Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California
Statistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2002-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/tsw.2002.86 |
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| author | Haiganoush K. Preisler Michael J. Arbaugh Andrzej Bytnerowicz Susan L. Schilling |
| author_facet | Haiganoush K. Preisler Michael J. Arbaugh Andrzej Bytnerowicz Susan L. Schilling |
| author_sort | Haiganoush K. Preisler |
| collection | DOAJ |
| description | Statistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predicted ozone exposure and explanatory variables, and to predict exposure at nonmonitored sites. The fitted model was also used to estimate probability maps for season average ozone levels exceeding critical (or subcritical) levels in the Sierra Nevada region. The explanatory variables — elevation, maximum daily temperature, and precipitation and ozone level at closest active monitor — were significant in the model. There was also a significant mostly east-west spatial trend. The between-site variability had the same magnitude as the error variability. This seems to indicate that there still exist important site features not captured by the variables used in the analysis and that may improve the accuracy of the predictive model in future studies. The fitted model using robust techniques had an overall R2 value of 0.58. The mean standard deviation for a predicted value was 6.68 ppb. |
| format | Article |
| id | doaj-art-8b4607916a624fa4b844c78fb9a10f29 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2002-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-8b4607916a624fa4b844c78fb9a10f292025-02-03T05:44:42ZengWileyThe Scientific World Journal1537-744X2002-01-01214115410.1100/tsw.2002.86Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, CaliforniaHaiganoush K. Preisler0Michael J. Arbaugh1Andrzej Bytnerowicz2Susan L. Schilling3Pacific Southwest Research Station, 800 Buchanan St., West Annex, Albany, CA 94710, USAPacific Southwest Research Station, Forest Fire Laboratory, 4955 Canyon Crest Drive, Riverside, CA 92507, USAPacific Southwest Research Station, Forest Fire Laboratory, 4955 Canyon Crest Drive, Riverside, CA 92507, USAPacific Southwest Research Station, Forest Fire Laboratory, 4955 Canyon Crest Drive, Riverside, CA 92507, USAStatistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predicted ozone exposure and explanatory variables, and to predict exposure at nonmonitored sites. The fitted model was also used to estimate probability maps for season average ozone levels exceeding critical (or subcritical) levels in the Sierra Nevada region. The explanatory variables — elevation, maximum daily temperature, and precipitation and ozone level at closest active monitor — were significant in the model. There was also a significant mostly east-west spatial trend. The between-site variability had the same magnitude as the error variability. This seems to indicate that there still exist important site features not captured by the variables used in the analysis and that may improve the accuracy of the predictive model in future studies. The fitted model using robust techniques had an overall R2 value of 0.58. The mean standard deviation for a predicted value was 6.68 ppb.http://dx.doi.org/10.1100/tsw.2002.86 |
| spellingShingle | Haiganoush K. Preisler Michael J. Arbaugh Andrzej Bytnerowicz Susan L. Schilling Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California The Scientific World Journal |
| title | Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California |
| title_full | Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California |
| title_fullStr | Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California |
| title_full_unstemmed | Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California |
| title_short | Development of a Statistical Model for Estimating Spatial and Temporal Ambient Ozone Patterns in the Sierra Nevada, California |
| title_sort | development of a statistical model for estimating spatial and temporal ambient ozone patterns in the sierra nevada california |
| url | http://dx.doi.org/10.1100/tsw.2002.86 |
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