Assessment of exposure determinants and exposure levels by using stationary concentration measurements and a probabilistic near-field/far-field exposure model

Abstract

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m3 room, no local ventilation, and NF ventilation of 1.6 m3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the exposure level was 12.5% of the OELV.

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template

“…For predictive exposure assessment, particle emission rate from the chamber to the room is a requirement entailing information of air flow rates, besides the concentration measurements. Predictive exposure assessment requires knowledge of the exposure determinants, while source characterization requires measurements of concentration according to the process parametrization and analysis of mass flows [10]. Data availability and reliability following a tiered approach defines the precision of the modelled emission rates.…”

“…The NECID database is missing some of the most relevant contextual information considering worker exposure, such as material process/handling rate, production rates, emission control efficiency and random air flow at workstations. For example, one of the most relevant exposure determinants is the random air flow at the workstation diluting the concentrations after LEV [10]. For LEV, it is relevant to know if replacement air is extracted from the workstation or supplied elsewhere.…”

“…FEM data can also provide insights into the factors affecting process emissions, exposure levels and risks. These insights can be used to quantify the safe conditions of storage and use of NOs and identify efficient risk control techniques [10]. FEM data can be divided into three main categories related to process and material parameters, environmental conditions and worker behavior.…”

“…Quantifying emissions according to the process parametrization makes it possible to predict the process impact on work area concentration under different operational conditions. This allows conditions of use for any work scenario to be set by using a probabilistic exposure model [10]. This is currently the most promising approach for setting evidence-based conditions of use for processes and materials in the supply chain, which is one of the objectives in the update of REACH chemical legislation (DG ENV B2, DG GROW F1 "D. Evidence Base, Data collection and Better Regulation Instruments") [11].…”

Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

“…In this regard, the REACH regulation [1] requires estimation of human exposure by all relevant routes to determine the appropriate risk management measures and prevent excessive exposure. The EN 689:2018+AC:2019 [2] standard provides indications in this sense and recommends a basic exposure characterization in order to decide if personal exposure measurements are needed, which can be obtained by using adequate exposure models as shown in Koivisto et al [3]. Mass-balance models have been proposed by several authors as useful tools for indoor particle exposure prediction [4][5][6][7].…”