Imprecise Uncertainty Modelling of Air Pollutant PM10

摘要：

The State of California sets very high standards for their air quality, and air pollutants are carefully monitored, one of which is PM10. The PM10 concentrations are recorded from 1989 to 2007, but during the 19 years, even though there are 213 sample sites, only a limited number of sites are sampled each year. In this study, we utilised the inverse distance weight methodology to fill in the locations with missing values. We developed a series of uncertain measure theory founded spatial-temporal methodology, including the inverse distance scheme, the kriging scheme, and the geometric canonical process based weighted regression analysis in order to extract the change information from the incomplete 1989-2007 PM10 records. The full completed data records for the 213 sites, for all 19 years, containing 4047 data records are converted to 19 ordinary kriging prediction maps and change maps for examination.

关键词： air pollution PM10 regression model inverse distance prediction kriging prediction uncertain canonical process temporal regression California

作者: Danni Guo Renkuan Guo Christien Thiart Yanhong Cui

作者单位: Climate Change and Bioadapation Division, South African National Biodiversity Institute, Cape Town, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa

会议类型: 国际会议

会议名称: The Second International Conference on Uncertainty Theory(ICUT)(第二届不确定理论国际会议)

会议地点: 拉萨

会议语种:英文

页码: 175-181

在线出版日期: 2011-08-06（万方平台首次上网日期，不代表论文的发表时间）