Polycyclic aromatic hydrocharbons (PAHs) are a class of highly toxic pollutants that are highly detrimental to the ecosystem. Landfill leechate emanated from municipal solid waste are reported to constitute significant PAHs. In the present investigation, three Fenton proceses, namely conventional Fenton, photo-fenton and electro-fenton methods have been employed to treat landfill leehcate for removing PAHs from a waste dumpig yard. Response surface methodology (RSM) and artificial neural network (ANN) methodologies were adopted to optimize and validate the conditions for optimum oxidative removal of COD and PAHs. The statistical analysis results showed that all independent variables chosen in the study are reported to have significant influence of the removal effects with P-values <0.05. Sensitivity analysis by the developed ANN model showed that the pH had the highest significance of 1.89 in PAH removal when compared to the other parameters. However for COD removal, H2O2 had the highest relative importance of 1.15, followed by Fe2+ and pH. Under optimal treatment conditions, the photo-fenton and electro-fenton processes showed better removal of COD and PAH compared to the Fenton process. The photo-fenton and electro-fenton treatment processes removed 85.32% and 74.64% of COD and 93.25% and 81.65% of PAHs, respectively. Also the investigations revelaed the presence of 16 distinct PAH compunds and the removal percentage of each of these PAHs are also reported. The PAH treatment research studies are generally limited to the assay of removal of PAH and COD levels. In the present investigation, in addition to the treatment of landfill leachate, particle size distribution analysis and elemental characterization of the resultant iron sludge by FESEM and EDX are reported. It was revealed that elemental oxygen is present in highest percentage, followed by iron, sulphur, sodium, chlorine, carbon and potassium. However, iron percentage can be reduced by treating the Fenton-treated sample with NaOH.
A large volume of produced water (PW) has been produced as a result of extensive industrialization and rising energy demands. PW comprises organic and inorganic pollutants, such as oil, heavy metals, aliphatic hydrocarbons, and radioactive materials. The increase in PW volume globally may result in irreversible environmental damage due to the pollutants' complex nature. Several conventional treatment methods, including physical, chemical, and biological methods, are available for produced water treatment that can reduce the environmental damages. Studies have shown that adsorption is a useful technique for PW treatment and may be more effective than conventional techniques. However, the application of adsorption when treating PW is not well recorded. In the current review, the removal efficiencies of adsorbents in PW treatment are critically analyzed. An overview is provided on the merits and demerits of the adsorption techniques, focusing on overall water composition, regulatory discharge limits, and the hazardous effects of the pollutants. Moreover, this review highlights a potential alternative to conventional technologies, namely, porous adsorbent materials known as metal-organic frameworks (MOFs), demonstrating their significance and efficiency in removing contaminants. This study suggests ways to overcome the existing limitations of conventional adsorbents, which include low surface area and issues with reuse and regeneration. Moreover, it is concluded that there is a need to develop highly porous, efficient, eco-friendly, cost-effective, mechanically stable, and sustainable MOF hybrids for produced water treatment.
PAHs is the group of emerging micro-pollutants present in most environmental matrices that has the tendency to bioaccumulate and cause carcinogenic effects to human health. The present research involved the quantification and treatment of leachate produced from secured landfill, to eliminate the PAHS. Electro-Fenton process, a class of advanced oxidation process, is adopted to degrade the PAHs using titanium electrodes as both anode and cathode. Artificial intelligence based statistical tool "Central Composite Design" a module of JMP -19 software was used to design the experiments and optimize the critical parameters involved in the research. It was observed that the value of P is significant (P