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Ozone-driven non-linear controls on nighttime ClNO2 formation: Unintended oxidant increases under NOx control

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Author(s)
Nam, WoohuiCho, ChangminLee, GahyunJeong, Sun-A.Park, Jeong-HooMin, Kyung-Eun
Type
Article
Citation
Journal of Hazardous Materials, v.514
Issued Date
2026-08
Abstract
Nitryl chloride (ClNO2) is an important but underrecognized driver of oxidant formation, linking nocturnal reactive chlorine chemistry to next-day ozone (O3) production by enhancing daytime oxidation capacity through the photolytic releases of NO2 and chlorine radicals. Despite its importance, its key controls remain difficult to isolate in ambient observations where chemical precursors and meteorological factors co-vary, limiting policy-relevant diagnosis of nocturnal chemistry. Here, we applied an explainable machine learning framework using seven routinely available predictors (O3, NO2, CO, PM2.5, temperature, relative humidity, and solar radiation) to quantify their contributions of wintertime ClNO2 formation in urban South Korea. The model reproduced ClNO2 (R2 = 0.96) and identified nocturnal O3 as the dominant control, exceeding the influence of NO2. Coupled O3–NOx effects produced non-linear transitions among NO2-, O3-, and NO-limited regimes for ClNO2 formation. Under observed wintertime long-term trends at this site—nocturnal O3 rising (+0.62 ppbv yr−1) and NO2 declining (-0.53 ppbv yr−1)— ClNO2 is projected to increase by 1–2% annually, suggesting a potential positive feedback with next-day O3 formation. These results suggest that, when ClNO2 production is O3-limited with the rise in nocturnal O3, NOx controls targeting NO2 may not yield the intended O3 response due to the enhanced ClNO2 and its photolysis products, thereby strengthening daytime oxidation. O3-control strategies should therefore explicitly account for nocturnal chemistry and chlorine activation alongside daytime photochemistry to avoid unintended increases in oxidant exposure under NOx emission reductions. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Publisher
Elsevier B.V.
ISSN
0304-3894
DOI
10.1016/j.jhazmat.2026.142772
URI
https://scholar.gist.ac.kr/handle/local/34279
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