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Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O(3) concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O(3) concentrations from 2000 a...

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Autores principales: Hsu, Chin-Yu, Wu, Jhao-Yi, Chen, Yu-Cheng, Chen, Nai-Tzu, Chen, Mu-Jean, Pan, Wen-Chi, Lung, Shih-Chun Candice, Guo, Yue Leon, Wu, Chih-Da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480950/
https://www.ncbi.nlm.nih.gov/pubmed/30978985
http://dx.doi.org/10.3390/ijerph16071300
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author Hsu, Chin-Yu
Wu, Jhao-Yi
Chen, Yu-Cheng
Chen, Nai-Tzu
Chen, Mu-Jean
Pan, Wen-Chi
Lung, Shih-Chun Candice
Guo, Yue Leon
Wu, Chih-Da
author_facet Hsu, Chin-Yu
Wu, Jhao-Yi
Chen, Yu-Cheng
Chen, Nai-Tzu
Chen, Mu-Jean
Pan, Wen-Chi
Lung, Shih-Chun Candice
Guo, Yue Leon
Wu, Chih-Da
author_sort Hsu, Chin-Yu
collection PubMed
description This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O(3) concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O(3) concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R(2) of 0.74 and a 10-fold cross-validated R(2) of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O(3) concentration variation in Asian areas.
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spelling pubmed-64809502019-04-29 Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration Hsu, Chin-Yu Wu, Jhao-Yi Chen, Yu-Cheng Chen, Nai-Tzu Chen, Mu-Jean Pan, Wen-Chi Lung, Shih-Chun Candice Guo, Yue Leon Wu, Chih-Da Int J Environ Res Public Health Article This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O(3) concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O(3) concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R(2) of 0.74 and a 10-fold cross-validated R(2) of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O(3) concentration variation in Asian areas. MDPI 2019-04-11 2019-04 /pmc/articles/PMC6480950/ /pubmed/30978985 http://dx.doi.org/10.3390/ijerph16071300 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hsu, Chin-Yu
Wu, Jhao-Yi
Chen, Yu-Cheng
Chen, Nai-Tzu
Chen, Mu-Jean
Pan, Wen-Chi
Lung, Shih-Chun Candice
Guo, Yue Leon
Wu, Chih-Da
Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title_full Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title_fullStr Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title_full_unstemmed Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title_short Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration
title_sort asian culturally specific predictors in a large-scale land use regression model to predict spatial-temporal variability of ozone concentration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480950/
https://www.ncbi.nlm.nih.gov/pubmed/30978985
http://dx.doi.org/10.3390/ijerph16071300
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