Background: Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence. Methods: We used data from 14 cohort studies in eight European countries. We geocoded baseline addresses and assessed air pollution with land-use regression models for eight elements (Cu, Fe, K, Ni, S, Si, V and Zn) in size fractions of PM2.5 and PM10. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effect models for meta-analysis. Results: The 245,782 cohort members contributed 3,229,220person-years at risk. During follow-up (mean, 13.1 years), 1878 incident cases of lung cancer were diagnosed. In the meta-analyses, elevated hazard ratios (HRs) for lung cancer were associated with all elements except V; none was statistically significant. In analyses restricted to participants who did not change residence during follow-up, statistically significant associations were found for PM2.5 Cu (HR, 1.25; 95% CI, 1.01-1.53 per 5 ng/m3), PM10 Zn (1.28; 1.02-1.59 per 20 ng/m3), PM10 S (1.58; 1.03-2.44 per 200 ng/m3), PM10 Ni (1.59; 1.12-2.26 per 2 ng/m3) and PM10 K (1.17; 1.02-1.33 per 100 ng/m3). In two-pollutant models, associations between PM10 and PM2.5 and lung cancer were largely explained by PM2.5 S. Conclusions: This study indicates that the association between PM in air pollution and lung cancer can be attributed to various PM components and sources. PM containing S and Ni might be particularly important.
Particulate matter air pollution components and risk for lung cancer
Ricceri F.;Migliore E.;Vineis P.
2016-01-01
Abstract
Background: Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence. Methods: We used data from 14 cohort studies in eight European countries. We geocoded baseline addresses and assessed air pollution with land-use regression models for eight elements (Cu, Fe, K, Ni, S, Si, V and Zn) in size fractions of PM2.5 and PM10. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effect models for meta-analysis. Results: The 245,782 cohort members contributed 3,229,220person-years at risk. During follow-up (mean, 13.1 years), 1878 incident cases of lung cancer were diagnosed. In the meta-analyses, elevated hazard ratios (HRs) for lung cancer were associated with all elements except V; none was statistically significant. In analyses restricted to participants who did not change residence during follow-up, statistically significant associations were found for PM2.5 Cu (HR, 1.25; 95% CI, 1.01-1.53 per 5 ng/m3), PM10 Zn (1.28; 1.02-1.59 per 20 ng/m3), PM10 S (1.58; 1.03-2.44 per 200 ng/m3), PM10 Ni (1.59; 1.12-2.26 per 2 ng/m3) and PM10 K (1.17; 1.02-1.33 per 100 ng/m3). In two-pollutant models, associations between PM10 and PM2.5 and lung cancer were largely explained by PM2.5 S. Conclusions: This study indicates that the association between PM in air pollution and lung cancer can be attributed to various PM components and sources. PM containing S and Ni might be particularly important.File | Dimensione | Formato | |
---|---|---|---|
Raaschou-Nielsenetal2016_EnvironIntern.pdf
Accesso aperto
Tipo di file:
POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione
758.41 kB
Formato
Adobe PDF
|
758.41 kB | Adobe PDF | Visualizza/Apri |
Ricceri_2_Particulate matter.pdf
Accesso riservato
Tipo di file:
PDF EDITORIALE
Dimensione
615.64 kB
Formato
Adobe PDF
|
615.64 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.