8/14/2019 Air Ware

1/19

AirWare: an Urban and Industrial Air Quality Assessment and

Management Information System

DDr. Kurt Fedra

Environmental Software & Services, A-2352 Gumpoldskirchen, AUSTRIA (kurt@ess.co.at, http://www.ess.co.at)

AAbstract

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

energy and material consumption and traffic on the other. While air quality modeling is a

well-established field of research and environmental engineering, the challenge is to integrate

scientific tools of analysis with the environmental policy-making and management process, to

involve a large and diverse audience as participants in the policy and decision-making

processes, and to support new functions such as the information of the public, mandated by

new laws and regulations. This requires us to embed air quality models in an operationalframework that includes and explicitly addresses policy-relevant elements such as monitoring

of ambient air quality and the compliance with standards, regular forecasts of air quality,

reporting and the information of the public, the control of emission sources including

economic criteria, and the assessment of impacts of current and potential future projects and

policies on human health and the environment.

AirWare is an integrated environmental information system for air quality assessment and

management. It was developed with major contributions from a series of international

research and development projects, starting with a EUREKA EUROENVIRON project, and a

set of EU sponsored Fourth and Fifth Framework projects including ECOSIM (FW4

Environmental Telematics: http://www.ess.co.at/ECOSIM), AIR-EIA (INFO2000:

http://www.ess.co.at/AIR-EIA), SUTRA (City of Tomorrow, http://www.ess.co.at/SUTRA),

the related LUTR cluster activity (http://www.lutr.net), ISIREMM, INCO Copernicus:

http://www.ess.co.at/ISIREMM) and most recently Env-e-City (http://www.env-e-city.org) e-

content project.

In the course of these research projects, the AirWare system has undergone a continuous and

progressive transition from a dedicated engineering system implemented on special hardware

in the technical division of a users institution, to be used by a few trained specialists, to an

Internet based distributed client-server system for a much broader user group with support for

public information systems, and an eventual ASP (Application Service Provider) businessmodel.

Introduction

Air quality remains one of the pressing problems of modern cities. While technological

advances continue to reduce unit emissions, especially for major industrial sources, growth of

urban areas, increasing per capita energy and material consumption, and in particular the

increase of urban transportation and passenger cars offset these reduction to an overall

increase of emissions in many places. This is particularly true for developing countries, where

rapid urbanisation continues at an ever increasing pace. Health impacts, as well as

environmental and material damages of considerable magnitude illustrate the socio-economicdimensions of the problem.

8/14/2019 Air Ware

2/19

The management of urban air quality includes a number of closely related tasks that can

broadly be grouped into monitoring, impact assessment, and emission control 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 thresholdsand alert levels are exceeded, as well as the regular reporting on the state of the environment.

Due to the usually small number of monitoring stations and the resulting difficulties of spatial

interpolation for complete spatial coverage and thus exposure and impact estimates, the

observations data can and should be augmented by simulation modelling as foreseen in the

Directive - that derives spatially distributed ambient concentrations from emission data,

topographic and land use information, and meteorological data (Fedra, 2000a,b).

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 http://www.ess.co.at/AIR-EIA, one of the

web sites of the Info 2000 project AIR-EIA.

In all these cases, the use of models provides for either descriptive or prescriptive analysis.

Descriptive analysis either supports air quality monitoring data for a better spatial coverage

and resolution or involves scenario analysis that 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 tomorrow's

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 likelyaccident 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 spatial interpolation.

Finally, there is an increasing number of both national and EU level regulations for public

access to environmental information, both as a passive right-to-know and as an active

mandate to inform the affected public by governmental institutions or companies.

The ultimate objective, however, must be to improve environmental planning, policy making

and management, end eventually, the environment, and the urban environment in particular.This decision support function addresses a broad audience, namely all the actors involved in

the policy and decision making processes as well as the institutions and individuals involved

8/14/2019 Air Ware

3/19

in operational environmental management. Better and shared information is one of the

elements of an improved decision making process. Problem awareness, an understanding of

causes and effects, but also the costs of impacts, and costs and benefits of alternative

strategies are the basic elements of this information, which must include technological,

environmental, socio-political, and economic criteria (Fedra and Haurie, 1999).

The rapid development of information and computer technology (ITC) and in particular the

potential of the Internet provide the infrastructure underlying a successful information and

decision support system that has to link real-time data acquisition, the relevant institutions,

various actors and interest groups, and the general public. Analysis and communication are

two inseparable components of the approach.

Functionality and architecture

To support the above set of tasks within the corresponding regulatory framework is the

objective of the AirWare system. AirWare was originally developed within the framework of

a EUREKA project, and was and is continuously updated and expanded during a number ofEU sponsored RTD projects. The basic design philosophy is integration, flexibility, and ease

of use, recognizing that the target user group is not necessarily interested in the technical and

scientific details of the solution, but rather in an efficient, reliable and useful solution in the

first place. This leads to an open, modular, and distributed architecture based on a set of

objects that together describe the air quality assessment and management domain (Fedra,

1999). The main advantage of object oriented design here is that it can map the concepts,

processes, and language of the user into an efficient and flexible implementation. These

modular objects are implemented as distributed information resources.

Figure 1. AirWare distributed client-server architecture.

8/14/2019 Air Ware

4/19

The AirWare architecture is based on a central server (that can be implemented on one or

more physical servers across any TCP/IP network) and a set of distributed information

resources such as various data bases, monitoring networks, and model server(s). Both local

and remote clients (including mobile WAP clients) are supported.

Air quality management involves a number of basic building blocks that form a conceptualframework for the analysis and formulation of management strategies and policies as the

ultimate goal, which can be represented by an object oriented paradigm:

Sources of emission, represented in various emission inventories for industrial,commercial, or domestic sources and the transportation system, as well as land-use related

sources (biogenic emissions of VOCs, particulate matter from soils and street surfaces);

Monitoring system observing ambient air quality and historical trends with emphasis onthe peak values that may exceed regulatory standards;

Dispersion and transformation processes, driven by emissions, meteorology, and localtopography, that translate emissions into the ambient concentrations, represented by airquality simulation models;

Impact assessment, which translates the ambient concentrations into costs in a generalsense (e.g., in terms of public health and environmental damage);

Control strategies which basically attempt to limit emissions, relocate them, or mitigateimpacts where that is possible, with fuel quality constraints, end of pipe technologies, or

temporary traffic restrictions being of the more noticeable instruments (Fedra and Haurie,

1999);

Communication tasks including various levels of regular reports, event driven warningssuch as smog alarms, as well as the continuous information of the public on ambient air

quality.

This range of objects and their associated functions needs to be managed by the target group

of the Air Quality Framework Directive, i.e., all agglomerations of 250,000 inhabitants or

more, but also, at least in part, by any operator or project proponent subject to the regulations

on environmental impact assessment (EIA).

The use of complex analytical functions and model in particular requires a good

understanding of the methods used, and their limitations, for a reliable interpretation ofresults. Consequently, a set of tools and models that is freely accessible to anybody over the

Internet carries the danger of use outside the design parameters and misinterpretation of the

results.

To address this problem, AirWare not only uses a fully interactive, graphical and symbolic

user interface, but incorporates a rule-based expert system that can guide and control user

requests and assure the completeness, consistency, and plausibility of data and scenario

assumptions.

Research projects

AirWare builds on a number of European and international RTD projects and pilot

applications, integrating the latest research results and developments as they become

8/14/2019 Air Ware

5/19

available. These projects, with different emphasis and a range of case study applications, span

a wide range of physiographic and meteorological conditions, institutional settings, and data

availability and quality.

Related RTD Projects include AIDAIR EUREKA EU 1388 EUROENVIRON, that involves

Austrian, Swiss, Turkish and Russian partners with the primary applications in Vienna,Geneva (Fedra et al., 1996), and Izmir. AIDAIR developed the base system with the

integration of monitoring time series analysis and simulation modeling. ECOSIM (Fedra et

al., 1996), a Telematics Applications project (http://www.ess.co.at/ECOSIM) extended the

base system into a distributed architecture with monitoring and compute servers for external

models distributed across Europe. Case studies included, Berlin (Mieth et al., 1994a,b),

Athens (Moussiopoulos et al., 1995, Kunz and Moussiopoulos, 1995), and Gdansk. Theconcept of distributed servers including high-performance parallel computers and clusters for

real-time forecasting was further develop in HITERM, (http://www.ess.co.at/HITERM) for

accidental release scenarios, and SIMTRAP (http://www.ess.co.at/SIMTRAP) for the real-

time forecasting of dynamic traffic (Schwerdtfeger, 1994), emissions, and the resulting urban

air quality including photochemical models (Schmidt et al., 1998). Both project were runningunder the ESPRIT HPCN umbrella. Another extension was explored in MUTATE under the

Educational Multi-Media framework (http://www.ess.co.at/MUTATE) where the air quality

simulation models were embedded into on-line interactive lectures on spatial analysis and

applied GIS, aimed at post-graduate or continuing education. A different audience, namely

professionals in the public and private environmental sector, was addressed by AIR-EIA

(http://www.ess.co.at/AIR-EIA) and Info 2000 project with emphasis on the multi-media

nature of the information provided. ISIREMM (http://www.ess.co.at/ISIREMM) under the

INCO-Copernicus framework is extending the tools for monitoring data analysis by adding

multi-dimension data and remote sensing information by airborne sensors to the classical

stationary point measurements. A case study in Tomsk, Siberia, is the testing ground for these

developments. SUTRA, a City of Tomorrow projects, concentrates on the relationship of land

use, the transportation system, and environmental quality in cities: Buenos Aires, Gdansk,

Geneva, Genoa, Lisbon, Tel Aviv, and Thessaloniki are the case studies where a range of

external models describing the energy system (Wene and Ryden, 1998), transportation,

regional ozone, and street canyons, are linked with the basic AirWare tools and models

including an expert system for environmental impact assessment. Accessibility through the

internet, and the distributed client-server implementation is taken yet another step towards a

full Application Service Provider (ASP) model in Env-e-City, an eContent project. In

applications for cities as different as Helsinki, Finland and Vitoria, Spain, a complete

outsourcing approach over the Internet is explored for a system primarily supporting the Air

Quality Framework Directive and related tasks of assessment and public information.

AirWare: a guided tour

AirWare is designed for a broad range of applications, including the support for the EU Air

Quality Framework Directive and its daughter directives. The main function groups that the

system supports are:

Data management and time series analysis (emission inventories, monitoring includingreal-time data acquisition)

Planning, design, impact assessment, optimization (emission control) Scenario analysis, forecasting (regular or event based) Communication: reporting and public information.

8/14/2019 Air Ware

6/19

They are supported by a corresponding set of main functions and numerous auxiliary generic

tools such as the fully integrated GIS (Fedra, 1996) and the embedded expert system, as well

as data import and export facilities.

Monitoring time series analysis

A central component, closely related to 96/62/EC is the support of monitoring stations and

networks, both for historical data and real-time data acquisition. Monitoring stations are

objects that contain one or more time series or data streams. The data sets are displayed and

analyzed under interactive control, analyzed e.g., for compliance with the respective

regulations, and are also used to provide meteorological inputs and comparison data for the

simulation models. They can also be used for various data assimilation schemes for real-time

forecasting. Monitoring data analysis functions include:

1. Selection, aggregation, display of time series data, base statistics, histograms, dataquality assurance (e.g., rule-based outlier detection)

2. Test for compliance with standards3. Station and parameter comparison, correlations4. Spatial homogeneity tests5. Pattern analysis (seasonality, trends)6. Spatial interpolation, animation.

Figure 2. Observation time series display.

8/14/2019 Air Ware

7/19

Figure 3. Comparison of several variables or stations.

Figure 4. Regression plot of a two-station comparison.

8/14/2019 Air Ware

8/19

Figure 5. Spatial interpolation of monitoring data.

Emission inventories

Emission inventories are supported for

industrial point sources, commercial/ residential area sources, street networks (line sources),

and, where applicable such as for airports, volume sources. Emission objects are stored not

only with the basic data required by the simulation models, but with an open list of properties

for administrative purposes. A major element is to capture the dynamics of emission sources

which may use explicit time-series such as for larger industrial stacks, or generic patterns that

can be used to construct an emission estimate for any arbitrary date and point in time.

Emission objects are georeferenced, linked to the embedded GIS (Fedra, 1996). Tools to

display, edit, and analyze the emission data including ranking and benchmarking provide

graphical display and analysis tools including estimation tools (rule-based expert system).

Emission objects provide automatic, complete and consistent input to the simulation models

both for scenario analysis and real-time forecasting.

8/14/2019 Air Ware

9/19

Figure 6. Emission inventory, point and area sources overview.

Figure 7. Details from the industrial point source emission inventory.

8/14/2019 Air Ware

10/19

Simulation models

Basic models in AirWare include a set of fast and efficient screening level models designed

for fully interactive use, including

ISC3/AERMOD (short term, 24 hours, seasonal long-term) DWM 3D diagnostic wind model TIMES 3D dynamic Eulerian model PBM photochemical box model.For more complex tasks that require computational efforts beyond the constraints of a fully

interactive response, AirWare provides links to external models including Lagrangian and 3D

dynamic photochemical models (e.g., MUSE, UAM-V, CAMx, etc.) and meteorological pre-

processors (MEMO, MM5). For these models, interactive scenario editors as well as tools for

the post-processing of results are provided, while the models themselves are solved as a batch

or background job, possibly on a remote high-performance compute server, compute cluster

or grid, or a parallel machine.

For very large number of (low level) sources including city-scale street or transportation

networks with thousands of links and nodes, a convolution method with a range of scaleable

computational kernels is used. This approach supports very high resolution in the meter range

for realistic near-field gradients, and overcome the limitations of the Gaussian approach by

using a near-source mixing-zone approach similar to the CALINE series of models.

Figure 8. Gaussian model with terrain corrections.

8/14/2019 Air Ware

11/19

Figure 9. 3D Dynamic Eulerian model results.

For complex terrain where Gaussian models are insufficient, a 3D diagnostic wind field

model is used (DWM): it provides input for a dynamic multi-layer Eulerian dispersion model,

TIMES. For regular or event driven real-time forecasts, this (or any of the other models) can

be embedded into a real-time expert system framework (Fedra and Winkelbauer, 1999) that

manages the compilation and pre-processing of all required inputs including rule-based

quality control and exception handling, running one or more of the models in cascade or

parallel, and the post-processing including, for example, web and WAP publishing.

8/14/2019 Air Ware

12/19

Figure 10. Model results draped over a 3D terrain map.

Figure 11. Direct comparison and deltas for two model scenarios.

8/14/2019 Air Ware

13/19

Figure 12. Near-field concentration gradients from traffic emissions.

Figure 13. Pseudo-3D display of traffic generated pollution.

8/14/2019 Air Ware

14/19

Post-processing: impact assessment

Once the basic concentration fields for the various pollutants have been computed, this

information is displayed in the form of topical mps over an appropriate background map, with

the color coding based on the applicable air quality standards, or defined interactively by the

user. In a subsequent step, the system can identify and displays areas where standards areexceeded, population exposure, or calculate air quality indices from a combination of model

results.

Figure 14. Spatial population exposure analysis from model results.

8/14/2019 Air Ware

15/19

Figure 15. Rule-based air quality indices derived from model results.

Optimisation: emission control strategies

If and when standards are violated or observed and predicted concentration too high,

measures have to be taken to reduce the ambient concentrations. This is usually done by

reducing conditionally or unconditionally, emissions. Depending on the type of emission

source, this may involve a combination of different mechanisms. In general, any such strategy

involves costs, for investment and for operation (Fedra and Haurie, 1999).

To design cost-efficient optimal strategies, an optimization model is used for processing the

results of the long-term (seasonal or annual) model results. For each source or source class,

cost functions and efficiencies for alternative emission reduction strategies are defined. Themodel then finds, based on a net present value concept, the best reduction strategy for e given

budget, or the least cost strategy to meet a given air quality standard.

8/14/2019 Air Ware

16/19

Figure 16. Emission control optimization: interim results for a low investment level.

Figure 17. Final results for a high investment level.

8/14/2019 Air Ware

17/19

Communication: web and WAP support

The functions of the AirWare system are also accessible over the Internet. This not only

provides support for distributed institutions without high-bandwidth connectivity,

Figure 18. Web display of air quality monitoring data.

8/14/2019 Air Ware

18/19

Figure 19. Java applet for the animation of 48 hour regional air quality forecasts.

it also offers the possibility for a range of information services for a wide range of different

user groups including the general public. In turn, efficient access through the Internet makes it

possible to offer all the functions and services of a system like AirWare in an internet-basedoutsourcing or ASP (Application Service Provider) model. The end users do not need to

obtain and maintain the technical infrastructure for complex data analysis, including modeling

and model based forecasts these services can be located with an appropriate provider. Cost

efficiency through the sharing of high-performance IT infrastructure, as well as the special

expertise required for more complex analysis, make this an attractive option for public-private

partnerships around environmental data and information services.

Discussion

The management of urban air quality is a complex undertaking that involves technological as

well as institutional components, combines data and uncertainties, facts as well as perceptions

and believes. Numerous conflicting objectives, multiple criteria, uncertainties, as well as

vested interests and political agenda make this a difficult decision problem where no optimal

solutions in the sense of classical operations research, but economically feasible politically

acceptable compromise is sought (Bell et al., 1978). This implies access to shared informationfor all actors, and a forum for information exchange to develop such compromise solutions.

The information system that can support this process involves much more than any computer

based approach can offer, including the media and numerous inter-institutional

communication channels. However, the increasing importance of electronic media and the

integration of computers, the Internet, and mobile communication as one of the backbones ofan emerging civic society transcending traditional institutional structures and policy making

processes poses a new challenge for research and development. Integrating environmental

8/14/2019 Air Ware

19/19

sciences, applied systems analysis, and information and communication technologies leads to

a new paradigm for a new approach to environmental management, that implements the

vision of Agenda 21 through the empowerment of all actors and participants in this process.

References

Bell D.E., R.L Keeney and H. Raiffa; [eds.] Conflicting Objectives in Decisions, 442 pp., John Wiley & Sons,Chichester (1978).

Fedra K; Model-based decision support for integrated urban air quality management, in: J.W. Longhurst, D.M.Elsom and H. Power [eds.].Air Quality Management243-260, WIT Press, Southampton (2000a).

Fedra K.; Environmental Information and Decision Support Systems, Informatik/Informatique 4/2000 (2000b)14-20.

Fedra K.; Urban Environmental Management: Monitoring, GIS and Modeling, Computer, Environment andUrban Systems, (1999).

Fedra K., K. Karatzas and N. Moussiopoulos; Integrated urban environmental management: Monitoring,simulation, decision support, in: N. Moussiopoulos [ed.]: Research in the Fields of Energy and the Environment,

Selected Scientific Articles of the Laboratory of Heat Transfer and Environmental Engineering, 101-109,Thessaloniki (1999).

Fedra K.; Distributed Models and Embedded GIS: Strategies and Case Studies of Integration, in: Goodchild,M.F., Steyart, L.Y., Parks, B.O., D. Johnston, D. Maidment, M. Crane and S. Glendinning [eds], GIS andEnvironmental Modeling: Progress and Research Issues, GIS World Books, Fort Collins, CO., (1996) 413417.

Fedra K., H. Greppin, A. Haurie, C. Hussy, H. Dao and R. Kanala; GENIE: An Integrated EnvironmentalInformation and Decision Support System for Geneva, Part I: Air Quality. Arch. Sci. Genve, 49 (3) (1996) 247263.

Fedra K. and A. Haurie; A Decision Support System for Air Quality Management: Combining GIS andOptimization Techniques,Int. J. Environ. and Pollution, 12 (1/2) (1999) 125-146.

Fedra K. and L. Winkelbauer; A hybrid expert system, GIS and simulation modeling for environmental andtechnological risk management, in: Environmental Decision Support Systems and Artificial Intelligence,Technical Report WS-99-07, pp 1-7, AAAI Press, Menlo Park, CA (1999).

Kunz R. and N. Moussiopoulos; Simulation of the wind field in Athens using refined boundary conditions,Atmos. Environ. 29 (1995) 35753591.

Mieth P., S. Unger and M.L. Jugel; An Environmental Simulation and Monitoring System for Urban Areas,Transactions of the Society for Computer Simulation International, 15(3) (1994) 115-121.

Mieth P., S. Unger and A. Sydow; "Scenario Analysis of a Summer Smog Episode in Berlin, in:Proc. SecondInternational Conference on Air Pollution, September 27-29, 1994, Barcelona, Spain (1994).

Moussiopoulos N., P. Sahm, A. Gikas, A. Karagiannidis, K. Karatzas and S. Papalexiou; Analysis of airpollutant transport in the Athens basin and in the Spata area with a three-dimensional dispersion model, in: N.Moussiopoulos, H. Power and C.A. Brebbia (eds), Air Pollution III, Vol 3: Urban Pollution ComputationalMechanics Publications, Southampton (1995) 141152.

Schmidt M., R.-P. Schfer and K. Nkel; SIMTRAP: Simulation of Traffic-Induced Air Pollution, Transactionof the Society for Computer Simulation International15(3) (1998) 122-132.

Schwerdtfeger T.; DYNEMO: A model for the Simulation of Traffic Flow in Motorway Networks,Transportation and Traffic Theory, VNU Science Press (1994) 6587.

Wene C.-O. and B. Ryden; A comprehensive energy model in the municipal energy planning process, EuropeanJ. of Operational Research, 33 (1998) 212222.