# Advanced satellite-based model for precise measurement of point source CO 2 emission

Yiyang Huang, Haowei Zhang, Xin Ma, Tianqi Shi, Ge Han, Zhipeng Pei, Huiqin Mao, Wei Gong

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Yiyang Huang, Haowei Zhang, Xin Ma, Tianqi Shi, Ge Han, et al.. Advanced satellite-based model for precise measurement of point source CO 2 emission. Geo-spatial Information Science, 2025, pp.1 - 16. 10.1080/10095020.2025.2550497. hal-05476936

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# Advanced satellite-based model for precise measurement of point source CO₂ emission

Yiyang Huang, Haowei Zhang, Xin Ma, Tianqi Shi, Ge Han, Zhipeng Pei, Huiqin Mao & Wei Gong

**To cite this article:** Yiyang Huang, Haowei Zhang, Xin Ma, Tianqi Shi, Ge Han, Zhipeng Pei, Huiqin Mao & Wei Gong (09 Oct 2025): Advanced satellite-based model for precise measurement of point source CO₂ emission, *Geo-spatial Information Science*, DOI: 10.1080/10095020.2025.2550497

**To link to this article:** https://doi.org/10.1080/10095020.2025.2550497

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# Advanced satellite-based model for precise measurement of point source CO₂ emission

Yiyang Huang IDª, Haowei Zhang IDᵇ, Xin Maᶜ, Tianqi Shi IDᵇ,ᵈ, Ge Hanª, Zhipeng Pei IDª, Huiqin Maoᵉ and Wei Gong IDª,f

ªSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China; ᵇAerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou, China; ᶜState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; ᵈLaboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France; ᶜMinistry of Ecology and Environment, Center for Satellite Application on Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, China; ᶜKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing, China

## ABSTRACT

Point source emissions account for over two-thirds of anthropogenic CO₂ emissions, and accurately assessing these emissions is crucial for understanding their impact on global climate change. The Orbiting Carbon Observatory-2 (OCO-2) satellite is capable of monitoring global CO₂ concentrations. Under specific orbital conditions, OCO-2 can effectively identify strong point sources and assess their CO₂ emission intensities, thereby providing near-real-time emission reports. To improve the accuracy and applicability of point source emission evaluations, this study introduces a novel model, EMI-GET. This model combines genetic algorithms with sequential quadratic programming methods, significantly reducing uncertainties introduced by predefined empirical parameters and meteorological data in existing models. The model was applied to 11 cases across China, comparing the CO₂ emissions estimated by OCO-2 with those reported in emission inventories. The results show that the emission differences ranged from 1.2 to 73.1 kg/s, with the EMI-GET model reducing uncertainty by approximately 14.4% compared to existing approaches, especially in point source assessments. This model provides valuable support for policymakers, aiding in the management of fossil fuel usage and the formulation of effective CO₂ emission reduction policies.

## ARTICLE HISTORY

Received 15 October 2024

Accepted 16 August 2025

## KEYWORDS

Remote sensing; CO₂ emissions; point sources, quantification model

## 1. Introduction

Since the Industrial Revolution, the escalating greenhouse effect has increased the likelihood of global natural disasters, such as heatwaves, droughts, floods, and sea level rise (Schiermeier 2021), further jeopardizing human food security and living environments (Jägermeyr et al. 2021; Minoli et al. 2022). In December 2015, nations worldwide signed the Paris Agreement, committing to limit the temperature rise to 1.5 °C above pre-industrial levels (Agreement 2015). As the most significant greenhouse gas, CO₂, reducing its emissions is imperative (Masson-Delmotte et al. 2021; Wang et al. 2018). Point sources, including factories, power plants, and chemical plants, contribute to over 40% of global annual anthropogenic CO₂ emissions through fossil fuel combustion (Shi, Han, Ma, Mao, et al. 2023; Yang et al. 2023). Accurate monitoring of such point source emissions can calculate fossil fuel combustion efficiency, thereby providing data to formulate emission reduction policies.

Currently, CO₂ monitoring of point sources primarily employs bottom-up (BU) and top-down (TD)

methods. The BU approach relies on emission inventory methods, reporting global or regional point source CO₂ emissions based on combustion efficiency (Xu et al. 2024). Its advantage is broad coverage, but it suffers from low timeliness. In contrast, TD methods enable near-real-time monitoring of specific point sources through concentration observation data, characterized by high timeliness. Various TD research projects have been initiated based on different detection systems. Nassar et al. used a 2-dimensional Gaussian diffusion model to estimate global power plant CO₂ emissions based on XCO₂ data downwind of emission sources monitored by OCO-2 (Das et al. 2023; Nassar et al. 2021; Li et al. 2024). Moreover, Zheng et al. based on OCO-2 observations, successfully observed XCO₂ enhancement in cities and industrial areas and inverted CO₂ emissions from strong CO₂-emitting cities and industrial areas in China (Zheng et al. 2020). Guo et al. quantified the CO₂ emissions of six power plants in China using 14 CO₂ plume datasets captured over seven years by OCO-2

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and two years by OCO-3 (Guo et al. 2023). These studies primarily utilized the Gaussian plume model and the $X_{CO_2}$ products from OCO-2/3 to estimate CO₂ emissions. In addition to the OCO-2/3 satellites, Han et al. used concentration data from the DQ-1 lidar satellite and employed a Gaussian transport model to invert the carbon emission rates of 17 power plants globally (Han et al. 2024). Apart from utilizing the Gaussian transport model, there are related studies that employ chemical transport models as an alternative to the Gaussian transport model (He et al. 2024; Zheng, Nassar, and Baxter 2019). With the increasing number of CO₂ monitoring satellites in orbit, recent studies have shown that images obtained from instruments such as the PRISMA satellite imaging spectrometer can also be used, along with algorithms like the IME method and CSF method, to estimate CO₂ emissions from power plants (Cusworth et al. 2023; Guanter et al. 2021). The divergence method, a lightweight approach based on solving the mass balance equation, has also become a prominent research focus (Beirle et al. 2023; Huang et al. 2024; Yuan et al. 2024). However, recent studies by RR Nelson et al. comparing the point-source emission reporting capabilities of various satellites have shown that, although methods based on areal observations, such as those using PRISMA or EMIT, are more effective for plume reconstruction, they are unable to quantify emissions from low-emission sources, which represents a significant limitation of these approaches. In contrast, the unparalleled detection accuracy of OCO-2/3 remains the irreplaceable mainstream method in the field of point-source detection (Nelson et al. 2024).

However, with the massive data from the OCO-2 satellite, selecting orbital data for monitoring point source CO₂ emissions requires visual interpretation, and the quantitative CO₂ emission model heavily relies on a priori diffusion parameters. Estimating CO₂ background concentrations near point sources also depends on statistical data, bringing inconvenience and uncertainty to point source emission assessment. To address these issues, this paper develops an innovative point source CO₂ emission quantitative model based on OCO-2 satellite data, named EMIT-GET. The model consists of several parts (2D Gaussian plume model, diffusion parameter adaptive method, and local robustness regression adaptive evaluation parameters), enabling the rational calculation of background concentrations and high-precision calculation of point source CO₂ emissions without relying on a priori diffusion parameters.

In Section 2, we provide a detailed introduction to the data used and the developed EMIT-GET model. In Section 3, we analyze single point source cases in China using OCO-2 data based on this model, furthermore, comparison between the retrived emission results with different TD methods and emission

inventories was conducted. We then discuss the performance of EMIT-GET in dual point source cases and special scenarios of repeated transit monitoring. Finally, we summarize the main findings in this work.

## 2. Data and method

### 2.1. Satellite data

The OCO-2 satellite was launched in July 2014 with an orbital altitude of about 705 km. The detection spatial resolution of the OCO-2 satellite is 1.29 × 2.25 km². In this paper, the $X_{CO_2}$ data from the OCO₂ _L2_Lite_FP_V11r product, which can be accessed via a link (https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_V11r/summary, Last accessed: 15 May 2023), are used to calculate the emission intensity of strong CO₂ sources (Zhao et al. 2023). To avoid the uncertainty of $X_{CO_2}$ in the OCO₂_L2_Lite_FP_V11r product from influencing the results of the emission calculations, the $X_{CO_2}$ data with a quality flag label of 0 (good quality) was used for the strong point source CO₂ emission calculations.

### 2.2. Meteorological data

The paper uses European Centre for Medium-Range Weather Forecasts Fifth Generation reanalysis dataset (ERA5) to re-analyze meteorological data (Hersbach et al. 2023). Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This dataset provides global grid data for 37 pressure layers (from 1000 hPa to 1 hPa) at a temporal resolution per hour, a spatial resolution of 0.25°, and its data is stored in NetCDF format. The 'ERA5 hourly data on pressure levels from 1940 to present' is used, which uses eight data fields, namely: 'longitude', 'latitude', 'time', 'Specific humidity', 'airTemperature', 'V-component of wind', 'U-component of wind' and 'pressure_level'. This data was obtained according to the corresponding link (https://cds.climate.copernicus.eu/cdsapp#!/dataset/rea analysis-era5-pressure-levels?tab=form, Last accessed: 15 May 2023). According to the spatial location of the emission sources, meteorological data from 0 m to 400 m above the emission outlets are selected to partici pate in the subsequent inversion of CO₂ emissions from point sources. To reduce the wind direction error, we allowed the wind direction to rotate within 45° on each side of the ERA5 local wind direction to optimize the spatial correlation between the Gaussian plume model and the OCO-2 observations of $X_{CO_2}$ (Nassar et al. 2021).

### 2.3. China CO₂ market enterprise list data

In March 2022, China's Ministry of Ecology and Environment's Environmental Information Platform
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reported the first compliance cycle (2019–2021) of the national CO₂ market. The national CO₂ market is compatible with the requirements of China’s milestone goal of achieving CO₂ peaking by 2030. This data was obtained according to the corresponding link (http://114.251.10.30/#/whole-list, Last accessed: 15 May 2023). The key emission units included in the first compliance cycle of the national CO₂ market are the power generation industry (including self-provisioned power plants of other industries), with a total of 2,162 key emission units and an annual coverage of about 4.5 billion tons of CO₂ emissions, which is the largest CO₂ market in the world in terms of the scale of covered emissions. The companies reported in the CO₂ emission list are divided into 3 main categories, power generation, chemicals, and manufacturing (aluminum, paper mills, etc.). The number of power plants is the highest with 1,321. The number of chemical companies is 113, and the remaining 728 are in the manufacturing category. The list reports the total annual CO₂ emissions (t) of individual strong emitters. To facilitate comparison with satellite-calculated CO₂ emission results, annual emissions reported by the list were converted to emission intensity (kg/s).

$$q = \frac{T \times 10^3}{365 \times 24 \times 3600} \quad (1)$$

where q is the emission intensity (kg/s) of the strong emitting firm and T is the total annual CO₂ emissions of the firm. Since satellite overpasses provide instantaneous emission report values, whereas the temporal resolution of existing meteorological reanalysis data is

hourly, we are forced to make the following assumption: that the wind field remains stable within each hourly period. Although this assumption may reduce the reliability of the inversion results, the current datasets do not offer meteorological information with a finer time resolution. In Section 2.7, the inversion model we developed utilizes an optimization algorithm to correct wind direction deviations, thereby mitigating the inversion errors caused by inaccuracies in the initial meteorological data.

## 2.4. Principle of OCO-2 detected CO₂ plume

The principle of OCO-2 quantifying source CO₂ emission is shown as Figure 1, OCO-2 monitor the $X_{CO2}$ enhancement in downwind direction of source. And we supposed the diffusion of CO₂ emitted by source follows Gaussian dispersion. To model it, we built a 2-D coordinate that the downwind direction as X axis and the direction vertical to X axis is Y, the location of source is set as coordinate origin.

## 2.5. Quantitative CO₂ emission modeling

An increase in CO₂ concentration in the region downwind of a strong point source causes a larger $X_{CO2}$ value of OCO-2 in the corresponding region, and CO₂ emission calculation model (EMIT-GET) was designed based on this effect in Figure 1. In this study, the CO₂ concentration in the downwind direction of the emission source is considered to be consistent with a vertically integrated Gaussian plume distribution in Eq. 2.

<figure><img src="image_17.png" /><figcaption>Figure 1. Scenario diagram for the application of OCO-2 satellite monitoring of CO₂ emissions from point sources.</figcaption></figure>
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$$V(x, y) = \frac{Q}{\sqrt{2\pi}\sigma_y(x)u} e^{-\frac{1}{2}\left(\frac{y}{\sigma_y(x)}\right)^2} + B \quad (2)$$

$$V(x, y) = \frac{X}{n} \qquad (3)$$

$$\sigma_y(x) = a \cdot \left(\frac{x}{x_0}\right)^b \qquad (4)$$

where V(x, y) is the vertically integrated CO₂ concentration (unit: g/m²), Q is the CO₂ emission intensity (kg/s), u is the wind speed (m/s), and x₀ is 1000 m. B is the background concentration of greenhouse gases in the measurement area. X is CO₂ in ppm, and n is the conversion factor to g/m², σ_y(x) is the horizontal diffusion parameter, a and b are the horizontal diffusion coefficients, which are usually determined according to the Pasquill-Gifford (atmospheric stabilization) guidelines (Magazine 1961) (Rege and Tock 1996). Atmospheric stability refers to the measure of whether disturbances superimposed on the background atmospheric field can intensify over time. It also refers to the degree to which a parcel of air in the atmosphere accelerates upward or downward due to buoyancy, which is caused by differences in density, temperature, and flow velocity between the air parcel and its surrounding atmosphere. The Pasquill-Gifford guidelines classify atmospheric stability into six levels, ranging from A to F. In previous studies, in Equations 2–4, different empirical values for parameters a and b are used depending on the atmospheric stability class. Theoretically, when the atmospheric stabilization is A, a = 213 and b = 0.894; and when F, a = 34 and b = 0.894. However, it is not reasonable to set a and b at fixed values because the effects caused by the environment around the point source cannot be ignored. The values of a and b corresponding to different stabilization levels are not necessarily the best values to match the 2D Gaussian plume model. Therefore, the values of a and b are set with a dynamic range of a from 1 to 1000 and b from 0.01 to 10. The detailed principles and technical details of the algorithm we developed will be presented in Chapter 2.7.

## 2.6. Assessment of background concentration

The parameter B can affect the magnitude of the X<sub>CO2</sub> enhancement and the accuracy of the emission rate quantification in Eq. 2. Nassar et al. confirmed the magnitude of the parameter B value by using manual judgment, namely the average X<sub>CO2</sub> value calculated from multiple orbital data of the satellite in the upwind region of the emission source. Another method is to average the X<sub>CO2</sub> values by using regions of the same orbit that are less affected by the emission source. However, the CO₂ fluxes

are inconsistent in regions with large differences in time or distance, and both methods may overestimate or underestimate the parameter B-value. To effectively extract the CO₂ background concentration in the neighborhood of the point source, a set of local robust regression and Gaussian Mixture Model (GMM) algorithms are designed to achieve the distinction between CO₂ enhanced and non-enhanced regions, and then extract the CO₂ background concentration under the available orbit more accurately.

The local robust regression is a commonly used method to obtain the baseline, which is widely used in spectral signal processing. Compared with the global regression, the local regression reduces the interference of the background signal, which makes the final estimated background signal closer to the real background signal. The strong point source downwind X<sub>CO2</sub> data monitored by the OCO-2 satellite can be represented by Eq. 5, where Y(xᵢ) represents the CO₂ concentration observed in xᵢ. The xᵢ represents the location observed by OCO-2 satellite. The b(xᵢ) is the background X<sub>CO2</sub> concentration in xᵢ, s(xᵢ) is the enhancement of CO₂ concentration due to the CO₂ emission from the strong point source, and eᵢ is the instrumental measurement error. Following the basic assumption of locally robust regression, b(x) is sufficiently smooth over a certain spatial range and can be expressed as b(x) = b(x; θ). Under this condition, by minimizing the loss function, the optimal estimate at each observation location can be obtained to obtain the background concentration at that location.

$$Y(x_i) = b(x_i) + s(x_i) + e_i, \qquad (5)$$

$$Loss = \sum_{i=1}^{n} w_s(x_i)w_r(x_i)K\left(\frac{x_i - x}{h}\right)\{y_i - b(x_i; \theta)\}^2, \qquad (6)$$

In Eq. 6, n denotes the number of X<sub>CO2</sub> observations, and K(xᵢ - x/h) denotes the number of measurements in the local regression of the background concentration at the evaluation location. The w<sub>r</sub>(xᵢ) represents the weight of the X<sub>CO2</sub> enhancement from strong point source CO₂ emissions in the measurements involved in the regression.

For background concentration estimation, the greater of the enhanced concentration s(xᵢ) in the observations, the smaller of w<sub>r</sub>(xᵢ), which can be calculated by Eq. 7.

$$w_r(x_i) = \begin{cases} 1 & \text{if } y_i - \hat{b}'(x_i) < 0 \\ \left[ \max\left\{1 - \left(\frac{y_i - b}{g * \sigma}\right)^2, 0\right\} \right]^2 & \text{otherwise,} \end{cases} \qquad (7)$$
<figure><img src="image_19.png" /></figure>

$$ \sigma = \text{median} \left( \sqrt{w_s(x_i)} | y_i - \hat{b}'(x_i) | \right) / 0.6745, \quad (8) $$

In Eq. 7, $\hat{b}'(x_i)$ is obtained from the global least squares regression estimate. The $w_s(x_i)$ represents the OCO-2 measurement error at $x_i$. By robust local regression, an optimal estimate of $\hat{b}(x)$ can be obtained at each measurement location $x_i$. The CO$_2$ background concentration in the neighborhood of the monitored emission sources is obtained by averaging the extracted concentration values. Although the local robust regression method treats the CO$_2$ concentration enhancement as an anomaly and reduces its weight in the regression, the regression process is still affected by the CO$_2$ concentration enhancement. Therefore, the CO$_2$ background concentration is further accurately calculated by subsequently screening the observations that are less affected by the strong CO$_2$ emission sources through GMM.

## 2.7. EMIT-GET model

The CO$_2$ concentration data from the OCO-2 satellite have spatial location information. Based on a 2-dimensional Gaussian diffusion model, an enhanced model of the gas concentration from each point source emission can be constructed (as shown in Figure 2). The CO$_2$ concentration from the OCO-2 satellite consists of a concentration increase and a background concentration from diffusion at different point sources. Therefore, a model for the superposition effect of multiple point sources was constructed based on the CO$_2$ concentration observed from the OCO-2 satellite in Eqs. 2–4. As discussed in Chapter 2.5, the values of a and b corresponding to

different stabilization levels are not necessarily the optimal values for matching the 2D Gaussian plume model. We have developed the ‘EMIT-GET’ model, and this chapter will provide a detailed introduction to the principles behind the model.

$$ f = \left( \frac{\sum_{k=1}^{m} V'(m) - V(m)}{V(m)} \right)^2, \quad (9) $$

The model is composed of Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) Algorithm. The unknown parameters (Q, a, b, and B) in formulas 2 to 4 are solved as follows: Firstly, reasonable logical upper and lower constraints are set for each unknown parameter, see Table 1, and an adaptation calculation Eq. 9 is established.

$U_{10}$ is the wind speed at altitude of 10 m, $U_{500}$ is the wind speed at altitude of 500 m; D is the mean value of wind direction from altitude of 10 m to 500 m.

Based on the GA algorithm, the aforementioned unknown parameters are solved, where $V'(m)$ is the simulated concentration calculated during the iterative matching parameter process. This analysis employs a Monte Carlo simulation technique, iterating the procedure 10,000 times to establish a robust solution space for the unknown parameters. Throughout these iterations, the ensemble of results is scrutinized to determine the maximum and minimum values associated with each unknown parameter. The average values of the corresponding unknowns are used as the initial input for the SQP algorithm. SQP (Sequential Quadratic Programming) is an iterative algorithm for solving nonlinear constrained optimization problem; detailed principle is shown as follows:

<figure><img src="image_20.png" /><figcaption>Figure 2. Framework of CO$_2$ emission calculation model.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Table.1.** Constraints setting in unknown parameters.</figcaption></figure>

<table><thead><tr><th>Parameters</th><th>Lower boundary</th><th>Upper boundary</th></tr></thead><tbody><tr><td>Q (kg/s)</td><td>0</td><td>inf</td></tr><tr><td>Wind speed (m/s)</td><td>U<sub>10</sub></td><td>U<sub>500</sub></td></tr><tr><td>Wind direction (°)</td><td>D-45°</td><td>D+45°</td></tr><tr><td>a</td><td>0</td><td>1000</td></tr><tr><td>b</td><td>0</td><td>100</td></tr></tbody></table>

$$ \begin{array}{l} \min f(x) \\ \text{subject to } c_i(x) = 0, i = 1, 2, \dots, m, \quad (10) \\ h_j(x) \ge 0, j = 1, 2, \dots, p \end{array} $$

where $f(x)$ is same to formula 9, and $h_j(x)$ are equality constraints and inequality constraints, in this step, $x$ is the unknown parameter. $h_j(x)$ means as the maximum and minimum values corresponding to the unknown parameters among the 10,000 solution values in GA.

The mean values of the corresponding unknown parameters in GA are used as the initial input ($x_k$) for the SQP algorithm. At the current initial input $x_k$, at which the quadratic programming subproblem is constructed with the objective of minimizing a quadratic function, as shown in Eq. 11.

$$ \min \frac{1}{2} \Delta x^T B_k \Delta x + \nabla f(x_k)^T \Delta x, \quad (11) $$

where $B_k$ is an approximation of the Hessian matrix of the objective function, $\nabla f(x_k)$ is the gradient of the objective function.

Constraint condition:

$$ h_j(x_k) + \nabla h_j(x_k)^T \Delta x = 0, j = 1, 2, \dots, p, \quad (12) $$

$$ x_{k+1} = x_k + \alpha \Delta x_k, \quad (13) $$

where $\alpha$ is the step size, when $x_{k+1}$ satisfies the tolerated value of $f(x)$, $x_{k+1}$ is the final result of the unknown parameters.

Figure 2 presents the flowchart of the EMI-GET method. In the diagram, the optimized parameter Q in the gray box represents the final emission reporting result, $u$ denotes the wind speed, and Q represents the wind direction. Detailed explanations of a and b can be found in Section 2.5 The initial values of these parameters are provided in Table 1.

## 2.8. Adaptive track data screening mechanism

Step 1. OCO-2 data at a range of 60 km around the target emission source are selected as $X_{CO2}$ data potentially available for point source CO$_2$ emission calculations, where the amount of high-precision (QA=0) $X_{CO2}$ should be greater than 150 (Peiro et al. 2022). The method for obtaining the geographic coordinates of point sources is presented in Section 2.3

Step 2. The $X_{CO2}$ data selected from OCO-2 in Step 1 should ensure that there are more than 20 footprint points with $X_{CO2}$ concentrations exceeding $2\sigma_c$, with $\sigma_c$ being the standard deviation of the selected range of $X_{CO2}$ data.

Step 3. Fitting the selected $X_{CO2}$ data with a standard Gaussian distribution function, calculating the azimuthal angle (w1) between the fitted $\mu$ (approximated as the location of the $X_{CO2}$ maximum) and the location of the emission source, and the w2 for the average of the ERA 5 wind directions obtained at different heights (10–400 m), and $|w1-w2|$ should be less than 60 °.

$$ f(x) = \frac{1}{\sqrt{2\pi}\sigma} \exp\left(-\frac{(x - \mu)^2}{2\sigma^2}\right) f, \quad (14) $$

Step 4. The correlation coefficient ($R$) between the measured $X_{CO2}$ enhancement data of OCO-2 and the simulated $X_{CO2}$ enhancement data is calculated according to Eq. 15, and the corresponding parameter is obtained from the EMIT-GET inversion. When $R^2>0.5$, the CO$_2$ emission intensity calculation of the target emission source can be regarded as successfully obtained, namely, the satellite monitoring data of this segment can be used to calculate the CO$_2$ emission of the point source.

$$ R = \frac{\operatorname{cov}(C, C)}{\sqrt{\operatorname{Var}(C) \cdot \operatorname{Var}(C)}}, \quad (15) $$

The $C$ is the measured $X_{CO2}$ from OCO-2, the $C$ is the simulated $X_{CO2}$ at the corresponding location based on the emission intensity calculated by EMIT-GET and the diffusion parameter values. The cov(C, C) is the covariance of C and C, and the Var(C) is the variance of C, and the Var(C) is the variance of C.

## 2.9. Accuracy assessment

To estimate the applicability of the model (GA-SPQ) in actual quantification cases, three evaluation indicators were used in this study, mean error (ME), correlation coefficient ($R^2$), root mean square error (RMSE).

$$ ME = \frac{1}{N} \cdot \sum (X - Y), \quad (16) $$

$$ R^2 = 1 - \frac{\sum (X - Y)^2}{\sum (Y - \bar{Y})^2}, \quad (17) $$

$$ \text{RMSE} = \sqrt{\frac{1}{N} \cdot \sum (X - Y)^2}, \quad (18) $$

where X represents the simulated $X_{CO2}$ values through GA-SPQ, Y represents the $X_{CO2}$ value measured by OCO-2, and $\bar{Y}$ is the mean value of OCO-2 retrievals.
<figure><img src="image_22.png" /></figure>

## 2.10. Uncertainty evaluation

EMIT-GET model will be calculated 1000 times repeatedly based on the concerned footprints of OCO-2 and meteorological data; then, the uncertainty and final retrieved emission rate could be defined by:

$$e = \sqrt{\frac{1}{N} \cdot \sum (Q_i - Q_m)^2} \quad (19)$$

$$Q_f = Q_m = \frac{1}{N} \cdot \sum (Q_i) \quad (20)$$

where e is the uncertainty of the retrieved emission rate, $Q_i$ is the ith retrieved emission rate, $i = 1, 2, 3... 1000$, $Q_m$ is the mean value of $Q_i$, N is the maximum number of iterations, which is usually set to 1000, and $Q_f$ is regarded as the retrieved emission rate.

<table><caption>Table 2. Summary of OCO-2 transit enterprise times.</caption><thead><tr><th>Num</th><th>Company Name</th><th>Lon (°)</th><th>Lat (°)</th><th>Time</th></tr></thead><tbody><tr><td>1</td><td>Inner Mongolia Mengwei Technology Company (IMMT)</td><td>117.0531</td><td>39.1518</td><td>2018.11.02</td></tr><tr><td>2</td><td>Yili Clean Energy Company (YILI)</td><td>108.7230</td><td>40.5032</td><td>2021.11.15</td></tr><tr><td>3</td><td>Ordos Haohua Guotai Chemical Company (OHGC)</td><td>108.9765</td><td>40.4723</td><td>2021.10.15</td></tr><tr><td>4</td><td>Tianjin Huaneng Yangliqing Thermal Power Company (THYT)</td><td>117.0531</td><td>39.1518</td><td>2020.10.24</td></tr><tr><td>5</td><td>Luxi Chemical Group Company (LUXI)</td><td>116.1540</td><td>36.3595</td><td>2017.12.10</td></tr><tr><td>6</td><td>Xinjiang Sixth Agricultural Division Coal and Power Company (XSAD)</td><td>87.6869</td><td>44.2727</td><td>2021.09.07</td></tr><tr><td>7</td><td>Inner Mongolia Jinglong Power Generation Company (IMJP)</td><td>113.1628</td><td>40.3995</td><td>2018.10.03</td></tr><tr><td>8</td><td>Shandong Sunshine Power Company (SSPC)</td><td>117.5807</td><td>36.1784</td><td>2018.8.25</td></tr><tr><td>9</td><td>(1) China Coal Shaanxi Yulin Energy Chemical Company<br>(2) Shanxi Huadian Yuheng Coal & Power Company (ZMHD)</td><td>109.5440</td><td>38.1326</td><td>2021.12.15</td></tr><tr><td>10</td><td>Guotou Hami Power Generation Company (HAMI)</td><td>93.3529</td><td>42.3088</td><td>2021.07.16<br>2020.04.24</td></tr><tr><td>11</td><td>Xinjiang Qiya Aluminum & Electric Company (XQAE)</td><td>89.0343</td><td>44.8789</td><td>2020.05.08<br>2020.10.15<br>2020.03.05</td></tr></tbody></table>

<figure><img src="image_23.png" /><figcaption>Figure 3. Spatial distribution of strong point source locations; the blue circular mark represents a power generation company; the red circular mark represents a chemical type company; the green represents a plant with two-point sources; the yellow represents an aluminum power plant.</figcaption></figure>

## 3. Results

According to the track data screening mechanism described in section 2.8, $X_{CO_2}$ data collected from the OCO-2 satellite during the period from September 2015 to December 2022 were utilized to select cases applicable for the quantification of strong point source $CO_2$ emissions. The ultimately selected cases encompass a variety of power plants, including combined heat and power, thermal power generation, and coal-fired power plants, ensuring that the chosen point sources are representative in the assessment of $CO_2$ emission intensity, as shown in Table 2; spatial distribution of these cases is presented in Figure 3.

### 3.1. Single strong point source case

The cogeneration branch of Yili Clean Energy Company in Inner Mongolia (YILI) includes natural gas cogeneration units, coal-fired cogeneration units, gas turbines, and waste heat generating units,
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 4.** CO₂ emission assessment results based on OCO-2 satellite data for YILI Company.</figcaption></figure>

primarily relying on coal for energy generation in Figure 4. On November 15, 2021, the OCO-2 detection orbit passed approximately 12 kilometers downwind of YILI. With a wind speed of U10 = 2.7 m/s on that day, CO₂ emissions from the company caused a relatively significant enhancement in CO₂ concentration according to the detection trajectory data. The plume-induced CO₂ concentration enhancement footprint comprised 45 points, with a background concentration calculated to be around 412.8 ppm. The detected concentration enhancement (ΔXCO₂) was as high as 8.4 ppm compared to the background concentration. Based on the EMIT-GET model, the CO₂ emission intensity from YILI point sources was estimated to be 103.3 ± 12.4 kg/s, compared to 74.2 kg/s recorded in the emission inventory, indicating a relative deviation of approximately 39.2%. This discrepancy is hypothesized to be influenced by the Erdos Guotai Chemical Plant, located upwind of YILI with an emission intensity of 21.2 kg/s. The reconstructed XCO₂ data from EMIT-GET, in comparison with the OCO-2 XCO₂

observations, yielded an R² of 0.54, RMSE of 0.86 g/m², ME of 0.01 g/m², and MAE of 0.09 g/m².

Luxi Chemical Group Company's Power Branch (LUXI), established in 1998, includes coal-fired generator sets, gas turbine generator sets, circulating fluidized bed boilers, steam turbine generator sets, and waste heat generator sets. These products are widely used in the electric power, steel, chemical, and petroleum industries. LUXI possesses multiple generator sets to power the Group's facilities, meeting the power requirements for the production of various products, and is classified as a cogeneration plant.

On 10 December 2017, the OCO-2 detection orbit transited approximately 14 kilometers downwind of LUXI, as shown in Figure 5. With a wind speed of U10 = 2.1 m/s on that day, the CO₂ released by the company caused a significant CO₂ concentration enhancement, based on the XCO₂ data from the detection track. The plume-induced CO₂ concentration enhancement footprint consisted of 102 points, with a fitted background concentration of about 408.5 ppm, and the

<figure><img src="image_26.png" /><figcaption>**Figure 5.** CO₂ emission assessment results based on OCO-2 satellite data for Luxi Chemical Group Company; (a) OCO-2 detection data in the downwind direction of the Power Branch of LUXI Chemical Group Company; (b) the XCO₂ (blue) involved in the quantification of CO₂ emissions and the XCO₂ (read) obtained from the EMIT-GET fitting.</figcaption></figure>
<figure><img src="image_27.png" /></figure>

detected enhancement ($\Delta X_{CO_2}$) reached up to 5.2 ppm. The strong point source $CO_2$ emission of LUXI was evaluated to be $334.2 \pm 31.1$ kg/s, with a relative deviation of only about $-6.5\%$ compared to the emission intensity of 357.35 kg/s recorded in the emission list. The reconstructed $X_{CO_2}$ data from EMIT-GET, compared with the $X_{CO_2}$ data from OCO-2 observations, showed an $R^2$ of 0.71, RMSE of 0.63 g/m$^2$, ME of 0.01 g/m$^2$, and MAE of 0.74 g/m$^2$.

## 3.2. Comparison with bottom-up inventories

Based on the $CO_2$ data observed by OCO-2 satellite, EMIT-GET is a top-down theory to evaluate the $CO_2$ flux. China's enterprise $CO_2$ emission inventory is a bottom-up theory, which determines the $CO_2$ emissions in different time periods through the statistically collected samples. Therefore, comparing the China $CO_2$ market enterprise list with $CO_2$ fluxes from EMIT-GET can measure the uncertainty of the EMIT-GET model. The point sources are strong point sources belonging to a single $CO_2$ emission in Table 1, with the exception of the ZMHD point source. Besides, the $CO_2$ emission intensity data of XQAE point sources are not recorded in the Chinese enterprise $CO_2$ market data. Therefore, the nine-point $CO_2$ emission intensities retrieved through EMIT-GET are shown in Figure 6 from 2018 to 2021.

There is a high correlation between the $CO_2$ emission intensity evaluated by EMIT-GET and the $CO_2$ emission intensity of the China $CO_2$ market enterprise list in Figure 6, with an $R^2$ of 0.83. This indicates that the $CO_2$ emission intensity evaluated from the EMIT-GET model is very close to the $CO_2$ emission intensity recorded from China's $CO_2$ market enterprises list.

Compared to the emission inventory, the maximum fluctuation of a single measurement was -28.3%, and the minimum was +6.5%. Given that the average reported emission value for the detection cases was 192.56 kg/s, we statistically conclude that the EMI-GET model provides nearly unbiased estimates overall, with a bias of -1.36%. However, the average absolute error between the EMI-GET model's reported values and the inventory's reported values is 18.51%, indicating that accurately estimating emissions from a power plant using a single OCO orbit remains challenging, even after model optimization and improvements. The $Q$ (kg/s) value in the EMI-GET inversion report represents the instantaneous emission intensity, but most point-source inventories only provide annual emission intensities (in units of Mt/year). Currently, only the U.S. EPA inventory provides detailed hourly emission reports (The download link is https://campd.epa.gov/data/). In contrast, China does not provide similar high-resolution temporal emission inventories. Considering that electricity consumption fluctuates with peaks and valleys, and power plant emissions are not constant, we use the sector-scale daily emission time series curve provided by the CarbonMonitor inventory to calculate the emission ratio for conversion (the download link is https://carbonmonitor.org/). Nevertheless, we still rely on the assumption of a constant daily emission rate. This assumption may not hold for certain power plants. For example, in China, thermal power plants are required to mitigate fluctuations in renewable energy generation. In recent years, with the rapid growth of solar power, Chinese thermal power plants typically operate at lower capacities during the day and increase their output at night. Since these plants do not disclose

<figure><img src="image_28.png" /><figcaption>**Figure 6.** Error distributions of China $CO_2$ Market Enterprise List Data versus EMIT-GET $CO_2$ emission intensity.</figcaption></figure>
<figure><img src="image_29.png" /></figure>

their hourly generation data, we are currently unable to correct for intra-day variations on a plant-by-plant basis. We continue to urge relevant departments to provide more granular emission inventories to facilitate transparent and open verification.

The color of the points represents the uncertainty of the CO₂ emission intensity from EMIT-GET. In Figure 6, the color from blue to grey represents a gradual increase in uncertainty. Besides, the uncertainty of CO₂ emission intensity of point sources increases with the increase of CO₂ emission intensity in Figure 6, and its uncertainty ranges from 1.2 to 73.1 kg/s. Converting to relative uncertainty, we evaluate the average uncertainty of the EMI-GET model in all successful inversion cases to be ±15.38%. In general, there is excellent accuracy in the CO₂ emission intensity evaluated by EMIT-GET.

### 3.3. Comparison with top-down methods

Similar models developed by Nassar and Zheng et al. also applied in quantify CO₂ emission by OCO-2 measurements. Therefore, to estimate the accuracy of EMIT-GET, the Nassar et al. and Zheng et al. methods were chosen to compare and calculate the above cases meanwhile. Nassar's definition of emission uncertainty is based on the error propagation formula, which decomposes the uncertainty into the square sum of five components: wind field, plume uplift, background concentration, concentration enhancement, and uncertainty in secondary emission

contributions. In contrast, Zheng et al. quantify uncertainty by representing the random error of a single retrieval of XCO₂ as the uncertainty in the net flux estimate. In Nassar's method, particularly for wind field uncertainty, the standard deviation of inversion results derived from different meteorological reanalysis data is used. For background and enhancement uncertainty, subjective region selection is employed for random calculation. This approach carries the risk of amplifying uncertainty and is subject to the "survivor bias" effect. Zheng's approach is similar to ours in that it assesses deviations in the final inversion results based on perturbations of initial variables. However, Zheng applies a subjective 45° rotation correction for wind direction, which could potentially affect the accurate assessment of algorithmic uncertainty. Figure 7 present the CO₂ emission results obtained by the three models in point source cases, with the error bars in the horizontal direction representing the CO₂ emission intensity uncertainty from EMIT-GET, and the error bars in the vertical direction representing the CO₂ emission intensity uncertainty from Nassar or Zheng et al. As is shown in Figure 7, EMIT-GET result shows high correlation with that of Nassar et al. and Zheng et al. with R² of 0.81 and 0.75, and RMSE of 133.97 kg/s and 102.39 kg/s, respectively. For all cases, we performed emission estimation using three different methods and, based on the uncertainty calculation methods provided by each algorithm, computed the uncertainty for each case under each method. The inversion results and uncertainties were

<figure><img src="image_30.png" /><figcaption>**Figure 7.** Error distributions of Nassar et al. and Zheng et al. CO₂ emission intensity versus EMIT-GET CO₂ emission intensity. The horizontal axis represents the CO₂ emission intensity from the EMIT-GET model, while the yellow and green vertical axes represent the CO₂ emission intensity from Nassar and Zheng, respectively.</figcaption></figure>
<figure><img src="image_31.png" /></figure>

categorized into three groups, and the uncertainty for each overpass within each group was statistically analyzed. Finally, we calculated the average absolute uncertainty across all single-track cases in the three groups. The results showed that the average uncertainty for EMI-GET was ±15.38%, while Nassar's uncertainty was ±17.69%, and Zheng's uncertainty was ±18.26%. EMIT-GET shows its lower uncertainty among the models, -13.04% and -15.75% for Nassar et al. and Zheng et al. respectively.

# 4. Discussion

## 4.1. Quantification in two adjacent point emission sources

China Coal Shaanxi Yulin Energy Chemical Company (YULIN), located in Yuyang District, Yulin City, Shaanxi Province, is a chemical enterprise mainly focusing on deep processing of coal. The company mainly produces coal-to-methanol, coal-to-gas, and coal-to-fertilizer products, with annual capacities of 300,000 tons, 3 billion cubic meters, and 400,000 tons, respectively. Shanxi Huadian Yuheng Coal & Power Company (HUADIAN) is a large-scale coal power company located in Hengshan District, Yulin City, Shaanxi Province, and is one of the key energy suppliers in the region. The Yuchang Power Plant covers an area of about 900 acres and has two 600 MW supercritical units with a total installed capacity of 1,200 MW and an annual generation capacity of 8 billion kWh.

On 15 November 2021, the detection track of OCO-2 was transiting in the region about 22km downwind from the above dual strong point source (ZMHD) in Figure 8. The dual emission source described above is approximated as a virtual strong point source and the location of this virtual point source is the average of the locations (latitude and longitude) of the two emission sources. The CO₂ release from this virtual strong

point source has caused a significant CO₂ enhancement value under the wind speed $U_{10} = 2.6$ m/s. There are 114 plume-induced CO₂ concentration enhancement footprint points, and the fitted background concentration is about 415.4 ppm. The maximum concentration enhancement value ($\Delta X_{CO_2}$) monitored was about 5.2 ppm. The CO₂ emission intensity of YULIN and HUADIAN recorded in the CO₂ emission list was 96.49 kg/s and 153.15 kg/s, respectively. The total emission intensity of the two-point sources was $342 \pm 29.2$ kg/s according to EMIT-GET, with a relative deviation of about 37.0%. The $X_{CO_2}$ reconstructed from EMIT-GET approximates a single point source in the downwind direction with $R^2 = 0.83$, RMSE = 1.1 g/m², ME = 0.01 g/m², and MAE = 0.87 g/m², when compared to the $X_{CO_2}$ obtained from OCO-2. When two-point sources are spatially close to each other, there exists a possibility of successfully calculating the total CO₂ emissions of both.

## 4.2. Repeated transit monitoring for certain point source

There is contingency in the assessment of CO₂ intensity of firms through the EMIT-GET model based on single-transit OCO-2 satellite data. Therefore, Xinjiang Qiya Aluminum and Power Company and Guotou Hami Power Company were selected because of the multiple transits of the OCO-2 satellite. The accuracy of the EMIT-GET model in evaluating CO₂ emission intensity can be further validated based on these two study areas.

The CO₂ emission intensity and error from the EMIT-GET model in Guotou Hami Power Generation Company (HAMI) and Xinjiang Qiya Aluminum and Electricity Company (XQAE) are shown in Figure 9. The OCO-2 satellite had two transits of Guotou Hami Power Generation Company on April 24, 2020 and July 16, 2021, and the CO₂ emission intensity evaluated by the EMIT-GET model was

<figure><img src="image_32.png" /><figcaption>**Figure 8.** Total CO₂ emissions from dual point sources (Chemical Branch of China Coal Shaanxi Yulin Energy & Chemical Company, and Yuhang Power Plant of Shaanxi Huadian Yuhang Coal & Electricity Company) are Estimated; (a) OCO-2 detection data in the downwind direction of the Power Branch of ZMHD Chemical Group Company; (b) the $X_{CO_2}$ (blue) involved in the quantification of CO₂ emissions and the $X_{CO_2}$ (red) obtained from the EMIT-GET fitting.</figcaption></figure>
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /><figcaption>Figure 9. Histogram of CO₂ emission intensity of two enterprises; the yellow line represents one standard deviation.</figcaption></figure>

259.02±57.04 kg/s and 166.21±29.13 kg/s in Figure 9. The corresponding emission inventory data of enterprises in China's CO₂ market were 154.12 kg/s and 149.21 kg/s. Although the model has overestimated the CO₂ intensity of the enterprises, by evaluating the existence of the relationship R² is 1.0 and MAE is 40.95 kg/s. Therefore, the model can be used to evaluate the CO₂ emissions of the enterprises. For Xinjiang Qiya Aluminum & Electric Company (XQAE), there were three transits of OCO-2 satellite on 5 March 2020, 8 May 2020 and 15 October 2020, and the CO₂ emission intensity results evaluated by the EMIT-GET model were 350.35 ± 85.25 kg/s, 452.36 ± 55.71 kg/s and 405.20 ± 58.91 kg/s. The results evaluated by the EMIT-GET model cannot be compared with the data from the China CO₂ Market Enterprise Emission Inventory because the annual CO₂ emission data of the enterprise is not recorded in the CMEI.

The different CO₂ emission intensities of the two enterprises assessed based on the EMIT-GET model for multiple transits are mainly affected by the combination of power supply intensity and surface wind speed in different months and the transit location. In general, the CO₂ intensity values from the three transit assessments are consistent and demonstrate the good robustness of the models.

### 4.3. Comparison of inversion methods based on transport models

Converting satellite-observed gas column concentrations into emission fluxes (or rates) from emission sources is an attribution problem, specifically an inverse problem. In atmospheric science, solving this

inverse problem typically involves the use of various atmospheric transport models to perform inverse modeling. In the current studies on quantifying point source emission rates based on satellite-borne sensors, particularly with strip-viewing observational satellites like OCO-2/3, the prevailing approach is to fit the observed concentrations with simulated concentrations using atmospheric transport models (Broquet et al. 2018; Jacob et al. 2016; Kaminski et al. 2022; Lauvaux et al. 2022; Wu et al. 2018). The use of transport models is generally categorized into three types: Gaussian plume model, Lagrangian dispersion model, and Eulerian dispersion model.

The Lagrangian dispersion model and Eulerian dispersion model describe the diffusion of pollutants in the atmosphere based on different physical assumptions (Wu et al. 2018). The Lagrangian model simulates the trajectory of individual particles and uses stochastic processes (such as Brownian motion) to describe pollutant diffusion, providing a detailed representation of pollutant transport pathways, making it particularly suitable for short-term, localized studies such as industrial emission monitoring or near-source dispersion analysis. However, the computational burden of the Lagrangian model is significant, and it requires high-precision initial conditions and real-time meteorological data, which makes it challenging for large-scale applications. Methods based on the Lagrangian dispersion model, such as WRF-STILT (Lauvaux et al. 2022; Wu et al. 2018), WRF-FLEXPART (He et al. 2024) and X-STILT (Yi et al. 2024), have been used for the quantification of point source emission rates. These models excel in capturing fine-scale transport processes and are particularly valuable when high-resolution meteorological data
<figure><img src="image_35.png" /></figure>

are available, though their computational intensity poses challenges for large-scale, near-real-time applications.

On the other hand, the Eulerian model discretizes space and solves the time evolution of pollutant concentrations based on a fixed grid system, making it suitable for addressing large-scale pollution problems with multiple emission sources and complex boundary conditions. Its grid-based approach enables comprehensive simulations over large spatial domains and extended time periods, making it a critical tool for regional and global emission assessments. While it offers higher spatial resolution and temporal variation information, it faces similar issues as the Lagrangian model, including strong grid dependence and high computational resource demands. In current studies, Eulerian-based models, such as WRF-Chem, have demonstrated their feasibility for point source CO₂ emission estimation (Zheng, Nassar, and Baxter 2019). However, numerical diffusion in Eulerian transport schemes can lead to significant errors when simulating the transport of narrow plumes (Rastigejev et al. 2010). Nevertheless, WRF-Chem's robust handling of complex meteorological fields and its capacity to incorporate chemical reactions make it indispensable for studies requiring detailed atmospheric chemistry and multi-source interactions, though the accuracy of the wind field remains crucial for reliable emission estimation (Brioude et al. 2013; Zheng, Nassar, and Baxter 2019).

In contrast, the 'EMI-GET' model proposed in this study is based on the Gaussian dispersion framework and integrates Genetic Algorithms (GA) with Sequential Quadratic Programming (SQP) methods. This approach significantly avoids the reliance on empirically determined parameters typical of traditional Gaussian dispersion models (Nassar et al. 2021, Das et al. 2023; Li et al. 2024) and overcomes the limitations of models that cannot optimize wind fields. In our model, emission flux, wind speed, wind direction, and horizontal diffusion coefficients are all precisely optimized through a combination of GA and SQP algorithms. This avoids the complexity of running chemical transport models while ensuring high accuracy. By leveraging optimization techniques, our model aims to closely replicate the real meteorological field, coupling simulated concentrations with observed concentrations. We employed the GA to estimate the parameters in the Gaussian dispersion model to ensure the reliability and stability of the results. First, we ran the GA 1000 times, as each run may yield slightly different results due to the heuristic search nature and randomness of the algorithm. This allowed us to determine the upper and lower bounds of each parameter, helping to understand the uncertainty and variability of the parameters. Next, we used the SQP algorithm to precisely calculate the unknown parameters in the Gaussian model. As a local optimization method, the SQP algorithm provides more accurate parameter values

within the determined ranges. However, the Gaussian framework assumes homogeneous atmospheric conditions and steady-state flow, which may limit its performance in environments with highly variable meteorological conditions or complex topographies. Additionally, while our optimization-based approach effectively calibrates key parameters, it does not account for nonlinear chemical reactions and feedback processes in the atmosphere, which are crucial in certain emission scenarios, such as those involving reactive gases.

The 'EMI-GET' model we developed has strong practical potential due to its simplicity and low computational demands, particularly in situations with limited computational resources. While EMI-GET is highly effective for rapid point-source emission verification and large-scale "top-down" assessments, it is less suited for scenarios requiring detailed chemical transformation modeling or complex multi-source interaction analysis. As discussed in Section 4.1, although the total emission values can be reported accurately, we are unable to further distinguish the individual emissions from multiple closely spaced point sources with higher precision. Nonetheless, its adaptability, ease of implementation, and computational efficiency make it a valuable tool for leveraging the growing availability of satellite-based concentration data, particularly in regions or applications where rapid emission estimation is prioritized over highly detailed atmospheric process modeling.

## 5. Conclusions

Based on the monitoring data from OCO-2 as well as the detection mechanism, emission quantifying model, EMIT-GET was designed in this paper. EMIT-GET has an automatic data screening mechanism to find the usable data of OCO-2 on point sources. Based on this mechanism, cases of strong point sources of CO₂ emissions were screened and analyzed. The deviation ranges from 1.2 to 73.1 kg/s between the CO₂ emission intensity calculated by OCO-2 satellite and the emission list. Its uncertainty also shows least among the current point source calculation models. It should be noted that, when dual point sources are in close proximity, the EMIT-GET can accurately quantify the total emissions of them. We also demonstrated that when the OCO-2 satellites have duplicate measurements for certain source, the mean value of estimations is close to the inventory report.

OCO-2 retrivals could also be influenced by cloud and aersol, the $X_{CO_2}$ detected by it is instantaneous measurement, which may not monitor the CO₂ concentration around the target point source, and it also can't monitor certain source freely due to its designed orbit or telescope monitoring angle. We ideally want to realize the effective assessment of CO₂ emissions from point sources through space, it could be achieved
<figure><img src="image_36.png" /></figure>

complementary by series satellites. In future, high performance satellites similar to OCO-2 could product CO₂ retrivals with higher spatial resolution, it would provide more enhanced information on distribution around point sources. Hyperspectral satellites (such as Earth Surface Mineral Dust Source Investigation, EMIT (Shi, Han, Ma, Pei, et al. 2023; Thorpe et al. 2023; Zhao et al. 2023)) could gain full plume of enhancement caused by point source, its wide coverage could help us estimate multiple point sources in same region. With the lunch of active LIDAR satellite DQ-1, it could measured CO₂ concentration both day and night, and less influence by aersol. It should be noted that EMIT-GET could be applied to the above-mentioned satellite point source emission monitorings. Under the enrichment of satellites measurements, EMIT-GET can help governments to acquire reliable emission data and design reasonable management to reduce CO₂ emission.

## Acknowledgments

This work was supported by the National Key R&D Program of China [Grant number 2024YFB3910203], the National Natural Science Foundation of china [Grant number 42475144], and the Collaborative Innovation Center of Geospatial Technology [Grant number 2042022dx0002].

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Notes on contributors

Yiyang Huang received his Bachelor's and Master's degrees in Engineering from the School of Remote Sensing and Information Engineering at Wuhan University, where he is currently pursuing his PhD His research focuses on the inversion of greenhouse gas fluxes.

Haowei Zhang received his PhD from Wuhan University and is currently with the Aerospace Information Research Institute, Henan Academy of Sciences, in Zhengzhou.

Xin Ma received his PhD in Engineering from Wuhan University and is currently with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University.

Tianqi Shi received his PhD from the School of Electronic Information at Wuhan University and is currently a post-doctoral researcher at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay), Gif-sur-Yvette, France.

Ge Han received the B.S. and M.S. degrees in geodetection and information technology from the China University of Geosciences (Wuhan) in 2009 and 2012, respectively, and the PhD degree from Wuhan University in 2015. He is currently an Associate Professor at Wuhan University. His research interests include atmospheric sounding and remote sensing of environment.

Zhipeng Pei received the B.S. degree from Wuhan University in 2019, where he is currently pursuing the PhD degree with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing.

Huiqin Mao is a Professor-level Senior Engineer and Head of the Greenhouse Gas Division in the Department of Atmospheric Environment at the Satellite Environment Center, Ministry of Ecology and Environment of China. Her research focuses on the application of satellite remote sensing in environmental monitoring.

Wei Gong is currently a Professor and PhD supervisor at Wuhan University. He received the B.S. degree from Huazhong University of Science and Technology and M.S. degree from the Institute of Modern Physics, Chinese Academy of Sciences. He completed his PhD at Huazhong University of Science and Technology in 1999. Afterward, he worked as a Postdoctoral Fellow at Hampton University, USA, and later as a Research Assistant Professor. In 2005, he returned to China and became an Associate Professor at Wuhan University before being promoted to Professor in 2006. He is mainly engaged in teaching and research of new laser, optical technology, and its remote sensing applications, and has made achievements in atmospheric Lidar and optical remote sensing in recent years.

## ORCID

Yiyang Huang http://orcid.org/0009-0008-9362-1725

Haowei Zhang http://orcid.org/0000-0002-2660-6794

Tianqi Shi http://orcid.org/0000-0003-4815-4175

Zhipeng Pei http://orcid.org/0000-0003-1474-7888

Wei Gong http://orcid.org/0000-0002-2276-8024

## Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request

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