Artificial Intelligence Application in Nonpoint Source Pollution Management: A Status Update

Almando Morain, Ryan Nedd, Kevin Poole, Lauren Hawkins, Micala Jones, Brian Washington and Aavudai Anandhi ()
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Almando Morain: Office of International Agriculture Programs, Florida Agricultural and Mechanical University, 1740 S Martin Luther King Jr Blvd, Tallahassee, FL 32307, USA
Ryan Nedd: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA
Kevin Poole: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA
Lauren Hawkins: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA
Micala Jones: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA
Brian Washington: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA
Aavudai Anandhi: Biological Systems Engineering, Florida Agricultural and Mechanical University, Tallahassee, FL 32307, USA

Sustainability, 2025, vol. 17, issue 13, 1-40

Abstract: Artificial intelligence (AI) has the potential to significantly advance the management of nonpoint source pollution (NPSP), a critical environmental issue characterized by diffuse sources and complex transport mechanisms. This study systematically examines current AI applications addressing NPSP through bibliometric and systematic analyses. A total of 124 studies were included after rigorous identification, screening, and eligibility assessments based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Key findings from the bibliometric analysis include publication trends, regional research contributions, author and journal contributions, and core concepts in NPSP. The systematic analysis further provided: (a) a comprehensive synthesis of NPSP characterization, covering pollution sources, key drivers, pollutants, transport pathways, and environmental impacts; (b) identification of emerging AI technologies such as the Internet of Things, unmanned aerial vehicles, and geographic information systems, and their potential applications in NPSP contexts; (c) a detailed classification of AI models used in NPSP assessment, highlighting predictors, predictands, and performance metrics specifically in water quality prediction and monitoring, groundwater vulnerability mapping, and pollutant-specific modeling; and (d) a critical assessment of knowledge gaps categorized into AI model development and validation, data constraints, governance and policy challenges, and system integration, alongside proposed targeted future research directions emphasizing adaptive governance, transparent AI modeling, and interdisciplinary collaboration. The findings from this study provide essential insights for researchers, policymakers, environmental managers, and communities aiming to implement AI-driven strategies to mitigate NPSP.

Keywords: bibliometric analysis; artificial intelligence; nonpoint source pollution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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