| 1. |
- Beelen, Rob, et al.
(författare)
-
Effects of long-term exposure to air pollution on natural-cause mortality : an analysis of 22 European cohorts within the multicentre ESCAPE project
- 2014
-
Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 383:9919, s. 785-795
-
Tidskriftsartikel (refereegranskat)abstract
- Background Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants. Methods We used data from 22 European cohort studies, which created a total study population of 367 251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2.5 mu m (PM2.5), less than 10 mu m (PM10), and between 10 mu m and 2.5 mu m (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buff er. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis. Findings The total study population consisted of 367 251 participants who contributed 5 118 039 person-years at risk (average follow-up 13.9 years), of whom 29 076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2.5 of 1.07 (95% CI 1.02-1.13) per 5 mu g/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I-2 p value=0.95). HRs for PM2.5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 mu g/m(3) (HR 1.06, 95% CI 1.00-1.12) or below 20 mu g/m(3) (1.07, 1.01-1.13). Interpretation Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value.
|
|
| 2. |
- de Hoogh, Kees, et al.
(författare)
-
Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data
- 2016
-
Ingår i: Environmental Research. - : Elsevier BV. - 0013-9351 .- 1096-0953. ; 151, s. 1-10
-
Tidskriftsartikel (refereegranskat)abstract
- Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.
|
|
