Construction of polluted aerosol in accumulation that affects the incidence of lung cancer

Heliyon

Kamonwan Jenwitheesuk¹  Kriangsak Jenwitheesuk²  Udomlack Peansukwech³ 

[1] Consultant of Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand;General Surgery Unit, Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand;;Research Manager &

关键词: Lung cancer;    Air pollution;    Polluted aerosol components;    Bayesian model;    MERRA-2;    Environmental science;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: This model demonstrated the correlation between lung cancer incidences and the parts of ambient air pollution according to the National Aeronautics and Space Administration (NASA)'s high resolution technology satellites. Methods: Chemical type of aerosols was investigated by the Aerosol Diagnostics Model such as black carbon, mineral dust, organic carbon, sea-salt and SO4. The model investigated associations between the six year accumulation of each aerosol and lung cancer incidence by Bayesian hierarchical spatio-temporal model. Which also represented integrated geophysical parameters. Results: In analyses of accumulated chemical aerosol component from 2010 – 2016, the incidence rate ratio (IRR) of patients in 2017 were estimated. We observed a significant increasing risk for organic carbon exposure (IRR 1.021, 95%CI 1.020–1.022), SO4, (IRR 1.026, 95% CI 1.025–1.028) and dust, (IRR 1.061, 95% CI 1.058–1.064). There was also suggestion of an increased risk with, every 1 ug/m3 increase in organic carbon compound is associated with 21% increased risk of lung cancer, whereas a 26% excess risk of cancer per 1 ug/m3 increase in mean SO4 and 61% increased risk of lung cancer for dust levels. The other variables were the negative IRR which did not increase the risk of the exposed group. Conclusion: With our results, this process can determine that organic carbon, SO4 and dust was significantly associated with the elevated risk of lung cancer.

【 授权许可】

Unknown