Spatially disaggregated emission inventory for anthropogenic NMVOC in Austria

Atmospheric EnvironmentVol. 27A, No. 16, pp. 2575-2590, 1993.

0004-6981/93 $6.00+0.00 © 1993 Pergamon Press Lid

Printed in Great Britain.

SPATIALLY DISAGGREGATED EMISSION INVENTORY FOR ANTHROPOGENIC NMVOC IN AUSTRIA WOLFGANG LOIBL, RUDOLF ORTHOFER a n d WILFRIED WINIWARTER Austrian Research Centre Seibersdorf, A-2444 Seibersdorf, Austria

(First received 17 December 1992 and in final form 13 June 1993) Abstract--Regional emission densities of anthropogenic non-methane volatile organic compounds (NMVOC) in Austria were calculated using statistical information on emission generation activities, emission factors from technical literature, and regional reference data. Total anthropogenic NMVOC emissions in Austria were estimated to be 467,000 metric tons for the base year 1987. However, due to the high uncertainties of the available emission factors, the range could be as much as 316,000-754,000 tyr-t. Anthropogenic NMVOC emissions consist of 32*/0 paraffin, 8% olefin, 20% aromatic, 13'/0 carbonyl, 6% photoehemically "non-reactive", and 20% other compounds. The total emissions from caeh source group were regionally disaggregated based on settlement densities, traffÉcdensities, and relevant regional source statistics. In total about 45,000 polygons were defined for an area of 84,000 km 2. While the theoretical average emission density for anthropogenic NMVOC in Austria would be around 5.6 t km -2 yr-t, the actual emission densities vary from 0 in unpopulated regions to 50-100 t kin-2 yr-~ in urban areas and upto 700 t km -2 yr-1 along major highways. National average values for emission densities fail thus to reveal the scale of emission densities in populated areas.

Key word index: Air pollution, volatile organic compounds, geographical information system, mapping, Austria.

1. INTRODUCTION 1.1. Usefulness of spatially disaggreoated emission inventories Spatially disaggregated emission inventories are the first step for regional effective air pollution management. Only if sources of air pollution are located and their contribution to overall pollution levels determined, cost-effective control actions can be introduced. Emission inventories are a major input for air pollution transformation and dispersion models, which allow the assessment of the risks for potential receptors. Overall nationwide emission figures--as they are frequently reported from various countries (e.g. UNECE, 1990; 1991)--might be useful for international comparisons and negotiations but have limited value for regional control actions. Small-scale differentiated emission data are necessary for the planning and implementation of regional effective air pollution control measures especially when photochemical oxidant formation is concerned. This is particularly important for a country with an extremely heterogeneous topography as Austria, where large areas of land are not inhabited and some other areas have a high concentration of population or industry. 1.2. Importance of N M VOC Non-methane volatile organic compounds (NMVOC) include a large number of different compounds, the majority of which are photochemically reactive and a number of them are toxic to human health and the environment. Many areas in Europe

exceed the critical levels of ozone for the protection of vegetation and human health (Grennfelt et al., 1988). Nitrogen dioxide (NO2) and photochemically reactive organic compounds (ROC) are major precursors for the production of ozone in the troposphere and in the planetary boundary layer (Fishman and Crutzen, 1978). While emissions of NOz have been sufficiently quantified in many countries, data about emissions of reactive organic compounds are still scarce. Frequently, available ROC emission inventories are not always reliable because inventory methods are unstandardized. Difficulties in the establishment of emission inventories of organic compounds arise because many different emission sources have to be included, and there are large amounts of diffuse emissions which cannot be attributed to a specific source category and which are difficult to quantify. Finally, there are still technical uncertainties which organic compounds should be considered as ROC, 1.3. Terminolooy While "classic" urban air pollutants are chemically definite substances (SO2, NO2, CO, etc.), photochemically reactive organic air pollutants are harder to classify. In some studies they are referred to as"hydrocarbons" or "organic emissions". Both terms would include semi-volatile and non-volatile compounds which are irrelevant for photochemistry. On the other hand, inventories considering specifically "photochemically reactive" compounds might leave out some compounds of low reactivity but with potential toxicity which are of considerable interest for any

2575

W. LoniL et al.

2576

national emission inventories and control options. In this article, we defined the compounds of interest as non-methane volatile organic compounds (NMVOC), and we considered as "volatile" all compounds which may be emitted into the atmosphere in gaseous form (Bouscaren et al., 1987). 1.4. Study objectives The objective of the study was to estimate the emissions of N M V O C in Austria using available data for the reference year 1987 and to disaggregate the nationwide emission inventory so that small-scale regional emission densities can be determined. A base year of 1987 reflects the situation in Austria before major N M V O C emission controls were implemented, and thus is representative for most of the 1980s. This article presents the results of the N M V O C emission inventory, and describes the methods and results of the spatial disaggregation. Throughout this article, only anthropogenic emissions will be considered. Biogenie emissions are not covered and will be the topic of further research. 1.5. Data situation in Austria Reliable emission inventories existed in Austria for the urban air pollutants SO2, NOx, C O since the early 1980s but the emissions of N M V O C were largely unknown. Official N M V O C emission data of 1987, which referred to combustion sources only, were 120,000 t y r - 1 (Onz et al., 1987), other estimates which included solvent evaporation were 250,000tyr -1 (OECD, 1987). Preliminary results from this study were the basis for official revisions of earlier estimates and were then reported to international programmes ( U N E C E , 1991). M E T H O D S OVERVIEW

2.1. Emission inventory 2.1.1. Emission calculation. Emissions are calculated as the product of the rate of an emission generating activity (e.g. electricity production from electric power plants; or total kilometres travelled by automobiles) and the appropriate emission factor (e.g. the average amount of NMVOC emitted by a hard-coal-fired power plant during the production of a unit of electricity: or the amount of NMVOC emitted with exhaust of a car with a specified engine rating over a certain distance). In this study, emission generating activity rates have been compiled from official national statistics, and emission factors were taken from technical literature. When emission factors from literature were found to have great variations, the most likely emission factor was taken, or, if this was inappropriate, an average emission factor was considered. As an overall strategy, the completeness of the inventory was given higher priority than the accuracy of single emission factors. Thus default emission factors were introduced for emission sources in which factors were unknown. Emission factors applied in this study are summarized in Table 1. In cases where emissions of some sources have already been quantified in other special studies, data were taken directly from those studies. As emission data are frequently found in less accessible literature, our compilation is certainly not complete. While the most important standard literature is covered, especially

some recent results may be missing. Additional data may widen the database, but do not necessarily improve the emission inventory. Also, a complex scheme for estimation of emissions, like the one available for traffic (Samaras and Zierock, 1989) cannot be broken down to emission factors to be compared here. 2.1.2. Uncertainties. Uncertainties of emission inventories result from inappropriate emission factors, and from missing statistical information on the emission-generating activities. While the uncertainties associated with emission factors usually can be assessed by comparing different factors of the technical literature, insufficient knowledge of emission generating activities often remains hidden, and may cause significant errors. In this inventory, uncertainties were estimated by calculating upper and lower emission limits by using the upper and lower values of available data. Usually, the arithmetic means were taken as the most "probable" values. The difference between the upper and lower limits as given in Table 2 reflects the uncertainties associated with the emission of each activity. A large difference indicates high uncertainties, while small differences show fairly good agreement of the available data and reliability of the estimates. 2.1.3. Emission composition. NMVOC emissions encompass tens of thousands of different compounds, whose only common feature is their organic chemical character and their volatility. Compounds have a molecular weight from as small as 26 up to large molecules with a molecular weight of around 300. NMVOC compounds can be low or very highly toxic, and the yearly emissions can be in the magnitude of few grams up to 10,000 kg or more. With present knowledge, a detailed quantification of all different NMVOC species emissions is not appropriate. Because of the high number of NMVOC compounds it is necessary to follow a manageable classification system. Several reactivity classification systems have been suggested in the literature (DOE, 1987; Grosjean and Fung, 1984; Fox, 1986), most of them are based on the photochemical reactivity of the compounds. In this study, NMVOC's were classified into 6 main groups covering a total of 18 classes, taking into account chemical properties and photochemical reactivity (cf. Table 3). The composition of the emissions was calculated by disaggregating total NMVOC emissions in accordance with published data for the composition of the various compounds (Table 4). The characterization of emissions in this system is useful for inputs to photochemical models and for assessment to emissions with respect to human and plant toxicity. It should be noted, however, that available data are fairly sparse and sometimes inconsistent. A compilation of data for stationary sources by Veldt (1991) yielded in some cases considerable differences to the profiles presented here. The evaluation and comparison of the different profiles found in the literature is beyond the scope of this paper. Therefore no uncertainty estimates for the chemical composition of emissions can be given currently. 2.2. Spatial disaogreoation of emission inventory Usually, emission inventories are calculated for defined areas, which can be very small (like industrial plants), or very large (like entire countries). Spatially differentiated emission inventories are frequently created by defining small subunits within a larger unit (e.g. grid cells, or administrative units like municipalities within a country), and calculating the emissions for each subunit ("bottom-up approach"). This means that first of all the geographical units are defined and then the emission of this subunit is calculated. This method is time-consuming and requires a detailed knowledge of emission generating activities in the subunits. The results of such a regionalization then reflect average emission densities of the predefined subunits rather than realistic local emission densities. The geographic accuracy of such inventories is very high, but the emission estimates might be inadequate due to lacking data on emission generating activities.

Anthropogenic NMVOC in Austria An alternative approach presented in this study is to calculate emission totals for a whole country (or other administrative districts) where sufficient emission data are available. The spatial disaggregation is then performed in a second step for every emission source group using source specific statistical data. These statistical data represent the regional and local distribution patterns related to geographical units, e.g. population density, industrial employment density, traffic density etc. ("top-down approach"). The spatial distribution of the regional emission sum can be easily calculated by uniting the source specific geographical databases. This approach has the advantage that the overall accuracy of the emission inventory is very high because of the good data quality for emission generating activities. The estimation of scenarios using different emission factors which lead to different national emission sums can be easily dis-

2577

aggregated relating to their actual spatial distribution. Thus the overall practical usefulness of such top-down inventories is very high. 2.2.1. Disaogreoation approach followed in this study. In this study, the regional disaggregation of the NMVOC emissions was performed following the "top-down" approach. For each pollution source, total emissions were calculated for the area with the most reliable data (which usually was an administrative unit, and in most cases, was Austria as a whole). The disaggregation was specifically performed for the major emission source categories (road traffic, stationary combustion, solvent evaporation and industrial processes), which make up 92% of all anthropogenic emissions. The remainder of small diffuse sources were not disaggregated as they have rather low relevance for a regional approach in view of the uncertainties associated with the emission inven-

Table 1. Average emission factors for the sources considered in the inventory Source category

Emission factor

Reference/ remarks

Traffic and mobile sources Transit road traffic Petrol-fuelled vehicles Refuelling losses Evaporation losses Running losses Cars until 1986 Engines< 1400 cm a Engines > 1400 crn 3 Cars since 1987 Engines < 1400 cm 3 Engines > 1400 cm a Diurnal losses Cars until 1986 Cars since 1987 Hot soak Cars until 1986 Engines < 1400 cm 3 Engines> 1400 cm 3 Cars since 1987 Engines< 1400 cm 3 Engines > 1400 em 3 Exhaust emissions Exhaust during driving Passenger vehicles without catalytic converter Light duty vehicles without catalytic converter Passenger vehicles with catalytic converter Cold start emissions Passenger vehicles without catalytic converter Light duty vehicles without catalytic converter Passenger vehicles with catalytic converter 2-wheelers Mopeds (2-stroke engines) Motorcycles Motorcycles (2-stroke) Motorcycles (4-stroke) Diesel vehicles Passenger vehicles Light duty vehicles Heavy duty vehicles and buses Tractors Trains Aircraft Ships and boats Commercial shipping Private boats Other mobile sources Lawn mowers Motor saws (2-stroke)

1.90 kg t - 1 fuel

a

0.16 g kin- t travelled 0.32 g kin- ~ travelled

b b

0.05 g kin- ~ travelled 0.10 g kin- ~ travelled

b b

2.0 g car- ~ day- 1 1.0 g car- 1 day- t

b b

3.2 g per hot soak 6.1 g per hot soak

b b

1.5 g per hot soak 3.0 g per hot soak

b b

1.75 g kin- 1 travelled 2.70 g kin- ~ travelled 0.17 g kin- ~ travelled

c d e

2.2 g per cold start 3.5 g per cold start 1.5 g per cold start

f f f

9.5 g km- 1 travelled

g

8.1 g km- ~ travelled 4.3 g km- 1 travelled

h i

0.40 g km- 1 travelled 0.46 g km-a travelled 3.4 g km- ~ travelled 11.8 kg t - ~ fuel consumed 14.0 kgt- ~ diesel fuel consumed 18.0 kg per take off/landing cycle

j f k 1 n o

14.0 kg t - ~ diesel fuel consumed 80.0 g •- 1 fuel consumed

n

110 g h - ~ 130 g C- 1 fuel consumed

p p

2578

W . LOIBL

et al.

Table L (Continued) Source category

Stationary combustion Small domestic stoves Coal/coke Hard coal Lignite Briquettes Coke Fuel oil Natural gas Fuelwood Small commercial stoves Coal/coke Hard coal Lignite Briquettes Coke Fuel oil Gaseous fuels Fuelwood Other fuels and waste Large industrial boilers Coal/coke Hard coal Lignite Briquettes Coke Fuel oil Gaseous fuels Natural, coke oven, refinery gas Blast furnace gas Fuelwood Other fuels and waste Thermal power plants Coal Fuel oil Natural gas Waste incineration plants Industrial processes Petroleum industry Natural gas production and distribution Plastic materials production Polyethylene production Ethylene production Polymerization Polypropylene production Polymerization Polyvinyi chloride production Vinyl chloride emissions Other (softeners) Polystyrol production Chemical industry Chlorinated hydrocarbons production Formaldehyde production Rubber and tyro production Iron and steel industry Sinter plants Coke plants Food industry Meat smoking Beer and wine production Beer White wine Red wine Coffee roasting Vegetable oil extraction

Emission factor

300 kgTJ -1 500 kg TJ -1 500 kg TJ -x 20 kgTJ -1 20 kgTJ -x 10 kgTJ -1 900 kgTJ - t 300 kgTJ -1 500 kgTJ -s 500 kgTJ -1 20 kgTJ -s 20 kgTJ -1 10 kg TJ -1 900 kgTJ -1 900 kgTJ -1

Reference/ remarks

q

q q r r

r s

r

r r r r

r r

20 kgTJ -1 30 kgTJ -1 30 kgTJ -1 10 kgTJ - t 10 kg TJ -s

1 ! 1 t

2 kgTJ -~ 60kg TJ -1 800 kgTJ -1 180 kgTJ -~

t t u t

3,40 kg T J - 1 6.80 kgTJ -1 0.50 kgTJ -1 7 m g C m -3 exhaust gas

1

1 1

1 v W W

1.2 kg t ' s produced 2.0 kg t- 1 produced

P

2.0 kg t- 1 produced

t

0.3 kg t- 1 produced 0.8 kg t - l produced 15.0 kg t -1 produced

t

4.0 kg t- 1 produced 3.0 kg t- ~ produced 100.0 kg per 1000 tyres produced

t

t t t x

0.1 kg t- 1 produced 0.55 kg t- ! produced

P P

0.1 kgt -1 produced

t

0.02 kg t- 1 produced 0.2 kg t- l produced 1.1 kgt -1 produced 1.0 kg t - 1 produced

f

r r

r w

2579

Anthropogenic NMVOC in Austria Table 1. (Continued) Source category

Fibre board production Pressing Drying Pulp/paper/cellulose Sulphate process Sulphite process

Emission factor

Reference/ remarks

0.12 kg m -3 produced 0.1 kgm -3

y t

1.0 kg t- ~ produced 1.5 kg t- ~ produced

z z

Solvents Solvent use

85% of total solvent use

Other sources Waste landfills Wastewater treatment plants Agricultural straw burning Tobacco smoking

1.0% of methane 28.0% of methane 15.0 kg t-1 straw 15.0 kg t- 1 tobacco

aa aa bb cc

Remarks: (a) Matsumura, 1974; factor varies from 1.6-2.8 (Braddock et al., 1986; UBA, 1981; OECD, 1986). (b) Modified after Concawe, 1987; carbon canisters are obligatory in Austria since 1987. (c) Calculated after Concawe, 1987; factor varies from 1.1-2.0 (Black, 1989; BFU, 1986; BMHGI, 1984). (d) Calculated from Concawe, 1987; factor varies from 1.7-2.4 (BFU, 1986; BMGHI, 1984). (e) Assumed as 10% of uncontrolled vehicles. (f) BFU, 1986. (g) Calculated after BFU, 1986, includes evaporation losses of 0.6 g per day; factor varies from 5.8-13.7 (BFU, 1986, BMHGI, 1984). (h) BFU, 1986, includes 2.1 gkm -x evaporation losses (Black, 1989). (i) Black, 1989, includes 2.1 g km-1 evaporation losses. (j) Calculated after OECD, 1986, as average for 33% city and 67% overland traffic; factor varies from 0.34-0.59 (BFU, 1986 and BMHGI, 1984). (k) Calculated from BFU, 1986 and BMHGI, 1984. (1) BMHGI, 1984. (n) Calculated after Brice and Derwent, 1978. (o) Emission factors given in OECD, 1986; emission calculations for Austria taken from Mueller and Alfons, 1986. (p) OECD, 1986. (q) Friedrich et al., 1987; factors can be as high as 1100 kgTJ-1 (God and Mugrauer, 1987). (r) Friedrich et al., 1987. (s) Friedrich et al., 1987; factors can be as high as 2300 kgTJ -t (God and Mugrauer, 1987). (t) UBA, 1981. (u) Calculated after Bocola and Cirillo, 1987. (v) Measured in Vienna incineration plants. (w) Emission data taken directly from industry inventories. (x) USEPA, 1980; Stockton and Stelling, 1987. (y) Jost, 1987. (z) Stockton and Stelling, 1987. (aa) USEPA, 1980, calculated after Orthofer, 1991. (bb) Calculated after Bocola and CiriUo, 1987; Brice and Derwent, 1978. (cc) Calculated as straw burning.

tory. For all of the mentioned major pollution sources, appropriate statistical reference data was used to describe the regional distribution of the emissions (see Section 3.2). Thus for each emission source, a map was calculated showing the respective spatial relevance. Using a geographic information system, those source-specific maps were combined to create an overall emission map. Emissions from large point sources were allocated to the administrative unit of their location, because due to the Austrian legal situation concerning confidentiality of industrial data it was not possible to allocate industrial emission to the exact location of the industrial plants. The digital databases were created using the geographic information system ARC/INFO. Data were acquired manually through digitizing, and automatically by scanning and using image processing classification software (ERDAS) and subsequent grid-to-vector conversion routines.

3. SPATIAL DISAGGREGATION OF EMISSIONS

The basis for the spatial disaggregation is the establishment of the geometric databases for source-televant areas. After they have been established, their quantitative connection with the source categories can be assigned. 3.1. Establishment of source-relevant areas

the

geometric

bases

for

The source-relevant areas were determined by using available statistical and m a p information. Relevant areas for each emission category are summarized in Table 5.

W. LOmL et al.

2580

Table 2. NMVOC emissions in Austria (rounded to nearest 100 t yr- 1, total emission rounded to the nearest 1000 t yr- 1). The difference between lower and upper limit estimate reflects the uncertainty Best estimate

Lower limit

(t yr -1)

(t yr -1)

Traffic and mobile sources Domestic road traffic Transit road traffic Railways Aircraft Ships and boats Other mobile sources

-

Upper limit (t yr -1)

134,800 8300 700 300 500 1900

93,0OO 5900 600 300 400 1700

-

206,200 12,800 800 1100 500 2100

Stationary combustion Small domestic stoves Small commercial stoves Large industrial boilers Thermal power and waste incineration plants

82,200 1000 8300 200

76,000 500 3000 200

-

206,000 1000 9400 300

Industrial processes Petroleum industry Natural gas Plastic materials Chemical industry Iron and steel industry Food industry Fibre boards production Pulp/paper/cellulose production

10,000 2300 5100 500 1500 200 300 1600

9100 800 3100 200 700 100 300 500

-

12,600 2800 8200 1100 2000 200 400 3000

Solvents Solvent evaporation

170,000

110,000

-

220,000

Other sources Waste landfills Wastewater treatment plants Agricultural straw burning Tobacco smoking Total emissions

800 25,000 12,000 200 467,000

400 4000 5500 100 316,000

-

1200 40,000 22,500 400 754,000

Table 3. Composition of NMVOC emissions in Austria (emission estimates rounded to nearest 100 t yr- 1) NMVOC group

NMVOC subgroup

NMVOC code

Emissions

(t yr-') Non-reactive

Ethane Ethine ( = acetylene)

NRE ETHANE NRE ETHINE

20,000 7400

Paraffins

Propane Higher paraffins

PAR PROPAN PAR CA + PAR

1800 146,800

Olefins

Ethene Propene Higher olefins

OLE ETHENE OLE PROPEN OLE C 4 + OLE

14,300 8500 16,600

Aromatics

Benzene Toluene Higher aromatics

ARO BENZEN ARO TOLUEN ARO C8 + ARO

7800 19,200 68,000

Carbonyis

Formaldehyde Acetaldehyde Higher aldehydes Ketones

CNY CNY CNY CNY

FORMAL ACETAL C3 + A L D KETONE

17,300 4200 4000 36,600

OTH OTH OTH OTH

OXO RCOOH HALO OTHER

67,500 0 24,600 2900

Other NMVOC Alcohols, esters, ethers Acids Halogenated compounds Other/undefined compounds

3.1.1. Administrative boundaries. M o s t regional statistical i n f o r m a t i o n which is necessary to calculate a n d disaggregate emissions, a n d to relate statistical

i n f o r m a t i o n to relevant areas, is provided for administrative units. F o r this study, digital databases for the b o u n d a r i e s of the country, the 9 provinces, the 99

Anthropogenic NMVOC in Austria districts, and the approximately 2300 municipalities were used, which was provided by the Federal Bureau of Statistics. 3.1.2. Major national roads. All major national roads were digitized from an official 1:500,000 map (BEV, 1986), which also indicated the road segments used for official traffic counts. Through this, a total of about 10,900 km of major roads on which about 50% of total traffic occurs was covered. The traffic densities from the traffic count (OeSTAT, 1985) were assigned to the respective road segments. To calculate emission densities, roads were given a standard width of 50 m. 3.1.3. Uninhabited areas (areas which are not suitable for settlement). Uninhabited areas include major roads, forests, rivers, lakes, and mountain areas above 1500 m a.s.1. This base map proved to be one of the most important for the emission disaggregation as (with the exception of roadside emissions) all major anthropogenic emissions come from outside uninhabited areas. Thus, all relevant source areas naturally were referred to the inhabited areas of provinces, districts, and municipalities only. The uninhabited areas were defined by analysing the available official land-use map of Austria with a scale of 1:1,300,000 (OeAW, 1960). The map was digitized with a colour scanner and the resulting digital image processed by classifying the various land use categories. However, with pixels after the image processing representing about 1 km 2 each, resulting maps were not sufficiently accu, ate to differentiate between inhabited and uninhabited areas in small alpine valleys. In those regions the accuracy of the base map was improved by overlaying with a digital vectorized forest map and road map with scales of 1:500,000 each (BEV, 1986). Areas above 1500 m a.s.l, were defined in more detail using a national digital elevation model for Austria. 3.1.4. Settlement reoions. Point location and population number of the about 3000 settlements (towns and villages) with a population of more than 500 were extracted from an existing digital database (UBA, 1989). These high-density settlements comprise about 2/3 of the overall population. To generate a settlement map on a 1:500,000 scale and to allocate the emissions, settlement areas were created by calculating a circular buffer round their centre points, with a radius depending on the number of inhabitants (e.g. 500 inh.: r = 700 m; 10,000 inh.: r = 2000 m; 250,000 inh.: r = 5000 m; 1,600,000 inh.: r = 8000 m). About 1/3 of the national population lives in disperse or in very small villages and is not covered by the settlement database. For the allocation of emissions, this distribution of disperse population was calculated for each municipality, and it was assumed that this population lives in the areas which are outside high density settlements, and outside uninhabited areas. 3.2. Disaggregation of emissions 3.2.1 Traffic and mobile sources. Emissions from motor vehicle traffic were disaggregated according to

2581

two different major traffic patterns: (i) highway traffic on major national roads, and (ii) local road traffic. About 40% of passenger vehicle traffic and 50% of duty vehicle traffic can be attributed to highway traffic (OeSTAT, 1985). In addition, most of the transit traffic through Austria occurs on the major road network. The emissions from highway traffic were assigned to the major national road network. For each road segment, emissions were calculated from the nationwide traffic statistics, taking into account the vehicle movements per road segment for passenger cars, buses and trucks. Emission densities were calculated for an average road width of 50 m. For local traffic off major highways it was assumed that local traffic numbers would follow the same pattern as local car registrations in the 99 districts (OeSTAT, 1989), and that emissions would be uniform over the settlements and the inhabited areas inside each district. As stated above, about 60% of passenger vehicle traffic and 50% of service vehicle traffic is local. Emissions in the respective relevant areas were calculated from the total nationwide emissions estimates for passenger and duty vehicles, taking into account the number of registered vehicles in each district. Emission densities are calculated for the inhabited areas of each district. Emissions caused by hot soak petrol evaporation, cold starts, mopeds, and tractors were fully attributed to local traffic. Emissions from aircraft, railways, lawn mowers, and motor saws are very small compared to vehicle emissions, and thus were not considered in the disaggregation procedures. 3.2.2. Stationary combustion. Most of the NMVOC emissions of this sector come from small domestic sources (see Table 2). While industrial boilers are a minor but still considerable source of emissions, other sources such as power plants were not considered significant for the spatial disaggregation. Emissions from domestic heating were related both to the high density and low density settlement areas, taking into account the respective number of population, and the type of fuel used. The latter proved to be most important, as NMVOC emissions from domestic sources depend very much on the fuel types. Fueluse patterns are very different in the various parts of Austria. While in some major cities and their surroundings natural gas is a dominant fuel, firewood is vital in rural regions, especially in many mountain valleys. It was assumed that there is a constant emission contribution per person for a given amount of fuel used. The relative quotas of different fuel types and of population in all municipalities were calculated using available census data on housing (OeSTAT, 1981). From these quotas, emissions for the various municipalities could be calculated from the total nationwide emissions. Those emissions within a municipal area were further disaggregated for high density and low density settlement areas. Emissions from industrial boilers were disaggregated for the 99 districts, using the respective numbers of industrial employees as disaggregation parameter (OeSTAT, 1981).

.

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distribution N a t u r a l gas exploitation and distribution Ethylene production Ethylene polymerization Polypropylene polymerization PVC production, V C emissions P V C production, additives Polystyrol p r o d u c t i o n Polym~'thane f o a m i n g

Petroleum products storage and

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(~%)

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NRE ETHIN

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(~%)

Coal/coke Fuel oil

Stationary combustion

~ a ~ c and m o b ~ sources P . e ~ l i n ~ g / e v a p o r a t i o n losses (petrol vehicles) Exhaust (vehicles w i t h o u t catalytic converter) Exhaust (vehicles with c a t a l y t i c converter) Exhaust from void s t a r t (all vehicles) 2-wheelars/2-stroke engines vehicle~ trains, commercial ships Air.aft Private boats, lawn m o w e r s and m o t o r saws

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(11 kmmptioe~ ( m ) UBX, I ~ t .

(!) O E C D , 19116; D e r w e n t a n d R o y , 1979 (modificxl). N M V O C c o m p o ~ t i o n a . m u n e d as for I~trol cars. (11) ~ r e t a / . 1991. (i) C o ~ a ~ 1986. 0) OECD, 19116( m o d i f m i ) . (k) H ~ l c l a n d Vitove~ 1990.

(d) Cakulated - , 50% cximust and 50% evaporation, (e) CAdculslcdas 25% exhaust, 75% evaporation (C. Veldt, p c ~ |

p.cmr~rd~.x~ (a) B r a d d o ~ , 1988; Nelson et a/~ 1983; O E C D , 1986. Co) N , ~ m a n d QuiMey, 1984. (c) U S E P A , 19110.

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Other

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2584

W. LOmLet al. Table 5. Relevant areas and their quantification characteristics for disaggregation of emission categories

Emission category Road traffic Stationary combustion

Solvents

Industrial processes

Relevant areas

Quantification

Major national roads Inhabited areas in districts Settlements over 500 population Inhabited areas in municipalities Inhabited areas in districts Settlements over 500 population Inhabited areas in municipalities Inhabited areas in districts Inhabited areas in districts Inhabited areas in municipalities, where industrial plants are located Inhabited areas in districts

3.2.3. Solvent evaporation. Emissions from solvent evaporation result from domestic and industrial solvent use. The various emission sources are not known in detail, and the emission densities of single sources can vary significantly. As a first estimate it was arbitrarily assumed that about 25% of solvents are used in the domestic sector, and 75% in industry. Domestic emissions were disaggregated according to the population distribution, assuming that solvent evaporation was of the same quantity for every inhabitant. Emissions were calculated for each municipality, and then further disaggregated for high density and low density settlement areas. Solvent emissions from industry were disaggregated for each of the 99 districts, using the relative number of industrial employees as a surrogate pattern for emission distribution (OeSTAT, 1981). The necessary assumption of uniform NMVOC emission rates for all industry branches is a source of considerable uncertainty. 3.2.4. Industrial processes. Due to the variety of emission sources, their characteristics, and the available information on their spatial distribution, there is no uniform disaggregation methodology. If the locations of the industrial plants were known (which was the case for most major industries), their emissions were allocated specifically to the inhabited area of the respective municipalities. This method is, of course, not a disaggregation of overall emission estimates but a proper bottom-up approach (see Section 2.2). Emissions from the oil and gas mining sector with many minor plants were allocated to the inhabited areas of the respective districts according to their share of production. Emissions from the distribution of natural gas were attributed to all municipalities with gas distribution systems for domestic heating. The reason for this approach is that most emissions come from small pipelines involved in final distribution rather than from the modern transit pipeline systems involved in primary distribution (Hackl and Vitovec, 1990). Emissions from petroleum products storage were allocated to the location of major storage facilities in proportion to their capacity. For the emis-

Traffic statistics Number of registered vehicles Population number Number of population not living in settlements Number of industrial workers Population number Number of population not living in settlements Number of industrial workers Number of industrial workers Plant-specific production data Oil/gas production data

sions from the distribution of petroleum products, 80% were allocated to the six existing sub-storage facilities in proportion of their capacities, and 20% were allocated to industrial activities in the 99 districts. Emissions from gas station fillings were allocated to the 99 districts according to their share of motor vehicle registration numbers, assuming that the distibution and turnover of petrol stations directly corresponds to the local motor vehicle densities. Emissions from the polystyrol, and polyurethane industry, which are caused by many small facilities whose locations are not known in detail, were disaggregated according to the number of industrial employees in each district. All industrial emissions were allocated to the high density and low density settlement areas of the districts and municipalities. 3.2.5. Total emissions and emission densities. Total emissions were calculated for each polygon through geometric overlay of all different layers containing the spatial patterns of the emission source groups. The resulting emissions for each spatial unit reflect the respective emission densities. Once the disaggregation has been managed in the geographical information system, and all relevant emission source areas have been defined, the system can be easily updated when new information becomes available, or for the visualisation of control scenarios. 4. RESULTS Detailed results for emission calculations of the various sources considered are summarized in Tables 2 and 3. 4.1. Traffic and mobile sources

Total emissions from mobile sources are 146,000tyr -1, with nearly 98% coming from road traffic (143,000 t yr-1). About 75% of the road traffic emissions come from vehicle exhaust, and the remainder from petrol evaporation and refuelling losses. Alternative calculations made with the preliminary version of the COPERT emission model from the

Anthropogcnic NMVOC in Austria European Community (Samaras and Zierock, 1989) showed that both estimates differed by only 7-8%. However, uncertainties of traffic emissions are generally very high due to the heterogeneity of sources and the thus generally unreliable emission factors, especially in the ease of fuel evaporation. The composition of NMVOC emissions reflects the high influence of motor vehicle exhaust with about 50% paraffins, 25% aromatics and 17% olefins. The highest emission densities occur along the main highways. Emission densities were around 700 t k m - 2 yr- 1 for main motorways, and about 50-100 tkm -2 yr- 1 for other major national roads. Typical values for emission densities from dispersed local traffic are 50-100 t k m - 2 y r -1 in urban areas and 5 - 1 0 t k m - 2 y r -1 in densely populated areas in alpine valleys. However, when assessing those high values, it must be taken into account that they refer to a road width of 50 m, so that 20 km of road corresponds to 1 km 2. 4.2. Stationary combustion

2585

(see Section 4.2), and industrial solvent evaporation with the solvent evaporation (see Section 4.4). Industry sectors considered were the petrol, natural gas, plastic, chemical, iron and steel, food, chip-board, and pulp and paper industries. The largest contribution to NMVOC emissions comes from the petroleum industry sector. In this sector, uncertainties were relatively small due to the fact that emission data were taken from a specific emission survey by the industry itself. Other industrial activities which cause significant contributions to overall emissions are the mining and distribution of natural gas, plastic and chemical industry, iron and steel industry (sinter and coke ovens), and the cellulose production. The spatial disaggregation of emissions from industrial processes reflects primarily the distinct location of the industrial plants. Although emissions sometimes are low compared with total emissions, the emission densities of single plants can be extremely high, e.g. more than 150 t km-2 yr-1 for the municipality area where the main refinery plant is located.

Emissions from stationary combustion are 92,000tyr -1. Small domestic stoves, particularly 4.4. Solvent evaporation The main purpose of the use of solvents is that they those which are fired with solid fuels such as coal or firewood, are the major causes for the high emissions, evaporate and disperse. Thus it has to be assumed because the combustion processes are very inefficient that--except for the defined quantities which are deand difficult to control. Nearly 80% of emissions from stroyed after use--what is used is lost. According to stationary combustion come from firewood-fuelled international estimates (Coneawe, 1986) it was asstoves. This very high proportion is due to the fact sumed that only 15% of used solvent might be dethat firewood use is widespread in rural Austria, espe- stroyed or otherwise immobilized in non-polluting cially in the alpine forest areas. According to available ways. From available statistics of solvent production fuel statistics, firewood provides about one-third of and imports/exports, it was calculated that in 1987 the total energy use for domestic heating. Coal-fired total nationwide volatile solvent use in Austria was small domestic stoves and large industrial boilers each about 170,000-200,000 t yr -1 (OeSTAT, 1988). Thus contribute about another 10% of the emission in this solvent evaporation is estimated to be about sector. Other sources like power plants and waste 170,000 t yr- 1. Of course, uncertainties associated incineration plants have only very low NMVOC with this estimate are high due to the inadequate emissions. Uncertainties of the estimates are high, knowledge about the rates of solvent recovery and because especially for the activities with highest destruction. NMVOC emissions composition is charemissions the validity of data is very poor. The acterized by a high portion of aromatics (23%), higher composition of NMVOC emissions reflects the high paraffins (18%), and earbonyls (17%), particularly portion of emissions from firewood combustion with acetone. Halogenated solvents contribute about 13 %. The spatial distribution of solvent evaporation reits relatively high proportion of aldehydes, particularly formaldehyde. Paraffin emission accounts for flects primarily the distribution of industrial activities in Austria. Emission densities are as high as 57% of emissions, and 22% are aldehydes. The areas of highest emission densities correspond 50 t km - 2 yr- 1 in major cities and 10-20 t k m - 2 yr- 1 with areas of dominant firewood use. Highest emis- in industrialized alpine valleys. In most areas with low sion densities in urban areas are 15 or more industrial activities, emission densities are below t km- 2 yr- 1. However, relatively high emission densities 5 t k m - 2 y r -1. also occur in some alpine valleys with almost exclusive use of firewood, where emission densities reach 4.5. Other anthropogenic emissions 5-15 t km-2 yr -1. Sources of interest not included in the above emission sectors are those which can be expected to 4.3. Industrial processes contribute major emissions (waste landfills, sewage 4.3.1. Emissions. Emissions from industrial pro- treatment plants, open straw burning in agriculture) cesses are about 21,000 tyr-1. This emission group as well as those which are frequently discussed publicrefers to the emissions which are directly associated ly but which have never been sufficiently quantified with process emissions only. Industrial boilers are (tobacco smoking). Of the sources considered, only considered together with the stationary combustion sewage treatment plants and agricultural open straw AE(A) 27:16-I

2586

W. LOIBLet al.

burning are major sources of NMVOC emissions. emission densities range from 50 tkm -2 yr- ~ in the Sewage treatment plants emit about 25,000 t yr -1, suburbs, to more than 2 5 0 t k m - 2 y r -1 in the mostly by-products from methanogenesis-related pro- highly populated city centre areas. Thus, even in urban cesses. Emissions from waste landfills are below areas, emission densities can vary by a factor of five. 1000 t yr-1. In the case of sewage treatment plants In rural areas emission densities vary from 3 to and waste landfills, uncertainties are high because 1 0 t k m - 2 y r -1, but they can be as high as emission factors depend highly on the substrate char- 20-50 t k m - 2 yr = 1 in some densely populated alpine acteristics and the specific treatment parameters, valleys. The case of the alpine valleys clearly demonwhich are not quantifiable. Additionally, the emission strates the usefulness of this study's approach to assign factors are derived from data on the chemical com- emissions to their relevant source areas, independent position and may be less reliable than other factors. of the administrative boundaries. The inhabited parts Accordingly, the difference between the upper and of these areas have high emission densities, whereas average emission densities for the respective whole lower limit estimate is fairly large. Burning of straw in the fields after harvest cause administrative areas including the (uninhabited) foremissions of about 12,000 t yr- 1, which gives cause for est and mountain areas would be unrealistically low. concern, as those emissions are concentrated on only 5. CONCLUSIONS a few weeks per year in late summer, when sunshine is strong enough for formation of photochemical smog. Results from this study show that until recently, A spatial disaggregation of this sector was not performed as sufficient statistical data for major areas of NMVOC emissions in Austria have been understraw combustion and areas of sewage treatment estimated. This was mainly due to the fact that several plant installations was not available at the time of the potent emission-generating activities were not at all considered in the inventories, and that the contribustudy. tion of some emission sources has not been taken into account sufficiently. In general, the completeness of 4.6. Total emissions emission inventories, i.e. that all major sources are The best estimate for total NMVOC emissions in actually included, is the major factor proving the Austria in the base year 1987 is 467,000 t yr- t. How- usefulness of an inventory. If all major emission sourever, uncertainties are very high and the actual figure ces are included in emission inventories, the causes of may range from 316,000 to 754,000 tyr -1 (see Table uncertainties are insufficient knowledge of the validity 2). About 36% of all emissions originate from the of emission factors, and insufficient data on emission evaporation of solvents in the domestic and industrial generating activities. Uncertainties of the NMVOC sector. Thirty-one percent of the emissions result from emission inventories are generally high, but unfortutraffic and mobile sources sector, and 20% from com- nately, reports on emission data frequently lack unmercial, industrial, and domestic stationary combus- certainty figures. In this study, the uncertainty was tion sources. Industrial processes contribute only estimated to range from - 3 0 to + 60% of the "best about 5% to overall emissions, but their local impact estimates" (see Section 2.1.2.). A comparison of anthropogenic NMVOC emiscan be high. It is worth noting that several of the major emission sectors have only one dominating sions in various European and North American counsource group. In the traffic sector, road traffic has by tries is given in Table 6 (UNECE, 1991). Austria's far the most dominant contribution. In the stationary yearly per capita emissions of 59 kg corresponds well combustion sector, firewood-fuelled domestic small with other countries like U.S.A., Canada, Greece, stoves have the biggest contribution to overall emis- Sweden, France or Switzerland, which are all in the sions. In the case of emissions from industrial pro- 50-80 kg per capita range. In industrialized countries, cesses, more than 80% of emissions originate from oil, per capita quotas lower than 40 kg could indicate that gas and plastics industry, which are three related one or several emission categories could have been left out of the inventories. Per capita emission quota of industries. Not all emission sources could be taken into ac- less than 20 or above 150 kg might indicate unrealistic count for the spatial disaggregation of the NMVOC emission estimates (e.g. in the case of Bulgaria, East emissions. However, of the total 467,000 t yr- 1, about Germany, or Portugal). The expression of anthropo91% (424,000 t yr- 1) were considered. The map show- genic NMVOC emissions as per area quotas (or avering the spatial disaggregation of NMVOC emissions age emission densities) is not useful for comparison is given in Fig. 1. The map shows very clearly the with different countries, as anthropogenic emissions importance of spatially disaggregated emission invent- per definition refer to population related activities and ories. The theoretical average emission density for the not to area related activities. In addition, numbers for whole country of 5.6 t k m - 2 yr- ~ applies by chance average NMVOC emission densities are frequently only for certain areas. The actual emission densities misleading, because actual emission densities are very range from 0 in many uninhabited areas up to more heterogeneous within one country. than 700 t kin- 2 yr- 1 along major motorways. In The spatial disaggregation of overall emission urban areas, e.g. in the capital Vienna, the typical inventories is necessary to sufficiently assess the

10

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Anthropogenic NMVOC in Austria

2589

Table 6. Comparison of estimates of NMVOC emissions in the years 1985-1988 in various countries. Emission data for Austria from this study. Emission data for Hungary from Molnar (1990). All other emission data from UNECE (1991)

Austria Bulgaria Canada Denmark Finland France Germany (East) Germany (West) Greece Hungary Ireland Italy Luxembourg Netherlands Norway Poland Portugal Sweden Switzerland U.K. U.S.A.

Total emissions ( 106 kg yr- 1)

Per capita emissions

467 2594 1783 184 181 2874 140 2460 657 439 140 1535 13 480 188 1651 156 460 339 1816 19,400

59 291 69 35 36 52 8 40 66 41 40 27 33 33 45 43 15 55 51 32 79

emission problems and to identify areas of specific problems. The method of the spatial disaggregation of emission inventories with the use of advanced geographic information systems as it is described here, is an effective tool for the creation of regionalized emission inventories. Once the geographical database for the major emission generating activities is established, it can be used not only for NMVOC, but also for other nationwide emission inventories with similar source characteristics. Compared to the often-used bottom-up method, it has many advantages. First of all, the estimation of overall nationwide emission data is more accurate than the estimation of predefined subunits, for which important data are usually lacking. Overall nationwide emission data can then be regionally analysed with only a few relevant statistical reference data, which are usually easily obtainable. The top-down system demonstrated here facilitates the updating of regional distribution, as soon as new data become available. Similarly, the regional effects of various control scenarios can be calculated and analysed. Acknowledoement--This study was partly funded by the Austrian Federal Ministry of Science and Research and by the Austrian Federal Ministry of Environment, Youth and Family Affairs. The authors gratefully acknowledge the Austrian Federal Environmental Agency for providing census data and the data on settlement centerpoints.

REFERENCES BEV (1986) Topographische Karte 1:500,000 (Topographic map of Austria). Austrian Federal Bureau of Survey, Vienna.

(kg yr- 1)

BFU (1986) Schadstoffemissionendes privaten Strassenverkehrs 1950-2000 (Pollutant emissions of private road traffic 1950-2000). Swiss Federal Agency for Environmental Protection, report No. 55, Berne. Black F. (1989) Motor vehicles as sources of compounds important to tropospheric and stratospheric ozone. In Atmospheric Ozone Research and its Policy Implications (edited by Schneider T. et al.), Studies in Environmental Science No. 35. Elsevier, Amsterdam. BMHGI (1984) Energiebericht 1984(Austrian energy report 1984). Federal Ministry of Economic Affairs, Vienna. Bocola W. and Cirillo M. C. (1987) Air pollutant emissions by combustion processes in Italy. ENEA report No. ENEA-RT/STUDI/87/4. Bouscaren R., Frank R. and Veldt C. (1987) Hydrocarbons. Identification of air quality problems in member states of the European Communities.Commission of the European Communities, report No. EUR 10646, Luxembourg. Braddock J. N., Gabele P. A. and Lemmon T. J. (1986) Factors influencingthe composition and quantity of passenger car refueling emissions, part 1. In International Fuels and Lubricants MeetinO and Exposition. Philadelphia PA, 6-9 October 1986. SAE Technical Paper Series 861558 Society of Automotive Engineers, Warrendale, Pennsylvania. Brice K.A. and Derwent R. G. (1978)Emissionsinventory for hydrocarbons in the United Kingdom. Atmospheric Environment 12, 2045-2054. Concawe (1986) Volatile organic compound emissions: an inventory for Western Europe. Concawe. Report No. 2/86, The Hague. Concawe (1987) An investigation into evaporative hydrocarbon emissions from European vehicles. Concawe. Report No. 87/60, The Hague. Derwent R. and Hov e. (1979) Computer modelling studies of photochemical air pollution formation in North West Europe. U.K. Atomic Energy Research Establishment Harwell. Report No. R-9434, Harwell. DoE (1987) Ozone in the United Kinodo~ United Kingdom Photochemical Oxidants Review Report, United Kingdom Department of Environment, South Rnislip.

2590

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Fishman J. and Crutzen P. J. (1978) The origin of ozone in the troposphere. Nature 274, 855-858. Fox D. L. (1986) The transformation of pollutants. In Air Pollution Vol. VI. Academic Press, New York. Friedrich R., Obermeier A. and Voss A. (1987) Emissionskataster ftr flfichtige organisehe Verbindungen (Emission inventory for volatile organic compounds). Kernforschungszentrum Karlsruhe. Report No. KfK-PEF 22, Karlsruhe. God Ch. and Mugrauer F. (1987) Umweltbelastuno durch kleine Einzelfeuerungen f~r feste Brennstoffe (Environmental effects of small domestic stoves for solid fuels). Institute for Energy Economics, University of Leoben, Austria. Grennfelt P., Saltbones J. and Schjoldager J. (1988) Oxidant data collection in OECD Europe 1985-87. Report on ozone, nitrogen dioxide, and peroxyacetyl nitrate. Norwegian Institute for Air Research. Report No. NILU-31, Lillestrom. Grosjean D. and Fang K. (1984) Hydrocarbons and carbonyls in Los Angeles air. J. Air Pollut. Control Ass. 34(5), 537-543. Hackl A. and Vitovec W. (1990) Die Kohlenwasserstoffemissionen der Mineraltlkette in ~)sterreich 1988 (Hydrocarbon emissions from the petroleum sector in Austria for 1988). OMV AG, Vienna. Jost D. (1987) Die neue TA-Luft (New technical standards for clean air). WEKA Publ., Kissing. Matsumura I. (1974) Evaporation loss of hydrocarbon in handling petroleum. Bull. Jap. Petrol. Inst. 16(2), 132-139. Molnar A. (1990) Estimation of volatile organic compounds (VOC) for Hungary. Atmospheric Environment 24A, 2855-2860. Mueller K. J. and Alfons G. (1986). Schadstoffemissionen des gewerblichen Fiugverkehrs in Osterreich (Pollutant emissions of commercial air traffic in Austria). Technical University Vienna, Institute of Steam and Gas Turbines, Communication Series. Report No. 12/1986, Vienna. Nelson P. F. and Quigley S. M. 0984) The hydrocarbon composition of exhaust emitted from gasoline fuelled vehicles. Atmospheric Environment 18, 79-87. Nelson P. F., Quigley S. M. and Smith M. Y. (1983) Sources of atmospheric hydrocarbons in Sydney--a quantitative determination using a source reconciliation technique. Atmospheric Environment 17, 439-449. Obermeier A., Friedrich R., John C. and Vo[3 A, (1991) Zeitlicher Verlauf and r~iumiiche Verteilung der Emissionen yon flfichtigen organischen Verbindungen und Kohlenmonoxid in Baden-Wfirttemberg (Temporal trend and spatial distribution of the emissions of volatile organic compounds and carbon monoxide in Baden-Wfirttemberg). Kernforschungszentrum Karlsruhe. Report No. KfK-PEF 78, Karlsruhe.

OeAW (1960) Atlas der Republik ~)sterreich. Karte Landnut. zung (Austrian Land Use Map). Austria Academy of Science, Vienna. OECD (1986) Control of major air pollutants--a status report. Report No. OECD-ENV/AIR/86.3, Paris. OECD (1987) Environmental Data Compendium 1987. OECD, Paris. OeSTAT (1981) GroBz~,hlung 1981--Detailergebnisse (Census 1981). Austrian Statistical Service, Vienna. OeSTAT (1985) StraBenverkehrsz~hlun0 1985 (Traffic census 1985). Austrian Statistical Service, Vienna. OeSTAT (1988) ~)sterreichs Aassenhandel 1988 (Foreign trade 1988). Austrian Statistical Service, Vienna. OeSTAT (1989) Bestandsstatistik der Kraftfahrzeuoe in Osterreich 1989 (Motor vehicle registration statistics 1989). Austrian Statistical Service, Vienna. Onz K. et al. (1987) Umweltbericht 1987. Teil 1: Luft (Report on the state of the environment, part 1: air). Austrian Federal Institute of Health, Vienna 1987. Orthofer R. (1991): Absch/itzung der Methan.Emissionen in Osterreich (Estimation of methane emissions in Austria). Austrian Research Centre Seibersdorf. Report No. OEFZS-A-1965, Seibersdorf. Samaras Z. and Zierock K. H. (1989) Summary report of the CORINAIR working group on emission factors for calculating 1985 emissions from road trat~c--Vol. 2: COPERT (Computer program to calculate emissions from road traffic). Economic European Community, DG XI. Brussels. Stockton M. B. and Steiling J. H. E. (1987) Criteria pollutant emission factors for the 1985 NAPAP emissions inventory. US Environmental Protection Agency. Report No. EPA600/7-87-015. UBA (1981) Luftreinhaltun 0 '81, Entwickluno-Stand- Tendenzen (Clean Air '81, development-status-trends) (edited by the German Federal Environmental Protection Agency). Erich Sehmidt, Berlin. UBA (1989) Digitized Settlement-centres from the Topographic Map of Austria 1: 500,000. Austrian Federal Environmental Agency, Vienna. UNECE (1990) Emissions from Volatile Organic Compounds from Stationary Sources and Possibilities of their Control. United Nations Economic Commission for Europe, VOC Task Force, Karlsruhe. UNECE (1991) Strategies and policies for air pollution abatement. United Nations Commission for Europe. Report No. ECE/EB.AIR/27. United Nations, New York. USEPA (1980) Volatile organic compound (VOC) species data manual. 2nd edition. US Environmental Protection Agency. Report No. EPA-450/4-80-015. Veldt C. (1991) Development of EMEP and CORINAIR emission factors and species profiles for emissions of organic compounds. Draft report 91-299, TNO, Apeldoorn, The Netherlands, September 1991.