Bioaerosol emissions from animal feeding operation (AFO) facilities are of increasing interest due to the magnitude of the emissions and their potential health effect on local communities. There is limited information about fate and transport of AFO bioaerosol emissions. In this study, concentrations of airborne bacteria and fungi were measured at four ambient stations in four wind directions surrounding an egg production farm through winter, spring and summer using Andersen six-stage samplers. Mean concentrations of ambient bacteria and fungi ranged from 8.7 × 10 2 CFU m -3 to 1.3 × 10 3 CFU m -3 and from 2.8 × 10 2 CFU m -3 to 1.4 × 10 3 CFU m -3, respectively. Ambient bacterial concentrations were not significantly different over the seasons, while ambient fungal concentrations were the highest in summer and the lowest in winter. There were significant differences between downwind and upwind bacterial concentrations (p < 0.0001). Downwind bacterial and fungal concentrations responded differently to the influencing factors. Bacterial concentrations were quadratically correlated with wind vector (combined effects of wind speed and direction) and emission rate, were positively correlated with temperature, and were negatively correlated with solar radiation. Fungal concentrations were positively correlated with temperature, RH, and emission rate, and were negatively correlated with wind vector.
While providing food for human-beings, animal feeding operations (AFOs) also emit significant amounts of bioaerosols. By definition, bioaerosols are airborne biological particles that may consist of bacteria, fungi, and other microorganisms. Once emitted, bioaerosols can travel short and long distances in the air [
Like other aerosols, the concentrations of bioaerosol in the ambient air vary due to the spatial and temporal effects [
Although characterizing bioaerosols in animal production environments has been a topic for numerous studies [
Airborne bacteria and fungi samples were collected at five locationson a commercial egg production farm (also known as the layer farm) in North Carolina. As illustrated in
To investigate temporal and spatial variations on ambient bioaerosol concentrations, a total of 14 sampling events (days) were conducted in winter (December 2010-January 2011), spring (March-May 2011), and summer (June-July 2011). During each sampling event, 12 sampling tests with 6 tests for bacteria and 6 tests for fungi were conducted from 10:00 a.m. to 2:25 p.m. The sample sizes are listed in
Andersen six-stage samplers (Tisch 1 ACFM Six-stage Viable Particle Sampler, Tisch Environmental, Inc., Village of Cleves, OH.) were used for the field bacteria and fungi sampling. To ensure the performance of the viable samplers, the samplers’ flow rates were calibrated to the design flow rate at 28.3 l∙min−1 before each sampling event, and were checked after sampling using a digital flow meter, Bios DryCal Defender 510-H (0.3 - 30 LPM) (Bios International Corporation, Butler, NJ). Sampling duration at all ambient locations was set for 10
Winter (5 days) | Spring (4 days) | Summer (5 days) | ||||
---|---|---|---|---|---|---|
Source | Ambient | Source | Ambient | Source | Ambient | |
Bacteria | 180 | 360 | 144 | 288 | 180 | 360 |
Fungi | 180 | 360 | 144 | 282 | 180 | 360 |
*Sample sizes (plates) are the products of [sampling day × test/day × 6 plates/test].
min per test for both bacteria and fungi samples without encountering any overloading issues.
The collection media for total bacteria was R2A agar, which is a non-selective agar that has been found to allow the culturing of many species of bacteria. The collection media for total fungi was Malt Extract Agar (MEA), which is commonly used for the isolation of fungi. To prevent bacteria growth on MEA agar, 1 ml 1000× streptomycin sulfate was added to each 1 L MEA agar after cooling and before dispensing. The final concentration of streptomycin sulfate in the plates was 0.1%. The prepared agar plates were labeled and stored at 4˚C until use to avoid background contamination.
As a quality control/quality assurance (QA/QC) procedure, a set of lab and field blanks for both collection media, R2A and MEA, were used on each sampling date. They were randomly chosen from the prepared plates before each sampling event. The lab blanks were stored in the refrigerator in the lab where the agar plates were prepared. The filed blanks were transported to the sampling site with other sampling plates in coolers at each sampling event. The blanks were settled without covers being removed and were incubated with all other sampled plates after sampling.
After each sampling event, the bacteria and fungi samples collected at the farm were transported back to the lab on the same day in coolers with ice packs. The samples were then incubated immediately after arrival in the lab under consistent temperatures to allow the colonies to grow. Bacteria samples were incubated for 48 hours at 30˚C, and fungi samples were incubated for 72 hours at 25˚C [
After the plate reading, bacterial or fungal concentration (C) in CFU m−3 was calculated using the following equation [
where, CFU is the colony forming unit, which indicates the numbers of total bacteria or fungi counts on the sampled plate; t is sampling duration; and Qs is sampler flow rate, the average value of pre-sampling flow rate and post-sampling flow rate.
Bacterial and fungal emission rates were calculated based upon measured in-house concentrations and the house ventilation rate. Often times, higher ventilation rate in hot weather led to higher emission rates. Detailed information about emission rate calculation and emission rate results is reported in Hu, et al. [
In total, 1512 non-selective bacterial sample plates and 1326 non-selective fungal sample plates were collected and analyzed over 3 seasons (winter, spring, and summer) at all four ambient stations to examine temporal and spatial variations on ambient bioaerosols concentrations under different meteorological conditions. ANOVA tests were applied to test the seasonal, time, and spatial variations of bioaerosol concentrations in the vicinity. Statistical analysis was applied to identify the significant influencing factors affecting ambient bacterial and fungal concentrations. Temperature, RH, wind vector (i.e., wind speed/direction), solar radiation, and emission rate were used as the five indicators to predict downwind bacterial and fungal concentrations in the vicinity. Statistical model was developed and selected through the following steps:
1) Plotted the relationship between the log-transformed downwind bioaerosol concentrations and each of the influencing factors.
2) Based on the relationship shown in the plotting, introduced transformed influencing factors as potential significant predictors.
3) Developed all possible regression models.
4) Model with the largest R-square and the smallest C(p) value was selected as the best predicting model.
All the statistical analyses were conducted using SAS9.2 software (SAS Institute, Inc., Cary, NC).
The mean concentrations of bacteria and fungi for all ambient stations (ST2-ST5) were computed by season. The mean concentrations ± SDs of bacteria were 869 ± 1003 CFU m−3, 1263 ±1955 CFU m−3, and 1193 ± 1497 CFU m−3 for winter, spring and summer, respectively. An ANOVA test indicates that there was no significant difference in ambient bacterial concentrations among three seasons (p = 0.33). The mean concentrations ± SDs of fungi were 280 ± 373 CFU m−3, 1403 ± 1461 CFU m−3, and 2558 ± 2276 CFU m−3 for winter, spring and summer, respectively. Mean ambient fungal concentrations were the highest in summer and the lowest in winter (p < 0.0001).
The lowest fungal concentration in winter may be due to the inactivation of fungi at low temperature and RH levels. Fungal concentrations were higher in summer than in spring although solar radiations were significantly higher in summer than in spring (p < 0.0001). Bacterial concentrations did not vary with season although atmospheric conditions changed significantly (
Bacterial and fungal concentrations at each ambient station were averaged by season to illustrate the seasonal effects at different stations.
Ambient temperature and RH changed not only with season, but also with time of day on each sampling day. In
Season | Temperature (˚C) | RH (%) | Solar Radiation (W/m2) | |||
---|---|---|---|---|---|---|
Mean* | SD | Mean | SD | Mean | SD | |
Winter | 5 | 4 | 39 | 15 | 366 | 156 |
Spring | 15 | 5 | 62 | 13 | 454 | 218 |
Summer | 34 | 3 | 54 | 11 | 783 | 189 |
*Mean of 166 measurement points.
Season | Sampling Location | Mean | SD | N |
---|---|---|---|---|
Winter | ST2 | 377 | 533 | 15 |
ST3 | 1525 | 1039 | 15 | |
ST4 | 1484 | 1044 | 15 | |
ST5 | 91 | 77 | 15 | |
Spring | ST2 | 2827 | 2257 | 12 |
ST3 | 555 | 1013 | 15 | |
ST4 | 100 | 75 | 9 | |
ST5 | 1456 | 2329 | 12 | |
Summer | ST2 | 761 | 1034 | 15 |
ST3 | 1258 | 1995 | 14 | |
ST4 | 1671 | 1396 | 15 | |
ST5 | 1087 | 1452 | 15 |
*From the two-way ANOVA test, it is shown that the weather and location were not significant (p = 0.46 and 0.25, respectively), but the interaction between them was significant (p = 1.83E−8).
Season | Sampling Location | Mean | SD | N |
---|---|---|---|---|
Winter | ST2 | 611 | 623 | 15 |
ST3 | 213 | 146 | 15 | |
ST4 | 163 | 110 | 15 | |
ST5 | 132 | 61 | 15 | |
Spring | ST2 | 1205 | 708 | 11 |
ST3 | 1550 | 2305 | 15 | |
ST4 | 1617 | 1068 | 9 | |
ST5 | 1241 | 864 | 12 | |
Summer | ST2 | 2510 | 1558 | 15 |
ST3 | 2889 | 1368 | 15 | |
ST4 | 1515 | 842 | 15 | |
ST5 | 3317 | 3862 | 15 |
*From the two-way ANOVA test, it is shown that the weather was significant (p = 3.28E−13), while the location and the interaction between them were not significant (p = 0.43 and 0.10, respectively).
order to estimate the time effect on ambient bioaerosol concentrations in different seasons, mean concentrations of bacteria and fungi at different times of day were computed by season.
Season | Time of Day | Mean* | SD | N |
---|---|---|---|---|
Winter | 10:00 AM | 869 | 860 | 10 |
10:30 AM | 818 | 1146 | 10 | |
11:00 AM | 781 | 878 | 10 | |
1:00 PM | 955 | 1138 | 10 | |
1:30 PM | 972 | 1131 | 10 | |
2:00 PM | 819 | 1075 | 10 | |
Spring | 10:00 AM | 1410 | 2002 | 8 |
10:30 AM | 1372 | 2480 | 8 | |
11:00 AM | 2014 | 2636 | 8 | |
1:00 PM | 1360 | 2280 | 8 | |
1:30 PM | 702 | 1015 | 8 | |
2:00 PM | 720 | 966 | 8 | |
Summer | 10:00 AM | 1296 | 1651 | 10 |
10:30 AM | 777 | 1161 | 10 | |
11:00 AM | 898 | 1138 | 10 | |
1:00 PM | 1119 | 1185 | 9 | |
1:30 PM | 1361 | 1345 | 10 | |
2:00 PM | 1702 | 2320 | 10 |
*Mean was calculated using measured concentrations at all four ambient stations. **From the two-way ANOVA test, it is shown that the time, weather, and the interaction were not significant (p = 0.99, 0.30, and 0.66, respectively).
Season | Time of day | Mean* | SD | N |
---|---|---|---|---|
Winter | 10:00 AM | 453 | 575 | 10 |
10:30 AM | 317 | 489 | 10 | |
11:00 AM | 341 | 484 | 10 | |
1:00 PM | 217 | 121 | 10 | |
1:30 PM | 168 | 66 | 10 | |
2:00 PM | 167 | 104 | 10 | |
Spring | 10:00 AM | 1099 | 512 | 8 |
10:30 AM | 2307 | 3063 | 8 | |
11:00 AM | 1131 | 598 | 8 | |
1:00 PM | 914 | 489 | 8 | |
1:30 PM | 1140 | 600 | 8 | |
2:00 PM | 2295 | 1403 | 8 | |
Summer | 10:00 AM | 2596 | 1444 | 10 |
10:30 AM | 2368 | 1181 | 10 | |
11:00 AM | 1812 | 1083 | 10 | |
1:00 PM | 2009 | 1150 | 9 | |
1:30 PM | 2705 | 2724 | 10 | |
2:00 PM | 3558 | 4317 | 10 |
*Mean was calculated using measured concentrations at all four ambient stations. **From the two-way ANOVA test, it is shown that the weather was significant (p = 2.44E−12), while the time and the interaction between them were not significant (p = 0.14 and 0.57, respectively).
slightly during the day. In spring, there was no consistent pattern observed for both bacteria and fungi. Bacteria concentrations were the lowest in afternoon, while fungi concentrations were the lowest in early morning and at noon. In summer, bacteria and fungi variations showed a similar pattern.
To investigate diurnal time effects on bacterial and fungal concentrations at different locations, mean concentrations of bacteria and fungi at different times of day were calculated by station.
To investigate spatial effects, data were grouped into the upwind and downwind classes. The overall mean concentrations of bacteria and fungi at downwind locations were 1856 ± 1688 CFU m−3 and 1155 ± 1179 CFU m−3, respectively. The overall mean concentrations of bacteria and fungi at upwind locations were 291 ± 606 CFU m−3 and 1690 ± 2321 CFU m−3, respectively. Mean concentration of bacteria was significantly higher downwind than upwind (p < 0.0001). In contrast, mean concentration of fungi at downwind was not significantly different from upwind (p = 0.1206). This observation indicated that wind vector (i.e., combined effects of wind direction
Station | Time of Day | Mean* | SD | N |
---|---|---|---|---|
ST2 | 10:00 AM | 1300 | 1868 | 8 |
10:30 AM | 709 | 1296 | 8 | |
11:00 AM | 1116 | 1929 | 8 | |
1:00 PM | 2184 | 2234 | 6 | |
1:30 PM | 1273 | 874 | 6 | |
2:00 PM | 876 | 965 | 6 | |
ST3 | 10:00 AM | 1121 | 949 | 7 |
10:30 AM | 350 | 291 | 7 | |
11:00 AM | 1371 | 1242 | 7 | |
1:00 PM | 1247 | 1530 | 8 | |
1:30 PM | 863 | 1076 | 7 | |
2:00 PM | 1612 | 2152 | 8 | |
ST4 | 10:00 AM | 1592 | 1551 | 7 |
10:30 AM | 1675 | 1457 | 7 | |
11:00 AM | 1068 | 1058 | 7 | |
1:00 PM | 1153 | 1061 | 6 | |
1:30 PM | 990 | 832 | 6 | |
2:00 PM | 836 | 788 | 6 | |
ST5 | 10:00 AM | 590 | 1025 | 6 |
10:30 AM | 1179 | 2346 | 6 | |
11:00 AM | 1150 | 2096 | 6 | |
1:00 PM | 556 | 616 | 8 | |
1:30 PM | 674 | 1125 | 8 | |
2:00 PM | 975 | 1645 | 8 |
*Mean was calculated using measured concentrations over three seasons at any given station stations. **From the two-way ANOVA test, it is shown that the time, weather, and the interaction between them were not significant (p = 0.94, 0.53, and 0.72, respectively).
Station | Time of Day | Mean* | SD | N |
---|---|---|---|---|
ST2 | 10:00 AM | 1713 | 1268 | 8 |
10:30 AM | 1799 | 1896 | 8 | |
11:00 AM | 1369 | 1090 | 8 | |
1:00 PM | 1799 | 1896 | 8 | |
1:30 PM | 1369 | 1090 | 8 | |
2:00 PM | 725 | 516 | 8 | |
ST3 | 10:00 AM | 1394 | 1301 | 7 |
10:30 AM | 1177 | 1051 | 7 | |
11:00 AM | 2524 | 3369 | 7 | |
1:00 PM | 1273 | 1441 | 8 | |
1:30 PM | 1452 | 1864 | 8 | |
2:00 PM | 1539 | 1788 | 8 | |
ST4 | 10:00 AM | 1064 | 863 | 7 |
10:30 AM | 1210 | 1361 | 7 | |
11:00 AM | 917 | 828 | 7 | |
1:00 PM | 770 | 477 | 7 | |
1:30 PM | 694 | 488 | 6 | |
2:00 PM | 1435 | 1596 | 6 | |
ST5 | 10:00 AM | 1181 | 1190 | 6 |
10:30 AM | 1564 | 1716 | 6 | |
11:00 AM | 1135 | 1222 | 6 | |
1:00 PM | 1025 | 1001 | 8 | |
1:30 PM | 1888 | 2967 | 8 | |
2:00 PM | 2506 | 5142 | 8 |
*Mean was calculated using measured concentrations over three seasons at any given station stations. **From the two-way ANOVA test, it is shown that the time, weather, and the interaction between them were not significant (p = 0.94, 0.53, and 0.72, respectively).
and wind speed) played an important role in affecting bacterial and fungal concentrations in the vicinity of the farm.
The overall mean concentrations of bacteria and fungi at downwind locations were computed by season.
The average downwind and upwind concentrations of bacteria and fungi at each ambient station were calculated to illustrate the spatial variation of their concentrations at different locations (
Season | Mean* | SD | N |
---|---|---|---|
Bacteria | |||
Winter | 1629a | 916 | 30 |
Spring | 2421a | 2235 | 24 |
Summer | 1732a | 1748 | 30 |
Fungi | |||
Winter | 185b | 130 | 30 |
Spring | 1131c | 803.7631 | 25 |
Summer | 2113d | 1346.257 | 31 |
*Means with the same letter are not significantly different.
Location | Bacteria | Fungi | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Downwind | Upwind | Downwind | Upwind | |||||||||
Mean* | SD | N | Mean | SD | N | Mean | SD | N | Mean | SD | N | |
ST2 | 2223b | 2045 | 18 | 457a | 850 | 24 | 1585c,d | 1278 | 17 | 1393d | 1390 | 24 |
ST3 | 1737b | 1547 | 25 | 284a | 685 | 19 | 870c | 1421 | 27 | 2727d | 2022 | 18 |
ST4 | 1911b | 1175 | 26 | 266a | 370 | 13 | 776c,d | 591 | 23 | 1368d | 997 | 15 |
ST5 | 1611b | 2042 | 20 | 133a | 224 | 22 | 1636c,d | 969 | 19 | 1685d | 3574 | 23 |
*Means with the same letter are not significantly different.
not significantly different (p = 0.28). The variations might be mainly because of the location of each station. As shown in
In comparison to bacterial and fungal concentrations at source (ST1), there were more factors affecting the concentrations at ambient stations in the vicinity of the farm. While various meteorological variables and emission rate were selected to examine their impact on downwind concentrations, distance from the source was not due to lack of resource and accessibility to the neighboring properties. By all means this fact should be included in future studies of fate and transport of bioaerosols.
As shown in
fungal concentrations were positively related to T, RH, solar radiation, and emission rate (
For bacterial concentration analysis, the linear regression analysis suggests that downwind bacterial concentration was only linearly dependent on wind vector, W (p = 0.0046) at a significant level of 0.05. However, it did not mean that other influencing factors (i.e., T, RH, solar radiation, and emission rate) were not important in impacting downwind bacterial concentration, because there were strong linear dependencies between them. Tests of the linear dependencies between the six variables indicate that T had significant linear dependencies with RH (p = 0.0022), solar radiation (p < 0.0001), and emission rate (p < 0.0001); RH had significant linear dependencies with T, wind vector W (p = 0.0022), and emission rate (p = 0.0169); wind vector W had significant linear dependencies with RH; solar radiation had significant linear dependencies with T and emission rate (p = 0.0002); and emission rate had significant linear dependencies with T, RH, and solar radiation.
For fungal concentration analysis, Pearson correlation coefficients suggested that downwind fungal concentration was significantly linearly dependent on all five influencing factors at a 0.05 level. Similarly, there were strong linear dependencies between the influencing factors. Temperature had significant linear dependencies
with RH (p = 0.0062), solar radiation (p < 0.0001), and emission rate (p < 0.0001); RH had significant linear dependencies with T and wind vector, W (p = 0.0488); wind vector W had significant linear dependencies with RH; solar radiation had significant linear dependencies with T and emission rate (p < 0.0001); and emission rate had significant linear dependencies with T and solar radiation.
The linear correlation between variables might cause some predictors to be not important when fitted in a linear regression model. To better study the impacts of influencing factors on downwind bacterial and fungal concentrations, log transformed bacterial and fungal concentrations were used as new responses. As suggested by residual plots (not shown), emission rate was also log transformed. Quadratic forms of wind vector (W2) and emission rate (emission2) were introduced as two new predictors for predicting downwind bacterial concentration. Among all subset models, the model included T, W, W2, solar radiation, emission rate, and emission2 was preferred by R-square and Mallow’s CP selections. For predicting downwind fungal concentration, residual plots suggested that the relationships between fungal downwind concentration and emission or wind vector (W) were not quadratic. Among all subset models, the model included every influencing factor but solar radiation was preferred by R-square and Mallow’s CP selections.
Estimate | Std. Error | p-Value | ||
---|---|---|---|---|
Bacteria* | Intercept | 56 | 19 | 0.0040 |
T | 0.034 | 0.016 | 0.037 | |
W | 3.0 | 0.46 | <0.0001 | |
W2 | −0.62 | 0.13 | <0.0001 | |
Solar Radiation | −0.0016 | 0.00058 | 0.0064 | |
Emission Rate | −6.0 | 2.3 | 0.0099 | |
Emission2 | 0.17 | 0.067 | 0.0131 | |
Fungi** | Intercept | 2.0 | 1.1 | 0.060 |
T | 0.045 | 0.013 | 0.0006 | |
RH | 0.021 | 0.0047 | <0.0001 | |
W | −0.19 | 0.078 | 0.0187 | |
Emission Rate | 0.24 | 0.11 | 0.0231 |
*Model DF = 6, total DF = 85, R2 = 0.5221; **Model DF = 4, total DF = 85, R2 = 0.7481.
of the models only indicated that when other factors were controlled, RH was no longer an important predictor of bacterial concentration downwind while solar radiation was no longer an important predictor of fungal concentration downwind. Due to the differences in size and specie characteristics, bacteria and fungi responded differently to the influencing factors. Temperature, wind vector, and emission rate were important predictors for both bacterial and fungal variations.
In this project, concentrations of non-selective bacteria and fungi were measured using Andersen six-stage sampler in the vicinity (4 stations) of an AFO facility for three seasons. Mean concentrations of ambient bacteria and fungi ranged from 8.7 × 102 CFU m−3 to 1.3 × 103 CFU m−3 and from 2.8 × 102 CFU m−3 to 1.4 × 103 CFU m−3, respectively. Ambient bacterial concentrations did not vary with season although atmospheric conditions changed significantly. The lowest ambient fungal concentration was observed in winter time. Ambient fungal concentrations were higher in summer than in spring although solar radiation was significantly higher in summer. There were significant differences between downwind and upwind bacterial and fungal concentrations. Downwind bacterial and fungal concentrations responded differently to the influencing factors. Bacteria concentrations were quadratic in response to wind vector and emission rate, positively correlated with temperature, and negatively correlated with solar radiation. Fungal concentrations were positively correlated with temperature, RH, and emission rate, and were negatively correlated with wind vector.
This project was supported in part by the NSF CAREER Award No. CBET-0954673 and USDA NRI Grant No. 2008-35112-18757. Help from Qianfeng Li and Manqing Ying for field sampling is also thankfully acknowledged. Authors would also like to thank the egg production farm for their generous support.