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Now available in PDF format: Abstract Book [7.4 Mb] (posted 10 November 2005)

Abstracts for Posters

Air Quality & Health (P-AQ)

P-AQ1.1

Integrating Climate Modeling and Remote Sensing Data
to Improve Public Health Decision Support Tools: Model Results – Part II

Dazhong Yin, University of Arizona, yin@atmo.arizona.edu

Brian Barbaris, University of Arizona

William A. Sprigg, University of Arizona

The December 15 and 16, 2003 dust storm described in a previous talk spread across New Mexico, west Texas, and northern Mexico. The Dust Regional Atmospheric Model (DREAM) model we used to simulate this storm is nested within the NWS/NCEP/Eta operational forecast system of the National Weather Service. Our aim is to produce an operational dust forecast and early warning system for health and air quality public services.

In this paper, DREAM model results of four climate scenarios as reflected in land cover will be presented. The four scenarios represent climate circa 1980; current climate; and two hypothetical future climates of drier and wetter conditions in the southwest. The results show that NASA earth science observations yield up-to-date land cover and, therefore, improve model results which are fed into public health decision support tools. The decadal changes of land cover due to climate variation and other reasons, such as agricultural activities, have great impact on the modeled air-borne dust concentrations. Model results of the two hypothetical climate scenarios demonstrate the potential of the prospective dust.

P-AQ1.2

Possibilities and Challenges in Using Satellite Data for PM2.5 Forecasts

Mian Chin, NASA Goddard Space Flight Center, Mian.Chin@nasa.gov

Hongbin Yu, GEST/NASA Goddard Space Flight Center

Allen Chu, JECT/NASA Goddard Space Flight Center

Satellite remote sensing has brought our observation of the Earth's atmosphere into a new era, and remote sensing capability could lead to a quantum leap in our ability of air quality monitoring and prediction. In terms of aerosols, the most common quantity from satellite retrieval is the atmospheric column aerosol optical thickness (AOT), and the most common quantity indicating air quality at the surface is the concentration of PM2.5. We present here the relationship between the column AOT and the surface PM2.5 from a global aerosol model GOCART and from satellite instruments MODIS and MISR and surface measurements from EPA and IMPROVE networks. We will discuss the possibilities and challenges in using satellite data for PM2.5 forecasts, and if model-satellite assimilation can improve the forecast quality. We will focus on the following questions:

• Can satellite observations of AOT be quantitatively used for surface PM2.5 monitoring and forecasting?
• Can model assimilation of satellite AOT data improve the quality of PM2.5 forecasting?

[Poster PDF]

P-AQ1.3

Satellite-Derived High Resolution Land Use/Land Cover Data
to Improve Urban Air Quality Model Forecasts and Decision Making

Dale Quattrochi, NASA, Marshall Space Flight Center, Huntsville, AL, dale.quattrochi@nasa.gov

William Crosson, Universities Space Research Association, National Space Science & Technology Center, Huntsville, AL

Maury Estes, Universities Space Research Association, National Space Science & Technology Center, Huntsville, AL

Maudood Khan, Georgia Environmental Protection Division, Air Quality Branch, Atlanta, GA

Under the Clean Air Act (CAA), local and state agencies are responsible for developing State Implementation Plans (SIPs) aimed at attaining and maintaining the National Ambient Air Quality Standards (NAAQS). Typically, the Decision Support Systems (DSSs) used for this purpose utilize numerical models to simulate the physical and chemical processes that govern the transport and transformation of criteria pollutants and their precursors within the region of interest. Within these models, the specification of land use plays an important role in controlling land surface energy and water fluxes, which in turn affect the near-surface meteorology and emissions. Accurate land use characterization has the potential to improve the accuracy of the modeling results and would thus be of great value to federal and state agencies. Researchers from the NASA Marshall Space Flight Center have worked with the Georgia Environmental Protection Division (GEPD) to incorporate an improved high-resolution land use characterization data set (LandPro99 merged with National Land Cover Data, NLCD) within the modeling system. The dataset provides a more accurate representation of the current land use. It also allows a more robust assessment of future land use changes in the region through the use of the Spatial Growth Model (SGM). Meteorological and air quality forecasts made using the high-resolution land use data for two summertime high ozone episodes in 1999 and 2000 were compared against the lower-resolution traditional land use data previously used in the AQMDSS. It was found that use of the high-resolution data improved performance of the meteorological model substantially, with the overall daytime cold bias reduced by over 30%. The air quality model performance for ozone did not show an improvement. Increased boundary layer mixing simulated using the high-resolution land use data negates the effects of warmer near-surface air temperatures, with the net effect on ozone being near zero. In additional, land use changes in the Atlanta area due to urbanization were predicted through 2030 using the Spatial Growth Model (SGM). Modeling simulations with the projected land use predicted higher urban air temperatures. The incorporation of urban heat island mitigation strategies (i.e., highly reflective roofing and increased tree canopy) partially offset this warming trend. Recommendations and lessons learned from this research are being incorporated by the GEPD to improve their air quality modeling DSS for the Atlanta metropolitan area.

[Poster PDF]

P-AQ1.4

Global Atmospheric Pollution Studies Using Space-Based Observations Alongside Global Modeling
and Data Assimilation Tools Developed in NASA's Global Modeling and Assimilation Office

Steven Pawson, NASA GSFC, spawson@gmao.gsfc.nasa.gov

Ivanka Stajner, NASA GSFC & SAIC

Julio Bacmeister, NASA GSFC & UMBC

Andrew Tangborn, NASA GSFC & UMBC

The paper addresses our ability to use available and planned observations to understand the distributions of atmospheric pollutants and their interactions with global climate and air quality. It is most relevant to the "Air Quality" theme of the workshop. The work strongly addresses the second topic of interest, "Evaluation of the current state of observations, modeling, or other research and its appropriateness for use in decision making at different scales," in that it directly addresses characteristics and quality of space-based data and aspects of transport in present-day models.

The foci of discussion will be ozone and carbon monoxide, which have different characteristics, making them sensitive to diverse aspects of model uncertainty and subject to different types of observations. For ozone, discussion will address the value and limitations of assimilating space-based
observations for enhancing our understanding of radiative forcing of climate, long-range transport, and air quality. Existing space-based ozone data give information on total and partial atmospheric column burdens (from the TOMS, SBUV, and OMI instruments), stratospheric profiles (from limb-emission measurements, such as the EOS-MLS instrument), or partial columns in the upper troposphere (from the AIRS and TES instruments). Future (active) sensors may be developed to detect near-surface ozone concentrations. Carbon monoxide can be deduced from infrared emission measurements, which give information about deep layers in the middle and upper troposphere (the MOPITT and AIRS instruments). Results will discuss:

• Estimates and error bars on ozone radiative forcing of climate, including sensitivity to the inhomogeneous ozone data record and the evolving network of meteorological observations.
• Confidence in the vertical profiles of ozone and carbon monoxide determined by data assimilation of space-based data, and the consequent value of these products to our understanding of long-range (inter- and cross-continental) transport of pollution and on air quality. Impacts of limitations in both the observations and the model processes will be evaluated.
• The likely role of future (active) measurements in addressing weaknesses of the present network, and the potential for observation system simulation experiments (OSSEs) to aid in the design of new space-based measurements.

P-AQ1.5

Effects of Urbanization on Meteorology, Biogenic Emissions, and Air Quality
in the Houston-Galveston Area (HGA) in Texas, USA

Daewon Byun, University of Houston, dbyun@mail.uh.edu

Soontae Kim, University of Houston

Fang-Yi Cheng, University of Houston

Changes in the land use (LU) and land cover (LC) not only affect physical characteristics involved in surface flux and radiative energy exchanges, but also influence the amount of biogenic emissions produced by the vegetation. Both the meteorological and emissions input data are important in determining the air quality in a region. Urbanization effects of the Houston-Galveston area (HGA), Texas, USA, on the meteorology, biogenic emissions, and air quality, are studied with a satellite derived high resolution LU/LC data. The siting of coastal cities makes them particularly vulnerable to climate change. We have developed the idea by exploring how shifts in land use and land cover affected local heat islands, land-sea breezes, and air temperatures. In the case of Houston, extensive and ongoing deforestation was leading to significant rises in ozone concentration with potential knock-on effects for public health. Meanwhile, any improvements in car emissions was more than off-set by growth in traffic and further changes in land cover thanks to the
construction of extra road surfaces. Only very reluctantly was a comprehensive assessment of land use and land cover being factored into policy making. Further we suggest far more work needed to be done in assessing the health effects of regional climate change and air quality. Using combined field plot information, satellite estimates of tree cover (stratified by the land use type specified in the new dataset), and detailed leaf mass density (LMD) data for HGA developed by the US Forest Service, we estimated changes in the biogenic emissions. The new biogenic emissions showed significant differences over the areas with large changes in the vegetation cover. Finally, we simulated air quality model with the new meteorological and emissions inputs to study the overall impacts of using the updated LU/LC dataset in the photochemical modeling of high ozone events in the Houston-Galveston nonattainment area. Comparative modeling simulations were performed with the 1990, 2000, and projected 2010 LU/LC datasets. This study demonstrated reduction of urban heat island effects by selectively planting more tree species that produce less isoprene emissions and increasing other vegetation area in the HGA has beneficial effects on improving ozone problems.

P-AQ1.6

Accounting for Uncertainty in Future Climate Change and Evaluating Its Effects on Regional Air Quality

Kasemsan Manomaiphiboon, School of Civil and Environmental Engineering,
Georgia Institute of Technology, Atlanta, GA, U.S.A., km110@mail.gatech.edu

Armistead G. Russell, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, U.S.A.

Chien Wang, Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, U.S.A.

Cassandra B. Roth, Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, U.S.A.

Lai-Yung Leung, Pacific Northwest National Laboratory, Richland, WA, U.S.A.

Jung-Hun Woo, Northeast States for Coordinated Air Use Management (NESCAUM), Boston, MA, U.S.A.

Praveen Amar, Northeast States for Coordinated Air Use Management (NESCAUM), Boston, MA, U.S.A.

Shan He, Northeast States for Coordinated Air Use Management (NESCAUM), Boston, MA, U.S.A.

Precise forecast of future climatic conditions has typically been difficult due partly to the presence of large uncertainty in estimating various factors that can affect climate, e.g. emissions released into atmosphere from natural sources and human activities. This leads to an unclear level of uncertainty in evaluating future regional air quality which is dependent on both meteorology and emissions in the future. In this modeling study, a paradigm of regional air quality modeling over the continental US has been set up using the PSU/NCAR MM5 model and the US EPA CMAQ model for control year 2001 and future year 2050. A scenario of projected future anthropogenic emissions is assumed and a nominal (or base) scenario of future meteorology adopted for use is based on the downscaled results from the NASA GISS global climate model. Uncertainty in climate change is incorporated into the modeling through 1) numerically coupling perturbation values suggested by the results from the MIT Integrated Global System Model (IGSM) to a nominal future meteorological field of interest (here, temperature field) and then 2) performing meteorological downscaling to the regional scale. This poster is to present the overview of how uncertainty in climate change is quantified and the preliminary results of its effects on regional ozone and fine particulate matter levels.

[Poster PDF]

P-AQ1.7

Examining the Impact of Climate Change and Variability on Regional Air Quality over the United States

Ellen Cooter, U.S. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL), Atmospheric Science Modeling Division (ASMD), Research Triangle Park, NC (In partnership with the U.S. Environmental Protection Agency Office of Research and Development), ellen.cooter@noaa.gov

Robert Gilliam, NOAA/ARL/ASMD, Research Triangle Park, NC

Alice Gilliland, NOAA/ARL/ASMD, Research Triangle Park, NC

William Benjey, NOAA/ARL/ASMD, Research Triangle Park, NC

Jenise Swall, NOAA/ARL/ASMD, Research Triangle Park, NC

Chris Nolte, NOAA/ARL/ASMD, Research Triangle Park, NC

The United States has established a series of standards for criteria and other air pollutants to safeguard air quality to protect human health and the environment. The Climate Impact on Regional Air Quality (CIRAQ) project, a collaborative research effort involving multiple Federal Agencies and academic institutions, examines global climate change scenarios as they might affect regional and urban tropospheric air quality in North America for ozone and fine particles. Global climate simulations have been derived from the NASA Goddard Institute for Space Studies (GISS) version II'(two prime) model assuming the IPCC Special Report on Emission Scenarios (SRES) A1B "business as usual" emission scenario. Scientists with the Department of Energy (DOE) Pacific Northwest National Laboratory have used these scenarios to provide boundary and initial conditions to a regional climate model (RCM) based on the Fifth Generation Pennsylvania State/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5). Finally, the RCM was used to generate10 years of present (~2000) and future (~2050) hourly climate scenarios for the continental U.S. over a grid of 36km by 36km cells. Results for analyses of RCM surface temperature, surface wind, precipitation and steering level transport patterns on various time scales (e.g., seasonal, annual, inter-annual) have been compared to historical point and gridded reanalysis datasets as well as to the future RCM scenario decade. These comparisons are used to identify some key model biases and uncertainties on temporal and spatial scales relevant to regional and national air quality assessment. In the next year, RCM simulations will be used as meteorological drivers in the development of 5-year time series of present and future climate-driven emissions and air quality scenarios generated through the Community Multiscale Air Quality (CMAQ) modeling system. Results of the CIRAQ project contribute to the U.S. Climate Change Science Program assessments addressing the effects of global change on human health and welfare and human systems.

The research reported here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic an Atmospheric Administration (NOAA) and under agreement number DW13921548 and contributes to NOAA's Air Quality and Climate Programs. Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect their policies and views.

[Poster PDF]

P-AQ1.8

Assessing Potential Health Impacts of Ozone and PM2.5 Under a Changing Climate

Patrick Kinney, Columbia University, Mailman School Of Public Health, plk3@columbia.edu

Michelle Bell, Yale University, School Of Forestry & Environmental Studies

Christian Hogrefe, University at Albany

Cynthia Rosenzweig, Goddard Institute For Space Studies at Columbia University

Kim Knowlton, Columbia University, Mailman School Of Public Health

Whereas potential impacts of climate change on heat-related illnesses have received considerable attention, less is known about climate-related changes in air quality and corresponding health effects. To address this need, the New York Climate & Health Project (NYCHP) developed and applied a modeling framework designed to generate downscaled estimates of ozone and PM2.5 air quality under alternative scenarios of global climate change, and to relate these changes to potential human health impacts. The modeling framework linked the global Goddard Institute for Space Studies (GISS) general circulation model (GCM) with regional climate (MM5) and air quality (Community Multiscale Air Quality - CMAQ) models to estimate hourly surface ozone and PM2.5 concentrations over the eastern U.S. on a 36 km grid for the summer seasons (June–August) for five consecutive mid-decadal years (e.g., 1993-1997) in the 1990s and 2050s. The outputs from the air quality simulations were used to evaluate the modeling system against observed ozone and PM2.5 data, to project future concentrations throughout the 36 km eastern U.S. modeling domain, and for assessing potential public health impacts within the study area. For assessing health impacts, mortality risks were estimated using exposure-response relationships derived from the literature. Results showed that both ozone and PM2.5 were sensitive to climate change, and that public health impacts could increase over time in the absence of mitigation and/or adaptation efforts. This modeling strategy could be applied in other metropolitan areas, for other climate change scenarios, and for other health outcomes to assess potential health impacts of air pollution under a changing climate.

[Poster PDF]

P-AQ1.9

Criteria for Evaluation of Heat Event Early Warning Systems

Kristie L. Ebi, Exponent Health Sciences Group, kebi@exponent.com

R. Sari Kovats, London School of Hygiene and Tropical Medicine

Introduction

Heat events are associated with marked short-term increases in mortality, particularly in mid-latitude countries. In regions where extreme hot weather is infrequent, major heat events are associated with excess mortality, such as in the midwestern U.S. and Europe. Studies quantifying the impacts of heat events in developed countries consistently find that the elderly are most at risk, although physiological studies suggest that cardiovascular fitness may be more important than age for individual thermoregulation. Early warning systems are being designed to alert the population and relevant authorities in advance about developing adverse meteorological conditions, then to implement effective measures to reduce adverse health outcomes. Although these systems are considered to be effective, there is very little published information on formal (quantitative or qualitative) assessments of the effectiveness of systems as a whole or of individual intervention measures.

Methods

We evaluated literature on heat event responses in Europe and the United States and interviewed stakeholders in Europe regarding response plans to formulate criteria for evaluation of such systems.

Results

Heat event early warning systems are difficult to evaluate, partly because heat events are rare events, with varying characteristics between events, and because high temperatures have different impacts in different populations. Most systems have not been operational for extended periods. The number of deaths avoided per heat event day is typically low and therefore it is difficult to quantitatively evaluate heat event warnings. The system components should be evaluated to ensure that the process of issuing a warning is effective. Criteria were developed for planning, implementing, and on-going evaluation of the system, including: describe the components and operation of the system; describe the resources used to operate the system; and evaluate the system for simplicity, acceptability, sensitivity, timeliness, and effectiveness of individual response measures.

Discussion

Heat events are a public health problem that have, until recently, received insufficient attention from both meteorological and public health agencies. Clear performance standards and regular performance evaluations can build public awareness and confidence in these systems, thus increasing their usefulness.

[Poster PDF]

P-AQ1.10

Integration of NASA Data into ArboNET/Plague Surveillance System

Jorge E. Pinzon, mailstop 923, NASA/GSFC, Greenbelt, MD 20771, USA, jorge.e.pinzon@nasa.gov

Compton J. Tucker, NASA/GSFC, compton@ltpmailx.gsfc.nasa.gov

Kenneth L. Gage, CDC/Fort Collins, klg0@cdc.gov

Russell E. Enscore, CDC/Fort Collins, rce0@cdc.gov

A basic understanding of a region's landscape ecology is useful for predicting the future course of plague outbreaks and identifying areas of high risk for humans. In the case of plague prevention and response efforts, the Center for Disease Control and Prevention (CDC) has partnered with NASA to explore the use of remote sensing products into the CDC's ArboNET plague surveillance system (PSS). The incorporation of Earth science data into the ArboNET/PSS was designed to employ different remote sensing models and techniques that will ultimately allow CDC to deploy these new
capabilities operationally in support of their plague science and management decision making. The use of remote sensing for epidemiological studies is not new. The temporal and spatial distribution of most infectious and vector-borne diseases are affected by a combination of environmental factors such as vegetation, temperature, landscape structure, and rainfall. This paper summarizes the relationships between climate variables as provided by MODIS, AVHRR, LandSAT, TRMM, and SRTM sensors and the frequency of human plague cases (1981-2002). Results show that temporal variations in plague risk can be estimated by monitoring these key climatic variables and allow a better characterization of the spatial and temporal dimensions of plague vector habitats.

[Poster PDF]