The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom
Exposure of phthalate has adverse effects on child health. Currently, the field measurement on PAEs concentration in children’s bedrooms were limited, and the test of PAEs is laborious. Based on the data of home detection in 454 residences from March 2013 to December 2014 in Shanghai, the associatio...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/23/e3sconf_roomvent2022_05032.pdf |
| _version_ | 1828118525061890048 |
|---|---|
| author | Sun Chanjuan Wang Qinghao Huang Chen Li Jingguang Zhang Jialing Zou Zhijun Tian Lang |
| author_facet | Sun Chanjuan Wang Qinghao Huang Chen Li Jingguang Zhang Jialing Zou Zhijun Tian Lang |
| author_sort | Sun Chanjuan |
| collection | DOAJ |
| description | Exposure of phthalate has adverse effects on child health. Currently, the field measurement on PAEs concentration in children’s bedrooms were limited, and the test of PAEs is laborious. Based on the data of home detection in 454 residences from March 2013 to December 2014 in Shanghai, the association of PAEs in children's bedroom and building characteristics, residents’ lifestyle and indoor environment characterization were built by Spearman correlation. According to the Spearman correlation coefficient method, the concentration of PAEs, such as residential area was significantly correlated with DMP, BBP and DiBP in children’s bedroom (sig <0.05, sig <0.01, sig <0.01; r> 0), and the use of chemicals was significantly associated with DEP and DiBP in children’s bedroom (sig <0.05, sig <0.05; r> 0). Then a gradient-boosting decision tree model with higher prediction accuracy is established. The influencing factors of the studied PAEs were determined by comprehensive consideration of the current study and literature review. 11 influencing factors of PAEs concentrations from three aspects were finally established in this study. The training model of GBDT has a reasonable accuracy( R2>0.9). This paper provides a reference for the prediction of PAEs concentration in the residential bedroom and the influence degree of influencing factors. |
| first_indexed | 2024-04-11T13:33:38Z |
| format | Article |
| id | doaj.art-c945a1f9d48c48e79a7a76068a18562a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-11T13:33:38Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c945a1f9d48c48e79a7a76068a18562a2022-12-22T04:21:43ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013560503210.1051/e3sconf/202235605032e3sconf_roomvent2022_05032The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroomSun Chanjuan0Wang Qinghao1Huang Chen2Li Jingguang3Zhang Jialing4Zou Zhijun5Tian Lang6University of Shanghai For Science And TechnologyUniversity of Shanghai For Science And TechnologyUniversity of Shanghai For Science And TechnologyShanghai Research Institute of Building Science Group Co., Ltd.University of Shanghai For Science And TechnologyUniversity of Shanghai For Science And TechnologyUniversity of Shanghai For Science And TechnologyExposure of phthalate has adverse effects on child health. Currently, the field measurement on PAEs concentration in children’s bedrooms were limited, and the test of PAEs is laborious. Based on the data of home detection in 454 residences from March 2013 to December 2014 in Shanghai, the association of PAEs in children's bedroom and building characteristics, residents’ lifestyle and indoor environment characterization were built by Spearman correlation. According to the Spearman correlation coefficient method, the concentration of PAEs, such as residential area was significantly correlated with DMP, BBP and DiBP in children’s bedroom (sig <0.05, sig <0.01, sig <0.01; r> 0), and the use of chemicals was significantly associated with DEP and DiBP in children’s bedroom (sig <0.05, sig <0.05; r> 0). Then a gradient-boosting decision tree model with higher prediction accuracy is established. The influencing factors of the studied PAEs were determined by comprehensive consideration of the current study and literature review. 11 influencing factors of PAEs concentrations from three aspects were finally established in this study. The training model of GBDT has a reasonable accuracy( R2>0.9). This paper provides a reference for the prediction of PAEs concentration in the residential bedroom and the influence degree of influencing factors.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/23/e3sconf_roomvent2022_05032.pdf |
| spellingShingle | Sun Chanjuan Wang Qinghao Huang Chen Li Jingguang Zhang Jialing Zou Zhijun Tian Lang The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom E3S Web of Conferences |
| title | The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom |
| title_full | The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom |
| title_fullStr | The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom |
| title_full_unstemmed | The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom |
| title_short | The gradient-boosting decision tree model can predict the concentration of PAEs in children bedroom |
| title_sort | gradient boosting decision tree model can predict the concentration of paes in children bedroom |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/23/e3sconf_roomvent2022_05032.pdf |
| work_keys_str_mv | AT sunchanjuan thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT wangqinghao thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT huangchen thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT lijingguang thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT zhangjialing thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT zouzhijun thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT tianlang thegradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT sunchanjuan gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT wangqinghao gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT huangchen gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT lijingguang gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT zhangjialing gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT zouzhijun gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom AT tianlang gradientboostingdecisiontreemodelcanpredicttheconcentrationofpaesinchildrenbedroom |