Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons

Triphasic partitioning of mixed Scenedesmus and Desmodesmus for nutrients' extraction and chlorophyll composition prediction for algae bloom

Overgrowth of microalgae will result in harmful algae blooms that can affect the aquatic ecosystem and human health. Therefore, the quantitation of chlorophyll pigments can be used as an indicator of algae bloom. However, it is difficult to monitor the geographical and temporal distribution of chlorophyll in the aquatic environment. Accordingly, an innovative and inexpensive method based on the red-green-blue (RGB) image analysis was utilized in this study to estimate the microalgae chlorophyll content. The digital images were acquired using a smartphone camera. The colour index was then evaluated using software and associated with chlorophyll concentration significantly. A regression model, using RGB colour components as independent variables to estimate chlorophyll concentration, was developed and validated. The Green in the RGB index was the most promising way to estimate chlorophyll concentration in microalgae. The result showed that acetone was the best extractant solvent with a high R-squared value among the four extractant solvents. Next, the isolation of useful biomolecules, such as proteins, fatty acids, polysaccharides and antioxidants from the microalgae, has been recognized as an alternative to regulating algae bloom. Microalgae are shown to produce bioactive compounds with a variety of biological activities that can be applied in various industries. This study evaluates the biochemical composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp. using the liquid triphasic partitioning (TPP) system. The findings from analytical assays revealed that the biomass consisted of varied concentrations of carbohydrates, protein, and lipids. Phenolic compounds and antioxidant activity were at 60.22 mg/L and 90.69%, respectively.