Distribution of particle and gas concentrations in Swine gestation confined animal feeding operations

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.

Particulate Matter in Swine Barns: A Comprehensive Review

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Due to the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. As PM in swine barns is primarily of biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

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.

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.

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.

Assessment of Interventions to Improve Air Quality in a Livestock Building

This study examined the effectiveness of engineering controls to reduce contaminant concentrations in a swine farrowing room during winter in the U.S. Midwest. Over two winters, changes in air quality were evaluated following installation of a 1700 m3 h-1 (1000 cfm) recirculating ventilation system to provide 5.4 air exchanges per hour. This system incorporated one of two readily available dust control systems, one based on filtration and the other on cyclonic treatment. A second treatment evaluated reductions in carbon dioxide (CO2) associated with replacement of standard, unvented gas-fired heaters with new vented heaters, installed between the two winter test periods. The concentrations of carbon monoxide and hydrogen sulfide were negligible in the test room. Although concentrations of ammonia increased over each winter test period, the increase was unrelated to increased air movement from the new recirculating ventilation system. The dust concentrations were significantly reduced by the ventilation system for both inhalable dust (23% to 44% with filtration, 33% with cyclone) and respirable dust (32% with filtration, 20% with cyclone), significant (p 0.024) for all except respirable dust using the cyclone (p = 0.141). The filtration unit is recommended to improve livestock building air quality because it was more effective than the cyclone unit at reducing respirable dust. Carbon dioxide concentrations were significantly lower with vented heaters (mean = 1400 ppm, SD = 330 ppm) compared to unvented heaters (mean = 2480 ppm, SD = 160 ppm). A 940 ppm reduction in CO2 was attributed to the use of the vented heater, after accounting for differences in outdoor temperatures and animal housing over both test periods. The benefits of readily available technology to significantly reduce concentrations of dust and CO2 demonstrates useful control options to improve air quality in swine buildings.

An inexpensive sensor for noise

Noise is a pervasive workplace hazard that varies spatially and temporally. The cost of direct-reading instruments for noise hampers their use in a network. The objectives for this work were to: (1) develop an inexpensive noise sensor (<$100) that measures A-weighted sound pressure levels within ±2 dBA of a Type 2 sound level meter (SLM; ∼$1,800); and (2) evaluate 50 noise sensors for use in an inexpensive sensor network. The inexpensive noise sensor consists of an electret condenser microphone, an amplifier circuit, and a microcontroller with a small form factor (28 mm by 47 mm by 9 mm) than can be operated as a stand-alone unit. Laboratory tests were conducted to evaluate 50 of the new sensors at 5 sound levels: (1) ambient sound in a quiet office; (2) 3 pink noise test signals from 65-85 dBA in 10 dBA increments; and (3) 94 dBA using a SLM calibrator. Ninety-four percent of the noise sensors (n = 46) were within ±2 dBA of the SLM for sound levels from 65-94 dBA. As sound level increased, bias decreased, ranging from 18.3% in the quiet office to 0.48% at 94 dBA. Overall bias of the sensors was 0.83% across the 75 dBA to 94 dBA range. These sensors are available for a variety of uses and can be customized for many applications, including incorporation into a stationary sensor network for continuous monitoring of noise in manufacturing environments.

Effect of low-level CO2 on innate inflammatory protein response to organic dust from swine confinement barns

BACKGROUND

Organic hog barn dust (HDE) exposure induces lung inflammation and long-term decreases in lung function in agricultural workers. While concentrations of common gasses in confined animal facilities are well characterized, few studies have been done addressing if exposure to elevated barn gasses impacts the lung immune response to organic dusts. Given the well documented effects of hypercapnia at much higher levels we hypothesized that CO2 at 8 h exposure limit levels (5000 ppm) could alter innate immune responses to HDE.

METHODS

Using a mouse model, C57BL/6 mice were nasally instilled with defined barn dust extracts and then housed in an exposure box maintained at one of several CO2 levels for six hours. Bronchiolar lavage (BAL) was tested for several cytokines while lung tissue was saved for mRNA purification and immunohistochemistry.

RESULTS

Exposure to elevated CO2 significantly increased the expression of pro-inflammatory markers, IL-6 and KC, in BAL fluid as compared to dust exposure alone. Expression of other pro-inflammatory markers, such as ICAM-1 and matrix metalloproteinase-9 (MMP-9), were also tested and showed similar increased expression upon HDE + CO2 exposure. A chemokine array analysis of BAL fluid revealed that MIP-1γ (CCL9) shows a similar increased response to HDE + CO2. Further testing showed CCL9 was significantly elevated by barn dust and further enhanced by CO2 co-exposure in a dose-dependent manner that was noticeable at the protein and mRNA levels. In all cases, except for ICAM-1, increases in tested markers in the presence of elevated CO2 were only significant in the presence of HDE as well.

CONCLUSIONS

We show that even at mandated safe exposure limits, CO2 is capable of enhancing multiple markers of inflammation in response to HDE.

STATIC AND ROVING SENSOR DATA FUSION FOR SPATIO-TEMPORAL HAZARD MAPPING WITH APPLICATION TO OCCUPATIONAL EXPOSURE ASSESSMENT

Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.

Simulation of air quality and operational cost to ventilate swine farrowing facilities in Midwest U.S. during winter

We have developed a time-dependent simulation model to estimate in-room concentrations of multiple contaminants [ammonia (NH3), carbon dioxide (CO2), carbon monoxide (CO) and dust] as a function of increased ventilation with filtered recirculation for swine farrowing facilities. Energy and mass balance equations were used to simulate the indoor air quality (IAQ) and operational cost for a variety of ventilation conditions over a 3-month winter period for a facility located in the Midwest U.S., using simplified and real-time production parameters, comparing results to field data. A revised model was improved by minimizing the sum of squared errors (SSE) between modeled and measured NH3 and CO2. After optimizing NH3 and CO2, other IAQ results from the simulation were compared to field measurements using linear regression. For NH3, the coefficient of determination (R2) for simulation results and field measurements improved from 0.02 with the original model to 0.37 with the new model. For CO2, the R2 for simulation results and field measurements was 0.49 with the new model. When the makeup air was matched to hallway air CO2 concentrations (1,500 ppm), simulation results showed the smallest SSE. With the new model, the R2 for other contaminants were 0.34 for inhalable dust, 0.36 for respirable dust, and 0.26 for CO. Operation of the air cleaner decreased inhalable dust by 35% and respirable dust concentrations by 33%, while having no effect on NH3, CO2, in agreement with field data, and increasing operational cost by $860 (58%) for the three-month period.

Effects of data sparsity and spatiotemporal variability on hazard maps of workplace noise

Personal sampling, considered a state-of-the-art technique to assess worker exposures to occupational hazards, is often conducted for the duration of a work shift so that time-weighted average (TWA) exposures may be evaluated relative to published occupational exposure limits (OELs). Such cross-shift measurements, however, provide little information on the spatial variability of exposures, except after a very large number of samples. Hazard maps, contour plots (or similar depiction) of hazard intensity throughout the workplace, have gained popularity as a way to locate sources and to visualize spatial variability of physical and chemical hazards within a facility. However, these maps are often generated from short duration measures and have little ability to assess temporal variability. To assess the potential bias that results from the use of short-duration measurements to represent the TWA in a hazard map, noise intensity measurements were collected at high spatial and temporal resolution in two facilities. Static monitors were distributed throughout the facility and used to capture the temporal variability at these locations. Roving monitors (typical of the hazard mapping process) captured spatial variability over multiple traverses through the facility. The differences in hazards maps generated with different sampling techniques were evaluated. Hazard maps produced from sparse, roving monitor data were in good agreement with the TWA hazard maps at the facility with low temporal variability. Estimated values were within 5 dB of the TWA over approximately 90% of the facility. However, at the facility with higher temporal variability, large differences between hazard maps were observed for different traverses through the facility. On the second day of sampling, estimates were at least 5 dB different than the TWA for more than half of the locations within the facility. The temporal variability of noise was found to have a greater influence on map accuracy than the spatial sampling resolution.

Performing T-tests to Compare Autocorrelated Time Series Data Collected from Direct-Reading Instruments

Industrial hygienists now commonly use direct-reading instruments to evaluate hazards in the workplace. The stored values over time from these instruments constitute a time series of measurements that are often autocorrelated. Given the need to statistically compare two occupational scenarios using values from a direct-reading instrument, a t-test must consider measurement autocorrelation or the resulting test will have a largely inflated type-1 error probability (false rejection of the null hypothesis). A method is described for both the one-sample and two-sample cases which properly adjusts for autocorrelation. This method involves the computation of an "equivalent sample size" that effectively decreases the actual sample size when determining the standard error of the mean for the time series. An example is provided for the one-sample case, and an example is given where a two-sample t-test is conducted for two autocorrelated time series comprised of lognormally distributed measurements.

Modeled effectiveness of ventilation with contaminant control devices on indoor air quality in a swine farrowing facility

Because adverse health effects experienced by swine farm workers in concentrated animal feeding operations (CAFOs) have been associated with exposure to dust and gases, efforts to reduce exposures are warranted, particularly in winter seasons when exposures increase due to decreased ventilation. Simulation of air quality and operating costs for ventilating swine CAFO, including treating and recirculating air through a farrowing room, was performed using mass and energy balance equations over a 90-day winter season. System operation required controlling heater operation to achieve room temperatures optimal to ensure animal health (20 to 22.5 °C). Five air pollution control devices, four room ventilation rates, and five recirculation patterns were examined. Inhalable dust concentrations were easily reduced using standard industrial air pollution control devices, including a cyclone, filtration, and electrostatic precipitator. Operating ventilation systems at 0.94 m3 s(-1) (2000 cfm) with 75 to 100% recirculation of treated air from cyclone, electrostatic precipitator, and shaker dust filtration system achieves adequate particle control with operating costs under $1.00 per pig produced ($0.22 to 0.54), although carbon dioxide (CO2) concentrations approach 2000 ppm using in-room ventilated gas fired heaters. In no simulation were CO2 concentrations below industry recommended concentrations (1540 ppm), but alternative heating devices could reduce CO2 to acceptable concentrations. While this investigation does not represent all production swine farrowing barns, which differ in characteristics including room dimensions and swine occupancy, the simulation model and ventilation optimization methods can be applied to other production sites. This work shows that ventilation may be a cost-effective control option in the swine industry to reduce exposures.

Simulation of air quality and cost to ventilate swine farrowing facilities in winter

We developed a simulation model to study the effect of ventilation airflow rate with and without filtered recirculation on airborne contaminant concentrations (dust, NH3, CO, and CO2) for swine farrowing facilities. Energy and mass balance equations were used to simulate the indoor air quality and operational cost for a variety of ventilation conditions over a 3-month winter period, using time-varied outdoor temperature. The sensitivity of input and output parameters on indoor air quality and operational cost were evaluated. Significant factors affecting model output included mean winter temperature, generation rate of contaminants, pit-air-exchange ratio, and recirculation ratio. As mean outdoor temperature was decreased from -2.5 °C to -12.5 °C, total operational costs were increased from $872 to $1304. Dust generation rate affected dust concentrations linearly. When dust generation rates changed -50% and +100% from baseline, indoor dust concentrations were changed -50% and +100%, respectively. The selection of a pit-air-exchange ratio was found critical to NH3 concentration, but has little impact on other contaminants or cost. As the pit-air-exchange ratio was increased from 0.1 to 0.3, the NH3 concentration was increased by a factor of 1.5. The recirculation ratio affected both IAQ factors and total operational cost. As the recirculation ratio decreased to 0, inhalable and respirable dust concentrations, humidity, NH3 and CO2 concentrations decreased and total operational cost ($2216) was 104% more than with pit-fan-only ventilation ($1088). When the recirculation ratio was 1, the total operational cost was increased by $573 (53%) compared to pit-fan-only. Simulation provides a useful tool for examining the costs and benefits to installing common ventilation technology to CAFO and, ultimately, making sound management decisions.

Wintertime factors affecting contaminant distribution in a swine farrowing room

An estimated 200,000 to 500,000 U.S. workers in concentrated animal feeding operations (CAFOs) are at risk of adverse respiratory outcomes from exposures to indoor contaminants. In the wintertime, general ventilation is minimized in the Midwest due to high heating costs required to maintain indoor temperatures optimal for animal production. Pit fans typically operate to exhaust under-floor manure pits, but little other fresh air intake exists. Many operators believe that these systems are sufficient to reduce contaminant concentrations within the building during winter. Investigating whether these pit fans provide sufficient protection against classic CAFO contaminants during minimal wintertime ventilation was warranted. Direct-reading instruments were used to measure and record concentrations of multiple contaminants using both fixed-area and mobile contaminant mapping in a farrowing room during a Midwest winter. With the exception of CO, concentrations were significantly (p<0.001) higher with the pit fan off compared with those with the pit fan on. Additional analyses identified that significant changes (p<0.001) in mean room concentrations of respirable dust (decreased, 77% with pit fan off and 87% with pit fan on) and CO2 (increased, 24%) over the 5-hr study periods and that multiple fixed-area monitors rather than the much-used, single center-of-room monitor provided a more conservative (e.g., protective) assessment of room concentrations. While concentrations did not exceed occupational exposure limits from OSHA or ACGIH for individual contaminants, recommended agricultural health limits from exposure-response studies suggested in the literature were exceeded for respirable dust, CO2, and NH3, indicating a need to consider personal exposures and control options to reduce contaminant concentrations in farrowing rooms. Pit fans reduced NH3 and H2S concentrations, but these fans may not be sufficient to control dust and eliminate the need for secondary exposure prevention methods.

Influence of analysis methods on interpretation of hazard maps

Exposure or hazard mapping is becoming increasingly popular among industrial hygienists. Direct-reading instruments used for hazard mapping of data collection are steadily increasing in reliability and portability while decreasing in cost. Exposure measurements made with these instruments generally require no laboratory analysis although hazard mapping can be a time-consuming process. To inform decision making by industrial hygienists and management, it is crucial that the maps generated from mapping data are as accurate and representative as possible. Currently, it is unclear how many sampling locations are necessary to produce a representative hazard map. As such, researchers typically collect as many points as can be sampled in several hours and interpolation methods are used to produce higher resolution maps. We have reanalyzed hazard-mapping data sets from three industrial settings to determine which interpolation methods yield the most accurate results. The goal is to provide practicing industrial hygienists with some practical guidelines to generate accurate hazard maps with 'off-the-shelf' mapping software. Visually verifying the fit of the variogram model is crucial for accurate interpolation. Exponential and spherical variogram models performed better than Gaussian models. It was also necessary to diverge from some of the default interpolation parameters such as the number of bins used for the experimental variogram and whether or not to allow for a nugget effect to achieve reasonable accuracy of the interpolation for some data sets.