Ozone (O3) is a gas that is one of the normal components of the atmosphere. In the stratosphere, it forms the ozone layer and protects the earth from the sun's ultraviolet rays. However, in the troposphere, ozone does not provide this same protective effect; it is instead identified as being a greenhouse gas and one of the main components of smog. This gas could therefore constitute certain risks to human health.
The aim of this document is to present approaches to modelling the environmental exposure of populations to ground-level ozone, specifically the approaches carried out on Québec's population by the Chair on Air Pollution, Climate Change and Health at the Université de Montréal. The ultimate purpose of the work carried out in Québec is to better estimate the health risks represented by this pollutant, and to provide scientific knowledge to professionals and decision makers in public health to further protect Québec's population.
Therefore, to briefly describe the context associated with the research work carried out in Québec, the first section presents the main characteristics of ozone as well as the conditions that favour its formation; the second section explains its short- and long-term health effects; and the third section addresses such things as ambient concentrations and the spatial and temporal distribution of ground-level ozone on Québec's territory.
Finally, the fourth section addresses more specifically the modelling of exposure, mainly by presenting the principal characteristics of different models for estimating exposure to ground-level ozone. The models presented are land use regression models as well as interpolation models, where the method is based on the proximity of the measuring stations, or kriging, or BME (Bayesian Maximum Entropy). This section ends with a short presentation of the objectives, methodology and a few results of the research work conducted in Québec.
In short, this study shows the success of the BME method developed by the Research Chair on Air Pollution, Climate Change and Health at the Université de Montréal. This method has the particular feature of making use of all of the network's data (just like kriging) as well as the data estimated by a land-use mixed regression model.