# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 1 Introduction  
Environmental contamination has contributed to a significant increase in cyanobacterial biomass (i.e., algae blooms) in water bodies, severely affecting water quality [1]. The term \"bloom\" refers to the rapid growth of blue-green algae, or cyanobacteria, which can produce harmful toxins [2]. The emergence and proliferation of Harmful Algal Blooms (HABs), primarily driven by cyanobacteria, has become a critical environmental concern worldwide. These blooms degrade water quality, threaten public health, and disrupt aquatic ecosystems. Key factors fueling the rise in HAB incidents include nutrient pollution, particularly from agricultural runoff and industrial waste [3], as well as climatic changes, such as rising water temperatures and shifts in water quality [4, 5]. HABs are known for producing hazardous toxins, undermining the recreational and aesthetic value of waterways, and complicating efforts to provide clean drinking water [6]. The recent surge in HAB occurrences has been linked to population growth, intensified agricultural practices [7, 8], increasing pollution levels, and climate change [9]. This trend underscores the urgent need for improved HAB monitoring, estimation, modeling, and prediction techniques to safeguard water resources and public health [10, 11, 12, 13]. Lake Erie, as part of the Great Lakes system, provides a compelling case study for investigating HABs. The Great Lakes represent the largest and most biodiverse freshwater system on Earth [14; 15]. With both industrial and agricultural regions in its basin, Lake Erie is the shallowest and smallest of the Great Lakes by volume, yet it holds ecological, cultural, and economic importance for approximately 12.5 million residents within its watershed. Lake Erie supports commercial and traditional fisheries, extensive freight transport, and a robust recreation and tourism industry [16]. Its western basin is particularly prone to nutrient overload, primarily due to its geographical setting [17]. Since 2002, chlorophyll-a (Chl-a) concentrations, a widely accepted indicator of eutrophication and HABs, have risen to unprecedented levels [17; 18]. Humans face exposure to HABs through ingestion, drinking water, and recreational activities [19; 20]. Therefore, predicting HAB occurrences is essential for minimizing health, economic, and environmental risks. Identifying the key factors driving these blooms is crucial for implementing effective mitigation strategies [21]. HAB formation typically results from the interplay of various factors that foster favorable growth conditions [22]. Eutrophication, poor water quality, especially elevated nitrogen and phosphorus levels, and climate change are among the primary contributors [23; 24]. While past studies have examined individual drivers such as nutrients, land use, or climate, few have explored the complex interactions between physical, chemical, and biological factors [25; 26; 27]. Previous studies, such as [PERSON] et al. [28], have highlighted key nutrient drivers like total phosphorus (TP) and iron in predicting cyanobacterial bloom intensity in Lake Torment, Nova Scotia, emphasizing the broader significance of nutrient management in mitigating HABs across freshwater systems. These approaches often rely on limited data, which can oversimplify predictions and introduce potential inaccuracies. Therefore, understanding the multifaceted drivers of HABs is critical for effective monitoring and prevention. Traditional HAB monitoring techniques, including laboratory analysis of chlorophyll-a, cyanobacteria, and various algal toxins, are labor-intensive and require specialized expertise [29; 30; 31]. Remote sensing technologies, such as satellite and Unmanned Aerial Vehicle (UAV) data, offer valuable spatial insights into understanding environmental drivers [32; 33], including bloom dynamics [34; 35; 36]. However, the relationship between chlorophyll-a levels and algal toxicity is complex, varying by location and environmental conditions [37; 38].