AMMONIA DISPERSION FROM MULTI-FLOOR VERSUS STANDARD SINGLE-FLOOR PIG PRODUCTION FACILITIES BASED ON COMPUTATIONAL FLUID DYNAMICS SIMULATIONS

Yicong XIN, Li RONG, Gunther SCHAUBERGER, Dejia LIU, Xiusong LI, Zhihua YANG, Songming ZHU, Dezhao LIU

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Front. Agr. Sci. Eng. ›› 2023, Vol. 10 ›› Issue (3) : 374-389. DOI: 10.15302/J-FASE-2023501
RESEARCH ARTICLE
RESEARCH ARTICLE

AMMONIA DISPERSION FROM MULTI-FLOOR VERSUS STANDARD SINGLE-FLOOR PIG PRODUCTION FACILITIES BASED ON COMPUTATIONAL FLUID DYNAMICS SIMULATIONS

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Highlights

● NH3 dispersion from a multi-floor pig building was compared to a single-floor building.

● NH3 dispersed much further from the multi-floor pig building.

● Wind speed, direction and source concentration were important for NH3 dispersion.

● NH3 tended to accumulate in the east and west yards of the multi-floor pig building.

● Higher wind speed was the likely cause of more NH3 accumulation in the yards.

Abstract

Multi-floor buildings for raising pigs have recently attracted widespread attention as an emerging form of intensive livestock production especially in eastern China, due to the fact that they can feed a much larger number of animals per unit area of land and thus alleviate the shortage of land available for standard single-floor pig production facilities. However, this more intensive kind of pig building will pose new challenges to the local environment in terms of pollutant dispersion. To compare the dispersion air pollutants (ammonia as a representative) emitted from multi- versus single-floor pig buildings, ammonia dispersion distance and concentration gradients were investigated through three-dimensional simulations based on computational fluid dynamics. The validation of an isolated cubic model was made to ensure the simulation method was effective. The effects of wind direction, wind speed and emission source concentration at 1.5 m (approximate human inhalation height) during summer were investigated. The results showed that the ammonia dispersion distance of the multi-floor pig building was far greater than that of the single-floor building on a plane of Z = 1.5 m. When the wind direction was 67.5°, the wind speed was 2 m·s−1 and the emission source concentration was 20 ppmv, the dispersion distance of the multi-floor pig building could reach 1380 m. Meanwhile, the ammonia could accumulate in the yard to 7.68 ppmv. Therefore, future site selection, wind speed and source concentration need to be given serious consideration. Based on the simulation used in this study with source concentration is 20 ppmv, the multi-floor pig buildings should be located 1.4 km away from residential areas to avoid affecting residents. The results of this study should guidance for any future development of multi-floor pig buildings.

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Keywords

pig building / computational fluid dynamics / ammonia / dispersion

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Yicong XIN, Li RONG, Gunther SCHAUBERGER, Dejia LIU, Xiusong LI, Zhihua YANG, Songming ZHU, Dezhao LIU. AMMONIA DISPERSION FROM MULTI-FLOOR VERSUS STANDARD SINGLE-FLOOR PIG PRODUCTION FACILITIES BASED ON COMPUTATIONAL FLUID DYNAMICS SIMULATIONS. Front. Agr. Sci. Eng., 2023, 10(3): 374‒389 https://doi.org/10.15302/J-FASE-2023501

References

[1]
Du L, Yang C, Dominy R, Yang L, Hu C, Du H, Li Q, Yu C, Xie L, Jiang X. Computational fluid dynamics aided investigation and optimization of a tunnel ventilated poultry house in China. Computers and Electronics in Agriculture, 2019, 159: 1–15
CrossRef Google scholar
[2]
Hu Y, Cheng H, Tao S. Environmental and human health challenges of industrial livestock and poultry farming in China and their mitigation. Environment International, 2017, 107: 111–130
CrossRef Pubmed Google scholar
[3]
Saha C K, Zhang G, Kai P, Bjerg B. Effects of a partial pit ventilation system on indoor air quality and ammonia emission from a fattening pig room. Biosystems Engineering, 2010, 105(3): 279–287
CrossRef Google scholar
[4]
Rebolledo B, Gil A, Pallarés J. A spatial ammonia emission inventory for pig farming. Atmospheric Environment, 2013, 64: 125–131
CrossRef Google scholar
[5]
Smit L A M, Heederik D. Impacts of intensive livestock production on human health in densely populated regions. GeoHealth, 2017, 1(7): 272–277
CrossRef Pubmed Google scholar
[6]
Benincà E, van Boven M, Hagenaars T, van der Hoek W. Space-time analysis of pneumonia hospitalisations in the Netherlands. PLoS One, 2017, 12(7): e0180797
CrossRef Pubmed Google scholar
[7]
Pohl H R, Citra M, Abadin H A, Szadkowska-Stańczyk I, Kozajda A, Ingerman L, Nguyen A, Murray H E. Modeling emissions from CAFO poultry farms in Poland and evaluating potential risk to surrounding populations. Regulatory Toxicology and Pharmacology, 2017, 84: 18–25
CrossRef Pubmed Google scholar
[8]
Zilio M, Orzi V, Chiodini M E, Riva C, Acutis M, Boccasile G, Adani F. Evaluation of ammonia and odour emissions from animal slurry and digestate storage in the Po Valley (Italy). Waste Management, 2020, 103: 296–304
CrossRef Pubmed Google scholar
[9]
Lateb M, Meroney R N, Yataghene M, Fellouah H, Saleh F, Boufadel M C. On the use of numerical modelling for near-field pollutant dispersion in urban environments—A review. Environmental Pollution, 2016, 208(Pt A): 271–283
[10]
Allegrini J, Dorer V, Carmeliet J. Coupled CFD, radiation and building energy model for studying heat fluxes in an urban environment with generic building configurations. Sustainable Cities and Society, 2015, 19: 385–394
CrossRef Google scholar
[11]
Blocken B, Stathopoulos T, Carmeliet J, Hensen J L M. Application of computational fluid dynamics in building performance simulation for the outdoor environment: an overview. Journal of Building Performance Simulation, 2011, 4(2): 157–184
CrossRef Google scholar
[12]
Botham-Myint D, Recktenwald G W, Sailor D J. Thermal footprint effect of rooftop urban cooling strategies. Urban Climate, 2015, 14: 268–277
CrossRef Google scholar
[13]
Skelhorn C, Lindley S, Levermore G. The impact of vegetation types on air and surface temperatures in a temperate city: a fine scale assessment in Manchester, UK. Landscape and Urban Planning, 2014, 121: 129–140
CrossRef Google scholar
[14]
Yang X, Zhao L, Bruse M, Meng Q. Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces. Building and Environment, 2013, 60: 93–104
CrossRef Google scholar
[15]
Gu Z L, Zhang Y W, Lei K B. Large eddy simulation of flow in a street canyon with tree planting under various atmospheric instability conditions. Science China. Technological Sciences, 2010, 53(7): 1928–1937
CrossRef Google scholar
[16]
Tan Z, Dong J, Xiao Y, Tu J. A numerical study of diurnally varying surface temperature on flow patterns and pollutant dispersion in street canyons. Atmospheric Environment, 2015, 104: 217–227
CrossRef Google scholar
[17]
Olivardia F G G, Zhang Q, Matsuo T, Shimadera H, Kondo A. Analysis of pollutant dispersion in a realistic urban street canyon using coupled CFD and chemical reaction modeling. Atmosphere, 2019, 10(9): 479
CrossRef Google scholar
[18]
Ntinas G K, Shen X, Wang Y, Zhang G. Evaluation of CFD turbulence models for simulating external airflow around varied building roof with wind tunnel experiment. Building Simulation, 2018, 11(1): 115–123
CrossRef Google scholar
[19]
Tominaga Y, Stathopoulos T. Numerical simulation of dispersion around an isolated cubic building: comparison of various types of k-ε models. Atmospheric Environment, 2009, 43(20): 3200–3210
CrossRef Google scholar
[20]
Li W W, Meroney R N. Gas dispersion near a cubical model building. Part I. Mean concentration measurements. Journal of Wind Engineering and Industrial Aerodynamics, 1983, 12(1): 15–33
CrossRef Google scholar
[21]
Rodriguez M R, Losada E, Besteiro R, Arango T, Velo R, Ortega J A, Fernandez M D. Evolution of NH3 concentrations in weaner pig buildings based on setpoint temperature. Agronomy, 2020, 10(1): 107
CrossRef Google scholar
[22]
Franke J, Hellsten A, Schlunzen K H, Carissimo B. The COST 732 Best Practice Guideline for CFD simulation of flows in the urban environment: a summary. International Journal of Environment and Pollution, 2011, 44(1−4): 419−427
[23]
Richards P J, Hoxey R P. Appropriate boundary conditions for computational wind engineering models using the kε turbulence model. Journal of Wind Engineering and Industrial Aerodynamics, 1993, 46−47: 145−153
[24]
Tominaga Y, Stathopoulos T. Numerical simulation of dispersion around an isolated cubic building: model evaluation of RANS and LES. Building and Environment, 2010, 45(10): 2231–2239
CrossRef Google scholar
[25]
Bazdidi-Tehrani F, Jadidi M. Large eddy simulation of dispersion around an isolated cubic building: evaluation of localized dynamic kSGS-equation sub-grid scale model. Environmental Fluid Mechanics, 2014, 14(3): 565–589
CrossRef Google scholar
[26]
Hanajima D, Kuroda K, Morishita K, Fujita J, Maeda K, Morioka R. Key odor components responsible for the impact on olfactory sense during swine feces composting. Bioresource Technology, 2010, 101(7): 2306–2310
CrossRef Pubmed Google scholar

Acknowledgements

The research was financially supported by the National Key R&D Program of China (2022YFE0115600) and the Key Research and Development Program of Zhejiang Province (2022C02045).

Compliance with ethics guidelines

Yicong Xin, Li Rong, Gunther Schauberger, Dejia Liu, Xiusong Li, Zhihua Yang, Songming Zhu, and Dezhao Liu declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2023. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
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