Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

Abstract

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, walking, and indoors at home; microenvironmental infiltration rates; and pollutant removal rates in indoor environments. Typically, exposure models rely upon ambient air concentration inputs from a sparse network of monitoring stations. Here we present a unique methodology for combining multiple types of air quality models (the Community Multi-Scale Air Quality [CMAQ] chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (the Hazardous Air Pollutant Exposure Model [HAPEM] or the Stochastic Human Exposure and Dose Simulation [SHEDS] model) to enhance estimates of air pollution exposures that vary temporally (annual and seasonal) and spatially (at census-block-group resolution) in an urban area. The results indicate that there is a strong spatial gradient in the predicted mean exposure concentrations near roadways and industrial facilities that can vary by almost a factor of 2 across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district than in the suburbs. This is mostly due to the importance of personal mobility factors whereby individuals living in the central area often move between microenvironments with high concentrations, as opposed to individuals residing at the outskirts of the city. Also, our results indicate 20-30% differences due to commuting patterns and almost a factor of 2 difference because of near-roadway effects. These differences are smaller for the median exposures, indicating the highly variable nature of the reflected ambient concentrations. In conjunction with local data on emission sources, microenvironmental factors, and behavioral and socioeconomic characteristics, the combined source-to-exposure modeling methodology presented in this paper can improve the assessment of exposures in future community air pollution health studies.

Emission Scenarios and the Health Risks Posed by Priority Mobile Air Toxics in an Urban to Regional Area: An Application in Nashville, Tennessee

“…This condition was followed for the scenario that did not consider on-road sources with a 27.5% contribution, where the main reductions were the result of air toxics contributions generated by gasoline LDVs, mainly benzene and 1,3-butadiene. Those values were similar with those found in other studies (Luecken et al, 2006;Cook et al, 2007;Isakov et al, 2007Isakov et al, , 2009). However, these results are different than those found in the NATA study for 1999, mainly because of the effect of biogenic emissions and secondary formation considered in our CMAQ study.…”

Sub-Grid Scale Plume Modeling

“…Isakov and Venkatram [56] used a hybrid modeling approach for a case study in Wilmington, California, a community that contains a diverse array of emissions sources, including petroleum refineries, heavily traveled freeways, distribution centers, and local businesses, all located in close proximity to or interspersed with residential and mixed-use development. Examples of hybrid modeling for other urban regions, such as Houston, Texas; Philadelphia, Pennsylvania; New Haven, Connecticut; and Detroit, Michigan are provided in [24,[57][58][59][60]. Cook et al [61] discuss the development of local scale emissions for hybrid modeling to simulate air quality near roadways.…”

The Use of AERMOD Air Pollution Dispersion Models to Estimate Residential Ambient Concentrations of Elemental Mercury

“…The elevation of sampling sites is often not considered when model building (Fruin et al 2008;Wheeler et al 2008). More recent investigations, however, have reported the significant impact that elevation has on exposure (Aguilera et al 2008;Isakov et al 2009;Ryan et al 2008). Inputs to AERMOD include the elevation of sources, receptors, buildings, structures and hills which may affect dispersion.…”