Journal of Arid Meteorology ›› 2022, Vol. 40 ›› Issue (2): 266-274.DOI: 10.11755/j.issn.1006-7639(2022)-02-0266

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Quantitative assessment of influence of meteorological conditions on ozone pollution in Guangzhou during 2014-2019

BU Qiaoli(), YU Lefu, CHEN Chen   

  1. Foshan Meteorological Bureau of Guangdong Province, Foshan 528000, Guangdong, China
  • Received:2021-04-19 Revised:2022-03-18 Online:2022-04-30 Published:2022-05-10

广州2014—2019年气象条件对O3污染影响的定量评估

步巧利(), 余乐福, 陈辰   

  1. 广东省佛山市气象局,广东 佛山 528000
  • 作者简介:步巧利(1987— ),女,硕士,工程师,研究方向为环境气象. E-mail: buqiaoli@126.com
  • 基金资助:
    “龙卷等致灾雷暴大风精细化探测机理研究”院士专家工作站(2021002);“2DVD雨滴谱特征分析及对双偏振雷达定量降水估测的改进”(GRMC2020M28)

Abstract:

The ozone mass concentration is affected by meteorological elements and emissions from air pollution sources. In order to quantitatively evaluate the effect of air pollution control measures, it is necessary to separate the contributions from air pollution sources. The Kolmogorov-Zurbenko filter was used to decompose the time series of daily ozone mass concentration during 2014-2019 as well as time series of meteorological factors in the same period in Guangzhou into long-term, short-term and seasonal components, and the variance contribution of each component to the total variance of the original ozone mass concentration data was calculated. Then multiple linear stepwise regression method was used to establish the relationship between ozone mass concentration data and meteorological variables for each time scale, the contributions of meteorological factors and pollutant emissions to ozone mass concentration were separated to obtain the contribution of ozone mass concentration influenced by meteorological conditions only. The results are as follows: (1) The long-term series of ozone mass concentration generally fluctuated and increased, the seasonal component showed a high value in late spring and early summer, a secondary peak in late summer and early autumn, and a trough in winter. (2) By analyzing the variance contribution rates of each component to the total variance of ozone mass concentration, the short-term component contributed the most, followed by the seasonal component, and the long-term component contributed the least, which indicated that the fluctuation of ozone mass concentration in Guangzhou was mainly caused by the short-term and seasonal changes of meteorological conditions and precursor emissions, and the long-term changes of emissions and climate conditions were not the main reasons for the fluctuation of ozone mass concentration. (3) In terms of explanatory variance, meteorological variables had the highest explanatory ability to the long-term component of ozone mass concentration, followed by the seasonal component. (4) The long-term component of ozone mass concentration which eliminated the influence of meteorological conditions by stepwise regression showed a fluctuating decreasing trend. Combined with the aggravation of ozone pollution from 2014 to 2019 in Guangzhou, the meteorological conditions were unfavorable for ozone diffusion in recent years.

Key words: KZ filter, Guangzhou, linear regression, ozone

摘要:

O3质量浓度受气象因子和污染源排放共同影响,为了定量化评估污染控制措施减排对广州市O3质量浓度影响的效果,需将由污染源排放的O3质量浓度数据分离出来。利用Kolmogorov-Zurbenko(KZ)滤波将广州市2014—2019年逐日O3质量浓度数据和同期气象数据分解为长期分量、短期分量和季节分量,计算各分量方差对原始时间序列方差的贡献率;结合多元线性逐步回归方法建立O3质量浓度各分量与相应尺度气象要素的线型回归模型,将气象因子和污染源排放对O3质量浓度的贡献分离开来,得到仅由气象条件影响的O3质量浓度贡献。分析结果表明:(1)广州O3质量浓度长期序列总体呈波动上升趋势,季节分量和短期分量波动基本一致,季节分量表现为春末夏初O3质量浓度出现高值,夏末秋初出现次高峰,冬季谷值;(2)分析各分量方差对O3质量浓度原始序列总方差的贡献率发现,短期分量方差贡献率最大,其次为季节分量,长期分量方差贡献最小,表明广州O3质量浓度的波动主要由前体物排放和气象条件的季节和短期变化引起,长期排放及气候条件的变化并不是引起监测波动的主要原因;(3)从解释方差来看,气象变量对O3质量浓度长期分量的解释能力最高,季节分量其次;(4)经逐步回归消除气象条件影响的O3质量浓度长期分量有波动下降趋势,结合2014—2019年O3污染有所加重,表明这几年广州气象条件对O3扩散不利。

关键词: KZ滤波, 广州, 逐步回归, O3

CLC Number: