Characterization and mapping of very fine particles in an engine machining and assembly facility

Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 2: Applications

Direct reading instruments (DRIs) for aerosols have been used in industrial hygiene practice for many years, but their potential has not been fully realized by many occupational health and safety professionals. Although some DRIs quantify other metrics, this article will primarily focus on DRIs that measure aerosol number, size, or mass. This review addresses three applications of aerosol DRIs that occupational health and safety professionals can use to discern, characterize, and document exposure conditions and resolve aerosol-related problems in the workplace. The most common application of aerosol DRIs is the evaluation of engineering controls. Examples are provided for many types of workplaces and situations including construction, agriculture, mining, conventional manufacturing, advanced manufacturing (nanoparticle technology and additive manufacturing), and non-industrial sites. Aerosol DRIs can help identify the effectiveness of existing controls and, as needed, develop new strategies to reduce potential aerosol exposures. Aerosol concentration mapping (ACM) using DRI data can focus attention on emission sources in the workplace spatially illustrate the effectiveness of controls and constructively convey concerns to management and workers. Examples and good practices of ACM are included. Video Exposure Monitoring (VEM) is another useful technique in which video photography is synced with the concentration output of an aerosol DRI. This combination allows the occupational health and safety professional to see what tasks, environmental situations, and/or worker actions contribute to aerosol concentration and potential exposure. VEM can help identify factors responsible for temporal variations in concentration. VEM can assist with training, engage workers, convince managers about necessary remedial actions, and provide for continuous improvement of the workplace environment. Although using DRIs for control evaluation, ACM and VEM can be time-consuming, the resulting information can provide useful data to prompt needed action by employers and employees. Other barriers to adoption include privacy and security issues in some worksites. This review seeks to provide information so occupational health and safety professionals can better understand and effectively use these powerful applications of aerosol DRIs.

Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 1: Instruments and good practices

With advances in technology, there are an increasing number of direct-reading instruments available to occupational health and safety professionals to evaluate occupational aerosol exposures. Despite the wide array of direct-reading instruments available to professionals, the adoption of direct-reading technology to monitor workplace exposures has been limited, partly due to a lack of knowledge on how the instruments operate, how to select an appropriate instrument, and challenges in data analysis techniques. This paper presents a review of direct-reading aerosol instruments available to occupational health and safety professionals, describes the principles of operation, guides instrument selection based on the workplace and exposure, and discusses data analysis techniques to overcome these barriers to adoption. This paper does not cover all direct-reading instruments for aerosols but only those that an occupational health and safety professional could use in a workplace to evaluate exposures. Therefore, this paper focuses on instruments that have the most potential for workplace use due to their robustness, past workplace use, and price with regard to return on investment. The instruments covered in this paper include those that measure aerosol number concentration, mass concentration, and aerosol size distributions.

Estimating personal exposures from a multi-hazard sensor network

Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O₃), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m³, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O₃, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.

Where Do Ultrafine Particles and Nano-Sized Particles Come From?

This paper presents an overview of the literature studies on the sources of ultrafine particles (UFPs), nanomaterials (NMs), and nanoparticles (NPs) occurring in indoor (occupational and residential) and outdoor environments. Information on the relevant emission factors, particle concentrations, size, and compositions is provided, and health relevance of UFPs and NPs is discussed. Particular attention is focused on the fraction of particles that upon inhalation deposit on the olfactory bulb, because these particles can possibly translocate to brain and their possible role in neurodegenerative diseases is an important issue emerging in the recent literature.

Early Effect Markers and Exposure Determinants of Metalworking Fluids Among Metal Industry Workers: Protocol for a Field Study

Background

Exposure to aerosols from metalworking fluids (MWF) has previously been related to a series of adverse health outcomes (eg, cancer, respiratory diseases). Our present epidemiological study focuses on occupational exposures to MWF and a panel of exposure and effect biomarkers. We hypothesize that these health outcomes are caused by particle exposure that generates oxidative stress, leading to airway inflammation and ultimately to chronic respiratory diseases. We aimed to assess whether MWF exposure, in particular as characterized by its oxidative potential, is associated with biomarkers of oxidative stress and inflammation as well as genotoxic effects.

Objective

The ultimate goal is to develop exposure reduction strategies based on exposure determinants that best predict MWF-related health outcomes. The following relationships will be explored: (1) exposure determinants and measured exposure; (2) occupational exposure and preclinical and clinical effect markers; (3) exposure biomarkers and biomarkers of effect in both exhaled breath condensate and urine; and (4) biomarkers of effect, genotoxic effects and respiratory symptoms.

Methods

At least 90 workers from France and Switzerland (30 controls, 30 exposed to straight MWF and 30 to aqueous MWF) were followed over three consecutive days after a nonexposed period of at least two days. The exposure assessment is based on MWF, metal, aldehyde, and ultrafine particle number concentrations, as well as the intrinsic oxidative potential of aerosols. Furthermore, exposure biomarkers such as metals, metabolites of polycyclic aromatic hydrocarbons and nitrosamine are measured in exhaled breath condensate and urine. Oxidative stress biomarkers (malondialdehyde, 8-isoprostane, 8-hydroxy-2’-deoxyguanosine, nitrates, and nitrites) and exhaled nitric oxide, an airway inflammation marker, are repeatedly measured in exhaled breath condensate and urine. Genotoxic effects are assessed using the buccal micronucleus cytome assay. The statistical analyses will include modelling exposure as a function of exposure determinants, modelling the evolution of the biomarkers of exposure and effect as a function of the measured exposure, and modelling respiratory symptoms and genotoxic effects as a function of the assessed long-term exposure.

Results

Data collection, which occurred from January 2018 until June 2019, included 20 companies. At the date of writing, the study included 100 subjects and 29 nonoccupationally exposed controls.

Conclusions

This study is unique as it comprises human biological samples, questionnaires, and MWF exposure measurement. The biomarkers collected in our study are all noninvasive and are useful in monitoring MWF exposed workers. The aim is to develop preventative strategies based on exposure determinants related to health outcomes.

International Registered Report Identifier (IRRID)

DERR1-10.2196/13744

Optimizing a Sensor Network with Data from Hazard Mapping Demonstrated in a Heavy-Vehicle Manufacturing Facility

Objectives

To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards.

Methods

Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance.

Results

The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs.

Conclusions

Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

The Overview of Methods of Nanoparticle Exposure Assessment

Nanotechnology is now widely used in industry as well as consumer products, such as electrical devices, cosmetics, medicine, and household appliances. In the life cycle of the nano-products, including production, use, and disposal, nanoparticles may be released to the environment. However, there is no current consensus on the best method for evaluating and characterizing nanoparticle exposure. Therefore, this chapter focuses on the nanoparticle exposure assessment methods and sampling techniques.

Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility

Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels ± standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 ± 0.2 mg m-3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m-3 for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks.

Occupational Exposure to Fine Particles and Ultrafine Particles in a Steelmaking Foundry

Several studies have shown an increased mortality rate for different types of tumors, respiratory disease and cardiovascular morbidity associated with foundry work. Airborne particles were investigated in a steelmaking foundry using an electric low-pressure impactor (ELPI+™), a Philips Aerasense Nanotracer and traditional sampling equipment. Determination of metallic elements in the collected particles was carried out by inductively coupled plasma mass spectrometry. The median of ultrafine particle (UFP) concentration was between 4.91 × 103 and 2.33 × 105 part/cm3 (max. 9.48 × 106 part/cm3). Background levels ranged from 1.97 × 104 to 3.83 × 104 part/cm3. Alveolar and deposited tracheobronchial surface area doses ranged from 1.3 × 102 to 8.7 × 103 mm2, and 2.6 × 101 to 1.3 × 103 mm2, respectively. Resulting inhalable and respirable fraction and metallic elements were below limit values set by Italian legislation. A variable concentration of metallic elements was detected in the different fractions of UFPs in relation to the sampling site, the emission source and the size range. This data could be useful in order to increase the knowledge about occupational exposure to fine and ultrafine particles and to design studies aimed to investigate early biological effects associated with the exposure to particulate matter in the foundry industries.

Composition of Metallic Elements and Size Distribution of Fine and Ultrafine Particles in a Steelmaking Factory

Background: The characteristics of aerosol, in particular particle size and chemical composition, can have an impact on human health. Particle size distribution and chemical composition is a necessary parameter in occupational exposure assessment conducted in order to understand possible health effects. The aim of this study was to characterize workplace airborne particulate matter in a metallurgical setting by synergistically using two different approaches; Methodology: Analysis of inhalable fraction concentrations through traditional sampling equipment and ultrafine particles (UFP) concentrations and size distribution was conducted by an Electric Low-Pressure Impactor (ELPI+™). The determination of metallic elements (ME) in particles was carried out by inductively coupled plasma mass spectrometry; Results: Inhalable fraction and ME concentrations were below the limits set by Italian legislation and the American Conference of Governmental Industrial Hygienists (ACGIH, 2017). The median of UFP was between 4.00 × 10⁴ and 2.92 × 10⁵ particles/cm³. ME concentrations determined in the particles collected by ELPI show differences in size range distribution; Conclusions: The adopted synergistic approach enabled a qualitative and quantitative assessment of the particles in steelmaking factories. The results could lead to a better knowledge of occupational exposure characterization, in turn affording a better understanding of occupational health issues due to metal fumes exposure.

Evaluation of coverall field dry aerosol decontamination methods using a manikin

A full-size manikin dressed in fire-resistant coveralls coated in 120 g of sodium bicarbonate was randomly given one of three treatments for dry aerosol decontamination. The three treatments were high-efficiency particulate air (HEPA) vacuum, a commercially available air shower, and the no treatment control. Immediately after the treatment, the coveralls were doffed and an air sample was taken in the breathing zone of the manikin to estimate airborne total and respirable dust concentrations to an unprotected worker post decontamination. Each treatment was applied four times for a total of 12 trials. Using analysis of variance (ANOVA) with alpha =.05 and Tukey's Honestly Significant Difference multiple comparison post-test, it was determined that HEPA vacuuming was not significantly different from the air shower for respirable dust, but only the air shower was significantly better than no decontamination (p =.037). For total dust, HEPA was not significantly different from the air shower, but both were significantly better than no treatment (p =.007, p =.004, respectively).

Sampling Strategies for Accurate Hazard Mapping of Noise and Other Hazards Using Short-Duration Measurements

Hazard mapping is an effective way to depict spatial variability in hazard intensity obtained with direct-reading instruments on a facility floor plan. However, the extent to which temporal variability affects map accuracy is unknown, and guidance on sampling strategies to minimize map bias is lacking. In this study, we evaluated the accuracy of hazard maps produced for simulated sources and sampling strategies in a hypothetical facility. Hazard maps were produced from sampled data at high, mid, and low spatial resolution and with and without replicates and compared to a reference time-weighted average hazard map using several map comparison metrics. In agreement with 'real-world' mapping datasets, the simulation showed that increasing the number of replicates improved the overall comparability of the hazard map produced from the sampled data with the time-weighted average hazard map more efficiently than increasing the sampling spatial resolution. However, if accurately capturing peak exposures near sources is of interest, increasing the spatial resolution of the measurements, particularly near sources, is needed. From these results, we formulated guidelines to use the preliminary assessment of the temporal variability of large and intermittent sources to inform the spatial resolution and need for replicate measures to minimize the bias in hazard maps.

Relationships between number, surface area, and mass concentrations of different nanoparticles in workplaces

No consistent metric for measuring exposure to nanoparticles has yet been agreed upon internationally. This study seeks to examine the relationship between the number concentration (NC), surface area concentration (SAC), and mass concentration (MC) of nanoparticles in workplaces. Real-time NC20-1000 nm, SAC10-1000 nm, and respirable MC100-1000 nm were determined for different nanoparticles. Concentration ratio (CR, activity: background), exposure ranking (ER), and between-metric correlation coefficients (R) were used to analyze the relationships between the three metrics. The ratio of cumulative percentage by number (APN) and cumulative percentage by mass (APM) was used to analyze whether the nanoparticle number is predominant, as compared with the nanoparticle mass. The CRs of NC20-1000 nm and SAC10-1000 nm for different nanoparticles at the corresponding work sites were higher than those of respirable MC100-1000 nm. The ERs of NC20-1000 nm for nano-Fe2O3 and nano-Al2O3 were the same as those of SAC10-1000 nm, but were inconsistent with those of respirable MC100-1000 nm. The order of correlation coefficients between NC20-1000 nm, SAC10-1000 nm, and respirable MC100-1000 nm was: RSAC and NC > RSAC and MC > RNC and MC. The ratios of APN and APM for nano-Al2O3 and grinding-wheel particles (less than 100 nm) at the same work site were 2.03 and 1.65, respectively. NC and SAC metrics are significantly distinct from the MC in characterizing exposure to airborne nanoparticles. Simultaneous measurements of the NC, SAC, and MC should be conducted as part of nanoparticle exposure assessment strategies and epidemiological studies.

Spatial and temporal variability of incidental nanoparticles in indoor workplaces: impact on the characterization of point source exposures

This study deployed a suite of direct-reading instruments in six locations inside one building to characterize variability of the background aerosol, including incidental nanoparticles (NP), over a six month period. The instrument suite consisted of a portable Condensation Particle Counter (CPC) and a Scanning Mobility Particle Sizer (SMPS) for assessing particle number concentrations and size distributions in the nano-scale range; an Aerodynamic Particle Sizer (APS) for assessing micron-scale particle number concentrations and size distributions; plus a desktop Aerosol Monitor (DustTrak DRX) and a Diffusion Charger (DC2000CE) for assessing total particle mass and surface area concentrations respectively. In terms of number concentration, NPs (<100 nm) were the dominant particles observed in the background aerosol, contributing up to 53-93% of the total particle number concentrations. The particle size distributions were bimodal with maxima around 19-79 nm and 50-136 nm, respectively, depending on workplace locations. The average detected background particle number, surface area and total mass concentrations were below 7.1 × 10(3) # cm(-3), 22.9 μm(2) cm(-3) and 33.5 μg m(-3), respectively in spring samples and below 1.8 × 10(3) # cm(-3), 10.1 μm(2) cm(-3) and 12.0 μg m(-3), respectively in winter samples. A point source study using an older model laser printer as the emission source indicated that NPs emitted from the investigated printer were distinguishable from background. However, more recent low emitting printers are likely to be indistinguishable from background, and chemical characterization (e.g. VOCs, metals) would be required to help identify emission sources.

Ultrafine particle characteristics in a rubber manufacturing factory

BACKGROUND

According to epidemiological research, exposure to rubber fumes can cause various types of cancer and can lead to an increase in death rate because of cardiovascular diseases.

OBJECTIVES

In this study, we have assessed the characteristics of ultrafine particles emitted into the air during the manufacturing of rubber products using waste tires.

METHODS

To assess the aerosol distribution of rubber fumes in the workplace from a product during curing, we have performed particle number concentration mapping using a handheld condensation particle counter. The particle number concentration of each process, count median diameter (CMD), and nanoparticle ratio (<100nm) were determined using an electrical low-pressure impactor (ELPI), and the surface area concentration was determined using a surface area monitor. The shape and composition of the sampled rubber fumes were analyzed using an ELPI-transmission electron microscopy grid method. Further, the rubber fume mass concentration was determined according to the Methods for the Determination of Hazardous Substances 47/2.

RESULTS

The results of particle mapping show that the rubber fumes were distributed throughout the air of the workplace. The concentration was the highest during the final process of the work. The particle number concentration and the surface area concentration were 545 000cm(-3) and 640 µm(2) cm(-3), respectively, approximately 10- and 4-fold higher than those in the outdoor background. During the final process, the CMD and the nanoparticle ratio were 26nm and 94%, respectively. Most of the rubber fume particles had a compact shape because of the coagulation between particles. The main components of these fumes were silicon and sulfur, and heavy metals such as zinc were detected in certain particles. The filter concentration of the rubber fumes was 0.22mg m(-3), lower than the UK workplace exposure limit of 0.6mg m(-3).

CONCLUSIONS

Therefore, the rubber manufacturing process is a potentially dangerous process that produces a high concentration of specific nanoparticles.

Comparison of the DiSCmini aerosol monitor to a handheld condensation particle counter and a scanning mobility particle sizer for submicrometer sodium chloride and metal aerosols

We evaluated the robust, lightweight DiSCmini (DM) aerosol monitor for its ability to measure the concentration and mean diameter of submicrometer aerosols. Tests were conducted with monodispersed and polydispersed aerosols composed of two particle types (sodium chloride [NaCl] and spark-generated metal particles, which simulate particles found in welding fume) at three different steady-state concentration ranges (Low, <10(3); Medium, 10(3)-10(4); and High, >10(4) particles/cm(3)). Particle number concentration, lung deposited surface area (LDSA) concentration, and mean size measured with the DM were compared with those measured with reference instruments, a scanning mobility particle sizer (SMPS), and a handheld condensation particle counter (CPC). Particle number concentrations measured with the DM were within 16% of those measured by the CPC for polydispersed aerosols. Poorer agreement was observed for monodispersed aerosols (±35% for most tests and +101% for 300-nm NaCl). LDSA concentrations measured by the DM were 96% to 155% of those estimated with the SMPS. The geometric mean diameters measured with the DM were within 30% of those measured with the SMPS for monodispersed aerosols and within 25% for polydispersed aerosols (except for the case when the aerosol contained a substantial number of particles larger than 300 nm). The accuracy of the DM is reasonable for particles smaller than 300 nm, but caution should be exercised when particles larger than 300 nm are present. [Supplementary materials are available for this article. Go to the publisher's online edition of the Journal of Occupational and Environmental Hygiene for the following free supplemental resources: manufacturer-reported capabilities of instruments used, and information from the SMPS measurements for polydispersed test particles.].

Occupational exposure assessment in carbon nanotube and nanofiber primary and secondary manufacturers: mobile direct-reading sampling

RESEARCH SIGNIFICANCE: Toxicological evidence suggests the potential for a wide range of health effects from exposure to carbon nanotubes (CNTs) and carbon nanofibers (CNFs). To date, there has been much focus on the use of direct-reading instruments (DRIs) to assess multiple airborne exposure metrics for potential exposures to CNTs and CNFs due to their ease of use and ability to provide instantaneous results. Still, uncertainty exists in the usefulness and interpretation of the data. To address this gap, air-monitoring was conducted at six sites identified as CNT and CNF manufacturers or users and results were compared with filter-based metrics.

METHODS

Particle number, respirable mass, and active surface area concentrations were monitored with a condensation particle counter, a photometer, and a diffusion charger, respectively. The instruments were placed on a mobile cart and used as area monitors in parallel with filter-based elemental carbon (EC) and electron microscopy samples. Repeat samples were collected on consecutive days, when possible, during the same processes. All instruments in this study are portable and routinely used for industrial hygiene sampling.

RESULTS

Differences were not observed among the various sampled processes compared with concurrent indoor or outdoor background samples while examining the different DRI exposure metrics. Such data were also inconsistent with results for filter-based samples collected concurrently at the same sites [Dahm MM, Evans DE, Schubauer-Berigan MK et al. (2012) Occupational exposure assessment in CNT and nanofiber primary and secondary manufacturers. Ann Occup Hyg; 56: 542-56]. Significant variability was seen between these processes as well as the indoor and outdoor backgrounds. However, no clear pattern emerged linking the DRI results to the EC or the microscopy data (CNT and CNF structure counts).

CONCLUSIONS

Overall, no consistent trends were seen among similar processes at the various sites. The DRI instruments employed were limited in their usefulness in assessing and quantifying potential exposures at the sampled sites but were helpful for hypothesis generation, control technology evaluations, and other air quality issues. The DRIs employed are nonspecific, aerosol monitors, and, therefore, subject to interferences. As such, it is necessary to collect samples for analysis by more selective, time-integrated, laboratory-based methods to confirm and quantify exposures.

Size distributions and exposure concentrations of nanoparticles associated with the emissions of oil mists from fastener manufacturing processes

The aims of the present study were set out to measure size distributions and estimate workers' exposure concentrations of oil mist nanoparticles in three selected workplaces of the forming, threading, and heat treating areas in a fastener manufacturing plant by using a modified electrical aerosol detector (MEAD). The results were further compared with those simultaneously obtained from a nanoparticle surface area monitor (NSAM) and a scanning mobility particle sizer (SMPS) for the validation purpose. Results show that oil mist nanoparticles in the three selected process areas were formed mainly through the evaporation and condensation processes. The measured size distributions of nanoparticles were consistently in the form of uni-modal. The estimated fraction of nanoparticles deposited on the alveolar (AV) region was consistently much higher than that on the head airway (HD) and tracheobronchial (TB) regions in both number and surface area concentration bases. However, a significant difference was found in the estimated fraction of nanoparticles deposited on each individual region while different exposure metrics were used. Comparable results were found between results obtained from both NSAM and MEAD. After normalization, no significant difference can be found between the results obtained from SMPS and MEAD. It is concluded that the obtained MEAD results are suitable for assessing oil mist nanoparticle exposures.

Prospects and pitfalls of occupational hazard mapping: 'between these lines there be dragons'

Hazard data mapping is a promising new technique that can enhance the process of occupational exposure assessment and risk communication. Hazard maps have the potential to improve worker health by providing key input for the design of hazard intervention and control strategies. Hazard maps are developed with aid from direct-reading instruments, which can collect highly spatially and temporally resolved data in a relatively short period of time. However, quantifying spatial-temporal variability in the occupational environment is not a straightforward process, and our lack of understanding of how to ascertain and model spatial and temporal variability is a limiting factor in the use and interpretation of workplace hazard maps. We provide an example of how sources of and exposures to workplace hazards may be mischaracterized in a hazard map due to a lack of completeness and representativeness of collected measurement data. Based on this example, we believe that a major priority for research in this emerging area should focus on the development of a statistical framework to quantify uncertainty in spatially and temporally varying data. In conjunction with this need is one for the development of guidelines and procedures for the proper sampling, generation, and evaluation of workplace hazard maps.

Developing korean standard for nanomaterial exposure assessment

Nanotechnology is now applied to many industries, resulting in wide range of nanomaterial-containing products, such as electronic components, cosmetic, medicines, vehicles, and home appliances. Nanoparticles can be released throughout the life cycle of nanoproducts, including the manufacture, consumer use, and disposal, thereby involving workers, consumers, and the environment in potential exposure. However, there is no current consensus on the best sampling method for characterizing manufactured-nanoparticle exposure. Therefore, this report aims to provide a standard method for assessing nanoparticle exposure, including the identification of nanoparticle emission, the assessment of worker exposure, and the evaluation of exposure mitigation actions in nanomaterial-handling workplaces or research institutes.

Mapping particulate matter at the body weld department in an automobile assembly plant

A respiratory health survey conducted in an assembly plant in 2000-2001 found that welders had elevated rates of self-reported respiratory symptoms compared with painters and assembly workers. Subsequently, the ventilation system was improved at the body weld department. In a follow-up study, particle spatial distributions were analyzed, following a mapping protocol developed specifically for this workplace, to evaluate the effectiveness of the changes. Significant temporal and spatial variations were observed. Temporal variation during a shift was monitored with over-shift stationary sampling at fixed locations. Spatial variation was evaluated with 1-min time-weighted average particle concentrations measured throughout the process areas (212 locations). The arithmetic spatial mean across 212 locations for the respirable particles varied from 305 microg/m(3) to 501 microg/m(3) on 6 sampled days, with a standard deviation of 71 microg/m(3), indicating that the difference between before and after countermeasures must be at least 191 microg/m(3) to be considered statistically significant at the given sample sizes. The available data were not sufficient to evaluate the reduction of the particle concentrations after the countermeasures. The map of particle mass concentration revealed several high concentration areas, requiring further investigation and potentially higher level of controls. Resistance welding needed to be effectively controlled, as it could be the major particle emitting sources in the facility. The map of submicrometer (0.014 microm to 1.0 microm) particle count concentration presented different patterns from that of respirable particle mass concentration, indicating that the submicrometer particles tended to be more evenly distributed over the process areas. Workers not in proximity to intensive welding operations might be exposed to fine particles at levels higher than had traditionally been thought. Mapping was demonstrated to be an effective method to assess particle spatial distributions. A well-designed sampling protocol is critical to achieving the specific aims of a mapping study.

Use of a condensation particle counter and an optical particle counter to assess the number concentration of engineered nanoparticles

There is a need to evaluate nanoparticle (< 100 nm) exposures in occupational settings. However, portable instruments do not size segregate particles in that size range. A proxy method for determining nanoparticle count concentrations involves subtracting counts made with a condensation particle counter (CPC) from those of an optical particle counter/sizer (OPC), resulting in an estimation of "very fine" particles < 300 nm, where 300 nm is the OPC lower detection limit. However, to determine size distributions from which particles < 100 nm may be estimated, the resulting count of particles < 300 nm can be used as an additional channel of count data in addition to those obtained from the OPC. To test these methods, the very fine number concentrations determined using a CPC and OPC were compared with those from SMPS measurements and were used to verify the accuracy of a very fine particle number concentration determined by an OPC and CPC. Two "size-distribution" methods, weighted-average and log-probit, were applied to reproduce particle size distributions from OPC and CPC data and were then evaluated relative to their ability to accurately estimate the nanoparticle number concentrations. Various engineered nanoparticles were used to create test aerosols, including titanium dioxide (TiO(2)), silicon dioxide (SiO(2)), and iron oxide (Fe(2)O(3)). These materials were chosen because of their different refractive indices and therefore may be measured differently by the OPC. The count-difference method was able to estimate very fine particle number concentrations with an error between 10.9 to 58.4%. In estimating nanoparticle number concentrations using the size-distribution methods, the log-probit method resulted in the lowest percent errors that ranged from -42% to 1023%. Percent error was lower than the instrument manufacturer's indicated level of accuracy when the test aerosol refractive index was similar to that used for OPC calibration standards. Accuracy could be increased if there was an increase in the size resolution for number concentrations measured by the CPC of very fine particles and mitigation of optical effects.

New evidence on the health hazards and control of metalworking fluids since completion of the OSHA advisory committee report

BACKGROUND

Metalworking fluids (MWF) are used in the manufacture of engines, transmissions, chassis parts and other products. In 2003, OSHA denied a union petition to promulgate a standard for MWF. The 3rd Circuit Court of Appeals rejected a union lawsuit to compel OSHA to regulate MWF. OSHA relied exclusively on the 1999 Metal Working Fluids Standards Advisory Committee report, therefore, only evidence available before 1999 was quoted supporting the denial. This review was conducted to identify studies published since 1998.

METHODS

Electronic reference sources were queried for the terms for metalworking fluids, machining fluids, cutting fluids, cutting oils, coolants, machining, and machinist. All items returned were reviewed for relevance to MWF regulation.

RESULTS

The review noted 227 reports in the peer reviewed literature directly relevant to regulation of MWF exposures. Of these, 26 addressed cancer; 58 respiratory effects; 32 skin effects or absorption; 45 microbial contaminants; and 76 exposure measurements and controls. Three major studies identified excess cancer including lung, liver, pancreatic, laryngeal, and leukemia associated with MWF exposures. Reports strengthened associations of asthma and hypersensitivity pneumonitis with recent exposure to MWF.

CONCLUSIONS

Material new evidence demonstrates significant risks to material impairment of health at prevailing exposure levels and feasibility of lower exposure limits.

Aerosol monitoring during carbon nanofiber production: mobile direct-reading sampling

Detailed investigations were conducted at a facility that manufactures and processes carbon nanofibers (CNFs). Presented research summarizes the direct-reading monitoring aspects of the study. A mobile aerosol sampling platform, equipped with an aerosol instrument array, was used to characterize emissions at different locations within the facility. Particle number, respirable mass, active surface area, and photoelectric response were monitored with a condensation particle counter (CPC), a photometer, a diffusion charger, and a photoelectric aerosol sensor, respectively. CO and CO₂ were additionally monitored. Combined simultaneous monitoring of these metrics can be utilized to determine source and relative contribution of airborne particles (CNFs and others) within a workplace. Elevated particle number concentrations, up to 1.15 × 10⁶ cm⁻³, were found within the facility but were not due to CNFs. Ultrafine particle emissions, released during thermal treatment of CNFs, were primarily responsible. In contrast, transient increases in respirable particle mass concentration, with a maximum of 1.1 mg m⁻³, were due to CNF release through uncontrolled transfer and bagging. Of the applied metrics, our findings suggest that particle mass was probably the most useful and practical metric for monitoring CNF emissions in this facility. Through chemical means, CNFs may be selectively distinguished from other workplace contaminants (Birch et al., in preparation), and for direct-reading monitoring applications, the photometer was found to provide a reasonable estimate of respirable CNF mass concentration. Particle size distribution measurements were conducted with an electrical low-pressure impactor and a fast particle size spectrometer. Results suggest that the dominant CNF mode by particle number lies between 200 and 250 nm for both aerodynamic and mobility equivalent diameters. Significant emissions of CO were also evident in this facility. Exposure control recommendations were described for processes as required.

Nanoparticle emission assessment technique (NEAT) for the identification and measurement of potential inhalation exposure to engineered nanomaterials--part A

There are currently no exposure limits specific to engineered nanomaterial nor any national or international consensus standards on measurement techniques for nanomaterials in the workplace. However, facilities engaged in the production and use of engineered nanomaterials have expressed an interest in learning whether the potential for worker exposure exists. To assist with answering this question, the National Institute for Occupational Safety and Health established a nanotechnology field research team whose primary goal was to visit facilities and evaluate the potential for release of nanomaterials and worker exposure. The team identified numerous techniques to measure airborne nanomaterials with respect to particle size, mass, surface area, number concentration, and composition. However, some of these techniques lack specificity and field portability and are difficult to use and expensive when applied to routine exposure assessment. This article describes the nanoparticle emission assessment technique (NEAT) that uses a combination of measurement techniques and instruments to assess potential inhalation exposures in facilities that handle or produce engineered nanomaterials. The NEAT utilizes portable direct-reading instrumentation supplemented by a pair of filter-based air samples (source-specific and personal breathing zone). The use of the filter-based samples are crucial for identification purposes because particle counters are generally insensitive to particle source or composition and make it difficult to differentiate between incidental and process-related nanomaterials using number concentration alone. Results from using the NEAT at 12 facilities are presented in the companion article (Part B) in this issue.

Relationships among particle number, surface area, and respirable mass concentrations in automotive engine manufacturing

This study investigated the relationships between particle number, surface area, and respirable mass concentration measured simultaneously in a foundry and an automotive engine machining and assembly center. Aerosol concentrations were measured throughout each plant with a condensation particle counter for number concentration, a diffusion charger for active surface area concentration, and an optical particle counter for respirable mass concentration. At selected locations, particle size distributions were characterized with the optical particle counter and an electrical low pressure impactor. Statistical analyses showed that active surface area concentration was correlated with ultrafine particle number concentration and weakly correlated with respirable mass concentration. Correlation between number and active surface area concentration was stronger during winter (R2 = 0.6 for both plants) than in the summer (R2 = 0.38 and 0.36 for the foundry and engine plant respectively). The stronger correlation in winter was attributed to use of direct-fire gas fired heaters that produced substantial numbers of ultrafine particles with a modal diameter between 0.007 and 0.023 mu m. These correlations support findings obtained through theoretical analysis. Such analysis predicts that active surface area increasingly underestimates geometric surface area with increasing particle size, particularly for particles larger than 100 nm. Thus, a stronger correlation between particle number concentration and active surface area concentration is expected in the presence of high concentrations of ultrafine particles. In general, active surface area concentration may be a concentration metric that is distinct from particle number concentration and respirable mass concentration. For future health effects or toxicological studies involving nano-materials or ultrafine aerosols, this finding needs to be considered, as exposure metrics may influence data interpretation.

Measurements of ultrafine particle concentrations and size distribution in an iron foundry

The number and surface area concentration of ultrafine particles in an iron foundry is of interest as freshly generated ultrafine particles are produced by metal melting, pouring and molding processes. This study measured the number and surface area concentrations of ultrafine particles and their size distributions in an iron foundry using a scanning mobility particle sizer (SMPS). The 10-100 nm ultrafine particle number concentrations (NC(0.01-0.1)) and surface area concentrations (SC(0.01-0.1)) measured at the iron foundry were 2.07 x 10(4) to 2.82 x 10(5)particles cm(-3) and 67.56 to 2.13 x 10(3)microm(2)cm(-3), respectively. The concentrations changed dramatically depending on on-site manufacturing conditions. The NC(0.01-0.1) levels in the iron foundry were approximately 4.5 times higher on average compared with those in the outdoor ambient environment. These measurement results indicate that the presence of extra particles in the workplace air is within the ultrafine range. Additionally, the analytical results suggest that the number mode diameter can be used to estimate the SC(0.01-0.1) levels using the NC(0.01-0.1) levels. Moreover, the ultrafine particle number mode diameter was found to be about 46.1 nm in the iron foundry.