Développement et évaluation d’approches géostatistiques à l’échelle urbaine pour l’estimation de l’exposition aux particules fines et à l’ozone troposphérique
Air pollution is a major environmental hazard with clearly demonstrable adverse effects on health and the environment. However, the measurement of populations’ exposure to pollutants such as particulate matter and ground-level ozone remains approximate due to the low density of sampling stations for these pollutants. Moreover, intra-urban spatial variation in the spatial modeling of pollutant concentrations has received little research attention. If anything, some studies have combined spatial interpolation and correlation with local factors; however, they do so without examining the effect of the weather regime (e.g., a temperature inversion) on the magnitude of these correlations. In order to overcome these shortcomings, this dissertation aims to evaluate new ways of characterizing the spatial and temporal distribution of fine particles (PM2.5) and ground-level ozone (O3) at the intra-urban scale. In particular, we developed a multivariable geostatistical model called “kriging with external drift” (KED) based on the integration of auxiliary variables into the process of estimating daily PM2.5 and O3 concentrations. Kriging is a form of spatial interpolation of data from measurement stations that are dispersed, while external drift is based on correlations between local conditions (road transport arteries, green spaces, etc.) and the concentration of atmospheric pollutants. In order to take account of temporal variations, especially those related to weather conditions, we designed these models around six synoptic weather classes based on daily meteorological data (such as temperature, wind, relative humidity and precipitation). v The results show that the integration of auxiliary variables (such as vegetation density and local industrial activity areas) in KED partly explains the intra-urban variations of PM2.5 on the island of Montreal, but that this contribution is variable depending on the weather conditions. Thus, when the correlations are very low, a simple spatial interpolation (such as the inverse distance weighting (IDW) method) is more accurate than multivariable interpolation, whereas for other synoptic conditions KED produces the most certain predictions. For this reason, we proposed a hybrid interpolation model (KED-IDW) that can adapt to different weather conditions. We have also shown, particularly in the case of O3, that KED improves the results obtained from ordinary kriging (without auxiliary variables). This dissertation also allowed to evaluate the contribution of a spatial-temporal model—BME (bayesian maximum entropy)—in the estimation of the short-term effect of exposure to O3 on deaths in Montreal. The results suggest that this spatio-temporal model under the determined conditions (e.g., based on data from 12 measurement stations, for a territory of 1 310 km2) does not offer significant improvements to the estimation of the effect of exposure. Overall, this dissertation contributes to the advancement of the empirical spatial modeling of air pollutants, namely by taking into account the adaptation to weather conditions as well as certain predictive meteorological factors. In this context, the dissertation opens up a promising path for improving the estimation of air pollution at the intra-urban scale and the capacity to assess population health risks through better characterization of exposure.
Air pollution Fine particles Ground-level ozone Health Geostatistics Kriging with external drift (KED) Urban environment