Salinity variations in the water resources fed by the Etnean volcanic aquifers (Sicily, Italy): natural vs. anthropogenic causes

In this paper, in an attempt to reveal possible changes connected to natural or anthropogenic causes, the main results of hydrogeochemical monitoring carried out at Mount Etna are evaluated. We report on the salinity contents of the groundwaters that flow in fractured volcanics, which make up the flanks of the volcano. These waters, analyzed for major ion chemistry, were sampled regularly from 1994 to 2004. Basing on nonparametric Sen??s slope estimator, time series of groundwater composition reveal that the salinity of most of the Etnean aquifers increased by 0.5% to 3.5% each year during this period. This change in the water chemistry is clearly referable to the overexploitation of the aquifers. This increasing trend needs to be inverted urgently; otherwise, it will cause a shortage of water in the near future, because the maximum admissible concentration of salinity for drinking water will be exceeded.     

Pollutants and sperm quality: a systematic review and meta-analysis

Environmental Science and Pollution Research - Male fertility and semen quality have declined over recent decades. Among other causes, exposure to environmental and occupational pollution has been...     

Removal of heavy metal ions from wastewaters using dendrimer-functionalized multi-walled carbon nanotubes

Dendrimer-functionalized multi-walled carbon nanotubes (MWCNT) for heavy metal ion removal from wastewaters were developed. Triazole dendrimers (TD) were built directly onto the carbon nanotube surface by successive click chemistry reactions affording the zero- and first-generation dendrimer-functionalized MWCNT (MWCNT-TD1 and MWCNT-TD2). The Moedritzer-Irani reaction carried out on the amino groups present on the MWCNT-TD2 sample gave the corresponding α-aminophosphonate nanosystem MWCNT-TD2P. Both MWCNT-TD2 and MWCNT-TD2P nanosystems have been characterized by physical, chemical, and morphological analyses. Their chelating abilities towards the toxic metal ions Pb²⁺, Hg²⁺, and Ni²⁺ and the harmless Ca²⁺ ion have been experimentally evaluated in the two different sets of experiments and at the salt concentrations of 1 mg/mL or 1 μg/mL by inductively coupled plasma mass spectrometry (ICP-MS). The results of these studies pointed out the interesting chelating behavior for the phosphonated nanosystem towards the Hg²⁺ ion. The complexation mode of the best chelating system MWCNT-TD2P with mercury was investigated through density functional theory (DFT) calculations, suggesting a chelation mechanism involving the two oxygen atoms of the phosphate group. The synthesized dendrimers, supported on the multi-walled carbon nanotubes, have shown the potential to be used for the selective toxic metal ion removal and recovery.     

Dieldrin uptake by vegetable crops grown in contaminated soils

The aim of these trials was to study the distribution of dieldrin in soil and its translocation to roots and the aerial parts of vegetable crops grown in greenhouses and fields. The main objectives were to characterize dieldrin accumulation in plant tissues in relation to the levels of soil contamination; uptake capability among plants belonging to different species, varieties and cultivars. The presence of the contaminant was quantified by gas chromatography-electron capture detector (GC-ECD) and confirmed by gas chromatography-mass spectrometer (GC-MS). The results showed a translocation of residues in cucurbitaceous fruits and flowers confirming that zucchini, cucumber and melon are crops with high uptake capability. The maximum level of dieldrin residue at 0.01 mg/kg was found to be a threshold value to safeguard the quality production of cucurbits. Tomato, lettuce and celery were identified as substitute crops to grow in contaminated fields.     

Climate change, drought risk and land capability for agriculture: implications for land use in Scotland

Land capability classification systems define and communicate biophysical limitations on land use, including climate, soils and topography. They can therefore provide an accessible format for both scientists and decision-makers to share knowledge on climate change impacts and adaptation. Underlying such classifications are complex interactions that require dynamic spatial analysis, particularly between soil and climate. These relationships are investigated using a case study on drought risk for agriculture in Scotland, which is currently considered less significant than wetness-related issues. The impact of drought risk is assessed using an established empirical system for land capability linking indicator crops with water availability. This procedure is facilitated by spatial interpolation of climate and soil profile data to provide soil moisture deficits and plant available water on a regular 1-km grid. To evaluate potential impacts of future climate change, land capability classes are estimated using both large-scale ensemble (multi-simulation) data from the HadRM3 regional climate model and local-scale weather generator data (UKCP09) derived from multiple climate models. Results for the case study suggest that drought risk is likely to have a much more significant influence on land use in the future. This could potentially act to restrict the range of crops grown and hence reduce land capability in some areas unless strategic-level adaptation measures are developed that also integrate land use systems and water resources with the wider environment.     

BIO_ALGAE 2: improved model of microalgae and bacteria consortia for wastewater treatment

Environmental Science and Pollution Research - A new set up of the integral mechanistic BIO_ALGAE model that describes the complex interactions in mixed algal-bacterial systems was developed to...     

An intelligent way for discerning plastics at the shorelines and the seas

Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.

     

Conservation state of two paintings in the Santa Margherita cliff cave: role of the environment and of the microbial community

Environmental Science and Pollution Research - The conservation of ancient paintings sited in humid environments is an actual challenge for restorers, because it needs the knowledge of the...     

Carbon deposition in soil rhizosphere following amendments with compost and its soluble fractions, as evaluated by combined soil-plant rhizobox and reporter gene systems

We determined the organic carbon released by roots of maize plants (Zea mays L.) when grown in soils amended with compost and its soluble fractions. In rhizobox systems, soil and roots are separated from the soil of a lower compartment by a nylon membrane. Treatments are applied to the upper compartment, while in the lower compartment luminescent biosensors measure the bioavailable organic carbon released by roots (rhizodeposition). The rhizobox-plants systems were amended with a compost (COM), its water extract (TEA), the hydrophobic (HoDOM) and hydrophilic (HiDOM) fractions of the dissolved organic matter (DOM) extracted from the compost. After root development, the lower untreated compartments were sampled and sliced into thin layers. The bioavailable organic carbon in each layer was assessed with the lux-marked biosensor Pseudomonas fluorescens 10586 pUCD607, and compared with total organic carbon (TOC) analyses. The TOC values ranged between 8.4 and 9.6 g kg(-1) and did not show any significant differences between bulk and rhizosphere soil samples in any treatment. Conversely, the biosensor detected significant differences in available C compounds for rhizosphere soils amended with various organic materials. Concentrations of available organic compounds in the first 2 mm of soil rhizosphere were 1.69 (control), 1.09 (COM), 2.87 (HiDOM), 4.73 (HoDOM) and 2.14 (TEA)micromol Cg(-1) soil g(-1) roots. The applied rhizobox-biosensor integrated method was successful in detecting and quantifying effects of organic amendments on organic carbon released by maize plant roots. This approach may become important in assessing the carbon cycle in agricultural soils and soil-atmosphere compartments.