Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

4th International Conference on Safe Production and Use of Nanomaterials

De Ipiña, J M López^1 ; Vaquero, C.^1 ; Gutierrez-Cañas, C.^2 ; Pui, D.Y.H.^3

TECNALIA - Parque Tecnológico de Alava, Miñano

01510, Spain^1

UPV, EHU, Dpt. Chem. and Environ. Eng, University of the Basque Country, Alameda Urquijo s/n Street, Bilbao

48013, Spain^2

UMN-PTL, 111 Church St SE, Minneapolis

MN

55455, United States^3

关键词: Analysis and modelling;    Background signals;    Industrial scenarios;    Occupational exposure;    Potential difference;    Quantitative assessments;    Signal-noise ratio;    Stochastic signals;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/617/1/012003/pdf
DOI  :  10.1088/1742-6596/617/1/012003

来源: IOP

【 摘 要 】

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzersin industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2by cold pressing was performed.