RAINS review 2004 The RAINS model: Health impacts of PM

Main issues Methodology for health impact assessment Dispersion modelling for PM Quantification of population exposure in cities Uncertainties

Estimating the loss of life expectancy in RAINS Approach Endpoint: –Loss in statistical life expectancy –Related to long-term PM2.5 exposure, based on cohort studies Life tables provide baseline mortality for each cohort in each country For a given PM scenario: Mortality modified through Cox proportional hazard model using Relative Risk (RR) factors from literature From modified mortality, calculate life expectancy for each cohort and for entire population

Input to life expectancy calculation Life tables (by country) Population data by cohort and country, Urban/rural population in each 50*50 km grid cell Air quality data: annual mean concentrations –PM2.5 (sulfates, nitrates, ammonium, primary particles), excluding SOA, natural sources –alternatively PMcoarse, PM10, black carbon –50*50 km over Europe, rural + urban background –for any emission scenario Relative risk factors

Critical assumptions reviewed by TF on Health Choice of appropriate RR and shape of C-R curve Mortality related to PM2.5 (mass) PM2.5 includes effects from SO 2, NO 2, carbonaceous, diesel Are ozone effects independent? (SOA are excluded, thus no potential double-counting of ozone effects) Extrapolation beyond 35 μg/m 3 PM2.5 Treatment of natural background Exposure calculation: (Urban) background concentrations (annual mean) * population No effects for younger than 30 years Quantification of uncertainties (CI of RR, alternative impact theories, potential biases, linearity, etc.)

Regional scale Urban scale Uncertainties Modelling of health-relevant PM formation and transport in the atmosphere

Atmospheric dispersion of PM Health-relevant metric: annual mean PM2.5 mass Performance of EMEP Eulerian model for PM –TFMM 2003 review: Rural sulphates: ok. Rural nitrates: observations missing, model probably ok. Anthropogenic primary PM: to be demonstrated Secondary organic aerosols: missing Natural contributions: missing –Thus: not able to reproduce observed total PM mass, but possible to track PM changes due to anthropogenic emissions

S-R relations for RAINS Linearity of changes in PM due to changes in emissions is crucial for the mathematical design of RAINS 87 model experiments with the new EMEP model: –Response of European PM2.5/10 concentrations to changes in SO 2, NO x, VOC, NH 3, PPM2.5/10 emissions –For German, Italian, Dutch, UK and European emissions –3 emission scenarios: CLE (current legislation 2010) = CAFE baseline for 2010 MFR (maximum technically feasible reductions 2010 UFR (ultimately feasible reductions) = MFR/2

Response of PM2.5 due to ΔPPM2.5 from German emissions

Response of SIA due to ΔSO 2 from German emissions

Response of SIA due to ΔNO x from German emissions

Response of SIA due to ΔNH 3 from German emissions

Response of SIA due to ΔVOC from German emissions

Response of SIA due to ΔSO 2 +ΔNO x +ΔNH 3 +ΔVOC+ ΔPPM

Response of PM2.5 due to ΔSO 2 +ΔNO x +ΔNH 3 +ΔVOC+ ΔPPM

–Regional scale –Urban scale –Uncertainties Modelling of health-relevant PM formation and transport in the atmosphere

City-Delta objectives Identify systematic differences in urban AQ results computed by – regional scale models – urban scale models. Identify differences in model results (deltas) across – Emissions (2000, 2010, maximum feasible reductions), – cities in Europe, – scales, – models, – pollutants (PM, O 3, for health-relevant metrics). 17 models, 8 cities, 9 scenarios

Changes in urban PM10 Results from City-Delta1

PM10 as a function of emission density

“Urban impact” on PM2.5 in Vienna Source: Puxbaum et al., 2003

–Regional scale –Urban scale –Uncertainties Modelling of health-relevant PM formation and transport in the atmosphere

Euro-Delta model inter-comparison Evaluate the performance of regional-scale atmospheric dispersion models against observations Identify differences in model results (deltas) across – emissions (2000, 2010, maximum feasible reductions), – regions in Europe, – models, – pollutants. Put the EMEP model performance into perspective, derive quantitative information for uncertainty analysis

Annual mean PM2.5 and sulphate levels (μg/m 3 ) 9 German sites, as computed by the Euro-Delta models PM2.5Sulphate Observations are shown in black

PM2.5 responses of the Euro-Delta models Receptor regions: 00.. Europe 01.. Austria 08.. France 09.. Germany 12.. Italy 14.. Netherlands 19.. Spain 22.. British Isles

Further work Develop regional source-receptor (S-R) relationships for PM Complete City-Delta analysis for PM, develop urban-regional SR relationships Investigate inter-annual meteorological variability Quantify uncertainties, explore use of ensemble-model Finalize uncertainty analysis for health-impact analysis