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

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124 Residential (1A4b) 0% Agr./for. (1A4c) 2% Railways (1A3c) 0% Comm./Inst. (1A4a) 44% Navigation (1A3d) 77% Residential (1A4b) 17% Navigation (1A3d) 3% Military (1A5) 8% Agr./for. (1A4c) 17% Railways (1A3c) 8% Industry- Other (1A2f) 26% Fisheries (1A4c) 0% �� Comm./Inst. (1A4a) 0% �� Railways (1A3c) 1% Civil Aviation (1A3a) 2% Fisheries (1A4c) 17% Industry- Other (1A2f) 1% Military (1A5) 1% Civil Aviation (1A3a) 1% Fisheries (1A4c) 4% �� Industry- Other (1A2f) 8% Military (1A5) 0% Navigation (1A3d) 8% Comm./Inst. (1A4a) 3% Residential (1A4b) 1% Civil Aviation (1A3a) 2% Agr./for. (1A4c) 49% Railways (1A3c) 6% Navigation (1A3d) 20% Military (1A5) 1% Comm./Inst. (1A4a) 55% Navigation (1A3d) 16% �� Residential (1A4b) 0% Industry- Other (1A2f) 22% �� Civil Aviation (1A3a) Agr./for. 1% (1A4c) 14% Railways (1A3c) 4% Military (1A5) 1% Industry- Other (1A2f) 28% Comm./Inst. (1A4a) 0% Agr./for. (1A4c) 25% Fisheries (1A4c) 24% Civil Aviation (1A3a) 2% Fisheries (1A4c) 1% Industry- Other (1A2f) 4% Military (1A5) 0% Navigation (1A3d) 5% Railways (1A3c) Residential 0% (1A4b) 20% �� Residential (1A4b) 1% Fisheries (1A4c) 8% Comm./Inst. (1A4a) 3% Agr./for. (1A4c) 39% Civil Aviation (1A3a) 0% Figure 3.74 SO2, NOX, NMVOC, CO, NH3 and PM emission shares pr vehicle type for other mobile sources in 2009. 1RQ H[KDXVW HPLVVLRQV RI 763 30 �� DQG 30�� Apart from the exhaust emission estimates of particulate matter (PM), the Danish emission inventories also comprise the non-exhaust PM emissions coming from road transport brake and tyre wear, and road abrasion. In Table 3.23, the non-exhaust TSP, PM10 and PM2.5 emissions for road transport are shown for 2009 in NFR sectors. The activity data, emission factors are also shown in Annex 2.B.14. Table 3.23 Emissions of TSP, PM10 and PM2.5 in 2009 from road transport and other mobile sources. NFR Sector TSP PM10 PM2.5 tonnes tonnes tonnes Road brake wear 848 509 356 Road tyre wear 564 553 220 Road abrasion 1 033 517 279 Total Road non-exhaust 2 445 1 578 855 The respective source category distributions for TSP, PM10 and PM2.5 emissions are identical for each of the non-exhaust emission type’s brake wear, tyre wear and road abrasion, and, hence, only the PM10 distributions are shown in Figure 3.75. Passenger cars caused the highest
emissions in 2009, followed by trucks, light-duty vehicles, buses and 2wheelers. PM10 Brake wear PM10 Tyre wear Trucks 20% Light duty vehicles 20% Buses 6% wheelers-2 1% Passenger cars 53% Trucks 25% Light duty vehicles 16% Buses 5% wheelers-2 2% Passenger cars 52% PM10 Road abrasion 2009 PM totals for road transport Trucks 24% Light duty vehicles 12% Buses 5% wheelers-2 0% Passenger cars 59% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% TSP PM10 PM2.5 Exhaust Tyre wear Brake wear Road abrasion Figure 3.75 Brake and tyre wear and road abrasion PM10 emission shares and PM exhaust/non-exhaust distributions for road traffic in 2009. Figure 3.75 also shows the exhaust/non-exhaust distribution of the total particulate emissions from road transport, for each of the size classes TSP, PM10 and PM2.5. The exhaust emission shares of total road transport TSP, PM10 and PM2.5 are 38, 49 and 64 %, respectively, in 2009. For brake and tyre wear and road abrasion the TSP shares are 14, 21 and 26 %, respectively. The same three sources have PM10 shares of 18, 16 and 17 %, respectively, and PM2.5 shares of 9, 15 and 12 %, respectively. In general, the non-exhaust shares of total particulate emissions are expected to increase in the future as total exhaust emissions decline. The latter emission trend is due to the stepwise strengthening of exhaust emission standards for all vehicle types. +HDY\ PHWDOV In Table 3.24, the heavy metal emissions for road transport and other mobile sources are shown for 2009 in NFR sectors. The emission figures in the time-series 1990-2009 are given in Annex 2.B.15 (NFR format) and are shown for 1990 and 2009 in Annex 2.B.14 (CollectER format). 125
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ANNUAL DANISH INfORMATIVE INVENTORY
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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
Page 76 and 77: 74 Fuel consumption, PJ SO2, Gg____
Page 78 and 79: As, Mg_____ Cd, Mg_____ Cr, Mg_____
Page 80 and 81: 78 SO2, Gg____ NOx, Gg_____ Fuel co
Page 84 and 85: 82 Fuel consumption, PJ 90 80 70 60
Page 86 and 87: 84 As, Mg_____ 0.00 0.05 0.10 0.15
Page 88 and 89: 86 Fuel consumption, PJ 1A4 Other S
Page 90 and 91: 88 As, Mg Cd, Mg Cr, Mg Cu, Mg Hg,
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 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
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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 146 and 147: 144 (YDSRUDWLYH HPLVVLRQV IURP JDVR
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
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174 5RDG WUDQVSRUW The total mileag
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176 Delvig, P. 2010: Unpublished da
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178 Winther, M. 2005: Kyoto notat -
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180 Table 3.41 Summary of the Danis
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182 2IIVKRUH DFWLYLWLHV Fugitive em
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184 Coal import, Gg . 16000 14000 1
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186 gasoline sales show an increase
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188 Venting in gas treatment and st
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190 and Kilde (1994) for the years
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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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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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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$3 VRXUFH FRUUHV
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340 Table 2A-2.2 Detailed fuel cons
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342 ��Ã
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344 ��Ã
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348 ��Ã
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pollutant fuel_type fuel nfr nfr_na
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Table 2A-4.9b PAH emission factors
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Table 2A-4.10 Dioxin and HCB emissi
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Table 2A-4.11a Dioxin and HCB emiss
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Table 2A-4.12 NH3 emission factors
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$QQH[ $ /DUJH SRLQW VRXUFHV Table 2
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02 MUNICIP. WASTES 44 STRAW 25 Hade
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396 national emissions in year t du
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400 ��Ã S
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402 ��Ã S
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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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416 however, included in the refere
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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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598 Emissions from clinical, indust
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