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Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

R. M. Knight, X. Tong, Z. Liu, S. Hong, L. Zhao


Published in Transactions of the ASABE 62(2): 415-427 (doi: 10.13031/trans.12950). Copyright 2019 American Society of Agricultural and Biological Engineers.


Submitted for review in June 2018 as manuscript number PAFS 12950; approved for publication by the Plant, Animal, & Facility Systems Community of ASABE in November 2018.

Mention of company or trade names is for description only and does not imply endorsement by the USDA. The USDA is an equal opportunity provider and employer.

The authors are Reyna M. Knight, Graduate Research Associate, and Xinjie Tong, Graduate Research Associate, Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, Ohio; Zhenyu Liu, Associate Professor, College of Information Science and Engineering, Shanxi Agricultural University, Taigu, Shanxi Province, China; Sewoon Hong, Assistant Professor, Department of Rural and Bio-Systems Engineering, Chonnam National University, Gwangju, South Korea; Lingying Zhao, Professor, Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, Ohio. Corresponding author: Lingying Zhao, 590 Woody Hayes Drive, Columbus, OH 43210; phone: 614-292-2366; e-mail: zhao.119@osu.edu.

Abstract. Poultry layer houses are a significant source of particulate matter (PM) emissions, which potentially affect worker and animal health. Particulate matter characteristics, such as concentration and size distribution inside layer houses, are critical information for assessment of the potential health risks and development of effective PM mitigation technologies. However, this information and its spatial and seasonal variations are lacking for typical layer facilities. In this study, two TSI DustTrak monitors (DRX 8533) and an Aerodynamic Particle Sizer (APS 3321) were used to measure PM mass concentrations and number-weighted particle size distributions in two typical manure-belt poultry layer houses in Ohio in three seasons: summer, autumn, and winter. Bimodal particle size distributions were consistently observed. The average count median diameters (mean SD) were 1.68 0.25, 2.16 0.31, and 1.87 0.07 m in summer, autumn, and winter, respectively. The average geometric standard deviations of particle size were 2.16 0.23, 2.16 0.18, and 1.74 0.17 in the three seasons, respectively. The average mass concentrations were 67.4 54.9, 289.9 216.2, and 428.1 269.9 g m-3 for PM2.5; 73.6 59.5, 314.6 228.9, and 480.8 306.5 g m-3 for PM4; and 118.8 99.6, 532.5 353.0, and 686.2 417.7 g m-3 for PM10 in the three seasons, respectively. Both statistically significant (p < 0.05) and practically significant (difference of means >20% of smaller value) seasonal variations were observed. Spatial variations were only practically significant for autumn mass concentrations, likely due to external dust infiltration from nearby agricultural activities. The OSHA-mandated permissible exposure limit for respirable PM was not exceeded in any season.

Keywords.Air quality, Particulate matter, Poultry housing, Seasonal variation, Spatial variation.

Particulate matter (PM) emitted from poultry facilities is a prominent air pollutant. It is primarily generated in the processes of feeding, building ventilation, bird movement, cleaning, and manure handling. The total suspended particles (TSP) concentrations in poultry facilities can range from 0.02 to 81.33 mg m-3 (Donham et al., 2002). Maghirang et al. (1991) found that 99% of the total number of particles in layer houses have a diameter of less than 10 m. The exact composition of poultry PM varies among facilities depending on factors such as housing and animal type (Ellen et al., 2000). However, it is generally a mixture of feed, manure, feathers, dander, and litter (Qi et al., 1992). The PM concentrations and size distributions are dependent on several factors, including ventilation rate, bedding type, air temperature, humidity, stocking density, and frequency of cleaning (Banhazi et al., 2008).

It has long been known that PM from poultry facilities can be hazardous to the health of workers. Bacteria and other potentially pathogenic microbes can attach to airborne dust particles within poultry facilities (Harry, 1978). Gram-negative bacteria that attach to dust particles ultimately result in endotoxin becoming a component of PM in poultry facilities, potentially causing inflammatory reactions in workers (Heederik et al., 2007). Exposure to poultry dust has been linked to many adverse health effects in workers, ranging in seriousness from airway irritation, coughing, and rhinitis to bronchitis, asthma, and organic dust toxic syndrome (Radon et al., 2001). The three main groups of PM that raise serious respiratory health concerns are thoracic particles (PM10, diameter = 10 m), respirable particles (PM4, diameter = 4m), and fine particles (PM2.5, diameter = 2.5 m) (Brown et al., 2013). Donham et al. (2000) found that decreased pulmonary function, potentially leading to further respiratory problems, occurred in workers exposed to PM4 concentrations of 162 g m-3 or greater.

In addition, high concentrations of PM can impact the overall health and welfare of the hens in layer houses. Within a facility, PM can serve as a vector of airborne bacterial transmission (Harry, 1978). Just et al. (2012) identified sev-eral potentially pathogenic bacteria present in bioaerosols sampled in poultry facilities, including Staphylococcus spp., Enterococcus spp., and Clostridium perfringens. Higher PM concentrations have also been linked to increased bird mortality rates (Guarino et al., 1999).

Particulate matter emitted from animal facilities can also be detrimental to the local environment. Toxic and odorous gases can be absorbed and carried by these airborne dust particles, and then be emitted from poultry facilities along with PM (Cambra-Lpez et al., 2010). Fine particulate matter can contribute to development of haze, severely impacting outdoor visibility (Sisler and Malm, 2000). Emitted PM can also reduce the absorbed fraction of solar radiation for nearby vegetation, potentially causing significant stress to the local ecosystem (Grantz et al., 2003). It has been found that PM from agricultural sources such as animal production is responsible for about half of the total anthropogenic air pollution in the U.S. and about 55% in Europe (Bauer et al., 2016). Particulate matter emissions generated in poultry production facilities thus constitute a potential hazard to human health, animal health, and environmental quality. Effective control of PM emissions from poultry facilities is an urgent need.

In order to properly design an effective PM control system for a poultry production facility, the environmental conditions and certain characteristics of the PM must be known. Relevant environmental conditions include indoor air temperature, relative humidity, and air velocity. Critical PM characteristics include particle size distribution, mass concentration, particle density, and electrical properties such as resistivity and dielectric constant.

Particulate matter studies have been conducted at various types of poultry facilities with a focus on measurement of PM concentrations to determine PM emission rates of poultry facilities in conjunction with building ventilation rates. As part of the U.S. National Air Emissions Monitoring Study (NAEMS), Wang-Li et al. (2013) and Li et al. (2013) quantified PM concentrations, particle size distributions, and emission rates at the exhausts of two traditional high-rise layer houses in North Carolina. Ni et al. (2017a, 2017b) determined emission factors of air pollutant emissions from two manure-belt layer houses and two traditional high-rise layer houses in the Midwestern state of Indiana. In addition, the U.S. egg production industry and related groups have supported studies for evaluation of traditional and new layer production systems in terms of animal welfare, production efficiency, and environmental impacts. In one of these comprehensive studies, sponsored by the Coalition for Sustainable Egg Supply (CSES), Zhao et al. (2015b) measured and compared PM concentrations in three types of layer houses by taking a limited number of measurements at the house center, outdoors, and the exhausts. In another CSES-sponsored study, Shepherd et al. (2015) measured and compared emission rates of PM from the same three types of layer houses, including a Midwestern tunnel-ventilated manure-belt poultry layer facility (Zhao et al., 2015a). Outside the U.S., Amador et al. (2016) studied size-segregated PM mass concentrations using a five-stage cascade impactor in a small, naturally ventilated aviary broiler house in southern Brazil under warm and humid conditions. In western Germany, Mostafa et al. (2016) examined mass concentrations of PM2.5, PM10, and total suspended particles (TSP) as well as particle density and shape factor in a mechanically ventilated aviary layer house in all seasons.

In addition to quantitative PM concentration values and emission rates obtained for various types of poultry facilities, the above studies also reveal that PM emission characteristics are significantly affected by animal facility type, weather conditions, in-house manure handling, and environmental management practices. Based on a review of the previous studies, spatial and seasonal variations in both PM mass concentrations and size characteristics in tunnel-ventilated manure-belt layer houses in the Midwestern U.S. are still not very clear. There is a pressing need to characterize the PM emissions in terms of concentrations and size distributions from typical manure-belt poultry layer houses in the Midwest, where the majority of layer houses are located, to provide fundamentally important information for health risk assessment and development of effective PM control technologies.

The specific objectives of this study were to (1) quantify airborne PM concentrations and size distributions in typical manure-belt layer houses in Ohio, (2) analyze the spatial and seasonal variations of the PM concentrations and size distributions in the layer houses, and (3) identify indoor air quality problems and potential PM exposure risks to poultry workers and animals.

Methodology

The study was conducted at a large commercial poultry production facility in central Ohio. The layer facility contains eight layer houses. Two of the layer houses (houses 1 and 2), each identical in size (19 m W 137 m L 6.7 m H), were selected for this study. The layout of the poultry houses is shown in figure 1. Each house has a nominal capacity of 201,600 Hyline W36 laying hens, each with seven rows of cages, with each row being 200 cages in length and two cages in width. The layer houses are divided into two levels, an upper and lower level, and have four tiers of cages on the lower level and five tiers on the upper level. The birds in house 1 were 48 weeks old at the beginning of the study and 81 weeks old at the end of the study, while the birds in house 2 aged from 60 weeks to 93 weeks during the study. A total of 187,535 hens were present in house 1, and 173,222 hens were present in house 2 at the conclusion of the study. In the layer houses, conveyer belts were used for egg collection and manure removal. Four fan rooms, one in each corner of the house, each contained eleven 132 cm diameter axial exhaust fans on the building end walls, and light traps installed on the wall separating the fans from the layer house. The central section of the roof contained 11 baffled air inlets, seven of which were 30.4 m long and four of which were 41.5 m long. The second and sixth cage rows (moving north to south) were each located beneath three inlets spanning the length of the rows, while the other five inlets were centered over the other cage rows. Manure was dried using recirculated air and removed from the houses every three to four days. Cleaning and bird inspections were performed every afternoon by one worker in each of the houses.

Measurement of PM Size Distribution and Mass Concentration

(a)
(b)
Figure 1. (a) Satellite image showing an overview of the poultry facilities and (b) a simplified geometric layout of a poultry house showing the ventilation system including exhaust fans and air inlets.

An Aerodynamic Particle Sizer (APS, model 3321, TSI, Inc., Shoreview, Minn.) was used to sample PM and measure the particle size distribution (PSD), including the geometric mean diameter and geometric standard deviation of particle size (GSD). The APS measures equivalent spherical diameter (ESD) using a light scattering intensity-based method and is able to quantify the particle size and size-specific number concentration of particles between 0.5 and 20 m. The ESD can be converted to an aerodynamic equivalent diameter (AED) if the density of the sampled particles is known. At 10 m, the APS has a resolution of 0.03 m and a particle coincidence error (when two or more particles simultaneously enter the detection zone of the sampler, affecting the sizing of those particles) rate less than 10% (TSI, 2012). The APS preparation process involved setting the sampling time to 1 min and connecting a laptop to use the Aerosol Instrument Manager (AIM) software to log the data recorded for each sample.

The PSD is commonly used to illustrate the ranges of par-ticle sizes and the relative amount (by number, surface area, or mass) of particles within each specific particle size range. The most commonly used distribution for particle size characterization is a log-normal distribution, which can be number-weighted, surface area-weighted, or mass-weighted. The two fitting parameters used to mathematically define a log-normal PSD are the GSD and the geometric mean diameter, which can take one of three forms. In number-weighted, surface area-weighted, and mass-weighted PSDs, the geometric mean diameter is referred to respectively as the count median diameter (CMD), surface median diameter (SMD), and mass median diameter (MMD) (Zhang, 2005). The AIM software for the APS automatically analyzed the particle size data from each sample and calculated the CMD, SMD, MMD, and GSD for each set of sampled particles. This study primarily uses number-weighted log-normal distributions with the CMD and number-weighted GSD to characterize the observed particle size distributions, with some data presented for the MMD and mass-weighted GSD. Calculations of CMD and number-weighted GSD were performed using equations 1 and 2, while calculations of MMD and mass-weighted GSD were performed using equations 3 and 4, respectively (TSI, 2006):

(1)

(2)

(3)

(4)

where

c = number of counted particles

Q = sample flow rate (m3 s-1)

t = sample time (s)

Dp = particle diameter (m)

?p = particle density (kg m-3).

Two DustTrak DRX monitors (model 8533, TSI, Inc., Shoreview, Minn.) were used to sample PM and measure size-segregated PM mass concentrations, including PM2.5, PM4, and PM10. The DustTrak DRX measures mass concentrations using a photometric method combined with single particle detection signal pulsing for size segregation. The DustTrak DRX has a resolution of 0.1% of the current reading or 1 g m-3, whichever is greater, and a flow rate accuracy of 5% (TSI, 2017). The DustTrak preparation process included performing a 2.5 m impactor calibration, a size calibration, a photometric calibration, and a zero-point calibration.

Maintenance and cleaning routines were followed in accordance with the procedures and schedules suggested by the manufacturer in the operating manuals for each device. The APS and two DustTraks were collocated on a mobile cart for easier transport between sampling locations. The devices were positioned such that sampling occurred at each location for 1 min with two replicates at a height of 135 cm, approximately the human breathing height when tilting the head down slightly while cleaning floors, inspecting cages, or performing other duties typical for workers in poultry houses. In winter, indoor air velocities were very low, so isokinetic sampling was not a concern. In summer and autumn, when tunnel ventilation resulted in higher indoor air velocities, rubber tubing connected to the sampling inlets was positioned to sample in alignment with the airstream to ensure isokinetic sampling. A sampling time of 1 min was chosen to limit the chance of a rapid increase in bird activity during sampling that might artificially inflate the instrument readings.

DustTrak monitors were chosen to measure PM mass concentrations due to their portability. More reliable PM monitors, such as the tapered element oscillating microbalance (TEOM 1400ab, Thermo Fisher Scientific, Waltham, Mass.), use a TEOM mass sensor and control units to provide real-time PM mass concentration measurements. However, TEOMs are bulky and cannot be moved around poultry houses, between poultry houses, and between floors in a quick and easy manner. The DustTraks were user-calibrated within the poultry houses before each round of preliminary sampling to ensure that the calibration conditions closely resembled the conditions during sampling. To reference the DustTraks to the more reliable TEOM method, preliminary sampling for calibration purposes was performed in which DustTraks were collocated with two TEOM 1400ab units, which have been designated by the U.S. Environmental Protection Agency (USEPA) as a federal equivalent method (EQPM-1090-079) for PM10 measurement (USEPA, 2017). This calibration allowed the DustTraks to be directly referenced to the TEOM units through correction factors and linear regression equations, as recommended by Cambra-Lpez et al. (2015). Following a statistical procedure similar to that of Yang et al. (2018), the average measured correction factors were found to be 0.447 for PM2.5 and PM4 and 2.669 for PM10, indicating undersampling of PM10 and oversampling of PM2.5 and PM4 by the DustTraks as compared to the TEOM units. The PM mass concentrations measured by the DustTraks were multiplied by the associated correction factors to determine TEOM-equivalent values of PM mass concentrations.

Measurement of Air Conditions

The temperature, relative humidity, and air velocity were measured at each sampling location using a TSI VelociCalc (model 9565-P, TSI, Inc., Shoreview, Minn.). The VelociCalc was not directly collocated with the APS and DustTrak units on the mobile cart but was held approximately 1 m from the cart at a height of 135 cm to prevent blockage or distortion of the airflow. The measurements of the air conditions at each sampling location were taken just prior to PM sampling to ensure that no interference occurred due to the locally altered airflow from PM sampling.

Experimental Design and Sampling Plan

Preliminary sampling was conducted at 30 locations within each level of both layer houses as an initial assessment of possible spatial variation. The PM mass concentrations, PSD, indoor temperature, relative humidity, and air velocity were measured at corresponding locations in the northern and southern halves of each house in summer, autumn, and winter to determine if symmetric PM characteristics and environmental conditions were present. The results from the preliminary sampling were compared using Tukey-Kramer tests, and the comparison showed that PM characteristics and environmental conditions in the northern and southern halves were not statistically significantly different. The exhaust fans were automatically controlled in a symmetric pattern and monitored continuously as part of a separate study at the same poultry production facility (Tong et al., 2018). Throughout the sampling periods, all fans functioned properly with no performance issues or malfunctions observed. Due to this symmetric pattern, only the northern half of each house was measured. Sampling was performed at 13locations on both the upper and lower levels of both houses for a total of 52 sampling points (fig. 2).

The indoor environments of animal facilities are significantly affected by ventilation systems, which operate in stages depending on seasonal weather conditions. To account for seasonal variation of PM characteristics in the layer houses, the PM sampling and measurements were taken over a two-week period (three days total) in late June 2016, a two-week period (three days total) in late October and early November 2016, and a one-week period (two days total) in early February 2017. Measurements were taken on sampling days between 10:00 a.m. and 4:00 p.m., when workers were present and birds were awake and active. Consequently, PM concentrations were high due to elevated bird activity levels and variations in PM concentrations were small because of stable ventilation operations affected by relatively stable outdoor environmental conditions during the sampling periods.

This experiment used a randomized complete block design with three blocking variables: season (summer, autumn, and winter), level (upper or lower), and house (1 or 2). Similar types of experimental design for PM sampling have been used in previous studies of PM in animal housing facilities (Zhao et al., 2015b).

Statistical Analysis

JMP 12.2.0 statistical software (SAS Institute Inc., Cary, N.C.) was used for data analysis. The standard least squares regression method with ANOVA and Tukey-Kramer testing was used to test for possible significant effects of season, level, house, and location, as well as interactions between these parameters, on CMD, GSD, PM2.5, PM4, and PM10. Standard mean-center coding of these parameters was used in the hierarchical regression model, and a = 0.05 was the significance level for all statistical tests and removal of parameters from the model.

Figure 2. Diagram of the 13 sampling locations on each level of each poultry house. The darker bars represent the layer cage rows. Points are labeled as aisle-position with F-NW and F-NE representing the northwestern and northeastern exhaust fan rooms, respectively.

In addition to statistical significance, a criterion for practical significance was used. The distinction of practically significant results is derived from a conservative interpretation of a study by Viana et al. (2015), in which it was found that the DustTrak DRX can experience random re-basing issues that may increase its relative error from 5% to 50%. Winkel et al. (2015a) observed differences between DustTrak and TEOM readings of approximately 20%, among the lowest of published studies. A total of 901 usable samples were taken, allowing statistically significant differences to be found between values with miniscule actual differences, so a means difference of 20% of the smaller value was chosen as an indicator of practically significant differences in the results.

Table 2. Summary of indoor environmental conditions.
SeasonAir Temperature
(C, mean SD)
Relative Humidity
(%, mean SD)
Ventilation Rate
(m3 s-1, mean)
LocationAir Velocity
(m s-1, mean SD)
Summer (n = 136)30.2 0.835.1 2.5400Aisle center0.13 0.07
1/4 aisle length1.23 0.26
Exhaust1.99 0.48
Autumn (n = 104)26.3 1.753.9 4.872.4Aisle center0.08 0.06
1/4 aisle length0.30 0.18
Exhaust0.53 0.27
Winter (n = 60)25.4 1.255.0 7.054.4Aisle center0.08 0.05
1/4 aisle length0.22 0.09
Exhaust0.29 0.12

Results and Discussion

The average ambient outdoor air temperature, relative humidity, whole house ventilation rate, and air velocity for each season during the time that measurements were taken are shown in table 1. The seasonal average indoor air temperatures and relative humidity values, along with the average air velocities at three different areas of the poultry house, are shown in table 2. As expected, both indoor and outdoor air temperatures were found to be highest in summer and lowest in winter. Autumn and winter showed significantly higher relative humidity values than summer both indoor and outdoor. Winter displayed the highest average outdoor wind speeds, while summer displayed the lowest. The indoor air velocity at the center of the aisles was consistently very low for each season. However, due to the increased ventilation requirements for reducing the indoor temperature in summer, significantly greater indoor air velocities and ventilation rates of the layer houses were measured in summer than in autumn and winter.

Table 1. Summary of outdoor environmental conditions.
SeasonAir
Temperature
(C, mean SD)
Relative
Humidity
(%, mean SD)
Air Velocity
(m s-1,
mean SD)
Summer (n = 6)26.0 2.241.5 9.63.48 2.32
Autumn (n = 6)12.4 1.958.8 7.94.82 1.78
Winter (n = 4)-0.8 1.858.4 11.95.18 2.22

Particle Size Distribution

Figure 3 shows examples of the number-weighted PSDs from representative summer, autumn, and winter samples. Samples typically exhibited a bimodal distribution, with the disparity between modes of the distribution becoming significantly larger during colder seasons. Bimodal distributions have been observed in other studies of PM in poultry houses (Manuzon, 2012; Mostafa et al., 2016). The particle sizes are represented as aerodynamic equivalent diameter (AED) as measured by the APS. The measurements of equivalent spherical diameter (ESD) were converted by the APS to AED measurements by specifying the particle density. An air pycnometer (Accupyc model 1330, Micromeritics Instrument Corp., Norcross, Ga.) was used to measure the particle density of poultry PM, which was found to be 1403kg m-3. This allowed the APS to also measure mass-weighted PSDs, which are shown in figure 4 for the same samples as shown in figure 3. However, as the focuses of this article are to characterize PM for effective control and iden-tify potential PM exposure risks to birds and workers, number-weighted PSDs are more relevant to these purposes than mass-weighted PSDs. As noted by Lee et al. (2006), number-weighted particle characteristics are important to PM risk exposure assessment due to the widespread focus on mass-weighted characteristics. This may lead to an underrepresentation of the actual number of fine particles, as these smaller particles tend to constitute a very small percentage of airborne PM by mass, but a significantly larger percentage of airborne PM in terms of overall particle counts. This can be seen in figures 3 and 4, where smaller particles dominate in terms of particle counts, but larger particles dominate in terms of particle mass. The number-weighted PSDs will be the primary focus of discussions on variations in PSDs in this article.

(a)
(b)
(c)
Figure 3. Bimodal number-weighted particle size distributions exhibited by representative PM samples taken in: (a) summer, with a CMD of 1.54m and a GSD of 2.19; (b) autumn, with a CMD of 2.09 m and a GSD of 2.05; and (c) winter, with a CMD of 1.93 m and a GSD of 1.72.

The measured particle characteristics given here include the CMD and associated GSD for each average PSD in dif-ferent seasons, as well as the overall average PSD. A summary of the CMD and number-weighted GSD (henceforth referred to as simply GSD) data, as well as the MMD and mass-weighted GSD data, is presented in table 3.

(a)
(b)
(c)
Figure 4. Mass-weighted particle size distributions exhibited by representative PM samples taken in: (a) summer, with a MMD of 7.39 m and a GSD of 1.76; (b) autumn, with a MMD of 9.80 m and a GSD of 1.74; and (c) winter, with a MMD of 8.61 m and a GSD of 1.98.
Table 3. Summary of average count and mass median diameters and number-weighted and mass-weighted geometric standard deviations of particle size.
SeasonCMD
(m, mean SD)
Number-Weighted GSD
(mean SD)
MMD
(m, mean SD)
Mass-Weighted GSD
(mean SD)
Summer (n = 408)1.68 0.252.16 0.237.88 2.411.74 0.12
Autumn (n = 312)2.16 0.312.16 0.189.80 1.331.71 0.10
Winter (n = 181)1.87 0.071.74 0.177.53 1.622.00 0.14
Overall (n = 901)1.85 0.312.06 0.278.19 2.271.78 0.16

The observed average CMDs ranged from 1.68 to 2.16m, while the observed average GSDs ranged from 1.74 to 2.16. Season showed a statistically significant effect on CMD, while level and house had no statistically significant effects. Season, house, and location all had statistically significant effects on GSD. However, only season had practically significant effects on CMD and GSD. These results are similar to the findings of previous research, such as Manuzon (2012), who found the average CMD and GSD in a poultry layer house to be 1.8 m and 2.2, respectively.

Figure 5. Average count median diameters (CMD) at the sampling locations during summer, autumn, and winter.

For development of PM mitigation technologies, particle size is an important factor in determining the PM collection efficiencies. The number-weighted PSDs observed in this study indicated that PM2.5 consistently accounted for over half of all airborne particles, suggesting that removal of PM2.5 should also be focused on in poultry production facilities. A promising PM mitigation method for efficient removal of PM2.5 is electrostatic precipitation, which has been examined for PM mitigation in poultry houses. Manuzon et al. (2014) preliminarily tested a small-scale electrostatic precipitator (ESP) in a poultry layer house that achieved a PM2.5 collection efficiency of 86%. Ru et al. (2017) tested an electrostatic spray scrubber (ESS) under laboratory conditions similar to those of a poultry facility and reported a PM2.5 removal efficiency of 88%. Strohmaier et al. (2018) tested an optimized dry filter in a layer house and achieved PM2.5 collection efficiencies of up to 74%, but expressed concerns about particle resuspension negatively affecting filter performance. Winkel et al. (2015b) tested a dry filter and an ESP, both independently and together, in a layer house and reported a PM2.5 removal efficiency of 45.3% by the ESP and insignificant PM2.5 removal by the dry filter, potentially due to clogging and particle resuspension. Because filtration devices achieve lower PM2.5 removal efficiencies and are prone to clogging and resuspension issues that require frequent cleaning or replacement, electrostatic precipitation technologies provide a less labor-intensive option and have the potential to achieve high PM2.5 removal efficiencies.

Seasonal Variations in PSD Characteristics

The largest particle CMDs were observed in autumn, followed by winter and then summer. GSDs of particle size were not significantly different between summer and autumn, but were significantly smaller for winter. The smaller size range of particles observed during winter is consistent with the study by Manuzon (2012). The smaller winter GSDs may be caused by the lower indoor air velocities that prevent resuspension of larger particles that have settled out of the air. This would allow a smaller size range of particles to remain airborne and potentially be sampled, leading to lower measurements of GSD in winter as compared to autumn or summer. One explanation for the larger CMDs observed in autumn is provided by Shaw (1994), who found that settled particles larger than 4 m have higher resuspension rates as air velocity increases beyond 0.2 m s-1. Thus, the larger average CMD in autumn as compared to winter may be due to the more frequent resuspension of larger settled particles, allowing these larger particles to be resampled, causing the measured CMD to increase. Additionally, the facility is located in a heavily agricultural area, with several nearby fields of crops. On two of the sampling days in autumn, a corn crop was being harvested in a field located less than 300 ft to the west of the poultry houses, with the wind blowing from west to east. This may have caused some larger external dust particles from the cornfield to infiltrate the facility, ultimately increasing the measured CMD in autumn.

Spatial Variations in PSD Characteristics

The CMD showed no significant spatial variation during winter measurements. During autumn measurements, CMDs were found to be larger in the western side of the poultry house as opposed to the eastern side (fig. 5). This may be a result of the sampling of additional larger dust originating from nearby agricultural activities to the west of the poultry house, causing larger dust to infiltrate the western side of the facility; winds can carry these external particles into the house, where they become a significant portion of sampled PM (Cambra-Lpez et al., 2011). During summer, CMDs were found to be significantly larger at the centers of the aisles than at the ends or by the exhaust fans. This is likely due to the presence of stagnant air zones, as shown by the lower air velocities measured at these points, allowing the larger particles to remain suspended. The GSD showed no practically significant spatial variation either overall or in each of the three seasons, although statistically significant spatial variations in GSDs were observed in summer (fig. 6). Tables 4 and 5 list the statistical significance of the examined factors on variations in CMD and GSD, respectively. It should be noted that no practically significant spatial variations were observed for either CMD or GSD, although statistically significant variations were found.

PM Mass Concentrations

Particulate matter mass concentrations were measured for the three size ranges of PM2.5 (fine), PM4 (respirable), and PM10 (thoracic). The mean and standard deviations of each PM mass concentration were averaged for summer, autumn, winter, and overall and are shown in table 6.

Figure 6. Average geometric standard deviations (GSD) of particle size at the sampling locations during summer, autumn, and winter.
Table 6. Average PM2.5, PM4, and PM10 mass concentrations.
SeasonPM2.5
(g m-3,
mean SD)
PM4
(g m-3,
mean SD)
PM10
(g m-3),
mean SD)
Summer (n = 408)67.4 54.973.6 59.5118.8 99.6
Autumn (n = 312)289.9 216.2314.6 228.9532.5 353.0
Winter (n = 181)428.1 269.9480.8 306.5686.2 417.7
Overall (n = 901)281.6 246.7310.3 272.3487.3 394.6

PM2.5

The observed average PM2.5 mass concentrations ranged from 67.4 to 428.1 g m-3. Winter was found to have the largest PM2.5 mass concentration, while summer was found to have the smallest, corresponding to periods of low and high ventilation rates, respectively. Significant spatial variations were observed in autumn only. This is likely due to the inconsistent ventilation patterns during autumn, as well as increased amounts of external agricultural dust infiltrating the facility.

PM4

The observed average PM4 mass concentrations ranged from 73.6 to 480.8 g m-3. The highest and lowest mass concentrations were observed in winter and summer, featuring low and high ventilation rates, respectively. Autumn was the only season to exhibit significant spatial variation, potentially due to the introduction of dust from outside the facility as well as inconsistent ventilation patterns during autumn.

PM10

The observed average PM10 mass concentrations ranged from 118.8 to 686.2 g m-3. The largest concentrations were observed in winter, with summer exhibiting the smallest. Again, lower ventilation rates in winter as compared to summer correspond to higher PM mass concentrations. Spatial variations were only found to be significant in autumn, when inconsistent ventilation patterns and external sources of agricultural dust were present.

PM2.5/PM10 Ratio

The APS measured PM2.5/PM10 ratios ranging from 0.020 to 0.382 with a mean of 0.101 0.074. Seasonal average PM2.5/PM10 ratios measured by the APS were 0.083 0.033, 0.055 0.023, and 0.196 0.095 for summer, autumn, and winter, respectively. Li et al. (2013) used a TEOM in high-rise layer houses in North Carolina, and the average PM2.5 and PM10 mass concentrations reported suggest approximate PM2.5/PM10 ratios of 0.100 and 0.078, which are extremely close to the overall average PM2.5/PM10 ratio measured by the APS in this study.

Seasonal Variations in PM Mass Concentrations

All three size-fractionated mass concentrations (PM2.5, PM4, and PM10) were smallest in summer and largest in winter, consistent with results from previous studies (Zhao et al., 2015b; Wang-Li et al., 2013; Mostafa et al., 2016). In previous studies of poultry facilities, the larger PM mass concen-trations observed in colder conditions have been found to occur due to lower ventilation rates, which cause lower air velocities within facilities (Lai et al., 2014). Winter was found to exhibit significantly lower indoor air velocities than autumn or summer, as well as the lowest mean ventilation rate, primarily affecting the seasonal variations in PM mass concentrations. It should also be noted that the seasonal mean PM concentrations each have large standard deviations relative to their respective means. This is due to the skewed distribution of measured concentrations, with uneven ventilation patterns, particularly within the fan rooms, causing a large overall range of concentration measurements for which the median value is much larger than the mean value. Thus, the lowest measured concentrations may be significantly smaller than the mean, while the gap between the mean and the largest measured concentrations may be several times larger because the lower end of the distribution is bounded by zero. This causes the distribution of measured PM mass concentrations to have relatively large standard deviations.

Spatial Variations in PM Mass Concentrations

(a)
(b)
(c)
Figure 7. Average mass concentrations of (a) PM2.5, (b) PM4, and (c) PM10 at each sampling location during summer, autumn, and winter.

The overall average PM concentrations for PM2.5, PM4, and PM10 were not statistically significantly different at any of the 13 sampling locations. The seasonal PM2.5, PM4, and PM10 mass concentrations at each sampling location are shown in figure 7. It should be noted that PM2.5, PM4, and PM10 exhibited the same pattern of statistical and practical significance in terms of spatial variations, as shown in table7. Comparisons of the least squares mean estimates from each of the three regression models using Tukey-Kramer tests showed that within summer and winter, none of the locations had significantly different average PM mass concentrations for either PM2.5, PM4, or PM10, suggesting relative spatial homogeneity with regard to PM mass concentrations throughout the poultry house. However, for both PM2.5 and PM4 in autumn, points 2-2, 2-3, and 4-2 had average concentrations that were significantly higher than other locations. As discussed earlier, these locations are nearest to the baffled air inlets that bring in outdoor air from west of the facility, and it is possible that the elevated PM mass concentrations at these locations are due to the additional sampling of dust from external sources that were brought into the facility by wind. Ventilation patterns changed throughout the day in autumn, as opposed to all exhaust fans running at full capacity in summer and only a few fans running for minimum ventilation in winter. Additionally, in autumn and winter, houses 1 and 2 showed practically significant difference in PM mass concentrations. This is likely caused by differences in cleaning by the workers assigned to each house, as well as one house being situated closer to the nearby crop fields, allowing more external dust to infiltrate. Because spatial variations of PM mass concentrations were only found to be practically significant during autumn, it is believed that spatial variations are more correlated to inconsistencies in ventilation patterns and external dust infiltration than to differing ventilation rates.

Potential Health and Welfare Risks

The Occupational Health and Safety Administration (OSHA) maintains a list of permissible exposure limits for various substances and specifies a limit of 5 mg m-3 (8 h time-weighted average) for respirable dust treated as particulates not otherwise regulated (OSHA, 2017). The workers at the poultry houses in this study work approximately 8 h shifts and spend nearly all of their working time in the poultry houses, exposed to PM while performing their daily tasks and activities. A comparison of the measured PM4 mass concentrations to this permissible exposure limit is taken as a best estimate of the potential exposure risks to workers under worst-case scenario working conditions. Respirable dust concentrations in poultry facilities vary, with Ellen et al. (2000) finding respirable PM concentrations ranging from 0.01 to 6.5 mg m-3, so not all facilities may meet these standards. Despite this, health concerns for workers may still exist, given that a previous study of dose-response relationships in poultry workers by Donham et al. (2000) found that exposure to respirable dust concentrations of as low as 162g m-3 is associated with significant decreases in pulmonary function. The overall mean PM4 mass concentration in the studied facility was found to be 310.3 g m-3, which is below the OSHA exposure limit but above the limit proposed by Donham et al. (2000), suggesting that the workers may experience a significant risk of decreased pulmonary function, potentially leading to other respiratory health complications. It is recommended that research on dust control methods for poultry houses be prioritized in the future, with a particular focus on electrostatic methods, as they appear well-suited for this application.

Potential Risks to Laying Hens

The primary points of concern for bird health are that poultry dust can serve as a reservoir of pathogen transmission and can lead to respiratory problems. It has been established that poultry PM can carry pathogens throughout a facility, potentially leading to outbreaks among the birds of diseases such as salmonellosis, campylobacteriosis, and avian influenza (Cambra-Lpez et al., 2010). Additionally, increased concentrations of TSP and respirable PM have been shown to be linked to increased numbers of bird mortalities (Guarino et al., 1999). Michel and Huonnic (2003) found that hens raised in aviaries with higher dust concentrations (up to 31.6 mg m-3) suffered pulmonary lesions with greater number and severity than hens raised in cage facilities with significantly lower dust concentrations (up to 2.3mg m-3). Wolfe et al. (1968) found that higher dust concentrations (25 to 35 mg m-3) were linked to an increased rate of airsacculitis in turkeys as compared to levels in a range of 7 to 21 mg m-3. The overall average PM10 mass concentration in this study was 487.3 g m-3, but dose-response relationships between PM concentration and poultry health are not well established, so further research is needed to determine whether the current PM concentrations in the visited facility pose an increased risk to poultry health.

Conclusions

Particulate matter in the manure-belt layer houses generally exhibited a bimodal particle size distribution. The mean CMDs were 1.68 0.25, 2.16 0.31, and 1.87 0.07 m in summer, autumn, and winter, respectively. The mean GSDs were 2.16 0.23, 2.16 0.18, and 1.74 0.17 in the three seasons, respectively. The mean mass concentrations were 67.4 54.9, 289.9 216.2, and 428.1 269.9 g m-3 for PM2.5; 73.6 59.5, 314.6 228.9, and 480.8 306.5 g m-3 for PM4; and 118.8 99.6, 532.5 353.0, and 686.2 417.7 g m-3 for PM10 in the three seasons, respectively.

Spatial variations for PSD in terms of CMD were not significant in winter, but significant in both autumn and summer due to sampling of additional larger dust originating from external agricultural fields in autumn and tunnel ventilation mode in summer. The GSD showed no practically significant spatial variation in each of the three seasons, alt-hough statistically significant spatial variations in GSDs were observed in summer.

Spatial variations for average PM concentrations were not significant in winter and summer. In autumn, PM mass concentration differences were found to be practically significant, giving means differences greater than 20%. This suggests that spatial variability in PM mass concentrations is more closely correlated with inconsistent ventilation patterns than with high or low ventilation rates.

Significant seasonal variations were observed for all PM mass concentrations as well as CMD and GSD. Winter had the highest mass concentrations of PM2.5, PM4, and PM10, followed by autumn, with summer exhibiting the lowest values. CMDs were lowest in summer and highest in autumn, while GSDs showed no significant difference between summer and autumn but were significantly lower in winter.

The observed concentrations of respirable PM suggest that long-term poultry workers may be at a significant risk of developing respiratory health issues, and that laying hens may be at risk of increased exposure to infectious disease agents transported by airborne PM. Technologies for PM control and mitigation can lessen this risk, and the data provided from this study will be used for future PM control research.

Acknowledgements

This study was supported by the USDA-NIFA Grant 2016-67021-24434. The authors would also like to thank The Ohio State University and the poultry production facility staff for their efforts, contributions, and assistance.

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