| 引用本文: | 沈劲,叶钰洁,黎柏良,林玉君,蔡日东,刘军,廖彤,陈多宏,卢清,赵志远.三维空气质量模型初始场VOCs同化对臭氧预报的影响[J].环境监控与预警,2023,15(5):24-29 |
| SHEN Jin,YE Yujie,LI Boliang,LIN Yujun,CAI Ridong,LIU Jun,LIAO Tong,CHEN Duohong,LU Qing,ZHAO Zhiyuan.The Influence of VOCs Assimilation in Initial Conditions in a 3D Air Quality Model on Ozone Prediction[J].Environmental Monitoring and Forewarning,2023,15(5):24-29 |
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| 摘要: |
| 于2023年2月15日—3月8日，采用中尺度数值预报模式/嵌套网格空气质量模式系统（WRF/NAQPMS），分析了初始场同化6项常规大气污染物及挥发性有机物（VOCs）对广东省臭氧（O3）预报的改进效果。 结果表明，同化6项常规污染物可显著降低O3预报的标准化平均偏差（NMB）和均方根误差（RMSE），NMB从-26%改善为-8%，RMSE从50.6μg/m3下降到35.0μg/m3。但对相关系数（r）的改善效果不佳，从0.51下降到0.49。相比于只同化常规6项污染物，同时同化VOCs对O3的预报效果改善较为明显，r从0.49提高到0.63。此外，对NMB和RMSE的改善效果也较好，NMB从-8%改善为-3%，RMSE从35.0μg/m3下降到30.1μg/m3。相比于不同化，同化6项常规污染物的改善效果显著，空气质量指数（AQI）等级预报准确率可提升10%以上，AQI范围预报准确率可提升40%以上。相比于仅同化6项常规污染物，再增加同化VOCs，AQI等级预报准确率和范围预报准确率均提升5%左右，改善程度不高。 |
| 关键词: 空气质量模型 初始场 挥发性有机物同化 臭氧预报 广东省 |
| DOI：10.3969/j.issn.1674-6732.2023.05.004 |
| 分类号:X51 |
| 基金项目:广东省重点领域研发计划（2020B1111360003）；2022年度国家环境保护区域空气质量监测重点实验室开放基金项目 |
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| The Influence of VOCs Assimilation in Initial Conditions in a 3D Air Quality Model on Ozone Prediction |
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SHEN Jin1， YE Yujie2， LI Boliang3， LIN Yujun1， CAI Ridong1， LIU Jun1， LIAO Tong1， CHEN Duohong1*， LU Qing4， ZHAO Zhiyuan5
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1.State Environmental Key Laboratory of Regional Air Quality Monitoring， Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research， Guangdong Ecological Environmental Monitoring Center， Guangzhou， Guangdong 510308， China；2.Guangzhou Hexin Instrument Co.， Ltd.， Guangzhou，Guangdong 510535， China； 3.Anhui Landun Photoelectron Co.， Ltd.， Tongling， Anhui 244000， China； 4.Guangdong Provincial Key Laboratory of Water and Air Pollution Control，South China Institute of Environmental Science， Ministry of Ecology and Environment， Guangzhou， Guangdong 510655， China；5.3 Clear Technology Co.，Ltd.， Beijing 100029， China
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| Abstract: |
| The improvement effect of assimilating six conventional atmospheric pollutants and VOCs in the initial conditions on ozone prediction in Guangdong was analyzed using WRF/NAQPMS. The evaluation period was from February 15，2023 to March 8，2023. Assimilation of six conventional pollutants could significantly reduce the normalized mean bias (NMB) and root mean square error (RMSE) of ozone prediction， improving NMB from -26% to -8%， and reducing RMSE from 50.6 μg/m3 to 35.0 μg/m3. However， the improvement on the correlation coefficient was poor， decreasing from 0.51 to 0.49. Compared with only assimilating six conventional pollutants， assimilating VOCs simultaneously improved the prediction of ozone significantly， with a correlation coefficient increased from 0.49 to 0.63. In addition， the improvement on NMB and RMSE was also good. NMB was improved from -8% to -3%， and RMSE was reduced from 35.0 μg/m3 to 30.1 μg/m3 . Compared with non assimilation， the improvement of the assimilation of six conventional parameters was significant. The accuracy of AQI level was increased by more than 10%， and the accuracy of AQI range was increased by more than 40%. Compared with only assimilating six conventional parameters， the improvement of adding assimilating VOCs was not significant， with an AQI level and range accuracy increase of about 5%. |
| Key words: Air Quality Model Initial Conditions VOCs Assimilation Ozone Prediction Guangdong Province |
