(funded by dg research 5th fp)

IER

Universität Stuttgart

Institut für Energiewirtschaft und Rationelle Energieanwendung

Multi-Pollutant Multi-Effect Modelling of European Air Pollution Control Strategies - an Integrated Approach

(funded by DG Research 5th FP)

IER



The MERLIN team:• IER University of Stuttgart (Co-ordinator)

• Aristotle University of Thessaloniki, Laboratory for Heat Transfer and Environmental Engineering (AUT/LHTEE)

• University College London (UCL)

• Norwegian Meteorological Institute (met.no)

• ECOFYS Energy and Environment

• Institute for Ecology of Industrial Areas (IETU)

• Energy Research Center (ERC) of Ostrava Technical University

• National Institute of Meteorology and Hydrology (NIMH)

• University of Ploiesti (Ploiesti, Romania)

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Objectives

Development and application of methodologies and toolsfor an integrated assessment of European air pollution control strategies

• multi-pollutant, multi-effect assessment

• cost-effectiveness and cost-benefit analysis

• application of advanced optimisation methods

• inclusion of non-technical measures

• assesment of sectoral reduction potentials

• macroeconomic effects and distributional burdens of air pollution control

• inclusion of accession countries

Features

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Acidification

Eutrophication

TroposphericOzone

Global Warming

primary & secondary Aerosols

Urban Air Quality

NOx

SO2

NMVOCCO

CO2 CH4NH3

N2O

Particulate Matter

(PM2.5 / PM10)

Multi-Pollutant Multi-Effect Analysis

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Reference Description EF1 EF2 .. EFn Costs Meta-InformationMeasure-Database (MDB)

Reference Description Stock (S) Activity (A) EF1 EF2 .. EFn Meta-InformationStock-Activity-Database (SADB)

unique ID

e = A * EFi

E = S * e

techn. measures(affecting EFs)

non-tech. measures(affecting S or A) Information on implementation,

interdependencies, i.e. AND, OR, XOR, ...

EF = Emission FactorS = Stock (e.g. # of vehicles)A = Activity (e.g. km/yr)e = source emissionsE = source-group emissions

Costs of implementation(typically with reference to Stock or Activity)

The Measure-Matrix approach illustrated

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Calculation example (I)

Stock Activities

Emission Factors

Emissions

X

No. of vehicles:

# [GER, PC, EURO2, gasoline, 2000]

annual mileage:

km/vehicle*year [GER, PC, EURO2, gasoline, 2000]

g/km [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...]

t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...]

concentrations/deposition

impacts exceedances

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Calculation example (II)

Stock Activities

Emission Factors

Emissions = f(M1)

X

No. of vehicles:


annual mileage:



t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...]Measure (M1)DPF PM


impacts

costs

benefits

CEA

CBAexceedancescompliance?

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Calculation example (III)

Stock Activities

Emission Factors

Emissions = f(M1,M2)

X

No. of vehicles:


annual mileage:



t/year [CO2, NOx, CO, NMVOC, PM2.5, PM10, ...]Measure (M1)DPF PM

Measure (M2)Scrapping ofEURO1 vehicles #


impacts benefits

CEA

CBAcosts

exceedancescompliance?

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Non-Technical Measures (I)

In MERLIN, non-technical measures such as:

(a) Higher motor fuel taxes

(b) Road congestion pricing

(c) Higher taxes on motor vehicle ownership

(d) Restructuring vehicle fuels taxes (eg the balance between diesel fuel and petrol)

(e) Accelerated scrapping incentives

(f) Parking charges

(g) Public transport subsidy

• are evaluated, which affect activity levels by increasing relative prices.

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Non-Technical Measures (II)

example for a price increase on fuel: Dead weight loss (DWL) as an means

to assess the costs of implementation for a 10% increase in fuel tax

Dead Weight Loss for a 10% increase in fuel tax (based on 2000 prices)

0

2

4

6

8

10

12

Au

str

ia

Be

lgiu

m

De

nm

ark

Fin

lan

d

Fra

nc

e

Ge

rma

ny

Gre

ec

e

Ire

lan

d

Ita

ly

Lu

xe

mb

ou

rg NL

Po

rtu

ga

l

Sp

ain

Sw

ed

en

UK

Bu

lga

ria

Cze

ch

R

Es

ton

ia

Hu

ng

ary

La

tvia

Lit

hu

an

ia

Po

lan

d

Ro

ma

nia

Slo

va

k R

Mill

ion

€

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Non-Technical Measures (III)

Different effects on increased fuel tax is distributed into

• modal change: change from private to public transport (urban/inter-urban rail/bus)

• activity reduction: reduction of annual mileage („not driving“)

• technology change: effects on fuel efficiency (l/km) long term effect

investigate joint assessment of technical andnon-technical measures in the same framework, avoiding double-counting and over/underestimationof effects

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Sectoral Studies

• potential to investigate questions such as the general spatial, temporal

and speciation differences of emissions of individual source sectors, e.g.

• environmental impacts of gasoline vs diesel vehicles

• emission control in mobile vs stationary sources

(emission height, VOC split etc.)

• impacts of structural changes in the energy sector on air pollutant

emissions and GHG emissions

• for this purpose, calculation of sector-specific Source-Receptor Matrices

• road transport (PC, LDV, HDV, MP, MC, evaporation)

• public power plants (by fuel)

• ...

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CEEC Information

• detailed analysis of implementation of

environmental policies in Accession countries

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Calibration/Evaluation

• first comparison of fuel consumption data for road transport fuels

(gasoline & diesel); R2 = 0.9867

0

500

1.000

1.500

2.000

2.500

3.000

AU

T

BE

L

DE

N

FIN

FR

A

GE

R

GR

E

IRE

ITA

LUX

NL

PO

R

SP

A

SW

E

UK

BU

L

CZ

R

ES

T

HU

N

LAT

LIT

PO

L

RO

M

SLO

SLV

Transport Fuel Consumption [PJ] MERLIN Transport Fuel Consumption [PJ] IIASA Baseline

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Calculate the resulting emissions for each country

Calculate the resulting emissions for each country

Crossbreed and mutate the surviving strategies

Crossbreed and mutate the surviving strategies

Remove strategies with worstperformance

Remove strategies with worstperformance

Evaluate results(emissions, concentrations,

costs and benefits)

Evaluate results(emissions, concentrations,

costs and benefits)

Calculate concentration changes on a 50 * 50 km grid

Calculate concentration changes on a 50 * 50 km grid

Generate the first set of solutions at random

Generate the first set of solutions at random

Compiling a set of measuresfrom the measure database

Source receptorMatrices (EMEP)

End optimisation, if targets areachieved (evaluation with EMEP Uni)

MERLIN Optimisation Approach (Genetic Algorithm) – the OMEGA Model

Stock activity databasemodified by measure set

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Model results (Ia): Ozone AOT40 crops [ppb.h]

2010 CLE (2003 met)

AOT40c (-50% NOx -50% NMVOC)

AOT40c (-50% NOx -50% NMVOC)

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Model results (Ib): Ozone AOT40 crops [ppb.h]

2010 CLE (2003 met)

AOT40c (-50% NOx -50% NMVOC) AOT40c (-50% NOx -50% NMVOC)

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Model results (Ic): Ozone AOT40 crops [ppb.h]

2010 CLE (2003 met)

AOT40c (-50% NOx -50% NMVOC) AOT40c (-50% NOx -50% NMVOC)

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Model results (IIa): Acid-Deposition [mg/m2]

2010 CLE (2003 met)

S-Deposition (-50% NOx -50% SO2)


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Model results (IIb): Acid-Deposition [mg/m2]

2010 CLE (2003 met)



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Model results (IIIa): N-Deposition [mg/m2]

2010 CLE (2003 met)

N-Deposition (-50% NOx -50% NH3)


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Model results (IIIb): N-Deposition [mg/m2]


2010 CLE (2003 met)


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Model results (IIIc): N-Deposition [mg/m2]


2010 CLE (2003 met)


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Model results (IVa): Optimisation example for 4 pollutants

UK Italy

GermanyFrance

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Model results (IVb): Optimisation example for 4 pollutants

France

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Conclusions

• OMEGA Model up and running• Source-receptor matrices for more years needed to

investigate scenarios on the basis of averaged values• calibration/evaluation of stock, activity and emission factors

vs. latest baseline scenario data vital to ensure comparability of results

• sectoral matrices for road transport and power plants (first) to allow for detailed sector assessment runs

• final evaluation of measure data with data available from EGTEI, Baseline Scenarios, experts

To do: define sets of targets (air quality & GHG emissions) and ...

... run, run, run, run run ...

(funded by dg research 5th fp)

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