Grantee Research Project Results

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.

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:

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:

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:

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.