Food waste is a critical global challenge, with the hospitality industry losing over $100 billion of food annually. In university dining halls, inefficiencies related to inventory, production, and consumer choices exacerbate waste. Although AI-driven tools like Winnow and LeanPath have shown potential for reducing food waste, their impact in universities is rarely discussed. This study analyzed University of California, Los Angeles (UCLA) Dining’s baseline data and reviewed Sustainability Action Research findings to identify operational gaps about consumer behaviors. Collaborations with Stanford researchers and direct meetings with Winnow guided a plan for AI-based waste measurement. Preliminary evidence suggests Winnow’s real-time tracking can significantly reduce edible waste, while offering actionable plans for menu design and inventory management. These findings support the feasibility and benefits of employing AI in university dining, establishing a framework for other universities to enhance both sustainability and operational scalability.

California is known for its hundreds of miles of Pacific coastline, large stretches of which have been industrialized for either safety or commercial purposes. At the Santa Monica Pier, a breakwater was constructed in 1933 to protect the shore from wave damage. It has degraded and been rebuilt several times, which will only happen more frequently with the acceleration of climate change. Meanwhile, many species of marine life native to the Santa Monica Bay have adapted to the manmade structure. Currently defunct, The Bay Foundation (TBF) nonprofit is working to revitalize the breakwater by turning it into a “living breakwater” where species can thrive. To determine species diversity, a different research group sampled water from six different sites near the breakwater and two control sites near Venice Beach. Our job was to use environmental DNA (eDNA) metabarcoding on each sample using three different primer sets: CO1, MiFish, and 16S Metazoan. After we analyze the results using the Anacapa toolkit, we we anticipate identifying a variety of invertebrate species that live at the breakwater, from the California Spiny Lobster (Panulirus interruptus) to the California Golden Gorgonian (Muricea californica). We expect the breakwater species diversity to be similar to the control site diversity. This analysis will be extremely valuable to determine if TBF’s efforts have been successful and further inform us if “living breakwaters” should receive greater attention from investors and conservationists.

Extreme heat, pollution exposure, and natural disaster response have been long-standing issues in LA County, however, there has not yet been extensive research on how a person’s movement through space and at certain times impacts their susceptibility to these risks. Current studies rely on static, census data, and cannot be used to analyze daytime exposures. Our project aims to understand the dynamics of population movement in Los Angeles County and to better individualize exposure records and quantify vulnerabilities among demographics and communities. We are utilizing mobile-phone location data “pings”, from the Summer of 2024 (via Outlogic platform), to geographically map human movement, and then machine learning techniques (specifically a dynamic, time warping KNN) to create an online database and consumer-friendly way to generalize the obtained results to a larger population. We used QGIS to generate preliminary maps of population movement patterns, which confirm consistent daily trends and suggest that our mobile phone data closely aligns with the census-based population distributions. Through this research, we aim to improve the efficiency of disaster response teams and support urban planners and transportation engineers in designing more equitable systems for daily movement and transportation across Los Angeles County. Additionally, our findings may inform policy recommendations — such as the placement of new cooling centers and decisions around future infrastructure development — made by elected officials.

Anaerobic digesters at wastewater treatment plants produce biogas primarily composed of methane (CH₄) and carbon dioxide (CO₂). To make biogas viable as an energy source, CO₂ must be removed. This project investigates biological scrubbing as a sustainable CO₂ removal method using pure microalgae(Chlorella vulgaris) and a mixed microbial community of microalgae and heterotrophic bacteria. We hypothesize that systems containing photosynthetically active organisms will exhibit greater CO₂ removal than the deionized (DI) water control due to biological CO₂ fixation, with slightly higher removal in the pure microalgae condition. Three systems were evaluated in this experiment. (i) A DI water control, (ii) microalgae and (iii) mixed microbial communities cultured in liquid growth media were exposed to a 50:50 CO₂:CH₄ gas mixture in airtight gas bags. Gas concentrations were measured using a Gas Chromatography Thermal Conductivity Detector (GC-TCD). All systems, including the control, showed significant and comparable CO₂ reductions, suggesting that CO₂ dissolution, rather than photosynthesis. To address this, CO₂ solubility was calculated, and a pretreatment step was proposed to saturate the microbial and control conditions with CO₂ prior to experimentation. This allows measurements to more accurately capture biological CO₂ fixation. Our results contribute to enhancing experimental design for evaluating biological CO₂ scrubbing and support the development of scalable, low-energy purification for circular wastewater-energy systems.

Projected increases in extreme heat due to climate change pose severe health risks, particularly for disadvantaged communities that already face systemic inequities in infrastructure, healthcare access, and environmental conditions. Despite this, extreme heat exposure continues to rise, disproportionately affecting those with the fewest resources to cope. This study maps extreme heat days using Cal-Adapt data, for mid-century (2040–2060) and late-century (2080–2099) projections under a high-emissions scenario, comparing them to historical baselines. Bivariate mapping overlays extreme heat projections with social vulnerability indicators—poverty levels, environmental health burdens, and asthma emergency visits—to identify high-risk areas. Results show a significant rise in extreme heat days, with the greatest increases in disadvantaged communities, compounding existing health disparities. These findings highlight the unequal burden of extreme heat and the urgency of targeted adaptation measures. Without intervention, climate change will further exacerbate extreme heat and health inequities.

Microplastics pose threats to ecosystems and human health through atmospheric transport and deposition. While previous research has quantified deposition rates across different regions, sampling duration remains an understudied factor affecting measurement accuracy. A past study found that protocols using sampling durations under 24 hours exhibited significant variability in deposition rates, potentially by several orders of magnitude. They recommended a minimum sampling duration of 10 days to reduce temporal variability but did not define a specific relationship between sampling duration and deposition accuracy. This study examines the correlation between microplastic deposition rates and sampling duration to determine how different durations impact measurement consistency. Using two passive samplers deployed in Westwood, Los Angeles, California, deposition rates were measured over varying time intervals. We also assess the influence of meteorological factors, including wind speed, temperature, humidity, and particulate matter (PM) concentrations, on deposition rates to determine their potential role as confounding variables. Our initial results suggests a negative correlation between sampling duration and deposition rate, with durations under 48 hours displaying the greatest variability. By clarifying the relationship between sampling duration and deposition rate variability, this study contributes to the development of standardized protocols for microplastic monitoring. Further data analysis is ongoing.

California is experiencing rising temperatures, prolonged droughts, and an increase in wildfires—factors that threaten native vegetation and diminish the natural regenerative capacity of ecosystems. This project examines the viability of native seeds under various pre-germination treatments to support ecological restoration and mitigate long-term environmental impacts. In collaboration with the National Park Service, the study evaluates germination success for native species collected from Paramount Ranch, including Asclepias fascicularis, Stipa pulchra, Stipa lepida, Eriogonum elongatum, Astragalus trichopodus, and Corethrogyne filaginifolia. Seeds were subjected to four treatment groups: control, cold stratification, water soak/stratification (substituted for liquid smoke), and a combination of cold stratification with soak. They were incubated on agar, adjusted to a pH of 5.6–5.8 using HCl/NaOH, and supplemented with gibberellic acid as a growth agent. Germination and mold development were monitored every 3–4 days over the course of several months. It is anticipated that treated seeds will demonstrate higher germination rates compared to controls. The results will inform the National Park Service on effective pre-germination techniques, aiding in the restoration of habitats impacted by wildfires and drought while enhancing carbon sequestration, soil health, and climate resilience across California.