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• Use of personal exposure modeling in risk assessment of air pollutants
  Prem S Sarin
  Published on: 25 March 2003

• Published on: 25 March 2003

  Use of personal exposure modeling in risk assessment of air pollutants

  • Prem S Sarin, Research Professor

  Dear Editor

  Kromhout and van Tangeren [1] raise important issues regarding the papers by Cherrie [2] and Harrison et al.[3] The major shortcomings of the paper by Harrison et al.[3] are the small size of the sample (6 subjects each) used in the extrapolation of results. The three groups studied were the children, elderly and subjects with preexisting disease. The sample size in the disease categor...

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  Dear Editor

  Kromhout and van Tangeren [1] raise important issues regarding the papers by Cherrie [2] and Harrison et al.[3] The major shortcomings of the paper by Harrison et al.[3] are the small size of the sample (6 subjects each) used in the extrapolation of results. The three groups studied were the children, elderly and subjects with preexisting disease. The sample size in the disease category used only 2 subjects each with chronic obstructive pulmonary disease (COPD), left ventricular failure (LVF) and severe asthma. These sample sizes are rather inadequate to draw any correlation. Thus the paper by Harrison et al.[3] does not adequately represent generalized level of exposure of the individuals to carbon monoxide (CO), nitrogen dioxide (NO₂) and particulate matter (PM₁₀).

  In risk assessment of ill-health association with air contaminants, uniform sampling of the pollutants at home, school, work and outdoor activities is important. In a recent article [4] detailing the effect of World Trade Center collapse, the authors emphasize that environmental monitoring for exposure assessment is a complex technical task that involves selection of pollutants for monitoring, location of monitors, sample collection methods, risk assessment standards, sampling results and data analysis. For example, the initial approach to the environmental sampling at the World Trade Center [4] was to locate monitors at the perimeter of the site, at locations where emergency and debris carrying vehicles were leaving the site, on the debris pile, and at locations in the surrounding community. Additional monitors were set up in the community to ensure a safer environment for workers and students returning to workplaces, homes and schools. Approximately, 66,000 results were entered in a database collected between September 11 to November 13 for subsequent analysis. Substances monitored included asbestos, particulate matter (PM_2.5 and PM₁₀), dioxins, PCBs, CO, heavy metals and volatile organic compounds (VOCs).

  As pointed out by Schneider [5] deterministic models are developed from equations based on mathematical principles, while statistical models are developed by fitting to observed data.[6] In the statistical modeling of inhalation exposure, mixed effect models have been useful. Linear Mixed Effects Model with AR-1 autoregressive correlation structures has recently been used by Levy et al.[7,8] in their studies.

  For example, due to the difficulties in the measurement of personal exposure, data on air pollution patterns in homogenous microenvironments linked with activity data are often used as surrogates (7). In these studies [7] PM_2.5 indoor-outdoor ratios were found to be greater than1 in settings with high levels of human activity. Cooking activities contributed significantly to elevation in levels of PM_2.5 along with other pollutants. Using Linear Mixed Effects Models with AR-1 autoregressive correlation structures, 10 minute average outdoor concentrations were generally weak predictors of indoor levels.

  As mentioned by Levy et al.[8] although ambient particulate matter has been associated with ill-health, the health risks for individuals depend in part on their daily activities. Information about levels of PM size distributions in indoor and outdoor microenvironments can help identify high-risk individuals. The authors used Linear Mixed Effects Models with an AR-1 autoregressive correlation structure to evaluate statistical significance of differences between microenvironments. Levels of larger particles were generally higher near significant human activity, and levels of smaller particles were higher near combustion sources. The indoor PM₁₀ concentrations were reported to be significantly higher than the outdoors on buses and trolleys. Statistical models showed significant variability among some indoor microenvironments.

  As pointed out by Chang et al.[9] simulation of activities performed by 65+ year olds indicate substantial variability in personal exposures of PM_2.5, O₃, CO and VOCs over a 12-hour period. For example, one hour personal CO exposures measured in vehicles were significantly higher than those measured in other microenvironments and the correlation between personal PM_2.5 exposures and ambient concentrations was lowest in the winter months for indoor non-residential microenvironment and highest in vehicle microenvironments.

  Thus the conclusions drawn by Harrison et al. [3] from the data on a relatively small sample of subjects utilizing microenvironment modeling of CO, NO₂ and PM may not adequately reflect the overall exposure patterns. The variance observed between measured and modeled values for PM₁₀ among elderly, COPD and children could be minimized by taking measurements in a larger sample both indoors and outdoors and during summer and winter months.

  References

  (1) Kromhout H, van Tongeren M. How important is personal exposure assessment in the epidemilogy of air pollutants? Occup Environ Med 2003; 60: 143-144.

  (2) Cherrie JW. How important is personal exposure assessment in the epidemiology of air pollutants? Occup Environ Med 2002; 59: 653-654.

  (3) Harrison RM, Thornton CA, Lawrence RG, Mark D, Kinnersley RP, Ayres JG. Personal exposure monitoring of particulate matter, nitrogen dioxide, and carbon monoxide, including susceptible groups. Occup Environ Med 2002; 59: 671-679.

  (4) Holtz TH, Leighton J, Balter S, Weiss D, Blank S, Weisfuse I, in Levy BS, Sidel VW (Eds). Terrorism and Public Health. A balanced approach to strengthening systems and protecting people Oxford University Press, Inc., Oxford, 2003, pp.19-48.

  (5) Schneider T. Improving exposure assessment requires measurements and modeling. Scand J Work Environ Health 2002; 28: 367-369.

  (6) Hornung RW, Griefe AL, Stayner LT, Steenland NK, Herrick RF, Elliott LJ et al. Statistical model for prediction of retrospective exposure to ethylene oxide in occupational mortality study. Am J Ind Med 1994; 25:825-836.

  (7) Levy JI, Dumyahn T, Spengler JD. Particulate matter and polycyclic aromatic hydrocarbon concentrations in indoor and outdoor microenvironments in Boston, Massachusetts. J Expo Anal Environ Epidemiol 2002; 12: 104-114.

  (8) Levy JI, Houseman EA, Ryan L, Richardson D, Spengler JD. Particle concentrations in urban microenvironments. Environ Health Perspect 2000; 108:1051-1057

  (9) Chang LT, Koutrakis P, Catalano PJ, Suh HH. Hourly personal exposures to fine particles and gaseous pollutants-results from Baltimore, Maryland. J Air Waste Manag Assoc 2000; 50: 1223-1235.

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  Conflict of Interest:
  None declared.

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