Site-specific dataset of mining and metallurgical residues for resource management.

This geospatial dataset provides a compilation of findings from an evidence-based review of site-specific resource assessments of mining and metallurgical residues. Information pertaining to location, target material, geological knowledge, extractability, resource classification and stakeholder perspectives was collected from publicly available reports, articles, academic theses, and databases. The dataset includes 44 relevant data attributes from 64 mining and metallurgical sites in 27 countries. Resource classification is available for 38 sites. The dataset can be used by evaluators of recovery projects, authorities that provide permits, as well as by decision makers in support of developing regulatory policies. The dataset facilitates future addition of sites by the research community and can be further used as a starting point to bridge the estimates on recoverable quantities to the United Nations Framework Classification (UNFC). The UNFC is a universally applicable scheme for the sustainable management of all energy, primary and secondary mineral resources. Its use is stimulated by the European Commission and is intended to be adopted by geological surveys to harmonize the data on the availability of primary and secondary raw materials in Europe in future.

(212)Pb as tracer for PM deposition on urban vegetation.

(212)Pb concentration in outdoor air is closely correlated with fine suspended particulate matter in the atmosphere. Thanks to this association, this isotope can be used to trace the sinking processes of particulate matter due to the vegetation, also providing accurate estimations of the deposition velocity on foliar surfaces. This approach is particularly effective in areas with high thoron fluxes and, consequently, high (212)Pb fluxes from soil. The contribution of vegetation to the improvement of air quality (AQImp) in the municipality area (MA) of Rome (Latium, Italy), almost entirely located on Th-enriched volcanic soils, was estimated by studying (212)Pb deposition velocity on the grasses (0.9-2.5mm·s(-1)) and on the most common tree classes, namely conifers (1.5-15mm·s(-1)), evergreen (1-4mm·s(-1)) and deciduous (0.2-1.5mm·s(-1)). (212)Pb activity in outdoor air was determined by gamma spectrometry after air pumping with accumulation on cellulose filters and after collection on artificial electrostatically charged surfaces (ECS). The high (212)Pb activity values obtained from this analysis (0.90±0.6Bqm(-3) and 0.58±0.15Bqm(-3), respectively near and far from the soil) are consistent both with the average regional thoron flux from volcanic soils (2.9·10(4)Bqm(-2)·h(-1)) and with the thoron flux measured in the volcanic soils of the study area. Thoron and (212)Pb fluxes were also measured both in laboratory and in the field under different soil moisture conditions. The total AQImp for the period from September 2014 to September 2015, calculated after the classification of the MA of Rome into six classes of vegetation, provided a value of 0.20 corresponding to 2.3 Tons per day of removed PM10. The role of grasslands in the PM10 removal, the contribution of the vegetation to the improvement of AQImp and the possibility of improving the sinking efficiency of green areas by increasing conifer trees coverage were also highlighted.

A multivariate approach for mapping fire ignition risk: the example of the National Park of Cilento (southern Italy).

Recent advances in fire management led landscape managers to adopt an integrated fire fighting strategy in which fire suppression is supported by prevention actions and by knowledge of local fire history and ecology. In this framework, an accurate evaluation of fire ignition risk and its environmental drivers constitutes a basic step toward the optimization of fire management measures. In this paper, we propose a multivariate method for identifying and spatially portraying fire ignition risk across a complex and heterogeneous landscape such as the National Park of Cilento, Vallo di Diano, and Alburni (southern Italy). The proposed approach consists first in calculating the fire selectivity of several landscape features that are usually related to fire ignition, such as land cover or topography. Next, the fire selectivity values of single landscape features are combined with multivariate segmentation tools. The resulting fire risk map may constitute a valuable tool for optimizing fire prevention strategies and for efficiently allocating fire fighting resources.

A methodology to assess the accuracy with which remote data characterize a specific surface, as a function of Full Width at Half Maximum (FWHM): application to three Italian coastal waters.

This methodology assesses the accuracy with which remote data characterizes a surface, as a function of Full Width at Half Maximum (FWHM). The purpose is to identify the best remote data that improves the characterization of a surface, evaluating the number of bands in the spectral range. The first step creates an accurate dataset of remote simulated data, using in situ hyperspectral reflectances. The second step evaluates the capability of remote simulated data to characterize this surface. The spectral similarity measurements, which are obtained using classifiers, provide this capability. The third step examines the precision of this capability. The assumption is that in situ hyperspectral reflectances are considered the "real" reflectances. They are resized with the same spectral range of the remote data. The spectral similarity measurements which are obtained from "real" resized reflectances, are considered "real" measurements. Therefore, the quantity and magnitude of "errors" (i.e., differences between spectral similarity measurements obtained from "real" resized reflectances and from remote data) provide the accuracy as a function of FWHM. This methodology was applied to evaluate the accuracy with which CHRIS-mode1, CHRIS-mode2, Landsat5-TM, MIVIS and PRISMA data characterize three coastal waters. Their mean values of uncertainty are 1.59%, 3.79%, 7.75%, 3.15% and 1.18%, respectively.