Research progress in online monitoring technologies for workplace dust concentration
10.20001/j.issn.2095-2619.20250820
- VernacularTitle:工作场所粉尘浓度在线监测技术研究进展
- Author:
Qiangzhi GUO
1
;
Yuntao MU
;
Jinning YU
;
Chuntao GE
;
Chen WANG
;
Zhiguo ZHOU
;
Xue JIANG
;
Yazhen WANG
;
Jinling LIU
;
Di LIU
;
Shibiao SU
Author Information
1. SINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Chemical Safety,Qingdao, Shandong 266104, China
- Publication Type:Review
- Keywords:
Dust;
Online monitoring;
Respirable dust;
Pneumoconiosis;
Research progress
- From:
China Occupational Medicine
2025;52(4):472-476
- CountryChina
- Language:Chinese
-
Abstract:
Occupational pneumoconiosis remains the most common occupational disease in China, with occupational mineral dust exposure being its primary causative factor. Although national standards for online monitoring and early warning systems of coal mine dust concentrations have been established, national occupational health standards for rapid and online monitoring of dust concentration and particle size distribution in other industries are still limited. Among dust concentration sensor technologies, the light scattering method is the preferred choice for online dust monitoring owing to its wide measurement range and low cost. The beta-ray absorption method is mature but highly sensitive to humidity. The electrostatic induction method offers high sensitivity, simple structure, and low maintenance costs but exhibits high errors in low-concentration dust monitoring. The tapered element oscillating microbalance method is highly sensitive but costly. Multi-sensor data fusion technology can improve monitoring reliability, however, mature domestic products are not yet available. For monitoring dust particle size distribution, sieving and sedimentation methods are cumbersome. The aerodynamic method shows broad prospects in the online monitoring of respirable dust but has obvious measurement errors for larger dust particles. The use of optical measurement method is limited by dust morphology and is not suitable for monitoring coal dust particle size distribution. The electrical mobility method is primarily applicable to submicron dust. Future research should focus on promoting the application of monitoring technology for respirable dust particle size distribution in online monitoring of industrial dust. By integrating Internet of Things, data mining, and artificial intelligence technologies, along with multi-sensor data fusion and numerical simulation, dust concentration prediction models can be established to achieve accurate dust concentration monitoring and early warning of exceedances. The advancements of technologies will provide scientific support for the assessment of industrial dust hazards and the prevention and control of occupational pneumoconiosis.
