Application of national pollutant inventories for monitoring trends on dioxin emissions from stationary industrial sources in Australia, Canada and European Union
Bibliographic record
Abstract
Industrial sources, including iron ore sintering, municipal waste incineration and non-ferrous metal processing have been prominent emitters of dioxins to the environment. With the expanding industrial sectors, many international conventions were established in order to reduce the emission of dioxins in the past two decades. The Stockholm convention, a global monitoring treaty, entered into force in 2004 with the aim to promote development of strategies to reduce or eliminate dioxin emissions. According to the convention, parties are required to develop national inventory databases to report emission levels and develop a national implementation plan (NIP) to reduce further dioxin emissions. In order to understand the trend of dioxin emissions since 1990s this study provides a comparative assessment of dioxin emissions from different industrial sources by deriving emission data from the national inventory databases of Australia, Canada and the 28 European countries (EU-28). According to the data collected, iron and steel production and electricity generation were the highest emitters of dioxins in 2017 for Europe, Canada and Australia, when compared to other stationary industrial sources. The change in the trend of dioxin emissions from the iron and steel industry and the public electricity sector was also assessed. The emission of dioxins during 1990-2017 from both iron and steel production and electricity generation revealed a relative decreasing trend, except for Spain and Italy who showed higher level of emissions from iron and steel production in 2017. Furthermore, comparing emission data for metal production revealed that the blast furnace process was the prominent emitter of dioxins comparing to electric arc furnace process. Further investigation was performed to compare the amount of dioxin emitted from three different fuel types, black coal, brown coal and natural gas, used for electricity generation in Australia. The study showed that dioxin emissions from brown coal were higher than black coal for the last two years, while power production from natural gas emits the lowest amounts of dioxins to the environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".