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<p>The accurate estimation of ammonia (NH<sub>3</sub>) emissions from livestock facilities is required to establish effective air quality management policies, yet significant uncertainty remains in current bottom-up inventories. This study thus presents a top-down inverse modelling approach for the estimation of NH<sub>3</sub> emission factors. We validated the performance of three atmospheric dispersion models—AERMOD, CALPUFF, and computational fluid dynamics (CFD)—against atmospheric NH<sub>3</sub> datasets collected over four seasons in a high-density pig farming region with aging, older-type facilities in South Korea. Evaluation of model performance revealed that the CFD model produced the most accurate local concentration distributions, with an index of agreement (IA) of 0.98, while CALPUFF effectively captured long-term temporal variation (IA = 0.89). Based on the CALPUFF results, the average NH<sub>3</sub> emission factor was estimated to be 4.17<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\:\pm\:\)</EquationSource></InlineEquation>1.33 kg animal<sup>− 1</sup> year<sup>− 1</sup>, with seasonal fluctuations ranging from 3.50&#xa0;kg animal<sup>− 1</sup> year<sup>− 1</sup> in autumn to 4.53&#xa0;kg animal<sup>− 1</sup> year<sup>− 1</sup> in winter. The estimated emission factor was around 80% of the legacy Clean Air Policy Support System value, falling between the modernised (2020) and legacy (2008) inventory factors, suggesting the potential of the top-down inverse approach as a complementary tool for refining bottom-up emission inventories. The proposed method can help improve the representativeness of emission estimates by incorporating receptor-based atmospheric observations from large-scale source clusters.</p>

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Refining ammonia inventories through top-down inverse modelling in high-density swine farming regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Chae-rin Lee,
  • Se-yeon Lee,
  • Ji-yeon Park,
  • Jinseon Park,
  • Rial Arifin Rajagukguk,
  • Yeonhoo Kim,
  • Mijung Song,
  • Beom-Keun Seo,
  • Jongho Kim,
  • Jinsik Kim,
  • Hyung-Do Song,
  • Chul Yoo,
  • Se-woon Hong

摘要

The accurate estimation of ammonia (NH3) emissions from livestock facilities is required to establish effective air quality management policies, yet significant uncertainty remains in current bottom-up inventories. This study thus presents a top-down inverse modelling approach for the estimation of NH3 emission factors. We validated the performance of three atmospheric dispersion models—AERMOD, CALPUFF, and computational fluid dynamics (CFD)—against atmospheric NH3 datasets collected over four seasons in a high-density pig farming region with aging, older-type facilities in South Korea. Evaluation of model performance revealed that the CFD model produced the most accurate local concentration distributions, with an index of agreement (IA) of 0.98, while CALPUFF effectively captured long-term temporal variation (IA = 0.89). Based on the CALPUFF results, the average NH3 emission factor was estimated to be 4.17\(\:\pm\:\)1.33 kg animal− 1 year− 1, with seasonal fluctuations ranging from 3.50 kg animal− 1 year− 1 in autumn to 4.53 kg animal− 1 year− 1 in winter. The estimated emission factor was around 80% of the legacy Clean Air Policy Support System value, falling between the modernised (2020) and legacy (2008) inventory factors, suggesting the potential of the top-down inverse approach as a complementary tool for refining bottom-up emission inventories. The proposed method can help improve the representativeness of emission estimates by incorporating receptor-based atmospheric observations from large-scale source clusters.