27-2 Investigating Heavy Metal Concentrations In Sediments Of The White River Watershed In Anderson, Indiana: A Preliminary Study

Session: Environmental Geochemistry and Health (Posters)

Poster Booth No.: 75

Presenting Author:

Paa Kwesi Mbroh

Authors:

Mbroh, Paa Kwesi¹, Neumann, Klaus²

(1) Environment, Geology and Natural Resources (EGNR) Department, Ball State University, 2000 W University Ave, Muncie, IN 47306, Muncie, Indiana, USA, (2) Environment, Geology and Natural Resources (EGNR) Department, Ball State University, 2000 W University Ave, Muncie, IN 47306, Muncie, Indiana, USA,

Abstract:

Industrial legacy of the American Rust Belt has left a lasting environmental impact, particularly in river systems where heavy metals accumulate in sediments, posing risks to aquatic ecosystems and human health. Much of this contamination predates the implementation of environmental legislation, most notably the Clean Water Actt (CWA) of 1972. Anderson, Indiana, once a prominent automotive manufacturing hub, shares this legacy. However, the extent of sediment contamination, which serves as a “sink” within its stretch of the White River, remains largely undocumented. This study conducts a preliminary investigation to quantify concentrations of key heavy metals (Pb, Cd, Cu, Ni, Hg, and As) in surface sediments of the White River within the Anderson region. The objectives are to assess spatial distribution patterns, infer possible pollution sources, and identify environmental risks associated with legacy contamination. Sampling sites were selected to reflect both high- and low-energy depositional environments to ensure representative data. Sediment samples were collected using the USEPA wading method, microwave-digested, and analyzed via Inductively Coupled Plasma Mass Spectrometry (ICP-MS) following USEPA Methods 3050B and 6020B. To evaluate contamination levels, the study will apply multiple sediment quality indices, including the Pollution Load Index (PLI), Contamination Factor (CF), Enrichment Factor (EF), and Geoaccumulation Index (Igeo). Results will be compared against EPA sediment quality benchmarks and findings from Muncie, a nearby industrial center, to trace potential anthropogenic sources. Statistical analyses and geospatial visualization using GIS tools will be used to map distribution patterns and identify contamination hotspots. Additionally, a machine learning approach will be employed to model pollution trends, predict contamination zones, and explore future scenarios under different watershed conditions. This multidisciplinary approach integrates environmental chemistry, spatial science, and data analytics to provide a robust understanding of sediment contamination. The research addresses a knowledge gap in Anderson’s watershed and supports the development of informed strategies for remediation, land use planning, and public health protection. Findings will contribute to broader discussions on the resilience of river systems impacted by historical industrial activity and inform policies to restore environmental quality in post-industrial urban landscapes.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-4845

© Copyright 2025 The Geological Society of America (GSA), all rights reserved.