多尺度视角下广东臭氧时空分异特征及驱动因素
Spatiotemporal variation characteristics and driving factors of near-surface ozone in Guangdong province from a multi-scale perspective
余锐 1步巧利 2陈辰 2麦博儒 3孙丽颖 4江铭诺 5邓若钊 6查进林 7符传博8
作者信息
- 1. 佛山市南海区气象局,广东 佛山 528200
- 2. 佛山市气象局,广东 佛山 528300
- 3. 中国气象局广州热带海洋气象研究所,广东 广州 510640
- 4. 广州市增城区气象局,广东 广州 511300
- 5. 广东省气象数据中心,广东 广州 510640
- 6. 广东省遂溪县气象局,广东 湛江 524000
- 7. 云南大学地球科学学院,云南 昆明 650500
- 8. 海南省气象科学研究所,海南 海口 570203
- 折叠
摘要
鉴于近地面臭氧(O3)浓度具有多尺度和复杂非线性的时空变化特征,本研究基于2015～2022年广东地区O3监测数据和同期气象资料,利用围绕中心点分割(PAM)聚类、Kolmogorov-Zurbenko(KZ)滤波和广义加性模型(GAM)等手段,探讨了不同区域多时间尺度O3浓度的变化特征及驱动因素.结果表明:广东地区近地面O3存在跨行政区划的区域性特征,通过对O3数据聚类分析可以划分出8个子区域;O3浓度时间序列经尺度分离后,短期分量对总方差贡献最大,其次为季节分量;气象因子与O3浓度变化的关系受到时间尺度和区域差异的影响;经气象调整后的O3峰值浓度序列大多在 2016 年或者2017 年后呈现下降的态势,珠三角地区是 O3 污染的突出区域,由不利气象条件和人为排放共同引起;O3 浓度时间序列的长期分量大多呈现波动上升的趋势,人为排放虽然是 O3 浓度的主要贡献,但气象条件才是驱动 O3 浓度变化的主导因素.本研究结果进一步提示 O3 污染防控工作需要因时因地制宜,建立基于气象状况变化的前体物减排预案对O3污染防控具有重要意义.
Abstract
The methods of Partitioning Around Medoids(PAM)clustering,Kolmogorov-Zurbenko(KZ)filtering and Generalized Additive Models(GAM)were used to explore the variability and driving factors of O3 concentration changes at various time scales and regions from 2015 to 2022 in Guangdong Province.The results were as follows:(1)Significant regional features of near-surface O3 across administrative divisions in Guangdong province were found,and 8subregions could be identified through O3 data clustering analysis.(2)The short-term component contributed the most to the total variance,followed by the seasonal component after the separation of the O3 concentration time series.(3)The correlation between meteorological variables and O3 concentration variation was influenced by both temporal scales and regional characteristics.(4)The peak concentration of O3 after meteorological adjustment mostly presented a declining trend after 2016 or 2017.The Pearl River Delta region was identified as a prominent area for O3 pollution,jointly caused by adverse meteorological conditions and anthropogenic emissions.(5)Whilst anthropogenic emissions were the main contributors to O3 concentrations,the long-term component of the O3 concentration time series generally exhibited a fluctuating upward trend,and meteorological conditions were the main drivers of O3.These findings suggest that appropriate O3 pollution prevention and control work needs to be tailored to specific times and locations,and the development of precursor reduction plans based on meteorological conditions is important in O3 pollution management.
关键词
臭氧/多尺度/气象调整/聚类分析/时空分异特征Key words
ozone/multi-scale/meteorological adjustment/cluster analysis/spatiotemporal characteristics引用本文复制引用
基金项目
国家自然科学基金资助项目(42375174)
国家自然科学基金资助项目(42005023)
国家自然科学基金资助项目(42065010)
广东省自然科学基金资助项目(2021A1515011494)
广东省自然科学基金资助项目(2022A1515010718)
云南省基础研究计划项目(202301AT070199)
广东省气象局科技创新团队项目(GRMCTD202003)
广东省气象局科研项目(GRMC2022M23)
广东省气象数据中心科研项目(2023A13)
佛山市气象局科技创新团队项目(202302)
佛山市气象局科研项目(202102)
佛山市气象局科研项目(202311)
出版年
2024
NETL
NSTL
万方数据