Application of data-driven models in exploring cyanobacterial bloom risks in Lake Mälaren

Sammanfattning: Cyanobacteria are a unique organism, a bacterium that develop photosynthesis, thus it contains chlorophyll, a pigment commonly associated to algae. For this reason, cyanobacteria are also known as blue-green algae. One of the secondary metabolites of cyanobacteria is cyanotoxin, a substance which is hepatoxic, neurotoxic, and dermatoxic. The frequency and intensity of cyanobacterial blooms have been of increasing concern in the last decades for drinking water supply. There is a need to improve monitoring of cyanobacteria content at source water for drinking water supply and its indicators and correlation with other chemical, physical and biological parameters. This study aims to identify the potential cyanobacterial bloom risk in Lake Mälaren by determining the influential chemical and physical parameters using Random Forest in classification mode. The classification was done using the WHO Alert Level Frameworks and study cases for lakes in Sweden. The data used to model was downloaded from the website of the Swedish University of Agricultural Science. It comprises 33 monitoring stations from 1964 to 2020, 21 chemical parameters, including cyanobacteria biovolume and chlorophyll content. Given the heterogeneity of data, the monitoring stations were grouped into Clusters. Using the data, statistical, correlation, time series, and principal component analysis were performed. Through these methods, spatial, distribution and temporal analysis were obtained. Afterwards, several models were determined using Random Forest. However, the mean values of cyanobacteria distributed over time indicated a medium risk, the maximum values suggested high risk in several areas of the Lake. Maximum concentrations were present at the west and northeast of the Lake, where the major inflows from the Watershed are discharged. As the water flows through the basin, the concentration of cyanobacteria reduces by half, which suggested that the large and deep bays act as sedimentation ponds. A very high correlation was found between the Cluster 5 and 6, east and middle northeast of the Lake, respectively. Finally, the contributing factors identified after modelling cyanobacteria as target factor were chlorophyll, month, water temperature, oxygen content, transparency, NO2NO3N, TN/TP, Ca, Mg and Cl.