A model to predict the breathing zone concentrations of particles emitted from surfaces

Our plain-English summary of A model to predict the breathing zone concentrations of particles emitted from surfaces, by J. Thornburg, J. Kominsky, G.G. Brown, et al (Journal of Environmental Monitoring, 2010).

The study

• Authors: J. Thornburg, J. Kominsky, G.G. Brown, P. Frechtel, W. Barrett, G. Shaul
• Journal: Journal of Environmental Monitoring
• Published: 2010;12(4):973-80
• Read it: PubMed · Publisher

Abstract

Activity based sampling (ABS) is typically performed to assess inhalation exposure to particulate contaminants known to have low, heterogeneous concentrations on a surface. Activity based sampling determines the contaminant concentration in a person's breathing zone as they perform a scripted activity, such as raking a specified area of soil, while wearing appropriate sample collection instrumentation. As an alternative approach, a probabilistic model based on aerosol physics and fluid dynamics was developed to predict the breathing zone concentration of a particulate contaminant emitted from a surface during activities of variable intensity. The model predicted the particle emission rate, tracked particle transport to the breathing zone, and calculated the breathing zone concentration for two scenarios. One scenario used an Eulerian model based on a Gaussian concentration distribution to quantify aerosol exposure in the trailing wake of a moving object. The second scenario modeled exposure in a quiescent environment. A Lagrangian model tracked the cumulative number of individual particles entering the breathing zone volume at a particular time. A Monte Carlo simulation calculated the breathing zone concentration probability distribution for each scenario. Both models predicted probability distributions of asbestos breathing zone concentrations that bracketed experimentally measured personal exposure concentrations. Modeled breathing zone concentrations were statistically correlated (p-value < 0.001) with independently collected ABS concentrations. The linear regression slope of 0.70 and intercept of 0.03 were influenced by the quantity of ABS data collected and model parameter input distributions at a site broader than those at other sites.

In plain English

This study built a model to estimate how much asbestos reaches a person's breathing zone when activity disturbs asbestos-containing soil. It was meant as a cheaper alternative to activity based sampling, in which a person performs a scripted task while wearing air samplers. The model covered two situations: riding behind a moving source, represented by motorcycle riding, and raking in low-turbulence conditions, where wind could carry particles away. Inputs came from seven US sites with elevated soil asbestos. Results were compared with breathing-zone samples that the US EPA collected independently. For motorcycle riding at Clear Creek Management Area, the modeled median was 0.59 asbestos structures per cubic centimeter. Measured values during motorcycle riding there were 0.14 to 2.0 structures per cubic centimeter. For raking at Swift Creek, half the model runs gave zero. The rest had a median of 0.031 structures per cubic centimeter, against measured values of 0.024 to 0.043. Modeled site medians, which excluded zero results, were statistically correlated with measured medians, with a regression slope of 0.70. The authors concluded that the model's ranges bracketed measured values and described it as a screening tool. They noted that some inputs were assumed rather than measured.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Probabilistic (Monte Carlo) modeling study based on aerosol physics and fluid dynamics, checked against independently collected field breathing-zone samples (activity based sampling)
• Who was studied: Seven US sites with elevated asbestos in soil: Clear Creek Management Area (King City, CA), Swift Creek (Nooksack, WA), North Ridge Estates (Klamath Falls, OR), Coalinga Operable Unit 2 (Coalinga, CA), Big Tex Site (San Antonio, TX), Vermont Asbestos Group (Eden, VT) and Sapphire Mine (Sapphire, NC). The article does not give sampling dates. The model simulated adults aged 16 to 60.
• Exposure: Asbestos in soil, disturbed by activity and measured as asbestos structures by transmission electron microscopy. The article does not name the fiber type for the sites. The model simulated motorcycle riding behind another rider and yard work (raking) in a quiescent, low-turbulence environment with ambient wind. Field sampling included motorcycle riding at Clear Creek, combined jogging, biking and walking at Swift Creek, and raking a 3 m by 3 m area for 120 minutes at all sites except Clear Creek.
• Main results:
  • Motorcycle riding at Clear Creek Management Area: the median modeled breathing zone concentration was 0.59 asbestos structures per cubic centimeter, and modeled values spanned four orders of magnitude. Measured values during motorcycle riding ranged from 0.14 to 2.0 structures per cubic centimeter (11 samples).
  • Raking scenario: in half of the model iterations at each site, ambient wind carried particles away before they reached the breathing zone, so no exposure occurred. At Swift Creek, half of the iterations produced a concentration of zero.
  • Raking at Swift Creek, non-zero model runs: the median was 0.031 structures per cubic centimeter, with a modeled range of 0.01 to 0.08. This range bracketed the measured values of 0.024 to 0.043 structures per cubic centimeter.
  • The authors compared modeled and measured median concentrations by linear regression across eight scenarios at the seven sites. Zero modeled values were excluded from the modeled medians. The regression was statistically significant (p less than 0.001). The slope was 0.70, which was statistically different from 1. The intercept was 0.03, which was not statistically different from zero.
  • Sensitivity analysis, motorcycle scenario: three inputs had the largest influence on modeled concentrations. These were the distance from the leading rider, the angle from the leading rider, and the asbestos content of the soil.
  • Sensitivity analysis, raking scenario: three inputs influenced modeled concentrations. These were the asbestos emission rate from the soil, the speed of the warm air rising from the body, and the ambient wind speed.
• The authors' conclusion: The authors concluded that the model predicted ranges of breathing zone concentrations that bracketed measured concentrations for motorcycle riding and raking. They described it as a screening tool to help decide whether field sampling is justified, and as applicable to any contaminant emitted from a surface.
• Limitations the authors state:
  • In the motorcycle scenario, the authors assumed a constant turbulent eddy velocity, which may have caused the model to underestimate breathing zone concentrations.
  • The emission equation used for the motorcycle scenario estimates dust from vehicles on unpaved roads, and it was applied outside its original context.
  • The two most important inputs for the motorcycle scenario, distance and angle, used assumed distributions. These were estimated from limited notes and from conversations with observers.
  • At Coalinga, the wide range of measured emission rates may have biased the modeled median upward.
  • At Big Tex, the small amount of field sampling data and the very low concentrations made it more likely that the measured median was biased low.
  • The sensitivity analysis found that soil silt, wind speed and moisture did not influence the motorcycle model output. The authors said this finding should be re-evaluated for other locations and activities.

Mines named here

Asbestos mines this study is about. Each mine's page says who ran it, when it worked, and what it produced.

• Belvidere Mountain quarry, Hyde Park / Eden, Vermont

All asbestos mines

Topics

Medical terms in this study, explained in our Lexicon: P-Value.

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