Future Prospects of Occupational Exposure Modelling of Substances in the Context of Time-Resolved Sensor Data.
chemical
exposure
models
occupational
real-time
sensor
sensor networks
substances
time-resolved
workplace
Journal
Annals of work exposures and health
ISSN: 2398-7316
Titre abrégé: Ann Work Expo Health
Pays: England
ID NLM: 101698454
Informations de publication
Date de publication:
22 04 2021
22 04 2021
Historique:
received:
04
12
2019
revised:
02
09
2020
accepted:
01
10
2020
pubmed:
21
11
2020
medline:
18
5
2021
entrez:
20
11
2020
Statut:
ppublish
Résumé
This commentary explores the use of high-resolution data from new, miniature sensors to enrich models that predict exposures to chemical substances in the workplace. To optimally apply these sensors, one can expect an increased need for new models that will facilitate the interpretation and extrapolation of the acquired time-resolved data. We identified three key modelling approaches in the context of sensor data, namely (i) enrichment of existing time-integrated exposure models, (ii) (new) high-resolution (in time and space) empirical models, and (iii) new 'occupational dispersion' models. Each approach was evaluated in terms of their application in research, practice, and for policy purposes. It is expected that substance-specific sensor data will have the potential to transform workplace modelling by re-calibrating, refining, and validating existing (time-integrated) models. An increased shift towards 'sensor-driven' models is expected. It will allow for high-resolution modelling in time and space to identify peak exposures and will be beneficial for more individualized exposure assessment and real-time risk management. New 'occupational dispersion models' such as interpolation, computational fluid dynamic models, and assimilation techniques, together with sensor data, will be specifically useful. These techniques can be applied to develop site-specific concentration maps which calculate personal exposures and mitigate worker exposure through early warning systems, source finding and improved control design and control strategies. Critical development and investment needs for sensor data linked to (new) model development were identified such as (i) the generation of more sensor data with reliable sensor technologies (achieved by improved specificity, sensitivity, and accuracy of sensors), (ii) investing in statistical and new model developments, (iii) ensuring that we comply with privacy and security issues of concern, and (iv) acceptance by relevant target groups (such as employers and employees) and stimulation of these new technologies by policymakers and technology developers.
Identifiants
pubmed: 33215191
pii: 5993016
doi: 10.1093/annweh/wxaa102
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
246-254Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.