Soil pollution with hydrocarbons is a consequence of activities associated with the petroleum industry and related sectors. The effects of petroleum pollution are devastating, making the remediation of contaminated sites imperative. Consequently, soil decontamination represents a significant and costly challenge for the petroleum industry. The article proposes a dual-recovery bioremediation solution that is both efficient and cost-effective, exploring the potential use of dehydrated sewage sludge from municipal wastewater treatment plants to treat petroleum-contaminated soils. Over the three-month bioremediation experiment, changes in the density of indigenous bacteria in petroleum-contaminated soil samples, treated or untreated with sludge, were monitored along with the reduction in petroleum hydrocarbon concentrations. In parallel, the evolution of other contaminants, such as heavy metals, was monitored during the bioremediation experiment. Geotechnical tests were also conducted to evaluate the feasibility of returning the treated soil to its original location after the bioremediation experiment. Our results demonstrate that the proposed method effectively addresses both the remediation of petroleum-contaminated soils (hazardous waste) and the reuse of sewage sludge from municipal wastewater treatment plants.

Romania has some huge ferrous slag stockpiles that are secondary resources of minerals. Although a sizable portion of ferrous slags is recycled for use in building roads and other infrastructure, a sizable portion is still dumped. By November 2026, Member States must submit information on the quantity of critical raw materials (CRMs) in their secondary resources, as well as the quantification techniques employed, in accordance with Regulation (EU) 2024/1252. Therefore, the XRFS reliable measurements carried on ferrous slags were addressed to prevent dissipative loss of CRMs in cases where an improper slag recycling route is operated. The main novelty of this paper is the method of ensuring the reliability of the XRFS results based on weighted arithmetic mean and on the maximum likelihood approach. Secondly, the XRFS measurements carried on ferrous slags demonstrate that they contain CRMs like Ba, Sr, Y, etc.; however, below the minimum cut-off grade for CRMs, recovery XRFS cannot detect light CRMs. Our preliminary LIBS measurements on ferrous slags disclosed the presence of Li and Be. The drawbacks of the XRFS technique impose further research to develop an integrated XRFS, LIBS, and XRD procedure for comprehensive and trustworthy CRMs screening in extractive waste piles.

Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps, and their reliable quantification plays a critical role for decision risk regarding effective waste management. The reliable quantification of trace elements is a very challenging issue. Hence, in this study, a new conservative approach for data analysis in screening for trace elements in waste dumps is presented. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of conventional approaches based on unproven hypotheses, such as binomial, Poisson, or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. This model fills the gap of data analysis when a set of analytical results are uniformly distributed. Our approach deals with results reported as being lower than the LOQ. The model was applied on XRFS results from studies carried out on tailings to emphasize the differences among classic, robust, and conservative data analyses. Classical analyses overestimate the concentration values and underestimate the associated uncertainties increasing the decision risk. This study demonstrates that a conservative approach is mandatory when screening for trace elements if the concentration values are uniformly distributed. The proposed model can be applied to any solid waste dump, regardless of the analytical method used for trace element screening.

The use of sewage sludge for the bioremediation of hydrocarbon-contaminated soils remains insufficiently documented, particularly regarding microbial dynamics and material behavior during treatment. Although petroleum-hydrocarbon contamination severely disrupts soil functions, sewage sludge—through its high organic matter content, active bacterial communities, and fine mineral fraction—offers potential for the sustainable remediation of such soils. Three soil–sludge mixtures (P1:N1, P2:N1, P1:N2) were monitored to assess hydrocarbon degradation, bacterial community dynamics, and material behavior. Hydrocarbon-degrading and hydrocarbon-tolerant bacteria remained active, while sludge-derived Enterobacteriaceae declined below detection limits. Enrichment cultures of the sludge yielded three hydrocarbon-degrading strains (Providencia alcalifaciens IBBN1, Klebsiella pneumoniae IBBN2, Acinetobacter tandoii IBBN3), highlighting the metabolic potential of the active microbial communities. A moderate increase in surfactant concentrations reflected both residual anionic surfactants and biosurfactant production by these consortia, facilitating hydrocarbon mobilization. Total petroleum hydrocarbons (TPH) decreased by 45–60% (IR), and GC-FID analysis showed preferential degradation of C10–C40 fractions. Heavy-metal concentrations remained stable, indicating no geochemical changes or inhibitory effects on bacterial activity. Overall, the results confirm the potential of sewage sludge as a sustainable amendment that accelerates hydrocarbon biodegradation and supports integrated soil-restoration strategies.

Soil pollution with hydrocarbons is a consequence of the activity of the oil industry and other related fields of activity. The effects of oil pollution are devastating; therefore, the remediation of polluted sites is mandatory. For this reason, soil decontamination is a major and expensive problem for the oil industry. The article proposes a dual-recovery bioremediation solution that is efficient and inexpensive. The possibility of using sludge resulting from municipal wastewater treatment plants for the treatment of soils contaminated with petroleum was studied. During the bioremediation experiment, the variation over time of the density of indigenous bacteria present in the mixtures of contaminated soil and sewage sludge, in various proportions was followed, simultaneously with the decrease in the concentrations of petroleum hydrocarbons. In parallel, the evolution of other pollutants from the two environments was analyzed, such as heavy metals (ICP - MS), etc. The study also includes geotechnical tests for returning the soil after bioremediation to the original locations. The obtained results lead to the conclusion that the proposed method can simultaneously solve both the problem of remediation of soils polluted with petroleum products (hazardous waste) and the recovery of sludge from municipal wastewater treatment plants.

Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps. Their reliable quantification plays a critical role for decision risk regarding effective waste management. Reliable quantification of trace elements is a very difficult issue. Hence, the paper addresses a new conservative approach for data analysis in screening for trace element in waste dumps. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of the conventional approaches that are based on unproved hypotheses, like binomial, Poisson or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. The model fills the gap of data analysis in case where a set of laboratory outcomes are uniform distributed. Our approach cope with results reported as lower than LOQ. The model was applied on XRFS results carried on tailings to emphasize the differences among classic, robust and conservative data analyses. Classical analyses overestimate the concentration values and sub-evaluate the associated uncertainties which enhances the decision risk. The paper demonstrates that conservative approach is mandatory in case of screening for trace elements if concentration values are uniform distributed. The model can be applied to any solid waste dump, regardless the analytical method used for trace element screening.