Assessment of soil contamination around an abandoned mine in a semi-arid environment using geochemistry and geostatistics: Pre-work of geochemical process modeling with numerical models

International audienceOne of the most serious environmental issues related to mining industry in Morocco and elsewhere around the world, is the pollution from abandoned mine sites. Mine wastes cause obvious sources of soil contaminations. Climatic effects such as heavy rainfall engender metal dispersion in semi-arid areas, since soils are typically and scarcely vegetated. In this study, extension and magnitude of soil contaminations with toxic elements from abandoned Kettara mine, in Morocco, are assessed using geochemical analysis and geostatistics for mapping. Soils and mine wastes are sampled and analyzed for 41 chemical elements (Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, U, Au, Th, Sr, Cd, Sb, Bi, V, Ca, P, La, Cr, Mg, Ba, Ti, Al, Na, K, W, Zr, Ce, Sn, Y, Nb, Ta, Be, Sc, Li, S, Rb and Hf). Based on enrichment factor (EF), only five elements of interest (Cu, Pb, Zn, As, and Fe) were selected in this research. Geochemical background is determined with exploratory data analysis and geochemical maps were elaborated using geostatistics in Geographic Information System (GIS) environment.The obtained results show that Kettara soils are contaminated with metals and metalloid that exceed the established geochemical background values (Cu ≈ 43.8 mg/kg, Pb ≈ 21.8 mg/kg, Zn ≈ 102.6 mg/kg, As ≈ 13.9 mg/kg and Fe ≈ 56,978 mg/kg). Geochemical maps show that the deposited mine wastes are responsible for soil contaminations with released metals and metalloid that have been dispersed downstream from the mine waste mainly, through water after rainfall. For sustainable development and environmental planning, the current study is expected to serve as a reference for politicians, managers, and decision makers to assess soil contaminations in abandoned mine sites in Morocco

A Remote Sensing-Assisted Risk Rating Study to Monitor Pinewood Forest Decline: The Study Case of the Castelporziano State Nature Reserve (Rome)

The Multi-Spectral Instrument (MSI) aboard the ESA Sentinel-2 (S-2) allows satellite the Normalized Different Vegetation Index (NDVI) to be measured at much higher spatial resolution (10 m) than has been previously possible with space-borne sensors such as Medium Resolution Imaging Spectrometer aboard ENVISAT or Enhanced Thematic Mapper Plus aboard Landsat. Therefore, multi-spectral analysis of remote sensing data today represents an efficient tool for monitoring vegetation in a Mediterranean environment, where spatial resolution often represents a limiting factor due to high fragmentation and spatial distribution of forest stand. The aim of this study has been to map the health conditions of the Castelporziano coastal pinewood forest (Roma). To this aim, we used a diachronic NDVI index, provided by ESA Sentinel-2 images and field observations, to monitor the health status in a historic pinewood forest that has recently been affected by a rapid diffusion of pests (Tomicus destruens Woll.). The monitoring performed allowed us to map the pinewood forest in risk classes and at the same time to provide data concerning the localization of areas showing a strong decline. Thus, we provide information useful for the correct management and planning of forestry thinning to preserve those areas of the pinewood forest not involved in the decline process.

Comparative Study on Swarm Intelligence Techniques for Biclustering of Microarray Gene Expression Data

Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component

Evaluating the Potential of Vegetation Indices in Detecting Drought Impact Using Remote Sensing Data in a Mediterranean Pinewood

© 2020, Springer Nature Switzerland AG. The Mediterranean ecosystem represents an important natural resource, being able to produce ecosystem services, has both economic and social repercussions, especially if located in urban and peri-urban areas. In the last decades, increased forest vulnerability is being reflected in a larger number of severe decline episodes associated mainly with drought conditions. In this context, the Mediterranean area shows high forest vulnerability and a subsequent decline in its natural renewal rate. In this context, the objective of this research is to evaluate the different vegetation indices to monitor the effect of drought on the health of the Castelporziano pine wood. For this purpose, we used the NDVI, NDII and NMDI, provided by ESA Sentinel-2 images and field observations, to monitor the health status of a historic pinewood that has recently been affected by a rapid spread of parasites (Tomicus destruens Woll.). The application of these indices, on the scale of the entire pinewood, showed that the NDVI and NDII indices differentiate better the changes in vegetative health status for the observed period than the NMDI. Moreover, NDVI and NDII were applied, based on the classifications made, to volume and age classes. Ultimately, these preliminary results require further studies to better understand the potential and limiting factors of the indices used in monitoring pinewoods under stress due to aridity