Reports and Papers
Model-based Decision Support
for Integrated Urban Air Quality Management
Dr. Kurt Fedra
Environmental Software & Services GmbH
A-2361 Gumpoldskirchen, AUSTRIA
Urban air quality management faces new and continuing
challenges, driven by new legislation and public awareness on the
one hand, and the growth of urban conglomerates and increases in
power consumption and traffic on the other.
While air quality modeling is a well established field, the challenge
is to integrate scientific tools of analysis with the environmental
policy making and management process, to involve a large and
diverse audience and participants in the policy and decision making
processes, and to support new functions such as the information of
the public. This requires to embed air quality models in a
conceptual framework that includes and explicitly addresses policy
relevant elements such as the control of emission sources including
economic criteria, monitoring of ambient air quality and the
compliance with standards, and impacts on human health and the
From an information and decision support point of view, the urban
air quality management problem is characterized by a number of
features. They include:
- multiple sources of information, ranging from census data
compiled every few years to continuous on-line monitoring
- a dynamic and spatially distributed structure with multiple
temporal and spatial scales for the complex dispersion and
transformation processes that translates emissions into ambient
air quality, which is the domain of air quality modeling proper;
- distributed (and mobile) emission sources with pronounced
temporal patterns that include industry, households, and traffic,
or, from a different point of view, an energy sector that can be
modeled as a large scale mathematical programming problem,
and a traffic sector that can be modeled as a network (dynamic)
- direct regulatory and indirect economic control on emission
sources, involving complex human behaviour;
- multiple objectives and criteria at different spatial and temporal
scales for the different actors and the regulatory framework.
This, obviously, defines a rather complex problem domain,
which also includes a broad range of actors, stake holders and
audiences in the decision and policy making process. With the shift
from more or less authoritarian and technocratic to participatory
decision models that characterise the political evolution of the last
several decades, technical and scientific information, and the free
and open access to this information, has become an important
element in the political process. Consequently, information
technology plays an increasingly important role where technical
and scientific issues are involved, as is certainly the case in urban
environmental management (Fedra ).
Decision Support for Air Quality Management
The management of urban air quality includes a number of
closely related tasks that can broadly be grouped into monitoring,
emission control, and impact assessment, together with related
reporting and public information provisions (90/313/EEC).
These tasks include the continuous monitoring of ambient air
quality for compliance with EU (96/62/EC) and national
regulations, including the appropriate responses if certain
thresholds and alert levels are exceeded, as well as the regular
reporting on the state of the environment. Related to the
monitoring, and in particular driven by any violation of standards
and thus failure to comply with existing regulations, is the
formulation of general policies and strategies to reduce emissions
and thus ambient concentrations (98/96/EC), or to comply with
emission-related standards such as CO2 protocols.
Major emission sources are controlled by another body of
regulations, (e.g., 88/609/EEC, 98/429/EEC, 89/369/EEC,
COM(96)538), usually related to the commissioning of industrial
plants, power plants, or waste incinerators. Mobile emission
sources again are regulated by a number of strategies including
general engine exhaust characteristics, vehicle inspection programs
and strategies affecting fleet composition, and fuel quality
requirements, e.g., in the Auto Oil Program (94/12/EC,
41/441/EEC, 96/69/EC). A third major group of regulatory tasks is
related to environmental impact assessment for a number of
projects and activities defined in (97/11/EC, 85/33/EEC). For a
recent compilation of information resources on air quality and
environmental impact assessment, see the web site of the Info 2000
project AIR-EIA: http://www.ess.co.at/AIR-EIA.
In all these cases, the use of models provides for either
descriptive or prescriptive analysis. Descriptive analysis or
scenario analysis explores WHAT-IF questions, forecasting the
expected behavior of the system in response to a set of changes (or
the lack thereof, i.e., a business as usual scenario) projected into the
future. The daily forecast of tomorrows expected ozone
concentration would be one example, the forecasting of the effect
of a new road construction on ambient air quality another. For the
case of accidental emissions, regulated by the so-called Seveso
Directive (96/82/EC), the requirements for external emergencies
specifically refers to scenario analysis by defining a set of credible
or most likely accident cases as the basis for safety analysis.
A specific use of descriptive modeling is in combination with
monitoring, where the Air Quality Framework Directive 96/62/EC
specifically addresses the use of models to supplement monitoring
programs, which amounts to a mass-budget based approach to
The conceptual framework
Air quality management involves a number of basic building
blocks that form a conceptual framework for the analysis and
formulation of management strategies and policies:
- Sources of emission, represented in various emission
inventories for industrial, commercial, or domestic sources and
the transportation system, as well as landuse related sources
(biogenic emissions of VOCs, particulate matter from soils and
- The monitoring system observing ambient air quality and
historical trends with emphasis on the peak values that may
exceed regulatory standards;
- The dispersion and transformation processes, driven by
emissions, meteorology, and local topography, that translate
emissions into the ambient concentrations, represented by air
quality models (e.g., Zanetti);
- Impact assessment, which translates the ambient concentrations
into costs (in terms of public health and environmental
- Control strategies which basically attempt to limit emissions,
relocate them, or mitigate impacts where that is possible.
It is these components and building blocks that any approach
to decision support must address and refer to.
The design of a comprehensive air quality strategy for an
urban region must consider the spatial distribution of emissions
and impacts, the distribution of cost to the economic agents, and
the role of constraints imposed by environmental laws and
regulations. Some decisions are discrete and of a design type (this
is in particular true in the traffic control strategies based on a
redesign of the network, of the setting of air quality standards),
while others are amenable to a treatment via economic calculus
based on comparing marginal costs.
A multi- tiered, iterative approach for the design of such strategies
has been proposed (Fedra and Haurie):
- Obtain emission scenarios for different sectors (energy,
transportation) through optimization models maintaining
economic efficiency and meeting sectoral objectives and
constraints. This is based on rational economic behavior of
realistic agents at the sectoral level. The sectoral optimization
approach is based on the use of models for energy, e.g.,
MARKAL optimizing an energy demand-supply balance for
the city including bounds on pollution emissions (Wene and
Ryden ), and for transportation, e.g., EMME/2 simulating
traffic equilibria (Hearn and Florian) which again constitute
an emission scenario.
- Get a representation of the resulting ambient air quality as a
function of these emissions for different averaging periods,
relating to air quality standards through solving of the
dispersion equations with spatially distributed air quality
- Obtain spatially distributed measures of environmental and
public health impacts based on land use information and a
population distribution, resulting in a spatially distributed
measure of vulnerability or damage function, for different
classes of pollutants.
- Minimize this distributed impact function subject to economic
constraints by distributing the maximum acceptable costs.
Alternatively, subject to environmental quality constraints,
allocate the maximum permissible emission levels to traffic
and energy uses while maintaining economic efficiency. This
global optimization takes emission scenarios (unmitigated or
sectorally optimized) as its starting point and is based on a
source-receptor matrix computed by a long-term air quality
- Use the permissible emission levels obtained as a constraint on
the previous sectoral optimization. Feedback loops from the
impact analysis and global optimization can help to redefine
objectives and constraints of the sectoral models.
- Repeat the above steps to obtain a number of (sectorally)
- Use a discrete multi-criteria tool to find a preferred compromise
solution that satisfies the objectives of all groups of actors.
Reflecting the iterative nature of the approach, an interactive
and graphical user interface supports the efficient comparative
evaluation of scenarios describing alternative plans and policies,
and thus the feedback loops linking the steps of the approach.
The translation of emissions into ambient pollution
concentrations is accomplished through an averaging of the
mathematical description of a complex stochastic dynamical
system (the dispersion process). Impact assessment estimates the
spatially distributed impacts of the ambient air quality (the
immission) resulting from pollutant emission on the environment
and human health; for example, comparing census data and
exposures or health risks at different locations, which is essentially
a GIS application.
The overall task necessitates a combination of energy and
technology choices and traffic design policies under a spatially
distributed constraint on pollution concentrations. The combination
of the power of large scale optimization, spatially distributed
simulation and GIS techniques is expected to help develop rational
and efficient air quality management strategies.
The technological framework
Urban air quality management addresses tough problems: they are
complex; dynamic, and involve spatially distributed 3D phenomena
and models. They also involve problems of communicating
difficult technical concepts and data to a largely non-technical
audience, and of assisting non-technical users with complex
A possible architecture to support this complexity efficiently
uses an object oriented client-server approach to integrate
distributed information resources, and provide an easy to use and
understand user interface (Figure 1). It is based on:
- A flexible client-server implementation for distributed and
decentralized use of information resources.
- Communication architecture based on the http protocol which
is used to integrate real-time data acquisition from monitoring
sites, as well as optional high-performance computing
resources such as supercomputers or workstation clusters;
primary consideration here is the scalability of applications
over a wide range of performance requirements.
- Multi-media user interface design to support an intuitive
understanding of results.
- Integration of GIS with data bases, monitoring results, and
spatially explicit simulation modeling (Fedra).
- Embedded rule-based expert systems for logical modeling and
- Integration of a range of simulation models.
- Optimization models that supplement the dynamic simulation
models for design and decision support tasks.
Figure 1: systems architecture
Conceptually, the system uses a central server that
coordinates the information resources and a number of display
clients. The elements include a number of data bases, which may
be linked to an on-line monitoring system, GIS data describing the
domain and providing spatially distributed model inputs, emission
inventories, and models scenarios and results.
The emission data may also be linked to external models of
the energy sector or of traffic, that create emission data according
to their respective scenarios.
The second major block of information resources are the
component simulation models, ranging from simple steady-state
screening models to dynamic, 3D photochemical models which are
implemented on parallel computers for better than real-time
performance. Depending on their computational requirements,
ranging up to supercomputers or workstation clusters, they may be
implemented locally or on a remote compute server.
The data bases contain both spatially distributed data (topical
maps) that are managed, processed, and displayed by the GIS
functionality; data bases for temporal data (air quality observations
and meteorological data), that are linked to on-line monitoring
systems for continuous updates; and emission inventories that may
be sets of point sources; polygons or regular matrices for area
sources; and networks for traffic generated emissions. Point,
polygon, and line sources may be converted to regular cell grids
(and be displayed in this format in the GIS) as one of the input
formats for some of the component models. Digital terrain data (for
3D wind field modeling), landuse (e.g., for roughness), and
population data for exposure complement the data requirements.
Sources of emission
Emission sources are the main input to any dispersion
modeling, and at the same time the major focus of any air quality
management strategy. They can be grouped, from a technical and
model oriented point of view into:
- Major industrial point sources (where stack parameters like
height, diameter, emission temperature and speed play an
important role for the computation of virtual stack height)
- Small point sources (e.g., from small industrial sources,
commercial sources, and domestic sources such as block
heating plants); depending on their size compared to the
model domain, they can be treated as point sources proper
or grouped into area sources;
- Area sources such as the thousands of individual chimneys
in a city, light industry districts; a special case here are
airports, which can be treated as either area sources of even
volume sources, depending on their size and relative
importance. Another group of area sources cover non-
pyrogenic emissions such as VOCs from the transportation
system, industry, commerce, and households, biogenic
emissions related to landuse, and entrainment areas for
- Line sources, which are basically the arcs of the
Figure 2,3: Emission inventories for point and area sources
From an administrative point of view, these sources are covered by
different regulations, different permit and monitoring schemes, and
will thus require different data base structures and analysis tools
(Figures 2,3). In addition to the basic emission characteristics
including their variability over time, technological and economic
information on alternative and emission reduction technologies are
required for source control optimisation.
Monitoring data integration
Observation data are used for several important purposes:
- To monitor the state of the atmospheric environment and in
particular, any possible violation of air quality standards
(e.g., 92/86/EC and daughter Directives);
- To analyse trends as one possible method for forecasting;
- To provide initial and boundary conditions for simulation
- For comparison with model results to establish their
validity and possible us the observations for calibration
The latter use of monitoring data, however, involves
numerous complications and potential pitfalls as the spatial and
temporal scales of monitoring and modeling are very different, and
a naive direct comparison can be very misleading.
Figures 4,5: Monitoring data analysis
To use the monitoring data in a decision support system effectively,
they must be integrated in a real-time manner, with direct links to
the monitoring network. At the same time, functionality for time
series analysis and spatial analysis is required (Figures 4,5), as well
as the possibility to automatically use the observation data together
with the simulation models .
The component models
The basic concept of the approach described above is its modular
structure and flexibility; several simulation models can be
integrated to provide a range of tools for different tasks. This
provides the flexibility to use the most appropriate model for any
task, depending on the nature of the problem (short or long-term,
dynamic or steady state, planning or operational control), the
pollutants of concern (conservative, simple chemistry,
transformation and decay or photochemical), the terrain and
meteorological conditions (simple or highly structured, sea
breezes), the necessary spatial resolution, etc.
The levels supported include:
- A screening level with classical Gaussian steady-state models
such as ISC3/AERMOD or photochemical box models or a
multi-puff model for dynamic problems;
- A forecasting level defined by the requirement of
computational performance at least an order of magnitude
better-than-real-time, including multi-layer dynamic Eulerian
models and Lagrangian models based on diagnostic wind
- A planning level including the use of fully 3D dynamic
- A decision support level that requires embedding the models
into an optimisation framework: this can be done directly for
simple steady-state models with linear emission - immission
characteristics, or will require a Monte-Carlo approach with
discrete mutli-criteria optimisation Zhao et al.,
The models generating emission data include energy models such
as MARKAL (Wene and Ryden) and traffic simulation models
such as EMME/2 (Hearn and Florian) or DYNEMO
The projects ECOSIM, AIDAIR and SIMTRAP all address aspects
of environmental management, the interactions between technology
and its impact on the human living conditions.
ECOSIM, an environmental telematics projects, is designed
to support urban environmental management, integrating on-line
monitoring with simulation modeling for both strategic planning
and operational management questions; the environmental domains
include air quality including photochemical smog (ozone), coastal
water quality, and groundwater quality. ECOSIM has validation
sites in Berlin, Germany; Athens, Greece; and Gdansk, Poland.:
As any other large urban conglomerates, cities like Berlin or
Athens are observing episodes of summer smog. Different models,
but the same software framework and client-server architecture was
used for air quality simulation in these two cities.
Ozone forecasting for Berlin was attempted in two of the
projects presented, and based on two different approaches: in
ECOSIM (http://www.ess.co.at/ECOSIM) this based on static
emission matrices which are scaled to temporal patterns and using
a multi-layer forecasting model, REGOZON, with the possibility of
a direct comparison of the model forecasts with the on-line
monitoring data from the BLUME monitoring network (Mieth et
al. [9,10]. In SIMTRAP, this is based on the direct coupling of the
dynamic traffic model DYNEMO with the DYMOS 3D ozone
model (Schmidt and Haenisch ).
An alternative approach using different models within the
same software framework and client-server architecture was used
for a case study in Athens. Whereas REGOZON is a combined
mesoscale meteorological and dispersion model, MUSE is a
dispersion model only, requiring the meteorological quantities
usually computed by MEMO (Flassak and Mussiopoulos ;
Mussiopoulos[11,12,13] ; Mossiopoulos et al..
The non-hydrostatic prognostic mesoscale model MEMO
(Kunz and Moussiopoulos) is a basic constituent of the
European Zooming Model (EZM, previously called EUMAC
Zooming Model). The EZM represents one of the most widely used
European air quality model systems for urban scale applications
(about 15 study cases in the last three years).
Figures 6,7: Emission matrices for REGOZON, and
comparison of results with the Berlin monitoring network data.
MEMO solves the conservation equation for mass,
momentum and several scalar quantities in terrain-influenced
coordinates. Non-equidistant grid spacing is allowed in all
directions. The numerical solution is based on second- order
discretization applied on a staggered grid.
AIDAIR, a EUREKA EUROENVIRON project, has similar
aims, but concentrates on air quality assessment and management,
within the new air quality framework Directive 96/62/EC as the
guiding principle. Here the emphasis is on the integration of
different sources of air pollution: industry, domestic sources, and
the transportation system, and the energy system as the general
framework describing them (Fedra). Linking models for energy
planning and optimisation with air quality simulation is one of the
objectives. Case studies include Vienna, Geneva (Fedra et al),
A typical application is an environmental impact assessment
for a district heating scheme in Vienna: the scenarios with and
without the scheme involving both an additional block at a power
plant and on the corresponding elimination of a large number of
individual heating systems in several areas of the city are compared
(Figure 8,9) and the changes caused by the project computed and
displayed for several alternative scenarios.
Figures 8,9: impact assessment for a distrcit heating scheme.
Another application is the estimation of traffic generated
urban scale or street level pollution, based on the results of steady-
state traffic equilibrium models (Figure 10,11).
SIMTRAP, an Esprit HPCN (High-performance Computing
and Networking) project specifically addresses transportation and
air quality, linking dynamic traffic simulation models with 3D,
dynamic photochemical air quality models. SIMTRAP application
sites include Milano, Italy; Vienna, Austria; Berlin, Germany, and
Maastricht, the Netherlands.
Figures 10,11: traffic generated pollution at city and
individual street level.
SIMTRAP (http://www.ess.co.at/SIMTRAP) integrates the
dynamic traffic flow model DYNEMO and the 3D dynamic
photochemical model system DYMOS (Schmidt et al.).
The traffic model DYNEMO is a simulation tool for road
networks, treating individual cars (up to 100,000) as the unit of
traffic flow. Car movements, however, are governed by average
traffic densities on the individual links of the network. From the
mean speed of the cars, and the fleet composition, their emissions
are computed and form the dynamic input into the air quality model
together with background emission from households, industry, and
DYMOS consists of three meteorology/transport models and
one air chemistry model for the calculation of photochemical
oxidants like ozone. The meteorology/transport models include
REWIMET - a hydrostatic mesoscale Eulerian model with a low
vertical resolution, GESIMA - a non-hydrostatic mesoscale
Eulerian model with a high vertical resolution, and one Lagrangian
model. The air chemistry model is CBM-VI dealing with 34
species in 82 reaction equations for simulating the photochemical
processes in the lower atmosphere.
Air pollution simulations require extremely large amounts of
computing time. In order to make the results of case studies
available to users within an acceptable period of time or to enable a
smog prediction to be made at all (computing time less than
simulation period), the DYMOS system was parallelised (Schmidt
and Haenisch). A message-passing version was developed
from the sequential program and implemented on various parallel
System Integration and Decision Support
From the viewpoint of computer implementation, all the
above models, their conceptual differences and range of
applications notwithstanding, share basic characteristics that allows
the designer to develop a common, generic interface: As model
input, they use vectors of parameters, time series, or matrices and
vector fields, the latter two also as time series or vertical slices of a
three dimensional discretisation. As output, the models generate
again time series of scalar variables (photochemical box model),
spatial matrices (steady state Gauss models), time series and sets of
matrices for different parameters and different vertical layers (all
dynamic models). This structural similarity makes the design of a
generic client-server interface, but also of a generic scenario editing
and results display possible. Simulation models can therefor be
integrated rather easily, depending on the specific requirements of
From a decision support point of view, it is important that
these features are presented to the user in a problem-oriented, not
in a tool-oriented way: the system must represent the
administrative, regulatory, technological, and economic features of
the system, including their socio-political boundary conditions, to
be truly useful and usable. Besides, while technical detail and
scientific precision are essential, simple and easy to understand and
communicate user interfaces and intuitively understandable formats
are important where direct inputs to the policy and decision making
process are the ultimate goal. Providing publicly accessible
environmental information over the Internet, including dynamic
model and monitoring results, is one such emerging application. In
summary, for an environmental decision support system, good
models are a necessary, but not a sufficient condition for a
 Fedra, K., ``Distributed Models and Embedded GIS: Strategies
and Case Studies of Integration.'' In: Goodchild, M.F., Steyart,
L.Y., Parks, B.O., Johnston, C., Maidment, D., Crane, M. and
Glendinning, S. [Eds.], GIS and Environmental Modeling:
Progress and Research Issues, pp. 413-417. GIS World Books,
Fort Collins, CO. 1996.
 Fedra, K. (1999) Urban Environmental Management:
Monitoring, GIS and Modeling. Computer, Environment and
Urban Systems (in print).
 Fedra, K., Greppin, H., Haurie, A., Hussy, C., Dao, Hy, and
Kanala, R. ``GENIE: An Integrated Environmental Information
and Decision Support System for Geneva. Part I: Air Quality.''
pp 247-263. Arch.Sci.Gen‚ve, Vol. 49, Fasc.3, 1996.
 Fedra, K and Haurie, A. (1999) A Decision Support System for
Air Quality Management: Combining GIS and Optimization
Techniques. Int. Journal Environment and Pollution, Vol. 12
 Flassak Th. and Moussiopoulos N., Direct solution of the
Helmholtz equation using Fourier analysis on the CYBER 205,
Environ. Software, 3, pp. 12-16, 1998.
 Hearn, D. and Florian, M; Network equilibrium models and
algorithms, Network routing, Handbooks in Operations
Research and Management Science, Vol. 8, North-Holland,
 Kunz R. and Moussiopoulos N., Simulation of the wind field in
Athens using refined boundary conditions, Atmospheric
Environment, 29, pp. 3575-3591, 1995.
 Majchrazak, J. ``DISCRETE: an interactive decision support
system for discrete alternatives multicriteria problems''. WP-88-
113, 20 pp, International Institute for Applied Systems
Analysis, A-2361 Laxenburg, Austria 1988.
 Mieth, P., Unger, S., and Jugel, M.L., An Environmental
Simulation and Monitoring System for Urban Areas,
Transactions of the Society for Computer Simulation
International, Vol. 15, 3, pp. 115-121, 1998.
 Mieth, P.,Unger, S. and Sydow, A , "Scenario Analysis of a
Summer Smog Episode in Berlin", Proc. Second International
Conference on Air Pollution, September 27 - 29, 1994,
Barcelona, Spain, 1994.
 Moussiopoulos N., An efficient scheme to calculate
radiative transfer in mesoscale models, Environ. Software, 2,
pp. 172-191, 1987.
 Moussiopoulos N., ed., The EUMAC Zooming Model,
EUROTRAC Special Publication, ISS, Garmisch-
 Moussiopoulos N., The EUMAC Zooming Model, a tool
for local-to-regional air quality studies, Meteorol. Atmos. Phys.,
57, pp. 115-133, 1995.
 Moussiopoulos N., Sahm P., Gikas A., Karagiannidis A.,
Karatzas, K. and Papalexiou S., Analysis of air pollutant
transport in the Athens basin and in the Spata area with a three-
dimensional dispersion model, in Air Pollution III, Vol 3:
Urban Pollution (N. Moussiopoulos, H. Power and C.A.
Brebbia, eds) Computational Mechanics Publications,
Southampton, pp. 141-152, 1995.
 Schmidt, M. and Haenisch, R., "Implementation of an air
pollution transport model on parallel hardware", Proc.
International Conference on Massively Parallel Processing,
June 21 - 23, 1994, Delft, The Netherlands, 1994.
 Schmidt, M., Sch„fer, R.-P, and N”kel, K., SIMTRAP:
Simulation of Traffic-Induced Air Pollution. Transaction of the
Society for Computer Simulation International Vol. 15, 3, pp.
 Schwerdtfeger, T., DYNEMO: A model for the Simulation
of Traffic Flow in Motorway Networks. Transportation and
Traffic Theory, VNU Science Press, pp. 65-87, 1984.
 Wene, C.-O. and Ryden, B., A comprehensive energy
model in the municipal energy planning process, European
Journal of Operational Research, 33, pp. 212-222, 1988.
 Zannetti, P. ``Air pollution modeling'' Theories,
computational methods, and available software. Van Nostrand
Reynolds, New York, 1990.
 Zhao, Ch., L. Winkelbauer, and K. Fedra, ``Advanced
Decision-oriented Software for the Management of Hazardous
Substances''. Part VI. The Interactive Decision-Support
Module. CP-85-50, International Institute for Applied Systems
Analysis, A-2361 Laxenburg, Austria 1985