Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China
Contribution of Particulate Nitrate Photolysis to Heterogeneous Sulfate Formation for Winter Haze in China
复制标题
颗粒硝酸盐光解对中国冬季雾霾中异质硫酸盐形成的贡献
DOI：
10.1021/acs.estlett.0c00368
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发表时间：
2020
影响因子：
10.9
通讯作者：
McElroy Michael B.
中科院分区：
文献类型：
--
作者：
Zheng Haotian;Song Shaojie;Sarwar Golam;Gen Masao;Wang Shuxiao;Ding Dian;Chang Xing;Zhang Shuping;Xing Jia;Sun Yele;Ji Dongsheng;Chan Chak K.;Gao Jian;McElroy Michael B.
硝酸盐和硫酸盐是大气颗粒物（PM）的两种主要成分。虽然硫酸盐的形成机制多种多样，但目前的空气质量模型普遍低估了北方冬季灰霾事件中硫酸盐的浓度和质量分数。另一方面，目前的模型通常高估了硝酸盐的质量分数。最近，实验室研究表明，硝酸盐颗粒光解产生的亚硝酸（N（III））可以氧化二氧化硫产生硫酸盐。在这里，我们第一次将这种异质机制参数化为最先进的社区多尺度空气质量（CMAQ）模型，并量化其对硫酸盐形成的贡献。我们发现，这一机制的意义主要取决于增强效应（1-3个数量级的建议，由现有的实验研究）的硝酸盐的光解速率常数（JNO 3-）在气溶胶液态水相比，在气相中。模式模拟和北京现场观测的比较表明，这条路径可以解释约15%（假设增强因子（EF）为10）至65%（假设EF = 100）的模式观测间隙硫酸盐浓度在冬季灰霾。我们的研究强烈呼吁未来的研究，以减少EF的不确定性。
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 parametrize this heterogeneous mechanism into a state-of-the-art Community Multiscale 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 the nitrate photolysis rate constant (JNO3–) 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 from 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.