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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6480950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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