CITY-DELTA Objectives, Methodology, and Results C. Cuvelier, P. Thunis, L. Rouïl on behalf of the steering group

Contents Background and Objectives Methodology Interpretation of the results Emission Inventories Model Validation Deltas Interpretation Conclusions Functional Relationships

Contents Background and Objectives Methodology Interpretation of the results Emission Inventories Model Validation Deltas Interpretation Conclusions Functional Relationships

Driving forces of health impacts WHO systematic review of health impacts from air pollution (2003/2004): Largest damage from long-term exposure to PM2.5 - Not yet possible to distinguish potency of individual PM components - No threshold can be identified - Thus larger health benefits from large-scale reductions of low concentrations than from peak concentrations at hot spots New evidence for mortality effects from ozone - From time series studies - No firm evidence for no-effect level, but larger uncertainties for effects at low concentrations - Thus also low ozone days are relevant

Modelling questions for CAFE What are the downscaling effects when a refined grid is used over cities instead of “regional” grid How to include sub-grid effects into an Europe-wide health impact assessment for PM/ozone? How to balance Europe-wide vs. local emission controls to reduce health impacts in a cost-effective way? NOT: How to model compliance with AQ limit values of AQ-Directives!

Contents Background and Objectives Methodology Interpretation of the results Emission Inventories Model Validation Deltas Interpretation Conclusions Functional Relationships Discussion

City Delta A model inter-comparison exercise for urban/regional dispersion models to identify: Systematic deltas between rural and urban AQ  Scale How these deltas depend on emissions  Emissions How these deltas vary across cities  Cities How these deltas vary across models  Models How these deltas vary for PM and O3  Pollutants

7 European-scale and … … 11 urban-scale models Model # of levels Level Domain Resolution CALGRID 11 10m CITYDELTA 5-10 CAMx CHIMERE local 6: surface-700hPa SL (50m) 5 CHIMERE regional Europe 50 EMEP Unified Model 20: surface-100hPa 1m EMEP EMEP-v.1 45m EPISODE 6: 25-2500m 2m 10 EUROS 4 25m 10-50 LOTOS local 3: 0-3500m ML LOTOS régional MOCAGE 47: surface-5hPa 1st level (0-50m) Paris, Milano MUSCAT 22: 0-4400m 1st level (0-33m) MUSE OFIS 2 REM3 local 4: 0-3000m SL REM3 regional STEM-FCM TRANSCHIM 50m

Cities: London Paris Prague Berlin Copenhagen Katowice Milan Marseille Comparisons are conducted for a number of European cities with distinct differences in climatic conditions, geographical settings, and emission densities. London Paris Prague Berlin Copenhagen Katowice Milan Marseille

Input Data: Monitoring data: Meteorological data: For the 8 cities delivered by the city-authorities: O3, NO2, PM2.5, PM10 Meteorological data: Provided to CityDelta by Meteo-France, or by the modelling group themselves Emission inventories: High-resolution (1 km to 5 km) city-emission inventories Low-resolution (50 km) EMEP-TNO emission inventory Boundary conditions: Provided by EMEP, or by the modelling group themselves

Emission Scenarios NOx 0 --- 1999 1 --- 2010 CLE: Current Legislation 0 --- 1999 1 --- 2010 CLE: Current Legislation 2 --- 2010 NOx MFR: Maximum Feasible Reduction 3 --- 2010 NOx (CLE+MFR)/2 4 --- 2010 VOC MFR 5 --- 2010 NOX and VOC MFR 6 --- 2010 PMcoarse MFR 7 --- 2010 PM2.5 MFR NOx CLE-1999 MFR-1999 Prague -34% -62% Milan -36% -53% Paris -42% -65% Berlin -38% -50%

OUTPUT Hourly values for O3 (and NO2) for 6 months (Summer) GAS PM Hourly values for O3 (and NO2) for 6 months (Summer) Daily values for PM2.5 and PM10 for 12 months

The JRC tool Monitoring data Validation with Obs. Scenario Deltas 2D Avg. Fields Scenario Deltas Emissions

Contents Background and Objectives Methodology Interpretation of the results Emission Inventories Model Validation Deltas Interpretation Conclusions Functional Relationships

Local vs EMEP Regional emission inventories MILAN NOx, CO, SOx estimates seems quite robust (uncertainty 20-30%) PM estimates show larger than 50% differences. CITY DELTA has contributed to a considerable revision of the regional emission data and resulted in the development of a new methodology for distributing emission data that secures consistency among pollutants and a traceable improvement in the emission distribution by sector.

Taylor plots: Berlin city centre CRMSE Corr. K.E.Taylor, 2001, JGR, 106, 7183-7192

The ENSEMBLE model OZONE PM10 Day1 Day2 Day3 Day4 ExcD60 MeanT

City variability O3 MeanT (CD) PM10 Winter (CD)

Summary of model validation Concentration levels Correlation Deviation from mean Difference between coarse and fine scale models Range O3 +/- 20 % No large differences, some additional titration with FS 0.4-0.8 NO2 0 to -80% Strong underestimates disappear with FS 0.2-0.6 PM10 -20 to -50% -3 to +20 μg/m3, 0.4-0.75

NOx Reduction O3 SOMO (CD) VOC Reduction

NOx Group Reduction PM10 Winter (CD) VOC Group Reduction

Key findings: Ozone Model results are highly sensitive to the quality of the emission inventories. Generally, - models reproduce well the present situation, - they agree on the ozone changes expected from CLE in 2010 Coarse and fine scale models - when averaged over the regional scale - agree over wide ranges of responses of ozone to emission controls. Fine-scale models are able to show important sub-grid effects, which are not captured by regional scale models. Models agree more on the response to VOC emission controls than on the effects of NOx cuts (titration effect modelling). The use of the ENSEMBLE model response provides a robust tool for analyzing the impacts of further emission reductions.

Key findings: PM Validation of PM is hampered by the lack of reliable (chemically speciated) observations All models underestimate total PM mass, both at urban and large scale. This is due to limited understanding of sources and processes. Models agree that a large part of PM found in urban background originates from the regional background. Urban PM levels can – to a large extent - be related to the density of primary urban low-level emissions, regional background, and city characteristics (e.g., ventilation).

Contents Background and Objectives Methodology Interpretation of the results Emission Inventories Model Validation Deltas Interpretation Conclusions Functional Relationships

Functional relationships: Basic Approach 50km City 50km Delta Conc = PMconc in a given 5x5km Grid - Average PMconc over whole Domain Delta Emis = ED in a given 5x5km Grid - Average ED over whole Domain Correlate: Delta Concentration vs Delta Emission Density

ENSEMBLE Base Case, CLE and MFR “Conc-Emis” correlations PARIS Paris: Slope = 0.23 R2 = 0.82 MILAN Milan: Slope = 1.64 R2 = 0.68

Slopes of individual cities against EMEP wind speed in city grid

Functional relationship for PM DPMsub-grid = (EDsub-grid - EDEMEP) * (k1 - k2*Vwind) DPMsub-grid .. Difference in PM concentration between sub-grid (urban/rural) area and EMEP grid average EDx … Emission density for low sources (x=urban/rural/EMEP grid average) Vwind … Annual mean wind speed in EMEP grid cell k1, k2 … Parameters derived from the City-Delta ensemble model

Modelling concept for urban background PM2.5 Take grid-average (50*50 km) PM2.5 concentrations from the regional scale EMEP model These include: primary PM and secondary inorganic aerosols and do not include: secondary organic aerosols PM from natural sources Add urban (sub-grid) increment, based on City-Delta functional relationships

PM implementation in RAINS ΔPMsub-grid = (EDsub-grid – EDEMEP) * (k1 - k2*Vwind) = (EDEMEP * (PDsub-grid / PDEMEP ) – EDEMEP) * (k1-k2*Vwind) = EDEMEP * (PDsub-grid / PDEMEP – 1)* (k1 - k2*Vwind) Necessary input data: Emission densities of low level sources in an EMEP cell (for an emission control scenario) Population densities in urban and rural areas for an EMEP cell Annual mean wind speed in an EMEP grid cell

Population density ratios Input data Wind speed Emission densities Population density ratios Urban increments

Anthropogenic contribution to PM2. 5 Grid average vs Anthropogenic contribution to PM2.5 Grid average vs. urban increments, 2000 [µg/m3]

Validation against observations Urban background PM2.5 [μg/m3]

Discussion Urgent need for validation with monitoring data, hampered by lack of PM2.5 twin sites. Presently, grid average wind speed used. No consideration of topography. City-specific wind speeds should be improved. Which emission/population density is representative for a city (how to draw city borders)? This determines directly the size of the urban increment. For which city size are the FR applicable? (introduce lower limit)

Conclusions (1) A first approach for addressing urban air quality for Europe-wide health impact assessment has been developed and implemented – based on observations and City-Delta results Major determinants: Regional background Urban emission densities of low level sources (traffic, heating) Meteorological and topographical information (wind speed) Geographical information (size of city, population)

Conclusions (2) First results are promising, further refinement is necessary More PM2.5 monitoring data is necessary for validation Uncertainty and sensitivity analyses not yet performed Only for health impact assessment, not yet suitable for compliance with AQ limit values

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