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基于EOF分解和KZ滤波的2019~2021年中国臭氧时空变化及驱动因素分析
引用本文:王浩琪,张裕芬,罗忠伟,王艳阳,戴启立,毕晓辉,吴建会,冯银厂.基于EOF分解和KZ滤波的2019~2021年中国臭氧时空变化及驱动因素分析[J].环境科学,2023,44(4):1811-1820.
作者姓名:王浩琪  张裕芬  罗忠伟  王艳阳  戴启立  毕晓辉  吴建会  冯银厂
作者单位:南开大学环境科学与工程学院, 中国气象局-南开大学大气环境与健康研究联合实验室, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300350
基金项目:国家自然科学基金项目(42177465);天津市科技计划项目(18PTZWHZ00120)
摘    要:基于我国337个地级行政区2019~2021年的3~8月O3逐时浓度数据及同期气象数据,使用经验正交函数(EOF)分解,分析了我国O3浓度的主要空间分布模态变化趋势及其主要气象驱动因素.选取31个省会城市,利用KZ滤波将O3及气象因子的时间序列分解为短期分量、季节分量和长期分量,结合逐步回归模型,建立O3与气象要素之间的关系,进而重构“气象调整”后O3浓度的长期分量时间序列.结果表明,在我国O3浓度变率的总体空间特征基本稳定的背景下,O3浓度的第一模态整体呈趋同性变化,即O3浓度变率的高值区域波动性减弱,低值区域波动性增强.气象调整前后不同城市O3浓度的变化趋势存在一定差异,大部分城市O3浓度经调整后的长期分量较调整前更“平缓”.其中,气象因素对石家庄、济南和广州等城市O3浓度长期变化的影响较大,而前体物排放变化对福州、海口、长沙、太原、哈尔滨和乌...

关 键 词:臭氧(O3)  时空特征  气象调整  经验正交函数(EOF)  KZ滤波
收稿时间:2022/4/7 0:00:00
修稿时间:2022/7/8 0:00:00

Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter
WANG Hao-qi,ZHANG Yu-fen,LUO Zhong-wei,WANG Yan-yang,DAI Qi-li,BI Xiao-hui,WU Jian-hui,FENG Yin-chang.Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter[J].Chinese Journal of Environmental Science,2023,44(4):1811-1820.
Authors:WANG Hao-qi  ZHANG Yu-fen  LUO Zhong-wei  WANG Yan-yang  DAI Qi-li  BI Xiao-hui  WU Jian-hui  FENG Yin-chang
Institution:State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Abstract:Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.
Keywords:surface ozone (O3)  spatial-temporal characteristics  meteorological adjustment  empirical orthogonal function (EOF)  KZ filter
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