Open-Access Forecasting System of the Health Effects of Air Pollution

Air pollution represents a major global environmental health problem. 99% of the world population lives in locations where international air quality guidelines are not met. Overall, ambient air pollution causes 4.2 million premature deaths every year worldwide, half a million of which in Europe alone. Available early warning systems of air quality are generally based on location-specific thresholds of air pollutant concentrations, they are entirely based only on forecasts representing the physical processes of atmospheric chemistry, and they do not account for the inequalities in vulnerability of the exposed populations. The ERC-funded project FORECAST-AIR will go beyond these limitations in health early warning systems by integrating air quality forecasting, environmental epidemiology and the inequalities in vulnerability to implement a new method to issue public health alerts. Towards this aim, I will estimate epidemiological models between air pollution observations and health records disaggregated by causes of disease and sociodemographic vulnerable groups; I will use these models to transform bias-corrected air quality forecasts into heath predictions; I will analyse and compare the window of predictability of forecasts and predictions; and I will use this comparative study to assess the predictability of the resulting health early warning systems, so that they generate trust among public health authorities and end-users. If successful, FORECAST-AIR will drive innovation by creating operational, fit-for-purpose, early warning systems representing the health impacts of several air pollutants, with a special focus on vulnerable populations.