| 摘要: |
| 基于WRF-CHEM模式模拟研究2016年11月、12月关中地区两次大气重污染事件期间咸阳市本地排放对当地PM2.5污染水平的贡献以及关中地区主要污染源排放对咸阳市PM2.5质量浓度的贡献。模式合理地模拟了研究时段内关中地区PM2.5质量浓度的时空变化特征，较好地再现了大气污染过程。敏感性试验结果表明：秋冬季重污染期间咸阳市本地排放对当地PM2.5的贡献约为30%，外源输送的贡献高达50%—60%。在关中地区的主要污染源中，居民源是秋冬季咸阳市PM2.5最主要的来源，在秋冬季的贡献分别为37.4%和60.6%；工业源和交通源对咸阳市秋季PM2.5的贡献分别为22.1%和11.2%，冬季的贡献分别为15.6%和9.8%；电厂源对秋冬季咸阳市PM2.5的贡献约为2.0%。因此，在秋冬季大气重污染期间，应该主要通过控制居民源排放来减轻咸阳市PM2.5污染。 |
| 关键词: WRF-CHEM PM2.5 关中地区 大气污染 |
| DOI：10.7515/JEE182084 |
| CSTR：32259.14.JEE182084 |
| 分类号: |
| 基金项目:科技部项目（Y7YF051437） |
| 英文基金项目:Ministry of Science and Technology of the People’s Republic of China (Y7YF051437) |
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| Simulating the sources of PM2.5 during heavy haze pollution episodes in the autumn and winter of 2016 in Xianyang City, China |
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LI Xia, WU Jiarui, LIU Lang, LI Guohui
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1. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
2. Key Laboratory of Aerosol Chemistry and Physics, Chinese Academy of Sciences, Xi’an 710061, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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| Abstract: |
| Background, aim, and scope Recently, frequent and persistent particulate pollution has been the most urgent air pollution problem in most regions and cities in China, causing serious impact on climate change and human health. Fine particulate matters (PM2.5) contribute to climate change directly by absorbing and scattering the solar radiation and indirectly by serving as cloud condensation nuclei (CCN) and ice nuclei (IN) to modify cloud properties. High concentrations of PM2.5 can reduce atmospheric visibility and exert deleterious impacts on air quality, ecosystem, and human health. According to previous studies, the occurrence of particulate pollution is considered to be closely related to the characteristics of PM2.5 and its chemical components. The further study of the formation process, reaction mechanism, and major sources of PM2.5 is currently one of the major bottlenecks in improving the air quality in China. In the past few decades, with the accelerating process of industrialization and urbanization, the emissions of pollutants in China have increased significantly, and the air pollution has become increasingly severe. The Guanzhong Basin (GZB) is located in northwestern China and surrounded by the Qinling Mountains in the south and the Loess Plateau in the north, with a warm-humid climate. The rapid increasing industries and city expansions, as well as the unique topography, have caused frequent occurrence of haze in the basin, which has drawn extensive attention to clarify its formation, sources, and influence. The main purpose of the present study is to quantitatively evaluate the contribution of the local emissions in Xianyang City and the major emission sources in GZB to the PM2.5 mass concentrations in Xianyang City based on the WRF-CHEM model during two heavy haze pollution episodes in GZB from 12 to 19 November 2016 and 16 to 21 December 2016, aiming at providing a reliable basis for local authorities to establish reasonable and effective comprehensive prevention and control strategies and pollution reduction measures for particulate pollution. Materials and methods A specific version of the WRF-CHEM model is used to investigate the air pollution formation in GZB, including a flexible gas-phase chemical module and the CMAQ aerosol module developed by US EPA. The wet deposition of aerosols follows the method used in the CMAQ module and the dry deposition of chemical species is parameterized following Wesely. The photolysis rates are calculated using the FTUV (fast radiation transfer model) module, considering the aerosol and cloud effects on photolysis. The inorganic aerosols are calculated using ISORROPIA Version 1.7. The secondary organic aerosol (SOA) is predicted using the volatility basis-set (VBS) modeling method, with contributions from glyoxal and methylglyoxal. The NCEP 1°×1° reanalysis data are used for the meteorological initial and boundary conditions, and the chemical initial and boundary conditions are interpolated from the 6 h output of MOZART. The SAPRC-99 chemical mechanism is used in the study. The anthropogenic emissions are from the MEIC emission inventory, including agriculture, industry, power generation, residential, and transportation sources. The biogenic emissions are calculated online using the MEGAN model. Results Compared to observations over the ambient monitoring sites in GZB, the WRF-CHEM model reasonably well reproduces the temporal variations and spatial distributions of the PM2.5 mass concentrations during the simulation period, indicating reasonable replications of the haze events. Discussion The sensitivity simulations show that the emissions in Xianyang City contribute about 30% of the local PM2.5 mass concentrations during the simulation period in the autumn and winter of 2016. Except for the background contribution of about 10%, the contribution of the regional transport to the PM2.5 mass concentration in Xianyang City is up to 50%—60%. Among the various emission sources in GZB, the residential source is the major contributor to the PM2.5 mass concentrations in Xianyang City, with contribution of 37.4% in autumn and 60.6% in winter. During the two simulation periods in the autumn and winter of 2016, the contribution of industrial and transportation emissions in GZB to the PM2.5 in Xianyang City is 22.1% and 11.2% and 15.6% and 9.8%, respectively; and the contribution of power emissions is only 2.0%. Conclusions During the heavy haze pollution episodes in the autumn and winter of 2016, the local emissions in Xianyang City contribute about 30% to the mass concentrations of PM2.5, and the contribution of regional transport to the PM2.5 mass concentration in Xianyang City is up to 50%—60%. Among the various emission sources in GZB, the residential source significantly contributes to the PM2.5 concentration in Xianyang City, with contribution of 37.4% in autumn and 60.6% in winter. The contribution of industrial and transportation emissions in GZB to the PM2.5 in Xianyang City is 22.1% and 11.2% in autumn and 15.6% and 9.8% in winter; and the contribution of power emissions is only 2.0%. Recommendations and perspectives Further studies need to be conducted to improve the WRF-CHEM model performance considering both the uncertainties in the meteorological simulations and the emission inventory. More sensitivity simulations of long time scales are also needed in future studies to provide more reliable guidance for the air quality improvements. |
| Key words: WRF-CHEM PM2.5 Guanzhong Basin air pollution |
