Landfill leachate (LFL) treatment is a severe challenge due to its highly viscous nature and various complex pollutants. Leachate comprises various toxic pollutants, including inorganic macro/nano components, xenobiotics, dissolved organic matter, heavy metals, and microorganisms responsible for severe environmental pollution. Various treatment procedures are available to achieve better effluent quality levels; however, most of these treatments are nondestructive, so pollutants are merely transported from one phase to another, resulting in secondary contamination. Anaerobic digestion is a promising bioconversion technology for treating leachate while producing renewable, cleaner energy. Because of its high toxicity and low biodegradability, biological approaches necessitate employing other techniques to complement and support the primary process. In this regard, pretreatment technologies have recently attracted researchers’ interest in addressing leachate treatment concerns through anaerobic digestion. This review summarizes various LFL pretreatment methods, such as electrochemical, ultrasonic, alkaline, coagulation, nanofiltration, air stripping, adsorption, and photocatalysis, before the anaerobic digestion of leachate. The pretreatment could assist in converting biogas (carbon dioxide to methane) and residual volatile fatty acids to valuable chemicals and fuels and even straight to power generation. However, the selection of pretreatment is a vital step. The techno-economic analysis also suggested the high economic feasibility of integrated-anaerobic digestion. Therefore, with the incorporation of pretreatment and anaerobic digestion, the process could have high economic viability attributed to bioenergy production and cost savings through sustainable leachate management options.

Azo dyes and hexavalent chromium (CrVI) are common pollutants in wastewater generated by the leather processing industry. This study was designed to develop a treatment strategy that can simultaneously remove azo dyes, their byproducts and CrVI using a biochar packed bioreactor. Various feedstock materials were evaluated, after which, biochar produced from pyrolysis of corn cobs at 400 °C was selected as a packing material for the reactor, based on its large surface area (1275 m² g⁻¹), microporosity (2–5 μm), and ability to support microbial biofilm formation. In the bioreactor experiments, simulated tannery wastewater containing 100 mg L⁻¹ Reactive Black-5 azo dye and 10–100 mg L⁻¹ aniline (byproduct of azo dye) was treated in the presence and absence of CrVI using the bacterium Pseudomonas putida strain KI. The results showed complete biodegradation of Reactive Black 5 (100 mg L⁻¹) within 5 h. Strain KI could also reduce 100 mg L⁻¹ dye and 10 mg L⁻¹ CrVI simultaneously in 24 h in a 2 L continuous packed bed bioreactor. Complete biodegradation of aniline (byproduct of Reactive Black 5) in the bioreactor was obtained within 24 h in the absence of CrVI, whereas degradation was decreased to 84% in the presence of CrVI. LC-MS analysis confirmed the biodegradation of Reactive Black 5 and aniline. This study clearly illustrates the feasibility of using bacterial strains having multifaceted functions for the biological treatment of tannery wastewater in bioreactors containing pyrolyzed carbon (biochar) as a support matrix for the bacterial cells.

Plant growth promoting (PGP) bacteria are known to enhance plant growth and protect them from environmental stresses through different pathways. The rhizosphere of perennial plants, including olive, may represent a relevant reservoir of PGP bacteria. Here, seven bacterial strains isolated from olive rhizosphere have been characterized taxonomically by 16S sequencing and biochemically, to evaluate their PGP potential. Most strains were identified as Pseudomonas or Bacillus spp., while the most promising ones belonged to genera Pseudomonas and Curtobacterium. Those strains have been tested for their capacity to grow under osmotic or salinity stress and to improve the germination and early development of Triticum durum subjected or not to those stresses. The selected strains had the ability to grow under severe stress, and a positive effect has been observed in non-stressed seedlings inoculated with one of the Pseudomonas strains, which showed promising characteristics that should be further evaluated. The biochemical and taxonomical characterization of bacterial strains isolated from different niches and the evaluation of their interaction with plants under varying conditions will help to increase our knowledge on PGP microorganisms and their use in agriculture.

The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.