2 Advances in characterizing groundwater, surface water, and their interactions

This session focuses on characterizing groundwater, surface water, and their interactions through fieldwork, modeling, and data synthesis. We welcome submissions addressing flow, transport, water quality, and water levels under both typical and extreme conditions.

Sunday, 22 March 2026
8:00 AM - 12:00 PM
CCC, Room 25

Evaluating Multiple Linear Regression Approaches to Predict Microplastic Concentrations in a Stream System Using a Suite of Environmental Variables

Paper number: 2-2
Program: Technical Sessions
Time: 8:25 AM – 8:45 AM

Authors

Abstract

We apply three linear regression approaches—stepwise selection (SW), elastic net (EN), and LASSO—to predict microplastic concentrations in Plum Run, a stream in southeastern Pennsylvania, from a set of environmental variables. Each model was evaluated using 10-fold cross-validation implemented in the caret package in R. Root mean squared error (RMSE) was averaged across cross-validation iterations and used as a robust metric of model performance. Sixty-six water column samples were collected in duplicate across an annual cycle. All three models found higher microplastic counts to be significantly correlated with either lower temperatures or stronger wind gusts. Root mean squared error (RMSE) values were similar across all three models, indicating comparable predictive performance for microplastic concentrations in Plum Run (SWRMSE = 13.90, ENRMSE = 14.16, LASSORMSE = 14.12). Separately, an independent validation was conducted using 14 additional water samples from Plum Run, with observed microplastic counts compared to values predicted by the stepwise regression model. Observed and predicted concentrations were significantly correlated (R² = 0.52, p < 0.01), with a validation RMSE of 5.8. Collectively, these results demonstrate that environmental variables significantly influence microplastic pollution in aquatic ecosystems.   

Geological Society of America Abstracts with Programs. Vol. 58, No. 2

doi: 10.1130/abs/2026NE-411196

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