AQ Scenario for AIP 2008 CFP

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Word Version of the Scenario

Since the scenario needs to be in this format, please edit the above document or send changes to David McCabe if you would prefer!

2.6.2 Air Quality Scenario

Summary

The air quality scenario envisions GEOSS facilitating two broad goals: building connections to facilitate movement of data between actors, and developing interoperable tools for intercomparison and fusion of a wide variety of atmospheric data. This brief description of the scenario contains hyperlinks to a more detailed version of the scenario.

The scenario is focused on three end users:

1. A policy-maker, needing synthesized information on the importance of intercontinental pollutant transport
2. An air quality manager, who needs to assess whether a regional pollution event was caused by an "exceptional event"
3. The public, needing information about air quality now and in the near future (via forecasts) to make activity decisions

While the scenario describes three distinct sets of end users, each depends upon common upstream actors and synthesized Earth observations. In fact, the common need for these synthesized atmospheric observations is a primary motivation for the structure of this scenario.

Given the wide variety of atmospheric processes at many scales, each of the above decisions needs an array of observations and models (listed below). Each type of data is significantly limited and not able to broadly document the state of the atmosphere. Synthetic fusion and intercomparison of the data will allow analysts to produce a far more complete and accurate description of the atmosphere than obtainable from any one type of data. There are a number of scientific approaches to this challenge, but technical tools for intercomparison, fusion, and processing of air quality data are not operationally available.

This scenario is consistent with GEO project HE-07-03: Integrated Atmospheric Pollution Monitoring, Modelling, and Forecasting in the GEO 2007-2009 Work Plan, and with the efforts of the CEOS Atmospheric Composition Constellation; the development of the GMES Atmospheric Service, as well as other major international collaboration efforts.

Context and pre-conditions

Actors, and the information they need

A number of actors process earth observations information upstream of the decision makers, who base their decisions on highly synthesized data.

While presented here in a matrix, a number of these actors have overlapping roles, and the same individuals will serve several downstream decision makers. Similarly, similar upstream information serves all of the end users in the scenario. Actors are enumerated in more detail in the full scenario.

Intercontinental pollution transport

• End use decision maker: Policy maker negotiating an agreement on intercontinental pollutant transport
  • Information needed: Synthetic assessment reports quantifying the impact of long-range pollutant transport
• Upstream information processor: Scientific advisory group
  • Information needed: Technical assessments of model experiments and synthesized datasets to assess transport
• Upstream information processor: Scientific task force assessing long-range transport
  • Information needed: Synthetic description of the atmosphere, using multiple observations and models
• Upstream information processor: Air quality data analysts
  • Information needed: Wide variety of atmospheric observations, synthetic integrations of this data

Exceptional pollution event

• End use decision maker: Air quality manager assessing pollution event: is it an exceptional event?
  • Information needed: Assessment reports which quantify the impact of transport on the region for that period
• Upstream information processor: Air quality data analysts
  • Information needed: Wide variety of atmospheric observations, synthetic integrations of this data

Member of the public planning activities

• End use decision maker: Member of the public
  • Information needed: Air Quality Index or similar health-based index for the local area
• Upstream information processor: Air quality forecasters / data collectors
  • Information needed: Wide variety of atmospheric observations, synthetic integrations of this data

Earth observations providers

Most of these earth observations are used in all three of the decision making information chains.

• Government agencies (National, State/Provincial/Tribal, and/or Local):
  • Environmental, Meteorological, Land management, Space agencies
• Industry, Consultants
• Academic and Other Research Institutes
• International cooperative fora (e.g. WMO, CEOS, EEA, ...)

Information already available for the scenario events

1. meteorological data, such as observations from ground-based networks, satellites, sondes, and forecasts from numerical models at the global and regional scales
2. geographical data (land use, demographics, emissions-related activity, etc.)
3. atmospheric composition (air quality) observations such as surface monitoring networks, satellite observations, sondes, ground-based remote sensors, and aircraft measurements
4. numerical air quality chemical transport models (at regional to global scales)

Specific processing and collaboration functionality needed

1. Community Catalog(s) for registering data and services to be harvested by the GEOSS Clearinghouse
2. Community Portal(s) for finding, accessing the data and services needed for the execution of the scenario,
3. Functionality for standard-based access to spatio-temporal data and metadata, and workflow software for for service orchestration
4. Community of Practice Workspace(s) where the actors in the scenario can communicate and coordinate their activities.

Additional functionality and facilities specific to the Air Quality Scenario should include tools for visualizing, and processing observational and modeling data for near real time and for historical analysis. These tools should facilitate:

1. Integration of multiple observational data sets to create rich n-dimensional descriptions of the atmosphere to improve understanding of atmospheric processes;
2. Comparison of observational data to numerical model estimates to improve numerical model descriptions of historical conditions (events or long-term trends);
3. Real-time assimilation of observational data into numerical models to improve numerical forecasts;
4. Effective mechanisms for distributing (in near real time) maps/images, descriptive information, and processed data health, emergency response, and air quality management authorities; to mass media; other research and assessment communities (e.g., health); and the general public.
   

Scenario Events

The cyberinfrastructure envisioned by this scenario will enable analysts to combine wide range of air quality observations, models, and other information, which will ultimately be used to produce a broad range of decision support products for a number of different audiences. The air quality and health scenario events include: Assessment of International and Intercontinental Transport of Air Pollution, Exceptional Event Analysis, and Informing the Public and the Health Sector About Air Quality. Current projects (e.g. AIRNow, AIRNow-International, 3D-AQS, PHASE, GEMS, IDEA) are significant building blocks along with the evolving data mediatiors (e.g. Datafed, GIOVANNIof the needed networks and tools. As above, the actors will benefit from constructing linkages between data sources and tools, but also from development and linking tools to facilitate comparison of models, observations, and emissions information.

Assessment of International and Intercontinental Transport of Air Pollution

Assessment of long range pollutant transport is currently underway by several bodies. GEOSS can assist these efforts be by:

• constructing linkages between the various databases and other existing air quality-related data hubs
• developing and linking tools to facilitate comparison of models, observations, and emissions data. Such visualization and analysis tools should build upon existing tools.

These capabilities are used to form a more complete and accurate description of the atmosphere than currently available from any one type of atmospheric observation. Researchers will use these datasets and models to quantitatively assess the importance of long-range pollutant transport. Research efforts will then be compiled into a detailed report of the task force. This report is then used as the basis for an synthesis report and executive summary which will finally be delivered to policymakers to inform their decision making process, as international conventions consider initiatives to address long range pollutant transport.

The connectivity and tools developed as part of this effort will be applicable to model evaluation and analysis at the regional scale as well, ultimately benefiting a large community of air quality managers and researchers.

Exceptional Event analysis

Air quality is periodically influenced by natural and anthropogenic events, such as wildfires and dust storms. For regulatory purposes, pollution episodes can be flagged as 'exceptional events' if an area would not have exceeded the pollution standard without the occurrence of a an uncontrollable and unusual natural or anthropogenic event.

An event might be obvious or subtle, so the impetus to examine a given event could come air quality managers or the wider community. Analysts at air management agencies or elsewhere would use models and ambient and satellite observations to identify potential events. Once an event is proposed, relevant data is compiled from those data sources to explore the origin and evolution of the pollution, with data and developing analysis shared in a virtual workspace. Synthesizing data from the various sources, analysts quantify the effect of the event on the receptor regions, and then compile this information into a report submitted to air quality managers.

Current projects are significant building blocks of the needed networks and tools. As above, the actors will benefit from constructing linkages between data sources and tools, and from developing and linking tools to facilitate comparison of models, observations, and emissions information.