Remediation Activities in Italian Superfund Sites: the case study of Naples - Bagnoli
Bibliographic record
Abstract
Until the 1990s, Italy was among the world's leading producers of raw asbestos fibres and Asbestos Containing Materials (ACM) and one of the most contaminated Countries in Europe.To reduce asbestos-related health effects, Italy has adopted many laws and regulations regarding exposure thresholds limits and remediation tools.The Italian Environmental Ministry (MASE) has identified 42 Italian Superfund sites, 11 of which are mainly contaminated by Asbestos.The highest levels of exposure occur during remediation activities in the 42 Superfund-Sites (SS) and during the management of asbestos containing waste in landfills, which requires specific procedures.INAIL-DIT, the Italian Governmental Occupational and Safety Institute, play a role as MASE scientific consultant on issues concerning workers protection, against risks caused by pollution, remediation and Asbestos Containing Waste (ACW) management.The aim is to identify suitable Emergency Safety Measures, to suggest specific best practices for remediation concerning on site monitoring, laboratory analysis, safety measures.Moreover, aim of INAIL research is testing the advanced technologies available for friendlier working activities and analytical methodologies.This paper describes the remediation of Bagnoli industrial facility (Naples), an Eternit factory which produced in '70s -80s asbestos cement products.The remediation has been analysed, considering a first phase focused on demolition of structures and facilities, and a second phase regarding the characterization, screening, removal and disposal of polluted soils.The project planned the complete removal of all industrial structures and asbestos dispersed in the soil/subsoil and the recovery of the clean fraction.This work highlights the remediation techniques used and the prevention and protection measures provided for workers and daily life areas.This study, considering the high number of asbestos cement factories in the world, can serve as an important reference for similar situations at European or international scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".