秦毅,刘旻霞,宋佳颖,于瑞新,李亮,苏贵金.粤港澳大湾区近地层O3浓度的时空变化特征[J].环境科学学报,2021,41(8):2987-3000
粤港澳大湾区近地层O3浓度的时空变化特征
- Temporal and spatial variation characteristics of O3 concentration in the surface layer over Guangdong-Hong Kong-Macao Greater Bay Area, China
- 基金项目：国家自然科学基金项目（No.31760135）
- 秦毅
- 西北师范大学地理与环境科学学院, 兰州 730070
- 刘旻霞
- 西北师范大学地理与环境科学学院, 兰州 730070
- 宋佳颖
- 西北师范大学地理与环境科学学院, 兰州 730070
- 于瑞新
- 西北师范大学地理与环境科学学院, 兰州 730070
- 李亮
- 西北师范大学地理与环境科学学院, 兰州 730070
- 苏贵金
- 中国科学院生态环境研究中心 环境纳米技术与健康效应重点实验室, 北京 100085
- 摘要：基于遥感卫星（OMI）反演数据，对2005—2019年粤港澳大湾区近地层的O3浓度数据进行提取及分析，探讨其时空变化特征和影响因素，同时利用后向轨迹（HYSPLIT）模型对O3来源进行解析.结果表明：①在空间分布上，臭氧浓度自北向南逐渐降低，高值区集中分布在肇庆、广州、佛山等地；低值区集中在东莞、深圳、香港等地.②在时间变化上，15年来，该区域O3浓度整体呈先上升后下降的趋势，2005—2010年O3浓度持续升高，2010—2019年O3浓度呈下降趋势，在2018年有小幅增长.季节变化表现为：春夏季O3浓度高于秋冬季，高值区在春夏季交替出现，且秋季略高于冬季；每年11月—次年2月出现低值区，4—7月出现高值区.③自然因素中，风向和风速对O3扩散和传输起重要作用；后向轨迹聚类分析表明：O3长距离的输送受到来自西伯利亚的寒冷气流影响，短距离的输送则受到来自太平洋的暖湿气流的影响.气温与O3浓度呈正相关；降水与O3浓度基本呈负相关.④人为因素中，O3浓度与GDP、人口密度的空间分布表现出显著相关性；NOx的影响中，电力源、交通源和工业源是主导因素，居民源的影响较弱；而VOCs的影响中，工业源是主控因素，交通源和居民源次之，电力源的影响最弱.⑤O3浓度与HCHO浓度的空间分布保持高度的一致性；NOx等污染物参与光化学反应，对O3浓度的变化起着一定作用；气溶胶对太阳辐射产生消光作用，使得O3浓度降低.
- Abstract：Based on Remote Sensing Satellite (OMI) inversion data, the O3 concentration data in the near layer of Guangdong, Hong Kong and Macao Bay area from 2005 to 2019 were extracted and analyzed, the temporal and spatial variation characteristics and influencing factors were discussed. At the same time, the source of O3 was analyzed by the backward trajectory (HYSPLIT) model. The results showed that: ① In spatial distribution, O3 concentration gradually decreased from north to south, the high value areas concentrated in Zhaoqing, Guangzhou, Foshan and other places; and the low value areas concentrated in Dongguan, Shenzhen, Hong Kong and other places.② In temporal variation, O3 concentration increased firstly and then decreased in the past 15 years. The concentration increased from 2005 to 2010, and decreased from 2010 to 2019, with a slight increase in 2018. O3 concentration was higher in spring and summer than in autumn and winter, high-value areas alternated in spring and summer, and autumn was higher than winter. Low-value areas appeared from November to February of each year while high-value areas appeared from April to July. ③ In natural factors, wind direction and wind speed played an important role in the diffusion and transport of O3; backward trajectory cluster analysis showed that long-distance transportation of O3 was affected by the cold airflow from Siberia, while short-distance transportation was affected by the warm and wet airflow from the Pacific. There was a positive correlation between temperature and O3 concentration, and a negative correlation between precipitation and O3 concentration.④ In human factors, the spatial distribution of O3 concentration, GDP and population density showed significant correlation; in the effects of NOx emissions, power, traffic and industrial sources were the dominant factors, while the impact of residential source was weak; in the influence of VOCs emissions, industrial source was the dominant factor, followed by traffic and residential sources, and the influence of power source was the weakest. ⑤ The spatial distribution of O3 concentration was highly consistented with HCHO concentration; NOx and other pollutants participated in photochemical reaction which played a certain role in the change of O3 concentration; aerosol had an extinction effect on solar radiation which reduced O3 concentration.
