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Transformation, Transport, and Storage of Major Elements in Municipal Solid Waste Disposal Sites

2024· preprint· en· W4391145188 on OpenAlexaff
Yao Wang, Chuanbin Zhou, Houhu Zhang, Shijun Ma, Ziyang Lou, Lutgarde Raskin, Dimitrios Zekkos, Frédéric Coulon, Steve Skerlos, Abid Hussain, Grzegorz Lisak, Mingliang Fang, Ke Yin, Hongping He, Xunchang Fei

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsCarleton University
FundersNanyang Technological University
KeywordsMunicipal solid wasteTransformation (genetics)Waste managementEnvironmental scienceBusinessEngineeringChemistry

Abstract

fetched live from OpenAlex

The predominant management approach for municipal solid waste (MSW) remains disposal, given significant increases in generation and disposal rates of MSW in recent decades. In addition to the well-documented carbon emissions from disposal sites, these sites accumulate numerous elements, the masses of which are substantial globally yet inadequately quantified. The unique combinations of waste constituents, elements, diverse environments, and confined spaces in disposal sites create distinct biogeochemical conditions, setting them apart from any other infrastructure or geological feature on Earth. This review first presents a global summary of the cumulative masses and disposal rates of MSW constituents and associated elements. The five dominant transformation and transport processes influencing disposed elements are examined: biochemical degradation, physicochemical transformation, gas migration, leachate migration, and solid spillage. The magnitudes and rates of the processes corresponding to major disposed elements, including carbon, nitrogen, sulfur, chlorine, and several metals, are systematically summarized. We examine and quantify the potential environmental impacts and health risks associated with element transformation and transport. We also explore the existing knowledge and techniques for resource recovery and site remediation of disposal sites. The distilled compilation of measurements and insights herein serves as a valuable primer for researchers, practitioners, and decision-makers involved in MSW management.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.314
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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Same venuePreprints.orgSame topicMunicipal Solid Waste ManagementFrench-language works237,207