| 2015年中国近地面臭氧浓度特征分析 |
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| 引用本文： | 段晓瞳,曹念文,王潇,张玉欣,梁静舒,杨思鹏,宋秀瑜. 2015年中国近地面臭氧浓度特征分析[J]. 环境科学, 2017, 38(12): 4976-4982 |
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| 作者姓名： | 段晓瞳 曹念文 王潇 张玉欣 梁静舒 杨思鹏 宋秀瑜 |
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| 作者单位： | 南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044,南京信息工程大学, 气象灾害教育部重点实验室, 气候与环境变化国际合作联合实验室, 气象灾害预报预警与评估协同创新中心, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 |
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| 基金项目： | 国家自然科学基金项目（41375044/D0503，41175033/D0503）；公益性行业科研专项（GYHY201006047-5） |
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| 摘 要： | 根据2015年全国189个城市的近地面臭氧浓度数据，使用ArcGIS等软件处理，从不同时空、地形特征、温度等方面分析得出中国近地面臭氧浓度的变化特征.2015年中国近地面的臭氧浓度变化呈先增高后降低的趋势，各季节中浓度大小关系呈夏季 > 秋季 > 春季 > 冬季的变化规律，且在7月达到全年最高值.中国各行政区中，华东、华南、华北地区的臭氧污染较为严重.在经纬度变化的影响方面，经度变化对近地面臭氧浓度的影响不大，而纬度变化使臭氧浓度变化明显；在同一纬度的3种不同地形对比中发现，不同的地形给近地面臭氧浓度带来的影响微乎其微.温度和近地面臭氧浓度的变化呈现良好的正相关关系.
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| 关 键 词： | 臭氧浓度 变化特征 相关性 时空变化 地形 温度 |
| 收稿时间： | 2017-03-06 |
| 修稿时间： | 2017-06-20 |
Characteristics Analysis of the Surface Ozone Concentration of China in 2015 |
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| DUAN Xiao-tong,CAO Nian-wen,WANG Xiao,ZHANG Yu-xin,LIANG Jing-shu,YANG Si-peng and SONG Xiu-yu. Characteristics Analysis of the Surface Ozone Concentration of China in 2015[J]. Chinese Journal of Environmental Science, 2017, 38(12): 4976-4982 |
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| Authors: | DUAN Xiao-tong CAO Nian-wen WANG Xiao ZHANG Yu-xin LIANG Jing-shu YANG Si-peng SONG Xiu-yu |
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| Affiliation: | Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China and Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China |
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| Abstract: | Surface ozone concentration data from 189 cities in China in 2015 were processed by ArcGIS software in order to obtain the characteristics of the surface ozone concentration, such as time and space, topographical features, temperature, etc. The trend for surface ozone concentration was a decrease followed by an increase in China in 2015. The concentrations during the four seasons followed the order:summer > autumn > spring > winter, and the maximum appeared in July. The ozone pollution of East China, South China, and North China were more serious than other regions in China. The variation of longitude had a small influence on the ozone concentration, while the influence of latitude is significant. According to the analysis contrasting three different topographies in the same latitude, the influence of topography on ozone concentration was negligible. Furthermore, the research found a significant positive correlation between surface ozone concentration and temperature. |
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| Keywords: | ozone concentration variation in characteristics correlation temporal and spatial variation topography temperature |
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