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Article

Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2

by
Shaoqing Zhang
1,2,
Liping Lei
1,
Mengya Sheng
1,2,
Hao Song
3,
Luman Li
1,2,
Kaiyuan Guo
1,2,
Caihong Ma
1,
Liangyun Liu
1 and
Zhaocheng Zeng
4,*
1
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100049, China
4
School of Earth and Space Sciences, Peking University, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(19), 5024; https://doi.org/10.3390/rs14195024
Submission received: 24 August 2022 / Revised: 28 September 2022 / Accepted: 30 September 2022 / Published: 9 October 2022
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions)
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Abstract

:
Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

1. Introduction

Controlling anthropogenic carbon dioxide (CO2) emissions, which lead to the increase in atmospheric CO2 concentration, is important to mitigate global warming for human sustainable development. Therefore, an accurate investigation of anthropogenic CO2 emissions is critical to evaluate the effects of emission reduction and control measures. Current gridded data of anthropogenic CO2 emissions are estimated based on the spatial and temporal attribution of national or regional totals in inventories by proxy data, such as population density, transportation network and enterprise emitting sources. These bottom-up gridded emission inventories, such as the Emissions Database for Global Atmosphere Research (EDGAR), Open-source Data Inventory for Anthropogenic CO2 (ODIAC) [1,2,3], a high-resolution inventory of CO2 emissions in China produced by The Ministry of Ecology and Environment of China (called CHRED hereafter) [4,5], and the emission inventory with various proxy data developed by Peking University [5] (called PKU hereafter), have been widely used. The used national or regional emissions are generally accurate in the counties/regions with reliable data sources and standardized statistical methods [6]. The gridded emissions, however, remain highly uncertain in space and time due to the imperfections of proxy data, which can be irregular and insufficient, used to disaggregate the national/regional emissions in space and time [7]. For example, the uncertainty (2σ) of gridded ODIAC is up to 120% on average and ranges from 4% to 190% [8]. The emissions from different inventories, moreover, are inconsistent at the same location and time. It is therefore important to quantify the uncertainties of these gridded inventories for the assessment of anthropogenic CO2 emissions and application in modeling global carbon cycle.
The uncertainty of inventories can be evaluated by cross-validation with more detailed regional emission inventory data [9,10] or the model simulation at the specific time and regions [11]. The column-averaged dry air mole fractions (XCO2) derived from satellite observations, such as the Greenhouse gases Observing Satellite (GOSAT) and Orbiting Carbon Observatories (OCO-2, OCO-3), have been developed to detect the magnitude of anthropogenic CO2 emissions from regional emissions and point sources. Satellite observations provide important ‘top-down’ information for evaluating bottom-up inventories and reducing uncertainty [12,13]. Many studies indicated that the emissions by human activities could lead to 1–3 ppm enhancements of XCO2 regionally [14,15,16,17] and up to 6 ppm enhancement over large power plants [18], as detected by the XCO2 retrievals derived from GOSAT, OCO-2, and OCO-3 observations. The spatial pattern of XCO2 derived from GOSAT is consistent with the spatial patterns of the inventories of ODIAC and CHRED [19,20]. The XCO2 variation explains more than 40% of ODIAC in the grids and up to more than 90% in the binned emissions based on clustering analysis of gridded XCO2 [21,22,23]. The anthropogenic CO2 emissions could be estimated using gridded XCO2 retrievals from GOSAT and OCO-2 observations by the generalized regression neural networks (GRNN) using training datasets combined with ODIAC data [22,23]. The estimated emissions were generally consistent with inventory emissions but showed a large difference with ODIAC in the urban areas, likely because of the uncertainty of spatial distribution of the emissions in ODIAC. Recently, Chevallier et al. [12] indicated that the large fossil fuel CO2 emissions derived from OCO-2 and OCO-3 explain more than one third of the inventory variance from EDGAR at the corresponding cells and hours, and the signal-to-noise ratio for the XCO2 retrieval tend to increase with the emission level. These studies suggested that the satellite observations, as a top-down measure, can be potentially applied to evaluate the uncertainty of bottom-up inventories [24]. As an attendant measure, the guidelines for the national emission survey improvement program in the IPCC report (2019) [25] suggest applying the satellite observations to verify and rectify the emission inventories. However, a comparison of the emission inventories, especially for the top two national emitters (China and the US), has been less explored.
Here, we use satellite observations of XCO2 from GOSAT and OCO-2 to evaluate the differences and uncertainties of bottom-up emission inventories in space and time, aiming to develop data-driven approaches for the verification of emission inventories using satellite observations. In the next section, we describe the datasets used, including satellite XCO2 observations and four widely used inventories (CHRED, PKU, ODIAC, and EDGAR) and the developed data-driven approaches. In Section 3, we demonstrate the results of how the emission inventories are correlated with the magnitude of observed XCO2 and the uncertainties of the emission inventories in space and time by comparing with the emissions predicted by a machine learning-based model. The discussion and conclusion are presented in Section 4 and Section 5, respectively.

2. Materials and Methods

2.1. Materials

2.1.1. Satellite Observations

  • Globally mapping XCO2 dataset based on GOSAT and OCO-2
GOSAT and OCO-2 have been observing and retrieving global XCO2 since 2009. Original GOSAT and OCO-2 level 2 retrieval data are available from the Goddard Earth Science Data Information and Services Center (GES DISC) [26]. The XCO2 retrievals are the latest ACOS level 2 Lite data product (v9r) for GOSAT and the latest level 2 lite data product (v10r) for OCO-2 [27,28,29]. In this paper, we used the global land monthly mapping XCO2 dataset (Mapping-XCO2) with a spatial resolution of 1° in latitude and longitude from April 2009 to December 2020. Mapping-XCO2 data are generated based on a spatiotemporal geostatistical method by using the integrated XCO2 retrievals obtained from GOSAT and OCO-2 through the corrections of the systemic errors between GOSAT (from April 2009 to August 2014) and OCO-2 (from September 2014 to December 2020), including sensor sensitivity, and the differences in observing time and space [17,30,31]. This Mapping-XCO2 dataset has shown its validation and effectiveness in detecting the anthropogenic CO2 emissions, variations of XCO2 in the global and regional scales by comparing with Total Column Carbon Observing Network (TCCON), and CarbonTracker model simulations [17,30,31,32,33].
  • SIF
We collected the solar-induced chlorophyll fluorescence (SIF) data, the Global SIF Dataset (GOSIF), for interpreting the amount of vegetation ecological CO2 uptake in XCO2. This dataset was developed by Xiao et al. [34] based on discrete OCO-2 SIF soundings, moderate Resolution Imaging Spectro radiometer (MODIS) data, and meteorological reanalysis data based on a data-driven approach. GOSIF covers the period 2000–2020 with 0.05° grid and 8 days’ intervals [34]. Previous studies have demonstrated that the SIF can be used to derive gross primary productivity (GPP) that is closely related to the magnitude of CO2 uptake [35,36]. We resampled the GOSIF (8 days/0.05°) to match the Mapping-XCO2 dataset (month/1°) by using the cubic convolution.
  • Other Testing Data
To obtain the optimal combination for predicting the anthropogenic emissions with the minimum deviation by using GRNN, we also used NDVI, GPP, wind speed, and night-time light for testing. We collected the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations. NDVI data from the MOD13C2 product have temporal and spatial resolutions of 0.05° and 2 days, respectively. The GPP data is from the MOD17A2H product with temporal and spatial resolutions of 500 m and 8 days [37,38]. The wind speed data are the horizontal speed of air moving at a height of ten meters above the surface of the Earth in meters per second, which is provided by ERA5 (the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate) [39]. The night-time light data were sourced from Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) stray light characterization and correction product [40].

2.1.2. Anthropogenic CO2 Emission Inventories

We collected two global emission inventories (ODIAC and EDGAR) and two regional emission inventories (CHRED and PKU) focusing on China. Table 1 presents the specifications of these inventories, and Figure 1 shows the spatial distribution of their total emissions in 2012 in China (Hong Kong, Macao, and Taiwan in Figure 1a show as “no data” as CHRED data were only produced over the mainland).
ODIAC is a gridded CO2 emission dataset generated by the spatial and temporal disaggregation of national emissions from the CO2 Information Analysis Centre (CDIAC) and British Petroleum (BP), using proxy data including satellite observed night-time lights and power plant emissions [1,2]. Similarly, EDGAR is a gridded CO2 emission dataset generated by disaggregating the national emission inventories. The national emissions are from the energy balance statistics of the International Energy Agency (IEA), and country-specific activity datasets from BP, United States Geological Survey (USGS), World Steel Association, Global Gas Flaring Reduction Partnership (GGFR)/U.S. National Oceanic and Atmospheric Administration (NOAA), and the International Fertilizer Association (IFA). The spatial disaggregation of national emissions was implemented using proxy data including population density, traffic networks, night-time lights, and CO2-emitting point sources [3]. EDGAR has multiple versions released, such as EDGARv5.0, EDGARv6.0, and EDGARv4.3.2_bp shown in Table 1. EDGARv5.0 provides monthly emissions only for the year 2015. EDGARv6.0 provides monthly emissions data from 2000–2018. EDGARv4.3.2_bp used in this study, which was produced based on EDGARv4.3 and BP statistics 2019 (Version 2.0) [41], is the monthly emissions updated to 2019 which facilitates a comparison with ODIAC from 2009 to 2019. The temporal changes in emissions were allocated based on the datasets from the monitoring atmospheric composition and climate—the Netherlands Organization (MACC-TNO) and the CO2 release and oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) [42,43,44,45]. It has been shown that bottom-up emission inventories have a large uncertainty in the Chinese region due to a lack of reliable and promptly updated statistical data, inappropriate emission factors (EF), and accurate proxy data [46]. In order to evaluate the uncertainty of these inventories, we also collected CHRED and PKU, which were produced by Chinese institutions with more accurate emissions statistics. CHRED is the annual anthropogenic CO2 emission gridded data in 10 km resolution generated by the Ministry of Ecology and Environment of China based on the statistics of regional CO2 emissions in 2012, and field investigations of point source emissions which involved 15 million factories and enterprises in the Chinese regions (excluding ocean islands) in 2012 [4]. PKU is a global CO2 emission inventory generated by Peking University using local emissions from fossil fuel combustions, populations, vegetations, and geographical data. The large database of subnational fuel consumption was used for 45 major countries in the PKU data. The fossil, biomass, and solid waste fuels were included and categorized into 64 types in 6 economic sectors, and the uncertainties of the CO2 emission maps were quantified. The relative uncertainty range of CO2 emissions could be reduced from 364% to 63.2% by using the sub-national disaggregation [5].
All of these four collected inventories described above are converted to unified Mt CO2 units, and integrated into same spatial (1 degree) and temporal (a month) scales.

2.2. Methods

2.2.1. Correlating Emission Inventories and Satellite Observed XCO2

The change of XCO2 signals, as obtained by satellite observations over a specific target, include the background CO2 that is globally mixed driven by the atmospheric circulation (CBK), the anthropogenic CO2 emissions (CAE), biological CO2 fluxes balanced by CO2 uptake and release from the biospheres (CBio), as well as CO2 fluxes of incoming and outgoing CO2 driven by wind field (CTran) and the error term (ε) [47,48], as shown in Equation (1):
XCO2 = CBK + CAE + CBio + CTran + ε
Accordingly, XCO2 is bound to correlate with the anthropogenic CO2 emissions. We can therefore use the XCO2 as a base to compare the emissions in the four inventories (CHRED, PKU, ODIAC and EDGAR) created based on different data sources and methods. We defined the spatial XCO2 patterns by the clustering analysis of the annual mean Mapping-XCO2 in 2012. The clustering analysis was implemented by slicing XCO2 from the minimum (390.4 ppm) to the maximum XCO2 (394.4 ppm) with 0.2 ppm step into 20 segments in the land region (excluding the ocean islands). The averages of XCO2 and emissions in CHRED, PKU, EGDAR and ODIAC are computed for each XCO2 segments, respectively. The XCO2 enhancement, moreover, is calculated by subtracting the minimum XCO2 from the XCO2 average of each segment. The corresponding differences of emissions in the four inventories corresponding to the same XCO2 level can indicate the consistencies of the four inventories.

2.2.2. Comparing Emission Inventories with Predicted Emissions by a Machine Learning-Based Model Based on Satellite Observations

Based on Equation (1), the anthropogenic CO2 emissions could be predicted by a neural network-based machine learning model trained using the data of XCO2, CBK, CBio, CTran, and the emission inventory [1,2,3,22,23]. Specifically, for the training datasets, we used: (1) the time series from satellite observations (from 2010 to 2019) for XCO2; (2) NDVI, GPP, and SIF data to represent variable CBio; and (3) wind speed data to represent variable CTran. We introduced the generalized regression neural network (GRNN) as a machine-learning simulator to predict the emissions using satellite observation data and emission inventories. The GRNN has the advantage of effectively avoiding the effects of artificial subjective assumptions on the prediction because the training of GRNN generally depends on the samples, less artificial adjustments for the operating parameters, with only one smoothing factor [49]. GRNN, moreover, has a strong nonlinear mapping capability and quick learning speed. These advantages of GRNN make it provide better predictions using less training data, such as the time series [22].
Firstly, we tested the performance of the multiple combinations of variables (XCO2, dXCO2, NDVI, GPP, SIF, wind speed, night-time light, emissions in EDGAR) to obtain the optimal combination for predicting the anthropogenic emissions with the minimum deviation from EDGAR by using GRNN built using temporal data for each grid. The dXCO2 is the background removed XCO2 by subtracting the latitudinal average of XCO2 at the same time which could enhance the signals induced by the anthropogenic emissions. As a result, the combination of XCO2 and SIF shows an optimal prediction among the multiple combinations of variables. This demonstrates that using SIF to represent the ecological flux associated with XCO2 can better predict the anthropogenic CO2 emissions. We, moreover, tested the GRNN that was built based on the training of all grids spatially using the same combination of variables for both the global grids and the regional grids in China, respectively. The results indicated that the predictions from the same GRNN for all of the grids showed large deviations.
Through the above testing and analysis, we used XCO2 and SIF to predict the anthropogenic CO2 emissions in 2012 and 2019, respectively. The processing flow is shown in Figure A1. The biases of the predicted emissions and inventory emissions could demonstrate the uncertainty of inventory emissions, because the predicting emissions’ base on GRNN are from the fitting of the long-time changing trend of emission inventory under the constraint of the satellite observations (Figure A2 and Figure A3).
The processing (Figure A1) includes the following two key points:
(1)
Predicting area and year
We selected the Northern Hemisphere (NH) with 10,312 grids as the study area. The NH included China, India, North America, and Europe, which accounted for most of the global anthropogenic CO2 emissions. The dynamic GRNN models were built for each grid using the time series data (2010–2019), counting 10,312 GRNN models in the NH, respectively. The predicting year was focused on the years 2012 and 2019. The prediction for the year 2012 was mainly for evaluating the inventories under different scenarios in China where these four inventories are available. The prediction was also implemented in the year 2019, which was the last year in the used time series, in order to compare with the predictions in 2012 and verify the validity of GRNN model;
(2)
GRNN structure
The neural network module of GRNN included generalized regression neural network in MATLAB. The GRNN network consisted of four layers [50], which were input layer, pattern layer, summation layer, and output layer. The number of neurons in the input layer was the same as the dimensions of sample vectors. The pattern layer was also called the hidden regression layer, the number of neurons in the pattern layer was the same as the number of training samples. The Euclidean distance square exponent between the training samples and the ith neural unit is calculated for the summation layer. The summation layer used two types of pattern summation, one was an arithmetic summation of the outputs of the pattern layer, and the other was a weighted summation by the transfer function of the link between the neurons in the pattern layer and the total neurons in the summation layer. In the final output layer, the predicting value was obtained by calculating the ratio of two neurons in the summing layer.

3. Results

3.1. Correlations between Emission Inventories and Satellite-Observed XCO2

The emission inventories, CHRED, PKU, ODIAC, and EDGAR, show differences in space which can be seen from Figure 1, as they used different emissions coefficients, proxy data sources, and spatially and temporally disaggregating methods (Table 1). Figure 2 demonstrates their correlations with the variations of XCO2 spatially which are described in Section 2.2.
The largest inconsistence between the four inventories is with the XCO2 value of 392 ppm corresponding to high emission levels where ODIAC and EDGAR are 60% and 50% higher than CHRED, respectively. ODIAC and EDGAR generally showed larger emissions than CHRED and PKU. The previous investigations indicated that ODIAC and EDGAR likely also overestimated the regional emissions in China [7]. Figure 1a demonstrates that the overestimations of emissions are also with the XCO2 value of 392 ppm and the emissions of 7–12 Mt CO2. EDGAR is much larger than the other inventories for the regions with XCO2 values of 392.6 ppm and 394.2 ppm, which are, respectively, the results from the abnormal magnitudes of emissions in Shenyang city (662 Mt CO2) and Shanghai city (833 Mt CO2). The emissions from the cities in EDGAR are mostly allocated by using the Carbon Monitoring for Action (CARMA) power plant database, which was a global database that gathered and presented the best available estimates of CO2 emissions for 50,000 power plants [51].
It can be seen from Figure 2b that all of the high emissions in the four inventories showed 3–4 ppm enhancements of XCO2. CHRED and PKU presented the strongest correlation to the spatial variations of XCO2 (R2 = 0.78) whereas EDGAR showed the lowest correlation (R2 = 0.59), and the largest RMSE (9.01 Mt CO2). This result indicates the spatial distributions of emissions in CHRED and PKU tend to be more accurate than the other inventories. CHRED is suggested to be more reasonable spatially as it used more detailed local data, and the survey of CO2 emissions from the point sources including the power plants burning fossil fuel and the air pollution monitoring network (Table 1).
Additionally, we implemented the same data processing as in Figure 2 for the ten years’ data (2010–2019) of XCO2 for ODIAC and EDGAR. The results, as shown in Figure 3, demonstrate that both ODIAC and EDGAR present similar responses to XCO2 with a slope (4.1–4.3 Mt CO2/ppm) from 2010–2019, and similar R2 of 0.55 and 0.61 for ODIAC and EDGAR, respectively. The high emissions induce the largest CO2 enhancements of 3.7 ppm in both ODIAC and EDGAR [17].

3.2. Spatial and Temporal Uncertainties of ODIAC and EDGAR

3.2.1. Spatial Distribution of Inventory Emissions

Figure 4 shows the average emissions in inventory, the predictions, and the biases (prediction minus inventory) for ODIAC and EDGAR in the year 2012. The predictions for ODIAC and EDGAR are derived using the satellite datasets of monthly XCO2 and SIF in 2012 by GRNN built by the training datasets of ODIAC, EDGAR, XCO2, and SIF during the period from 2010 to 2019 as described in Section 2.2.2. Similarly, the predicted results for the year 2019 are shown in Figure A4.
It can be clearly seen from Figure 4 that, in addition to Figure A4, both of the biases in ODIAC and EDGAR are opposite between China (negative) and the USA (positive). The magnitudes of bias are larger in China than USA either in 2012 or in 2019 (Figure A5 and Table A1). The mean biases in 2012 and 2019 in China are −0.28 ± 0.78 Mt CO2 and −0.35 ± 3.10 Mt CO2 for ODIAC and −0.19 ± 0.74 Mt CO2 and −0.39 ± 1.04 Mt CO2 for EDGAR, respectively. The large biases are mostly located around the big cities such as Shanghai, Wuhan, and Shenyang. The biases in the USA are much less than those in China and are positive either in 2012 or 2019, which are 0.15 ± 0.27 Mt CO2 and 0.05 ± 0.16 Mt CO2 for ODIAC and 0.16 ± 0.41 Mt CO2 and 0.02 ± 0.47 Mt CO2 for EDGAR, respectively. These results indicate that both inventories probably overestimated emissions in China and slightly underestimated emissions in the USA. This implies that the uncertainty for both inventories tend to be higher in China than the USA. The biases in India, moreover, are negative for ODIAC but positive for EDGAR in 2012.
Figure 5 illustrates the biases corresponding to the emissions in ODIAC and EDGAR in 2012 by the scatter plot of all grids in China and the USA, respectively. The biases of both ODIAC and EDGAR, as shown in Figure 5 and Figure A6, present much stronger linear correlations with the emissions in China (R2 = 0.83–0.91) than the USA (R2 = 0.01–0.51), either in 2012 or in 2019, which imply that the uncertainty of emissions in inventories systematically depend on the emissions in China but randomly in the USA. The two grids, located at Shanghai and Shenyang, with abnormal high emissions in China shown in Figure 5c (and Figure A5c) for EDGAR, showed extremely large biases, where the magnitude of emissions in the year 2012 and 2019 are 833 Mt CO2 and 1064 Mt CO2 in Shanghai, and 662 Mt CO2 and 873 Mt CO2 in Shenyang, respectively. EDGAR allocates abnormally large emissions to these two cities using the source-emitting data of coal-fired power plants from CARMA. Our field investigations and verifications for CARMA data found that nearly half of points’ sources were geographically dislocated or non-existent in CARMA, which is consistent with other investigations [5]. Another investigation found that CARMA has ignored about 1300 small power plants in China [52]. Our investigations found as well that the locations of ground heat sources detected by the satellite observations (Figure A6b) mostly corresponded with the power plants and the chemical industry better than CARMA (Figure A6a). The CARMA data are likely one of the key reasons inducing the large uncertainties of emissions in inventories spatially as the point source emissions accounted for a large proportion of the total emissions [53]. ODIAC allocates the emissions to the point sources better than EDGAR as it filtered the abnormal and likely irrational emitting points in CARMA.

3.2.2. Seasonal Variation of Emissions in Inventories

We investigated the differences in seasonal cycles by the monthly mean in the year 2012 and 2019 for ODIAC and EDGAR in China and in the USA, which are shown in Figure 6. The trends of seasonal variations are consistent between inventory and predictions either in ODIAC or EDGAR. The seasonal inventory emissions in ODIAC are generally larger (6.9% per month) than the predictions from April/May to December in China. The seasonal emissions in EDGAR are generally larger (4.4% per month) than predictions from August to February in the following year while they are less than the predictions in the rest of the months in China. The seasonal inventory emissions in both ODIAC and EDGAR showed a smaller value in the USA (3.4% per month in ODIAC; 5.1% per month in EDGAR) than the predications. However, EDGAR showed the largest bias in July 2019, which was 9.1% less than prediction. The seasonal variations, moreover, presented significantly larger differences between ODIAC and EDGAR in China compared with the USA where EDGAR clearly showed a seasonal cycle with the peak value in December and the trough in July whereas ODIAC only showed an increasing trend up to a maximum in December without a cyclic variation. The seasonal cycles in the USA presented both in EDGAR and ODIAC showed two peaks in January and August, and two troughs in May and October.
The monthly time series of inventory emissions in ODIAC were created from 2011 to 2020 using the climatological seasonality based on the 2013 version of the CDIAC monthly gridded emissions which only covered from 2000 to 2010. The monthly CDIAC data from 2000 to 2010 were constructed using a proportional proxy methodology of monthly available statistical data from the 21 largest fossil fuel CO2 (FFCO2)-emitting countries to extend the utility of those data to the globe and to a longer time series [54]. Therefore, the monthly emissions time series from ODIAC, which mostly depend on the statistical CO2 emission data used in CDIAC, have probably large uncertainties because of the statistical uncertainty of the data in CDIAC which likely did not account for the changes in CO2 emissions in the recent years [2]. The monthly variations in ODIAC for the Chinese region in Figure 6 demonstrated this issue as well.
The seasonality in EDGAR is the aggregated temporal profiles of the sectors including power generation, agricultural emissions, shipping and aviation emissions for each greenhouse gas in the Northern hemisphere. It is constructed based on the modulations of monthly distributions in emissions from the sectors data which are from the previous studies and the Long Term Ozone Simulation (LOTOS) database of Builtjes [3]. Figure 6 shows that the seasonal variations in EDGAR tend to be more reasonable than ODIAC according to the seasonal cycle of human CO2 emission activities in China, such as the increase in FFCO2 for heating in winter and the decrease in summer in Northern China which account for the largest emissions in China. These results indicate that the monthly allocated approaches of emissions in ODIAC need to be rectified in China.
We, moreover, collected the areas with large biases especially in 2019 as the regions of interest (ROIs) in China (Figure 7), to evaluate the uncertainty of seasonal variations of emissions in the ROIs. Figure 8 presents the time series of mean emissions from 2009 to 2019 in ODIAC and EDGAR for each ROI, and the monthly variation of predictions in 2012 and 2019, respectively.
As shown in Figure 8, the emissions in the three cities that are much higher than the other areas are in the order of Shanghai, Beijing, and Shenyang in ODIAC. However, the emissions in EDGAR are in the order of Shenyang, Shanghai, and Wuhan, and all of these cities also show the largest biases. The differences of emissions between ODIAC and EDGAR in the cities are likely due to spatial allocating issues of emissions using the CARMA data as described above. The biases are larger in EDGAR than in ODIAC. The largest biases in EDGAR are −3.96 Mt CO2 and −5.92 Mt CO2 in Shenyang in October in 2012 and 2019, respectively. The largest biases in ODIAC, however, are −2.22 Mt CO2 and −2.38 Mt CO2 in Shanghai in December in 2012 and 2019, respectively.
The annual growth rate of emissions from 2009 to 2019 in EDGAR, 0.048 Mt CO2 year−1 (R2 = 0.85), is greater than that of ODIAC, 0.012 Mt CO2 year−1 (R2 = 0.48) in the three cities with the largest emissions. The annual growth rate in ODIAC is probably more reasonable than EDGAR as the growth rate of anthropogenic CO2 emissions has been generally slowing down by the activities of reducing CO2 emissions through development of clean energy, switching from coal to gas, and moving large air-polluting factories out of megacities such as Beijing and Shanghai after 2010, the starting year of the new 5-year economic development plan in China [55].

4. Discussion

The overestimations of emissions in China and underestimations in the USA, shown in Figure 4 and Figure A4 as well as Table A1 in Appendix A, are most likely caused by the uncertainty of inventory emissions as we can assume that the uncertainties of satellite-observed data are consistent spatially at a global scale due to their unified instrumental characteristics and retrieval algorithms. We also evaluated the performances of ODIAC and EDGAR by comparing them with CHRED. The emissions in CHRED are believed to be the most detailed and reasonable in representing the spatial pattern in China as it applied more detailed survey data of anthropogenic CO2 emissions from point sources for the spatial allocation of the national total. Accordingly, using CHRED as the criterion, the spatial contrasts of inventory emissions between CHRED with PKU, ODIAC, and EDGAR are plotted for all of the grids which are shown in Figure 9, and for the means calculated based on XCO2 segments (segmenting-XCO2-based) and based on the provincial regions (province-based), respectively, which are shown in Figure 10.
The spatial distributions of emissions in PKU, ODIAC, and EDGAR are not highly correlated with CHRED in the grid unit where all R2 are approximately 0.5, and the standard deviations (Std) are from 15 Mt CO2 to 17 Mt CO2 (indicated in Figure 9). It can be seen from Figure 10 that all inventories show better consistency with CHRED in the segmenting-XCO2-based emissions (where R2 is 0.88–0.96 and the Std are 1.5–3.1 Mt CO2) than those in the province-based emissions (where R2 is 0.53–0.84 and the Std are from 9 Mt CO2 to 130 Mt CO2). PKU emissions have the minimum deviation to CHRED while EDGAR shows the largest deviation (130 Mt CO2) in the province-based emissions that is much larger than PKU (9 Mt CO2), and ODIAC (39 Mt CO2). The better agreement of PKU, ODIAC, and EDGAR with CHRED in the segmenting-XCO2 emissions implies that spatial allocations of emissions in these inventories could be constrained by the XCO2 as the clustering analysis of XCO2 integrates the strong emitting sources and weak sources regions. The large inconsistency in the province-based emissions, especially in EDGAR, indicates its high uncertainty in the spatial disaggregation by using the proxy data. The predicted emissions for ODIAC and EDGAR, moreover, only demonstrate minor variations to the original emissions as shown in Figure 9 and Figure 10.

5. Conclusions

In this study, we applied two data-driven approaches that use satellite observations from GOSAT and OCO-2 to evaluate the performance of emission inventories from the widely used CHRED, PKU, ODIAC, and EDGAR. The evaluation was carried out by quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations, and comparing emission inventories with emissions predicted by a machine learning-based model GRNN trained using satellite-observed XCO2. In addition, we assessed the uncertainties of ODIAC and EDGAR for their spatial distribution and seasonal variations in 2012 and 2019, respectively, by GRNN.
Our results show that the spatial characteristics of four inventories have a good correlation with the spatially clustering XCO2. However, EDGAR has the lowest correlation with the XCO2 enhancements and at the same time has the largest deviation from CHRED based on the province-based emissions. This is due to the inexact attribution of the large emitting sources spatially. The inventories from both ODIAC and EDGAR show larger uncertainties in Asia, especially in megacities, than in Europe and the USA. The prediction biases in either ODIAC or EDGAR show systemic errors which are linearly dependent on the magnitude of emissions in China, whereas they are weakly dependent on the emissions in the USA. Generally, the biases in EDGAR are spatially larger than those in ODIAC whereas the seasonal variations in EDGAR tend to be more reasonable than ODIAC. The long-term growing trend in EDGAR, however, is likely overestimated comparing with ODIAC in 2019.
The emissions’ inventories have large uncertainty in their spatial and temporal disaggregation of the national emissions. This is due to their intense use of proxy datasets to represent anthropogenic emissions, such as the fossil fuel consumption from industries which are the primary point emission sources, however, satellite-observed night lights, population density, and many other proxy datasets are not accurate enough to characterize the anthropogenic fossil fuel emissions in China [56]. The results in this study indicate that the satellite observations can reveal the spatial and temporal uncertainty of inventory emissions. With more and more satellite data becoming available, the errors from using satellite-observed XCO2 can be continuously reduced [57]. Therefore, satellite observations have great potential to improve the spatial and temporal disaggregation of emissions in inventories. The application of satellite observations as a top-down data-driven approach, as demonstrated in this study, would be expected to assist in verifying the emission inventories, as suggested in the IPCC report.

Author Contributions

Conceptualization, L.L. (Liping Lei) and S.Z.; Data curation, L.L. (Liping Lei), Z.Z. and C.M.; Formal analysis, M.S., K.G., L.L. (Luman Li) and L.L. (Liping Lei); Methodology, L.L. (Liping Lei), L.L. (Liangyun Liu) and S.Z.; Software, S.Z., M.S. and H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (Grant No. 2020YFA0607503), the Key Program of the Chinese Academy of Sciences (Grant No. ZDRW-ZS-2019-1), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19080303).

Acknowledgments

We are grateful for the ACOS-GOSAT v9r data and OCO-2 v10r data, which were provided by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology and obtained from the ACOS/OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. The datasets of ODIAC, EDGAR, PKU, and CEADs are freely available from http://db.cger.nies.go.jp/dataset/ODIAC/ (accessed on 20 January 2021), https://meta.icos-cp.eu/collections/unv31HYRKgullLjJ99O5YCsG (accessed on 20 January 2021) and http://inventory.pku.edu.cn/download/download.html (accessed on 22 January 2021), respectively. CHRED is available from the data developers upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Statistics of inventory emissions results in different regions.
Table A1. Statistics of inventory emissions results in different regions.
Units (Mt CO2)ChinaUSAIndiaEurope
2012ODIACInventory9696476318273453
Prediction9396490417513423
Total bias−300141−76−30
Mean bias−0.270.15−0.28−0.05
Maximum bias0.322.240.111.54
Minimum bias−11.42−0.19−6.30−3.16
Standard deviation0.780.270.590.32
EDGARInventory11,546484428434119
Prediction11,334499728834049
Total bias−21215341−70
Mean bias−0.190.160.15−0.11
Maximum bias1.223.706.791.57
Minimum bias−13.68−0.81−1.51−7.75
Standard deviation0.740.410.530.54
2019ODIACInventory10,372470724553068
Prediction9947475423633252
Total bias−42547−92184
Mean bias−0.380.05−0.340.28
Maximum bias0.222.710.018.64
Minimum bias−12.37−0.45−5.36−2.15
Standard deviation1.040.160.620.73
EDGARInventory12,996475235043777
Prediction12,641477332243889
Total bias−35521−280113
Mean bias−0.320.02−1.030.17
Maximum bias3.123.211.208.15
Minimum bias−73.43−4.87−31.06−1.35
Standard deviation3.000.482.320.66
Number of sampling grids1105964273664
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Figure A1. Workflow for training the GRNN neural network model and evaluating emission inventory using the predicted emissions based on GRNN trained using the satellite XCO2 observations.
Figure A1. Workflow for training the GRNN neural network model and evaluating emission inventory using the predicted emissions based on GRNN trained using the satellite XCO2 observations.
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Figure A2. The gridded mean of (a) XCO2 and (b) SIF in the Northern Hemisphere from April 2009 to December 2020.
Figure A2. The gridded mean of (a) XCO2 and (b) SIF in the Northern Hemisphere from April 2009 to December 2020.
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Figure A3. Temporal variations of (a) averaged emissions of ODIAC, EDGAR, and (b) XCO2 and SIF in the Northern Hemisphere.
Figure A3. Temporal variations of (a) averaged emissions of ODIAC, EDGAR, and (b) XCO2 and SIF in the Northern Hemisphere.
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Figure A4. Averages of monthly inventories and predicted emissions based on the GRNN using satellite observations in 2019 for (a) original ODIAC, (b) predicted emissions for ODIAC, (c) original EDGAR, (d) predicted emissions for EDGAR, (e) bias for ODIAC, and (f) bias for EDGAR.
Figure A4. Averages of monthly inventories and predicted emissions based on the GRNN using satellite observations in 2019 for (a) original ODIAC, (b) predicted emissions for ODIAC, (c) original EDGAR, (d) predicted emissions for EDGAR, (e) bias for ODIAC, and (f) bias for EDGAR.
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Figure A5. Scatter plots of the bias corresponding to the inventory emissions in 2019 for (a) ODIAC in China; (b) EDGAR in China where red color is the result of excluding two grids with abnormal emissions in the cities of Shanghai and Shenyang; (c) ODIAC in the USA, and (d) EDGAR in the USA.
Figure A5. Scatter plots of the bias corresponding to the inventory emissions in 2019 for (a) ODIAC in China; (b) EDGAR in China where red color is the result of excluding two grids with abnormal emissions in the cities of Shanghai and Shenyang; (c) ODIAC in the USA, and (d) EDGAR in the USA.
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Figure A6. The distributions of emitting point sources from (a) CARMA and (b) heat source detected by satellite observations in China.
Figure A6. The distributions of emitting point sources from (a) CARMA and (b) heat source detected by satellite observations in China.
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Figure 1. Spatial distribution of the total CO2 emissions in the year 2012 (0.1 degree grid) for four inventories in China: (a) CHRED, (b) PKU, (c) ODIAC, and (d) EDGAR.
Figure 1. Spatial distribution of the total CO2 emissions in the year 2012 (0.1 degree grid) for four inventories in China: (a) CHRED, (b) PKU, (c) ODIAC, and (d) EDGAR.
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Figure 2. Correlation between emissions from bottom-up inventories and XCO2 from satellites in 2012. (a) Averaged emissions corresponding to different XCO2 in each segment of XCO2; (b) Correlation between bottom-up inventory emissions and satellite-observed XCO2 enhancement.
Figure 2. Correlation between emissions from bottom-up inventories and XCO2 from satellites in 2012. (a) Averaged emissions corresponding to different XCO2 in each segment of XCO2; (b) Correlation between bottom-up inventory emissions and satellite-observed XCO2 enhancement.
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Figure 3. The correlation between bottom-up inventory emissions from ODIAC and EDGAR and satellite-observed XCO2 enhancement for ten years from 2010 to 2019. Different colors represent different inventory emissions data, where red represents EDGAR and blue represents ODIAC.
Figure 3. The correlation between bottom-up inventory emissions from ODIAC and EDGAR and satellite-observed XCO2 enhancement for ten years from 2010 to 2019. Different colors represent different inventory emissions data, where red represents EDGAR and blue represents ODIAC.
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Figure 4. Averages of monthly inventories and predicted emissions based on GRNN using satellite observations in 2012 for (a) original ODIAC, (b) predicted emissions for ODIAC, (c) original EDGAR, (d) predicted emissions for EDGAR, (e) bias for ODIAC, and (f) bias for EDGAR.
Figure 4. Averages of monthly inventories and predicted emissions based on GRNN using satellite observations in 2012 for (a) original ODIAC, (b) predicted emissions for ODIAC, (c) original EDGAR, (d) predicted emissions for EDGAR, (e) bias for ODIAC, and (f) bias for EDGAR.
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Figure 5. Scatter plots of the bias corresponding to the emissions in inventory in 2012: (a) ODIAC in China, (b) EDGAR in China where red color is the result excluding two grids with abnormal emissions in the cities, Shanghai and Shenyang, (c) ODIAC in the USA, (d) EDGAR in the USA.
Figure 5. Scatter plots of the bias corresponding to the emissions in inventory in 2012: (a) ODIAC in China, (b) EDGAR in China where red color is the result excluding two grids with abnormal emissions in the cities, Shanghai and Shenyang, (c) ODIAC in the USA, (d) EDGAR in the USA.
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Figure 6. Seasonal cycles of emissions and predictions in ODIAC and EDGAR illustrated by the monthly average in 2012 for (a) China and (b) the USA, and in 2019 for (c) China and (d) the USA.
Figure 6. Seasonal cycles of emissions and predictions in ODIAC and EDGAR illustrated by the monthly average in 2012 for (a) China and (b) the USA, and in 2019 for (c) China and (d) the USA.
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Figure 7. Locations of ROIs in China overlapped with the prediction biases for EDGAR in 2019 where the red circles tagged by C1–C7 are around the cities, and S1–S4 are around towns and villages.
Figure 7. Locations of ROIs in China overlapped with the prediction biases for EDGAR in 2019 where the red circles tagged by C1–C7 are around the cities, and S1–S4 are around towns and villages.
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Figure 8. Time series of averaged inventory emissions in the ROIs from 2009 to 2019 (left column) and the monthly prediction biases in the ROIs for 2012 (middle column) and 2019 (right column) for (a) ODIAC and (b) EDGAR.
Figure 8. Time series of averaged inventory emissions in the ROIs from 2009 to 2019 (left column) and the monthly prediction biases in the ROIs for 2012 (middle column) and 2019 (right column) for (a) ODIAC and (b) EDGAR.
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Figure 9. Scatter plot of gridded emissions in 2012 in China from CHRED emissions (x-axis) in contrast to the PKU, ODIAC, and EDGAR emissions (y-axis).
Figure 9. Scatter plot of gridded emissions in 2012 in China from CHRED emissions (x-axis) in contrast to the PKU, ODIAC, and EDGAR emissions (y-axis).
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Figure 10. CHRED (x-axis) in contrast to PKU, ODIAC, and EDGAR (y-axis) based on regional mean emissions in 2012 in China, in which (a) is the segementing-XCO2-based emissions which are from the clustering analysis of XCO2, and (b) is the province-based emissions which are averaged values for each province in China.
Figure 10. CHRED (x-axis) in contrast to PKU, ODIAC, and EDGAR (y-axis) based on regional mean emissions in 2012 in China, in which (a) is the segementing-XCO2-based emissions which are from the clustering analysis of XCO2, and (b) is the province-based emissions which are averaged values for each province in China.
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Table
Table 1. Specifications of four selected anthropogenic CO2 emission inventories.
Table 1. Specifications of four selected anthropogenic CO2 emission inventories.
SpecificationCHREDPKUODIACEDGAR
Covering areaChinaGlobalGlobalGlobal
Period used201220122009–20192009–2019
Resolution
(Time/space)		Monthly/10 kmMonthly/0.1 degMonthly/1 kmMonthly/0.1 deg
Emission factor for raw coal
(tC per ton coal)		0.5180.5180.7460.713
Uncertainty8%19%17.5%15%
Energy statistic sourceCESYCESYIEAIEA
Point sourceFCPSCCARMA 2.0CARMA 2.0CARMA 3.0
Line sourcenational road, railway, navigation network, and traffic flowsNot availableNot availableOpenStreetMap and OpenRailway Map, Int.aviation and bunker
Area sourcePopulation density, land use, human activityNight-time light, Population density, VegetationNight-time lightNight-time light, Population density
VersionCHREDPKU-CO2-v2ODIAC2020EDGARv4.3.2_bp
Released sourceData developerhttp://inventory.pku.edu.cn/download/download.html (accessed on 10 January 2021)http://db.cger.nies.go.jp/dataset/ODIAC/ (accessed on 10 January 2021)https://meta.icos-cp.eu/collections/unv31HYRKgullLjJ99O5YCsG (accessed on 10 January 2021)
ReferencesCai et al. (2018) [4]Wang et al. (2013) [5]Oda et al. (2018) [2]Janssens-Maenhout (2017) [3]
Note: FCPSC: the First China Pollution Source Census; CESY: China Energy Statistical Yearbook.
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Zhang, S.; Lei, L.; Sheng, M.; Song, H.; Li, L.; Guo, K.; Ma, C.; Liu, L.; Zeng, Z. Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2. Remote Sens. 2022, 14, 5024. https://doi.org/10.3390/rs14195024

AMA Style

Zhang S, Lei L, Sheng M, Song H, Li L, Guo K, Ma C, Liu L, Zeng Z. Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2. Remote Sensing. 2022; 14(19):5024. https://doi.org/10.3390/rs14195024

Chicago/Turabian Style

Zhang, Shaoqing, Liping Lei, Mengya Sheng, Hao Song, Luman Li, Kaiyuan Guo, Caihong Ma, Liangyun Liu, and Zhaocheng Zeng. 2022. "Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2" Remote Sensing 14, no. 19: 5024. https://doi.org/10.3390/rs14195024

APA Style

Zhang, S., Lei, L., Sheng, M., Song, H., Li, L., Guo, K., Ma, C., Liu, L., & Zeng, Z. (2022). Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2. Remote Sensing, 14(19), 5024. https://doi.org/10.3390/rs14195024

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