A strategy for ammonia odor monitoring, prediction, and reduction from livestock manure wastes in Korea: a short review

IF 1.5 Q3 GEOSCIENCES, MULTIDISCIPLINARY Geosystem Engineering Pub Date : 2022-03-04 DOI:10.1080/12269328.2022.2120094
Sang-hun Lee, S. Bae
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引用次数: 0

Abstract

ABSTRACT Ammonia is a malodorous substance, even at low concentrations, that has harmful effects on humans and the environment. Ammonia is considered an alternative energy source for hydrogen energy transport. Most ammonia is emitted from livestock facilities, as the main sources of ammonia odor. Therefore, this study focused on the emission and dispersion of ammonia as the main compound in ammoniacal odor substances from livestock manure facilities, as well as on the treatment and recovery of ammonia near livestock facilities. First, this study reviewed the monitoring of ammoniacal odor recommended by multiple low-cost monitoring sensors integrated with information technology (IT). To enhance the monitoring data quality, periodic calibration and validation through reference instruments is essential. To estimate odor emissions, emission factors should be classified by considering various scenarios and conditions. The application of modelling is desirable for predicting the dispersion of ammonia and odors, but a simpler model should be developed for non-experts in the preliminary stage. For ammonia treatment and resource recovery, it is necessary to support the development of high-efficiency and eco-friendly treatment technologies, which should be tested in upscaled systems with actual odors under field conditions.
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韩国畜禽粪便中氨气味的监测、预测和减少策略:简要回顾
摘要氨是一种恶臭物质,即使浓度很低,也会对人类和环境产生有害影响。氨被认为是氢能源运输的一种替代能源。大多数氨是从畜牧设施排放的,是氨气味的主要来源。因此,本研究重点研究了畜禽粪便设施中氨性气味物质中主要化合物氨的排放和扩散,以及畜禽设施附近氨的处理和回收。首先,本研究回顾了多个低成本监测传感器与信息技术(IT)相结合所推荐的氨性气味监测。为了提高监测数据的质量,通过参考仪器进行定期校准和验证至关重要。为了估计气味排放,应通过考虑各种情景和条件对排放因素进行分类。建模的应用对于预测氨和气味的分散是可取的,但在初步阶段应该为非专家开发一个更简单的模型。对于氨处理和资源回收,有必要支持开发高效环保的处理技术,这些技术应在现场条件下,在具有实际气味的大型系统中进行测试。
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来源期刊
Geosystem Engineering
Geosystem Engineering GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
2.70
自引率
0.00%
发文量
11
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