Science Inventory

Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China

Citation:

Sarwar, G., H. Zheng, S. Song, M. Gen, S. Wang, D. Ding, X. Chang, J. Xing, Y. Sun, D. Ji, C. Chan, J. Gao, AND M. McElroy. Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, 7(9):632-638, (2020). https://doi.org/10.1021/acs.estlett.0c00368

Impact/Purpose:

Similar to all air quality models, CMAQ under-predicts observed sulfate in China. A new chemical pathway is implemented in CMAQ and its impact on sulfate is examined.

Description:

Nitrate and sulfate are two key components of airborne particulate matter (PM). While multiple formation mechanisms have been proposed for sulfate, current air quality models commonly underestimate its concentrations and mass fractions during northern China winter haze events. On the other hand, current models usually overestimate the mass fractions of nitrate. Very recently, laboratory studies have proposed that nitrous acid (N(III)) produced by particulate nitrate photolysis can oxidize sulfur dioxide to produce sulfate. Here, for the first time, we parameterize this heterogeneous mechanism into the state-of-the-art Community Multi-scale Air Quality (CMAQ) model and quantify its contributions to sulfate formation. We find that the significance of this mechanism mainly depends on the enhancement effects (by 1–3 orders of magnitude as suggested by the available experimental studies) of nitrate photolysis rate constant ("J" _(〖"NO" 〗_"3" ^- )) in aerosol liquid water compared to that in the gas phase. Comparisons between model simulations and in-situ observations in Beijing suggest that this pathway can explain about 20% (assuming an enhancement factor (EF) of 10) to 75% (assuming EF = 100) of the model–observation gaps in sulfate concentrations during winter haze. Our study strongly calls for future research on reducing the uncertainty in EF.

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:09/08/2020
Record Last Revised:09/15/2020
OMB Category:Other
Record ID: 349700