title: Estimation of particulate mass and manganese exposure levels among welders.
abstract: BACKGROUND
  Welders are frequently exposed to Manganese (Mn), which may increase the risk of neurological impairment. Historical exposure estimates for welding-exposed workers are needed for epidemiological studies evaluating the relationship between welding and neurological or other health outcomes. The objective of this study was to develop and validate a multivariate model to estimate quantitative levels of welding fume exposures based on welding particulate mass and Mn concentrations reported in the published literature.
  
  
  METHODS
  Articles that described welding particulate and Mn exposures during field welding activities were identified through a comprehensive literature search. Summary measures of exposure and related determinants such as year of sampling, welding process performed, type of ventilation used, degree of enclosure, base metal, and location of sampling filter were extracted from each article. The natural log of the reported arithmetic mean exposure level was used as the dependent variable in model building, while the independent variables included the exposure determinants. Cross-validation was performed to aid in model selection and to evaluate the generalizability of the models.
  
  
  RESULTS
  A total of 33 particulate and 27 Mn means were included in the regression analysis. The final model explained 76% of the variability in the mean exposures and included welding process and degree of enclosure as predictors. There was very little change in the explained variability and root mean squared error between the final model and its cross-validation model indicating the final model is robust given the available data.
  
  
  CONCLUSIONS
  This model may be improved with more detailed exposure determinants; however, the relatively large amount of variance explained by the final model along with the positive generalizability results of the cross-validation increases the confidence that the estimates derived from this model can be used for estimating welder exposures in absence of individual measurement data.
authors.affiliation: Department of Neurology, Washington University, St Louis, MO 63116, USA.
authors.forename: Angela
authors.initials: A
authors.lastname: Hobson
authors.forename: Noah
authors.initials: N
authors.lastname: Seixas
authors.forename: David
authors.initials: D
authors.lastname: Sterling
authors.forename: Brad A
authors.initials: BA
authors.lastname: Racette
mesh_terms: D000395:Air Pollutants, Occupational / Q000032:analysis*; D006801:Humans; D008345:Manganese / Q000032:analysis*; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016273:Occupational Exposure / Q000032:analysis*; D052638:Particulate Matter / Q000032:analysis*; D012044:Regression Analysis; D014896:Welding
chemical_list: D000395:Air Pollutants, Occupational; D052638:Particulate Matter; D008345:Manganese