding to unveil community perspectives and their connections between racial oppression and climate justice.The project’s significance lies in advocating for tailored, place-based disaster preparedness and climate adaptation interventions rooted in the community’s experiences and perceptions. Ultimately, this research seeks to empower the Harlem Park Community by generating actionable data for climate and racial justice.

The Colorado River Basin in the arid southwest is fraught with controversy over dwindling water supplies. This conflict underscores the need to understand how water management and climate change impact the hydrologic dynamics of this watershed. ParFlow-CLM—a model integrating groundwater, surface water, and land surface processes—is well-suited to addresses complex hydrological processes in the Upper Colorado River Basin (UCRB), which supplies the majority streamflow in the Colorado River system.

Groundwater pumping may threaten UCRB streamflow but is inconclusively studied. This research explores various pumping scenarios in ParFlow-CLM, using data synthesized from multiple well databases, land cover maps, and consumptive use estimates. These inputs help estimate irrigation demand and groundwater use in croplands. Model outputs for total water storage are compared with GRACE satellite data, and inflows to reservoirs will be highlighted.

Climate change poses an additional threat, but few studies examine climate change scenarios with a groundwater/surface water model. ParFlow-CLM provides a unique opportunity to track water balance changes under varying climate conditions and provide mechanistic insights into the interactions between atmospheric, surface, and subsurface water and energy fluxes.

Ultimately, our fully-developed ParFlow-CLM model will support wide-ranging research on future UCRB hydrology under evolving climate and policy scenarios.

Microbial evolution is significantly influenced by shifting environmental conditions, and understanding these changes is essential for predicting evolutionary outcomes. A pressing example is the rise of antimicrobial resistance (AMR), a growing public health crisis that could cause up to 10 million deaths annually from resistant bacterial infections. Although AMR mechanisms and spread have been well-studied, the role of environmental factors, particularly climate change, on resistance evolution remains less understood. This study analyzes fitness data from Pseudomonas stutzeri mutants across 176 conditions to assess climate change’s impact on antibiotic resilience. Fitness values, measured as changes in mutant abundance over one growth cycle, were analyzed under thermal, salinity, and metal stress conditions to simulate climate stressors. Our findings show that numerous antibiotic-resistant (ABR) mutants gain fitness advantages in these environments, suggesting specific molecular mechanisms linking AMR and climate change. Notably, climate stressors reduce the fitness cost associated with ABR, effectively “rescuing” these mutants. These results imply that as climate change progresses, AMR mutants may more easily persist and spread, potentially worsening the AMR crisis.

The Mid-Atlantic region is set to be one of the first and largest contributors to the offshore wind energy goals of the United States. This region is home to a diverse marine ecosystem comprising important marine species such as the critically endangered North Atlantic right whale. To support the development and operation of the planned offshore wind farms, there is a need for high-resolution modeling of NARW presence. Toward this, we leverage highly localized observations from nine glider deployments in the Mid-Atlantic to propose a machine-learning approach for modeling NARW presence conditioned on a diverse set of glider- and satellite-based oceanic, physical, and contextual information. We find that tree- and ensemble-based models achieve the highest levels of accuracy while maintaining a sensible balance of missed and false alarms. Interpretation of the machine-learnt features reveals interesting insights on the relative value of well-resolved satellite surface measurements to well-resolved vertical information from glider sampling in explaining the species-habitat patterns of NARWs. Our work constitutes the first machine learning attempt to jointly leverage glider- and satellite-based information for the modeling of NARWs.

This study investigates the long-term variability and trends in offshore wind characteristics along the U.S. Mid-Atlantic coast, aiming to address the critical knowledge gap in hub-height offshore wind data essential for sustainable wind energy development. With offshore wind capacity goals of up to 39 GW from Virginia to Massachusetts, understanding the long-term behavior of wind resources is crucial for the optimal design and operation of wind farms. This project leverages the NOW-23 reanalysis dataset, spanning from 1940 to the present, alongside the NOW-23 dataset by the National Renewable Energy Laboratory (NREL), to analyze historical wind patterns at the 160-meter turbine hub height. The focus period for this project is 2000-2020. Through analysis, this research seeks to address discrepancies in data resolution, assessing both long-term trends and interannual variability in wind conditions at the turbine level. Validation efforts will be conducted using observational data from offshore lidar buoys, ensuring reliable analysis of wind characteristics. Findings from this project are expected to offer insights into the atmospheric boundary layer dynamics, enabling more accurate forecasting and informing climate-resilient design strategies for offshore wind farms, thereby contributing to the reliable expansion of renewable energy resources.

The oxygen reduction reaction (ORR) is integral to emerging renewable energy technologies, serving as the reduction half-reaction for fuel cells. Developing non-noble metal systems for ORR and understanding how catalyst structure and ligand design govern the ORR selectivity for water versus hydrogen peroxide are important research areas. The majority of studies into molecular transition metal complexes based on earth-abundant metals have utilized macrocyclic ligands, while studies to explore the possible roles of redox-active ligands for this reaction are scarce. In particular, redox-active ligands with protic sites may engage in proton-coupled electron transfer with oxygen to favor water formation, opening new avenues for catalyst design. We have prepared a dicationic cobalt complex supported by the o-phenylenediamine ligand and have shown that this complex reacts with O2 or air via ligand-centered two-hydrogen atom transfer. Electrochemical studies in the presence of trifluoroacetic acid demonstrate electrocatalytic current enhancement for ORR at mild potentials. Spectrochemical studies using decamethylferrocene as a chemical reductant also confirm O2 reduction activity, yielding water as the 4e−/4H+ reaction product with high selectivity. Further studies to elucidate the catalytic rate law and reaction mechanism under electrochemical and chemical conditions are currently underway.

Groups whose stance on political or social issues place them outside of the mainstream can gain support for their positions by entering into coalitions that increase power and urgency for their causes. The success of these collaborations is linked to both the amount and quality of of communicative activity among these groups. The allegations that marine mammal deaths are linked to offshore wind exploration, made by a loosely formed coalition of five groups in January 2023, presented an opportunity for further collaboration and expansion to include additional like-minded groups. While the misinformation about marine mammal deaths did not result in an enduring collaboration, it galvanized public opinion, leading to a significant drop in support for wind energy among the populace. Preliminary analyses of Facebook data collected during a 15-month period from December 2022 to February 2024 indicate that despite a significant increase in communication centered around this misinformation, the failure to collaborate is correlated to low levels of communicative engagement within these groups. However, the misinformation itself was enough to unify a wider stakeholder community.

Flash flooding is an increasingly serious issue, causing death and property destruction in New Jersey. One cause of this is likely increased storm potency, historic urbanization patterns in which many NJ towns are in low-lying areas next to rivers, as well as the possibility of multiplier effects of new construction and increased flood intensity. These trends are echoed globally. In this project, we ask: where do flash floods happen, and what types of novel data can be included to more accurately predict potential flooding location? Using a mixed-methods approach, one component of the project uses reported flood locations to build a predictive model of risk that includes six factors and prioritizes accuracy for flash flood occurrences. This model is then contextualized with less systematic sources of information, such as alerts, local community-based networks and organizations among others. Our initial findings highlight a novel approach to modeling flash flooding – as well as further questions on how these sources of knowledge can be combined and layer and intersect such that they make sense and are used by residents, decision-makers, and community organizations that are all ‘owners’ and often at the frontlines of response and harm.

Flowering is crucial for plant reproduction, biomass, and seed production, with its timing synchronized to developmental stages, weather conditions, and pollinator availability. Disruptions to flowering time can negatively impact plant productivity, species survival, and ecosystem stability. While elevated CO2 and rising temperatures are known to impact flowering time, the underlying mechanisms remain unclear.
MicroRNAs (miRNAs) play a key role in regulating gene expression and are highly responsive to environmental changes. Using sequencing, we identified miRNA-regulated pathways affected by doubled atmospheric CO2 or a 3–6°C temperature increase. The miR156/157-regulated transcriptional network emerged as central to early flowering induced by elevated CO2 1.
We further investigated the robustness of the miR156/157 network in response to varying CO2 levels and its impact on flowering time 2. Our analysis showed that CO2 concentrations of 200–300 ppm dramatically advanced flowering time, while 400–800 ppm had a mild effect. We confirmed the buffering role of feedback regulation, identified an inverse relationship between flowering time and its variance, and highlighted sensitive features within the network.
Our study provides a promising strategy for predicting and engineering flowering time to enhance plant adaptation and resilience to climate changes.

Carbon neutrality is the balance between anthropogenic greenhouse gas (GHG) emissions and removals. While numerous studies compare measures to mitigate climate change, research case studies evaluating both renewable energy and carbon sequestration measures, with the latter mitigation based on measurements and modeling, are rare. The main objective of this study was to develop a carbon footprint for Duke Farms, a farm and environmental education center, to assess the contribution of two renewable energy projects (solar and geothermal) and carbon sequestration from beyond business-as-usual management practices (i.e., tree plantings, rotational vs. continuous grazing of pasture). Results found a carbon footprint of 1880 Mg CO2-eq. for the year 2016 not considering carbon sequestration (= base case). Building operation contributed 75% of the carbon footprint followed by employee commuting (27%), land-based systems (10%), and vehicles and equipment for general use (4%). Solar on-site energy production reduced the emissions of the base case by 16%. Carbon sequestration reduced the carbon footprint by additional 13%. Carbon neutrality has not been reached, and therefore further GHG reductions (e.g., from buildings and employee commuting) and removals (e.g., tree plantings in floodplain) need to be implemented. GHG reductions are more permanent than removals.

My name is Dan Lucal and I was encouraged to apply to this poster event by design professor Atif Akin during a studio visit yesterday. Rather than a poster, I would like to submit a sculptural artwork– one that has floated in the Raritan River and the East River, and been on display in the Mason Gross Galleries and Neighbors Gallery (Brooklyn). The sculpture is made of steel and foam. Please see images below of the sculpture. It is approximately 6 feet tall and 30 inches wide and 30 inches deep. I believe it will function as a form of communication about climate change, albeit in a less literal sense than some posters.
This work is part of a series of floating artworks I’ve been working on as an MFA candidate at Mason Gross. Two of my other works are currently moored in the boat basin at the Rutgers university Marine Field Station in Tuckerton NJ. You can see photos of those on my website Danlucal.com.

Factory farming is one of the major contributors to climate change. Moreover, this captive system causes thousands of animals to suffer daily. Many animal activists and environmental campaigns use direct documentation to raise awareness of these ethical issues. However, meat consumption continues to increase globally. As an artist, I explore the role of art in promoting actions to fight factory farming.
This poster, divided into two sections, will illustrate the ethical impact of the viewer imagining from the perspective of an artist who is morally attuned to animal and environmental ethics. First, I will discuss how to discern whether the artist is morally attuned by examining the marks, colors, composition, and conceptual content in her artwork. I analyze a drawing of animals in a slaughterhouse by English-American artist Sue Coe as an example. In the second part, I illustrate how this viewing exercise prepares the viewer to imagine from Sue Coe’s perspective and even the ones of the captive animals. Each imagination step is accompanied by an image. This process allows the viewer to gain a personal understanding of factory farming and its negative consequences.

In New Jersey, the transition from fossil fuels to renewable energy generation involves the use of solar, wind, and bioenergy. While most of the state’s wind resources are found offshore, solar energy will mostly be produced onshore. Farmland is typically considered a prime location for solar energy generation, but typical solar farms often prevent continued farming on the land. Agrivoltaics offers an alternative solution, allowing land to be used for both farming and solar energy, enhancing land efficiency and potentially increasing economic viability compared to farming alone.
Members of the Rutgers Agrivoltaics Program (RAP) are investigating the opportunities and challenges associated with agrivoltaics in New Jersey. The RAP team designed and installed agrivoltaic research and demonstration systems at three Rutgers/NJAES research farms and is finishing up the first season of research trials studying production of forage with beef cattle, hay, soybeans, tomatoes, eggplants, and peppers. Furthermore, the RAP team assists the NJ Board of Public Utilities (NJBPU) with the development and implementation of the NJ Dual-Use Solar Energy Pilot Program. The RAP team is also actively engaged in outreach activities targeting farmers, solar developers, regulators, Extension personnel, researchers, and other solar industry stakeholders.

Climate change significantly affects the hydrologic regime of catchments, especially over complex terrains like High Mountain Asia (HMA), affecting the livelihood of billions of people. Owing to the need to understand the effects of changing precipitation and temperature on future streamflow characteristics of glacierized catchments in HMA, four statistically downscaled CMIP6 GCMs under SSP5-8.5 scenario are used for simulating streamflow over basins in Pakistan, Nepal, and Bhutan. Hydrological Model for Distributed Systems (HYMOD_DS) is used to simulate projections of total streamflow and components (snowmelt, ice melt, and rainfall-runoff) over mid-century (2041-2070) and end-century (2071-2100). The future projections are compared against the historic simulations (1990-2014) at annual and monthly timescales. While the streamflow simulations from basin in Pakistan are influenced by precipitation and temperature, the simulations over basins in central and eastern HMA being rainfall-dominated basins, are influenced more by precipitation. The overall water availability across the basins is projected to increase along with an increase in rainfall-runoff. The contribution of snowmelt component to total streamflow over central and eastern basins was found to decrease in the future. The results reveal that depending on the choice of GCM, the direction and magnitude of future climate varies thereby affecting the hydrological projections.

Climate change poses major challenges to populations in different parts of the world. The shift to a warmer world brings along increasing frequency and intensity of hydrologic extremes, overloading infrastructures, threatening food security and endangering people’s lives, especially in vulnerable communities. The region comprising the mountain ranges in Central-South Asia, known as High Mountain Asia (HMA), is a unique area of study, as it includes complex topography, the highest number of glaciers outside the poles, and provides water supply to billions of people. In this study we utilized downscaled climate projections to quantify future changes in the severity of droughts, heatwaves and intense precipitation over HMA, estimating the exposure of general and vulnerable communities to these hazards. Results show notable changes in severity for all three extremes, indicating higher risk for the affected population. Vulnerable communities living at higher elevations will also be affected, especially for intense precipitation and its possible cascading hazards, such as landslides and glacier outbursts. Drought increasing exposure is more pronounced at the North-Western Portion of HMA (Kazakhstan to North Pakistan) while the heatwaves exposure will have a widespread increase over the region.

With climate change becoming an increasingly critical issue to combat, green energy solutions such as solar arrays have become more prevalent. This has created a new issue surrounding land-use, as much of the best land for solar farms is also ideal land for agricultural farms. The solution to this land-use issue is known as agrivoltaics, the dual use of land for both farming and solar power generation. Existing solar arrays used in agrivoltaic applications are often either single-axis tracking arrays, generally raised above the height of the crops, or vertical bifacial arrays. These each have their benefits and drawbacks, with single axis trackers producing more energy but being more difficult to install and maintain and vertical bifacial panels being simpler and effective at times of day when electricity demand is highest but producing less energy overall. However, the two can be effectively combined into a new array type designated as tilting near-vertical bifacial, or TNVBF. This new array design takes the traditional vertical bifacial design and incorporates small angle tilting at optimized angles in the morning and afternoon to enhance energy production by over 20% while still maintaining ample row spacing for agriculture.

With climate change becoming an increasingly critical issue to combat, green energy solutions such as solar arrays have become more prevalent. This has created a new issue surrounding land-use, as much of the best land for solar farms is also ideal land for agricultural farms. The solution to this land-use issue is known as agrivoltaics, the dual use of land for both farming and solar power generation. Existing solar arrays used in agrivoltaic applications are often either single-axis tracking arrays, generally raised above the height of the crops, or vertical bifacial arrays. These each have their benefits and drawbacks, with single axis trackers producing more energy but being more difficult to install and maintain and vertical bifacial panels being simpler and effective at times of day when electricity demand is highest but producing less energy overall. However, the two can be effectively combined into a new array type designated as tilting near-vertical bifacial, or TNVBF. This new array design takes the traditional vertical bifacial design and incorporates small angle tilting at optimized angles in the morning and afternoon to enhance energy production by over 20% while still maintaining ample row spacing for agriculture.

Forests are major carbon sinks but global climate change influences vegetation and impacts soil microbial communities that play important roles in nutrient cycling as they decompose soil organic matter. Abundance, composition, and activity within the soil microbial community changes in response to environmental variables such as season, vegetation, and soil pH; Acidobacteriota dominate acidic soils. The poorly characterized Acidobacteriota represents over half of some soil bacterial communities, however diversity within this phylum is understudied. Cultivated species can break down complex carbon in nutrient limited environments. This research aims to characterize bacterial communities in Arctic tundra and boreal forest soils and elucidate major factors for diversity within Acidobacteriota. It was hypothesized that Acidobacteriota communities would differ by geographic separation and soil type. Soil cores were collected across Finland in a North- South transect focusing on different forest types (pine and spruce) with varying soil moisture and organic/mineral content. DNA was extracted from organic and mineral layers and rRNA operon PCR amplicons were sequenced using the MinION. BLAST was performed using a curated database with sequences from RefSeq. Analysis of community structures by rRNA operons indicate that forest type and associated vegetation impact community composition and analysis reveals previously unseen diversity.

The need for future flood risk assessments has greatly risen in locations across the globe particularly for vulnerable populations that lack the wherewithal to design large-scale flood mitigation structures. Conventionally, hydrologic models are calibrated and used to guide decision-making that optimize mitigation strategies. The data and computational demands of these models implemented across large spatial scales make them an inefficient tool for resource-limited countries to access and utilize. Machine learning (ML) based models are a viable alternative that counters these drawbacks. The potential exists for employing ML models to spatially inform on future flood risk under various climate scenarios. The flood peak prediction ML model developed is based on the ensemble-based Histogram Gradient Boosting Regressor (HGBR) and the hydrologic model is the Ensemble Framework For Flash Flood Forecasting (EF5). Four global circulation models for shared socio-economic pathways (SSP-2.45 and SSP-5.85) from LOCA-CMIP6 provide the meteorological input for both models. Results reveal that the model uncertainty from HGBR and EF5 is well within the climate-forcing uncertainty. More importantly, the relative change (future vs historic) from the two models is comparable; a result that could serve communities at the global scale that lack the resources to access, develop and operate hydrologic models.

In response to growing urban populations, rising energy consumption, and increasing environmental degradation, scientists and policymakers alike have prioritized the approach to modern urban development. Aiming for environmentally and economically sustainable designs, two urban strategies have risen in recent years: Smart and Green cities. However, Smart and Green cities are often conflated due to their inherent link to sustainability, despite the former existing in the dimension of social and economic benefit while the latter points towards environmental improvement. Therefore, we propose a Connected framework, influenced by successful existing Smart and Green strategies, in order to provide an urban development strategy with potentially unilateral benefit. The aim of this research is to (1) analyze the effectiveness of existing policies and technologies and (2) build a reduced list of strategies potentially usable for the aforementioned Connected framework. Based on the index analysis of the four described cities, (Berlin, Osaka, Helsinki, and Prague, we proposed the Connected framework containing the following key points: promotion of Public-Private partnerships and funding, investment in unique structural materials, satisfaction of at least two of the following three goals: software Implementation, education/Transparency, and physical Infrastructure.

Tree mortality is projected to escalate as land surface temperatures induce drought-related stress. Periods of depleted moisture disproportionately impact semi-arid ecosystems–– regions characterized by limited water availability. Vegetation in semi-arid environments are drought-resistant, meaning these plants have adapted to seasonal depletion of moisture. Drought resistance comes with the long-term trade-off of increased sensitivity to the timing of dry and wet seasons. Scientific consensus suggests understanding water availability is imperative to assessing and predicting droughts and their influence on tree die-off. Despite the proposed importance of forecasting drought-stress, discrepancies persist between observed drought-induced mortality and simulated drought models. The goal of my proposed research is to explore the gaps in research that challenge existing methodologies when studying drought resistance. I leveraged Parflow-TREES, a coupled model that integrates plant hydraulics with hydrology into a continuum of dynamic water fluxes between the atmosphere, soil and vegetation. This approach to modeling seeks to simulate the movement of water while approximating plant traits responsible for responding to drought. I hypothesize that 1 water table depth (WTD) selects for plant traits that improve drought resistance; 2 drought responses vary along a hillslope gradient relative to WTD; 3 and individual drought responses extend to ecosystemic drought response.

How do library closures impact sustainability in communities and access to climate action and resilience resources? This poster represents the beginning of my examination of this research question as a first-year Ph.D. student in Library Information Science. Using the case study of the Trenton Free Public Library branch closings, from the six original branches at the beginning of the 20th century to the one existing branch in 2024, I examine some of the social and environmental services that were once provided to the community of Trenton and imagined opportunities for a more climate resilient present and future. Through discourse examination of early 20th century newspapers from archivist scrapbooks and geographical mapping with ArcGIS FEMA data, I show how the now closed library branches once provided for past Trenton communities, how they could serve present communities in terms of cooling centers, elderly check-in buddy programs, and resources for civic engagement. I also speculate if early 20th century WPA New Deal initiatives offer a model for potential ways to re-establish climate action support in Trenton today, outline future research goals, and welcome feedback as I continue this project.

Space and field observations show the formation of impermeable layers in glacial firn at various scales, from miniature ice lenses to kilometer-scale slabs. The presence of ice layers in glacial firn controls meltwater retention and runoff. Ice layers could be flat or irregularly shaped called chunks. Understanding the underlying mechanism of meltwater infiltration and formation of these impermeable layers as well as their modeling is essential to accurately predicting glacier mass balance in a warming climate. Meltwater infiltration and ice layer formation are a multidimensional phenomenon. However, the state-of-the-art models are essentially one-dimensional (along the z-axis), lacking physical rigor to fully explain the observed datasets. Here we present a large scale, multidimensional, multi-phase thermo-mechanical model and utilize high-fidelity field data to understand the melt water partitioning into runoff, liquid storage and refreezing at different lateral scales (from meters to tens of kilometers).

Timing insecticide applications with Sparganothis fruitworm (Sparganothis sulfureana) peak flight is critical for its management in New Jersey cranberry. The annual peak flight of S. sulfureana has been modeled using a degree-day model with a biofix date of March 1st; however, this biofix is not suitable for New Jersey where winter and spring temperatures are warmer and cranberry web flooding the practices differ. We present two models for predicting S. sulfureana peak flight in New Jersey: one with a biofix of April 15th, when drainage of cranberry beds occurs on average, and another using individual bed drainage dates. These models project peak flight at 525.5 and 521.0 degree-days, respectively, and are suitable for regional grower guidance. Furthermore, differences in S. sulfureana peak flight were observed across cranberry varieties; however, the effect of variety was influenced by year (significant variety-by-year interaction). This year-to-year variation in peak flight was strongly associated with spring (April–May) temperatures. Using these models, we project that with climate change, the peak flight of S. sulfureana in New Jersey cranberry beds may occur up to a week earlier by 2050. The use of regionally-specific and variety-specific models will help improve timing of management strategies against this pest.

Cryophiles are well-adapted natives of the planet’s Arctic and Antarctic biomes.
Microorganisms that live in permafrost or seasonally frozen environments have adapted all
aspects of their biological functions in order to survive and maintain metabolic activity at sub-
zero temperatures. While there has been significant progress in the study of cryophilic microbes,
the impact of these under-studied microorganisms on carbon and nitrogen cycling is not well
understood and presents a substantial knowledge gap for accurate climate modeling. Studying
microbial life that exists and thrives at temperatures below the freezing point of water, in subzero
conditions, presents a significant challenge. This project aims to partner cultivation of
representative cryo-active species with ribosomal rRNA amplicon sequencing of the community
in order to gain a deeper understanding of polar soil microbiomes and how the communities will
respond to a changing climate. The subzero active community members are targeted using novel
culturing techniques through the use of osmotic pressure, to emulate the water activity at subzero
temperatures, selective enrichment and isolation of potential subzero active bacteria. One major
benefit of this approach is culturing at temperatures above 0 °C allowing for higher growth rates
and cultivation. Enrichment and isolation of cryophilic organisms has been achieved using these
techniques with soil from Arctic tundra and the Antarctic. Enrichment cultures and isolates
growing at 4 °C have been screened for novel strains of interest by rRNA operon sequencing.
Several potentially new species having been identified, with full genome sequencing of
representative strains for detailed characterization of cry-adaptation. Two novel Pseudomonas
strains, most closely related to the known ice-nucleating Pseudomonas borealis, were enriched
from Finnish tundra soil. An Antarctic soil isolate was obtained from high osmotic soil
enrichments, with a remarkably rapid growth rate at 4°C. This study underscores the importance
of combining targeted enrichment/cultivation with molecular community analysis for a deeper
understanding of the sub-zero active polar microbiome.

The Federated States of Micronesia (FSM) is an island nation in the north Pacific that is extremely vulnerable to climate change. FSM has a population of 110K people speaking 17 distinct languages across 607 islands within one million square miles of ocean and is home to some of the most biodiverse terrestrial and marine ecosystems in the world. FSM is threatened by sea level rise, altered rainfall, rising temperatures, and increased storm frequency/intensity all of which directly impacts local food production. FSM’s indigenous population has become dependent on imported, unhealthy processed foods and non-communicable, diet related diseases are their greatest health threat. Thus, developing a sustainable local food system in this rapidly changing climate has become a national priority.

This PhotoVoice study was conducted to explore community perceptions of air quality and heat stress in Elizabeth, NJ, through visual storytelling and participatory workshops. Partnering with Rutgers University, Groundwork Elizabeth, and the Housing Authority of the City of Elizabeth (HACE), participants utilized photography to document their lived experiences regarding environmental changes, focusing on air quality and heat stress. The study spanned from July to August, with a 14-day photo-taking period bookended by two workshops, where community members discussed their observations.
Key findings revealed that perceptions of good air quality were associated with clear skies, urban green spaces, and waterfront areas, while poor air quality was linked to industrial activities, vehicle emissions, and visible air pollution. Heat stress was characterized by symptoms such as dying plants and empty streets, with residents expressing concerns about its impact on their environment and well-being. By combining visual evidence with community discussions, the project emphasized the importance of addressing environmental health challenges in urban areas, particularly for vulnerable populations. This study provides a platform for residents’ voices to inform future interventions on air quality and heat stress.

Despite occupying a small geographic area, water properties along and near the Antarctic margin play an important role in the global climate system. Nutrient distributions and exchange between the Antarctic continental shelf and open-ocean impact ocean ecosystems and air-sea CO2 fluxes. Shelf nutrients are influenced by regional circulation (in particular, the Antarctic Slope Current and Antarctic Coastal Current), seasonal sea ice coverage, upwelling and the delivery of offshore nutrients, ocean mixing, and biological processes. The Antarctic margin can be broken up into distinct “zones” or “shelf types” based on regional dynamics governing the physical properties in the region and connections between the waters on and off-shore of the shelf. Here we examine the mean-state and variability of nutrient content along the Antarctic margin utilizing a solution of the Biogeochemical Southern Ocean State Estimate (B-SOSE), a numerical simulation constrained by available observations in the Southern Ocean. We utilize monthly output of biogeochemical tracers from an iteration of B-SOSE at 1/6th- degree horizontal resolution that spans 2013 through 2023. Our analysis quantifies shelf iron, nitrate, and chlorophyll content and variability on monthly, seasonal, and inter-annual timescales to build a mechanistic understanding of drivers of changes in nutrient content along the shelf. Our results shed light on the physical and biogeochemical processes setting the mean-state and driving variability in nutrient budgets in five distinct shelf sectors. Advances in our understanding of the dynamics governing Antarctic margin nutrient distributions are required to create a better understanding of global ocean biogeochemical processes and for predicting changes as the climate continues to warm.

The surface climate of the Antarctic continent and surrounding regions plays a crucial role in shaping Southern Ocean dynamics, particularly through its influence on ocean circulation and sea ice patterns. This study assesses near-Antarctic surface temperature, precipitation, and wind patterns as simulated by three coupled climate models — GFDL-CM4, GFDL-ESM4, and GFDL-CM4Xp25 — each differing in atmospheric resolution (100 km for GFDL-CM4 and ESM4, and 50 km for GFDL-CM4Xp25). We compare the simulated surface metrics in historical simulations against ERA5 reanalysis and direct observations from Antarctic Automatic Weather Stations (AWS). Regional analysis of surface air temperature, precipitation, wind speed, and wind constancy highlights discrepancies, especially in wind patterns, although temperature and precipitation are more accurately represented. Our work aims to provide insight into causes and consequences of model differences in simulated surface climate metrics. Refining near-Antarctic surface climate modeling is vital to understanding the ocean-atmosphere interactions along the Antarctic margin and enhancing climate projections.

Biomass is a renewable carbon source from which bio-oil can be produced via pyrolysis for sustainable fuel production. However, raw bio-oil must be upgraded to improve its shelf life for storage and transportation, as the presence of small carbonyl compounds leads to condensation and polymerization reactions that contribute to its chemical instability. As such, hydrotreatment via carbonyl hydrogenation is considered an effective upgrading method, but the required harsh conditions have motivated the search for alternative, milder processing methods. An electrocatalytic hydrogenation scheme operating under ambient conditions would circumvent the need for hydrogen gas or stoichiometric reductant, and enable direct integration with renewable electricity. To date, molecular electrocatalysts for hydrogenation are scarce and require forcing potentials and/or strong Brønsted acids that limit reaction efficiency. To address these issues, we are targeting milder reducing agents based on transition metal-hydride complexes that can be electrochemically regenerated under mild conditions. We have shown that an iridium-hydride complex reacts stoichiometrically with acetophenone, a model carbonyl substrate, in the presence of simple carboxylic acids to generate 1-phenylethanol at ambient conditions, with significant rate enhancements upon addition of Lewis acidic salts. Preliminary electrocatalytic studies by controlled-potential electrolysis show high substrate conversion using 1 mol % iridium catalyst.

Surface winds along the Antarctic margin significantly influence global climate through their impact on Southern Ocean (SO) circulation and sea ice dynamics. Notably, strong katabatic winds, driven by density differences and shaped by steep topography, play a critical role in Dense Shelf Water (DSW) formation. Despite their importance, the representation of near-Antarctic surface winds in climate models has been relatively understudied. This study evaluates the representation of Antarctic surface winds and specifically Katabatic winds in three fully coupled climate models—GFDL-CM4, GFDL-ESM4, and GFDL-CM4Xp25 relative to ERA5 reanalysis data. The models vary in atmospheric resolution, with CM4 and ESM4 having a 100 km grid spacing and GFDL-CM4Xp25 a finer 50 km grid spacing. Future wind changes under the high-emission SSP5-8.5 scenario are also examined, with a regional focus on the Antarctic continent. This research seeks to enhance our understanding of near-surface and Katabatic wind representation and the mechanisms influencing projected changes under continued warming. Improved modeling of the Antarctic surface climate is critical for accurately capturing near-Antarctic ocean-atmosphere interactions and reducing uncertainty in projected changes in this key region of the climate system.

Small community water systems (CWS) in Puerto Rico, known as non-PRASA systems, face substantial challenges in delivering safe and reliable drinking water amid climate change and natural disasters. This study aimed to identify key barriers to the resilience of these decentralized water systems and explore alternative solutions for adaptive water supply in Puerto Rico’s non-PRASA communities, with a particular emphasis on hurricane impacts. Data, especially perishable information following Hurricane Fiona, was gathered through various methods, including online and in-person surveys, workshops, and site visits, to pinpoint primary technical, economic, and social factors compromising water supply resilience. Stormwater-to-drinking water (STDW), a concept extending beyond traditional rainwater harvesting, emerged as a promising, well-accepted alternative for water supply in these small communities. The resilience of STDW stems from its water source diversification, decentralization, and modular design. Insights from Puerto Rico’s experience offer valuable guidance for current and future efforts to develop resilient water supply solutions in increasingly vulnerable coastal, island, and other regions.

Offshore wind is poised to become a pivotal contributor to the U.S. climate change mitigation efforts. Yet, the reliable integration of GW-scale offshore wind projects is contingent on accurate short-term wind forecasts of the wind resource and power. We present the AI-powered Rutgers University Weather Research and Forecasting (AIRU-WRF), an AI-powered model that merges physics-based numerical weather predictions (NWPs) with site-specific observations to provide short-term (minutes to hours ahead) forecasts that are of high resolution, both spatially (site-specific) and temporally (minute-level). Tested on actual measurements from the NY/NJ Bight—in vicinity to multiple large-scale offshore wind project developments—the forecasts made via AIRU-WRF are shown to significantly outperform prevalent benchmarks in the wind forecasting literature and practice.

The increasing demand for electricity and the aging infrastructure of power distribution systems have raised significant concerns about future system reliability. Failures in distribution systems, closely linked to system usage and environmental factors, are the primary contributors to electricity service interruptions. The integration of distributed energy resources (DER) presents an opportunity to enhance system reliability through optimized operations.
This poster presents a novel approach that explicitly incorporates both decision- and context-dependent reliability into the optimization of control setpoints for DERs in active distribution systems. The proposed model captures how operational decisions and ambient temperature impact the likelihood of component failures, enabling a balanced approach to cost efficiency and reliability. By leveraging a logistic function model for component failure rates and employing a sequential convex programming method, the model addresses the challenges of non-convex optimization under decision-dependent uncertainty. Numerical case study on a modified IEEE 33-bus test system demonstrates the effectiveness of the model in dynamically adjusting power flows and enhancing system robustness under varying environmental conditions and operational loads. The results highlight the potential of DERs to contribute to distribution system reliability by efficiently managing power flows and responding to fluctuating energy demands.