Grantee Research Project Results
Final Report: Advanced Modeling System for Forecasting Regional Development, Travel Behavior, and Spatial Pattern of Emissions
EPA Grant Number: R831835Title: Advanced Modeling System for Forecasting Regional Development, Travel Behavior, and Spatial Pattern of Emissions
Investigators: Rodriguez, Daniel , Hanna, Adel , Frey, H. Christopher , Morton, Brian J. , Khattak, Asad , Rouphail, Nagui , Arunachalam, Sarav , Song, Yan
Institution: University of North Carolina at Chapel Hill , North Carolina State University
EPA Project Officer: Chung, Serena
Project Period: September 1, 2004 through November 14, 2009
Project Amount: $680,000
RFA: Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions (2004) RFA Text | Recipients Lists
Research Category: Climate Change , Air
Objective:
Through simulation modeling of land use, transportation, emissions and air quality, this research investigated whether and how regional development significantly affects the quantity and spatial characteristics of health- and environment-damaging emissions. The final product accommodates model-ready emission inventories, such as CMAQ (Community Multi-scale Air Quality system). The research rigorously tested the hypothesis that various development patterns, over a planning horizon of 50 years, significantly influence the quantity and location of direct and indirect emissions from on- and off-road mobile sources and thus affect air quality. Patterns of interest included both the type (e.g., transit-oriented, pedestrian- and bicycle-supportive, dense mixed use, etc.) and location of development within the regional landscape (e.g., greenfield, infill, etc.). The model produced different results in travel behavior and emissions for distinctly different development patterns.
The research team, comprising social and physical scientists as well as engineers, developed a simulation model with land use, travel behavior, and emissions components. The model used data for Charlotte—the largest city in North Carolina, and surrounding Mecklenburg County. This rapidly growing region is making major investments that will affect regional travel patterns, including a tolled beltway and rail expansions. The area, which only recently has seen the emergence of decentralization, has a radial-corridor regional structure, with employment highly centralized in the CBD. In the coming decades, the area will face significant growth challenges, including water scarcity, congestion, and rising housing costs.
The modeling system was used to forecast emissions. We compared scenarios and propagated the emissions changes through SMOKE (Sparse Matrix Operator Kernel Emissions) and CMAQ, which resulted in varying air pollutant concentrations and potential population exposures. The final outputs of the simulation model are spatially distributed air quality concentrations for multiple scenarios. The work yielded a general objective method to test hypotheses about how various development forms may affect emissions.
Summary/Accomplishments (Outputs/Outcomes):
The team comprised researchers from several institutions: the Institute for Transportation Research and Education at North Carolina State University, and the Institute for the Environment and the Center for Environmental Modeling for Policy Development, both at the University of North Carolina—Chapel Hill.
The team developed a state-of-the-art simulation model comprising the following three modules:
• Cross-sectional land-market equilibrium model
• Behavioral travel forecasts, including non-motorized modes and environmental measures
• Modal approach to estimating emissions based on EPA’s MOVES system
Research components
The Charlotte area was chosen because of available data on parcels, land market and transportation network; recent multimodal travel survey; photochemical modeling of the area using a 4-km grid; status as an ozone non-attainment area; and its energetic and collaborative approach to regional modeling and policy. The major components from this project include:
• Typology of regional development and built environment. We categorized neighborhoods in terms of land use and transportation attributes for use in regional development scenarios. Factor analysis applied to a set of urban form attributes yielded several dimensions that capture essential differences in urban form: walkability, accessibility, agglomeration, industry, and property values. Factor scores served as inputs to a cluster analysis to identify eight distinct neighborhood types, from CBD to rural greenfields.
• Regional development scenarios. Scenarios include a 2000 baseline, and two scenarios for land and transport supply for 2050, representing two distinctly different sets of choices about regional growth management. The typology of built environment was used to identify the zones most appropriate for future development according to two scenarios: sprawl ("Business as Usual") and strict growth management ("Smart Growth"). The former entailed outward growth, while the latter applied stringent criteria to direct higher-density growth away from key undeveloped areas, with attendant strategic mass transit investments. Scenarios used the same population and employment growth factors, and varied only in location. In both scenarios, road capacity increased only when actual road demand was high.
• Land-use and transportation modeling. The modeling platform used was TRANUS, an integrated land use and transportation model that simulates land markets and transportation networks on urban and regional scales. The model was calibrated for the base year, comparing model outputs with observed conditions for the study area; then 10-year increments were run to the year 2050. Scenarios were created and run in TRANUS with specified transport, land use, population and employment conditions characteristic of Business as Usual or Smart Growth. The model accounted for non-motorized modes. and incorporated built environment typology and its influence on non-motorized travel behavior.
• Emissions estimation. While models such as MOBILE6 are cycle-based, the integrated model developed here yielded link-based vehicle data compatible with travel demand models. Using meso-scale emissions factors for various Levels of Service and for a range of vehicles of varying size and fuel type, the team developed link-based tailpipe emissions models; coupled them with vehicle activity data to estimate an emissions inventory; and estimated the impacts of alternative technologies on emissions for multiple scenarios. The Triangle Regional Model provided data to prove the method, with the Raleigh/Durham/Chapel Hill metro area as a test case, using the modeling month of July—peak season for high ambient air pollutant levels. The same test was reproduced for the Charlotte baseline (2000) and was completed for the 2050 scenarios. Other steps included emission rates and adjustments for HC, CO, and NOx, and basic emission rates for CO2, with corrections for technology and attendant emissions drops and fuel economy gains.
• Air quality modeling. The model-ready emissions inventory proved the feasibility of using this modeling system to create emissions scenarios for use in air quality models. SMOKE was used to allocate links to grid cells defined in the CMAQ modeling domain, and to spatially and temporally allocate and chemically speciate all emissions for input into CMAQ. The team used an MM5-SMOKE-CMAQ application at a 4-km grid resolution that spanned the entire state of North Carolina and parts of neighboring states. Existing MOBILE6-based on-road emissions estimates for Mecklenburg County were replaced with link-based estimates from this project, to predict ozone, HC, NOx and CO concentrations. Results The final location results for the two scenarios estimates using TRANUS show two distinct regional urban forms. Even though both scenarios contain light rail systems, the larger transit system combined with land controls in the Smart Growth scenario produced a significantly more polycentric pattern. While in both scenarios the downtown area is an important job center, in the Smart Growth scenario additional job centers formed along the transit corridors, as expected. The travel behavior implications of the two future scenarios were examined with TRANUS from the demand and supply perspectives. From the demand side, transit passenger-km (all transit modes) is about 50% higher in Smart Growth 2050 relative to Business as Usual for the same year and car-based passenger-km is 6.3% lower in the Smart Growth scenario relative to the Business as Usual scenario. Walking is also 54% higher, and driving is between 8 and 9% lower (depending on how carpooling and high occupancy vehicles are counted). The mode share for transit and walking in 2050 Smart Growth more is more than twice the mode share for Business as Usual. In terms of supply, transit supply is about 60% higher in Smart Growth than in Business as Usual, and overall road supply is very similar in both scenarios. In other words, even though road supply (car equivalent miles) is similar, the location of activities and residences, and transit investments, persuade individuals to walk and take transit. For the baseline, the link-based and MOBILE6 emissions for Mecklenburg County show similar total CO and NOx allocations, although allocation of the emissions to the local geography suggests important spatial variations. The link-based approach produced higher NOx concentrations in the urban center of the county and lower in the periphery, compared to MOBILE6. For the model day, link-based data improved model performance substantially in the southern edge of the county over the MOBILE6 emissions. The link-based prediction for daily maximum 8-hour O3 was similar to MOBILE6 at two of three stations. For the 2050 scenarios, we found that reductions in HC, CO, NOx and CO2 ranged between 5.5 and 7.8% for Smart Growth Scenario, relative to the Business as Usual Scenario. The savings achieved appear to be greater than those due to the market penetration assumed for alternative technologies. Furthermore, we found that complete retirement of old Tier 0 and Tier 1 vehicles in the future will have significant positive impacts on certain emissions, relative to current emissions. The exception is CO2. Compared to the baseline scenario, CO2 emissions of all future scenarios will significantly increase. Modest improvement in fuel economy is completely offset by significant VMT increases plus network speed reductions. Air quality results for 2050 are being finalized.
Conclusions:
This project developed an integrated and multi-level model capable of simulating changes in land use, transportation infrastructure, economic activity, travel behavior, and technological innovation to estimate current and forecast future emissions and model air quality. The modeling system improves on earlier efforts by incorporating quantitative measures of the built environment and accounting for changes in vehicle technology. This project successfully demonstrated several innovations:
• A method to measure the built environment and incorporate it into scenario planning
• An assessment of strategies to increasing walking and transit use while decreasing auto use
• A demonstration of vehicle-specific factors used to account for link type, speed and fleet mix
• Use of a meso-scale approach that improves on current emissions inventory practices
The air quality component of our results showed spatial and temporal variability in pollutants, and demonstrated how the link-based approach may improve on current approaches, with implications for modeling applications used to demonstrate NAAQS attainment. Overall, this research developed and demonstrated a general, objective method for exploring hypotheses about the leverage that Smart Growth and other development strategies may have on the spatial pattern and quantity of emissions from on-road mobile sources. In the future, the modeling system can explore scenarios with varying built environment features and differing penetration of technology and policy levers, and predict their effectiveness for reducing emissions. The method may be applied in other cities and metropolitan areas. The model performed well, generating plausible, defensible and policy relevant differences in travel behavior as well as emissions in distinctly different scenarios. The scenario exercise demonstrated the ability of our modeling system to generate emissions estimates that can be processed into a "model-ready" emissions inventory, and it provides the most policy-relevant measure of the significance of the emission reductions that may be achieved with Smart Growth. The modeling system we developed supports additional future benefits, including land use forecasts to better allocate anthropogenic emissions (non-road and area sources in particular) and land-use forecasts to better forecast deforestation, which will help forecast biogenic emissions (extremely important for ozone formation). Other promising areas for extending this research include a deeper looks at technology and automotive innovations, exposure and associated health risks, and environmental impacts and climate change.
Journal Articles on this Report : 15 Displayed | Download in RIS Format
Other project views: | All 44 publications | 15 publications in selected types | All 15 journal articles |
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Cho EJ, Rodriguez D, Song Y. The role of employment subcenters in residential location decisions. Journal of Transport and Land Use 2008;1(2):121-151. |
R831835 (2008) R831835 (Final) |
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Coelho MC, Frey HC, Rouphail NM, Zhai H, Pelkmans L. Assessing methods for comparing emissions from gasoline and diesel light-duty vehicles based on microscale measurements. Transportation Research Part D:Transport and Environment 2009;14(2):91-99. |
R831835 (Final) |
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Frey HC, Rouphail NM, Zhai H. Speed- and facility-specific emission estimates for on-road light-duty vehicles on the basis of real-world speed profiles. Transportation Research Record 2006;1987:128-137. |
R831835 (Final) |
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Frey HC, Rouphail NM, Zhai H, Farias TL, Goncalves GA. Comparing real-world fuel consumption for diesel-and hydrogen-fueled transit buses and implication for emissions. Transportation Research Part D:Transport and Environment 2007;12(4):281-291. |
R831835 (2007) R831835 (2008) R831835 (Final) |
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Frey HC, Rouphail NM, Zhai H. Link-based emission factors for heavy-duty diesel trucks based on real-world data. Transportation Research Record 2008;2058:23-32. |
R831835 (2007) R831835 (2008) R831835 (Final) |
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Frey HC, Zhai H, Rouphail NM. Regional on-road vehicle running emissions modeling and evaluation for conventional and alternative vehicle technologies. Environmental Science & Technology 2009;43(21):8449-8455. |
R831835 (Final) |
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Shay E, Fan Y, Rodriguez DA, Khattak AJ. Drive or walk? Utilitarian trips within a neotraditional neighborhood. Transportation Research Record 2006;1985:154-161. |
R831835 (2006) R831835 (Final) |
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Shay E, Khattak AJ. Automobiles, trips, and neighborhood type:comparing environmental measures. Transportation Research Record 2007;2010:73-82. |
R831835 (2007) R831835 (2008) R831835 (Final) |
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Shay E, Rodriguez DA, Cho G, Clifton KJ, Evenson KR. Comparing objective measures of environmental supports for pedestrian travel in adults. International Journal of Health Geographics 2009;8:62 (12 pp.). |
R831835 (Final) |
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Shay E, Khattak AJ. Toward sustainable transport: conventional and disruptive approaches in the U.S. context. International Journal of Sustainable Transportation 2010;4(1):14-40. |
R831835 (Final) |
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Shay E, Khattak AJ. Household travel decision chains: residential environment, automobile ownership, trips and mode choice. International Journal of Sustainable Transportation 2011;6(2):88-110. |
R831835 (Final) |
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Wilson B, Song Y. Comparing apples with apples: how different are recent residential development patterns in Portland and Charlotte? Journal of Urbanism 2009;2(1):51-74. |
R831835 (Final) |
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Zhai H, Frey HC, Rouphail NM. A vehicle-specific power approach to speed-and facility-specific emissions estimates for diesel transit buses. Environmental Science & Technology 2008;42(21):7985-7991. |
R831835 (Final) |
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Zhai H, Frey HC, Rouphail NM, Goncalves GA, Farias TL. Comparison of flexible fuel vehicle and life-cycle fuel consumption and emissions of selected pollutants and greenhouse gases for ethanol 85 versus gasoline. Journal of the Air & Waste Management Association 2009;59(8):912-924. |
R831835 (Final) |
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Zhai H, Frey HC, Rouphail NM. Development of a modal emissions model for a hybrid electric vehicle. Transportation Research Part D: Transport and Environment 2011;16(6):444-450. |
R831835 (Final) |
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Supplemental Keywords:
land use, built environment, multi-modal, travel, air quality, emissions, mobile, risk, health effects, VOC, oxidants, nitrogen oxides, sustainable development, public policy, modeling, North Carolina, EPA Region 4, business , RFA, Scientific Discipline, PHYSICAL ASPECTS, Air, Ecosystem Protection/Environmental Exposure & Risk, climate change, Air Pollution Effects, Monitoring/Modeling, Environmental Monitoring, Physical Processes, Urban and Regional Planning, Atmosphere, ecosystem models, infrastructure systems, emissions monitoring, land use model, motor vehicle emissions, ozone , Emissions Inventory Modeling System, human activities, exposure, traffic patterns, air quality model, human exposue, green house gas concentrations, modeling, mobile source emissions, mobile sources, atmospheric pollutant loads, regional emissions model, tropospheric ozone, climate model, ecological models, global warming, predicting ecological response, alternative vehicle technology, air quality, ambient air pollution, climate variability, community structure, Global Climate ChangeRelevant Websites:
http://www.ie.unc.edu/cempd/projects/EPA-Charlotte/index.cfm
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- 2009
- 2008 Progress Report
- 2007 Progress Report
- 2006 Progress Report
- 2005 Progress Report
- Original Abstract
15 journal articles for this project