Exposure of the Austrian Population to Pm10 in 1996

Jürgen SCHNEIDER

Reports

R-163

EXPOSURE OF THE AUSTRIAN POPULATION TO PM10 IN 1996

Wien/Vienna, 1999 Author Jürgen Schneider (Federal Environment Agency – Austria) with contributions from an internal Report prepared by Wolfgang Loibl, Austrian Research Center, Seibersdorf Rudolf Orthofer, Austrian Research Center, Seibersdorf Project Team Austria Baumann Ruth (Project Leader), Federal Environment Agency – Austria Loibl Wolfgang, Austrian Research Center, Seibersdorf Orthofer Rudolf, Austrian Research Center, Seibersdorf Schneider Juergen, Federal Environment Agency – Austria Spangl Wolfgang, Federal Environment Agency – Austria

The work conducted within this study was part of a tri-lateral research project on the HEALTH COSTS DUE TO ROAD TRAFFIC-RELATED AIR POLLUTION prepared for the WHO Ministerial Conference for Environment and Health in London in June 1999.

Acknowledgement The author would like to express his gratitude to • Paul Filliger (Swiss Agency for the Environment, Forests and Landscape, Berne, Switzerland) for providing valuable help and input throughout the work, • Manfred Ritter (Federal Environment Agency - Austria) for providing Austrian CORINAIR emission data, • the Offices of the Federal Governments of the Federal Provinces in Austria for providing air quality monitoring data, • the Central Statistical Office in Austria for providing data on population density, • the whole tri-lateral project team for good collaboration within the project.

Impressum/Imprint Medieninhaber und Herausgeber: Umweltbundesamt GmbH (Federal Environment Agency Ltd.), Published by: Spittelauer Lände 5, A-1090 Wien (Vienna), Austria Druck/Printed by: Riegelnik

Contents

Summary ...... 5

Background of the project...... 5 Objective ...... 5 Methodology ...... 5 Results ...... 6 Discussion...... 7 Technical Recommendations ...... 7

1 Introduction ...... 9

1.1 Context...... 9 1.2 Objective ...... 9 1.3 Mapping of Air Pollution Exposure...... 12 1.3.1 Selection of a pollution indicator...... 12 1.3.2 Aim and Structure of this Report...... 13 1.3.3 The ‘at least approach’ ...... 13

2 General Remarks on Particulate Matter in Ambient Air...... 14

2.1 Size distribution of particles in ambient air...... 14 2.2 Measurement techniques for PM...... 15 2.3 Chemical composition of PM...... 17 2.4 Major sources of PM in the atmosphere ...... 18

3 Concept of this study...... 21

4 Results...... 22

4.1 Modelling of NOx levels...... 23 4.1.1 Establishment of a spatial disaggregated NOx emission inventory for Austria ...... 23 4.1.2 Modelling of NOx dispersion...... 32 4.1.3 Maps of NOx ambient air concentrations ...... 38 4.1.4 Validation with measurement results...... 42

4.2 Calculation of NO2 levels...... 47 4.3 Modelling of TSP/PM10 levels...... 49 4.3.1 Measurement data of TSP...... 49 4.3.2 Estimation of TSP regional background concentrations...... 51 4.3.3 Modelling of local induced PM10/TSP concentrations using NOx as surrogate ...... 54 4.3.4 Contributions to TSP levels ...... 57 4.4 Comparison of modelled TSP to measurement data ...... 59

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 4 Exposure of the Austrian Population to PM10 in 1996

5 Exposure of the population...... 62

5.1 Population density data...... 62

5.2 Exposure to NO2 and PM10 ...... 63

6 Uncertainties ...... 65

6.1 Uncertainties of input data...... 65 6.2 Uncertainties of model assumptions...... 66

7 Conclusions ...... 68

7.1 General Conclusions...... 68 7.2 Research Recommendations ...... 69

8 Literature...... 70

9 Glossary...... 73

Annex I Emissions database and area allocation ...... 75 Annex II Input data for calculation of traffic emissions with the ARCS model ...... 84 Annex III Wind data used for Gaussian pollutant dispersion from point sources...... 85 Annex IV GIS neighbourhood functions...... 86 Annex V NOx concentrations at Austrian measurement sites...... 87 Annex VI Equipment of Austrian TSP measurement sites ...... 91 Annex VII Classification of the environment of Austrian TSP measurement sites ...... 95 Annex VIII Classification scheme of measurement sites ...... 99 Annex IX TSP concentrations at Austrian measurement sites ...... 100

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Summary

Background of the project In preparation to the Transport, Environment and Health segment of the Ministerial Confer- ence on Environment and Health in London in June 1999, a tri-lateral project was carried out by Austria, France and Switzerland. The project was launched to assess the health costs of road traffic-related air pollution in the three countries using a common methodological framework and was based on an interdisciplinary co-operation in the fields of air pollution, epidemiology and economy. The tasks of the three domains can be summarised as follows: a) Air pollution: Evaluation of the (road-traffic related) exposure b) Epidemiology: Evaluation of the exposure-response relationship between air pollution and health impacts1 c) Economy: Evaluation of the road-traffic related health impacts and their monetarisation2 This report presents the results of the air pollution part of Austria.

Objective The international working group selected PM10 as air pollution indicator. The main task of the air pollution part was to estimate the population exposure to the annual mean of PM10 (particulate matter with an aerodynamic diameter of less than 10 µm). The population exposure was calculated for total PM10 as well as for the PM10 concentration caused by road traffic.

Methodology A general methodological framework was defined. This involved four main steps : a) collection and analysis of the available data on ambient concentration of particulate matter; b) PM10 mapping by empirical dispersion models or statistical methods; c) estimation of the road traffic-related part of PM10; and d) calculation of the population exposure by superposing the PM10 map with a population distribution map. Currently, in agreement with national legislation particulate matter in Austria is measured as Total Suspended Particulates (TSP) at more than 110 sites, whereas PM10 measurements are not yet available. Therefore, spatial TSP concentrations were modelled and used to estimate PM10 levels. As a working hypothesis, it was assumed that ambient air TSP (and subsequently PM10) levels can be attributed to the contribution of local sources and regional background concentrations. They were modelled separately. For the modelling of local contributions the starting point was the availability of a spatially disaggregated emission inventory for nitrogen oxides (NOx). An empirical dispersion model was established for NOx whose results could be compared with an extended network of NOx monitors. The spatial distribution of NOx was converted into PM10/TSP concentrations, using source specific TSP/NOx conversion factors. The regional background TSP levels, which even contribute significantly to total TSP

1 Künzli N., Kaiser R., Medina S., Studnicka M., Oberfeld G., Horak F. (1999), Health Costs due to Road Traffic- related Air Pollution, Air Pollution Attributable Cases. 2 Sommer H., Seethaler R., Channel O., Herry M., Vergnaud J.-C. (1999), Health Costs due to Road Traffic- related Air Pollution, Economic Evaluation. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 6 Exposure of the Austrian Population to PM10 in 1996 concentrations in urban areas, were estimated from measurements and superimposed with the contributions from local sources. These results were compared to measured TSP data. This comparison showed that the modelling results are in reasonable agreement with the measurement data. On average, the TSP mapping showed a tendency towards underestimation. This is in line with the selected 'at-least' approach of the overall project. Finally, PM10 concentrations were derived from TSP values by applying source specific TSP/PM10 conversion factors. The model is able to provide an estimate of the traffic-related part of PM10 concentration.

Results The main results are summarised in the following table and figure:

Table S1: Population weighted PM10 averages PM10 concentration in µg/m3 Total PM10 26.0 PM10 without share attributable to road traffic 18.0 PM10 due to road traffic 8.0

Figure S1: PM10 population exposure; Total exposure (blue) and exposure without road traffic share (red)

25 Total PM10

PM10 population 15 exposure without share attributable to road traffic 10

0 <5 5 - <10 10 - 15 - 20 - 25 - 30 - 35 - 40 - 45 - >=50 <15 <20 <25 <30 <35 <40 <45 <50

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It can be seen that road traffic contributes strongly to PM10 concentrations. In city centres, the relative contribution of road traffic to total PM10 is higher than in rural areas. This exposure can be linked to significant adverse health effects.

Discussion During the work on this study several difficulties had to be overcome. The most serious was a lack of PM10 measurement data. Therefore, many assumptions and estimates had to be made to calculate population exposure. Anyhow, the results for total PM10 exposure as well as for the road traffic fraction are reasonable and can be regarded as the best available estimate on the basis of the now available measurements and emission data. Further improvements of the results will be possible once improved input data are available (under the forthcoming EU regulations a PM10 monitoring network will be established in the near future). Regardless of the uncertainty linked to the results, this study shows that a considerable part of the Austrian population is likely to be exposed to high PM10 levels. These levels can be linked to considerable health effects (see Sommer et al., 1999).

Technical Recommendations In building up PM10 measuring networks, it will be crucial that the PM10 samplers used are compatible with the new European reference method, thus ensuring full comparability with other countries. Besides PM10, other indicators of particulate matter such as PM2.5 and even smaller fractions should be included as well. Additionally, information on the chemical composition of particulate matter is crucial to determine the origin of particulate matter in ambient air. The establishment of reliable PM10 emission inventories is also necessary. Little is known about PM10 emissions from different sources, e.g. road dust re-suspension, construction activities etc. Special attention has to be given to these sources to get reliable emission factors. Receptor studies which try to apportion the sources by using measurement data on the chemical composition of PM10 are a necessary supplement to the emission inventories. Such studies should be initiated.

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999)

Exposure of the Austrian Population to PM10 in 1996 9

1 INTRODUCTION

1.1 Context

In addition to its positive impact on the growth and prosperity of the national economy and its importance for satisfying our individual needs for mobility, road transport also has adverse effects: accidents, noise, air pollution, harm to health, crop damage, traffic jams, etc. In the last 10 to 20 years an increasing awareness may be observed for these negative effects of transport. Congestion, air pollution and noise affect more and more people. Their impact on health and welfare, the damage to buildings and the natural environment are considerable, just like the material and intangible costs caused by them. These costs are mainly external costs which means that they are not covered by the polluters (the motorists) but that they are imposed on everybody. External costs cause a problem to the economy, as they are not included in the market price which leads to wrong decisions and to a wasting of scarce and vital resources (clean air, silence, clean water, etc.). Motorists behave as if those costs do not exist, since they have not to pay for them. By including the external costs, such trips may have produced higher total costs than the total benefit. As a consequence, many trips would have been avoided if all the external costs had to be considered by the driver. In order to stop the wasting of scarce resources, the government has to take action and put a price on clean air and other environmental ‘products’. As a result, negative impacts of road transport have to be paid for by the polluter. The usual terminology for this process is ‘inter- nalisation of externalities’. A condition for such an environmental and transport policy is a knowledge about the negative impacts of road traffic and their monetary quantification. With the present study, an important part of the external traffic-related costs, namely the negative impacts of road-traffic related air pollution on human health, is evaluated and quantified in monetary terms.

1.2 Objective

In order to quantify the road traffic related health costs due to air pollution, Austria, France and Switzerland have co-operated in a tri-lateral research project. One objective is the choice of a common methodological framework and the evaluation of results that are comparable for the three countries. Of course, within the common methodological framework, some specific features of each country (data availability, health system, etc.) must be considered. The results of this co-operation provide an input for the WHO Ministerial Conference in June 19993. The research project is based on an interdisciplinary co-operation in the fields of air pollution, epidemiology and economy. The tasks of the three domains may be summarised as follows: a) Air pollution: Evaluation of the (road-traffic related) exposure For the three countries Austria, France and Switzerland, the exposure of the residential population had to be assessed. The result had to present a fine register that describes to which level of concentration the number of persons living in each geographic unit are exposed. It had to be considered that the emission sources is not only transport but other sources as well, such as industry and households.

3 Third WHO Ministerial Conference of Environment & Health, London, 16-18 June 1999. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 10 Exposure of the Austrian Population to PM10 in 1996 b) Epidemiology: Evaluation of the exposure-response relationship between air pollution and health impacts The relationship between air pollution and health had to be assessed. Thereby it had to be shown, to which extent different levels of air pollution affect a population’s morbidity and mortality. This evaluation had to be based on the latest scientific state of the art presented in the epidemiologic literature. c) Economy: Evaluation of the road-traffic related health impacts and their monetarisation By combining the exposure-response relationship with the exposure to PM10 in each country, the impacts of traffic related air pollution on human health had to be quantified (number and type of additional cases of morbidity, number of additional cases of prema- ture death). With adequate methods, these health effects finally had to be valued in monetary terms.

The present project is building on the former research in Switzerland4. In the framework of this trilateral co-operation, several methodological questions were further discussed within the international and interdisciplinary groups and have partly resulted in new approaches. Furthermore, the most recent scientific results have been adopted and in addition, several methodological calculation steps have been modified in order to make the common methodological framework applicable for the evaluation of health costs in other countries also. A short overview of the study design is given in figure 1.1. An important condition of the study was the demand to present the results at the 3rd Ministe- rial Conference on Transport, Environment and Health in London (June 1999). With the project starting in early 1998, a very strict time-table resulted from this condition and the selected methods had to consider this.

4 ECOPLAN (1996), Monetarization of the external health costs attributable to transport; Künzli N. et al., Teilbericht Epidemiologie: Synthesebericht Monetarisierung der verkehrsbedingten Gesundheitskosten; Künzli N. et al (1997), Luftverschmutzung in der Schweiz - Quantifizierung gesundheitlicher Effekte unter Verwendung epidemiologischer Daten; Wanner H.U. et al. 1996: Monetarisierung der verkehrsbedingten externen Gesund- heitskosten, Teilbericht Lufthygiene. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 11

Figure 1.1: Study design

Air pollution map Population cadastre Air Pollution

Exposure of the population

number of cases Exposure-Response relationship between air pollution and number of mortality and morbidity cases

PM10 concentration 3 10 20 30 40 50 60 in µg/m Epidemiology

Number of mortality and morbidity cases

Health costs per case Economy

External health costs due to air pollution

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1.3 Mapping of Air Pollution Exposure

This report presents the principles, methods and results of the air pollution section and summarises the method and main findings for Austria. A more general descriptions including the results concerning population exposure from the co- operating partners France and Switzerland can be found in Filliger et al. (1999). Künzli et al. (1999) report on the cases of morbidity and mortality attributable to air pollution. An economic valuation of the health outcome is given in Sommer et al. (1999).

1.3.1 Selection of a pollution indicator

In the air pollution section, the main task is to estimate the exposure of the population to a selected air pollution indicator. The air pollution indicator was selected by the international working group. Air pollution is a mixture of many known and unknown substances. The basis for any impact assessment is the establishment of quantitative dose response functions, which link exposure to defined health outcomes. Such functions can be derived from epidemiologic studies. In air pollution epidemiology, the exposure has to be clearly defined. The usual approach consists in the measurement of at least one specific pollutant, considered to be an indicator of the complex mixture. The closer the indicator of the pollutant correlates with the true health relevant aspect(s) of air pollution, the better the indicator will be associated with the health effect. Furthermore, for some pollutants such as ozone or sulphur oxides, it has been clearly shown to be a direct cause of health effects at concentrations observed in real ambient air. Therefore, the total impact of air pollution on health may be considered the sum of 1) all independent effects of specific pollutants, 2) the effects of mixtures and 3) the additional effects due to interactions between pollutants. Epidemiologic studies mostly report the associations of some pollutant indicator with health, whereas the independent effects are not published. Furthermore, many air pollutants are highly correlated in the ambient air due to their common sources. As a consequence, impact assessment has to select one or few indicators of air pollution. To simply sum up the pollutant specific impact would be a misconception, leading to overestimation of the overall impact. From a health effects point of view, ‘general ambient air pollution’ (characterised by pollutants such as PM2.5, PM10, TSP, NO2, SO2 and others) may be distinguished from the additional air quality problem observed in summer only, i.e., oxidant pollution. Within this study it was decided to estimate the impact for one single indicator. The impact assessment ought to rely on indicators of air pollution for which epidemiological evidence is strong and for which effect estimates are available. The epidemiologist involved in the international group favoured the selection of PM10 as indicator. For particulate matter up to 10 µm in diameter (PM10), a broad and sound epidemiological literature exists to extract effect estimates (for a summary see: WHO, 1999). Contrary to larger particles, PM10 is able to enter the pulmonary tract, where it interferes with the defence system of the bronchial tree, and can cause chronic inflammation. For a further discussion of the selection of air pollution indicator in this study, see the Technical Report on Epidemiology (Künzli et al., 1999). Beside the question of the selection of an air pollution indicator, it was agreed that each country selects 1996 as reference year for the exposure assessment. As a consequence, all data refer to 1996. If no data of that particular year were available, data from different years were taken. Such exceptions are indicated in the text.

PM10 was used as an indicator of air pollution for this study 1996 was used as reference year

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1.3.2 Aim and Structure of this Report

As stated previously, this report presents the principles, methods and results of the population exposure to PM10 in Austria. Population exposure is given for total (from all sources) PM10 as well as for PM10 excluding road traffic.

Chapter 2 provides some basic information on PM10. Chapter 3 gives an overview on the general methodological principles chosen within this study. In Chapters 4 and 5 results are presented and discussed in detail. Uncertainties are discussed in Chapter 6 and some very general conclusions are given in Chapter 7. Chapters 8 and 9 contain the literature and a glossary, respectively.

1.3.3 The ‘at least approach’

The overall project involves numerous assumptions and decisions in the field of air pollution, epidemiology and economics. It was therefore decided to follow an ‘at least’ approach, that is to choose methodological assumptions in such a way that the impact and resulting costs are ‘at least’ attributable to air pollution. In this report the approach is specified as follows: The main goal in air pollution modelling is to simulate the real situation as nearly as possible. The ‘at least’ approach is only used if input data is completely lacking and has to be estimated based on expert knowledge. ‘At least’ assumptions are then chosen5. This means that there is a tendency for the results to be underestimated rather than overestimated. In the context of the air pollution part, this can be called a conservative estimate.

5 In the field of air pollution, we would prefer to speak of a ‘best estimate’, which is the best available estimate based on the available input and existing knowledge under the conditions imposed on the study. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 14 Exposure of the Austrian Population to PM10 in 1996

2 GENERAL REMARKS ON PARTICULATE MATTER IN AMBIENT AIR

The aim of the project was, as described in the introductory chapter, to estimate the exposure of the Austrian population to PM10 and the traffic related part of this exposure. This implies that not only information on the spatial distribution of PM10 concentrations is needed, but quantitative also estimates on the sources of PM10. Such an estimation is quite complex and challenging because PM10 is not an uniform pollutant, but a complex, heterogeneous mixture of airborne particles of different size, composition, chemical and physical properties etc. This complexity does not only lead to problems in the measurement of PM10 (see next section), but also in the spatial interpolation of measured values and the attribution of levels to different sources. To illustrate these difficulties, it is necessary to give some background information on particulate matter (PM).

2.1 Size distribution of particles in ambient air

A key property of PM, which is believed to have major influence on its biological effectiveness, is the size distribution. Figure 2.1 provides information on an ideal size distribution of PM found in ambient air.

Figure 2.1: Mass distribution of PM depending on the particle size

Three modes can be distinguished. A nucleation mode (size: 0,01 µm – 0,1 µm), which consists mainly of particles either primary emitted or from gas to particle conversions; an accumulation mode (size 0,1 µm – 2,5 µm) and a coarse mode (> 2,5 µm). The relative importance of the coarse mode seems to decline in the past years due to measures implemented to reduce the emission of particles of that size. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 15

The size is of primary interest because it determines how far particles can penetrate into the respiratory system. Table 2.1 provides a summary of important definitions.

Table 2.1: Classification of PM fractions according to their ability to enter the respiratory system Name Description inhalable can enter extrathoratic respiratory system thoracic can penetrate the larynx and enter the thoracic region respirable can penetrate into alveolar region

Specific sampling devices were developed to mimic the impactation characteristics of the thoracic region. Generally, they are designed to have a 50 % sampling efficiency at an aerodynamic diameter of 10 µm. The corresponding fraction is called PM10. This is illustrated by figure 2.2 (ISO, 1995).

Figure 2.2: The inhalable, thoracic and respirable convention according to ISO (1995).

It can be seen that the sampling efficiency is very high at low diameters, 50 % at 10 µm and then rapidly decreasing towards bigger particles.

2.2 Measurement techniques for PM

Today a great variety of different measurement techniques are applied to get information on PM concentrations in ambient air and the chemical and physical characteristics of this pollutant. In general, the measurement of PM comprises often two independent steps: Sampling and the analysis.

Sampling During sampling, a more or less well defined fraction of the suspended particulate matter in air is impacted on a selected medium (often on filters). Table 2.2 summarises the most common indicators determined in ambient air measurements.

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Table 2.2: Definition of different PM sampling inlets Name Description TSP (Total suspended Particulates) PM sampled with conventional sample inlets PM10 Sampling adapted to mimic the impactation characteristics of the thoratic region. 50 % sampling efficiency at an aerodynamic diameter of 10 µm. PM2,5 50 % sampling efficiency at an aerodynamic diameter of 2,5 µm

For PM10 an European standard has been established (EN, 1999). Within this standard, some reference methods and the determination of equivalence measurement methods are described. Anyhow, no prescription on the analysis is given. For PM2,5, there is currently no CEN standard available.

Analysis In a subsequent step, certain properties like the mass or the chemical composition of the sampled material can be determined. Some of the compounds of particulate matter are semivolatile under ambient air conditions. This means that such compounds can either condense from the vapour phase or evaporate from the liquid/solid phase. Such substances include water, VOC and ammonium nitrate (NH4)NO3. The extent of volatilisation depends on the temperature, the vapour pressure of the substance and its concentration in the different phases. It is quite evident that the process of volatilisation can crucially influence the result of the mass determination. The simplest way to determine the mass of the filter is by weighting it. Anyhow, the disadvantage of this procedure is that the results cannot be obtained immediately. To make the results comparable, it is necessary to condition filters under well defined and controlled circumstances for a certain period of time. Anyhow, it has to be emphasised that this procedure will change the original composition of the sample, e.g. the water content. Alternatively, some measurement techniques have been developed to obtain data continuously and online. The most common techniques are beta attenuation and TEOM sampler. The beta attenuation is using the absorption of beta–rays from an internal source by the sample collected on filter material. The change in absorption is used to estimate the mass of the material sampled on the filter. The Tapered Element Oscillating Microbalance (TEOM) consists of an oscillating tapered tube with a filter on its free end. The change in mass due to aerosol collected on the filter gives a change in the oscillation frequency which is translated into a mass difference. Both methods interfere in principle strongly with the humidity of the sampled air. Therefore, beta attenuation instruments have usually heated air inlets. In TEOM devices, the filter is usually heated. Anyhow, this reduces not only the water content of the sample (and makes therefore direct comparisons with measurements using filters difficult), but can also lead to evaporation of volatile components like certain organic compounds and ammonium nitrate. The quantitative extent of this process is strongly influenced by the temperature applied.

There is a large body of evidence that the method used for the determination of even one single indicator of PM, as PM10, influences the results. Several parallel measurements using automated monitors and gravimetric determination of filter mass have indicated that the differences in the results may be quite significant (EPA, 1996; UK Airborne Particle Expert Group, 1998).

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As an example, measurement data obtained by beta attenuation in the Air Quality monitoring network in Berlin are believed to give 20 % lower results than measurements done using gravimetric determination of filter mass. For TEOM, this can be illustrated by field comparisons to PM10 concentrations measured by a gravimetric method (Partisol Air Sampler) in the UK. Table 2.3 provides the ratio of parallel measurement results (UK Airborne Particle Expert Group, 1998).

Figure 2.3: Correlation between measurement data obtained by TEOM and a gravimetric method for different sites in the UK. Site Correlation Correlation coefficient Ribble Valley Grav = 1,15*TEOM – 0,05 0,85 South Yorkshire Grav = 1,3*TEOM 0,8 Cornwall Grav = 1,55*TEOM – 6,09 0,89 London Grav = 1,2*TEOM – 3,1 0,85

2.3 Chemical composition of PM

As stated above, PM is a complex mixture of various particles of different sizes, origin and chemical composition. All these factors can vary within time and space and within different fractions. The main components which contribute to PM mass are: • inorganic ions (mainly sulphate, nitrate and ammonium, but also sea salt etc.) • crustal elements (often as oxides) • elemental carbon • organic compounds • biogenic particles (like pollen, plant debris) and • water

Figure 2.3 shows a typical example of the contribution of different chemical components to the total mass in rural areas of North Western Europe.

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Figure 2.3: Idealised example of contributions to aerosol mass (PM10) in rural areas in Europe

See-salt aerosols

Biogenic aerosols Ammoniumsulphate

Particles from photochemical processes

Organic compounds from combustion

Ammoniumnitrate

Elemental carbon

Primary particles with inorganic chemical composition

2.4 Major sources of PM in the atmosphere In general, primary and secondary particulate matter can be distinguished. Primary components are directly emitted into the air, while secondary particulate matter is formed from gaseous precursors by various processes in the atmosphere.

Primary sources of PM include • aerosols from combustion processes • process emissions (from industry, mining, etc., but also brake wear) • dust (including road dust; fugitive dust; dust from agriculture and forestry) • erosion • sea spray • bioaerosols (like pollen, spores) • organic debris (fragments from plants or insects) and • re-suspension Emissions of particulate matter can either be of anthropogenic origin or result from natural processes.

Secondary aerosols Secondary aerosols can be formed in the atmosphere from certain precursor substances; inorganic particulates mainly originate from precursors NOx, SO2 and NH3 (forming nitrate, sulphate and ammonium). Organic compounds (of anthropogenic and biogenic origin) can be oxidised and undergo gas to particle conversions. Additionally, certain semivolatile organic compounds can condense on particle surfaces.

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Estimations of particle emissions in Austria Currently, there is no up to date information on the emission of PM in Austria. Anyhow, there are some older crude estimations on the emissions of PM in the year of 1991 available (UBA, 1993).

Table 2.4: Estimation of emissions of particulate matter of undefined aerodynamic diameter in 1990 in Austria (UBA, 1993). Source Amount in 100 t Energy and transformation 12 Industry (combustion and process) 110 Non-industrial combustion 127 Traffic 130 Sum 379

Additional information is available on the tailpipe emissions of road traffic from diesel engines (UBA, 1998d). Figure 2.4 and table 2.5 provide information on the trend of these emissions in recent years.

Figure 2.4:Trend of tail-pipe emissions from diesel vehicles in Austria

9 8 7 Cars 6 5 Light duty veh. 4 3 Heavy duty veh. 2 1 Sum 0 1980 1982 1984 1986 1988 1990 1992 1994

Table 2.5: Trend of tail-pipe emissions and emission factors from diesel vehicles in Austria (UBA, 1998d) Source emissions [1000 t] 1980 1985 1988 1990 1991 1992 1993 1994 1995 Diesel cars 0,53 0,66 1,14 1,30 1,42 1,42 1,46 1,51 1,55 light duty vehicles <3,5 t 0,32 0,48 0,75 0,83 0,92 0,95 0,98 1,09 1,01 heavy duty vehicles >3,5t buses 7,00 4,39 3,43 2,99 2,65 2,52 2,43 2,48 2,17 sum 7,85 5,52 5,32 5,11 4,99 4,88 4,87 5,07 4,74 Source emission factors for PM [g/km] 1980 1985 1988 1990 1991 1992 1993 1994 1995 Diesel cars 0,41 0,30 0,22 0,17 0,17 0,16 0,15 0,13 0,13 light duty vehicles <3,5 t 0,76 0,68 0,64 0,50 0,44 0,40 0,37 0,34 0,32 heavy duty >3,5t and buses 1,58 1,11 0,83 0,66 0,52 0,47 0,44 0,40 0,37

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It should be emphasised again that these calculations do only include tailpipe emissions and not ¾ emissions from brake and tyre wear ¾ asphalt wear and road dust ¾ road salting ¾ re-suspension and ¾ particles formed from precursors emitted from road traffic Estimations performed within several international studies indicate that these emissions might readily exceed tail-pipe emissions. E.g., within the course of this tri-lateral study Switzerland established a PM10 emission inventory showing that tailpipe emissions account for only 16% of total traffic emissions.

Estimation of PM10 emissions for 1990 and 1993 Berdowski et al. (1996) have performed some estimations on the emissions of PM in several European countries including Austria. Table 2.6 summarises the most important results.

Table 2.6: Estimation of PM10 emissions in Austria 1990 in t 1993 in t Source PM10 PM2,5 PM0,1 PM10 PM2,5 PM0,1 total stationary 12000 7600 890 13000 8100 920 combustion Total power generation 1300 850 100 1300 800 79 Total industrial 2400 1500 180 2400 1500 180 combustion Total small combustion 8400 5200 610 9700 5800 660 sources Transport 9100 6000 1800 11000 7200 2200 road transport 8600 5700 1700 10000 6800 2000 Total process emissions 8600 4500 1300 7500 3900 1000 Storage and handling 350 0,51 0 370 0,54 0 Production processes 8200 4500 1300 7200 3900 1000 Agriculture 7700 3700 0 9100 4400 0 Waste processing plants 7,2 5 1,1 7,2 5 1,1 Sum 37000 22000 4000 41000 24000 4100

This numbers might be taken as indication of the order of magnitude of PM emissions in Austria.

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 21

3 CONCEPT OF THIS STUDY

The development of a strategy to estimate the PM10 exposure of the Austrian population was rather challenging. There were neither measurement data on PM10 available nor any up-to-date information on emissions of particulates6. Due to severe time constraints it was not possible to conduct representative PM10 measurements in Austria or to obtain a reliable emission inventory. Only TSP data from about 120 monitoring sites were available when starting the project (UBA, 1998a). A simple interpolation model starting from measured TSP data was not considered to be sufficient, since interpolation by itself does not give any indication about the sources of ambient air particulate concentrations. Therefore a concept was developed which takes into account different contributions to ambient air concentrations of TSP. Within the model developed, a significant part of the TSP levels is attributed to a spatially variable regional background7, which was mainly derived from TSP measurements. Additionally, it was assumed that there is a part due to local emission sources. The contribution of the local sources is estimated using NOx as a surrogate, since for this pollutant an emission inventory was available and a dispersion model could be adapted. Factors to convert NOx to TSP and further to PM10 levels for different source categories were derived from literature.

The underlying concept is described briefly:

1. A spatially disaggregated emission inventory for NOx for various source categories was developed. 2. Based on this inventory a model for simulating source specific dispersion was developed and validated by NOx measurement data.

3. The spatial distribution of NOx concentrations was converted to TSP concentrations utilising source specific NOx/TSP/PM10 factors (derived from literature and ambient air measurements from appropriate sites). 4. The contribution of the different sources to the TSP/PM10 levels were summed up grid by grid. 5. Regional background concentrations of TSP/PM10 were derived from measurement data and superimposed with source-specific contributions. 6. The results were validated using measurement data for TSP. 7. The exposure of the population was estimated by combining population data with modelled PM10 values. The main steps are summarised in the flow chart in figure 3.1.

6 The emission estimates described in the previous chapters were not regarded as a sufficient basis for the establishment of a spatially disaggregated inventory within the time frame of the project. 7 In this sense, background is defined as the TSP levels not caused by local emissions Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 22 Exposure of the Austrian Population to PM10 in 1996

Figure 3.1: Main steps for calculating the exposure to PM10

data on NOx- TSP- chemical emissions measure- composition ment data

NOx-levels

Assessment of contributions to TSP loads Source-specific NOx-dispersion

Determination of background

NOx-maps

International Source-specific data on interpolation PM10/TSP- ratios

spatial TSP- concentrations Determination of source/location specific factors

spatial PM10 exposure Population distribution

Assessment of Exposure

: Data

: Process

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 23

4 RESULTS

4.1 Modelling of NOx levels

4.1.1 Establishment of a spatial disaggregated NOx emission inventory for Austria

Emissions are generally calculated by multiplying specific emission factors with data on emission generating activities as illustrated in figure 4.1.

Figure 4.1: General scheme of the calculation of emissions.

The establishment of a spatial disaggregated NOx emission inventory was based on the official Austrian CORINAIR emission data (UBA, 1999a) and information on the spatial pattern of emission generating activities. In addition, selected point sources were included and emission data harmonised. The final emission source database produced for this study contains separate tables for the CORINAIR area sources ("CA"), the CORINAIR point sources ("CP"), and other point sources ("OP"). The total emissions in Austria are CA+CP, which is equivalent to (CA-OP)+CP+OP. A full documentation of the emission database is given in Annex I. Table 4.1 provides an overview of the emission database, categorised into emission source groups used for the emission allocation.

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 24 Exposure of the Austrian Population to PM10 in 1996

Table 4.1: Major source categories included in the NOx emission inventory

CORINAIR CORINAIR OTHER TOTAL TOTAL TOTAL

Area Emis Point Emis Point Emis Emis Area Emis Point Emis (CA) (CP) (OP) (CA+CP) (CA-OP) (CP+OP) Description (t) (t) (t) (t) (t) (t)

Agriculture Tractors, soil 13.796 13.796 13.796 Forests Forest soils, forestry machinery 1.104 1.104 1.104

Various population related emissions Railroad, off-road machinery 4.802 4.802 4.802 Residential combustion Fossil solid fuels 2.045 2.045 2.045 Gaseous fuels 4.495 4.495 4.495 Fuel oils 6.092 6.092 6.092

Wood+biogenic fuels 10.755 10.755 10.755 Production & Industry

Production related emissions 9.476 1.290 9.476 8.186 1.290 Brick/cement/ceramics industry 6.403 6.403 6.403

Chemical Industry 4.400 4.400 4.400

Thermal power plants 992 6.875 7.867 992 6.875 Metal industry 5.158 5.101 5.158 57 5.101

Oil refinery 3.494 3.389 3.494 105 3.389

Pulp & paper industry 2.567 1.777 2.567 790 1.777

Wood and timber production 588 588 588 Traffic

Road traffic on highways 34.868 a) 34.868 34.868 Road traffic- rural roads 24.495 a) 24.495 24.495

Urban road traffic 25.129 a) 25.129 25.129 Aircraft take-off/landing cycles 648 648 648 Aircraft cruising 6.879 6.879 6.879 Commercial shipping 1.085 1.085 1.085 Total 169.271 6.875 11.557 176.146 157.714 18.432 a) These CORINAIR numbers for emissions from road traffic were not used for allocation of emissions (see later) The emissions from road traffic were calculated separately for the major road network and for total traffic. Major road network includes freeways ("Autobahnen") and all federal interregional roads outside of city centers ("Bundesstrassen"). Emissions from major roads were calculated using traffic count data for 2.421 sections with a total length of 11.750 km (from 1995) and emission factors for the 1996 vehicle fleet, derived from the Handbook of Emissions Factors (HBEFA 1.1A, "Handbuch der Emissionsfaktoren", UBA 1998e).

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 25

Emissions of the total Austrian motor vehicle population were calculated by a model developed by the ARCS (‚ARCS traffic emission model‘). This model is based on official 1995 statistical data for vehicle registration. The model simulates the motor vehicle fleet and the respective vehicle-kilometers-traveled (VKT) of 11 gasoline and 13 diesel vehicle categories with different emission characteristics. Emissions of Non-Austrian vehicles on Austrian roads are also included. Emission factors were derived from the HBEFA 1.1A. Emission factors were used for the calculation of the overall NOx emissions on all roads. Table 4.2 summarises the results from the road traffic NOx emission calculation. A documentation of the model input parameters is given in Annex II.

Table 4.2: Summary of activities, emission factors and NOx emissions from road traffic in Austria

A. Vehicle-kilometres-travelled (VKT)

"ALL" "NON-A" "HWY" "URB & RUR"

Vehicle Total VKT VKT from VKT on major VKT on minor Registration Non-A Vehcl road network roads & settl (Number) (Mio km) (% of ALL) (Mio km) (Mio km)

Pass Vehicles 3.593.450 45.876 7,3 % 31.084 14.792

2-Wheelers 546.412 1.474 3,8 % 532 942

Buses 9.752 413 13,8 % 365 48

LDV 195.955 4.392 1,9 % 2.237 2.155

HDV (& Trail) 109.538 4.806 5,9 % 3.581 1.225

Others 465.383 465 0 95 370

Total 4.920.490 57.426 6,6 % 37.893 19.533 (66,0%) (34,0 %)

B. NOx-emissions

"ALL" "NON-A" "HWY" "URB & RUR"

NOx Total NOx NOx from NOx on maj NOx on min Emis Factor Non-A Vehcl road netwrk roads & settl (g/km) (t/yr) (% of ALL) (t/yr) (t/yr)

Pass Vehicles 0,61 27.984 7,3% 18.961 9.023

2-Wheelers 0,28 427 3,8% 154 273

Buses 10,63 4.390 13,8% 3.877 513

LDV 1,26 5.534 1,9% 2.819 2.715

HDV (& Trail) 8,12 39.012 6,0 % 31.968 7.044

Others 10,25 4.766 0,0% 971 3.796

Total 82.114 6,2% 58.750 23.364 (71,5%) (28,5%)

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 26 Exposure of the Austrian Population to PM10 in 1996

Even though the overall results from these calculations are in good agreement with the numbers within the CORINAIR inventory, there are large differences between the two data sets concerning road and driving mode types. For instance, it was calculated that 71,5 % of all NOx is emitted along major highways. In contrast, the CORINAIR data indicates that only 41,3 % of NOx emissions come from highway traffic. The reason for this discrepancy is that CORINAIR includes into its "HWY" category only a subset of the Austrian national highway system. Thus, for the emission allocation done in this study, the CORINAIR emission data for road traffic were replaced with the emission data from the ARCS model. The split of the emissions from the minor roads & settlements into "rural" and "urban" traffic had to be introduced because different dispersion was assumed for these two sub-sets (see later). In this study, "rural" traffic refers to traffic outside the settlements on the secondary road network ("Landesstrassen")8 and on other minor roads that could not be located (diffuse traffic). It was estimated that of all non-highway emission about 30 % (7.009 t) would occur on minor roads (ROA-RUR) and about 70 % (16.355 t) in the settlements (ROA-URB).

Spatial allocation of emissions to emission-relevant areas (ERA) The actual distribution of emissions in Austria depends on the patterns of emission- generating activities. Previous work at ARCS (Loibl et al. 1993, Orthofer and Loibl, 1996) has proved the usefulness of a top-down emission disaggregation into emission-relevant areas (ERA) using data related to emission-generating activities (or surrogate data if directly related data are not available). All emissions were allocated to appropriate emission-relevant areas. The spatial allocations and analyses were done using a predefined grid system of 250 m * 250 m over all of Austria.

The spatial resolution of the model is 250 m * 250 m

The emission sources were divided into point sources, area sources and line sources. Each of these groups need a different approach for spatial allocation dispersion modelling (see later).

Point sources

78 individual point sources with an emission total of 18.432 t NOx were identified, mainly thermal power plants and industrial combustion and processing plants. These emissions were directly allocated to their geographic locations.

Line & area sources All CORINAIR SNAP3 emission categories were related into specific emission-relevant areas (ERA). A few SNAP codes could not be directly allocated to relevant areas, and assumptions and surrogate procedures had to be applied9. The ERA used for this study and the emissions allocated to them are summarised in Table 4.3 and Table 4.4. A full documentation of the allocation of all CORINAIR SNAP groups is given in Annex I. Emissions from aircraft cruise traffic do not contribute significantly to the ground-level ambient concentrations. Thus these emissions (which were about 90 % of all emissions from air traffic) were eliminated from the allocation and dispersion model.

8 The whole network of secondary roads (22.850 km) was digitized but no traffic count data were available. 9 For instance, emissions from SNAP 01-02-03 ("District Heating - Combustion Plants < 50 MW (boilers) with biogenic fuels") were allocated to the area for residential heating with firewood (POP-WOOD); it was assumed that small biofuel-fired district heating plants show the distribution pattern as domestic heating with firewood. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 27

Table 4.3: Major Source Categories for NOx emissions in emission-relevant areas

a) Source Categories ERA CODENOx Emissions Area Point

Not relevant for dispersion (aircraft cruise) - 6.879 0 Agricultural areas AGR 13.796 0 Airports AIR 648 0 Shipping on river Danube DAN 1.085 n.a. Forest areas and nature FOR 1.104 0 Populated areas (general) POP 4.802 n.a. Populated areas w/ coal fuel residential heating POP-COAL 2.045 0 Populated areas w/ gaseous fuels residential heating POP-GAS 4.495 0 Populated areas w/ fuel oil residential heating POP-OIL 6.092 0 Populated areas w/ wood & bio fuels res. heating POP-WOOD 10.755 0 Industrial areas (general) PRO 8.186 1.290 Clinker, cement, glass & ceramics industry PRO-CERA 6.403 0 Chemical & pharmaceutical industry PRO-CHEM 4.400 0 Energy production industry & utilities PRO-ENER 992 6.875 Iron and non-iron metal industry PRO-METL 57 5.101 Oil industry & refineries PRO-OIL 105 3.389 Pulp & paper Industries PRO-PAPR 790 1.777 Timber & wood cutting and manufacturing industry PRO-TIMB 588 0 Road Traffic on major highways ROA-HWY 34.868 n.a. 58.750 b) Road Traffic on secondary roads ROA-RUR 24.495 n.a. 7.009 b) Road Traffic in cities ROA-URB 25.129 n.a. 16.355 b)

Total emissions 157.714 18.432 a) Sum of area & point emissions given here is the same as in CORINAIR data set (176.146 t), but it includes more point sources. Original CORINAIR data are 169.271 t for area sources and 6.875 t for point sources. b) Emissions estimates for road traffic (82.114 t) are about 3 % lower than CORINAIR estimates (84.492 t). The large differences between ROA-HWY in CORINAIR and ARCS data set comes from a different definition of what to consider as "highway".

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 28 Exposure of the Austrian Population to PM10 in 1996

Table 4.4: Emissions generating areas used for disaggregation of sector-specific emission sources. Source category Amount ERA NOx in t Agriculture 13 776 Crop production areas Aviation10 648 Main Airports (Vienna Schwechat 65%, Salzburg 10%, Innsbruck 8%, Linz 7%, Graz 6% and Klagenfurt 4%) Shipping 1085 Uniform along the river Danube Forest emissions 1086 Uniformly over forest areas Residential Heating Census data on existing stove/fuel types and usage; these data were adjusted and preprocessed Coal 2045 Gas 4495 Oil 6092 Wood + biogenic 10755

General Production 9476 Municipalities with industrial employment quota processes Specific industrial sectors Sectoral disaggregation to municipalities with sector-specific share of employment Brick cement and ceramics 6403 Chemical & pharmaceutical 4400 Energy production 992 Iron and non-iron metal 5158 Oil Industry and Refineries 3494 Pulp & paper 2567 Timber & wood cutting 588 Major road network According to count sections (HWY) 58 750 Minor roads – urban ROA- Human settlements; pre-processing was applied URB 5009 Minor roads – rural ROA- Secondary road network RUR 2000 Unspecific 4802 related to general population density

The results are shown in figure 4.2. Results are given in kg per grid cell (250 m * 250 m).

10 Landing and Takeoff R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 29

Figure 4.2: NOx Emission inventory for 1996

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 30 Exposure of the Austrian Population to PM10 in 1996

The highest emission densities are calculated along the major roads, mainly along freeways within Vienna as well as in other areas with high density of population and/or industrial employees. The maximum emission densities not from major road network occur in the cities of Vienna, Linz, Graz and Salzburg. The emission sources contributing much to these non- line emissions are non-highway road traffic, residential heating and industrial activities. In rural areas the emissions come usually from residential heating, occasionally also from industrial installations or power plants.

Figure 4.3 shows the results of the regional NOx emission inventory from road traffic (which make up 47 % of all NOx emissions). It can be seen that the overall emission densities are around 1-10 kg per grid cell per year (i.e. 0,016-0,16 t/km²) in the populated areas outside the Alps. Much higher emission densities occur in cities. The highest emission densities (32.000 kg per grid cell or 2 t/km²) occur along highways. It is interesting to note that the traffic along the A23 in Vienna ("Südosttangente", which is the main Vienna thoroughfare) has more average emissions than traffic along the transit routes like A1 ("Westautobahn"), A2 ("Südautobahn") or A10 ("Tauernautobahn"). This means that the contribution of the intra- urban transit to the overall traffic is much higher than the interregional traffic just passing Vienna or other major cities. The disaggregation of the emissions caused by road traffic on the secondary road network was modelled with population density as a surrogate data set. The allocation is based on the assumption that the traffic density along the secondary road network is similar to the population density of these regions. This assumption reflects the traffic density as a response of population density as the main driving force for road traffic.

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 31

Figure 4.3: Regional emission densities for NOx emissions from road traffic 1996. Results given in kg per grid cell (250 m * 250 m).

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 32 Exposure of the Austrian Population to PM10 in 1996

4.1.2 Modelling of NOx dispersion

Dispersion of NOx was simulated using specific dispersion profiles. The profiles were derived from Keller11 et al. (1997) and were adapted during the work of this study. The model predicts annual average NOx concentrations in the vicinity of a standardised emission source for several emission/stack heights and different landscape characteristics. The original model was used to calculate ambient air dispersion profiles of a standardised source strengths of 1 t NOx per year in a 200 m * 200 m grid. To achieve an area-type of emission, the emissions are simulated as uniform 25 point sources spread regularly over the 200 m * 200 m source grid. Within this study all dispersion profiles were adapted for application to the 250 m * 250 m grid cell sizes. Furthermore, dispersion profiles from point sources were separately modelled (see later). The spatial implementation of the dispersion model is based on a grid cell system using the functionality of a Geographic Information System (GIS). This grid- based GIS approach allows to calculate the specific contribution of given NOx emissions in a source grid cell to the ambient air NOx concentration of other (target) grid cells in the vicinity of the source grid through cell-by-cell modelling and summing-up all cell results. Figure 4.4 gives an example of how the cell-by-cell dispersion model works.

Figure 4.4: Modelling air pollutant dispersion from emission sources in a grid cell system.

Location of hypothetical emission sources in the grid cell system. (Units: t NOx per year per grid)

b) Results of grid cell dispersion modelling using a given exemplary dispersion profile (here: an emission of 1 t NOx per grid cell produces an annual average ambient concentration of 4 µg/m³ in the source cell, 2 µg/m³ in the 1st neighbour-distance, 1 µg/m³ in the 2nd neighbour-distance, and zero beyond that). Units: µg/m³ annual average NOx concentration per grid. Note: The example shows how emissions in two neighbouring grid cells contribute to average ambient air st NOx concentrations of all neighbouring cells. E.g. "4+1" means: 4 µg/m³ from source 1 in 1 neighbour distance and 1µg/m³ from source 2 in 2nd neighbour distance.

111 222

12221 2 4 4+1 4+1 2+1

12421 2 4+1 8+2 4+2 2+2 1

12221 2 4+1 4+2 4+4 2+2 1

111 2 2 2+2 2+2 2 1

1 1 1

11This model is based on ambient air measurements and detailed studies for a monitoring site in Payerne using 22 classes of specific meteorological characteristics. For further details see: Rotach and de Haan (1995); Gryning et al. (1987) R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 33

Figure 4.5 summarises the standard dispersion profiles for standard emission sources with an annual emissions strength of 25 g/m² (=1 t per 200 m * 200 m or 1,56 t per 250 m * 250 m source grid). In settlements with many street canyons the dispersion is steep and the profile is short with high values at the source grids, while in the open landscape ("countryside") there is much wider and smoother dispersion. Emissions from residential combustion and from industry come usually from higher levels. The higher the stacks, the wider is the dispersion of air pollutants. Figure 4.6 summarises the dispersion profiles for standard emission strengths from sources with "2-20 m" and "20-50 m" stack height.

Figure 4.5. Profiles used for modelling the dispersion of emissions from stationary point sources with stacks >2 m

10,0

1,0

0,1 NOx concentration (µg/m³)

0,0 -125 125 375 625 875 1.125 1.375 1.625 Source grid Distance from source grid center (m) center 2 - 20 m stack height 20 -50 m stack height

Figure 4.6: Dispersion profiles for emissions from sources with emission heights < 2 m.

10,0

1,0

0,1 NOx concentration (µg/m³)

0,0 -125 125 375 625 875 1.125 1.375 1.625 Source grid Distance from source grid center (m) center < 2 m settlements < 2 m major roads < 2 m countryside

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 34 Exposure of the Austrian Population to PM10 in 1996

Dispersion profiles for point sources The Gaussian plume model ISCST312 has been used to model the dispersion of a reference 13 emission source with a standard stack emission rate of 1.000 t NOx per year. The dispersion was modelled for hypothetical stack heights of 50, 75, 100, 150 and 200 m. Several wind speeds and stability classes were tested in order to provide best estimates for the single emission sources. It was found that plume sizes and concentration maxima depend strongly on stack height, wind speed and air layer stability. Stability class 4 and a wind speed of 5 m/sec was used as standard default dispersion parameters for all point sources. All model runs were made with only one fixed standard wind direction to produce downwind dispersion patterns. As an example, Figure 4.7 shows such "rectangle downwind" dispersion patterns for a stack height of 100 m. The overall dispersion plume stretches over 50 km downwind but only the first 35 km have considerable concentrations and were used in the model. The concentration maximum is about 8 km downwind of the emission source. The influence of prevailing wind directions for the single point source was simulated using wind frequency data from the nearest representative meteorological monitoring sites. Wind directions were considered in 1/8th circle (= 45°) sectors. Thus all "rectangle downwind" Gaussian concentrations were "translated" into 1/8th circle sector concentrations. This was achieved by "spreading" the concentration values at the plume centerline over the respective 1/8th circle sector. Figure 4.8 summarises the resulting dispersion profiles for the 1/8th-circle sector concentrations for a standard point emission source of 1000 t/year with different stack heights. The model results were resolved for 1 km distance increments instead of 250 m.

Figure 4.7: Annual average NOx concentrations (in µg/m³) at ground level downwind calculated for a reference point source with an emission rate of 1000 t/year with ISCST3 plume model. Location of the emission source is at column 1/row 4. The X-axis shows the downwind distance from sources in km, the Y-axis reflects the cross section distance through the plume in km

C ro ssw ind d ista nc e fro m sourc e (km)

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Downwind distance from source (km)

0-2 2-4 4-6 6-8 8-10 NOx concentration (µg/m³)

An example for the application of such multiple 1/8th-circle sector dispersion figures with different wind directions is given in Figure 4.9.

12Calculations were done with the ISCST3 model from Trinity Consultants, which is based on the ISCST3 code by USEPA. The model has a similar algorithm as the Gauss dispersion standards defined by the Austrian standard (OeNORM). 13A ”real” major emission source in Austria for which many data are available – the Dürnrohr coal fired power plant – was used to model the ”standard” NOx dispersion. The applicable wind fields were taken from the nearest representative meteorological monitoring site. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 35

Figure 4.8: Dispersion profiles for point source emissions. The figure shows annual average concentrations (µg/m³) in 1/8th circle sectors "downwind" of a point source with an emission strength of 1000 t per year with different stack heights. Dispersion profiles are based on model runs for "rectangle-downwind" Gaussian dispersion, translated into 1/8th circle sectors. default dispersion parameters in Gaussian model are average wind speed of 5- m/sec and stability class 4.

100

50 m 10 100 m

100 m 1

0,1 150 m

200 m 0,01 NOx concentrations (µg/m³)

0,001

0,0001 0 5 10 15 20 25 30 35 Radial distance from source

Figure 4.9: Example for wind-direction specific NOx-dispersion from a point source (screenshot from ArcView). Note that in this example dispersion during calm episodes has not yet been included.

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 36 Exposure of the Austrian Population to PM10 in 1996

Simulation of dispersion during calm episodes During calm episodes (wind speed < 0,8 m/sec) the Gaussian plume model cannot be applied. Thus default dispersion profiles described for point sources by Keller et al. (1998) were taken. These profiles are centred around the emission sources and have very condensed dispersion profiles. These profiles were available only for maximum stack heights of 20-50 m, therefore, modified dispersion profiles were constructed for sources with taller stacks. The results from the Gaussian model were used to establish a relation between stack height and maximal ground level NOx concentrations (see Figure 4.10) for the standard source. Assuming that similar ratios between concentrations and stack heights are indicative for other type of dispersion profiles, these ratios were taken to adapt the distance-dependent radial concentration for calm episodes at various stack heights (Figure 4.11). It has to be emphasised that these dispersion profiles are rather uncertain.

Figure 4.10: Influence of stack heights and wind speeds on average NOx concentrations about 8-12 km downwind of a point source (Calculated for a standard release of 1000 t NOx per year with dispersion stability class 4, and wind speed of 5 m/sec)

10 Maximum NOx concentration (µg/m³) 5

0 0 50 100 150 200 250 Stack height (m)

Figure 4.11: Dispersion profiles for radial dispersion from point sources during calm conditions. Dispersion profiles are based on Keller et al. (1997) and were adapted for tall stacks.

1000

Stack 50 m 100 Stack 75 m

Stack 100 m 10

Stack 150 m 1

Stack 200 m

0,1 NOx concentration (µg/m³)

0,01

0,001 01234 Distance from point source (km)

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 37

Table 4.5 shows the application of different dispersion profiles for specific sources categories.

Table 4.5: Usage of dispersion profiles for NOx dispersion modelling Source category Dispersion Profile Area sources Agricultural sources < 2 m "countryside" Aircraft take-off and landing 2-20 m Populated areas 2-20 m Residential heating 2-20 m Industrial areas 2-20 m Urban road traffic < 2m "major roads" (Diffuse) Rural road traffic < 2m "major roads" Line sources Highway road traffic (outside settlements) < 2 m "major roads" Highway road traffic (freeways in settlements) < 2 m "major roads" Highway road traffic (fed highways in settlements) < 2 m "settlements" Rural road traffic (secondary road network) < 2 m "major roads" Commercial shipping (along Danube) < 2 m "countryside" Point sources (stack height classes 50/75/100/150/200 m) Point sources during wind episodes Gaussian dispersion Point sources during calm episodes Radial dispersion

Modelling of dispersion using a GIS Dispersion modelling is based on the GIS managed grid cell system. Dispersion was calculated for all NOx dispersion classes with the cell-by-cell modelling and summing-up method. Cartographic modelling functions like "FOCALSUM" and irregular kernels applied as convolution filter matrices were used to support the spatial model14. For specific emission source groups specific annulus-shaped filter kernels and their weights simulate the decrease of NOx concentration with radial distance from the emission source. To consider the different dispersion characteristics of road traffic emissions due to landscape influences (e.g. street canyon characteristics), it was necessary to separate the major roads into roads within settlement areas and roads outside areas. For instance, freeways within settlements ("Stadtautobahnen") were modelled as "major roads", because within cities freeways are usually lifted and dispersion is not hindered by built-up structures.

For each of the approximately 70 point sources in Austria the NOx dispersion has been modelled separately considering their stack height and the predominant wind characteristics. The emission rates of each point source were expressed as quotas of the 1000 t/year standard source. Wind direction data for 1996 were taken from the nearest representative meteorological monitoring site. These wind data are documented in Annex III. The wind direction frequencies were calculated from the half-hourly data, considering the 8 wind sectors as well as calms using the dispersion profiles described above. NOx concentrations were calculated for 1 km * 1 km grid cells within a circle of 35 km. To calculate wind-direction-specific dispersion, the emissions from each point source are divided into relative fractions for each wind situation (8 wind directions and 1 calm)15. Disper-

14 The cartographic modelling approach is based on Tomlin (1990). For an explanation of GIS neighborhood functions see Annex 4. 15 An example: If a point source has total emissions of 800 t NOx, and the NW wind account for 15 % of all days, then the emissions for calculating the NOx concentrations in the NW sector are assumed to be 15 % of 800 t (= 120 t). These emissions are again 12 % of the standard 1000 t basis of the dispersion profiles. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 38 Exposure of the Austrian Population to PM10 in 1996 sion for each wind-direction specific emissions are calculated using the 1/8th circle sector dispersion method presented above. The calculation for the dispersion during calm periods is done using the dispersion profiles described above. The concentrations for the 8 wind sectors and for the calms are added to give one resulting dispersion pattern for each point source. Results for all Austrian point sources were finally moved to their geographic locations and added to one nation-wide point- source dispersion map.

Background concentrations As stated previously, the modelling of dispersion of NOx was crucial to estimate the contributions of local sources to TSP concentrations. Anyhow, to validate the model with measurement data it was necessary to include the contribution from long range transport (which is assumed to be responsible for regional background concentrations) to ambient air concentrations of NOx.

The background contribution to ambient air NOx concentration was estimated based on measurement data. (NOx concentrations as annual mean values from all Austrian monitoring sites are summarised in Annex V.) Typical rural background stations outside the Alps have average annual mean values of NOx of 10 – 14 µg/m³. Therefore, 12 µg/m³ were taken as representative for regions outside the Alps. Within Alpine regions, background concentrations vary between 5 – 14 µg/m³ ; 8 µg/m³ as annual mean were assumed as representative in the Alps (including the Alpine valleys). The spatial patterns of the suggested background concentrations were generated by using a topographic map and a digital elevation model. The border of the 12 µg/m³ background level region was set to the Alpine borders with a continuous decrease to 8 µg/m³ in a transition zone of 20 km. The transition was performed using FOCALMEAN filtering16. Measurement data indicate that NOx background concentrations decrease with increasing altitude. Anyhow, within this project no altitude dependent function was used. This was not considered as major drawback, since measurement stations at rural background sites are less important for validating the dispersion model.

4.1.3 Maps of NOx ambient air concentrations

Figure 4.12 shows the NOx concentrations in ambient air. The highest values occur in the centres of cities, particularly along major roads with high traffic densities and nearby industrial sources. It is interesting to note that there are also considerable NOx concentrations in some of the alpine valleys, particularly in the vicinity of road segments with high traffic densities. Figure 4.13 shows the NOx concentrations caused by road traffic emissions (which account for 47 % of all NOx emissions in Austria). The general NOx concentration pattern is dominated by the major road network. Emissions in the rural regions do not lead to significant NOx concentrations within the resolution of the model. It has to be stressed that the model is not suited to predict kerb-site concentrations.

Figure 4.14 shows the concentrations of NOx in ambient air caused by industrial area and point sources. It is interesting to note that contributions from point sources seem to be rather small, compared to the part caused by area sources. Point sources have usually high stacks which allow an effective dispersion of pollutants, and thus contribute little to local NOx annual concentrations.

16 For an explanation of FOCALMEAN function see Annex 4. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 39

Figure 4.12: NOx concentrations in ambient air at ground level as calculated with the emission allocation and dispersion model.

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Figure 4.13: NOx concentrations in ambient air at ground level caused by road traffic (Calculated with the emission allocation and dispersion model).

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Figure 4.14: NOx concentrations in ambient air at ground level for emissions from industry (area and point sources) as calculated with the emission allocation and dispersion model

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 42 Exposure of the Austrian Population to PM10 in 1996

4.1.4 Validation with measurement results

General Remark The model results were compared with NOx monitoring data from more than 130 stations of the Austrian monitoring network. The data were obtained as annual mean values for the year of 1996. Annex V contains a table with all measured values. There are some basic difficulties in comparing modelled data with data obtained by measurement. Monitoring data provide a complete time series of the concentration of a pollutant at a given location. The representativity may range from a few meters (e.g. at certain kerb sites; the measurements might be representative for the concentrations along the street, but not for locations more distant from the driving lane) to several kilometres (for selected rural background sites). On the other side, models provide information on the concentration of certain cell sizes (in this case: 250 m x 250 m). This means that homogenous dispersion is assumed within each grid cell. Normally this will not be the case in the vicinity of major sources (e.g. larger roads). Both modelling and measurements have their unique advantages. Measurements are necessary to monitor the levels and the temporal variability of air quality at a given location, often at a point where maximum concentrations occur. Modelling, in contrast, can be used to obtain information on the average spatial concentrations of air pollutants; this information is often crucial to estimate human exposure. Therefore an unreflected and superficial comparison of monitoring data and model results might result in false conclusions. This is especially true for areas near to major sources, where air pollution is highly variable even within small distances. However, comparison of model results and monitoring data was necessary to assess the overall performance of the model. The comparison was performed for grid cells with monitoring sites (about 130 out of ~ 2,900.000 cells) in it. The comparison steps have been repeated several times in order to constantly fine-tune and improve the model. This feedback iteration requires that all steps of the model (that includes emission categorisation, emission allocation to areas, emission allocation parameters, dispersion profiles) are checked and – if necessary – updated and redone. During the project it was tried to characterise the monitoring sites according to their predominating influences from certain emission categories. However, it was found that there are very few sites that can be attributed to single predominating emission influences. Most monitoring sites are located in urban areas, partly close to major roads, or close to industrial sites, with several sources contributing to ambient air concentrations. Additional, the precise location of the monitoring site is crucial, particularly for such sites in the vicinity of emission sources. A difference of 100 m in the apparent location of a monitoring site from the location of an emission source can lead to a difference in NOx concentration by a factor of about 5. Comparison of modelled and measured concentrations Figure 4.15 presents a scatter plot of the measured NOx concentrations versus the modelled NOx concentrations.

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 43

Figure 4.15: Comparison of observed and modelled NOx at grid cells which have monitoring sites. Solid lines indicate levels for 50 % under- and overestimation.

350

300

+50 % 250 overestimation

200 underestimation Rinnbö 150 Währ Gü Gaud Gü - 50 % AKH 100 Hietz Kai Linz B

Modeled NOx concentrations (µg/m³) Linz H Ternitz Hallein Rudolfspl 50 Klagenft

St Veit Lienz Völkerm Landeck 0 0 50 100 150 200 250 300 350 Observed NOx concentrations (µg/m³)

Comparisons of model results are quite satisfactory. Of approximately 130 monitoring sites for which NOx data for 1996 were available, 72 sites (= 54 %) are within ± 15 µg/m³ of the model results, and 101 sites (76 %) sites are within ± 25 µg/m³. Stations with more than 25 µg/m³ deviation (higher or lower) are mainly in larger cities. Outside major cities the differences are generally much smaller (in absolute figures). On average, the model underpredicts the measurement data. The average observed NOx concentrations were 50 µg/m3, while the average concentrations within grid cells with monitoring sites were 37 µg/m3. Further analyses were performed to give some indications for over- and underestimation at certain types of monitoring locations. For instance, stations with high underestimation have significantly more NOx contribution from traffic, while stations with higher overestimation rates have slightly more contributions from residential heating and from industry. This could mean that the model tends to underestimate the air pollution contribution from traffic and overesti- mates the contribution from residential heating and from industry. Figure 4.16 shows the contribution of several emission source classes to the total NOx concentration at grids with monitoring sites with the highest modelled average NOx concentrations. It can be seen that the sites with the highest NOx concentrations are dominated by road traffic and – to a lesser extent – by residential heating. Contributions from agriculture, aircraft, and ships are negligible. The contributions from industry are generally less important but are high in the city of Linz.

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Figure 4-16: Contribution of main NOx emission sources to modelled NOx concentrations at the grid cells of the 23 monitoring sites with the highest modelled NOx concentrations. Note that for some sites the modelled concentrations can be different from the actual observed concentrations at the monitoring site.

150

120

60 NOx concentration (µg/m³)

Rinn Währ Gaud AKH Wels Linz B Hallein Liesing Taborstr Floridsd StadlauHall i.T. Linz 24 Linz UrsGraz M Hietz Kai Belgradpl KendlerstrLinz ORF Rudolfspl Stephanspl Linz Hauser

Resid Heating Road Traffic Industry Other (pop related) Background

Sites from Southern Austria have a high share of underestimation (Figure 4.17). This can be explained by the fact that Southern Alpine basins have unfavourable dispersion conditions with many winter inversion episodes. It could be well the case that the dispersion profiles used in this study are not suitable to correctly describe the actual dispersion situation in such regions. Figure 4.15 also shows that there are several sites with large differences of modelled and observed concentrations ("outliers") which deserve some specific discussion. For instance, there are significant underestimations for the Viennese site Hietzinger Kai ("Hietz Kai"), and the site Rudolfsplatz ("Rudolfspl") in Salzburg, as well as for Hallein ("Hallein"), Klagenfurt Völkermarkterstrasse ("Klagenfurt"), Lienz ("Lienz") and St.Veit ("St Veit"). All these sites are in the immediate vicinity of major roads, often close to junctions with frequent traffic jams during rush hours. Such situations can not be modelled adequately with a resolution of 250 m x 250 m. The highest underestimation (-79 %) was for Weiz ("Weiz") in Styria. This could be partly due to the influence of a biomass district heating plant in the area which was not considered specifically within the emission model. On the other hand, there are sites that were highly overestimated. One such site is Vienna Rinnböckstrasse ("Rinnbö") which is located near to a spatially elevated freeway. The concentrations are overpredicted since the freeway is in the same grid as the monitoring site. The site Vienna AKH ("AKH") is on the rooftop of a 15-floor high building (low measured ambient air concentrations) within a densely populated area with high traffic emissions. The concentrations are also overestimated at sites in the industrial town Linz (sites Hauserhof "Linz H", Berufsschulzentrum, "Linz B"). One of the reasons could be that the sector- dependent allocation of industrial emissions to the urban area of Linz is incorrect. The high overprediction for the Viennese site Währingergürtel ("Währ Gü") can be explained by the fact that the location, though near a densely populated area with heavy traffic, is situated in a narrow street and therefore sheltered from the surrounding emission sources.

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In the following paragraphs some possible reasons for the differences between the model results and the measurement data are summarised. • In some cases, the emission model – particularly for some of the ”general” allocation classes (such as for the PRO, POP categories) is probably not sufficiently accurate at certain locations, particularly if surrogate disaggregation parameters had to be used. CORINAIR does not always indicate a detailed description of the activities (cf. section 2.1.3), so that for some sources the allocation is somewhat arbitrary. These difficulties could be solved by an improved allocation of emissions, particularly for important non- uniformly distributed sources (such as small biomass-fired district heating plants). • There are no traffic count data on major urban trunk roads. Thus the distribution of road traffic in urban areas had to be assumed to follow the population density patterns. This leads to an underestimation of the actual traffic-related emissions on major city roads; and as a consequence, to an underestimation of the pollutant concentrations. • The resolution of the model is 250 m * 250 m. Variations within each grid (‘sub grid effects’) cannot be resolved. Anyhow, for an assessment of population exposure, the resolution of the model seems sufficient.

• The highest contribution of a single emission source category to NOx concentrations is road traffic. These emissions are allocated to a digital road network digitised from large- scale maps. Inaccurate locations of monitoring sites or of roads in the model can explain possible differences. An incorrect identification of the distance between a road and a monitoring site of 250 m can lead to an uncertainty of about one order of magnitude17. Furthermore, the road traffic density does not always reflect the emission intensity. The same car number in traffic jam situations is causing more emissions due to stop and go traffic leading to longer time spending within the same road segment than the car number under fluently traffic during the whole day. Similarly, the model at present cannot differentiate between cold start emissions and warm engine emissions, which will be specifically geographically distributed (cold start emissions will be overproportional in residential areas). • Dispersion profiles, which were applied to the model grid, are step-curves. The concentration varies between two model raster fields in the range of a factor of 3-10. Thus, within one model grid cell of 250*250 m the uncertainty of the concentrations in the vicinity of a receptor point can be in a similar range (depending whether the receptor point within the grid is on the near end or on the far end of an emission source; see section 2.2.2). • The representativity of monitoring site might be smaller than the grid size used by the model. Every monitoring station has its own characteristics and is influenced by its immediate environment (such as a wall, a tree will have effects). Thus the measurement data although "accurate" for the very location might not be representative for the overall location18. • Inaccurate distances between the location of monitoring sites and the location of emission sources (mainly roads) lead to seemingly "wrong" NOx model results.

17 One example of such an incorrect location is the Vienna/Floridsdorf monitoring site, where the distance between the highway and the site is much closer in the spatial model than in reality. 18 One such example is the Vienna AKH station (see above), where the monitoring site is on the roof of the building and thus not "typical" for the ground-level situation. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 46 Exposure of the Austrian Population to PM10 in 1996

Figure 4.17: Spatial analysis of difference between modelled NOx concentrations in the grid cells and the observed NOx concentrations at monitoring sites. Underpredictions (blue dots) are frequent in South-eastern Austria.

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4.2 Calculation of NO2 levels

Even though not the main task within this study, spatial NO2 concentrations were estimated. NO2 levels were calculated using an empirical relationship derived from measurement data from more than 130 Austrian stations. Observed annual mean NO2/NOx ratios were related to NOx concentrations. A logarithmic function was used to estimate NO2 values from modelled levels of NOx. Figure 4.18 shows the data set used to derive the function to estimate NO2 concentrations from NOx concentrations. NO2 concentrations [NO2] can be calculated for all NOx concentrations [NOx] through the equation:

[NO2] = [NOx] * {(-0,2) * ln([NOx]) + 1,35}

This formula is suitable for the calculation of NO2 in all grid cells. At very low NOx concen- 3 trations (NOx < 5 µg/m ) the formula should not be applied, because NO2/NOx ratios are slightly above 1,0. However, in practice this overestimation is irrelevant, since minimum NOx background levels are 8 µg/m³ (see above) and thus above the critical NOx concentrations.

Figure 4.18: Relationship between the ratio NO2/NOx and the NOx concentrations as measured at the stations of the Austrian monitoring network. Solid line: Function used for calculation of NO2 with formula indicated.

1,0

0,8

0,6

0,4

NO2/NOx (µg per µg) NO2/NOx (µg y = -0,2Ln(x) + 1,35

0,2

0,0 0 50 100 150 200 250 300 NOx concentration (µg/m³)

Figure 4.19 shows a NO2 concentration map derived from the NOx map and the empirical relationship between NOx and NO2. The pattern is similar to the pattern of NOx; anyhow, the surface is smoother.

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Figure 4.19: NO2 concentrations for Austria as calculated from NOx concentrations and NO2/NOx relations. Top map: NO2 concentration pattern in Austria as a result of all NOx emissions. Bottom map: NO2 concentration in Austria; only road traffic NOx emissions are considered.

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4.3 Modelling of TSP/PM10 levels

4.3.1 Measurement data of TSP

Currently, TSP is measured in Austria at approximately 120 sites. These sites are run by the Governments of the Federal Provinces and the Federal Environment Agency. The main purposes of the measurements are • compliance checking; the Austrian Air Quality Protection Act (Federal Gazette 115/97) defines a limit value of 0,15 mg/m3 as daily mean value for TSP. The location and number of the sites is laid down in a Federal Ordinance. • Trend analysis. • Estimation of population exposure. • Identification of major sources.

Figure 4.20 shows the monitoring sites in 1996.

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 50 Exposure of the Austrian Population to PM10 in 1996

Figure 4.20: TSP monitoring sites in Austria in 1996

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 51

As stated previously, the monitoring sites are equipped with automated monitors for continuos registration of TSP concentrations. Two types of monitors are used, devices based on beta attenuation (mainly FH 62) and TEOM (Ruprecht-Patachnik) sampler. In Annex VI the monitoring equipment of the sites is given.

Classification of monitoring sites In order to obtain information on the potential sources of TSP levels measured at the different monitoring sites, all stations were classified according to their immediate and local environment. The classification scheme was derived from EU Ozone Directive 92/72/EC. Results are summarised in Annex VII, the scheme is described in Annex VIII.

4.3.1.1 TSP concentrations in 1996 In table 4.6, important statistical values of measured TSP concentrations are displayed.

Table 4.6: Summary of Austrian TSP measurement data from 1993 - 1997. Annual mean value in mg/m3 1993 1994 1995 1996 1997 Min 0,008 0,013 0,012 0,017 0,013 5-percentile 0,018 0,022 0,020 0,026 0,021 mean value 0,038 0,037 0,035 0,039 0,034 median 0,038 0,036 0,033 0,037 0,032 95-percentile 0,058 0,059 0,051 0,054 0,052 Max 0,076 0,076 0,089 0,092 0,082

It can be seen that TSP concentrations were high in 1996 in contrast to 1995 and 1997 The reason for the high TSP levels in 1996 is not clear. Annex IX contains a table with annual mean values measured at Austrian TSP-monitoring sites.

4.3.2 Estimation of TSP regional background concentrations

Within this study, background is defined as the concentration not caused by local sources. The determination of the background concentration is of crucial importance within this project. This part of TSP is derived from measurement data; in contrast, local contributions are modelled using NOx as leading substances. Most of the Austrian TSP monitoring sites are not located in areas defined as background. This can be explained by the fact that the main purpose of monitoring TSP is compliance checking. Measurements are therefore predominately carried out at locations where highest concentrations are expected and where the population is likely to be exposed. Anyhow, some significant differences can be seen in different areas of Austria. Especially, there is a gradient between Eastern and Western parts and Alpine and non Alpine regions. • In the Eastern parts of Austria (Vienna, Lower Austria and Burgenland) all annual mean values were higher than 30 µg/m3. • Upper Austria shows generally lower values than Lower Austria. • In Styria and Carinthia, the situation is somewhat complex. Stations in Graz and in the highly industrialised Mur valley show high concentrations, also sites in the central basins of Carinthia. The main reason for that are the unfavourable dispersion conditions within such areas.

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• Salzburg has basically no monitoring sites in its Alpine areas. • In Tyrol and Vorarlberg, several values below 30 µg/m3 can be found, even though there are no true background sites. This information was used to derive the pattern of background concentrations. The concentration of the background was also derived from measurements. For the Eastern parts of Austria, a value of 25 µg/m3 was derived, for the Western parts 13 µg/m3. Between these areas, a smooth transition zone was assumed. Especially the background concentration for the Eastern parts of Austria seems quite high. Anyhow, these data are directly derived from measurements and are consistent with modelling results obtained by other institutions. Results from Tarrason and Tsyro (1998) obtained with the EMEP model indicate that inorganic secondary particulates can contribute more than 15 µg/m3 to PM2,5 concentrations as annual mean value in Eastern Austria. The model might overestimate nitrate, but is in good agreement with measurement results of sulphate. ApSimon and Gonzales (1999, personal communication) estimate that long range transport of primary PM10 may contribute up to 6,6 µg/m3 to background concentrations in the Eastern parts of Austria and 3,4 µg/m3 in the Western parts. Theses estimations are based on a simple dispersion model distinguishing between fine and coarse particles and the PM10 emissions inventory established by Berdowski et al. (1996). Additional contributions to background concentrations can be expected from secondary organic particles.

Regional TSP background concentrations vary between 13 and 25 µg/m3 as annual mean value.

The regional background concentration of TSP in Austria in 1996 is shown in Figure 4.21.

Figure 4.21: Spatial distribution of background TSP concentration in Austria 1996

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Traffic contribution to background concentrations The estimation of the traffic contribution to PM10 background concentrations was based on the following considerations: • Secondary inorganic particles contribute 70% to background PM10. As an average, 37,5 % can be attributed to sulphate and nitrate, 25 % to ammonia. • The relative share, but not absolute concentrations, of different contributions is similar all over Austria (in reality, the relative contribution of sulphate could be higher in Eastern parts of Austria, while the same is true for nitrate in the Western parts of Austria. Anyhow, there were no sufficient data to quantify this differences). • The contribution of traffic to secondary particles is assumed to be proportional to the traffic related portion of the precursor emissions. • Secondary organic compounds contribute on average 10 % to background PM10. • Primary particles contribute 20 % to background PM10. • For primary particles the traffic share was derived from data on PM2,5 emissions in Austria and its neighbouring countries. The contribution of different (chemical) fractions is summarised in Table 4.7.

Table 4.7: Contribution of different fractions to PM10 background concentration in %. Substance Contribution to Traffic share of Traffic contribution background PM10 precursor to background PM10 in % emissions in % Nitrate 26,5 63 %* 17 Sulphate 26,5 6 %* 2 Ammonia 17 2 %* 0 OC including secondary 10 46 %* 5 primary particles 20 33 % ** 7 100 31 * CORINAIR (1994) ** Berdowski et al. (1996)

Altitude dependence of TSP concentrations There are some clear indications that TSP concentrations are generally lower at higher altitudes (Filliger et al., 1999). Anyhow, • no effect could be seen for stations below 1000 m • for 1996 measurement data for only one station located above 1000 m are available. The correlation between the altitude and TSP concentrations as annual mean values from 1996 is shown in Figure 4.22. Therefore, it was not possible to derive a quantitative estimate of an altitude-dependent function of the concentration. Therefore, the results might have a tendency to overestimate the TSP concentrations at higher altitudes and should not be considered as representative for areas higher than 1000 m. However, the main goal of this study was to identify population exposure, and most of the population lives in altitudes lower than 1000 m.

No altitude dependent correction function was used.

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 54 Exposure of the Austrian Population to PM10 in 1996

Figure 4.22: Correlation of TSP (annual mean value in mg/m3) and the altitude of the monitoring site for selected background sites in Austria.

0,05

0,045

0,04

0,035

0,03 TSP (as annual mean in mg/m3) 0,025

0,02

0,015 0 200 400 600 800 1000 1200 1400 Altitude

4.3.3 Modelling of local induced PM10/TSP concentrations using NOx as surrogate

As indicated above, the aim of the project was to estimate the exposure of Austrian population to PM10. Anyhow, no PM10 data were available; therefore, an emission- dispersion model for NOx was applied to PM10 using assumptions for the overall relation between NOx and PM10 concentrations in the vicinity of defined source categories. PM10 concentrations in ambient air were estimated using the NOx dispersion model results. Information from the Austrian TSP monitoring network should be used to support the consistency of results. The underlying assumptions for the approach are:

• The emission ratios between PM10 and NOx are more or less constant for the emission source groups used in this study (such as aircraft, agriculture, highway road traffic, etc.).

• The PM10 dispersion profiles follow a similar pattern than NOx. This assumption is clearly only an approximation of the reality. The dispersion of fine particles (< 2,5µm) will be quite similar to NOx; anyhow, coarse particles will deposit more rapidly.

• The ratios between ambient concentrations of PM10 (and TSP) and NOx in an area which receives its ambient air pollution from only one source group are constant. • The PM10 concentrations in a grid cell can be estimated by applying the appropriate PM10/NOx ratios to all NOx contributions from the defined NOx emission source groups, and by adding the appropriate background levels. It is quite evident that some of the assumptions are only coarse approximations of the reality. However, as long as the geographic distribution for all defined emission source groups is correct and the dispersion patterns of these emissions are appropriately estimated (which was achieved through the establishment and successive fine-tuning of the NOx model), it should be possible to get a rough approximation of the distribution pattern of PM10.

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Default PM10/NOx ratios for all important emission source groups were established (Table 4.8). These are based on known emission ratios19, on selected literature data20, on expert judgements, and on observed TSP/NOx concentrations at selected suitable sites from the Austrian monitoring network. It is important to note that these PM10/NOx ratios are NOT the emission ratios proper, but they do reflect the likely concentration ratios in the vicinity of emission sources (that are covered by the dispersion profiles). Thus these ratios are used to directly convert the NOx concentrations into PM10 concentrations. Special ratios have been introduced for traffic on minor roads (ROA-HWY b), and for one particular major point source in an alpine valley (Donawitz, PRO-METL b) which has special unfavourable dispersion conditions.

Table 4.8: TSP/NOx and PM10/TSP ratios for concentrations in ambient air (µg/m³ per µg/m³) for different emission source groups. These values are assumed to be valid in the vicinity (< 5 km) of the emission sources.

Code Name TSP/NOx PM10/TSP PM10/NOx Area Sources: AGR Agriculture 5 0,5 2,5 AIR Aircraft 0,02 1,0 0,02 DAN Danube Shipping 0,2 0,8 0,16 FOR Forest areas a) -- - POP Population related sources 0,5 0,9 0,45 POP-WOOD Residential - Wood 2 0,3 0,6 POP-GAS Residential - Gas 0,01 1,0 0,01 POP-OIL Residential - Oil 0,5 1,0 0,5 POP-COAL Residential - Coal 1 0,7 0,7 PRO Production (general) 0,5 0,9 0,45 PRO-CERA Glass/Lime/Cement 1 0,3 0,30 PRO-CHEM Chemical Industry 0,5 0,9 0,45 PRO-ENER Non-Point Energy Production 0,5 0,9 0,45 PRO-METL Metal Industry 1 0,8 0,8 PRO-OIL Refineries 0,05 0,9 0,045 PRO-PAPR Pulp & Paper Industry 0,5 0,8 0,4 PRO-TIMB Wood and Timber Industry 0,5 0,9 0,45 ROA -HWY a Highway Road Traffic 0,15 0,8 0,12 ROA -HWY b Minor Road Traffic 0,5 0,9 0,45 ROA -RUR Rural Road Traffic 0,5 0,8 0,4 ROA -URB Urban Road Traffic 0,7 0,8 0,56 Point Sources: PRO-ENER Utilities/District Heating 0,05 1,0 0,05 PRO-METLa Iron & Steel Linz 1,0 0,8 0,8 PRO-METLb Iron & Steel Donawitz 5,0 0,4 2,0 PRO-PAPR Pulp & Paper 0,50 0,8 0,4 PRO-OIL Refinery 0,05 0,9 0,045 Background: 3 3 NOx: in alpine areas 8 µg/m , all other areas 12 µg/m TSP: in alpine areas 13 µg/m3, all other areas 25 µg/m3 Background PM10/TSP = 0,9 a) Emissions from forest areas were not considered in the NOx dispersion model.

19 From existing data collections (EEA 1997; EPA 1997) 20 Such as measured PM10/TSP ratios in Switzerland (EMPA, 1998) Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 56 Exposure of the Austrian Population to PM10 in 1996

For agriculture, a high PM10/NOx ratio was assumed. This is mainly supported by measurement data obtained in the vicinity of agricultural areas. Such stations have relatively low NOx concentrations (often near background levels); in contrast, TSP levels are often significantly higher than the concentration attributed to background. The sites Pillersdorf and Illmitz, e.g., have the highest TSP/NOx ratios from all Austrian monitoring sites. For residential heating, the values are derived from emission for NOx (CORINAIR) and estimations from Berdowski (1997). Total emissions are estimated with 23000 t NOx and 9700 t PM10. The PM10/NOx ratios vary from 0,7 (coal) to 0,01 (gas). For traffic, different ratios were applied. For highway traffic, it was estimated that re-suspension plays a minor role. This is supported by measurement data from sites in the vicinity of highways. A ratio of 0,12 was derived. A lower ratio would not be in line with data on soot and NO obtained on monitoring site near a highway in Lower Austria. Figure 4.23 shows the result of this measurement campaign. NO and elemental carbon (EC), one compound of traffic related PM10, show a very similar pattern (which is a good indication of common sources). The mean ratio of the two components is 1: 0,11 (expressed in µg/m3). Bearing in mind that traffic emits also other particulate substances than EC, a ratio of 0,12 seems rather conservative.

Figure 4.23: Correlation between the concentration of Elemental Carbon (EC) and NO in the vicinity of a highway in Lower Austria

For urban roads, a ratio of 0,56 was applied. This value is considerably higher than the estimate for highways. Anyhow, it is supported by measurement data at sites near medium roads within cities. The high ratio is mainly explained by the dominant role of re-suspension within build up areas in cities (there are measurement and emission data from different countries indicating that non-tailpipe emissions can be 5 to 10 times higher than tail-pipe emissions. See: Filliger et al., 1999) and by differences of emissions from paved roads. Venkatram et al. (1999) found differences of PM emissions of up to a factor of 10 between city roads and freeways.

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The ratios were calculated for TSP/NOx and PM10/TSP. The reason for this two-step approach was that TSP levels could be calculated, which were subsequently compared to measured concentrations. It is clear that this method for the calculation of PM10 has a considerable level of uncertainty in it. The assumptions for the PM10/NOx ratios cannot be validated in a true sense, because there are no routine measurements of PM10. Technically this approach was quite straightforward: in any receptor grid cell, the contribution of the various emission source categories to ambient concentrations is known from the NOx dispersion model. Through the GIS data set, the PM10 concentrations in these grid cells can then be calculated by applying the appropriate TSP/NOx and PM10/TSP ratios to the contributions to NOx concentrations for the relevant area, line and point emission source groups. The contributions of the different sources to the TSP and PM10 concentration loads were summed up grid by grid.

4.3.4 Contributions to TSP levels

Figure 4.24 shows the modelled contribution of different sources to total TSP levels at those sites with the highest total TSP. Even though these sites have relatively high contributions from local sources, the class ‘Other including background’ is dominating at most locations. The contribution of Industry at the site Donawitz is an exception which is due to high emission densities and unfavourable dispersion conditions.

Figure 4.24: Modelled contribution of the main sources to TSP concentrations at selected sites in Austria in 1996. Residential Heating, Industry and Traffic do only include the local contribution from that sources categories.

100,000 OTHER INCL. BACKGORUND 90,000 RESID HEATING 80,000 INDUSTRY 70,000 TRAFFIC

60,000

50,000 µg/m3

40,000

30,000

20,000

10,000

0,000

Wels Liesing Taborstr. Belgradpl. Kendlerstr. Graz Mitte Floridsdorf Graz West Laaer Berg Graz Nord Gaudenzdorf Rinnböckstr. Hietzinger Kai Linz Hauserhof Linz 24erLinz Turm BH-Urfahr Leoben Donawitz Linz Ursulinenhof Linz ORF-Zentrum Stephansplartz (Wien)

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Contributions from traffic were compared to measurement data of elemental carbon (EC) obtained at selected Viennese sites (Table 4.9; UBA, 1998). EC originates from tail-pipe emissions and partly from tyre wear. Anyhow, at least at the site Belgradplatz, domestic heating might also significantly contribute to EC levels. These results do not contain contributions from brake and tyre wear, asphalt wear and road dust, road salting and re- suspension, which might be readily significant higher than the contribution of tail-pipe emissions. On the other hand, the site Hietzinger Kai is in the immediate environment of a busy street and the results might not be representative for a 250 m * 250 m grid. These two effects could compensate each other; therefore it can be concluded that for these sites the contribution from traffic is in a correct range. Results from studies from other countries (Filliger et al., 1999; UK Airborne Particles Expert Group 1999) indicate that the contribution from traffic often exceeds 50 %. Assuming that the situation in Austria is comparable, the estimations obtained in this study are rather conservative concerning the traffic share of PM10.

Table 4.9: Concentration of Elemental (EC), Organic (OC) and Total Carbon (TC) as annual mean value at a few selected sites in Vienna.

Site EC OC TC (µg EC/m3) (µg OC/m3) (µg TC/m3) Hietzinger Kai 12,6 12,7 25,3 Spittelauer Lände 9,9 5,3 15,2 Rinnböckstraße 7,8 11 18,9 Gaudenzdorfergürtel 5,0 9,2 14,2 Belgradplatz 4,4 8,3 12,7

The PM10 maps were originally calculated with a spatial resolution of 250 m x 250 m. Since population data were not available at such a high resolution, and to avoid overestimating the exposure in cells in the vicinity of very busy roads, these data were aggregated into grids of 1,25 km x 1,25 km (see later) . Some results are summarised in Table 4.10.

Table 4.10: Minimum, average and maximum PM10 concentrations modelled in Austria Statistical quantity PM10 in µg/m3 Minimum 11 5-Percentile 11 Mean 22 Median 22 95-Percentile 29 Maximum 59

Within this report, no maps of PM10 data are provided. The main reason for that is that currently no measurement data are available for validation of model results. Anyhow, this does not mean that the results are totally uncertain. There are a couple of good indications that the results are reliable. • The results are consistent with the available monitoring data for TSP and emission data (for NOx). • The background concentrations were derived from measurement data and are supported by measurements of selected chemical compounds. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 59

• The calculated PM10 concentrations were translated to TSP concentrations and compared to measurement data (see next section). The agreement of the data sets is reasonable. • The results are in line with results from foreign studies using completely different methodologies. This is true for the absolute concentrations, but also for the traffic related part.

4.4 Comparison of modelled TSP to measurement data

As mentioned above, the comparison between model results and observed TSP data has to take into account the general difficulties of comparing site-specific measurement data with concentrations calculated for 250 m * 250 m grid cell values (see section 4.1.4). Additional, the coarse fraction of TSP has certainly different dispersion characteristics than gaseous pollutants or PM10. In general large particles are subject to more rapid sedimentation and deposition. Figure 4.25 shows the comparison between model results and point measurement data for TSP. The model results underestimate the measured concentrations at most monitoring sites. While the average concentration of the 108 monitoring sites is 40 µg/m3, the average concentration of the respective grid cells is 32 µg/m³. Interestingly, this average apparent underestimation of 20 % is less than it was for NOx (for which it was 28 %), but the tendency is much more pronounced. On average, the model tends to underestimate the TSP concentrations. 76 of 108 sites are within a range of +/- 50%. Overestimations were close to 50 % at two sites in Vienna, Gaudenzdorf and Stephansplatz. While the Gaudenzdorf ("Gaud") site has already been overestimated for NOx to a similar extent, the Stephansplatz has not yet been included in the NOx comparison, because no NOx data were available. The Stephansplatz site is a good example which demonstrates some of the small-scale shortcomings of the model. The Stephansplatz monitoring station is located directly at St.Stephens cathedral in the centre of Vienna; the model predicts that this site is significantly influenced by traffic and residential combustion. In reality this site is located within a pedestrian area with only little residential population. Furthermore this site is shielded from the traffic in the vicinity through buildings.

Figure 4.25: Comparison of observed and modelled TSP concentrations from 108 grid cells with monitoring sites inside.

100

+50 % overestimation 80

Gaud Gü underestimation

60 - 50 % Donawitz Stephanspl

Klagenfurt Völk B Vös St Pölten Modeled TSP concentrations (µg/m³) Kapfenb 20 Weiz

0 0 20406080100 Observed TSP concentrations (µg/m³)

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A few selected sites are discussed here as an example for an underestimation: The highest underestimations occur at sites in Weiz ("Weiz") and Klagenfurt Völkermarkterstrasse ("Klagenfurt"). Both sites have already been underestimated for NOx, and have consequently also lower calculated TSP levels. A site which is surprisingly highly underestimated is Bad Vöslau ("B Vös"). This site with vineyards in the surroundings was underpredicted by -30 % for NOx but is underpredicted by -108 % for TSP. There are also two industrial sites in Styria ("Donawitz", "Kapfenberg") which are underestimated. Both locations have metal industries, where relatively high TSP is associated with the NOx emissions. Donawitz in particular has steel works with a metal sintering plant, whose particle emissions are known to be very high21. Figure 4.26 shows the spatial analysis of difference between modelled TSP concentrations in the grid cells and the observed TSP concentrations at monitoring sites. Possible explanations for the systematic underestimation include: • NOx, which was used as leading substance, was already underestimated at several sites. • Monitoring sites might be located in ‘hot spots’ within grid cells (especially in the vicinity of traffic sources) and are therefore not representative for the average condition within the grid cell. • The contribution of local PM was modelled using NOx as leading substance. Anyhow, there are some sources of PM which have no or negligible NOx emissions such as mining or construction work. These sources are subsequently not or inadequately covered. • The contribution of local biogenic sources to measured TSP data is unclear, but might be significant at certain locations (near trees,..). As mentioned in previous sections, an at least approach was followed within the project. Therefore, no excessive model tuning was performed to decrease the average underestimation of actual TSP levels.

21 The underestimation for the Donawitz site was even more pronounced in first versions of the TSP/NOx ratio tables. The TSP/NOx was then modeled separately for the Donawitz site to somehow account for the particular situation. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 61

Figure 4.26: Spatial analysis of the difference between modelled TSP concentrations in the grid cells and the observed TSP concentrations at monitoring sites. High underpredictions (blue dots) are frequent in Southern and Eastern Austria.

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5 EXPOSURE OF THE POPULATION

In principle, population exposure can be calculated on the basis of a) the place of residence, or b) personal exposure. In the overall study, epidemiology based exposure-response functions were used. In the research studies from which these functions are derived, the correlation of health outcomes with ambient average level of air pollution rather than with personal exposure is given (for a further discussion, see Künzli et al. 1999) In view of this, the place of residence had to be selected as the criterion for calculating population exposure, and the PM10 map has to represent the urban background conditions typical of the residence areas of the population. The use of mean concentrations within grid cells with a size of 1,25 km * 1,25 km (see later) is consistent with the way most epidemiological studies derive exposure-response functions. The choice of peak PM10 concentrations in the vicinity of the streets would not be consistent with these studies. The results for calculated ambient air concentrations in all 250 m * 250 m grid cells were averaged to 1,25 km * 1,25 km grid cells using the "BLOCKSUM"-function22 within the GIS.

5.1 Population density data The population number for those grid cells was provided by the Austrian Statistical Service (OESTAT). The population numbers for all 2,5 km * 2,5 km grid cells were calculated through aggregation of the population number inhabiting the Austrian census districts. All inhabitants living within one census district are allocated to that grid square to which the census district centroid belongs23. This leads to a concentration of the population number within the respective settlement centres; spreading over the whole municipal area is avoided (which often have large forest and agricultural areas that would inaccurately lead to a very low population density over large areas) (Figure 5.1). The population data allocated to the 2,5 km *2,5 km grid cells were divided by 4, assuming an average population density within the four 1,25 km * 1,25 km cells located within.

Figure 5.1: Population densities in Austria (1,25 km * 1,25 km grid cells)

22 For an explanation of BLOCKSUM function see Annex 4. 23 Each municipality is divided into census districts if their population number is exceeding 2.500 people; the ~3.000 municipalities are divided into ~8.000 census districts. For this study census districts are not provided as polygons but as census district centroids. R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 63

5.2 Exposure to NO2 and PM10

For the final analysis of population exposure, the population grids were superimposed with the air pollutant concentration grids in the final resolution of 1,25 km * 1,25 km. As a result, the number of people exposed to the annual average concentrations of NOx, NO2, TSP, and PM10 (from the various contributing emission sources) can be determined. The population within the (virtual) 1,25 km * 1,25 km squares was "moved" to centre points of the respective grid cells containing the averaged concentration through a spatial relation within the GIS. Doing this step cell-by-cell the overall exposure of the population can be monitored and summarised to exposure classes. Table 5.1 summarises the exposure results.

Table 5.1: Exposure of Austrian population to PM10. Traffic-induced exposure for PM10 includes exposure to concentrations caused by local traffic plus the contribution of national and international traffic to the background concentrations. µg/m³ No. of persons No. of persons No. of persons exposed to total exposed to PM10 exposed to PM10 PM10 without traffic caused by traffic <5 - - 950 002 5 - <10 - 739 483 4 769 744 10 - <15 887 291 1 704 531 1 462 519 15 - <20 1 105 749 2 547 675 606 987 20 - <25 1 779 728 1 826 500 - 25 - <30 2 158 546 405 168 - 30 - <35 661 766 257 390 - 35 - <40 363 901 160 314 - 40 - <45 260 569 148 191 - 45 - <50 265 884 - - >50 305 818 - - Sum 1991 7 789 252 7 789 252 7 789 252 Population 1996 8 059 385

It was estimated that about 1,8 million people (= approx. 25 % of the total population) are exposed to PM10 levels > 30 µg/m3, and 0,6 million people (= 6 % of the total population) to PM10 levels > 50 µg/m3. The additional contribution by traffic is less than 10 µg/m3 for nearly 6 million people or 85 % of the population. Anyhow, traffic is the most important local source of PM10 and also a significant contributor to PM10 background concentrations.

In Switzerland, an ambient air quality standard of 20 µg PM10/m3 as annual mean value was established. It is obvious that most of the population in Austria is exposed to higher levels. In the European Union, a limit value of 40 µg/m3 (to be met from 2005 on) will be established, which could be lowered to 20 µg/m3 in 2010 (Common Position (EC) 57/98).

The exposure of the Austrian population to NO2 is summarised in Table 5.2.

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Table 5.2: Exposure of Austrian population to NO2. µg/m³ No. of persons exposed to NO2 <5 - 5 - <10 1 459 180 10 - <15 2 970 478 15 - <20 962 430 20 - <25 656 987 25 - <30 520 497 30 - <35 381 473 35 - <40 341 480 40 - <45 347 830 ≥45 148 897 Sum 1991 7 789 252

3 About 1 million people (= 15 % of the total population) are exposed to NO2 levels >30 µg/m , 3 and 150.000 people (= 2 % of the total population) to NO2 levels ≥ 45 µg/m . An exposure assessment of the population to NO2 was not a central part of this study. Anyhow, it is interesting to note that the Austrian Academy of Science has established Air Quality Criteria 3 for NO2 for the protection of human health (OEAW, 1998). A concentration of 30 µg/m was derived as annual mean value. This means that more than 1 million people are exposed to NO2 levels exceeding this concentration.

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6 UNCERTAINTIES

Within this chapter, several potential sources of uncertainty are summarised. This description is not given to weaken the results of the study (which are in reasonable agreement with measurement data and results from similar international studies) but to provide some guidance for potential improvement of the methodology used within this study. No quantitative estimations on the extent of uncertainties are provided, but some indication on the reliability of different input data and methods applied within this work should be given.

6.1 Uncertainties of input data

Within this study a variety of different data sources were used. The emission data for NOx can be regarded as rather reliable. The method of calculating emissions is well established; emission factors are derived from international measurements and estimations, while data on emission generating activities are derived from official statistical surveys. A more serious problem is the spatial disaggregation of emission inventories. Within this study, a resolution of 250 m * 250 m was chosen. It is evident that not all relevant information was available with such a high resolution. In some cases, the CORINAIR emission data did not allow a clear identification of the emission sources. Thus assumptions had to be made to achieve an indicative allocation of the emissions to surrogate areas (such as population density, employment, etc.). Improvements of the emission disaggregation and allocation can be achieved if the nation-wide emissions are defined more accurately in terms of emission sources, and with a more detailed spatial reference. The emission allocation is performed through application of emission activity patterns based on appropriate land-use and population density data. The allocation accuracy could be greatly improved by having a more detailed land-use model. It would be helpful to have a more detailed inventory of emission-relevant areas, such as industrial areas, population centres etc. Important uncertainties include: • The digitised point source location was not always accurate. Most major discrepancies have been eliminated during this study but the accurate location could not be verified for all point sources. • The digitised road network was based on a 1:500.000 map, which is not always precise. Thus the digitised road network might differ from the actual road network by 100 - 300 m. While this will not have major impacts on the validity of the Austrian-wide spatial model results, it has lead to some problems within the comparisons of model results with monitoring data. • Digital data about major roads in cities were not available. Thus within the cities, a default allocation of road traffic emissions had to be made using the population density patterns. This procedure will necessarily underestimate the actual emissions along major roads, while overestimate the emissions in areas with traffic restrictions. • The land use model that was used for the definition of emission-relevant areas was based on a 100 m * 100 m grid re-sampled to the 250 m * 250 m model grid cells. The model includes generalisation steps that result in a loss of some small entities. This can lead to the definition of inaccurate locations or to missing out some land use classes. The validity of the emission allocation cannot be easily checked because the emission- generating activities are not always known in detail. The comparison can only be performed by applying dispersion modelling and a comparison of measured and modelled results.

Compared to previous work (Loibl and Orthofer 1995), the spatial allocation of NOx emissions has been greatly improved in this study. Particularly the integration of the additional land use classes like settlement areas or agricultural areas allow a much better allocation of emissions to their respective sources. Furthermore, the inclusion of population

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 66 Exposure of the Austrian Population to PM10 in 1996 and employment density data for more than 8.000 Austrian census districts has helped to increase the allocation accuracy. Some errors in the emission allocation have been identified and corrected through comparison of the dispersion model results with monitoring data. It can be assumed that the emission allocation is in good agreement with the actual situation, even though some local discrepancies can occur. The road traffic emissions can be calculated satisfactory for average traffic situations. However, the higher emissions due to stop and go traffic during rush hours cannot be calculated separately at present. This shortcoming has impacts on the accuracy of the emission allocation (and the dispersion modelling) in cities. With a better data to identify the emissions in all emission-relevant areas, many other smaller improvements of the emission allocation are. In sum these improvements will lead to an increase of the quality of the NOx emission inventory base map which will also help to improve spatial emission inventory for other air pollutants. Ambient air measurement data were mainly used to check the validity of the model results. For NOx, the data can be regarded as reliable. The representativity of the monitoring sites can be more problematic. This is especially true for sites either sheltered from emissions sources or in the immediate environment of such sources. Measurement data for TSP are not as reliable as data for NOx. This can be explained by the fact that (a) the measurement of particulate matter is per se more difficult than the measurement of classical gaseous pollutants and (b) different devices (run under different conditions) are used within the Austrian monitoring network to obtain TSP data. Anyhow, some harmonisation can be expected in the near future. The estimation of TSP background is a very sensitive parameter, since background contributes a significant part of the total TSP concentration. Currently, not too many monitoring sites are located at background sites. Therefore, data from a few sites had to be generalised. No PM10 measurement data for Austria were available. Results from Switzerland and Germany were used within several assumptions within modelling. Currently it is not perfectly clear, if these data can be regarded as representative for Austria.

6.2 Uncertainties of model assumptions

A crucial step of the modelling procedure is the simulation of pollutant dispersion to yield ambient air concentrations from emission densities. Dispersion was calculated for standard situations assuming equal dispersion in all directions (with the exception of the dispersion from large point sources, where wind direction dependent functions were applied). The dispersion profiles used were derived from a former Swiss study. There were no independent measurements for validating the applicability of the profiles for Austrian conditions. For instance, some results for areas within (Alpine) basins indicate that the standard dispersion profiles are not adequate. Concentrations for NOx are underestimated for several monitoring sites; this means that dispersion profiles have to be adapted to the special conditions in basins. Anyhow, this was not possible within this study. It is important to note that the translation of emissions into ambient concentrations has considerable uncertainties. First of all, the dispersion from emission sources is never uniform. Prevailing winds will have a major influence on directions and extent of dispersion. Gaussian dispersion, for instance, results in peak concentrations in a certain distance downwind of an emission source. Furthermore, dispersion will be dependent on local physical conditions; land-cover and topography have important influences. It will certainly not be possible to account for all potential influences on dispersion of pollutants in such a study. There is currntly no alternative to using default standard dispersion profiles for specific conditions. It should be useful, however, to devote more work for reviewing the validity of the dispersion profiles for Austrian conditions.

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A crucial assumption within the modelling of PM10 was that NOx could be taken as leading substance to estimate local contributions to PM10 concentrations. Since the ratio of NOx to PM10/TSP is different for different source categories, different factors had to be applied. These factors were derived from measurement data (preferable from stations predominantly influence by one source category) and from emission estimates (either for single sources or for source categories). It is evident that such an approach has considerable uncertainties, which are especially pronounced for those sources which have not well defined emissions or for which no measurement data are available. This approach also implies implicitly similar dispersion conditions of a gaseous pollutant like NOx and different (size) fractions of particulate matter, which is clearly a significant simplification of the reality. A real improvement can be realised if emission inventories for PM10 will be available, which would make the use of NOx as leading substance unnecessary. For those emissions, special dispersion profiles should be established. The determination of the traffic related part of air pollution is clearly as uncertain as the modelling of different contributions to PM10/TSP levels. There are also some uncertainties concerning the contribution of (European) traffic to background concentrations. Such estimations can only be derived from modelling results.

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7 CONCLUSIONS

7.1 General Conclusions

Within this study, the exposure of the Austrian population to PM10 was estimated. PM10 was considered as suitable indicator for air pollution related health effects by the tri-lateral working group, since numerous epidemiological studies link the exposure to this pollutant to several serious health effects, including morbidity and mortality. As stated several times, serious problems had to be tackled within this work. The most serious was the lack of PM10 measurement data from Austria. Several open questions and uncertainties are attached to the many assumptions and methodological decisions which were required to estimate the exposure of the population to PM10. Anyhow, Künzli et al. (1999) describes some convincing arguments which were decisive to contribute to the research project: For the following reasons it was nevertheless agreed to participate and contribute required data to this project: 1. There is abundant evidence that current levels of air pollution have adverse health effects. From a public health perspective it is therefore an ethical consequence to estimate and communicate the impact to the public. This will allow societies to make decisions which also include and apply research findings, usually established through public funding. 2. Policy makers and societies have to make important decisions all the time. To abstain from impact assessment, given the many uncertainties, would promote decision making based exclusively on individual interests without considering general aspects of public health. This is particularly true for environmentally sensitive decisions. We consider the participation of epidemiologists in this interdisciplinary process as crucial (Samet et al, 1998). 3. In general, scientific uncertainty should not be taken as leading argument to entirely ignore current knowledge. 4. We are well aware that similar impact assessments are conducted by numerous groups. However, no common method has been developed so far, thus the results may grossly vary across different assessments. We would like to contribute and encourage the discussion to develop a common methodology.

In general, the choice of the method was dictated by the availability of suitable input data. Since neither PM10 measurement data nor up-to-date emission inventories for particulate matter were available, modelling of local PM10 was done using NOx as leading substance. This situation is far from ideal; anyhow, the results can be regarded as good indication of the real situation. To avoid an overestimation of actual concentrations, an ‘at least’ approach was selected. This means that the authors believe that the results are rather conservative. Within this report, no maps of PM10 data are provided. The main reason for that is that currently no measurement data are available for validation of model results. Anyhow, this does not mean that the results are totally uncertain. There are a couple of good indications that the results are reasonable reliable (see Section 4.2). Improvements of the results are possible if improved input data are available. Many assumptions and estimates had to be made to calculate population exposure. The results for total PM10 exposure as well as for the road traffic fraction as reasonable and the best available estimate on the basis of the now available measurements and emission data. Regardless the uncertainty linked to the results, this work shows that a considerable part of the Austrian population is likely to be exposed to high PM10 levels. This levels can be linked to considerable health effects (see Sommer et al., 1999).

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7.2 Research Recommendations

From this study, the following conclusions concerning research needs can be derived: • PM10 measurements: The establishment of a PM10 monitoring network covering different site types (including rural background sites) is necessary. TSP measurements should be carried out on selected sites to obtain conversion factors. The comparability of the measurements is a very important point. At the moment the comparability of particle measurements made by different methods is not guaranteed. QA/QC is also essential to ensure the establishment of reliable measurement data. • Chemical analysis of PM10 components: The knowledge on the chemical composition of PM10 is an indispensable tool to interpret PM10 values. Chemical analysis of PM10 should be conducted routinely at a few selected sites. • Beside PM10, also other indicators of particulate matter such as PM2.5 and even smaller fractions should be included into monitoring systems. Additionally, information should be gathered on the chemical composition of particulate matter as well as on particle number and particle surface area. This is crucial to assess the origin, the characteristics and the effects of particulate matter in ambient air. • Emission inventory: Currently, no detailed up-to-date emission inventory is available in Austria. Work on emission factors and emission generating activities should be initiated. This also includes the spatial allocation of emission generating activities. • Dispersion of particulate matter: Ideally, different functions should be used to calculate the dispersion of different size fractions of particulate matter. Such functions should be developed and checked by measurements. • Long-range transport: Whereas information about the long-range transported secondary components is quite good, the amount of long-range transported fine primary particles is not well known in Europe. • In addition, receptor studies are necessary to give valuable additional information on PM10 sources and should be launched.

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8 LITERATURE

ADEME UP7-LED (1999): Évaluation de l'exposition des populations à la pollution de l'air en France (in preparation). Berdowski J.J.M., Mulder W., Veldt C., Visschedijk A.J.H. and Zandveld P.Y.J. (1997): Par- ticulate matter emissions (PM10 - PM2.5 - PM0.1) in Europe in 1990 and 1993. Netherlands Organisation for Applied Scientific Research TNO-report, TNO-MEP - R 96/472. CEN (1998): Air quality - Determination of the PM10 fraction of suspended particulate matter - Reference method and field test procedure to demonstrate reference equivalence of measurement methods, EN 12341:1998, European Committee for Standarisation, Brussels. CORINAIR (1994): http://www.aeat.co.uk/netcen/corinair/94/summned.htm ECOPLAN (1996): Monétarisation des coûts externes de la santé imputables aux transports. Service d'étude des transports, Mandat SET no 272, Berne (report in German and summary in English available). EEA 1997: EMEP/CORINAIR Atmospheric Emission Inventory Guidebook, European Environment Agency, Copenhagen 1997 (CD ROM Version) EMEP (1995): European transboundary acidifying air pollution: Ten years calculated fields and budgets to the end of the first Sulphur Protocol. EMEP/MSC-W Report 1/95. Norwegian Meteorological Institute, Oslo. EMEP (1997): Transboundary Air Pollution in Europe. MSC-W Report 1/97, Part 1 and 2. Norwegian Meteorological Institute, Oslo. EMEP (1998): Long-range transport of fine secondary particles, as presently estimated by the EMEP Lagrangian model. EMEP/MSC-W Note 2/98. Norwegian Meteorological Institute, Oslo. EMPA (1998): Vergleich von PM10 und PM2.5 Schwebstaubmessungen mit TSP Messungen im NABEL. EMPA-Report 168 107. Zürich. EPA (1996): Air Quality Criteria for Particulate Matter. EPA/600/P-95/001bF. U.S. Environmental Protection Agency, Research Triangle Park, N.C., May 1998). EPA (1997): AIR CHIEF CD ROM Version 5.0, Publ. by the US Environmental Protection Agency, Research Triangle Park, N.C. EPA (1998): Particulate Matter Controlled Emissions Calculator. (by E.H. Pechan & Ass. Inc.) U.S. Environmental Protection Agency, Research Triangle Park, N.C., May 1998). European Commission, DG XI (1997): Economic evaluation of air quality targets for sulphur dioxide, nitrogen dioxide, fine and suspended particulate matter and lead. Institute for Environmental Studies IVM, vrije Universiteit Amsterdam. Federal Gazette (1997): Immissionsschutzgesetz Luft. BGBl. I 115/97.Vienna. Filliger P, Puybonnieux-Texier V. and Schneider J. (1999) Health Costs due to Road Traffic related Air Pollution, PM10 Population Exposure. Technical Report on Air Pollution. GVF-Report, Bern. Grosjean D. and Seinfeld J.H. (1989): Parameterization of the Formation Potential of Secon- dary Organic Aerosols. Atmos. Environ., 23, 1733-1747. Gryning, S.E., A.A.M.Holstag; J.S.Irwin; B.Sievertsen (1987): Applied Dispersion Modeling based on Meteorological Scaling Parameters, Atmospheric Envrionment 21(1), 79-89 (1997). Hausberger (1998): Dr. Hausberger TU Graz, personal communication HBEFA (1998): Handbuch der Emissionsfaktoren CD ROM (HBEFA V 1.1A), BMUJF 1998. Wien.

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ISO (1995). Air quality – Particle size fraction definitions for health related sampling. ISO 7708. International Organisation for Standardisation. Geneve.

Keller, M., Heldstab J. and Künzle T. (1997): NO2-Immissionen in der Schweiz 1990-2010. Schriftenreihe Umwelt Nr. 289 Luft. Herausgegeben vom Bundesamt für Umwelt, Wald und Landschaft (BUWAL), Bern. Künzli N., Kaiser R., Medina S., Studnicka M., Oberfeld G. and Horak F. (1999): Health Costs due to Road Traffic related Air Pollution, Air Pollution Attributable Cases, Technical Report on Epidemiology. GVF-Report, Bern. Loibl, W.; R.Orthofer (1995): Kleinräumige Disaggregation der NOx-Emissionen aufbauend auf dem Emissionskatasters des Umweltbundesamtes; Umweltbundesamt Wien Report, UBA-95--121 39 p., Wien. Loibl, W.; R.Orthofer; W.Winiwarter (1993): Spatially Disaggregated Emission Inventory for Anthropogenic NMVOC in Austria; Atmos Environ, Vol. 27, No 16, pp 2575-2590, 1993 Lurmann F.W., Wexler A.S., Pandis S.N., Musarra S., Kumar N. and Seinfeld J.H. (1997): Modelling Urban and Regional Aerosols – II. Application to California’s South Coast Air Basin. Atmos. Environ., 31, 2695-2715. MLuS-92 (1996): Merkblatt über Luftverunreinigungen an Straßen. Teil: Straßen ohne und mit lockerer Randverbauung. Ausgabe 1992, geänderte Fassung 1996. Forschungs- gesellschaft für Strassen- und Verkehrswesen, Köln. OEAW (1998). Stickstoff in der Atmosphäre. Wirkung auf den Menschen. Bundesministerium für Umwelt, Jugend und Familie. Wien. Orthofer R. and Loibl W. (1996): GIS Aided Spatial Disaggregation of Emission Inventories. In: The Emission Inventory Programs and Progress. (Proceedings Speciality Conference of the Air & Waste Management. Assoc. Research Triangle Park, NC (USA) 11-13 Oct 1995), p. 502-513, Air & Waste Management Association, Pittsburgh, PA (USA). Orthofer R.; W. Loibl (1996): GIS Aided Spatial Disaggregation of Emission Inventories. In: The Emission Inventory Programs and Progress. (Proceedings Speciality Conference of the Air & Waste Management. Assoc. Research Triangle Park, NC (USA) 11-13 Oct 1995), p. 502-513, Air & Waste Management Association, Pittsburgh, PA (USA). Rotach M.; P.de Haan (1995): Immissionen einer Flächenquelle unter Berücksichtigung von mittleren meterologischen Bedingungen, Internes Arbeitspapier der geographischen Instituts der ETH Zürich, Zürich. Sommer H. et al. (1999): Health Costs due to Road Traffic related Air Pollution, Report of the Economy Group, GVF Report, Bern. Steinnocher K. (1996): Integration of Spatial and Spectral Classification Methods for Building a Land-Use Model of Austria; International Archives of Photogrammetry and Remote Sensing, Vol. 31, Part B4, pp. 841-846. Tomlin C.D. (1990): Geographic Information Systems and Cartographic Modeling, Prentice Hall Inc., Englewood Cliffs, N.J. UBA (1993): Umweltsituation in Österreich - Umweltkontrollbericht der Bundesministerin für Umwelt, Jugend und Familie an den Nationalrat, Umweltbundesamt Wien. UBA (1998a): Luftgütemeßstellen in Österreich. UBA-BE-131. Umweltbundesamt, Wien. UBA (1998c): Ruß- und Schwermetallimmissionen im Bereich einer Autobahn. UBA-BE-113. Umweltbundesamt, Wien. UBA (1998d): Umweltsituation in Österreich. 5. Umweltkontrollbericht. BMUFJ, Wien. UBA (1998e): Handbuch der Emissionsfaktoren. HBEFA 1.1A. BMUJF. Wien. UBA (1999a): Luftschadstofftrends in Österreich. UBA-BE-146. Umweltbundesamt, Wien. UBA (1999b): Rußimmissionen in Wien. In preparation. Umweltbundesamt, Wien. Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 72 Exposure of the Austrian Population to PM10 in 1996

UK Airborne Particle Expert Group (1998): Source Apportionment of Airborne Particulate Matter in the United Kingdom. Interim Report. Draft 3. Venkatram A., D. Fitz, K. Bumiler, S. Du, M. Boeck and C. Ganguly. (1999). Using a dispersion model to estimate emission rates of particulate matter from paved roads. Atmos. Environment 33. 1093-1102. Wanner H.U. A. Fuchs, M. Karrer and D. Kogelschatz: Monetarisierung der verkehrsbedingten Gesundheitskosten, Teilbericht Lufthygiene. Dienst für Gesamtverkehrsfragen, GVF- Auftrag Nr. 274, Bern WHO(1999). Air Quality Guidelines for Europe. In preparation. WHO Regional Office Bilthoven, The Netherlands.

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9 GLOSSARY

Area sources Sources, where emissions are designated to spatial areas.

Background concentration In a general sense, this term means a concentration that is not influenced by local sources. Urban background concentrations are measured at sites that stand apart from streets (e.g. backyard, residential area). Regional background concentrations are measured at rural sites which are not influenced by near sources.

CORINAIR Core Inventory of Air pollutants. Contains the annual emissions from several hundred selected emissions sources.

Dispersion profile Idealised function to simulate the dispersion of emitted pollutants.

Elemental Carbon (EC) Component of PM, consisting graphite-like elemental carbon. Is mainly formed during incomplete combustion processes.

Emission relevant area (ERA) Important parameter for the spatial disaggregation of emissions. Emissions are designated to areas where emission generating activities are located: ERAs

Epidemiolog Study of the analysis of the distribution of illnesses, physiological variables and social consequences of illnesses in human population groups, as well as factors influencing this distribution

External Costs Costs borne not by those responsible for them, but by others.

Fine particles Particulate matter with less than 2.5 µm aerodynamic diameter. In contrast to coarse particles.

Gaussian model Model to estimate the dispersion of pollutants in the atmosphere.

GIS Geographical Information System. Tool to calculate spatial distribution of selected parameters.

Line source E.g., roads and streets.

Nox Total oxides of nitrogen. Conventionally the sum of nitrogen monoxide (NO) and 3 nitrogen dioxide (NO2), often expressed as mg NO2./m .

Particulate Matter (PM) Suspension of tiny particles in gases. An aerosol particle is any substance, except pure water, that exists as a liquid or solid in the atmosphere under normal conditions and is of microscopic or submicroscopic size but larger than molecular dimensions.

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PM10 Particulate matter less than 10 µm aerodynamic diameter (or, more strictly, particles which pass through a size selective inlet with a 50 efficiency cut-off at 10 µm aerodynamic diameter). PM10 is the thoratic fraction (particles that pass the larynx) of the particulate matter in the atmosphere.

PM10 components Specific chemical species that are part of the PM10 concentration in the air (e.g. elemental and organic carbon, earth crust elements, secondary particles, etc.).

Point source Major source emitting pollutants from high stacks.

Precursor pollutants Gaseous pollutants that form secondary pollutants in the atmosphere by chemical transformation. The most important precursor pollutants to form secondary particles are sulphur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3) and volatile organic components (VOC).

Primary particles Particles that are directly emitted at the sources.

Re-suspension Return of previously deposited PM and road dust to the atmosphere.

Secondary particles Particles that are formed in the atmosphere through the action of atmospheric chemical 2- reactions. The most important secondary components are sulphate (SO4 ), nitrate (NO3), + ammonium (NH4 ) and organic aerosols.

SNAP Selected Nomenclature of Air Pollutants. Standardised list of selected emission sources. Within the EMEP format, 11 main categories can be distinguished. Each of these contain several sub-categories in two levels.

Tail-pipe emissions Exhaust emissions from traffic due to combustion processes.

TEOM Tapered Element Oscillating Mircobalance. Automated monitor to measure the concentration of PM in ambient air.

TSP Total suspended particulate matter.

µg/m3 Microgram per cubic meter (unit measure the mass content of particles in the atmosphere)

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Annex I: Emissions database and area allocation A. CORINAIR area sources

ID SNAP 94 Description Sub Fuel NOxEMIS DISAG (t) CA0332 080503 OTHER MOBILE & MACH.- DOMESTIC CRUISE TRAFFIC (> 1000 m) 206B Petr 82 no alloc CA0333 080504 OTHER MOBILE & MACH.- INTERNATIONAL CRUISE TRAFFIC(> 1000 m) 206B Petr 6.797 no alloc 6.879 NO ALLOC Total CA0276 060412 OTHER (preservation of seeds, ...) 0 AGR CA0334 080600 OTHER MOBILE & MACH.- AGRICULTURE 2050 Dies 7.588 AGR CA0335 080600 OTHER MOBILE & MACH.- AGRICULTURE 2080 Gasol 0 AGR CA0352 090700 OPEN BURNING OF AGRICULTURAL WASTES (except on field 100300) 15 AGR CA0357 091003 SLUDGE SPREADING 0 AGR CA0361 100101 CULTURE WITH FERTILIZERS - PERMANENT CROPS 190 AGR CA0362 100102 CULTURE WITH FERTILIZERS - ARABLE LAND CROPS 3.356 AGR CA0363 100104 CULTURE WITH FERTILIZERS - MARKET GARDENING 49 AGR CA0364 100105 CULTURE WITH FERTILIZERS - GRASSLAND 2.306 AGR CA0365 100106 CULTURE WITH FERTILIZERS - FALLOWS 0 AGR CA0366 100202 CULTURE WITHOUT FERTILIZERS - ARABLE LAND CROPS 0 AGR CA0367 100203 CULTURE WITHOUT FERTILIZERS - RICE FIELD 0 AGR CA0368 100205 CULTURE WITHOUT FERTILIZERS - GRASSLAND 278 AGR CA0369 100206 CULTURE WITHOUT FERTILIZERS - FALLOWS 9 AGR CA0370 100300 ON-FIELD BURNING OF STUBBLE, STRAW, ... 5 AGR CA0371 100401 ENTERIC FERMENTATION - DAIRY COWS 0 AGR CA0372 100402 ENTERIC FERMENTATION - OTHER CATTLE 0 AGR CA0373 100403 ENTERIC FERMENTATION - OVINES 0 AGR CA0374 100404 ENTERIC FERMENTATION - FATTENING PIGS 0 AGR CA0375 100405 ENTERIC FERMENTATION - HORSES 0 AGR CA0376 100407 ENTERIC FERMENTATION - GOATS 0 AGR CA0377 100408 ENTERIC FERMENTATION - LAYING HENS 0 AGR CA0378 100409 ENTERIC FERMENTATION - BROILERS 0 AGR CA0379 100410 ENTERIC FERMENTATION - OTHER POULTRY (ducks, gooses, etc.) 0 AGR CA0380 100412 ENTERIC FERMENTATION - SOWS 0 AGR CA0381 100415 ENTERIC FERMENTATION - OTHER 0 AGR CA0382 100501 MANURE MANAGEMENT - DAIRY COWS 0 AGR CA0383 100502 MANURE MANAGEMENT - OTHER CATTLE 0 AGR CA0384 100503 MANURE MANAGEMENT - FATTENING PIGS 0 AGR CA0385 100504 MANURE MANAGEMENT - SOWS 0 AGR CA0386 100505 MANURE MANAGEMENT - OVINES 0 AGR CA0387 100506 MANURE MANAGEMENT - HORSES 0 AGR CA0388 100507 MANURE MANAGEMENT - LAYING HENS 0 AGR CA0389 100508 MANURE MANAGEMENT - BROILERS 0 AGR CA0390 100509 MANURE MANAGEMENT - OTHER POULTRY (ducks, gooses, etc.) 0 AGR CA0391 100511 MANURE MANAGEMENT - GOATS 0 AGR CA0392 100515 MANURE MANAGEMENT - OTHER 0 AGR CA0393 100600 USE OF PESTICIDES 0AGR 13.796 AGR Total CA0329 080501 OTHER MOBILE & MACH.- DOM. AIRPORT TRAFFIC(LTO cycles<1000m) 206B Petr 34 AIR CA0330 080501 OTHER MOBILE & MACH.- DOM. AIRPORT TRAFFIC(LTO cycles<1000m) 2080 Gasol 103 AIR CA0331 080502 OTHER MOBILE & MACH.-INTERN. AIRPORT TRAF.(LTO cycles<1000m) 206B Petr 511 AIR 648 AIR Total

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CA0328 080304 OTHER MOBILE & MACH.- INLAND GOODS CARRYING VESSELS 2050 Dies 1.085 DAN 1.085 DAN Total CA0336 080700 OTHER MOBILE & MACH.- FORESTRY 2050 Dies 0 FOR CA0337 080700 OTHER MOBILE & MACH.- FORESTRY 2080 Gasol 18 FOR CA0394 100700 MANAGED DECIDUOUS FORESTS 181 FOR CA0395 100800 MANAGED CONIFEROUS FORESTS 638 FOR CA0396 101105 LUWC-WOOD STOCK/GROWTH - TEMPERATE FORESTS/COMMERCIAL Conif 0 FOR CA0397 101105 LUWC-WOOD STOCK/GROWTH - TEMPERATE FORESTS/COMMERCIAL Decid 0 FOR CA0398 101201 LUWC-WOOD STOCK/HARVEST - BIOMASS IN COMMERCIAL HARVEST 0 FOR CA0399 101202 LUWC-WOOD STOCK/HARVEST - TRADITIONAL FUELWOOD 0FOR CONSUMED CA0400 101602 LUWC-LAND ABAND.<20y/ABOVEGROUND BIOMASS - TEMPERATE 0 FOR FORESTS CA0401 101702 LUWC-LAND ABAND.<20y/SOIL CARBON UPTAKE - TEMPERATE 0FOR FORESTS CA0402 101802 LUWC-LAND ABAND.>20y/ABOVEGROUND BIOMASS - TEMPERATE 0 FOR FORESTS CA0403 101902 LUWC-LAND ABAND.>20y/SOIL CARBON UPTAKE - TEMPERATE 0FOR FORESTS CA0404 110100 NON-MANAGED DECIDUOUS FORESTS 50 FOR CA0405 110200 NON-MANAGED CONIFEROUS FORESTS 217 FOR CA0406 110300 FOREST FIRES 0 FOR CA0407 110400 NATURAL GRASSLAND 0 FOR CA0408 110500 WETLANDS (marshes-swamps) 0 FOR CA0409 110601 WATERS - LAKES 0FOR CA0410 110605 WATERS - RIVERS 0 FOR CA0411 110702 ANIMALS - MAMMALS 0FOR 1.104 FOR Total CA0234 050501 GASOLINE DISTRIBUTION - REFINERY DISPATCH STATION 0 POP CA0235 050502 GASOLINE DIST.-TRANSPORTS & DEPOTS (except service stations) 0 POP CA0236 050503 GASOLINE DIST. - SERVICE STATIONS (incl. refuelling of cars) 0 POP CA0239 060102 PAINT APPLICATION - CAR REPAIRING 0 POP CA0241 060104 PAINT APPLICATION - DOMESTIC USE (except 060107) 0 POP CA0248 060202 DRY CLEANING 0 POP CA0271 060407 UNDERSEAL TREATMENT AND CONSERVATION OF VEHICLES 0 POP CA0272 060408 DOMESTIC SOLVENT USE (other than paint application) 0 POP CA0273 060409 VEHICLES DEWAXING 0 POP CA0275 060411 DOMESTIC USE OF PHARMACEUTICAL PRODUCTS 0 POP CA0277 060501 USE OF N2O FOR ANAESTHESIA 0 POP CA0322 080201 OTHER MOBILE & MACH.- SHUNTING LOCS 2050 Dies 740 POP CA0323 080202 OTHER MOBILE & MACH.- RAIL-CARS 2050 Dies 518 POP CA0324 080203 OTHER MOBILE & MACH.- LOCOMOTIVES 101A HardC 5 POP CA0325 080203 OTHER MOBILE & MACH.- LOCOMOTIVES 2050 Dies 222 POP CA0326 080303 OTHER MOBILE & MACH.- PERSONAL WATERCRAFT 2050 Dies 0 POP CA0327 080303 OTHER MOBILE & MACH.- PERSONAL WATERCRAFT 2080 Gasol 3 POP CA0338 080900 OTHER MOBILE & MACH.- HOUSEHOLD AND GARDENING 2050 Dies 0 POP CA0339 080900 OTHER MOBILE & MACH.- HOUSEHOLD AND GARDENING 2080 Gasol 2 POP CA0340 081000 OTHER MOBILE & MACH.- OTHER OFF-ROAD 2050 Dies 3.308 POP CA0341 081000 OTHER MOBILE & MACH.- OTHER OFF-ROAD 2080 Gasol 0 POP CA0342 081000 OTHER MOBILE & MACH.- OTHER OFF-ROAD 303A LPG 0 POP CA0343 090201 INCINERATION OF DOMESTIC OR MUNICIPAL WASTES 114B Waste 0 POP CA0344 090201 INCINERATION OF DOMESTIC OR MUNICIPAL WASTES 114C IndWaste 0 POP

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CA0345 090201 INCINERATION OF DOMESTIC OR MUNICIPAL WASTES 118A SewSlu 0 POP CA0346 090201 INCINERATION OF DOMESTIC OR MUNICIPAL WASTES 204D HeavOil 0 POP CA0347 090201 INCINERATION OF DOMESTIC OR MUNICIPAL WASTES 301A NatGas 0 POP CA0348 090205 INCINERATION OF SLUDGES FROM WASTE WATER TREATMENT 0 POP CA0350 090207 INCINERATION OF HOSPITAL WASTES 0 POP CA0353 090901 INCINERATION OF CORPSES 4 POP CA0354 090902 INCINERATION OF CARCASSES 0 POP CA0356 091002 WASTE WATER TREATMENT IN RESIDENTIAL AND COMMERCIAL 0 POP SECTORS CA0358 091004 LAND FILLING 0POP CA0359 091005 COMPOST PRODUCTION FROM WASTE 0 POP CA0360 091007 LATRINES 0POP 4.802 POP Total CA0044 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 101A HardC 0 POP-COAL CA0045 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 105A BrC 0 POP-COAL CA0046 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 106A Briqu 0 POP-COAL CA0064 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 101A HardC 15 POP-COAL CA0065 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) HZH 105A BrC 136 POP-COAL CA0066 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 106A Briqu 0 POP-COAL CA0067 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 107A Coke 236 POP-COAL CA0079 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 101A HardC 316 POP-COAL CA0080 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 101A HardC 33 POP-COAL CA0081 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 105A BrC 200 POP-COAL CA0082 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 105A BrC 10 POP-COAL CA0083 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 106A Briqu 0 POP-COAL CA0084 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 106A Briqu 0 POP-COAL CA0085 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 107A Coke 765 POP-COAL CA0086 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 107A Coke 79 POP-COAL CA0097 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 101A HardC 54 POP-COAL CA0098 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 105A BrC 24 POP-COAL CA0099 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 106A Briqu 0 POP-COAL CA0100 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 107A Coke 177 POP-COAL 2.045 POP-COAL Total CA0054 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 301A NatGas 412 POP-GAS CA0055 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 303A LPG 3 POP-GAS CA0056 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 304A Koksgas 0 POP-GAS CA0057 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 305A Gichtgas 0 POP-GAS CA0077 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 301A NatGas 1.004 POP-GAS CA0078 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 303A LPG 57 POP-GAS CA0094 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 301A NatGas 1.030 POP-GAS CA0095 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 301A NatGas 990 POP-GAS CA0096 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 303A LPG 110 POP-GAS CA0104 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 301A NatGas 889 POP-GAS 4.495 POP-GAS Total CA0049 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 204A XLightOil 4 POP-OIL CA0050 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 204B LightOil 896 POP-OIL CA0051 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 204C MedOil 43 POP-OIL CA0052 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 204D HeavOil 0 POP-OIL CA0053 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 2050 Dies 0 POP-OIL CA0071 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 204A XLightOil 374 POP-OIL

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CA0072 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 204B LightOil 1.827 POP-OIL CA0073 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 204C MedOil 30 POP-OIL CA0074 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 204D HeavOil 0 POP-OIL CA0075 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 206B Petr 27 POP-OIL CA0076 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 2080 Gasol 0 POP-OIL CA0089 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 204A XLightOil 2.283 POP-OIL CA0090 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 204A XLightOil 145 POP-OIL CA0091 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 204B LightOil 133 POP-OIL CA0092 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 204B LightOil 0 POP-OIL CA0093 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 204C MedOil 13 POP-OIL CA0102 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 204A XLightOil 317 POP-OIL CA0103 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 204B LightOil 0 POP-OIL 6.092 POP-OIL Total CA0047 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 111B Biogen 2.661 POP-WOOD CA0048 010203 DISTRICT HEAT. - COMBUSTION PLANTS < 50 MW (boilers) 114A Waste 0 POP-WOOD CA0068 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 111A Wood 3.134 POP-WOOD CA0069 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 111B Biogen 823 POP-WOOD CA0070 020103 COMM./INSTIT. - COMBUSTION PLANTS < 50 MW (boilers) 114A Waste 29 POP-WOOD CA0087 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) HZH 111A Wood 3.474 POP-WOOD CA0088 020202 RESIDENTIAL - COMBUSTION PLANTS < 50 MW (boilers) WZH 111A Wood 42 POP-WOOD CA0101 020205 RESIDENTI. - OTHER EQUIPMENTS (stoves,fireplaces,cooking...) EO 111A Wood 591 POP-WOOD CA0105 020303 AGRIC./FORES./AQUA. - STATIONARY GAS TURBINES 113A Torf 1 POP-WOOD 10.755 POP-WOOD Total CA0106 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 101A HardC 0 PRO CA0108 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr. 105A BrC 130 PRO CA0110 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 106A Briqu 0 PRO CA0112 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr. 107A Coke 0 PRO CA0115 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr. 111B Biogen 2.139 PRO CA0117 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr. 114A Waste 26 PRO CA0119 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 204B LightOil 341 PRO CA0121 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 204C MedOil 0 PRO CA0123 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 204D HeavOil 1.005 PRO CA0125 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 2050 Dies 0 PRO CA0127 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 224A RefinPr 0 PRO CA0129 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 301A NatGas 1.420 PRO CA0131 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 303A LPG 0 PRO CA0136 030105 COMB. MANU. IND.- STATIONARY ENGINES 2050 Dies 8 PRO CA0138 030205 COMB. MANU. IND.- OTHER FURNACES 101A HardC 0 PRO CA0139 030205 COMB. MANU. IND.- OTHER FURNACES 105A BrC 0 PRO CA0140 030205 COMB. MANU. IND.- OTHER FURNACES 106A Briqu 0 PRO CA0141 030205 COMB. MANU. IND.- OTHER FURNACES 107A Coke 0 PRO CA0142 030205 COMB. MANU. IND.- OTHER FURNACES 111A Wood 0 PRO CA0143 030205 COMB. MANU. IND.- OTHER FURNACES 111B Biogen 20 PRO CA0144 030205 COMB. MANU. IND.- OTHER FURNACES 114A Waste 141 PRO CA0145 030205 COMB. MANU. IND.- OTHER FURNACES 204B LightOil 18 PRO CA0146 030205 COMB. MANU. IND.- OTHER FURNACES 204C MedOil 68 PRO CA0147 030205 COMB. MANU. IND.- OTHER FURNACES 204D HeavOil 635 PRO CA0148 030205 COMB. MANU. IND.- OTHER FURNACES 224A RefinPr 0 PRO CA0149 030205 COMB. MANU. IND.- OTHER FURNACES 301A NatGas 1.007 PRO CA0167 030326 COMB. MANU. IND.- OTHER other 101A HardC 0 PRO

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CA0168 030326 COMB. MANU. IND.- OTHER other 105A BrC 5 PRO CA0169 030326 COMB. MANU. IND.- OTHER other 106A Briqu 0 PRO CA0172 030326 COMB. MANU. IND.- OTHER other 111A Wood 12 PRO CA0173 030326 COMB. MANU. IND.- OTHER other 111B Biogen 658 PRO CA0174 030326 COMB. MANU. IND.- OTHER other 114A Waste 16 PRO CA0175 030326 COMB. MANU. IND.- OTHER other 204B LightOil 18 PRO CA0176 030326 COMB. MANU. IND.- OTHER other 204C MedOil 68 PRO CA0177 030326 COMB. MANU. IND.- OTHER other 204D HeavOil 635 PRO CA0178 030326 COMB. MANU. IND.- OTHER other 206B Petr 1 PRO CA0179 030326 COMB. MANU. IND.- OTHER other 224A RefinPr 44 PRO CA0180 030326 COMB. MANU. IND.- OTHER other 301A NatGas 1.007 PRO CA0181 030326 COMB. MANU. IND.- OTHER other 303A LPG 54 PRO CA0218 040605 OTHER PROCESSES - BREAD 0 PRO CA0219 040606 OTHER PROCESSES - WINE 0 PRO CA0220 040607 OTHER PROCESSES - BEER 0 PRO CA0221 040608 OTHER PROCESSES - SPIRITS 0 PRO CA0222 040610 OTHER PROCESSES - ROOF COVERING WITH ASPHALT MATERIALS 0 PRO CA0223 040611 OTHER PROCESSES - ROAD PAVING WITH ASPHALT 0 PRO CA0227 040615 OTHER PROCESSES - BATTERIES MANUFACTURING 0 PRO CA0229 040700 COOLING PLANTS 0PRO CA0238 060101 PAINT APPLICATION - MANUFACTURE OF AUTOMOBILES 0 PRO CA0240 060103 PAINT APPLICATION - CONSTRUCTION AND BUILDINGS (exc.060107) 0 PRO CA0242 060105 PAINT APPLICATION - COIL COATING 0 PRO CA0243 060106 PAINT APPLICATION - BOAT BUILDING 0 PRO CA0245 060108 OTHER INDUSTRIAL PAINT APPLICATION 0 PRO CA0246 060109 OTHER NON INDUSTRIAL PAINT APPLICATION 0 PRO CA0249 060203 ELECTRONIC COMPONENTS MANUFACTURING 0 PRO CA0250 060204 OTHER INDUSTRIAL CLEANING 0 PRO CA0262 060312 TEXTILE FINISHING 0 PRO CA0263 060313 LEATHER TANNING 0PRO CA0265 060401 GLASS WOOL ENDUCTION 0 PRO CA0266 060402 MINERAL WOOL ENDUCTION 0 PRO CA0267 060403 PRINTING INDUSTRY 0 PRO CA0268 060404 FAT EDIBLE AND NON EDIBLE OIL EXTRACTION 0 PRO CA0269 060405 APPLICATION OF GLUES AND ADHESIVES 0 PRO CA0278 060502 OTHER USE OF N2O 0PRO CA0355 091001 WASTE WATER TREATMENT IN INDUSTRY 0 PRO 9.476 PRO Total CA0151 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 101A HardC 0 PRO-CERA CA0152 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 105A BrC 0 PRO-CERA CA0153 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 107A Coke 0 PRO-CERA CA0154 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 114A Waste 0 PRO-CERA CA0155 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 204B LightOil 0 PRO-CERA CA0156 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 204C MedOil 0 PRO-CERA CA0157 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 204D HeavOil 0 PRO-CERA CA0158 030311 COMB. MANU. IND.- CEMENT (except decarbon. in 040612) 301A NatGas 0 PRO-CERA CA0159 030317 COMB. MANU. IND.- OTHER GLASS (except decarb. in 040613) 107A Coke 0 PRO-CERA CA0160 030317 COMB. MANU. IND.- OTHER GLASS (except decarb. in 040613) 204D HeavOil 9 PRO-CERA CA0161 030317 COMB. MANU. IND.- OTHER GLASS (except decarb. in 040613) 301A NatGas 119 PRO-CERA CA0162 030317 COMB. MANU. IND.- OTHER GLASS (except decarb. in 040613) 303A LPG 3 PRO-CERA

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 80 Exposure of the Austrian Population to PM10 in 1996

CA0163 030319 COMB. MANU. IND.- BRICKS AND TILES 204B LightOil 6 PRO-CERA CA0164 030319 COMB. MANU. IND.- BRICKS AND TILES 204C MedOil 0 PRO-CERA CA0165 030319 COMB. MANU. IND.- BRICKS AND TILES 204D HeavOil 138 PRO-CERA CA0166 030319 COMB. MANU. IND.- BRICKS AND TILES 301A NatGas 136 PRO-CERA CA0224 040612 OTHER PROCESSES - CEMENT (decarbonizing) 4.714 PRO-CERA CA0225 040613 OTHER PROCESSES - GLASS (decarbonizing) 1.262 PRO-CERA CA0226 040614 OTHER PROCESSES - LIME (decarbonizing) 0 PRO-CERA CA0351 090208 INCINERATION OF WASTE OIL 16 PRO-CERA 6.403 PRO-CERA Total CA0198 040401 INORGANIC CHEMICAL - SULFURIC ACID 0 PRO-CHEM CA0199 040402 INORGANIC CHEMICAL - NITRIC ACID 0 PRO-CHEM CA0200 040403 INORGANIC CHEMICAL - AMMONIA 0 PRO-CHEM CA0201 040405 INORGANIC CHEMICAL - AMMONIUM NITRATE 0 PRO-CHEM CA0202 040407 INORGANIC CHEMICAL - NPK FERTILISERS 0 PRO-CHEM CA0203 040408 INORGANIC CHEMICAL - UREA 0 PRO-CHEM CA0204 040411 INORGANIC CHEMICAL - GRAPHITE 0 PRO-CHEM CA0205 040413 INORGANIC CHEMICAL - CHLORINE PRODUCTION 0 PRO-CHEM CA0206 040414 INORGANIC CHEMICAL - PHOSPHATE FERTILIZERS 0 PRO-CHEM CA0207 040416 INORGANIC CHEMICAL - OTHER 4.400 PRO-CHEM CA0208 040506 ORGANIC CHEMICAL - POLYETHYLENE LOW DENSITY 0 PRO-CHEM CA0209 040509 ORGANIC CHEMICAL - POLYPROPYLENE 0 PRO-CHEM CA0210 040513 ORGANIC CHEMICAL - STYRENE-BUTADIENE LATEX 0 PRO-CHEM CA0211 040517 ORGANIC CHEMICAL - FORMALDEHYDE 0 PRO-CHEM CA0212 040521 ORGANIC CHEMICAL - ADIPIC ACID 0 PRO-CHEM CA0213 040527 ORGANIC CHEMICAL - OTHER (phytosanitary, ...) 0 PRO-CHEM CA0251 060301 POLYESTER PROCESSING 0 PRO-CHEM CA0252 060302 POLYVINYLCHLORIDE PROCESSING 0 PRO-CHEM CA0253 060303 POLYURETHANE PROCESSING 0 PRO-CHEM CA0254 060304 POLYSTYRENE FOAM PROCESSING 0 PRO-CHEM CA0255 060305 RUBBER PROCESSING 0 PRO-CHEM CA0256 060306 PHARMACEUTICAL PRODUCTS MANUFACTURING 0 PRO-CHEM CA0257 060307 PAINTS MANUFACTURING 0 PRO-CHEM CA0258 060308 INKS MANUFACTURING 0 PRO-CHEM CA0259 060309 GLUES MANUFACTURING 0 PRO-CHEM CA0261 060311 ADHESIVE, MAGNETIC TAPES, FILMS AND PHOTOGRAPHS 0 PRO-CHEM MANUFACT. CA0264 060314 CHEMICALS PRODUCTS MANUFACTURING OR PROCESSING - OTHER 0 PRO-CHEM CA0274 060410 PHARMACEUTICAL PRODUCTS MANUFACTURING 0 PRO-CHEM 4.400 PRO-CHEM Total CA0001 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 101A HardC 0 PRO-ENER CA0002 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 105A BrC 0 PRO-ENER CA0003 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 204B LightOil 0 PRO-ENER CA0004 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 204C MedOil 0 PRO-ENER CA0005 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 204D HeavOil 0 PRO-ENER CA0006 010101 PUBLIC POWER - COMBUSTION PLANTS >= 300 MW (boilers) 301A NatGas 0 PRO-ENER CA0007 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 101A HardC 0 PRO-ENER CA0008 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 105A BrC 0 PRO-ENER CA0009 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 204B LightOil 0 PRO-ENER CA0010 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 204C MedOil 0 PRO-ENER CA0011 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 204D HeavOil 0 PRO-ENER

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 81

CA0012 010102 PUBLIC POWER -COMBUSTION PLANTS >= 50 AND < 300 MW (boilers) 301A NatGas 0 PRO-ENER CA0013 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 101A HardC 0 PRO-ENER CA0014 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 105A BrC 0 PRO-ENER CA0015 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 106A Briqu 0 PRO-ENER CA0016 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 111B Biogen 21 PRO-ENER CA0017 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 114A Waste 0 PRO-ENER CA0018 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 204A XLightOil 3 PRO-ENER CA0019 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 204B LightOil 509 PRO-ENER CA0020 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 204C MedOil 0 PRO-ENER CA0021 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 204D HeavOil 0 PRO-ENER CA0022 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 2050 Dies 0 PRO-ENER CA0023 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 206B Petr 0 PRO-ENER CA0024 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 224A RefinProd 0 PRO-ENER CA0025 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 301A NatGas 459 PRO-ENER CA0026 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 303A LPG 0 PRO-ENER CA0027 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 304A CokeGas 0 PRO-ENER CA0028 010103 PUBLIC POWER - COMBUSTION PLANTS < 50 MW (boilers) 305A BlastFGas 0 PRO-ENER CA0029 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 101A HardC 0 PRO-ENER CA0030 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 114A Waste 0 PRO-ENER CA0031 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 204B LightOil 0 PRO-ENER CA0032 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 204C MedOil 0 PRO-ENER CA0033 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 204D HeavOil 0 PRO-ENER CA0034 010201 DISTRICT HEAT. - COMBUSTION PLANTS >= 300 MW (boilers) 301A NatGas 0 PRO-ENER CA0035 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 101A HardC 0 PRO-ENER CA0036 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 105A BrC 0 PRO-ENER CA0037 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 106A Briqu 0 PRO-ENER CA0038 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 111B Biogen 0 PRO-ENER CA0039 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 204B LightOil 0 PRO-ENER CA0040 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 204C MedOil 0 PRO-ENER CA0041 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 204D HeavOil 0 PRO-ENER CA0042 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 301A NatG 0 PRO-ENER CA0043 010202 DISTRICT HEAT. - COMB. PLANTS >= 50 MW AND < 300 MW (boil.) 310A LandfillG 0 PRO-ENER CA0237 050600 GAS DISTRIBUTION NETWORKS 0 PRO-ENER 992 PRO-ENER Total CA0062 010406 SOLID FUEL TRANS. - COKE OVEN FURNACES 101A HardC 0 PRO-METL CA0133 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 304A CokeGas 0 PRO-METL CA0134 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 305A BlastFGas 0 PRO-METL CA0137 030203 COMB. MANU. IND.- BLAST FURNACE COWPERS 305A BlastFGas 0 PRO-METL CA0150 030301 COMB. MANU. IND.- SINTER PLANTS 107A Coke 372 PRO-METL CA0171 030326 COMB. MANU. IND.- OTHER other 107A Coke 160 PRO-METL CA0170 030326 COMB. MANU. IND.- OTHER Iron 107A Coke 0 PRO-METL CA0182 030326 COMB. MANU. IND.- OTHER other 304A CokeGas 0 PRO-METL CA0184 040201 IRON/STEEL & COLLIERY- COKE OVEN (door leakage & extinction) 0 PRO-METL CA0185 040202 IRON/STEEL & COLLIERY - BLAST FURNACE CHARGING 4.439 PRO-METL CA0186 040204 IRON/STEEL & COLLIERY - SOLID SMOKELESS FUEL 0 PRO-METL CA0187 040205 IRON/STEEL & COLLIERY - OPEN HEARTH FURNACE STEEL PLANT 0 PRO-METL CA0188 040206 IRON/STEEL & COLLIERY - BASIC OXYGEN FURNACE STEEL PLANT 0 PRO-METL CA0189 040207 IRON/STEEL & COLLIERY - ELECTRIC FURNACE STEEL PLANT 142 PRO-METL CA0190 040208 IRON/STEEL & COLLIERY - ROLLING MILLS 0 PRO-METL CA0191 040209 IRON/STEEL & COLLIERY- SINTER PLANT (except comb. in 030301) 0 PRO-METL

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CA0192 040210 IRON/STEEL & COLLIERY - OTHER 28 PRO-METL CA0193 040210 IRON/STEEL & COLLIERY - OTHER 0 PRO-METL CA0194 040304 NON-FERROUS METAL - MAGNESIUM PROD. (except 030323) 0 PRO-METL CA0195 040309 NON-FERROUS METAL - OTHER Alum Foundry 16 PRO-METL CA0196 040309 NON-FERROUS METAL - OTHER HeavM 1 PRO-METL Foundry CA0197 040309 NON-FERROUS METAL - OTHER 0 PRO-METL CA0247 060201 METAL DEGREASING 0 PRO-METL 5.158 PRO-METL Total CA0260 060310 ASPHALT BLOWING 0 PRO-MIN 0 PRO-MIN Total CA0058 010301 PETROLEUM REF. - COMBUSTION PLANTS >=300 MW (boilers) 204B LightOil 0 PRO-OIL CA0059 010301 PETROLEUM REF. - COMBUSTION PLANTS >=300 MW (boilers) 204D HeavOil 0 PRO-OIL CA0060 010301 PETROLEUM REF. - COMBUSTION PLANTS >=300 MW (boilers) 308A RefinGas 0 PRO-OIL CA0061 010306 PETROLEUM REF. - PROCESS FURNACES 308A RefinGas 0 PRO-OIL CA0063 010503 COAL/OIL/GAS EXT. - COMBUSTION PLANTS < 50 MW (boilers) 301A NatGas 15 PRO-OIL CA0135 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Ind. Electr 308A RefinGas 0 PRO-OIL CA0183 040100 PROCESSES IN PETROLEUM INDUSTRIES 3.479 PRO-OIL CA0228 040616 OTHER PROCESSES - EXTRACTION OF MINERAL ORES 0 PRO-OIL CA0230 050100 EXTRACTION AND 1ST TREATMENT OF SOLID FOSSIL FUELS 0 PRO-OIL CA0231 050200 EXTRACTION, 1ST TREAT. AND LOADING OF LIQUID FOSSIL FUELS 0 PRO-OIL CA0232 050301 EXTRACTION OF GASEOUS FOSSIL FUELS- LAND-BASED 0PRO-OIL DESULFURATION CA0233 050302 EXTRACTION OF GAS - LAND-BASED ACTIV. (other than desulfur.) 0 PRO-OIL CA0349 090206 FLARING IN GAS AND OIL EXTRACTION 0 PRO-OIL 3.494 PRO-OIL Total CA0107 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 101A HardC 349 PRO-PAPR CA0109 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 105A BrC 317 PRO-PAPR CA0111 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 106A Briqu 0 PRO-PAPR CA0113 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 107A Coke 0 PRO-PAPR CA0114 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 111A Wood 0 PRO-PAPR CA0116 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 111B Biogen 213 PRO-PAPR CA0118 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 114A Waste 0 PRO-PAPR CA0120 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 204B LightOil 6 PRO-PAPR CA0122 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 204C MedOil 0 PRO-PAPR CA0124 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 204D HeavOil 845 PRO-PAPR CA0126 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 206B Petr 0 PRO-PAPR CA0128 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 224A RefinProd 4 PRO-PAPR CA0130 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 301A NatGas 295 PRO-PAPR CA0132 030103 COMB. MANU. IND.- COMBUSTION PLANTS < 50 MW (boilers) Paper Proc 303A LPG 2 PRO-PAPR CA0215 040602 OTHER PROCESSES - PAPER PULP (kraft process) 202 PRO-PAPR CA0216 040603 OTHER PROCESSES - PAPER PULP (acid sulfite process) 334 PRO-PAPR CA0217 040604 OTHER PROC.-PAPER PULP (neutral sulphite semi-chemical pro.) 0 PRO-PAPR 2.567 PRO-PAPR Total CA0214 040601 OTHER PROCESSES - CHIPBOARD 588 PRO-TIMB CA0244 060107 PAINT APPLICATION - WOOD 0 PRO-TIMB CA0270 060406 PRESERVATION OF WOOD 0 PRO-TIMB 588 PRO-TIMB Total CA0317 070600 GASOLINE EVAPORATION FROM VEHICLES Conv 2080 Gasol 0 ROA CA0318 070600 GASOLINE EVAPORATION FROM VEHICLES Cat 2080 Gasol 0 ROA CA0319 070700 AUTOMOBILE TYRE AND BRAKE WEAR 0 ROA

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0 ROA Total CA0279 070101 PASSENGER CARS - HIGHWAY DRIVING 2050 Dies 2.628 ROA-HWY CA0280 070101 PASSENGER CARS - HIGHWAY DRIVING Conv 2080 Gasol 5.820 ROA-HWY CA0281 070101 PASSENGER CARS - HIGHWAY DRIVING Cat 2080 Gasol 4.908 ROA-HWY CA0288 070201 LIGHT DUTY VEHICLES < 3.5 t - HIGHWAY DRIVING 2050 Dies 1.302 ROA-HWY CA0289 070201 LIGHT DUTY VEHICLES < 3.5 t - HIGHWAY DRIVING Conv 2080 Gasol 883 ROA-HWY CA0290 070201 LIGHT DUTY VEHICLES < 3.5 t - HIGHWAY DRIVING Cat 2080 Gasol 58 ROA-HWY CA0297 070301 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - HIGHWAY DRIVING Conv 2050 Dies 18.035 ROA-HWY CA0298 070301 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - HIGHWAY DRIVING Buses Conv 2050 Dies 980 ROA-HWY CA0299 070301 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - HIGHWAY DRIVING HDV Conv 2080 Gasol 166 ROA-HWY CA0300 070301 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - HIGHWAY DRIVING HDV Cat 2080 Gasol 0 ROA-HWY CA0311 070501 MOTORCYCLES > 50 CM3 - HIGHWAY DRIVING Conv 2080 Gasol 88 ROA-HWY CA0312 070501 MOTORCYCLES > 50 CM3 - HIGHWAY DRIVING Cat 2080 Gasol 0 ROA-HWY 34.868 ROA-HWY Total CA0282 070102 PASSENGER CARS - RURAL DRIVING 2050 Dies 2.320 ROA-RUR CA0283 070102 PASSENGER CARS - RURAL DRIVING Conv 2080 Gasol 4.028 ROA-RUR CA0284 070102 PASSENGER CARS - RURAL DRIVING Cat 2080 Gasol 2.346 ROA-RUR CA0291 070202 LIGHT DUTY VEHICLES < 3.5 t - RURAL DRIVING 2050 Dies 1.322 ROA-RUR CA0292 070202 LIGHT DUTY VEHICLES < 3.5 t - RURAL DRIVING Conv 2080 Gasol 746 ROA-RUR CA0293 070202 LIGHT DUTY VEHICLES < 3.5 t - RURAL DRIVING Cat 2080 Gasol 55 ROA-RUR CA0301 070302 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - RURAL DRIVING HDV Conv 2050 Dies 12.069 ROA-RUR CA0302 070302 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - RURAL DRIVING Buses Conv 2050 Dies 934 ROA-RUR CA0303 070302 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - RURAL DRIVING HDV Conv 2080 Gasol 115 ROA-RUR CA0304 070302 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - RURAL DRIVING HDV Cat. 2080 Gasol 0 ROA-RUR CA0309 070400 MOPEDS AND MOTORCYCLES < 50 CM3 Conv 2080 Gasol 9 ROA-RUR CA0310 070400 MOPEDS AND MOTORCYCLES < 50 CM3 Cat 2080 Gasol 4 ROA-RUR CA0313 070502 MOTORCYCLES > 50 CM3 - RURAL DRIVING Conv 2080 Gasol 120 ROA-RUR CA0314 070502 MOTORCYCLES > 50 CM3 - RURAL DRIVING Cat 2080 Gasol 0 ROA-RUR CA0320 080100 OTHER MOBILE & MACH.- MILITARY 2050 Dies 362 ROA-RUR CA0321 080100 OTHER MOBILE & MACH.- MILITARY 2080 Gasol 65 ROA-RUR 24.495 ROA-RUR Total CA0285 070103 PASSENGER CARS - URBAN DRIVING 2050 Dies 2.913 ROA-URB CA0286 070103 PASSENGER CARS - URBAN DRIVING Conv 2080 Gasol 2.636 ROA-URB CA0287 070103 PASSENGER CARS - URBAN DRIVING Cat 2080 Gasol 4.310 ROA-URB CA0294 070203 LIGHT DUTY VEHICLES < 3.5 t - URBAN DRIVING 2050 Dies 1.250 ROA-URB CA0295 070203 LIGHT DUTY VEHICLES < 3.5 t - URBAN DRIVING Conv 2080 Gasol 473 ROA-URB CA0296 070203 LIGHT DUTY VEHICLES < 3.5 t - URBAN DRIVING Cat 2080 Gasol 43 ROA-URB CA0305 070303 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - URBAN DRIVING HDV Conv 2050 Dies 11.421 ROA-URB CA0306 070303 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - URBAN DRIVING Buses Conv 2050 Dies 1.941 ROA-URB CA0307 070303 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - URBAN DRIVING HDV Conv 2080 Gasol 122 ROA-URB CA0308 070303 HEAVY DUTY VEHICLES > 3.5 t AND BUSES - URBAN DRIVING HDV Cat 2080 Gasol 0 ROA-URB CA0315 070503 MOTORCYCLES > 50 CM3 - URBAN DRIVING Conv 2080 Gasol 20 ROA-URB CA0316 070503 MOTORCYCLES > 50 CM3 - URBAN DRIVING Cat 2080 Gasol 0 ROA-URB 25.129 ROA-URB Total 169.271 TOTAL

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Annex II: Input data for calculation of traffic emissions with the ARCS model

Austr Vhcls Fuel Ecnmy Usage per Usage in Non-Austr Vhcl NOx Emis Fac Vhcl Austr Traffic

(Number) (l/100km) (km/yr) (% of Usage) (% of A-VKT in A) (g/km) Gasoline

MoPeds (2-stroke) 281.670 2,3 1.700 100% 0% 0,04 MoPeds (2-stroke Cat) 89.835 2,3 1.700 100% 0% 0,01 MoCycles (2-stroke) 17.491 5,2 4.500 100% 7% 0,10 MoCycles (4-stroke) 157.416 5,2 4.500 100% 7% 0,30 Cars Gasoline 1.200.594 10,0 6.800 98% 15% 1,51 Cars Gasoline Cat US83 1.186.847 9,5 14.000 95% 10% 0,57 Cars Gasoline Cat 32KDV 379.469 9,0 21.500 95% 0% 0,32 Cars Gasoline Cat EUROII 0 8,5 0 0% 0% 0,25 LDV < 3,5 Gasoline 38.667 17,0 21.000 95% 6% 3,61 LDV < 3,5 Gasoline KDV (89) 17.123 16,0 20.000 95% 0% 0,85 LDV < 3,5 Gasoline EURO II 0 15,0 0 0% 0% 0,48 Diesel Cars Diesel 158.282 9,0 10.000 92% 15% 0,73 Cars Diesel US Reg 411.551 7,5 11.500 92% 10% 0,31 Cars Diesel 32KDV 256.707 7,5 22.000 92% 0% 0,18 Cars Diesel EURO II 0 7,5 0 0% 0% 0,09 Busses 9.752 34,0 43.000 85% 16% 9,44 LDV <3,5 t Diesel 41.671 20,0 15.000 95% 6% 2,35 LDV <3,5 t Diesel KDV (89) 98.494 16,0 28.000 95% 0% 0,47 LDV <3,5 t Diesel EURO II 0 15,0 0 0% 0% 0,26 HDV 3,5-15 t Diesel 57.474 29,0 40.000 85% 5% 8,05 HDV Diesel >15 t 39.447 39,0 60.000 85% 7% 10,82 Sattelzug Diesel 12.617 39,0 63.000 70% 8% 10,82 Tractors 390.383 65,0 1.000 100% 0% 18,04 Other 75.000 39,0 1.000 100% 0% 10,82

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Annex III: Wind data used for Gaussian pollutant dispersion from point sources

NOx- Stack emissions height class Wind station (1995 Point Source (t/yr) (m) and 1996) Wind direction frequencies (°/oo) N NE E SE S SW W NW Calms EBS Simmering 103 50 Fischamend 87 25 90 255 60 57 121 255 50 ESG Linz FHKW Mitte 330 200 Asten 29 62 200 38 27 62 257 23 301 ESG Linz FHKW Süd 119 50 Asten 29 62 200 38 27 62 257 23 301 EVN KW Korneuburg 141 50 Streithofen 60 58 147 38 65 286 165 58 124 EVN KW Theiß 217 150 Streithofen 60 58 147 38 65 286 165 58 124 EVN/VKG KW Dürnrohr 886 200 Streithofen 60 58 147 38 65 286 165 58 124 FHKW Kirchdorf 12 50 Lenzing 14 170 91 2 8 208 143 25 339 HBW FHKW Arsenal 42 75 Fischamend 87 25 90 255 60 57 121 255 50 HBW FHKW Flötzersteig 11 100 Fischamend 87 25 90 255 60 57 121 255 50 HBW FHKW Spittelau 47 150 Fischamend 87 25 90 255 60 57 121 255 50 HBW FWKW Kagran 46 150 Fischamend 87 25 90 255 60 57 121 255 50 Jungbunzlauer Pernhofen 69 50 Mistelbach 105 98 92 163 58 45 170 227 43 Lenzing AG Energieanlage I 446 150 Lenzing 14 170 91 2 8 208 143 25 339 Lenzing AG Energieanlage IIa 431 75 Lenzing 14 170 91 2 8 208 143 25 339 ÖDK KW St. Andrä 179 75 St.Andrae/Lavanttal 145 39 31 140 156 44 10 33 401 ÖDK KW Voitsberg 711 200 Voitsberg 8 5 28 220 8 11 78 84 558 ÖDK KW Zeltweg 301 75 Zeltweg 15 22 101 35 26 32 56 25 690 OKA KW Riedersbach 269 75 Mattsee 19 59 226 50 21 172 257 82 114 OKA KW Riedersbach 596 200 Mattsee 19 59 226 50 21 172 257 82 114 OKA KW Timelkam 646 50 Lenzing 14 170 91 2 8 208 143 25 339 OKA KW Timelkam 11 75 Lenzing 14 170 91 2 8 208 143 25 339 OMV Schwechat RS08 942 50 Fischamend 87 25 90 255 60 57 121 255 50 OMV Schwechat RS13 2448 75 Fischamend 87 25 90 255 60 57 121 255 50 PF Hallein Hallein K 4 47 50 SalzburgFernheizwerk161 35 18 51 103 141 91 82 318 PF Hallein Hallein K 5 109 75 SalzburgFernheizwerk161 35 18 51 103 141 91 82 318 PF Hamburger Wirbelschicht 4 107 75 WienerNeustadt 86 77 128 27 105 117 77 114 269 PF Leykam Kohlekessel 206 100 Judendorf 23 13 194 31 25 104 87 52 472 PF Leykam Laugenverbr 206 75 Judendorf 23 13 194 31 25 104 87 52 472 PF Nettingsdorfer K4 193 50 Asten 29 62 200 38 27 62 257 23 301 PF Neusiedler Theresiental 56 50 Amstetten 14 31 319 28 22 24 378 28 156 PF Patria Strahlungskessel 455 75 St.Andrae/Lavanttal 145 39 31 140 156 44 10 33 401 PF Pöls Zellstoff K1 0 75 Zeltweg 15 22 101 35 26 32 56 25 690 PF Steyrermühl K1 290 50 Lenzing 14 170 91 2 8 208 143 25 339 PF Steyrermühl K6 8 75 Lenzing 14 170 91 2 8 208 143 25 339 PWA Ortmann SCA Hygiene K1 99 75 N-E-Ausrichtung 0 0 350 0 0 0 400 0 250 Semperit Reifen AG 88 75 Gumpoldskirchen 144 46 29 61 80 71 128 295 146 Solvay K3+K5 131 50 SalzburgFernheizwerk161 35 18 51 103 141 91 82 318 STEWEAG FHKW Graz 22 50 Graz Schloßberg 44 67 123 143 82 33 30 361 116 STEWEAG FHKW Mellach 513 150 Graz Schloßberg 44 67 123 143 82 33 30 361 116 STEWEAG KW Neudorf 56 150 Graz Schloßberg 44 67 123 143 82 33 30 361 116 STEWEAG KW Pernegg 92 50 Judendorf 23 13 194 31 25 104 87 52 472 STW Klagenfurt FHKW 225 75 Klagenfurt 64 52 100 144 60 35 72 337 137 STW Salzburg FHKW Mitte 137 75 SalzburgFernheizwerk161 35 18 51 103 141 91 82 318 STW Salzburg FHKW Nord 21 75 SalzburgFernheizwerk161 35 18 51 103 141 91 82 318 STW St.Pölten FHKW NORD 135 75 Streithofen 60 58 147 38 65 286 165 58 124 VA Donawitz Kombikessel 60 t 16 50 Donawitz 1 8 124 64 5 33 128 9 630 VA Stahl Linz ME 1 Blockkess 274 100 Asten 29 62 200 38 27 62 257 23 301 VKG KW Korneuburg 181 150 Streithofen 60 58 147 38 65 286 165 58 124 WEW KW Donaustadt 91 150 Fischamend 87 25 90 255 60 57 121 255 50 WEW KW Leopoldau 46 50 Fischamend 87 25 90 255 60 57 121 255 50 WEW KW Simmering 688 150 Fischamend 87 25 90 255 60 57 121 255 50 ZF Agrana Hohenau K4 23 50 Mistelbach 105 98 92 163 58 45 170 227 43 ZF Agrana Leopoldsdorf 77 50 Mistelbach 105 98 92 163 58 45 170 227 43 VA Stahl Linz (zusätzl.) 274 50 Asten 29 62 200 38 27 62 257 23 301 VA Donawitz (zusätzl.) 738 50 Donawitz 1 8 124 64 5 33 128 9 630

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Annex IV: GIS neighbourhood functions

A "focalfunction" is an algebraic grid cell operation that includes the neighbourhood in a specific way and copies the requested results (sum, average, maximum etc.) to the neighborhood focus. The neighbourhood focus is the grid cell in the center of the respective neighbourhood. These operations work cell-by-cell moving through the grid row-by-row from the top left to the bottom right. The function is part of a raster cell modelling language developed by Tomlin (1990) actually known as MAPALGEBRA which is today implemented in many GIS software systems that allow grid cell operations. The shape of the neighbourhood can be a square, a circle, a donut ("annulus") and even an irregular area of any shape, to be defined by a 0/1 array, where 1 means the grid that cells have to be included into the operation. Using a 3*3 neighbourhood the 8 adjacent grid cells surrounding the center grid cell are included into the operation. Using a 5*5 neighbourhood the 8 adjacent first-neighbours and the 16 second-neighbours – which are adjacent to the 8 first-neighbours – are included into the operation. Greater neighbourhoods are treated similar. Using circles or donut shapes the neighbours to be included into the operation are different: Circle shapes exclude the grid cells located at the square corners in order to build a (gridded) circle, donut shapes are the neighbourhood center, too. In this study the FOCALMEAN function has often been used. The focalmean calculates the average within a neighbourhood and copies the result to the position of the neighbourhood focus into the output grid. The result is a grid with a smoother "surface" than the original one. All peaks and minima are clipped through the averaging process considering the values of the neighbouring grid cells. A FOCALSUM function was used for dispersion modelling. The FOCALSUM function together with an irregular shap that defines correcvt dispersion ranges allow to sum up the contribution of each emission source in the vicinity the grid cell-specific pollutant concentration. "Blockfunctions" are similar to focalfunctions. BLOCKMEAN copies the result of the respective focal operation (in this study the FOCALMEAN) to all neighbours within the neighbourhood "window". The operation is not performed cell-by-cell but block-by-block. The increment (the "jumping distance" to the next grid cell) is not 1 but the size of the block- square. The neighbourhoods for the MAPALGEBRA operations do not overlap. This means the BLOCKMEAN results are the same within each block, e.g. when calculating a BLOCKMEAN using a 5*5 window the value within each 5*5 window is the same. The BLOCKMEAN function is used to calculate the final results for the population exposure.

Figure: MAPALGEBRA functions: blockfunction and focalfunction (Figure from ARCView online- help pages)

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Annex V: NOx concentrations at Austrian measurement sites

3 NOx concentrations (in mg NO2/m ) as annual mean value from Austrian measurement sites

Province Monitoring Site 1993 1994 1995 1996 1997 Burgenland Eisenstadt 0,020 0,031 0,034 Carinthia Bleiburg 0,038 0,036 0,031 0,039 0,037 Carinthia Feldkirchen 0,058 0,052 0,044 0,040 0,034 Carinthia Ferlach 0,028 0,022 0,026 0,028 0,027 Carinthia Fürnitz 0,038 0,026 0,032 0,035 0,035 Carinthia Hermagor 0,043 0,034 0,030 0,032 0,034 Carinthia Klagenfurt Koschatstr. 0,082 0,067 0,061 0,051 0,041 Carinthia Klagenfurt Völkermarkterstr. 0,118 0,126 0,124 0,112 Carinthia Oberdrauburg 0,032 0,033 0,026 0,024 0,028 Carinthia Obervellach 0,028 0,020 0,017 0,018 0,021 Carinthia Spittal a.d.D. 0,062 0,051 0,046 0,046 0,046 Carinthia St. Andrä 0,055 0,052 0,052 0,044 0,043 Carinthia St. Georgen Herzogberg 0,015 0,013 0,016 0,014 0,016 Carinthia St. Veit a.d.G. Oktoberpl. 0,116 0,095 0,091 0,090 0,093 Carinthia Villach 0,091 0,082 0,078 0,072 0,069 Carinthia Völkermarkt 0,087 0,071 0,069 0,069 0,067 Carinthia Vorhegg 0,008 0,009 0,005 Carinthia Wolfsberg 0,085 0,085 0,079 0,072 0,069 Lower Austria Amstetten 0,048 0,044 0,039 0,039 0,045 Lower Austria Bad Vöslau 0,032 0,027 0,025 0,033 0,031 Lower Austria Brunn a.G. 0,023 0,061 0,043 0,039 0,050 Lower Austria Deutsch Wagram 0,025 0,022 0,026 0,034 Lower Austria Dunkelsteinerwald 0,021 0,019 0,022 0,020 0,023 Lower Austria Fischamend 0,031 0,027 0,035 0,027 0,024 Lower Austria Forsthof 0,017 0,020 0,017 0,013 0,020 Lower Austria Gänserndorf 0,034 0,026 0,020 0,023 0,023 Lower Austria Großenzersdorf 0,036 0,030 0,027 0,023 0,028 Lower Austria Großgöttfritz 0,013 0,014 0,011 0,012 0,016 Lower Austria Hainburg 0,030 0,026 0,024 0,023 0,023 Lower Austria Heidenreichstein 0,017 0,016 0,013 0,016 0,014 Lower Austria Himberg 0,032 0,034 0,032 0,034 0,033 Lower Austria Klosterneuburg 0,036 0,010 0,031 0,034 0,008 Lower Austria Kollmitzberg 0,026 0,017 0,019 0,027 Lower Austria Korneuburg 0,048 0,044 0,036 0,033 0,036 Lower Austria Krems 0,044 0,042 0,043 0,034 0,035

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Province Monitoring Site 1993 1994 1995 1996 1997 Lower Austria Langenzersdorf 0,037 0,036 0,040 0,039 Lower Austria Mannswörth 0,046 0,032 0,043 0,033 0,034 Lower Austria Mistelbach 0,025 0,022 0,018 0,004 Lower Austria Mödling 0,052 0,017 0,041 0,033 0,037 Lower Austria Neusiedl i.T. 0,022 0,024 0,020 0,022 0,026 Lower Austria Payerbach 0,004 0,012 0,008 0,009 Lower Austria Pillersdorf 0,021 0,012 0,010 0,013 0,012 Lower Austria Schwechat 0,070 0,042 0,032 0,039 0,034 Lower Austria St. Leonhard 0,018 0,012 0,012 0,012 0,015 Lower Austria St. Pölten 0,013 0,052 0,040 0,046 0,043 Lower Austria St. Valentin 0,041 0,030 0,032 0,031 0,030 Lower Austria Stixneusiedl 0,019 0,020 0,017 0,017 0,018 Lower Austria Stockerau 0,045 0,041 0,038 0,040 0,044 Lower Austria Streithofen 0,026 0,006 0,018 0,022 0,019 Lower Austria Ternitz 0,028 0,034 0,026 0,030 0,032 Lower Austria Traisen 0,033 0,029 0,025 0,010 Lower Austria Traismauer 0,008 0,030 0,023 0,025 0,027 Lower Austria Trasdorf 0,015 0,018 0,020 0,023 0,023 Lower Austria Tulbinger Kogel 0,019 0,015 0,015 0,019 0,016 Lower Austria Tulln 0,061 0,033 0,047 0,049 0,053 Lower Austria Vösendorf 0,068 0,057 0,038 0,035 0,019 Lower Austria Wiener Neustadt 0,046 0,052 0,044 0,046 0,036 Lower Austria Wiesmath 0,015 0,004 0,007 0,002 Lower Austria Wolkersdorf 0,034 0,034 0,028 0,029 0,028 Lower Austria Zwentendorf 0,017 0,020 0,020 0,023 0,025 Upper Austria Asten 0,049 0,048 0,049 0,044 0,043 Upper Austria Bad Ischl 0,019 0,018 0,017 Upper Austria Braunau 0,037 0,036 0,031 0,038 0,045 Upper Austria Grünbach b.F. 0,010 Upper Austria Hochburg-Ach 0,010 0,013 0,011 0,014 0,015 Upper Austria Lenzing 0,029 0,020 0,022 0,023 0,017 Upper Austria Linz 24er Turm 0,113 0,092 0,083 0,078 0,084 Upper Austria Linz Berufsschulzentrum/Neue Welt 0,074 0,059 0,051 0,049 0,078 Upper Austria Linz BH-Urfahr 0,095 0,088 0,073 0,077 0,065 Upper Austria Linz Hauserhof 0,067 0,053 0,040 0,038 0,025 Upper Austria Linz Kleinmünchen 0,077 0,048 0,031 0,049 0,041 Upper Austria Linz ORF-Zentrum 0,083 0,069 0,056 0,056 0,062 Upper Austria Linz Ursulinenhof 0,081 0,056 0,037 0,038 Upper Austria Perg 0,034 0,015 0,028 0,028 Upper Austria Schöneben 0,008

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Province Monitoring Site 1993 1994 1995 1996 1997 Upper Austria Steyr 0,039 0,040 0,034 0,042 0,040 Upper Austria Steyregg 0,045 0,041 0,030 0,025 0,027 Upper Austria Traun 0,053 0,059 0,039 0,040 0,048 Upper Austria Wels 0,071 0,063 0,058 0,057 0,062 Salzburg Hallein Hagerkreuzung 0,151 0,132 0,142 0,170 0,149 Salzburg Hallein Winterstall 0,034 0,019 0,019 Salzburg Salzburg Itzling 0,085 0,075 0,079 Salzburg Salzburg Lehen 0,060 0,055 0,046 0,064 0,063 Salzburg Salzburg Mirabellplatz 0,094 0,074 0,080 0,075 Salzburg Salzburg Rudolfsplatz 0,247 0,207 0,210 0,214 0,195 Salzburg Tamsweg 0,011 0,031 0,029 0,035 0,038 Styria Arnfels 0,011 0,010 0,009 Styria Bockberg 0,017 0,013 0,007 0,010 0,011 Styria Bruck a.d.M. 0,046 0,038 0,045 0,042 0,039 Styria Deutschlandsberg 0,043 0,037 0,041 0,043 0,043 Styria Fohnsdorf 0,031 0,027 0,032 0,031 0,029 Styria Graz Mitte 0,132 0,133 0,115 0,120 0,115 Styria Graz Nord 0,043 0,032 0,028 0,052 0,056 Styria Graz Ost 0,071 0,069 0,069 0,074 0,076 Styria Graz Süd 0,100 0,090 0,099 0,084 0,092 Styria Graz Südwest 0,106 0,097 0,086 0,075 0,079 Styria Graz West 0,096 0,077 0,076 0,072 0,066 Styria Hartberg 0,017 0,033 Styria Höchgößnitz 0,009 0,005 0,006 0,009 0,011 Styria Hörgas 0,019 0,017 0,018 0,018 0,022 Styria Judenburg 0,030 0,027 0,032 0,033 0,029 Styria Judendorf 0,052 0,052 0,051 0,050 0,047 Styria Kapfenberg 0,070 0,048 0,037 0,036 0,033 Styria Kindberg-Wartberg 0,028 0,031 0,044 Styria Knittelfeld 0,052 0,060 0,055 0,052 0,049 Styria Köflach 0,058 0,060 0,061 0,057 0,053 Styria Leoben Donawitz 0,044 0,049 0,038 0,038 0,032 Styria Leoben Göß 0,057 0,084 0,062 0,061 0,083 Styria Leoben Zentrum 0,050 0,049 0,047 0,040 0,044 Styria Liezen 0,041 0,042 0,041 0,034 Styria Masenberg 0,009 0,005 0,006 0,005 0,006 Styria Peggau 0,028 0,060 0,061 0,068 0,057 Styria Piber 0,012 0,015 0,017 0,017 0,019 Styria Pöls Ost 0,021 0,020 0,023 0,023 0,018 Styria Straßengel 0,043 0,042 0,047 0,045 0,046

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 90 Exposure of the Austrian Population to PM10 in 1996

Province Monitoring Site 1993 1994 1995 1996 1997 Styria Voitsberg Freibad 0,048 0,051 0,045 0,043 0,042 Styria Voitsberg Krems 0,068 0,069 0,065 0,063 0,062 Styria Weiz 0,050 0,056 0,045 0,043 0,040 Styria Wildon 0,044 0,032 0,030 0,025 0,016 Styria Zeltweg 0,058 0,054 0,054 0,054 0,047 Tyrol Gärberbach 0,123 0,102 0,148 Tyrol Hall i.T. 0,169 0,156 0,162 0,135 0,149 Tyrol Innsbruck Nordkette 0,006 0,005 0,005 0,006 0,006 Tyrol Innsbruck Olympisches Dorf 0,095 0,084 0,095 0,084 0,095 Tyrol Innsbruck Reichenau 0,127 0,098 0,095 0,102 0,108 Tyrol Innsbruck Zentrum 0,123 0,108 0,102 0,098 0,112 Tyrol Kufstein Zentrum 0,094 0,084 0,082 0,079 0,076 Tyrol Landeck 0,126 0,089 0,092 0,092 0,099 Tyrol Lienz Dolomitenkreuzung 0,129 0,119 0,117 0,117 Tyrol Vomp 0,296 Tyrol Wörgl 0,085 0,073 0,076 0,077 0,293 Vorarlberg Bludenz 0,038 0,037 0,032 Vorarlberg Dornbirn 0,035 0,032 0,038 Vorarlberg Feldkirch 0,051 0,048 0,056 0,055 0,051 Vorarlberg Lustenau 0,031 0,027 0,033 0,033 0,029 Vorarlberg Wald a.A. 0,030 0,026 Vienna AKH Dach 0,065 0,051 0,053 0,051 0,055 Vienna Belgradpl. 0,098 0,073 0,068 0,069 0,068 Vienna Floridsdorf 0,087 0,062 0,061 0,058 0,057 Vienna Gaudenzdorf 0,080 0,063 0,068 0,073 0,067 Vienna Hermannskogel 0,025 0,018 0,020 0,021 0,025 Vienna Hietzinger Kai 0,350 0,321 0,317 0,303 0,278 Vienna Hohe Warte 0,055 0,053 0,055 0,048 0,051 Vienna Kaiserebersdorf 0,071 0,086 0,064 0,061 0,056 Vienna Kendlerstr. 0,070 0,077 0,066 0,059 0,055 Vienna Laaer Berg 0,074 0,060 0,063 0,059 0,059 Vienna Liesing 0,080 0,063 0,076 0,067 0,063 Vienna Lobau 0,023 0,017 0,022 0,022 0,021 Vienna Rinnböckstr. 0,120 0,111 0,104 0,094 0,092 Vienna Schafbergbad 0,047 0,036 0,035 0,037 0,035 Vienna Stadlau 0,066 0,057 0,061 0,058 0,060 Vienna Stephansplatz 0,079 0,057 0,060 0,029 0,073 Vienna Taborstr. 0,116 0,108 0,134 0,119 0,104 Vienna Währinger Gürtel 0,070 0,060 0,058 0,017 0,216 Vienna Währinger Straße 0,068

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 91

Annex VI: Equipment of Austrian TSP measurement sites

Province Station Name Instrum. Bend Temp Heated Temp Sample Heating in °C Tube in °C flow in m3/h Burgenland Illmitz FH62IN 50 3 Carinthia Arnoldstein - Gailitz FH62IN yes 50 3 Carinthia Bleiburg FH62IN yes 50 3 Carinthia Feldkirchen FH62IN yes 50 3 Carinthia Ferlach FH62IN yes 50 3 Carinthia Fürnitz FH62IN yes 50 3 Carinthia Hermagor Gailstalstr. FH62IN yes 50 3 Carinthia Klagenfurt Koschatstr. FH62IN yes 50 3 Carinthia Klagenfurt Völkermarkterstr. FH62IN yes 50 3 Carinthia Oberdrauburg FH62IN yes 50 3 Carinthia Obervellach FH62IN yes 50 3 Carinthia Radenthein Erdmannsiedlung FH62IN yes Carinthia Radenthein Evangel. Kirche FH62IN yes Carinthia Soboth FH62IN Carinthia Spittal a.d.Drau FH62IN yes 50 3 Carinthia St. Andrä FH62IN yes 50 3 Carinthia St. Georgen FH62IN yes 50 3 Carinthia St. Paul - Hundsdorf FH62IN yes 3 Carinthia St. Veit a.d.Glan FH62IN yes 50 3 Carinthia Villach FH62IN yes 50 3 Carinthia Vorhegg FH62IN yes 50 3 Carinthia Völkermarkt FH62IN yes 50 3 Carinthia Wietersdorf FH62IN yes 3 Carinthia Wolfsberg FH62IN yes 50 3 Carinthia Frantschach - Voderwölch FH62IN yes 1 Lower Austria Brunn a. Gebirge TEOM 1 Lower Austria Deutsch Wagram TEOM 1 Lower Austria Fischamend TEOM 1 Lower Austria Großenzersdorf TEOM 1 Lower Austria Hainburg FH62I yes 50 3 Lower Austria Himberg TEOM 1 Lower Austria Heidenreichstein FH62I yes 50 3 Lower Austria Klosterneuburg TEOM 1 Lower Austria Korneuburg TEOM 1 Lower Austria Krems FH62I yes 50 1

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 92 Exposure of the Austrian Population to PM10 in 1996

Province Station Name Instrum. Bend Temp Heated Temp Sample Heating in °C Tube in °C flow in m3/h Lower Austria Langenzersdorf TEOM 1 Lower Austria Mannswörth TEOM 1 Lower Austria Neusiedl FH62I Lower Austria Pillersdorf FH62IN yes 50 3 Lower Austria Schwechat TEOM 1 Lower Austria St.Pölten FH62I yes 50 1 Lower Austria St.Valentin FH62I yes 50 1 Lower Austria Stixneusiedl FH62I yes 50 3 Lower Austria Stockerau TEOM 1 Lower Austria Streithofen FH62I Lower Austria Traismauer FH62I Lower Austria Trasdorf FH62I Lower Austria Tulln FH62I Lower Austria Vösendorf TEOM 1 Lower Austria Wiener Neustadt FH62I yes 50 1 Lower Austria Zwentendorf FH62I 1 Upper Austria Asten FH62IN yes 40 1 Upper Austria Bad Ischl FH62IR yes 40 1 Upper Austria Braunau FH62IR no no 1 Upper Austria Grünbach TEOM Upper Austria Lenzing FH62IN yes 40 1 Upper Austria Linz 24er Turm FH62IN yes 40 1 Upper Austria Linz BH-Urfahr FH62IN yes 40 1 Upper Austria Linz Hauserhof FH62IN yes ca 50° no 1 Upper Austria Linz Kleinmünchen FH62IR yes 40 1 Upper Austria Linz Neue Welt FH62IN yes 40 1 Upper Austria Linz ORF-Zentrum FH62IN yes 40 1 Upper Austria Linz Römerberg FH62IN yes 40 1 Upper Austria Linz Ursulinenhof Upper Austria Perg Upper Austria Schöneben Upper Austria Steyr FH62IN yes 40 1 Upper Austria Steyregg Weih FH62IN yes 40 1 Upper Austria Traun FH62IN yes 40 1 Upper Austria Vöcklabruck FH62IN yes 40 1 Upper Austria Wels FH62IN yes 40 1 Salzburg Hallein Hagerkreuzung FH62IN yes 40 1 Salzburg Salzburg Lehen FH62IN yes 40 1

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 93

Province Station Name Instrum. Bend Temp Heated Temp Sample Heating in °C Tube in °C flow in m3/h Salzburg Salzburg Mirabellplatz FH62IN yes 40 1 Salzburg Salzburg Rudolfsplatz FH62IN yes 40 1 Salzburg Tamsweg FH62IN yes 40 1 Styria Bockberg TEOM yes 50 3 Styria Bruck an der Mur FH62IN yes 80 3 Styria Deutschlandsberg FH62IN yes 80 3 Styria Fohnsdorf FH62IN yes 80 3 Styria Gratkorn FH62IN yes 50 3 Styria Graz Mitte FH62IN yes 50 3 Styria Graz Nord FH62IN yes 50 3 Styria Graz Süd FH62IR yes 50 1 Styria Graz Südwest FH62IN yes 80 3 Styria Graz West FH62IR yes 50 1 Styria Judenburg FH62IN yes 80 3 Styria Kapfenberg TEOM yes 50 2l/min Styria Knittelfeld FH62IN yes 80 3 Styria Köflach FH62IN yes 50 3 Styria Leoben Donawitz TEOM yes 50 2l/min Styria Leoben Göß FH62IN yes 50 3 Styria Leoben Zentrum FH62IN yes 80 3 Styria Masenberg TEOM yes 50 2l/min Styria Mobile I FH62IN yes 50 3 Styria Mobile II FH62IN yes 50 3 Styria Peggau FH62IN yes 50 3 Styria Pöls Ost FH62IR yes 50 1 Styria Straßengel TEOM yes 50 2l/min Styria Voitsberg FH62IN yes 80 3 Styria Weiz TEOM yes 50 2l/min Styria Wildon FH62IN yes 80 3 Styria Zeltweg FH62IN yes 80 3 Tyrol Gärberbach FH62IN yes ? 3 Tyrol Hall i.T. FH62IR yes 40 1 Tyrol Innsbruck Olymp. Dorf FH62IN yes ? 3 Tyrol Innsbruck Reichenau FH62IN yes ? 3 Tyrol Innsbruck Zentrum APDA 351 yes 50 3 Tyrol Kufstein Zentrum FH62I2 yes ? 1 Tyrol Landeck FH62IN yes ? 3 Tyrol Lienz Dolomitenkreuzung APDA 351 yes 50 3

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 94 Exposure of the Austrian Population to PM10 in 1996

Province Station Name Instrum. Bend Temp Heated Temp Sample Heating in °C Tube in °C flow in m3/h Tyrol Vomp FH62I yes ? 1 Tyrol Wörgl FH62I yes ? 1 Vorarlberg Bludenz FH62I yes 40 no 1 Vorarlberg Dornbirn FH62I yes 40 no 1 Vorarlberg Feldkirch FH62I yes 40 no 1 Vorarlberg Lustenau FH62I yes 40 no 1 Vienna Belgradplatz FH62IN no yes 50 3 Vienna Floridsdorf FH62IN no yes 50 1 Vienna Gaudenzdorf FH62IN no yes 50 3 Vienna Hermannskogel FH62IN no yes 50 1 Vienna Hietzinger Kai FH62IN no yes 50 3 Vienna Hohe Warte FH62IN no yes 50 1 Vienna Kaiserebersdorf FH62IN no yes 50 3 Vienna Kendlerstraße FH62IN no yes 50 3 Vienna Laaer Berg FH62IN no yes 50 1 Vienna Liesing FH62IN no yes 50 3 Vienna Rinnböckstraße FH62IN no yes 50 3 Vienna Schafbergbad FH62IN no yes 50 1 Vienna Stadlau FH62IN no yes 50 3 Vienna Stephansplatz FH62IN no yes 50 1 Vienna Taborstrasse FH62IN no yes 50 1 Vienna Währinger Gürtel FH62IN no yes 50 1

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Annex VII: Classification of the Environment of monitoring sites

Classification of the Environment of Austrian TSP monitoring sites according to the scheme described in the EU Ozone Directive 92/72/EC.

Province Station Name Stat. Immediate Local Environment Type Environment Burgenland Illmitz EU_R I8,I9,I92 LH,LI,LK Carinthia Arnoldstein EU_U I2c,I92 LBb,LE,LH Carinthia Bleiburg EU_R I7,I92 LCb,LE,LI Carinthia Feldkirchen EU_U I1b,I92 LCb,LE,LI Carinthia Ferlach EU_R I2c,I7 LCb,LE,LI Carinthia Fürnitz EU_R I2c,I8 LBb,LCb,LE,LI Carinthia Hermagor Gailstalstr. EU_S I1b, LCb,LE,LI,LJ Carinthia Klagenfurt Koschatstr. EU_S I1b,I92 LCb,LE,LH Carinthia Klagenfurt Völkermarkterstr. EU_S I1a,I99 LAc,LAd,LCa Carinthia Oberdrauburg EU_S I1b, LCb,LE,LI,LJ Carinthia Obervellach EU_R I7,I92 LCb,LE,LH,LI,LJ Carinthia Radenthein EU_R I2c LBb,LCb,LE,LI Erdmannsiedlung Carinthia Radenthein Evang. Kirche EU_R I2c,I7 LBb,LCb,LE Carinthia Soboth EU_R I92 LH,LI Carinthia Spittal a.d.Drau EU_U I2b,I7,I8 LCb,LE,LI Carinthia St. Andrä EU_U I7 LBc,LE,LI Carinthia St. Georgen EU_R I92 LH,LI Carinthia St. Paul EU_R I9,I92 LCb,Le,LI Carinthia St. Veit a.d.Glan EU_S I2a,I99 LCa,LE,LH,LI Carinthia Villach EU_S I1a,I99 LAc,LCa,LH Carinthia Vorhegg EU_R I8,I92 LCc,LE,LH,LJ Carinthia Völkermarkt EU_S I1a,I99 LCa,LE,LI Carinthia Wietersdorf EU_R I8 LE,LH,LI Carinthia Wolfsberg EU_U I2b,I7 LBb,LCa,LE,LJ Lower Austria Amstetten EU_U I1c LAd,LI Lower Austria Brunn a. Gebirge EU_U I4,I7,I8 LBc,LCa,LE,LH Lower Austria Deutsch Wagram EU_R I9,I92 LCb,LE,LH,LI Lower Austria Fischamend EU_R I2c,I9,I92 LCa,LE,LI Lower Austria Großenzersdorf EU_R I2c,I8,I92 LCb,LE,LH,LI Lower Austria Hainburg EU_R I1a,I7,I9,I92 LAd,LCa,LH,LI Lower Austria Himberg EU_R I2b,I9,I92 LCa,LE,LI

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 96 Exposure of the Austrian Population to PM10 in 1996

Province Station Name Stat. Immediate Local Environment Type Environment Lower Austria Irnfritz EU_R I2c,I8,I92 LCc,LE,LH,LI Lower Austria Klosterneuburg EU_U I99 LAd,LCa,LD,LH Lower Austria Korneuburg EU_U I7 LAd,LCa,LH,LI Lower Austria Krems EU_U I1a,I2c,I8,I92 LAd,LE Lower Austria Langenzersdorf EU_R I99 LCa,LE,LH,LI Lower Austria Mannswörth EU_R I8,I92 LBb,LE Lower Austria Mödling EU_U I2c,I7 LAd,LCb,LH Lower Austria Neusiedl EU_R I92 LBb,LCc,LE,LI Lower Austria Schwechat EU_U I7,I9,I92 LBb.LCa,LE,LG Lower Austria St.Pölten EU_U I2c,I7,I92 LAd,LCb,LH Lower Austria St.Valentin EU_R I9,I92 LCc,LE,LI Lower Austria Stixneusiedl EU_R I8,I92 LE,LH,LI Lower Austria Stockerau EU_U I7,I92 LAd,LCa,LH,LI Lower Austria Streithofen EU_R I92 LBb,LCc,LE,LI Lower Austria Traismauer EU_R I92 LCb,LE,LH,LI Lower Austria Trasdorf EU_R I92 LBb,LCc,LE,LI Lower Austria Tulln EU_S I1b LAa,LAc,LBb,LCb Lower Austria Vösendorf EU_S I2b LCa,LE,LI Lower Austria Wiener Neustadt EU_U I7,I92 LAd,LBc,LCb, Lower Austria Zwentendorf EU_R I9 LCc,LE,LH,LI Upper Austria Asten EU_R I9 LH, LI Upper Austria Bad Ischl EU_U I8,I92, I2b LCa, LE, LH Upper Austria Braunau EU_S I1a, I2c LBc, LCa, LE, LI Upper Austria Grünbach EU_R I92 LE,LI Upper Austria Lenzing EU_R I2c LBa,LCb,LE,LH Upper Austria Linz 24er Turm EU_S I1a, I92 LAc,LCa Upper Austria Linz BH-Urfahr EU_S I1a,I5 LAc,LCa Upper Austria Linz Hauserhof EU_U I1a, I6 LAd, LCa Upper Austria Linz Kleinmünchen EU_U I1a, I2c LAc, LCa Upper Austria Linz Neue Welt EU_U I1a LAd,LBa,LCa Upper Austria Linz ORF-Zentrum EU_U I1a LAd, LCa Upper Austria Linz Ursulinenhof EU_U I7, I1a LAc, LCa Upper Austria Perg EU_R I2c, I92 LBc, LI Upper Austria Schöneben EU_R I8,I92 LH,LI Upper Austria Steyr EU_U I2c LBc,LCb,LE,LI Upper Austria Steyregg Weih EU_R I8,I92 LBa,LE,LH,LI Upper Austria Traun EU_U I7 LCc,LE,LI

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 97

Province Station Name Stat. Immediate Local Environment Type Environment Upper Austria Vöcklabruck EU_U I2a, I4, I8, I91,LBb, LCc, LE, LH I92 Upper Austria Wels EU_S I1a, I7 LAc, LBc, LCa, LD, LH Salzburg Hallein Hagerkreuzung EU_S I1a LAd,LCa Salzburg Salzburg Itzling EU_S I2b LAc,LCb Salzburg Salzburg Lehen EU_U I2c,I6 LAc,LCc Salzburg Salzburg Mirabellplatz EU_S I1b LAa,LCb,LH Salzburg Salzburg Rudolfsplatz EU_S I1a LAa,LCa Salzburg St. Koloman EU_R I92 LH,LJ Salzburg Tamsweg EU_U I2b LAc Styria Arnfels - Remschnigg EU_R I8,I92 LE,LH,LI,LJ Styria Bockberg EU_R I92 LBb,LE,LH,LI Styria Bruck an der Mur EU_U I4 LAd,LCb,LH Styria Deutschlandsberg EU_R I2b LAc,LCb,LH Styria Fohnsdorf EU_R I92 LBb,LH,LI Styria Graz Mitte EU_U I3b LAa,LAc,LCb Styria Graz Nord EU_U I2c,I92 LBc,LCb,LE,LI Styria Graz Ost EU_U I2b LCb,LE,LH Styria Graz Südwest EU_U I2c,I92 LCc,LE,LH Styria Graz West EU_U I2c,I92 LCb,LE,LH Styria Hörgas EU_R I92 LE,LI Styria Judenburg EU_U I2c LBc,LCb,LE,LH Styria Kapfenberg EU_U I2c LAd,LCb,LH Styria Knittelfeld EU_U I2c,I7 LAa,LAc,LCb Styria Köflach EU_U I2b LAd,LCb Styria Leoben Donawitz EU_U I2b LAd,LBaLCb,LH Styria Leoben Göß EU_U I2c,I7 LAd,LCb,LH Styria Leoben Zentrum EU_U I7 LAd,LCb,LH Styria Masenberg EU_R I8,I92 LH,LJ Styria Mellachberg EU_R I8,I92 LBb,LE,LH,LI Styria Peggau EU_R I2c LCa,LE,LH,LI Styria Pöls Ost EU_R I92 LBb,LE,LI Styria Straßengel EU_R I2c,I92 LBa,LCa,LE,LI Styria Voitsberg EU_R I2c,I7 LAd,LCb,LH,LI Styria Weiz EU_U I8 LE,LH,LI Styria Wildon EU_R I8 LBb,LE,LH,LI Styria Wundschuh EU_R I92 LBb,LE,LH,LI

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 98 Exposure of the Austrian Population to PM10 in 1996

Province Station Name Stat. Immediate Local Environment Type Environment Styria Zeltweg EU_U I1b LAd,LCb, Tyrol Gärberbach EU_S I1a LCa,LH,LI,LJ Tyrol Hall i.T. EU_S I1b LAa,LAc,LCa,LI, LJ, LH Tyrol Innsbruck Olymp. Dorf EU_U I1c LAc,LCa,LI, LJ, LH Tyrol Innsbruck Reichenau EU_U I1b LAc,LCa,LH, LI, LJ Tyrol Innsbruck Zentrum EU_U I3b LAa,LAc,LCb,LH, LI, LJ Tyrol Kufstein Zentrum EU_U I1b LAc, LCa, LI, LJ, LH Tyrol Landeck EU_S I2b LAc,LCa, LI, LJ, LH Tyrol Lienz Dolomitenkreuzung EU_S I2b LAd,LCb, LI, LJ, LH Tyrol Vomp EU_S I1a LCa,LE,LI, LJ, LH Tyrol Wörgl EU_U I2b LAc,LCa,LI,LJ, LH Vorarlberg Bludenz EU_U I2b,I5 LAd,LCb,LH Vorarlberg Dornbirn EU_U I1b LAd,LCb Vorarlberg Feldkirch EU_S I3a LAa,LAc,LCa,LH Vorarlberg Lustenau EU_U I2c,I9,I92 LCa,LE,LH,LI Vienna Belgradplatz EU_U I2c,I8 LAc,LCb,LH Vienna Floridsdorf EU_U I2c LAd,LCa Vienna Gaudenzdorf EU_S I1a LAc,LCa,LH Vienna Hermannskogel EU_R I6,I8 LCc,LH Vienna Hietzinger Kai EU_S I1a,I5 LAc,LCa Vienna Hohe Warte EU_U I2c,I5 LAc,LCb,LE,LH Vienna Kaiserebersdorf EU_U I2b LAd,LBb,Lca Vienna Kendlerstraße EU_U I1b LAc,LCb Vienna Laaer Berg EU_U I1b LCa,LE,LH Vienna Liesing EU_U I1b LAd Vienna Lobau EU_R I8,I92 LBb,LH,LI,LK Vienna Rinnböckstraße EU_S I2b LAd,LCa Vienna Schafbergbad EU_U I2b,I92 LE,LH Vienna Stephansplatz EU_U I3d,I5 LAa,LAc,LCb Vienna Taborstrasse EU_S I3b LAc,LCb Vienna Währinger Gürtel EU_S I1a LAc,LCa

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Annex VIII: Classification scheme Classification scheme for the description of the environment of monitoring sites according to the EU Ozone Directive 92/72/EC Station Type EU_R type rural EU_S type street EU_U type urban background EU_O type other Immediate Environment (within a radius of 0 to 100 m) I1a large street heavy traffic I1b large street medium traffic I1c large street low traffic I1d large street pedestrian zone I2a small street heavy traffic I2b small street medium traffic I2c small street low traffic I2d small street pedestrian zone I3a canyon street heavy traffic I3b canyon street medium traffic I3c canyon street low traffic I3d canyon street pedestrian zone I4 footway I5 front of building I6 terrace, tower, belfry I7 interior court, school, hospital I8 trees I9 large flat area I91 channel I92 meadow, field I99 other Local Environment (within a radius of 100 m to some km) LAa urban commercial LAb urban industrial LAc urban residential LAd mixture of commercial, industrial and residential LBa insutrial heavy concentration LBb industrial medium concentration LBc industrial light concentration LCa road traffic heavy LCb road traffic medium LCc road traffic light LD commercial LE residential (isolated houses) LF harbour LG airport LH park, forest, natural field LI agricultural area LJ mountains, valleys LK sea side or lake side LL other

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 100 Exposure of the Austrian Population to PM10 in 1996

Annex IX: TSP concentrations at Austrian measurement sites

TSP concentrations in mg/m3 as annual mean value from Austrian measurement sites

Province Monitoring Site 1993 1994 1995 1996 1997 Burgenland Illmitz 0,028 0,032 0,026 Carinthia Bleiburg 0,039 0,043 0,039 0,042 0,039 Carinthia Feldkirchen 0,023 Carinthia Ferlach 0,038 0,034 0,031 0,034 0,030 Carinthia Fürnitz 0,037 0,027 0,032 0,043 0,035 Carinthia Hermagor 0,039 0,041 0,028 0,033 0,065 Carinthia Klagenfurt Koschatstr. 0,046 0,047 0,041 0,043 0,040 Carinthia Klagenfurt Völkermarkterstr. 0,074 0,073 0,076 0,065 Carinthia Oberdrauburg 0,030 0,032 0,026 0,030 Carinthia Obervellach 0,030 0,026 0,021 0,026 0,020 Carinthia Radenthein Erdmannsiedlung 0,026 0,024 0,020 Carinthia Radenthein Evangelische Kirche 0,030 0,028 0,027 Carinthia Spittal a.d.D. 0,038 0,042 0,032 0,036 0,028 Carinthia St. Andrä 0,048 0,046 0,048 0,046 0,040 Carinthia St. Georgen Herzogberg 0,027 0,029 0,027 0,030 0,023 Carinthia St. Paul Hundsdorf 0,032 0,030 0,024 Carinthia Villach 0,068 0,076 0,050 0,047 0,041 Carinthia Völkermarkt 0,054 0,054 0,047 0,053 0,048 Carinthia Vorhegg 0,015 0,012 Carinthia Wietersdorf Pemberg 0,033 0,026 Carinthia Wolfsberg 0,066 0,066 0,050 0,053 0,045 Lower Austria Amstetten 0,030 0,038 0,037 Lower Austria Brunn a.G. 0,037 0,038 0,037 0,041 0,028 Lower Austria Deutsch Wagram 0,040 0,047 0,041 Lower Austria Fischamend 0,045 0,033 0,032 0,035 0,031 Lower Austria Großenzersdorf 0,039 0,038 0,041 0,037 Lower Austria Hainburg 0,033 0,029 Lower Austria Heidenreichstein 0,025 Lower Austria Himberg 0,035 0,032 0,037 0,028 Lower Austria Irnfritz 0,031 0,031 0,027 Lower Austria Klosterneuburg 0,038 0,035 0,035 0,043 0,034 Lower Austria Korneuburg 0,046 0,037 0,036 0,040 0,031 Lower Austria Krems 0,037 0,034 0,035 Lower Austria Langenzersdorf 0,037 0,037 0,036 0,040 0,033 Lower Austria Mannswörth 0,044 0,038 0,034 0,026 0,036

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 101

Province Monitoring Site 1993 1994 1995 1996 1997 Lower Austria Mistelbach 0,031 0,027 Lower Austria Mödling 0,041 0,038 0,034 0,039 0,031 Lower Austria Neusiedl i.T. 0,032 0,033 0,041 Lower Austria Pillersdorf 0,036 0,030 0,026 0,031 0,027 Lower Austria Schwechat 0,036 0,038 0,041 0,029 Lower Austria St. Pölten 0,050 0,059 0,055 Lower Austria St. Valentin 0,035 0,031 0,033 0,031 Lower Austria Stixneusiedl 0,029 0,032 0,037 0,030 Lower Austria Stockerau 0,042 0,039 0,036 0,034 Lower Austria Streithofen 0,016 0,029 0,037 0,028 Lower Austria Traisen 0,031 Lower Austria Traismauer 0,029 0,033 0,029 Lower Austria Trasdorf 0,022 0,025 0,032 0,025 Lower Austria Tulln 0,036 0,044 0,048 0,042 Lower Austria Vösendorf 0,039 0,034 0,034 0,036 Lower Austria Wiener Neustadt 0,034 0,029 0,036 0,041 0,034 Lower Austria Zwentendorf 0,027 0,033 0,037 0,030 Upper Austria Asten 0,035 0,029 0,027 0,035 0,031 Upper Austria Bad Ischl 0,033 0,029 0,023 Upper Austria Braunau 0,030 0,026 0,023 0,026 0,022 Upper Austria Grünbach 0,017 0,015 Upper Austria Hochburg-Ach 0,021 0,022 0,020 0,026 Upper Austria Lenzing 0,025 0,022 0,031 0,025 Upper Austria Linz 24er Turm 0,043 0,036 0,031 0,039 0,035 Upper Austria Linz Berufsschule/Neue Welt 0,047 0,041 0,038 0,045 0,038 Upper Austria Linz Hauserhof 0,042 0,032 0,027 0,032 0,032 Upper Austria Linz Kleinmünchen 0,037 0,031 0,031 0,038 0,031 Upper Austria Linz ORF-Zentrum 0,047 0,042 0,041 0,043 0,044 Upper Austria Linz Urfahr 0,043 0,035 0,033 0,038 0,037 Upper Austria Linz Ursulinenhof 0,045 0,040 0,035 0,039 0,035 Upper Austria Perg 0,030 0,026 0,023 0,026 Upper Austria Schöneben 0,015 0,013 0,015 Upper Austria Steyr 0,034 0,026 0,025 0,035 0,025 Upper Austria Steyregg 0,041 0,032 0,029 0,031 0,028 Upper Austria Traun 0,033 0,027 0,024 0,029 0,026 Upper Austria Vöcklabruck 0,034 0,024 0,024 0,028 0,025 Upper Austria Wels 0,051 0,034 0,029 0,032 0,027 Salzburg Hallein Hagerkreuzung 0,024 0,020 0,036 0,050 0,042 Salzburg Salzburg Itzling 0,035 0,030 0,026 Salzburg Salzburg Lehen 0,023 0,019 0,020 0,032 0,027

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999) 102 Exposure of the Austrian Population to PM10 in 1996

Province Monitoring Site 1993 1994 1995 1996 1997 Salzburg Salzburg Mirabellplatz 0,032 0,031 0,037 0,033 Salzburg Salzburg Rudolfsplatz 0,025 0,038 0,044 0,054 0,048 Salzburg St. Koloman 0,012 Salzburg Tamsweg 0,035 0,033 0,038 0,039 Styria Arnfels 0,022 0,023 0,018 0,020 Styria Bockberg 0,025 0,029 0,023 Styria Bruck a.d.M. 0,030 0,028 0,029 0,025 Styria Deutschlandsberg 0,035 0,037 0,035 0,040 0,031 Styria Fohnsdorf 0,033 0,033 0,036 0,026 Styria Graz Mitte 0,058 0,059 0,053 0,055 0,053 Styria Graz Nord 0,042 0,043 0,042 0,044 0,041 Styria Graz Ost 0,044 0,050 0,044 0,047 0,043 Styria Graz Süd 0,052 0,048 0,044 0,050 0,050 Styria Graz Südwest 0,056 0,056 0,046 0,048 0,044 Styria Graz West 0,044 0,045 0,040 0,043 0,045 Styria Hörgas 0,032 0,033 0,026 0,033 0,026 Styria Judenburg 0,025 0,027 0,025 0,021 Styria Kapfenberg 0,040 0,042 0,042 0,049 0,035 Styria Knittelfeld 0,034 0,036 0,047 0,034 Styria Köflach 0,039 0,045 0,048 0,049 Styria Leoben Donawitz 0,056 0,062 0,089 0,092 0,082 Styria Leoben Göß 0,049 0,048 0,043 0,042 0,037 Styria Leoben Zentrum 0,034 0,038 0,042 0,036 0,036 Styria Masenberg 0,008 0,013 0,015 0,017 0,013 Styria Mellachberg 0,031 Styria Peggau 0,057 0,044 0,045 0,044 Styria Straßengel Kirche 0,030 0,034 0,026 Styria Voitsberg Freibad 0,042 0,046 0,032 Styria Weiz 0,054 0,045 0,039 0,050 0,038 Styria Wildon 0,043 0,039 0,036 0,041 0,032 Styria Wundschuh 0,031 0,036 Styria Zeltweg 0,050 0,046 0,044 0,035 Tyrol Brixlegg Bahnhof 0,030 0,026 Tyrol Brixlegg Innweg 0,029 0,026 0,030 0,036 Tyrol Gärberbach 0,027 0,022 Tyrol Hall i.T. 0,035 0,031 0,027 0,029 Tyrol Innsbruck Olympisches Dorf 0,039 0,028 0,026 0,031 Tyrol Innsbruck Reichenau 0,042 0,028 0,027 0,032 0,021 Tyrol Innsbruck Zentrum 0,038 0,030 0,029 0,033 0,026 Tyrol Kufstein Zentrum 0,028 0,026 0,025 0,028

R-163 (1999) Umweltbundesamt/Federal Environment Agency - Austria Exposure of the Austrian Population to PM10 in 1996 103

Province Monitoring Site 1993 1994 1995 1996 1997 Tyrol Landeck 0,029 0,031 0,040 0,030 Tyrol Lienz Dolomitenkreuzung 0,042 0,045 0,051 0,040 0,030 Tyrol Vomp 0,040 0,020 Tyrol Wörgl 0,027 0,024 0,027 0,031 Vorarlberg Bludenz 0,031 Vorarlberg Dornbirn 0,030 0,035 Vorarlberg Feldkirch 0,053 0,034 0,031 0,034 Vorarlberg Lustenau 0,017 0,014 0,015 0,022 Vienna Belgradplatz 0,043 0,042 0,054 0,042 Vienna Floridsdorf 0,060 0,046 0,039 0,043 0,037 Vienna Gaudenzdorf 0,045 0,039 0,040 0,047 0,035 Vienna Hermannskogel 0,019 0,038 0,029 0,031 0,025 Vienna Hietzinger Kai 0,042 0,035 0,046 0,053 0,067 Vienna Hohe Warte 0,040 0,035 0,032 0,036 0,031 Vienna Kaiserebersdorf 0,044 0,038 0,038 0,047 0,042 Vienna Kendlerstr. 0,057 0,056 0,053 0,053 0,043 Vienna Laaer Berg 0,051 0,040 0,036 0,039 0,032 Vienna Liesing 0,070 0,055 0,051 0,056 0,047 Vienna Lobau 0,028 0,031 0,025 Vienna Rinnböckstr. 0,052 0,042 0,037 0,043 0,029 Vienna Schafbergbad 0,039 0,033 0,030 0,037 0,031 Vienna Stadlau 0,052 0,044 0,042 0,047 0,038 Vienna Stephansplatz 0,035 0,036 0,033 0,036 0,027 Vienna Taborstr. 0,076 0,065 0,069 0,069 0,052 Vienna Währinger Gürtel 0,052 0,046 0,039 0,041 0,032 Mean 0,038 0,037 0,035 0,039 0,034 95-Percentile 0,058 0,059 0,051 0,054 0,052 5-Percentile 0,018 0,022 0,02 0,026 0,021 Maximum 0,076 0,076 0,089 0,092 0,082 Minimum 0,008 0,013 0,012 0,017 0,013 Median 0,038 0,036 0,033 0,037 0,032

Umweltbundesamt/Federal Environment Agency – Austria R-163 (1999)