Jill A. Engel-Cox

The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

Abstract Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.

An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth

Abstract Using satellite observations of aerosol optical depth (AOD) to estimate surface concentrations of fine particulate matter (PM2.5) is a well-established technique in the air quality community. In this study, the relationships between PM2.5 concentrations measured at five monitor locations in the Baltimore, MD/Washington, DC region and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Imaging Spectroradiometer (MISR), and Geostationary Operational Environmental Satellite (GOES) were calculated for the summer of 2004 and all of 2005. Linear regression methods were used to determine the direct quantitative relationships between the satellite AOD values and PM2.5 concentration measurements. Results show that correlations between AOD and surface PM2.5 concentrations range from 0.46 to 0.84 for the analyzed time period. Correlations with AOD from MODIS and MISR were higher than those from GOES, likely because of variations in the algorithms used by the different instruments. To determine the relative usefulness of platform- and season-specific AOD PM2.5 regression analysis, the results from this study were used to estimate surface PM2.5 concentrations for two representative case studies. This analysis of case studies demonstrates that it is necessary to include season and satellite platform information for more accurate estimates of surface PM2.5 concentrations from satellite AOD data. Consequently, tools that currently use a constant relationship to estimate surface PM2.5 concentrations from satellite AOD data, such as the Infusing satellite Data into Environmental Applications (IDEA) website, may need to be revised to include parameters that allow the relationships to vary with season and satellite platform to provide more accurate results.

Applications of the three-dimensional air quality system to western U.S. air quality: IDEA, smog blog, smog stories, airquest, and the remote sensing information gateway.

Abstract A system has been developed to combine remote sensing and ground-based measurements of aerosol concentration and aerosol light scattering parameters into a three-dimensional view of the atmosphere over the United States. Utilizing passive and active remote sensors from space and the ground, the system provides tools to visualize particulate air pollution in near real time and archive the results for retrospective analyses. The main components of the system (Infusing satellite Data into Environmental Applications [IDEA], the U.S. Air Quality Web log [Smog Blog], Smog Stories, U.S. Environmental Protection Agency’s AIR Quest decision support system, and the Remote Sensing Information Gateway [RSIG]) are described, and the relationship of how data move from one system to another is outlined. To provide examples of how the results can be used to analyze specific pollution episodes, three events (two fires and one wintertime low planetary boundary layer haze) are discussed. Not all tools are useful at all times, and the limitations, including the sparsity of some data, the interference caused by overlying clouds, etc., are shown. Nevertheless, multiple sources of data help a state, local, or regional air quality analyst construct a more thorough picture of a daily air pollution situation than what one would obtain with only surface-based sensors.

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.

Conceptual model of comprehensive research metrics for improved human health and environment.

Objective Federal, state, and private research agencies and organizations have faced increasing administrative and public demand for performance measurement. Historically, performance measurement predominantly consisted of near-term outputs measured through bibliometrics. The recent focus is on accountability for investment based on long-term outcomes. Developing measurable outcome-based metrics for research programs has been particularly challenging, because of difficulty linking research results to spatially and temporally distant outcomes. Our objective in this review is to build a logic model and associated metrics through which to measure the contribution of environmental health research programs to improvements in human health, the environment, and the economy. Data sources We used expert input and literature research on research impact assessment. Data extraction With these sources, we developed a logic model that defines the components and linkages between extramural environmental health research grant programs and the outputs and outcomes related to health and social welfare, environmental quality and sustainability, economics, and quality of life. Data synthesis The logic model focuses on the environmental health research portfolio of the National Institute of Environmental Health Sciences (NIEHS) Division of Extramural Research and Training. The model delineates pathways for contributions by five types of institutional partners in the research process: NIEHS, other government (federal, state, and local) agencies, grantee institutions, business and industry, and community partners. Conclusions The model is being applied to specific NIEHS research applications and the broader research community. We briefly discuss two examples and discuss the strengths and limits of outcome-based evaluation of research programs.

Compilation and assessment of recent positive matrix factorization and UNMIX receptor model studies on fine particulate matter source apportionment for the eastern United States.

Abstract In 1997, the U.S. Environmental Protection Agency (EPA) revised its particulate matter standards to include an annual standard for fine particulate matter (PM2.5; 15 μg/ m3) and a 24-hr standard (65 μg/m3). The 24-hr standard was lowered to 35 μg/m3 in 2006 in an effort to further reduce overall ambient PM2.5 concentrations. Identifying and quantifying sources of particulate matter affecting a particular location through source apportionment methods is now an important component of the information available to decision makers when evaluating the new standards. This literature compilation summarizes a subset of the source apportionment research and general findings on fine particulate matter in the eastern half of the United States using Positive Matrix Factorization. The results between studies are generally comparable when comparable datasets are used; however, methodologies vary considerably. Commonly identified source categories include: secondary sulfate/coal burning (sometimes over 50% of total mass), secondary organic carbon/mobile sources, crustal sources, biomass burning, nitrate, various industrial processes, and sea salt. The source apportionment tools and methodologies have passed the proof-of-concept stage and are now being used to understand the ambient composition of particulate matter for sites across the United States and the spatial relationship of sources to the receptor. Recommendations are made for further and standardized method development for source apportionment studies, and specific research areas of interest for the eastern United States are proposed.

Conceptual model of comprehensive research metrics for improved human health and environment

Performance measurement predominantly consisted of near-term outputs measured through bibliometrics, but the recent focus is on accountability for investment based on long-term outcomes. Our objective is to build a logic model and associated metrics through which to measure the contribution of environmental health research programs to improvements in human health, the environment, and the economy. We developed a logic model that defines the components and linkages between extramural environmental health research grant programs and the outputs and outcomes related to health and social welfare, environmental quality and sustainability, economics, and quality of life, focusing on the environmental health research portfolio of the National Institute of Environmental Health Sciences (NIEHS) Division of Extramural Research and Training and delineates pathways for contributions by five types of institutional partners in the research process. The model is being applied to specific NIEHS research applications and the broader research community. We briefly discuss two examples and discuss the strengths and limits of outcome- based evaluation of research programs.A avaliacao de desempenho compreendia predominantemente resultados de curto prazo avaliados atraves de bibliometria, mas recentemente a enfase voltou-se a prestacao de contas dos investimentos com base em resultados a longo prazo. Nosso objetivo e criar um modelo logico e metricas associadas atraves dos quais possamos avaliar a contribuicao de programas de pesquisa em saude ambiental para melhorar a saude humana, o meio ambiente e a economia. Desenvolvemos um modelo logico que define os componentes e elos entre os programas de pesquisa em saude ambiental extramuros subsidiados e os resultados relacionados a saude e ao bem-estar social, qualidade ambiental e sustentabilidade, economia e qualidade de vida, com enfase no portfolio de pesquisa em saude ambiental do National Institute of Environmental Health Sciences (NIEHS), divisao de pesquisa e treinamento extramuros, delineando caminhos para as contribuicoes de cinco tipos de parceiros institucionais no processo de pesquisa. O modelo esta sendo usado em aplicacoes especificas do NIEHS e na comunidade de pesquisa como um todo. Analisamos brevemente dois exemplos e os pontos fortes e limitacoes da avaliacao baseada em resultados dos programas de pesquisa.

Correlating seasonal averaged in situ monitoring of fine PM with satellite remote sensing data using geographic information system (GIS)

Satellite remote sensing data are another source of information to study air quality, supplementing the in situ monitoring networks. Satellite data have primarily been used to study specific events that affect air quality, such as wildfires, biomass burning, dust storms, and volcanoes. In this exploratory analysis we have used the monthly averaged aerosol optical depth (AOD) product of the MODIS sensor data from the Terra satellite platform to study fine particulate matter throughout the contiguous U.S. While most of the previous quantitative work has focused on hourly correlations between in situ monitors and satellite AOD data, we have attempted to quantify monthly, seasonal, and annual correlations. Our analysis of 2001 monthly data found that correlations do exist, but not throughout the entire study period or area. The best correlations were seen in the northeast and industrial Midwest during the summer months.

3D-AQS, a Three-Dimensional Air Quality System

In 2006, we began a three-year project funded by the NASA Integrated Decisions Support program to develop a three-dimensional air quality system (3D-AQS). The focus of 3D-AQS is on the integration of aerosol-related NASA Earth Science Data into key air quality decision support systems used for air quality management, forecasting, and public health tracking. These will include the U.S. Environmental Protection Agency (EPA)s Air Quality System/AirQuest and AIRNow, Infusing satellite Data into Environmental Applications (IDEA) product, U.S. Air Quality weblog (Smog Blog) and the Regional East Atmospheric Lidar Mesonet (REALM). The project will result in greater accessibility of satellite and lidar datasets that, when used in conjunction with the ground-based particulate matter monitors, will enable monitoring across horizontal and vertical dimensions. Monitoring in multiple dimensions will enhance the air quality communitys ability to monitor and forecast the geospatial extent and transboundary transport of air pollutants, particularly fine particulate matter. This paper describes the concept of this multisensor system and gives current examples of the types of products that will result from it.