亚硝酸(HONO)在年夜气中羟基自由基(OH)的预算中起着重要的感化。然而,由于缺乏高分辨率的主要HONO排放清单,当前的化学迁移模型(CTM)通常会低估HONO的环境浓度。为了解决这个问题,我们已经为广东省的CTM建立了高度解决的自下而上的HONO排放清单,利用国内现有的最佳实测排放因子和新获得的活动数据。我们的结果表明,2020年各种来源的排放,包括土壤,道路交通,非道路交通,生物质燃烧,和静止燃烧,估计为21.5、10.0、8.2、2.5和0.7kt,分别。值得注意的是,珠江三角洲(PRD)和非PRD地区的HONO排放结构有所不同。具体来说,交通来源是珠三角HONO排放的主要贡献者(62%),而非珠三角地区的土壤来源占多数(65%)。在道路交通来源中,柴油车发挥了重要作用,贡献99.7%。与以前方法的比较表明,柴油车的HONO排放被低估了大约2.5倍。更高的HONO排放,以土壤排放为主,在夏季观察到,尤其是在8月。此外,柴油车辆排放在夜间公布,可能导致夜间HONO的积累和OH的早晨高峰。本研究中开发的排放清单可以直接应用于广泛使用的CTM,例如CMAQ,CAMx,WRF-Chem,和NAQPMS,支持OH形成和二次空气污染的模拟。
Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (OH) in the atmosphere. However, current chemical transport models (CTMs) typically underestimate ambient concentrations of HONO due to a dearth of high resolution primary HONO emission inventories. To address this issue, we have established a highly resolved bottom-up HONO emission inventory for CTMs in Guangdong province, utilizing the best available domestic measured emission factors and newly obtained activity data. Our results indicate that emissions from various sources in 2020, including soil, on-road traffic, non-road traffic, biomass burning, and stationary combustion, were estimated at 21.5, 10.0, 8.2, 2.5, and 0.7 kt, respectively. Notably, the HONO emissions structure differed between the Pearl River Delta (PRD) and the non-PRD regions. Specifically, traffic sources were the dominant contributors (62 %) to HONO emissions in the PRD, whereas soil sources accounted for the majority (65 %) of those in the non-PRD. Among on-road traffic sources, diesel vehicles played a significant role, contributing 99.7 %. Comparisons with previous methods suggest that HONO emissions from diesel vehicles are underestimated by approximately 2.5 times. Higher HONO emissions, dominated by soil emissions, were observed in summer months, particularly in August. Furthermore, diesel vehicle emissions were pronounced at night, likely contributing to the nighttime accumulation of HONO and the morning peak of OH. The emission inventories developed in this study can be directly applied to widely used CTMs, such as CMAQ, CAMx, WRF-Chem, and NAQPMS, to support the simulation of OH formation and secondary air pollution.