Reports and Papers
Fedra, K. (in print) AirWare: an urban and industrial air quality assessment and management information system
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 operational framework 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.
INTRODUCTIONAir 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-economic dimensions of the problem.
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 thresholds and 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 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 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 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 ARCHITECTURETo 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 of EU 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
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 conceptual framework for the analysis and formulation of management strategies and policies as the ultimate goal, which can be represented by an object oriented paradigm:
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 of results. 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 projectsAirWare builds on a number of European and international RTD projects and pilot applications, integrating the latest research results and developments as they become 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. The concept 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 running under 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. Acessability 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 tourAirWare 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:
Monitoring time series analysisA 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:
Figure 2: Observation time series display; Figure 3: Comparison of several variables or stations; Figure 4: Regression plot of a two-station comparison; Figure 5: Spatial interpolation of monitoring data.
Emission inventoriesEmission inventories are supported for
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.
Figure 6: Emission inventory, point and area sources overview; Figure 7: Details from the industrial point source emission inventory.
Simulation modelsBasic models in AirWare include a set of fast and efficient screening level models designed for fully interactive use, including
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; 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.
Figure 10: Model results draped over a 3D terrain map; Figure 11: Direct comparison and deltas for two model scenarios; Figure 12: Near-field concentration gradients from traffic emissions; Figure 13: Pseudo-3D display of traffic generated pollution.
Post-processing: impact assessmentOnce 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 are exceeded, population exposure, or calculate air quality indices from a combination of model results.
Figure 14: Spatial population exposure analysis from model results; Figure 15: Rule- based air quality indices derived from model results.
Optimisation: emission control strategiesIf 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. The model 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.
Figure 16: Emission control optimization: interim results for a low investment level; Figure 17: Final results for a high investment level.
Communication: web and WAP supportThe functions of the AirWare system are also accessible over the Internet. This not only provides support for distributed institutions without high-bandwidth connectivity, 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-based outsourcing 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.
Figure 18: Web display of air quality monitoring data; Figure 19: Java applet for the animation of 48 hour regional air quality forecasts.
DiscussionThe 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 information for 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 of an emerging civic society transcending traditional institutional structures and policy making processes poses a new challenge for research and development. Integrating environmental 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.
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