| 长三角城市群臭氧浓度的时空分异及驱动因素 |
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| 引用本文： | 黄小刚,邵天杰,赵景波,曹军骥,宋永永. 长三角城市群臭氧浓度的时空分异及驱动因素[J]. 长江流域资源与环境, 2019, 28(6): 1434-1445. DOI: 10.11870/cjlyzyyhj201906018 |
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| 作者姓名： | 黄小刚 邵天杰 赵景波 曹军骥 宋永永 |
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| 作者单位： | 陕西师范大学地理科学与旅游学院,陕西西安710119;山西师范大学地理科学学院,山西临汾041004;陕西师范大学地理科学与旅游学院,陕西西安,710119;陕西师范大学地理科学与旅游学院,陕西西安710119;中国科学院地球环境研究所气溶胶化学与物理重点实验室,陕西西安710061;中国科学院地球环境研究所气溶胶化学与物理重点实验室,陕西西安,710061 |
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| 基金项目： | 中央高校基本科研业务费专项;重点实验室项目;国家自然科学基金 |
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| 摘 要： | 运用克里金插值、空间自相关分析、冷热点分析和地理探测等定量分析方法，对长三角城市群2015~2017年O3浓度的时空分异特征及驱动因素进行了探讨。结果表明：（1）2015~2017年长三角城市群O3浓度呈上升趋势，O3日最大8 h滑动平均值第90百分位数平均浓度由149 μg/m3上升到166 μg/m3，平均超标率由9.3%上升到12.1%，以O3为首要污染物的天数占超标总天数的比例由32.3%上升到46.4%。（2）受气温和降水量年际波动的影响，各年份O3月均浓度变化曲线形状不同。但O3超标都主要发生在4~9月，超标天数分别占2015、2016、2017年的88.3%、98.2%和97.0%。（3）由于安徽O3浓度快速上升，长三角城市群O3浓度空间分布格局由东高西低演变为北高南低，且同质化增强、异质性减弱。（4）随着O3浓度的上升，O3浓度热点区由环太湖地区向南京都市圈扩展，冷点区在安徽有明显收缩。（5）地理探测表明，长三角城市群O3浓度空间分异主要受经济规模、城市化和排放源等社会经济因素驱动，且均呈正向影响。自然因素中的降水量和风速呈负向影响，分别对O3有显著的清除和扩散作用。
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| 关 键 词： | 臭氧浓度 时空分异 驱动因素 地理探测器 长三角城市群 |
Spatio-temporal Differentiation of Ozone Concentration and Its Driving Factors in Yangtze River Delta Urban Agglomeration |
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| HUANG Xiao-gang,SHAO Tian-jie,ZHAO Jing-bo,CAO Jun-ji,SONG Yong-yong. Spatio-temporal Differentiation of Ozone Concentration and Its Driving Factors in Yangtze River Delta Urban Agglomeration[J]. Resources and Environment in the Yangtza Basin, 2019, 28(6): 1434-1445. DOI: 10.11870/cjlyzyyhj201906018 |
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| Authors: | HUANG Xiao-gang SHAO Tian-jie ZHAO Jing-bo CAO Jun-ji SONG Yong-yong |
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| Affiliation: | (1. School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China; 2. College of Geographical Sciences, Shanxi Normal University, Linfen 041004, China; 3. Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’ an 710061, China); |
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| Abstract: | This study presents the spatio-temporal differentiation of ozone concentration and its driving factors in the Yangtze River Delta urban agglomeration from 2015 to 2017 via Kriging interpolation, spatial auto-correlation, hot and cool spot analysis, and geographical detection. The results show that: 1) the concentration of ozone rises during this period with 90th percentile of maximum daily 8-h average ozone concentration rising from 149 μg/m3 to 166 μg/m3, the percentage of days which exceed standard (GB 3095-2012) going up from 9.3% to 12.1%, and the proportion of days with ozone as primary pollutant to total polluted days increasing from 32.3% to 46.4%; 2) impacted by the large inter-annual fluctuations of temperature and precipitation, the curves of monthly ozone concentration differ slightly in different years; the days with over-limit ozone concentration mainly occurred from April to September, which account for 88.3%, 98.2% and 97.0% of the year 2015, 2016 and 2017, respectively; 3) due to the rapid increase of ozone concentration in Anhui province, the spatial distribution pattern of ozone concentration evolves from high in East and low in West to high in North and low in South, but the concentration is more homogenized than before; 4) with the rise of ozone concentration, the hot spots expand from Taihu Lake area to Nanjing metropolitan area, while the cool spots shrink evidently in Anhui Province; 5) the analysis of geographical detector shows that socioeconomic factors such as economic scale, urbanization and emission source have a significantly positive impact on the spatial distribution of ozone concentration, while precipitation and wind have a negative impact on the spatial distribution of ozone concentration since the precipitation scavenges ozone and wind promotes the dispersion of ozone. |
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