An efficient approach to spatiotemporal analysis and modeling of air pollution data

DOI10.1007/s13253-011-0057-7zbMath1306.62353OpenAlexW2129993097MaRDI QIDQ2261015

Georgios Tsiotas, Athanassios A. Argiriou

Publication date: 6 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-011-0057-7

zbMATH Keywords

KrigingGaussian maximum likelihood estimatorspatiotemporal modelinginnovation algorithmurban air pollution

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32) Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76) Meteorology and atmospheric physics (86A10)

Cites Work

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• An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields
• Ozone Exposure and Population Density in Harris County, Texas
• ON STATIONARY PROCESSES IN THE PLANE

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