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In recent years, the study of anthropogenic sinkholes in densely urbanized areas has attracted the attention of both researchers and land management entities. The city of Naples (Italy) has been frequently affected by processes generating such landforms in the last decades: for this reason, an update of the sinkhole inventory and a preliminary susceptibility estimation are proposed in this work. Starting from previous data, not modified since 2010, a total of 270 new events occurred in the period February 2010–June 2021 were collected through the examination of online newspapers, local daily reports, council chronicle news and field surveys. The final consistence of the updated inventory is of 458 events occurred between 1880 and 2021, distributed through time with an increasing trend in frequency. Spatial analysis of sinkholes indicates a concentration in the central sector of the city, corresponding to its ancient and historic centre, crossed by a dense network of underground tunnels and cavities. Cavity-roof collapse is confirmed as one of the potential genetic types, along with processes related to rainfall events and service lines damage. A clear correlation between monthly rainfall and the number of triggered sinkholes was identified. Finally, a preliminary sinkhole susceptibility assessment, carried out by Frequency Ratio method, confirms the central sector of city as that most susceptible to sinkholes and emphasizes the predisposing role of service lines, mostly in the outermost areas of the city. [ABSTRACT FROM AUTHOR]

• Historic center of Naples is one of the UNESCO word heritage sites. • Cathedrals, churches and chapels are subject to cavity collapse. • Places of worship are above a connected network of underground cavities. • DInSAR analysis suggest the presence of anomalous areas. • 95 places of worship of the historic center of Naples are susceptible to sinkholes. The historic center of Naples (southern Italy) is characterized by a variety of elements which, forming its cultural asset, have contributed to its recognition as a UNESCO World Heritage site. Among these, cathedrals, churches and chapels are particularly relevant due to their diffusion and differentiation in age. A singular feature of these places of worship is the presence of a frequently connected network of underground cavities. Such cavities, excavated for quarrying tuff blocks, are susceptible to sudden collapses generating sinkholes which in a number of cases have been responsible for the loss of human lives. On this basis and considering the importance of such elements as attractors of visitors, an analysis of susceptibility to sinkholes of cathedrals, churches and chapels has been here carried out. A sinkhole, cavities and places of worship inventory was prepared using available data and was used, in association to ongoing ground deformation data obtained by DInSAR. Results indicated that 40% of the inventoried sinkholes are a product of cavity collapses and in 45 cases cavities lie underneath places of worship. Geodetic data obtained by Differential Interferometric SAR technique indicate that approximately 1 km2 of the historic center of Naples is subject to ongoing subsiding deformation that in several cases is localized above cavities. The susceptibility map derived from available data indicate the presence of a zone where no cathedral, church and chapel can be subject to sinkholes (1) and 3 zones where they can be subject to sagging (2), potential future cavity collapses (3) and future cavity collapses anticipated by ongoing deformation (4); in detail, 34 places of worship are in zone 2, 57 in zone 3 and 9 in zone 4. Considering an increasing susceptibility level of the identified zones, the 9 places of worship located in zone 4 require a quick response in terms of characterization, stability analysis and real time monitoring have been identified. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new procedure for Groundwater Flooding Hazard (GFH) assessment has been developed. • A newly developed MATLAB™ code for hazard mapping has been provided. • The procedure is based on a probability model depicting long-term water table fluctuation. • A sixty-two-years piezometric time series and basement depths data were used. • The procedure has been tested and validated in a semi-urban aquifer. Many areas of the world are experiencing groundwater flooding (GF) events with serious impacts on the environment and human health. In Europe, GF has received specific attention with the Directive 2007/60/EC, which requires Member States to map GF hazard and risk and propose mitigation measures. In Italy, despite the commitment assigned by the Legislative Decree 49/2010 to River Basin District Authorities, no models have been developed for groundwater flooding hazard (GFH) evaluation also for the lack of standard methods based on commonly available data. In this paper, a new method has been developed through a specific procedure that accounts for i) probability of GF events estimation based on available time series of groundwater levels and ii) probability mapping through a newly developed MATLAB™ code, high-resolution topographic data and groundwater table model. The methodology has been tested and validated in the Metropolitan City of Naples (Italy), affected by GF since 2010, for which i) sixty-two-year long time series of groundwater levels, ii) high-resolution Digital Elevation and Digital Terrain Models and iii) basement/building geometry, are available. The results show that about 59% of the buildings present in the study area have potentially floodable underground structures. In the 2010–2021 period, only 2% of these buildings were flooded and most of them were built in the period 1980–2000, when the piezometric levels were deeper due to over-pumping of the groundwater. The obtained GFH map shows that only 2% of the built area is involved by 1- to 2- year events, while 6% is affected by 1- to 10- year events. A good spatial correspondence between the return period and locations of GF events occurred during 2010–2021 period was observed, thus indicating the significant performance of the model. The obtained GFH map output can support decisions of authorities and policy-makers to reduce the GF risk. [ABSTRACT FROM AUTHOR]