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An assessment of uncertainty in COPERT4 & managing differences arising from model development: from COPERTII to COPERT4. Leonidas Ntziachristos ETC/ACM. Updated version of: Road transport emission inventory uncertainties – GHGs and APs, EEA, Copenhagen, 18 November 2010
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An assessment of uncertainty in COPERT4 & managing differences arising from model development:from COPERTII to COPERT4 Leonidas Ntziachristos ETC/ACM Updated version of: Road transport emission inventory uncertainties – GHGs and APs,EEA, Copenhagen, 18 November 2010 http://www.eionet.europa.eu/events/transport%20uncertainties/
Agenda of the Nov. 18, 2010 meeting TFEIP Stockholm
Projected emission factors • Emission reductions for future vehicle technologies generally follow the rule: • Limitation: • Real-world behaviour does not (always) follow emission standards TFEIP Stockholm
Example: Euro V trucks NOx • Emission Level • EF over ES ratios TFEIP Stockholm
Impacts • Uncertainty of projection increases due to inability to predict real-world behaviour beforehand • Difficulties to meet targets may originate from such uncertainty in setting targets • Best example: NECD TFEIP Stockholm
Towards NECD: Current Assessment Source: Nitrogen oxide (NOx) distance-to-target for EEA member countries, EEA, Oct. 2010. TFEIP Stockholm
Responsible: Model or Regulation? • Model projects what regulations wished to achieve • Reality proves that regulations failed to achieve • Manufacturers followed “letter” not “spirit” of law! • Improvements required to regulations • Different driving profile? • Non-to-exceed approach? TFEIP Stockholm
Quantifying uncertainties • Use new knowledge • Models • Activity data • Compare with • Old models • Old activity data • Objective: Explain uncertainty due to model and activity data differences TFEIP Stockholm
Approach to quantify uncertainty: input data • RAINS activity and emission factor data used to set the NECD targets • Cost-effective Control of Acidification and Ground-Level Ozone. Part A: Methodology and Databases. Sixth Interim Report to the European Commission, IIASA 1998. • Actual excel files received by J. Cofala, Oct. 2010. • FLEETS/EC4MACS data • Updated datasets used by GAINS in the framework of LIFE EC4MACS • Based on original data by individual MSs • Four countries used as examples:DE, FR, IE, NL TFEIP Stockholm
Approach to quantify uncertainty: models • COPERT II (1997) • Used to provide removal efficiencies to RAINS • COPERT 4 v8.0 (Nov. 2010) • Most updated version, implementing HBEFA 3.1 HDV EFs TFEIP Stockholm
Runs executed (Germany) • Run 1: Original RAINS calculation • Run 2: COPERT 2 + RAINS Input • Run 3: COPERT 2 + EC4MACS Input • Run 4: COPERT 4 + RAINS Input • Run 5: COPERT 4 + EC4MACS Input TFEIP Stockholm
DE: Activity TFEIP Stockholm
DE: Technology penetration TFEIP Stockholm
34% activity 75% EF DE: NOx Emissions TFEIP Stockholm
DE 2010: Technology responsibility TFEIP Stockholm
IE: Activity TFEIP Stockholm
131% activity 64% EF IE: NOx Emissions TFEIP Stockholm
IE 2010: Technology responsibility TFEIP Stockholm
Summary • Differences between target and reality result from both emission factors and activity data • 65-75% higher emissions due to EFs • 19-131% higher emissions due to activity data • Emission factors • Practically all Euro 3 / III and later diesel EFs • Conventional/E1 GPC! • Activity data: • Misallocation of HD, LD diesel consumption • Relative increase of DPC consumption • Too fast scrappage of old vehicles assumed TFEIP Stockholm
More Info http://acm.eionet.europa.eu/reports/ETCACC_TP_2010_20_Copert2vsCopert4 TFEIP Stockholm