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Characterisation of emission and exposure to diesel engine exhaust from trackless mobile machinery in underground South African Platinum Mines: Evaluating strategies to prevent and control exposure
(University of the Witwatersrand, Johannesburg, 2024) Manyike-Modau, Amukelani Portia; Brouwer, Derk
Background: Mining remains one of the major economic drivers in South Africa, as is evident through continued focus on long-term investments in mining and the mechanisation of mining operations. Mechanisation uses diesel-powered machinery; such machinery offers greater versatility than electric and battery-operated vehicles due to their ability to cover greater distances and move between different working sections (1). Diesel powered machines are most preferred because of their high energy efficiency and low carbon monoxide and carbon dioxide emissions compared to gasoline equipment (2). Using diesel-powered machines in mechanised mining has introduced a new risk to mine workers working in confined spaces underground. Diesel engine exhaust (DEE) can increase significantly due to wear or breakdown of the engine components and after-treatment systems. The impact on emissions varies depending on the engine type, age, and state of wear and tear (3). Significant evidence demonstrates a correlation between DEE exposure and respiratory outcomes in mine workers. DEE is a known carcinogen. In 2012, the International Agency for Research on Cancer (IARC) classified DEE as being carcinogenic to humans (class I) when inhaled, based on sufficient evidence that exposure is associated with an increased risk for lung cancer (4). There has been an incredible drive to manage diesel Particulate matter (DPM) in the South African mining industry (SAMI). The approach, however, has been focused on monitoring personal exposure to DPM rather than a multifaceted approach that includes eliminating or reducing the pollutant at the source. Efforts should be made to ensure that management plans for engineered solutions and risk-based approaches are in place when monitoring DPM (5). As a result, research in the mining domain has focused on developing integrated control strategies/solutions to prevent exposure to diesel engine exhaust. South Africa shares the same concern with other countries regarding the DPM challenges in mining operations. The South African legal framework requires the employer to conduct a risk assessment and implement measures to prevent employees from overexposure to harmful airborne pollutants (MHSA 1996). Therefore, without local guidelines and regulations, SAMI can use the available scientific knowledge to control the exposure of DPM to employees and to ensure continuous monitoring of employees while working at the mines. Currently, no specific systems guide sets standards or limits for personal or occupational exposure or tailpipe emissions of DPM (6,7). Further, even though enough knowledge is The Occupational Cancer Research Centre Report published in 2017 presented different control strategies for DPM following the hierarchy of control principles (8). These ranged from the proactive (most effective) to reactive (less effective) controls and included the following controls: elimination using alternative energy such as electric or battery-powered machines, substitution such as replacing, repowering, or engine rebuilds, and this would typically include retrofitting the engines with engine after-treatment systems; Engineering controls which may include retrofitting the engines with after-treatment technologies, improving general ventilation systems, idling technologies, installation of protective cabs; Administrative controls which may include preventative maintenance, idling policies, operator training and planned schedule for the site such as planning the number of machines required to operate in a working place; and lastly the use of personal protective equipment such as respirators. These controlscan be implemented in a multifaceted approach to reduce or prevent employees from being overly exposed (3,8,9). Diesel emission is a complex mixture and may require multiple control strategies to minimise employee exposure. In the study conducted by Bugarski et al, 2009, they highlighted different monitoring and control strategies ranging from emissions monitoring, including undiluted emissions measurements, i.e., Both ‘tailpipe output’ and ‘engine out’ (upstream of after- treatment systems), and the installation of after-treatment strategies and higher tier standard engine emissions. The control strategies can help identify and distinguish between engine maintenance issues and emission control device failures and assist in estimating ventilation requirements (10). In addition, in a study by Hines in 2019, significant improvements were achieved in reducing tailpipe emissions (reduction at source) by implementing an emission- based maintenance (EBM) program. This has resulted in a reduction of Carbon Monoxide (CO) by more than 80% and DPM by more than 47% on personal exposure, showing the direct impact the EBM has on reducing exposure of DPM to employees working underground. The study successfully reduced tailpipe emissions by introducing the EBM program at the mines. Further, fuel usage was also reduced by 7-20%, showing that when machines are well maintained, there are improvements in efficiencies and even utilisation and availability of machines for production (11). Objective: The overarching aim of the research project was to determine the characteristics of diesel engine exhaust emissions at the source (aerodynamic size fractions) and evaluate how maintenance, maintenance plus installation of diesel particulate filters (DPF), and ventilation will impact the levels of DEE at the source and in the workplace. Methods: A quantitative, quasi-experimental study, designed with an intervention component, was conducted in two Platinum underground mines in South Africa. DEE was measured at the source, and DPM was personally exposed to employees. DPM dispersion modelling was conducted underground, and different control strategies were evaluated to determine their role in reducing the pollutant underground. Results: The concentration of median particles significantly decreased post the interventions, achieving an efficacy of 90-96% and 20-40% (p-value=0.001) for the machines that underwent maintenance plus installation of the DPF and machines that underwent maintenance only, respectively. Most particles emitted were in the ultrafine aerodynamic range, with a diameter between ≥0.01<0.1μm and an aerodynamic fine size of ≥0.1<1μm. Conclusion: A combination of control strategies (maintenance, retrofitting of machines with DPF, and ventilation) has shown great potential to reduce DEE in underground mines. Therefore, focused effort is required to implement integrated strategies to prevent or minimise exposure to DEE. Future studies to link dispersion models with real-time monitors are recommended to improve DEE's risk-based management
Occupational Exposure to Chrysotile Asbestos in the Chrysotile Asbestos Cement Manufacturing Industry in Zimbabwe
(University of the Witwatersrand, Johannesburg, 2023-08) Mutetwa, Benjamin; Brouwer, Derk; Moyo, Dingani
Introduction: Asbestos is a generic term for a group of naturally occurring silicates that principally include serpentine variety (white chrysotile asbestos) and the amphibole variety, consisting of crocidolite (blue asbestos), amosite (brown asbestos), anthophyllite, actinolite and tremolite. Asbestos exposure has drawn much international, regional and national attention as it presents significant public and occupational health concerns. All asbestos types are known to cause asbestos related disease. Objectives: The objectives of this PhD were: 1. To analyse trends in airborne chrysotile asbestos fibre exposure data obtained by the chrysotile asbestos cement manufacturing factories for the period 1996 to about 2016. 2. To establish a job exposure matrix (JEM) to estimate occupational exposure levels in the Zimbabwe chrysotile asbestos industry using available exposure data. 3. To predict asbestos related diseases (ARDs) namely lung cancer, mesothelioma, gastrointestinal cancer and asbestosis in the chrysotile asbestos cement manufacturing industry through exposure levels obtained in the factories. 4. To assess amphibole contaminants in the chrysotile asbestos fibre being used by the factories in the manufacture of asbestos cement (AC) products. 5. To examine approaches for prevention of exposure to chrysotile asbestos fibre and some perspectives on the debate on asbestos ban. Methodology: A retrospective cross-sectional study using the factories personal chrysotile exposure data was designed to evaluate exposure patterns over time. Analysis involved close to 3000 personal exposure measurements extracted from paper records in the two-asbestos cement (AC) manufacturing factories in Harare and Bulawayo, covering the period 1996-2020. Exposure trends were characterised according to three to four time periods and calendar years to gain insight into exposure trends over time. Operational areas for which personal exposure data were available were saw cutting, fettling table, kollergang, moulded goods, ground hard waste, laundry room, and pipe making operations in the case of the Bulawayo factory. The standard method of the Asbestos International Association (AIA) Recommended Technical Membrane Filter Reference Method (AIA, 1982) was reported to be used to collect personal chrysotile asbestos fibre in various operational areas over the years. Quantitative personal exposure chrysotile fibre concentration data collected by the two factories over the considered period were used to construct the JEM. Analysis of amphiboles in locally produced and imported raw chrysotile fibre samples used in the manufacturing processes was done using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (SEM). Prediction of asbestos related diseases (ARDs) was done by combining the JEM converted to cumulative exposures, with OSHA’s linear dose effect model in which asbestos related cancers was derived using linear regression equations established for lung cancer, mesothelioma and gastrointestinal cancer by plotting estimates of cancer mortality cases versus respective cumulative exposures. The linear regression equations were applied to establish estimates of possible cancer mortality while for asbestosis, the linear in cumulative dose equation, Ra = m(f)(d), where Ra – predicted incidence of asbestosis, m – slope of linear regression taken as 0.055, f – asbestos fibre concentration and d – duration of exposure, was used to estimate possible asbestosis cases over the respective duration of exposure at 1, 10, 20 and 25 years. To examine arguments for approaches used for prevention of exposure to chrysotile asbestos and examine some perspectives on the debate on asbestos ban, a literature search was conducted. Literature materials that advocated for the complete ban of all forms of asbestos including chrysotile as the only means of control of exposure and that, which argues for the controlled use approach, were reviewed. Search words used in literature search were chrysotile asbestos exposure, asbestos-cement, ban asbestos, controlled use, asbestos related disease, mesothelioma, lung cancer and asbestosis. Data analysis was conducted using IBM SPSS version 26. For analysis, monthly averaged personal exposure levels for the factories were used. Mean personal airborne chrysotile fibre concentrations were analysed per operational area per factory and trends in airborne fibre concentrations over the years were displayed graphically. ANOVA was applied with the aim categories and determine whether there was a statistically significant difference in exposure concentrations between four time-periods for various jobs. Additionally, a Tukey Post Hoc Test (Tukey’s Honest Significance Difference test) was run to find out which specific group means of time periods (compared with each other) were different. Results and Discussion: Trends in airborne chrysotile asbestos fibre concentrations in asbestos cement manufacturing factories in Zimbabwe from 1996 to 2016. Mean personal exposure chrysotile asbestos fibre concentrations generally showed a downward trend over the years in both factories. Exposure data showed that over the observed period 57% and 50% of mean personal exposure chrysotile asbestos fibre concentrations in the Harare and Bulawayo factories, respectively, were above the Zimbabwean OEL of 0.1 f/mL, with overexposure generally being exhibited before 2008. Overall, personal exposure asbestos fibre concentrations in the factories dropped from 0.15 f/mL in 1996 to 0.05–0.06 f/mL in 2016, a decrease of 60–67%. Statistically significant relationships were observed over time between exposure levels and calendar year and time periods (p<0.001) for all occupational categories other than fettling table operations in Harare. The general decline in exposure over time from 1996 to 2016 suggests good occupational safety and health (OSH) framework being implemented by the two factories over the years, with the years after 2008 showing much lower exposure levels below the OEL particularly for the Bulawayo factory. However, for the period 2018 to 2020 exposures in the Harare factory were much higher than the proceeding time period of 2009 to 2016 due to movement of trucks within the factory as they come to load concrete tiles and other products making it possible for residual chrysotile fibre left during manufacture of AC products to become airborne. The company reported no clean-up of asbestos in the factory or wetting of the floors to control dust, hence the possible increased levels of chrysotile asbestos fibre for the period 2009 to 2016. The general decreasing trends in exposure to chrysotile asbestos fibre may also be viewed from the fact that industry was responding to anticipated lowering of chrysotile OEL as a result of increased calls to ban all forms asbestos, triggering the scaling up of exposure controls in the factories. Job Exposure Matrix for chrysotile asbestos fibre in the asbestos cement manufacturing (ACM) industry in Zimbabwe. On average, all jobs/occupations in both factories had annual mean personal exposure concentrations exceeding the OEL of 0.1 f/ml, except for the period 2009 to 2016 in the Harare factory and for the time-periods 2009 to 2020 in the Bulawayo factory. Despite Harare factory having no AC manufacturing activity since 2017, personal exposure concentrations showed elevated levels for the period 2018-2020. Amphiboles were detected in almost all presently collected bulk samples of chrysotile asbestos analysed. The established JEM, which was successfully generated from actual local quantitative exposure measurements, can be used in evaluating historical exposure to chrysotile asbestos fibre, to better understand, inform and predict occurrence of ARDs in future. Prediction of Asbestos Related Diseases (ARDs) and chrysotile asbestos exposure concentrations in asbestos-cement (AC) manufacturing factories in Zimbabwe. The results show that more cancer and asbestosis cases were likely to be experienced among those workers exposed before 2008 as exposure levels (0.11-0.19 f/ml) and subsequently cumulative exposures were generally much higher than those experienced after 2008 (0.04-0.10 f/ml). After a possible working exposure period of 25 years, overall cancer cases, i.e., estimates of possible cancer cases in a factory for each respective duration of exposure, predicted in the Harare factory were 325 cases per 100 000 workers while for the Bulawayo factory 347 cancer cases per 100 000 workers exposed may be experienced. Asbestosis cases likely to be detected after 25-years duration of exposure ranged from 50 to 260 cases per 100 000 workers (0.05 to 0.26% incidence of asbestosis) for various jobs. Possible high numbers of ARDs are likely to be associated with specific tasks/job titles, e.g., saw cutting, kollergang, fettling table, ground hard waste and possibly pipe making operations as cumulative exposures though lower than reported in other studies may present higher risk of health impairment. Examining approaches for prevention of exposure to chrysotile asbestos and some perspectives on the debate on ban of asbestos. Different perspectives on approaches to the prevention of exposure to asbestos have been presented. One position argues that there exist major differences in health risk between amphiboles and chrysotile asbestos, that low exposure and risk experienced under today’s workplace conditions are completely different to high-risk exposures experienced in the past where occupational hygiene conditions were very poor and levels of education, awareness and training in the asbestos industry was low compared to the present situation. It is further argued that there are low levels of exposure below which risk of health impairment becomes insignificant, hence controlled use approach as a measure of exposure control can be successfully applied. However, the other position holds that all forms of asbestos including chrysotile are equipotent, that there is no safe level of exposure, that controlled use is not practical and that there is no merit in continuing use of chrysotile asbestos in light of safer alternatives available today. Both positions appear plausible. Banning as a form of control measure occupies a high level in the hierarchy of controls with potential to eliminate the hazard and risk; nonetheless, the banning of chrysotile may imply substitution with materials that have been reported to carry health risk of cancer and other health impairments. On balance, banning may possibly not be the panacea of elimination of ARDs, in view of the fact that some other forms of mining such as diamond and gold mining have been associated with exposure to amphibole asbestos. The controlled use approach may provide real possibilities of prevention of exposure to levels that presents minimal risk to health if effectively implemented as applied to a range of occupational hazards with success. Conclusion: Not much is known about exposure to airborne chrysotile asbestos fibre exposure in Zimbabwe chrysotile asbestos cement (AC) manufacturing industry. This study may constitute the single largest characterisation of personal exposure chrysotile asbestos fibre concentrations data set in Zimbabwe in which about 3000 airborne personal exposure measurements collected from company records spanning a period of about 25 years, were used in assessing exposure trends over time, building a job exposure matrix, and predicting possible ARDs namely lung cancer, mesothelioma, gastrointestinal cancer and asbestosis in Zimbabwe AC manufacturing industry. The study adds considerably to future epidemiological studies, gives insights into possible magnitude of ARDs that may be observed in AC factories and possibly analysis of exposure response relationships that may be linked to exposure episodes in the distant past. The study also gives some insights into possible amphibole contaminants that may be associated with local and imported chrysotile asbestos that is used in the AC manufacturing processes and thus providing support for a more comprehensive investigation into the presence of amphiboles in chrysotile asbestos in Zimbabwe. The study also provides some perspectives on approaches to prevention of exposure to asbestos and some aspects on the call to ban all forms of asbestos including chrysotile. Personal exposure chrysotile fibre concentration data in the two AC manufacturing factories showed a downward trend over the years, and that overexposure as evaluated against the OEL of 0.1 f/ml were being exhibited largely before 2008. The job categories with high exposure levels were saw cutting, fettling, ground hard waste, laundry room and multi-cutter operator and such jobs are likely to be associated with high risk of ARDs particularly for exposures happening before 2008. Moulded goods operators were associated with low exposures as process is generally a wet process. Despite exposure concentrations being high in the earlier time periods of 1996 to 2008, declines over time particularly for Bulawayo factory which has continued to use chrysotile to date, suggests that controlled use approach may yield exposures that may present minimal risk to health of those exposed to chrysotile asbestos. While banning can still be considered as a way to eliminating ARDs, it may not necessarily be the panacea for prevention of ARDs, as controlled use approach may perhaps still present real possibilities of prevention of exposure to levels that may present minimal risk to health impairment if effectively implemented as applied to a range of hazards with some success. Banning would possibly imply substitution by materials reported to be hazardous to health. These results can be used in future epidemiological studies, and in predicting the occurrence of asbestos-related diseases in Zimbabwe.
Preventing Coal Mine Dust Lung Disease: Application of Bayesian Hierarchical Framework for Occupational Exposure Assessment in The South African Coal Mining Industry
(University of the Witwatersrand, Johannesburg, 2023-10) Made, Felix; Brouwer, Derk; Lavoue, Jerome; Kandala, Ngianga-Bakwin
Background: The world's largest energy source is coal with nearly 36% of all the fuel used to produce power. South Africa is the world's top exporter and the seventh-largest producer of coal. In the upcoming years, it is expected that South Africa's coal production output rate will rise. Coal mine dust lung disease (CMDLD) is an irreversible lung disease caused by the production of coal, the emission of dust, and prolonged exposure to the dust. When conducting safety evaluation, exposure is typically reported as an eight-hour time-weighted average dust concentration (TWA8h). In occupational exposure contexts, occupational exposure limits (OEL) are often used as a threshold where workers can be exposed repeatedly without adverse health effects. The workers are usually grouped into homogenous exposure groups (HEGs) or similar exposure groups (SEGs). In South Africa, a HEG is a group of coal miners who have had similar levels and patterns of exposure to respirable crystalline silica (RCS) dust in the workplace. Several statistical analysis methods for compliance testing and homogeneity assessment have been put into use internationally as well as in South Africa. The international consensus on occupational exposure analysis is based on guidelines from the American Industrial Hygiene Association (AIHA), the Committee of European Normalisation (CEN), and BOHS British and Dutch Occupational Hygiene Societies' guidelines (BOHS). These statistical approaches are based on Bayesian or frequentist statistics and consider the 90th percentile (P90) and 95th percentile (P95), with- and between-worker variances, and the lognormal distribution of the data. The current existing practices in South Africa could result in poor or incorrect risk and exposure control decision-making. Study Aims: The study aimed to improve the identification of coal dust overexposure by introducing new methods for compliance (reduced dust exposure) and homogeneity (similar dust exposure level) assessment in the South African coal mining industry. Study Objectives: The objectives of this study were: 1. To compare compliance of coal dust exposure by HEGs using DMRE-CoP approach and other global consensus methods. 2. To investigate and compare the within-group exposure variation between HEGs and job titles. 3. To determine the posterior probabilities of locating the exposure level in each of the OEL exposure categories by using the Bayesian framework with previous information from historical data and compare the findings and the DMRE-CoP approach. 4. To investigate the difference in posterior probabilities of the P95 exposure being found in OEL exposure category between previous information acquired from the experts and the current information from the data using Bayesian analysis. Methods: The TWA(8h) respirable coal dust concentrations were obtained in a cross-sectional study with all participants being male underground coal mine workers. The occupational hygiene division of the mining company collected the data between 2009 and 2018. The data were collected according to the South African National Accreditation System (SANAS) standards. From the data, 28 HEGs with a total of 728 participants were included in this study. In objectives 1 and 2, all 728 participants from the 28 HEGs were included in the analysis. For exposure compliance, the DMRE-CoP accepts 10% exceedance of exposure above the OEL (P90 exposure values from HEGs should be below the OEL). The 10% exceedance was compared to the acceptability criterion from international consensus which uses 5% exceedance above the OEL (P95 exposure is below the OEL) of the lognormal exposure data. For exposure data to be regarded as homogenous, the DMRE-CoP requires that the arithmetic mean (AM) and P90 must fall into the same DMRE-CoP OEL exposure category. The DMRE-CoP on assessment of homogeneity was also compared with the international approaches which include the Rappaport ratio (R-ratio) and the global geometric standard deviation (GSD). A GSD greater than 3 and an R-ratio greater than 2 would both indicate non-homogeneity of the exposure data of a HEG. The GSD and DMRE-CoP criteria were used to assess the homogeneity of job titles exposure within a HEG. In objective 3 a total of nine HEGs which have 243 participants, were included in the analysis. To investigate compliance, a Bayesian model was fitted with a Markov chain Monte Carlo (MCMC) simulation. A normal likelihood function with the GM and GSD from lognormal data was defined. The likelihood function was updated using informative prior derived as the GM and GSD with restricted bounds (parameter space) from the HEGs' historical data. The posterior probabilities of the P95 being located in each DMRE exposure band were produced and compared with the non-informative results and the DMRE approach DMRE-CoP using a point estimate inform of the 90 percentiles. In objective 4, a total of 10 job titles were analysed and selected. The selection of the job titles was based on if they have previous year's data so it can be used to develop prior information in the Bayesian model. The same job titles were found across different HEGs, so to ensure the mean is not different across HEGs, the median difference of a job title exposure distribution across HEGs was statistically compared using the Kruskal-Wallis test, a non-parametric alternative to analysis of variance (ANOVA). Job titles with statistically non-significant exposure differences were included in the analysis. Expert judgements about the probability of the P95 located in each of the DMRE exposure bands were elicited. The IDEA (Investigate", "Discuss", "Estimate" and "Aggregate) expert elicitation procedure was used to collect expert judgements. The SHELF tool was then used to produce the lognormal distribution of the expert judgements as GM and GSD to be used as informative prior. A similar Bayesian analysis approach as in objective 3 was used to produce the probability of the P95 falling in each of the DMRE exposure bands. The possible misclassification of exposure arising from the use of bounds in the parameter space was tested in a sensitivity analysis. Results: There were 21 HEGs out of 28 in objectives 1 and 2 that were non-compliant with the OEL across all methods. According to the DMRE-CoP approach, compliance to the OEL, or exposure that is below the OEL, was observed for 7 HEGs. The DMRE-CoP and CEN both had1 HEG with exposures below the OEL. While the DMRE-CoP showed 6 homogeneous HEGs, however, based on the GSDs 11 HEGs were homogeneous. The GSD and the DMRE-CoP agreed on homogeneity in exposures of 4 (14%) HEGs. It was discovered that by grouping according to job titles, most of the job titles within non-homogenous HEGs were homogenous. Five job titles had AMs above their parent HEG. For objective 3, the application of the DMRE-CoP (P90) revealed that the exposure of one HEG is below the OEL, indicating compliance. However, no HEG has exposures below the OEL, according to the Bayesian framework. The posterior GSD of the Bayesian analysis from non-informative prior indicated a higher variability of exposure than the informative prior distribution from historical data. Results with a non-informative prior had slightly lower values of the P95 and wider 95% credible intervals (CrI) than those with an informative prior. All the posterior P95 findings from both non-informative and informative prior distribution were classified in exposure control category 4 (i.e., poorly controlled since exceeding the OEL), with posterior probabilities in the informative approach slightly higher than in the non-informative approach. Job titles were selected as an alternative group to assess compliance in objective 4. The posterior GSD indicated lower variability of exposure from expert prior distribution than historical data prior distribution. The posterior P95 exposure was very likely (at least 98% probability) to be found in exposure control category 4 when using prior distribution from expert elicitation compared to the other Bayesian analysis approaches. The probabilities of the P95 from experts' judgements and historical data were similar. The non-informative prior generally showed a higher probability of finding the posterior P95 in lower exposure control categories than both experts and historical data prior distribution. The use of different parameter values to specify the bounds showed comparable results while the use of no parameter space at all put the posterior P95 in exposure category 4 with 100% probability. Conclusions: In comparison to other approaches, the DMRE-CoP tend to show that exposures are compliant more often. Overall, all methods show that the majority of HEGs were non-compliant. The HEGs that suggest non-homogeneity revealed that the constituent job titles were homogenous. Application of the GSD criterion indicated that HEGs are more likely to be considered as homogeneous than when using the DMRE-CoP approach. When using the GSD and the DMRE-CoP guidelines, alternative grouping by specific job titles showed a greater agreement of homogeneity. The use of job titles showed that using HEGs following the DMRE-CoP current guidelines might not show high-exposure job titles and would overestimate compliance. Additionally, since job titles within a HEG may be homogeneous or have a different exposure to the parent HEG, exposure variability is not properly recorded when using HEGs. In compliance assessment, it is important to use the P95 of the lognormal distribution rather than the DMRE-CoP approach that use the empirical P90. Our findings suggest that the subgrouping of exposure according to job titles within a HEG should be used in the retrospective assessment of exposure variability, and compliance with the OEL. Our results imply that the use of a Bayesian framework with informative prior from either historical or expert elicitation may confidently aid concise decision making on coal dust exposure risk. Contrary to informative prior distribution derived from historical data or expert elicitation, Bayesian analysis using the non-informative uniform prior distribution places HEGs in lower exposure categories. Results from noninformative prior distributions typically show high levels of uncertainty and variability, so a decision on dust control would be reached with less confidence. The Bayesian framework should be used in the assessment of coal mining dust exposure along with prior knowledge from historical data or professional judgment, according to this study. For exposure, findings are to be reported with high confidence and for sound decisions to be reached about risk mitigation, an exposure risk assessment should be considered while using historical data to update the current data. The study also promotes the use of experts in situations where it is necessary to combine current data with historical data, but the historical data is unavailable or inapplicable.
Reporting Silica Dust Exposure Measurements in South African Gold and Coal Mines: 2005 to 2016
(University of the Witwatersrand, Johannesburg, 2023-10) Mongoma, Brian Tshepo; Nelson, Gill; Brouwer, Derk
Background: Arising from the Mine Health and Safety Act 29 of 1996 (MHSA), one of the measures to protect mine workers is monitoring exposure to airborne pollutants. Mines are statutorily required to report airborne pollutant concentrations to the Department of Mineral Resources and Energy (DMRE) on a regular basis. Based on the DMRE's 2013 report, it was determined that 76% of workers were exposed to airborne pollutants at concentrations less than 10% of their respective occupational exposure limits (OELs). Using the same exposure data from the DMRE, the Chamber of Mines of South Africa reported a 14% improvement in the exposure to the airborne pollutants from 2005 to 2013. However, these reported reduced exposures to airborne pollutants are based on the summation of all airborne pollutant exposures by the DMRE. The annual reports refer to the percentage of employees exposed to the combined airborne pollutants, rather than to specific pollutants, such as silica dust – a hazard that is high on the occupational health agenda of the mining industry. From these reports, broad (and perhaps incorrect) conclusions are reached with regard to trends in silica dust and other exposures. The limitations of the SAMI include inaccurate data, self-regulation, incomplete employment and exposure records, and historical biases, which hinder its ability to effectively handle occupational health risks. This emphasizes the immediate need for clear and consistent regulations, accurate data collection, and impartial research approaches to protect the health of mine workers. Objectives: The objectives of this study were to describe trends in combined airborne pollutant and silica dust concentrations from 2005 to 2016, and to evaluate the DMRE Mandatory Code of Practice (MCoP) and the EN 689 methods (for testing exposure levels in the workplace against the OEL of 0.1 mg/m3) as published by the European Committee for Standardization (CEN), using reported silica dust concentrations from 2015 and 2016. Methods: This was a cross-sectional study in which secondary airborne pollutants exposure data, reported to the DMRE by coal and gold mining members of the Minerals Council, were analysed. The 282 870 data points were pooled together to describe trends in airborne pollutant exposures as they comprised 69 airborne pollutants reported by different mines with various mining methods, activities, and occupations. The exposure data was categorized into coal and gold mines, and further into four three-yearly periods (i.e. period 1: 2005-2007, period 2: 2008-2010, period 3: 2011-2013, and period 4: 2014-2016). This was conducted in order to have a consistent metric to allow for uniform assessment across different pollutants with varying OELs. Dividing the exposure concentration by its OEL provided a ratio, similarly to the way that an Air Quality Index is calculated. As a result, the data was normalized by dividing each pollutant exposure concentration by its occupational exposure limit (OEL) to obtain a ratio, termed Q. The arithmetic mean, standard deviation, geometric mean, and geometric standard deviation of the Qs were calculated for each of the three groups i.e. coal and gold mines combined, b) coal mines, and c) gold mines, for each period. Jeffreys’s Amazing Statistics Program was used to analyse the Qs and silica dust concentrations. The Kruskal–Wallis test was used to identify statistically significant differences among the four time periods for each commodity group. Additionally, Scheffe’s post-hoc test in JASP was conducted for further analysis and comparison of differences across all observed periods. Two methods, namely the EN 689 and the method required by the DMRE MCoP, were used to assess compliance. EXPOSTATS Tool 1 was used to calculate the arithmetic mean (AM), median, standard deviation (SD), geometric mean (GM), geometric standard deviation (GSD), and 90th and 95th percentiles of the exposure data derived from EN 689. Microsoft Excel was used to calculate the 90th and 95th percentiles of the exposure data based on MCoP method. A total of 127 014 silica dust data points from 2005 to 2016 out of the 282 870 were utilized to describe silica dust exposure trends, and 44 990 data points from the 127 014 were used to assess compliance for the years 2015 and 2016. Results: A total of 282 870 personal airborne pollutant concentrations from 2005 to 2016, obtained from DMRE, were included the analysis. Analysis of the pooled airborne pollutant exposure concentrations indicated that there was a high variability (data points were far apart and also far from the GM) as the GSDs ranged from 6.37 to 7.53, 7.8 to 8.43, and 5.7 to 6.16 for the coal and gold mines combined, coal mines alone, and gold mines alone, respectively. The variabilities of the silica dust concentrations were less than that of the pooled airborne pollutant data. The GSDs of the silica dust concentrations were < 3.5 for all three groups compared to the GSDs calculated from the pooled airborne pollutants concentrations, where the lowest GSD was 5.7. The trends in the pooled airborne pollutant exposure concentrations over the 12-year period, for all three groups, showed that there was a reduction in reported exposures to combined airborne pollutants. The AMs of the ratios (Q) indicated that the reduction in exposures for coal and gold mines combined, gold mining alone and coal mining alone, were 57%, 55% and 26%, respectively. The corresponding GMs of the ratios (Q) for gold mining alone, coal and gold mines combined, and coal mining alone, reduced by 64%, 45% and 15%, respectively, from 2005 to 2016. The distribution of the airborne pollutant data was skewed, which affected AM more than GM, and resulted in differences between the two measures. This was evident in the gold mining data, where the AM decreased by 55% but the GM decreased by 64%. Data for the period 2005-2007 had the highest AM (1.54) and standard deviation (2.75), suggesting that there were outliers. In this period, ratios (Q) ranged from 0.003 to 7.7, impacting the AM and creating a gap between median and AM values. From 2008 to 2010, the AM (1.26) and SD (2.04) decreased, showing reduced variability. A similar trend was observed from 2011 to 2013, with increased numbers of observations and further reduced variability. In 2014-2016, the AM decreased to 0.67 and SD to 1, indicating stability. The GMs for the coal and gold mines combined, coal mines alone and gold mines alone ranged from 0.17 to 0.31, from 0.22 to 0.28, and from 0.16 to 0.45, respectively. The trends in reported silica dust concentrations in all three groups showed a reduction over the 12-year period. The AMs indicated that the reductions for coal and gold mines combined, gold mining alone and coal mining alone, were 61%, 38% and 34%, respectively. The GMs of the silica dust concentrations indicated that the reductions in exposures for coal and gold mines combined, coal mining alone, and gold mining alone, were 54%, 35% and 31%, respectively. The AMs of the silica dust concentrations for coal and gold mines combined ranged from 0.17 to 0.44 mg/m3, while the coal mines ranged from 0.67 to 1.02 mg/m3 from 2005 to 2016. For gold mines, the AMs ranged from 0.13 to 0.23 mg/m3. Similarly, the GMs of the silica dust concentrations for the coal and gold mines combined ranged from 0.11 to 0.24 mg/m3, whereas coal mines ranged from 0.41 to 0.63 mg/m3, and gold mines ranged from 0.09 to 0.13 mg/m3. The 90th percentiles for the silica dust concentrations almost correlated with the AMs as they reduced by 67%, 40% and 34% for coal and gold mining combined, gold mining alone, and coal mining alone, respectively. The 90th percentiles for silica dust concentrations for the coal and gold mines ranged from 1.64 to 2.48 mg/m3, and 0.29 to 0.51 mg/m3, respectively. Although the trends indicated a reduction in exposure to silica dust concentrations, the AM, GM, 90th and 95th percentiles exceeded the OEL of 0.1 mg/m3 for the entire study period for the three groups, except for the gold mines alone in 2016. In that year, the GM was 0.09 mg/m3 (rounded to 0.1 mg/m3). For coal mining only, the 90th percentiles ranged from 1.64 to 2.48 mg/m3, whereas the 95th percentiles ranged from 2.16 to 3.16 mg/m3. For gold mining only, the 90th percentiles ranged from 0.29 to 0.51 mg/m3, and the 95th percentiles ranged from 0.35 - 0.63 mg/m3. A total of 44 990 silica dust concentrations were used from 2015 to 2016 to compare the 95th percentiles according to EN 689, and the 90th percentiles according to the MCoP. The DMRE MCoP method was shown to underestimate the exceedance of the occupational exposure limit by 5-26%, when compared with the EN 689 method. Conclusion: Despite the variabilities and challenges associated with pooling the airborne pollutants concentrations in the coal and gold mining industries, exposures to the airborne pollutants in the three commodity groups decreased from 2005 to 2016. However, reporting employee exposure as pooled airborne pollutants concentrations is flawed and obscures exposures to individual pollutants such as silica dust. The three commodity groups showed a decrease in silica dust exposure measurements from 2005 to 2016. However, there was still overexposure to silica dust in the three groups (greater than the OEL of 0.1 mg/m3). Inhalation of particles containing silica was higher in the coal than the gold mines, which is contradictory to what is known about the silica content of the ores in which the two commodities are found. The DMRE MCoP approach to compliance with silica dust levels underestimated the exceedance of the OEL in comparison to the EN 689’s approach. The current DMRE reporting methodology, i.e. the pooling of all data, does not allow accurate reporting of silica dust exposures and as a result, it does not provide direction or support for carrying out measures to decrease exposure to silica dust. The MCoP method for compliance testing revealed higher 90th-percentiles for coal mining compared to the 90th-percentile estimated for the population (EN 689). For gold mining it was the opposite. The EN 689 method is a more precise means of estimating OEL compliance, which is crucial for managing silica dust and specific pollutant health hazards and should be used in favour of the method in the MCoP.