Spatial Analysis of Heavy Metal Emissions in Residential, Commercial and Industrial Areas Adjacent to a Scrap Metal Shredder in Winnipeg, Canada
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".