| 摘要: |
| 本文利用WRF-CHEM模式对关中地区2015年7月25日至30日的一次O3污染事件进行了数值模拟。通过与地面观测数据对比发现，WRF-CHEM模式基本上可以合理模拟西安和咸阳城市群O3和NO2的质量浓度的时空分布。敏感性试验表明，在臭氧生成的峰值期（12:00 — 18:00 LT），交通源是城市重要的O3源，无论在高浓度臭氧条件下还是低浓度臭氧条件下，贡献量都高于15 µg ∙ m−3，平均贡献量均高于24 µg ∙ m−3；工业源仅在臭氧峰值生成时期贡献明显；生物源无论在高浓度还是低浓度臭氧的条件下，平均贡献都在16 µg ∙ m−3以上；居民源的贡献基本低于10 µg ∙ m−3；能源生产源有降低O3质量浓度的作用，但在臭氧生成的峰值时期，能源生产源可以增加O3质量浓度。随着交通源排放量的增加，O3的质量浓度逐渐增加，尤其在臭氧的峰值期。在臭氧生成峰值期，当氮氧化物（NOx）减少50%时，除城市中心臭氧浓度略增加，其他地区臭氧质量浓度均在下降；当挥发性有机物（VOCs）减少50%时，城市群内臭氧质量浓度都在下降；当NOx和VOCs同时减少50%时，臭氧质量浓度都呈现下降趋势，减少量可达20 µg ∙ m−3以上。在整个研究区域内，H2O2 / HNO3比值均在0.6以上，这表明西安和咸阳城市群属于NOx控制区。 |
| 关键词: O3 NOx WRF-CHEM 西安 |
| DOI：10.7515/JEE201706007 |
| 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） |
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| Simulations of summertime ozone in Xi’an and surrounding areas |
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BEI Naifang1, FENG Tian2, WU Jiarui2,3, LI Guohui2,3
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1. School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710054, 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 & Physics, Chinese Academy of Sciences, Xi’an 710061, China
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| Abstract: |
| Background, aim, and scope Ozone (O3) is a key species in the atmosphere due to its role in controlling the photochemistry in the stratosphere and troposphere since O3 and its photochemical derivative, OH, are the major oxidants for most reduced gases. High levels of surface O3 exert deleterious impacts on ecosystems and human health and O3 is one of the criteria pollutants regulated by the environmental agencies in many countries, such as the US Environmental Protection Agency (US EPA) and China’s Ministry of Environmental Protection (China MEP). The objective of this study is to simulate summertime O3 concentration in Xi’an and its surrounding areas and examine the main influence factors and precursor contributors. Materials and methods We simulate an O3 pollution event during the period from 25 to 30 July 2015 in the Guanzhong region, China using the WRF-CHEM model. Statistics including mean bias, root mean square error, and index of agreement is used to evaluate the model performance. Results Compared with the ground-based measurements, the model generally reasonably reproduces the spatiotemporal patterns of O3 and NO2 concentrations over the city clusters including Xi’an and Xianyang. Sensitivity studies show that transportation emission contributes the most to urban O3 levels during peak episodes (12:00 — 18:00 LT) and the contribution is higher than 15 µg ∙ m−3 during both high- and low-level O3 episodes with an average of >24 µg ∙ m−3; biological emission contributes >16 µg ∙ m−3 during both high- and low-level O3 episodes; the contribution from industrial emission is remarkable only at O3 peak time; the contribution from residential sources is <10 µg ∙ m−3. Discussion The emissions from power plants generally suppress O3 production but enhance O3 formation during peak periods. O3 concentration is increased along with the increasing emission from transportation, particularly at O3 peak time. If the emitted nitrogen oxides(NOx) were reduced by 50%, in general, peak O3 concentration would decrease except for urban centers; if anthropogenic volatile organic compounds (VOCs) emission were reduced by 50%, peak O3 concentration would decline without exception; if both NOx and VOCs emissions were slashed by 50%, peak O3 concentration would decrease by >20 µg ∙ m−3 over the city clusters. The H2O2 / HNO3 ratio over the city clusters is greater than 0.6, indicative of a NOx-limited regime. Conclusions O3 contributions from different precursor sources vary widely and transportation emissions contributes the most to urban O3 levels during peak episodes. Projected reductions in NOx and VOCs emissions would lead to different spatial patterns of O3 decrease for urban areas. A NOx-sensitive regime is presented over the city clusters by the high H2O2 / HNO3 ratio. Recommendations and perspectives This study provides scientific basis for O3 mitigation strategies in Xi’an and surrounding areas. It is worth noting that, although the WRF-Chem model generally performs well in the simulations of the gas-phase species and aerosols compared to measurements, it still sometimes underestimates or overestimates the observations. Future studies need to improve the meteorological field simulations and update the emission inventory in Xi’an and surrounding areas for a better assessment of O3 formation. |
| Key words: O3 NOx WRF-CHEM Xi’an |
