Cultural heritage is widely recognized to be at risk due to the impact of climate change and associated hazards, such as events of heavy rain, flooding, and drought. User-driven solutions are urgently required for sustainable management and protection of monumental complexes and related collections exposed to changes of extreme climate. With this purpose, maps of risk-prone areas in Europe and in the Mediterranean Basin have been produced by an accurate selection and analysis of climate variables (daily minimum and maximum temperature—Tn and Tx, daily cumulated precipitation—RR) and climate-extreme indices (R20mm, R95pTOT, Rx5 day, CCD, Tx90p) defined by Expert Team on Climate Change Detection Indices (ETCCDI). Maps are available to users via an interactive Web GIS (Geographic Information System) tool, which provides evaluations based on historical observations (high-resolution gridded data set of daily climate over Europe—E-OBS, 25 km) and climate projections (regional climate models—RCM, ~12 km) for the near and far future, under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The tool aims to support public authorities and private organizations in the decision making process to safeguard at-risk cultural heritage. In this paper, maps of risk-prone areas of heavy rain in Central Europe (by using R20mm index) are presented and discussed as example of the outputs achievable by using the Web GIS tool. The results show that major future variations are always foreseen for the 30-year period 2071–2100 under the pessimistic scenario (RCP 8.5). In general, the coastal area of the Adriatic Sea, the Northern Italy, and the Alps are foreseen to experience the highest variations in Central Europe.

Some studies suggested a role of the atmospheric particulate matter (PM) and of its oxidative potential (OP) in determining adverse health effects. Several works have focused on characterisation of source contributions to PM OP, mainly using three approaches: correlation between OP and chemical markers of specific sources; use of OP as input variable in source apportionment with receptor models; and multi-linear regression (MLR) between OP and source contributions to PM obtained from receptor models. Up to now, comparison of results obtained with different approaches on the same dataset is scarce. This work aims to perform a OP study of PM_2.5 collected in an industrial site, located near a biogas production and combustion plant (in southern Italy), comparing different approaches to investigate the contributions of the different sources to OP. The PM_2.5 samples were analysed for determining ions, metals, carbonaceous components, and OP activity with the DTT (dithiotreitol) assay. Results showed that OP normalised in volume (DTT_V) is correlated with carbonaceous components and some ions (NO₃⁻, and Ca²⁺) indicating that PM of combustion, secondary, and crustal origin could contribute to the OP activity. The source apportionment, done with the Environmental Protection Agency (EPA)—Positive Matrix Factorization (PMF5.0) model, identified six sources: secondary sulphate; biomass burning; industrial emissions; crustal; vehicle traffic and secondary nitrate; and sea spray. A MLR analysis between the source’s daily contributions and the daily DTT_V values showed a reasonable agreement of the two approaches (PMF and MLR), identifying the biomass burning and the vehicle traffic and secondary nitrate as the main sources contributing to DTT_V activity.