Urban CO2 and CH4 atmospheric measurements in the Milan city area (northern Italy)

Northern Italy is one of the most polluted and densely populated areas in Europe. The diversity of land use in the Po basin makes this region an important contributor to greenhouse gas emissions from different sources. Medium and large cities, as well as industrialised areas, contribute significantly to emissions from industrial processes, combustion, waste management and natural gas distribution.

As part of the H2020 RI-URBANS project (https://riurbans.eu/) and in synergy with PNRR “ITINERIS” Project, a pilot study has been carried out in the Milan urban area with the aim of supporting the local authorities to evaluate the effectiveness of air quality policies and the effects of pollutants on human health. A one-year long measurement campaign was carried out using a mobile platform in the premises of the CNR facility (AdRMi1, 45°28’47"N 9°13’54"E; 120 m a.s.l.), located in in the urban area of Milan, in the University district. The mobile platform has been equipped with a suite of instruments (ACSM, Vocus Chemical Ionization TOF-MS, Aethalometer, NOx CLD) to provide near real time (NRT) information on aerosol source partitioning and to characterise nanoparticle contributions from urban hot spots. From July 2023 to March 2024, the mobile platform has been equipped with a Cavity Ring Down Analyser for continuous observations of carbon dioxide (CO₂) and methane (CH₄).

In this work we will provide a first characterization of the diel cycles and seasonal variations (from late summer to early spring) of atmospheric CO₂ and CH₄ in the urban area of Milan. Thanks to the combined analysis of other atmospheric tracers, first insights about the influence by atmospheric processes (i.e. PBL dynamics) and emission sources (fossil fuel and biomass burning combustion, biogenic, waste management) will be discussed. Finally, we provided a preliminary “top-down” estimate of CH₄ emission for the Milan urban area by using the interspecies correlation approach: the obtained values were higher (but still within the range of uncertainties) than emissions provided by statistical “bottom-up” inventories (e.g. INEMAR).

Acknowledgments:  RI-URBANS t has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036245. This reserach was partially supported by the European Unione- Next Generation EU, Missione 4 Componente 2 - CUP B53C22002150006 -  IR0000032 – ITINERIS, Italian Integrated Environmental Research Infrastructures System