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Enregistrement W2118058055 · doi:10.1097/jom.0000000000000134

Do Existing Empirical Models for Welding Fumes Estimate Exposure to Ultrafine Particles Among Canadian Welding Apprentices?

2014· letter· en· W2118058055 sur OpenAlexafffundabout
Eva Suarthana, Maximilien Debia, Igor Burstyn, Hans Kromhout

Notice bibliographique

RevueJournal of Occupational and Environmental Medicine · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueOccupational exposure and asthma
Établissements canadiensUniversité de MontréalHôpital du Sacré-Cœur de MontréalFonds de Recherche du Québec - Santé
Organismes subventionnairesUniversité de Montréal
Mots-clésWeldingMedicineApprenticeshipMetallurgyMaterials science

Résumé

récupéré en direct d'OpenAlex

To the Editor: Welders are at risk of a wide range of respiratory health problems, including bronchitis, airway irritation, and lung function changes.1–5 Despite short duration of exposure, an inception cohort study of apprentice welders in Quebec documented a significant respiratory function decline, incidence of welding-related respiratory symptoms suggestive of occupational asthma and sensitization to metallic salts.6 It is important to understand how these effects relate to exposures experienced by apprentices in order to develop adequately protective exposure standards. Welders may be exposed to numerous chemical hazards associated with welding and cutting processes, including welding fumes, inert gases, gas mixtures, and solvents.7 Welding fumes consist of metallic oxides and gaseous vapors as well as ultrafine particles (UFP; size <100 nm).8 The type and quantity of fumes generated greatly depend on many factors, including (but not limited to) the welding process, and within the process, electrodes, fluxing agents, coatings on the base metal, base metals, and the power configuration of the welding machine.9 An exposure study in Quebec showed that apprentices in welding profession have a high level of exposure to UFP during the whole training period.10 Nevertheless, investigation of welding exposure at vocational schools is hardly performed. Empirical exposure models may be useful tools to provide estimates of exposure levels in this setting. Several models for estimating welding exposure exist11–13 and have been used to investigate associations between welding exposure and respiratory symptoms.5 These models were developed and validated using mass concentration data without consideration of the UFP fraction of the aerosol. Moreover, these exposure models have never been applied in the population of apprentices. Thus, the objective of this study was to evaluate how well existing empirical models for welding fumes estimate exposure to UFP among welding apprentices. We used an existing exposure database of 136 UFP measurements collected by Debia and colleagues10 from two welding vocational schools in Quebec. We used three exposure models (Table 1)11–13 to estimate UFP exposure among the apprentice welders studied by Debia and colleagues.10 The first model was developed by Kromhout and colleagues11 for inhalable dusts and fumes; the second model by Lehnert and colleagues12 for respirable dusts and fumes; and the third model by Liu and colleagues13 for the total particulate matter. Pearson correlation coefficients (rp) were calculated between the estimated exposure to welding fumes on the basis of the three models and measured UFP concentrations.14 All analyses were performed using SPSS 20.0 for Windows (Statistical Package for Social Sciences, Chicago, IL).TABLE 1: Exposure Models for Welding ExposureAs shown in Table 2, we found low correlation coefficients between the measured UFP concentrations and the estimated welding fume concentrations from the three exposure models that ranged from 0.11 to 0.22. Lehnert and colleagues12 found a higher correlation coefficient (0.42) between ultrafine and respirable particles in welding fumes. Low correlations may be found because different components of welding fumes have different predictors. It is also important to note that UFP concentrations were derived in a standard apprentices' cohort, whereas the exposure models were derived in a large group of welders; the two populations have very different exposure profiles in terms of duration of exposure and welding frequency.TABLE 2: Correlation Coefficients (r p) of the Estimated Exposure to Welding Fumes and the Measured UFP ConcentrationCorrelations coefficients between the estimates of the three models were much higher and ranged from 0.41 to 0.74 (Table 2). This finding was somewhat expected. According to Lenhert et al,12 respirable particles comprised about half of the mass of the inhalable particles in the welding fume. Measuring it with a respirable, inhalable, or total dust sampler will therefore not result in differences in estimated concentrations. In conclusion, current empirical models for exposure to welding fumes are insufficient for predicting exposure to UFP among welding apprentices. More UFP measurements are needed to derive UFP-specific empirical models. These models are crucial for controlling exposure, which is of increasing importance as evidence suggests that UFP may contribute to adverse respiratory and cardiovascular outcomes.15 Eva Suarthana, MD, PhD Research Centre, Hôpital du Sacré-Coeur de Montréal Montreal, Quebec, Canada Department of Social and Preventive Medicine, Université de Montréal, Canada Maximilien Debia, PhD Department of Environmental and Occupational Health, Université de Montréal, Canada Igor Burstyn, PhD Drexel University School of Public Health, Philadelphia, Penn. Hans Kromhout, PhD Institute for Risk Assessment Sciences, Utrecht University, the Netherlands ACKNOWLEDGMENTS We thank Denyse Gautrin of the Université de Montréal for her critical review of the manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,180
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,078
Tête enseignante GPT0,340
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission3
Résumé présentoui

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