Abstract
Numerous epidemiological studies have confirmed the negative influences of air pollutants on human health, where fine particles (PM2.5) and nitrogen dioxide (NO2) cause the highest health risks. However, the traditional studies have only involved the ambient concentration for a short to medium time period, which ignores the influence of indoor sources, the individual time-activity pattern, and ...
Abstract
Numerous epidemiological studies have confirmed the negative influences of air pollutants on human health, where fine particles (PM2.5) and nitrogen dioxide (NO2) cause the highest health risks. However, the traditional studies have only involved the ambient concentration for a short to medium time period, which ignores the influence of indoor sources, the individual time-activity pattern, and the fact that the health status is impacted by the long-term accumulated exposure. The aim of this paper is to develop a methodology to simulate the lifelong exposure (rather than outdoor concentration) to PM2.5 and NO2 for individuals in Europe. This method is realized by developing a probabilistic model that integrates an outdoor air quality model, a model estimating indoor air pollution, an exposure model, and a life course trajectory model for predicting retrospectively the employment status. This approach has been applied to samples of two population studies in the frame of the European Commission FP7-ENVIRONMENT research project HEALS (Health and Environment-wide Associations based on Large Population Surveys), where socioeconomic data of the participants have been collected. Results show that the simulated exposures to both pollutants for the samples are influenced by socio-demographic characteristics, including age, gender, residential location, employment status and smoking habits. Both outdoor concentrations and indoor sources play an important role in the total exposure. Moreover, large variances have been observed among countries and cities. The application of this methodology provides valuable insights for the exposure modelling, as well as important input data for exploring the correlation between exposure and health impacts.
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