Emissions of acidifying components

T Schneider (Editor), Acidification Research Evaluation and Policy Applications @ 1992 Elsevier Science Publishers El V All rights resewed

65

Emissions of acidifying components Markus Amann International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria

Abstract Emissions of SOZ, NO, and ammonia are major contributors to acidification of natural ecosystems. The paper reviews purpose and methods of emission inventories and compares the major international inventories in Europe. For each of the pollutants the major gaps in current knowledge and discrepancies in existing data are briefly discussed. Finally, national differences in per-capita emissions are analyzed for the year 1985. 1. INTRODUCTION Emissions of sulfur dioxide, nitrogen oxides and ammonia are important contributors to acidification of ecosystems that has been of growing concern in many countries in Europe and North America. Due to the complexity of the interaction of emission generation, atmospheric dispersion of pollutants and environmental impacts improved understanding of the major processes seems necessary to design efficient emission reduction strategies. An important prerequisite for such a better understanding is the accurate knowledge of emission rates. This paper summarizes the status of some important aspects of emission accounting in Europe and tries to identify the major gaps in current knowledge.

2. THE PURPOSE OF EMISSION INVENTORIES Attempts to estimate anthropogenic emissions have been made since harmful effects of emissions have been recognized. Early efforts had exploratory character and tried to estimate magnitudes of emission releases. Over time techniques have been refined and emission estimating changed from a scientific adventure to bureaucratic routine, often performed with passion. In order to put resources spent for emission inventorying into relation to the final use of the results the ultimate purpose of emission accounting should be clarified. In general two different objectives can be identified: Emission inventories are considered as an important element of national environmental statistics. They are used to compare present emission rates against historical records and thereby to demonstrate the success (or the failure) of environmental policies. Recent economic research tries to incorporate data on the release of harmful substances to the environment into the general system of national accounts to

66 quantify also negative effects of economic activities. 0

Emission data are an essential input to atmospheric dispersion models, which aim at the understanding of chemical processes and the behavior of air pollutants in the atmosphere.

With the first objective no natural limitation does exist for the ultimate resolution and accuracy of emission estimates. Currently a general trend exists towards a continuous improvement of inventories, e.g., by increasing the temporal, sectoral and spatial resolution of inventories. Resources required to achieve results grow simultaneously. If emission data should provide input to model calculations, the appropriate level of resolution and accuracy can be derived from the specific model structure. It has to be stated that up to now only few cases have been reported in which ’officially’ compiled inventories have actually been used for model calculations (the EMEP model for longrange transport of sulfur and nitrogen compounds [ll]is one example). The majority of modelling studies could not rely on inventories covering their actual data demand and had therefore to estimate the necessary data themselves. Although emission inventories have improved substantially over the last years (not at least with the aim to provide reliable input data for model calculations) it seems questionable if an ’all-purpose’ inventory satisfying all modeller’s demands could ever be developed. Scientific models do by their nature aim at exploring new and unknown aspects of air pollution and will therefore in many cases be one step ahead of institutionalized emission reporting systems. 3. METHODS OF EMISSION INVENTORIES Emission estimates can be established by two different methods: 0

0

The release of pollutants can be directly monitored at each emission source. In order to obtain reliable overall data continuous measurements over time of all emission sources have to be performed. Such measurement programs are expensive and therefore only applicable to important point sources. Monitoring of small and distributed sources is difficult and resource intensive. More often emissions rates are calculated based on surrogate data. Most commonly, the simple formula

EMISSION = A C T I V I T Y . EMISSION FACTOR

(1)

is applied to estimate total emission from a specific source or source category. Activity rates are usually available from statistics. Proper emission factors can be extrapolated from measurements, extracted from literature, derived from the plant’s commissioning license or calculated based on mass balance approaches. Recently, comprehensive information on emission factors has been compiled in handbooks [7]. The estimation of emissions according to this formula contains a number of inherent uncertainties and potential sources of inaccuracies. Often activity rates are considered as more accurate than emission factors. However, experience shows that also the quality of

67 activity data rapidly declines with finer temporal and spatial resolution. In many eastern European countries statistical data have been classified for a long time and statistics published now are still contradictory in many cases. 4. INTERNATIONAL EMISSION INVENTORIES

The long residence time of many air pollutants in the atmosphere results in strong international interdependencies of air pollution in Europe. Strategies to improve air quality should therefore address domestic and transboundary sources of emissions. Consequently, also emission accounting as one basis for decision making should provide international consistency. In recognizing this demand several efforts were made in Europe to establish international emission inventories for the major air pollutants. The following two tables provide a survey of the most important (completed or envisaged) inventories that are based on nationally submitted data. Although each of these inventories establishes comparability among countries, commonalities among the inventories are limited. Differences occur in their time horizons, the pollutants covered, the sectoral aggregation of emission sources, the spatial resolution, etc. Table 3 compares emission estimates for SOz and NO, for the year 1985 from different sources. Data listed under UN/ECE have been submitted officially to UN/ECE or have been estimated by EMEP [21], [ll]. CORINAIR data are retrieved from the final document of the CORINAIR project, which was carried out for the Commission of the European Communities [6]. For comparison two estimates of independent sources are Table 1: Spatial resolution of international emission inventories. (Table based on Joerss [13].) Collecting Bodies Area Resolution 150 x 150 km according to EMEP system UN-ECE Convention ECE countries

UN Statistical

ECE countries

National totals

CEC-CORINAIR 85

EC countries

Territorial Units according to administrative regions

CEC-CORINAIR 90

ECE countries

Territorial Units according to administrative regions

OECD/EUROSTAT

OECD countries National totals

OECD/MAP

OECD countries 50 x 50 km according to EMEP system

IPCC (assumed)

global

Commission ECE

National totals

68 Table 2: Comparison of international emission inventories: Types of pollutants covered by the 9.

Pollutant so2

NO.

co

Data T

S T S

T

UN-ECE/ EMEP

UN’

X

x

X X

x

X X

Inventory CORINAIR OECD/ OECD/ 1985 1990 EURO- MAP STAT 1980 x

X

X

X

x

X

X

X

X

X S X X T NH3 X S x x X NM-VOC T X X S T X CHr T NzO X T co2 X T PM X T Pb X T Hg T X Cd Table based on Joerss [13] T: National Totals Inventory S: Spatial Inventory

X

X

X

IPCC (assumed) X

X

X

X

X

X X

X

X

X

X X

X X X

X

X

X

X

X

X

X

X

NM-VOC Non-Methane Hydrocarbons PM Particulate Matters 1 UN/ECE Statistical Commission provided which are calculated with more aggregated information: The inventory of the IIASA-RAINS model for SO1 and NO, emissions is based national energy statistics and fuel characteristics [l].Estimates of European NO, emissions have been compiled by the Norwegian Institute for Air Research (NILU) [18].

69 Table 3: Estimates of emissions of SO2 (in k t SO2) and NO, (in kt NOz) for the year 1985. SO1 (kt SO1) NO, (kt NOz) UN/ECE CORIN- IIASA UN/ECE CORIN- IIASA NILU EMEP AIR RAINS EMEP AIR RAINS Country 1111 161 PI 1111 [GI 111 1181 Albania 50’ 123 92 34 30 Austria 230 240 178 250 199 281 452 Belgium 392 342 478 416 317 Bulgaria 1069 150 284 1034 367 1127 3150 3100 544 769 CSFR Denmark 258 307 340 268 330 333 270 Finland 251 382 287 230 353 1615 1470 France 1806 1481 1605 1761 1796 2450 Germany, West 2315 2930 2617 2830 2407 1715 Germany, East 7013 5340 5080 876 a75 746 276 500 Greece 526 288 500 308 1404 Hungary 1546 262 265 273 Ireland 141 85 91 140 136 97 89 1573 1595 1486 2504 Italy 2509 1563 2089 Luxembourg 22 19 16 16 16 27 33 Netherlands 200 544 462 276 280 588 471 Norway 203 212 98 100 166 1500 1374 4300 Poland 3638 1248 Portugal 198 96 147 198 263 157 390’ 1738 Romania 680 1900 604 950 1065 2190 2220 Spain 2190 991 394 270 Sweden 329 271 339 214 Switzerland 210 96 90 203 2278 3562 2322 3767 2311 3676 UK 11110 19190 3369 5532 USSR’ 7111 Yugoslavia 1500 1364 400 420 494 Notes: European part of USSR within the EMEP area EMEP estimate 1987

5. CURRENT GAPS AND UNCERTAINTIES OF EMISSION ESTIMATES 5.1 Inventories of SO2 emissions

Since SO2 emission inventories have a relatively long tradition and emission factors are mainly determined by fuel quality and emission control equipment, accuracy of estimates is generally high. Uncertainties have been estimated to be in a range of f 10 percent 18). Aircraft measurements and air monitoring data seem to confirm these evaluation, at least as long as aggregated figures are considered. However, factors discussed above cause

70

important discrepancies and discontinuities in submitted data for some eastern European countries. According to earlier official submissions to UN-ECE/EMEP Romania’s sulfur emissions in the year 1980 amounted to 200 kt of S 0 2 . In 1990, this number was officially increased by a factor of nine to 1800 kt [21]. Major interannual variations in emissions are often caused by economic instabilities. As an example, the development of Romania’s SO2 emissions between 1980 and 1990 are displayed in Table 4. According to these data emissions declined within only two years (between 1988 and 1990) by 40 percent.

...

...

1985 1912 1986 1802 1987 1762 1988 2397 1989 1646 1990 1430 Source: Romanian Ministry for Environment. Uncertainties are not only associated with quantitative estimates of emissions, but also with their spatial distribution. As an example Figures 1 and 2 compare the spatial distribution of Ukrainian emissions for the year 1985 (as estimated in 1988) and for the year 1988, as it has been officially submitted in 1990 when the critical loads approach

came into discussion. Whereas in 1985 an emission peak occurred in the EMEP grid 32/27, in 1988 these emissions appear distributed over four neighboring grids cells. The overall accuracy of emission inventories does not only depend on the reliability of data on known emission sources. Maybe more important is the completeness of the inventories. Over the last years increasing attention has been attributed to emissions from ships [4]which turned out to be an important source in north-west Europe, but which have not been considered by earlier inventories. Marine emissions on the North-Sea overrule e.g. the Norwegian land based SO1 emissions by more than a factor of two (Table 5). First estimates of biogenic sulfur emissions from the North Atlantic Ocean indicate also non-negligible amounts of Dimethyl sulphide (DMS) from the marine troposphere (Table 5). 5.2 Inventories of NO, emissions

Estimates of NO, emissions are still associated with major uncertainties. Several methods to estimate emissions from different sectors are currently in use, and even measurements on plant level suggest a wide range of emission factors for the same type of technical equipment. Therefore, also in cases with comparably good statistical material available (e.g. for the Netherlands) the accuracy of overall estimates is considered to be in the range between 10 to 20 percent [3].

71

2 zn o

40

114

5

I 10

27 0

26 0

2

150

~

25.0

Figure 1: Sulfur emissions in the Ukraine in 1985 (in kt S), Source: Eliassen et al., 1988

28 o

27 0

2cC

-.

117

c ~

517

2s c

Figure 2: Sulfur emissions in the Ukraine in 1988 (in kt S). Source: Iversen et al., 1990

72

Table 5: Comparison of anthropogenic and biogenic emissions in the North-West of Europe. Sulfur emissions (kt S’, Yearly Monthly Emission area 11988) emissions Anthropogenic: 121 10 Denmark Ireland 76 6 Norway 33 3 United Kingdom 1832 152 Int. trade, North Sea

87

Biogenic: North Sea (DMS) 21 North Atlantic (DMS) 1035 Source: Tarrason, 1991

7 0.1 - 5 10 - 188

Similar to SO1 substantial discrepancies occur among different estimates for eastern European countries. One striking example concerns the total amount of NO, emissions from the CSFR. Western experts consider official data on NO, emissions from Czechoslovakia (1127 kt NO, in 1985 [21]) as much too high and suggest significantly lower figures (e.g., Pacyna et al. [18] estimate a national total of 544 kt). Large disagreement exists also for emissions from the Soviet Union. Official data report e.g for 1985 3369 kt of NO, for the European part of the Soviet Union [21]. Analysis recently undertaken at IIASA, which is based on new statistical information on regional energy consumption of the Soviet Union computes 60 percent higher emissions (5530 kt NO,), if average emission factors are being applied [19]. Major uncertainties of NO, estimates are associated with emissions from mobile sources. At the moment two different approaches are applied to compute emissions from the transport sector: The more simple method applies a single surrogate emission factor to the amount of fuel consumption. This surrogate emission factor reflects the average of all driving modes and is assumed as representative for the entire car fleet and all driving conditions. Consequently, considerable differences occur in such emission factors used by individual countries. The other approach calculates emissions as composite of numerous driving modes with specific emission factors for individual car categories, driving cycles, etc. This approach has been used by some countries to estimate transport emissions for the CORINAIR inventory. However, this method puts high demand on the availability of accurate statistical data on traffic conditions (e.g., mileage, fleet composition, etc.) which can not always be satisfied. The low accuracy of the underlying statistical material caused serious reservations against this ‘bottom-up’ approach and no general consensus on which approach to prefer does exist at the moment.

73 5.3 Inventories of NHs emissions

Whereas emission estimatesfor SO2 and NO, have some history and are now performed mostly on a routine basis the estimation of ammonia emissions is a relatively new field for most countries. Uncertainties are not only caused by the lack of experience, but also, as recent research indicates, by the general stochasticity of emission factors: NH3 emissions are depending inter alia on the nitrogen content of fodder, on the weather conditions at the time of manure application and are influenced by particular managements practices (e.g., rotational or continuous grazing, etc.). Table 1: Estimates of emissions of NH3 for the year 1987. Inventory Buijsman IIASA Asman EMEP National [51 ~ 4 1 ~ 4 1 121 1101 estimates2 Country 1980/83 1980 1987 1987 1988 Year 21 25 27 32 24 Albania Austria 107 85 72 79 79 94 Belgium 123 82 102 105 Bulgaria 123 147 126 122 120 219 170 CSFR 200 200 197 128-222 1981 144 Denmark 129 116 103 111 155-196 1980186 Finland 61 44 43 56 49 52 1984186 974 841 France 679 650 782 1985 709 Germany, West 718 371 380 529 533 348-641 1986/88 274 Germany, East 242 228 239 90-157 1980/85 207 Greece 111 95 112 88 100 151 179 156 155 130 Hungary 90-157 1976187 117 Ireland 130 188 128 128 Italy 426 435 359 366 36 1 422 1987 Luxembourg 6 7 5 5 5 276 Netherlands 224 239 218 150 154-258 1982187 Norway 41 38 37 47 36 64 1987 Poland 561 570 528 478 405 Portugal 55 76 66 65 47 Romania 297 340 350 387 301 232 Spain 365 251 317 273 Sweden 74 66 59 62 52 Switzerland 74 64 59 62 53 64 1987 482 492 548 UK 478 405 451-560 1983/87 1256 1543 2288 2446 3182 USSR' 198 214 217 235 235 Yugoslavia Notes: European part of USSR within the EMEP area According to [14] I

'

14

Since at the moment national estimates do not exist for all countries in Europe international inventories have been compiled based on centrally available information ([2], [5], [14]). In all these studies emission factors are primarily based on experience gathered in the Netherlands; only in few cases emission factors have been slightly modified to consider national differences. As Menzi has pointed out for the Alpine situation a simple extrapolation of Dutch emission factors to other countries does ignore country specific conditions and agricultural practices and consequently may result in significant inaccuracies of the estimates [15]. Not surprisingly, the similar emission factors applied in these studies limit differences in national estimates to f 15 percent, which is significantly lower than the general uncertainties of such estimates are thought to be. 6. PER-CAPITA EMISSIONS IN EUROPE

Figure 3 displays the potential contribution of S 0 2 , NO, and ammonia emissions in 1985 to acidification on a per-capita basis. Over all of Europe, SO2 emissions contributed roughly 60 percent to potential acidity; the remaining fraction originated from NO, and NH3 emissions at almost equal shares. Particularly high per-capita emissions occur in eastern European countries, mainly caused by the utilization of brown coal leading to substantial ,502 emissions. All six countries with highest per-capita SO2 emissions are located in eastern Europe. This distribution gives some indication on which potential inaccuracies in emission estimates have highest influence on the estimation of total European emissions. 7. CONCLUSIONS

Inventories of acidifying emissions exist for all of Europe, though with variable accuracies. Whereas the sources of the largest contribution to acidification through SO2 emissions are already relatively well documented, still substantial uncertainties exist for estimates of NO, and ammonia emissions. Further refinement of emission inventory techniques seems necessary for these two pollutants.

References [l] Amann M., Sorensen L., The RAINS Energy and Sulfur Emission Database, Status

1991. WP-91-xx, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1991 (forthcoming).

[2] Asman W., Ammonia emissions in Europe: updated emission and seasonal variation. Report DMU-Luft-A132, National Research Institute DMU, Roskilde, Denmark, 1990. [3] Baars H.P., Accuracy of emission inventories. Methodology and preliminary results of the Dutch NO, inventory. TNO Report P90/031, TNO Delft, Netherlands, 1990.

75 QDR CSFR HUN POL BUL USR DK LUX IRE FIN ROM UK 9 PA QRE BEL YU F RQ FRA ITA SW E AUS NOR NET AL TK SWI POR

0

2

4

6

I

I

I

8

10

12

10-9 moles =SO2

O N O x

Figure 3: Potential acidity of emissions in Europe on a per-capita basis, 1985 (4) Bremnes P.K., Calculations of exhaust gas emissions from sea transport. Methodology and results. Proceedings of the EMEP Workshop on Emissions from Ships. Oslo, 7-8 June 1990, Norway. (51 Buijsman E., Maas H., Asman W., Anthropogenic NH3 emissions in Europe. Atmos.Env. 32: 1009-1022, 1987.

[6] Commission of the European Communities, Resultats du programme CORINE. SEC(91)958, Commission of the European Communities, Brussels, 1991.

[7] CORINAIR Inventory: Default Emission Factors Handbook. Prepared by CITEPA, Paris, 1991. [8] Egglestone H.S., Accuracy of national air pollutant emission inventories. IIASA/NILU Task Force Meeting on accuracy of emission inventories, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, 1991. [9] Eliassen A., Hov O., Iversen T., Saltbones J., Simpson D., Estimates of Airborne Transboundary Transport of Sulphur and Nitrogen over Europe. EMEP/MSC-W Report 1/88, Norwegian Meteorological Institute, Oslo, 1988.

76 [lo] Iversen T., Halvorsen N., Saltbones J., Sandnes H., Calculated Budgets for Airborne

Sulphur and Nitrogen in Europe. EMEP/MSC-W Report 2/90, Norwegian Meteorological Institute, Oslo, 1990.

T.,Halvorsen N., Mylona S., Sandnes H., Calculated Budgets for Airborne Acidifying Components in Europe, 1985, 1987, 1988, 1989, 1990. EMEP/MSC-W Report 1/91, Norwegian Meteorological Institute, Oslo, 1991.

[ll] Iversen

[I21 Jarvis S.C., Pain B.F., Ammonia Volatilisation from Agricultural Land. Proceedings No. 298, The Fertilizer Society, Peterborough, UK, 1990.

[13] Joerss K.E., Proceedings of the EMEP Workshop on Emission Inventory Techniques, Regensburg, July 1991. [I41 Klaassen G., Past and future emissions of ammonia in Europe. SR-91-01, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1991. [I51 Menzi H.,Neftel A., J.-M. Besson, Stadelmann F.X., Special conditions influencing ammonia emission factors in Switzerland. In: G. Klaassen (ed.): Ammonia emissions in Europe: emission factors and abatement costs. CP-gI-xx, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1991 (forthcoming). [I61 OECD, Emission Inventory of Major Air Pollutants in OECD European Countries. Environment Monographs No. 21, OECD Paris, 1990. [17] Pacyna J., NO, emission from stationary sources in Eastern Europe in 1985. NILU Report 78/88, Norwegian Institute for Air Research, Lillestrom, Norway, 1988. [18] Pacyna J., Larssen S., Semb A., European survey for NO, emissions, 1985. NILU Report 26/89, Norwegian Institute for Air Research, Lillestrom, Norway, 1989. [19] Popov S., The energy- and emission data base of the RAINS model for the European part of the Soviet Union. WP-gl-xx, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1991 (forthcoming). [20] Tarrason L., Biogenic Sulphur Emission from the North-Atlantic Ocean. EMEP/MSC-W Note 3/91, Norwegian Meteorological Institute, Oslo, 1991. [21] UN/ECE, Major review of Strategies and Policies. ECEIEB.AIRI27, United Nations Economic Commission for Europe, Geneva, Switzerland, 1990.