Occupational inhalation exposure during surface disinfection-exposure assessment based on exposure models compared with measurement data

Abstract

Background: For healthcare workers, surface disinfections are daily routine tasks. An assessment of the inhalation exposure to hazardous substances, in this case the disinfectant´s active ingredients, is necessary to ensure workers safety. However, deciding which exposure model is best for exposure assessment remains difficult.

Objective: The aim of the study was to evaluate the applicability of different exposure models for disinfection of small surfaces in healthcare settings.

Methods: Measurements of the air concentration of active ingredients in disinfectants (ethanol, formaldehyde, glutaraldehyde, hydrogen peroxide, peroxyacetic acid) together with other exposure parameters were recorded in a test chamber. The measurements were performed using personal and stationary air sampling. In addition, exposure modelling was performed using three deterministic models (unsteady 1-zone, ConsExpo and 2-component) and one modifying-factor model (Stoffenmanager®). Their estimates were compared with the measured values using various methods to assess model quality (like accuracy and level of conservatism).

Results: The deterministic models showed overestimation predominantly in the range of two- to fivefold relative to the measured data and high conservatism for all active ingredients of disinfectants with the exception of ethanol. With Stoffenmanager® an exposure distribution was estimated for ethanol, which was in good accordance with the measured data.

Impact statement: To date, workplace exposure assessments often involve expensive and time consuming air measurements. Reliable exposure models can be used to assess occupational inhalation exposure to hazardous substances, in this case surface disinfectants. This study describes the applicability of three deterministic and one modifying-factor model for disinfection of small surfaces in healthcare settings, in direct comparison to measurements performed and will facilitate future exposure assessments at these workplaces.

Keywords: Exposure assessment; Exposure modelling; Inhalation exposure; Measurement data; Model quality; Surface disinfection.

Fig. 1. Surface disinfection and measurements.

A Performance of surface disinfection in the test chamber. B Exposure to disinfectant active ingredients was measured using a personal air sampling system (person and clothes rack/ dummy with vest) and stationary air sampling system on the stand. Photos taken by and shown in Wegscheider et al. [41].

Fig. 2. Visualised measured and modelled values of surface disinfection.

Visualisation of mean values of measured data from personal and stationary air sampling and modelled values of the unsteady 1-zone model, the ConsExpo model and the 2-component model for (A) ethanol (No. 1), B formaldehyde and glutaraldehyde (No. 2), C glutaraldehyde (No. 3) and D hydrogen peroxide and peroxyacetic acid (No 4).

Fig. 3. Selected scatter plots of measured to modelled data.

Measured data from ethanol (No. 1) of personal air sampling (▲) and stationary air sampling (x) plotted against the modelled data of (A) the unsteady 1-zone model and B ConsExpo (Tier 2 calculation) as well as measured data from hydrogen peroxide (No. 4) of personal air sampling (▲) and stationary air sampling (x) plotted against the modelled data of (C) ConsExpo (Tier 1 calculation) and D ConsExpo (Tier 2 calculation), shown as selected examples. Each scatter plot includes data for the three different sizes of the disinfected surface.