Evaluating Human Exposure to Fine Particulate Matter Part II: Modeling

Abstract

Exposure modeling has become a fundamental component of exposure analysis as it provides an efficient and economical means for assessing exposure of individuals to populations over a variety of spatial and temporal scales for past, current, future, or hypothetical conditions. For airborne particulate matter, traditional modeling approaches typically utilize ambient concentration data to assign exposure levels across an area of interest for a given period of time. Technological advancements have allowed for more sophisticated and innovative modeling approaches that combine exposure measurements and ⁄ or models to integrate the strengths of individual methods. The purpose of this article is to provide a general overview of both conventional and novel approaches of modeling exposure to fine particulate matter.

Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: A review

“…Mass balance models that have been applied in indoor environments have usually taken into consideration various combustion related activities like home heating and cooking; and also activities such as cleaning and infiltration of outdoor air resulting in a contribution from outdoor sources (Millar et al, 2010). CMB analyses have made use of different combinations of source profiles for the estimation of the contribution of food cooking emissions to ambient particle concentrations.…”

Ensemble classification for identifying neighbourhood sources of fugitive dust and associations with observed PM 10

“…Land-use regression (LUR) models are a popular tool in air pollution exposure assessments (Jerrett et al, 2005;Millar et al, 2010;Zwack et al, 2011). LUR models achieve the objective of mapping air quality by using the correlation between pollutants of interest and land-use indicators such as population density, traffic intensity and distances to known pollution sources (Beelen et al, 2009;Zwack et al, 2011).…”

Spatial statistical modelling of urban particulate matter

“…Land-use regression (LUR) models are a popular tool in air pollution exposure assessments (Jerrett et al, 2005;Millar et al, 2010b;Zwack et al, 2011). LUR models achieve the objective of mapping air quality by using the correlation between pollutants of interest and land-use indicators such as population density, traffic intensity and distances to known pollution sources (Beelen et al, 2009;Zwack et al, 2011).…”