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基于时空指示克里格的PM2.5不确定性分布
引用本文:梅杨,张文婷,杨勇,赵玉,李露露. 基于时空指示克里格的PM2.5不确定性分布[J]. 中国环境科学, 2018, 38(1): 35-43
作者姓名:梅杨  张文婷  杨勇  赵玉  李露露
作者单位:1. 宁波市镇海规划勘测设计研究院, 浙江 宁波 315200;2. 华中农业大学资源与环境学院, 湖北 武汉 430070;3. 农业部长江中下游耕地保育重点实验室, 湖北 武汉 430070
基金项目:国家自然科学基金资助项目(41671217);中央高校基本科研业务费专项资金资助项目(2662017PY038)
摘    要:以山东省2014年PM2.5浓度监测数据为对象,利用时空指示克里格理论和方法,实现对PM2.5时空分布的不确定性分析.结果表明,山东省境内PM2.5的空间自相关范围大于100km,时间自相关范围为3d左右.此外,山东省境内各空间位置全年空气质量以大于0.8的概率达到空气质量"优"级别的时空占比为7%,以大于0.8的概率达到轻度污染级别的时空占比为34%,以大于0.8的概率超过严重污染级别的时空占比为1%;东部沿海地域空气质量达到轻度污染的概率明显高于中西部,夏季空气质量也明显优于其它季节.

关 键 词:时空指示克里格  不确定性分析  PM2.5  
收稿时间:2017-06-05

Uncertainty assessment of PM2.5 probability mapping by using spatio-temporal indicator kriging
MEI Yang,ZHANG Wen-ting,YANG Yong,ZHAO Yu,LI Lu-lu. Uncertainty assessment of PM2.5 probability mapping by using spatio-temporal indicator kriging[J]. China Environmental Science, 2018, 38(1): 35-43
Authors:MEI Yang  ZHANG Wen-ting  YANG Yong  ZHAO Yu  LI Lu-lu
Affiliation:1. Zhenhai Urban Planning and Survey Research Insitute of Ningbo, Ningbo 315200, China;2. College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China;3. Key Laboratory of Arable Land Conservation, Middle and Lower Reaches of Yangtse River, Ministry of Agriculture, Wuhan 430070, China
Abstract:As a new indicator of the air pollution, PM2.5 is attracting more and more attentions from the society and academia. In China, with the rapid industrialization and urbanization, part of region is experiencing severe air pollution problems. Thus, understanding the spatio-temporal (ST) variation and trends of air pollution is a key element of an improved understanding of the underlying physical mechanisms and the implementation of the most risk assessment and environmental policy in the region. However, most of existing studies merely focused on the change of concentration, and the probability of PM2.5 exceeding ones certain concentration is rarely studied. Within this context, a method of ST Indicator Kriging (STIK) based on a non-separable ST semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations monitored during 2014 in Shandong Province, China were used as the experimental dataset. Spatial auto-correlation extent of PM2.5 was more than 100km, and the temporal auto-correlation range was about 3days. We also found that 7% of the place in Shandong province in 2014maintains probability of attaining excellent air quality larger than 0.8, 34% of the place in Shandong province in 2014 maintains the probability of attaining slight polluted air quality larger than 0.8, and only 1% of the place in Shandong province in 2014 maintains the probability of attainting severe polluted air quality larger than 0.8. Spatially, the probability of attaining slight polluted air quality in the eastern coastal was significantly higher than that of in Midwest; temporally, the air quality in summer was obviously better than those of in other seasons.
Keywords:spatio-temporal indicator Kriging  uncertainty analysis  PM2.5  
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