본 연구는 돼지 간 거리(PD), 돈사 내 상대 습도(RRH), 돈사 내 이산화탄소(RCO2) 세 가지 변수를 사용하여, 네 개의 데이터 세트를 구성하고, 이를 다중 선형 회귀(MLR), 서포트 벡터 회귀(SVR) 및 랜덤 포레스트 회귀(RFR) 세 가지 모델 기계학습(ML)에 적용하여, 돈사 내 온도(RT)를 예측하고자 한다. 2022년 10월 5일부터 11월 19일까지 실험을 진행하였다. Hik-vision 2D카메라를 사용하여, 돈사 내 영상을 기록하였다. 이후 ArcMap 프로그램을 사용하여, 돈사 내 영상에서 추출한 이미지 안 돼지의 PD를 계산하였다. 축산환경관리시스템(LEMS) 센서를 사용하여, RT, RRH 및 RCO2를 측정하였다. 연구 결과 각 변수 간 상관분석 시 RT와 PD 간의 강한 양의 상관관계가 나타났다(r > 0.75). 네 가지 데이터 세트 중 데이터 세트 3을 사용한 ML 모델이 높은 정확도가 나타났으며, 세 가지 회귀 모델 중에서 RFR 모델이 가장 우수한 성능을 보였다.

This study evaluated the effectiveness of odor reduction when spraying inside the Bio-curtain (hereinafter referred to as curtain) according to the exhaust fan operating rate. Spraying is a main factor affecting the ability to odor reduction of curtains. The curtain (total area: 37.9m3) was constructed with two layers of light-shielding screens stretched over a rectangular parallelepiped structure installed around an exhaust fan (630 mm) on the side wall of a pig barn. Air samples for odor analysis were collected from inside the pig barn and outside the curtain. The main odorous compounds such as volatile fatty acids, phenols, indoles, and ammonia were measured. The odor reduction effectiveness was evaluated by total odor activity values (TOAVs) summed to the odor activity values of each odorous compounds. Depending on the exhaust fan operating rate, the reduced rate of TOAVs gradually decreased to the range between 15.67% and 68.80%. Because the contact time between the spraying liquid and the air velocity of the exhaust fan becomes shorter (or there is a reduction in liquid to gas flow ratio) as the exhaust fan operating rate increases. The results of this study can be used as basic data for research into spraying conditions to improve the odor reduction effectiveness of curtains.

The distance between livestock facilities and residential spaces is decreasing. Moreover, livestock odor complaints are increasing due to the large-scale and concentrated livestock breading industry. In order to reduce odor from livestock facilities, bio-curtain that are easy to install and inexpensive are commonly used in Korea. However, there is a lack of basic data on design standards and operation manuals for bio-curtains. The installation density of the bio-curtain material is an important factor that affects the odor reduction rate, increment of the load on the ventilation fans, and the structural stability of the curtain. There are limitations on deriving the design conditions of the bio-curtain by only field experiments targeting invisible air. Therefore, aerodynamic simulation such as CFD (computational fluid dynamics) can be used to obtain quantitative data according to various environmental conditions. Bio-curtain is a porous medium with a complex structure, and it is necessary to derive aerodynamic coefficients to analyze it. In this study, the wind speed and pressure difference according to the design density of the bio-curtain were monitored using the experimental chamber. Using the field results, a pressure resistance curve was created for each flow velocity and installation density. The viscosity and resistance coefficient of the bio-curtain were calculated through the derived resistance curve.

The annual number of odor complaints increased about 10 times over 14 years from 4,302 in 2005 to 40,854 in 2019, in Korea. Especially, livestock facilities account for more than 50% of the odor complaints and the swine farms account for the most odor complaints among livestock. It is therefore necessary to manage swine farms as the major odor emission source. In this study, a real-time odor monitoring system equipped with PTR-TOF-MS (proton transfer reaction time-of-flight mass spectrometric) was used to measure the odorous substances in two swine farms. Odorous substances emitted from outlets were sampled and measured at the two types of swine farms. In addition, the boundary spots were designated as measurement points. As a result, the rankings of the odorous substances in order, from highest to lowest, were ammonia, acetaldehyde, methyl mercaptan, fatty acids, etc. and the level of odor intensity was 0.8-4.4 at the outlet of the swine farms. The concentration at the boundary decreased between 1/100 ~ 1/10000 compared to the concentration emitted from outlets. Base on the results of evaluating odor activity values, Skatole and p-Cresol were estimated as major odor substances in swine farms.

The present trial verified the effects of spraying microbial agents on odor reduction in commercial pig farms of different operating sizes and barn types. Farms without microbial agent spraying and those sprayed with microbial agents at two different intervals were compared. The treatments included spraying of water alone every day or a mixture of water plus microbial agent at 24 and 72 h intervals. The experimental farms were divided according to size into 1,000-, 3,000-, and 5,000-head farms and according to barn type into gestation, farrowing, nursery, and grower-finisher farms. To compare odor concentration within each housing barn, ammonia and hydrogen sulfide gas levels were measured. The average concentrations of ammonia (p<0.01) and hydrogen sulfide (p<0.05) gas were the lowest in all types of farms sprayed with the microbial agent at a 24 h interval. In farms sprayed with the microbial agent at a 24 h interval, the decrease in ammonia concentration according to barn type was in the following order: farrowing (p<0.01) (11.0 to 1.8 ppm), nursery (p<0.05) (17.0 to 9.2 ppm), grower-finisher (15.3 to 8.8 ppm), and gestation (9.7 to 6.4 ppm) farms. Moreover, spraying the microbial agent at a 24 h interval significantly (p<0.01) decreased ammonia concentration from 19.9 to 10.4 ppm, from 11.1 to 4.1 ppm, and from 8.8 to 5.1 ppm in 5,000-, 3,000-, and 1,000-head farms, respectively. Overall, spraying microbial agents every day may be the most effective method to reduce odor in commercial pig farms.

Odor is a type of sensory pollution that can stimulate the human sense of smell when it occurs, causing discomfort and making it difficult to create a pleasant environment. For this reason, there is a high possibility of complaints regarding odors if odors occur in pigsties near residential properties, and the number of such complaints is also increasing. In addition, odors emanating from pigsties around military installations can cause physical and psychological harm, not only to the soldiers living in these type of facilities but also to the families belonging to military personnel living there as well. Because the concentration of odors varies due to diverse factors such as temperature, humidity, wind direction, wind speed, and interaction between causative materials, predicting odors based on only one factor is not proper or appropriate. Therefore, in this work, we sought to construct models that are based on several regression techniques of machine learning using data collected in field. And we selected and utilized the model that has the highest-accuracy in order to notify and warn residents of odors in advance. In this work, 3672 data items were used to train and test the model. The several machine learning algorithms to build the models are polynomial regression, ridge regression, K-nearest neighbor regression (KNN Regression), and random forest. Comparing the performance of models based on each algorithm, the study found that KNN Regression was the most suitable model, and the result obtained from KNN regression was significant.

구조와 사육환경이 동일한 3개의 돈방(room A~C)에서 48일 동안 비육돈의 암모니아 농도 및 환기량을 모니터링하여 배출계수를 산정하였다. 실험 결과, 온도 22.5℃, 습도 53.9% 환경에서 평균 암모니아 순발생 농도 5.93 ppm, 환기량 23.7 m3/h·pig로 나타났다. 일별 상관관계 분석결과, 암모니아 농도는 온도와 음의 상관관계(R2: -0.65 ~ -0.53)를 가지는 것으로 나타났으며, 환기량은 암모니아 농도에 거의 영향을 미치지 않는 것으로 나타났다. 암모니아 농도는 이른 오전을 기점으로 서서히 증가 경향을 보이다가 12~13시경 최댓값에 도달하였고, 상호 상관도가 높은 온도, 습도, 환기량의 경우 14~15시에 최댓값을 갖는 것으로 분석되었다. 시간별 데이터 상관관계 분석결과, 암모니아 배출량에 영향을 미치는 요소는 암모니아 농도(R2=0.71)와 환기량(R2=0.61)으로 이 중, 암모니아 농도가 더 상관성이 높은 것으로 분석되었다. 암모니아 배출계수는 2.28 g/d·pig로 분석되었다.

In this study, the main odorous substances were selected for each swine facility by investigating the concentration and occurrence characteristics of odorous substances according to farm facilities. The objective was to find a solution to manage odor effectively in swine farms. Samples collected from the boundary site, manure storage, fan, and indoor the swine building were analyzed for concentration, odor activity value (OAV), and odor contribution. As a result, there was a difference in the concentration of odorous substances as well as the tendency of OAV in each swine facility. Also, the main substances of odor in the farms were similar, but odor contribution differed from facility to facility. Therefore, it is considered that the odor management efficiency will be improved only if the proper odor reduction method is applied according to the types of main odorous substances in swine facilities.

The characteristics of ammonia during the growing period of pigs in a facility with a mechanical ventilation system were analyzed, and the emission factor was calculated. Real-time ammonia concentration was measured using photoacoustic spectroscopy equipment, and a ventilation measuring device was fabricated to measure the amount of air vented from an exhaust fan according to the operation rate. All data were collected as one-hour averages. The mean ammonia concentration, indoor temperature, and ventilation rate was 1.44~2.08 ppm, 25.5~26.4oC, and 24~32 m3/h per pig, respectively. Both concentration and ventilation rate are important factors in terms of emission management, but correlation analysis shows that the impact of concentration is higher than that of ventilation. Using ammonia concentration and ventilation data, the ammonia emissions per pig were calculated by considering the number of pigs (0.25~1.74 g/day·pig). The final ammonia emission factor yielded a value of 0.81 g/day·pig.

Due to lack of established operating conditions, the swine manure management process circulates bio-liquor between the slurry pit and the bioreactor process cannot be effectively used yet. Therefore, a lab scale study comprising a single bio-reactor and slurry pit was conducted to investigate the optimal operating conditions. The main experiment was performed after conducting a preliminary study on the operating conditions. In the preliminary study, the volume ratio of the bioreactor to the slurry pit was fixed at 1 and hydraulic retention time (HRT) of the bioreactor was set as 5, 10 and 15 d. In the main experiment, the HRT of the bioreactor was fixed at 5 d based on preliminary results and the ratio of bioreactor to slurry pit was set at 1:3, 1:5, 1:7 and 1:10. Since, a decrease in bioreactor performance occurred when NH4-N loading rate reached 60 g/m3/d, the loading rate of NH4-N was required to be maintained below 55 g/m3/d to achieve stable operation. Although manure excretion can definitely increase the loading rate into the bioreactor as well as NH4-N concentration in the slurry pit, the NH4-N in slurry pit can be kept consistent with the circulation rate above 9.5Q (ratio to manure excretion). The optimal volume ratio of the bioreactor to the slurry pit and HRT of the bioreactor to fulfill these operating conditions was 1:3 and 5d, respectively. Notably, studying of the individual farm situation is very important to establish an ideal method to apply the optimal operation conditions suggested in this study.

The annoyance potential for odor sources can be evaluated by separation distances. A separation distance between a standard pig farm and a residential area was investigated by the AERMOD model. The studied area comprised four sites in Korea. The study sites were Paju, Yangpyeong, Suwon, and Icheon, respectively. The separation distances criteria of the three reference Odor Impact Criteria (OIC) were used to evaluate the separation distance. Results show that separation distances for the four sites were calculated 20 m from the fence in the existing pig farm criteria case [exceedance probability P (%) = 2.0% and concentration = 6 OU] in Ireland. In the case of the new pig farm criteria [(exceedance probability P (%) = 2.0% and concentration = 3 OU) of Ireland, results show that the separation distances of the four locations were between 120 m and 145 m from the fence. These values were about 3.0~4.5 times larger than those of the existing pig farm criteria case. In the case of a concentration of 1 OU and the exceedance probability P (%) of a 2.0% criteria, the separation distances of the four sites ranged from 250 m to 290 m.