The sampling bag is used as a storage container for odor gas samples. It is known that the substances recovery rate of odor bags decreases during storage time, and the degree of recovery varies depending on the characteristics of the gas sample and the material of the bag. This study investigated the recovery rate of VFA (ACA, PPA, BTA, VLA) in PEA bags during storage time. In addition, a model was developed to estimate the recovery rate of each substance as a function of time. Standard gas (ACA, PPA, BTA, VLA mixed) recovery rate was used for the model development. The concentration of the compound in the bag was measured by SIFT-MS at intervals of 1 to 2 hours. The recovery rate according to the storage time was calculated as the ratio to the initial concentration. The recovery rate of each substance according to the storage period (12h, 24h, 36h, 48h) was ACA (66.2%, 62.8%, 55.6%, 52.0%), PPA (77.6%, 72.1%, 63.0%, 58.1%, 86.6%), BTA (86.6%, 81.3%, 71.6%, 66.9%), VLA (94.8%, 89.0%, 76.6%, 71.7%). The recovery rate continued to decrease over the course of 48 hours of storage time. ACA, PPA, and BTA showed the greatest decrease within the initial 12 hours, which is form of exponential decrease. Therefore, we considered a 1~3 degree polynomial regression model and a 1~2 degree exponential decay model. Each developed model was evaluated by r², RMSE, MAPE, AIC, and then a model for each substance was selected. Selected models were tested with recovery rate data from swine farm odor samples. Only the ACA model exhibited a good performance (r² = 0.76).

This ammonia prediction study was performed using the time-series artificial neural network model, Long-short term memory (LSTM), after long-term monitoring of ammonia and environmental factors (ventilation rate (V), temperature (T), humidity (RH)) from a slurry finishing pig farm on mechanical ventilation system. The difference with the actual ammonia concentration was compared through prediction of the last three days of the entire breeding period. As a result of the analysis, the model which had a low correlation (ammonia concentration and humidity) was confirmed to have less error values than the models that did not. In addition, the combination of two or more input values [V, RH] and [T, V, RH] showed the lowest error value. In this study, the sustainability period of the model trained by multivariate input values was analyzed for about two days. In addition, [T, V, RH] showed the highest predictive performance with regard to the actual time of the occurrence of peak concentration compared to other models . These results can be useful in providing highly reliable information to livestock farmers regarding the management of concentrations through artificial neural network-based prediction models.

Several analytical measurement techniques have been developed over the years for ammonia (NH3). However, the field monitoring of NH3 still remains a significant challenge owing to the wide range of possible environmental conditions and NH3 concentration. In this regard, it is imperative to ensure the quality control of techniques to measure the NH3 emission levels reliably. A present study was conducted to compare the five analytical methods for the measurement of atmospheric NH3 via validation tests under laboratory and field conditions. The analytical instruments applied in the present study were based on wet chemistry, gas detection tube, electrochemical sensor, photoacoustic spectroscopy, and cavity ring-down spectroscopy. The reproducibility and linearity of all the analyzed methods were observed to be high with the relative standard deviation and coefficient of determination (R2) being 10% and > 0.9, respectively. In the case of wet chemistry and high NH3 concentration, the measured NH3 results were found to be close to the actual standard gas levels. Response times of electrochemical sensor showed faster from the instruments utilized more than one year and the high NH3 concentrations. In the field tests, NH3 concentration showed higher in the manure storage tank compared with the pig-pen. In both cases, the NH3 concentration levels measured by gas detection tube were found to be quite different from that of wet chemistry. It was proposed that such differences in NH3 concentration could arise due to the inherent instrumental characteristics and the variations in air velocity during sampling/measurement. The periodic instrumental maintenance, verification, replicate analyses, and suitable consideration of environmental factors should be considered for a more reliable measurement of NH3 concentration under real field conditions.

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.