Improving air pollution exposure assessment by integrating spatial-temporal dynamics of pollution concentration with people's space-time activity patterns


Dr. Desheng Liu, Department of Geography
Rank at time of award: Associate Professor 



Accurate assessment of human exposures to air pollution (e.g. nitrogen dioxide and fine particulate matter) is critical to a wide range of health studies such as modeling the associations between air pollution exposure and  health outcomes (e.g. asthma, premature mortality,  and cancer), evaluating health impacts or disease burdens in different population groups, and determining  environmental  injustices  that  may  be  occurring  with  respect  to  exposures (Brunekreef and Holgate, 2002). Health studies typically assess air pollution exposure by assigning temporally averaged (e.g. annual mean) pollution concentration at people's residential locations (Jerrett et al, 2005). However, air pollution exposure is much more complicated as the exposure process is determined by the interaction of two geographic processes: air pollution concentration and people's activity patterns, both vary in space and time in highly complex ways (e.g., Kwan, 2000, 2004). In general, as air pollution sources are closely associated  with particular types of productive activities (e.g., manufacturing) and traffic, exposure tends to be low during the night when people stay at home. In contrast, peak exposure to air pollution occurs in the daytime when people are at work or making journeys to and from them (Briggs, 2005).
Consequently, air pollution exposures of different individuals exhibit significant spatial-temporal variability; and the static exposure assessment in past studies ignores the temporal dimension of both pollution concentration and people's daily activity-travel patterns and thus can result in substantial errors in exposure estimation. To quantify air pollution exposure more accurately, it is important to take the temporal variation of both air pollution concentration and people's mobility into consideration (Briggs, 2005; Hoek et al, 2008).
To date, there have been very limited efforts in exposure assessment that take into account the complex interaction between the temporal variation of air pollution concentration and people's activity patterns. One exception is the studies by Gulliver and Briggs (2005 and 2007) who analyzed journey-time exposures in two urban areas in the United Kingdom. However, air pollution exposure assessment based on people's detailed activity patterns has rarely been done to the best of our knowledge. The main purpose of the proposed project is to improve air pollution exposure assessment by integrating the spatial-temporal dynamics of pollution concentration and detailed space-time activity patterns of individuals. Specifically, we  focus on the assessment of  exposure  to fine  particulate matter, PM2.5 (i.e. all  particulate matter  2.5 microns or less) in Franklin County, Ohio. This is based on two considerations: (1) PM 2.5 has strong spatial-temporal variability and has been related to a wide variety of health effects (e.g. Pope et al, 2002); and (2) the availability of a detailed activity-travel diary dataset collected in the study area (NuStats 2000).
This project has the following three objectives:
1)  To estimate the spatial-temporal variability of PM2.5 concentration in Franklin County by developing a land use regression model that incorporates a temporal component;
2)  To assess people's exposures to PM2.5 by integrating the spatial-temporal dynamics of PM2.5 concentration with detailed space-time activity patterns of individuals in Franklin County; and
3)  To   understand   the   variation   in   exposure   with   respect to people's demographic characteristics and the particular land use context of the study area.

Publications resulting from this seed grant

Chapters in Edited Volumes

2015. Kwan, M.P., D. Liu, and J. Vogliano. Assessing dynamic exposure to air pollution. In Space-Time Integration in Geography and GIScience: Research Frontiers in the US and China. Mei-Po Kwan, Douglas Richardson, Donggen Wang and Chenghu Zhou (eds). Dordrecht: Springer.