UROP Project

PFAS Contamination in the Lower Suwannee River Basin

PFAS, Laboratory work, Machine Learning, Deep Learning, Citation Analysis

Research Mentor: Mr. Shahin Alam , Shaheen
Department, College, Affiliation: Civil & Environmental Engineering, FAMU-FSU College of Engineering
Contact Email: ma23ch@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Environmental Engineering; Environmental Science / Environmental Chemistry; Data Science / Environmental Data Science; Chemical Engineering (Environmental or Materials Track); Geosciences / Hydrology; Information Science / Scientometrics / Bibliometrics; Computational Science / Applied AI
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

This research project focuses on addressing the complex and persistent issue of Per- and Polyfluoroalkyl Substances (PFASs) contamination in aquatic and terrestrial environments. PFASs, often referred to as "forever chemicals," are synthetic compounds widely used in industrial and consumer products for their resistance to heat, water, and oil. However, their environmental persistence, bioaccumulation potential, and toxicological risks pose significant challenges to public health and ecosystem sustainability. The core objective of this research is to investigate PFAS contamination in surface water and sediments, understand their sources and distribution patterns, and develop predictive tools for effective monitoring and management.

This project integrates advanced laboratory analyses with state-of-the-art machine learning (ML) and deep learning (DL) techniques. Field samples from strategically selected watershed regions are collected and analyzed using high-resolution instruments such as LC-MS/MS and GC-MS to quantify PFAS concentrations and determine physicochemical properties. These empirical data sets serve as the foundation for developing robust predictive models that can identify PFAS sources, simulate spatial distribution patterns, and assess contamination risks. By combining GIS-based spatial modeling with ML algorithms such as random forests, support vector machines, and neural networks, the project aims to uncover hidden patterns in large-scale environmental datasets.

Additionally, the research includes a bibliometric and citation analysis of global PFAS-related studies to identify key trends, research hotspots, influential authors, institutions, and emerging methodologies. This meta-analytical component provides critical insights into the evolution of PFAS research and highlights knowledge gaps that require urgent attention. By mapping the scientific landscape, the project ensures that the developed models and frameworks align with cutting-edge research and policy priorities.

The interdisciplinary nature of this work—bridging environmental engineering, analytical chemistry, geospatial science, and data analytics—enables a comprehensive approach to tackling one of the most pressing environmental challenges of our time. The outcomes are expected to contribute to more informed decision-making, targeted remediation strategies, and the development of early warning systems for PFAS contamination in vulnerable watersheds.

Research Tasks: literature review, data collection, data analysis, Lab Help, Field Trip

Skills that research assistant(s) may need: Attention to detail

Ability to work in interdisciplinary teams

Critical thinking and problem-solving mindset

Time management and task prioritization