| 18 Jun 2026
Reactivity Over Abundance: Unveiling the True Kinetic Drivers of Urban Ozone Using Process-Informed Machine Learning
Abstract. Ground-level ozone remains a persistent challenge in East Asian urban centers, where concentrations continue rising despite significant reductions in precursor emissions. Designing effective mitigation is complicated by the non-linear relationship between precursor abundance and reactivity. Standard data-driven approaches often suffer from "survivor bias," systematically undervaluing highly reactive precursors that are rapidly depleted. We introduce a process-informed machine learning framework that uses net chemical consumption (ΔVOC) rather than ambient concentrations to resolve this attribution failure. Applied to measurements from a NOₓ-saturated roadside site in Seoul, South Korea, the approach reveals a robust quantitative relationship between intrinsic hydroxyl radical reactivity (kOH) and ozone formation sensitivity.
Across a comprehensive suite of precursors—including oxygenated VOCs (e.g., formaldehyde, acetone) and aromatics—the framework identifies reactive aromatics (trimethylbenzenes, xylenes) and biogenics (isoprene, monoterpenes) as the dominant kinetic drivers. Whereas static metrics such as OFP rank precursors by stoichiometric capacity under idealized accumulated conditions, the process-informed framework shows that realized ozone production in fresh urban plumes is governed by kinetic turnover rather than abundance, with this kinetic selectivity further amplified during high-ozone episodes. These results indicate that mass-based VOC inventories and OFP-style rankings, when applied without kinetic context, can systematically misallocate control priorities in NOₓ-saturated urban regimes. Because the framework requires only sub-hourly co-located VOC and ozone observations and no prescribed mechanism, it offers a complementary empirical pathway in settings where explicit mechanism-based modeling is constrained by incomplete VOC speciation or unmeasured radical precursors.
This paper investigates the ozone photochemical production by a machine learning model where the key variable is formed by the volatile organic compounds. The main role is devoted to the chemical processes whereas physical processes are ignored. The measurement campaign extended during one month, from 1 to 30 April 2022 and the study site is inside the city, although the sampling site is surrounded by vegetation and close to a hill (the Namsan Mountain). In consequence, some minor changes should be introduced to determine the paper restrictions.
Since the measurement extension is one month, some comments about the data representativeness should be included. For instance, the authors should consider the prevailing atmospheric circulation with the suitable meteorological charts. Moreover, the authors should comment if the measurements and the model response are similar under varied meteorological conditions, such as front passages or precipitation events.
The sampling site is close to a hill. The authors should comment if the airflow is modified by this hill. In addition, the wind rose presented in Fig. S1c shows that the prevailing wind directions come from this hill, whereas the city contribution looks like secondary.
Moreover, the study site is surrounded by vegetation. Perhaps the precursor composition does not respond to that from the city, i.e. measurements may be quite local.
The authors present a comparison between measured and predicted O3 deltas. Perhaps, potential readers would wonder about the reason for such comparison instead of measured and predicted concentrations. The authors highlight Dt=18 minutes in the text. Perhaps, they should indicate the reason, since Dt=30 minutes determines a better correlation in Fig. S7.
Some of the graphs in Fig. 5 may be misunderstood since most of the space in them is white, without dots. To see a clear result Y axes should be modified, between -5 and 5 in Fig. (b), perhaps between -7.5 and 5 in Fig. (c) and perhaps -2 and 3 in Fig. (c). These changes would allow the linear relationship to be observed and, perhaps, questioned.
Finally, the authors should determine the potential readers of this research, which is quite focused, and they should establish the way to increase them.