Climate and extensive anthropisation of the area make the Po Valley one of Europe’s most polluted regions, despite emissions being actually comparable to those of other industrialised districts. Particulate matter, or fine dust, heads the list of the most critical polluting agents.
Long-term exposure to high concentrations of particulate matter increases the incidence of both cardiovascular and respiratory diseases. Industries, traffic and domestic heating are some of the leading sources of fine dust emissions. However, even intensive livestock breeding and agricultural activities can contribute to the dissemination of this harmful pollutant. To date, few studies have been conducted on the topic.
The D-DUST project (Data-driven moDelling of particUlate with Satellite Technology aid) aims to bridge this gap by providing important data to investigate the impact of emissions from agricultural and livestock activities on our health. D-DUST, funded by Fondazione Cariplo’s ‘Data Science for Science and Society’ call for proposals, counts on Politecnico di Milano, Department of Civil and Environmental Engineering (DICA) as lead partner, in partnership with Fondazione Politecnico di Milano, the Department of Electronics, Information and Bioengineering (DEIB) and Università degli Studi dell’Insubria (DiSAT) as scientific partners.
Maria Antonia Brovelli, our Geographical Information Systems professor who is coordinating the project, explained that
the D-DUST project will test new analytical and predictive procedures for the generation and diffusion mechanisms of particulate matter from the agricultural sector. These procedures are solely based on the vast wealth of environmental data and observations now available as open data, with particular focus on the potential contribution of new satellite missions designed to monitor air quality.
The study will also make use of the Sentinel satellite platforms of the European Copernicus programme, including the Sentinel 5P satellite, which provides open data measurements of the main atmospheric pollutants on a global scale, together with the study of spatial predictive models based on machine learning techniques. Models will be developed taking into account data from the fixed ground-based monitoring stations of the ARPA Lombardy network, and data from the detection and chemical characterisation campaigns of particulate matter combined with data on the incidence of cardiovascular and respiratory diseases. Professor Brovelli further emphasises that
the research aims to increase local knowledge of particulate matter even in areas not covered by ground-based measurement stations, in order to provide estimates and forecasts that could be replicated and used to monitor and analyse population exposure to this pollutant.
In parallel to the research described above, educational activities will be organised, mainly involving students from agricultural senior high schools through awareness-building workshops and direct participation in monitoring campaigns. The project will also involve non-profit organisations and foundations actively participating in research, education and dissemination projects on environmental issues.
