Artificial Intelligence in Addressing Air Pollution: A Scoping Review of Policies

Hossein Bouzarjomehri; Ali Asghar  Ebrahimi; Mohammad Hassan  Ehrampoush; Mobina  Ardani; Zahra Soltanianzadeh; Yasaman  Herandi; Mohsen  Khosravi; Seyed Masood  Mousavi; Mohammad  Ranjbar

doi:10.18502/jehsd.v11i1.21163

Hossein Bouzarjomehri Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Ali Asghar Ebrahimi Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Mohammad Hassan Ehrampoush Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Mobina Ardani Department of Occupational Health, School of Public Health, Shahid Sadoughi Univercity of Medical Sciences, Yazd, Iran.
Zahra Soltanianzadeh Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Yasaman Herandi Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Mohsen Khosravi Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran.
Seyed Masood Mousavi Department of Health Care Management, Health Policy and Management Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Mohammad Ranjbar National Center for Health Insurance Research, Tehran, Iran.

DOI: https://doi.org/10.18502/jehsd.v11i1.21163

Keywords: Artificial Intelligence, Air Pollution, Environmental Policy, Scoping Review, Public Health, Public Policy.

Abstract

Introduction: Air pollution remains the leading environmental risk factor for human health. Although artificial intelligence (AI) has demonstrated strong technical potential for air quality monitoring and prediction, its integration into environmental policy and governance remains unclear. This study examines how AI is currently addressed in policy-oriented literature on air pollution management.

Methods: A scoping review was conducted following Arksey and O’Malley’s framework and its extension by Levac et al. Systematic searches of major scientific databases and policy sources identified English-language documents published between 2015 and 2025 that addressed the use of AI in air pollution control from a policy, governance, or strategic perspective. Thematic analysis was used to synthesize the findings.

Results: Eight policy-relevant documents met the inclusion criteria of this review. The analysis identified four core themes: applications of AI, perceived benefits, governance and ethical concerns, and policy strategies. AI applications have primarily been framed around real-time monitoring, predictive modeling, and data-driven policymaking. The reported benefits included improved accuracy, responsiveness, and decision support, whereas the key concerns were related to data quality, privacy, energy use, transparency, and institutional capacity. Policy strategies emphasized regulatory frameworks, digital infrastructure, capacity building, cross-sector collaboration, and international coordination.

Conclusions: The limited number of policy-oriented studies highlights a significant governance gap between technical AI development and environmental policy-making. Integrating AI into air pollution management requires evidence-based, transparent, and accountable governance. Future research should focus on policy design, implementation, and evaluation to support the responsible and sustainable adoption of AI in environmental governance.