@article{22164,
  author = {Agnes Bodnar and Randy L Maddalena and Thomas E McKone},
  title = {Addressing locally grown foods in cumulative exposure  assessments},
  abstract = {<p>Both laboratory and field studies confirm the importance of vegetation for scavenging semi-volatile organic  chemicals (SVOCs) from the atmosphere and a number of exposure studies have found that the dietary  pathway is often a significant contributor to cumulative exposure for these chemicals. Exposure  calculations based on published concentration data for foods indicate that the potential intake through  ingestion is up to 1000 times that of inhalation for several persistent SVOCs. However, little information  exists on the source-to-dietary intake linkage for SVOC's.  Because of higher SVOC emissions to urban  regions, this linkage is particularly important for foods that are grown, distributed and consumed in or near  urban regions.  The food pathway can also contribute to dietary exposure for populations that are remote  from a pollutant source if the pollutants can migrate to agricultural regions and subsequently to the  agricultural commodities distributed to that population. We use the characteristic travel distance (CTD) and  available data within the CalTOX multimedia model framework to assess the contribution of local food  markets to the fraction of cumulative food intake that is attributable to local sources. For a set of three  representative multimedia SVOCs- benzo(a)pyrene, fluoranthene, and 2,3,7,8-TCDD, we explore the  contribution of airborne SVOC's to cumulative uptake through the local food consumption pathway. We  use the population based intake fraction (iF) to determine how SVOC intake varies among food  commodities and compares to inhalation. The approach presented here provides a useful framework and  starting point for source-to-intake assessments for the air-to-dietary exposure pathway.</p>},
  year = {2002},
  journal = {Journal of Exposure Analysis and Environmental Epidemiology},
  volume = {14},
  number = {1},
  pages = {60-73},
  language = {eng},
}