atmosphere Article Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories Yaqin Hu 1,2 and Yusheng Shi 1,* 1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; [email protected] 2 University of Chinese Academy of Sciences, Beijing 101408, China * Correspondence: [email protected]; Tel.: +86-138-1042-0401 Abstract: The concentration of atmospheric carbon dioxide (CO2) has increased rapidly world- wide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capac- ity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be Citation: Hu, Y.; Shi, Y. Estimating 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method CO2 Emissions from Large Scale ± Coal-Fired Power Plants Using for the Waigaoqiao Power Plant, and 14.58 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power OCO-2 Observations and Emission Plant, respectively. These estimates were compared with three standard databases for validation: Inventories. Atmosphere 2021, 12, 811. the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, https://doi.org/10.3390/atmos and the Carbon Brief database. The comparison found that previous emission inventories spanning 12070811 different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying Academic Editor: Rafael Borge CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up Received: 27 April 2021 statistical methods. Accepted: 21 June 2021 Published: 24 June 2021 Keywords: carbon dioxide; Gaussian model; point source; Orbiting Carbon Observatory-2 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- 1. Introduction iations. Anthropogenic carbon dioxide (CO2) emissions from industries (including fossil fuel burning) are the dominant contributors to the observed enhancement in global atmospheric CO2 concentration [1], and the increase of CO2 emissions account for approximately 78% of the total increase in greenhouse gas (GHG) emissions. As the most important GHG, Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. CO2 is the main driver of global climate change [2,3]. To date, 189 member states of the This article is an open access article United Nations Framework Convention on Climate Change (UNFCCC) have achieved distributed under the terms and consensus on the Paris Agreement, which aims to reduce the incremental increase of the ◦ conditions of the Creative Commons average temperature of earth’s surface to no more than 2 C higher than when global Attribution (CC BY) license (https:// industrialization began [4]. China has been the largest emitter of CO2 worldwide since creativecommons.org/licenses/by/ 2006, and its emissions have been more than twice that of the next highest country, the 4.0/). United States, as of 2018 [5]. Atmosphere 2021, 12, 811. https://doi.org/10.3390/atmos12070811 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 811 2 of 18 Based on the Emissions Database for Global Atmospheric Research (EDGAR) [6], the Science for Policy report by the Joint Research Center (JRC) indicates that of the main emission-producing sectors, the power industry has been the largest contributor to CO2 emissions since 1990 [5]. In China, electrical equipment lags in efficiency; thus, CO2 emissions from coal-fired power stations account for 80% of the nation’s electricity sector emissions (the world average is 40%). Solving the environmental problems caused by the coal electric power industry has great significance for sustainable development and the mit- igation of global climate change [7]. Consequently, the methods for estimating coal-burning power plants CO2 emissions at the unit level must be improved to provide a reasonable scientific reference for the formulation of CO2 emission reduction countermeasures for countries without well-established emission reporting systems [8]. There is increasing interest in estimating the CO2 emissions from power plants in China. However, no global observation system for accurately monitoring point-source CO2 emissions was previously available [9]. National CO2 emission inventories of power plants are primarily obtained by statistical methods on account of factors such as the consumption of fossil fuel, the type of consumption, the purity and mix of fuel, and efficiency [10,11]. Therefore, the estimation of anthropogenic emissions at fine scales generally has a large uncertainty regarding the collection of accurate consumption data. With the launch of high-precision carbon satellite missions, such as Japan’s Greenhouse Gases Observing Satellite (GOSAT) [12], the Orbiting Carbon Observatory-2 (OCO-2) [13] of the National Aeronautics and Space Administration (NASA), and other satellites, the remote sensing top-down retrieval method for quantifying CO2 emissions has proven to be an effective method. OCO-2 incorporates three high-resolution spectrometers that take synchronous measurements of reflected sunlight; for CO2, measurements are taken at the 1.61 and 2.06 µm bands in the shortwave infrared (SWIR) region, while measurements for oxygen (O2) are taken at the 0.76 µm band in the near-infrared (NIR) region. These measurements are used to retrieve the average dry air mole fraction of the CO2 column (herein, XCO2). The precisions of GOSAT and OCO-2 are approximately 1–2 ppm and 1 ppm (or 0.25%), respectively [12,14]. Multiple studies have focused on estimating anthropogenic CO2 flux on relatively larger spatial scales using GOSAT and OCO-2 data [15–19]. However, there is currently insufficient research on the estimation of emissions at point scales using remote sensing inversion methods, especially for power plants with high carbon emission intensities in China. Initially, a promising concept proposed by Bovensmann et al. [20] suggested that remote sensing satellites could detect strong localized CO2 point sources and quantify their emissions. However, this achievement demands imaging with a spatial resolution 2 of 2 × 2 km for satellites to map the concentration distribution of the atmospheric CO2 column with an accuracy of 0.5% (2 ppm) or better. The CO2 emission problem was solved on the scale of a single facility (power plant) for the first time by Nassar et al. [21] using a Gaussian plume model based on OCO-2 data with an assumed constant wind field. The gap between the estimated emissions of American power plants and the daily reported emissions was 1–17%. Following [21], Krings et al. [22] inferred the CO2 emission estimation of a group of coal-fired power plants in a complex study area by combining the mass balance and inverse Gaussian plume model method. The data were collected through aerial remote sensing observation and airborne in-situ measurements of CO2 with the MAMAP instrument, which is an airborne double-channel NIR–SWIR grating spectrometer that is used to accurately measure column-averaged gradients of CO2 and methane concentrations. Further, a high-resolution chemical transport model has been applied to evaluate the CO2 emissions of power plants. Zheng et al. [23] used the Weather Research and Forecasting-Chem (WRF-Chem) simulation method, which provided a framework for addressing different wind fields, a method which could be extended to urban-level emission estimation. In addition, the WRF-Chem modeling system of complex components was designed to simulate the increase of CO2 [23], yet its estimates of CO2 emissions for the Atmosphere 2021, 12, 811 3 of 18 target power plant did not show improved consistency with the reported value compared to Nassar’s research [21], which used a relatively simple plume model. As for Krings’ research [22], although more reliable observation data was obtained from the MAMAP instrument, which had higher precision (approximately 0.4 ppm) compared to OCO-2 data, air-based observations were not available for large-scale measurement; thus, national range point emissions estimates could not be achieved. Therefore, this study adopted an improved Gaussian plume model method to estimate the CO2 emissions at the unit level. By applying