基于时空模型的PM(2.5)预测与插值  

Prediction and interpolation of PM_(2.5) based on space-time model

作　　者：徐文[1] 黄泽纯[2,3,4] 张倩宁 

机构地区：[1]武汉大学测绘学院,湖北武汉430079 [2]西南交通大学地球科学与环境工程学院,四川成都610031 [3]西南交通大学高速铁路运营安全空间信息技术国家地方联合实验室,四川成都610031 [4]西南交通大学轨道交通安全协同创新中心,四川成都610031

出　　处：《江苏师范大学学报（自然科学版）》2016年第3期70-75,共6页Journal of Jiangsu Normal University：Natural Science Edition

基　　金：中央高校基本科研业务费专项资金资助项目(2682014CX017);长江学者和创新团队发展计划资助项目(IRT13092)

摘　　要：PM_（2.5）已对我国的空气质量构成了严重威胁,对其预警、预报具有重要的意义.由于PM_（2.5）数据同时具有时间与空间属性,而目前的研究缺少对其时空属性的探索与挖掘.以2015年10~12月华北地区58个城市的日均PM_（2.5）浓度数据作为实验数据,利用时空自回归移动平均（STARMA）模型及只考虑时间属性的自回归移动平均（ARMA）模型对PM_（2.5）进行预测,并利用时空克里金插值与只考虑空间属性的普通克里金插值对PM_（2.5）进行插值.结果表明,STARMA模型与ARMA模型的预测相比,时空克里金与普通克里金的插值相比,PM_（2.5）预测及插值精度均有所提升,且具有时空灵活性.The air quality of China has been seriously polluted by PM_（2.5）.It is necessary to warn and forecast PM_（2.5）in China.The data of PM_（2.5）has time and space dual properties,but currently,there is little study in spatio-temporal data mining of PM2.5.The daily average PM_（2.5）concentration datum of 58 cities in North China during October to December 2015 were acquired as experimental data,then the PM_（2.5） data were respectively predicted by space-time autoregressive moving average（STARMA）and ARMA model which just considered the time property of the data,and the PM_（2.5） were interpolated by space-time Kriging（ST-Kriging）and ordinary Kriging which just considered the space property of the data.Experimental results show that compared with ARMA and ordinary Kriging model,STARMA and ST-Kriging with the flexibility of time and space have considered the space-time correlation of PM_（2.5）data and could improve the prediction and interpolation accuracy.

关 键 词：PM_(2.5)时空分析 时空自回归移动平均模型 时空克里金插值 

分 类 号：X823[环境科学与工程—环境工程]