norwegian meteorological institute met.no 7 th joint unece task force & eionet ws on emission...

Norwegian Meteorological Institute met.no

7th Joint UNECE Task Force & EIONET WS on Emission Inventories and Projections, Thessaloniki 31 Oct – 2 Nov 2006

Vigdis Vestreng, EMEP/MSC-W

MSC-W Emission Estimates: Why and How?

PM2.5: SNAP total

0 %

20 %

40 %

60 %

80 %

100 %

2000 2001 2002 2003 2004

Gaps

Replaced

Reported


Methodology for gap filling and replacements of reported emission data

I. Inventory Review 2006. Emission data reported to LTRAP Convention and NECD. EMEP note 1/2006ETC-ACC: Elisabeth Rigler, Martin AdamsMSC-W: Vigdis Vestreng

II. Chapter 2 in EMEP joint report 1/2006; Emissions: progress towards the emission ceilings in the Gothenburg ProtocolETC/ACC:Elisabeth Rigler

MSC-W: Vigdis Vestreng, Leonor Tarrasón,Heiko Klein, Anna Carlin Benedictow

III. Chapter 3 in EMEP report 1/2004JRC: phillip Thunis; CONCAWE: Les whiteMSC-W: Leonor Tarrasón, Heiko Klein, Vigdis Vestreng

IV. MSC-W presentation in the Projection WSMSC-W: Jan Eiof Jonson


Motivation & Requirements

• Assessments of pollution impact on human health, exceedances of critical loads and climate

• Need complete and good quality emission data in the whole of the modelling domain

• Spatially distributed consistent sector emission data


Step 1 of 6. Input from Stage 1 and 2 review

• Review on NFR level flag problems with consistency and comparability.

• IIRs needed to verify replacement of reported data


Step 2 of 6. Identification of data format and gaps

NOx: SNAP sector 7

0 %

20 %

40 %

60 %

80 %

100 %

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Gap 07

SNAP 07

NFR1 07

NFR2 07

PM2.5: SNAP sector 2

0 %

20 %

40 %

60 %

80 %

100 %

2000

2001

2002

2003

2004

Gap 02

SNAP 02

NFR1 02

NFR2 02


Step 3 of 6. Conversion to SNAP

SNAP sectors in the modelling work (NFR to SNAP, table

IIIB)

A. we wish to use as much as possible gridded data reported by countries and up to now most reported gridded data is in SNAP (only 5 countries reported in NFR)

B. we wish to be able to compare directly with the emissions gridded data estimated in cooperation with IIASA for CAFÉ (1990, 1995, 2000, 2010, 2020 data)


Step 4 of 6. Identification of possible inconsistent data

Test sector data across sectors, by looking at how the contribution of a sector to the reported national total varies over the years. This was done to see if the flagged dips and jumps in the time series was due to varying allocation of emissions to different sectors (e.g. between S1 and S3/S4)

Both PM 2.4 and PM10 needs to be reported


Step 5 of 6. IIR consultation; Example: Denmark

• SNAP 1 seems inconsistent across time. Possible outliers in 1991 and 1996

• IIR explanation: e.g. High fuel consumption in 1996 due to large electricity export. Also 2003 vs 2004: Low rainfall in Norway and Sweden in 2003.

0

20

40

60

80

100

120

140

160

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

Gg

NO

2

SNAP 1 NOX

0

100

200

300

400

500

600

700

800

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fue

l con

sum

ptio

n

-60

-40

-20

0

20

40

60

80

100

Ele

ctri

city

exp

ort

Fossil fuel consumption [PJ]

Coal consumption [PJ]

Electricity export [PJ]


Step 6 of 6. Sources of emission data to complete official data

A. Linear interpolation between reported emission values if maximum of 5 years apart reliable reported values B. For lack of reported emission values after a given point in time, trends are filled inn in agreement with the sector trends for countries with similar economic structures and fuel split. This was assumed more realistic than to assume a linear trend, because we observe that the officially reported emission reductions decrease with time. If a maximum of two years are missing in the end of a timeseries, gapfilling is performed by extrapolation

C. When no trend information is available, we use IIASA Café values and if necessary linear interpolation between 1990, 1995, 2000, 2005. IIASA 2005 data was used for 2004. IIASA provided in addition emissions of CO, and PM emissions for Armenia, Georgia and Kazakhstan. D. EDGAR version 3.2 emission data was used for a few countries (Armenia, Azerbaijan , Georgia and Iceland)

E. For a very few countries NH3 data from GEIA and data from a similar country scaled with population was used.


Results statistics

Pollutant% reported(average)

% replaced(average)

% gaps(average)

SOx 57 20-34 (30) 9-23 (13)

NOx 48-64 (57) 23-41 (30) 9-27 (13)

NMVOC 36-59 (45) 23-41 (34) 18-30 (20)

NH3 34-48 (41) 25-45 (26) 18-32 (23)

PM2.5 34-43 (38) 7-23 (14) 41-57 (48)

PM10 27-36 (33) 14-30 (23) 41-52 (45)

Amount of reported, replaced and gaps of national total emission in the EMEP inventory (Unit: %, average values in brackets)


Use of ancillary data to distribute sector emissions:

Large Point Source Information (LPS, both location and

intensities)

Population distribution (POP, common with IIASA)

Information from the CEPMEIP project (TNO, land-use and

road maps)

Information on national gridded sector emissions (GS)

The method secures the consistency of emission sector distribution across

Europe per component. Precursor gases and primary PM emissions are

consistently distributed.

Methodology to spatially distribute emission data


Conclusions

Strong need for MSC-W estimates due to

A. Lack of officially reported data (e.g. only 13 countries reported gridded sector data for at least one year)

B. Inconsistent and incomplete (in terms of sources included) reporting

MSC-W estimates are needed to be able to perform impact studies for health, air

pollution and climate.

norwegian meteorological institute met.no 7 th joint unece task force & eionet ws on emission...

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