Unveiling the HONO Offsetting Effect: Rethinking NO_x Emission Controls during Urban Ozone Pollution Episodes.

Area of Science:

• Atmospheric Chemistry
• Environmental Science
• Machine Learning Applications

Background:

• Traditional ozone (O3) control strategies focus on nitrogen oxides (NOx) and volatile organic compounds (VOCs).
• The role of nitrous acid (HONO) in atmospheric photochemistry and ozone formation is often underestimated.
• Existing models may not fully capture complex O3-NOx-VOCs interactions, especially during high-pollution events.

Purpose of the Study:

• To investigate the influence of HONO on ozone production during high-pollution episodes.
• To integrate machine learning-derived HONO-NOx relationships into photochemical modeling.
• To re-evaluate the effectiveness of NOx reduction strategies considering HONO's impact.

Main Methods:

• Utilized machine learning (ML) to establish HONO-NOx reduction relationships from real-world atmospheric data.
• Integrated ML findings into the photochemical model (OBM-MCM) for simulations in Shenzhen, China.
• Employed the empirical kinetic modeling approach (EKMA) to assess shifts in ozone production rates.

Main Results:

• OBM simulations incorporating observed HONO showed a 95% increase in net ozone production rates.
• HONO demonstrated a strong anticorrelation with NOx, indicating a greater NOx-driven O3 increase corresponds to a greater HONO-driven O3 decrease.
• ML predictions indicated a 10% NOx reduction could decrease HONO and VOCs, shifting net ozone production from a 28% increase to a 14% decrease.

Conclusions:

• HONO plays a critical role in ozone formation and can offset ozone increases from NOx reduction.
• Findings challenge traditional EKMA frameworks, suggesting NOx control can be effective even in VOC-limited regimes when HONO is considered.
• This study highlights the necessity of including HONO dynamics in air pollution control strategies for accurate ozone management.