Engineering topics
Publications and source records attributed to Crosson, William.
This slide presentation examines the effect of cities, the accompanying heat island effect, and other impacts that urbanization has had on the environment. Various satellite views of several urban areas are shown.
In 2007, the National Research Council's committee on Extending Observations and Research Results to Practical Applications recommended that NASA's Applied Science Program (ASP) directly engage with a broader community of users - not just federal agencies. Soon afterwards, scientists at the NASA Marshall Space Flight Center began discussions on a collaborative research project with EMBARQ - the World Resource Institute's Center for Sustainable Transport. The discussions initially focused on how best to utilize satellite observations and atmospheric models for assessing the impact of a proposed transportation project on land use and air quality. Discussions exposed the participants to a broad spectrum of science and policy challenges that these diverse organizations face on a routine basis. It brought into clear focus the need for an observation-modeling system that will allow a proactive approach towards development planning, and the fact that satellite systems do not always provide the spatial and temporal resolution useful for urban-scale applications, underscoring the need for earth system models to bridge this gap. Realizing the significant risk that unplanned urbanization and climate change pose to the social and functional stability of large cities, both organizations decided to expand the scope of their preliminary discussion to include water resources and agriculture. A pilot project, funded by NASA ASP, EMBARQ and Istanbul Technical University focused on quantifying the magnitude and extent of urbanization in Istanbul, and analyzed the combined effect of urbanization and projected climate change on local climate, air quality, and its consequent effects on agricultural productivity. Preliminary results show that Istanbul has undergone a significant amount of Land Use/Land Cover change over the past two decades. While some forested areas have been lost to urban-landscapes, urbanization has mostly occurred over former croplands due to the fact that in contrast to forested areas, croplands have flat terrain, making them a more attractive target for conversion to urban land. The reduction in land area for agricultural and increase in temperature has reduced agricultural production in the vicinity of the city. The United Nations expects the world population to reach 9.2 billion by 2050. Most of this increase will be absorbed by countries that are least able to sustain it. Rapid economic growth is likely to continue and will require massive infrastructure investments. If not properly managed, the magnitude and extent of these initiatives will overwhelm sensitive ecosystems around the world. Successful relationships such as the one described here are critically important if we are to stabilize the threatened ecosystems on which human systems ultimately rely. The challenges, needs, culture, and operating environment of development planning organizations are fundamentally different from a research organization. An appreciation of these differences is a prerequisite for any successful collaboration.
Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.
Aerosol optical depth (AOD), derived from satellite measurements using Moderate Resolution Imaging Spectrometer (MODIS), offers indirect estimates of particle matter. Research shows a significant positive correlation between satellite-based measurements of AOD and ground-based measurements of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5). In addition, satellite observations have also shown great promise in improving estimates of PM2.5 air quality surface. Research shows that correlations between AOD and ground PM2.5 are affected by a combination of many factors such as inherent characteristics of satellite observations, terrain, cloud cover, height of the mixing layer, and weather conditions, and thus might vary widely in different regions, different seasons, and even different days in a same location. Analysis of correlating AOD with ground measured PM2.5 on a day-to-day basis suggests the temporal scale, a number of immediate latest days for a given run's day, for their correlations needs to be considered to improve air quality surface estimates, especially when satellite observations are used in a real-time pollution system. The second reason is that correlation coefficients between AOD and ground PM2.5 cannot be predetermined and needs to be calculated for each day's run for a real-time system because the coefficients can vary over space and time. Few studies have been conducted to explore the optimal way to apply AOD data to improve model accuracies of PM2.5 surface estimation in a real-time air quality system. This paper discusses the best temporal scale to calculate the correlation of AOD and ground particle matter data to improve the results of pollution models in real-time system.
Describes the public health surveillance efforts of NASA, in a joint effort with the Center for Disease Control (CDC). NASA/MSFC and the CDC are partners in linking nvironmental and health data to enhance public health surveillance. The use of NASA technology creates value - added geospatial products from existing environmental data sources to facilitate public health linkages. The venture sought to provide remote sensing data for the 5-country Metro-Atlanta area and to integrate this environmental data with public health data into a local network, in an effort to prevent and control environmentally related health effects. Remote sensing data used environmental data (Environmental Protection Agency [EPA] Air Quality System [AQS] ground measurements and MODIS Aerosol Optical Depth [AOD]) to estimate airborne particulate matter over Atlanta, and linked this data with health data related to asthma. The study proved the feasibility of linking environmental data (MODIS particular matter estimates and AQS) with health data (asthma). Algorithms were developed for QC, bias removal, merging MODIS and AQS particulate matter data, as well as for other applications. Additionally, a Business Associate Agreement was negotiated for a health care provider to enable sharing of Protected Health Information.
HELIX-Atlanta was developed to support current and future state and local EPHT programs to implement data linking demonstratio'n projects which could be part of the EPHT Network. HELIX-Atlanta is a pilot linking project in Atlanta for CDC to learn about the challenges the states will encounter. NASA/MSFC and the CDC are partners in linking environmental and health data to enhance public health surveillance. The use of NASA technology creates value - added geospatial products from existing environmental data sources to facilitate public health linkages. Proving the feasibility of the approach is the main objective
As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELiX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM2.5). HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM2.5 concentrations that can be linked with clinic visits for asthma. While use of the Air Quality System (AQS) PM2.5 data alone could meet HELIX-Atlanta specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. We are using NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data for estimating daily ground level PM2.5 at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.
As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELIX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM(2.5)) HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA (Clayton, Cobb, DeKalb, Fulton, and Gwinnett counties). Under HELIX-Atlanta, pilot projects are being conducted to develop methods to characterize exposure; link health and environmental data; analyze the relationship between health and environmental factors; and communicate findings. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM(2.5) concentrations that can be linked with clinic visits for asthma. From 1999-2000 there were over 9,400 hospitalizations per year in Georgia with asthma as the primary diagnosis. The majority of these hospitalizations occurred in medical facilities in the five most populous Metro-Atlanta counties. Hospital charges resulting from asthma in Georgia are approximately $59 million dollars annually. There is evidence in the research literature that asthmatic persons are at increased risk of developing asthma exacerbations with exposure to environmental factors, including PM(2.5). Thus, HELIX-Atlanta is focusing on methods for characterizing population exposure to PM(2.5) for the Atlanta metropolitan area that could be used in on-going surveillance. While use of the Air Quality System, (AQS) PM(2.5) data alone could meet HELIX Atlanta, specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. Also, the AQS ground observations are made at time intervals ranging from one hour to six days leaving some temporal gaps. NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data have the potential for estimating daily ground level PM(2.5) at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.
Planning is an integral element of good management and necessary to anticipate events not merely respond to them. Projecting the quantity and spatial distribution of urban growth is essential to effectively plan for the delivery of city services and to evaluate potential environmental impacts. The major drivers of growth in large urban areas are increasing population, employment opportunities, and quality of life attractors such as a favorable climate and recreation opportunities. The spatial distribution of urban growth is dictated by the amount and location of developable land, topography, energy and water resources, transportation network, climate change, and the existing land use configuration. The Atlanta region is growing very rapidly both in population and the consumption of forestland or low-density residential development. Air pollution and water availability are significant ongoing environmental issues. The Prescott Spatial Growth Model (SGM) was used to make growth projections for the metropolitan Atlanta region to 2010,2020 and 2030 and results used for environmental assessment in both business as usual and smart growth scenarios. The Prescott SGM is a tool that uses an ESRI ArcView extension and can be applied at the parcel level or more coarse spatial scales and can accommodate a wide range of user inputs to develop any number of growth rules each of which can be weighted depending on growth assumptions. These projections were used in conjunction with meteorological and air quality models to evaluate future environmental impacts. This presentation will focus on the application of the SGM to the 13-County Atlanta Regional Commission planning jurisdiction as a case study. The SGM will be described, including how rule sets are developed and the decision process for allocation of future development to available land use categories. Data inputs required to effectively run the model will be discussed. Spatial growth projections for ten, twenty, and thirty year planning horizons will be presented and results discussed, including regional climate and air quality impacts.
The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.
The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.
The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.
The growth of cities, both in population and in areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80% of the world's population will live in cities. One of the more egregious side effects of urbanization is the deterioration in air quality as a result of increased vehicular traffic, industrialization and related activities. In the United States alone, under the more stringent air quality guidelines established by the U.S. Environmental Protection Agency (EPA) in 1997, nearly 300 counties in 34 states will not meet the new air quality standards for ground level ozone. The mitigation of one the physical/environmental characteristics of urbanization known as the urban heat island (UHI) effect, is now being looked at more closely as a possible way to bring down ground level ozone levels in cities and assist states in improving air quality. The UHI results from the replacement of "natural" land covers (e.g., trees, grass) with urban land surface types, such as pavement and buildings. Heat stored in these surfaces is released into the air and results in a "dome" of elevated air temperatures that presides over cities. The effect of this dome of elevated air temperatures is known as the UHI, which is most prevalent about 2-3 hours after sunset on days with intense solar radiation and calm winds. Given the local and regional impacts of the UHI, there are significant potential affects on human health, particularly as related to heat stress and ozone on body temperature regulation and on the cardiovascular and respiratory systems. In this study we are using airborne and satellite remote sensing data to analyze how differences in the urban landscape influence or drive the development of the UHI over four U.S. cities. Additionally, we are assessing what the potential impact is on risks to human health, and developing mitigation strategies to make urban areas more environmentally sustainable.
The growth of cities, both in population and in areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80% of the world's population will live in cities. One of the more egregious side effects of urbanization is the deterioration in air quality as a result of increased vehicular traffic, industrialization and related activities. In the United States alone, under the more stringent air quality guidelines established by the U.S. Environmental Protection Agency (EPA) in 1997, nearly 300 counties in 34 states will not meet the new air quality standards for ground level ozone. The mitigation of one the physical/environmental characteristics of urbanization known as the urban heat island (UHI) effect, is now being looked at more closely as a possible way to bring down ground level ozone levels in cities and assist states in improving air quality. The UHI results from the replacement of "natural" land covers (e.g., trees, grass) with urban land surface types, such as pavement and buildings. Heat stored in these surfaces is released into the air and results in a "dome" of elevated air temperatures that presides over cities. The effect of this dome of elevated air temperatures is known as the UHI, which is most prevalent about 2-3 hours after sunset on days with intense solar radiation and calm winds. Given the local and regional impacts of the UHI, there are significant potential affects on human health, particularly as related to heat stress and ozone on body temperature regulation and on the cardiovascular and respiratory systems. In this study we are using airborne and satellite remote sensing data to analyze how differences in the urban landscape influence or drive the development of the UHI over four U.S. cities. Additionally, we are assessing what the potential impact is on risks to human health, and developing mitigation strategies to make urban areas more environmentally sustainable.
Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.
It is well known that soil moisture is a characteristic of the land surface that strongly affects the partitioning of outgoing radiation into sensible and latent heat which significantly impacts both weather and climate. Detailed land surface schemes are now being coupled to mesoscale atmospheric models in order to represent the effect of soil moisture upon atmospheric simulations. However, there is little direct soil moisture data available to initialize these models on regional to continental scales. As a result, a Soil Hydrology Model (SHM) is currently being used to generate an indirect estimate of the soil moisture conditions over the continental United States at a grid resolution of 36 Km on a daily basis since 8 May 1995. The SHM is forced by analyses of atmospheric observations including precipitation and contains detailed information on slope soil and landcover characteristics.The purpose of this paper is to evaluate the utility of initializing a detailed coupled model with the soil moisture data produced by SHM.
Through the CaPE Hydrometeorology Project, we have developed an understanding of some of the unique data quality issues involved in assimilating data of disparate types for regional-scale hydrologic modeling within a GIS framework. Among others, the issues addressed here include the development of adequate validation of the surface water budget, implementation of the STATSGO soil data set, and implementation of a remote sensing-derived landcover data set to account for surface heterogeneity. A model of land surface processes has been developed and used in studies of the sensitivity of surface fluxes and runoff to soil and landcover characterization. Results of these experiments have raised many questions about how to treat the scale-dependence of land surface-atmosphere interactions on spatial and temporal variability. In light of these questions, additional modifications are being considered for the Marshall Land Surface Processes Model. It is anticipated that these techniques can be tested and applied in conjunction with GCIP activities over regional scales.
This research is the MSFC component of a joint MSFC/Pennsylvania State University Eos Interdisciplinary Investigation on the global water cycle extension across the earth sciences. The primary long-term objective of this investigation is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates change on both global and regional scales. Significant accomplishments in the past year are presented and include the following: (1) water vapor variability; (2) multi-phase water analysis; (3) global modeling; and (4) optimal precipitation and stream flow analysis and hydrologic processes.