# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 2 Materials and Methods
### Study Area and Dataset Preprocessing  
As the nutrient load and HAB occurrence in Lake Erie take place mostly in the western part, our study focused on the western basin of Lake Erie (shown in Figure 2), which encompasses the western part of the lake to Point Pelee, ON, Canada, and Cedar Point, OH, USA. In this study, we used water quality data collected by the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) from multiple monitoring stations (Figure 2) on the US side of the western part of Lake Erie. The data span the years 2013 to 2020, with a near-daily resolution during the bloom-prone months (typically May through September) [17]. A comprehensive description of the sampling methodology, including sampling frequency, station characteristics, equipment setup, calibration routines, and deployment strategies, can be found in [PERSON] et al. [17], which details NOAA's HAB monitoring framework in western Lake Erie. We specifically focused on monitoring stations closest to Maumee River inflow (i.e., WE06 and WE09),which reflect the various nutrient and sediment input into the western basin of Lake Erie as well as representing the areas that are prone to HAB consistently compared to other stations [17; 18]. All data preprocessing was performed by the authors and included the removal of duplicate entries, handling of missing values, and consistency checks to ensure high-quality inputs for statistical analysis. In terms of missing data handling, records with missing values in either the target variables (Chl-a, TSS) or in any of the key predictor variables selected for regression modeling were excluded from the analysis. Due to the high temporal resolution of the NOAA monitoring dataset, these omissions represented a small fraction of the total data and did not introduce significant bias. We opted not to perform imputation to avoid introducing artificial variance or assumptions into the regression models.  
Figure 1: Workflow of the proposed study for analyzing the key drivers of HABs.  
Figure 2: Location and description of western Lake Erie water quality monitoring stations operated by NOAA’s Great Lakes Environmental Research Laboratory. Retrieved from [[https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/rtMonSQL_php](https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/rtMonSQL_php)]([https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/rtMonSQL_php](https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/rtMonSQL_php)) (accessed on 16 October 2024).  
Based on the available data, we selected physical, chemical, and biological variables, shown in Table 1. Physical variables include Secchi Depth (SD), CTD Temperature (T), CTD Specific Conductivity (Cond), and Turbidity (Turb). Chemical drivers also include CTD Dissolved Oxygen (DO), Total Phosphorus (TP), Total Dissolved Phosphorus (TDP), Ammonia (A), Nitrate + Nitrite (NOx), Particulate Organic Carbon (POC), Particulate Organic Nitrogen (PON), and Total Suspended Solids (TSS). Chlorophyll-a (Chl-a) is also categorized under the biological variables.