| 摘要: |
| 本文应用WRF-CHEM模式模拟分析了2016年6月西安市大气污染过程。模式准确地模拟了西安地区大气臭氧（O3）、细颗粒物（PM2.5）以及二氧化氮（NO2）的时空变化趋势，较好地再现了天气形势以及大气污染的演变过程。根据近年来西安市交通排放量的增加制定敏感性试验，结果表明：西安市20%交通排放量在研究时段内平均PM2.5质量浓度贡献量为4.5 µg ∙ m−3，模拟时段内O3平均贡献量为4.8 µg ∙ m−3，西安市20%交通排放量在研究时段内的平均NO2贡献量为2.7 µg ∙ m−3，而且污染物浓度越高，交通源排放量的影响越显著。 |
| 关键词: 交通源 大气污染 西安市 WRF-CHEM |
| DOI：10.7515/JEE201706005 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41275101，41275153，41430424）；中央高校基本科研业务费专项资金（2013jdhz25，?zdyf2017001） |
| 英文基金项目:National Natural Science Foundation of China (41275101, 41275153, 41430424); Fundamental Research Funds ?for the Central Universities of China (2013jdhz25, zdyf2017001) |
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| Contribution of transportation emission to summertime air quality in Xi’an, China |
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EI Naifang, WU Jiarui, FENG Tian, LI Guohui
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1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710054, China
2. Key Laboratory of Aerosol Chemistry & Physics, Chinese Academy of Sciences, Xi’an 710061, China
3. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China
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| Abstract: |
| Background, aim, and scope Rapid growth of industrialization, urbanization, and transportation has caused persistent and severe air pollution in China. Ozone (O3) and fine particulate matter (PM2.5) are considered to be the major air pollutants of concern during summertime. Air pollution significantly affects the regional and global climate and exerts deleterious impacts on ecosystems and human health. Xi’an, located in the Guanzhong Basin, is the largest city in northwestern China. The basin is nestled between the Qinling in the south and the Loess Plateau in the north with a warm-humid climate. The unique topography is not favorable for the dispersion of pollutants. In addition, the rapid growth of industrialization, city expansion and transportation has caused frequent air pollution in Xi’an city in recent years. The purpose of the present study is to quantitatively investigate the contribution of transportation emission to the summertime (June 2016) air quality in Xi’an city using the WRF-CHEM model. Materials and methods A specific version of WRF-CHEM model is used in this study, with a new flexible gas-phase chemical module and the CMAQ aerosol module developed by the US EPA. The wet deposition follows the method used in the CMAQ and the surface deposition of chemical species is also parameterized. The photolysis rates are calculated using the FTUV, in which the effects of aerosols and clouds on photolysis are considered. The inorganic aerosols are predicted in the WRF-CHEM model using ISOPROPIA Version 1.7. The secondary organic aerosol (SOA) formation is calculated using a non-traditional SOA module. The NCEP 1°×1° reanalysis data is used to obtain the meteorological initial and boundary conditions. The chemical initial and boundary conditions are interpolated from the 6 h output of MOZART. The anthropogenic emissions are developed by Zhang et al (2009). The biogenic emissions are calculated online using the MEGAN (model of emissions of gases and aerosol from nature) model. Results In general, the simulated temporal variations of PM2.5, O3, and NO2 concentrations agree well with observations in Xi’an city, but the model biases still exist, which are perhaps caused by the uncertainties of simulated meteorological conditions and the emission inventory. The model also successfully reproduces the spatial distribution of PM2.5, O3, and NO2 concentrations compared with measurements. Discussion During the study episode, when the PM2.5 concentration is lower than 35 µg ∙ m−3, 20% transportation emission can contribute approximately 1.6 µg ∙ m−3 to the PM2.5 concentration in Xi’an city; when the PM2.5 concentration is between 35 µg ∙ m−3 and 75 µg ∙ m−3, the average contribution of 20% transportation emission can be 3.0 µg ∙ m−3; when the PM2.5 concentration is higher than 75 µg ∙ m−3, the average contribution of 20% transportation emission is about 8.8 µg ∙ m−3 in Xi’an city. As for O3 pollution, when the O3 concentration in Xi’an city is lower than 100 µg ∙ m−3, 20% transportation emission can decrease the O3 concentration during the study episode, with value of −0.8 µg ∙ m−3; when the O3 concentration is between 100 µg ∙ m−3 and 160 µg ∙ m−3, the average contribution of 20% transportation emission can be 4.2 µg ∙ m−3; when the O3 concentration is between 160 µg ∙ m−3 to 200 µg ∙ m−3, 20% transportation emission can contribute about 5.9 µg ∙ m−3 to the O3 level on average; when the O3 concentration is higher than 200 µg ∙ m−3,the average contribution of 20% transportation emission to the O3 concentration can be 9.7 µg ∙ m−3 in Xi’an city. The transportation emission contributes little to the NO2 concentration in Xi’an city during the study episode. When the NO2 concentration in Xi’an city is lower than 20 µg ∙ m−3, 20% transportation emission contributes little to the NO2 concentration during the study episode, with value of 0.4 µg ∙ m−3; when the NO2 concentration is between 20 µg ∙ m−3 and 40 µg ∙ m−3, the average contribution of 20% transportation emission to the NO2 level can be 1.6 µg ∙ m−3; when the NO2 concentration is between 40 µg ∙ m−3 to 60 µg ∙ m−3, 20% transportation emission can contribute about 3.5 µg ∙ m−3 on average to the NO2 level; when the NO2 concentration is higher than 60 µg ∙ m−3,the average contribution of 20% transportation emission to the NO2 level in Xi’an city can be 5.3 µg ∙ m−3. Conclusions The analysis indicates that during the study episode, 20% transportation emission can contribute 4.5 µg ∙ m−3 to the PM2.5 concentration, 4.8 µg ∙ m−3 to the O3 concentration and 2.7 µg ∙ m−3 to the NO2 concentration on average in Xi’an city. The influence of transportation emission is more significant with the aggravated air pollution. Recommendations and perspectives Future studies need to be conducted to improve the WRF-CHEM model simulations and the emission inventory should be updated in future studies. This study quantitatively investigates the contribution of transportation emission to the summertime (June 2016) air quality in Xi’an city using the WRF-CHEM model. Model simulations of long time scales in different cities are also needed in future studies. |
| Key words: transportation emission air pollution Xi’an city WRF-CHEM |
