Open AccessContribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China
Author(s) -
Haotian Zheng,
Shaojie Song,
Golam Sarwar,
Masao Gen,
Yafei Wang,
Dian Ding,
Xing Chang,
Shuping Zhang,
Jia Xing,
Yele Sun,
Dongsheng Ji,
Chak Keung Chan,
Jian Gao,
Xinyu Chen
Publication year - 2020
Publication title -
environmental science and technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 58
ISSN - 2328-8930
DOI - 10.1021/acs.estlett.0c00368
Subject(s) - haze , particulates , sulfate , nitrate , cmaq , environmental chemistry , aerosol , chemistry , environmental science , nitrous acid , air quality index , sulfate aerosol , atmospheric sciences , sulfur , sulfur dioxide , meteorology , air pollution , inorganic chemistry , organic chemistry , geology , physics
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 ( JNO 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 15% (assuming an enhancement factor (EF) of 10) to 65% (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.