Mathematical models for large-scale air pollution studies consist of systems of partial differential equations (PDEs).
Some results, which are obtained when several versions of a large-scale air pollution model are run on different parallel architectures, will be presented in this paper.
The number of equations in these systems of PDEs is equal to the number of chemical compounds (the number of chemical compounds involved in the current large-scale air pollution models varies from 20 to about 200).
We introduce the concept and preliminary calculations of a new integrated model — the GLASS model (G̱lobal A̱ssessment of S̱ecurity) — designed to link extreme global environmental changes (such as droughts, flooding, and large-scale air pollution episodes) and the risk to society due to these changes.
Deforestation remains a serious issue in many countries, causing large-scale air pollution, loss of biodiversity, and affecting the livelihoods of local people.
Exposure to large-scale air pollution was assessed with data from monitoring stations.
The geographical air mass origin variable is thus not just a surrogate for LRT air pollution and its composition, but a combination of both the large-scale meteorological conditions as well as the large-scale air pollution transportation pattern.
The geographical air mass origin variable is not just a surrogate for LRT air pollution and its composition, but a combination of both the large-scale meteorological conditions as well as the large-scale air pollution transportation pattern.
The emergence of remote sensing based estimates of ground-level PM2.5 (van Donkelaar et al. 2010) now makes it possible to include rural areas in large-scale air pollution studies, as was recently demonstrated in a Canadian national-level cohort study (Crouse et al. 2012).
In the environmental research setting, novel smartphone-based software that records people's movements and physical activity levels in the urban environment and is integrated with spatial temporal maps of air pollution is already being developed to enhance large-scale air pollution exposure data collection in a cost-effective, accurate unobtrusive way (de Nazelle et al. 2013).
