The Effect of Sampling Inlet Direction and Distance on Particle Source Measurements for Dispersion Modelling

Abstract

The source rate is the single most critical input parameter in dispersion models. Determining accurate source rates from workplace processes can be challenging due to interference with work operation and poorly known dilution between the outlet of the particle generator and the measurement point. In this work, we measured the aerosol source rate in a chamber with a steady release of TiO 2 particles generated by an aerosol brush generator. The number concentrations measured directly from the particle generator and in the source position near the source spanned three orders of magnitude depending on the relative location and orientation to the source. Moreover, a dispersion factor was calculated based on a single mode fit of the obtained source rates. The dispersion factor takes into account the dispersion and dilution occurring between the measurement point and the source outlet for subsequent modelling. The particle emission rates were implemented in a previously published multi-box aerosol dispersion model using a one-box layout. The modelled concentrations were compared with concentrations measured in three locations in the chamber. We found that using a dispersion factor of one, meaning that at-source dilution or dispersion was not accounted for, the modelled concentrations were 1 to 3 orders of magnitude lower than measured concentrations for all source rates except the source rates measured directly from the aerosol generator. When applying the calculated dispersion factor, thereby correcting the source rate for initial dilution and dispersion, the concentrations were within 0.5 to 2 times the measured concentrations suggesting the use of such a factor to correctly estimate the source rate, and hence the occupational exposure.

Modeling of High Nanoparticle Exposure in an Indoor Industrial Scenario with a One-Box Model

“…Because of this limitation, the assessment of the root mean squared logarithmic error (RMSLE) is proposed to not over-penalize differences between predicted and actual values, when concentrations are high [36]. This is because the RMSLE mainly takes into account the ratio of change, rather than the actual concentrations.…”

“…This would mean that, according to the ranges suggested by Jayjock et al (2011), the performance of the one-box model for the industrial scenario assessed in this work would be considered as comparable. In the literature, several authors have reported underestimations by the one-box model [31,35,36,57,58] as it assumes homogeneous concentrations through all the room. This is consistent with our results, even though the high airflows measured in the industrial scenario allowed us to assume adequate mixing, on average.…”

“…Emission rates are known to be the most critical exposure determinant, and thus should be calculated based on experimental data collected as close (physically) to the source as possible with known dilution and mixing. Otherwise, large differences may be detected depending on the location and position of the monitoring instrumentation [36]. These authors demonstrated that emission rates can be corrected by using an adjusting coefficient to compensate for unknown flow distributions.…”

“…Key challenges for model application are source characterization, local controls and air mixing rates [30,32,33,34,35,36]. In addition, there is a need to test model performance under real-world conditions.…”

“…However, as in the case of understanding exposure determinants in thermal spraying [6,7], emission rates must be quantified from measured concentrations using methods such as the convolution theorem or mathematical mass balance [27,38]. The use of estimated emission rates from actual monitored concentrations has been seen to provide good modeling results in laboratory studies [35,36,39]. However, literature related to emission rates of unintentional NP at industrial scale is limited [15,40].…”