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Record W3029866507 · doi:10.4236/gep.2020.85023

Spatial Analysis of Heavy Metal Emissions in Residential, Commercial and Industrial Areas Adjacent to a Scrap Metal Shredder in Winnipeg, Canada

2020· article· en· W3029866507 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Geoscience and Environment Protection · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEnvironmental scienceContaminationPollutionScrapEnvironmental chemistryArsenicSnowZincEnvironmental engineeringHydrology (agriculture)MetallurgyChemistryGeographyGeologyMeteorologyMaterials science

Abstract

fetched live from OpenAlex

A spatial analysis of air pollution in the South St. Boniface (SSB) and Mission Industrial Areas (MIA) of Winnipeg, in Manitoba, Canada, was conducted by mapping the results for 23 composite snow samples. Heavy metals were analyzed in the snow samples by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Higher concentrations closer to the shredder were significant for every metal, but, not for arsenic, in regression modeling R squared (0.585 for Cd, 0.462 for Pb, 0.423 for Zn, 0.343 for Cr, 0.343 for Ni, 0.244 for Mn, and 0.069 for As). Heavy metal concentrations were significantly higher in the industrial zone, with the next highest being the roadside zone, then the commercial zone and finally the residential/parkland zone, at p-value < 0.01 statistical significance levels according to the non-parametric Kruskal-Wallis H- test. The metals concentrations mapped on Arc-GIS with ArcMap 10.6 using kriging interpolation, display that all toxic metal concentrations, but particularly Pb, Ni, Zn, Cr, and Hg, are highest proximate to the scrap metal shredder. Furthermore, pollution indices, specifically contamination factor (CF), degree of contamination (DOC), and pollution load index (PLI), were undertaken registering high contamination. The CF registered high for lead, zinc, and nickel in all areas compared to the background levels, but the highest levels were nearby to the scrap metal shredder. The DOC values showed that the industrial contamination is nearly five times greater than that for the road or commercial areas and almost 20 times more contaminated compared to the residential/parkland. With PLI levels above 1 considered contaminated, the shredder (4.1), roadside (2.2), and commercial areas (1.9) were polluted. These findings point to the shredder as the cause of present-day contamination for all areas, including residential/parkland, traffic, and commercial areas. High levels of toxic metal air pollution emissions warrant further study of human exposure and health risk posed by multiple sources from the air, water, and land. Enforcement and enclosure of the outdoor shredder should be considered to reduce heavy metal exposure to the public.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.235
Teacher spread0.203 · 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