Technology hybridization for real time particulate matter monitoring

While the monitoring and management of air pollution remains a challenge at a regional level, it also becomes more and more sensitive at very local scale. Both, as hot spot sources, can be very localized, but vulnerable population (in schools, hospital…) due to fluidic effect, especially in cities with tall buildings, can lead to very significant disparities in local pollution levels. For all those reasons, accurate real time measurements of local pollution is increasingly of interest for all stakeholders. So far, no satisfactory solution (microsensors, modeling...) has proven both reliable and affordable on its own as an alternative to reference analyzers. The solution can thus only come from an appropriate combination of various technologies and algorithms. This paper presents and shows the benefit of an innovative approach to combine well known dust air quality monitoring proven equivalent method, such as beta attenuation and indicative sensors based on light scattering as a first step to allow wider hybrid networks over particulate matter monitoring and extend it to other parameters such as gases (SO2, NO2...), which would provide simultaneously a cost effective, reliable, and hyperlocal solution especially when combined to mathematical modelling software.