Central Italy Research Articles

Adolescents’ attitudes, habits, identity and social support in relation to physical activity after the COVID-19 pandemic

This study focuses on adolescents’ cognitive processes, behaviors and social support (SS) as they relate to physical activity (PA) before and after the pandemic. The aims of the study were: (1) to investigate the changes in adolescents’ engagement in moderate and vigorous physical activity (MVPA), and examine the changes in PA-related attitudes and behaviors before and after the COVID-19 pandemic; (2) to analyze the correlations between the significant changes that were found, PA engagement, and SS. The survey targeted third-year middle school students of Italian nationality, attending male and female mixed classes, residents in urban, periphery and sub-urban areas, living in families with different incomes, and different habits of engaging in PA. A longitudinal study was developed using a standardized questionnaire. The questionnaire was administered in April–May 2023 to a sample of 952 students aged 11/14 residing in the Marche region in Central Italy. Increasing values were found in the post-COVID-19 phase for all the cognitive processes and attitudes, in particular, those regarding habits (0.66 vs 0.50, + 32%) and identity (0.70 vs 0.55, + 27%) related to PA. Significant correlations were found between these values and VPA engagement and between the values of the same indicators and SS (p < 0.01). The strongest relationship was found with the dimension of identity (r = 0.51; r = 054).

Geostatistical Analysis of Lineament Domains: The Study Case of the Apennine Seismic Province of Italy

Regional-scale swarms of subparallel linear topographic features, known as lineament domains, are a common feature of planetary surfaces. Lineament domains are superficial manifestations of the crustal stress field trajectory. Notably, one of the effects of active tectonics is seismicity. Italy is one of the most seismically active regions in the Mediterranean, with many destructive earthquakes that have occurred in past centuries. Here, we assess the seismic meaning of the main lineament domain in the tectonically active region of Central Italy. We describe the use of an automated analysis of satellite imagery coupled with spatial grid analysis to identify three lineament domains of the Central Apennines. Spatial and azimuthal comparisons of the main lineament domain (i.e., the Apennine Domain), with the known locations of earthquakes (moment magnitude of Mw &gt; 5.5) that occurred during the past century, revealed the most seismically active tectonic areas and their spatial distributions. Further, we present a conceptual seismo-geodynamic model for the Central Apennines, which is characterized by regional arching and explains the presence of an extensional tectonic regime in the upper crustal layer of the active Apennines fold-and-thrust belt.

Phenology, Yield and Nut Traits Evaluation of Twelve European Hazelnut Cultivars Grown in Central Italy

European hazelnut (Corylus avellana L.) cultivation has grown worldwide in recent years, even though the world’s production is still fulfilled by a few dozen cultivars well adapted to the local environments of their selection. After Turkey, Italy is the second largest producer of hazelnuts, and the province of Viterbo, in central Italy, is recognized as one of the most suitable districts for hazelnut cultivation. The production in this area relies almost entirely on local cultivar ‘Tonda Gentile Romana’, whereas cultivar ‘Nocchione’ is used as the main pollinizer. More recently, cultivar ‘Tonda di Giffoni’ has been introduced in new local plantations. Cultivation of many cultivars in different areas is of fundamental importance to test their adaptability to different environmental conditions, a need currently accentuated by ongoing climate change. With this aim, a European hazelnut collection field was established in 2000 in Caprarola municipality (Viterbo province), where plants of twelve hazelnut cultivars were observed over four growing seasons for their phenological, agronomic and nut traits. The cultivars studied, namely ‘Barcelona’, ‘Camponica’, ‘Ennis’, ‘Merveille de Bollwiller’, ‘Negret’, ‘Nocchione’, ‘Riccia di Talanico’, ‘San Giovanni’, ‘Tombul’, ‘Tonda di Giffoni’, ‘Tonda Gentile’ and ‘Tonda Gentile Romana’, showed a high diversity in their phenological behaviour, yield efficiency and nut and kernel traits. Cultivars ‘Negret’, ‘Tombul’ and ‘Tonda Gentile’, with poor yields over the years of research, do not seem suitable to be cultivated as main cultivars in the studied environment, contrary to ‘Camponica’ and ‘San Giovanni’, which produced abundantly, reaching a total of almost 30 and 25 kg plant—1 of in–shell hazelnuts, respectively. Cultivars ‘Tonda Gentile Romana’ and ‘Nocchione’ confirmed their yield consistency, whereas ‘Ennis’, ‘Barcelona’ and ‘Tombul’ seem suitable to be introduced as pollinizers thanks to their male blooming overlapping with the female blooming of the main cultivar ‘Tonda Gentile Romana’. Cultivar ‘Tonda di Giffoni’ did not perform well in the studied environment, despite being recognized as one of the highest-yield cultivars.

Assessing teratogenic risks of gadolinium in freshwater environments: Implications for environmental health

Gadolinium (Gd) is among the rare earth elements extensively utilized in both industrial and medical applications. The latter application appears to contribute to the rise in Gd levels in aquatic ecosystems, as it is excreted via urine from patients undergoing MRI scans and often not captured by wastewater treatment systems. The potential environmental and biological hazards posed by gadolinium exposure are still under investigation. This study aimed to assess the teratogenic risk posed by a gadolinium chelate on the freshwater cnidarian Hydra vulgaris. The experimental design evaluated the impact of pure Gadodiamide (25 μg/l, 50 μg/l, 100 μg/l, 500 μg/l) and its commercial counterpart compound (Omniscan®; 100 μg/l, 500 μg/l, 782.7 mg/l) at varying concentrations using the Teratogenic Risk Index (TRI). Here we showed a moderate risk (Class III of TRI) following exposure to both tested formulations at concentrations ≥ 100 μg/l. Given the potential for similar concentrations in aquatic environments, particularly near wastewater discharge points, a teratogenic risk assessment using the Hydra regeneration assay was conducted on environmental samples collected from three rivers (Tiber, Almone, and Sacco) in Central Italy. Additionally, chemical analysis of field samples was performed using ICP-MS. Analysis of freshwater samples revealed low Gd concentrations (≤ 0.1 μg/l), despite localized increases near domestic and/or industrial wastewater discharge sites. Although teratogenic risk in environmental samples ranged from high (Class IV of TRI) to negligible (Class I of TRI), the low Gd concentrations, particularly when compared to higher levels of other contaminants like arsenic and heavy metals, preclude establishing a direct cause-effect relationship between Gd and observed teratogenic risks in environmental samples. Nevertheless, the teratogenic risks observed in laboratory tests warrant further investigation.

Assessment of the Potential Contribution of the Urban Green System to the Carbon Balance of Cities

Reducing greenhouse gas emissions is a crucial challenge in urban areas characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars have analyzed both the ability of species-specific and layout-specific green infrastructure to increase carbon sequestration and the best location sites for new green infrastructure to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement based on vegetation species-specific performance and the local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of urban park performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between building demand and tree supply to support local decisions about the best locations for new green infrastructure and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructure based on the demanded ecosystem service.

Risk Categorization in On-Farm Welfare in Different-Sized Dairy Sheep Flocks

The objective of the present work was to categorize the risks in the on-farm welfare of dairy sheep in semi-extensive systems in order to highlight if risks in welfare vary according to the farm size. To the best of our knowledge, this study constitutes one of the few categorizations of the risks in the welfare of dairy sheep reared semi-extensively. The survey was conducted on 12 semi-extensive dairy sheep farms in Tuscany (Central Italy), which were classified according to flock size: &lt;500, from 500 to 1000, and &gt;1000. The results showed an adequate rating for all the farms included in the study. The most critical issues concerned the ratio between the number of stockpersons and the number of animals within the farms, the small decubitus areas, the lack of udder cleaning procedures during milking operations, and, in terms of biosecurity, the lack of prevention, control, and eradication plans for the most important infectious ovine diseases. The results show that different items in the five areas evaluated, including the assessments of management of the flock, the number of daily inspections, and the hygiene of the water supplies are associated with the farm size. In large farms, the assessment of management of the flock was better, but the number of daily inspections and the hygiene of the water supplies were worse.

A novel, continuous high-resolution palaeoecological record from central Italy suggests comparable land-use dynamics in Southern and Central Europe during the Neolithic

A novel, continuous high-resolution palaeoecological record from central Italy suggests comparable land-use dynamics in Southern and Central Europe during the Neolithic

Evapotranspiration Analysis in Central Italy: A Combined Trend and Clustering Approach

Climate change is increasingly influencing the water cycle, hindering the effective management of water resources in various sectors. Lazio, central Italy, exhibits a wide range of climatic conditions, stretching from the Tyrrhenian coast to the Apennines. This study assessed a crucial aspect of climate change, focusing specifically on reference evapotranspiration (ETo) and its associated hydrological variables. The seasonal Mann–Kendall (MK) test was used to assess trends in gridded data. The K-means algorithm was then applied to divide Lazio into four homogeneous regions (clusters), each characterized by distinct trends in hydrological variables. The analysis revealed statistically significant increasing trends (p ≤ 0.01) in temperature, solar radiation, and ETo, with more marked effects observed in the coastal and hilly clusters. In contrast, statistically significant decreasing trends (p ≤ 0.01) were observed for relative humidity, while no statistically significant trends (p &gt; 0.01) were observed for precipitation. This study’s methodology, combining trend analysis and clustering, provides a comprehensive view of ETo dynamics in Lazio, aiding in pattern recognition and identifying regions with similar trends.

The estimation of b-value of the frequency-magnitude distribution and of its one-sigma intervals from binned magnitude data

Summary The estimation of the slope (b-value) of the frequency magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature, this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b-value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude dataset. In this work we provide the complete theoretical formulation including i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions; and iii) the corresponding formulas for the parameter b. We deduce iv) the standard one-sigma intervals for the estimated b. Moreover, we are able v) to quantify the error associated with the Utsu minimum-magnitude correction. Furthermore, we have discussed the formulas to produce statistically independent magnitude differences. We tested extensively the b-value estimators on simulated synthetic datasets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next. We have also analysed the real aftershock sequence of the 30/10/2016 Norcia (central Italy) to integrate the finding of the simulations. To judge the performance of the various estimators we have introduced an index p that can be seen as a non-parametric extension of the Student's t index. The main outcomes of this paper are that 1) the b-value estimators devised for continuous magnitude data are not adequate for binned magnitudes, 2) for complete datasets, estimators based on magnitudes and on magnitude differences provide substantially equivalent results, 3) for incomplete magnitude datasets, estimators based on magnitude differences provide better results, 4) for incomplete aftershock sequences there is no evidence that methods based on positive magnitude differences are superior than other methods using differences. This conclusion is further confirmed by our analysis of the above-mentioned Norcia seismic sequence. This last finding contrasts with the van der Elst's claim that the so called ${b}_ + $ method is the most adequate to treat real aftershock sequences.

Mapping the occurrence of Eustrongylides spp. in fish species caught from six lakes in Central Italy (Tuscany and Latium regions): Implications for local fishery supply chains

In the present study, in order to expand the knowledge on the distribution of parasites of the genus Eustrongylides in never investigated Italian lacustrine areas of Tuscany and Latium, fish specimens from four Tuscan lakes (Bilancino, Chiusi, Montedoglio and San Cipriano) and two Latium lakes (Bolsena and Bracciano) were collected and analyzed. The parasitological analysis, consisting of a visual inspection followed by a chloro-peptic digestion, was performed on 1650 specimens belonging to 17 species: European perch (Perca fluviatilis), Pumpkinseed (Lepomis gibbosus), Pike-perch (Sander lucioperca), Largemouth black bass (Micropterus salmoides), Pike (Esox lucius), Big-scale sand smelt (Atherina boyeri), Tench (Tinca), European Whitefish (Coregonus lavaretus), Channel catfish (Ictalurus punctatus), Chub (Squalius cephalus), Rudd (Scardinius erythrophthalmus), Common bleak (Alburnus alborella), South European roach (Sarmarutilus rubilio), South European nase (Protochondrostoma genei), Italian riffle dace (Telestes muticellus), Goldfish (Carassius auratus), and Freshwater bream (Abramis brama). Eustrongylides spp. larvae were only found in one specimen of European perch caught in Bracciano Lake and purchased from a local fishmonger (P = 3.3%; MI = 1; MA = 0.03). The only isolated Eustrongylides spp. larva was submitted to molecular analysis of the ITS gene region and identified as E. excisus. This finding adds Bracciano Lake to the list of the several Italian lakes in which nematodes belonging to this genus have been reported. Even if the observed prevalence is currently low, the presence of the parasite in this local freshwater fish supply chain requires FBOs to adopt risk management measures to prevent contaminated products from reaching final consumer. Moreover, future investigations will show if the parasite expands in these areas.

Characterisation of microbial communities and quantification of antibiotic resistance genes in Italian wastewater treatment plants using 16S rRNA sequencing and digital PCR

The spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in humans, animals and environment is a growing threat to public health. Wastewater treatment plants (WWTPs) are crucial in mitigating the risk of environmental contamination by effectively removing contaminants before discharge. However, the persistence of ARB and ARGs even after treatment is a challenge for the management of water system.To comprehensively assess antimicrobial resistance dynamics, we conducted a one-year monitoring study in three WWTPs in central Italy, both influents and effluents. We used seasonal sampling to analyze microbial communities by 16S rRNA, as well as to determine the prevalence and behaviour of major ARGs (sul1, tetA, blaTEM, blaOXA-48, blaCTX-M-1 group, blaKPC) and the class 1 Integron (int1). Predominant genera included in order: Arcobacter, Acinetobacter, Flavobacterium, Pseudarcobacter, Bacteroides, Aeromonas, Trichococcus, Cloacibacterium, Pseudomonas and Streptococcus. A higher diversity of bacterial communities was observed in the effluents compared to the influents. Within these communities, we also identified bacteria that may be associated with antibiotic resistance and pose a significant threat to human health. The mean concentrations (in gene copies per liter, gc/L) of ARGs and int1 in untreated wastewater (absolute abundance) were as follows: sul1 (4.1 × 109), tetA (5.2 × 108), blaTEM (1.1 × 108), blaOXA-48 (2.1 × 107), blaCTX-M-1 group (1.1 × 107), blaKPC (9.4 × 105), and int1 (5.5 × 109). The mean values in treated effluents showed reductions ranging from one to three log. However, after normalizing to the 16S rRNA gene (relative abundance), it was observed that in 37.5 % (42/112) of measurements, the relative abundance of ARGs increased in effluents compared to influents. Furthermore, correlations were identified between ARGs and bacterial genera including priority pathogens. This study improves our understanding of the dynamics of ARGs and provides insights to develop more effective strategies to reduce their spread, protecting public health and preserving the future efficacy of antibiotics.

Occurrence of Antimicrobial-Resistant Enterococcus spp. in Healthy Chickens Never Exposed to Antimicrobial Agents in Central Italy

Occurrence of Antimicrobial-Resistant Enterococcus spp. in Healthy Chickens Never Exposed to Antimicrobial Agents in Central Italy

Comparative performance evaluation of four different methods for diagnosing Giardia infection in dogs and zoonotic assemblages’ identification

Giardia duodenalis (syn. G. intestinalis or G. lamblia) is a parasitic protozoan that infects the upper intestinal tract of a broad range of hosts, including humans and domestic animals. Thus, it has raised concerns about the public health risk due to companion animals. Recently, with the improvement of living standards and increasing contacts between pets and humans, the zoonotic transmission of Giardia has dramatically increased. From a genetic point of view, G. duodenalis should be viewed as a complex species that includes eight different species-specific genetic assemblages. The laboratory diagnosis is mainly based on the finding of microscopic cysts in stool samples by coprological examination. Other methods include the detection of antigens, immunoassays or PCR protocols, which allow the identification of Giardia assemblages. The study aimed to compare the performance of Direct Fluorescence Antibody test (DFA), zinc sulfate flotation technique (ZnSO4), rapid diagnostic test (RDT), end-point PCR amplification (PCR) for the detection of Giardia and to identify the concerning assemblages in a canine population from Central Italy. Direct fluorescence antibody test is the reference standard for laboratory diagnosis of Giardia in fecal samples from dogs, despite the microscopic examination after flotation remains the most useful method in many veterinary diagnostic centers. The present findings demonstrate the high performance of DFA and ZnSO4 in detecting Giardia, while RDT may be useful as alternative or complementary method to the DFA and ZnSO4. PCR performance was low, but it allowed determining Giardia B zoonotic assemblage in 25% of the PCR-positive specimens (15 out of 60), while the remaining PCR-positive isolates belonged to the dog-specific assemblage C. The 26% prevalence of G. duodenalis detected by DFA in owned dogs and the identification of potentially zoonotic assemblages underline the potential risk for public health and indicate frequent cross-species transmission of the parasite between humans and dogs.

Machine-learning-aided regional post-seismic usability prediction of buildings: 2016-2017 Central Italy earthquakes

This study addresses the development of machine learning models for predicting the post-seismic buildings usability within regions prone to frequent earthquakes. The analysis leverages on field data from 2016-2017 Central Italy earthquakes. Several machine learning techniques are employed for this task, namely K-nearest neighbors, linear support vector machine, radial basis function support vector machine, decision tree, random forest, neural network, adaptive boosting, naive Bayes, quadratic discriminant analysis, logistic regression, and linear discriminant analysis. The input variables include both building attributes and seismic intensity measures. Since the database turns out to be strongly imbalanced, the potential influence of two preprocessing techniques is examined, namely principal component analysis and synthetic minority oversampling technique. Several metrics are considered to evaluate the performance of the resulting predictive machine learning models. Moreover, this study investigates the optimal machine learning model’s robustness against uncertainties, quantifies the importance of its features, and investigates how usability classes clustering can impact its performance. Every step of the implemented procedure is deeply explained and discussed to provide useful guidelines for similar applications.

Phytosanitary problems in elephant garlic (Allium ampeloprasum var. holmense) in the “Val di Chiana” area (Central Italy), and evaluation of potential control strategies

Allium ampeloprasum var. holmense (elephant garlic) is traditionally cultivated in “Val di Chiana”, an area between Umbria and Tuscany regions of Central Italy, under the name “Aglione della Valdichiana”. This product has recently increased in importance, becoming a key economic resource for local farmers. In 2019, phytosanitary problems of elephant garlic cloves ready for transplanting emerged in this cultivation area. Symptom/sign observations and fungal isolations were performed for cloves divided into four components (tunic, basal plate, reserve tissue and shoot) from six farms in the “Val di Chiana” area. Isolates obtained were identified, using partial β-tubulin (BenA) and calmodulin (CaM) or translation elongation factor 1α (tef1α) genes sequences, as belonging to Penicillium [P. allii (95%), P. citrinum (4%), P. brevicompactum (1%)] or Fusarium [F. oxysporum (81%), F. proliferatum (19%)]. Fusarium spp. were mainly associated with clove tunics and basal plates, while Penicillium spp. with basal plates, reserve tissues and shoots. Fungi often also developed from asymptomatic components, but a correlation was found between isolated pathogens and disease symptoms. Pathogenicity and virulence towards elephant garlic cloves were verified for a representative isolate of each identified species, and Penicillium allii was the most virulent. Strategies to control Fusarium and Penicillium spp. on cloves were assessed, including chemicals, a biocontrol agent, surface sterilization and heat treatment. Among these, treatments with Patriot Gold® (active ingredient [a.i.] Trichoderma asperellum TV1, approved in organic farming on crops similar to elephant garlic), or Signum® (a.i. boscalid + pyraclostrobin, approved for Integrated Pest Management systems on crops similar to elephant garlic), were effective in simultaneous reduction of Penicillium spp. and Fusarium spp. Transplanting of asymptomatic cloves combined with the use of the above treatments showed promising effects for pathogens control, and to assist elephant garlic crop establishment.

Using Beerkan Procedure to Estimate Hydraulic Soil Properties under Long Term Agroecosystems Experiments

The BEST (Beerkan Estimation of Soil Transfer parameters) method was used to compare the hydraulic properties of the soils in two Long-term Agroecosystem Experiments (LTAEs) located at the FIELDLAB experimental site of the University of Perugia (central Italy). The LTAE “NewSmoca” consists of a biennial maize-durum wheat crop rotation under integrated low-input cropping systems with (i) inversion soil tillage (INT) or (ii) no-tillage (INT+) and (iii) under an organic cropping system with inversion soil tillage (ORG). ORG and INT+ involve the use of autumn-sown cover crops (before the maize cycle). Pure stand durum wheat was grown in INT and INT+, while a faba bean–wheat temporary intercropping was implemented in ORG. The LTAE “Crop Rotation” consists of different crop rotations and residue management, a continuous soft winter wheat and biennial rotations of soft winter wheat with maize or faba bean. Each rotation is combined with two modes of crop residue management: removal or burial. For INT+, despite the high-bulk density (&gt;1.50 g/cm3), we found that conductivity, sorptivity and available water are comparable to those of INT, probably due to a more structured and efficient micropore system. ORG soils show the highest conductivity, sorptivity and available water content values, probably due to the recent spring tillage occurring in the wheat inter-row with the faba bean incorporation into the soil. For LTAE Rotation, the residue burial seems to influence the capacity-based indicators positively. However, the differences in the removal treatment are minor, and this could be due to the inversion soil tillage, which limits the progressive accumulation of organic matter.

Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches

ABSTRACT In contemporary agriculture and environmental management, the need for precise and accurate crop maps has never been more vital. Although object-based (OB) methods within Google Earth Engine (GEE) improve accuracy and output quality in contrast to pixel-based approaches, their application to crop classification remains relatively rare. Therefore, this study aimed to develop an OB classification methodology for crops located in central Italy’s Lake Trasimeno area. This methodology employed spectral bands, spectral indices (Normalized Difference Vegetation Index and Modified Radar Vegetation Index), and textural information (Gray-Level Co-occurrence Matrix) derived from Sentinel-2 L2A (S2) and Sentinel-1 GRD (S1) data within the GEE platform. Moreover, European Common Agricultural Policy (CAP) data associated with cadastral parcels were employed and served as ground information during the training and validation stages. The CAP crop classes were aggregated into three levels (Level 1–3 crop types, Level 2–5 crop types, and Level 3–7 crop types). Subsequently, optimized Random Forest (RF) classifiers were applied to map crops effectively. Feature selection analysis highlighted the importance of certain textural features. Additionally, findings demonstrated high overall accuracy results (89% for Level 1, 86% for Level 2, and 82% for Level 3). It was found that winter crops achieved the highest F-score at Level 1, while specific subclasses, such as winter cereals and warm-season cereals, excelled at Level 2. Overall, this study provides a promising approach for improved crop mapping and precision agriculture in the GEE environment.

Analysis of Wildfire Susceptibility by Weight of Evidence, Using Geomorphological and Environmental Factors in the Marche Region, Central Italy

Fires are a growing problem even in temperate climate areas, such as those in Central Italy, due to climate change leading to longer and longer periods of drought. Thus, knowing the fire susceptibility of an area is crucial for good planning and taking appropriate countermeasures. In this context, it was decided to use only causal factors of a geomorphological and environmental nature in order to obtain a fire susceptibility analysis that can also be applied to climatically under-sampled areas. Vector data of fires in Central Italy from 2005 to 2023 were collected, and the correct areal extent was calculated for each. At the same time, six factors were selected that could have an influence on fire development, such as ecological units, topographic wetness index (TWI), geology, slope, exposure, and altitude. The model was obtained by means of the weight of evidence statistical method, which takes into account past data by reinterpreting them in a future-oriented way on the basis of the identified factors and classes. The model was validated with a test sample and shows an area under the curve (AUC) value of 0.72 with a reliability that can be described as good considering the total absence of climatic factors that are known to play a major role in fire development. Furthermore, the identified causal factors were divided into classes, and these were carefully weighted in order to define their relative influence in the study area. Particularly Ecological Units with characteristic and well-defined contrast (C) values, which could lead to a more complete definition of forests that tend to increase fire susceptibility and those that tend to decrease it, allowing the latter to be exploited as a hazard mitigation agent.

Geochemical Surveys of Ground and Surface Waters in the Abandoned Hg-Mine of Abbadia San Salvatore (Central Italy): A Preparatory Investigation before Remediation

Since 2013, 34 surveys of surface and ground waters within and outside the former Hg-mine of Abbadia San Salvatore (Italy), which is currently under remediation, were performed for determining Hg, As, Sb, and main and minor solutes. The water quality is rather poor since most waters show relatively high Hg concentrations (up to 695 µg/L). Differently, As and Sb only overcome the Italian law thresholds in a few sites. A high geochemical variability was observed for most groundwaters without any clear relationship between wet and dry periods. The main source of chalcophile elements is likely related to: (i) the interaction between meteoric waters and soils contaminated by the previous production of mercury; or (ii) the interaction between meteoric waters and the anthropic filling material of a former paleo-valley near the furnaces edifices. While the remediation is expected to be concluded in 2025, the aquifer contamination still remains a problem. Our investigation, including geochemical/hydrogeological modeling, is prodromal to future activities aimed at reducing the Hg content. Currently, the construction of a hydraulic barrier is apparently the most suitable solution to minimize the interaction processes between water–rock and man-made material, which are responsible for the 10-year concentration variability.

Added value of amateur observational network for high-resolution climatological analysis: a case study in the aterno valley, abruzzo, Italy

The ecological transition calls for an increasing need for local climate services. A fine spatial characterization of atmospheric relevant quantities (temperature, precipitation, humidity, wind, solar radiation, etc.) at long-term climatological scales is typically based on observational networks run by public entities such as the European Union (e.g. Copernicus services) and national and regional Agencies (e.g. National Met Office, Hydrographic Offices). The aim of this work is to verify if the density of these networks is adequate to represent the variability over the territory, with particular regard to a complex terrain area such as the Aterno river Valley in Abruzzo, Central Italy. We use a combination of public networks and the available dense amateur network of weather stations. We subject the database to careful data quality check both in terms of temporal and spatial anomalies. We found that the public network is generally adequate to represent the spatial and temporal variability over the area in terms of temperature and precipitation, but this is not the case for wind and relative humidity for the lack of sensors. We suggest that an integration of public and non-institutional observational networks is desirable for a finer climatological characterization of a complex territory and for allowing the description of more phenomena, in order to better inform adaptation measures with respect to climate change.