Xu Bufan, Jin Jianbing, Fang Li, Pang Mijie, Xia Ji, Li Baojie, Liao Hong
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
Sci Total Environ. 2024 Feb 20;912:169053. doi: 10.1016/j.scitotenv.2023.169053. Epub 2023 Dec 12.
Atmospheric ammonia has great environmental implications due to its important role in ecosystem and nitrogen cycle, as well as contribution to formation of secondary particles. China is recognized as a hotspot of NH pollution owing to agricultural and livestock intensification. In the quest to achieve a comprehensive understanding of atmospheric ammonia load and to quantify its environmental impacts in China, relying solely either on existing measurements or on model simulations falls short. Their limitations, either in spatial coverage and integrity or in data quality, fails to meet the needs. Available reanalysis products exhibit a marked deficiency in ammonia data. We therefore aim to propose an integrated ammonia reanalysis product in China, adeptly melding satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) NH retrievals with chemical transport model simulation, capitalizing on the robust Ensemble Kalman Filter (EnKF) data assimilation methodology. The product is validated in high quality via the comparison against independent measurements from ground monitoring stations. Spanning a decade from 2013 to 2022, our reanalysis uncovers not just the spatial intricacies of NH concentrations but also their temporal dynamics. Our findings pinpointed the spatial disparities in atmospheric ammonia intensities, highlighting regional hotspots in the NCP, SCB, and Northeast China, and identified annual and seasonal patterns. Our research provides crucial insights for shaping future NH pollution prevention and control strategies in China.
大气氨在生态系统和氮循环中发挥着重要作用，并且对二次颗粒物的形成有贡献，因此具有重大的环境意义。由于农业和畜牧业的集约化，中国被认为是氨污染的热点地区。为了全面了解中国大气氨负荷并量化其环境影响，仅依靠现有测量或模型模拟都存在不足。它们在空间覆盖范围和完整性或数据质量方面的局限性无法满足需求。现有的再分析产品在氨数据方面存在明显不足。因此，我们旨在提出一种中国大气氨综合再分析产品，巧妙地将红外大气探测干涉仪（IASI）氨反演的卫星观测数据与化学传输模型模拟相结合，利用强大的集合卡尔曼滤波（EnKF）数据同化方法。该产品通过与地面监测站的独立测量数据进行比较，得到了高质量的验证。我们的再分析涵盖了2013年至2022年的十年，不仅揭示了氨浓度的空间复杂性，还揭示了其时间动态。我们的研究结果指出了大气氨强度的空间差异，突出了华北平原、长三角地区和中国东北地区的区域热点，并确定了年度和季节性模式。我们的研究为制定中国未来的氨污染防治策略提供了关键见解。
