The arable land was enriched in P2O5, while the highest values for K2O and MgO were found in wasteland soils. The mean C content of all the soils was about 2%, with N (about 0.2%), C/N ratio (about 12) and pH (about 6.9) in the order arable land less then meadow less then wasteland. The highest DHA and Ure activity was determined in the reference (unpolluted) soils, while much higher IA was present in the wasteland soils. PCA model focused on factors connected with different soil uses, while RF model emphasised the natural resistance of the studied soils to degradation. Our results indicate that the driving factors of the soil quality index were controlled by the inherent properties of the loess parent material, rather than soil pollution.Mining in Tunisia generates a large amount of tailings charged with toxic minerals. As these tailings have a wide spread distribution, it is important to characterize and estimate their impact on soil contamination. This study examines the potential of field hyperspectral spectroscopy and SENTINEL-2 Multispectral data in estimating and mapping seven minerals content, including three toxic minerals (fluorite, barite and sphalerite), within soils around Hammam Zriba mine in Northen Tunisia. 69 soil and dike surface samples were collected, field Visible, Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) reflectance spectra were measured on these surfaces. The X-ray diffraction (XRD) method was used to identify the types of mineral and their associated contents on each collected soil samples. The mineral contents were predicted using the partial least squares regression (PLSR) method using i) field VNIR-SWIR spectra at raw spectral resolution, ii) field VNIR-SWIR spectra aggregated to the SENTINEL-2 spectral resolution and then iii) SENTINEL-2 spectra. This study shows 1) an accurate prediction of four of the seven minerals using field VNIR-SWIR spectroscopy, 2) a slight decrease of performances due to spectral resolution degradation (SENTINEL-2 simulated spectra) and 3) a significant decrease of performances due to spatial resolution degradation, except for fluorite. This work paves the way for large-scale mapping of minerals with high pollution potential using SENTINEL-2 data. In addition, the high frequency of SENTINEL-2 data may be used to monitor the spatial distribution of some minerals with high pollution potential in soils.This study explores the factors affecting the biodiversity of diatoms, vegetation with focus on bryophytes, and invertebrates with focus on water mites, in a series of 16 spring-habitats. The springs are located primarily from the mountainous part of the Emilia-Romagna Region (Northern Apennines, Italy), and two pool-springs from agricultural and industrial lowland locations. Overall, data indicate that biological diversity (Shannon-Wiener, α-diversity) within individual springs was relatively low, e.g. Sdiatoms = 0-46, Swater-mites = 0-11. However, when examined at the regional scale, they hosted a very high total number of taxa (γ-diversity; Sdiatoms = 285, Swater-mites = 40), including several new or putatively-new species, and many Red-List taxa. This pattern suggested there is high species turnover among springs, as well as high distinctiveness of individual spring systems. A key goal was to assess the hydrogeological and hydrochemical conditions associated with this high regional-pool species richness, ied from an ecohydrogeological perspective, are excellent systems in which to further investigate and understand geo-biodiversity relationships.In recent years, soil pollution is a major global concern drawing worldwide attention. Earthworms can resist high concentrations of soil pollutants and play a vital role in removing them effectively. Sodium oxamate order Vermiremediation, using earthworms to remove contaminants from soil or help to degrade non-recyclable chemicals, is proved to be an alternative, low-cost technology for treating contaminated soil. However, knowledge about the mechanisms and framework of the vermiremediation various organic and inorganic contaminants is still limited. Therefore, we reviewed the research progress of effects of soil contaminants on earthworms and potential of earthworm used for remediation soil contaminated with heavy metals, polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), pesticides, as well as crude oil. Especially, the possible processes, mechanisms, advantages and limitations, and how to boost the efficiency of vermiremediation are well addressed in this review. Finally, future prospects of vermiremediation soil contamination are listed to promote further studies and application of vermiremediation in contaminated soils.We propose and exemplify a framework to assess Natural Background Levels (NBLs) of target chemical species in large-scale groundwater bodies based on the context of Object Oriented Spatial Statistics. The approach enables one to fully exploit the richness of the information content embedded in the probability density function (PDF) of the variables of interest, as estimated from historical records of chemical observations. As such, the population of the entire distribution functions of NBL concentrations monitored across a network of monitoring boreholes across a given aquifer is considered as the object of the spatial analysis. Our approach starkly differs from previous studies which are mainly focused on the estimation of NBLs on the basis of the median or selected quantiles of chemical concentrations, thus resulting in information loss and limitations related to the need to invoke parametric assumptions to obtain further summary statistics in addition to those considered for the spatial analysis. Our work enables one to (i) assess spatial dependencies among observed PDFs of natural background concentrations, (ii) provide spatially distributed kriging predictions of NBLs, as well as (iii) yield a robust quantification of the ensuing uncertainty and probability of exceeding given threshold concentration values via stochastic simulation. We illustrate the approach by considering the (probabilistic) characterization of spatially variable NBLs of ammonium and arsenic detected at a monitoring network across a large scale confined groundwater body in Northern Italy.