Public-health impact of outdoor and traffic-related air pollution: a European assessment

147

An association between particulate air pollution and morbidity and mortality is well established. However, little is known about which sources of particulate matter contribute most to the adverse health effects. Identification of responsible sources would merit more efficient control. For a 6-year period (01 January 1999 to 31 December 2004), we examined associations between urban background PM 10 in the presence of gaseous pollutants (CO, NO 2 ) and hospital admissions due to cardiovascular and respiratory disease in the elderly (ageZ65), and asthma in children (age 5-18) in Copenhagen, Denmark. We further studied associations between fractions of PM 10 assigned to six sources (biomass, secondary, oil, crustal, sea salt, and vehicle) and admissions during a 1 1 2 -year campaign. We used Poisson generalized additive time-series model adjusted for season, day of the week, public holidays, influenza epidemics, grass pollen, school holidays, and meteorology, with up to 5 days lagged air pollution exposure. We found positive associations between PM 10 and the three health outcomes, with strongest associations for asthma. The PM 10 effect remained robust in the presence of CO and NO 2 . We found different PM 10 sources to be variably associated with different outcomes: crustal and secondary sources showed strongest associations with cardiovascular, biomass with respiratory, and vehicle with asthma admissions. These novel results may merit future research of potential mechanism, whereas at present, no single PM 10 source can be attributed to all morbidity.

Section: Discussioncontrasting

confidence: 85%

Section: Discussioncontrasting

confidence: 69%

Ambient particle source apportionment and daily hospital admissions among children and elderly in Copenhagen

Andersen

Wåhlin²,

Raaschou-Nielsen

et al. 2007

147

“…Acute effects of ambient air pollution on mortality and hospital admissions have been well documented in both Australia and elsewhere (Simpson et al, 1997(Simpson et al, , 2000Sunyer et al, 1997;Morgan et al, 1998a, b;Spix et al, 1998;Kunzli et al, 1999;Schwartz, 1999;Samet et al, 2000;Pope et al, 2002). Fewer studies have investigated associations between ambient air pollutants and emergency department (ED) attendances and the majority of these have reported on the associations between air pollution and ED attendances for asthma (Schwartz et al, 1993;Castellsague et al, 1995;Smith et al, 1996;Stieb et al, 1996;Tenias et al, 1998;Fauroux et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Associations between ambient air pollution and daily emergency department attendances for cardiovascular disease in the elderly (65+ years), Sydney, Australia

Jalaludin

Morgan

Lincoln

et al. 2005

There are no reported studies on the effects of ambient air pollution on emergency department (ED) attendances in Sydney, Australia. This study aimed to determine associations between ambient air pollutants and ED attendances for cardiovascular disease (CVD) in those aged 65 þ years. We constructed daily time series of hospital ED attendances, air pollutants and meteorological factors for the Sydney metropolitan area from 1 January 1997 to 31 December 2001. We used generalised linear models to determine associations between daily air pollution and daily ED attendances and controlled for the effects of long-term trends, seasonality, weather and other potential confounders. Increased ED attendances for all CVD, cardiac disease and ischaemic heart disease were seen with 24-h particulate pollution, 1-h NO 2 , 8-h CO and 24-h SO 2 . Air pollutants were associated with decreased ED attendances for stroke. The effects of air pollutants on CVD, cardiac disease and stroke attendances were generally greater in the cool period compared to the warm period. The single-pollutant effects of CO, O 3 , NO 2 and SO 2 were essentially unchanged in two-pollutant models. Although air pollution levels in Sydney are relatively low compared to similar cities, we have demonstrated associations between ambient air pollutants and ED attendances for CVD in people aged 65 þ years. Our study adds to the growing evidence for the effects of ambient air pollution on CVD outcomes even at relatively low ambient concentrations.

“…The literature examining the effects of motor vehicle emissions on respiratory and cardiovascular health is extensive (e.g., Ku¨nzli et al, 2000;Brauer et al, 2002Brauer et al, , 2007Brunekreef and Holgate, 2002;Hoek et al, 2002a, b;Gauderman et al, 2005;Krzyzanowski et al, 2005;Wilhelm and Ritz, 2005). Geographic estimates of ambient concentration distributions enable assessment of within-city variability of traffic-related pollutants for epidemiological studies of their health impacts.…”

Section: Introductionmentioning

confidence: 99%

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

Poplawski

Gould

Setton

et al. 2008

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO 2 ) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa s passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R 2 ¼ 0.51) than in Seattle (R 2 ¼ 0.33). Scenario 2 produced improved R 2 -values in both cities (Victoria ¼ 0.58, Seattle ¼ 0.65), with further improvement achieved under Scenario 3 (Victoria ¼ 0.61, Seattle ¼ 0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.