Imaging-based assessment of lung function in a population cooking indoors with biomass fuel: a pilot study.

Biomass fuels (wood) are commonly used indoors in underventilated environments for cooking in the developing world, but the impact on lung physiology is poorly understood. Quantitative computed tomography (qCT) can provide sensitive metrics to compare the lungs of women cooking with wood vs. liquified petroleum gas (LPG). We prospectively assessed (qCT and spirometry) 23 primary female cooks (18 biomass, 5 LPG) with no history of cardiopulmonary disease in Thanjavur, India. CT was obtained at coached total lung capacity (TLC) and residual volume (RV). qCT assessment included texture-derived ground glass opacity [GGO: Adaptive Multiple Feature Method (AMFM)], air-trapping (expiratory voxels ≤ -856HU) and image registration-based assessment [Disease Probability Measure (DPM)] of emphysema, functional small airways disease (%AirTrapDPM), and regional lung mechanics. In addition, within-kitchen exposure assessments included particulate matter <2.5 µm(PM2.5), black carbon, ß-(1, 3)-d-glucan (surrogate for fungi), and endotoxin. Air-trapping went undetected at RV via the threshold-based measure (voxels ≤ -856HU), possibly due to density shifts in the presence of inflammation. However, DPM, utilizing image-matching, demonstrated significant air-trapping in biomass vs. LPG cooks (P = 0.049). A subset of biomass cooks (6/18), identified using k-means clustering, had markedly altered DPM-metrics: greater air-trapping (P < 0.001), lower TLC-RV volume change (P < 0.001), a lower mean anisotropic deformation index (ADI; P < 0.001), and elevated % GGO (P < 0.02). Across all subjects, a texture measure of bronchovascular bundles was correlated to the log-transformed ß-(1, 3)-d-glucan concentration (P = 0.026, R = 0.46), and black carbon (P = 0.04, R = 0.44). This pilot study identified environmental links with qCT-based lung pathologies and a cluster of biomass cooks (33%) with significant small airways disease.NEW & NOTEWORTHY Quantitative computed tomography has identified a cluster of women (33%) cooking with biomass fuels (wood) with image-based markers of functional small airways disease and associated alterations in regional lung mechanics. Texture and image registration-based metrics of lung function may allow for early detection of potential inflammatory processes that may arise in response to inhaled biomass smoke, and help identify phenotypes of chronic lung disease prevalent in nonsmoking women in the developing world.

Design and testing of a personalized noise monitoring system.

Agricultural workers are more prone to noise-induced hearing loss than are many other workers. Hearing protection device use among agricultural workers is low, but training can increase hearing protection device use. This work proposes a system designed to automatically inform agricultural workers when they were exposed to noises that exceed the National Institute for Occupational Safety and Health (NIOSH) recommended exposure level. The smartphone-based system worn on the arm uses a noise dosimeter to measure noise exposures throughout the day to within ±2 A-weighted decibels of a Class 2 sound level meter. The device collects location and audio data, which are transferred to a server and presented to the worker on a locally hosted website. The website details noise exposure and helps the worker identify where exposure occurred and what specific tasks exceed NIOSH's recommended exposure limit, putting them at higher risk of noise-induced hearing loss. With this understanding, the worker is expected to adopt behavior changes and better hearing protection use at critical places and times. This pilot study evaluates the accuracy of the noise dosimeter and GPS relative to gold-standard instruments. The system was tested on a farm with outputs compared with gold-standard instruments. A-weighted, 1-sec averaged sound pressure levels and position data were collected while users were performing a variety of tasks indoors and outdoors. The smartphone's external noise dosimeter read within ±2 A-weighted decibels of the Class 2 reference dosimeter 59% of the time. The positioning devices had an average error of sub-4 m. While not perfectly matching gold-standard instruments, the device is capable of identifying and collecting information relative to loud noise events that promote noise-induced hearing loss.

Assessment of university classroom ventilation during the COVID-19 pandemic.

Ventilation plays an important role in mitigating the risk of airborne virus transmission in university classrooms. During the early phase of the COVID-19 pandemic, methods to assess classrooms for ventilation adequacy were needed. The aim of this paper was to compare the adequacy of classroom ventilation determined through an easily accessible, simple, quantitative measure of air changes per hour (ACH) to that determined through qualitative "expert judgment" and recommendations from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the American Conference of Governmental Industrial Hygienists (ACGIH)®. Two experts, ventilation engineers from facilities maintenance, qualitatively ranked buildings with classrooms on campus with regard to having "acceptable classroom ventilation." Twelve lecture classrooms were selected for further testing, including a mix of perceived adequate/inadequate ventilation. Total air change per hour (ACH) was measured to quantitatively assess ventilation through the decay of carbon dioxide in the front and rear of these classrooms. The outdoor ACH was calculated by multiplying the total ACH by the outdoor air fraction. The classrooms in a building designed to the highest ASHRAE standards (62.1 2004) did not meet ACGIH COVID-19 recommendations. Four of the classrooms met the ASHRAE criteria. However, a classroom that was anticipated to fail based on expert knowledge met the ASHRAE and ACGIH criteria. Only two classrooms passed stringent ACGIH recommendations (outdoor ACH > 6). None of the classrooms that passed ACGIH criteria were originally expected to pass. There was no significant difference in ACH measured in the front and back of classrooms, suggesting that all classrooms were well mixed with no dead zones. From these results, schools should assess classroom ventilation considering a combination of classroom design criteria, expert knowledge, and ACH measurements.

Combining physics-based and Kriging models to improve the estimation of noise exposure.

Worker exposure to occupational hazards is traditionally measured by equipping workers with wearable exposure monitors. An emerging alternative measurement first generates time-varying hazard maps from permanent monitors within the facility, then estimating worker exposure by integrating hazard levels traversed in map, following the tracked movement of workers. Complex environments may require many monitors to produce a hazard map with the necessary accuracy, but effective interpolation functions can reduce the required number of monitors needed. This work assesses the effectiveness of three models for accurately interpolating hazard levels among monitors: a traditional Kriging model, a physics-based model, and a hybrid model that combines the Kriging and physics-based models. The effectiveness of each interpolation function was tested with sound levels collected in four environmental settings. These detailed experimental data were used to generate over 10,000 simulation trials, where each trial configured the experimental data into a unique arrangement of simulated monitoring and sampling positions. For each simulation trial, the effectiveness of the three models was assessed with the root mean square error of the sound levels at the simulated sampling positions, using the simulated monitoring positions as input. The interpolated values between the monitored positions were analyzed separately from the extrapolated values beyond the monitored positions. The hybrid model consistently reported among the lowest errors in each trial. The Kriging model performed best for the densest networks (those with the most monitors). Even in these cases, the hybrid model performed within 10% of the Kriging model with less than a third of the monitors. The experiment demonstrates that the hybrid model is highly effective at estimating hazardous sound levels; future work may demonstrate similar advantages for gas and aerosol hazards.

Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

Assessment of occupational personal sound exposures for music instructors.

Daily activities performed by music instructors generate high sound levels that could potentially lead to overexposure. Adverse outcomes associated with high-exposure to sound, such as hearing loss and tinnitus, can be especially devastating to music instructors as hearing is essential to both job performance and career reward. The primary objective of this study was to compare sound exposures of music instructors to recommended exposure limits. Secondary objectives were to identify high-exposure activities and to evaluate potential similar exposure groups by examining between- and within-worker exposure variability. Personal sound exposure measurements from music instructors were collected using dosimeters during full workdays for up to 4 weeks over multiple semesters at a university's school of music. Study participants completed an activity log to record work-related activities throughout each day of sampling. Dosimeters logged 1-sec sound equivalent levels in A-weighted decibels. These data were used to calculate 8-hr time-weighted averages, daily dose, and activity-specific contributions to that dose to determine if daily exposures exceeded the recommended limit of 85 dBA and to identify high-exposure activities that could be targeted for future intervention. Seventeen participants were sampled for a total of 200 days. Approximately one-third of daily exposures exceeded recommended limits. The groups with the highest exposures were brass and conducting instructors. Conductors experienced the highest between-day variability in daily exposures. Activities that contributed the most to daily dose included group rehearsals, personal practice sessions, and performances, while classes and administrative work did not substantially contribute to daily dose. Daily exposures were highly variable, ranging from 60-95 dBA (mean = 81 dBA, sd = 8 dBA), and were influenced by instructional area and musical activity. Future exposure assessments for music instructors should include sampling for multiple days, and those above-recommended limits should be placed into hearing conservation programs.

Comparing respirator laboratory protection factors measured with novel personal instruments to those from the PortaCount.

A quantitative fit test is performed using a benchtop instrument (e.g., TSI PortaCount) to assess the fit factor provided by a respirator when assigned to a worker. There are no wearable instruments on the market to measure protection factors while the respirator is in use. The aim of this study is to evaluate two new, wearable, quantitative instruments-a dual-channel optical particle counter (DC OPC) and a dual-channel condensation particle counter (DC CPC)-that would enable in-situ, real-time measurement of respirator workplace protection factor. Respirator laboratory protection factors measured by the new instruments were compared to those measured with the TSI PortaCount on one test subject for three test aerosols (sodium chloride, incense, ambient) at target laboratory protection factors of 100, 300, and 1,000 for sodium chloride and ambient, and 75 and 500 for incense. Three replicates were performed for each test condition. Data were analyzed with a two-sided paired t-test at a significance level of 0.05. Laboratory protection factors measured with the DC CPC agree with those measured with the PortaCount whereas those from the DC OPC generally do not. Mean laboratory protection factors derived from the DC CPC are only statistically significantly different for mean values of a laboratory protection factor at ambient conditions for a target laboratory protection factor of 300 (p = 0.02) and for incense at a target laboratory protection factor of 75 (p = 0.03). Although statistically significant, the difference in laboratory protection factors derived from the DC CPC are not substantial in practice and may be explained by systematic uncertainty. In contrast, the DC OPC reports substantially larger mean laboratory protection factors, differing by about half an order of magnitude in extreme cases, and statistically significantly different mean laboratory protection factors for the sodium chloride aerosol for target laboratory protection factors of 100 and 300 (p = 0.01 and p = 0.01).

Lung function of primary cooks using LPG or biomass and the effect of particulate matter on airway epithelial barrier integrity.

BACKGROUND: Cooks exposed to biomass fuel experience increased risk of respiratory disease and mortality. We sought to characterize lung function and environmental exposures of primary cooking women using two fuel-types in southeastern India, as well as to investigate the effect of particulate matter (PM) from kitchens on human airway epithelial (HAE) cells in vitro. METHODS: We assessed pre- and post-bronchodilator lung function on 25 primary female cooks using wood biomass or liquified petroleum gas (LPG), and quantified exposures from 34 kitchens (PM2.5, PM < 40 µm, black carbon, endotoxin, and PM metal and bacterial content). We then challenged HAE cells with PM, assessing its cytotoxicity to small-airway cells (A549) and its effect on: transepithelial conductance and macromolecule permeability (NuLi cells), and antimicrobial activity (using airway surface liquid, ASL, from primary HAE cells). RESULTS: Lung function was impaired in cooks using both fuel-types. 60% of participants in both fuel-types had respiratory restriction (post bronchodilator FEV1/FVC>90). The remaining 40% in the LPG group had normal spirometry (post FEV1/FVC = 80-90), while only 10% of participants in the biomass group had normal spirometry, and the remaining biomass cooks (30%) had respiratory obstruction (post FEV1/FVC<80). Significant differences were found in environmental parameters, with biomass kitchens containing greater PM2.5, black carbon, zirconium, arsenic, iron, vanadium, and endotoxin concentrations. LPG kitchens tended to have more bacteria (p = 0.14), and LPG kitchen PM had greater sulphur concentrations (p = 0.02). In vitro, PM induced cytotoxicity in HAE A549 cells in a dose-dependent manner, however the effect was minimal and there were no differences between fuel-types. PM from homes of participants with a restrictive physiology increased electrical conductance of NuLi HAE cells (p = 0.06) and decreased macromolar permeability (p ≤ 0.05), while PM from homes of those with respiratory obstruction tended to increase electrical conductance (p = 0.20) and permeability (p = 0.07). PM from homes of participants with normal spirometry did not affect conductance or permeability. PM from all homes tended to inhibit antimicrobial activity of primary HAE cell airway surface liquid (p = 0.06). CONCLUSIONS: Biomass cooks had airway obstruction, and significantly greater concentrations of kitchen environmental contaminants than LPG kitchens. PM from homes of participants with respiratory restriction and obstruction altered airway cell barrier function, elucidating mechanisms potentially responsible for respiratory phenotypes observed in biomass cooks.

Continuous in-home PM2.5 concentrations of smokers with and without a history of respiratory exacerbations in Iowa, during and after an air purifier intervention.

BACKGROUND: Americans spend most of their time indoors. Indoor particulate matter (PM) 2.5 µm and smaller (PM2.5) concentrations often exceed ambient concentrations. Therefore, we tested whether the use of an air purifying device (electrostatic precipitator, ESP) could reduce PM2.5 in homes of smokers with and without respiratory exacerbations, compared with baseline. METHODS: We assessed PM2.5 concentrations in homes of subjects with and without a recent (≤3 years) history of respiratory exacerbation. We compared PM2.5 concentrations during 1 month of ESP use with those during 1 month without ESP use. RESULTS: Our study included 19 subjects (53-80 years old), nine with a history of respiratory exacerbation. Geometric mean (GM) PM2.5 and median GM daily peak PM2.5 were significantly lower during ESP deployment compared with the equivalent time-period without the ESP (GSD = 0.50 and 0.37 µg/m3, respectively, p < 0.001). PM2.5 in homes of respiratory exacerbators tended (p < 0.14) to be higher than PM2.5 in homes of those without a history of respiratory exacerbation. CONCLUSIONS: Subjects with a history of respiratory exacerbation tended to have higher mean, median, and mean peak PM2.5 concentrations compared with homes of subjects without a history of exacerbations. The ESP intervention reduced in-home PM2.5 concentrations, demonstrating its utility in reducing indoor exposures. NOVELTY OF STUDY: Our work characterizes PM air pollution concentrations in homes of study subjects with and without respiratory exacerbations. We demonstrate that PM concentrations tend to be higher in homes of participants with respiratory exacerbations, and that the use of an inexpensive air purifier resulted in significantly lower daily average PM concentrations than when the purifier was not present. Our results provide a helpful intervention strategy for purifying indoor air and may be useful for susceptible populations.

Sources of error and variability in particulate matter sensor network measurements.

The quality of mass concentration estimates from increasingly popular networks of low-cost particulate matter sensors depends on accurate conversion of sensor output (e.g., voltage) into gravimetric-equivalent mass concentration, typically using a calibration procedure. This study evaluates two important sources of variability that lead to error in estimating gravimetric-equivalent mass concentration: the temporal changes in sensor calibration and the spatial and temporal variability in gravimetric correction factors. A 40-node sensor network was deployed in a heavy vehicle manufacturing facility for 8 months. At a central location in the facility, particulate matter was continuously measured with three sensors of the network and a traditional, higher-cost photometer, determining the calibration slope and intercept needed to translate sensor output to photometric-equivalent mass concentration. Throughout the facility, during three intensive sampling campaigns, respirable mass concentrations were measured with gravimetric samplers and photometers to determine correction factors needed to adjust photometric-equivalent to gravimetric-equivalent mass concentration. Both field-determined sensor calibration slopes and intercepts were statistically different than those estimated in the laboratory (α = 0.05), emphasizing the importance of aerosol properties when converting voltage to photometric-equivalent mass concentration and the need for field calibration to determine slope. Evidence suggested the sensors' weekly field calibration slope decreased and intercept increased, indicating the sensors were deteriorating over time. The mean correction factor in the cutting and shot blasting area (2.9) was substantially and statistically lower than that in the machining and welding area (4.6; p = 0.01). Therefore, different correction factors should be determined near different occupational processes to accurately estimate particle mass concentrations.

Size, composition, morphology, and health implications of airborne incidental metal-containing nanoparticles.

There is great concern regarding the adverse health implications of engineered nanoparticles. However, there are many circumstances where the production of incidental nanoparticles, i.e., nanoparticles unintentionally generated as a side product of some anthropogenic process, is of even greater concern. In this study, metal-based incidental nanoparticles were measured in two occupational settings: a machining center and a foundry. On-site characterization of substrate-deposited incidental nanoparticles using a field-portable X-ray fluorescence provided some insights into the chemical characteristics of these metal-containing particles. The same substrates were then used to carry out further off-site analysis including single-particle analysis using scanning electron microscopy and energy-dispersive X-ray spectroscopy. Between the two sites, there were similarities in the size and composition of the incidental nanoparticles as well as in the agglomeration and coagulation behavior of nanoparticles. In particular, incidental nanoparticles were identified in two forms: submicrometer fractal-like agglomerates from activities such as welding and supermicrometer particles with incidental nanoparticles coagulated to their surface, herein referenced as nanoparticle collectors. These agglomerates will affect deposition and transport inside the respiratory system of the respirable incidental nanoparticles and the corresponding health implications. The studies of incidental nanoparticles generated in occupational settings lay the groundwork on which occupational health and safety protocols should be built.

Evaluation of a Portable Aerosol Collector and Spectrometer to measure particle concentration by composition and size.

We evaluated a newly developed Portable Aerosol Collector and Spectrometer (PACS) in the laboratory. We developed an algorithm to estimate mass concentration by size and composition with a PACS. In laboratory experiments, we compared particle size distributions measured with the PACS to research instruments for multi-modal aerosols: two-mode generated by spark discharge, consisting of ultrafine (fresh Mn fume) and fine particles (aged Cu fume); and three-mode produced by adding coarse particles (Arizona road dust) to the two-mode. Near-real-time size distributions from the PACS compared favorably to those from a scanning mobility particle sizer and an aerodynamic particle sizer for the three-mode aerosol (number, bias=9.4% and R2=0.96; surface area, bias=17.8%, R2=0.77; mass, bias=-2.2%, R2=0.94), but less so for the two-mode aerosol (number, bias=-17.7% and R2=0.51; surface area, bias=-45.5%, R2=0; for mass, bias=-81.75%, R2=0.08). Elemental mass concentrations by size were similar to those measured with a nano micro-orifice uniform deposition impactor for coarse-mode particles, whereas agreement was considerably poorer for ultrafine- and fine-mode particles. The PACS has merit in estimating multi-metric concentrations by size and composition but requires further research to resolve discrepancies identified for two-mode aerosol.

Design and Evaluation of a High-Flowrate Nanoparticle Respiratory Deposition (NRD) Sampler.

A high-flow (10 L/min) nanoparticle respiratory deposition (NRD) sampler was designed and evaluated to achieve reduced limits of quantification (LOQs) for metal nanoparticles. The high-flow NRD consists of an inlet, impactor stage, diffusion stage, and a final filter. An impactor stage with 12 nozzles was designed from theory to achieve a cut-off diameter of 300 nm at 50% particle collection efficiency (d50). Various depths of 37-mm-diameter polyurethane foam cylinders were tested for the diffusion stage to obtain a collection efficiency curve similar to the deposition of nanoparticles in the human respiratory tract, known as the nanoparticulate matter (NPM) criterion. The objective for the final filter was a collection efficiency of near 100% with minimal pressure drop. The collection efficiencies by size and pressure drops were measured for all NRD sampler components. The final design of the impactor stage nozzle achieved a d50 of 305 nm. The collection efficiency for the diffusion stage with a depth of 7 cm when adjusted for presence of the impactor was the closest to the NPM curve with a R2 value of 0.96 and d50 of 43 nm. Chemical analysis of the metal content for foam affirmed that the high-flow NRD sampler required less sampling time to meet metal LOQs than the 2.5 L/min NRD sampler. The final filter with a modified support pad had a collection efficiency near 100%. The overall pressure drop of the sampler of 8.5 kPa (34 in. H2O) could not be handled by commercial personal sampling pumps. Hence the high-flow NRD sampler can be used as an area sampler or without the final filter for collection of nanoparticles.

Indoor Particulate Matter From Smoker Homes Induces Bacterial Growth, Biofilm Formation, and Impairs Airway Antimicrobial Activity. A Pilot Study.

Background: Particulate matter (PM) air pollution causes deleterious health effects; however, less is known about health effects of indoor air particulate matter (IAP). Objective: To understand whether IAP influences distinct mechanisms in the development of respiratory tract infections, including bacterial growth, biofilm formation, and innate immunity. Additionally, we tested whether IAP from Iowa houses of subjects with and without recent respiratory exacerbations recapitulated the National Institute of Standards and Technology (NIST) IAP findings. Methods: To test the effect of NIST and Iowa IAP on bacterial growth and biofilm formation, we assessed Staphylococcus aureus growth and Pseudomonas aeruginosa biofilm formation with and without the presence of IAP. To assess the effect of IAP on innate immunity, we exposed primary human airway surface liquid (ASL) to NIST, and Iowa IAP. Lastly, we tested whether specific metals may be responsible for effects on airway innate immunity. Results: NIST and Iowa IAP significantly enhanced bacterial growth and biofilm formation. NIST IAP (whole particle and the soluble portion) impaired ASL antimicrobial activity. IAP from one Iowa home significantly impaired ASL antimicrobial activity (p < 0.05), and five other homes demonstrated a trend (p ≤ 0.18) of impaired ASL antimicrobial activity. IAP from homes of subjects with a recent history of respiratory exacerbation tended (p = 0.09) to impair ASL antimicrobial activity more than IAP from homes of those without a history respiratory exacerbation. Aluminum and Magnesium impaired ASL antimicrobial activity, while copper was bactericidal. Combining metals varied their effect on ASL antimicrobial activity. Conclusions: NIST IAP and Iowa IAP enhanced bacterial growth and biofilm formation. ASL antimicrobial activity was impaired by NIST IAP, and Iowa house IAP from subjects with recent respiratory exacerbation tended to impair ASL antimicrobial activity. Individual metals may explain impaired ASL antimicrobial activity; however, antimicrobial activity in the presence of multiple metals warrants further study.

Physicochemical properties of air discharge-generated manganese oxide nanoparticles: Comparison to welding fumes.

Exposures to high doses of manganese (Mn) via inhalation, dermal contact or direct consumption can cause adverse health effects. Welding fumes are a major source of manganese containing nanoparticles in occupational settings. Understanding the physicochemical properties of manganese-containing nanoparticles can be a first step in understanding their toxic potential following exposure. In particular, here we compare the size, morphology and Mn oxidation states of Mn oxide nanoparticles generated in the laboratory by arc discharge to those from welding collected in heavy vehicle manufacturing. Fresh nanoparticles collected at the exit of the spark discharge generation chamber consisted of individual or small aggregates of primary particles. These nanoparticles were allowed to age in a chamber to form chain-like aggregates of primary particles with morphologies very similar to welding fumes. The primary particles were a mixture of hausmannite (Mn3O4), bixbyite (Mn2O3) and manganosite (MnO) phases, whereas aged samples revealed a more amorphous structure. Both Mn2+ and Mn3+, as in double valence stoichiometry present in Mn3O4, and Mn3+, as in Mn2O3 and MnOOH, were detected by X-ray photoelectron spectroscopy on the surface of the nanoparticles in the laboratory nanoparticles and welding fumes. Dissolution studies conducted for these two Mn samples (aged and fresh fume) reveal different release kinetics of Mn ions in artificial lysosomal fluid (pH 4.5) and very limited dissolution in Gamble's solution (pH 7.4). Taken together, these data suggest several important considerations for understanding the health effects of welding fumes. First, the method of particle generation affects the crystallinity and phase of the oxide. Second, welding fumes consist of multiple oxidation states whether they are amorphous or crystalline or occur as isolated nanoparticles or agglomerates. Third, although the dissolution behavior depends on conditions used for nanoparticle generation, the dissolution of Mn oxide nanoparticles in the lysosome may promote Mn ions translocation into various organs causing toxic effects.

Identifying determinants of noise in a medical intensive care unit.

Continuous and intermittent exposure to noise elevates stress, increases blood pressure, and disrupts sleep among patients in hospital intensive care units. The purpose of this study was to determine the effectiveness of a behavior-based intervention to reduce noise and to identify determinants of noise in a medical intensive care unit. Staff were trained for 6 weeks to reduce noise during their activities in an effort to keep noise levels below 55 dBA during the day and below 50 dBA at night. One-min noise levels were logged continuously in patient rooms 8 weeks before and after the intervention. Noise levels were compared by room position, occupancy status, and time of day. Noise levels from flagged days (>60 dBA for >10 hr) were correlated with activity logs. The intervention was ineffective, with noise frequently exceeding project goals during the day and night. Noise levels were higher in rooms with the oldest heating, ventilation, and air-conditioning system, even when patient rooms were unoccupied. Of the flagged days, the odds of noise over 60 dBA occurring was 5.3 dBA higher when high-flow respiratory support devices were in use compared to times with low-flow devices in use (OR = 5.3, 95% CI = 5.0-5.5). General sources, like the heating, ventilation, and air-conditioning system, contribute to high baseline noise and high-volume (>10 L/min) respiratory-support devices generate additional high noise (>60 dBA) in Intensive Care Unit patient rooms. This work suggests that engineering controls (e.g., ventilation changes or equipment shielding) may be more effective in reducing noise in hospital intensive care units than behavior modification alone.

Particle Concentrations in Occupational Settings Measured with a Nanoparticle Respiratory Deposition (NRD) Sampler.

There is an increasing need to evaluate concentrations of nanoparticles in occupational settings due to their potential negative health effects. The Nanoparticle Respiratory Deposition (NRD) personal sampler was developed to collect nanoparticles separately from larger particles in the breathing zone of workers, while simultaneously providing a measure of respirable mass concentration. This study compared concentrations measured with the NRD sampler to those measured with a nano Micro Orifice Uniform-Deposit Impactor (nanoMOUDI) and respirable samplers in three workplaces. The NRD sampler performed well at two out of three locations, where over 90% of metal particles by mass were submicrometer particle size (a heavy vehicle machining and assembly facility and a shooting range). At the heavy vehicle facility, the mean metal mass concentration of particles collected on the diffusion stage of the NRD was 42.5 ± 10.0 µg/m3, within 5% of the nanoMOUDI concentration of 44.4 ± 7.4 µg/m3. At the shooting range, the mass concentration for the diffusion stage of the NRD was 5.9 µg/m3, 28% above the nanoMOUDI concentration of 4.6 µg/m3. In contrast, less favorable results were obtained at an iron foundry, where 95% of metal particles by mass were larger than 1 µm. The accuracy of nanoparticle collection by NRD diffusion stage may have been compromised by high concentrations of coarse particles at the iron foundry, where the NRD collected almost 5-fold more nanoparticle mass compared to the nanoMOUDI on one sampling day and was more than 40% different on other sampling days. The respirable concentrations measured by NRD samplers agreed well with concentrations measured by respirable samplers at all sampling locations. Overall, the NRD sampler accurately measured concentrations of nanoparticles in industrial environments when concentrations of large, coarse mode, particles were low.

Low-Cost, Distributed Environmental Monitors for Factory Worker Health.

An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The R² for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 μg/m³. The R² for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R² for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.

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.

Development of a Portable Aerosol Collector and Spectrometer (PACS).

This article presents the development of a Portable Aerosol Collector and Spectrometer (PACS), an instrument designed to measure particle number, surface area, and mass concentrations continuously and time-weighted mass concentration by composition from 10 nm to 10 µm. The PACS consists of a six-stage particle size selector, a valve system, a water condensation particle counter to detect number concentrations, and a photometer to detect mass concentrations. The stages of the selector include three impactor and two diffusion stages, which resolve particles by size and collect particles for later chemical analysis. Particle penetration by size was measured through each stage to determine actual collection performance and account for particle losses. The data inversion algorithm uses an adaptive grid-search process with a constrained linear least-square solver to fit a tri-modal (ultrafine, fine, and coarse), log-normal distribution to the input data (number and mass concentration exiting each stage). The measured 50% cutoff diameter of each stage was similar to the design. The pressure drop of each stage was sufficiently low to permit its operation with portable air pumps. Sensitivity studies were conducted to explore the influence of unknown particle density (range from 500 to 3,000 kg/m3) and shape factor (range from 1.0 to 3.0) on algorithm output. Assuming standard density spheres, the aerosol size distributions fit well with a normalized mean bias of -4.9% to 3.5%, normalized mean error of 3.3% to 27.6%, and R2 values of 0.90 to 1.00. The fitted number and mass concentration biases were within ±10% regardless of uncertainties in density and shape. However, fitted surface area concentrations were more likely to be underestimated/overestimated due to the variation in particle density and shape. The PACS represents a novel way to simultaneously assess airborne aerosol composition and concentration by number, surface area, and mass over a wide size range.