Key Takeaways & Executive Findings
- •• • Direct vehicle emissions contributed 49.7% to HONO concentration during winter haze in Guangzhou, highlighting traffic as the dominant source; controlling vehicular HONO precursors could significantly reduce secondary nitrate formation. • • Heterogeneous photosensitized reaction of NO2 on aerosol surfaces accounted for 23.0% of HONO, indicating that aerosol surface chemistry is a major non-vehicular source; models must include this pathway to avoid underestimation. • • Incorporating seven additional HONO sources increased simulated daytime HONO from (0.3±0.1) to (6.5±2.3) μg·m−3, matching observations; this improvement elevated ·OH concentrations by 1.2-fold and nitrate production rate by 3.5-fold, demonstrating the sensitivity of oxidative capacity to HONO. • • The simulated-to-observed nitrate ratio improved from 21% to 81% after optimizing HONO sources, indicating that accurate HONO representation is critical for reproducing nitrate levels; sensitivity tests suggest nitrate photolysis enhancement factor of 100 yields best results.
Abstract
Nitrous acid (HONO) is a critical precursor of hydroxyl radicals (·OH) in the atmosphere, influencing oxidative capacity and secondary pollutant formation. However, model simulations often underestimate HONO concentrations, and its role in nitrate formation remains unclear. This study investigates a typical winter haze episode in Guangzhou (January 2021, peak PM2.5: 243.0 μg·m−3) using observational data and a box model to quantify HONO sources and assess their impact on ·OH and particulate nitrate. HONO concentrations increased from (1.0±1.0) μg·m−3 during clean periods to (9.2±3.8) μg·m−3 during polluted periods, while nitrate rose from (6.4±3.4) to (43.3±20.0) μg·m−3 (6.8-fold). Incorporating seven additional HONO sources improved simulated daytime HONO from (0.3±0.1) to (6.5±2.3) μg·m−3, matching observations. Source apportionment showed direct vehicle emissions dominated (49.7%), followed by heterogeneous photosensitized reaction of NO2 on aerosol surfaces (23.0%), ground surface reaction (10.7%), and nitrate photolysis (8.7%). With optimized HONO, simulated daytime ·OH increased from (0.6±0.3)×10^6 to (1.5±0.8)×10^6 molec·cm−3 (1.2-fold), and nitrate production via ·OH+NO2 increased from (3.4±1.2) to (15.3±8.5) μg·m−3·h−1 (3.5-fold). The simulated-to-observed nitrate ratio improved from 21% to 81%. Sensitivity tests indicated that setting nitrate photolysis enhancement to 100 times gaseous nitric acid yielded better HONO and nitrate simulations. This study underscores the importance of refining HONO sources for accurate simulation of atmospheric oxidation and nitrate formation, aiding pollution control strategies.
1. Introduction
Atmospheric nitrous acid (HONO) is a primary source of hydroxyl radicals (·OH) in the troposphere, driving photochemical oxidation cycles that produce secondary pollutants such as ozone and particulate nitrate. Despite its importance, current chemical transport models systematically underestimate HONO concentrations, often by factors of 2-10, leading to underpredicted oxidative capacity and inaccurate secondary aerosol formation. This discrepancy arises from incomplete representation of HONO sources, including direct emissions, heterogeneous reactions on ground and aerosol surfaces, and photolytic pathways. In polluted urban environments like Guangzhou, where haze events are frequent, the underestimation of HONO directly impacts the simulation of nitrate, a major component of PM2.5, thereby hindering effective air quality management.
This study addresses the bottleneck by integrating observational data with a box model to optimize HONO source parameterizations for a typical winter haze episode in Guangzhou. The authors systematically added seven HONO source mechanisms to the base model, including vehicle emissions, NO2 heterogeneous reactions on aerosol and ground surfaces, and nitrate photolysis with enhanced coefficients. The refined model achieved excellent agreement with observed HONO concentrations, improving from (0.3±0.1) to (6.5±2.3) μg·m−3. This optimization not only enhanced ·OH levels by 1.2-fold but also increased nitrate production rates by 3.5-fold, raising the simulated-to-observed nitrate ratio from 21% to 81%. The findings provide a quantitative framework for accurately simulating HONO and its impacts, essential for designing targeted emission controls and mitigating secondary pollution in megacities.
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YANG Suxia, HUANG Jizhang, YE Ziming, PEI Chenglei, FANG Kunxin, LI Mei, CHENG Chunlei (2026). Optimized Simulation of HONO Sources and Its Impact on Nitrate Formation in Guangzhou. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025081103
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Frequently Asked Questions
What are the dominant HONO sources during winter haze in Guangzhou, and how do their contributions vary between clean and polluted periods?
During the polluted period, direct vehicle emissions contributed 49.7% to HONO, followed by NO2 heterogeneous photosensitized reaction on aerosol surfaces (23.0%), ground surface reaction (10.7%), and nitrate photolysis (8.7%). In clean periods, HONO concentrations were much lower (1.0±1.0 μg·m−3) compared to polluted periods (9.2±3.8 μg·m−3), indicating that source strengths intensify with pollution levels.
How does the model performance improve after incorporating the seven additional HONO sources, and what are the implications for simulating atmospheric oxidation?
Simulated daytime HONO increased from (0.3±0.1) to (6.5±2.3) μg·m−3, matching observations. This led to a 1.2-fold increase in ·OH concentration (from (0.6±0.3)×10^6 to (1.5±0.8)×10^6 molec·cm−3) and a 3.5-fold increase in nitrate production rate via ·OH+NO2 (from (3.4±1.2) to (15.3±8.5) μg·m−3·h−1). Accurate HONO simulation is thus critical for reproducing oxidative capacity and secondary nitrate formation.
What sensitivity tests were conducted regarding nitrate photolysis, and what enhancement factor yielded the best results?
Sensitivity tests varied the nitrate photolysis enhancement factor relative to gaseous nitric acid. Setting it to 100 times produced the best agreement between simulated and observed HONO and nitrate concentrations, indicating that particulate nitrate photolysis is a significant HONO source that must be parameterized with high enhancement factors.
How do the findings inform emission control strategies for reducing nitrate pollution in Guangzhou?
Since direct vehicle emissions are the largest HONO source (49.7%), controlling vehicular NOx and HONO emissions would directly reduce HONO levels, thereby decreasing ·OH production and nitrate formation. Additionally, reducing aerosol surface area or reactivity could mitigate the heterogeneous HONO source (23.0%), further lowering secondary nitrate.
What are the limitations of the box model approach used in this study, and how might these affect the generalizability of the results?
The box model assumes a well-mixed air parcel and does not account for spatial heterogeneity or transport, which may limit applicability to regional scales. However, the source apportionment and sensitivity findings are based on observational constraints and provide valuable insights for improving larger-scale models. The methodology can be adapted to other urban areas with similar pollution characteristics.
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