Quantifying Nitrate Formation Pathways in Highly Polluted Environments Using Observations of 17O excess (Δ17O) and an Air Quality Model

Abstract:

The non-linear response of wintertime PM_2.5 to emission controls poses a critical challenge to pollution management in many metropolitan areas. In North China, high levels of nitrate persist in winter despite a steady reduction in NO_x emissions from vehicles and factories in recent years. Nitrate is becoming a dominant component of PM_2.5, especially during haze events, highlighting the need to understand its formation pathways in polluted environments. Observations of anomalous abundance of oxygen isotopes in atmospheric species, ¹⁷O excess in nitrate (Δ¹⁷O(NO₃^-)) shed light on the relative importance of different nitrate-producing mechanisms. We compile recent wintertime observations of Δ¹⁷O(NO₃^-), aerosol composition, and trace gases in Beijing and categorize them according to different haze regimes. The GEOS-Chem chemical transport model (GC) is then used to re-interpret these observations. The model has a low bias in Δ¹⁷O and a high bias in the nitrogen oxidation ratio (≡ [total nitrate]/([NO₂]+[total nitrate])), suggesting too rapid nitrate production in the model via low-Δ¹⁷O mechanisms. We show that the model biases can largely be corrected by using a much smaller reactive uptake coefficient for NO₂ reactions on aerosols than currently assumed, which has implications for both nitrate and HONO production in polluted environments. We propose several changes to the model’s parametrizations for improving the simulation of nitrate production, implement the chemistry updates into the GC model, analyze the results from different control experiments, and discuss the cascading effects of these updates on the other secondary aerosols and the oxidizing capacity of urban air. Our findings indicate that more detailed studies of the chemical mechanisms of NO₂ uptake on aerosols and the drivers for the variability of wintertime ozone during haze events are essential for improving the prediction of urban air quality in winter and the design of emission mitigation in the future.