Exposure characterization and risk assessment of ultrafine particles from the blast furnace process in a steelmaking plant

Abstract

Objectives This study aimed to clarify the exposure characteristics and risks of ultrafine particles from the blast furnace process and to provide a reasonable control strategy for protecting the health of workers. Methods The blast furnace location of a steelmaking plant was selected as a typical investigation site. A membrane‐based sampling system was used to collect ultrafine particles to analyze their morphology and elemental compositions. A real‐time system was used to monitor the total number concentration (NC), total respirable mass concentration (MC), surface area concentration (SAC), and size distribution by number. The risk level of ultrafine particles was analyzed using the Stoffenmanager‐Nano model. Results The total NC, total MC, and SAC increased significantly relative to background concentrations after slag releasing started and decreased gradually after the activity stopped. The three highest total concentrations during slag releasing were 3‐10 times higher than those of the background or non‐activity period. The ultrafine particles were mainly gathered at 10.4 or 40 nm, and presented as lump‐like agglomerates. The metal elements (Al and Pt) in the ultrafine particles originated from slag and iron ore. The risk level of the ultrafine particles was high, indicating the existing control measures were insufficient. Conclusions The blast furnace workers are at high risk due to exposure to high levels of ultrafine particles associated with working activity and with a bimodal size distribution. The existing control strategies, including engineering control, management control, and personal protection equipment need to be improved.

RETRACTED: Health risks and respiratory intake of submicron particles in the working environment: A case study

“…Size distribution analysis revealed that the predominant size of the submicron particles in the workshop air during the activity period ranged between 30 and 200 nm. The size distribution of the submicron particles during the activity period was bimodal (Figure 3C), which is in agreement with our previous study that reported a bimodal size distribution of submicron particles in a steelmaking plant (Gao et al, 2021).…”

Exposure Concentrations and Inhalation Risk of Submicron Particles in a Gasoline Station—A Pilot Study

“…The variation in the size of personal NC during working time was bimodal with values of 40 and 60 nm (Figure 4b), which suggested the primary size of particles and the agglomerate state or aerosol nucleation. This is consistent with a previously reported bimodal size distribution of submicron particles in a steel-making plant [51].…”

“…For the worker selected in this study, the ADDs in the olfactory area and brain were 2.19 × 10 −7 mg/kg•d −1 and 4.35 × 10 −8 mg/kg•d −1 , respectively. Previous studies have reported on the effects of submicron particles in the brain [51,52]. Submicron particles that accumulate in the brain not only translocate and directly damage neural tissue but also affect autonomic function [52,53].…”

A new hybrid approach based on probability distribution and an improved machine learning for multivariate risk assessment

“…The conformance probability is as follow using equation (7), with 𝑚 → ∞, 𝑛 = 𝑇 𝑈 , and note that 𝜑(−∞) = 0:…”

“…The pig iron temperature, circular pressure, and hot wind temperature are the most important indicators of blast furnace production and hazard; so by only predicting the multivariate risk of those parameters can approximately control blast furnace process. Nowdays there are many ways for blast furnace risk assessment research, we can cite failure modes and effects analysis (FMEA) [17], ultrafine particle risk assessment in the blast furnace production process [7], hazardous amounts of cement based solidification process are produced when fly ash and blast furnace slag from municipal solid waste incinerators are combined [12], forecast of blast furnace gas production using data driven and mechanism techniques [11] and [16] and modeling and identifying metallurgical systems using multivariate statistical control and data mining techniques [7,8,10,19].…”