State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2020 Mar 1;706:135735. doi: 10.1016/j.scitotenv.2019.135735. Epub 2019 Nov 26.
Chinese coal-fired power plants (CFPPs) are experiencing large-scale and rapid retrofitting of ultralow emission (ULE), causing significant changes in emission level of particulate matter (PM) from CFPPs. In this study, based on coal ash mass balance over the whole process, an integrated emission factors (EFs) database of three size-fractioned particulate matters (PM, PM, and PM) for CFPPs is constructed, which covers almost all typical ULE technical routes installed in CFPPs. To verify the reliability of PM EFs established in this study, we compare those with related results based on field tests. Overall, the gaps in the EFs of PM, PM, and PM obtained by the two methods are not outrageous within a reasonable range. By combined with the refined size-fractioned PM EFs and unit-based activity level database, a detailed high-resolution emission inventory of PM, PM, and PM from Chinese CFPPs in 2017 is established, with the corresponding total emissions of 143, 207, and 267 kt, respectively. Our estimation of PM emission is comparable to the official statistics announced by China Electricity Council (CEC), which further demonstrates the reliability of PM EFs constructed in this study. Moreover, potential reductions of PM from CFPPs at two stages before and after 2017 are assessed under three application scenarios of major ULE technical routes. We forecast the final annual emissions of PM, PM, and PM until 2020 will be reduced further, which fall within the range of 86-111 kt, 120-157 kt, and 142-184 kt, respectively, if all CFPPs achieve ULE requirements under the three scenarios. We believe our integrated database of PM EFs of CFPPs has good universality, and the forecast results will be helpful for policy guidance of ULE technologies, emissions inventory compilation, and regional air quality simulation and management.
中国燃煤电厂（CFPPs）正在大规模快速进行超低排放（ULE）改造，导致电厂颗粒物（PM）排放水平发生显著变化。在本研究中，基于全过程的煤灰质量平衡，构建了涵盖 CFPPs 中安装的几乎所有典型 ULE 技术路线的三种不同粒径颗粒物（PM、PM 和 PM）综合排放因子（EF）数据库。为了验证本研究中建立的 PM EF 的可靠性，我们将其与基于现场测试的相关结果进行了比较。总体而言，两种方法得到的 PM、PM 和 PM EF 之间的差距在合理范围内并不离谱。通过结合精细化的粒径 PM EF 和基于机组的活动水平数据库，建立了 2017 年中国 CFPPs 中 PM、PM 和 PM 的详细高分辨率排放清单，相应的总排放量分别为 143、207 和 267 kt。我们对 PM 排放的估计与中国电力企业联合会（CEC）公布的官方统计数据相当，进一步证明了本研究中建立的 PM EF 的可靠性。此外，在 ULE 主要技术路线的三个应用场景下，评估了 2017 年前后两个阶段 CFPPs 中 PM 的减排潜力。我们预测，如果所有 CFPPs 在这三种情景下都能达到 ULE 的要求，到 2020 年，PM、PM 和 PM 的年排放量将进一步减少，分别在 86-111 kt、120-157 kt 和 142-184 kt 的范围内。我们相信，我们的 CFPPs PM EF 综合数据库具有良好的通用性，预测结果将有助于 ULE 技术的政策指导、排放清单编制以及区域空气质量模拟和管理。
