Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Marrone, Babetta L.; Banerjee, Shounak; Talapatra, Anjana; Gonzalez-Esquer, C. Raul; Pilania, Ghanshyam

doi:10.1021/acsestwater.3c00369

Title: Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Journal Article · 30 November 2023 · ACS ES&T Water

DOI: https://doi.org/10.1021/acsestwater.3c00369 · OSTI ID:2222996

^[1]; Banerjee, Shounak ^[1]; Talapatra, Anjana ^[1]; Gonzalez-Esquer, C. Raul ^[1]; Pilania, Ghanshyam ^[1]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater. Nutrient runoff from adjacent watersheds is also a major driver of cyanoHAB formation. While some of the physicochemical factors behind cyanoHAB dynamics are known, there are still major gaps in our understanding of the conditions that trigger and sustain cyanoHABs over time. In this perspective, we suggest that sufficient data sets, as well as machine learning (ML) and artificial intelligence (AI) tools, are available to build a comprehensive model of cyanoHAB dynamics based on integrated environmental/climate, nutrient/water chemistry, and cyanoHAB microbiome and ‘omics data to identify key factors contributing to HAB formation, intensity, and toxicity. By taking a holistic approach to the analysis of all available data, including the rapidly growing number of biological data sets, we can provide the foundational knowledge needed to address the increasing threat of cyanoHABs to the security of our water resources.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; 20230279ER

OSTI ID:: 2222996

Alternate ID(s):: OSTI ID: 2274922

Report Number(s):: LA-UR-23-26846

Journal Information:: ACS ES&T Water, Vol. 4, Issue 3; ISSN 2690-0637

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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