Annual Danish informative inventory report to UNECE. Emission ...

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144 (YDSRUDWLYH HPLVVLRQV IURP JDVROLQH YHKLFOHV For each year, evaporative emissions of hydrocarbons are simulated in the forecast model as hot and warm running losses, hot and warm soak loss and diurnal emissions. For evaporation, no updates are made to the COPERT IV methodology, and the calculation approach is the same as in COPERT III. All emission types depend on RVP (Reid Vapour Pressure) and ambient temperature. The emission factors are shown in Ntziachristos et al. (2000). Running loss emissions originate from vapour generated in the fuel tank while the vehicle is running. The distinction between hot and warm running loss emissions depends on engine temperature. In the model, hot and warm running losses occur for hot and cold engines, respectively. The emissions are calculated as annual mileage (broken down into cold and hot mileage totals using the β-factor) times the respective emission factors. For vehicles equipped with evaporation control (catalyst cars), the emission factors are only one tenth of the uncontrolled factors used for conventional gasoline vehicles. 5 = 1 ⋅ 0 ⋅ (( 1− β ) ⋅ +5 + β ⋅:5) (10) � � � � � � , , , where R is running loss emissions and HR and WR are the hot and warm running loss emission factors, respectively. In the model, hot and warm soak emissions for carburettor vehicles also occur for hot and cold engines, respectively. These emissions are calculated as number of trips (broken down into cold and hot trip numbers using the β-factor) times respective emission factors: 6 � �, � 0 �, � = 1 �, � ⋅ ⋅ (( 1− β ) ⋅ +6 + β ⋅:6) (11) O �� where S C is the soak emission, ltrip = the average trip length, and HS and WS are the hot and warm soak emission factors, respectively. Since all catalyst vehicles are assumed to be carbon canister controlled, no soak emissions are estimated for this vehicle type. Average maximum and minimum temperatures pr month are used in combination with diurnal emission factors to estimate the diurnal emissions from uncontrolled vehicles E d(U): ( � �, � ( �, � � 8 ) = 365⋅ 1 ⋅ H ( 8 ) (12) Each year’s total is the sum of each layer’s running loss, soak loss and diurnal emissions. )XHO XVH EDODQFH The calculated fuel consumption in COPERT III must equal the statistical fuel sale totals according to the UNFCCC and UNECE emissions reporting format. The statistical fuel sales for road transport are derived from the Danish Energy Authority data (see DEA, 2010). The DEA data are further processed for gasoline in order to account for e.g. non road and recreational craft fuel consumption, which are not directly stated in the statistics, please refer to paragraph 1.1.4 for further information regarding the transformation of DEA fuel data.
The standard approach to achieve a fuel balance in annual emission inventories is to multiply the annual mileage with a fuel balance factor derived as the ratio between simulated and statistical fuel figures for gasoline and diesel, respectively. This method is also used in the present model. � �� Ã� � � �Ã� � � � � � � � 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00 )XHO VFDOH IDFWRUV EDVHG RQ IXHO VDOHV 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 Gasoline Diesel Figure 3.85 DEA:NERI Fuel ratios and diesel mileage adjustment factor based on DEA fuel sales data and NERI fuel consumption estimates. � �� Ã� � � �Ã� � � � � � � � )XHO VFDOH IDFWRUV EDVHG RQ IXHO FRQVXPSWLRQ 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 Gasoline Diesel Figure 3.86 DEA:NERI Fuel ratios and diesel mileage adjustment factor based on DEA fuel consumption data and NERI fuel consumption estimates. In the figures 3.85 and 3.86 the COPERT IV:DEA gasoline and diesel fuel use ratios are shown for fuel sales and fuel consumption from 1985- 2009. The data behind the figures are also listed in Annex 3.B.8. The fuel consumption figures are related to the traffic on Danish roads by Danish vehicles and foreign trucks. Pr fuel type, all mileage numbers are equally scaled in order to obtain fuel equilibrium, and hence the mileage factors used are the reciprocal values of the COPERT IV:DEA fuel consumption: fuel sales ratio. The reasons for the differences between DEA sales figures and bottomup fuel estimates are mostly due to a combination of the uncertainties related to COPERT IV fuel use factors, allocation of vehicle numbers in sub-categories, annual mileage, trip speeds and mileage splits for urban, rural and highway driving conditions. 145
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ANNUAL DANISH INfORMATIVE INVENTORY
Page 3 and 4:
AU ANNUAL DANISH INfORMATIVE INVENT
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&RQWHQWV 6XPPDU\ , %DFNJURXQG LQIRU
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Annex 2A-11 QA/QC for stationary co
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8 ,, 7UHQGV LQ HPLVVLRQV $FLGLI\LQJ
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10 reduced emissions is mainly incr
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12 Danish energy statistics have, f
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14 ,QGXVWULDO SURFHVVHV Recalculati
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16 ,, 8GYLNOLQJHQ L HPLVVLRQHU )RUV
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18 kilde til emission af kobber er
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20 6¡IDUW Brændselsforbrug for sk
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22 en stigning i NH3 emissionen for
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24 UNECE-LRTAP Convention of the An
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26 Table 1.1 List of current data s
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28 A list of all subsectors at the
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30 )XJLWLYH HPLVVLRQV IURP QDWXUDO
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32 Please refer to Chapter 5 for fu
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34 Table 1.2 Danish uncertainty est
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36 NR is used for pollutants prior
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38 ��Ã�� Acid-
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40 ��Ã��
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42 ��Ã��
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44 �� Industrial Processes 1% N
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46 �Ã�� Non-industrial
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48 Table 3.1 NFR source categories
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50 (PLVVLRQ VRXUFH FDWHJRULHV In th
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52 Fuel consumption, PJ Fuel consum
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54 sumption in ,QGXVWU\ in 2009 add
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56 (PLVVLRQV 62� Stationary combu
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58 the reduced emission from electr
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60 NMVOC, Gg 25 20 15 10 5 0 1990 1
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62 The primary sources of PM emissi
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64 PM, Gg PM 2.5, Gg 30 25 20 15 10
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66 Table 3.11 Decrease in heavy met
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68 3$+ Stationary combustion plants
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70 Benzo(a)pyrene Benzo(a)pyrene, M
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72 plants has increased due to incr
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74 Fuel consumption, PJ SO2, Gg____
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As, Mg_____ Cd, Mg_____ Cr, Mg_____
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78 SO2, Gg____ NOx, Gg_____ Fuel co
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82 Fuel consumption, PJ 90 80 70 60
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84 As, Mg_____ 0.00 0.05 0.10 0.15
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86 Fuel consumption, PJ 1A4 Other S
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88 As, Mg Cd, Mg Cr, Mg Cu, Mg Hg,
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Page 96 and 97: 94 software for inventories. The me
Page 98 and 99: 96 Both traded and non-traded fuels
Page 100 and 101: 98 ƒ Christiansen et al. (1997).
Page 102 and 103: 100 The emission factors for decent
Page 104 and 105: 102 Table 3.19 Danish uncertainty e
Page 106 and 107: 104 Danish Energy Agency (DEA), 201
Page 108 and 109: 106 Miljøstyrelsen (MST), 2010: An
Page 110 and 111: 108 Table 3.20 SNAP - NFR correspon
Page 112 and 113: 110 sumption development is due to
Page 114 and 115: 112 these types of machinery, the D
Page 116 and 117: 114 � � 5 4,5 4 3,5 3 2,5 2 1,5
Page 118 and 119: 116 Table 3.22 Emissions of SO2, NO
Page 120 and 121: 118 7RQQHV 600000 500000 400000 300
Page 122 and 123: 120 7RQQHV 8000 7000 6000 5000 4000
Page 124 and 125: 122 The TSP emission development fo
Page 126 and 127: 124 Residential (1A4b) 0% Agr./for.
Page 128 and 129: 126 Table 3.24 Heavy metal emission
Page 130 and 131: 128 Navigation (1A3d) 79% Navigatio
Page 132 and 133: 130 Table 3.25 Dioxin and PAH emiss
Page 134 and 135: 132 Residential (1A4b) 2% Railways
Page 136 and 137: 134 0HWKRGRORJLFDO LVVXHV The descr
Page 138 and 139: 136 1200 1000 � � Ã � 800 Ã
Page 140 and 141: 138 2000 � 1600 � Ã� Ã� 1
Page 142 and 143: 140 Table 3.28 Overview of the exis
Page 144 and 145: 142 8') 8 ⋅ 07& + 8 = , MTC < UMA
Page 148 and 149: 146 The final fuel use and emission
Page 150 and 151: 148 Figure 3.87 Most frequent domes
Page 152 and 153: 150 No. No. 30000 25000 20000 15000
Page 154 and 155: 152 [No] 4500 4000 3500 3000 2500 2
Page 156 and 157: 154 ��
Page 158 and 159: 156 Table 3.30 Domestic ferry route
Page 160 and 161: 158 national sea transport in the D
Page 162 and 163: 160 Table 3.32 Overview of the EU E
Page 164 and 165: Table 3.35 Current certification li
Page 166 and 167: 164 Table 3.37 Current legislation
Page 168 and 169: 166 Table 3.38 Fuel based emission
Page 170 and 171: 168 1RQ URDG ZRUNLQJ PDFKLQHU\ DQG
Page 172 and 173: 170 ( = )& ⋅ () (27) where E = em
Page 174 and 175: 172 tions, transport to Greenland a
Page 176 and 177: 174 5RDG WUDQVSRUW The total mileag
Page 178 and 179: 176 Delvig, P. 2010: Unpublished da
Page 180 and 181: 178 Winther, M. 2005: Kyoto notat -
Page 182 and 183: 180 Table 3.41 Summary of the Danis
Page 184 and 185: 182 2IIVKRUH DFWLYLWLHV Fugitive em
Page 186 and 187: 184 Coal import, Gg . 16000 14000 1
Page 188 and 189: 186 gasoline sales show an increase
Page 190 and 191: 188 Venting in gas treatment and st
Page 192 and 193: 190 and Kilde (1994) for the years
Page 194 and 195: 192 Table 3.48 Emission factors for
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194 Table 3.54 Emissions of NMVOC f
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196 and initialisation of new facil
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198 Table 3.59 Estimated emission u
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200 Extraction of oil and gas: fugi
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202 OleQGU\ QVNL � ' EVNL % 6NR V
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204 Table 4.1 Survey of industrial
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206 Table 4.2 Time-series for pollu
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208 financial crises. During the sa
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210 1 1 1 1 1 0 0 0 0 0 &KHPLFDO LQ
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212 they have not been subject to t
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214 Denmark. The emission of TSP an
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216 Table 4.3 Emission factors for
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218 2WKHU SURGXFWLRQ FRQVXPSWLRQ VW
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220 Nielsen, O.-K., Lyck, E., Mikke
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222 products is derived from Table
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224 kt NMVOC/year 60 50 40 30 20 10
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226 tained from the respective comp
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228 over several years so there wil
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230 8QFHUWDLQWLHV DQG WLPH VHULHV F
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232 powder consists of about 75 % o
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234 Passant et al. (2003) and Fyrve
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236 Table 5.9 Emission from firewor
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238 sumentfyrverkerier, Miljö Göt
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240 $JULFXOWXUH 1)5 VHFWRU 2YHUYLHZ
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242 Another important cause of the
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244 The emissions from agricultural
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246 Table 6.3 Livestock categories
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248 Table 6.4 Livestock production
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250 creasing. This is due to measur
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252 Table 6.7 Activity data used to
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254 Table 6.10 Activity data used t
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256 Table 6.13 Livestock categories
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258 Table 6.16 PM emission from hou
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260 swine production, etc to optimi
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262 ning, as given in the CLRTAP Em
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264 implemented NH3 reduction techn
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266 :DVWH 1)5 VHFWRU The waste sect
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268 0HWKRGRORJ\ +XPDQ FUHPDWLRQ The
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270 Even though the total number of
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272 statistic data show an increase
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274 an emission factor of 28 µg pr
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276 Table 7.8 Emission factors for
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278 Table 7.9b Total national emiss
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280 Table 7.10a Total national emis
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282 Table 7.11 Estimated uncertaint
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284 Table 7.13 Overview of yearly s
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286 Data Processing level 1 1. Accu
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288 Data Storage level 2 The full d
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290 Date=06%2F30%2F2004&_sk=9994499
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292 Amount composted, Gg 1200 1000
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294 • 50 kg waste pr year will in
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296 Affaldsstatistik 2007 og 2008 (
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298 and 5 % of a large fire respect
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300 for fire-fighting and will typi
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302 Table 7.21 Different types of n
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304 Table 7.22 Average vehicle mass
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306 Table 7.23 Emission factors bui
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308 material estimated in Persson e
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310 Table 7.26 Emission factors veh
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Table 7.27a National emissions from
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314 Table 7.28b National emissions
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316 UNEP toolkit, 2005: United Nati
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318 4$ 4& DQG YHULILFDWLRQ 4$ 4& SU
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320 These references exist in the d
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322 Data Processing level 1 For the
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324 2WKHU DQG QDWXUDO HPLVVLRQV Den
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326 similar methodology was used fo
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328 the localisation of the Danish
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330 • The national energy statist
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332 the Energy Statistics for 2009
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334 $QQH[ ± �H\ FDWHJRU\ DQDO\VL
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336 $QQH[ $ ,3&& 61$3 VRXUFH FRUUHV
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338 $QQH[ $ )XHO UDWH Table 2A-2.1
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340 Table 2A-2.2 Detailed fuel cons
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342 ��Ã
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344 ��Ã
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346 ��Ã
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348 ��Ã
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Table 56 Time-series for calorific
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$QQH[ $ (PLVVLRQ IDFWRUV Table 2A-4
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14. Danish Gas Technology Centre 20
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pollutant fuel_type fuel nfr nfr_na
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Ã ÃÃ ÃÃ ÃÃ ÃÃ ��Ã
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Table 2A-4.4a PM emission factors,
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Table 2A-4.5 HM emission factors (m
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378 Ã ÃÃ ÃÃ ÃÃ ÃÃ Benzo(a)
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Table 2A-4.9a PAH emission factors
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Table 2A-4.9b PAH emission factors
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Table 2A-4.10 Dioxin and HCB emissi
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Table 2A-4.11a Dioxin and HCB emiss
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Table 2A-4.12 NH3 emission factors
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$QQH[ $ /DUJH SRLQW VRXUFHV Table 2
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02 MUNICIP. WASTES 44 STRAW 25 Hade
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�� 049 Ren
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396 national emissions in year t du
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398 ��Ã S
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400 ��Ã S
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402 ��Ã S
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�� 20205 0
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406 • Monthly reporting by e-mail
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408 ��Ã��Ã �
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410 ��Ã��Ã �
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412 $QQH[ $ 7LPH VHULHV Fuel consum
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Table 6.1 List of external data sou
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416 however, included in the refere
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418 Data Processing level 1 3.Compl
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420 Some automated checks have been
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Basis factors for 2-stroke gasoline
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CH4 shares of VOC for diesel, gasol
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Annual working hours, load factors
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Operational data for the most impor
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Stock data for household and garden
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Stock data for small boats and plea
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Ferry data: Sailing time (single tr
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Year Source Category TSP PM10 PM2.5
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$QQH[ % 8QFHUWDLQW\ HVWLPDWHV Uncer
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Uncertainty estimation, NH3. 578 Ga
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Uncertainty estimation, Chromium. 5
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Uncertainty estimation, Lead. 582 G
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Uncertainty estimation, Flouranthen
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Uncertainty estimation, Benzo(g,h,i
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588 Table 2C.1a Nitrogen excretion
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590 (PLVVLRQ RI 1+ � +RXVLQJ The
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592 $QQH[ & %DFNJURXQG LQIRUPDWLRQ
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594 $QQH[ & %DFNJURXQG LQIRUPDWLRQ
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596 $QQH[ ± &RPSOHWHQHVV DQG XVH R
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598 Emissions from clinical, indust
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NERI DMU National Environmental Res
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