Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
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 t...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4824 |
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| author | Omer Mermer Ibrahim Demir |
| author_facet | Omer Mermer Ibrahim Demir |
| author_sort | Omer Mermer |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-b68c931cdcec4236a38da4ddf8a26ef4 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b68c931cdcec4236a38da4ddf8a26ef42025-08-20T03:52:56ZengMDPI AGApplied Sciences2076-34172025-04-01159482410.3390/app15094824Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake ErieOmer Mermer0Ibrahim Demir1IIHR Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USADepartment of River-Coastal Science and Engineering, Tulane University, New Orleans, LA 70118, USAHarmful 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 R<sup>2</sup> 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.https://www.mdpi.com/2076-3417/15/9/4824HABChlorophyll-atotal suspended solidswater quality parameterslinear regression modelPearson’s correlation coefficient |
| spellingShingle | Omer Mermer Ibrahim Demir Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie Applied Sciences HAB Chlorophyll-a total suspended solids water quality parameters linear regression model Pearson’s correlation coefficient |
| title | Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie |
| title_full | Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie |
| title_fullStr | Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie |
| title_full_unstemmed | Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie |
| title_short | Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie |
| title_sort | multivariate regression analysis for identifying key drivers of harmful algal bloom in lake erie |
| topic | HAB Chlorophyll-a total suspended solids water quality parameters linear regression model Pearson’s correlation coefficient |
| url | https://www.mdpi.com/2076-3417/15/9/4824 |
| work_keys_str_mv | AT omermermer multivariateregressionanalysisforidentifyingkeydriversofharmfulalgalbloominlakeerie AT ibrahimdemir multivariateregressionanalysisforidentifyingkeydriversofharmfulalgalbloominlakeerie |