Wu Nana, Geng Guannan, Qin Xinying, Tong Dan, Zheng Yixuan, Lei Yu, Zhang Qiang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China.
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
ACS Environ Au. 2022 May 17;2(4):363-372. doi: 10.1021/acsenvironau.2c00014. eCollection 2022 Jul 20.
Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO, PM, NO , and SO, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.
每日排放估算对于追踪排放源的动态变化至关重要。在这项工作中，我们通过结合基于机组的中国燃煤电厂排放数据库（CPED）的信息和连续排放监测系统（CEMS）的实时测量数据，估算了2017 - 2020年期间中国燃煤电厂的每日排放量。我们开发了一种逐步方法来筛选异常值并插补CEMS数据中的缺失值。然后，将从CEMS获得的电厂层面的烟气量和排放的每日分布与CPED的年度排放量相结合，以得出每日排放量。在排放变化与可用统计数据（即月度发电量和每日煤炭消耗量）之间发现了合理的一致性。CO、PM、NO 和SO的每日电力排放量分别在6267 - 12994、0.4 - 1.3、6.5 - 12.0和2.5 - 6.8 Gg范围内，冬季和夏季由于供暖和制冷需求导致排放量较高。我们的估算能够捕捉典型社会经济事件期间每日电力排放的突然下降（例如与新冠疫情封锁和短期排放控制相关的下降）或增加（例如与干旱相关的增加）。我们还发现，与之前的研究相比，CEMS的每周模式没有明显的周末效应。每日电力排放将有助于改进化学传输模型并促进政策制定。
