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Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
Journal article   Open access   Peer reviewed

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

Omer Mermer and Ibrahim Demir
Applied sciences, Vol.15(9), 4824
04/26/2025
DOI: 10.3390/app15094824
url
https://doi.org/10.3390/app15094824View
Published (Version of record) Open Access

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, turbidity, and particulate organic carbon 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.
 HAB Chlorophyll-a total suspended solids water quality parameters linear regression model Pearson's correlation coefficient ANOVA test relative importance

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