Assessing regional intake fractions in North America

Abstract

This paper develops the IMPACT North America model, a spatially resolved multimedia, multi-pathway, fate, exposure and effect model that includes indoor and urban compartments. IMPACT North America allows geographic differentiation of population exposure of toxic emissions for comparative risk assessment and life cycle impact assessment within U.S. and Canada. It looks at air, water, soil, sediment and vegetation media, and divides North America into several hundred zones. It is nested within a single world box to account for emissions leaving North America. It is a multi-scale model, covering three different spatial scales — indoor, urban and regional — in all zones in North America. Model results are evaluated against monitored emissions and concentrations of benzo(a)pyrene, 2,3,7,8-TCDD and mercury. Most of the chemical concentrations predicted by the model fall within two orders of magnitude of the monitored data. The model shows that urban intake fractions are one order of magnitude higher than rural intake fractions. The model application and importance is demonstrated by a case study on spatially-distributed emissions over the life cycle of diesel fuel. Depending on population densities and agricultural intensities, intake fractions can vary by eight orders of magnitudes, and even limited indoor emissions can lead to intakes comparable to those from outdoor emissions. To accurately assess these variations in intake fraction, we require the essential three original features described in the present paper: i) inclusion of the continental model within a world box for persistent pollutants, ii) addition of an urban box for short- and medium-lived substances (for grid size larger than 100 km), and iii) assess indoor emissions. This model can therefore be used to screen chemicals and assess regionalized intake fractions within North America for population-based human exposure assessment, life cycle impact assessment, and comparative risk assessment. The model can b.