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Multivariate Regression Analysis for Identifying Key Drivers  of Harmful Algal Bloom in Lake Erie

Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie

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Authors

Omer Mermer, Ibrahim Demir

Abstract

Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted by HABs, largely due to nutrient pollution and climatic changes. This study aims to identify key physical, chemical, and biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Lake Erie from 2013 to 2020, were analyzed to develop predictive models for chlorophyll-a (Chl-a) and total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen (PON), turbidity, and particulate organic carbon (POC) were the most influential variables for predicting Chl-a and TSS concentrations. Two regression models were developed, achieving high accuracy with R² values of 0.973 for Chl-a and 0.958 for TSS. This study demonstrates the robustness of multivariate regression techniques in identifying significant HAB drivers, providing a framework applicable to other aquatic systems. These findings will contribute to better HAB prediction and management strategies, ultimately helping to protect water resources and public health.

DOI

https://doi.org/10.31223/X5P133

Subjects

Engineering, Environmental Engineering, Environmental Indicators and Impact Assessment, Multivariate Analysis

Keywords

Algal bloom, Chlorophyll-a, total suspended solids, multivariate regression, Pearson’s correlation coefficient, ANOVA test, relative importance

Dates

Published: 2025-03-28 15:14

Last Updated: 2025-03-28 15:14

License

CC BY Attribution 4.0 International