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Review of Recent Researches on Occupational Health Assessment in China
Available online at www.sciencedirect.com
Procedia Engineering 43 (2012) 464 – 471
International Symposium on Safety Science and Engineering in China, 2012 (ISSSE-2012)
Review of Recent Researches on Occupational Health Assessment in China Minyan Lia,b, Deyin Huangb , Mao Liua,* a
b
Centre for Urban Public Safety Research,Nankai University,No.94, Weijin Road,Nankai District,Tianjin,300071,China Institute of Occupational Health,Tianjin Bohai Chemical Industry Group Co.Ltd,No.2, Shashi Dao,Heping District,Tianjin,300051,China
Abstract During recent years, some progress in the technology of occupational health assessment in our country have been achieved. The Centre for Urban Public Safety Research of Nankai University has contributed to research on the quantitative risk assessment. This paper reviews the centre’s achievements of quantitative risk assessment about acute intoxication and chronic diseases, such as carcinogenesis and noncarcinogenesis caused by occupational exposure, which could provide scientific basis for occupational risk assessment and health management.
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology. . Keywords: occupational risk; acute intoxication; chronic exposure; physiologically-based pharmacokinetic (PBPK) model; dose-response relationship
1. Introduction Health risk assessment researches in China began from 1990s, which are basically about introducing and applying other countries’ results. Currently, the health risk assessment technology in China is based on the four-step process recommended by USEPA, focusing on monitoring of exposure and epidemiological study. The environmental monitoring concentrations are usually used as the dose data of dose-response relationship. In recent years, the Centre for Urban Public Safety Research of Nankai University has developed a lot of cooperations with the Institute of Occupational Health from Tianjin Bohai Chemical Industry Group Co. Ltd in doing a series of researches on acute intoxication lethal and chronic diseases risk assessment, advancing the technology of exposure assessment, doseresponse model, risk characterization and uncertainty calculation.
* Corresponding author. Tel.: +1-360-212-2038; fax: +0-862-350-7765. E-mail address: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.080
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Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
2. Acute intoxication risk assessment 2.1. Acute intoxication accident risk assessment method and procedure Li Wang, Deyin Huang, Huixiang Chen et al. [1-5] introduced the method and procedure for accidental acute poisoning risk assessment, and analyzed the risk of the acute intoxication accident caused by chlorine gas occurring in the chemical plant as a case study. This acute risk analysis procedure consists of three parts: (1) hazards identification, (2) probability and consequence assessment, and (3) individual and social risk calculation. In the probability assessment part, accidents can be divided into big accidents, small accidents and medium accidents according to the leakage, thus the accident probability could be determined. Then, take the advantage of Eq. (1) to calculate the acute intoxication lethal probability.
P
1 (2S )1/2
³
5
f
exp(
u2 )du 2
(1)
Where, P is the lethal probability, and u is the variable of integration. The probability variable Y is
Y
A B ln((22.4 X / M )n t )
(2)
Y is the random variable submitting Gaussian distribution, with mean=5, standard deviation=1; the constants A, B and n of different chemicals which depend on the toxic nature have different values. X, M and t are contact poison concentration under standard condition (mg/m3), mole mass of the substance (g/mol), and contact poison time (min), respectively. Since Y and P have direct relations, they could be interconverted by table lookup. The risk consequence assessment includes the determination criterion of the involved area of acute poisoning caused by toxic substances (i.e. toxicity assessment index) and the concentration distribution of toxic substances diffusion. In our country,we mainly adopts the minimum lethal concentration (MLC) as foundation to divide the poisoning level. The leakage and diffusion analysis of the toxic substances includes leakage source type analysis and concentration analysis, and the latter one can use different mathematical model according to different characteristics. Finally, on the basis of quantitative risk analysis, the average individual risk can be expressed as the sum of lethal probabilities of all the consequences.
Average individual risk
n
¦fN i
i 1
i
/ PT (3)
In Eq. (3), n refers to the number of the varieties of consequences. fi is the frequency that the consequence i occurs and Ni is the death count caused by i. PT is the number of people in interest. Social risk is expressed as the frequency F of all the accidents with at least N fatalities in unit time in the entire industrial hazards area considered. The formula to calculate the social risk can be written as:
F (N )
¦ P[r (i)] n
(4)
Where, rn (i ) t N . F˄N˅ in the formula above implies summing up the probability of all the accidents which cause at least N fatalities. 2.2. Health risk assessment for acute exposure In the exposure stage, Jie Shi [6-7] first did research on the diffusion of poison gas on site in her thesis. With the theoretical study and computer simulation, she took advantage of SLAB software developed by US Department of Energy (USDOE) to model the diffusion process of the poison gas. Based on this, she transferred the gas diffusion model into an exposure dose model
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Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
to compute the exposure dose distribution based on diffusion simulation. And then, she introduced a PBPK model to calculate the toxicant concentration distribution in human body after metabolism. Trichloroethylene (TCE) was chosen as the substance for case study. After setting the accidental leaking scenario, she simulated and computed the exposure doses, and tried to find out the distribution of the internal doses by using the PBPK model when exposing on downwind site of the leaking source. Then she transferred the threshold doses of the two acute intoxication criterions (i.e. Emergency Response Planning Guidelines (ERPGs) and Acute Exposure Guideline Levels (AEGL)) into internal doses. After summarizing the toxicity test data of TEC, she analyzed the regressions to get the dose-response relationships under different exposure parameters for rats and human (Eq. (5)), and found the risk distributions on the downwind site of the leaking sources, based on which the emergency rescue was discussed.
P(Y t s C, T )
H {D s E1 f1 (C ) E2 f 2 (T )}
(5)
Where, P refers to the probability, with the level of severe Y (e.g. s=0, 1, 2), concentration C and exposure time T. The interceptα s,β 1 andβ 2 are the parameters of the regression analysis, of whichα s is intercept. β 1 andβ 2are coefficients of concentration and time. H is a probability function with the value between 0 and 1. The inverse function of H used to estimate parameters is called correlation function, which can be a logistic function, a probability density function or a logarithm-logarithm function. f1 and f2 are functions to transfer the concentration and time, generally in a 10-based logarithm form. 3. Chronic exposure risk assessment 3.1. Semi-quantitative risk assessment In the process of occupational risk assessment, Jing Zhang et.al [8] evaluated the health risk by a semi-quantitative health risk classified method, which contained three steps. (1) Hazards identification and assessment, (2) Exposure assessment, and (3) Risk ranking and evaluation. In the first step, it is necessary to indentify the substances used and produced, their location, the affected population, and their peril. Hazards rank can be determined based on the toxic effect or the acute intoxication. The larger value should be selected if the two are different. In the exposure assessment, the hazards of a substance depend on its characters and exposure factors, e.g. the degree, frequency, time and exposure pathways. According to the factors above, we can determine the exposure index and calculate the exposure rank by Eq. (6).
ER [ EI1 u EI 2 u ... u EI n ]1/ n
(6)
Where n denotes the number of the exposure factors. In the risk ranking and evaluation step, risk can be expressed in the form below.
Risk [ HR u ER]1/2
(7)
Based on Eq. (7), risk has 5 ranks, the value of which is an integer. Risk evaluation is used to find whether the risk is acceptable, which measures should be taken and their priority. 3.2. Quantitative risk assessment In the later health risk assessment researches of the centre, the essential framework is based on the four-stage method recommended by USEPA to evaluate the risk quantitatively, including (1) the risk identification, (2) dose-response assessment, (3) exposure assessment, and (4) the risk characterization.
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Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
3.2.1 The PBPK model and solution Yang Wang [9] used the PBPK model to describe the processes of absorption, distribution and metabolism after inhaling. In this model, the body is divided into the following compartments: alveolar gas-exchange region, fat, slowly perfused tissues, rapidly perfused tissues, liver and bone marrow. These parameters of the model can be described mathematically by using the mass-balance equations. To solve the equations, the Excel software is available. To do research on the chronic carcinogenic risk assessment quantitative method for benzene exposure in a styrene production project, Qian Zhang and Jing Zhang et.al [10] selected the ERDEM software to describe the PBPK model of benzene to get the internal dose. Qian Zhang [11-12] introduced the PBPK model to describe the metabolism of 1,3-butadiene and its metabolites in vivo in her thesis. By using the ERDEM software, she simulated the PBPK model for 1,3-butadiene to explain how to use the software to establish the model and get the simulated results. Minyan Li [13] used the MCSim software for Monte Carlo simulation to establish the PBPK model for benzene. Meanwhile, she considered the enzyme mechanism and interacts of the variety metabolites of benzene. 3.2.2 Dose-response relationship In most studies of the centre, the multistage model (also called the linear multistage (LMS) model) recommended by USEPA is chosen for the dose-response model about the chronic exposure of benzene and 1, 3-butadiene. K
F (d ) [1 exp(¦ ai d i )] i 0
(8)
Where d denotes dose; K is the number of stages that normal cells should go through before they turned into malignant cells. On the carcinogenic risk assessment of 1, 3-butadiene, Qian Zhang [11] did some epidemiological queue researches to find the dose-response relationship which caused death by leukemia, analyzed the carcinogenic effects to occupational population exposed, and then characterized the risk. Moreover, based on the bio-experiment, she used the multistage model to analyze the probability that the tumors occurred in the population exposed to 1, 3-butadiene. Since the mechanism of carcinogenesis had still not been clear, there was no accurate model to describe the progress. To determine the dose-response model, Minyan Li [13] compared the multistage model and the model-free methods. The use of the latter could reduce the uncertainties in the process of extrapolation from the high doses to the low, and could estimate the upper limit of the carcinogenic risk. 3.2.3 Exposure assessment Yunlong Zhang and Yang Yang et al. [14] simulated the benzene vapor concentrations on site, examined the risk for workers exposed, and then applied the BP neural network method and the statistical data to establish quantitative risk assessment expressions using MATLAB. By comparing the measured data and simulated results, the research realized the feasibility of the occupational benzene exposure risk assessment based on BP neural network. Yang Wang [15] introduced the functions of the release rates and mathematical models of indoor pollutants dispersion in detail. The most common models included well-mixed room model, two-region model, and Eddy current model, etc. She used the two-region model for exposure assessment, and contrasted the simulation values to the actual measured values. In the benzene exposure assessment, Liming Hu and Minyan Li [13] simulated the concentration distribution by using FLUENT software based on the environmental monitoring data, and finally computed the external doses for workers exposed to benzene on several joint exposure conditions. At last, the simulated statistical data were treated by using SPSS to get the external dose distribution. 3.2.4 Uncertainty analysis In the studies of Yang Wang, Jing Zhang and Qian Zhang et al. [9,11,16], the Crystal Ball software was applied to do Monte Carlo simulation to research on the uncertainties. Yang Wang and Li Wang then found out which distribution the simulated carcinogenic risk obeyed.
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Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
Yang Wang [15] applied the Monte Carlo simulated method to find the distribution range to reduce the uncertainty in the assessment results and to do sensitive analysis to see how much the variables influenced the benzene concentrations in working place. Minyan Li [13] used MCsim software to forecast the internal dose distribution for the workers exposed to benzene based on the uncertainties of the metabolism parameters and the inhaled doses in a PBPK model. In a waste incineration dioxin health risk assessment, Jie Yang [17] used Crystal Ball to do Monte Carlo simulation and calculated the carcinogenic and non-carcinogenic risk, because uncertainties existed in the related factors (the concentration of contaminant in air and soil, the four pathways that dioxin accessed the body, including soil intake, air inhalation, skin exposure and food ingestion) in the expressions of the dioxin concentration. 4. The odor assessment To assess the odor effect of the hydrogen sulfide, methyl mercaptan and ethyl mercaptan in the condensate oil refining factory, Liming Hu [18] applied the odor intensity method and the degree-annoyance method to establish the mathematical relationship between the concentration and the odor intensity or degree of annoyance, and then visualized by FLEUNT. The odor intensity could be calculated by the Weber-Fechner expression.
I
kw lg(C / C0 ) const
(9)
Where I is the dimensionless odor intensity with the parameter of the Weber-Fechner expression kw, odor concentration C and detection threshold (1 odor unit) C0. const is the parameter related to the mean intensity level determined by the best fit curve of each odor. And the total odor intensity Y is
Y
log(¦10 yi )
(10)
Where, yi is the individual odor intensity for the substance i. In the assessment of degree of annoyance, James A. Nicell proposed a 10-degree method. The dose-response expression for degree of annoyance is expressed as Eq. (11).
A
10 1D C 1 ( 5 AU ) D C
(11)
The degree of annoyance A (in annoyance unit au) indicates the degree of annoyance of population ranging from 0 to 10. C5AU corresponds to the odorant concentration where the population annoyance has a value of 5 au. The dimensionless parameter α is the persistence. For mixtures of hydrogen sulfide, methyl mercaptan and ethyl mercaptan, the total degree of annoyance Am is
Am 1 (
C1 C5 AU 1
10 1D 1 )D C3 C2 C5 AU 2 C5 AU 3
(12)
Where C5AU1, C5AU2, C5AU3 are the concentrations of hydrogen sulfide, methyl mercaptan and ethyl mercaptan (mg/m3) corresponding the annoyance value of 5 au, respectively. And C1, C2, C3 are the concentrations of them (mg/m3), respectively. Liming Hu used FLUENT to realize 3D visualization of odor intensity and degree of annoyance (see Fig.1 and Fig.2).
Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
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Fig. 1. Total odor intensity distribution of plant area under sampling port leakage
Fig. 2. Total degree of annoyance distribution of plant area under normal operation condition leakage
5. Occupational health management and protective measures in workshops Yingjun Jiang [19] did research on the prevention of the occupational exposure hazards factors of the etching area of the semiconductor manufacturing plants during preventive maintenance. The research consisted of three parts: at-the-source, alongthe-path and at-the-workers. The first research focused on the source control to optimize the pump-purge cycles and reduce the hazards emission before opening process chambers. The research method was using portable gas detectors to monitor the moment hazardous gases concentration in different pump-purge cycles and then established relationship models between concentration and pump-purge cycles. The second research was identifying the possible hazardous gases emitting area and defining the forbidden area for preventing the exposure of non-maintenance personnel during chamber maintenance. The research was to set up the sampling points and the models of the relationship between distance and hazardous gases concentration. The third research was designing the local hood to effectively remove the hazardous gases emission and then monitoring the hazards abatement rate. The forth research was standardizing the personal breathing equipment for etching chambers maintenance. After reviewing the experimental data, international codes, the characteristics of hazards, historical incident/fatal records, effectiveness and cost, he defined the Powered Air-Purifying Respirator (PAPR) to be the best option for etching process tool maintenance. He then [20] studied the organization, responsibilities, logics and operation principles of US Incident Command System (ICS), referred to the responsibilities of semiconductor business organization chart, analyzed and summarized the gaps between them, then planned and established the semiconductor manufacturing plants’ emergency response organization and flow chart. This emergency response concept and flow chart was helpful for mitigating the business impact of incidents.
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Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
On the basis of Yingjun Jiang’s study, Qing Mu [21] started from preventative angle, conducted a research on the diffusion of toxic substances escaped from clean room during preventive maintenance of metal etch reaction chamber of the semiconductor manufacturing plants, and then discussed occupational exposure of toxic substances in clean room. Qing Mu combined numeric simulation method of interior air distribution with the diffusion of point source, chose metal etching area where the effusion happened as original simplified geometric object, then set boundary conditions and installed corresponding physical model. Using CFD software FLUENT, he simulated the diffusion of toxic substance and worked out the scope of diffusion (see Fig 3).
Fig. 3. Division of hazardous area
Through this method, Qing Mu assessed quantitatively the relationship between the frequency of vacuum sweep and the concentration of HCl in the most sensitive position. The above studies focused on the hazards of toxic substances while ignored anoxia problem because of excessive nitrogen. For this problem, Binbin Wang [22] used CFD to simulate the diffusion of nitrogen in clean room and get the profile of O2 (see Fig 4).
Fig. 4. The 3D distribution cloud chart of volume fraction of O2 in the clean room
Binbin Wang simulated three different working conditions in the encapsulation workshop of a semiconductor plant: normal condition, extreme condition and double condition. Preprocess software GAMBIT was used to establish threedimension model of the clean room and generate mesh. Then, corresponding parameters and models were set in the solver FLUENT to simulate three conditions above. Lastly, TECPLOT was used to generate velocity vector diagrams, concentration distribution diagrams of N2 and O2, and hypoxia area diagrams. By analyzing the diagrams above, diffusion rules of N2 and changing conditions of hypoxia area could be found. Binbin Wang found that increasing the rate of air-inlet could not solve the hypoxia problem in the clean room, even might be counterproductive. Nevertheless, changing the position of air-outlet could reduce the area of hypoxia effectively. This result could contribute to the design modification of clean room and occupational health management of semiconductor plant.
Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
6. Summary and outlook In recent years, some progress in the technology of occupational health assessment in our country have been achieved, especially, Jie Shi, Yang Wang and Qian Zhang have established a related perfect method, but there is still room for improvement. (1) Lack of occupational data of epidemiological study and biological monitoring; (2) Lack of the parameters in PBPK model for people in China; (3) Further discussions about the dose-response relationship for chronic carcinogenic risk; (4) More researches on the PBPK model simulation software (e.g. ERDEM), the Monte Carlo simulation software (e.g. MCSim), the exposure concentration simulation software (e.g. FLUENT) and the related mathematical diffusion models; (5) Further researches on fit parameters in the expressions of odor intensity and degree of annoyance for different odorants in odor assessment. Acknowledgements Professor Liu, Deyin Huang and the fellow classmates of the Centre for Urban Public Safety Research of Nankai University are gratefully acknowledged. References [1] Deyin Huang, Mao Liu, Li Wang et al., 2007. Application of the Risk Analysis in the Pre-evaluation for the Occupational Acute Intoxication Accident, Chinese Journal of Industrial Medicine 3, p. 200-202. [2] Deyin Huang, Mao Liu, Yali Bo et al., 2008. Risk Analysis of Acute Poisoning of a New Oil Refining Project in Petrochemical Company and Suggestion of Countermeasures, Occupational Health and Emergency Rescue 26, p. 141-145. [3] Deyin Huang, Mao Liu, Jinyan Sun et al., 2007. Study on Risk of Acute Poisoning Accident in Synthetic Ammonia Equipment, Occupational Health and Emergency Rescue 25, p. 115-119. [4] Deyin Huang, Mao Liu, 2010. Risk Analysis Technology for the Occupational Acute Intoxication Accident based on Computer S imulation, Occupational Health and Emergency Rescue 28, p. 251-254. [5] Deyin Huang, Mao Liu, Qian Sun, 2010. Introduction of Areal Locations of Hazardous Atmosphere Software and its Application in the Simulation of Acute Intoxication Accident, Chinese Journal of Industrial Medicine 23, p. 238-240. [6] Jie Shi, Mao Liu, 2009. Categorically Regressive Analysis on the Acute Inhalation Toxicity of Tetrachloroethylene, Journal of Safety and Environment 49, p. 5-9. [7] Jie Shi, Analysis of Acute Health Risk Brought by Accidental Toxic Gas Leakage [D], Nankai University, 2009. [8] Deyin Huang, Jing Zhang, Mao Liu, 2009. “Application of a Health Risk Classification Method to Assessing Occupational Hazard in China,” Bioinformatics and Biomedical Engineering, ICBBE 2009, 3rd International Conference, 11-13 June 2009, pp.1-5. [9] Yang Wang, Mao Liu, Deyin Huang, 2009. “Health risk assessment for benzene occupational exposure using physiologically based pharmacokinetic model and dose-response model,” Bioinformatics and Biomedical Engineering, ICBBE 2009,3rd International Conference,11-13 June 2009, pp.1-4. [10] Deyin Huang, Qian Zhang, Zhang Jing et al., 2011. “Modelling of Occupational Exposure to Benzene and Health Risk of Workers in Styrene Production,” Bioinformatics and Biomedical Engineering, ICBBE 2011, 5th International Conference ,10-12 May 2011,pp. 1-5. [11] Qian Zhang, Occupational Health Risk Assessment on 1, 3-Butadiene [D], Nankai University, 2011. [12] Deyin Huang, Qian Zhang, Mao Liu, 2011, Occupational exposure assessment and carcinogenic risk simulation in benzene workers. Chinese Journal of Industrial Medicine 24. [13] Minyan Li, MCSim-based Occupational Health Risk Assessment on Low-dose Benzene [D], Nankai University, 2012. [14] Yunlong Zhang, Mao Liu, Yang Yang, 2009. Research of Benzene Occupation Exposure Based on BP Neural Network and MATLAB, China Public Security 2, p. 131-134. [15] Yang Wang, Study of occupational health risk assessment of benzene exposure [D], Nankai University, 2009. [16] Mao Liu, Deyin Huang, Li Wang et al., 2010. “The Application of the Crystal Ball and Monte Carlo Simulation Method in the Carcinogenic Risk Assessment of Benzene,” Proceedings of the Annual Occupational Health Conference. [17] Jie Yang, Mao Liu, Minyan Li, Use of Monte Carlo Simulation for health risk assessment of Dioxin/Fs produced by municipa l solid waste incineration, Journal of Safety and Environment 11, p. 234-238. [18] Liming Hu, FLUENT Simulation and Assessment of Odour in the Process of Condensate Gas Refinery [D], Nankai University, 2012. [19] Yingjun Jiang, Occupational hazard prevention research for etching area of semiconductor manufacturing plant during preventive maintenance period [D], Nankai University, 2008. [20] Yingjun Jiang, Mao Liu, 2007, Comparison Study of Emergency Response Organization and Operation Flowchart for Semiconductor Manufacturing Plants Based on the Incident Command System of USA. China Public Security 2, p. 110-116. [21] Qing Mu. Numerical simulation of hazardous substance diffusion and prevention and control of occupational hazard in clean room of chip plants [D], Nankai University, 2009. [22] Binbin Wang, Research of oxygen concentration in the clean room of semiconductor plants based on FLUENT simulation [D], Nankai University, 2011.
471
Procedia Engineering 43 (2012) 464 – 471
International Symposium on Safety Science and Engineering in China, 2012 (ISSSE-2012)
Review of Recent Researches on Occupational Health Assessment in China Minyan Lia,b, Deyin Huangb , Mao Liua,* a
b
Centre for Urban Public Safety Research,Nankai University,No.94, Weijin Road,Nankai District,Tianjin,300071,China Institute of Occupational Health,Tianjin Bohai Chemical Industry Group Co.Ltd,No.2, Shashi Dao,Heping District,Tianjin,300051,China
Abstract During recent years, some progress in the technology of occupational health assessment in our country have been achieved. The Centre for Urban Public Safety Research of Nankai University has contributed to research on the quantitative risk assessment. This paper reviews the centre’s achievements of quantitative risk assessment about acute intoxication and chronic diseases, such as carcinogenesis and noncarcinogenesis caused by occupational exposure, which could provide scientific basis for occupational risk assessment and health management.
© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Capital University of Economics and Business, China Academy of Safety Science and Technology. . Keywords: occupational risk; acute intoxication; chronic exposure; physiologically-based pharmacokinetic (PBPK) model; dose-response relationship
1. Introduction Health risk assessment researches in China began from 1990s, which are basically about introducing and applying other countries’ results. Currently, the health risk assessment technology in China is based on the four-step process recommended by USEPA, focusing on monitoring of exposure and epidemiological study. The environmental monitoring concentrations are usually used as the dose data of dose-response relationship. In recent years, the Centre for Urban Public Safety Research of Nankai University has developed a lot of cooperations with the Institute of Occupational Health from Tianjin Bohai Chemical Industry Group Co. Ltd in doing a series of researches on acute intoxication lethal and chronic diseases risk assessment, advancing the technology of exposure assessment, doseresponse model, risk characterization and uncertainty calculation.
* Corresponding author. Tel.: +1-360-212-2038; fax: +0-862-350-7765. E-mail address: [email protected]
1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.08.080
465
Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
2. Acute intoxication risk assessment 2.1. Acute intoxication accident risk assessment method and procedure Li Wang, Deyin Huang, Huixiang Chen et al. [1-5] introduced the method and procedure for accidental acute poisoning risk assessment, and analyzed the risk of the acute intoxication accident caused by chlorine gas occurring in the chemical plant as a case study. This acute risk analysis procedure consists of three parts: (1) hazards identification, (2) probability and consequence assessment, and (3) individual and social risk calculation. In the probability assessment part, accidents can be divided into big accidents, small accidents and medium accidents according to the leakage, thus the accident probability could be determined. Then, take the advantage of Eq. (1) to calculate the acute intoxication lethal probability.
P
1 (2S )1/2
³
5
f
exp(
u2 )du 2
(1)
Where, P is the lethal probability, and u is the variable of integration. The probability variable Y is
Y
A B ln((22.4 X / M )n t )
(2)
Y is the random variable submitting Gaussian distribution, with mean=5, standard deviation=1; the constants A, B and n of different chemicals which depend on the toxic nature have different values. X, M and t are contact poison concentration under standard condition (mg/m3), mole mass of the substance (g/mol), and contact poison time (min), respectively. Since Y and P have direct relations, they could be interconverted by table lookup. The risk consequence assessment includes the determination criterion of the involved area of acute poisoning caused by toxic substances (i.e. toxicity assessment index) and the concentration distribution of toxic substances diffusion. In our country,we mainly adopts the minimum lethal concentration (MLC) as foundation to divide the poisoning level. The leakage and diffusion analysis of the toxic substances includes leakage source type analysis and concentration analysis, and the latter one can use different mathematical model according to different characteristics. Finally, on the basis of quantitative risk analysis, the average individual risk can be expressed as the sum of lethal probabilities of all the consequences.
Average individual risk
n
¦fN i
i 1
i
/ PT (3)
In Eq. (3), n refers to the number of the varieties of consequences. fi is the frequency that the consequence i occurs and Ni is the death count caused by i. PT is the number of people in interest. Social risk is expressed as the frequency F of all the accidents with at least N fatalities in unit time in the entire industrial hazards area considered. The formula to calculate the social risk can be written as:
F (N )
¦ P[r (i)] n
(4)
Where, rn (i ) t N . F˄N˅ in the formula above implies summing up the probability of all the accidents which cause at least N fatalities. 2.2. Health risk assessment for acute exposure In the exposure stage, Jie Shi [6-7] first did research on the diffusion of poison gas on site in her thesis. With the theoretical study and computer simulation, she took advantage of SLAB software developed by US Department of Energy (USDOE) to model the diffusion process of the poison gas. Based on this, she transferred the gas diffusion model into an exposure dose model
466
Minyan Li et al. / Procedia Engineering 43 (2012) 464 – 471
to compute the exposure dose distribution based on diffusion simulation. And then, she introduced a PBPK model to calculate the toxicant concentration distribution in human body after metabolism. Trichloroethylene (TCE) was chosen as the substance for case study. After setting the accidental leaking scenario, she simulated and computed the exposure doses, and tried to find out the distribution of the internal doses by using the PBPK model when exposing on downwind site of the leaking source. Then she transferred the threshold doses of the two acute intoxication criterions (i.e. Emergency Response Planning Guidelines (ERPGs) and Acute Exposure Guideline Levels (AEGL)) into internal doses. After summarizing the toxicity test data of TEC, she analyzed the regressions to get the dose-response relationships under different exposure parameters for rats and human (Eq. (5)), and found the risk distributions on the downwind site of the leaking sources, based on which the emergency rescue was discussed.
P(Y t s C, T )
H {D s E1 f1 (C ) E2 f 2 (T )}
(5)
Where, P refers to the probability, with the level of severe Y (e.g. s=0, 1, 2), concentration C and exposure time T. The interceptα s,β 1 andβ 2 are the parameters of the regression analysis, of whichα s is intercept. β 1 andβ 2are coefficients of concentration and time. H is a probability function with the value between 0 and 1. The inverse function of H used to estimate parameters is called correlation function, which can be a logistic function, a probability density function or a logarithm-logarithm function. f1 and f2 are functions to transfer the concentration and time, generally in a 10-based logarithm form. 3. Chronic exposure risk assessment 3.1. Semi-quantitative risk assessment In the process of occupational risk assessment, Jing Zhang et.al [8] evaluated the health risk by a semi-quantitative health risk classified method, which contained three steps. (1) Hazards identification and assessment, (2) Exposure assessment, and (3) Risk ranking and evaluation. In the first step, it is necessary to indentify the substances used and produced, their location, the affected population, and their peril. Hazards rank can be determined based on the toxic effect or the acute intoxication. The larger value should be selected if the two are different. In the exposure assessment, the hazards of a substance depend on its characters and exposure factors, e.g. the degree, frequency, time and exposure pathways. According to the factors above, we can determine the exposure index and calculate the exposure rank by Eq. (6).
ER [ EI1 u EI 2 u ... u EI n ]1/ n
(6)
Where n denotes the number of the exposure factors. In the risk ranking and evaluation step, risk can be expressed in the form below.
Risk [ HR u ER]1/2
(7)
Based on Eq. (7), risk has 5 ranks, the value of which is an integer. Risk evaluation is used to find whether the risk is acceptable, which measures should be taken and their priority. 3.2. Quantitative risk assessment In the later health risk assessment researches of the centre, the essential framework is based on the four-stage method recommended by USEPA to evaluate the risk quantitatively, including (1) the risk identification, (2) dose-response assessment, (3) exposure assessment, and (4) the risk characterization.
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3.2.1 The PBPK model and solution Yang Wang [9] used the PBPK model to describe the processes of absorption, distribution and metabolism after inhaling. In this model, the body is divided into the following compartments: alveolar gas-exchange region, fat, slowly perfused tissues, rapidly perfused tissues, liver and bone marrow. These parameters of the model can be described mathematically by using the mass-balance equations. To solve the equations, the Excel software is available. To do research on the chronic carcinogenic risk assessment quantitative method for benzene exposure in a styrene production project, Qian Zhang and Jing Zhang et.al [10] selected the ERDEM software to describe the PBPK model of benzene to get the internal dose. Qian Zhang [11-12] introduced the PBPK model to describe the metabolism of 1,3-butadiene and its metabolites in vivo in her thesis. By using the ERDEM software, she simulated the PBPK model for 1,3-butadiene to explain how to use the software to establish the model and get the simulated results. Minyan Li [13] used the MCSim software for Monte Carlo simulation to establish the PBPK model for benzene. Meanwhile, she considered the enzyme mechanism and interacts of the variety metabolites of benzene. 3.2.2 Dose-response relationship In most studies of the centre, the multistage model (also called the linear multistage (LMS) model) recommended by USEPA is chosen for the dose-response model about the chronic exposure of benzene and 1, 3-butadiene. K
F (d ) [1 exp(¦ ai d i )] i 0
(8)
Where d denotes dose; K is the number of stages that normal cells should go through before they turned into malignant cells. On the carcinogenic risk assessment of 1, 3-butadiene, Qian Zhang [11] did some epidemiological queue researches to find the dose-response relationship which caused death by leukemia, analyzed the carcinogenic effects to occupational population exposed, and then characterized the risk. Moreover, based on the bio-experiment, she used the multistage model to analyze the probability that the tumors occurred in the population exposed to 1, 3-butadiene. Since the mechanism of carcinogenesis had still not been clear, there was no accurate model to describe the progress. To determine the dose-response model, Minyan Li [13] compared the multistage model and the model-free methods. The use of the latter could reduce the uncertainties in the process of extrapolation from the high doses to the low, and could estimate the upper limit of the carcinogenic risk. 3.2.3 Exposure assessment Yunlong Zhang and Yang Yang et al. [14] simulated the benzene vapor concentrations on site, examined the risk for workers exposed, and then applied the BP neural network method and the statistical data to establish quantitative risk assessment expressions using MATLAB. By comparing the measured data and simulated results, the research realized the feasibility of the occupational benzene exposure risk assessment based on BP neural network. Yang Wang [15] introduced the functions of the release rates and mathematical models of indoor pollutants dispersion in detail. The most common models included well-mixed room model, two-region model, and Eddy current model, etc. She used the two-region model for exposure assessment, and contrasted the simulation values to the actual measured values. In the benzene exposure assessment, Liming Hu and Minyan Li [13] simulated the concentration distribution by using FLUENT software based on the environmental monitoring data, and finally computed the external doses for workers exposed to benzene on several joint exposure conditions. At last, the simulated statistical data were treated by using SPSS to get the external dose distribution. 3.2.4 Uncertainty analysis In the studies of Yang Wang, Jing Zhang and Qian Zhang et al. [9,11,16], the Crystal Ball software was applied to do Monte Carlo simulation to research on the uncertainties. Yang Wang and Li Wang then found out which distribution the simulated carcinogenic risk obeyed.
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Yang Wang [15] applied the Monte Carlo simulated method to find the distribution range to reduce the uncertainty in the assessment results and to do sensitive analysis to see how much the variables influenced the benzene concentrations in working place. Minyan Li [13] used MCsim software to forecast the internal dose distribution for the workers exposed to benzene based on the uncertainties of the metabolism parameters and the inhaled doses in a PBPK model. In a waste incineration dioxin health risk assessment, Jie Yang [17] used Crystal Ball to do Monte Carlo simulation and calculated the carcinogenic and non-carcinogenic risk, because uncertainties existed in the related factors (the concentration of contaminant in air and soil, the four pathways that dioxin accessed the body, including soil intake, air inhalation, skin exposure and food ingestion) in the expressions of the dioxin concentration. 4. The odor assessment To assess the odor effect of the hydrogen sulfide, methyl mercaptan and ethyl mercaptan in the condensate oil refining factory, Liming Hu [18] applied the odor intensity method and the degree-annoyance method to establish the mathematical relationship between the concentration and the odor intensity or degree of annoyance, and then visualized by FLEUNT. The odor intensity could be calculated by the Weber-Fechner expression.
I
kw lg(C / C0 ) const
(9)
Where I is the dimensionless odor intensity with the parameter of the Weber-Fechner expression kw, odor concentration C and detection threshold (1 odor unit) C0. const is the parameter related to the mean intensity level determined by the best fit curve of each odor. And the total odor intensity Y is
Y
log(¦10 yi )
(10)
Where, yi is the individual odor intensity for the substance i. In the assessment of degree of annoyance, James A. Nicell proposed a 10-degree method. The dose-response expression for degree of annoyance is expressed as Eq. (11).
A
10 1D C 1 ( 5 AU ) D C
(11)
The degree of annoyance A (in annoyance unit au) indicates the degree of annoyance of population ranging from 0 to 10. C5AU corresponds to the odorant concentration where the population annoyance has a value of 5 au. The dimensionless parameter α is the persistence. For mixtures of hydrogen sulfide, methyl mercaptan and ethyl mercaptan, the total degree of annoyance Am is
Am 1 (
C1 C5 AU 1
10 1D 1 )D C3 C2 C5 AU 2 C5 AU 3
(12)
Where C5AU1, C5AU2, C5AU3 are the concentrations of hydrogen sulfide, methyl mercaptan and ethyl mercaptan (mg/m3) corresponding the annoyance value of 5 au, respectively. And C1, C2, C3 are the concentrations of them (mg/m3), respectively. Liming Hu used FLUENT to realize 3D visualization of odor intensity and degree of annoyance (see Fig.1 and Fig.2).
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Fig. 1. Total odor intensity distribution of plant area under sampling port leakage
Fig. 2. Total degree of annoyance distribution of plant area under normal operation condition leakage
5. Occupational health management and protective measures in workshops Yingjun Jiang [19] did research on the prevention of the occupational exposure hazards factors of the etching area of the semiconductor manufacturing plants during preventive maintenance. The research consisted of three parts: at-the-source, alongthe-path and at-the-workers. The first research focused on the source control to optimize the pump-purge cycles and reduce the hazards emission before opening process chambers. The research method was using portable gas detectors to monitor the moment hazardous gases concentration in different pump-purge cycles and then established relationship models between concentration and pump-purge cycles. The second research was identifying the possible hazardous gases emitting area and defining the forbidden area for preventing the exposure of non-maintenance personnel during chamber maintenance. The research was to set up the sampling points and the models of the relationship between distance and hazardous gases concentration. The third research was designing the local hood to effectively remove the hazardous gases emission and then monitoring the hazards abatement rate. The forth research was standardizing the personal breathing equipment for etching chambers maintenance. After reviewing the experimental data, international codes, the characteristics of hazards, historical incident/fatal records, effectiveness and cost, he defined the Powered Air-Purifying Respirator (PAPR) to be the best option for etching process tool maintenance. He then [20] studied the organization, responsibilities, logics and operation principles of US Incident Command System (ICS), referred to the responsibilities of semiconductor business organization chart, analyzed and summarized the gaps between them, then planned and established the semiconductor manufacturing plants’ emergency response organization and flow chart. This emergency response concept and flow chart was helpful for mitigating the business impact of incidents.
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On the basis of Yingjun Jiang’s study, Qing Mu [21] started from preventative angle, conducted a research on the diffusion of toxic substances escaped from clean room during preventive maintenance of metal etch reaction chamber of the semiconductor manufacturing plants, and then discussed occupational exposure of toxic substances in clean room. Qing Mu combined numeric simulation method of interior air distribution with the diffusion of point source, chose metal etching area where the effusion happened as original simplified geometric object, then set boundary conditions and installed corresponding physical model. Using CFD software FLUENT, he simulated the diffusion of toxic substance and worked out the scope of diffusion (see Fig 3).
Fig. 3. Division of hazardous area
Through this method, Qing Mu assessed quantitatively the relationship between the frequency of vacuum sweep and the concentration of HCl in the most sensitive position. The above studies focused on the hazards of toxic substances while ignored anoxia problem because of excessive nitrogen. For this problem, Binbin Wang [22] used CFD to simulate the diffusion of nitrogen in clean room and get the profile of O2 (see Fig 4).
Fig. 4. The 3D distribution cloud chart of volume fraction of O2 in the clean room
Binbin Wang simulated three different working conditions in the encapsulation workshop of a semiconductor plant: normal condition, extreme condition and double condition. Preprocess software GAMBIT was used to establish threedimension model of the clean room and generate mesh. Then, corresponding parameters and models were set in the solver FLUENT to simulate three conditions above. Lastly, TECPLOT was used to generate velocity vector diagrams, concentration distribution diagrams of N2 and O2, and hypoxia area diagrams. By analyzing the diagrams above, diffusion rules of N2 and changing conditions of hypoxia area could be found. Binbin Wang found that increasing the rate of air-inlet could not solve the hypoxia problem in the clean room, even might be counterproductive. Nevertheless, changing the position of air-outlet could reduce the area of hypoxia effectively. This result could contribute to the design modification of clean room and occupational health management of semiconductor plant.
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6. Summary and outlook In recent years, some progress in the technology of occupational health assessment in our country have been achieved, especially, Jie Shi, Yang Wang and Qian Zhang have established a related perfect method, but there is still room for improvement. (1) Lack of occupational data of epidemiological study and biological monitoring; (2) Lack of the parameters in PBPK model for people in China; (3) Further discussions about the dose-response relationship for chronic carcinogenic risk; (4) More researches on the PBPK model simulation software (e.g. ERDEM), the Monte Carlo simulation software (e.g. MCSim), the exposure concentration simulation software (e.g. FLUENT) and the related mathematical diffusion models; (5) Further researches on fit parameters in the expressions of odor intensity and degree of annoyance for different odorants in odor assessment. Acknowledgements Professor Liu, Deyin Huang and the fellow classmates of the Centre for Urban Public Safety Research of Nankai University are gratefully acknowledged. References [1] Deyin Huang, Mao Liu, Li Wang et al., 2007. Application of the Risk Analysis in the Pre-evaluation for the Occupational Acute Intoxication Accident, Chinese Journal of Industrial Medicine 3, p. 200-202. [2] Deyin Huang, Mao Liu, Yali Bo et al., 2008. Risk Analysis of Acute Poisoning of a New Oil Refining Project in Petrochemical Company and Suggestion of Countermeasures, Occupational Health and Emergency Rescue 26, p. 141-145. [3] Deyin Huang, Mao Liu, Jinyan Sun et al., 2007. Study on Risk of Acute Poisoning Accident in Synthetic Ammonia Equipment, Occupational Health and Emergency Rescue 25, p. 115-119. [4] Deyin Huang, Mao Liu, 2010. Risk Analysis Technology for the Occupational Acute Intoxication Accident based on Computer S imulation, Occupational Health and Emergency Rescue 28, p. 251-254. [5] Deyin Huang, Mao Liu, Qian Sun, 2010. Introduction of Areal Locations of Hazardous Atmosphere Software and its Application in the Simulation of Acute Intoxication Accident, Chinese Journal of Industrial Medicine 23, p. 238-240. [6] Jie Shi, Mao Liu, 2009. Categorically Regressive Analysis on the Acute Inhalation Toxicity of Tetrachloroethylene, Journal of Safety and Environment 49, p. 5-9. [7] Jie Shi, Analysis of Acute Health Risk Brought by Accidental Toxic Gas Leakage [D], Nankai University, 2009. [8] Deyin Huang, Jing Zhang, Mao Liu, 2009. “Application of a Health Risk Classification Method to Assessing Occupational Hazard in China,” Bioinformatics and Biomedical Engineering, ICBBE 2009, 3rd International Conference, 11-13 June 2009, pp.1-5. [9] Yang Wang, Mao Liu, Deyin Huang, 2009. “Health risk assessment for benzene occupational exposure using physiologically based pharmacokinetic model and dose-response model,” Bioinformatics and Biomedical Engineering, ICBBE 2009,3rd International Conference,11-13 June 2009, pp.1-4. [10] Deyin Huang, Qian Zhang, Zhang Jing et al., 2011. “Modelling of Occupational Exposure to Benzene and Health Risk of Workers in Styrene Production,” Bioinformatics and Biomedical Engineering, ICBBE 2011, 5th International Conference ,10-12 May 2011,pp. 1-5. [11] Qian Zhang, Occupational Health Risk Assessment on 1, 3-Butadiene [D], Nankai University, 2011. [12] Deyin Huang, Qian Zhang, Mao Liu, 2011, Occupational exposure assessment and carcinogenic risk simulation in benzene workers. Chinese Journal of Industrial Medicine 24. [13] Minyan Li, MCSim-based Occupational Health Risk Assessment on Low-dose Benzene [D], Nankai University, 2012. [14] Yunlong Zhang, Mao Liu, Yang Yang, 2009. Research of Benzene Occupation Exposure Based on BP Neural Network and MATLAB, China Public Security 2, p. 131-134. [15] Yang Wang, Study of occupational health risk assessment of benzene exposure [D], Nankai University, 2009. [16] Mao Liu, Deyin Huang, Li Wang et al., 2010. “The Application of the Crystal Ball and Monte Carlo Simulation Method in the Carcinogenic Risk Assessment of Benzene,” Proceedings of the Annual Occupational Health Conference. [17] Jie Yang, Mao Liu, Minyan Li, Use of Monte Carlo Simulation for health risk assessment of Dioxin/Fs produced by municipa l solid waste incineration, Journal of Safety and Environment 11, p. 234-238. [18] Liming Hu, FLUENT Simulation and Assessment of Odour in the Process of Condensate Gas Refinery [D], Nankai University, 2012. [19] Yingjun Jiang, Occupational hazard prevention research for etching area of semiconductor manufacturing plant during preventive maintenance period [D], Nankai University, 2008. [20] Yingjun Jiang, Mao Liu, 2007, Comparison Study of Emergency Response Organization and Operation Flowchart for Semiconductor Manufacturing Plants Based on the Incident Command System of USA. China Public Security 2, p. 110-116. [21] Qing Mu. Numerical simulation of hazardous substance diffusion and prevention and control of occupational hazard in clean room of chip plants [D], Nankai University, 2009. [22] Binbin Wang, Research of oxygen concentration in the clean room of semiconductor plants based on FLUENT simulation [D], Nankai University, 2011.
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