Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization
Agibayeva A. Muratuly A. Ibragimova O.P. Tursun K. Mukhamediya A. Zollanvari A. Almagambetov A. Yenisoy S. Baimatova N. Karaca F.
2025Elsevier B.V.
Atmospheric Pollution Research
2025
Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), constitute up to 70% of atmospheric particulate matter (PM) and significantly impact human health, climate forcing, and air quality. Despite their significance, accurate characterization of OC and EC fractions remains challenging due to aerosols’ complex and variable composition and the constraints in conventional analytical techniques. This review aims to assess how artificial intelligence (AI) can advance OC/EC characterization by enhancing data interpretation, automation, and measurement reliability. Recent developments in photo-optical methods for OC/EC quantification, including real-time monitoring, destructive, and non-destructive techniques, are comprehensively analyzed. The review further examines emerging efforts to integrate AI into conventional analytical frameworks, critically evaluating the potential of semi-supervised learning, active learning, incremental learning, TinyML, explainable AI, and large language models (LLMs) for improving OC/EC analysis. Notably, the underexplored potential of these AI approaches in source apportionment and real-time monitoring is emphasized, alongside a proposed roadmap for implementing AI-driven strategies in carbonaceous aerosol analysis and air quality assessments. AI integration with existing OC/EC analytical techniques can minimize measurement uncertainties and enable real-time, cost-effective monitoring systems. This approach facilitates high-resolution source apportionment with broad applications in environmental monitoring research, ultimately improving urban air quality diagnostics and evidence-based pollution control strategies.
Airborne particulate , Carbonaceous aerosols , Machine learning , Optical analysis , Organic/elemental carbons
Text of the article Перейти на текст статьи
Department of Civil and Environmental Engineering, School of Engineering and Digital Science, Nazarbayev University, Astana, 010000, Kazakhstan
Faculty of Chemistry and Chemical Technology, Center of Physical Chemical Methods of Research and Analysis, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Environmental and Analytical Chemistry Laboratory, Al-Farabi Kazakh National University, Almaty, 050038, Kazakhstan
Department of Electrical and Computer Engineering, School of Engineering and Digital Science, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Electrical and Computer Engineering, Utah Valley University, 800 West University Parkway, Orem, 84058, UT, United States
Department of Computer, Electrical, and Software Engineering, College of Engineering, Embry-Riddle Aeronautical University, Prescott, 86301, AZ, United States
Department of Chemistry, Bolu Abant Izzet Baysal University, Bolu, 14030, Turkey
Department of Civil and Environmental Engineering
Faculty of Chemistry and Chemical Technology
Environmental and Analytical Chemistry Laboratory
Department of Electrical and Computer Engineering
Department of Electrical and Computer Engineering
Department of Computer
Department of Chemistry
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026