Optimization Of Mercury Remediation From A Contaminated Industrial Park Soil Via Thermo Desorption: An Experimental Approach
Bibliographic record
Abstract
Environmental contamination caused by mercury -due to its mobility and long residence time in the soil and atmosphere [1] -is an emerging problem worldwide [2].To treat and remove the contaminant from the soil, different techniques have been implemented, both in laboratory, pilot and full-scale applications.One of the most promising approach for mercury removal is thermal desorption, a treatment technology that utilizes heat to increase the volatility of contaminants which are subsequently removed from the solid matrix and treated in an off-gas treatment system [3].In this work we analysed and treated a soil from an industrial area with high levels of mercury contamination, mostly in the forms of elemental mercury (24-67%) and insoluble inorganic mercury (32-73%).To understand the most effective remediation strategy, a series of tests have been performed on a different number of soil samples.The soil was collected via core drilling up to a depth of 5 metres, and each 1 metre layer was characterized in terms of total mercury contamination, dry residue, humidity, sieve fraction (less than 2 cm and more than 2 mm), and a mercury speciation was performed.After a first characterization, the layer with the highest mercury contamination was identified and 10 kg of material was selected for the subsequent analysis.A composite sample was obtained via mixing of the whole layers cored, including the high polluted stratum, and 30 kg of material was collected for analysis.A series of laboratory tests were performed on the samples from both the most contaminated layer and the composite, with the aim of investigating the most efficient strategy for the thermal desorption, In Situ Thermal Desorption (ISTD) or Ex-situ Thermal Desorption (ESTD), using a rocking oven.Small fractions of the samples were analysed through thermogravimetry (TGA) tests.The tests were performed on both unaltered state (14.5% humidity) and dry samples.In the first step of thermogravimetric analysis (TGA), the sample was continually weighted while heated between 25°C and 700°C, as an inert gas atmosphere was passed over it.Characteristic temperatures for the desorption of the different mercury species present in the soil were identified through the analysis of the thermogram -specifically the temperature intervals that show the highest weight loss and the differential scanning calorimetry (DSC) analysis.A second thermogravimetry analysis in isothermal conditions for a duration of 2 hours was performed, to investigate the mercury losses at the inflection temperature identified with the DSC analysis.The total mercury analysis showed a drastic reduction of the contamination, that fell under the Italian CSC (Contamination Threshold Concentrations) for mercury in sites suitable for industrial or commercial use (5 mg/kg) [4].A series of desorption tests with rotating oven were performed to scale up the process and recreate the real treatment conditions, and to investigate the two different remediation strategies: semi-static mode for the in-situ thermal treatment and rocking mode for the off-site treatment.From preliminary tests, both the advantages of the joint laboratory analysis and pilot approach, as well as the efficacy of the thermal treatment of mercury contaminated soils are evident.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".