School of Economics and Management, Beihang University, Beijing, 100191, China.
School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China.
Sci Data. 2020 Oct 5;7(1):325. doi: 10.1038/s41597-020-00665-1.
To meet the growing electricity demand, China's power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO and NO emissions from power plants using systematic actual measurements monitored by China's continuous emission monitoring systems (CEMS) network over 96-98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.
为满足日益增长的电力需求，中国的发电行业已成为空气污染物的一个重要来源。具体的控制政策制定需要一个反映电厂排放整体、异质、时变特征的清单。由于缺乏全面的实际测量，现有的清单依赖于平均排放因子，这些因子受到许多假设和高度不确定性的影响。本研究首次利用中国连续排放监测系统（CEMS）网络对全国 96-98%的总火电容量进行的系统实际测量，开发了电厂颗粒物（PM）、SO 和 NO 排放清单。本研究利用全国范围内、基于污染源的实时 CEMS 监测数据，直接估算排放因子和绝对排放量，避免使用间接平均排放因子，从而降低不确定性水平。该数据集提供了电厂级别的绝对排放量、燃料使用、发电能力、地理位置等信息。该数据集有助于进行电力排放特征描述和制定清洁空气政策，并且其他国家也可以采用基于 CEMS 的估算方法来对其电力排放进行监管。
