Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion

IF 6.7 Q1 ENGINEERING, ENVIRONMENTAL

ACS Environmental Au Pub Date : 2022-05-17 DOI:10.1021/acsenvironau.2c00014

Nana Wu, Guannan Geng, Xinying Qin, Dan Tong, Yixuan Zheng, Yu Lei and Qiang Zhang*,

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引用次数: 3

Abstract

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 CO₂, PM_2.5, NO_x, and SO₂, 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.

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基于多源数据融合的中国燃煤电厂日排放模式

每日排放估算对于跟踪排放源的动态变化至关重要。在这项工作中，我们结合了基于单位的中国燃煤电厂排放数据库(CPED)和连续排放监测系统(CEMS)的实时测量数据，估算了2017-2020年中国燃煤电厂的日排放量。我们开发了一种循序渐进的方法来筛选异常值并为CEMS数据输入缺失值。然后，将从CEMS获得的工厂级烟气量和排放量的每日概况与CPED的年排放量相结合，得出每日排放量。在排放变化和现有统计数据(即每月发电量和每日煤炭消耗量)之间发现了合理的一致。CO2、PM2.5、NOx、SO2的日排放量分别为6267 ~ 12994、0.4 ~ 1.3、6.5 ~ 12.0、2.5 ~ 6.8 Gg，其中冬夏两季因采暖制冷需求而排放量较高。我们的估计可以捕捉到典型社会经济事件期间每日电力排放量的突然减少(例如，与COVID-19封锁和短期排放控制有关的减少)或增加(例如，与干旱有关的增加)。我们还发现，与以往的研究相比，CEMS的每周模式没有明显的周末效应。每日电力排放数据将有助于改进化学运输模型，促进政策制定。

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来源期刊

ACS Environmental Au 环境科学-

CiteScore

7.10

自引率

0.00%

发文量

期刊介绍： ACS Environmental Au is an open access journal which publishes experimental research and theoretical results in all aspects of environmental science and technology both pure and applied. Short letters comprehensive articles reviews and perspectives are welcome in the following areas:Alternative EnergyAnthropogenic Impacts on Atmosphere Soil or WaterBiogeochemical CyclingBiomass or Wastes as ResourcesContaminants in Aquatic and Terrestrial EnvironmentsEnvironmental Data ScienceEcotoxicology and Public HealthEnergy and ClimateEnvironmental Modeling Processes and Measurement Methods and TechnologiesEnvironmental Nanotechnology and BiotechnologyGreen ChemistryGreen Manufacturing and EngineeringRisk assessment Regulatory Frameworks and Life-Cycle AssessmentsTreatment and Resource Recovery and Waste Management