基于Kolmogorov-Zurbenko滤波法分析2015—2021年京津冀地区大气颗粒物变化趋势

doi:10.19741/j.issn.1673-4831.2022.0875

摘要/Abstract

摘要： 京津冀地区是我国近年针对大气颗粒物污染治理的重点区域。采用Kolmogorov-Zurbenko(KZ)滤波分析2015-2021年京津冀地区13个城市细颗粒物(PM_2.5)、可吸入颗粒物(PM₁₀)和粗颗粒物(PM_2.5-10)的时间序列。结果表明,PM_2.5和PM₁₀年均浓度总体呈下降趋势,其下降速率分别为5.07和8.10 μg·m^-3·a^-1。KZ滤波分析结果表明,PM_2.5、PM₁₀和PM_2.5-10的短期分量、季节分量和长期分量之间相互独立。短期分量贡献占比(大于50%)最高,其次为季节分量,说明原始时间序列的波动主要受控于污染源排放以及气象条件的短期变化和季节变化。针对KZ滤波分解出的浓度序列,建立逐步多元回归线性模型,定量评估气象和减排对变化趋势的贡献。逐城分析结果表明,秦皇岛市气象条件对PM_2.5浓度的影响(14.86%)最大,衡水市减排措施对PM_2.5浓度的影响(96.77%)最大;秦皇岛市气象条件对PM₁₀浓度的影响(13.69%)最大,廊坊市减排措施对PM₁₀浓度的影响(93.96%)最大;沧州市气象条件对PM_2.5-10浓度的影响(26.23%)最大,廊坊市减排措施对PM_2.5-10浓度的影响(91.80%)最大。整体分析结果表明,减排对2015-2021年京津冀大气颗粒物污染改善起着至关重要的作用。

关键词: 大气颗粒物, KZ滤波法, 逐步多元线性回归, 变化趋势, 京津冀地区

Abstract: The Beijing-Tianjin-Hebei Region has become a key focus area for addressing atmospheric particulate matter pollution in recent years in China. In this study, Kolmogorov-Zurbenko (KZ) filtering analysis was applied to analyze the time series of fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and coarse particulate matter (PM_2.5-10) in 13 cities in the Beijing-Tianjin-Hebei Region from 2015 to 2021. The results show that the annual average concentrations of PM_2.5 and PM₁₀ had generally decreased, with a decline rate of 5.07 and 8.10 μg·m^-3·a^-1, respectively. The KZ filtering analysis also revealed that the short-term, seasonal, and long-term components of PM_2.5, PM₁₀, and PM_2.5-10 are independent of each other. The highest contribution to the fluctuations in the original time series was found to be from the short-term component (over 50%), followed by the seasonal component, indicating that the fluctuations are mainly influenced by short-term and seasonal variations in pollutant emissions and meteorological conditions. Using the KZ-filtered concentration series, a stepwise multiple regression linear model was established to quantitatively evaluate the contributions of meteorological factors and emission reduction measures to the trends observed. The city-by-city analysis results show that the impact of meteorological conditions on PM_2.5 concentrations was highest in Qinhuangdao (14.86%), while emission reduction measures had the greatest impact on PM_2.5 concentrations in Hengshui (96.77%). The impact of meteorological conditions on PM₁₀ concentrations was highest in Qinhuangdao (13.69%), while emission reduction measures had the greatest impact on PM₁₀ concentrations in Langfang (93.96%). The impact of meteorological conditions on PM_2.5-10 concentrations was highest in Cangzhou (26.23%), while emission reduction measures had the greatest impact on PM_2.5-10 concentrations in Langfang (91.80%). Overall, the analysis results suggest that emission reduction measures plays a crucial role in improving particulate matter pollution in the Beijing-Tianjin-Hebei Region from 2015 to 2021.

Key words: atmospheric particulate matter, KZ filtering, stepwise multiple linear regression, trend change, Beijing-Tianjin-Hebei Region

中图分类号:

占青, 张运江, 陈红, 张可馨, 盖鑫磊. 基于Kolmogorov-Zurbenko滤波法分析2015—2021年京津冀地区大气颗粒物变化趋势[J]. 生态与农村环境学报, 2023, 39(9): 1133-1143.

ZHAN Qing, ZHANG Yun-jiang, CHEN Hong, ZHANG Ke-xin, GE Xin-lei. Trend Analysis of Atmospheric Particulate Matter over the Beijing-Tianjin-Hebei Region During 2015-2021 Using the KZ Filtering Approach[J]. Journal of Ecology and Rural Environment, 2023, 39(9): 1133-1143.

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