2006 Progress Report: Predicting the Relative Impacts of Urban Development Policies and On-Road Vehicle Technologies on Air Quality in the United States: Modeling and Analysis of a Case Study in Austin, Texas

EPA Grant Number: R831839
Title: Predicting the Relative Impacts of Urban Development Policies and On-Road Vehicle Technologies on Air Quality in the United States: Modeling and Analysis of a Case Study in Austin, Texas
Investigators: McDonald-Buller, Elena , Allen, David T. , Kockelman, Kara , Parmenter, Barbara
Institution: The University of Texas at Austin
EPA Project Officer: Chung, Serena
Project Period: December 20, 2004 through December 19, 2007 (Extended to December 19, 2008)
Project Period Covered by this Report: December 20, 2005 through December 19, 2006
Project Amount: $650,000
RFA: Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions (2004) RFA Text |  Recipients Lists
Research Category: Global Climate Change , Climate Change , Air

Objective:

Robust forecasts of land use, emissions, and activity patterns are essential for air quality model predictions.The objectives of this project are to:

  1. Apply an integrated transportation-land use model (ITLUM) to investigate the impacts of regional development scenarios on the magnitude and spatial distribution of emissions of ozone precursors using the Austin, Texas Metropolitan Statistical Area as a case study. ITLUM-based forecasts will be compared with four pre-determined metropolitan development scenarios developed through a regional “visioning” initiative known as Envision Central Texas (ECT): i) ECT A: low-density, segregated-use development based on extensive highway provision; ii) ECT B: concentrated, contiguous regional growth within 1-mile of transportation corridors; iii) ECT C: concentrated growth in existing and new communities with distinct boundaries; iv) ECT D: high-density development and balanced-use zoning.
  2. Compare the air quality impacts of regional development scenarios on predicted ozone concentrations and human exposure patterns using a photochemical grid model.
  3. Test the hypothesis that predicted human exposure patterns based on ITLUM emission forecasts will differ from those based on the U.S. EPA’s post-Clean Air Act Amendment emission scenario projections.
  4. Test the hypothesis that changes in land use and dry deposition patterns have at least as significant an impact on future air quality as changes in on-road vehicle emission control technologies.

The objectives of the project have not changed from the original application.

Progress Summary:

Urban development results in changes to land use and land cover and, consequently, to biogenic and anthropogenic emissions, meteorological processes, and processes such as dry deposition. The hypotheses described above are being investigated using a case study in the five-county Austin, Texas Metropolitan Statistical Area (MSA). Austin was among the first areas to prepare an Early Action Compact or voluntary State Implementation Plan (SIP) under the National Ambient Air Quality Standard (NAAQS) for ozone concentrations averaged over 8-hours. The Austin Metropolitan Statistical Area (MSA), which includes Travis, Williamson, Hays, Bastrop and Caldwell Counties, is located in Central Texas with a population of approximately 1.25 million people and an economic sector based on computer hardware and semiconductor manufacturing, software development, education, and state government. The area is growing rapidly; Williamson (5th), Hays (26th), Bastrop (30th), and Caldwell (51st) counties were among the 100 fastest growing counties by percent change in the country, while Travis (32nd) County was one of 100 fastest growing counties by numeric change in the country between 2000 and 2001.

This on-going project contrasts the emissions and air quality impacts of four urban growth scenarios developed through a regional “visioning” initiative known as Envision Central Texas with a scenario based on an integrated transportation-land use model. Visioning is a highly community-oriented planning technique used to create regional land use and transportation goals (FHWA 1996). In contrast, land use models are based on historical trends and attempt to forecast or predict what future land use patterns will look like based on those trends (along with changes in any policy, land use, travel cost or other variable that the analyst has incorporated into the model). While fundamentally different, both the visioning and modeling processes carry benefits (Lemp, et al., 2006). Results to date focus on the four ECT Scenarios and the air quality impacts due to changes in biogenic emissions and dry deposition versus anthropogenic emissions. Development of the ITLUM is also described.

ECT Model Development and Air Quality Predictions

All four of the ECT land use “visions” are based on the assumption of a doubling of population within 20 to 40 years in the five-county Austin area and introduction of the same number of new jobs. However, the scenarios assume very different types of growth. Biogenic and anthropogenic emission inventories for the four scenarios were developed for use in the Comprehensive Air Quality Model with extensions (CAMx), which is currently the photochemical model used by the State of Texas for attainment demonstrations. CAMx was used to predict the spatial and temporal patterns of ozone concentrations, and the results for the ECT scenarios were contrasted with a Base Case that included 2007 emission inventories for anthropogenic sources, emission controls adopted for Austin’s Early Action Compact, and a biogenic emissions inventory and dry deposition estimates based on a land cover/land use database developed by Wiedinmyer, et al. (2000, 2001). Although the emission estimates varied between the scenarios, all of the model simulations were conducted using meteorology from a September 13-20, 1999 historical ozone episode that was originally developed for Austin’s Early Action Compact.

Biogenic sources and, because they have been scaled initially with population, area sources were predicted to remain the most significant sources of VOC emissions in the five-county area for all of the ECT scenarios. Differences in LULC led to 2-6% reductions in daily biogenic emissions in the 5-county Austin area that were consistent with the loss of vegetative cover in developing areas. Although VMT is predicted to continue increasing, emissions of NOx and VOCs from on-road mobile sources are predicted to decrease through approximately 2025 due to the phase-in of new emission standards. Similarly, NOx emissions from non–road mobile sources are also predicted to decrease due to the phase-in of new emission standards, while VOC emissions are predicted to increase by 5-9%.

Inter-comparison of air quality predictions for the scenarios is complex and multifaceted, i.e., differences can be evaluated from a number of perspectives including their temporal impacts, spatial impacts, differences in magnitude across the five-county region versus only in areas with high ozone concentrations, and influence on population exposure. With other factors remaining unchanged, future changes in daily maximum 1-hour averaged ozone concentrations for the five-county area due to both biogenic emissions and dry deposition due to a doubling of population (ECT A) resulted in changes of -0.94 ppb to +0.12 ppb relative to the Base Case. Although the changes in air quality due to the impacts of urbanization on biogenic emissions and deposition appear small, they are comparable in magnitude to some commonly employed air pollution control measures that were adopted as part of Austin’s Early Action Compact.

Differences in maximum daily 1-hour averaged ozone concentrations due to changes in anthropogenic emissions only were far more significant, ranging from -6.97 ppb to -1.3 ppb for ECT A. Daily maximum ozone concentrations in the Austin region have historically been predicted to be most responsive to NOx emissions reductions, and reductions in ozone concentrations for the ECT scenarios were consistent with decreases in NOx emissions from the phase-in of new standards for mobile sources. Differences in maximum daily ozone concentrations between the ECT scenarios were smaller than the differences between the ECT scenarios and the Base Case.

Development and Application of an Integrated Transportation-Land Use Model

Household and employment counts (by type) are key inputs to models of travel demand, on-road mobile and area source emissions, and air quality. As part of our integrated land use-transportation modeling effort, a statistically sophisticated land use intensity model was calibrated using year-2000 household and employment counts and GIS-encoded Austin maps. This three-stage least-squares seemingly-unrelated-regression (SUR)-equations model is designed to follow the team’s upstream model of land use change at the zonal or parcel level (Zhou and Kockelman 2006), while recognizing spatial dependence (using spatial lags and spatial errors). As expected, local land use conditions offer substantial predictive power, and transportation access plays a role. Such equations provide the key inputs for travel demand analyses, with transportation system performance feedback. A final version of this modeling approach will soon be applied with the travel demand modeling system, for an integrated modeling forecast of 2030, for comparison with the ECT scenarios, and the following approach. In addition, a SUR model for panel data (over time) also was specified and calibrated. Such models are applicable in circumstances where three-dimensional correlation exists – across time, space and equations. The estimation techniques are a mixture of generalized least squares (GLS) and maximum likelihood estimation (MLE), in order to tackle the complicated correlation patterns. Such models may make great sense in a dynamic setting, as with land use changes.

In addition to the rather complex statistically-driven approaches described above, Putman’s residential and employment allocation model (with a land consumption model) was coded, calibrated and applied. Based on a gravity-model specification, this simplistic and spatially aggregate (1245 zones for Austin) was linked to the travel demand models used to evaluate the ECT scenarios, in order to update travel conditions while providing household and employment forecasts (at five-year intervals). These land use models were calibrated using year-2000 and 2005 demographic data and year-2005 land use data for the region. Based on initial results, households and employment distributions are predicted to be relatively similar to ECT’s trend scenario A. More analyses will be forthcoming.

This research has made very apparent the different nature and objectives, benefits and limitations of mathematical models, vis-à-vis a visioning approach, such as the ECT scenarios. Visioning is a highly community-oriented planning technique used to describe regional goals. In contrast, land use models are based on historical trends, relying profoundly on data for model calibration and application, rather than community opinion and aspirations. While fundamentally different, the visioning and modeling processes appear quite complementary (Lemp, et al., 2006).

Models of Travel Demand

As part of our integrated transportation-land use modeling effort, travel demand models (TDMs) have been applied in coordination with land use models. To date, the TDM used for these purposes was calibrated using the 1996-1997 Austin Travel Survey (ATS) by Smart Mobility, Inc. (SMI) as part of the Envision Central Texas (ECT) visioning project. The model uses a variation of the traditional four-step travel demand modeling framework. Two other TDMs also have been calibrated for the region, rather recently; and these serve as enhancements to SMI’s approach, while offering a valuable point of TDM comparison. The first is similar to SMI’s TDM in that it follows the traditional four-step framework. However, trip distribution is performed in a destination choice framework (instead of SMI’s simpler gravity-based approach) and feedback procedures incorporate the method of successive averages (MSA) (rather than SMI’s less efficient, naïve feedback approach). The second TDM uses an activity-based, microsimulation structure, which differs substantially. This model simulates the behavior of each individual in the region distinctly, and uses a tour- (rather than trip-) based approach, generally considered to be more behaviorally and theoretically sound. The activity-based, microsimulation TDM is more sensitive to changes land use and transportation system variables and will be integrated with the land use models in the coming year.

Future Activities:

During 2007, the team will compare the emissions and air quality predictions from the four pre-determined ECT metropolitan development scenarios with those based on the ITLUM forecast. Sensitivity studies will be conducted around assumptions about the preservation of tree cover during development as well as assumptions used to generate the anthropogenic emission inventories for the ECT scenarios. Human exposure patterns based on the ECT and ITLUM forecasts will be compared by coupling predicted ozone concentrations from CAMx with demographic forecasts to generate population exposure metrics. The team will test the hypothesis that predicted human exposure patterns based on the ITLUM or ECT emission forecasts will differ from those based on the U.S. EPA’s post-Clean Air Act Amendment emission scenario projections. The magnitude and spatial distributions of exposure metrics will be mapped with ArcGIS and compared to quantify differences and support or reject the hypothesis.

The TDM specifications will be enhanced and more scenarios tested (e.g., road pricing) for inter-model comparisons. Commercial vehicles will be modeled directly, rather than their origin-destination patterns taken as given (the traditional approach). These TDMs will be integrated with the land use models, allowing for integrated-model forecasts of Austin’s future land use and traffic patterns, and the results will be compared in detail with the ECT visions.

Several land use modeling enhancements will be pursued. For example, dynamic spatial ordered probit models of land use intensities will be specified, estimated and applied, offering a new tool for spatial data analysis when observations are clustered/grouped (into sub-regions, for example). In addition, microsimulation of residential and commercial land uses will be undertaken, offering another perspective on land use modeling approaches. If data sets allow, firm life cycles (i.e., firm formation, firm growth, firm relocation and firm dissolution) will be modeled using simultaneous equation models (SEM) and discrete choice models. Competitive bidding and developer profit maximizing activities will be key components of such models.


Journal Articles on this Report : 2 Displayed | Download in RIS Format

Other project views: All 51 publications 14 publications in selected types All 13 journal articles
Type Citation Project Document Sources
Journal Article Wang X, Kockelman K. Tracking land cover change in a mixed logit model:recognizing temporal and spatial effects. Transportation Research Record 2006;1977:112-120. R831839 (2005)
R831839 (2006)
R831839 (2007)
R831839 (Final)
  • Abstract: Transportation Research Board-Abstract
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  • Other: University of Texas-Prepublication Paper
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  • Journal Article Wang X, Kockelman KM. Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China. Transportation 2007;34(3):281-300. R831839 (2006)
    R831839 (2007)
  • Abstract: SpringerLink-Abstract
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  • Supplemental Keywords:

    urban air quality, land use models, travel demand models, emission forecasts, ozone, air quality models, central Texas, air, isoprene, dry deposition,, RFA, Health, Scientific Discipline, PHYSICAL ASPECTS, Air, Ecosystem Protection/Environmental Exposure & Risk, RESEARCH, Health Risk Assessment, climate change, Air Pollution Effects, Risk Assessments, Monitoring/Modeling, Monitoring, Environmental Monitoring, Physical Processes, Ecological Risk Assessment, Atmosphere, ecosystem models, integrated assessments, particulate matter, air quality modeling, atmospheric measurements, model-based analysis, remote sensing, motor vehicle emissions, fine particles, automobile exhaust, exposure, global change, model assisted estimation, air pollution, green house gas concentrations, air quality model, modeling, human exposure, climate models, environmental stressors, human activity, landscape characterization, air quality assessments, airborne urban contaminants, human health risk, land use, air quality, ambient air pollution, public health effects, ozone concentrations, transportation, atmospheric chemistry

    Progress and Final Reports:

    Original Abstract
  • 2005 Progress Report
  • 2007 Progress Report
  • Final Report