Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

<p>Abstract</p> <p>Background</p> <p>The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM_2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles.</p> <p>Methods</p> <p>Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties.</p> <p>Results</p> <p>The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties.</p> <p>Conclusion</p> <p>When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.</p

Occupational and consumer risk estimates for nanoparticles emitted by laser printers

Several studies have reported laser printers as significant sources of nanosized particles (&lt;0.1 &mu;m). Laser printers are used occupationally in office environments and by consumers in their homes. The current work combines existing epidemiological and toxicological evidence on particle-related health effects, measuring doses as mass, particle number and surface area, to estimate and compare the potential risks in occupational and consumer exposure scenarios related to the use of laser printers. The daily uptake of laser printer particles was estimated based on measured particle size distributions and lung deposition modelling. The obtained daily uptakes (particle mass 0.15&ndash;0.44 &mu;g d&minus;1; particle number 1.1&ndash;3.1 &times; 109 d&minus;1) were estimated to correspond to 4&ndash;13 (mass) or 12&ndash;34 (number) deaths per million persons exposed on the basis of epidemiological risk estimates for ambient particles. These risks are higher than the generally used definition of acceptable risk of 1 &times; 10&minus;6, but substantially lower than the estimated risks due to ambient particles. Toxicological studies on ambient particles revealed consistent values for lowest observed effect levels (LOELs) which were converted into equivalent daily uptakes using allometric scaling. These LOEL uptakes were by a factor of about 330&ndash;1,000 (mass) and 1,000&ndash;2,500 (particle surface area) higher than estimated uptakes from printers. This toxicological assessment would indicate no significant health risks due to printer particles. Finally, our study suggests that particle number (not mass) and mass (not surface area) are the most conservative risk metrics for the epidemiological and toxicological risks presented here, respectively