Risk Factors and Health Effects of Workers in Foundry Process

Transcription

1 Risk Factors and Health Effects of Workers in Foundry Process Jong-hyeon Jung 1, Sang-man Lee 2, Young-gyu Phee 3, Hye-jeong Bae 4, Mi-ran Nam 5, Yu-jin Jung 6, Byung-hyun Shon 7 1,3 Faculty of Health Science, Daegu Haany University, Daegu, South Korea 2 IESH-Solution CO. Ltd, Daegu, South Korea 4,5 Institute for Inrial Health, Daegu Haany University, Daegu, South Korea 6 C.E.Tech Co. Ltd., R&D Center, Changwon, South Korea 7 Dept. of Environmental Engineering, Hanseo University, Seosan, South Korea Abstract - In this study, we analyzed emission characteristics and concentrations of hazardous pollutants generated in unit processes (casting, molding, core, welding and finishing) of a foundry inry, and assessed the excess rates of each process by using Bayesian model. In addition, we analyzed results of special health check of workers to assess the health protection and the working environment for them. Main pollutants generated in the foundry included mineral, crystalline silica and iron oxide and they were relatively higher than pollutants generated in general manufacturers. In particular, concentrations of mineral in the core process and levels of mineral and iron oxide in the finishing process were high. As the results of prediction analysis using Bayesian model, the excess rates of mineral, iron oxide and crystalline silica were 99.8 %, 94.8 % and 82.2 %, respectively. The excess rates of mineral in the core process and the molding process were 98.7 % and 87.4 %, respectively. Keywords Foundry process, Bayesian model, Crystalline silica, Mineral, Iron oxide. I. INTRODUCTION In Korea, the health care system for workers is not well established because health and hygiene work for employees working in most foundries is managed by consigned administration [1-5]. Hazardous pollutants generated in the foundry are various kinds of (TSP, PM 10, PM 2.5, PM 1, inhalable particulate matter, thoracic particulate matter, respirable particulate matter, etc), heavy metals (lead, nickel, cadmium, chromium, manganese, tin, barium, talc, aluminum, beryllium, etc), metal fume, iron oxide and silica. In particular, workers exposed to organic and inorganic may have some diseases such as silicosis and pneumoconiosis. In addition, it has been reported that workers have various diseases such as lung cancer, skin cancer and hypopharyngeal cancer together with heavy metal intoxication, organic solvent poisoning, pulmonary edema, pneumonia, bronchitis and various respiratory diseases [6-14]. In this study, we investigated process characteristics and hazardous factors in each unit process for health protection of employees working in the foundry and improvement in working environment, and analyzed results of special health check of workers. In addition, we determined the excess rates of each process by using Bayesian model. It is expected to make many contributions to take care of the health of employees working in the casting process or similar processes upon application of plans for improvement in the working environment presented by the results of this study. II. MATERIALS AND METHODS A. Subjects and process analysis In this study, we characterized the emission of pollutants in each unit process to identify hazardous factors in the working environment and analyzed the effects on health of employees working in the foundry. This study was conducted from June 2012 to December Subjects were 8 foundries located in Gyeongsang Province. We compared pollutants generated in the foundries to those generated in general workplaces in Daegu and Gyeongbuk areas in order to identify the emission characteristics of pollutants generated in unit processes in the foundries. In particular, this study was focused on mineral, crystalline silica and iron oxide which were critical factors in the assessment process of occupational lung diseases. B. Special health check of workers in the casting process We analyzed the results of special health check of employees working in 4 foundries in order to examine the effects on health of workers in the foundries. 48

2 C. Prediction analysis using Bayesian model There are some methods of measuring the levels of exposure to the working environment in the workplace as follows: i) a method to directly compare concentrations of pollutants measured in the working environment with exposure standards, ii) a method to compare it with exposure standards with consideration of sampling analysis errors. However, there are some limitations in assessing the levels of exposure. Because it is compared with the levels measured on one day, environmental variables or other variables on another day when it is not measured are not reflected. Therefore, AIHA (American Inrial Hygiene Association) is using the results of assessment using the Bayesian model together with levels measured in the working environment to compensate such problems [2]. The Bayesian model uses IHDA (IH Data Analyst, Version 1.27, Exposure Assessment Solutions, Inc., USA). IHDA provides the data analysis based on Bayesian statistics as a tool for decision making in inrial hygiene field using BDA model. The purpose of this analysis is to provide the mechanism to clearly include the judgment of experts in statistical analysis and description of the occupational exposure data, and to estimate the probability by classifying the accurate exposure profile into particular categories or exposure grades. The exposure grades use the exposure categories of AIHA shown in Table 1. AIHA efficiently utilizes the results by presenting the exposure levels and possibility of exceeding permissible standards as the probability. In addition, AIHA provides the qualitative and quantitative information (Prior) by applying the Bayesian model to the existing workplace. Moreover, it is complemented on the basis of partial measurements in the working environment (Likelihood) [2]. The Bayesian model derives 3 decision charts on the basis of judgment of experts and data from actual measurements. Therefore, we identified emission characteristics in each process and analyzed the possibility of exceeding the standards with hazardous pollutants generated in unit processes in the foundry by using the Bayesian model. Bayesian model is the model to be efficiently used to analyze current measurement data using a scientific method on the basis of past measurement data, opinions of experts and model predictions when actual on-site measurement data are not sufficient. On the other hand, exposure categories of AIHA are specifically classified and shown in Table 1 [15]. The purpose of analyzing the Bayesian model is to estimate the probability to classify the direct exposure data into particular categories or exposure grades through judgment of experts and statistical analysis. 49 Table 1 shows the probability of data which belongs to each category in accordance with the exposure category proposed by AIHA. Exposure category a Table 1 AIHA exposure categorization scheme Rule of thumb description b are trivial to nonexistent are highly controlled are well controlled are controlled are poorly controlled Recommended statistical interpretation c X % 1 % <X % 10 % <X % 50% <X % 100 % <X 0.95 Qualitative description, if they occur, infrequently exceed 1% of the OEL infrequently exceed 10% of the OEL infrequently exceed 50% of the OEL and rarely exceed the OEL infrequently exceed the OEL frequently exceed the OEL a An exposure category can be assigned to a SEG whenever the true 95th percentile exposure (X0.95) falls within the specified range. b The Rule-of-thumb descriptions were based on similar descriptions published by the AIHA. c X 0.95 = the true group 95th percentile exposure. III. RESULTS AND DISCUSSIONS A. Analysis on health hazardous factors and special health check of workers in the foundries Workers in foundries are being exposed to various hazardous factors such as various heavy metals, aromatic hydrocarbons, crystalline silica, HCHO, CO, etc [16]. It has been known that the induction of hypoxia and risks of heart diseases are increased in workers exposed to such hazardous factors [17]. High levels of are known to be associated with the decrease of lung function and various respiratory symptoms [18]. It has been reported that mortality caused by respiratory diseases is higher in employees working in the foundries than that in workers who are not working in the foundries [19]. In addition, lung cancer mortality of employees working in the foundries is 5 times as high as other workers. It has been reported that the risk of death caused by lung cancer is increased by 2~3 times in workers in the casting, injection of molten metals, and finishing processes [20-21]. Table 2 shows health hazardous factors of workers in each unit process in the foundries.

3 Table 2 Exposure contaminants and risk factors of workers in foundry process Classification A plant B plant C plant D plant Unit process Exposure contaminants Special health diagnoses Casting & Molding Crystalline quartz HCHO Methyl alcohol Shakeout & Finishing Crystalline quartz Molding Methyl alcohol Core HCHO Methyl alcohol Molding Methyl alcohol HCHO Core Methyl alcohol Phenol Molding, Shakeout & Finishing Crystalline quartz Crystalline quartz Core HCHO Methyl alcohol Phenol Table 3 Exposure risk factors and worker symptoms of special health diagnoses of workers at welding operation in foundry process Plant Unit process Exposure contaminants Diagnosis opinion of chest X-ray function test Iron oxide Thickening of the pleura Normal Iron oxide Hypertrophiacordis Normal E-1 Cutting Iron oxide Normal Rhinolalia Iron oxide Normal function E-2 Welding Fume Hypertrophiacordis function Fume Normal function Phrenic nerve paralysis Rhinolalia F Welding Fume Thickening of the pleura Normal Fume Scoliosis Normal G Welding Fume fibrosis function Hypertrophiacordis Normal H-1 Welding Fume Hypertrophiacordis Rhinolalia H-2 Cutting Iron oxide Hypertrophiacordis Normal Table 3 shows the health hazardous factors and effects of workers in 4 foundries. It was found that workers in Company E had non-tuberculous diseases, circulatory diseases and pulmonary, and workers in Company F had circulatory diseases and pulmonary. It was also found that workers in Company G had nontuberculous diseases, thickening of the pleura, circulatory diseases, hypertrophiacordis, and pulmonary, and workers in Company H had non-tuberculous diseases, thickening of the pleura, circulatory diseases, hypertrophiacordis, pulmonary, and increased bronchial shades [2-5]. Table 4 Results of special health diagnosis of workers in foundry process Unit process Empl- Paoyees tients Diagnosis opinion of chest X-ray Nontuberculous disease Inactive Circulatory system disease (Unit: person) function test Rhinolalia function Cutting Welding Total Table 4 shows the results of special health check of 42 employees working in the emission process of pollutants such as mineral, crystalline silicate, iron oxide, heavy metals and welding Fume (Fe, Mn). According to the results, only 19 workers (45.2 %) were healthy. 23 workers (54.7 %) had various diseases as follows: non-tuberculous diseases (7 workers), inactive tuberculosis (4 workers) and circulatory diseases (12 workers). In addition, 15 workers (36.7 %) showed moderate severe levels of FVC (Forced Vital Capacity). In particular, when they were compared with other workers in general manufacturers, the rates of health problems of workers in the foundries was relatively higher than those of other workers. B. Analysis and assessment using Bayesian model Table 5 shows levels of main pollutants in each process in the foundries and the excess rates using Bayesian model. The excess rate of pollutants means the probability (%) to exceed the standards upon measuring the working environment of the target process in the future. Concentrations (mean±s.d.) of mineral and crystalline silica in the molding process were ±910 mg/m 3 and 102±073 mg/m 3, respectively. Concentrations of mineral and crystalline silica in the core process were ± mg/m 3 and 099±079 mg/m 3, respectively. Concentration of iron oxide was ± mg/m 3. Concentrations of mineral, crystalline silica and iron oxide in the finishing process were ± mg/m 3, 172±102 mg/m 3 and ± mg/m 3, respectively. These levels were found to be relatively higher than those of pollutants generated in general workplace. 50

4 A concentration of mineral and iron oxide in the finishing process and the level of mineral in the core process were relatively higher than those in other processes, it is required, to actively establish the systematic management system for protection of the health of workers. Table 5 Exposure level and risk factors of workers in foundry process Unit process Molding Core Welding Shakeout & finishing Hazardous materials Mineral Crystalline quartz Mineral Crystalline quartz Iron oxide Mineral Crystalline quartz Iron oxide Excess rate (%) Mean (mg/m 3 ) Exposure level SD (mg/m 3 ) G.M. (mg/m 3 ) G.S.D (mg/m 3 ) Table 6 shows the levels of mineral, crystalline silicate and iron oxide discharged from 33 general workplaces in Daegu and Gyeongbuk areas, and the excess rate using the Bayesian model. Levels of mineral, crystalline silica and iron oxide in the finishing process were 460± mg/m 3, 083±054 mg/m 3 and 790± mg/m 3, respectively. Table 6 Exposure level and risk factors of workers at general inry process in Daegu and Gyeongbuk Unit process General inry Exposure level Hazardous Materials Excess Mean S.D. G.M. G.S.D. rate (%) (mg/m 3 ) (mg/m 3 ) (mg/m 3 ) (mg/m 3 ) Mineral Crystalline quartz Iron oxide In this chapter, we conducted the assessment work with the scientific methods to identify and improve the problems in the target processes by applying the Bayesian model [2]. We showed the pollutants generated in the foundries and results of analysis using the Bayesian model in Fig. 1~Fig. 6. Although workers in the foundries were being exposed to various metals and hazardous pollutants, only images and measurements of lung capacity were used to determine whether occupational lung diseases occurred upon special health check of workers. It is pointed out as the limitation. 51 Therefore, we analyzed the excess rates depending on the levels of pollutants by using the Bayesian model in order to overcome the limitation. In this study, we were focused on the most important hazardous factors such as mineral, crystalline silicate and iron oxide in the process of diagnosis and assessment of occupational lung diseases. When GSD (Geometric Standard Deviation) calculated in the Bayesian modeling was less than 1.5, it meant that the workplace had the working environment with a few variations of analysis data and the management levels were good. When GSD fell into 2~3.5, it meant that the working environment and management levels were moderate. When GSD was higher than 3.5, the working environment and management levels were poor TLV(mg/m 3 ) : Fig. 1. Concentrations of mineral at molding unit process in foundry inry. Prior Posterior Likelihood Fig. 2. Bayesian modeling and assessment result of mineral concentration at molding unit process in foundry process. Fig. 1 shows the levels of mineral of 12 samples collected from the molding process. Some samples exceed the standards (TLV 2 mg/m 3 ) of mineral. Level of mineral in the molding process is ±910 mg/m

5 Minimal level is mg/m 3 and maximal level is mg/m 3. The GM (geometric mean) is mg/m 3 and GSD is mg/m 3. Fig. 2 shows the results of predictions using the Bayesian model based on the results from analysis of mineral identified in the molding process. Statistical analysis of the Bayesian model is the method to statistically infer the value on the basis of post-information obtained by combining prior known information and observed data. AIHA provides the qualitative and quantitative information (Prior) by applying the Bayesian model in the conventional workplace. It is complemented on the basis of partial measurements in the working environment. The Bayesian model of Fig. 2 (A) ~ Fig. 2 (C) shows 3 decision charts on the basis of judgment of experts and actually measured results. Fig. 2(A) shows that the Bayesian model is applied in the molding process. It is used when it cannot be predicted due to no prior information on the process. Because we do not have prior knowledge about the molding process, we assume that exposure grades of each process are the same as follows. Category P(Pop i) 0-trivial 0 1-highly controlled 0 2-well controlled 0 3-controlled 0 4-poorly controlled 0 Fig. 2(B) shows the values complemented on the basis of partial measurements in the working environment. LDS (Likelihood decision distribution) shows the probability of exposure grades of each process calculated using collected data. Fig. 2(C) presents exposure levels and possibility of exceeding permissible standards in the target workplace as the probability recommended by AIHA. Fig. 2(C) shows the final decision probability with consideration of prior probability assigned to each category and likelihood probability as the product of Fig. 2(A) and Fig. 2(B). The main reason why Bayesian methodology is recently being used in many areas is that statistical inference can be made as it reflects the probability distribution of prior information unlike the traditional statistical analysis. The traditional method statistically infers characteristics of the population from actually measured data. However, the Bayesian statistical method expresses prior information reflecting opinions of experts, research achievements in similar topics and generally known fact as the probability distribution. Thus, it can make more meaningful inference when many measured data are not collected. As shown in Fig. 2(B)~Fig. 2(C), it is found that levels of mineral which is the main hazardous factor of the molding process are relatively high compared to levels of hazardous pollutants generated in general manufacturers. As the results of examination based on exposure categories proposed by AIHA, it belongs to the highest grade (grade 4) of exposure categories. It is required to establish the systematic working environment management system. In addition, according to the results of predictions of each unit process using the Bayesian model, the excess rate of mineral in the molding process is 87.4 % TLV(mg/m 3 ) : Fig. 3. Concentrations of mineral at core unit process in foundry inry. Prior Likelihood Posterior Fig. 4. Bayesian modeling and assessment result of mineral concentration at core unit process in foundry process. Fig. 3 shows the concentrations of mineral in 12 samples collected from the core process. Some samples exceed the standards of mineral. Level of mineral in the core process is ± mg/m 3. Minimal level is 890 mg/m 3 and maximal level is mg/m 3. The GM is mg/m 3 and GSD is mg/m

6 Fig. 4 shows the results of predictions using the Bayesian model based on the results from analysis of mineral identified in the core process. As shown in Fig. 4(B)~Fig. 4(C), it is found that concentrations of mineral in the core process are relatively high compared to levels of hazardous pollutants generated in general manufacturers. As it belongs to the highest grade (grade 4) of exposure categories proposed by AIHA, it is required to establish the systematic working environment management system. In addition, according to the results of predictions of each unit process using the Bayesian model, the excess rate of mineral in the core process is 98.7 %. 3.0 Levels of mineral, crystalline silicate and iron oxide in the core process are ± mg/m 3, 172±102 mg/m 3 and ± mg/m 3, respectively. Minimal levels of mineral, crystalline silicate and iron oxide are 890 mg/m 3, mg/m 3 and 007 mg/m 3, respectively. Maximal levels of mineral, crystalline silicate and iron oxide are mg/m 3, mg/m 3 and 304 mg/m 3, respectively. The GM of mineral, crystalline silicate and iron oxide are 700 mg/m 3, 126 mg/m 3 and mg/m 3, respectively. The GSDs of mineral, crystalline silicate and iron oxide are mg/m 3, mg/m 3 and mg/m 3, respectively TLV(mg/m 3 ) : (A)Mineral (A) Prior (A) Likelihood (A) Posterior (B)Crystalline silicate 22 6 TLV(mg/m 3 ) : (C)Iron oxide Fig. 5. Concentrations of mineral (A), crystalline silicate(b) and iron oxide (C) at shakeout and finishing unit process in foundry inry. Fig. 5 shows the concentrations of mineral, crystalline silicate and iron oxide of 12 samples collected from the shakeout and finishing process. Some samples exceed the standards of mineral. (B) Prior (B) Likelihood 22 (B) Posterior 53

7 International Journal of Emerging Technology and Advanced Engineering (C) Prior (C) Posterior (C) Likelihood Fig. 6. Bayesian modeling and assessment result of mineral (A), crystalline silicate(b) and iron oxide (C) concentration at shakeout and finishing process in foundry process. Fig. 6 shows the results of predictions using the Bayesian model based on the results from analysis of mineral, crystalline silicate and iron oxide identified in the finishing process. As shown in Fig. 6(B)~Fig. 6(C), it is found that levels of mineral, crystalline silicate and iron oxide in the finishing process are relatively high compared to levels of hazardous pollutants generated in general manufacturers. The excess rates of mineral, crystalline silicate and iron oxide are 99.8 %, 82.2 %, and 94.8 %, respectively. As shown in this study, as the results of assessment using the Bayesian model, it is found that levels of mineral and iron oxide generated in the finishing process and levels of mineral generated in the core process are relatively high. In addition, as the results of health examination of workers in the foundries, the rates of health problems of workers in the foundries are relatively higher than those in workers in general manufacturers. Therefore, it is required to have local exhaust ventilation in various types in order to prevent workers in the molding process from being exposed to particulate matters such as and fugitive. Moreover, it is required to give risk management training associated with hazardous pollutants to workers, and to present the management plans for prevention of occupational diseases in various types. IV. CONCLUSION In this study, we have obtained conclusions as follows The main hazardous factors of the foundry are mineral, crystalline silicate and iron oxide. In particular, concentrations of mineral and iron oxide in the shakeout and finishing process and levels of mineral in the core process are relatively high compared to those in other processes. Levels of hazardous pollutants generated in the unit process of the foundry are relatively higher than those in general manufacturers. 2. As the results of predictions about unit processes using the Bayesian model, the excess rates of mineral in the finishing process and the core process are 99.8 % and 98.7 %, respectively. The excess rate of iron oxide in the finishing process is 94.8 %. The excess rate of mineral in the molding process is 87.4 %. The excess rate of crystalline silicate in the finishing process is 82.2 %. As the results of analysis based on the exposure categories proposed by AIHA, it belongs to the highest grade (grade 4) of exposure categories. Thus, it is required to take fast actions. 3. As the results of special health check of employees working in the foundries, it is found that they have non-tuberculous diseases, thickening of the pleura, circulatory diseases, hypertrophiacordis, and pulmonary. When compared with workers in general manufacturers, the rates of health problems of workers in the foundries are relatively high. Acknowledgment This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No R1A1A4A ). REFERENCES [1] Jung Y. J., Shon B. H., Lee S. M. and Jung J. H., A Numerical Study on Performance Improvement of Canopy Hood in Melting Process, The Korea Academia-Inrial Cooperation Society, Vol. 14, No. 3, , [2] Jung J. H., Jung Y. J. Lee S. M, Lee J. H., Shon B. H. and Lim H. S., Health Risk Factors and Ventilation Improvements in Welding Operation at Large-sized Casting Process, Clean Technology, Vol. 20, No. 2, , [3] Jung J. H, Jung Y. J. Lee S. M, Bae H. J., Phee Y. G., Shon B. H. and Lim H. S., Health Risk Assessments of Workers in Casting Process, Proceedings of the Summer Conf., Korean Ind. Hyg. Assoc. J. Seoul, pp. 225, [4] Jung J. H, Jung Y. J., Lee S. M, Lee J. H., Shon B. H., Lim H. S. and Ham W. S., Ventilation Improvements in Operation Spot at Largesized Casting Process, Proceedings of the Summer Conf., Korean Ind. Hyg. Assoc. J. Seoul, pp. 224,

8 [5] Bae H. J. Nam M. R., Lee S. M, Jung Y. J, Shon B. H., Phee Y. G. and Jung J. H, Exposure Characteristics of Hazards Chemical Compounds in Casting Process, Proceedings of the Fall Conf., Korean Ind. Hyg. Assoc. J. Busan, (2014) [6] Jung J. H. Lee S. W, Lee S. M, Shon B. H., Lee J. H. and Jung Y. J., Improvement of capturing velocity in the fume hood using computational fluid dynamics(i)-uniform flow, The Korea Academia-Inrial Cooperation Society, Vol. 14, No. 2, , [7] Jung Y. J., Park K.W., Shon B. H. and Jung J. H., A numerical study on performance improvement of enclosed hood in a reverberatory furnace, Journal of the Korean Society of Urban Environment, Vol. 13, No. 3, , [8] Jung J. H., A study on reaction characteristic of SO 2/NOx simultaneous removal for alkali absorbent/additive in FGD and waste incinerator process, Pusan National University, Ph.D Dissertation, [9] Jung J. H., Health risk assessments and concentrations of environmental pollutants in an inrial complex, Dongguk University, Ph.D Dissertation, [10] Lee, S. M., Phee, Y. G., and Jung, J. H., Contaminant Investigation and Reduce Plan of Indium Process, Korean J. Ind. Heal, Vol. 7, , [11] Jung J. H., Effects of air pollutants on the health/environmental risk assessment and weathering of stone cultural properties in Gyeongju and its vicinities, Daegu Haany University, Ph.D Dissertation, [12] Jung, J. H., Risk factors of environmental diseases in an Inrial Complex Area, Korean J. Ind. Heal, Vol. 5, 69-77, [13] Park J. S., Development and Field Installation of a System of Simultaneously Removing Dust and Volatile Organic Compounds from Furan Process in Foundry, Korean Chem. Eng. Res., Vol. 44, No. 2, , [14] Phee Y. G., Poh Y. M., Lee K. M. Kim H. A., Kim Y. W., Won J. I. Kim H. W.. Analysis or quartz content and particle size distribution of airborne from selected foundry operations, Korean Ind. Hyg. Assoc. J. Vol. 7, N0. 2, , [15] Paul Hewett, Perry Logan, John Mulhausen, Gurumurthy Ramachandran, Sudipto Banerjee. Rating Exposure Control Using Bayesian Decision Analysis, Journal of Occupational and Environmental Hygiene, Vol. 3, , [16] Park R. M. Ahn YS, Stayner LT, Kang SK, Jang JK. Mortality of iron and steel workers in Korea, Am. J Ind. Med., Vol. 48, , [17] Steenland K. Epidemiology of occupation and coronary heart disease: research agenda, Am. J Ind. Med., Vol. 30, , [18] Gomes J, Lloyd OL, Norman NJ, Pahwa P. Dust exposure and impairment of lung function at a small iron foundry in a rapidly developing country, Occup. Environ. Med., Vol. 58, , [19] Yoon J. H, and Ahn Y. S. Cause-Specific Mortality Due to Malignant and Non-Malignant Disease in Korean Foundry Workers, PLoS One, Vol. 9, No. 2, 88264, [20] Shaw F. M., Foundry control the basic considerations, (in the working environment in iron foundries, University of Warwick, March [21] Schumacher J. S., A new control system for foundries, American Inrial Hygiene Association Journal, Vol. 39, 73-78,