Engineering evidence for carbon monoxide toxicity cases

Unintentional carbon monoxide poisonings and fatalities lead to many toxicity cases. Given the unusual physical properties of carbon monoxide-in that the gas is odorless and invisible-unorganized and erroneous methods in obtaining engineering evidence as required during the discovery process often occurs. Such evidence gathering spans domains that include building construction, appliance installation, industrial hygiene, mechanical engineering, combustion and physics. In this paper, we attempt to place a systematic framework that is relevant to key aspects in engineering evidence gathering for unintentional carbon monoxide poisoning cases. Such a framework aims to increase awareness of this process and relevant issues to help guide legal counsel and expert witnesses.

Stochastic modeling of short-term exposure close to an air pollution source in a naturally ventilated room: an autocorrelated random walk method

For an actively emitting source such as cooking or smoking, indoor measurements have shown a strong "proximity effect" within 1 m. The significant increase in both the magnitude and variation of concentration near a source is attributable to transient high peaks that occur sporadically-and these "microplumes" cause great uncertainty in estimating personal exposure. Recent field studies in naturally ventilated rooms show that close-proximity concentrations are approximately lognormally distributed. We use the autocorrelated random walk method to represent the time-varying directionality of indoor emissions, thereby predicting the time series and frequency distributions of concentrations close to an actively emitting point source. The predicted 5-min concentrations show good agreement with measurements from a point source of CO in a naturally ventilated house-the measured and predicted frequency distributions at 0.5- and 1-m distances are similar and approximately lognormal over a concentration range spanning three orders of magnitude. By including the transient peak concentrations, this random airflow modeling method offers a way to more accurately assess acute exposure levels for cases where well-defined airflow patterns in an indoor space are not available.

Modeling exposure close to air pollution sources in naturally ventilated residences: association of turbulent diffusion coefficient with air change rate

For modeling exposure close to an indoor air pollution source, an isotropic turbulent diffusion coefficient is used to represent the average spread of emissions. However, its magnitude indoors has been difficult to assess experimentally due to limitations in the number of monitors available. We used 30-37 real-time monitors to simultaneously measure CO at different angles and distances from a continuous indoor point source. For 11 experiments involving two houses, with natural ventilation conditions ranging from <0.2 to >5 air changes per h, an eddy diffusion model was used to estimate the turbulent diffusion coefficients, which ranged from 0.001 to 0.013 m² s⁻¹. The model reproduced observed concentrations with reasonable accuracy over radial distances of 0.25-5.0 m. The air change rate, as measured using a SF₆ tracer gas release, showed a significant positive linear correlation with the air mixing rate, defined as the turbulent diffusion coefficient divided by a squared length scale representing the room size. The ability to estimate the indoor turbulent diffusion coefficient using two readily measurable parameters (air change rate and room dimensions) is useful for accurately modeling exposures in close proximity to an indoor pollution source.