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Development and Evaluation of Land-Use Regression Models Using Modeled Air Quality Concentrations
Citation:
ISAKOV, V., J. TOUMA, M. M. Johnson, AND H. A. OZKAYNAK. Development and Evaluation of Land-Use Regression Models Using Modeled Air Quality Concentrations. Chapter 117, Douw G.Steyn & Silvia Trini Castelli (ed.), NATO - Air Pollution Modeling and its Application, XXI. Springer Netherlands, , Netherlands, Series C:717-722, (2011).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Abstract Land-use regression (LUR) models have emerged as a preferred methodology for estimating individual exposure to ambient air pollution in epidemiologic studies in absence of subject-specific measurements. Although there is a growing literature focused on LUR evaluation, further research is needed to identify strengths and limitations of LUR modeling and strategies for improvement. In particular, LUR models have several limitations and among these are the needs for comprehensive monitoring data from a large number of sites, and the inability to link sources of emissions with measured elevated concentrations. In contrast, air quality models are designed to provide this linkage and have a long history of use by regulatory agencies in developing pollution mitigation strategies. Thus, the linkage of LUR techniques with available air quality modeling tools may benefit evaluation and enhancement of LUR techniques. In this study, we evaluated the fitted LUR models in several different ways and examined the implications of alternate LUR development strategies on model performance for benzene, particulate matter (PM2.5), and nitrogen oxides (NOx).