AIP AQ Unified Scenario

From Federation of Earth Science Information Partners

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This Air Quality Scenario integrates various themes. It emphasizes the common goals and needs of the various themes, while specifying 4 goals:

  1. Real-time large scale event analysis
  2. Assessment of international and intercontinental transport of air pollution
  3. Assimilation of observations for air quality forecasting
  4. Informing the public and the health sector about air quality in real-time or near-real-time

Overview

Air pollution is a global problem that causes premature mortality and morbidity, damages crops and ecosystems, and contributes to climate change. In the United States alone, poor air quality is estimated to cause tens of thousands of deaths and cost society more than $100 billion annually. Globally, air pollution contributes to the deaths of more than 800 thousand people per year, most in the developing world.

This scenario describes how air quality and related earth observations could be used to inform a wide spectrum of decision making; it is structured around three decision-making end users:

  1. A policy-maker, needing synthesized information on the importance of intercontinental pollutant transport
  2. An air quality compliance manager, who needs to assess whether a regional pollution event was caused by transport of pollution from a distant fire, dust storm, etc.
  3. The public, needing information about air quality now and in the near future to make activity decisions

Air pollution does not respect jurisdictional boundaries and is affected by sources and processes over local, regional, intercontinental, and global scales. Understanding the causes of specific instances of air pollution and predicting air quality in any area therefore requires descriptions of atmospheric processes valid and useful on a wide range of scales. Given the wide variety of relevant observations at many scales, each of the above decisions ultimately needs an array of observations and models to describe the atmosphere.

Earth observing data are needed from a wide variety of sources:

  • ambient monitors, measuring the concentrations of pollutants near the ground
  • radiosondes and other instruments which profile an atmospheric column
  • chemical transport models
  • satellite measurements, which report either column densities of pollutants or, with limited vertical resolutions, 3-D fields
  • meteorological data and models
  • emissions data and models

Decision makers, and those that inform them, need access to these data. Moreover, 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 data source, or any one class of data. There are a number of scientific approaches to this challenge, but the technical tools for intercomparison, fusion, and processing of air quality data are not operationally available. Such tools would not make data fusion and intercomparison automatic, but could make it operationally feasible.

Therefore, this scenario a) documents actors' need for access to exisiting information, which GEOSS will facilitate and b) describes needed integrated air quality information, which is not operationally available, but which could be facilitated with interoperable, service-oriented tools for data fusion and intercomparison that will be promoted by GEOSS. To describe the needed information and tools, the scenario also documents the upstream users, and the information they need, in the data value chain.

Summary

Air pollution is a global problem that causes premature mortality and morbidity, damages crops and ecosystems, and contributes to climate change. Furthermore, air pollution does not respect jurisdictional boundaries and is affected by sources and processes over local, regional, intercontinental, and global scales. In the United States alone, poor air quality is estimated to cause tens of thousands of deaths and cost society more than $100 billion annually. Globally, air pollution contributes to the deaths of more than 800 thousand people per year, most in the developing world. Recent scientific and technical advancements, including new observing and information technologies and insights into atmospheric processes, have created opportunities to better assess and manage air pollution and its impacts. Improved information about air quality enables policy-makers and environmental managers to develop more effective policies and plans to improve public health and well being, protect critical ecosystems, and maintain a vital economy. Enhanced air quality forecasts allow communities and individuals, especially those suffering from asthma, allergic diseases, cardiovascular disease, or pulmonary disease, to more effectively limit exposure and the adverse effects of poor air quality.

To better understand, forecast, and manage air pollution, there is a need to bring together information about

  • a variety of atmospheric constituents from different observational platforms (surface monitoring networks, satellites, sondes, ground-based remote sensors, aircraft, ...)
  • nonlinear chemical and physical atmospheric processes from meteorological and chemical transport models
  • emissions and emissions-generating activities
  • population demographics, exposure-related behavior, and health impacts

For scientific assessment and analysis of management strategies, this integration can be done using historical datasets. For air quality forecasting to inform the public and manage individual air pollution episodes or events, it is necessary to perform this integration in near real time.

This air quality use scenario envisions a cyberinfrastructure for air quality information that facilitates access, integration, and use of the information described above for purposes of air quality assessment and forecasting. A particular emphasis is placed on:

  • the near real time analysis of large air pollution events (such as those associated with large fires, dust storms, and regional air pollution episodes)
  • the assimilation of satellite observations to improve numerical forecast models and provide forecasts where ground-based monitors do not exist
  • the assessment of the international or intercontinental transport of air pollution
  • the provision of relevant information to the health community and the general public.

This scenario is consistent with project HE-07-03: Integrated Atmospheric Pollution Monitoring, Modelling, and Forecasting in the GEO 2007-2009 Work Plan, which includes efforts to:

  • Advocate a stable and improved in-situ and space-based observing system of global air quality in line with the Integrated Global Atmospheric Composition Observations (IGACO) recommendations.
  • Support WMO efforts related to increased spatial and temporal resolution. As a priority, evaluate and recommend strategies for an integrated sampling frame for air pollution.
  • Coordinate and facilitate appropriate activities and consortia that complement UNECE CLRTAP HTAP activities and pursue implementation of projects integrating Earth observation data on long range transport with other data, such as health and socio-economic data, to improve decision making.
  • Support the development of international systems for both sand and dust storm warning and biomass burning monitoring.
  • Coordinate the construction of a high spatial and temporal resolution monitoring and forecasting system including atmospheric, terrestrial and oceanic observations, modelling and chemical data assimilation for global and local air quality.
  • Organise appropriate symposia in 2007.

This scenario is also consistent with the efforts of the CEOS Atmospheric Composition Constellation; the development of the GMES Atmospheric Service, GEMS, and its planned extensions; the evolution of AIRNow, IDEA, 3D-AQS, and Datafed; and other ongoing efforts.


Context and pre-conditions

Actors in the scenario, 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.

Actor Grid.png

While presented here in a matrix, a number of these actors have overlapping roles, and in reality the same individuals will serve several downstream decision makers. In fact, the common need for integrated atmospheric observations is a primary motivation for the structure of this scenario.

Intercontinental pollution transport

  • End use decision maker: Policy maker negotiating an international agreement on long-range pollutant transport
    • Information needed: Synthetic assessment reports which quantify the impact of intercontinental pollutant transport on various nations
  • Upstream information processor: National or internation scientific advisory group
    • Information needed: Technical assessments of model experiments and synthesized historical observational datasets designed to assess long-range transport
  • Upstream information processor: Scientific task force assessing long-range transport
    • Information needed: Synthetic description of the atmosphere, using multiple observations and models (satellites, ambient, etc.)
  • Upstream information processor: Air quality data analysts
    • Information needed: Chemical transport models, satellite observations, ambient observations, emissions inventories and models, meteorological data, data integrating all of the above

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: Chemical transport models, satellite observations, ambient observations, emissions inventories and models, meteorological data, data integrating all of the above

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: Chemical transport models, meteorological data, ambient observations when available, data integrating all of the above. For regions of the world without ambient monitors, integrated satellite-model data are particularly necessary.

Earth observations providers

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

  • National, State/Provincial, Local Environmental Management Agencies
  • National Meteorological Agencies
  • National Space Agencies
  • National Land Management Agencies
  • Industry
  • Consultants
  • Academic and Other Research Institutes
  • International cooperative fora (e.g. WMO, CEOS, EEA, ...)

Starting Information

Information available before scenario begins:

  • Meteorological data
    • Observations from ground-based networks, satellites, sondes
    • Forecasts from numerical models at the global and regional scales
  • Geographical data (land use, demographics, emissions-related activity, ...)
  • Atmopsheric Composition (Air Quality) Observations
    • Surface Monitoring Networks
    • Satellite Observations
    • Sondes
    • Ground-based remote sensors
    • Aircraft Measurements
  • Numerical Air Quality Chemical Transport Models (at regional to global scales)

Processing and Collaboration Functionality

  • Facilities to register all the components/services needed for the execution of the scenario.
  • Convenient portals for finding, accessing, visualizing, and processing observational and modeling data by analysts in near real time and for historical analysis
  • Data sharing and integration functionality including (1) registry/catalog for finding resources (2) standard-based access to spatio-temporal data and metadata, workflow software for integrating Service Components
  • Integration of multiple observational data sets to create rich 4-dimensional descriptions of the atmosphere to improve understanding of atmospheric processes
  • Comparison of observational data to numerical model estimates to improve numerical model descriptions of historical conditions (events or long-term trends)
  • Real-time assimilation of observational data into numerical models to improve numerical forecasts
  • A workspace to support the activities of the Air Quality Community of Practice participating in the Scenario
  • 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 is illustrated with the following events. However, since it will enable analysts to combine wide range of air quality observations, models, and other information, it will ultimately be used to produce a broad range of decision support products for a number of different audiences.

Assessment of International and Intercontinental Transport of Air Pollution

Assessment of long range pollutant transport is currently underway by several bodies. The Task Force on Hemispheric Transport of Air Pollution has organized a series of cooperative analysis efforts including a model intercomparison exercise, which to date involves more than 25 global modeling groups and for which a data server has been established at FZ Juelich; a compilation of relevant surface observations, which is being developed by NILU as a component of EBAS; a compilation of relevant aircraft campaign observations, which is being developed by NASA Langley; and an updated version of the EDGAR global emissions inventory.

These cooperative efforts would be enhanced by:

  • constructing linkages between the various databases and other existing air quality-related data hubs (e.g., Datafed and GIOVANNI) and
  • developing and linking tools to facilitate comparison of models, observations, and emissions. Such visualization and analysis tools may build upon existing tools (e.g., AMET, RSIG, and HemiTap Tool).

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.

While these efforts will directly benefit the HTAP assessment, 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.

Real-Time Large Scale Event Analysis (e.g. FASTNET, IDEA, SmogBlog)

Large-scale emission events (e.g., large fires, dust storms) and meteorological conditions can produce significant air pollution episodes on regional to intercontinental scales. Designated analysts will use a 'virtual observatory' to monitor air quality conditions over assigned regions of the world, using real-time satellite images, data from surface monitors, and other information. Once an ‘interesting’ event occurs, the analysts will compile relevant data to explore the origin and evolution of the pollution and share their analysis on a virtual workspace. Combining air quality observations and meteorological forecasts, analysts may be able to qualitatively forecast the evolution of the episode. Using standardized Common Notification Protocols, analysts may issue alerts to trigger exposure or impact mitigation actions or to trigger additional intensive monitoring, forecast modeling, or other additional analyses.


Assimilation of Observations for Air Quality Forecasting (e.g. GEMS, RAQMS)

To improve the accuracy of air quality forecasting, it is necessary to build upon the real-time connectivity and model-data comparisons discussed above and to assimilate observations into numerical simulation models in real-time. There are a number of existing efforts to use satellite observations to provide the initial conditions for numerical models producing air quality forecasts for the next 24-72 hours. Developing standard approaches and protocols for such processes will help expand the use of assimilation techniques, improving air quality forecasts for the benefit of all. Assimilation of satellite observations into numerical models may also enable “nowcasting” surface air quality in areas that do not have surface air quality monitors, which is the situation in much of the developing world.

Informing the public and the health sector about air quality in real-time or near-real-time
(e.g. AIRNow, PHASE)

In addition to supporting the analytical work of air quality managers and scientists, the envisioned cyberinfrastructure will also facilitate the provision of useful information to the health community and the general public. By providing real-time and forecasted air quality information to the public, individuals can make decisions to protect themselves from harmful exposures. Historical, real-time, and forecasted air quality information can also help health authorities assess public health impacts of air pollution episodes and respond to extreme events. For the general public, a number of existing programs (e.g. AIRNow) provide color-coded air quality indices based on real-time surface monitoring and quantitative and qualitative forecasts. This easy-to-understand information can be distributed via mass media, internet, and telecommunications (e.g. EnviroFlash), using Common Notification Protocols. For the health community, some pilot efforts have been made to understand and provide data that is most useful for exposure and health impact assessment (e.g. PHASE). Efforts to capture lessons learned from these existing efforts and to develop standard approaches and protocols will help air quality management agencies expand the amount of air quality information available to the public and interested communities.

Archive: Older Scenario Versions

  1. GEOSS_AIP_Pilot_-_Initial_Scenario
  2. AIP AQ Scenario A: Smoke and Dust
  3. AIP AQ Scenario B: Model - Data Synthesis
  4. AIP AQ Scenario C: forecasts