5

Optimizing Monitoring and Sampling Strategies

In response to the committee’s statement of task (see Appendix A), the preceding chapter of this report discussed the efficacy of current monitoring technologies and sampling approaches for respirable coal mine dust (RCMD) in U.S. mines, which are primarily driven by regulatory requirements. The statement of task also calls for the committee to address optimal monitoring and sampling strategies to aid mine operators’ decision making related to lowering RCMD exposures. The alarming increase in disease prevalence and severity—following a historical downward trend after enactment of federal regulations—was a focus of the committee’s considerations. Given current uncertainties about the cause of that increase, the committee noted the possibility that high rates of operator compliance with the 2014 dust rule requirements may not guarantee that RCMD exposures will be controlled adequately or that future disease rates will decline.

In the committee’s view, optimal strategies would augment or enhance the outcomes expected from compliance with the current regulatory requirements. In presenting guidance on optimal monitoring and sampling strategies that go beyond regulatory compliance, this chapter begins with a discussion of an idealized program. Then, in recognition of practical constraints, it discusses key features of more-realistic strategies that exhibit elements of the idealized program. The chapter also describes specific implementation opportunities in the near term and research and development needs to support the long-term success of the strategies.

AN IDEAL MONITORING AND SAMPLING PROGRAM

One approach to developing an optimal strategy for RCMD monitoring and sampling is to begin by envisioning an ideal program: to start fresh and define the central objectives, resources, and procedures of a program with few practical constraints, such as those related to cost, availability of technology, existing regulatory requirements, or program acceptance by various stakeholders. For the purpose of this discussion, the two primary stakeholders are mine workers and mine operators. However, other stakeholders (for example, government agencies, technology developers, and researchers) can certainly play a role in a RCMD monitoring and sampling program.

Figure 5-1 illustrates a possible development and implementation process for an ideal program. The process is methodical, with a clear sequence of procedural elements and interdependence of human, technical, and information resources. Moreover, the cyclical depiction of the process recognizes that all program drivers are dynamic in nature. As knowledge, needs, and capabilities evolve, so must the program. Such cyclic processes are frequently described for a range of environmental management and other data-driven programs, where iterative feedback is key to the program efficacy (Raymond et al., 2010; Williams and Brown, 2014). That feature is often termed continual or iterative improvement, learning, or assessment.

Program Objectives

Defining objectives is fundamental to establishing a program’s boundaries and components. The ultimate goal of any RCMD monitoring and sampling program is to protect worker health via mitigation of disease risk. Thus, there is an underlying assumption that surveillance of

some dust metric(s) will facilitate control of exposures within a range that should minimize risk for development of disease. Indeed, the Coal Mine Health and Safety Act of 1969 and the 2014 dust rule (79 Fed. Reg. 24,814, 2014) explicitly connect the RCMD exposure limitation and desired health outcomes. Also, particular RCMD measurements are required to demonstrate that exposure limits are achieved and maintained.

In addition to occupational health protection, other program objectives might include

• Tracking temporal and spatial trends in airborne RCMD concentrations to correlate dust metrics with other metrics (such as production or operational variables, geologic or environmental conditions, and worker behaviors). The trends information may be used for historical or predictive analysis or coordinating smart systems (such as ventilation on demand);
• Evaluation of new RCMD control measures or exposure prevention strategies;
• Enhanced training and education of various stakeholders; and
• Documentation of RCMD metrics to assess accountability (for example, regulatory compliance or other obligations).

It is important to note that, although an ideal RCMD monitoring and sampling program may include demonstration of regulatory compliance as an objective, the goal of worker health protection is the primary impetus.

Exposure Metrics and Tools

In an ideal program, careful consideration is given to selection and prioritization of RCMD metrics that are to be monitored. In the United States and elsewhere, RCMD mass concentration and quartz mass fraction have become the two primary metrics for monitoring. Those are the focus of U.S. regulatory exposure limits and related compliance monitoring and enforcement efforts. The legal impetus for tracking and reducing exposures by using those two metrics has resulted in significant decreases in the incidence of occupational lung disease (Seixas et al., 1992; NIOSH, 2008; Suarthana et al., 2011; Vallyathan et al., 2011). However, recent and unexpected observations of many new and severe cases of coal mine dust lung disease suggest the importance of other characteristics of RCMD particles, which had not been monitored widely in the past. In essence, those recent observations have raised a question about whether the important aspects of exposure are being monitored.

As indicated in previous chapters of this report, RCMD can contain a range of constituents (Walton et al., 1977; NIOSH, 1995; IARC, 1997), particle sizes, and shapes (NIOSH, 1995; Seixas et al., 1995; IARC, 1997); other characteristics, such as those associated with coal rank,¹ can also vary (Walton et al., 1977; NIOSH, 1995; IARC, 1997). However, detailed data on such RCMD particle characteristics are limited, and research on associations of specific occupational exposures with disease outcomes is nearly nonexistent. Nonetheless, coupling available RCMD information with what is known in general about coal mine dust lung disease allows for some inferences about RCMD metrics of potential interest:

• Mass fractions of silicate minerals, diesel particulate matter (DPM), and total or specific metals (IARC, 1997; Schatzel, 2009; Laney et al., 2010, Cauda et al., 2015; Cohen et al., 2016; Barrett et al., 2017);
• Distributions of particle size for the RCMD mass and the fibrogenic potential of specific constituents (for example, quartz, silicates, and metal-bearing minerals) (Huggins et al., 1986; Seixas et al., 1995; IARC, 1997; Sapko et al., 2007; Johann-Essex et al., 2017); and
• Distribution of particle surface features and ambient air pollution that have implications for health effects (for example, reactive oxygen species and free radicals) (Wallace et al., 1994, 2002; Castranova et al., 1995; Harrison et al., 1997; Vallyathan et al., 1998; Li et al., 2003; Araujo et al., 2008).

Appropriate sampling and analytical tools are needed for each metric selected for inclusion in a monitoring program. The objective is to collect samples for analysis that are representative of the airborne RCMD to which miners are or may be exposed. Sampling is done on either a personal basis (device is worn by a miner) or an area basis (device is at a fixed location in the mine), as discussed later. Because the tools and methods used to analyze the collected dust samples usually cannot exclude particles outside of the respirable size fraction, the sampling method itself is designed to do so. For that reason, RCMD sampling, like other size-specific particle sampling, is conducted generally with a particle-size selector. The cyclones currently used in RCMD sampling trains (that is, sampling equipment connected in sequence) were chosen originally so that the size distributions of airborne respirable particles sampled in coal mines approximately match the size distribution of particles able to penetrate into the distal airway and gas exchange regions of the lung (such that the cyclone reliably samples the respirable particle-size range, as discussed in Chapter 4). However, it is unclear whether there have been changes in the particle-size distributions of airborne RCMD over time that might have significantly affected that match. If so, RCMD samples may be less representative of particles penetrating into the lung than they were decades ago, when the particular cyclones used for sampling were first adopted.

From an analytical standpoint, all of the RCMD metrics listed above could be included in a monitoring and sampling program, as methods already exist, or could be adapted, for making the desired measurements. Electron microscopy (for example, scanning electron microscopy with energy dispersive x-ray spectroscopy [SEM-EDX]) can be used to estimate particle size, shape, and mineralogy number distributions in filter samples (Sellaro et al., 2015; Johann-Essex et al., 2017). Other methods can be used for determining mass concentrations of specific particle constituents, including x-ray diffraction (Page, 2006; Schatzel, 2009; MSHA, 2013a; Harper et al., 2014); infrared spectroscopy (IR) (Ainsworth, 2013; MSHA, 2013b); Fourier-transform infrared (FTIR) spectroscopy (NIOSH, 2003a; Schatzel, 2009; MSHA, 2013b; Cauda et al., 2016; Miller et al., 2017); x-ray fluorescence (XRF) (Schatzel, 2009; Wang et al., 2015); mass spectrometry (for example, inductively coupled plasma mass spectrometry or high-performance liquid chromatography mass spectrometry (Freedman and Sharkey, 1972; Lintelmann et al., 2006; Buiarelli et al., 2017); thermogravimetric analysis (TGA) (Scaggs et al., 2015; Phillips, 2017; Phillips et al.,

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¹ Coal rank is a classification of coal based on fixed carbon, volatile matter, and heating value. It indicates the progressive geological alteration (coalification) from lignite to anthracite.

2017); and thermal-optical analysis (Chow et al., 2001, 2007; Birch, 2003). Analytical methods are also available for determining free radicals (Delal et al., 1989) and other surface characteristics (Harrison et al., 1997) in RCMD.

Most analytical methods for filter samples require considerable processing time because of sample preparation requirements, the analysis itself, or the need for specialized laboratory instrumentation, which is not widely available, or field deployable. The need for processing time effectively means there may be lag times of days to weeks between sample collection and availability of analytic results. However, in some cases, methods and tools may be available to allow relatively quick analysis. For instance, NIOSH has developed a quartz monitor prototype, which is undergoing field testing (Cauda et al., 2015, 2016; Lee et al., 2017). The monitor is intended to provide results from a sample that is collected during a miner’s entire shift, which means the quartz mass fraction in a dust sample could be measured immediately after a filter sample is collected. Clearly, the sooner analytic results are available, the more effectively they can be correlated to the conditions under which sampling occurred.

Beyond postcollection filter analysis, the potential exists for continuous measurement of some RCMD metrics (that is, a sample is analyzed during the time it is being collected, as opposed to analysis after collection has occurred). The continuous personal dust monitor (CPDM) provides that capability for measuring the RCMD mass concentration; other instruments might offer capabilities for additional metrics. For example, near-real-time particle counters and size analyzers are being used in other environmental monitoring applications (Hutchins, 2016; Scherließ, 2016), and instruments have been developed or demonstrated for continuous DPM monitoring in metal/nonmetal mines (Barrett et al., 2017; Volkwein et al., 2016, 2017).

Monitoring Protocol

After selecting the RCMD metrics and appropriate tools and methods for sampling and analysis, a monitoring protocol can be designed and implemented. For that, the frequency and location of sample collection are important. In addition, appropriate training and education of miners is a critical component of implementation.

Frequency and Location of Sample Collection

In a truly ideal program, all RCMD metrics could be monitored continuously to allow exposures to be identified and addressed in real time. When and where that capability exists, exposure mitigation is certainly the highest aim. However, given the real-world limitations of analytic methods and instrumentation, continuous monitoring might never be possible for some metrics. When analysis must occur sometime after a sample is collected, sampling would be frequent enough to provide meaningful information about exposure, such that the sampling frequency could change with risk level or uncertainty. For example, changes in mine and operational conditions may lead to changes in the characteristics of RCMD particles that have serious health implications. Likewise, some miners might perform job duties or behave in other ways that cause his or her RCMD exposure to be higher than the exposure of other miners. In both of those examples, frequent sampling could help to identify and reduce RCMD exposures relatively quickly.

Furthermore, an ideal monitoring and sampling program should offer the flexibility and support to integrate additional (or replace outdated) RCMD metrics and tools, as new scientific information and technologies become available. Early on, monitoring of new metrics may occur infrequently, for example, to survey for specific risks. If significant risks are identified, a monitoring protocol that includes more routine measurements may be adopted as an understanding of the risk factors and mitigation strategies evolve. As discussed later, risk management and a commitment to best practices are key features of an optimal (as well as an ideal) monitoring and sampling program too.

The two primary monitoring approaches personal monitoring and area monitoring, serve two very different purposes. Personal monitoring serves to track an individual’s RCMD exposures and, in the interest of health protection, a monitoring program could ideally include every individual on every work shift. Area monitoring serves to track airborne RCMD trends in particular locations (for example, close to primary dust generation sources and key ventilation splits) can provide feedback data for assessing the performance of process control measures. When a process is controlled to minimize dust generation or aerosolization, workers in the vicinity would likely be exposed to minimal process-related dust, thus receiving an indirect means of health protection. Moreover, comprehensive and frequent (if not continuous) area monitoring can allow for a correlation between RCMD measurements and mine environment or operating conditions. Such correlations are extremely important in developing approaches for forecasting risks of possible RCMD exposures, and for evaluating dust mitigation strategies. It is important to note, however, that area monitoring results should not be conflated with personal exposure measurements.

For reference, the current regulatory approach for U.S. coal mines is monitoring designated occupations (DOs).² According to the approach, the exposure of a group of miners is controlled by using data from the personal monitoring of selected members of the groups. The assumption is that the DO’s RCMD exposure is the maximum expected exposure of any individual in the mining unit. As discussed in Chapter 4, however, that assumption may not be valid with use of the CPDM or any other direct-reading continuous personal monitor. Since the individual in the DO is provided with near-real-time data on personal exposure (that is, CPDM measurements of RCMD mass concentrations), he or she has the opportunity to take steps to limit personal exposures. That opportunity for reaction is a primary benefit of a CPDM, but it means an individual wearing the CPDM may not reflect exposures to others in that group.

If all RCMD metrics could be measured in real time on every individual, an ideal program might focus first on personal monitoring to support direct protection of individuals. Nevertheless, real-time analysis may be impossible for some metrics, especially using wearable monitors. It is more realistic to expect that continuous or semicontinuous analytic methods for RCMD would be developed and implemented for stationary monitoring locations (for example, using tube bundle or similar systems (Zipf et al., 2013; Brady et al., 2015) to draw air samples for analysis using a centrally located instrument). In that case, area monitoring would provide data for assessing the performance of process control measures thereby indicating changes in RCMD concentrations.

In the absence of continuous instruments, the question of personal versus area monitoring is still important. Personal exposure data can be related more directly to an individual’s health outcomes, and such data are more relevant for assessing an individual’s health risk, as those data reflect changes in the location and activities of an individual. Area-monitoring data are critical for understanding environmental and operational factors that influence the concentration and particle characteristics of RCMD in underground mines. An ideal monitoring protocol would therefore include both personal and area sampling.

Training and Education

Education and training are critical for achieving a monitoring strategy’s goal of providing accurate information on miners’ RCMD exposures to inform exposure control decisions. The words education and training are often used interchangeably, but in the committee’s view, they mean two different things. Training teaches the methods and procedures for completing a task or operation. Education conveys a deeper understanding of the task, including its importance and

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² Designated occupation is the occupation on a mechanized mining unit (MMU) that has been determined by results of RCMD samples to have the greatest respirable dust concentration. In addition, other occupations on an MMU that are designated for sampling are referred to as other designated occupations. Designated areas are specific locations in the mine where samples will be collected to measure sources of airborne RCMD in the active workings (that is, any place in a coal mine where miners are normally required to work or travel) (see 30 CFR 70.2).

context, as well as the circumstances for which the specific training may not apply. In other words, training is about how and education addresses why.

Training in the proper use of the CPDM is important for the miners who will wear the device. Equally important is education concerning

• Characteristics and risks of coal mine dust lung diseases;
• Components of coal mine dust;
• Respective hazards of coal dust, rock dust, and silica;
• Relevant Mine Safety and Health Administration (MSHA) regulations;
• RCMD and silica control, both industry wide and in their mines;
• Strategies for RCMD monitoring and sampling;
• How to use the CPDM read-outs to identify dusty locations and operations;
• The mine’s procedure for addressing hazardous conditions; and
• The miners’ right to file a complaint to the mine operator, the miners’ representative, or MSHA without fear of retaliation.

Done correctly, that kind of education would lead to better overall understanding and increased motivation to use the CPDM. The monitoring device is heavy and bulky for a miner already laden with heavy equipment, such as the self-contained self-rescuer. Miners will wear the CPDM and use its data more willingly if they perceive the monitor is helping to save their lives and the lives of their co-workers (Lindell, 1994).

How education and training are conducted is also important. Adults learn best when education is hands-on, interactive, based on using newly acquired knowledge to solve problems, and preferably in small groups (OSHA, 2010, 2015; ILO, 2012). Too often, safety and health instruction is delivered through lectures, videos, and slide presentations without accounting for how adults learn best.

As one of the objectives of the program is providing exposure information to the miner wearing the CPDM, that individual miner may be able to move to a less dusty location or otherwise adjust work practices. However, such an action only protects that one miner. CPDM readings could be used to identify the areas and operations where RCMD concentrations are highest, for permanently reducing those concentrations through engineering and work practice controls. Proper education empowers miners to take action, not just for themselves, but also for their fellow miners (Weinstock and Slatin, 2012).

Unfortunately, there is little published research on the effectiveness of mine safety and health training. (There is a more extensive literature on safety and health training in general [Cohen and Colligan, 1998; Sokas et al., 2009; Robson et al., 2010].) One study looked at the impact of MSHA Part 46 of title 30 of the Code of Federal Regulations training in stone, sand, and gravel mines before and after such training was first required in 1999 (Monforton and Windsor, 2010). The authors found a statistically significant decrease in permanently disabling injuries, but not in overall injury rates or other nonfatal injury categories. They concluded that although a causal relationship between the regulatory intervention (MSHA Part 46 training) and the decline in the most-serious injuries was plausible, the existence of such a relationship was called into question by the lack of a decline in other injury rates. In addition, Monforton and Windsor (2010) observed that the Part 46 regulation was written by lawyers, economists, and regulatory specialists instead of experts in adult education; the regulation was grounded more in the need to achieve consensus with the industry and reduce paperwork than in objective evidence; and that many stone, sand, and gravel mine operators were already providing training. The authors also pointed out that training and education alone will not make the workplace safe. Equally important are management commitment, worker involvement, worksite analysis, and hazard recognition. Education and training can facilitate, but not replace, those actions.

Data Management and Decision Making

In the context of an RCMD monitoring and sampling program, data management encompasses all the processes and structures for the collection, storage, processing, dissemination, and use of information. Good data management is a comprehensive and vital element of any monitoring program that provides a foundation for informed decision making. In contrast, poor data management can lead to ineffective, and perhaps counterproductive, decision making—or even a tendency toward nonparticipation of key program stakeholders. Beyond data quality (that is, reliability of measurements), the value of data with respect to program objectives is determined largely by its availability and usability. If data are not available to the appropriate stakeholders in a timely manner, or if their method of collection or presentation format are not easily understood, they would have little value in ensuring worker health protection, supporting process control, or predicting conditions that may result in elevated RCMD exposures.

For the purpose of this discussion, the primary data types of concern are RCMD metrics, which might be collected (semi)continuously (for example, via a CPDM worn by a worker or placed in a stationary location), periodically (for example, measurements of quartz or other constituents on regularly collected filter samples) or intermittently (for example, detailed characterization of RCMD), and related contextual information such as sampling locations and frequencies. Additionally, data related to environmental and operational conditions during RCMD sampling and other periods may be available. These may include measurements related to ventilation (for example, velocity at the mining face or in other locations, curtain positioning), dust control (for example, spray volumes and pressures, loading rate of dust collectors), and mining conditions (for example, total mining height, coal seam thickness, roof rock characteristics, production or advance rate, bit replacement rate); and, again, these data may be collected on range of time bases (that is, from continuously to intermittently) and in various locations. Especially important for continuous measurements, data transmission in coal mines has been a topic of much research, including for personnel tracking and communications and for atmospheric gas monitoring applications (for example, see Dubaniewicz and Chilton, 1995; Novak et al., 2009; Griffin et al., 2011; Sunderman and Waynert, 2012; Damiano et al., 2014; Zhang, 2014; Rowland et al., 2018)—and an optimal RCMD sampling and monitoring program would take advantage of existing data transmission capabilities or develop further capabilities.

For RCMD metrics, data availability is generally related to the sample analysis method. Where continuous measurements are possible, and the user has immediate access to the data, relatively quick, albeit reactive, decision-making is supported. For example, a miner wearing a CPDM might make a decision to change positions in order to limit further dust exposure during a work shift; and based on experience or other data might be able to identify and correct a condition that is allowing an inordinate amount of dust into his work area.

On the other hand, post-collection RCMD sample analysis means there is a lag period before results are available. Lag times also may occur when obtaining other non-continuous data types (such as descriptions of operational conditions), or when continuous data is not immediately transmitted to the user. An example of the latter situation might be post-shift analysis of CPDM data to study how a worker’s exposure changed over the shift. Given the dynamic nature of mining and mine environments, consideration of such data availability issues is critical for processing to support further analysis and interpretation. For instance, determining correlations between RCMD metrics and ventilation and/or dust control conditions requires careful accounting of the relevant measurement lag periods, as well as the location of measurements. In such instances, a system would be in place for processing data to match measurements temporally (and, where necessary, spatially). An output of this might be time-series plotting to visualize trends in different data types, identifying when a specific metric crosses a certain threshold, and ultimately correlating RCMD metrics to mine environmental or operating conditions.

In using sophisticated processing that integrates RCMD monitoring data with ventilation, dust control, or other data, a possible eventual outcome is a fully automated smart system that

makes or recommends operational changes to optimize the mine environment. Smart systems for environmental monitoring and control have been the topic of research for several decades. As reviewed by Cook and Das (2007), a wide range of applications have been considered and developed to varying degrees, including residential safety, comfort and efficiency; intelligent work and educational spaces; and even health monitoring. But the key elements are common across smart systems: an array of sensors (i.e., data collection instruments), hardware that allows data transmission to a centralized data processor, software to process and analyze data, and feedback controllers to adjust environmental conditions. A host of information on these elements and their integration is available in the literature, including for industrial applications (for example, see Ramamurthy et al., 2007; Pillai et al., 2010; Wang et al., 2010; Bhattacharya et al., 2012; Lee et al., 2014).

Whether or not such smart systems materialize, the ability for real users to see and interpret data is critical. Data usability refers to the meaningfulness of data, as presented, to the intended user, and thus it determines the types of decisions that can be made, and who can make them. Commonly, the purpose of wearable and continuous monitors, such as the CPDM, is to afford individual workers an opportunity to see and react to measurement data in real-time. For this purpose, the CPDM display serves as the primary data presentation and is generally usable by the worker wearing the monitor. Ideally, however, the collected data would also become part of a more extensive database that other workers and mine operators could access and use, which would require an open and understandable presentation format. For instance, brief summaries showing time-series exposures for annotated work shifts could be used to exemplify potentially high-dust activities during training sessions, and exposure comparisons between different job categories might be used to help evaluate dust-specific health risks. Similarly, multiple stakeholders would benefit from access and usability of RCMD measurements gathered from area sampling efforts, and corollary analysis describing relationships between various mine conditions and RCMD metrics.

It is worth noting that, beyond the technological challenges associated with data collection (e.g., measurement of specific RCMD metrics), transmission, processing and analysis, data presentation carries its own challenges, especially in systems where multiple users are intended (Wolfe, 2013; Hoffer, 2014). In an ideal RCMD monitoring and sampling program, design of presentation platforms (such as for instrument displays and reporting) would be thoughtful and flexible. Such platforms have yet to be designed for this specific application, though some examples are available for other mine monitoring applications such as atmospheric monitoring and ventilation on demand systems (for example, Agioutantis et al., 2014; Wallace et al., 2015). Moreover, user training and education would be a top priority, both in terms of how to interpret data and make decisions, and the rights and responsibilities of various stakeholders to do so. The importance of training and education is in ensuring an efficacious program cannot be overstated.

Program Evaluation and Improvement

Periodic evaluation of an RCMD monitoring and sampling program is imperative. Output data need to be reviewed to determine whether objectives are being met, and to identify and prioritize areas for improvement (for example, locations associated with high RCMD exposures). Specific program elements, including analytic tools, monitoring protocols, and data integration systems, also need to be reviewed to assess how each contributes to attainment of objectives. Again, where deficiencies are spotted, improvements are developed (such as modified sampling frequencies and adoption of new tools).

In an ideal RCMD monitoring and sampling program, evaluation and improvement would be explicitly included as procedural elements themselves (Figure 5-1). Target outputs and elements for evaluation, the timeline, and responsible stakeholder(s) would be defined. Also, the necessary resources would be committed for conducting the evaluation and making improvements. Further, the procedures for evaluation and improvement would include consideration of

current information concerning RCMD exposures and related disease. The health implications of observations based on monitoring data would be assessed using the latest knowledge of aspects, including factors that contribute to health effects (such as RCMD exposure metrics and nonoccupational factors), mine and operational factors that control RCMD metrics, and specific health outcomes of workers in the mine(s) being monitored. Knowledge of those aspects is woefully incomplete at present.

OPTIMAL MONITORING AND SAMPLING STRATEGIES

The previous discussion envisioned an ideal monitoring and sampling program that would be implemented with few limitations. However, from a real-world perspective, the objectives of an ideal program cannot be achieved fully because of a variety of real-world constraints. This section considers optimal approaches for implementing aspects of an ideal program in the context of practical constraints.

A mathematical approach to optimization could involve the use of techniques (such as linear, dynamic, nonlinear, or goal programming) to develop an objective function that is to be maximized or minimized. After the constraints on the function are specified, a mathematical region is described, where maximum or minimum values for the function can be determined, considering the pertinent variables and parameters (Taha, 2007).

In the case of optimizing monitoring and sampling strategies mathematically, the objective would be to elucidate the most effective real-world strategy for quantifying the critical RCMD exposures causing lung diseases. However, the substantial complexities and uncertainties associated with performing a mathematical optimization in this context would not likely allow for meaningful solutions.

Thus, in the context of this study, an optimal strategy involves monitoring and sampling strategies that enable continued, actual progress to be made toward the elimination of diseases associated with RCMD exposure. In that context, a broad range of conditions and constraints need to be considered in developing optimal monitoring and sampling strategies, including

• Current regulatory requirements;
• The state of monitoring and sampling capabilities, how they are being used, and the feasibility of making improvements;
• The state of dust control approaches, how they are used, and the feasibility of making process improvements that would reduce airborne RCMD concentrations;
• Miners’ ability to avoid or reduce exposures;
• Effectiveness of current medical surveillance programs for detecting lung diseases and the feasibility of improving them;
• Ability to identify the relative importance of RCMD exposure characteristics with respect to causes of deleterious health effects; and
• Likelihood that operators and miners would undertake activities that go beyond regulatory requirements.

At present, the CPDM and a gravimetric personal sampler are used to conduct mass-based personal and area monitoring, as prescribed by regulations. Improved strategies would embrace voluntary additional monitoring and sampling to gain information on potentially important aforementioned parameters affecting miners’ health as well as temporal and spatial variation of RCMD.

As new scientific evidence links deleterious health effects with specific characteristics of RCMD particles and new monitoring techniques become available, operators may choose to revise the objectives of optimization and institute additional monitoring technologies and sampling protocols based on newly evolved technology. That is, an optimal program would be flexible and improve when constraints are substantially reduced or eliminated. That approach would be consistent with the process diagram shown in Figure 5-1. Various monitoring techniques and their

expected capabilities are shown in Table 5-1. For example, new technology for more-rapid measurement of quartz exposure would necessitate changes in an optimal program for which the capability did not exist before. Analysis of monitoring data would provide feedback for the subsequent improvement of the overall monitoring and sampling processes.

While some proactive operators might adopt additional monitoring technologies voluntarily and use a more robust sampling protocol to obtain information aimed at reducing lung disease among their miners, other operators might tend to focus on mitigating exposures only by complying with MSHA regulations. However, the coal mine industry had used beyond-compliance efforts in the past to pursue a goal of zero accidents and injuries. In response to recommendations from the Mine Safety Technology and Training Commission (2006), the industry espoused widespread adoption of a comprehensive safety management systems approach and recognized that regulatory compliance was only a starting point. Several years later in an assessment of five underground coal mines, Kosmoski (2014) found behaviors among the participating mines and their companies that indicated a strong commitment to safety in their operations. Considering industrywide statistics, the National Institute for Occupational Safety and Health (NIOSH, 2016a,b) observed decreased nonfatal injuries in underground mine workers since 2006; however, the number of fatalities for coal operators at underground work locations varied widely, with 36 deaths in 2006, 6 deaths in 2009, 40 deaths in 2010, 14 deaths in 2013, and 8 deaths in 2015.

Near-Term Opportunities for Implementing Optimal Strategies

A number of near-term opportunities exist for improving the understanding of relationships between RCMD exposures and health effects and providing information to support decision making for exposure control. Table 4-1 in Chapter 4 describes potential outcomes of the monitoring and sampling requirements of the 2014 dust rule for mine operators and underlying assumptions for which there are important uncertainties. Table 5-2 identifies components of optimal monitoring and sampling strategy that could help achieve various potential outcomes from required monitoring and sampling methods.

RCMD Monitoring

RCMD monitoring is expected to ensure that efforts for minimizing the incidence of disease are focused on reducing RCMD exposures to all miners in underground coal mines in a maintainable manner, not only those wearing a monitoring instrument. There is a need for monitoring, not only to help prevent high-exposure episodes but also to help reduce long-term exposures of all miners as low as reasonably possible. Evaluations of RCMD sampling data should consider whether results from different sampling locations are sufficiently informative for making decisions for controlling exposures. Maguire (2006) discusses risk-based decision making for achievement of dust exposures as low as reasonably practicable in light of technical and economic constraints.

TABLE 5-1 Monitoring Techniques and Expected Capabilities for RCMD Exposures

Use of CPDMs for Nonregulatory Purposes

The current CPDM technology represents an innovative step toward better understanding the exposure conditions in coal mines. The stored data provide a time-related record of airborne RCMD concentrations. When compared with operational information, dust sources can be identified and the impact of worker positioning or control technologies can be evaluated.

TABLE 5-2 Potential Outcomes from Required Monitoring and Sampling Methods, Assumptions, and Components of Optimal Monitoring and Sampling Strategies

^aDesignated occupation (DO) is the occupation on a mechanized mining unit (MMU) that has been determined by results of RCMD samples to have the greatest respirable dust concentration. In addition, other occupations on an MMU that are designated for sampling are referred to as other designated occupations. Designated areas (DAs) are specific locations in the mine where samples will be collected to measure sources of airborne RCMD in the active workings (that is, any place in a coal mine where miners are normally required to work or travel) (see 30 CFR 70.2).

The use of the available CPDM technology by mine operators has been limited mostly to compliance control purposes. Although the instruments are well suited for such purposes, there are other possible applications. For example, mine operators could apply CPDMs in a stationary role for monitoring certain areas in a mine that have the potential for high RCMD concentrations, depending on technical conditions of machinery, geologic conditions, and the type of RCMD control measures being used. See Box 5-1 for other potential uses of CPDM data.

Under current regulations, these additional applications of the CPDMs would require notification but not the permission of MSHA.³ That requirement might discourage engineering applications and make them less attractive to mine operators. However, it may be preventing mine operators from purchasing additional instruments for the indicated purposes. Therefore, a first step for improving the application of the CPDMs would be for MSHA to encourage mine operators to use them in scenarios beyond the scope of compliance control measurements with assurances that the additional data will not be used as an enforcement tool.

Less Expensive Direct-Reading Instruments

CPDM cost is one of the major limitations for the use of the device for control and area monitoring purposes. Because CPDMs are relied upon for demonstration of regulatory compliance, they need to be highly reliable and provide data of high quality. Consequently, the cost for a single instrument is high. For many of the area monitoring purposes (see Chapter 4), measurements of very high quality are not of prime consideration, because area monitoring is primarily concerned with trends in the data over longer periods. As analytical chemists have known for centuries, measurements do not need to be as good as possible, just as good as necessary.

As discussed below, it is important to evaluate area monitors for possible use in coal mining environments. Mine operators would need scientific support to identify criteria for determining the successful use of the monitors. It is likely that the use of the lower-cost sensors would not be available for compliance control in the near future.

Worker Behavior Modification

Based on operator sampling data reported to MSHA, it is apparent that miners, who wear a CPDM during a working shift, are adjusting their work behavior (such as positioning in the work area in response to elevated RCMD concentrations) to manage exposure during the shifts when a CPDM is worn. An optimal program would facilitate measurement of these behavioral modifications when the CPDM is not present. In addition, CPDM data can be used by operators to investigate whether dust control measures should be adjusted.

Once an optimal monitoring and sampling strategy is implemented, it needs to be well executed. How well it accomplishes the goals depends on good execution at all organizational levels, including in the mine and particularly at the designated sampling locations. This means that an effective training campaign is necessary to ensure buy-in by all personnel, including miners, as recommended by the report of the Secretary of Labor’s Advisory Committee on the Elimination of Pneumoconiosis among Coal Mine Workers (1996). If the sampling strategy is executed well at all levels, then the data on dust exposures that will be forthcoming can be trusted as accurate and meaningful, as planned. Use of the results to aid the operator and miner in making decisions for reduction of dust exposure will be well founded and robust.

Process Control

An optimal system of dust prevention and control is designed to guarantee sustainably low RCMD exposure concentrations for all miners during their entire time working underground. Current approaches used in various countries tend not to focus on a maintainable strategy of exposure control for the long term. Instead, they tend to obtain short-term measurements within comparably small groups of miners and use those results (or the results of selected stationary measurements) as a surrogate for the exposure of much larger groups of miners.

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³ An operator is required to provide written notification to the MSHA district manager when a RCMD sample is collected using a CPDM only for engineering purposes (30 CFR 70.210(d),71.207(d), and 90.208(d)). The CPDM must be programmed to indicate that engineering samples are being collected. The samples are not uploaded to the MSHA database.

Efforts to augment that approach would focus on dust measurements, which try to monitor RCMD concentrations and silica concentrations nearly continuously in all relevant sites of the mines. That augmented approach is analogous to current monitoring systems for identifying dangerous concentrations of carbon monoxide and methane in coal mines. Those systems do not rely on a few expensive monitors that provide high-quality individual measurement result. Rather monitoring of those gases involves the use of an array of many lower-cost sensors with an appropriate level of reliability. The monitor readings would not be used for determining regulatory compliance; instead, they would be used for identifying high RCMD concentrations at specific sites that might warrant additional dust control actions.

In order to be useful for assessing process control effectiveness in coal mines, direct-reading instruments do not need to follow the performance specifications of RCMD regulatory monitoring, but one should know how the readings of those monitors relate to readings obtained from compliance monitors. The supplemental monitors need to be reasonably sensitive in the size range of respirable particles (for example, 1-10 µm). They would need not to be too cross sensitive under coal mining conditions (water sprays would be a consideration here). They would not need to give mass concentration results (say in mg/m³⁾ directly or indirectly after calibration. Instead their electrical signals could be directly used for comparison of “high” or “low” concentrations depending on the situation in a given mine environment. Those instruments would need to be intrinsically safe, such that their use in coal mine environments would not present an explosion hazard and their use in coal mining environments would require additional effort to enable them to withstand dust loading with minimal maintenance and calibration efforts.

Until very recently those types of instruments were not available (Thompson, 2016; Clements et al., 2017; Raj et al., 2017; Crilley et al., 2018). Due to recent demands for RCMD monitoring in environmental settings in recent years, many lower-cost sensors have become available. The sensors almost exclusively use optical principles for measuring airborne RCMD (light scattering of aerosols or single particles). Their electrical signals may not easily be recalculated or translated into mass concentrations of RCMD. Light-scattering signals depend on more airborne particle properties than mass (or number) concentration. Particle size distribution, particle shape, various cross sensitivities, and optical properties of the particles are the most important factors. However, if all these factors for airborne particles are kept constant, the monitors provide cheap, plentiful, simultaneous, and immediately available direct-reading information for assessing process control effectiveness on a relative scale. For example, a longwall operation could be equipped before and after the longwall. If signals behind the operation are increasing without a simultaneous increase before it, that would point to problems within the operation itself. Intervention could start immediately. Due to the fact that the sensors themselves are quite inexpensive, numerous units could be installed.

RCMD Characterization

Current regulations require gravimetric sampling and analysis for total mass and silica content. The total mass data are used to confirm results captured by CPDM measurements. As discussed earlier in this chapter, some factors potentially causing lung disease in coal miners have been hypothesized to include submicron-size fractions, number of particles, and DPM.

There may be other factors yet to be recognized. Identifying the presence and concentration of such RCMD-relevant factors is a challenge when using mass-based monitoring and sampling strategies. Other monitoring technology and analytical techniques do exist to seek more information on some of those factors, but they have not been linked causally to lung disease. Without scientific evidence of the contribution of RCMD components to lung disease, regulators and many mine operators are not likely to pursue the development of new technologies. Some operators may desire to monitor a factor or factors to manage their risk for disease progression among their miners, but the cost involved in doing so may be prohibitive at this point in time.

While very frequent and detailed characterization of RCMD on large numbers of samples is perhaps a longer-term aim, some available data and ready or near-ready analytical techniques may offer a better understanding of RCMD compositions in specific regions or mine environments. For example, considering the recent reports of significant numbers of coal workers’ pneumoconiosis (CWP) cases in geographic hot spots (Laney et al., 2010) and specific clinics (Blackley et al., 2016, 2018), it seems prudent to correlate basic work history of affected miners with any existing dust records collected as part of regulatory compliance activities. Such an effort could shed light on possible RCMD exposure factors associated with disease development (for example, trends in quartz concentrations, perhaps correlated with particular mining practices).

Furthermore, it is currently possible to survey a range of characteristics of RCMD particles and assess the entire composition of RCMD using available analytical techniques on filter samples. A recent study by Johann-Essex et al. (2017) applied a computer-controlled SEM-EDX routine to determine size, shape, and mineralogical class distributions in RCMD mine samples. XRF can also be used for direct elemental quantification on filter samples (for example, see Harper et al., 2007), as can digestion methods followed by analysis by ICP-MS or other mass spectrometry methods (for example, see ASTM D7439-14 [2014]). A simpler method using TGA has been used to estimate the mass fractions of coal, carbonate, and noncarbonate minerals in RCMD samples (Phillips et al., 2017). For mines with diesel equipment, the mass fraction of DPM in RCMD can be determined using submicron elemental carbon as a surrogate (Birch and Noll, 2004), which can be collected using an appropriate size selector during sampling and measured by the NIOSH 5040 standard method (Birch, 2003). While the time and costs associated with those and other possible methods for RCMD characterization are likely prohibitive for very routine monitoring, they may provide valuable opportunities to gather detailed information for critical questions or decision making (for example, comparing RCMD control strategies and diagnosing specific RCMD sources). Moreover, comprehensive RCMD characterization at discreet time intervals or in different areas of a mine could support monitoring of particular temporal or spatial trends, and surveys across many mines may help identify particular conditions or practices that influence RCMD exposure risks.

Specific to silica (quartz) monitoring, recent work by NIOSH may provide a real opportunity to bridge the gap between the current model of very infrequent quartz monitoring and the long-term goal of continuous monitoring. At present, quartz content in RCMD is measured following collection of filter samples and using the MSHA P-7 standard method (MSHA, 2013b) or the similar NIOSH 7603 standard method (NIOSH, 2003b), both of which use infrared spectroscopy. As mentioned above, due to the complex steps required to prepare samples for analysis and use of centralized laboratories, there is generally a significant lag time between sample collection and results. The need for options that can provide a more timely assessment, either for personal or area sampling, has been well established (NIOSH, 2015).

NIOSH has been working on two methods for this, both of which are aimed at providing an end-of-shift quartz measurement. The first method uses the “fast quartz” monitor mentioned earlier, which is a field FTIR instrument to analyze a dust sample collected on a standard filter using a gravimetric sampler (Miller et al., 2015; Cauda et al., 2016, Lee et al., 2017). The analysis time is short (a few minutes) and, since this method is nondestructive, the filter could be shipped to a laboratory for further analysis if desired. This method is currently in the field-testing stage. The second method NIOSH has been working on more recently is to measure quartz content directly on the CPDM filter, which is facing two primary challenges. Because the filter is attached to a stub, which allows the CPDM’s tapered-element oscillating microbalance (TEOM) to work, it is unsuitable for FTIR analysis (Miller et al., 2015). This has prompted NIOSH to consider XRF analysis, but the current CPDM filter media (borosilicate fiberglass) has the potential to cause interference for the quartz analysis. NIOSH identified a novel filter media (polyester-backed nylon fiber) that may allow this method for end-of-shift analysis on CPDM filters to be fully developed (Tuchman et al., 2008). While neither of the two methods currently in development for “fast quartz” analysis is envisioned for real-time monitoring, their availability would significantly im-

prove mine operators abilities to understand RCMD conditions and make informed decisions to reduce hazardous exposures.

Silica Monitoring

As has been stated above, crystalline silica is a highly relevant constituent in RCMD regarding lung disease in miners. At present, no direct-reading instruments are available to monitor crystalline silica that would replace the gravimetric sampling approach that is currently being used. Although the feasibility of continuously monitoring silica content in real time is under debate, progress is being made and prototypes are under development. It is important that efforts to develop a real-time crystalline silica monitor continue, and that NIOSH continue its efforts to develop an end-of-shift quartz monitor, as discussed earlier in this chapter.

Until a real-time silica instrument becomes available for routine monitoring, available data on RCMD composition could be used to gain a better understanding of mining areas with the potential for elevated silica exposures.

Correlation of Dust Surveillance and Medical Surveillance Data

Coupling surveillance information with monitoring and sampling information helps to attain the ultimate goal of disease eradication. Therefore, a system of RCMD exposure prevention needs to be accompanied by a suitable and acceptable system of medical surveillance.

As outlined in previous chapters, mounting evidence from medical surveillance of U.S. coal miners shows a substantial increase in the prevalence of coal mine dust lung diseases, especially the more disabling and rapidly progressive forms of CWP. Also, research on RCMD particle characteristics suggests that particle size, shape, and composition, along with changes in mining production, may be important risk factors for those diseases. Better linkage of medical data with exposure monitoring data to identify specific risk factors that may account for the upswing in pneumoconiosis cases is essential to ensure effective RCMD control and disease prevention.

RCMD sampling for compliance with MSHA-permissible exposure limits is not synonymous with exposure monitoring for epidemiology and prevention purposes. In order to explore ways to improve understanding of exposure-disease linkages, it is useful to consider currently existing data sources, data that have been collected, and where more-comprehensive data collection and integration opportunities exist. It is important to seek exposure data that accurately reflect a range of exposures including, but not limited to, worst-case exposures.

The major source of medical surveillance data on disease trends is the NIOSH Coal Workers’ Health Surveillance Program. Participation rates by active miners has been stable over many years, estimated at 40 to 60 percent. The vital information provided by that program would be enhanced by higher miner participation rates. Early cases of silicosis could be identified and progression could be prevented. Investigation into reasons driving miners’ reluctance to participate is needed to address disincentives where possible.

Similarly, there is little available information on disincentives for participation in the MSHA Part 90 Program. That program was designed to provide low-exposure mining jobs for miners whose chest radiograph results indicate the presence of early CWP and the risk for disease progression from ongoing exposure. A recent study provided data and analyses on participation rates (Reynolds et al., 2017). Additional exploration is needed on reasons for the low participation rates and the integration of Part 90 Program findings with specific exposure data would likely enhance the understanding of exposure-disease relationships. It is important that Part 90 data collected by MSHA are available to NIOSH researchers for pursuing that information.

Information from the more recently implemented Enhanced Coal Workers’ Health Surveillance Program (including details of miners’ occupational histories, job duties, specific mine locations, duration of employment, use of personal protective equipment, and other factors that are important for understanding exposure) needs to be integrated with more-comprehensive exposure

monitoring data obtained for epidemiologic purposes. Access to dust sampling data or to the mines themselves for sampling purposes would support that goal. Ongoing analysis of findings from chest imaging, lung function testing, arterial blood gas testing, and other factors that may be important in disease pathogenesis (such as smoking and family histories) is essential for linking particular exposures to particular disease outcomes. For example, in-depth characterization of miners’ exposures to respirable crystalline silica is likely important in understanding risk for rapidly progressive pneumoconiosis. Analysis of RCMD particle size and shape may be important in understanding dust deposition in a person’s airways and risk for chronic obstructive pulmonary disease.

In addition to NIOSH medical surveillance data, other data sources on trends in lung disease, particularly in retired coal miners, may be available through the Department of Labor Office of Black Lung Compensation Programs. Information from the Health Resources and Services Administration-funded Black Lung Clinics Program could also be used for cases of coal mine dust lung disease by integrating it into general medical surveillance data sets for understanding disease trends. Currently, there are few systems in place to collect and integrate those data sources for prevention research. Recent studies (for example, Almberg et al., 2017 and Graber et al., 2017) have begun to link those disparate datasets and provide insight into variable rates of participation in programs that shed light on coal mine dust lung disease prevalence and risk factors. Further such efforts are needed.

TECHNOLOGY DEVELOPMENT NEEDS

Alternative Tools for RCMD Monitoring

While the CPDM represents a significant advancement in RCMD monitoring capabilities, several drawbacks make more widespread use for either personal or area monitoring a real challenge. An ideal monitoring program would provide every individual with a real-time monitoring device, but the cost of the CPDM and the ergonomic issues (see Chapter 4) would be prohibitive. However, that technology is only currently available for coal mine environments as the CPDM device itself, which is now mandated for the determination of regulatory compliance. That circumstance sets up an undesirable situation for mine operators wishing to apply the near-real-time monitoring capability for area monitoring: To use their compliance-designated CPDM units for noncompliance purposes, they must notify MSHA, which could create a perceived additional burden. Alternatively, the mine operator could acquire additional monitors to be designated for engineering (that is, nonregulatory) purposes only. To address both the inherent and procedural challenges to more widespread use of monitors with capabilities similar to the CPDM, alternative technologies for near-real-time RCMD monitoring are a real need.

As a precursor to the CPDM, a machine-mounted continuous RCMD monitor (MMCRDM) was previously developed by NIOSH using the same TEOM sensing technology (NIOSH, 1997). It could continuously monitor, display, and record RCMD concentrations for at least 30 days without servicing. Results from the field testing of the machine-mounted instrument indicated that, to be mine worthy, its reliability needed substantial improvement (Kissell and Thimons, 2001). Reliability was addressed as the TEOM technology was further developed for application in the CPDM, but the MMCRDM was effectively abandoned. Nevertheless, a TEOM device could be used for area monitoring—be it machine mounted or otherwise—particularly if it required only infrequent intervention for filter changing, had a constant power supply (instead of battery power), and could transmit RCMD data for integration into decision-making systems (for example, concerning ventilation and other RCMD control processes).

Another approach to RCMD monitoring is embodied by the respirable dust dosimeter (RDD), also developed by NIOSH in the late 1990s (Volkwein et al., 1999). It utilizes the principle that changes in pressure drop across a filter can provide a measurement of dust mass accumulated on the filter. The device itself consists of two lengths of foam, inside a glass tube, which

successively trap oversized, nonrespirable particles, allowing the respirable particles to deposit on the filter. A number of publications describe the development, testing, and applications of several versions of the RDD in laboratory and mine settings (Volkwein et al., 1999; Volkwein and Thimons, 2001; Ramani et al., 2001, 2002; Hall et al., 2006). The initial development was aimed at its use as a personal sampler, and problems identified with the device for this application include the need for calibration to specific environments (that is, given different characteristics of RCMD). However, the device has potential for use in real-time area RCMD monitoring applications, where overall trends in RCMD concentrations may be of great interest. In this case, the RDD might be able to provide sufficiently reliable results at a relatively low cost compared to a TEOM device.

As particulate matter pollution is a major occupational and public health hazard around the world, a considerable amount of research and development is being devoted to the monitoring of this hazard (Koehler and Peters, 2015). Advances are occurring in sensors, batteries, pumps, information storage and telemetry devices, some of which might be transferrable or adaptable for applications in coal mines. For instance, a number of personal and environmental (that is, area) monitoring devices (such as, dust monitors, and particle counters) are currently available that contain suitable technology with respect to analytical needs, but may require modifications to meet the intrinsically safe requirement for operation in permissible areas of a coal mine).

Tools and Techniques for Routine Characterization of RCMD

As discussed earlier, most RCMD sampling, and all compliance sampling in U.S. mines, has focused on the RCMD mass concentration and the mass fraction of quartz in the RCMD. For these, progress has been made to enhance monitoring efforts resulting in the ability to measure RCMD mass concentration in near real time (via the CPDM) and to advance the potential for more timely quartz assessment (via the end-of-shift quartz measurement). However, it is good practice to question not only the adequacy of tools and techniques for the metrics presently in use, but also the adequacy of the metrics themselves to contribute to an optimal monitoring and sampling program of the future. Indeed, as more is learned about the cause-and-effect relationships of RCMD exposure factors and disease outcomes—including what key uncertainties remain—there is a need for routine, and ideally real-time, RCMD characterization as a long-term goal. This section discusses techniques that are currently available or in development for monitoring particle sizes and numbers, quartz, and DPM. Those are among the characteristics of RCMD particles that appear to be of highest concern at present. As more is learned about other RCMD constituents that may be important in a health context, development of monitoring techniques for those constituents would become future needs.

Real-time analysis of airborne mineral particles is of great interest in many fields, including mineral processing and outdoor air quality management. A range of monitors, based on different operating principles, has been demonstrated for particle counting and sizing applications. Optical particle sizers, which use light-scattering techniques, are popular in the range of respirable-sized dust; they are currently used in a variety of environmental monitoring applications to complement measurements of fine dust concentrations (for example, PMR₁₀ and PMR_2.5) (Grimm and Eatough, 2009; Burkart et al., 2010; TSI, 2017a). Those instruments generally have a lower particle-size limit of about 1 µm and relatively fast data collection speeds (for example, one reading per second), but in mixed-dust environments their sizing accuracy can degrade because different mineral or dust types can yield different light scattering because of their refractive indices (Müller et al., 2011). Possible degradation of sizing accuracy for area monitoring in coal mines might represent only a minor shortcoming for use in tracking general trends in particle sizes and numbers over time. For more accurate particle sizing in mixed dust, other options might be available. For example, Liu et al. (2016) have recently demonstrated an alternative particle sizer that uses a microfluidic multichannel Coulter counter. There are also options for monitoring nanoparticles, which is a need that was previously identified (NRC, 2007). Nanoparticle monitoring is common-

ly carried out by using spectrometers with condensation particle counters, which are now commercially available as field monitors (TSI, 2017b). Perhaps the biggest challenge for using or adapting instruments designed for dust monitoring in noncoal applications is meeting safety and other permissibility requirements for equipment to be used in U.S. coal mines. Another challenge is making relatively sensitive instruments rugged enough to work without inordinate maintenance requirements in tough mining environments.

Beyond the ability to count particles and measure their size distributions, long-term potential also exists for continuous monitoring of specific constituents. While NIOSH has indicated that it is not developing a real-time quartz monitor internally, it issued a research solicitation announcement in November 2017 calling for proposals on this topic. Also, Chalmers and Harb (2017) presented a proof-of-concept demonstration for using a cavity ring-down spectrometer for real-time quartz analysis on airborne RCMD samples collected on a filter. That and other options for quartz monitoring may be challenged by the problem of small sample size relative to the critical mass of sample needed to perform a particular analysis. However, even a semicontinuous approach may be very valuable for either personal or area monitoring applications in coal mines. (As currently designed, the CPDM actually functions as a semicontinuous monitor, providing exposure metric readouts as 15- or 30-minute averages.)

Real-time monitors are also available for tracking DPM. The aethalometer measures black carbon, which is a major component of DPM, using a laser absorbance technique. While it was developed for relatively low-level black carbon monitoring applications (for example, urban air pollution and forest fires), this instrument has been demonstrated several times in non–coal mining environments (Volkwein et al., 2016, 2017; Barret et al., 2017). Using the same principle of measurement, NIOSH developed a personal DPM monitor for metal/nonmetal miners, which is now commercially available (Noll and Janisko, 2013; Noll et al., 2013, 2014; FLIR, 2017). It is calibrated against the NIOSH 5040 standard method to read elemental carbon (EC; effectively analogous to black carbon).⁴ NIOSH has shown that coal dust–sourced EC and DPM-sourced EC might generally be differentiated on a size basis (that is, coal dust is frequently supramicron and DPM is mostly submicron) (Birch and Noll, 2004). This means that, with use of an appropriate size selector (for example, an impactor or cyclone) during sampling, the aethalometer or other types of monitors might practically be applied in coal mines to monitor DPM. Again, these technologies would have to be certified as intrinsically safe for permissible environments or be adapted to meet this requirement.

CONCLUSIONS

1. Given current uncertainties about the cause of the increase in coal mine dust lung disease prevalence and severity, the committee noted the possibility that high rates of operator compliance with the 2014 dust rule requirements may not guarantee that RCMD exposures will be controlled adequately or that future disease rates will decline.
2. Optimal strategies would embrace additional voluntary monitoring and sampling to gain information on potentially important factors affecting miners’ health as well as the temporal and spatial variation of RCMD within a mine. In the committee’s view, optimal strategies would augment or enhance the outcomes expected from compliance with the current regulatory requirements.
3. The primary impetus for optimal sampling and monitoring strategies is the protection of worker health through reduction in disease risk. The strategies are conceived to enhance the reduction of health risks in recognition of practical constraints (such as costs, avail

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⁴ It cannot be used to demonstrate regulatory compliance with personal exposure limits since these have been set to limit total carbon (that is, determined by the 5040 method as elemental plus organic carbon). In coal mines, the coal dust itself has the potential to cause interference with measurements of elemental carbon and thus total carbon, and so DPM is regulated on the basis of equipment emissions (that is, measured at the tailpipe) rather than on a personal exposure basis.

4. ability of technology, existing regulatory requirements, and program acceptance by various stakeholders).

5. Optimal sampling and monitoring strategies manifest as programs that, in principle, exhibit these attributes:
   • Aiding mine operators’ decision making related to reducing RCMD exposures in a maintainable manner with data that are representative of high-exposure episodes and cumulative exposures over the long-term for all workers throughout the mine, not only the ones wearing a monitoring instrument.
   • Supporting the decision-making ability of individual mine workers to protect themselves through training in the use of CPDMs and education concerning important factors that affect exposure-response relationships.
   • Monitoring characteristics of RCMD particles that are directly related to the risk of occupational cardiopulmonary disease. Using appropriate tools and methods to collect samples that are representative of the dust to which mine workers are or may be exposed.
   • Applying various monitoring technologies in engineering studies of RCMD exposure variability and exposure mitigation approaches.
   • Integrating RCMD monitoring data with associated contextual information, such as sampling locations and frequencies, environmental and operational conditions during sampling and other periods, and general knowledge of the health risks associated with the RCMD exposure metrics being monitored.
   • Involving a suitable and acceptable system of medical surveillance that provides regular, no-cost medical examinations for all miners to help assess the efficacy of exposure reduction efforts.
   • Making integrated data readily available, accessible, and usable for timely decision making.
   • Striving for continuous improvement in disease risk reduction, including periodic performance review and necessary modifications, and reaction to changes that remove or eliminate previous constraints.
6. Research and development efforts are needed for better understanding of relationships between miners’ exposures and disease, including studying effects of changes in mining methods, improving monitoring approaches, and increasing participation in medical surveillance programs. Likewise, enhanced worker education and mine operators’ monitoring and sampling efforts would help ensure that all coal miners’ exposures are adequately controlled, in addition to those whose individual exposures are being measured for regulatory compliance purposes.

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