1. Reinhard Mechler, Markus Amann, Wolfgang Schöpp International Institute for Applied Systems Analysis A methodology to estimate changes in statistical life expectancy due to the control of particulate matter air pollution A study sponsored by the Netherlands Ministry for Housing, Spatial Planning and the Environment (VROM) and the Swiss Agency for the Environment, Forests and Landscape (BUWAL)

2. 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

3. Available cohort studies Seventh-day Adventists study Abbey et al. 1991, 1999 PM10, 6338 individuals 1977-1992 RR=1.12 (1.01-1.24) for 10 μg/m 3 PM10 Harvard six cities study Dockery et al., 1993 Krewski et al., 2000 PM2.5, 8000 individuals 1974-1991 RR=1.13 (1.04-1.24) HEI-reanalysis: RR=1.14 American Cancer Society (ACS) study Pope et al. 1995, 2000, 2002 PM2.5, 552138 individuals 1979-2000 RR=1.07 (1.04-1.11) 2002 reanalysis: RR=1.06 (1.02-1.11)

4. 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)

5. Cox proportional hazards model y number fatalities y 0 baseline fatalities PM PM concentrations β functional parameter, determined by epidemiological studies Relative risk (RR): Approximation for small β:

6. An example life table

7. Example implementation RAINS PM2.5 scenarios for 1990, CLE 2010, MFR RAINS SO 2, NO x, VOC and NH 3 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) RAINS population data, UN population projections RR of Pope et al., 2002

8. 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

9. Assumptions Primary PM in cities 25% above rural background RR of 1.06 [1.02-1.11] for 10 μg/m 3 PM2.5 (Pope et al., 2002) American RR applicable to Europe No effects below 5 μg/m 3 PM2.5 Linear extrapolation beyond 35 μg/m 3 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

10. Illustrative results Rural background PM2.5 [ μg/m 3 ] 1990CLE 2010MFR 2010

11. Illustrative results Losses in avg. life expectancy [months] 1990CLE 2010MFR 2010

12. Illustrative results Losses in avg. life expectancy [days]

13. 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 Mean95% CI 1990496168-888 CLE27894-497 MFR19265-344

14. 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 O 3 reductions?

15. 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