Big data analytic based personalized air quality health advisory model

Abstract

Ambient air pollution has been a worldwide concern with a devastating impact on the health of the population. Assessing health effects of air pollution is vital for protecting individual health. This study proposes a big data analytic based personalized air quality health advisory model to address the issues of sparse air pollution monitoring sites, pollution mixture effects and lack of personalized air quality health guidance. The main components of the proposed model lie in three aspects: (i) estimating high resolution concentrations of air pollution with big data analytics based on enormous structured and unstructured data; (ii) quantifying the health effects of both single pollutant and pollutant mixtures; (iii) designing the personalized health advisory model based on individual characteristics and exposure information. A real example is provided to demonstrate the implementation and reasonability of the model based on data collected from Shenzhen city, China.