| 基于广义相加模型的广东省典型城市臭氧浓度气象影响因素分析 |
| 摘要点击 1134 全文点击 154 投稿时间：2024-02-25 修订日期：2024-04-30 |
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| 中文关键词 广东省 典型城市 臭氧（O3） 气象影响 广义相加模型（GAM） |
| 英文关键词 Guangdong typical city ozone （O3） meteorological impact generalized additive model （GAM） |
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| 中文摘要 |
| 基于2015~2020年广东省地面观测资料和再分析资料,分析广东省和4个典型城市臭氧（O3）浓度变化特征,并利用广义相加模型（GAM）探讨了不同季节气象要素对不同城市臭氧日最大8h滑动平均（O3_8h）浓度的影响. 结果表明：①2015~2019年广东省O3_8h浓度明显上升,2020年略有下降. 广东省秋季O3_8h浓度和污染总天数明显高于其他季节,4个典型城市呈现不同的季节变化特征：广州夏秋季最高,河源春夏季最高,揭阳春秋季最高,茂名秋季最高. ②回归模型对O3_8h浓度变化具有良好的拟合能力,季节模型整体上优于全年模型. 不同城市不同季节最优模型拟合结果存在较大差异,最优模型调整判定系数R2介于0.52~0.83之间,方差解释率（IRV）介于55.5%~86.9%之间. ③不同城市不同季节对O3_8h浓度变化具有显著影响的气象因子存在明显差异,所有气象因子均进入重要性排名前3. 相对湿度是影响各城市O3_8h浓度变化最重要的气象因子,其次为经向风速. 当相对湿度>45%时,O3_8h浓度随相对湿度增大而降低；当风速>2 m·s-1时O3浓度较高,说明存在污染输送现象,提示区域联防联控的重要性. |
| 英文摘要 |
| Based on the observation and reanalysis data in Guangdong from 2015 to 2020, the variation characteristics of ozone （O3） concentration in Guangdong and four typical cities were analyzed, and the effects of meteorological factors on O3 concentration in different cities and different seasons were revealed based on the generalized additive model （GAM）. The daily maximum 8-hour average O3 concentration （O3_8h） increased significantly from 2015 to 2019, with a trend of 5.0 μg·m-3·a-1, and decreased slightly in 2020 in Guangdong. The O3_8h concentration and the total number of polluted days were substantially higher in autumn than those in other seasons in Guangdong and showed different variation characteristics in four typical cities. The highest values in Guangzhou, Heyuan, Jieyang, and Maoming occurred in summer and autumn, spring and summer, spring and autumn, and autumn, respectively. The regression model had a good fit for the variation in O3_8h concentration, and the seasonal models were generally better than the annual models. As for the seasonal models, the average R2 values were 0.78, 0.69, 0.70, and 0.65, and the mean interpretation rate of variance （IRV） values were 79%, 71%, 73%, and 67% in Guangzhou, Heyuan, Jieyang, and Maoming, respectively. The equation fitting degrees of the optimal models varied considerably in different cities and different seasons, with the R2 values ranging from 0.52 to 0.83 and the IRV values ranging from 55.5% to 86.9%. The O3_8h concentration showed a nonlinear relationship with meteorological factors. The meteorological factors that had a significant impact on the variation of O3_8h concentration in different cities and seasons differed considerably, and all of the meteorological factors were in the top three lists of importance. Relative humidity was the most important meteorological factor affecting the variation in O3_8h concentration in different cities, followed by the V-component of wind. When the relative humidity was below 45%, the O3_8h concentration was relatively higher. When the relative humidity was above 45%, the O3_8h concentration decreased with the increase in relative humidity. Higher O3 concentrations appeared when the wind speed was greater than 2 m·s-1, indicating the regional transport of pollutants and emphasizing the importance of regional joint prevention and control. |
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