Chen Jianan, Du Xiaohui, Liu Xuejun, Xu Wen, Krol Maarten
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, National Observation and Research Station of Agriculture Green Development (Quzhou, Hebei), China Agricultural University, Beijing 100193, China.
Meteorology and Air Quality Group, Wageningen University & Research, Wageningen 6708 PB, Netherlands.
Environ Sci Technol. 2025 May 27;59(20):9991-10000. doi: 10.1021/acs.est.4c10878. Epub 2025 May 15.
An accurate ammonia (NH) emission inventory is crucial for policymakers developing air pollution mitigation strategies. Both satellite observations and bottom-up estimates identify significant NH emission hotspots in China. However, bottom-up NH emission inventories are highly uncertain due to the lack of localized emission factors, while large and uncertain errors in IASI satellite NH columns have hindered their direct application in top-down emission inversion methods. In this study, we perform a top-down optimization of monthly NH emissions over China using IASI-derived surface NH concentrations with well-evaluated error estimates, combined with the CAMx model at a 36 km resolution. Our posterior NH emissions for 2020 (12.3 [10.9-13.6] Tg N yr) are significantly higher than prior estimates from the MEIC inventory (7.6 Tg N yr), which primarily underestimates emissions during the warm months in hotspot areas (e.g., NCP and MLYR). We employ multiple approaches to comprehensively evaluate our inversion results. Our study highlights that error estimates for low-value observations are a particularly critical factor in the inversion setup, significantly influencing the reliability of emission optimization.
准确的氨(NH₃)排放清单对于制定空气污染缓解策略的政策制定者至关重要。卫星观测和自下而上的估算都确定了中国存在显著的氨排放热点地区。然而,由于缺乏本地化的排放因子,自下而上的氨排放清单具有高度不确定性,而IASI卫星氨柱浓度存在较大且不确定的误差,这阻碍了它们在自上而下的排放反演方法中的直接应用。在本研究中,我们利用IASI反演得到的具有良好误差估计的地表氨浓度,并结合36公里分辨率的CAMx模型,对中国的月度氨排放进行了自上而下的优化。我们得到的2020年后验氨排放量(12.3 [10.9 - 13.6] Tg N yr⁻¹)显著高于MEIC清单的先前估计值(7.6 Tg N yr⁻¹),后者主要低估了热点地区(如华北平原和长江中下游地区)温暖月份的排放量。我们采用多种方法全面评估我们的反演结果。我们的研究强调,低价值观测的误差估计是反演设置中的一个特别关键因素,对排放优化的可靠性有显著影响。