Department of Environmental & Sustainable Engineering Abstracts

Presenter(s): Vincent Scavullo, Thomas Pascarella, Eldgy Florvil

Showcase Advisor: Paul Millard

Abstract: Climate change is expected to increase the frequency and intensity of precipitation which can directly affect aquifer levels at historical remediation sites like ServAll Laundry in Bay Shore, New York. Increased precipitation makes underground aquifer levels susceptible to interacting with contaminants like volatile organic compounds (VOCs) that have leaked into soil.  In collaboration with NYSDEC, this study presents an analysis of how historical precipitation impacts groundwater levels at the remediation site and presents a climate-resilient engineering system to control groundwater levels to prevent further contamination. The system would use an automated pump to extract groundwater when sensors levels reach a set threshold. The groundwater would then be treated with a combined filtration and granular activated carbon (GAC) column and then be held in a storage tank to control when the groundwater would be injected back into the aquifer.

Presenter(s): Samantha Finlay, Soma Purbueva

Showcase Advisor: Paul Millard

Abstract: This project evaluates long‑term groundwater elevation trends at the inland NYSDEC remediation site at 100 Oser Avenue in Hauppauge, New York, and determines whether climate‑driven hydrologic changes could influence the protectiveness of the existing remedial system. The site is managed through engineering controls, including a sub‑slab Soil Vapor Extraction system, and institutional controls that prevent exposure to tetrachloroethylene contamination from former dry‑cleaning operations. Because these measures were developed under historical hydrologic conditions, the study also considers the need for a Climate‑Resilient Hybrid Remediation System that integrates SVE upgrades, hydrologic controls, and a downgradient reactive barrier.

Regional research shows rising groundwater levels, increasing precipitation, and more frequent extreme rainfall across the Northeastern United States. Multi‑year groundwater data and storm‑event responses were analyzed to determine whether these trends appear at the site scale. Results indicate gradual groundwater rise and storm‑driven fluctuations that may reduce vadose‑zone thickness and increase stress on existing controls.

Presenter(s): Rujal Kc

Showcase Advisor: Aynul Bari

Abstract: This study presents a calibration framework for TelosAir DUET PM  
2.5 sensors, evaluated over three months at contrasting sites: rural Whiteface Mountain and urban Queens College, NYC. To mitigate biases from meteorological variability and light-scattering limitations, the framework integrates temperature and relative humidity with novel source-apportionment variables: the PM 2.5 / PM 10 size ratio and the black carbon to brown carbon (BC/BrC) optical ratio. While meteorological corrections address drift and hygroscopic growth, these integrated ratios serve as proxies for aerosol microphysical shifts. The PM2.5 /PM10 ratio captures size distribution changes, whereas the BC/BrC ratio indicates refractive properties, accounting for light-absorption biases in urban combustion soot. Utilizing co-located reference monitors and machine learning, this dual-site framework enhances sensor accuracy. It provides a practical foundation for deploying composition-aware, low-cost air quality monitoring networks capable of adjusting to diverse environmental conditions and varying aerosol sources in real time.

Presenter(s): Behnia Bitaraf

Showcase Advisor: Yanna Liang

Abstract: To remove per- and poly-fluoroalkyl substances (PFAS) from water, this study focused on synthesizing a sawdust-based adsorbent through KMnO4 oxidation and coating m-phenylenediamine (mPD) onto the sawdust’s surface. The resulting sawdust/MnO2/PmPD was able to remove >90% of nine target PFAS and >80% of GenX spiked at 10 ppb in deionized water. When added to river water samples, the capture of long-chain PFAS remained basically the same. The low-cost nature of this sorbent was further strengthened by its regenerability and reusability for at least five cycles. Overall, at this stage, the sawdust/MnO2/PmPD material is ready to be used for removing PFAS in surface water.

Presenter(s): Zhuohang Wu

Showcase Advisor: Yaoze Liu

Abstract: To address the harmful algal bloom (HAB) problems in water bodies, agricultural best management practices (BMPs) are needed to reduce excess nutrient loadings. Wetlands, a popular type of agricultural BMP, are water covered areas with vegetation that can settle sediment, remove nutrients, and provide other ecosystem services. Understanding how the impacts of wetlands on agroecosystem sustainability respond to various driving factors is vital. The case study area in this study is the AXL watershed, an agricultural watershed in the Maumee River watershed, which drains into the western basin of Lake Erie and causes HAB problems. The Soil and Water Assessment Tool (SWAT) was improved to better simulate wetlands. And the improved SWAT was used to evaluate the responses of wetland performance to various driving factors, including internal driving factors and external driving factors. These results will better assist decision makers in implementing wetlands that improve agroecosystem sustainability.

Presenter(s): Yilin Huang

Showcase Advisor: Lu Li

Abstract: Indoor air quality is a key component of indoor environmental quality in buildings and directly affects occupant health, comfort, and cognitive performance. Traditional IAQ management focuses on monitoring pollutant concentrations and implementing control strategies such as ventilation and air filtration. However, recent advances in artificial intelligence and machine learning have enabled predictive approaches that can forecast indoor pollutant levels before they become harmful. This academic showcase will introduce fundamental concepts of indoor air quality, including major contaminants and evaluation methods and summarize recent research developments in ML-based IAQ prediction models. These ML technologies have the potential to transform building operation from passive monitoring to proactive environmental control, supporting healthier and more energy-efficient buildings.

Presenter(s): Samantha Finlay, Stephen Ackerman

Showcase Advisor: Lu Li

Abstract: In New York State, residential buildings use significant amounts of energy, and inefficient operation increases costs while harming the environment. Many buildings still depend on static or rule based controls, which leads to unnecessary waste even though sensor data, weather information, and building management system outputs are widely available. This study predicts short term residential energy consumption using machine learning methods applied to publicly accessible datasets, including building metadata and the ASHRAE Great Energy Predictor III dataset. Building characteristics, temporal variables, and meteorological data are used as predictors in models such as gradient boosting, random forest, and linear regression. Results show that nonlinear models perform better than linear regression in capturing complex energy use patterns, with the most influential factors being building size, primary use, and outdoor temperature. The study demonstrates how AI driven energy prediction can support sustainable energy practices, improve efficiency, and guide occupant centered building management.

Presenter(s): Vongai Sibanda

Showcase Advisor: Lu Li

Abstract: Fine particulate matter (PM2.5) poses significant risks to public health and urban air quality, making accurate concentration prediction essential for environmental decision-making. This project develops machine learning models to analyze and predict PM2.5 concentrations across New York State using publicly available daily monitoring data from the U.S. Environmental Protection Agency (EPA). Historical PM2.5 measurements, meteorological conditions, seasonal patterns, and geographic location are integrated as predictive features. Following data collection and preprocessing, exploratory analysis identifies key temporal and spatial trends across monitoring stations statewide. Supervised learning algorithms are then trained and evaluated to forecast PM2.5 levels with high accuracy. Results are interpreted to highlight the most influential environmental drivers of particulate pollution. This work demonstrates how artificial intelligence can enhance air quality assessment, support public health interventions, and inform planning for healthier, more sustainable built environments across New York State.

Presenter(s): Jiawei Wang

Showcase Advisor: Lu Li

Abstract: Many existing PM2.5 and aerosol products are available at coarse spatial resolution, which limits their ability to represent fine-scale spatial variability in PM2.5 across New York State during Canadian wildfire smoke impacts. This study presents an integrated method that targets improved spatial mapping. First, aerosol optical depth (AOD) is retrieved from Landsat imagery, taking advantage of Landsat’s higher spatial resolution to capture local aerosol spatial structure. Next, the Landsat-derived AOD is combined with background information from a physical model, and a Random Forest model is used to learn the relationship between aerosol conditions and near-surface PM2.5. The resulting estimates provide fine-scale PM2.5 spatial fields and allow spatial gap filling in areas affected by clouds or missing observations. Overall, the method emphasizes leveraging high-resolution satellite aerosol information together with physically consistent background context to better depict the spatial patterns of PM2.5 during smoke-impacted periods.

Presenter(s): Julian Mattison, Erik Nurminen

Showcase Advisor: Paul Millard

Abstract: Closed landfills are increasingly being repurposed for utility scale solar installations, creating a need to evaluate their impacts on landfill hydrology, vegetation and leachate management. The Fort Edward Landfill has been closed and capped for several years. Since 2024, a solar array has been built on the site using ballast systems to avoid penetrating the landfill cap and causing pollutant leaks. Waste continues to produce leachate, while precipitation that does not infiltrate the cap becomes runoff. This study compares leachate volume, flow, and composition before and after solar installation, analyzes freezing and thawing to assess shading effects on surface temperature, and examines vegetation impacts on evapotranspiration and precipitation uptake. Solar panel tilt is evaluated to optimize energy production, and vegetation composition is adjusted to prioritize grasses with a smaller proportion of wildflowers. Additionally, a pilot program using sheep grazing instead of mowing is assessed for economic and environmental benefits.

Presenter(s): Madhav Kharel

Showcase Advisor: Weilan Zhang

Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent pollutants frequently detected in wastewater treatment plant (WWTP) biosolids, raising concerns about their land application and potential transfer into crops and food chains. While reusing biosolids provides significant agronomic and sustainability benefits, uncertainties remain regarding PFAS bioavailability, plant absorption, and entry into the food supply. This study explores whether modified biochar can act as a sustainable soil amendment to reduce PFAS mobility, bioavailability, and plant uptake in farms affected by biosolids. Greenhouse experiments will involve growing tomato, radish, and soybean in soils amended with biosolids and biochar under various conditions. This research will assess how designer biochar and its application rates influence eleven PFAS compounds, PFAS distribution in soil, transfer to plants, and accumulation in edible parts. Findings will improve understanding of PFAS behavior in agricultural systems and suggest practical strategies to reduce PFAS transfer from biosolids to crops.

Presenter(s): Amit Lama, Madhav Lama

Showcase Advisor: Weilan Zhang

Abstract: 6-PPD and its transformation product, 6-PPD-quinone, are organic chemicals widely used as stabilizing additives(anti-degradants), in rubbers. They have been in use since the 1960s, including in natural rubber, styrene-butadiene, and butyl rubber, which are common in vehicle tires. They are attracting attention due to their presence and potential ecological risks. However, their uptake and bioaccumulation in crop plants remain poorly understood. This study aims to investigate the bioaccumulation of these compounds in soybean vegetative tissues grown in a controlled hydroponic system. Soybean plants were exposed to 0.1, 1, and 10 ng/mL of each compound for 3-4 weeks. At the end of the exposure, roots and shoots were harvested separately and analyzed using LC/MS-MS to evaluate the concentration-dependent uptake and distribution of 6-PPD and 6-PPD-quinone. The findings will provide new insights into the behavior of tire-wear particles in agricultural plants and support food safety assessments.

Presenter(s): Avery Jackson

Showcase Advisor: Weilan Zhang

Abstract: N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) is a tire antioxidant that is continuously released into the environment through tire wear particles. Its transformation product, 6PPD-quinone (6PPD-Q), has recently emerged as a contaminant of concern due to its environmental persistence and documented acute toxicity to aquatic organisms. Despite growing awareness of roadway-derived contaminants, little is known about the plant uptake and bioaccumulation potential of these compounds. This study investigates whether soybean (Glycine max), a widely used model and agricultural crop species, can take up and accumulate 6PPD and 6PPD-Q from contaminated growth media. In parallel, we aim to develop and validate an analytical workflow for extracting these compounds from plant tissues and quantifying them using liquid chromatography - tandem mass spectrometry (LC-MS/MS). This work will clarify the potential for terrestrial plants to act as sinks or transfer pathways for tire-derived contaminants and provide a robust analytical method for monitoring these emerging pollutants in plant matrices.

Presenter(s): Syed Muhammad Arsh Hussain

Showcase Advisor: Lu Li

Abstract: While meteorological conditions are frequently monitored by the public, the localized variations in respiratory health risks often remain under-analyzed. This research investigates air quality trends across the New York-New Jersey metropolitan area utilizing 2025 environmental data from the EPA’s monitoring network. By implementing a Python-based analytical framework, this study identifies specific "hotspots" urban regions characterized by prolonged and intensive episodes of fine particulate matter PM2.5 pollution.

The analysis quantifies the duration and severity of these pollution episodes and maps their geographic distribution to highlight areas of significant public health concern. By identifying seasonal peaks and correlating localized PM2.5 concentrations with health-based Air Quality Index (AQI) standards, this project demonstrates the utility of data science in converting raw environmental monitoring into actionable insights for urban planning and community health advocacy.