Detecting and sourcing GHGs and atmospheric trace gases in a municipal waste treatment plant using coupled chemistry and isotope compositions
Bibliographic record
Abstract
• Landfill quarters and composting facilities identified as high CH 4 emission zones. • Atmospheric 13 C-depleted CH 4 indicates contamination from emission hotspots. • δ 13 C-CH 4 signatures identify no predominant CH 4 production pathway. • δ 13 C-DIC of leachates is linked to the initial methanogenesis phase of active quarter. • Difference in traces concerns NO 2 , SO 2 , chlorocarbons, alkanes, benzene and alcohols. Landfill operations and waste processing facilities are important and highly heterogeneous sources of both greenhouse gases (GHGs) and non-GHG air pollutants in the atmosphere. This arises the need for detailed apportionment of waste sources in order to locate and subsequently reduce emissions from landfills. Here, a time series of in situ measurements of atmospheric trace gases and spatial allocation of specific emission source types under different processing phases and environmental conditions were conducted in and in the surroundings of a Municipal Solid Waste Treatment Plant (MSWTP) in south-western Poland. Results revealed that several individual GHG sources dominated across the waste processing facility and that GHGs concentrations displayed spatial seasonality. An increase in the ground-level CH 4 concentrations, from ∼ 30.3 to 56.3 ppmv, was observed close (∼5 – 10 m) to the major emission sources within the MSWTP. While hotspot areas generally yielded elevated CH 4 concentrations near the soil surface, these were relatively low (2.4 to 8.9 ppmv) along the facility’s fence line. The study of the corresponding δ 13 C delineated the extent of dispersion plumes downwind emission hotspots, characterized by a 13 C depletion (around 4.0 ‰) in the atmospheric CH 4 and CO 2 . For CH 4 , emissions were isotopically discriminated between the extraction wells at active quarters/cells (δ 13 C = –58.3 ± 1.1 ‰) and biogas produced in the biological waste treatment installation (δ 13 C = –62.7 ± 0.7 ‰). Most of the trace compounds (non-methane hydrocarbons, halocarbons, oxygen-bearing organic gases, ketones, nitrogenous and sulphurous gases, and other admixture compounds) detected at the ground surface were linked to the CH 4 - and CO 2 -rich spots. Despite the relatively high variability in the concentrations of organic and inorganic compounds observed at the MSWTP active zones, our results suggest that they do not have a meaningful impact on the surrounding air quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".