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A methodology to estimate impacts of particulate matter on life expectancy, incorporating time series and cohort studies, implemented in RAINS modeling tool. Provides illustrative results, sensitivity analysis, and conclusions for European settings.
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Reinhard Mechler, Markus Amann, Wolfgang SchöppInternational Institute for Applied Systems Analysis A methodology to estimate changes in statistical life expectancy due to thecontrol of particulate matter air pollution
Mortality impacts of PM • Time series studies • Relate daily PM with observed daily mortality • Many studies available (APHEA, etc.) • Chronic effects captured? • Cohort studies • Follow cohorts over decades, relate cohort mortality with PM exposure. Several sites necessary. • Only few studies available, all in US • Capture acute and chronic effects • Measures of mortality: • Cases of premature deaths • Life expectancy - adopted for RAINS
Methodology • Life tables provide baseline mortality for each cohort • For a given PM emission scenario: modified mortality through Cox proportional hazard model • From modified mortality, calculate life expectancy for each cohort • With population age statistics: Average life expectancy for entire population • Following report of WHO Working Group on Health Impact Assessment (WHO, 2001)
Example implementation • RAINS PM2.5 scenarios for 1990, CLE 2010, MFR • RAINS SO2, NOx, VOC and NH3 scenarios • Dispersion of primary PM: EMEP PPM model • Formation of secondary PM: EMEP Lagrangian model (to be substituted by Eulerian model) • Urban primary PM: assumed 25% above rural background (awaiting input from CITY-DELTA) • RR of Pope et al., 2002 • RAINS population data, UN population projections
Population data in RAINS • Urban and rural population for 50*50 km EMEP grid • Compiled from a variety of sources • Geo-statistical data for 2000 • Development up to 2050 based on UN projections • Time-dependent life tables and age structures from UN • Time-dependent country-specific mortality rates derived
Assumptions • Primary PM in cities 25% above rural background • RR of 1.06 [1.02-1.11] for 10 μg/m3 PM2.5 (Pope et al., 2002) • American RR applicable to Europe • No effects below 5 μg/m3 PM2.5 • Linear extrapolation beyond 35 μg/m3 PM2.5 • No effects for younger than 30 years • For each scenario constant exposure 2010-2080, cohorts followed up to end of their life time • Constant urban/rural population ratios
Illustrative resultsRural background PM2.5 [μg/m3] 1990 CLE 2010 MFR 2010
Illustrative resultsLosses in avg. life expectancy [months] 1990 CLE 2010 MFR 2010
Sensitivity analysis • Preliminary analysis limited to uncertainties of RR (95% CI 1.02-1.11) identified by Pope et al. (2002) • Loss in life expectancy (days): • Other uncertainties: Extrapolation beyond range of evidentiary studies, transferability, population projections, emission and dispersion calculations, etc. • In principle, error propagation (Suutari et al.) is possible
Implementation in RAINS • Hard-wired into RAINS • Provides environmental endpoint for PM health effects • Integrated in multi-pollutant/multi-effect framework • How useful is life expectancy for target setting? • Morbidity impacts not addressed because of methodological and data problems • Quantification of ozone morbidity effects? What will drive O3 reductions?
Conclusions • Methodology for impacts of PM on life expectancy developed • Example implementation in RAINS available • Losses in life expectancy are significant in Europe (~1.5 [0.5-2.5] years), should improve by 2010, and further improvements still possible • Further uncertainty and sensitivity analysis necessary • Life expectancy as additional endpoint in multi-pollutant/multi-effect strategies • Open how to handle morbidity effects in IA
