title: Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data.
abstract: Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal 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 recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.
authors.affiliation: Department of Resoure and Environmental Information, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.|Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Institue of Island and Coastal Ecosystems, Ocean College, Zhejiang University, Hangzhou, Zhejiang 310027, China.|Department of Geography, San Diego State University, San Diego, Califiornia 92182, USA.
authors.forename: George
authors.initials: G
authors.lastname: Christakos
authors.affiliation: Department of Resoure and Environmental Information, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.|Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.affiliation: Department of Resoure and Environmental Information, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.|Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China.
authors.forename: Chengda
authors.initials: C
authors.lastname: Lin
authors.affiliation: Department of Resoure and Environmental Information, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.|Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China.
authors.forename: Peihong
authors.initials: P
authors.lastname: Fu
authors.affiliation: Department of Resoure and Environmental Information, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.|Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtse River), Ministry of Agriculture, Wuhan 430070, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Mei
mesh_terms: D000397:Air Pollution; D002681:China; D003198:Computer Simulation; D005544:Forecasting; D015233:Models, Statistical; D052638:Particulate Matter / Q000032:analysis*; D062211:Spatio-Temporal Analysis; D035501:Uncertainty
chemical_list: D052638:Particulate Matter