Application of a scenario-based modeling system to evaluate the air quality impacts of future growth

The structure and design of future urban development can have significant adverse effects on air pollutant emissions as well as other environmental factors. When considering the future impact of growth on mobile source emissions, we generally model the increase in vehicle kilometers traveled (VKT) as a function of population growth. However, diverse and poorly planned urban development (i.e., urban sprawl) can force higher rates of motor vehicle use and in return increase levels of pollutant emissions than alternative land-use scenarios. The objective of this study is to develop and implement an air quality assessment tool that takes into account the influence of alternative growth and development scenarios on air quality.The use of scenario-based techniques in land use planning has been around since the late 1940s and been tested in many different applications to aid in decision-making. In this study, we introduce the development of an advanced interactive scenario-based land use and atmospheric chemistry modeling system coupled with a GIS (Geographical Information System) framework. The modeling system is designed to be modular and includes land use/land cover information, transportation, meteorological, emissions, and photochemical modeling components. The methods and modularity of the developed system allow its application to both broad areas and applications.To investigate the impact of possible land use change and urbanization, we evaluated a set of alternative future patterns of land use developed for a study area in Southwest California. Four land use and two population variants (increases of 500k and 1M) were considered. Overall, a Regional Low-Density Future was seen to have the highest pollutant emissions, largest increase in VKT, and the greatest impact on air quality. On the other hand, a Three-Centers Future appeared to be the most beneficial alternative future land-use scenario in terms of air quality. For all cases, the increase in population was the main factor leading to the change on predicted pollutant levels.