Quantitative prediction of toxin-producing<i>Aphanizomenon</i>cyanobacteria in freshwaters using Sentinel-2 satellite imagery

Gunawardana, M. H. M. A. S. V.; Sanjaya, Kelum; Atapaththu, Keerthi Sri Senarathna; Mudiyanselage, Ajith; Masakorala, Kanaji; Gamage, Shirani M. K. Widana

doi:10.2166/wh.2022.093

Cited by 2 publications

References 68 publications

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Carotenoids; a unique biomarker for monitoring Peridinium dinoflagellate blooms in freshwaters

Mudiyanselage,

Sanjaya,

Atapath

et al. 2024

Preprint

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Peridinium is a rare but, toxic bloom-forming dinoflagellate in freshwaters. Its toxic effects were reported from several countries including Sri Lanka although not-much attened. In this study, we developed a remote sensing-based empirical model to quantify Peridinium using Maussakelle Reservoir in Sri Lanka as the model. Since carotenoids are the major light-harvesting accessary pigments of Peridinium and many other dinoflagellates, it serves as a unique biomarker. Thus, spectral signatures of carotenoids allowed us to distinguish Peridinium in the background of chlorophyll-dominated mix population of phytoplankton. Ground data and Sentinel-2 satellite images were collected when a high density of Peridinium and carotenoid pigment levels were present and a set of linear regression models were developed. Among the models, that developed with B2 and B3 bands of Sentinel-2 better regressed with measured carotenoid (R2 = 0.93, p < 0.001). The relationship between measured and model-predicted carotenoid concentrations displayed a correlation (R2) of 0.86 and root mean squared error (RMSE) of 2.82. Further, a second regression model was developed to predict Peridinium cell density using carotenoid as a proxy. The established relationship was strong and significant (R2 = 0.85, p < 0.001). Then a final empirical model was derived by coupling the two regression models to quantify Peridinium cell density (R2 = 0.71, p < 0.001). We highlight that this method would be a novel approach that directs reliable and accurate prediction and quantification of carotenoid pigments and Peridinium cell density in freshwaters.

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Carotenoids; a unique biomarker for monitoring Peridinium dinoflagellate blooms in freshwaters

Mudiyanselage,

Sanjaya,

Atapath

et al. 2024

Preprint

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Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

Wijayaweera,

Gunawardhana,

Bamunawala

et al. 2024

Water

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With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

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Quantitative prediction of toxin-producingAphanizomenoncyanobacteria in freshwaters using Sentinel-2 satellite imagery

Cited by 2 publications

References 68 publications

Carotenoids; a unique biomarker for monitoring Peridinium dinoflagellate blooms in freshwaters

Carotenoids; a unique biomarker for monitoring Peridinium dinoflagellate blooms in freshwaters

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

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