SIXTH FRAMEWORK PROGRAMME

Project no: 502687NEEDS

New Energy Externalities Developments for Sustainability

INTEGRATED PROJECTPriority 6.1: Sustainable Energy Systems and, more specifically,

Sub-priority 6.1.3.2.5: Socio-economic tools and concepts for energy strategy.

RS 1d - Deliverable n. T3.1 “Datasets on reference environment and

technology”

Due date of deliverable: 30 April 2007Actual submission date: 1 August 2008

Start date of project: 1 September 2004 Duration: 48 months

Organisation name for this deliverable: AEKI, CDER, CUEC, LEGI-EPT, MEERI, NREA, OME, PROFING, SEI, UNWE

Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)

Dissemination LevelPU Public XPP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

Table of Contents

Summary..........................................................................................................................................31 Reference environment database.............................................................................................4

1.1 Population........................................................................................................................41.1.1 Hungary...................................................................................................................51.1.2 Czech Republic........................................................................................................71.1.3 Estonia.....................................................................................................................81.1.4 Slovakia.................................................................................................................101.1.5 Bulgaria..................................................................................................................121.1.6 Poland....................................................................................................................131.1.7 Tunisia...................................................................................................................151.1.8 Morocco.................................................................................................................171.1.9 Egypt......................................................................................................................181.1.10 Neigbouring countries...........................................................................................18

1.2 Risk group fractions.......................................................................................................191.3 Crops..............................................................................................................................20

1.3.1 Hungary.................................................................................................................211.3.2 Czech Republic......................................................................................................231.3.3 Estonia...................................................................................................................231.3.4 Slovakia.................................................................................................................241.3.5 Bulgaria..................................................................................................................241.3.6 Poland....................................................................................................................251.3.7 Tunisia...................................................................................................................271.3.8 Morocco.................................................................................................................271.3.9 Egypt......................................................................................................................281.3.10 Neighbouring countries.........................................................................................28

1.4 Materials........................................................................................................................282 Reference technology database..............................................................................................30

2.1 Data requirements..........................................................................................................302.2 Tool for emission calculation based on fuel type and technology.................................30

2.2.1 Model description..................................................................................................312.3 Country-specific technological data..............................................................................352.4 Allocation of external costs of heat and electricity generation......................................35

2.4.1 Emission allocation in CHP with back preasure turbine.......................................362.4.2 Emission allocation in CHP with extraction turbine.............................................392.4.3 Conclusion.............................................................................................................41

2

Summary

In order to extend the geographical coverage of the EcoSense model, the databases on the receptors (population, crops, materials), emission sources (technological data of the energy generation utilities) as well as monetary values had to be extended and updated. Data collection was performed by each partner for their respective countries, which had to be performed in accordance with the data requirement of the EcoSense model (presented to the partners by USTUTT.TFU at Workshop 1).

The collection of country-specific data needed for the extension of the geographical coverage of the EcoSense model is divided to three tasks:

1. Reference environment database, which includes receptor data such as population, crops and materials data.2. Reference technology database, which contains data of energy generation technologies selected for evaluation.

3

1 Reference environment database

Data were collected for respective countries and if relatively easily available, for the neighbour countries (which aren’t partners in NEEDS) as well. As a priority, data were collected for the selected base year 2005, but in case of unavailability of detailed data for 2005, the last year with detailed data with the highest possible spatial resolution was selected for data collection.

1.1 Population

Since damages in human health were found to be one of the most important drivers of external costs, damages should be calculated in a relevant way not only for regional scale, but also for local scale. For this reason population data were collected at the highest possible spatial resolution.

The reference environmental database of the EcoSense model is built on the EMEP grid 1 that is based on a polar-stereographic projection. The USTUTT.TFU group generated a grid with the resolution 10×10 km² (5×5 km²). The population data could be provided in a table with the geographical position referred as the EMEP10_X and EMEP10_Y coordinates. The grid definitions (grid_definitions.zip) were provided by the USTUTT.TFU group.

Another possibility was to provide the population data on the basis of administrative units, Nomenclature of Territorial Units for Statistics (NUTS) for European countries.2,3 NUTS Level 4 is now called LAU level 1 and NUTS Level 5 is now the LAU level 2.4 In the latter case, the USTUTT.TFU group had to convert the data based on the administrative units to data based on the EMEP grid. As digital maps (i.e. GIS-maps) of the contours of the NUTS Level 3 administrative units of all European countries were not available for the USTUTT.TFU group, population data based on administrative units had to be collected with a digital map (in shape format) of the contours of the respective administrative units. The absolute population (not population density) data were collected at the LAU2 administrative level (where available), as well as the GIS map of the contours of the administrative units (preferably) in the WGS84 coordinate system.

In order to harmonize of the data collection performed by the different partners, an EXCEL template was prepared and filled with the respective data provided by each partners. The description of the data headers is the following:

Country – two-letter country code, e.g. HU for HungaryEMEP10_Xa – X coordinate in the 10×10 km² EMEP gridEMEP10_Ya – Y coordinate in the 10×10 km² EMEP gridRegionb – code of the region at the administration level of the dataYear – year of data1 http://www.emep.int/grid/griddescr.html2 http://ec.europa.eu/comm/eurostat/ramon/nuts/codelist_en.cfm?list=nuts3 http://simap.eu.int/nomen_nuts/7148f4fa-ad24-9e4d-03e427e0aca09bad_en.html4 http://ec.europa.eu/comm/eurostat/ramon/nuts/home_regions_en.html

4

Population – absolute number of population in the respective region Quality – indicator of data quality (A: best, e.g. official data; E: worst, e.g. estimation)Commentb – label of the region at the administration level of the dataSource – source of the data

a for data collection based on the EMEP grid cellsb for data collection based on administrative units

In the next subsections the collected data are described in more details for each country.

1.1.1 Hungary

Population data were collected for every settlement in Hungary (LAU2 administrative level). Although the base year of the study was agreed as 2005, the most detailed population data were available only for the year of the last national census carried out in 2001. The data were obtained from the Hungarian Central Statistical Office.5 In order to prepare the data utilizable for the USTUTT.TFU group developing the EcoSenseWeb software utility, the raw data for the statistical units were combined with a digital map of the administrative units of Hungary that was available at AEKI. The map was in the special Hungarian projection EOV that had to be converted to the geographical coordinate system WGS84. Although the collected database includes absolute number of population for 3096 settlements, the illustrative map below shows population density map for Hungary (absolute number of population divided by the area of the administrative unit). Part of the summary table of the collected population data is included in Table 1.

Population density (1/ km2)0 - 250250 - 500500 - 10001000 - 20002000 - 4000

5 Population Census 2001, Hungarian Central Statistical Office, 2004, http://www.nepszamlalas2001.hu/eng/index.html

5

Fig. 1. Population density map of Hungary at the LAU2 administrative level

Table 1. Part of the population dataset for Hungary at the LAU2 administrative level (first and last 10 lines)

Country Region Year Population Quality Comment SourceHU 1357 2001 1777921 A Budapest Hungarian Central Statistical Office HU 330 2001 2135 A Iklad Hungarian Central Statistical Office HU 480 2001 1958 A Domony Hungarian Central Statistical Office HU 913 2001 3969 A Bag Hungarian Central Statistical Office HU 959 2001 8043 A Tura Hungarian Central Statistical Office HU 1394 2001 2975 A Hévízgyörk Hungarian Central Statistical Office HU 1618 2001 6428 A Aszód Hungarian Central Statistical Office HU 1950 2001 2527 A Galgahévíz Hungarian Central Statistical Office HU 2248 2001 1428 A Verseg Hungarian Central Statistical Office HU 3069 2001 5711 A Kartal Hungarian Central Statistical Office ...HU 3170 2001 7803 A Sándorfalva Hungarian Central Statistical Office HU 3336 2001 174135 A Szeged Hungarian Central Statistical Office HU 783 2001 1791 A Derekegyház Hungarian Central Statistical Office HU 1445 2001 31638 A Szentes Hungarian Central Statistical Office HU 1723 2001 3435 A Nagymágocs Hungarian Central Statistical Office HU 1906 2001 609 A Árpádhalom Hungarian Central Statistical Office HU 1997 2001 2278 A Fábiánsebestyén Hungarian Central Statistical Office HU 2299 2001 665 A Eperjes Hungarian Central Statistical Office HU 2917 2001 521 A Nagytoke Hungarian Central Statistical Office HU 3248 2001 4913 A Szegvár Hungarian Central Statistical Office

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1.1.2 Czech Republic

Population data were collected by CUEC for every settlement in Czech Republic (LAU2 administrative level). Data were bought from the Czech Statistical Office for year 2005. In order to prepare the data utilizable for the USTUTT.TFU group developing the EcoSenseWeb software utility, the data for the statistical units were collected together with a digital map of the administrative units of Czech Republic. Although the collected database includes absolute number of population for 6352 settlements, the illustrative map below shows population density map for Czech Republic. Part of the summary table of the collected population data is included in Table 2.

Population density (1/km2)0 - 250250 - 500500 - 10001000 - 2000 2000 - 30000

Fig. 2. Population density map of Czech Republic at the LAU2 administrative level

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Table 2. Part of the population dataset for Czech Republic at the LAU2 administrative level (first and last 10 lines)

Country Region Year Population Quality Comment SourceCZ 500259 2005 1931 A Veřovice Czech Central Statistical OfficeCZ 500291 2005 2468 A Vřesina Czech Central Statistical OfficeCZ 500496 2005 100381 A Olomouc Czech Central Statistical OfficeCZ 500526 2005 1974 A Bělkovice-Lašťany Czech Central Statistical OfficeCZ 500623 2005 1088 A Bílá Lhota Czech Central Statistical OfficeCZ 500801 2005 577 A Blatec Czech Central Statistical OfficeCZ 500852 2005 2462 A Bohuňovice Czech Central Statistical OfficeCZ 500861 2005 1463 A Bouzov Czech Central Statistical OfficeCZ 500879 2005 603 A Bystročice Czech Central Statistical OfficeCZ 501476 2005 1803 A Dlouhá Loučka Czech Central Statistical Office...CZ 536008 2005 807 A Katusice Czech Central Statistical OfficeCZ 565971 2005 18841 A Louny Czech Central Statistical OfficeCZ 566624 2005 5002 A Postoloprty Czech Central Statistical OfficeCZ 574252 2005 2778 A Meziměstí Czech Central Statistical OfficeCZ 535443 2005 4910 A Bělá pod Bezdězem Czech Central Statistical OfficeCZ 563340 2005 1048 A Spořice Czech Central Statistical OfficeCZ 565768 2005 1700 A Třebenice Czech Central Statistical OfficeCZ 561495 2005 5103 A Doksy Czech Central Statistical OfficeCZ 568201 2005 1200 A Řehlovice Czech Central Statistical OfficeCZ 568015 2005 4235 A Chlumec Czech Central Statistical Office

1.1.3 Estonia

Population data were collected for every settlement in Estonia (LAU2 administrative level), for 2005 from the land Board of Estonia. A very detailed table of 241 LAU2 units was provided by SEI, but unfortunately without a digital map of the contours of the administrative units. Digital map in shape format could be obtained only at the LAU1 level (from GADM6), therefore the illustrative map below shows population density map for Estonia at the LAU1 level only. Part of the summary table of the collected population data is included in Table 3.

6 GADM – Global Administrative Areas, http://biogeo.berkeley.edu/gadm/

8

Population density (1/km2)0 - 2020 - 4040 - 8080 - 100100 - 120

Fig. 3. Population density map of Estonia at the LAU1 administrative level

Table 3. Part of the population dataset for Estonia at the LAU2 administrative level (first and last 10 lines)

Country Region Year Population Quality Comment SourceEE 0296 2005 9386 A Keila Land Board of EstoniaEE 0424 2005 3469 A Loksa Land Board of EstoniaEE 0446 2005 16570 A Maardu Land Board of EstoniaEE 0580 2005 4190 A Paldiski Land Board of EstoniaEE 0728 2005 5067 A Saue Land Board of EstoniaEE 0784 2005 396193 A Tallinn Land Board of EstoniaEE 0112 2005 905 A Aegviidu Land Board of EstoniaEE 0140 2005 6244 A Anija Land Board of EstoniaEE 0198 2005 6786 A Harku Land Board of EstoniaEE 0245 2005 5197 A Jõelähtme Land Board of Estonia...EE 0389 2005 1773 A Lasva Land Board of EstoniaEE 0460 2005 1195 A Meremäe Land Board of EstoniaEE 0468 2005 811 A Misso Land Board of EstoniaEE 0493 2005 1031 A Mõniste Land Board of EstoniaEE 0697 2005 2045 A Rõuge Land Board of EstoniaEE 0767 2005 1914 A Sõmerpalu Land Board of EstoniaEE 0843 2005 1433 A Urvaste Land Board of EstoniaEE 0865 2005 1294 A Varstu Land Board of EstoniaEE 0874 2005 2141 A Vastseliina Land Board of EstoniaEE 0918 2005 4807 A Võru Land Board of Estonia

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1.1.4 Slovakia

Population data were collected by PROFING for every community in Slovakia (LAU2 administrative level). The data obtained from the Staistical Office of Slovak Republic7 for year 2005 were reorganized to the required format. In order to prepare the data utilizable for the USTUTT.TFU group developing the EcoSenseWeb software utility, a digital map of the administrative units of Slovakia was also obtained. Unfortunately the shapefile contains only the contours of the LAU1 administrative units. Although the collected database includes absolute number of population for 2929 communities, the illustrative map below shows population density map for Slovakia. Part of the summary table of the collected population data is included in Table 4.

Population density (1/km2)0 - 250250 - 500500 - 10001000 - 20002000 - 4500

Fig. 4. Population density map of Slovakia at the LAU1 administrative level

7 Statistical Office SR, Demographic and social policy in SR by the communities 2005, code of publication 020606

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Table 4. Part of the population dataset for Slovakia at the LAU2 administrative level (first and last 10 lines)

Country Region Year Population Quality Comment SourceSK 528595 2005 42241 A Bratislava - Staré Mesto Statistical Office SRSK 529311 2005 19977 A Bratislava - Podunajské Biskupice Statistical Office SRSK 529320 2005 69674 A Bratislava - Ružinov Statistical Office SRSK 529338 2005 18996 A Bratislava - Vrakuňa Statistical Office SRSK 529346 2005 37040 A Bratislava - Nové Mesto Statistical Office SRSK 529354 2005 20357 A Bratislava - Rača Statistical Office SRSK 529362 2005 4331 A Bratislava - Vajnory Statistical Office SRSK 529401 2005 1005 A Bratislava - Devín Statistical Office SRSK 529371 2005 15629 A Bratislava - Devínska Nová Ves Statistical Office SRSK 529389 2005 34540 A Bratislava - Dúbravka Statistical Office SR...SK 543951 2005 2097 A Vojčice Statistical Office SRSK 543969 2005 516 A Vojka Statistical Office SRSK 543977 2005 777 A Zatín Statistical Office SRSK 543985 2005 291 A Zbehňov Statistical Office SRSK 543993 2005 404 A Zemplín Statistical Office SRSK 544001 2005 964 A Zemplínska Nová Ves Statistical Office SRSK 544019 2005 1487 A Zemplínska Teplica Statistical Office SRSK 544027 2005 1138 A Zemplínske Hradište Statistical Office SRSK 544035 2005 629 A Zemplínske Jastrabie Statistical Office SRSK 544043 2005 489 A Zemplínsky Branč Statistical Office SR

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1.1.5 Bulgaria

Population data were collected for every sub-region in Bulgaria (LAU1 administrative level), for 2005 from the Bulgarian National Statistical Office. A very detailed table containing data for 264 sub-regions was provided by UNWE, but unfortunately without a digital map of the contours of the administrative units. Digital map in shape format could be obtained only at the NUTS3 level (from GADMError: Reference source not found), therefore the illustrative map below shows population map for Bulgaria at the NUTS3 level only. Part of the summary table of the collected population data is included in Table 5.

Population0 - 250000250000 - 500000500000 - 750000750000 - 10000001000000 - 1250000

Fig. 5. Population map of Bulgaria at the NUTS3 administrative level

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Table 5. Part of the population dataset for Bulgaria at the LAU1 administrative level (first and last 10 lines)

Country Region Year Population Quality Comment SourceBG BLG01 2005 13114 A Bansko Bulgarian National Statistical InstituteBG BLG02 2005 9518 A Belitza Bulgarian National Statistical InstituteBG BLG03 2005 77462 A Blagoevgrad Bulgarian National Statistical InstituteBG BLG11 2005 32022 A Gotze Delchev Bulgarian National Statistical InstituteBG BLG13 2005 14593 A Gurmen Bulgarian National Statistical InstituteBG BLG28 2005 5852 A Kresna Bulgarian National Statistical InstituteBG BLG33 2005 57102 A Petritch Bulgarian National Statistical InstituteBG BLG37 2005 21591 A Razlog Bulgarian National Statistical InstituteBG BLG40 2005 42299 A Sandanski Bulgarian National Statistical InstituteBG BLG42 2005 17428 A Satovcha Bulgarian National Statistical Institute...BG SHU21 2005 6796 A Nikola Kozlevo Bulgarian National Statistical InstituteBG SHU22 2005 19141 A Novi Pazar Bulgarian National Statistical InstituteBG SHU23 2005 15855 A Veliki Preslav Bulgarian National Statistical InstituteBG SHU25 2005 7818 A Smjadovo Bulgarian National Statistical InstituteBG SHU30 2005 101515 A Shumen Bulgarian National Statistical InstituteBG JAM03 2005 4856 A Boljarovo Bulgarian National Statistical InstituteBG JAM07 2005 18303 A Elhovo Bulgarian National Statistical InstituteBG JAM22 2005 14538 A Straldja Bulgarian National Statistical InstituteBG JAM25 2005 29083 A Tundja Bulgarian National Statistical InstituteBG JAM26 2005 79314 A Yambol Bulgarian National Statistical Institute

1.1.6 Poland

Population data were collected by MEERI for every settlement in Poland (LAU2 administrative level), for year 2004 from the Central Statistical Office of Poland. In order to prepare the data utilizable for the USTUTT.TFU group developing the EcoSenseWeb software utility, the data for the statistical units were converted directly to the 50×50, 10×10 and 5×5 km2 grids at the EMEP projection compatible with the database incorporated in the EcoSenseWeb utility. An illustrative map below shows population distribution of Poland in the 5×5 km2 EMEP grid.

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Fig. 6. Population map of Poland in the 5×5 km2 EMEP grid.

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1.1.7 Tunisia

Population data were collected by LEGI-EPT for administrative units “Gouvernorat” equivalent to the NUTS3 level in Europe. Data were obtained from the Institut National de la Statistique (INS), for year 2005. Digital map including the contours of the same administrative units in the WGS84 coordinate system was also provided. An illustrative map below shows population map for Tunisia. The collected population data are tabulated in Table 6.

Population0 - 200000200000 - 400000400000 - 600000600000 - 800000800000 - 1000000

Fig. 7. Population map of Tunisia based on administrative units “Gouvernorat” equivalent to NUTS3 in Europe.

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Table 6. Population dataset for Tunisia based on andministrative units “Gouvernorat”

Country Region Year Population Quality Comment SourceTN TN.AN 2005  435900 A Ariana Institut National de la Statistique (INS)TN TN.BJ 2005  304000 A Béja Institut National de la Statistique (INS)TN TN.BA 2005  520200 A Ben Arous Institut National de la Statistique (INS)TN TN.BZ 2005  529300 A Bizerte Institut National de la Statistique (INS)TN TN.GB 2005  345900 A Gabés Institut National de la Statistique (INS)TN TN.GF 2005  326000 A Gafsa Institut National de la Statistique (INS)TN TN.JE 2005  418100 A Jendouba Institut National de la Statistique (INS)TN TN.KR 2005  548200 A Kairouan Institut National de la Statistique (INS)TN TN.KS 2005  415700 A Kasserine Institut National de la Statistique (INS)TN TN.KB 2005  144400 A Kébili Institut National de la Statistique (INS)TN TN.KF 2005  258500 A Le Kef Institut National de la Statistique (INS)TN TN.MH 2005  381900 A Mahdia Institut National de la Statistique (INS)TN TN.MN 2005  341800 A Manubah Institut National de la Statistique (INS)TN TN.ME 2005  436700 A Médenine Institut National de la Statistique (INS)TN TN.MS 2005  466300 A Monastir Institut National de la Statistique (INS)TN TN.NB 2005  705000 A Nabeul Institut National de la Statistique (INS)TN TN.SF 2005  869400 A Sfax Institut National de la Statistique (INS)TN TN.SZ 2005  398400 A Sidi Bou Zid Institut National de la Statistique (INS)TN TN.SL 2005  233900 A Siliana Institut National de la Statistique (INS)TN TN.SS 2005  557100 A Sousse Institut National de la Statistique (INS)TN TN.TA 2005  143900 A Tataouine Institut National de la Statistique (INS)TN TN.TO 2005  98500 A Tozeur Institut National de la Statistique (INS)TN TN.TU 2005  986700 A Tunis Institut National de la Statistique (INS)TN TN.ZA 2005  163300 A Zaghouan Institut National de la Statistique (INS)

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1.1.8 Morocco

Population data were collected by CDER for every province in Morocco (equivalent to LAU1 administrative level in Europe). Data were obtained from the Direction de la Statistique, for year 2004. The dataset includes population data for 61 provinces. Digital map including the contours of the same administrative units was also provided. An illustrative map below shows population map for Morocco. Parts of the summary table of the collected population data are included in Table 7.

Population0 - 200000200000 - 400000400000 - 600000600000 - 800000800000 - 1000000

Fig. 8. Population map of Morocco based on administrative units provinces, equivalent to LAU1 in Europe.

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Table 7. Part of population dataset for Morocco based on administrative units provinces.

Country Region Year Population Quality Comment SourceMA 01.066 2004 20513 A Aousserd Direction de la StatistiqueMA 01.391 2004 78854 A Oued Ed-Dahab Direction de la StatistiqueMA 02.121 2004 46129 A Boujdour Direction de la StatistiqueMA 02.321 2004 210023 A Laâyoune Direction de la StatistiqueMA 03.071 2004 43535 A Assa-Zag Direction de la StatistiqueMA 03.221 2004 60426 A Es-Semara Direction de la StatistiqueMA 03.261 2004 166685 A Guelmim Direction de la StatistiqueMA 03.521 2004 70146 A Tan-Tan Direction de la StatistiqueMA 03.551 2004 121618 A Tata Direction de la StatistiqueMA 04.001 2004 487954 A Agadir-Ida ou Tanane Direction de la Statistique...MA 14.451 2004 259577 A Sefrou Direction de la StatistiqueMA 14.591 2004 150422 A Moulay Yacoub Direction de la StatistiqueMA 15.051 2004 395644 A Al Hoceïma Direction de la StatistiqueMA 15.531 2004 668232 A Taounate Direction de la StatistiqueMA 15.561 2004 743237 A Taza Direction de la StatistiqueMA 16.151 2004 524602 A Chefchaouen Direction de la StatistiqueMA 16.227 2004 97295 A Fahs Anjra Direction de la StatistiqueMA 16.331 2004 472386 A Larache Direction de la StatistiqueMA 16.511 2004 762583 A Tanger-Assilah Direction de la StatistiqueMA 16.571 2004 613506 A Tétouan Direction de la Statistique

1.1.9 Egypt

No data received so far from Egypt.

1.1.10 Neigbouring countries

Gridded population density data are available at the SEDAC website8, with a spatial resolution of 2.5 minutes. The source and year of the raw data are well documented at the website. The average input resolution shows different levels of resolution for different countries. According to the USTUTT.TFU group, this data source is sufficient if the RS1d partners don’t have easily available data with higher resolution. The figure below shows screenshot for Latvia.

8 Gridded Population of the Word and the Global Rural-Urban Mapping Project, http://sedac.ciesin.columbia.edu/gpw/

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Fig. 9. Screenshot of the population density map of Latvia based on the SEDAC data (green: low density, blue: high density).

1.2 Risk group fractions

In order to assess morbidity based on diseases connected to different age/symptom groups, information regarding the so called “Risk-Group-Fraction” (RGF), i.e. percentage of population in the country for each age/symptom groups were collected for several partner countries. The data collection results are summarized in Table 8. Default values for RGF available in EcoSense are average values for EU15 countries.

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Table 8. Summary of risk group fractions in the RS1d countries (sources of data are commented)

Name Age group [%] default

[%] BG

[%] CZ

[%] HU

[%] MA

[%] SK

[%] TN

Infants 0-11 months 0.9 0.95b 1.0d 1.85g 1.00h  Children 0 – 14 years 17 14.94b 16.6d 31.2g 16.59h 24.00i

5 – 14 years 11.2 10.38b 11.8d 21.3g 11.77h 10.51i,j

asthmatics2.24 2.34a

2.53c1.5e; 4–6f

 

Adults „adults“ 81.7 81.25b  15 – 64 years 67.2 71.02b 68.2d 63.2g 71.67h 66.39i

18 – 64 years 64 67.21b 64.4d 56.8g 67.22h 55.66i,k

27 years and above 70 68.33b 65.4d 44.7g 64.04h 45.58i,l

asthmaticsuse

defaultc 

20 years and above with chronic respiratory symptoms

24.5use

defaultc

 

Asthmaticsasthmatics of total popul.

4.5 use defaultc 1.58d

 

Elderly 65 years and above 15.8 15.05b 15.2d 5.6g 11.74h 6.89i

Baseline mortality

0.00990.0105b 0.0108d 0.01h

0.0059i

a National Health Information Center, Bulgaria, 2005b Czech Statistical Officec Institute of health information and statistics of the Czech Republicd Hungarian Central Statistical Office, 2001e School report, Hungary, 2002f Estimation, Hungary, 2002g RGPH, Morocco, 2004h Statistical Office SR, 2005i Institut National de la Statistique (INS), Tunisia, 2005j 0-9 yearsk 20-64 yearsl 25-64 years

1.3 Crops

Crops production as well as total crops area data were also collected, but at lower spatial resolution than population data, because crops yield losses have less importance in energy generation related external costs. The requirements for the spatial resolution of the data were min. 50 x 50 km² for a grid or at least NUTS Level 1 for administrative units.

In order to harmonize of the data collection performed by the different partners, an EXCEL template was prepared and filled with the respective data provided by each partners. The description of the data headers is the following:

Country – two-letter country code, e.g. HU for HungaryEMEP50_Xa – X coordinate in the 50×50 km² EMEP grid

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EMEP50_Ya – Y coordinate in the 50×50 km² EMEP gridRegionb – code of the region at the administration level of the dataYear – year of dataBarley – production of barley in [dt (= 100 kg)] (fresh weight) Oats – production of oats [dt]Potato – production of potato [dt]Rice – production of rice [dt]Rye – production of rye [dt]Sugar beet – production of sugar beet [dt]Sunflower seed – production of sunflower seed [dt]Tobacco – production of tobacco [dt]Wheat – production of wheat [dt]Total crops – total crops area [ha]Quality – indicator of data quality (A: best, e.g. official data; E: worst, e.g. estimation)Commentb – label of the region at the administration level of the dataSource – source of the dataa for data collection based on the EMEP grid cellsb for data collection based on administrative units

In the next subsections the collected data are described in more details for each country.

1.3.1 Hungary

Data were collected by AEKI for the most important crops in Hungary for the base year 2005 from the Hungarian Central Statistical Office, where data were available at the NUTS3 level. For information purposes, the map of Hungary including the NUTS3 regions is plotted in Figure 10. For barley, rice, rye and tobacco, regional data were not available at the Hungarian Central Statistical Office, therefore 2004 data from the Eurostat Regio9 database were used, which were available for the NUTS2 level. The collected data are listed in Table 9.

9 http://epp.eurostat.ec.europa.eu/portal/page?_pageid=1335,47078146&_dad=portal&_schema=PORTAL

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HU331

HU311

HU321

HU323

HU232

HU322

HU332

HU213

HU231

HU211

HU333

HU223

HU233

HU312

HU222

HU313

HU212

HU331

HU311

HU102

HU321

HU323

HU232

HU322

HU332

HU213

HU231

HU211

HU333

HU221

HU223

HU233

HU312

HU222

HU313

HU212HU101

Fig. 10. Map of the NUTS level 3 regions of Hungary

Table 9. Crops production and total crops area data for Hungary

Country Region Year Potato

Sugar beet

Sunflower seed Wheat

Total crops Quality Comment Source

HU HU101 2005 61030 114750 73350 350760 41000 B Budapest HCSOa

HU HU102 2005 888620 1126250 702570 2842700 263800 B Pest HCSOa

HU HU211 2005 68620 1776610 959560 3870710 252000 B Fejér HCSOa

HU HU212 2005 148930 768330 162380 1385100 104100 B Komárom-Esztergom HCSOa

HU HU213 2005 91190 60230 183020 1384050 145500 B Veszprém HCSOa

HU HU221 2005 525590 4982600 444200 3053350 231900 B Győr-Moson-Sopron HCSOa

HU HU222 2005 84850 3130020 183140 1635700 151600 B Vas HCSOa

HU HU223 2005 251910 455230 91070 939250 127100 B Zala HCSOa

HU HU231 2005 148540 1930810 296380 2443320 227500 B Baranya HCSOa

HU HU232 2005 171550 490830 385780 2227490 253600 B Somogy HCSOa

HU HU233 2005 99030 1462340 625320 2997790 214800 B Tolna HCSOa

HU HU311 2005 393460 1192680 854650 3315740 262700 B Borsod-Abaúj-Zemplén HCSOa

HU HU312 2005 21980 325400 673200 2127810 153600 B Heves HCSOa

HU HU313 2005 71450 72960 152900 764700 80100 B Nógrád HCSOa

HU HU321 2005 553850 7779230 649960 3207710 331500 B Hajdú-Bihar HCSOa

HU HU322 2005 92240 2397160 1497800 5002260 358400 B Jász-Nagykun-Szolnok HCSOa

HU HU323 2005 418670 1232910 847060 1379110 282400 B Szabolcs-Szatmár-Bereg HCSOa

HU HU331 2005 987230 1309500 879840 3615060 381600 B Bács-Kiskun HCSOa

HU HU332 2005 60770 2837540 926150 5480690 391800 B Békés HCSOa

HU HU333 2005 1019280 1588400 432750 2767400 258100 B Csongrád HCSOa

a Hungarian Central Statistical Office

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Table 9. Crops production and total crops area data for Hungary (continued)

Country Region Year Barley Rice Rye Tobacco Quality Comment SourceHU HU10 2004 563000 0 135000 0 B Közép-Magyarország Eurostat Regio 2006HU HU21 2004 1529000 0 117000 0 B Közép-Dunántúl Eurostat Regio 2006HU HU22 2004 2340000 0 130000 0 B Nyugat-Dunántúl Eurostat Regio 2006HU HU23 2004 1854000 0 72000 0 B Dél-Dunántúl Eurostat Regio 2006HU HU31 2004 2026000 0 67000 3000 B Észak-Magyarország Eurostat Regio 2006HU HU32 2004 1950000 33000 348000 96000 B Észak-Alföld Eurostat Regio 2006HU HU33 2004 3872000 63000 382000 15000 B Dél-Alföld Eurostat Regio 2006

1.3.2 Czech Republic

Crops data were collected by CUEC from the Czech Statistical Office for year 2005, on the NUTS3 administrative level. Regional data for sunflower seed production were not available, the production in the whole country was 114,508 dt in 2005. The collected data are tabulated in Table 10.

Table 10. Crops production and total crops area data for Czech Republic

Country Region Year Barley Oats Potato Rye

Sugar beet Wheat

Total crops Quality Comment Source

CZ CZ010 2005 69700 2720 7337 90 196540 210590 11130 B Hl. m. Praha CSOb

CZ CZ020 2005 4465990 207740 2359855 355050 8981620 8759100 500806 B Středočeský CSOb

CZ CZ031 2005 2014830 287750 1778976 326970 0 3937310 274655 B Jihočeský CSOb

CZ CZ032 2005 1705260 195040 554331 150550 0 3179340 216609 B Plzeňský CSOb

CZ CZ041 2005 309200 64080 108313 40690 0 628110 42289 B Karlovarský CSOb

CZ CZ042 2005 1550430 65070 287682 132180 1943120 3162930 154425 B Ústecký CSOb

CZ CZ051 2005 324180 61300 152274 64410 244480 586810 42745 B Liberecký CSOb

CZ CZ052 2005 1141670 80860 443219 121780 4846360 2804060 172524 B Králové-hradecký CSOb

CZ CZ053 2005 1324720 104510 548602 76390 2645320 2605580 182054 B Pardubický CSOb

CZ CZ061 2005 2492420 195560 3861260 404460 96970 3253370 282656 B Vysočina CSOb

CZ CZ062 2005 2744980 50610 638882 112770 4370080 5585760 325058 B Jihomoravský CSOb

CZ CZ071 2005 1999760 59800 243840 89580 7097550 2950210 186190 B Olomoucký CSOb

CZ CZ072 2005 759300 52090 220687 28190 1425320 1751640 100267 B Zlínský CSOb

CZ CZ080 2005 1051320 83410 354702 64440 3108750 2035580 137354 BMoravsko-slezský

CSOb

bCzech Statistical Office

1.3.3 Estonia

Crops production data were collected for the whole country that is regarded also as one NUTS1 region, for years 2000, 2005 and 2006, from the Central Statistical Office of Estonia. Total crops area was not provided by SEI.

23

Table 11. Crops production data for Estonia

Country Region Year Rye Wheat Barley Oats Quality Comment SourceEE EE0 2000 608 1468 3475 1171 EstoniaEE EE0 2005 204 2634 3656 842 EstoniaEE EE0 2006 179 2144 2947 620 Estonia

1.3.4 Slovakia

A very detailed crops database was collected by PROFING for year 2005, at the LAU1 administrative level of Slovakia, from the National Statistical Office. Total crops area data were not provided. Because of the extended dataset, Table 12 contains only the first and last 10 lines of the whole data table.

Table 12. Crops production data for Slovakia

Country Region Year Wheat Rye Barley Ovos Potato Sun-flower Tobacco Sugar-

beet Quality Comment SourceSK 104 2005 109705 16127 27362 2299 2619 8540 0 0 B Bratislava IVSK 106 2005 151146 74023 64603 16513 41149 21202 0 0 B MalackySK 107 2005 141423 9971 64091 5239 3397 46842 0 0 B PezinokSK 108 2005 290164 471 159861 2103 167525 57125 0 937374 B Senec

SK 201 2005 959997 1213 395562 4345 32912 96801 0 1987951 BDunajská Streda

SK 202 2005 528250 4318 303454 4300 185613 107501 0 1742188 B GalantaSK 203 2005 232877 60 115493 1234 29134 40021 438964 B HlohovecSK 204 2005 335483 4509 248483 1020 28695 48855 0 982339 B PiešťanySK 205 2005 321759 61164 158125 15141 43708 37055 0 304879 B SenicaSK 206 2005 219121 15276 145180 3149 31396 28671 369516 B Skalica...SK 711 2005 20374 653 3180 7063 6569 0 0 0 B StropkovSK 712 2005 19543 759 4706 8809 14387 0 0 B Svidník

SK 713 2005 183476 1209 39570 8227 39219 26016 0 0 BVranov nad Topľou

SK 801 2005 3248 107 18 31 8729 0 0 0 B Gelnica

SK 806 2005 537224 18954 228823 20062 151280 63689 0 0 BKošice - okolie

SK 807 2005 408217 9915 160587 5748 39863 47178 791 8832 B MichalovceSK 808 2005 61592 3350 16091 5001 16552 1692 0 0 B RožňavaSK 809 2005 193374 2729 46445 8360 14554 10110 0 0 B Sobrance

SK 810 2005 64118 6170 44611 3396 53700 0 0 0 BSpišská Nová Ves

SK 811 2004 558645 12373 194824 7383 65344 68950 760 0 B Trebišov

1.3.5 Bulgaria

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Crops production and total crops area data were collected by UNWE from the Bulgarian National Statistical Institute for year 2005 at the NUTS2 administrative level. Production of sugar beet, tobacco and rice was only available for the whole country, 247310, 201630, and 583360 dt, respectively. The data collected by regions are listed in Table 13.

Table 13. Crops production and total crops area data for Bulgaria

Country Region Year Barley Oats Potato Rye

Sunflower seed Wheat

Total crops* Quality Comment Source

BG BG11 2005 435840 143060 34110 6030 1038940 3402690 216162   North-west NSIc

BG BG12 2005 1738650 103380 112760 14630 2444350 7763010 504051   North-central NSIc

BG BG13 2005 1868970 100660 76600 11720 3719230 12686380 650424   North-east NSIc

BG BG23 2005 1492020 41940 162110 6530 1064820 4788270 295511   South-east NSIc

BG BG22 2005 900680 63270 665490 63540 995830 4977060 313937  South-central

NSIc

BG BG21 2005 142470 49070 213550 33720 85380 1163250 65992   South-west NSIc

* by region (NUTS2) without data for areas with sugar beet, tobacco and rice

c National Statistical Institute

1.3.6 Poland

Crops production and total crops area data were collected by MEERI from the Polish Central Statistical Office for year 2004 at the NUTS2 administrative level. Digital map in shape format could be obtained at the NUTS2 level (from GADMError: Reference source not found). For information purposes, the map is shown in Fig. 11, including the NUTS2 codes of the regions. The data collected by regions are listed in Table 14.

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PL12PL41

PL62

PL31

PL42

PL34

PL63

PL51

PL11

PL61

PL32PL21

PL43

PL22

PL33PL52

Fig. 11. Map of the NUTS level 2 regions of Poland

Table 14. Crops production and total crops area data for Poland

Country Region

Year

Wheat Rye Barley Oats Potatoes SugarbeetTotal crops

Quality Comment Source

PL PL51 2004 13761000 1465000 2931000 683000 7599000 12050000 539800 Dolnoślaskie CSO*

PL PL61 2004 8278000 2670000 4016000 369000 7449000 23123000 540200Kujawsko-pomorskie

CSO*

PL PL31 2004 9437000 2921000 3829000 2075000 12372000 18718000 705600 Lubelskie CSO*

PL PL43 2004 2572000 1589000 1031000 351000 2628000 1315000 187400 Lubuskie CSO*

PL PL11 2004 3272000 5375000 1524000 1075000 13920000 4374000 467100 Łódzkie CSO*

PL PL21 2004 3790000 308000 1549000 542000 9832000 860000 251900 Małopolskie CSO*

PL PL12 2004 4848000 7795000 1555000 2388000 18446000 9725000 764500 Mazowieckie CSO*

PL PL52 2004 8539000 736000 2635000 287000 3527000 9360000 326600 Opolskie CSO*

PL PL32 2004 4289000 797000 832000 790000 10913000 2925000 291200 Podkarpackie CSO*

PL PL34 2004 1327000 2853000 527000 1110000 7515000 2457000 273700 Podlaskie CSO*

PL PL63 2004 6889000 2145000 1714000 746000 7408000 5843000 403700 Pomorskie CSO*

PL PL22 2004 2595000 1037000 1321000 425000 4890000 1158000 187600 Śląskie CSO*

PL PL33 2004 2607000 1061000 1731000 407000 7747000 4516000 262700 Świętokrzyskie CSO*

PL PL62 2004 6846000 1215000 1372000 541000 4102000 2002000 351800Warmińsko-mazurskie

CSO*

PL PL41 2004 10539000 7554000 6255000 1382000 15526000 23164000 854000 Wielkopolskie CSO*

PL PL42 2004 9335000 3286000 2886000 1134000 6113000 5714000 534900Zachodnio-pomorskie

CSO*

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*Central Statistical Institute

1.3.7 Tunisia

Crops production data were collected by LEGI-EPT for administrative units “Gouvernorat” equivalent to the NUTS3 level in Europe. Data were obtained from the Observatoire National de l’Agriculture, for year 2005. Total crops area data were not provided. The collected crops production data are tabulated in Table 15.

Table 15. Crops production data for Tunisia

Country Region Year Wheat Barley Dates Grapes Tobacco Quality Comment SourceTN TN.AN 2005 1872000 331000 148900 Ariana ONAd

TN TN.BJ 2005 37765000 3080000 5320 Béja ONAd

TN TN.BA 2005 1411000 362000 147000 Ben Arous ONAd

TN TN.BZ 2005 19976000 1556000 55000 7660 Bizerte ONAd

TN TN.GB 2005 14000 210000 1660 Gabés ONAd

TN TN.GF 2005 101000 45000 54200 Gafsa ONAd

TN TN.JE 2005 12426000 2017000 10660 Jendouba ONAd

TN TN.KR 2005 8443000 6192000 Kairouan ONAd

TN TN.KS 2005 3691000 3059000 Kasserine ONAd

TN TN.KB 2005 580000 Kébili ONAd

TN TN.KF 2005 22580000 8175000 Le Kef ONAd

TN TN.MH 2005 1730000 2621000 Mahdia ONAd

TN TN.MN 2005 5555000 1115000 148900 Manouba ONAd

TN TN.ME 2005 38000 Mednine ONAd

TN TN.MS 2005 165000 271000 Monastir ONAd

TN TN.NB 2005 4401000 4900000 120000 5180 Nabeul ONAd

TN TN.SF 2005 117000 228000 Sfax ONAd

TN TN.SZ 2005 2683000 1942000 Sidi Bouzid ONAd

TN TN.SL 2005 22851000 4215000 Siliana ONAd

TN TN.SS 2005 3186000 2779000 Sousse ONAd

TN TN.TA 2005 9000 Tataouine ONAd

TN TN.TO 2005 3000 1000 336000 Tozeur ONAd

TN TN.TU 2005 299000 48000 Tunis ONAd

TN TN.ZA 2005 13419000 4024000 Zaghouan ONAd

d Observatoire National de l’Agriculture

1.3.8 Morocco

Crops production tion data were collected by CDER for every province in Morocco (equivalent to LAU1 administrative level in Europe). Data were obtained from the Ministère de l'Agriculture, for years 2004–2005. The dataset includes population data for 39 provinces. Parts of the summary table of the collected population data are included in Table 16. Because of the extended dataset, Table 16 contains only the first and last 10 lines of the whole data table. Production data for other crops were not collected by regions, data were provided for the whole country, e.g. maize 501200 dt, rice 428400 dt, sugar cane 7822900 dt.

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Table 16. Crops production and area data for Morocco

Country Region Year Wheat BarleyTotal crops* Quality Comment Source

MA 03.261 2004-2005 3800 6700 640 Guelmim Ministère de l'AgricultureMA 03.551 2004-2005 3300 28200 420 Tata Ministère de l'AgricultureMA 04.001 2004-2005 282100 309000 9900 Agadir Ministère de l'AgricultureMA 04.401 2004-2005 112400 69600 1520 Ouarzazate Ministère de l'AgricultureMA 04.541 2004-2005 427200 370700 12340 Taroudante Ministère de l'AgricultureMA 04.581 2004-2005 42300 264900 7870 Tiznit Ministère de l'AgricultureMA 05.281 2004-2005 2054600 256400 10820 Kenitra Ministère de l'AgricultureMA 05.481 2004-2005 4069400 287200 20560 Sidi Kacem Ministère de l'Agriculture

MA 06.111 2004-2005 940400 188100 8930Ben Slimane Ministère de l'Agriculture

MA 06.311 2004-2005 172400 352200 16140 Khouribga Ministère de l'Agriculture...MA 13.301 2004-2005 707200 122000 10900 Khenifra Ministère de l'AgricultureMA 14.131 2004-2005 73200 105600 4160 Boulemane Ministère de l'AgricultureMA 14.231 2004-2005 1437800 295800 15760 Fes Ministère de l'AgricultureMA 15.051 2004-2005 170000 639000 8170 Al Hociema Ministère de l'AgricultureMA 15.531 2004-2005 2157900 456000 22450 Taounate Ministère de l'AgricultureMA 15.561 2004-2005 1007000 554400 11240 Taza Ministère de l'AgricultureMA 16.151 2004-2005 413500 203400 6490 Chefchouen Ministère de l'AgricultureMA 16.331 2004-2005 683500 32700 4030 Larache Ministère de l'AgricultureMA 16.511 2004-2005 191500 64000 2370 Tanger Ministère de l'AgricultureMA 16.571 2004-2005 345800 179000 3370 Tetouan Ministère de l'Agriculture

*Area for wheat and barley

1.3.9 Egypt

No data received so far from Egypt.

1.3.10 Neighbouring countries

The use of the Eurostat Regio databaseError: Reference source not found is recommended for updating the crops production and area data for the neighbouring countries that are not RS1d partners, if necessary.

1.4 Materials

In order to harmonize of the data collection performed by the different partners, an EXCEL template was prepared. The description of the data headers is the following:

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Country – two-letter country code, e.g. HU for HungaryEMEP50_Xa – X coordinate in the 50×50 km² EMEP gridEMEP50_Ya – Y coordinate in the 50×50 km² EMEP gridRegionb – code of the region at the administration level of the dataYear – year of dataZinc – surface area [m2]Galvanized steel – surface area [m2]Sandstone – surface area [m2]Limestone – surface area [m2]Natural stone – surface area [m2]Mortar – surface area [m2]Rendering – surface area [m2]Paint – surface area [m2]Quality – indicator of data quality (A: best, e.g. official data; E: worst, e.g. estimation)Commentb – label of the region at the administration level of the dataSource – source of the dataa for data collection based on the EMEP grid cellsb for data collection based on administrative units

Attempt was made for collection of data for surface areas of different building materials for each partner country, but without success. For this reason the default data are recommended to use in the geographically extended EcoSenseWeb model.

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2 Reference technology database2.1 Data requirements

A list of substances [emission per year]The input into EcoSense will depend on the substance, e.g., [g/a] or [mg/a].

The absolute minimums are the following “classical” air pollutants:

Carbon dioxide (fossil) & Methane (fossil) Nitrogen oxides NMVOC PM2.5 PM10 Sulfur dioxide.

In the Excel spread sheet: NEEDS_Emission-List_061110.xls substances are listed for which external costs can be assessed.

o technical parameters EcoSense input parameters:

- effective stack height (height of release)(function of stack height, diameter, flue gas volume and temperature)- stack height, diameter, flue gas volume and temperature- location (longitude and latitude) - elevation at site (above sea level)- Anemometer height: for local modelling only

parameters for result aggregation: Energy sent out [kWh/a].

Reference technology data are allocated to a certain site. In case of the operation of a coal fired power plant this is mainly the site of the power plant itself. However, emissions caused, for example by electricity supply due to wind energy converters are caused by different processes, e.g. the steel production. Therefore, the external costs should be calculated at these locations.

2.2 Tool for emission calculation based on fuel type and technology

In order to calculate the environmental externalities for thermal power plants, the most important input data are the atmospheric emissions of – at least classical – pollutants. The EcoSenseWeb model requires these data as pollutant concentrations in flue gas [mg/Nm3], and flue gas volume [Nm3/h]. Emissions are, however, most of the cases reported on yearly basis, i.e. in [t/a] units. In order to calculate the concentrations needed for EcoSenseWeb using the reported yearly emission data, the yearly availability (full load hours per year) [h/a] of the power plant is also required.

Input Data of yearly emission amount for pollutant j – Emj [t/a]

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Flue gas volume stream – Vg [Nm3/h] Full load hours per year – T [h/a]

The concentration values can be calculated the following way:

Cj [mg/Nm3] = Emj [t/a] /Vg [Nm3/h] /T [h/a] *109

Usually the problem is that data reported to the authorities are not complete and not consistent, but the availability of fuel consumption as well as energy output data can help us to prepare consistent input data for the EcoSenseWeb calculations. For example, if only the yearly emissions of pollutants are given, the flue gas volume stream Vg can be calculated from the yearly fuel consumption data.

Input : Fuel consumption Qfi [GJ/a] for fuel iFull load hours per year T [h/a]Specific flue gas volume per energy unit Vei [Nm3/GJ] for fuel i

Specific flue gas volume is calculated from fuel composition at defined oxygen content in flue gases. At multiply fuel input the gas volume is calculated for the O2 content [%] defined forfuel with largest thermal input – O2ref.

Vg = Σi Qfi * V ei * (20.95 – O2i)/(20.95-O2ref)T

2.2.1 Model description

In order to enable the preparing the input data for required by the EcoSenseWeb software, a model in MS EXCEL environment has been prepared by PROFING and disseminated for Stream 1d participants. The model enables to calculate consistent data of electricity output, flue gas stream etc. Together with input data of basic pollutants like SO2, NOx PM, it enables on the basis of default data to calculate required emission data of PM10, PM2.5 and heavy metals. The model uses a reference database of typical fuel types, used by thermal power plants. In addition, the model enables to allocate the external cost on the electricity and heat in CHP units (see Section 2.4 for details), using EcoSenseWeb output data .

Fuel database

A database of most commonly used fuels is incorporated in the model. The user can use these data as defaults or can change it according domestic values. The database is split on the two types of fuels. Solid and liquid fuels, where fuel characteristics as elemental composition and calorific values are expressed on weight basis - % C,H,N,O,S and MJ/kg. Gaseous fuels, where the characteristics are expressed on volume basis. The program automatically calculates the calorific values. One should note that gaseous fuels can be included in the first group if their characteristics are given on weight basis. The fuel database can be modified or updated using the Fuel_DB macro, which calculates the specific flue gas volume per MJ as well as sulfur and ash content for calculation of SO2 and PM concentration as input to EcoSenseWeb model. It asks for

31

required input data as are fuel composition and calorific values. Some of the default data for solid and liquid (Table 17) as well as gaseous (Table 18) fuels are listed below. Data in tables are based on average data of fuels used in Slovakia, nevertheless they have to be replaced by specific national data of the relevant country.

Table 17. Database of typical solid and liquid fuels

abbr Name of fuel C [%] H [%] N [%] O [%] S [%] Ad [%] Sd [%] W [%] Ar [%]BC Brown coal 36.40 6.30 0.70 8.90 1.10 28.80 1.47 25.00 21.6Lig Lignite 27.80 2.43 0.49 12.36 1.81 24.19 3.06 40.77 14.33HC Hard coal 71.80 4.41 0.34 5.59 0.73 8.53 0.81 9.40 7.73CK Coke 71.33 0.84 0.88 1.15 0.63 12.74 0.73 14.24 10.93BM Dry wood 48.49 5.90 0.71 41.20 0.00 0.58 0.00 3.14 0.56HFO Heavy fuel oil 86.34 11.20 0.46 0.00 2.00 0.00 2.00 0.00 0LFO Light fuel oil 87.00 12.00 0.20 0.00 0.80 0.00 0.80 0.00 0

abbr Name of fuel [MJ/kg] [kgS/GJ] VSH VSR [kgC/GJ]EFCO2

[kg/GJ]mVs

[Nm3/MJ] O2 [%]Type of

fuelBC Brown coal 12.00 0.92 8.23 4.39 30.33 110.11 0.51 6.00 coalLig Lignite 9.87 1.83 6.09 2.73 28.16 102.21 0.39 6.00 coalHC Hard coal 24.53 0.30 8.72 7.23 29.27 106.25 0.41 6.00 coalCK Coke 24.39 0.26 8.75 6.55 29.25 106.16 0.38 6.00 coalBM Dry wood 18.81 0.00 4.67 4.50 25.78 0.00 0.50 11.00 biomassHFO Heavy fuel oil 40.60 0.49 10.16 10.16 21.27 77.59 0.29 3.00 oilLFO Light fuel oil 42.00 0.19 10.34 10.34 20.71 75.19 0.35 3.00 oil

Table 18. Database of typical gaseous fuels

Abbr Name of fuel CH4 [%] C2H2 [%] C2H4 [%] C2H6 [%] C3H6 [%] C4H10 [%] C5H12 [%] CO2 [%]

NG Natural gas 97.53 0.00 0.00 1.03 0.34 0.11 0.02 0.13COG Coking gas 22.30 0.08 1.76 0.59 0.32 0.00 0.00 2.14CNG Conveyor gas 0.01 0.00 0.00 0.00 0.00 0.00 0.00 16.57BFG Blast furnace gas 0.40 0.00 0.00 0.00 0.00 0.00 0.00 21.41

Abbr Name of fuel CO [%] H2 [%] N2 [%]LHV [MJ/Nm3]

mVs [Nm3/MJ]

EFCO2 [kg/GJ]

S [kgS/GJ]

Ash [kg/GJ]

NG Natural gas 0.00 0.00 0.82 36.14 0.28 55.18 0.00 0.00COG Coking gas 6.71 61.42 4.01 17.25 0.25 42.20 0.00 0.02CNG Conveyor gas 62.31 2.28 18.68 8.12 0.32 191.12 0.00 0.04BFG Blast furnace gas 23.00 0.00 52.74 3.05 0.55 289.66 0.02 0.10

Comment: The macro does not prepare data for calculation of PM10, PM2.5 and heavy metals. In the row of default data for typical fuels as are BC (brown coal), lignite, HC (hard coal), BM (biomass), NG (natural gas), HFO (heavy fuel oil) and LFO (light fuel oil) are included main characteristics needed for calculation of these emissions. Therefore if new characteristics for these representative typical fuels are included, the number of respective row at using the Macro Fuel_DB should be kept.

Treatment of fuel consumption data

The fuel consumption data are processed with the macro Fuelmix. It asks for type of boiler, and number and fractions of fuels used. It automatically uses the data from the fuel database and

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calculates the volume of flue gas per energy unit [Nm3/MJ], S and ash flow per energy unit [kgS/GJ] and [kgA/GJ] taking in consideration the S retention in ash and ash ratio deposited in boiler. These data are calculated for the actual technology and the actual fuel mix used. The size fractions of the formed PM due to different technologies are listed in Table 19.10

Table 19. Technology dependence of the size distribution of particulate matter (before filter)

   dry bottom 

wet bottom 

fluid 

PM from oil&gas

abbrName of fuel %PM10 %PM2.5 %PM10 %PM2.5 %PM10 %PM2.5 [kg/t]

BC Brown coal 35.0 10.0     72.9 27.6  Lig Lignite 35.0 10.0     72.9 27.6  HC Hard coal 23.0 6.0 23.0 6.0 72.9 27.6  CK Coke 23.0 6.0 23.0 21.0 72.9 27.6  BM Dry wood 89.0 77.5         15.0

HFOHeavy fuel oil 100.0 100.0         2.9

LFO Light fuel oil 100.0 100.0         2.1

Preparation of input data for EcoSenseWeb

Macro ECO_In is used for calculation of following input data for ECOSENSEWEB model, using several options:

Electricity production there are two options of input data:1 Direct input in GWh2 From net installed capacity MW and rated operation hour per year GWh = MW * h/a /1000

Full load hours per year: there are again two options1 Direct input in GWh2 From net installed capacity MW and rated operation hour per year h/a = GWh /MW *1000

Flue gas volume streamThere are the following options:1 direct input [Nm3/hour]2 calculation from total fuel consumption [TJ] and specific flue gas volume of fuel mix

[Nm3/MJ]3 calculation from electricity generated [GWh] , ratio of electricity[/total fuel consumption [%]

and specific flue gas volume of fuel mix [Nm3/MJ]

SO2 concentration mg/Nm 3 There are the following options:10 Zbigniew Klimont, Janusz Cofala, Imrich Bertok, Markus Amann, Chris Heyes and Frantisek Gyarfas: Modelling

Particulate Emissions in Europe. Interim Report IR-02-076

33

1 direct input [mg SO2/Nm3]2 calculation from total emission rate [t SO2/a] and flue gas volume stream [ Nm3/h]3 calculation from specific sulfur flow [kgS/GJ], specific flue gas volume of fuel mix

[Nm3/MJ] as well as SO2 abatement efficiency in %

NOx concentration mg/Nm 3 There are the following options:1 direct input [mg NOx/Nm3]2 calculation from total emission rate [t NOx/a] and flue gas volume stream [ Nm3/h]

TSP concentration mg/Nm 3 There are the following options:1 direct input [mg PM/Nm3]2 calculation from total emission rate [t TSP/a] and flue gas volume stream [ Nm3/h]3 calculation from specific ash flow [kgA/GJ], specific flue gas volume of fuel mix [Nm3/MJ]

as well as applied filter removal efficiency

Calculation of PM10, PM2.5 and micropollutant emissions

In the next step of the program TSP values are converted to concentrations of PM10 and PM2.5

as well as concentrations of heavy metals (Table 20). For this reason the filter type should be given as input, that determines the efficiency of PM10 and PM2.5 removal. If the actual filter has different efficiency as the default one, data are normalized on the actual input of efficiency. The default abatement efficiencies of the commonly used filters in the different size fractions are listed in Table 21.Error: Reference source not found

Table 20. Default heavy metal content of typical solid and liquid fuels

abbrName of fuel Units Pb Cd Cr Cr(VI) Hg Ni

BC Brown coal g metal/t PM 87.77 3.43 109.25 87.40 0.21 108.27Lig Lignite g metal/t PM 23.02 0.73 71.58 57.26 4.49 37.18HC Hard coal g metal/t PM 58.31 2.26 114.46 91.57 3.57 103.90CK Coke g metal/t PM 58.31 2.26 114.46 91.57 3.57 103.90BM Dry wood g metal/t PM 7.00 0.30 0.00 0.00 0.20  

HFOHeavy fuel oil g metal/t oil 0.60 0.05 1.20 0.96 0.20 20.00

LFO Light fuel oil g metal/t oil 0.54 0.05 1.08 0.86 0.18 18.00

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Table 21. Filter efficiencies as a function of particle size

Particle diameter [um] > 10 2.5 - 10 < 2.5  Particle removal efficiency [%]Cyclone CYC 90.00 70.00 30.00Wet scrubber WSCRB 99.90 99.00 96.00Electrostatic precipitator 1 field ESP1 97.00 95.00 93.00Electrostatic precipitator 2 fields ESP2 99.90 99.00 96.00Electrostatic precipitator 3 fields and more ESP3P 99.95 99.90 99.00Wet electrostatic precipitator 99.95 0.00 99.00Fabric filters FF 99.98 0.00 99.00Without filter 0.00 0.00 0.00

2.3 Country-specific technological data

Detailed technological data were collected by each partner for their specific case studies. In case of data unavailability, the tool described in the previous section was used for calculation of the remaining data. The detailed data will be published in Paper 5.1, with the country-specific results of the externality calculations.

2.4 Allocation of external costs of heat and electricity generation

At combined heat and power generation the question is, how to allocate the externalities on the heat and electricity produced. Considering the externalities of gaseous emission from fuel combustion, the next rule needed to be imagine: emissions are directly tied with the fuel consumption. Therefore the distribution of fuel consumption on the part of heat and electricity generation (heat or fuel rate) has to be expressed and emissions are directly related to this value. The next examples illustrate three ways of this allocation, based on the thermal balance in order to analyze if some simple approach gives relevant results, comparing with detailed energy balance of steam/water cycle. In this case CHP represents the steam boiler and steam turbine. The large difference in electricity and heat fuel rate is between the case of back-pressure and extraction turbine application. In the second case large energy losses are connected with the heat released in condenser and both fuel and emission rate is higher per electricity unit.

In the next parts the different approaches of emission allocation on heat and electricity generated are described. In energy balance, illustrated on the fig. 1 and fig. 2 the boiler represents the black box of whole heat consumption in steam/water cycle of boiler/turbine combination.

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2.4.1 Emission allocation in CHP with back preasure turbine

Option 1

Detailed energy balance is available as seen from Fig. 12.

SO2,CO2,NOx,PM

Pin,Tin, iib

Pout Tout iout

FuelQiGJ/t

Pfeed Tfeed ifeed

MWt

el

MWe

in

exch

Po To io

boiler

Fig. 12. Energy balance of boiler with back pressure turbine

The fuel consumption as well as emission can be distributed on the electricity and heat generated. The enthalpy drop in turbine and heat exchanger can be used.

Table 22. Energy balance of back pressure turbine with heat exchanger

  T oC P MPa i kJ/kg

s kj/kg/grad

in    

in 535 13.5 3423 6.53424     out 252 2 2908 6.55660 98%   MWe 50 i 515el 92%t/s 0.105Exchanger To Po i          60 1.98 2657vym 98%MWt 275

The enthalpy drop in turbine and heat exchanger can be used in our case by the following way:

Em el sh = itb/( itb + iexch ) = 515/(515+2657) = 16.24%

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Em heat sh = 1 -Em el sh = 73.76 %

whereEm el sh - share of emission produced in boiler allocated to electricity generationEm heat sh -share of emission produced in boiler allocated to heat generationitb - enthalpy drop in back pressure turbineiexch - enthalpy drop in heat exchanger

Option 2

The energy balance of turbine and heat exchanger is not available. In this case we can apply a simplified evaluation using the following, commonly available data:

1. Boiler efficiency boiler If value of the source in question is not available or if a group of sources with the same fuel is considered, some default values can be applied. For example for coal boiler 80%, oil boiler 85%, gas boiler 90%

2. Heat exchanger efficiency exch usually 98%3. Thermal output of boiler Qout [MWt]4. Thermal output of boiler Qin = Qout /hboiler [MWt]5. Heat supplied capacity Qheat [MWt]

In our case the input data for the same energy balance as previous are listed in Table 23.

Table 23. Input data for option 2

Boiler  

input MWt 394boiler 85%ourput MWt 335

Exchanger MWt 275 exchr  98%

Em heat sh =Nheat /exch/Nin = 275/98%/335 = 73.76 %Em el sh = 1 - Em heat sh = 16.24%Nint =Nout /boiler

whereEm el sh - share of emission produced in boiler allocated to electricity generationEm heat sh -share of emission produced in boiler allocated to heat generationNheat - Thermal input capacity [MWt] Nin - Thermal output capacity [MWt] exch - Efficiency of heat exchangerboiler - Efficiency of boiler

Using the simplified approach, identical results as in Option 1 are obtained.

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Option 3

The available data are the yearly input of fuel and the yearly output of heat and electricity. Input energy data are presented in Table 24, from the same energy balance as in the case of Option 1 and Option 2.

Table 24. Balance at the case of Option 3

Symbol data uniotsGeneration T 4000 h/yearload factor LF 0.9 fractionfuel Qfuel 5102 TJheat Qheat 3559 TJelectricty EL 200 GWhboiler efficiency boiler 85 %Heat exchanger efficiency exch 98 %

Em heat sh =Qheat /exch/Qiuel = 3559/98%/5102 = 73.76 %Em el sh = 1 - Em heat sh = 16.24%

Identical results for externality allocation are obtained in all three options (Table 25):

Table 25. Share of emissions as allocated to heat and electricity generation, using the three options

share electricityheatOption1 16.24% 83.76%Option2 16.24% 83.76%Option3 16.24% 83.76%

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2.4.2 Emission allocation in CHP with extraction turbine

Option 1

Detailed energy balance is available as seen from Figure 13.

SO2,CO2,NOx,PM

Pin,Tin, iin min

Pout Toutiout mout

FuelQiGJ/t

Pfeed Tfeed ifeed

MWt

el

MWe

in

exch

Po To io

Pextr Textr iextrmextr

boiler

Fig. 13. Energy balance of boiler with extraction turbine

The applied energy balance is shown in the next table.

Table 26. Energy balance of extraction turbine

  T oC P MPa i kJ/kg

s kj/kg/grad

in el MWtb MWodb t/h turbine

t/h extraction

Input data 535 13.53423.2 6.534 94% 94% 16.4  134.7 Extraction 260 22928.2 6.595 94% 92% 33.0 3.110 130.7 4.00Output 30.8  1877.5 6.201 72%         MWnominal 50      94% 92% 49.3 3.3   ExchangerTo

[oC]30.8io

kJ/kg128.95input output

  30.8vym 98%MWt 3.11 3.05

Similarly as in the case of back pressure turbine, the enthalpy drop can be used, nevertheless the steam output from boiler and steam amount withdrawn/extracted from the flow in turbine must be taken into account in the energy balance.

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Em heat sh = mextr iexch / (miniboiler ) = mextr iexch - io) / (miniin - io)io = T [oc] * 4.1868 = 128.95Em el sh =4*(2928.2-128.95)/(134.7*(3423-128.95)) =2.52%Em el sh = 1 -Em el sh = 97.48 %whereEm el sh - share of emission produced in boiler allocated to electricity generationEm heat sh -share of emission produced in boiler allocated to heat generationiboiler - enthalpy drop of steam/water cycleiexch - enthalpy drop in heat exchangermin - steam flow on the input of turbinemexch - steam flow of steam input in turbine

Option 2

Identical approach has been used as in the case of back pressure turbine.

Table 27. Input data for Option 2

Boiler input MWt 145boiler 85%  ourput MWt 123Exchanger MWt 3.05 exchr  98%

Em heat sh =Nheat /exch/Nin = 3.05/98%/145 = 2.52%Em el sh = 1 - Em heat sh = = 97.48 %Nint =Nout /boiler

whereEm el sh - share of emission produced in boiler allocated to electricity generationEm heat sh -share of emission produced in boiler allocated to heat generationNheat - Thermal input capacity [MWt] Nin - Thermal output capacity [MWt] exch - Efficiency of heat exchangerboiler - Efficiency of boiler

Option 3

Using the same approach as applied for back pressure turbine, and using input data listed in Table 28, the approach resulted in the same emission allocation as using the detailed energy balance.

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Table 28. Balance at the case of Option3

Symbol data unitsGeneration T 4000 h/yearload factor LF 0.9 fractionfuel Qfuel 1879 TJHeat Qheat 39.5 TJElectricity EL 197 GWhboiler efficiency exch 85 %Heat exchanger efficiency exch 98 %

Em heat sh =Qheat /exch/Qiuel = 39.5/98%/1879 = 2.52%Em el sh = 1 - Em heat sh = = 97.48 %

2.4.3 Conclusion

As it can be seen from the previous subsections, the simplified approaches (Options 2 and 3) for the emission allocation give the same results as the detailed energy balance. Therefore simplified approaches can be used for steam cycle with back pressure as well as extraction turbines. Only top/down energy balance is needed together with data of heat exchanger and boiler thermal efficiencies, nevertheless the use of default efficiency values will not introduce substantial errors. The same simplified approaches should be used in the case of combined cycle.

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