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A Danish decision-support GIS tool for management of urban air quality and human exposures
Transportation Research Part D 6 (2001) 229±241
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A Danish decision-support GIS tool for management of urban air quality and human exposures Steen Solvang Jensen a,*, Ruwim Berkowicz a, Henning Sten Hansen b, Ole Hertel a a
Department of Atmospheric Environment, National Environmental Research Institute (NERI), Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark b Department of Policy Analysis, National Environmental Research Institute (NERI), Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark
Abstract A new prototype model system named AirGIS has been developed to support local authorities in air quality management for big Danish cities. The system is based on the Danish operational street pollution model (OSPM), technical and cadastral digital maps and Danish national administrative databases on buildings, cadastres and populations. It applies a geographic information system (GIS). AirGIS estimates ambient air pollution levels at high temporal and spatial resolutions. The model system enables mapping of trac emissions, air quality levels and human exposures at residence addresses, at workplace addresses and in streets. Mapping and scenario results can be compared with air quality limits. Impact assessment of trac air pollution abatement measures can also be carried out. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Air pollution; Trac; Human exposures; Decision support; GIS
1. Introduction During the past few decades, trac has become the dominant source of air pollution in larger Danish cities. Numerous studies have indicated that exposure to air pollution increases risks of developing cancer, respiratory and allergy diseases, and aggravates the condition of people suffering from respiratory or heart diseases. A Danish review study based on primarily international reports (Larsen et al., 1997) suggests that the impact of ®ne particles on mortality in Denmark can be of the same magnitude as trac accidents (about 500 annual deaths in a population of 5.2 million). The air pollutants that raise health concerns under Danish conditions are ®ne particles, *
Corresponding author. Tel.: +45-4630-1281; fax: +45-4630-1214. E-mail address: [email protected] (S.S. Jensen).
1361-9209/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 1 - 9 2 0 9 ( 0 0 ) 0 0 0 2 6 - 2
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nitrogen dioxide (NO2 ), ozone (O3 ), polycyclic aromatic hydrocarbones (PAHs), benzene, 1,3butadiene, ethene and propene, and aldehydes (formaldehyde, acrolein, acetaldehyde). Fine particles pose the greatest health impact on mortality and morbidity, especially to highly exposed and sensitive persons (World Health Organisation, 1995, 1996; Hester and Harrison, 1998). New stricter European union (EU) air quality limit values will have to be met by 2005 and 2010 for 13 air pollutants, and air quality assessment and management are required for large cities with more than 250,000 inhabitants and areas that otherwise justify assessment. Urban air quality management has to encompass monitoring, assessment (e.g., by modelling), and information to the public (European Commission, 1996). Current Danish urban air quality management mainly involves monitoring and alert and information systems (Kemp et al., 1999). Comprehensive emission inventories, mapping of air quality and action plans are almost entirely missing. Decision-support systems are not applied in Denmark, although such systems are in operation in other Nordic and European medium-sized and larger cities. Examples of systems for local authorities are the Norwegian AirQUIS, the Swedish EnviMan, and the Austrian AirWare. However, present systems often have low spatial resolutions, crude exposure assessment (if any), and they do not take full advantage of geographic information system (GIS) and administrative databases. The application of decision-support tools is an opportunity to improve air quality planning for the larger Danish cities. Comprehensive systems should ideally include the following main elements air quality monitoring, emission inventories, air quality and exposure mapping, impact assessment of dierent trac abatement measures, information to the public as well as forecasts for, e.g., next-day levels. Compared with existing decision-support systems the presented model system has a higher spatial resolution, uses available digital maps and administrative databases for automatic generation of street con®guration data, applies more GIS features, and provides improved exposure assessment (Jensen, 1998a, 1999). 2. Methodology The model system combines modelled ambient air pollution levels at postal address locations with the presence of people at the address to estimate human exposures (Jensen, 1999). The address point of either the residence or the workplace is used as an exposure indicator. The method is an indirect exposure determination based on a microenvironment approach (Fig. 1). The Danish Municipality of Middelfart has served as a case study area due to easy access of the needed data and since a small municipality of about 19,000 inhabitants is sucient for developing and testing the methodology although air pollution levels are relatively low. 2.1. Air pollution modelling Concentration levels are determined using the operational street pollution model (OSPM) (Hertel and Berkowicz, 1989a,b; Berkowicz et al., 1997a,b; Berkowicz, 1998). The OSPM model calculates ambient hourly concentration levels of CO, NO2 ; NOx NO NO2 ; O3 and benzene, based on inputs of street con®gurations (physical appearance of the street) and hourly inputs of trac, meteorological parameters and urban background concentrations. Modelled levels
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Fig. 1. Conceptual outline of the methodology of the human exposure model.
represent ground-level concentrations, since the receptor points are located in front of the facades in heights of 2±3 m. The model describes the physical and chemical processes governing the concentrations in the street, and computes pollution levels as the sum of the direct contribution from trac and the contribution from the wind generated recirculating of air pollution inside the street environment. The direct contribution is described by a plume dispersion model and the recirculation by a box model that takes into account the exchange with the urban background air. The model also includes simple photochemistry between NO, NO2 and O3 (Palmgren et al., 1996). Trac emissions are estimated using emission factors and hourly trac data. The OSPM model uses default values for emission factors for each vehicle category. These factors are de®ned as emission per km travelled depending on the travel speed. The emission factors are from Sorenson and Schramm (1992) and Jensen (1995). Emission factors for benzene have been estimated from air quality measurements and inverse model calculations (Palmgren et al., 1999). The trac data are based on average daily trac (ADT) for passenger cars, vans, lorries and buses, and the travel speed for the dierent vehicle categories for each street in the municipality. In order to obtain hourly trac inputs for the OSPM model, an empirical method was developed based on trac data for selected locations. Default values for the temporal variations in cold starts have also been established empirically (Jensen, 1997). A semi-empirical background model was developed to provide hourly urban background concentrations for the OSPM model for cities with no monitoring. This model is based on standardised urban and rural background concentrations of NO2 ; NOx ; O3 and CO derived from monitor stations. The method is based on simple photochemistry and takes into account the exchange between rural and urban air. Levels in smaller cities are based on a simple dispersion formula to downscale observations in Copenhagen, and the decline in levels from the city centre to the outskirts is taken into account empirically (Jensen, 1998b). An urban background dispersion model is under development for application in AirGIS (Berkowicz, 2000).
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Fig. 2. Street con®guration parameters generated by the urban landscape model for use in the OSPM model to calculate air pollution levels.
The street con®guration data are static data that describe the physical environment around the receptor points. Examples of street con®guration data are street orientation, street width, building heights in wind sectors etc. An urban landscape model was developed using GIS (ArcView), the associated object-oriented programme Avenue, digital maps and data from a national database on buildings and dwellings to automatically generate street con®guration input parameters required by the OSPM model (Fig. 2). A GIS-based method was also developed to geocode the buildings in order to identify buildings to be able to draw information from the Building and Dwelling Database, e.g., building heights (Hansen et al., 1997). Meteorological parameters (wind direction and speed, temperature and global radiation) are obtained from a mast at roof level in urban areas, if available. Otherwise data are used from a nearby airport. 2.2. Digital maps and databases The authorities manage a number of comprehensive national databases for administrative purposes. These databases have two important features: All objects are uniquely identi®ed, and it is possible to combine data from dierent databases using common keys. An example of a common key is the postal address. The geographic objects and administrative databases used in the model system are illustrated in Fig. 3.
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Fig. 3. Geocoded addresses (points), buildings (polygons), streets (polylines) and property limits (polygons) in the digital maps and data from administrative databases. Data can be linked to the geographic objects.
The Building and Dwelling Register is managed by the Ministry of Housing. It contains detailed information about all buildings and homes. A property number and a building number identify buildings, and the database is used to estimate the heights of buildings. The Ministry of Housing is also in charge of the digital cadastral map and the Parcel Register, but they are managed by the National Survey and Cadastre Denmark (NSCD). Property limits are identi®ed by the cadastral district number and the cadastral number. The cadastral map with property limits is used in the process of geocoding buildings. NSCD has initiated the development of a new register containing the coordinates of addresses (address points). The address database is expected to become national by the end of year 2000. The Central Population Register is managed by the Ministry of Home Aairs. The database stores information about each Danish citizen. AirGIS uses the number of people living at each residence address, as well as their genders and ages for exposure assessment to ambient air pollution. The Ministry of Economic Aairs manages the Central Business Register, which contains information on all public and private companies. The database includes the total number of employees working at each workplace address. The database is used for exposure assessment of working people to ambient air pollution. The administrative databases do not include digital maps so addresses are used to link the data to digital maps. The Parcel Register is the only database that includes both property limit maps and attribute information. Each municipal road has a unique name and a unique four digit identi®cation code. Most municipalities maintain a road and trac database for trac planning and road maintenance purposes. However, at present most municipalities do not have links between the digital street network and the road and trac data. Therefore, a method was developed to establish such links (Jensen, 1999). There are plans to develop a national road and trac database that covers all types of roads.
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Most municipalities have a technical digital map with the objects required for the model (buildings, roads, address points), and data have to be obtained through the municipalities. However, a national digital map is under development by the NSCD that includes the building and road objects. Currently, it covers parts of the country and will be completed in 2001. 2.3. Exposure assessment Exposure is de®ned as a person's contact to an air pollutant. The outdoor air pollution levels at the residence addresses and the workplace addresses are used to provide a crude assessment of the exposure to ambient air pollution. A detailed exposure assessment can be carried out taking into account the time people spend indoors and outdoors. The indoor exposures are estimated using empirical indoor/outdoor ratios for the dierent pollutants, and average diurnal time pro®les are used to estimate the time spent by persons indoors and outdoors (Jensen, 1999). 3. Results and discussion The OSPM model has been extensively validated on data at locations in Denmark and abroad under dierent conditions of meteorology, background concentrations, trac and street con®gurations. A validation test of the presented model system to predict outdoor concentrations at street level was carried out using measured data from a street monitoring station in Copenhagen (Jagtvej) (Vignati et al., 1997). The test included inputs generated by the standardised trac method (Jensen, 1997) and the semi-empirical background method (Jensen, 1998b) mentioned above. The urban landscape model for estimation of street con®guration data (Hansen et al., 1997) was not applied since the required data have not yet been established for the model system for this area. Instead, crude street con®guration data were applied from a questionnaire ®lled out by the municipality. The uncertainty introduced by using generated compared with measured inputs is illustrated in Fig. 4. Overall, applying generated inputs increases the scatter. Detailed analysis showed that the annual means of NOx and NO2 were within 3% and 1%, respectively. The monthly means of NOx and NO2 were within 9% and 3%, respectively. Larger uncertainty is experienced for shorter averaging times and especially extreme values as indicated by the scatter in Fig. 4. The analysis also showed that for generated trac data the scatter increased although high values were not aected much. Application of generated background concentrations also added to the scatter and further smoothened out high values. The generated crude street con®guration data also increased the scatter but annual and monthly means were not aected much. Application of the urban landscape model will reduce the latter problem because it generates accurate street con®guration data. In summary at street level, the system predicts well the outdoor concentrations for especially longer averaging times with higher uncertainty for shorter averaging times. In the present design of the model system and with the limitation of data on human time-activity patterns, human exposure is estimated by combining outdoor concentrations with population data at address level, taking the address as an exposure indicator. This approach is useful for air quality management since local authorities regulate outdoor concentrations. However, the
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Fig. 4. Scatter plots of measured and modelled hourly NOx concentrations in ppb at Jagtvej, Copenhagen. Left: with measured inputs. Right: with generated inputs. (Vignati et al., 1997).
address is only a crude indicator of personal exposure since personal exposure takes into account the person's time-activity pattern, that is, the time spent in dierent microenvironments (e.g., at home, in streets, at workplace etc.). The address as an exposure indicator for personal exposure is further discussed in Jensen (1999). Urban air management is an ongoing process that includes the main elements in the planning process: mapping, setting of goals, assessment of abatement measures, implementation and evaluation. The model system may be used for mapping of trac emission inventories (NOx , CO, benzene), mapping of air quality NO2 ; O3 , CO, benzene) and mapping of exposures at residences (total numbers, age, gender), at workplaces (total number of employees) and in streets (road users). The comprehensive features of GIS are also available for visualisation and analysis of data. Visualisation of the density of, e.g., benzene concentrations based on levels at residence addresses can provide an overview of areas with high and low levels within the entire municipality. The highest levels are found in streets with a combination of high trac loads and buildings along the street resulting in poor dispersion conditions (Fig. 5). The high geographic resolution of the model system may, e.g., be visualised for benzene concentrations, number of people and exposures at address level for a close-up of the city of Middelfart. An exposure index is de®ned as concentrations times number of people at each address, and the addresses with high concentrations and many people will have a high index (Fig. 6). Exposure data may be shown as an accumulated distribution function of the number of people exposed to ambient annual benzene levels with the residence address as exposure indicator, and the percentage of people exposed to certain levels of benzene may be derived (Fig. 7). The road user benzene exposure intensity may be visualised (Fig. 8) taking into account the occupancy of vehicles and the time spent on the road segments. However, the indoor±outdoor
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µ
Fig. 5. Upper: Density of benzene concentration levels in 1996 for the entire municipality with a grid size of 100 m. Lower: Density of benzene concentration levels for a close-up of the town of Middelfart with a grid size of 5 m (lg m 3 km 2 ).
ratio for the dierent vehicle categories are not taking into account and the exposure of pedestrians and bicyclists are not considered due to lack of data. The highest exposure of road users are at the roads with the highest trac loads and concentrations. Two streets that have the same
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Fig. 6. Upper: Outdoor annual benzene levels (lg m 3 ) in 1996. Middle: People at residence addresses. Lower: Simple exposure index (lg m 3 persons).
trac loads and the same concentration levels can have very dierent road user exposures depending on the vehicle composition (e.g., passenger cars versus buses) and hence the number of people passing through the street. However, the trac conditions in Middelfart are entirely
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µ
µ Fig. 7. Accumulated distribution function of the number of people exposed to annual means of outdoor benzene at their residence addresses in the Municipality of Middelfart during 1996. Benzene levels have strongly decreased since 1996 due to a reduction of the benzene content in petrol and the penetration of cars equipped with catalyst converters.
Fig. 8. Annual road user benzene exposure intensity in 1996 visualised for all road users: passenger cars, vans, lorries and buses (1000 ppbpersonhours km 1 ).
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dominated by passenger cars and the in¯uence of bus trac on road user exposures cannot be observed as would be the case for a large city with much more public transportation. As illustrated, the AirGIS model system enables not only emission and air quality mapping but also crude exposure assessment for identi®cation of priority areas for further action. Mapping and scenario results of air quality levels as averages or percentiles can be compared with the new EU air quality limits or World Health Organisation (WHO) guidelines in order to assess compliance with limit values or municipal goals for the urban air quality. The exposure model is suitable to assess the impacts of dierent abatement measures on emissions, ambient levels and exposures provided that the changes in trac emission factors, trac loads and vehicle composition in the road network, and street con®guration data are available. Such measures could be new stringent emission standards, increase in the use of catalytic converters, alternative fuels, or urban and trac planning measures. Assessment of dierent abatement measures may be carried out to evaluate impacts and to compare with stated goals. The model system may also be used to evaluate dierent options for location of, e.g., a new kindergarten to minimise children's exposure to air pollution (Fig. 9). A systematic evaluation of the eects of implementation of an action plan is an important element in the planning process and should address the progress in achieving goals and provide a foundation for possible revision of goals and/or abatement measures. Apart from modelling of air pollution, monitoring stations are also crucial for evaluation of progress. A validation of the developed methods for generation of input parameters for the OSPM model and the applied digital maps and databases has been given in Jensen (1999).
Fig. 9. Annual benzene levels in 1996 at the locations of kindergartens (3±6 years).
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4. Conclusions A new prototype air pollution exposure system with high spatial and temporal resolutions has been developed for application by local authorities in big Danish cities as a decision-support tool for urban air quality management. The system integrates available digital maps, administrative databases, a street pollution model and GIS for air quality and exposure estimation at address level. Compared with existing urban air quality management tools, the presented model system has higher spatial resolution, makes use of digital maps and administrative databases for automatic generation of street con®guration data, adds GIS features using standard GIS, and provides improved exposure assessment.
Acknowledgements The model system was developed as part the ®rst author's Ph.D. study, which was funded by the Danish Transport Council, the Danish Research Academy and the National Environmental Research Institute, with matriculation at the University of Roskilde, Denmark. Activities related to the model development have also been undertaken under the Danish National Environmental Research Programme. The following institutions have supplied data Municipality of Middelfart, Ministry of Housing, National Survey and Cadastre Denmark, Ministry of Home Aairs, and Ministry of Economic Aairs. References Berkowicz, R., Hertel, O., Sùrensen, N.N., Michelsen, J.A., 1997a. Modelling air pollution from trac in urban areas. In: Perkins, R.J., Belcher, S.E. (Eds.), IMA Proceedings on Flow and Dispersion Through Obstacles, Cambridge. Marts 28±30, 1994, pp. 121±142. Berkowicz, R., Hertel, O., Larsen, S., Sùrensen, N.N., Nielsen, M., 1997b. Modelling trac pollution in streets. NERI Technical Report, National Environmental Research Institute, Roskilde. Berkowicz, R., 1998. Street scale models. In: Fenger, J., Hertel, O., Palmgren, F. (Eds.), Urban Air Pollution ± European Aspects, Kluwer Academic Publishers, Dordrecht, pp. 223±251. Berkowicz, R., 2000. A simple model for urban background pollution. In: Proceedings of the Second International Conference on Urban Air Quality, Measurement, Modelling & Management, 3±5 March 1999. Madrid, Journal of Environmental Monitoring and Assessment (accepted). European Commission, 1996. Directive 96/62/EC on ambient air quality assessment and management. Ocial Journal L296, 55. Hansen, H.S., Jensen, S.S., Berkowicz, R., 1997. Estimating street air quality using a 21=2 dimensional urban landscape model. In: Proceedings of AM/FM-GIS Nordic Conference, 29±31 October 1997. Kolding. Hertel, O., Berkowicz, R., 1989a. Modelling NO2 concentrations in a street canyon. NERI Technical Report No. A-131, National Environmental Research Institute, Roskilde. Hertel, O., Berkowicz, R., 1989b. Modelling pollution from trac in a street canyon. Evaluation of data and model development. NERI Technical Report No. A-129, National Environmental Research Institute, Roskilde. Hester, R.E., Harrison, R.M. (Eds.), 1998. Air Pollution and Health. Issues in Environmental Science and Technology, 10. Jensen, S.S., 1995. Driving patterns and emissions from dierent types of roads. The Science of the Total Environment 169, 123±128.
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Jensen, S.S., 1997. Standardised trac inputs for use in the operational street pollution model (OSPM). NERI Technical Report No. 197, National Environmental Research Institute, Roskilde. Jensen, S.S., 1998a. Mapping human exposure to trac air pollution using GIS. Journal of Hazardous Materials 61 (1±3), 385±392. Jensen, S.S., 1998b. Background concentrations for use in the operational street pollution model (OSPM). NERI Technical Report No. 234, National Environmental Research Institute, Roskilde. Jensen, S.S., 1999. A geographic approach to modelling human exposure to trac air pollution using GIS. Ph.D. Thesis. National Environmental Research Institute, Roskilde. Kemp, K., Palmgren, F., Manscher, O.H., 1999. The Danish Air Quality Monitoring Network. Annual Report for 1998, NERI Technical Report No. 296, National Environmental Research Institute, Roskilde. Larsen, P.B., Larsen, J.C., Fenger, J., Jensen, S.S., 1997. Evaluation of health impacts of air pollution from road trac. Danish Environmental Protection Agency Report No. 352, Copenhagen (In Danish with English summary). Palmgren, F., Berkowicz, R., Hertel, O., Vignati, E., 1996. Eects of reducing NOx and NO2 levels in urban streets. Science of the Total Environment 189/190, 409±415. Palmgren, F., Berkowicz, R., Ziv, A., Hertel, O., 1999. Actual car ¯eet emissions estimated from urban air quality measurements and street pollution model. Science of the Total Environment 235, 101±109. Sorenson, S., Schramm, J., 1992. Individual and public transportation ± emissions and energy consumption models. Report RE 91-5, Laboratory for Energetics, Technical University of Denmark, Lyngby. World Health Organisation, 1995. Updating and revision of the air quality guidelines for Europe. Meeting of the working group on classical air pollutants, Bilthoven, The Netherlands, 11±14 October 1994. WHO Regional Oce for Europe, Copenhagen. World Health Organisation, 1996. Updating and revision of the air quality guidelines for Europe. Report on a WHO working group on volatile organic compounds, Brussels, Belgium, 2±6 October 1995. WHO Regional Oce for Europe, Copenhagen. Vignati, E., Hertel, O., Berkowicz, R., Raaschou-Nielsen, O., 1997. Generation of input data for the OSPM. A sensitivity analysis of a method based on a questionnaire. NERI Technical Report No. 188, National Environmental Research Institute, Roskilde.
www.elsevier.com/locate/trd
A Danish decision-support GIS tool for management of urban air quality and human exposures Steen Solvang Jensen a,*, Ruwim Berkowicz a, Henning Sten Hansen b, Ole Hertel a a
Department of Atmospheric Environment, National Environmental Research Institute (NERI), Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark b Department of Policy Analysis, National Environmental Research Institute (NERI), Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark
Abstract A new prototype model system named AirGIS has been developed to support local authorities in air quality management for big Danish cities. The system is based on the Danish operational street pollution model (OSPM), technical and cadastral digital maps and Danish national administrative databases on buildings, cadastres and populations. It applies a geographic information system (GIS). AirGIS estimates ambient air pollution levels at high temporal and spatial resolutions. The model system enables mapping of trac emissions, air quality levels and human exposures at residence addresses, at workplace addresses and in streets. Mapping and scenario results can be compared with air quality limits. Impact assessment of trac air pollution abatement measures can also be carried out. Ó 2001 Elsevier Science Ltd. All rights reserved. Keywords: Air pollution; Trac; Human exposures; Decision support; GIS
1. Introduction During the past few decades, trac has become the dominant source of air pollution in larger Danish cities. Numerous studies have indicated that exposure to air pollution increases risks of developing cancer, respiratory and allergy diseases, and aggravates the condition of people suffering from respiratory or heart diseases. A Danish review study based on primarily international reports (Larsen et al., 1997) suggests that the impact of ®ne particles on mortality in Denmark can be of the same magnitude as trac accidents (about 500 annual deaths in a population of 5.2 million). The air pollutants that raise health concerns under Danish conditions are ®ne particles, *
Corresponding author. Tel.: +45-4630-1281; fax: +45-4630-1214. E-mail address: [email protected] (S.S. Jensen).
1361-9209/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 1 - 9 2 0 9 ( 0 0 ) 0 0 0 2 6 - 2
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nitrogen dioxide (NO2 ), ozone (O3 ), polycyclic aromatic hydrocarbones (PAHs), benzene, 1,3butadiene, ethene and propene, and aldehydes (formaldehyde, acrolein, acetaldehyde). Fine particles pose the greatest health impact on mortality and morbidity, especially to highly exposed and sensitive persons (World Health Organisation, 1995, 1996; Hester and Harrison, 1998). New stricter European union (EU) air quality limit values will have to be met by 2005 and 2010 for 13 air pollutants, and air quality assessment and management are required for large cities with more than 250,000 inhabitants and areas that otherwise justify assessment. Urban air quality management has to encompass monitoring, assessment (e.g., by modelling), and information to the public (European Commission, 1996). Current Danish urban air quality management mainly involves monitoring and alert and information systems (Kemp et al., 1999). Comprehensive emission inventories, mapping of air quality and action plans are almost entirely missing. Decision-support systems are not applied in Denmark, although such systems are in operation in other Nordic and European medium-sized and larger cities. Examples of systems for local authorities are the Norwegian AirQUIS, the Swedish EnviMan, and the Austrian AirWare. However, present systems often have low spatial resolutions, crude exposure assessment (if any), and they do not take full advantage of geographic information system (GIS) and administrative databases. The application of decision-support tools is an opportunity to improve air quality planning for the larger Danish cities. Comprehensive systems should ideally include the following main elements air quality monitoring, emission inventories, air quality and exposure mapping, impact assessment of dierent trac abatement measures, information to the public as well as forecasts for, e.g., next-day levels. Compared with existing decision-support systems the presented model system has a higher spatial resolution, uses available digital maps and administrative databases for automatic generation of street con®guration data, applies more GIS features, and provides improved exposure assessment (Jensen, 1998a, 1999). 2. Methodology The model system combines modelled ambient air pollution levels at postal address locations with the presence of people at the address to estimate human exposures (Jensen, 1999). The address point of either the residence or the workplace is used as an exposure indicator. The method is an indirect exposure determination based on a microenvironment approach (Fig. 1). The Danish Municipality of Middelfart has served as a case study area due to easy access of the needed data and since a small municipality of about 19,000 inhabitants is sucient for developing and testing the methodology although air pollution levels are relatively low. 2.1. Air pollution modelling Concentration levels are determined using the operational street pollution model (OSPM) (Hertel and Berkowicz, 1989a,b; Berkowicz et al., 1997a,b; Berkowicz, 1998). The OSPM model calculates ambient hourly concentration levels of CO, NO2 ; NOx NO NO2 ; O3 and benzene, based on inputs of street con®gurations (physical appearance of the street) and hourly inputs of trac, meteorological parameters and urban background concentrations. Modelled levels
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Fig. 1. Conceptual outline of the methodology of the human exposure model.
represent ground-level concentrations, since the receptor points are located in front of the facades in heights of 2±3 m. The model describes the physical and chemical processes governing the concentrations in the street, and computes pollution levels as the sum of the direct contribution from trac and the contribution from the wind generated recirculating of air pollution inside the street environment. The direct contribution is described by a plume dispersion model and the recirculation by a box model that takes into account the exchange with the urban background air. The model also includes simple photochemistry between NO, NO2 and O3 (Palmgren et al., 1996). Trac emissions are estimated using emission factors and hourly trac data. The OSPM model uses default values for emission factors for each vehicle category. These factors are de®ned as emission per km travelled depending on the travel speed. The emission factors are from Sorenson and Schramm (1992) and Jensen (1995). Emission factors for benzene have been estimated from air quality measurements and inverse model calculations (Palmgren et al., 1999). The trac data are based on average daily trac (ADT) for passenger cars, vans, lorries and buses, and the travel speed for the dierent vehicle categories for each street in the municipality. In order to obtain hourly trac inputs for the OSPM model, an empirical method was developed based on trac data for selected locations. Default values for the temporal variations in cold starts have also been established empirically (Jensen, 1997). A semi-empirical background model was developed to provide hourly urban background concentrations for the OSPM model for cities with no monitoring. This model is based on standardised urban and rural background concentrations of NO2 ; NOx ; O3 and CO derived from monitor stations. The method is based on simple photochemistry and takes into account the exchange between rural and urban air. Levels in smaller cities are based on a simple dispersion formula to downscale observations in Copenhagen, and the decline in levels from the city centre to the outskirts is taken into account empirically (Jensen, 1998b). An urban background dispersion model is under development for application in AirGIS (Berkowicz, 2000).
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Fig. 2. Street con®guration parameters generated by the urban landscape model for use in the OSPM model to calculate air pollution levels.
The street con®guration data are static data that describe the physical environment around the receptor points. Examples of street con®guration data are street orientation, street width, building heights in wind sectors etc. An urban landscape model was developed using GIS (ArcView), the associated object-oriented programme Avenue, digital maps and data from a national database on buildings and dwellings to automatically generate street con®guration input parameters required by the OSPM model (Fig. 2). A GIS-based method was also developed to geocode the buildings in order to identify buildings to be able to draw information from the Building and Dwelling Database, e.g., building heights (Hansen et al., 1997). Meteorological parameters (wind direction and speed, temperature and global radiation) are obtained from a mast at roof level in urban areas, if available. Otherwise data are used from a nearby airport. 2.2. Digital maps and databases The authorities manage a number of comprehensive national databases for administrative purposes. These databases have two important features: All objects are uniquely identi®ed, and it is possible to combine data from dierent databases using common keys. An example of a common key is the postal address. The geographic objects and administrative databases used in the model system are illustrated in Fig. 3.
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Fig. 3. Geocoded addresses (points), buildings (polygons), streets (polylines) and property limits (polygons) in the digital maps and data from administrative databases. Data can be linked to the geographic objects.
The Building and Dwelling Register is managed by the Ministry of Housing. It contains detailed information about all buildings and homes. A property number and a building number identify buildings, and the database is used to estimate the heights of buildings. The Ministry of Housing is also in charge of the digital cadastral map and the Parcel Register, but they are managed by the National Survey and Cadastre Denmark (NSCD). Property limits are identi®ed by the cadastral district number and the cadastral number. The cadastral map with property limits is used in the process of geocoding buildings. NSCD has initiated the development of a new register containing the coordinates of addresses (address points). The address database is expected to become national by the end of year 2000. The Central Population Register is managed by the Ministry of Home Aairs. The database stores information about each Danish citizen. AirGIS uses the number of people living at each residence address, as well as their genders and ages for exposure assessment to ambient air pollution. The Ministry of Economic Aairs manages the Central Business Register, which contains information on all public and private companies. The database includes the total number of employees working at each workplace address. The database is used for exposure assessment of working people to ambient air pollution. The administrative databases do not include digital maps so addresses are used to link the data to digital maps. The Parcel Register is the only database that includes both property limit maps and attribute information. Each municipal road has a unique name and a unique four digit identi®cation code. Most municipalities maintain a road and trac database for trac planning and road maintenance purposes. However, at present most municipalities do not have links between the digital street network and the road and trac data. Therefore, a method was developed to establish such links (Jensen, 1999). There are plans to develop a national road and trac database that covers all types of roads.
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Most municipalities have a technical digital map with the objects required for the model (buildings, roads, address points), and data have to be obtained through the municipalities. However, a national digital map is under development by the NSCD that includes the building and road objects. Currently, it covers parts of the country and will be completed in 2001. 2.3. Exposure assessment Exposure is de®ned as a person's contact to an air pollutant. The outdoor air pollution levels at the residence addresses and the workplace addresses are used to provide a crude assessment of the exposure to ambient air pollution. A detailed exposure assessment can be carried out taking into account the time people spend indoors and outdoors. The indoor exposures are estimated using empirical indoor/outdoor ratios for the dierent pollutants, and average diurnal time pro®les are used to estimate the time spent by persons indoors and outdoors (Jensen, 1999). 3. Results and discussion The OSPM model has been extensively validated on data at locations in Denmark and abroad under dierent conditions of meteorology, background concentrations, trac and street con®gurations. A validation test of the presented model system to predict outdoor concentrations at street level was carried out using measured data from a street monitoring station in Copenhagen (Jagtvej) (Vignati et al., 1997). The test included inputs generated by the standardised trac method (Jensen, 1997) and the semi-empirical background method (Jensen, 1998b) mentioned above. The urban landscape model for estimation of street con®guration data (Hansen et al., 1997) was not applied since the required data have not yet been established for the model system for this area. Instead, crude street con®guration data were applied from a questionnaire ®lled out by the municipality. The uncertainty introduced by using generated compared with measured inputs is illustrated in Fig. 4. Overall, applying generated inputs increases the scatter. Detailed analysis showed that the annual means of NOx and NO2 were within 3% and 1%, respectively. The monthly means of NOx and NO2 were within 9% and 3%, respectively. Larger uncertainty is experienced for shorter averaging times and especially extreme values as indicated by the scatter in Fig. 4. The analysis also showed that for generated trac data the scatter increased although high values were not aected much. Application of generated background concentrations also added to the scatter and further smoothened out high values. The generated crude street con®guration data also increased the scatter but annual and monthly means were not aected much. Application of the urban landscape model will reduce the latter problem because it generates accurate street con®guration data. In summary at street level, the system predicts well the outdoor concentrations for especially longer averaging times with higher uncertainty for shorter averaging times. In the present design of the model system and with the limitation of data on human time-activity patterns, human exposure is estimated by combining outdoor concentrations with population data at address level, taking the address as an exposure indicator. This approach is useful for air quality management since local authorities regulate outdoor concentrations. However, the
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Fig. 4. Scatter plots of measured and modelled hourly NOx concentrations in ppb at Jagtvej, Copenhagen. Left: with measured inputs. Right: with generated inputs. (Vignati et al., 1997).
address is only a crude indicator of personal exposure since personal exposure takes into account the person's time-activity pattern, that is, the time spent in dierent microenvironments (e.g., at home, in streets, at workplace etc.). The address as an exposure indicator for personal exposure is further discussed in Jensen (1999). Urban air management is an ongoing process that includes the main elements in the planning process: mapping, setting of goals, assessment of abatement measures, implementation and evaluation. The model system may be used for mapping of trac emission inventories (NOx , CO, benzene), mapping of air quality NO2 ; O3 , CO, benzene) and mapping of exposures at residences (total numbers, age, gender), at workplaces (total number of employees) and in streets (road users). The comprehensive features of GIS are also available for visualisation and analysis of data. Visualisation of the density of, e.g., benzene concentrations based on levels at residence addresses can provide an overview of areas with high and low levels within the entire municipality. The highest levels are found in streets with a combination of high trac loads and buildings along the street resulting in poor dispersion conditions (Fig. 5). The high geographic resolution of the model system may, e.g., be visualised for benzene concentrations, number of people and exposures at address level for a close-up of the city of Middelfart. An exposure index is de®ned as concentrations times number of people at each address, and the addresses with high concentrations and many people will have a high index (Fig. 6). Exposure data may be shown as an accumulated distribution function of the number of people exposed to ambient annual benzene levels with the residence address as exposure indicator, and the percentage of people exposed to certain levels of benzene may be derived (Fig. 7). The road user benzene exposure intensity may be visualised (Fig. 8) taking into account the occupancy of vehicles and the time spent on the road segments. However, the indoor±outdoor
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Fig. 5. Upper: Density of benzene concentration levels in 1996 for the entire municipality with a grid size of 100 m. Lower: Density of benzene concentration levels for a close-up of the town of Middelfart with a grid size of 5 m (lg m 3 km 2 ).
ratio for the dierent vehicle categories are not taking into account and the exposure of pedestrians and bicyclists are not considered due to lack of data. The highest exposure of road users are at the roads with the highest trac loads and concentrations. Two streets that have the same
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Fig. 6. Upper: Outdoor annual benzene levels (lg m 3 ) in 1996. Middle: People at residence addresses. Lower: Simple exposure index (lg m 3 persons).
trac loads and the same concentration levels can have very dierent road user exposures depending on the vehicle composition (e.g., passenger cars versus buses) and hence the number of people passing through the street. However, the trac conditions in Middelfart are entirely
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µ Fig. 7. Accumulated distribution function of the number of people exposed to annual means of outdoor benzene at their residence addresses in the Municipality of Middelfart during 1996. Benzene levels have strongly decreased since 1996 due to a reduction of the benzene content in petrol and the penetration of cars equipped with catalyst converters.
Fig. 8. Annual road user benzene exposure intensity in 1996 visualised for all road users: passenger cars, vans, lorries and buses (1000 ppbpersonhours km 1 ).
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dominated by passenger cars and the in¯uence of bus trac on road user exposures cannot be observed as would be the case for a large city with much more public transportation. As illustrated, the AirGIS model system enables not only emission and air quality mapping but also crude exposure assessment for identi®cation of priority areas for further action. Mapping and scenario results of air quality levels as averages or percentiles can be compared with the new EU air quality limits or World Health Organisation (WHO) guidelines in order to assess compliance with limit values or municipal goals for the urban air quality. The exposure model is suitable to assess the impacts of dierent abatement measures on emissions, ambient levels and exposures provided that the changes in trac emission factors, trac loads and vehicle composition in the road network, and street con®guration data are available. Such measures could be new stringent emission standards, increase in the use of catalytic converters, alternative fuels, or urban and trac planning measures. Assessment of dierent abatement measures may be carried out to evaluate impacts and to compare with stated goals. The model system may also be used to evaluate dierent options for location of, e.g., a new kindergarten to minimise children's exposure to air pollution (Fig. 9). A systematic evaluation of the eects of implementation of an action plan is an important element in the planning process and should address the progress in achieving goals and provide a foundation for possible revision of goals and/or abatement measures. Apart from modelling of air pollution, monitoring stations are also crucial for evaluation of progress. A validation of the developed methods for generation of input parameters for the OSPM model and the applied digital maps and databases has been given in Jensen (1999).
Fig. 9. Annual benzene levels in 1996 at the locations of kindergartens (3±6 years).
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4. Conclusions A new prototype air pollution exposure system with high spatial and temporal resolutions has been developed for application by local authorities in big Danish cities as a decision-support tool for urban air quality management. The system integrates available digital maps, administrative databases, a street pollution model and GIS for air quality and exposure estimation at address level. Compared with existing urban air quality management tools, the presented model system has higher spatial resolution, makes use of digital maps and administrative databases for automatic generation of street con®guration data, adds GIS features using standard GIS, and provides improved exposure assessment.
Acknowledgements The model system was developed as part the ®rst author's Ph.D. study, which was funded by the Danish Transport Council, the Danish Research Academy and the National Environmental Research Institute, with matriculation at the University of Roskilde, Denmark. Activities related to the model development have also been undertaken under the Danish National Environmental Research Programme. The following institutions have supplied data Municipality of Middelfart, Ministry of Housing, National Survey and Cadastre Denmark, Ministry of Home Aairs, and Ministry of Economic Aairs. References Berkowicz, R., Hertel, O., Sùrensen, N.N., Michelsen, J.A., 1997a. Modelling air pollution from trac in urban areas. In: Perkins, R.J., Belcher, S.E. (Eds.), IMA Proceedings on Flow and Dispersion Through Obstacles, Cambridge. Marts 28±30, 1994, pp. 121±142. Berkowicz, R., Hertel, O., Larsen, S., Sùrensen, N.N., Nielsen, M., 1997b. Modelling trac pollution in streets. NERI Technical Report, National Environmental Research Institute, Roskilde. Berkowicz, R., 1998. Street scale models. In: Fenger, J., Hertel, O., Palmgren, F. (Eds.), Urban Air Pollution ± European Aspects, Kluwer Academic Publishers, Dordrecht, pp. 223±251. Berkowicz, R., 2000. A simple model for urban background pollution. In: Proceedings of the Second International Conference on Urban Air Quality, Measurement, Modelling & Management, 3±5 March 1999. Madrid, Journal of Environmental Monitoring and Assessment (accepted). European Commission, 1996. Directive 96/62/EC on ambient air quality assessment and management. Ocial Journal L296, 55. Hansen, H.S., Jensen, S.S., Berkowicz, R., 1997. Estimating street air quality using a 21=2 dimensional urban landscape model. In: Proceedings of AM/FM-GIS Nordic Conference, 29±31 October 1997. Kolding. Hertel, O., Berkowicz, R., 1989a. Modelling NO2 concentrations in a street canyon. NERI Technical Report No. A-131, National Environmental Research Institute, Roskilde. Hertel, O., Berkowicz, R., 1989b. Modelling pollution from trac in a street canyon. Evaluation of data and model development. NERI Technical Report No. A-129, National Environmental Research Institute, Roskilde. Hester, R.E., Harrison, R.M. (Eds.), 1998. Air Pollution and Health. Issues in Environmental Science and Technology, 10. Jensen, S.S., 1995. Driving patterns and emissions from dierent types of roads. The Science of the Total Environment 169, 123±128.
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Jensen, S.S., 1997. Standardised trac inputs for use in the operational street pollution model (OSPM). NERI Technical Report No. 197, National Environmental Research Institute, Roskilde. Jensen, S.S., 1998a. Mapping human exposure to trac air pollution using GIS. Journal of Hazardous Materials 61 (1±3), 385±392. Jensen, S.S., 1998b. Background concentrations for use in the operational street pollution model (OSPM). NERI Technical Report No. 234, National Environmental Research Institute, Roskilde. Jensen, S.S., 1999. A geographic approach to modelling human exposure to trac air pollution using GIS. Ph.D. Thesis. National Environmental Research Institute, Roskilde. Kemp, K., Palmgren, F., Manscher, O.H., 1999. The Danish Air Quality Monitoring Network. Annual Report for 1998, NERI Technical Report No. 296, National Environmental Research Institute, Roskilde. Larsen, P.B., Larsen, J.C., Fenger, J., Jensen, S.S., 1997. Evaluation of health impacts of air pollution from road trac. Danish Environmental Protection Agency Report No. 352, Copenhagen (In Danish with English summary). Palmgren, F., Berkowicz, R., Hertel, O., Vignati, E., 1996. Eects of reducing NOx and NO2 levels in urban streets. Science of the Total Environment 189/190, 409±415. Palmgren, F., Berkowicz, R., Ziv, A., Hertel, O., 1999. Actual car ¯eet emissions estimated from urban air quality measurements and street pollution model. Science of the Total Environment 235, 101±109. Sorenson, S., Schramm, J., 1992. Individual and public transportation ± emissions and energy consumption models. Report RE 91-5, Laboratory for Energetics, Technical University of Denmark, Lyngby. World Health Organisation, 1995. Updating and revision of the air quality guidelines for Europe. Meeting of the working group on classical air pollutants, Bilthoven, The Netherlands, 11±14 October 1994. WHO Regional Oce for Europe, Copenhagen. World Health Organisation, 1996. Updating and revision of the air quality guidelines for Europe. Report on a WHO working group on volatile organic compounds, Brussels, Belgium, 2±6 October 1995. WHO Regional Oce for Europe, Copenhagen. Vignati, E., Hertel, O., Berkowicz, R., Raaschou-Nielsen, O., 1997. Generation of input data for the OSPM. A sensitivity analysis of a method based on a questionnaire. NERI Technical Report No. 188, National Environmental Research Institute, Roskilde.