Spatiotemporal Modelling of Exposure to Airborne Pollutants

Abstract

WM3-PD-05 Introduction: Health consequences of environmental pollution depend on the pollution source, its dispersion, and mobility. Given that humans move within their environment and between settings (home, work, leisure), exposure estimates should integrate pathways simultaneously through both space and time. Estimation of exposure is often difficult in environmental epidemiology studies. We present methods for estimating exposure that integrate across the space-time domain of human movement patterns while allowing for socioeconomic factors over the life course. Methods: We show how changes in residential locations of 541 women from Teesside interact with the changing industrial landscape over time to generate individual based exposure estimates. We obtained data on life-long residential, smoking, and socioeconomic history interview. We digitized maps of industrial land use from 1930 to 1990. We abstracted the positions of chimneys from 6 major polluting industries using them to create maps of indices of exposure. We combined the data on residential locations with exposure indices to derive cumulative life-time exposures. Results: Pollution sources and types changed considerably over the study period: For example the inverse distance weighting of exposure estimates for metal works had a maximum of 6885 in the 1930s and of 432 in the 1990s. Asbestos and chemical exposure estimates peaked in the 1960s, whereas values from waste incineration were highest in the 1990s. Women had been living an average of 56.4 (cases) and 55.3 years (controls) in the area. Given the average age of 65 years this indicated a stable population. However, they were mobile within the study area with an average of 6 moves. We observed greater mobility in high social classes in early life compared with more moves in low social classes in later life. This individual mobility led to highly skewed exposure estimates for some pollution types. Discussion: Exposure estimates varied greatly and were depending on personal factors such as changes in address and social class. There is a need to integrate the spatial and temporal information for individuals to obtain precise exposure estimates that are valid proxies of true exposure. Our results suggest that this is particularly important if the health outcomes under study are determined by cumulative exposure. This kind of approach is particularly important if pollution types and sources change dramatically over time as has been the case in many parts of the world.