Spatiotemporal modelling of ozone distribution in the State of California |
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| Authors: | P. Bogaert G. Christakos M. Jerrett H.-L. Yu |
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| Affiliation: | 1. Dept. of Environmental Sciences & Land Use Planning, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;2. Dept. of Geography, San Diego State University, San Diego CA, USA;3. Dept. of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA;4. Dept. of Bioenvironmental Systems Engineering, National Taiwan University, No. 1 Roosevelt Rd., Sec. 4, Taipei 10617, Taiwan;1. Department of Environmental Sciences and Engineering, UNC, 135 Dauer Drive, Chapel Hill NC27599-7431, USA;1. Division of Nephrology, Atlanta VA Medical Center and Emory University, Atlanta, Georgia;2. Division of Nephrology and Hypertension, University of California Medical Center, Orange, California;1. TU Bergakademie Freiberg, Institut für Markscheidewesen und Geodäsie, Reiche Zeche, Fuchsmühlenweg 9, D 09599 Freiberg, Germany;2. Delft University of Technology, Department of Geosciences and Engineering, Stevinweg 1, 2628 CN Delft, P.O. Box 5048, 2600 GA Delft, The Netherlands;3. IHC Merwede, Smitweg 6, 2961 AW, P.O. Box 8, 2960AA Kinderdijk, The Netherlands;1. Institute of Islands and Coastal Ecosystems, Ocean College, Zhejiang University, Zhoushan, China;2. Department of Geography, San Diego State University, San Diego, CA, USA;1. Department of Epidemiology, University of California, Los Angeles (UCLA) School of Public Health, Los Angeles, CA, USA;2. Pediatric Surgery, Children''s Hospital Los Angeles, Los Angeles, CA, USA;3. Division of Pediatric Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA;4. Cancer Prevention and Genetics, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA;5. Division of Endocrinology, Gerontology, & Metabolism, School of Medicine, Stanford University, Stanford, CA, USA;6. VA Palo Alto Health Care System, Palo Alto, CA, USA;7. Departments of Epidemiology, Environmental Health Sciences, and Urology, Schools of Public Health and Medicine, and Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA;8. Division of Pulmonary and Critical Care Medicine, UCLA David Geffen School of Medicine, Los Angeles, CA, USA;9. Pulmonary and Critical Care Section, New Mexico VA Healthcare System, Albuquerque, NM, USA;10. Department of Preventive Medicine, USC Keck School of Medicine at University of Southern California, Los Angeles, CA, USA;11. Department of Pathology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA;12. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA |
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| Abstract: | This paper is concerned with the spatiotemporal mapping of monthly 8-h average ozone (O3) concentrations over California during a 15-years period. The basic methodology of our analysis is based on the spatiotemporal random field (S/TRF) theory. We use a S/TRF decomposition model with a dominant seasonal O3 component that may change significantly from site to site. O3 seasonal patterns are estimated and separated from stochastic fluctuations. By means of Bayesian Maximum Entropy (BME) analysis, physically meaningful and sufficiently detailed space–time maps of the seasonal O3 patterns are generated across space and time. During the summer and winter months the seasonal O3 concentration maps exhibit clear and progressively changing geographical patterns over time, suggesting the existence of relationships in accordance with the typical physiographic and climatologic features of California. BME mapping accuracy can be superior to that of other techniques commonly used by EPA; its framework can rigorously assimilate useful data sources that were previously unaccounted for; the generated maps offer valuable assessments of the spatiotemporal O3 patterns that can be helpful in the identification of physical mechanisms and their interrelations, the design of human exposure and population health models, and in risk assessment. As they focus on the seasonal patterns, the maps are not contingent on short-time and locally prevalent weather conditions, which are of no interest in a global and non-forecasting framework. Moreover, the maps offer valuable insight about the space–time O3 concentration patterns and are, thus, helpful for disentangling the influence of explanatory factors or even for identifying some influential ones that could have been otherwise overlooked. |
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