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Air pollution has many recognized effects on human health. Numerous studies worldwide investigate the relationship between pollution and health, but few are interested in the modeling process that has coupled environmental data with medical data.However, measuring air pollution in the city is complicated, because it cannot be reduced to a specific pollutant (rather is consists of a mix of many pollutants). In addition, it has a spatial heterogeneity which must be taken into account before estimating impact on health, especially if one wishes to measure the effects of short-term exposure.This article aims to offer a critique on the modeling process that links environment and health,without denying the fact that air pollution has a negative impact on citizens’ health. First, we discuss how air pollution is modeled. We explain why it is important to model pollutant concentrations in the air because of a lack of permanent stations making actual measurements. Then the advantages and disadvantages of the different families of deterministic models (Gaussian, Lagrangian, and Eulerian) available to improve air quality monitoring in cities are discussed.The uncertainties of those models are evaluated so as to choose the better model for understanding air pollution dispersion (immissions) in cities. Immissions are the share of pollution that must be connected with health data. This is the crux of the process because it is difficult to know the ambient air pollution levels in real time at any point in space. Gaussian models have simplified equations which mean that the concentrations are just a decreasing function of the distance (basic model). Lagrangian models are useful to follow a particle or group of particles but cannot be used if the number of components remains important (the cost of the model is proportional to the number of followed particles). Eulerian dispersion models can be used in complex urban morphology to estimate air pollutants concentrations in all places, even at a fine scale (some meters). This is the best approach to modeling based on the information of permanent stations and the computing time is independent of the number of particles followed. After validating the choice of Eulerian models which are more efficient at fine scale (a street or a district), we compared the results of a simple model and a complex model (including wind direction and velocity) from a case study: the Bonaparte street in Paris. Advantages of each model for the purpose of health impact evaluation are identified and discussed. Second, the article proposes the concept of health routes in cities so as to reduce the exposition to air pollutants in the streets, in real time. Numerous studies have shown some links between air pollution and health impacts. However, it is necessary to deepen knowledge of the pollution and to better understand individual paths (routes, medical history, and differentiated daily exposure). Because we spend eighty percent of our time indoors,the links between outdoor and indoor pollution must also be deepened.Uncertainties are present in all steps of the evaluation process (emissions, immissions, and individual exposure). As such, we have to be prudent when estimating risk (quantitative measurements) between pollution and health. |
