基于时空指示克里格的PM<sub>2.5</sub>不确定性分布

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Abstract As a new indicator of the air pollution, PM_2.5 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 PM_2.5 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 PM_2.5 distributions in a contaminated atmosphere. PM_2.5 concentrations monitored during 2014 in Shandong Province, China were used as the experimental dataset. Spatial auto-correlation extent of PM_2.5 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.

Key words： spatio-temporal indicator Kriging uncertainty analysis PM_2.5

Received: 05 June 2017

PACS:

X513

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	MEI Yang
	ZHANG Wen-ting
	YANG Yong
	ZHAO Yu
	LI Lu-lu

Cite this article:

MEI Yang,ZHANG Wen-ting,YANG Yong等. Uncertainty assessment of PM_2.5 probability mapping by using spatio-temporal indicator kriging[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(1): 35-43.

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http://manu36.magtech.com.cn/Jweb_zghjkx/EN/ OR http://manu36.magtech.com.cn/Jweb_zghjkx/EN/Y2018/V38/I1/35

[1]	王庚辰,王普才.中国PM2.5污染现状及其对人体健康的危害[J]. 科技导报, 2014,32(26):72-78.
[2]	戴海夏,宋伟民.大气PM2.5的健康影响[J]. 环境卫生学杂志, 2001,28(5):299-303.
[3]	张文丽,崔九思,徐东群,等.大气中细颗粒物的污染特征及其生物效应[J]. 中国环境卫生, 2003,(1):90-102.
[4]	覃知还.关于PM2.5的十个问答[J]. 证券导刊, 2011,(46):93-96.
[5]	Weichenthal S, Villeneuve P J, Burnett R T, et al. Long-term exposure to fine particulate matter:association with nonaccidental and cardiovascular mortality in the agricultural health study cohort[J]. Environmental health perspectives, 2014,122(6):609-15.
[6]	李梓铭,孙兆彬,邵勰,等.北京城区PM2.5不同时间尺度周期性研究[J]. 中国环境科学, 2017,37(2):407-415.
[7]	Gehrig R, Buchmann B. Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data[J]. Atmospheric Environment, 2003,37(19):2571-2580.
[8]	庄欣,黄晓锋,陈多宏,等.基于日变化特征的珠江三角洲大气污染空间分布研究[J]. 中国环境科学, 2017,37:2001-2006.
[9]	周甜,闫才青,李小滢,等.华北平原城乡夏季PM2.5组成特征及来源研究[J]. 中国环境科学, 2017,37(9):3227-3236.
[10]	梅杨,党丽娜,杨勇,等.基于时空克里格的PM2.5时空预测及分析[J]. 环境科学与技术, 2016,39(7):157-163.
[11]	Christakos G, Yang Y, Wu J P, et al. Improved space-time mapping of PM2.5 distribution using a domain[J]. Ecological Indicators, 2018,85:1273-1279.
[12]	Begum B A, Biswas S K, Hopke P K. Temporal variations and spatial distribution of ambient PM2.5 and PM10 concentrations in Dhaka, Bangladesh[J]. Scie of the Total Envi[J], 2006,358(1-3):36-45.
[13]	Yu H L, Chiting C, Lin S D, et al. Spatiotemporal analysis and mapping of oral cancer risk in Changhua County (Taiwan):an application of generalized Bayesian maximum entropy method.[J]. Annals of Epidemiology, 2010,20(2):99.
[14]	Hu J, Wang Y, Ying Q, et al. Spatial and temporal variability of PM2.5, and PM10, over the North China Plain and the Yangtze River Delta, China[J]. Atmospheric Environment, 2014,95(1):598-609.
[15]	Kloog I, Chudnovsky A A, Just A C, et al. A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data[J]. Atmospheric Environment, 2014, 95(1):581-590.
[16]	中国环境科学研究院.环境空气质量标准[M]. 北京:中国环境科学出版社, 2012.
[17]	环境保护部环境监测司,中国."十二五"环境监测工作手册[M]. 中国环境科学出版社, 2012.
[18]	Yang Y, Christakos G. Uncertainty assessment of heavy metal soil contamination mapping using spatiotemporal sequential indicator simulation with multi-temporal sampling points[J]. Environmental Monitoring & Assessment, 2015,187(9):571.
[19]	杨勇,梅杨,张楚天,等.基于时空克里格的土壤重金属时空建模与预测[J]. 农业工程学报, 2014,30(21):249-255.
[20]	Cressie N, Huang H C. Classes of Nonseparable, SpatioTemporal Stationary Covariance Functions[J]. Publications of the American Statistical Association, 1999,94(448):1330-1339.
[21]	陆雅静,王辉,倪爽英,等.河北省2013~2014年环境空气质量现状对比分析[J]. 安徽农业科学, 2015,(14):271-274.
[22]	康桂红,孙兴池,韩永清,等.山东省大气污染时空分布特征分析[J]. 山东气象, 2016,36(1):13-17.
[23]	成亚利,王波,上海市PM2.5的时空分布特征及污染评估[J]. 计算机与应用化学, 2014,31(10):1189-1192.
[24]	山东省环保保护厅.2014年山东省环境状况公报[EB/OL]. http://www.zhb.gov.cn/gkml/hbb/qt/201506/t20150604_302942.html.