| 摘要: |
| 近年来我国多地区发生重大的霾污染，细颗粒物（PM2.5）浓度极高，来源复杂。有机气溶胶（OA）是PM2.5的重要组成成分之一，占北半球PM2.5质量浓度的20%—90%。在重霾期间二次有机气溶胶（SOA）对OA的贡献率可达44%—71%。因此，本文利用WRF-Chem模式模拟了2017年10月23—28日京津冀地区（BTH）一次重霾污染事件，评估了亚硝酸（HONO）的非均相反应、低温影响下羟基自由基（·OH）的反应速率以及挥发性有机化合物（VOCs）对SOA生成的影响。模式较好地再现了BTH的大气污染物与SOA的时空变化特征。敏感性实验分析表明：在研究期间，非均相HONO源可使区域平均SOA浓度增加30.0%；提高·OH反应的速率可使平均SOA浓度增加16.8%；同时提高排放清单中VOCs的排放量与·OH的反应速率时，SOA平均浓度可增加33.6%。并且随着污染程度的加重，SOA在OA中的占比逐渐增加。因此，除了减少一次颗粒物排放外，减少SOA前体物的排放，也是改善空气质量的重要途径之一。 |
| 关键词: WRF-Chem模式 二次有机气溶胶 大气污染 |
| DOI：10.7515/JEE212029 |
| CSTR： |
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| 基金项目:国家重点研发计划（2017YFC0210003） |
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| Simulating the sources and formation of secondary organic aerosol in Beijing-Tianjin-Hebei |
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GONG Xuehong, WU Jiarui, HAN Yongming, LI Guohui, AN Zhisheng
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1. Interdisciplinary Research Center of Earth Science Frontier, Beijing Normal University, Beijing 100875, China
2. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
3. Key Laboratory of Aerosol Chemistry and Physics, Chinese Academy of Sciences, Xi’an 710061, China
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| Abstract: |
| Background, aim, and scope Organic aerosol (OA) is one of the most important components of fine particulate matter (PM2.5), constituting 20%—90% of the PM2.5 mass concentration in the Northern Hemisphere. Traditionally, OA consists of two main types: primary OA (POA) and secondary OA (SOA). Observations have shown that SOA plays a critical role in haze pollution in China. In recent years, China has been suffering from severe haze pollution especially over the Beijing-Tianjin-Hebei (BTH) region. The BTH is located in the northern part of the North China Plain (NCP) and surrounded by the Bohai Sea in the east, the Yanshan Mountains in the north and the Taihang Mountains in the west. Previous studies have investigated SOA formation in China and generally tend to underestimate SOA concentrations. The main purpose of the present study is to quantitatively analyze the formation and influencing factors of SOA based on the WRF-Chem model during a heavy haze pollution episode in the BTH from 23 to 28 October 2017, aiming at providing a reliable basis for local governments to establish reasonable and effective particle pollution comprehensive prevention and control strategies. Materials and methods A specific version of the WRF-Chem model is utilized to investigate the SOA formation in the BTH. The aerosol module uses the CMAQ module developed by the US Environmental Protection Agency, the inorganic aerosols are calculated using ISORROPIA version 1.7, and the organic aerosol module is predicted using the non-traditional Volatility Basis Set (VBS) method. The meteorological initial and boundary conditions are from the NCEP 1°×1° reanalysis data. The Model for Ozone and Related Chemical Tracers (MOZART) output with a 6 h interval is used as the chemical initial and boundary conditions. The anthropogenic emission inventory is taken from the MEIC emission inventory, including agriculture, industry, power generation, residential, and transportation sources. The Model of Emissions of Gas and Aerosols from Nature (MEGAN) is used to calculate the biogenic emissions. Results Compared to the observations over the ambient monitoring sites in the BTH, the WRF-Chem model reasonably reproduces the temporal distributions and spatial evolution of hourly mass concentrations of PM2.5 and SOA. Discussion Sensitivity studies show when heterogeneous HONO sources are considered, the regional average SOA concentrations are increased by about 30.0% during the simulation period. With the increase of ·OH reaction rate, the average SOA concentrations are increased by 16.8%. The average SOA concentration are increased by 33.6% as the emission of VOCs increases by 25% and the reaction rate of ·OH increases. Conclusions During the episode, the regional average SOA concentrations are increased by about 30.0% due to the heterogeneous HONO sources. Both the reaction rate of ·OH and the emission of VOCs play an important role in SOA formation, which can cause the regional average SOA concentrations to change by 15%—35%. Recommendations and perspectives In order to better simulate the formation of SOA, SOA formation mechanisms need to be further studied to reasonably represent the formation of SOA in the atmosphere. In addition, reducing uncertainty in meteorological fields and chemical species is also essential for improving SOA simulations. |
| Key words: WRF-Chem SOA air pollution |
