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Article

Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction

by
Kaiyuan Guo
1,2,
Liping Lei
1,2,
Hao Song
3,
Zhanghui Ji
1,2 and
Liangyun Liu
1,2,*
1
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
3
Guangxi Key Laboratory of Culture and Tourism Smart Technology, Guilin Tourism University, Guilin 541006, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(5), 739; https://doi.org/10.3390/rs17050739
Submission received: 1 January 2025 / Revised: 12 February 2025 / Accepted: 16 February 2025 / Published: 20 February 2025
(This article belongs to the Special Issue Using Remote Sensing Technology to Quantify Greenhouse Gas Emissions)
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Abstract

:
Anthropogenic CO2 emissions are one of the primary drivers of the increase in atmospheric CO2 concentrations. It has been indicated that reducing emitted pollution gases can simultaneously bring out anthropogenic CO2 reduction, known as the synergistic effects of pollution and carbon reduction for controlling increases in CO2 and pollution gas concentrations. This study aims to assess these synergistic effects, which are still not clearly understood, by analyzing the mechanisms of atmospheric CO2 and NO2 concentration variability in response to human emission reduction activities. We utilize satellite-observed NO2, which is a short-lived anthropogenic pollution gas with the same emission sources as CO2, along with CO2 concentration data to detect their simultaneous response to anthropogenic CO2 emissions, thereby assessing and comparing the synergistic effects of pollution and carbon reduction in the two study areas of China and the United States, as well as in a special scenario of abrupt reductions in anthropogenic CO2 emissions. The results show that the synergistic effects of pollution and carbon reduction in the United States are likely better than those in China, as the United States demonstrates a stronger response (R2 = 0.53) between atmospheric NO2 and anthropogenic CO2 emission compared with China (R2 = 0.36). This difference is attributable to the CO2 emissions from coal-fired power generation in China are much more than those in the United States, where oil and natural gas dominate. Furthermore, the analysis of special scenarios during the COVID-19 pandemic (2020–2022) in China demonstrates that the types of anthropogenic emission sources are the main factors influencing the synergistic effects of pollution and carbon reduction. Specifically, the megacity regions, where fossil fuel power plants and transportation are the main emission sources, presented stronger synergistic effects of pollution and carbon reduction than those regions dominated by coal-based metallurgical and chemical plants.

1. Introduction

The increase in carbon dioxide (CO2) is a major driver of current and future climate change [1]. To mitigate climate change, countries worldwide are adopting measures to reduce and control anthropogenic carbon emissions [2,3]. However, atmospheric CO2 concentrations continue to rise significantly because of increased anthropogenic CO2 emissions from multi-energy consumption driven by economic growth [4,5]. Fossil fuel consumption, the primary source of carbon emissions, contributes more than 85% of total emissions across all sources [6,7]. On the other hand, the increase in pollutant gases, such as nitrogen dioxide (NO2), has been closely linked to the rise in anthropogenic NO2 emissions, which largely share the same emission source as fossil fuel consumption. The synergistic reduction in air pollution gases and carbon emissions has been promoted as an effective strategy for controlling carbon emissions. Therefore, it is essential to investigate and assess the effectiveness of this synergistic reduction in controlling carbon emissions, which is critical for decision-making on carbon emission control and air pollution management and for better understanding the mechanisms of synergistic reduction.
Satellite observations, with advantages such as standardized and systematic measurements, long time series, and large spatial coverage, can simultaneously detect and monitor variations in CO2 and pollutant gases. A series of greenhouse gas (GHG) monitoring satellites, such as GOSAT, OCO-2, and OCO-3, along with pollutant gas observation satellites such as Sentinel-5 Precursor/Tropospheric Monitoring Instrument (S5P/TROPOMI) [8,9,10,11], have accumulated multi-year datasets of atmospheric CO2 and NO2 concentrations [12,13,14]. NO2, a pollutant gas co-emitted with anthropogenic carbon emissions, has a short lifetime of only a few hours to several days in the troposphere [15,16,17]. Studies have shown that satellite-based observations of NO2 concentrations near emission sources and surrounding areas typically exceed background levels by approximately two orders of magnitude (~3.4 × 10−4 mol/m2) [18]. Many studies have indicated that NO2 is an effective tracer for anthropogenic CO2 emissions in areas with shared emission sources, and it can be used to estimate anthropogenic CO2 emissions through relationships between satellite observations of XCO2 (columnar average of molar fractions of carbon dioxide in dry air) and the proxy species NO2 [19,20,21,22]. For example, Hakkarainen et al. used TROPOMI NO2 data to retrieve anthropogenic carbon emissions from power plants in South Africa, and the comparison with emission inventories demonstrated good consistency [23]. Yang et al. established an empirical relationship between NO2 observed by TROPOMI and CO2 observed by OCO-3. Using this relationship and NO2 fields, they derived CO2 fields (NDCFs) and ultimately estimated CO2 emissions from NDCFs based on a mass balance approach [24]. These studies solely rely on the characteristic of NO2 as an effective tracer for anthropogenic carbon emissions to directly or indirectly estimate carbon emissions through the synergistic relationship between carbon and pollution emissions, but they lack an in-depth exploration of the co-variation relationship between NO2 and carbon emissions.
In addition, some studies have used satellite NO2 observations to reveal the spatial and temporal variations in regional atmospheric NO2 concentrations and their correlation with human activities, particularly during specific events. Wang analyzed and pointed out that the changes in NO2 concentrations are closely related to human activities, such as the relocation of polluting enterprises and emission controls during major events such as the Olympics [25]. Zhang et al. utilized satellite remote sensing observations to analyze the variations in atmospheric NO2 concentrations in North China and similarly found that air pollution control policies implemented during events such as the Beijing Olympics, OPEC meetings, National Day military parades, and the Nanjing Youth Olympic Games led to reductions in both NO2 and CO2 concentrations [26]. Sheng et al.’s study, as well as others, also found a decreasing trend in atmospheric NO2 and CO2 concentrations during the COVID-19 period [27,28]. These studies have highlighted variations in NO2 concentrations and the correlation between NO2 and CO2 concentrations with anthropogenic CO2 emissions. Moreover, these studies have demonstrated abnormal changes in atmospheric NO2 and CO2 concentrations during the special period of human activities. However, they lack a mechanistic analysis of how atmospheric pollutant concentrations respond to changes in anthropogenic CO2 emissions.
The synergistic effects of atmospheric pollution reduction and carbon emission reduction provide important insights for the formulation of air pollution control and emission reduction policies. Most previous studies also relied on observations of pollutants and carbon emissions to analyze their spatiotemporal distribution and influencing factors, which does not provide an accurate understanding of the relationship between them. Moreover, how the co-variation in NO2 and CO2 concentrations respond to the activity of industrial production emissions remains unclear. The unraveling of this issue can help us better assess the synergistic effects of reducing air pollution gases and carbon emissions, which is the purpose of this study. Therefore, faced with the urgent need for greenhouse gas emission reduction and control, we propose that accurately analyzing the spatiotemporal variations in atmospheric NO2 concentrations can trace emission sources and analyze the response of carbon emissions, enabling the evaluation of the synergistic effects of pollution and carbon reduction.
China and the United States are the two largest contributors to global CO2 emissions, collectively accounting for more than two-fifths (38.8%) of total global carbon emissions as of 2023 [6]. The synergistic control of carbon and air pollution emissions in these two countries significantly influences global energy conservation and emission reduction efforts. Therefore, in this study, we leverage satellite-observed CO2 and NO2 concentration data to quantitatively analyze the variation features of NO2 and CO2 concentrations simultaneously in response to anthropogenic carbon emission and to reveal the driving factors of the synergistic effects of pollution and carbon reduction. Through a comparison of the two study areas (China and the United States), which exhibit distinctly different characteristics in emission sources of pollutant gases and CO2, as well as special scenarios involving industrial production emission controls in China. We aim to reveal the driving factors behind the synergistic effects of pollution and carbon reduction. This research may provide a valuable reference for evaluating the synergistic effects and efficiency improvements of pollution and carbon reduction in the region.

2. Materials and Methods

2.1. Study Area and Data

This study selects the land area of China and the contiguous 48 states of the United States as the research regions.
The national energy structure primarily consists of the fossil fuels from coal consumption in China, particularly in energy-intensive industries, which are mainly distributed in the eastern and central regions, the centers of industrialization and urbanization where the population is highly concentrated. Coal still accounts for more than 50% of China’s energy consumption despite the rapid development of renewable energy in recent years [29]. The major sources of air pollution and carbon emissions include coal-fired power plants, industrial production, and transportation, which result in high NO2 and CO2 concentrations. China has gradually strengthened control over fossil energy use through policy guidance, such as the “Blue Sky Protection Campaign” and the “Peak Carbon Emissions and Carbon Neutrality” goals, energy structure optimization, industrial and technological upgrading, etc. [30,31]. These efforts have not only improved air quality across the country but also made a significant contribution to global carbon reduction.
The United States had historically relied on fossil fuels for its energy structure. The share of natural gas and renewable energy, however, has been increasing significantly while coal consumption has drastically declined in recent years. The control of air pollution benefited from the implementation of the Clean Air Act, gradually establishing stringent emission standards and a regulatory framework [32]. The emissions of major air pollutants, such as NO2 and SO2, have significantly decreased, and carbon emissions in the industrial and transportation sectors have also been controlled through policy measures and technological advancements. The United States has actively been promoting low-carbon emission initiatives also through carbon markets, energy transitions, and the deployment of environmental technologies.
We collected data in the two study areas based on observations from multiple satellite sources, as shown in Table 1, including atmospheric NO2 concentration data from TROPOMI, XCO2 data retrieved from multiple CO2 observation satellites (GOSAT [33], OCO-2 [34], and OCO-3 [35]), and power plant data related to emissions. To trace and analyze the emission characteristics of atmospheric NO2 concentration changes, we also gathered two internationally recognized anthropogenic emission inventory datasets, namely, ODIAC (Open-source Data Inventory for Anthropogenic CO2, ODIAC) [36] and EDGAR (Emissions Database for Global Atmospheric Research) [37].
NO2 data are a level 3 OFFL NO2 data product from January 2019 to December 2022, which is the total vertical column concentration (NO2) in units of mol/m2 derived by the satellite-based observing raw NO2 data retrieved and reprocessed. These raw NO2 data are derived from the observations of TROPOMI on board the S5P satellite in Copernicus Ecosystem, a global atmospheric pollution monitoring satellite. TROPOMI sensors have been greatly improved, with the signal-to-noise ratio increased by a factor of 1 to 5 compared with previous sensors for monitoring atmospheric composition [38]. The level 3 OFFL NO2 data products are generated by reprocessing raw NO2 data using the data processing system, including optimizing the algorithm based on inversion–assimilation modeling. Data from TROPOMI observations has been used to investigate urban-scale air pollution and monitor air quality for major events, providing a scientific data basis for policy decisions related to pollution emissions [39,40]. The anomalous below −0.001 mol/m2 in the collected data are removed as some effect of data observation noise introduced the negative values in the clean region recommended in the document of data description. We calculated the monthly-averaged atmospheric NO2 data in a 0.01° grid for the study area using online processing at the GEE platform and converted them to common units of molec/cm2 by multiplying 6.02214 × 1019.
XCO2 data were collected during the same period of 2019–2022 as NO2 from the Mapping-XCO2 dataset, which was generated by using the spatiotemporal geostatistical approach of the XCO2 retrievals derived from the observations of multiple satellites, including GOSAT, OCO-2, and OCO-3 [41,42]. The XCO2 retrievals are generated by the retrieval algorithms developed by the Japanese GOSAT team and the ACOS (Atmospheric CO2 Observations from Space) algorithm developed by NASA’s OCO team, respectively. The Mapping-XCO2 filled the gaps of XCO2 retrievals in space and time due to the cloudy and observing mode using the spatiotemporal geostatistical approach of XCO2 retrieval during the observing period from 2009 to 2022, GOSAT (April 2009 to August 2014), OCO-2 (September 2014 to December 2020), and OCO 3 (August 2019 to December 2022) [43,44]. This dataset is available on the HARVARD Dataverse (https://dataverse.harvard.edu/, accessed on 17 August 2021) [27]. The Mapping-XCO2 dataset, which exhibited a −0.29 ± 1.04 ppm deviation compared with the TCCON(Total Atmospheric Carbon Column Observation Network), enabled the examination of temporal and spatial changes at global and regional scales [45,46].
ODIAC data represent the latest version of the ODIAC2023 emissions dataset, covering the period from 2000 to 2022. It provides global monthly emissions data at a spatial resolution of 1 km × 1 km. These emissions data files include the total monthly carbon emissions for land regions, available in GeoTIFF format, with values expressed in tons. This dataset is based on the Carbon Dioxide Information Analysis Centre (CDIAC) data archive for 2000–2022 and the 2021–2022 World Energy Statistics, utilizing multiple spatial proxy data for emission spatial distribution. These proxies include the geographical locations of point sources (such as power plants), satellite-observed nighttime lights, and aircraft and shipping trajectories. The ODIAC emissions dataset has been widely used by research teams both domestically and internationally for various carbon cycle research applications, such as CO2 flux inversion [47,48] and urban emissions estimation [49,50,51], among others.
EDGAR data are published by the European Commission’s Joint Research Centre (JRC). It is a gridded CO2 emission dataset generated by disaggregating the national emission inventories. It provides gridded total emissions and sector-specific emissions at a spatial resolution of 0.1° × 0.1° globally [37,52]. In this study, we use the EDGAR v8.0 dataset, which covers total emissions in units of kg/m²/s. Emission data in this version includes all fossil CO2 sources, such as fossil fuel combustion and non-metallic production processes. CO2 emissions from fossil fuel combustion were primarily estimated using IEA data and based on publicly available IEA major fuel types (coal, oil, and gas), CO2 emissions (IEA, 2022), and 2021–2022 BP statistics [53].
To assist in the analysis of carbon emission source characteristics, we used global power plant data from the Global Power Plant Database, which is a comprehensive open-source database created by the World Resources Institute (WRI) and its partners. We used the latest v1.30 version of the power plant parameter data. The database covers approximately 30,000 power plants. In the study regions of China and the United States, the number of power plants is 4099 and 9833, respectively. The primary types of power plants include thermal power plants (e.g., coal, natural gas, oil, nuclear, biomass, waste, and geothermal) and renewable energy plants (e.g., hydropower, wind, and solar). Notably, coal-fired power plants dominate in the Chinese region, while oil and natural gas are the primary energy sources for power plants in the United States region.

2.2. Data Reprocessing and Analysis

(1)
Data integration processing
Data used in this study, with varying specifications in spatial and timely resolutions shown in Table 1, are integrated into the same temporal and spatial resolutions to meet the requirements of data analysis.
The units of ODIAC and EDGAR inventory data differ, so the values of both datasets were converted into MtCO2 units. ODIAC data have a 1 km × 1 km resolution and were unified to a 0.1° grid, consistent with EDGAR data considering the curvature of the Earth; 1 km2 emission data falling within the 0.1° grid were extracted and calculated to generate 0.1° grid ODIAC data.
(2)
Clustering of NO2 concentration spatiotemporal features in the study area
The combustion of fossil fuels, industrial production activities, and transportation simultaneously emit CO2 and NO2, inevitably inducing changes in atmospheric NO2 in space and time. Accordingly, we can take advantage of NO2 concentrations to extract the exact information of anthropogenic CO2 emissions with the objectivity of satellite observations. We implemented the clustering analysis of spatiotemporal NO2 features by the K-means clustering algorithm using the monthly NO2 from January 2019 to December 2022 in the two study areas, respectively, aiming to reveal the segmentation characteristics of carbon emissions. As an unsupervised learning classification algorithm, K-means iteratively clusters data points with similar characteristics into K-distinct clusters. As a result, the clustering analysis for the spatiotemporal NO2 varying features divided this study into 14 clusters. We calculated the average values of NO2, XCO2, and anthropogenic CO2 emissions from ODIAC and EDGAR for each clustered area. The correlations among these parameters are statistically analyzed by the clustered areas to find a rule of NO2 and CO2 variation to anthropogenic CO2 emission.
(3)
Analysis of the response of NO2 and CO2 concentrations to anthropogenic CO2 emissions to assess the synergistic effects of pollution and carbon reduction
We extracted the clustered areas with high NO2 levels, which are closely linked to CO2 emissions because of their common sources, to investigate the anthropogenic CO2 emission characteristics in highly polluted regions. The annual averages and increments of NO2 and anthropogenic CO2 emissions in these high-pollution clusters, that is, high-emission areas, are used to analyze correlations and responses between these variables to assess the co-variation in atmospheric NO2 and CO2 concentrations responding to the anthropogenic CO2 emissions. Hereby, we assessed the synergistic effects of pollution and carbon reduction in China and the United States by collaborative analysis and comparison of NO2 concentration variations in response to anthropogenic CO2 emissions between China and the United States.
Additionally, we selected two key regions in China, the Beijing–Tianjin–Hebei area and the Yangtze River Delta, to detect the abnormal changes through the temporal variations in NO2 and CO2 concentrations and the response of NO2 variations to anthropogenic CO2 emissions in special and unique scenarios associated with human activities drastically reduced in 2019 and 2022 due to COVID-19.
We focused on the spatial and seasonal variations in CO2 and NO2 in response to anthropogenic CO2 emissions of ODAIC, as ODIAC data are available monthly. EDAGR, which is only available yearly, is used to compare and validate the analysis results of CO2 and NO2 responding to ODIAC in the spatial and interannual variation in the discussion section.

3. Results

3.1. Spatial and Timely Responding Patterns of NO2 and CO2 Concentrations to Anthropogenic CO2 Emissions

3.1.1. Responding Pattern of Spatial Variations

Figure 1 and Figure 2 show the grid-based response of NO2 and XCO2 to anthropogenic CO2 emissions in the two study areas of China and the United States, respectively, which are the spatial variations in annual averages (2019–2022) in NO2, XCO2 and anthropogenic CO2 emission of ODIAC, and the interrelationships between them which are NO2 and ODIAC, XCO2 and ODIAC, NO2, XCO2 and ODIAC.
It can be seen from Figure 1 and Figure 2 that the NO2 concentration responds more strongly to the anthropogenic CO2 emissions in the spatial pattern of annual averages and correlations compared with CO2 in both China and the United States. The correlations of NO2 to anthropogenic CO2 emissions are significantly greater than that of XCO2 in China (R2 = 0.42) and the United States (R2 = 0.44).
It, however, can be seen from Figure 2 that the spatial pattern of XCO2 in the United States is less consistent with NO2 and anthropogenic CO2 emissions compared with that in China, as shown in Figure 1. The correlation between satellite-observed NO2 and XCO2 is greater in China (R2 = 0.35) than that in the United States (R2 = 0.01). Additionally, it can be seen that the model-simulated XCO2 using CAMS (see Figure A1) in China is similar to satellite-based XCO2, while the model-simulated XCO2 is neither consistent in NO2 and anthropogenic nor similar to XCO2 in the United States. It is probably due to the stronger influence of atmospheric transport in the United States, as the continental United States is bordered by oceans on both sides, while mainland China is bordered by oceans to the east and land to the west. This spatial pattern of CO2 concentrations in the United States requires further conformed and validation.
Using the objectivity of satellite NO2 observations, a K-means clustering analysis of the spatiotemporal characteristics of NO2 from 2019 to 2022 was conducted. Figure 3 presents the results of the spatiotemporal variation characteristics of NO2 concentrations during this period. As shown in Figure 3, fourteen clustering regions (C1–C14) were identified within the study areas of China and the United States, respectively. The C#-value in the figure represents the clustering region number and the corresponding mean NO2 concentration (2019–2022). The mean NO2 values for China and the United States study regions were calculated as 0.65 × 1016 molec/cm2 and 0.43 × 1016 molec/cm2, respectively. Using these means as thresholds, clusters with values exceeding the mean were identified as heavily polluted areas.
Figure 4 shows that the cluster-based NO2, which segmented the spatiotemporal variations in NO2 concentration, demonstrates great correlations (R2) with anthropogenic CO2 emissions, reaching up to 0.92 in China and 0.82 in the United States. These correlations (R2) are much greater than those derived from grid-based statistics (Figure 1 and Figure 2), indicating the impacts of transportation in wind fields on atmospheric NO2 and the uncertainty of spatial allocation of anthropogenic CO2 emissions from point sources in ODIAC.
The above results suggest that NO2 is better at responding to anthropogenic CO2 emissions. This can be deduced from the characteristics of signals captured by satellite observations, including NO2 and CO2 emitted from ground sources such as coal-fired plants. The signals of NO2, a short-lived gas, in the satellites observing data can track and quantify the ground-emitted NO2 while the signals of CO2, a long-lived gas, include not only locally emitted CO2 from the ground anthropogenic sources and natural sources (land ecological fluxes) but also CO2 accumulated over time. The satellite-observed CO2 significantly weakens the intensity of CO2 signals emitted from anthropogenic sources, as shown in Figure 1d and Figure 2d. This also illustrates the low correlations between NO2 and CO2 in Figure 1 and Figure 2.
Additionally, comparing the response differences in China and the United States, it can be found that the cluster-based NO2 concentration shows a slightly greater correlation with anthropogenic CO2 emissions in China (R2 = 0.92) than in the United States (R2 = 0.82). This result is likely attributed to the differences in CO2 emission sources between the two study areas, as can be seen in Figure 5, which presents the types of power plants.
The number of coal-fired power plants in China is much greater than in the United States, as shown in Figure 5. Specifically, China has 891 coal-fired power plants, accounting for 66.4% of its total generating capacity, while the United States has only 270 coal-fired power plants, accounting for 20.8% of its total generating capacity. In contrast, the number and the percentage of gas- and oil-fired power plants in the United States (732, 46.2% of total generating capacity) are much higher than those (275, 3.4% of total generating capacity) in China. It is known that coal-fired power plants emit significantly more CO2 and NO2 compared with other types of power plants, such as gas- and oil-fired power plants. Therefore, NO2 concentrations, CO2 concentrations, and anthropogenic CO2 emissions are significantly higher in China than in the United States, as evidenced by comparing Figure 1a,e with Figure 2a,e. Both NO2 concentrations and anthropogenic CO2 emissions are greater in China than in the United States. The average NO2 concentration (2019–2022) was 0.65 × 1016 molec/cm2 in China and 0.41 × 1016 molec/cm2 in the United States, while the maximum values were 3.31 × 1016 molec/cm2 and 1.52 × 1016 molec/cm2, respectively. The average anthropogenic CO2 emissions (2019–2022) were 0.28 MtCO2 in China and 0.07 MtCO2 in the United States, with maximum values of 33.7 MtCO2 and 22.9 MtCO2, respectively.

3.1.2. Time Variability with Human Emission Activity

It is known that the atmospheric CO2 concentration is enhanced yearly with its accumulation of CO2 emitted from the ground by human and nature–ecological activity. Meanwhile, NO2 concentration changes with the variations in human industrial production activity, such as anthropogenic CO2 emissions. Figure 6 and Figure 7 show the time-series variation in NO2 concentration and ODIAC anthropogenic CO2 emissions from 2019 to 2022 in the two study areas.
The results shown in Figure 6 and Figure 7 show that the temporal variation in NO2 in China is more closely aligned with anthropogenic CO2 emissions compared with the United States, particularly in the trend of annual increment (upper part of Figure 6 and Figure 7).
The seasonal peaks in NO2 concentrations and anthropogenic CO2 emissions both occur in winter (November to January) in China. This is likely attributed to the combined effects of human activities (e.g., increased heating energy consumption) and meteorological conditions (e.g., increased frequency of temperature inversions and lower boundary layer heights) during winter [54]. This seasonal difference highlights the coupling effect of meteorology and emissions.
The seasonal variations in NO2 in the United States present a distinct periodic seasonal variation, with peaks in summer (July to August) and troughs in spring (March to April) (Figure 7). The summer peak in NO2 is closely associated with increased traffic emissions [55] and higher electricity demand due to air conditioning use under high temperatures. Conversely, the minimum in spring is linked to the end of the heating season, increased vegetation uptake, and frequent spring precipitation leading to wet deposition effects.
The NO2 variations present a dual-mode characteristic of “baseline seasonality” and “event-driven disturbance“ in China (Figure 6), while it is dominated by stable interannual periodicity (Figure 7) in the United States. These differences are likely due to the emitting CO2 sources and meteorological conditions [56,57].
The extremely anomalous low values, moreover, were observed in China from January to February 2020 and April 2022 (lower panel of Figure 6), which are caused by the dramatic decreases in human industrial production activity. Industrial and transportation emissions sharply declined due to COVID-19 from January to March 2020, which resulted in 32.7% and 19.6% decreases in NO2 concentrations and anthropogenic CO2 emissions compared with the same period in 2019. This special scenario of drastic change provided us with an extreme case to reveal the impacts of human activities on NO2 concentration and anthropogenic CO2 emissions (see Section 3.2 for detailed quantitative analysis). Similarly, in the United States, NO2 concentrations and anthropogenic CO2 emissions decreased by 26.7% and 21.6% year-on-year from March to April 2020 because of COVID-19.
We extracted larger NO2 values as high emissions and heavy pollution areas using the clustered areas shown in Figure 3 to evaluate NO2 responses to anthropogenic CO2 emissions. Clusters C9–C14, as shown in Figure 3, which show higher than average NO2 overall, are extracted in the two study areas and represent high-pollution areas, accounting for 77.5% and 68.8% of anthropogenic CO2 emissions, respectively, in China and the United States. Figure 8 demonstrates the correlation in the annual increments between NO2 and anthropogenic CO2 emissions in the clustered C9–C14 areas from 2019 to 2022, in which the differences between years effectively removed observational systematic biases in these data.
Figure 8 demonstrates that the change slopes in China and the United States study areas are 0.04 and 0.24, respectively, with R2 values of 0.36 and 0.53, indicating that the changes in NO2 in the United States are more sensitive to anthropogenic CO2 emissions than in China. The results shown in Figure 8 reveal that CO2 emissions reduce by less than −0.01 MtCO2 when NO2 decreases by less than −0.04 × 1016 molec/cm2 in the United States, which indicates strong synergy effects of pollution reduction and CO2 reduction. The CO2 emissions, however, remain unchanged almost even when NO2 decreases to less than −0.3 × 1016 molec/cm2 in China. These results are probably related to the types of emission sources. Coal-dominated power plants and various chemical pollution industries, especially coal chemical enterprises, significantly contribute to pollutant emissions in China. In contrast, the United States primarily depends on gas-based energy and has fewer polluting industries, which results in lower NO2 emissions compared with China. This indicates that the pollution control co-benefits for emissions from chemical pollution industries may not significantly contribute to carbon reduction within a certain time frame. However, the efforts in carbon control could achieve substantial decreases in NO2 when considering the impact of carbon emission controls on NO2 reductions. These results imply that we need great effort to reduce the NO2 emitted from those chemical pollution industries as well as coal-dominated power plants, so as to achieve better synergy effects of carbon reduction and pollution control in China.
Additionally, Figure 9 presents the changes in the year-on-year differences in NO2 and ODIAC in the same month from 2020 to 2022 for C9–C14 in China, aiming to detect the magnitude of NO2 changes in high-pollution emission areas. It can be found that variations in NO2 and anthropogenic CO2 emissions showed more significant differences than the overall differences shown in Figure 6, with a significant reduction during January–February 2020, particularly in the C14 that covers the regions of Beijing–Tianjin–Hebei and Shanghai. We can quantify the changes in NO2 concentrations and anthropogenic CO2 emissions using these special scenarios of the extreme reduction in human activities.

3.2. Co-Response of NO2 and CO2 Concentrations to Anthropogenic CO2 Emissions in the Special Scenarios of Human Activity

We focused on the Yangtze River Delta urban agglomeration (YRD), which was most severely impacted by the COVID-19 control periods of January–February 2020 and April-May 2022 in terms of reducing human activities and Beijing–Tianjin–Hebei and neighboring regions (BTH) (see the Figure 10a). It can be seen that the decreases in CO2 and NO2 during the COVID-19 controlled period by the year-on-year differences in XCO2 and NO2 in the same month from 2020 to 2022 in Figure 10. We assess the varying responses of NO2 and CO2 to different emission sources, such as industrial pollution facilities and transportation-related emissions, which are underscored in the previous findings [27].
Figure 11 shows the spatial distribution of the difference in NO2 concentration between April and May 2022 and the same period in 2021 for the YRD and BTH regions. As seen in Figure 11a, the largest decrease in NO2 concentration is centered around Shanghai in the YRD, with a gradual reduction in NO2 concentration radiating outward from the city. During this period, the significant reduction in human activities, including transportation, urban economic activities, and power generation in large power plants in the YRD, especially in Shanghai, resulted in a maximum decrease in NO2 concentration by −0.73 to −0.93 × 1016 molec/cm2. In the BTH region, the reduction in NO2 concentration is smaller, and the area of reduction is also more limited. Notable decreases in NO2 are observed only in major cities such as Beijing, Tianjin, and Tangshan, and cities such as Handan and Xingtai. In the BTH region, the reduction in human activities was less pronounced; the maximum decrease in NO2 concentration was −0.53 to −0.73 × 1016 molec/cm2.
Figure 12 shows the response relationship between the NO2 differences and the anthropogenic CO2 emission differences in January–February 2020 and April–May 2022 to the previous year in the same period calculated based on the administrative boundaries. Figure 12a,b is similar to the regional response results investigated by Sheng et al. in the January–February 2020 scenario, which also showed the regional characteristics of the response relationship between NO2 concentration and anthropogenic CO2 emission changes, closely related to pollution-emitting industries, as well as emission sources such as transportation.
The results in the YRD, as shown in Figure 12c, show two types of different response characteristics regionally. The regions, represented by Jiangsu Province and Shanghai, show a correlation of 0.91 between NO2 and CO2 emissions, with a slope of 0.1025 for CO2 emissions as a function of NO2 changes. This is higher than the correlation of 0.65 and the slope of 0.0341 observed in the regions represented by Anhui Province and Zhejiang Province. This indicates that the synergistic effect of pollution and CO2 emission reduction is more noticeable in Shanghai and Jiangsu Province compared with Anhui and Zhejiang. This result is related to the types of emission sources in these regions. In the regions of Jiangsu and Shanghai, coal-fired power plants, steel smelting plants, and transportation are the primary sources of emissions. Shanghai showed the largest reduction in NO2 (−0.56 × 1016 molec/cm2) and anthropogenic CO2 emissions (−0.04 MtCO2), which responded to the sharp decrease in anthropogenic CO2 emissions during this period.
We investigated the sector-specific anthropogenic CO2 emissions for each province and municipality within the BTH region and the YRD region in 2021 (see Table A1). It can be found that three sectors, metal smelting, power production, and transportation, account for the highest proportion of emissions in Shanghai and Jiangsu Province. In the Anhui and Zhejiang Provinces, excluding power production, the three sectors with the highest proportion of emissions are coking, chemical production, and metal smelting. This is consistent with the results shown in Figure 12. Studies have also shown that energy-intensive industries (e.g., power generation, steel production, and transportation) in Jiangsu Province contribute over 70% of CO2 and 60% of NOx emissions, indicating a strong coupling between industrial and transportation activities in terms of pollutant and carbon emissions [58,59]. The average production capacity of coking and chemical industry enterprises in Anhui is only one-third of that in Jiangsu. While NOx emissions from these industries account for 28% of the province’s total industrial emissions, their contribution to CO2 emissions is less than 15% (due to their scattered and non-energy-intensive nature) [60,61]. In contrast, the Anhui and Zhejiang provinces, where the main emission sources are coking plants, chemical smelting plants, and steel smelting plants, have smaller-scale facilities, resulting in less significant changes in NO2 and CO2 emissions.
The BTH region in Figure 12d also shows two regional response characteristics, with two major clusters: one dominated by Beijing, Tianjin, and Shandong and the other by Hebei, Shanxi, and Henan. These clusters show similar responses in NO2 to CO2 emissions, with R2 values of 0.86 and 0.81, respectively, but the slopes of CO2 emissions in relation to NO2 changes differ at 0.0995 and 0.0841. This variation is also related to the emission source types in the two agglomerations. The emissions in Beijing, Tianjin, and Shandong Province primarily come from fossil fuel power plants and transportation, while Hebei, Shanxi, and Henan Province mainly have steel smelting plants, coal-fired power plants, and coke smelting and chemical factories. From Figure 12b, it is evident that NO2 reductions in Beijing, Tianjin, and Shandong correspond more closely to CO2 emission reductions than in Hebei, Shanxi, and Henan. For example, in Beijing, NO2 decreased by −0.1882 × 1016 molec/cm2, corresponding to a CO2 emission change of −0.0161 MtCO2. In contrast, in Handan, Hebei, where NO2 similarly decreased by −0.1911 × 1016 molec/cm2, the corresponding CO2 emission change was only −0.0056 MtCO2. This indicates that the pollution and carbon reduction synergy is stronger in the Beijing, Tianjin, and Shandong regions compared with other regions. Similarly, the transportation and power production sectors account for the highest proportion of emissions in Beijing, Tianjin, and Shandong (see Table A1). In contrast, in Hebei, Henan, and Shandong, the highest emissions are attributed to metal smelting, coking, chemical production, and power production.
Additionally, we also compared the relationship between atmospheric NO2 and CO2 concentrations. Figure 13 shows a correlation between NO2 differences and CO2 differences, which are the values in April–May in 2022 minus the values in the same period in 2021. It is evident that the correlation between NO2 and CO2 shows consistency in Figure 12c,d.
In summary, the results above indicate that the response of atmospheric NO2 to anthropogenic CO2 emissions varies depending on the emission source types within a region. Areas dominated by fossil fuel power plants and transportation, especially large urban areas, show a stronger synergy between pollution reduction and CO2 reduction compared with regions where coal, smelting, and chemical plants are the primary emission sources. Therefore, based on the regional emission source characteristics, we can utilize satellite-observed atmospheric NO2 and CO2 to assess and monitor the synergistic effects of pollution reduction and CO2 reduction and provide decision-making support for pollution and CO2 reduction synergistic control strategies.

4. Discussion

We used ODIAC data to evaluate and analyze the response of NO2 concentrations to anthropogenic CO2 emissions. However, ODIAC anthropogenic CO2 emissions data, derived from emission inventories, contain certain uncertainties, particularly after the COVID-19 pandemic in 2020 [62,63]. The complex and dynamic nature of human activities during this period further increased the uncertainty in quantifying anthropogenic CO2 emissions with ODIAC data.
We used another emission dataset, EDGAR, which contains only annual data, to further validate the relationship between NO2 and ODIAC anthropogenic CO2 emissions. Figure 14 shows the spatial and interannual variations in atmospheric NO2 concentrations and their response to EDGAR in both China and the United States.
As shown in Figure 14c–f, the annual response characteristics of NO2 to EDGAR are consistent with those of NO2 to ODIAC shown in Figure 1, Figure 2 and Figure 5. The results again indicate that the response in China is higher than that in the United States, confirming that, in general, the synergistic effect of pollution reduction and CO2 reduction in China is better than that in the United States.
As shown in Figure 15, the interannual variation in NO2 concentration in high-pollution areas (C9–C14 clustering regions shown in Figure 4b) also reveals a similar response of NO2 to EDGAR emissions with ODIAC analysis results. The results indicate that the R2 in China (0.56) is lower than that in the United States (0.87), implying that the synergistic effect of pollution and carbon reduction in high-pollution areas is lower in China compared with the United States. This finding indicates that more than 50% of the pollution reduction is needed to reach the same effects in CO2 emissions reduction in the high-pollution areas because of heavy air pollution in China.
In addition, the R2 values for EDGAR are consistently higher than those for ODIAC. Table 2 presents a comparison of the response of atmospheric NO2 to EDGAR and ODIAC in both China and the United States. As shown in Table 2, the response of NO2 to EDGAR is higher than to ODIAC, particularly in China, where the difference between atmospheric NO2 responses to EDGAR and ODIAC is greater than those in the United States. These results imply that the uncertainty associated with EDGAR data tends to be lower compared with ODIAC. Other comparative analyses of anthropogenic CO2 emissions inventories [64] also indicate that the uncertainty in ODIAC emissions is higher in China than in the United States.

5. Conclusions

Investigations have indicated that reducing the emitted anthropogenic pollution gases can simultaneously enhance the effect of carbon reduction, that is, the synergistic effects of pollution and carbon reduction. Therefore, the synergistic advancement of pollution and carbon reduction has been proposed as an effective emission reduction strategy. However, the mechanisms by which the variability in reduced pollution gases can induce carbon reduction and how their concentrations in the atmosphere will respond are unclear. In this study, we use satellite-observed NO2, which is one of the pollution gases with the same emitting sources as CO2, along with CO2 concentration data, to detect their simultaneous response to anthropogenic CO2 emissions and assess the synergistic effects of pollution and carbon reduction, with China and the United States selected as the study areas.
The results indicate that atmospheric NO2 strongly responds to anthropogenic CO2 emissions. Overall, the correlation between NO2 and anthropogenic CO2 emissions in China and the United States is 0.92 and 0.83, respectively. Meanwhile, the correlation between CO2 and anthropogenic CO2 emissions implies that CO2 concentration in China is significantly more influenced by ground emissions compared with the United States, where CO2 is more affected by external atmospheric transport. In high-pollution areas, the United States shows a stronger response (R2 = 0.53) than China (R2 = 0.36). We found that the reason for the different responses of NO2 and CO2 to anthropogenic CO2 emissions between China and the United States is that there is a much larger proportion of coal-fired power in China than in the United States.
We further quantified the response of NO2 and CO2 concentrations to anthropogenic CO2 emissions in special scenarios associated with sharply reduced human activity during 2020 and 2022 in response to COVID-19 in China. The results indicate that the types of anthropogenic emission sources are the important impacting factors for the synergistic effects of pollution and carbon reduction. The megacity regions, where fossil fuel power plants and transportation are the main emission sources, presented stronger synergistic effects of pollution and carbon reduction than the regions where coal-based metallurgical and chemical plants are the primary sources of emissions. In conclusion, we can apply satellite-observed variations in atmospheric NO2 and CO2 to detect the characteristics of regional anthropogenic CO2 emissions sources, assess the monitor synergistic effects of pollution and carbon reduction, and provide scientific support for making the synergistic strategies of pollution and carbon reduction.

Author Contributions

Conceptualization, K.G. and L.L. (Liping Lei); Methodology, K.G.; Software, K.G., H.S. and Z.J.; Validation, K.G.; Formal analysis, K.G.; Data curation, K.G.; Writing—original draft, K.G.; Writing—review and editing, K.G., L.L. (Liping Lei) and L.L. (Liangyun Liu). All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Key Research and Development Program Earth Observation and Navigation Key Project (grant no. 2023YFB3907404).

Data Availability Statement

Research data presented in this study are available on request from the corresponding author. These data are not publicly available as this project is still in the research phase.

Acknowledgments

We thank the European Space Agency (ESA) and Google Earth Engine for providing Sentinel-S5P NO2 products and the World Data Centre for Greenhouse Gases (WDCGG) for providing global atmospheric CO2 data. The datasets of ODIAC and EDGAR are freely available from http://db.cger.nies.go.jp/ (accessed on 5 August 2024) and https://edgar.jrc.ec.europa.eu (accessed on 21 September 2023).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Remotesensing 17 00739 g0a1
Figure A1. Mean XCO2 concentration simulated by the CAMAS model in China and the United States for the period 2019–2020. (a) China and (b) the United States.
Figure A1. Mean XCO2 concentration simulated by the CAMAS model in China and the United States for the period 2019–2020. (a) China and (b) the United States.
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Table A1. Sectoral anthropogenic CO2 emissions and their proportional contributions in the provinces/municipalities of the YRD and BTH Regions.
Table A1. Sectoral anthropogenic CO2 emissions and their proportional contributions in the provinces/municipalities of the YRD and BTH Regions.
Emission
Subsector		BeijingTianjinHebeiAnhuiJiangsu
Total Emissions159.76311.091771.01770.701635.36
Petroleum Processing and Coking1.42/0.899.17/2.9510.5/0.59110.07/14.286.57/0.40
Raw Chemical Materials and Chemical Products0.21/0.132.61/0.8420.17/1.1470.54/9.1526.39/1.61
Nonmetal Mineral Products2.42/1.527.14/2.3085.32/4.8220.69/2.68104.20/6.37
Smelting and Pressing of Ferrous Metals0.04/0.033.12/1.00752.66/42.50220.11/28.56368.50/22.53
Smelting and Pressing of Nonferrous Metals0.01/0.010.80/0.261.20/0.071.76/0.233.56/0.22
Metal Products0.50/0.311.47/0.472.05/0.122.42/0.312.54/0.16
Production and Supply of Electric Power, Steam, and Hot Water62.42/39.07139.86/44.96684.19/38.63283.88/36.83921.72/56.36
Production and Supply of Gas0.24/0.150.12/0.040.10/0.011.710.223.49/0.21
Construction1.47/0.927.34/2.360.55/0.037.77/1.010.97/0.06
Transportation, Storage, Post and Telecommunication Services37.95/23.7617.58/5.6528.32/1.6040.75/5.29191.73/11.72
Emission
Subsector		ZhejiangShanghaiShandongShanxiHenan
Total Emissions884.41388.141894.331227.46967.48
Petroleum Processing and Coking240.35/27.1811.30/2.9156.33/2.9783.33/6.795.47/0.57
Raw Chemical Materials and Chemical Products80.90/9.155.38/1.3924.46/1.296.66/0.545.79/0.60
Nonmetal Mineral Products102.47/11.594.94/1.27140.27/7.4051.76/4.2291.07/9.41
Smelting and Pressing of Ferrous Metals32.23/3.6443.68/11.25207.63/10.96305.82/24.91198.72/20.54
Smelting and Pressing of Nonferrous Metals2.25/0.257.62/1.9676.73/4.0527.84/2.2727.34/2.83
Metal Products3.13/0.350.89/0.2311.80/0.622.41/0.202.17/0.22
Production and Supply of Electric Power, Steam, and Hot Water259.82/29.38188.94/48.681158.47/61.15741.92/60.44510.15/52.73
Production and Supply of Gas0.22/0.034.79/1.230.97/0.050.36/0.031.23/0.13
Construction8.92/1.014.15/1.075.26/0.283.50/0.2916.02/1.66
Transportation, Storage, Post and Telecommunication Services59.39/6.7290.56/23.33171.57/9.0629.75/2.4269.16/7.15

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Figure 1. Response of NO2 and XCO2 to anthropogenic CO2 emissions in China (a) annual average of NO2 (b) correlation between NO2 and ODIAC in grids (c) annual average of XCO2 (d) correlation between XCO2 and ODIAC in grids (e) annual average of ODIAC, and (f) correlation between NO2 and XCO2 in grids.
Figure 1. Response of NO2 and XCO2 to anthropogenic CO2 emissions in China (a) annual average of NO2 (b) correlation between NO2 and ODIAC in grids (c) annual average of XCO2 (d) correlation between XCO2 and ODIAC in grids (e) annual average of ODIAC, and (f) correlation between NO2 and XCO2 in grids.
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Figure 2. Response of NO2 and XCO2 to anthropogenic CO2 emissions in the United States (a) annual average of NO2 (b) correlation between NO2 and ODIAC in grids (c) annual average of XCO2 (d) correlation between XCO2 and ODIAC in grids (e) annual average of ODIAC, and (f) correlation between NO2 and XCO2 in grids.
Figure 2. Response of NO2 and XCO2 to anthropogenic CO2 emissions in the United States (a) annual average of NO2 (b) correlation between NO2 and ODIAC in grids (c) annual average of XCO2 (d) correlation between XCO2 and ODIAC in grids (e) annual average of ODIAC, and (f) correlation between NO2 and XCO2 in grids.
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Figure 3. Clustering of NO2 spatiotemporal variations based on satellite-observed monthly NO2 data (2019–2022) (a) China and (b) the United States.
Figure 3. Clustering of NO2 spatiotemporal variations based on satellite-observed monthly NO2 data (2019–2022) (a) China and (b) the United States.
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Figure 4. Correlation of NO2 to anthropogenic CO2 emissions and XCO2 in clustered areas (a) China and (b) the United States.
Figure 4. Correlation of NO2 to anthropogenic CO2 emissions and XCO2 in clustered areas (a) China and (b) the United States.
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Figure 5. Types of power plants in the study areas: (a) China and (b) the United States.
Figure 5. Types of power plants in the study areas: (a) China and (b) the United States.
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Figure 6. Time variation in monthly averaged NO2 and ODIAC emissions (lower), and the relative variation (upper) calculated as the year-on-year difference in the same month divided by the value of the same month in the previous year, in China from 2019 to 2022.
Figure 6. Time variation in monthly averaged NO2 and ODIAC emissions (lower), and the relative variation (upper) calculated as the year-on-year difference in the same month divided by the value of the same month in the previous year, in China from 2019 to 2022.
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Figure 7. Time variation in monthly averaged NO2 and ODIAC emissions (lower), and the relative variation (upper) calculated as the year-on-year difference in the same month divided by the value of the same month in the previous year, in the United States from 2019 to 2022.
Figure 7. Time variation in monthly averaged NO2 and ODIAC emissions (lower), and the relative variation (upper) calculated as the year-on-year difference in the same month divided by the value of the same month in the previous year, in the United States from 2019 to 2022.
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Figure 8. Annual incremental response relationship between NO2 and anthropogenic CO2 emission in high-pollution areas in China and the United States (a) China and (b) United States.
Figure 8. Annual incremental response relationship between NO2 and anthropogenic CO2 emission in high-pollution areas in China and the United States (a) China and (b) United States.
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Figure 9. High-pollution emission clustered areas in China (a) and the year-on-year differences in NO2 and anthropogenic CO2 emissions in the same month from 2020 to 2022 (b).
Figure 9. High-pollution emission clustered areas in China (a) and the year-on-year differences in NO2 and anthropogenic CO2 emissions in the same month from 2020 to 2022 (b).
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Figure 10. The YRD and BTH study areas and the year-on-year differences in XCO2 (b) and NO2 (c) in the same month from 2020 to 2022 in both areas. The base map in (a) is the four-year mean concentration of NO2 from 2019 to 2022, with black dots indicating the locations of prefecture-level cities in China.
Figure 10. The YRD and BTH study areas and the year-on-year differences in XCO2 (b) and NO2 (c) in the same month from 2020 to 2022 in both areas. The base map in (a) is the four-year mean concentration of NO2 from 2019 to 2022, with black dots indicating the locations of prefecture-level cities in China.
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Figure 11. Spatial distribution of NO2 differences between April and May 2022 and the same period in 2021 for (a) YRD and (b) BTH.
Figure 11. Spatial distribution of NO2 differences between April and May 2022 and the same period in 2021 for (a) YRD and (b) BTH.
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Figure 12. Response of the changes in the mean NO2 concentrations and ODIAC emissions for (a) YRD and (b) BTH between January and February 2020 and the same period in 2019 (c) YRD and (d) BTH, between April and May 2022 and the same period in 2021.
Figure 12. Response of the changes in the mean NO2 concentrations and ODIAC emissions for (a) YRD and (b) BTH between January and February 2020 and the same period in 2019 (c) YRD and (d) BTH, between April and May 2022 and the same period in 2021.
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Figure 13. Correlation of NO2 differences to CO2 differences, which is the values in April–May in the year 2022 minus the values in April–May in the year 2021 for (a) YRD (b) BTH.
Figure 13. Correlation of NO2 differences to CO2 differences, which is the values in April–May in the year 2022 minus the values in April–May in the year 2021 for (a) YRD (b) BTH.
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Figure 14. Response of NO2 to EDGAR emissions in China and the United States (a,b) the spatial distribution of annual mean EDGAR emissions (2019–2022) in China and the United States (c,d) the grid-based response of NO2 to EDGAR in China and the United States (e,f) the cluster-based response of NO2 to EDGAR in China and the United States.
Figure 14. Response of NO2 to EDGAR emissions in China and the United States (a,b) the spatial distribution of annual mean EDGAR emissions (2019–2022) in China and the United States (c,d) the grid-based response of NO2 to EDGAR in China and the United States (e,f) the cluster-based response of NO2 to EDGAR in China and the United States.
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Figure 15. Interannual response of NO2 increments to EDGAR emission increments in high-pollution areas of China and the United States (a) China and (b) the United States.
Figure 15. Interannual response of NO2 increments to EDGAR emission increments in high-pollution areas of China and the United States (a) China and (b) the United States.
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Table 1. Summary table of used data.
Table 1. Summary table of used data.
Parameter NameData SourceTemporal/Spatial ResolutionTime PeriodProduct Release Source
Atmospheric NO2 ConcentrationTROPOMI-S5PMonthly/0.01°2019.1–2022.12Google Earth Engine
XCO2Mapping XCO2 based on the geostatistical method of XCO2 retrievals from multi-source satellitesMonthly/0.5°2019.1–2022.12https://dataverse.harvard.edu/ (accessed on 18 November 2023)
Anthropogenic Emission InventoryODIACMonthly/1km2019.1–2022.12CGER (http://db.cger.nies.go.jp/) (accessed on 5 August 2024)
EDGARYearly/0.1°2019–2022JRC (https://edgar.jrc.ec.europa.eu) (accessed on 21 September 2023)
Power Plant DataThe Global Power Plant Database-/point2021World Resources Institute
https://datasets.wri.org/datasets/global-power-plant-database (accessed on 16 October 2024)
Table 2. Atmospheric NO2 concentration and anthropogenic CO2 emissions response relationship in China and the United States study areas.
Table 2. Atmospheric NO2 concentration and anthropogenic CO2 emissions response relationship in China and the United States study areas.
Response Relationship (R2)ChinaUnited States
ODIACEDGARODIACEDGAR
Grid-based 0.420.460.440.44
Cluster-based0.920.920.820.83
In high-pollution areas0.360.560.530.87
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Guo, K.; Lei, L.; Song, H.; Ji, Z.; Liu, L. Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction. Remote Sens. 2025, 17, 739. https://doi.org/10.3390/rs17050739

AMA Style

Guo K, Lei L, Song H, Ji Z, Liu L. Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction. Remote Sensing. 2025; 17(5):739. https://doi.org/10.3390/rs17050739

Chicago/Turabian Style

Guo, Kaiyuan, Liping Lei, Hao Song, Zhanghui Ji, and Liangyun Liu. 2025. "Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction" Remote Sensing 17, no. 5: 739. https://doi.org/10.3390/rs17050739

APA Style

Guo, K., Lei, L., Song, H., Ji, Z., & Liu, L. (2025). Co-Response of Atmospheric NO2 and CO2 Concentrations from Satellites Observations of Anthropogenic CO2 Emissions for Assessing the Synergistic Effects of Pollution and Carbon Reduction. Remote Sensing, 17(5), 739. https://doi.org/10.3390/rs17050739

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