Volume 11, Issue 1 (March 2026)
J Environ Health Sustain Dev 2026, 11(1): 2891-2903 |
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Bouzarjomehri H, Ebrahimi A A, Ehrampoush M H, Ardani M, Soltanianzadeh Z, Herandi Y, et al . Artificial Intelligence in Addressing Air Pollution: A Scoping Review of Policies. J Environ Health Sustain Dev 2026; 11 (1) :2891-2903
URL: http://jehsd.ssu.ac.ir/article-1-1047-en.html
URL: http://jehsd.ssu.ac.ir/article-1-1047-en.html
Hossein Bouzarjomehri 
, Ali Asghar Ebrahimi * , Mohammad Hassan Ehrampoush , Mobina Ardani , Zahra Soltanianzadeh , Yasaman Herandi , Mohsen Khosravi , Seyed Masood Mousavi , Mohammad Ranjbar
, Ali Asghar Ebrahimi * , Mohammad Hassan Ehrampoush , Mobina Ardani , Zahra Soltanianzadeh , Yasaman Herandi , Mohsen Khosravi , Seyed Masood Mousavi , Mohammad Ranjbar
Environmental Sciences and Technology Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract: (11 Views)
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.
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.
Keywords: Artificial Intelligence, Air Pollution, Environmental Policy, Scoping Review, Public Health, Public Policy.
Type of Study: Systematic Review |
Subject:
Air and waste management
Received: 2025/11/15 | Accepted: 2026/01/20 | Published: 2026/03/23
Received: 2025/11/15 | Accepted: 2026/01/20 | Published: 2026/03/23
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