Big data for innovative air-pollution assessments in the era of verifiable regulatory decisions

Classical air-quality modelling has been outdated. The outcomes of the several international initiatives have returned limited achievements as one can easily confirm from the reported measurements of air pollution concentrations. New cloud applications handling big data and social media allows accurate real-time geographical representation of population exposure. This work focuses on realistic utilization of photochemical air-quality data from measurements that allows a new era for regulatory applications in several geographical scales. It eliminates inherent levels of uncertainty and allows a realistic representation of atmospheric processes. Such applications can provide real-time representation of risks and permits the health impact assessment. With such data sets we could also revisits the concept when and where the emission reductions could be effective and why the dangers of exposure from urban atmospheric pollution have been over-estimated.