Authors:
Takahiro Otani
1
;
Kunihiko Takahashi
1
;
Ayano Takeuchi
2
and
Mari Asami
3
Affiliations:
1
Department of Biostatistics, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya and Japan
;
2
School of Medicine, Keio University, Shinjuku-ku, Tokyo and Japan
;
3
Department of Environmental Health, National Institute of Public Health, Wako-shi, Saitama and Japan
Keyword(s):
Spatial Interpolation, Exposure Assessment, Air Pollutants, Nuclear Substance.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In response to accidents and disasters involving the proliferation of pollutants to the environment, performing exposure assessments across a region of impact is important for evaluating health effects. Owing to the typical unavailability of the spatially continuous data of pollutant concentrations immediately after accidents, various spatial interpolation methods have been studied to assess exposures using limited available data. In this study, we compared representative spatial interpolation methods based on the estimation of the distributions of exposures through a case study of the Fukushima Daiichi nuclear disaster initiated by the Great East Japan earthquake and subsequent tsunamis. The nearest neighbour method, inverse distance weighted method, and ordinary kriging method were compared in the context of exposure assessments. Even though estimated air dose rates were slightly different depending on the method used, different interpolation methods produced significantly equivale
nt estimates of the distribution of cumulative exposure over one year.
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