Reconstructing the VOC–Ozone Research Framework Through a Systematic Review of Observation and Modeling

Xiangwei Zhu, Huiqin Wang, Yi Han, Donghui Zhang, Senhao Liu, Zhijie Zhang and Yansheng Liu ()
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Xiangwei Zhu: College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China
Huiqin Wang: College of Engineering, China University of Petroleum (Beijing) at Karamay, Karamay 834000, China
Yi Han: Karamay Ecological Environment Bureau, Karamay 834000, China
Donghui Zhang: Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China
Senhao Liu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zhijie Zhang: School of Geography, Development and Environment, The University of Arizona, Tucson, AZ 85719, USA
Yansheng Liu: College of Chemical Engineering and Environment, China University of Petroleum, Beijing 102249, China

Sustainability, 2025, vol. 17, issue 16, 1-42

Abstract: Tropospheric ozone (O 3 ), a secondary pollutant of mounting global concern, emerges from complex, nonlinear photochemical reactions involving nitrogen oxides (NO x ) and volatile organic compounds (VOCs) under dynamically evolving meteorological conditions. Accurately characterizing and effectively regulating O 3 formation necessitates not only precise and multi-dimensional precursor observations but also modeling frameworks that are structurally coherent, chemically interpretable, and sensitive to regime variability. Despite significant technological progress, current research remains markedly fragmented: observational platforms often operate in isolation with limited vertical and spatial interoperability, while modeling paradigms—ranging from mechanistic chemical transport models (CTMs) to data-driven machine learning approaches—frequently trade interpretability for predictive performance and struggle to capture regime transitions across heterogeneous environments. This review provides a dual-perspective synthesis of recent advances and enduring challenges in the VOC–O 3 research landscape. We first establish a typology of ground-based, airborne, and satellite-based VOC monitoring systems, evaluating their capabilities, limitations, and roles within a vertically structured sensing architecture. We then examine the evolution of O 3 modeling strategies, from empirical and semi-mechanistic models to hybrid frameworks that integrate physical knowledge with algorithmic flexibility. By diagnosing the structural decoupling between observation and inference, we identify key methodological bottlenecks and advocate for a system-level redesign of the VOC–O 3 research paradigm. Finally, we propose a forward-looking framework for next-generation atmospheric governance—one that fuses cross-platform sensing, regime-aware modeling, and policy-relevant diagnostics into an integrated, adaptive, and chemically robust decision-support system.

Keywords: volatile organic compounds (VOCs); tropospheric ozone; photochemical regimes; multi-platform observation; chemical transport models; machine learning; atmospheric sensitivity; integrated governance (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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