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Improved Jayaweera-Mikkelsen model to quantify ammonia volatilization from rice paddy fields in China

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Abstract

Current estimates of China’s ammonia (NH3) volatilization from paddy rice differ by more than twofold, mainly due to inappropriate application of chamber-based measurements and improper assumptions within process-based models. Here, we improved the Jayaweera-Mikkelsen (JM) model through multiplying the concentration of aqueous NH3 in ponded water by an activity coefficient that was determined based on high-frequency flux observations at Jingzhou station in Central China. We found that the improved JM model could reproduce the dynamics of observed NH3 flux (R2 = 0.83, n = 228, P < 0.001), while the original JM model without the consideration of activity of aqueous NH3 overstated NH3 flux by 54% during the periods of fertilization and pesticide application. The validity of the improved JM model was supported by a mass-balance-based indirect estimate at Jingzhou station and the independent flux observations from the other five stations across China. The NH3 volatilization losses that were further simulated by the improved JM model forced by actual wind speed were in general a half less than previous chamber-based estimates at six stations. Difference in wind speed between the inside and outside of the chamber and insufficient sampling frequency were identified as the primary and secondary causes for the overestimation in chamber-based estimations, respectively. Together, our findings suggest that an in-depth understanding of NH3 transfer process and its robust representation in models are critical for developing regional emission inventories and practical mitigation strategies of NH3.

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Acknowledgments

This study was supported by the National Key Research and Development Program of China (2018YFC0213304), the National Natural Science Foundation of China (41671464), the China Postdoctoral Science Foundation (2017M620503), Interdisciplinarity Fund of Peak Discipline from Shanghai Municipal Education Commission (0200121005/053), and the 111 Project (B14001). We appreciated Jianping Wang, Yumin An, Zaizhen Zhang, Jinghong Tan, Gongyou Yu, Sheng Wang, and Bingyu Chen for collecting and analyzing samples and Prof. Bin Yin for designing dynamic chambers.

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  1. Xiaoying Zhan and Chuan Chen contributed equally to this work.

Authors and Affiliations

  1. Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, People’s Republic of China

    Xiaoying Zhan, Qihui Wang, Feng Zhou, Yonghua Wang, Yali Wu, Jin Fu, Shuoshuo Gao, Xikang Hou & Yan Bo

  2. Agricultural Clean Watershed Research Group, Chinese Academy of Agricultural Sciences, Institute of Environment and Sustainable Development in Agriculture, Beijing, 100081, People’s Republic of China

    Xiaoying Zhan

  3. Institute of International Rivers and Eco-security, Yunnan University, Kunming, 650091, People’s Republic of China

    Chuan Chen

  4. Division of Biogeochemical Cycles, National Agriculture and Food Research Organization, Institute for Agro-Environmental Sciences, 3-1-3, Kannondai, Tsukuba, Ibaraki, 305-8604, Japan

    Kentaro Hayashi

  5. College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, People’s Republic of China

    Xiaotang Ju

  6. School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Shu Kee Lam

  7. College of Agriculture, Yangtze University, Jingzhou, 434025, People’s Republic of China

    Luping Zhang, Qixia Wu & Jianqiang Zhu

  8. Key Laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture, Chinese Academy of Agricultural Sciences, Institute of Agricultural Resources and Regional Planning, Beijing, 100081, People’s Republic of China

    Dan Zhang & Hongbin Liu

  9. Jingzhou Agrometeorological Experimental Station, Jingzhou, 434025, People’s Republic of China

    Kaiwen Liu & Rongrui Su

  10. School of Ecology and Environmental Science, Yunnan University, Kunming, 650091, People’s Republic of China

    Changliang Yang

  11. Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, People’s Republic of China

    Chaomeng Dai

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  1. Xiaoying Zhan
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Correspondence to Feng Zhou.

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Quantification of activity coefficient, Table S1-S4, and Fig. S1-S2 are all available online.

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Zhan, X., Chen, C., Wang, Q. et al. Improved Jayaweera-Mikkelsen model to quantify ammonia volatilization from rice paddy fields in China. Environ Sci Pollut Res 26, 8136–8147 (2019). https://doi.org/10.1007/s11356-019-04275-2

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