The characterization of prospective plants is one of the critical issues in the efficiency and success of the phytoremediation process. Due to adaption and tolerance to different environmental stresses, native plant species have priority in this method. This study examined fifty plants of five species, namely Launaea acanthodes, Artemisia sp., Cousinia congesta, Peganum harmala, and Stipa sp., growing near a smelter and refinery in Iran to identify potential species for phytoextraction and phytostabilization. Therefore, Pb, Ni, Mn, Mo, S, and Cu concentrations in sampled plants and soils were analyzed. Three different pollution indices, namely metal accumulation index (MAI), translocation factor (TF), and bioconcentration factor (BCF) were used for evaluating the metal concentrations in roots and shoots of each plant species. The results indicated that Artemisia sp., with values of 3.21, 1.09, and 1.14 for MAI, BCF, and TF, respectively, is appropriate for phytoextraction in the study area. Plants such as Launaea acanthodes and Cousinia congesta with high BCF and low TF values showed the potential for phytostabilization. Investigating the indices for different elements demonstrated that Launaea acanthodes had a BCF value greater than 1 and a TF value less than 1; therefore, this plant could be used in the phytoremediation of arsenic through the phytostabilization technique. Furthermore, copper has very low bioavailability in these plant species. In addition, these native plant species were highly capable of accumulating sulfur from the soil because the BCF and TF indices for all inspected species were higher than 1; for Launaea acanthodes, the relevant TF value was about 10. The proposed native plant could be applied in practical applications of phytoremediation for soil remediation of contaminated sites around the metal factories and mines in southeastern Iran.

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R²) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10⁻⁴, and the values of R² = 0.944 and MSE = 2.4 × 10⁻⁴ were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

In this study, a plant-based adsorbent was used in order to remove lead, nickel, cobalt and cadmium metals from a wastewater sample collected from Sungun mine real wastewater. The biosorbent was one of the most abundant native plants of the Sungun region, named Chrysopogon zizanioides (C. zizanioides). The root powder of C. zizanioides was used in order to remove heavy metals from the wastewater sample. The biosorbent was characterized by FTIR, SEM, HR-TEM, EDS, BET and ZPC analyses. The effect of pH, initial metals concentrations, contact time and temperature on the biosorption process were accurately investigated. The metal concentrations were significantly reduced to lower concentrations after the biosorption process, which indicated that the C. zizanioides root powder removal efficiency was more than 95% for the metals from the wastewater sample, with maximum adsorption capacities of 31.78, 21.52, 26.69 and 27.81 mg/g, for Pb(II), Co(II), Cd(II) and Ni(II) ions, respectively. Furthermore, the adsorption kinetic results showed that the pseudo-second-order kinetic model correlated with the experimental data well, with correlation coefficient values of 1 for all metals. Isotherm studies illustrated that the Freundlich and Dubinin-Radushkevich (D-R) isotherm models could describe the obtained equilibrium data well. Moreover, from the D-R model, it was found that the biosorption type was physical. The thermodynamic studies demonstrated that the metals’ biosorption was an exothermic and spontaneous process. Moreover, the reusability of the biosorbent to be used in several successive cycles, and also the percentage of recovery of adsorbed metals from the biosorbent, was investigated. Altogether, being simple and cost-effective and having a high adsorption rate, fast kinetics, easy separation and high reusability prove that C. zizanioides root powder shows significant performance for the removal of heavy metals from waste effluents.

Tracer testing is a mature technology used for characterizing aquatic flow systems. To gain more insights from tracer tests a combination of conservative (non-reactive) tracers together with at least one reactive tracer is commonly applied. The reactive tracers can provide unique information about physical, chemical, and/or biological properties of aquatic systems. Although, previous review papers provide a wide coverage on conservative tracer compounds there is no systematic review on reactive tracers yet, despite their extensive development during the past decades. This review paper summarizes the recent development in compounds and compound classes that are exploitable and/or have been used as reactive tracers, including their systematization based on the underlying process types to be investigated. Reactive tracers can generally be categorized into three groups: (1) partitioning tracers, (2) kinetic tracers, and (3) reactive tracers for partitioning. The work also highlights the potential for future research directions. The recent advances from the development of new tailor-made tracers might overcome existing limitations.