Chromium is a potentially toxic metal occurring in water and groundwater as a result of natural and anthropogenic sources. Microbial interaction with mafic and ultramafic rocks together with geogenic processes release Cr (VI) in natural environment by chromite oxidation. Moreover, Cr (VI) pollution is largely related to several Cr (VI) industrial applications in the field of energy production, manufacturing of metals and chemicals, and subsequent waste and wastewater management. Chromium discharge in European Union (EU) waters is subjected to nationwide recommendations, which vary depending on the type of industry and receiving water body. Once in water, chromium mainly occurs in two oxidation states Cr (III) and Cr (VI) and related ion forms depending on pH values, redox potential, and presence of natural reducing agents. Public concerns with chromium are primarily related to hexavalent compounds owing to their toxic effects on humans, animals, plants, and microorganisms. Risks for human health range from skin irritation to DNA damages and cancer development, depending on dose, exposure level, and duration. Remediation strategies commonly used for Cr (VI) removal include physico-chemical and biological methods. This work critically presents their advantages and disadvantages, suggesting a site-specific and accurate evaluation for choosing the best available recovering technology.

Fourier transform infrared (FTIR) is a spectroscopy technique widely used to identify organic materials. It has recently gained popularity in microplastic (MP) pollution research to determine the chemical composition of unknown plastic fragments. However, it could also be used to evaluate the degree of ageing of MPs collected from the environment. In this context, the principal aim of our research has been to qualitatively evaluate the natural weathering of environmental MPs collected in an Italian freshwater body (the Ofanto River) using ATR-FTIR technology. Furthermore, we compared environmental particles to weathered artificial MPs under controlled light and temperature conditions and to unaltered pristine materials to assess the results. FTIR spectra were acquired using a Nicolet Summit FTIR (ThermoFisher Scientific) equipped with an Everest ATR with a diamond Crystal plate and a DTGS KBr detector (wavenumber range 4000–500 cm⁻¹, 32 scans per spectrum, spectral resolution of 4 cm⁻¹). The degree of ageing was assessed using three different indexes known to be related to changes in MPs: Carbonyl Index (CI), Hydroxyl Index (HI), and Carbon-Oxygen Index (COI). The overall results showed that the regions reflecting changes (hydroxyl groups, peaks from 3100 to 3700 cm⁻¹, alkenes or carbon double bonds, 1600 and 1680 cm⁻¹, and carbonyl groups, 1690 and 1810 cm⁻¹) appeared significantly modified in artificial and natural weathered particles compared to the pristine materials. The indexes calculated for polymers degraded under the artificial photo and thermo ageing conditions displayed a general tendency to increase with the time in contact with irradiation time. Particular enhancements of CI of PS fragment and PE pellet, HI of PE and PS fragments and PE pellet, and COI of PS fragment were observed. Otherwise, the following incubation of the same particles at a constant temperature of 45 °C did not further affect the chemical composition of the particles. Moreover, new unique peaks were also observed in the freshwater particles, almost all in the fingerprint region (1500–500 cm⁻¹). Differences in CI, HI, and COI were evidenced among the different morphological MP shapes. On the one hand, the CI calculated for the environmental PE pellets showed values ranging from 0.05 to 0.26 with a mean value of 0.17 ± 0.10. Most samples (57%) presented a CI with values between 0.16 and 0.30. On the other hand, fragments presented slicer modifications in the carbonyl region with CI values lower than pellets (0.05 ± 0.05). This index helps evaluate the degradation of PE MPs by UV light, increasing with enhancing residence time in the environment. Conversely, fragments showed greater values of HI (5.90 ± 2.57) and COI (1.04 ± 0.48) than pellets, as well as lines, which presented the maximum value of HI (11.51). HI is attributed to the bond vibrations of hydroxyl, carboxyl, or phenol groups. In contrast, COI is frequently attributed to the vibrations of C_O bonds found in carbohydrates, alkanes, secondary alcohols, and ketones. In conclusion, our results showed characteristics spectra acquired from environmental particles compared to pristine and artificial aged ones. The interpretation of our main results emphasizes the need to conduct ecotoxicological experimental studies using naturally weathered particles due to the unicity of their properties, which are more helpful for understanding microplastic pollution effects.

Microplastics have recently been stated as being remarkable contaminants of all environmental matrices. The lack of consistent and standardised methods and protocols used to evaluate and quantify microplastics present in riverine systems made a comparison among different studies a critical issue. Based on literature research and the practical expertise of the authors, this work presents a complete collection and analysis of procedures concerning the monitoring of microplastics in riverine environments, focusing on their sampling and analytical protocols to identify, quantify, and characterise them. Further details regarding the advantages and disadvantages of each analytical technique described, such as general recommendations and suggestions, are provided to give practical support for analytical procedures. In particular, microplastics studies consist firstly of their sampling from the aquatic compartment (aqueous and solid phase). Based on the goal of the research, specific devices can be used to collect particles from different matrices. It follows their quantification after extraction from the environmental matrix, adopting different protocols to isolate microplastics from a large amount of organic matter present in a riverine system. In the end, additional qualitative analyses (e.g., RAMAN and FTIR spectroscopy, GC-MS) are required to identify the chemical composition of particles for a better image regarding the abundance of polymer types, their origin, or other information related to manufacturing processes.

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.