III. Energy of Adsorption of Some Saturated Hydrocarbons on y-Aluminium Oxide,

The heats of adsorption on weakly activated γ-aluminium oxide for a number of linear alkanes (from n-butane to n-nonane) and for 2.2.4-trimethylpentane have been determined by GSC and extrapolated to zero surface coverage. The dependance of the adsorption energy on the number of carbon atoms is discussed on the basis of the bidimensional gas model: it is shown that the interactions of the adsorbates with the oxidic surface are mainly due to London dispersion forces

Editorial: The Benefits of Nature-Based Solutions to Psychological Health

Nature-based solutions (NBS) have been defined by the European Commission as actions aiming to provide environmental, social, and economic benefits through the inclusion of natural features in the urban environment. The exposure to natural environments, including NBS in urban contexts, has been associated with a large number of health benefits (Ulrich et al., 1991; Berman et al., 2008; Spano et al., 2020), particularly mental health and well-being among those most studied. Earlier studies on such benefits have been mainly experimental, investigating the short-term effects of brief exposure to natural environments on stress reduction and cognitive restoration (Kaplan and Kaplan, 1989; Berto, 2005; Nilsson et al., 2010; Carrus et al., 2017). More recently, large-scale epidemiological studies have provided further evidence of the long-term effects of sustained exposure to green spaces on mental health and well-being throughout the life course (Hartig et al., 2014; Gascon et al., 2015; McCormick, 2017; de Keijzer et al., 2020). Several dimensions characterize the human–nature interaction. In this sense, the present Research Topic was intended to provide an overview of studies focusing on the association of exposure to natural environments in urban, peri-urban, and rural settings with psychological well-being and mental health from different perspectives

RESPONSE OF BEETLE COMMUNITIES FIVE YEARS AFTER WILDFIRE IN MEDITERRANEAN FOREST ECOSYSTEMS

Wildfires are one of the most important drivers of forest composition and biodiversity in the Mediterranean Basin. Many studies have demonstrated that fires can affect insect diversity by altering the functional traits of species groups. We examined the 5-year response of beetles to wildfires by assessing patterns of community composition across a gradient from forest interior to forest edge to burnt forest area in Southern Italy. Our objective was to characterize the relationship between distance from the forest edge and occurrence of beetle taxonomic assemblages. We analyzed the composition, similarity, and dominance of ground beetle communities in randomly selected plots located along the forest-to-burned-area gradient. We found a negative relationship between community similarity and distance from the forest edge; moreover, the composition of species assemblages (within each family) became increasingly similar with proximity to the forest edge. As the distance from the forest edge into the burned area became greater the dominance of few species increased, and species composition shifted toward habitat generalists. The results partially support the notion that the differences in beetle communities probably are driven by habitat changes caused by fires, especially for those taxa with many specialist species in feeding and oviposition habitats. Understanding the biological effects of wildfires is necessary prior to design management strategies and policies for counteracting the loss of biodiversity at the global, regional and national levels

Soil conditions under a Fagus sylvatica CONECOFOR stand in Central Italy: an integrated assessment through combined solid phase and solution studies.

As soil solution represents the major phase of soil chemical reactions, its study is a powerful tool for ecological investigations. Soil solution chemical composition gives a realistic idea about the soil chemical components immediately available in the environment, mainly in relation to the soil ecosystem reaction to the disturbance due to acidifying loads. Within the CONECOFOR Program, the monitoring of forest soil conditions was performed in a level II plot (ABR I), under a Fagus sylvatica (European beech) stand, through the study of throughfall and soil solutions collected from depths ranging between the base of the litter layers and 90 cm. To be able to investigate solution contents of nutrients, acidifying agents and DOC throughout the profile, both zero tension and tension lysimeters were used. The first ones were inserted below the organic horizons, while tension lysimeters were placed within the mineral horizons at 15, 25, 55 and 90 cm depth. Sampled solutions were analyzed for Na, K, Ca, Mg, NH4, Cl, F, NO3, SO4, and DOC. The results evidence a clear seasonal pattern, mainly for macronutrients and inorganic N components. Acidic pulses were mostly evident below the organic horizons, in relation to strong nitric N releases from litter; these last were not always immediately neutralized by basic cations. Acid solutions leaving the organic horizons were invariably neutralized in the surface mineral horizons, within 15 cm depth. Temporal patterns of sulphate retention and release suggest that the soil has low retention capability for this anion. Such behaviour can be explained by the composition of the solid phase, where potential anion adsorbants appear strongly linked with organic matter in long residence time complexes. Sulphate and nitrate loading of this soil appear, anyway, to be mostly non-anthropogenic, but rather linked to natural mineralization pulses and, for sulphate, to aeolian solid transport from the south

The wildland-urban interface map of Italy: A nationwide dataset for wildfire risk management

A wildland-urban interface (WUI) raster map was created for the Italian peninsula with a resolution of 30 m per pixel. The map creation process consisted of three fundamental steps: (1) selection of buildings within the wildland-urban interface areas and subsequent classification of these into isolated, scattered, and clustered buildings; (2) creation of the tree canopy cover layer; (3) generation of WUI map by the intersection of two previous products. According to the WUI map, more than half of the total area of Italy is occupied by interface areas. Areas with buildings classified as clustered (24.61%) and scattered (19.15%) predominate on the territory compared to isolated buildings (14.93%). Most of the buildings are located in areas with a tree cover canopy between up to 64%. This map is functional to the implementation of forest fire prevention plans and to the identification of buildings that are close to fire risk areas such as forests, grasslands, and pastures

The ethics of isolation, the spread of pandemics, and landscape ecology

The debate around the SARS-CoV-2 pandemic has raised multiple and incompletely answered questions regarding how zoonoses are transmitted from wild populations to humans, how they spread within human communities, over regions and across continents, how countries and societies can fight or counter pandemics and how landscapes will have to be effectively managed for limiting the spread of diseases keeping communities safe and healthy.info:eu-repo/semantics/publishedVersio

Lessons learned from the past: forestry initiatives for effective carbon stocking in Southern Italy

Calabria (Italy) is a particularly interesting region of the Mediterranean basin from the perspective of forest management due to the extension of reforestation activities aimed at soil conservation. According to international agreements, these reforestation activities fulfill other functions as well, including carbon storage. Thus, Calabria was selected as a representative area for a study on the different typologies of forest plantations to verify the effects of these functions. Results showed a significant increment in carbon stock compared to the previous land use (i.e. arable land and pastures) and how the average carbon stock per hectare varies in relation to the species considered at the above- and below-ground levels. Carbon stock was higher in conifers (Calabrian pine, Douglas fir) and lower in broad-leaved trees (Turkey oak, European chestnut). The study analyses demonstrate how, based on different intensities of thinning, the carbon eliminated by trees is reconstituted over time in quantities larger than those eliminated by cutting. This latter aspect is relevant, as forest management allows the partial removal of biomass produced without negatively affecting carbon stock. Consequently, reforestation and sustainable forms of forest management are powerful strategies for mitigating the effects of climate change

Machine learning techniques for fine dead fuel load estimation using multi‐source remote sensing data

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1‐h fuel load using standard fuel parameters or site‐specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi‐source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1‐h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1‐h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel‐1), optical (Sentinel‐2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1‐h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1‐h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management

Estimating stand volume and above-ground biomass of urban forests using LiDAR

Assessing forest stand conditions in urban and peri-urban areas is essential to support ecosystem service planning and management, as most of the ecosystem services provided are a consequence of forest stand characteristics. However, collecting data for assessing forest stand conditions is time consuming and labor intensive. A plausible approach for addressing this issue is to establish a relationship between in situ measurements of stand characteristics and data from airborne laser scanning (LiDAR). In this study we assessed forest stand volume and above-ground biomass (AGB) in a broadleaved urban forest, using a combination of LiDAR-derived metrics, which takes the form of a forest allometric model. We tested various methods for extracting proxies of basal area (BA) and mean stand height (H) from the LiDAR point-cloud distribution and evaluated the performance of different models in estimating forest stand volume and AGB. The best predictors for both models were the scale parameters of the Weibull distribution of all returns (except the first) (proxy of BA) and the 95th percentile of the distribution of all first returns (proxy of H). The R2 were 0.81 (p < 0.01) for the stand volume model and 0.77 (p < 0.01) for the AGB model with a RMSE of 23.66 m3·ha−1 (23.3%) and 19.59 Mg·ha−1 (23.9%), respectively. We found that a combination of two LiDAR-derived variables (i.e., proxy of BA and proxy of H), which take the form of a forest allometric model, can be used to estimate stand volume and above-ground biomass in broadleaved urban forest areas. Our results can be compared to other studies conducted using LiDAR in broadleaved forests with similar methods