Q2 Environmental Science
Ting-Yuan Li, Jing Tang, Jin Shen, Jing-Yang Chen, Yu Gong
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引用次数: 0

摘要

基于广东省2015-2020年的观测和再分析资料,分析了广东省及4个典型城市臭氧(O3)浓度的变化特征,并基于广义相加模式(GAM)揭示了不同城市、不同季节气象因子对臭氧(O3)浓度的影响。浓度的变化特征,并基于广义相加模式(GAM)分析了不同城市、不同季节气象因子对臭氧浓度的影响。从2015年到2019年,O3日最大8小时平均浓度(O3_8h)从 2015 年到 2019 年明显上升,趋势为 5.0 μg-m-3-a-1,2020 年广东略有下降。广东省秋季O3_8h浓度和污染总天数均明显高于其他季节,且在四个典型城市呈现不同的变化特征。广州、河源、揭阳和茂名的最高值分别出现在夏秋季、春夏季、春秋季和秋季。回归模型对 O3_8h 浓度变化的拟合效果较好,季节模型的拟合效果普遍好于年度模型。季节模型的平均 R2 值分别为 0.78、0.69、0.70 和 0.65,平均方差解释率(IRV)分别为 79%、71%、73% 和 67%。不同城市、不同季节最优模型的方程拟合度差异较大,R2 值在 0.52 至 0.83 之间,IRV 值在 55.5%至 86.9%之间。O3_8h 浓度与气象因子呈非线性关系。对不同城市、不同季节 O3_8h 浓度变化有显著影响的气象因子差异较大,所有气象因子的重要程度都排在前三位。相对湿度是影响不同城市 O3_8h 浓度变化的最重要气象因子,其次是风的 V 分量。当相对湿度低于 45% 时,O3_8h 浓度相对较高。当相对湿度高于 45% 时,O3_8h 浓度随着相对湿度的增加而降低。当风速大于 2 m-s-1 时,O3_8h 浓度较高,这表明污染物具有区域迁移性,强调了区域联防联控的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Impact of Meteorological Factors on Ozone Concentration in Four Typical Cities of Guangdong Based on Generalized Additive Model].

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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来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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