Recalcitrant compounds in soils and dredged sediments: new biotechnological approaches for the recover to the public use
Bibliographic record
Abstract
The objective of this research was the development of innovative biotechnological strategies for the removal of toxic compounds from different contaminated environmental matrices using mycoremediation approaches. The main goal was to find microorganisms able to deplete the contaminants and to restore conditions in contaminated soils that are compatible with the survival of living beings, producing an environmental matrix whose chemical and physical characteristics resume those of safe soils that may be reintroduced in the environment. The myco-based approach was designed so that it may be applied in already consolidated methods of treatments used for contaminated soils, e.g: landfarming and dynamic biopiles. Landfarming is a form of bioremediation that, with the proper controls, can be a practical, effective, durable, and cost-effective method for treating soils contaminated by petroleum derivatives. Landfarms are engineered bioremediation systems that utilize naturally occurring aerobic microorganisms in the soil to biodegrade the contamination. The enhancing of biodegradation rate can be accomplished through the introduction of oxygen and nutrients to the soil in treatment. Tilling can both introduce oxygen to the soil and also increase rates of evaporation of volatile compounds. Moreover, adding soil amendments, such as manure and fertilizers can provide the necessary macronutrients to stimulate microbial growth rates. Typically, the soil or sediment to be treated is spread out and then tilled/turned on a consistent basis so that the petroleum hydrocarbons can volatilize and/or oxidised. Landfarming is actually similar in management strategy to dynamic biopiles. In dynamic biopiles, contaminated soils/sediments are piled on a consistent basis in the treatment area, additives are mechanically mixed with the contaminated matrices, and the biopiles are then cultivated via turning with an excavator on a regular basis to allow the oxygenation of the piles. In this study, the use of autochthonous and/or allochthonous fungal strains able to deplete the contamination was exploited by bioaugmentation of the matrix. New tests for ecotoxicology and mutagenesis studies, dedicated to the validation of the process(es) and testing of the effective reduction in toxicity of the matrices at the end of the treatment, will be adopted. Different ecotoxicological assays were also applied to follow the biodegradation processes from the toxicological point of view. This thesis is divided into three sections, in relation to the class of contaminant to be removed and the matrix to be treated. More precisely, the research described in this thesis is focused on: 1) the removal of Polychlorinated Byphenols (PCB) and Polycyclic Aromatic Hydrocarbons (PAH) in soils; 2) the removal of Total Petroleum Hydrocarbons (TPH) contamination in sediments; 3) mycoremediation for the treatment of landfill leachates. These goals were achieved by the use of autochthonous and allochthonous fungal strains with metabolic activities of interest, such as, oxidative capacity towards different classes of organic pollutants and their metabolic intermediates, resilience to harsh environmental conditions related to contamination, and potential for exploitability in other applications (leachate treatment). The underlying hypothesis of this objective was that autochthonous or allochthonous fungal strains are adapted to harsh conditions in hydrocarbon contaminated soils and are able to oxidize contaminant compounds and produce oxidative enzymes for the removal of pollutants, rendering them either non-toxic, or less toxic. The null hypothesis was that although autochthonous or allochthonous fungi may be adapted to harsh conditions in hydrocarbon contaminated soils, they are unable to remove or detoxify the contaminating compounds and they produce oxidative enzymes for the removal of pollutants. The experimental approach was designed to achieve process innovation: new bio-based protocols that may be transferable on a real-scale for the improvement of already exploited industrial processes such as landfarming and/or biopiles for the abatement of recalcitrant compounds in soils and sediments. To the end, a pilot plant assessment of the bench scale experimentation is described for most of the matrices analysed in this work. Contaminated soils to be treated were obtained from a disused industrial site: one of the first petroleum refineries built in Italy. The Concession Decree of 1935 marked the beginning of cutting-edge industrial activities in the field of refining mineral oils. The official opening of the plant took place in 1937. Right from the beginning, the working plan produced a wide-range of products (lubricants for industry and transport, paraffin, diesel, fuel oil, etc...). The last expansion began in 1972, increasing the productive and storage capacity, from 2.3 million to 5 million tonnes of crude per year. At the end of 1985 the refinery, ceased its activities. The area represents an historical contamination site containing high concentrations of petroleum derivatives. In some areas, where lubricants were produced, PCB contamination was recorded. Due to the dimensions of the site, the sustainability of the intervention in term of costs is crucial. Bio-based best practices such as landfarming and biopile for the treatments of soils are recommended, however the implementation of the approaches is desirable. The goal is the recovery of the soil to the public use. At the same time a bio-based approach for the decontamination of dredged sediments was studied, with the aim of transforming the clayey contaminated matrix into a productive soil that may be used for environmental restoration. The contaminated sediments derive from the Navicelli Channel, Pisa, Italy, a waterway that connects the city of Pisa to the Harbour of Livorno in Tuscany. However, the technology could find application in the same industrial area above mentioned, because of the programmed construction of an infrastructural logistics terminal, with docks for mooring of merchant ships. Finally, the feasibility of a myco-based approaches to contaminated wastewaters was also studied using a batch scale process for the treatment of landfill leachates, that, at least with reference to the landfill leachates studied (obtained at Summit Road, Winnipeg, Canada) can be contaminated also by petroleum derivatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".