|Home CCSP Workshop, November 2005 Abstracts Session 4: Air Quality Management: Application of Climate Science (AQ), Sub-Theme 2: Linking Climate Change Research to Air Quality Decisions|
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Abstracts for Speakers: Session 4
Air Quality Management: Application of Climate Science (AQ)
Sub-Theme 2: Linking Climate Change Research to Air Quality Decisions
Towards an Integrated Observing System for Air Quality Decision Making
Doreen Neil, NASA, Doreen.O.Neil@nasa.gov
R.B. Pierce, NASA
James Szykman, U.S. EPA
Jack Fishman, NASA
The recent U.S. CCSP Strategic Plan declares a goal to increase knowledge of the interactions among (air) pollutant emissions, long range transport, climate change, and air quality management. Under UNESCO, the international science community has outlined a decennial plan (Integrated Global Atmospheric Chemistry Observations) for an atmospheric chemistry observing system, the tools to integrate observations and models, and a structure to make these results accessible for decision making. On a regional scale, the U.S. Environmental Protection Agency (EPA) sets National Ambient Air Quality Standards for Criteria Pollutants [ozone, carbon monoxide, nitrogen oxides, sulfur dioxide, mercury, and aerosols] (Clean Air Act, 1970 and Amendments), and provides surface monitoring in urban areas. NASA's Applied Science program has supported the technical evaluation of space based observations of criteria and related pollutants in partnership with U.S. EPA (NASA National Applications Program Plan and Agency Deliverables). This work includes evaluating satellite products from NASA's newest missions, and prototyping the use of satellite data in daily efforts to predict, manage, and mitigate the harmful effects of air pollution.
Aerosol and trace gas pollution from human activities and large-scale wildfires rise above the boundary layer and can travel great distances, sometimes returning to the surface at sites geographically and jurisdictionally distant from the sources, due to the effects of weather and chemical processes. This complex behavior of trace atmospheric constituents that affect human health and ecosystems can be understood fully only with an integrated observing system that comprises process studies, source/point data, global observations, and time-resolved observations.
This paper reports specific efforts toward integrating observations of atmospheric chemistry from space and Earth's surface with adequately resolved models of atmospheric chemistry and transport, to attain the ultimate goal of air quality decision making support: ubiquitous, near-surface atmospheric trace gas concentration. In the past, climate change studies in atmospheric composition have focused on the composition of the upper atmosphere (ozone hole), with large spatial scales and long time scales. From the perspective of public and individual decision making, air quality is boundary layer composition throughout the day. We will discuss the capability of present systems to meet this challenge and offer suggestions for future investments to support decision making.
Integrating Climate Modeling and Remote Sensing Data
Stanley Morain, EDAC, University of New Mexico, email@example.com
Amelia Budge, EDAC, University of New Mexico
Karl Benedict, EDAC, University of New Mexico
William Hudspeth, EDAC, University of New Mexico
Thomas Budge, EDAC, University of New Mexico
Gary Sanchez, EDAC, University of New Mexico
William Sprigg, University of Arizona
On December 15 and 16, 2003, a dust storm occurred over New Mexico and west Texas as a strong Pacific cold front brought gale-force winds through the region. Combined with existing dry conditions, this system caused one of the worst dust storms in the area in recent years. Dust events such as this can adversely affect patients with known respiratory conditions. Early warning of these events would better prepare clinics, hospitals, and health care officials in responding to the need of these patients.
NASA's partners at the University of New Mexico, University of Arizona, Texas Tech University, and Sandia National Laboratories are using an NCEP/Eta-based model and satellite data to develop a dust forecasting tool that alerts public health officials to environmental events affecting patients with high risk respiratory conditions. Dust storms and smoke from forest fires can extend for hundreds of miles over a region. Data collected by a host of satellites and in-situ ground stations, coupled with weather models used by the National Weather Service and visualization technologies, offer a new dimension of information for providing health risk alerts. Working closely with the public health communities in Arizona, New Mexico, and Texas, the project aims to provide a module that can be used within existing desk-top decision support tools such as the Rapid Syndrome Validation Project (RSVP).
This presentation will provide an overview of the project and its relationship to the goals and objectives of the CCSP Strategic Plan on "Decision Support Resources Development." The discussion also will describe how NASA data are being introduced to improve a dust forecast model, the Dust Regional Atmospheric Model (DREAM), which is producing products for improving a public health decision support tool developed by Sandia National Laboratories. Finally, methods for communicating this scientific information will be discussed, including web mapping and visualization
Supporting Long-Term Regional Air Quality Management in Response to Global Change
Dan Loughlin, U.S. EPA – ORD/NRMRL/APPCD, Loughlin.Dan@epamail.epa.gov
Gary Kleiman, NESCAUM
Bryan Hubbell, U.S. EPA – OAR/OAQPS/EMAD
Darryl Weatherhead, U.S. EPA – OAR/OAQPS/AQSSD
Energy often enters into local, state, and regional planning. For example, the ability of industries to adapt to future changes in fuel availability and prices is important in ensuring that an area will be economically competitive in the future. Energy is also important in environmental planning; the energy system plays a critical role in urban- and regional-scale air quality management since combustion is the major anthropogenic source of nitrogen oxides (95%), carbon monoxide (95%), sulfur oxides (89%), ammonia (62%), and mercury (87%). Thus, any policies targeted at acid rain, tropospheric ozone, fine particulate matter, or mercury will undoubtedly affect the energy system. Similarly, land use planning and water resource management often have energy implications.
In this context, decision-makers should have sound tools for evaluating energy in environmental planning. Developing such tools, with a focus on air quality, is an objective of EPA Office of Research and Development's Global Change Air Quality Assessment. EPA entered into a cooperative agreement with the Northeast States for Coordinated Air Use Management, or NESCAUM, to develop a regional version of the MARKet ALlocation energy system model and technology database (NE-MARKAL). NE-MARKAL incorporates characterizations of energy supplies, energy demands, and energy-related technologies for the six New England states. Sectors represented include electricity generation, industrial, commercial, residential, and transportation. The model identifies cost-effective technology pathways for meeting future energy demands and emissions constraints. NESCAUM plans to apply
To facilitate regional analyses, EPA has developed a prototype decision support system that links NE-MARKAL to a response surface model, or RSM, and a health benefits model, BenMAP. The RSM is a statistical representation of a regulatory-scale photochemical grid model. BenMAP is an EPA model for estimating the health benefits associated with changes in air quality. This linkage allows quick screening of future energy system technology scenarios to estimate and visualize their impacts.
During the presentation, NE-MARKAL will be described and its linkage to the RSM and BenMAP within a prototype decision support system will be demonstrated. Future directions will be discussed.
Application of an Integrated Modeling System
Xin-Zhong Liang, Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA, firstname.lastname@example.org
Ho-Chun Huang, Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA
Allen Williams, Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA
Michael Caughey, Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA
Kenneth Kunkel, Illinois State Water Survey, University of Illinois at Urbana-Champaign, USA
Donald Wuebbles, Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, USA
A state-of-the-art integrated modeling system has been developed to provide credible information at regional to local scales on climate and air quality, including their variability, change and impact, as a scientific basis for decision makers to select optimal pathways. This system is currently being applied, with support from the EPA Science to Achieve Results (STAR) Program, to quantify and understand the uncertainties of the individual and combined impacts of global climate and emission changes on U.S. air quality, from the present to 2020, 2050 and 2100. We present recent results over the U.S. that demonstrate the system skill in downscaling the present climate (precipitation and surface air temperature) and air quality (ground-level ozone), as well as the uncertainties and credibility of future projections. The appropriateness and limitations of the modeling system for decision making at regional to local scales are evaluated.
It is shown that the dynamic downscaling can significantly reduce biases of the driving global models in simulating the present climate/air quality
Development and Evaluation of a Methodology for Determining Air Pollution Emissions
Allen Williams, Illinois State Water Survey, email@example.com
Geoffery Hewings, University of Illinois, Dept of Geography
Zhining Tao, Illinois State Water Survey
Kieran Donaghy, University of Illinois, Dept of Urban and Regional Planning
Donald Wuebbles, University of Illinois, Dept of Atmospheric Sciences
The evaluation of future air quality critically depends on the specification of future emissions. Technological change, which is typically reflected in different scenarios, can have a large impact on future emissions. A less well appreciated factor that influences emissions is structural economic changes such as outsourcing of goods and services. The focus of the present research is on developing an Emissions Inventory Modeling System (EIMS) that uses regional econometric models and emissions development tools to formulate future emissions inventories for different social and climate change scenarios. Changes in population, economy, policy and regulations, technology development, transportation systems, energy systems, landscape and land-use, and vegetation and land cover are considered. Results are reported for analysis of future emissions in the Chicago area in a format consistent with the USEPA's National Emissions Inventory (NEI), and the methods are being expanded to develop emissions for the Midwest. A baseline case incorporating future economic development out to 2030 with present emission technologies is presented to isolate the effects of structural economic change on emissions. Future total emissions and the impacts from each economic sector are analyzed. In addition, an analysis of the impact of heavy duty diesel rule on economy and emissions is conducted. The methodologies used to interface emissions and control technologies with the econometric model are discussed.