Season and weather factors matter, but not enough: a machine learning-based study on predicting incremental lifetime cancer risk of polycyclic aromatic hydrocarbons.
With the intensification of urbanization, air pollution has garnered global concern. This study aims to predict the incremental lifetime cancer risk (ILCR) of polycyclic aromatic hydrocarbons (PAHs) in atmospheric PM2.5. Utilizing machine learning regression algorithms and data from six cities in Ji...
| Publicado en: | International Journal of Environmental Health Research Vol. 35; no. 10; pp. 3006 - 3018 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189081195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189081195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09603123 57L jtl: International Journal of Environmental Health Research issn: 09603123 maglogo: Y pubinfo: dt: Oct2025 vid: 35 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 189081195 183049219 189081195 189081195 10.1080/09603123.2025.2467182 189081195 ppf: 3006 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Season and weather factors matter, but not enough: a machine learning-based study on predicting incremental lifetime cancer risk of polycyclic aromatic hydrocarbons. aug: au: Li, Chenjia Deng, Yuxiang Chen, Nuo Luo, Junyao Ji, Yan Yuan, Anjie Wang, Li Tan, Lifeng Sun, Hong Wang, Shou-Lin Chen, Chao affil: Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, P. R. China sug: subj: Neoplasms Risk Factors Particulate Matter Adverse Effects Polycyclic Hydrocarbons, Aromatic Adverse Effects Seasons Weather Machine Learning Risk Assessment Environmental Exposure Adverse Effects Human China Temperature Prediction Models Boosting Machine Learning Algorithms Linear Regression Random Forest Descriptive Statistics Funding Source ab: With the intensification of urbanization, air pollution has garnered global concern. This study aims to predict the incremental lifetime cancer risk (ILCR) of polycyclic aromatic hydrocarbons (PAHs) in atmospheric PM2.5. Utilizing machine learning regression algorithms and data from six cities in Jiangsu Province in 2018, we established models to investigate the relationship between ILCR and various factors, with a special emphasis on seasonal and meteorological data. After model training, SHapley Additive exPlanation (SHAP) analysis revealed that seasonal factors were even more influential than PM2.5 in predicting ILCR. Models were then validated using 2019 data, resulting in an R2 of 0.42, which indicated a decrease in accuracy compared to the 2018 test set R2 of 0.74 but still represented an improvement over using PM2.5 alone (R2 = 0.2). This suggests that while seasonal and related factors are crucial, additional factors are needed to build a robust model for future ILCR predictions. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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