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Temperature is the principal driver of global atmospheric formaldehyde (HCHO) and its primary oxidation precursor biogenic volatile organic compounds (BVOCs). We revisit such a temperature (T‐) dependency globally, leveraging TROPOMI HCHO column data. We find substantial variations in the T‐dependency of biogenic HCHO across plant functional types (PFTs), with the highest over Broadleaf Evergreen Tropical Trees (doubling every 6.0 K ± 4.1 K) and lowest over Arctic C3 Grass (doubling every 30.8 K ± 9.6 K). The GEOS‐Chem model interprets HCHO columns' T‐dependency at the PFT level (r = 0.87), with a 16% discrepancy on average. The discrepancy can be explained by BVOC emissions T‐dependency for Broadleaf Evergreen Tropical Trees and Warm C4 Grass and can be attributed to the insensitivity of HCHO columns to BVOC emissions for other PFTs. Our findings underscore a potentially magnified variation of BVOC emissions by GEOS‐Chem and MEGAN therein, particularly in regions experiencing greater temperature variations. Plain Language Summary: We use remote sensing data from an up‐to‐date monitor to examine the temperature (T‐) dependency of biogenic formaldehyde (HCHO), a proxy of a series of volatile organic gases released by plants, in a global manner. We find that the effect of temperature on HCHO varies significantly between different types of plants, with tropical evergreen trees showing the most sensitivity to temperature and Arctic grasses showing the least. The GEOS‐Chem, a state‐of‐the‐art chemical transport model, interprets such temperature sensitivity among plants with nonnegligible discrepancies. The sensitivity of volatile organic gases released by plants to temperature explains the sensitivity of HCHO to temperature for some plants, such as tropical evergreen trees and warm‐season grasses. Key Points: Temperature (T‐) dependency of biogenic HCHO columns varies substantially across plant functional types (PFTs)The GEOS‐Chem model with the MEGAN module implemented primarily interprets the T‐dependency of HCHO columns at the PFT levelThe T‐dependency of biogenic volatile organic compound (BVOC) emissions mainly accounts for that of HCHO columns in Broadleaf Evergreen Tropical Trees and Warm C4 Grass regions [ABSTRACT FROM AUTHOR]

This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH4, which highlight the impact of coal mining emissions as a major CH4 source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO2 and XCH4 data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO2 and 15.40 ± 31.60 ppb for XCH4. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources. [ABSTRACT FROM AUTHOR]

Nitrogen oxides, particularly NO2, are emitted through a variety of industrial and transport processes globally. The world's continuous economic development, including in developing countries, results in an increasing concentration of those gases in the atmosphere. Yet, there is scant information on the current state and recent evolution of these atmospheric pollutants over a range of spatial and temporal scales, especially in Africa. This, in turn, hinders the assessment of the emissions and the evaluation of potential risks or impacts on societies and their economies, as well as on the environment. This study attempts to fill the gap by leveraging data from a Pandora-2S ground-based, column-integrating instrument located in Wakkerstroom in the Mpumalanga Province of South Africa and space-based remote sensing data obtained from the TROPOMI instrument onboard the ESA Sentinel-5P satellite. We compare these two spatially (horizontal) representative data sets using statistical tools to investigate the concentrations of emitted and transported NO2 at this particular location, expecting that a significant positive correlation between the NO2 tropospheric vertical column (TVC) data might justify using the TROPOMI data, available globally, as a proxy for tropospheric and boundary layer NO2 concentrations over the Highveld of South Africa more generally. The data from the two instruments showed no significant difference between the interannual mean TVC-NO2 in 2020 and 2021. The seasonal patterns for both instruments were different in 2020, but in 2021, both measured peak TVC-NO2 concentrations in late winter (week 34). The instruments both detected higher TVC-NO2 concentrations during transitions between seasons, particularly from winter to spring. The TVC-NO2 concentrations measured in Wakkerstroom Mpumalanga are mostly contributed to by the emission sources in the low troposphere, such as biomass burning and emissions from local power stations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly influenced various aspects of society, including environmental factors such as methane emissions. This study investigates the changes in methane concentrations in Seoul, South Korea, from 2019 to 2023, using TROPOMI satellite data and rigorous statistical analyses. The normality of the sample data is first assessed using the Shapiro-Wilk (S-W) and Kolmogorov-Smirnov (K-S) tests, indicating that the data can be considered to come from a normal distribution. The S-W test demonstrated superior discriminative power (highest statistical power: 0.8668) compared to the K-S test (highest statistical power: 0.4002), confirming the validity of parametric tests for our data. The S-W test shows better discriminative power than the K-S test in terms of sensitivity to departures from normality, particularly for small sample sizes. Based on this confirmation, parametric tests such as analysis of variance (ANOVA) and post-hoc tests (Bonferroni correction, Tukey's HSD, Scheffe's method) are employed to identify significant differences in methane levels across different years. The ANOVA results show a statistically significant difference in methane concentrations across years (p-value: 2.02 × 10 - 13 , F-value: 26.572). Post-hoc analyses reveal no significant difference in methane concentrations between 2019 and 2020 (p-values: Bonferroni - 0.1045, Tukey's HSD - 0.397, Scheffe's - 0.1045), and no significant difference between 2020 and 2021 (p-values: Bonferroni - 0.917, Tukey's HSD - 0.840, Scheffe's - 0.917). However, a significant increase in methane levels is observed from 2022 to 2023 (p-values: Bonferroni - 0.0001, Tukey's HSD - 0.0002, Scheffe's - 0.0001), correlating with the "new normal" policy implemented in South Korea starting in November 2021 and effectively from the beginning of 2022. This suggests that changes in industrial activities and transportation patterns due to the "new normal" have contributed to higher methane emissions. Student's t-test and Welch's t-test were used to validate the ANOVA results. Permutation tests showed no significant difference between 2019 and 2020 (test statistic: -0.0096, p-values: 0.1191 for Student's and 0.1156 for Welch's). However, a significant difference was found between 2022 and 2023 (test statistic: -0.0172, p-value: 0.0001), confirming ANOVA results that indicated increased methane levels post-pandemic. This study provides a robust quantitative assessment of the pandemic's impact on methane levels and sets a methodological statistical approach for future research in the environmental research community. [ABSTRACT FROM AUTHOR]

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a potent greenhouse gas and the second highest anthropogenic emissions are recorded from CH4 on Earth. Considering its high global warming potential, the monitoring of source locations is inadvertent. The paper presented here is the first attempt (to the best of our knowledge) to comprehensively analyse the methane emissions over multiple Indian locations using satellite data. It outlays a brief background of methane emission sensors and studies carried out worldwide for estimation of the GHG. It further enumerates the potential of Earth Surface Mineral Dust Source Investigation (EMIT) and TROPOspheric Monitoring Instrument (TROPOMI) in highlighting the potential point sources of methane emissions and its concentration/emission flux in India. 17 unique plumes were identified using EMIT in the states of Maharashtra (06), Rajasthan (04), Punjab (02), Gujarat (03) and Assam (02). Gujarat, Surat, Assam Uttar Pradesh and Haryana using TROPOMI were also studied. The hotspots showcase emission sources from solid waste landfill sites, sewage treatment plants, wetlands/marshy agriculture, city sewage outlets, oil and gas fields, oil refinery and textile industry. It was observed that EMIT can effectively be used for point source identification, monitoring and enhancement while TROPOMI is best suited for regional level methane monitoring. A sewage outlet plume in Maharashtra produced the maximum emission of 6202.9 ± 691.94 kg/hr followed by solid waste (SW) sites located in Pirana Landfill, Ahmedabad and Khajod Landfill, Surat in Gujarat. Methane monitoring is an important step towards mitigating enormous methane emissions and anomalous methane sources. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Abstract The COVID-19 pandemic has significantly influenced various aspects of society, including environmental factors such as methane emissions. This study investigates the changes in methane concentrations in Seoul, South Korea, from 2019 to 2023, using TROPOMI satellite data and rigorous statistical analyses. The normality of the sample data is first assessed using the Shapiro-Wilk (S-W) and Kolmogorov-Smirnov (K-S) tests, indicating that the data can be considered to come from a normal distribution. The S-W test demonstrated superior discriminative power (highest statistical power: 0.8668) compared to the K-S test (highest statistical power: 0.4002), confirming the validity of parametric tests for our data. The S-W test shows better discriminative power than the K-S test in terms of sensitivity to departures from normality, particularly for small sample sizes. Based on this confirmation, parametric tests such as analysis of variance (ANOVA) and post-hoc tests (Bonferroni correction, Tukey’s HSD, Scheffe’s method) are employed to identify significant differences in methane levels across different years. The ANOVA results show a statistically significant difference in methane concentrations across years (p-value: $$2.02\times 10^{-13}$$ 2.02 × 10 - 13 , F-value: 26.572). Post-hoc analyses reveal no significant difference in methane concentrations between 2019 and 2020 (p-values: Bonferroni - 0.1045, Tukey’s HSD - 0.397, Scheffe’s - 0.1045), and no significant difference between 2020 and 2021 (p-values: Bonferroni - 0.917, Tukey’s HSD - 0.840, Scheffe’s - 0.917). However, a significant increase in methane levels is observed from 2022 to 2023 (p-values: Bonferroni - 0.0001, Tukey’s HSD - 0.0002, Scheffe’s - 0.0001), correlating with the “new normal” policy implemented in South Korea starting in November 2021 and effectively from the beginning of 2022. This suggests that changes in industrial activities and transportation patterns due to the “new normal” have contributed to higher methane emissions. Student’s t-test and Welch’s t-test were used to validate the ANOVA results. Permutation tests showed no significant difference between 2019 and 2020 (test statistic: -0.0096, p-values: 0.1191 for Student’s and 0.1156 for Welch’s). However, a significant difference was found between 2022 and 2023 (test statistic: -0.0172, p-value: 0.0001), confirming ANOVA results that indicated increased methane levels post-pandemic. This study provides a robust quantitative assessment of the pandemic’s impact on methane levels and sets a methodological statistical approach for future research in the environmental research community.

Abstract A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.

Nitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite‐based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best‐performing regression models to estimate surface NO2 at 0.01° by 0.01° resolution, and we term this estimate as quasi‐NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross‐validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non‐Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications. Plain Language Summary: Nitrogen dioxide (NO2) is an air pollutant which causes cardiovascular and respiratory illnesses and reacts in the atmosphere to form other harmful pollutants. This necessitates accurate and reliable quantification of NO2 concentrations in the air. Ground monitors directly observe NO2 concentrations near the Earth's surface. However, monitors do not have sufficient spatial coverage to quantify NO2 at large scales. Satellite‐based instruments capture NO2 amounts across the Earth at increasingly high spatial resolution. However, satellite instruments cannot directly observe surface NO2 concentrations. In this study, we compare regression methods for estimating surface NO2 over the continental U.S. using satellite data and auxiliary land‐use variables. We find that NO2 estimated using multivariate regression models with transforms applied to inputs result in the highest agreement with surface NO2 among the regression methods we investigated. We then use the regression models to quantify surface NO2 concentration across the U.S. at 0.01° by 0.01° spatial resolution. Our work leverages the precision of ground observations and the high resolution of satellite data to accurately quantify surface NO2. The interpretable, generalizable, and easily applicable methods used in our study will facilitate the use of satellite data for air quality and human health assessments. Key Points: We compare regression methods to estimate surface nitrogen dioxide concentrations at 0.01° resolution using satellite and land use dataMultivariate linear regression with Anscombe‐transformed inputs has strongest agreement with surface nitrogen dioxide measurementsRegression methods provide accurate, low‐bias concentration estimates with minimal computational and data requirements [ABSTRACT FROM AUTHOR]

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH4) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH4 than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH4 difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH4 difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH4 (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH4 differences comparable to or smaller than those from ground-based remote sensing. [ABSTRACT FROM AUTHOR]

Gas flaring (GF) is a long-term issue in the oil/gas industries and has a critical effect on the environment. In the last decade, remote sensing technology has shown resounding capabilities to detect and characterize GF. Iran has many natural oil/gas processing plants and petrochemical companies that are located in the southern regions. The main goal of this research is estimation of the volume of GF for two years (2018–2019) by day/nighttime radiation and air pollutant data. We used Decision Tree modeling/Exhaustive CHAID (Chi-squared Automatic Interaction Detector) based on remote sensing data such as shortwave infrared (SWIR) and thermal infrared (TIR) of Landsat 8/ M10 of VIIRS (Visible Infrared Imaging Radiometer Suite) / air pollutants of TROPOMI (Tropospheric Monitoring Instrument) in three types of models. Results showed that R2 values for model 1 (based on all variables/SWIR, TIR, Pollution products), model 2 (based on SWIR bands and pollution data), and model 3 (based on SWIR and TIR bands) is 0.52, 0.50, and 0.51, respectively. The results of sensitivity analysis showed that the shortwave infrared band for two sensors OLI (Operational Land Imager) /VIIRS (Visible Infrared Imaging Radiometer Suite) had the most important role in the estimation of gas flaring volume. The valuable findings of this research represent the important effect of the shortwave infrared bands of the sensors in estimating the GF volume at the local/global scale by hierarchical decision scheme modeling. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration. [ABSTRACT FROM AUTHOR]

A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach. [ABSTRACT FROM AUTHOR]

Gas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2 values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model. Plain Language Summary: Gas flaring (GF) is an essential process that used to dispose the unwanted gases in oil/gas processing plants and petrochemical companies. Obviously, burning gases has a huge and negative impact on the environment, climate, and human health. So, the best way to manage these effects are monitoring, detecting, characterizing, and estimating the volume of gas flaring. Remote sensing has enough potential to provide the useful data in all parts of the research on GF in industrial areas. In this study, we estimated the volume of gas flaring in one of the gas industries of Iran, the Pars Special Economic Energy Zone (PSEEZ) by using remotely sensed data (B6, B7, B10, B11/landsat8‐ M10/VIIRS‐ NO2, CO, O3, SO2/Sentinel‐5P) and ground data based on two kinds of models, artificial neural network (ANN), and multivariate regression. Since there was a non‐linear relationship between the variables and the volume of gas flaring, the ANN model showed the most acceptable results. Among all the variables, bands 10 and 11 of Landsat 8 played a very important role in estimating the gas flaring volume. Key Points: Air pollutants and remote sensing radiation used for estimation of gas flaring volumeThermal infrared bands of Landsat 8 showed the most important role in GF volume estimationNO2 showed more significant role in GF volume estimation compared to others pollutants [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is strongly correlated with gross primary productivity (GPP). Satellite-observed canopy SIF (SIFobs) captures only a part of the total leaf-emitted SIF (SIFtotal); therefore, SIFobs may hinder the interpretation of the physiological mechanism for GPP estimation. Furthermore, there are still significant discrepancies in the estimated SIFobs escape ratio (fesc) from the canopy to the leaf level with current methods. Here, we selected several vegetation canopy variables and downscaled SIFobs based on the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model from the canopy to the leaf level using machine learning (ML) algorithms and then applied our method to the TROPOspheric Monitoring Instrument (TROPOMI) near-infrared (NIR) SIFobs. The results showed that simulating the fesc with SIFobs, TROPOMI NIR reflectance, and the fraction of photosynthetically active radiation (FPAR) avoided the effects of different sun-sensor geometry conditions introduced by different sensors and was more suitable for satellite-observed SIFobs downscaling. Our downscaled SIFtotal also correlated well with the flux site GPP in areas with sparse vegetation types. SIFtotal better reflected the photosynthetic differences among vegetation types and showed an enhanced relationship with absorbed photosynthetically active radiation (APAR) compared with SIFobs. We provide an efficient canopy-to-leaf SIFobs downscaling method improved SIFtotal and GPP estimation, and our results also demonstrated the potential for using SIFobs as vegetation information in sparse coverage areas.

Abstract Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.

Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts. [ABSTRACT FROM AUTHOR]

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas. [ABSTRACT FROM AUTHOR]

Methane is the second largest anthropogenic greenhouse gas, and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks. Therefore, the monitoring of CH 4 concentration changes and the assessment of underlying driving factors can provide scientific basis for the government's policy making and evaluation. China is the world's largest emitter of anthropogenic methane. However, due to the lack of ground-based observation sites, little work has been done on the spatial-temporal variations for the past decades and influencing factors in China, especially for areas with high anthropogenic emissions as Central and Eastern China. Here to quantify atmospheric CH 4 enhancements trends and its driving factors in Central and Eastern China, we combined the most up-to-date TROPOMI satellite-based column CH 4 (xCH 4) concentration from 2018 to 2022, anthropogenic and natural emissions, and a random forest-based machine learning approach, to simulate atmospheric xCH 4 enhancements from 2001 to 2018. The results showed that (1) the random forest model was able to accurately establish the relationship between emission sources and xCH 4 enhancement with a correlation coefficient (R²) of 0.89 and a root mean-square error (RMSE) of 11.98 ppb; (2)The xCH 4 enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018, with a relative change of 3.27%±0.13%; (3) The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH 4 enhancement, contributing 68.00% and 31.21%, respectively, and the decrease of animal ruminants contributed -6.70% of its enhancement trend. [Display omitted] [ABSTRACT FROM AUTHOR]

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask. [ABSTRACT FROM AUTHOR]

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the emission rate to contextualize mitigation actions and track progress. Herein, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emission rates in a case study of the City of Calgary, Alberta, Canada. Due to low and variable annual observational coverage, we integrated valid TROPOMI observations over three years (2020–2022) and used mass balance modeling to derive a long-term mean estimate of the emission rate. The resulting column-mean dry-air mole fraction (XCH4) enhancement over Calgary was small (4.7 ppb), but within the city boundaries, we identified local hot spots in the vicinity of known emission sources (wastewater treatment facilities and landfills). The city-level emission estimate from mass balance was 215.4 ± 132.8 t CH4/d. This estimate is approximately four times larger than estimates from Canada's gridded National Inventory Report of anthropogenic CH4 emissions and six times larger than the Emissions Database for Global Atmospheric Research (EDGAR v8.0). We note that valid TROPOMI observations are more common in warmer months and occur during a narrow daily overpass time slot over Calgary. The limited valid observations in combination with the constrained temporal observational coverage may bias the emission estimate. Overall, the findings from this case study highlight an approach to derive a screening-level estimate of city-level methane emission rates using TROPOMI data in settings with low observational coverage. [ABSTRACT FROM AUTHOR]

Atmospheric methane is one of the major greenhouse gases and has a great impact on climate change. To obtain the polluted levels of atmospheric methane in the ground-level range, this study used satellite observations and vertical profile features derived by atmospheric chemistry model to estimate the ground methane concentrations in first. Then, the improved daily ground-level atmospheric methane concentration dataset with full spatial coverage (100%) and 5-km resolution in mainland China from 2019 to 2021 were retrieved by station-based observations and gaussian regression model. The overall estimated deviation between the estimated ground methane concentrations and the WDCGG station-based measurements is less than 10 ppbv. The R by tenfold cross-validation is 0.93, and the R2 is 0.87. The distribution of the ground-level methane concentrations in the Chinese region is characterized by high in the east and south, and low in the west and north. On the time scale, ground-level methane concentration in the Chinese region is higher in winter and lower in summer. Meanwhile, the spatial and temporal distribution and changes of ground-level methane in local areas have been analyzed using Shandong Province as an example. The results have a potential to detect changes in the distribution of methane concentration. [ABSTRACT FROM AUTHOR]

The Outer Continental Shelf of the Gulf of Mexico (GOM) is populated with numerous oil and natural gas (ONG) platforms which produce NOx (NOx = NO + NO2), a major component of air pollution. The Bureau of Ocean Energy Management (BOEM) is mandated to ensure that the air quality of coastal states is not degraded by these emissions. As part of a NASA‐BOEM collaboration, we conducted a satellite data‐based analysis of nitrogen dioxide (NO2) patterns and trends in the GOM. Data from the OMI and TROPOMI sensors were used to obtain 18+ year records of tropospheric column (TrC) NO2 in three GOM regions: (a) Houston urban area, (b) near shore area off the Louisiana coast, and a (c) deepwater area off the Louisiana coast. The 2004–2022 time series show a decreasing trend for the urban (−0.027 DU/decade) and near shore (−0.0022 DU/decade) areas, and an increasing trend (0.0019 DU/decade) for the deepwater area. MERRA‐2 wind and TROPOMI NO2 data were used to reveal several NO2 hotspots (up to 25% above background values) under calm wind conditions near individual platforms. The NO2 signals from these deepwater platforms and the high density of shallow water platforms closer to shore were confirmed by TrC NO2 anomalies of up to 10%, taking into account the monthly TrC NO2 climatology over the GOM. The results presented in this study establish a baseline for future estimates of emissions from the ONG hotspots and provide a methodology for analyzing NO2 measurements from the new geostationary TEMPO instrument. Plain Language Summary: Oil and natural gas operations emit nitrogen oxides (NOx), which are major air pollutants and precursors to ground‐level ozone. The Bureau of Ocean Energy Management (BOEM) agency is responsible for managing planned oil and natural gas (ONG) activity on the outer continental shelf, and is mandated to ensure related emissions do not degrade air quality of coastal states. In collaboration with BOEM, we used satellite data from the OMI and TROPOMI sensors to construct an 18+ year record of tropospheric nitrogen dioxide (NO2), a proxy for NOx, in the Gulf Coast region. These time series focused on three areas: (a) Houston urban, (b) off the Louisiana coast, and (c) deepwater Gulf off Louisiana. These regions experienced changes in tropospheric column NO2 of −13.7%, −5.8%, and +5.4% per decade, respectively. We also identified NO2 hotspots from ONG platforms using TROPOMI NO2 averages under calm wind conditions. The ONG deepwater platforms enhance NO2 background amounts by 7%–13% on average, and up to 25% for the Mars and Olympus platforms combined. The results presented here will facilitate our work on emissions estimates from these sources and on applications to the recently launched TEMPO instrument. Key Points: Satellite NO2 records and trends of urban, coastal and deep water areas from 2005 to 2022, are presentedClassifying NO2 over the Gulf of Mexico (GOM) under various wind conditions highlights typical patterns in average NO2 valuesGOM NO2 hotspots from deepwater platforms were identified by TROPOMI under calm wind conditions, the largest of which is over Mars/Olympus [ABSTRACT FROM AUTHOR]

This study conducts a spatio-temporal analysis of tropospheric nitrogen dioxide (NO2) and total carbon monoxide (CO) concentrations in the Punjab and Haryana regions of India and Pakistan, using datasets from the Sentinel 5-Precursor (S5P) satellite. These regions, marked by diverse economic growth factors including population expansion, power generation, transportation, and agricultural practices, face similar challenges in atmospheric pollution, particularly evident in major urban centers like Delhi and Lahore, identified as pollution hotspots. The study also spotlights pollution associated with power plants. In urban areas, tropospheric NO2 levels are predominantly elevated due to vehicular emissions, whereas residential activities mainly contribute to CO pollution. However, precisely attributing urban CO sources is complex due to its longer atmospheric residence time and intricate circulation patterns. Notably, the burning of rice crop residue in November significantly exacerbates winter pollution episodes and smog, showing a more pronounced correlation with total CO than with tropospheric NO2 levels. The temporal analysis indicates that the months from October to December witness peak pollution, contrasted with the relatively cleaner period during the monsoon months of July to September. The severe pollution in the OND quarter is attributed to factors such as variations in boundary layer height and depletion of OH radicals. Furthermore, the study highlights the positive impact of the COVID-19 lockdown on air quality, with a significant decrease in NO2 concentrations during April, 2020 (Delhi: 59%, Lahore: 58%). However, the reduction in total CO columns was less significant. The study also correlates lockdown stringency with tropospheric NO2 columns (R2: 0.37 for Delhi, 0.25 for Lahore, 0.22 for Rawalpindi/Islamabad), acknowledging the influence of various meteorological and atmospheric variables. The research highlights the significant impact of crop residue burning on winter pollution levels, particularly on total CO concentrations. The study also shows the notable effect of the COVID-19 lockdown on air quality, significantly reducing NO2 levels. Additionally, it explores the correlation between lockdown stringency and tropospheric NO2 columns, considering various meteorological factors. [ABSTRACT FROM AUTHOR]

Methane is a significant greenhouse gas (GHG), and it is imperative to understand its spatiotemporal distribution and primary sources in areas with higher methane concentrations, as such insights are essential for informing effective mitigation policies. In this study, we employed TROPOMI satellite retrievals to analyze the spatiotemporal patterns of methane distributions and identify major emission sources in South Korea over the period from August 2018 to July 2019. Additionally, we examined the spatial correlations between satellite methane retrievals and emission sources to characterize regions with higher methane levels on an annual basis. Concerning spatial distributions, concentrations exceeding 1870 ppb were predominantly observed in western non-mountainous regions, particularly in rice paddy areas. Moreover, sporadic concentrations exceeding 1880 ppb were detected in large ports and industrial zones, primarily located in coastal regions of South Korea. Our spatial correlation analysis, conducted using the SDMSelect method, identified specific emissions contributing to regions with higher methane concentrations. There were some areas with relatively strong correlations between high XCH4 and emissions from the domestic livestock industry, fossil fuel utilization (specifically, the oil and gas sector), landfills, and rice paddies. This analysis, incorporating domestic emission inventories and satellite data, provides valuable insights into the characteristics of regional methane concentrations. In addition, this analysis can assess national methane emissions inventories, where there is limited information on the spatial distributions, offering critical information for the prioritization of domestic regional policies aimed at reducing greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021–2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI’s superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO2), methane (XCH4) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH4, which highlight the impact of coal mining emissions as a major CH4 source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO2 and XCH4 data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO2 and 15.40 ± 31.60 ppb for XCH4. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources.

Nitrogen oxides, particularly NO2, are emitted through a variety of industrial and transport processes globally. The world’s continuous economic development, including in developing countries, results in an increasing concentration of those gases in the atmosphere. Yet, there is scant information on the current state and recent evolution of these atmospheric pollutants over a range of spatial and temporal scales, especially in Africa. This, in turn, hinders the assessment of the emissions and the evaluation of potential risks or impacts on societies and their economies, as well as on the environment. This study attempts to fill the gap by leveraging data from a Pandora-2S ground-based, column-integrating instrument located in Wakkerstroom in the Mpumalanga Province of South Africa and space-based remote sensing data obtained from the TROPOMI instrument onboard the ESA Sentinel-5P satellite. We compare these two spatially (horizontal) representative data sets using statistical tools to investigate the concentrations of emitted and transported NO2 at this particular location, expecting that a significant positive correlation between the NO2 tropospheric vertical column (TVC) data might justify using the TROPOMI data, available globally, as a proxy for tropospheric and boundary layer NO2 concentrations over the Highveld of South Africa more generally. The data from the two instruments showed no significant difference between the interannual mean TVC-NO2 in 2020 and 2021. The seasonal patterns for both instruments were different in 2020, but in 2021, both measured peak TVC-NO2 concentrations in late winter (week 34). The instruments both detected higher TVC-NO2 concentrations during transitions between seasons, particularly from winter to spring. The TVC-NO2 concentrations measured in Wakkerstroom Mpumalanga are mostly contributed to by the emission sources in the low troposphere, such as biomass burning and emissions from local power stations.

The urban environment is at substantial risk due to the pace of urbanization. There are two major components of the urban environment viz. urban heating and urban pollution. The outbreak of the COVID-19 pandemic and the associated lockdown provided a stability to the deteriorating environment and all associated parameters. The study focuses on understanding the changes in the surface urban heat island (SUHI) and atmospheric/near-surface urban pollution island phenomenon (AUPI/NSUPI) as a result of lockdown in 2020 and 2021 w.r.t. averaged values of 2017–19, through parameters like LST, AOD, NO2 and PM2.5 for the Delhi region (Delhi urban area or DUA and non-urban area surrounding DUA) using in situ and remote sensing satellite measurements. A remarkable reduction in mean daytime/nighttime LST (1.99 °C/1.80 °C) using MODIS datasets and 5.46 °C using Landsat datasets was observed in LD-1 of 2020. SUHII values were 0.35 °C higher in 2020 as compared to 2017–19 due to thriving vegetation. Similarly, annual NO2/PM2.5 reduced by 61.39%/11.78% and satellite-based NO2/AOD reduced by 28.87%/9% in 2020 compared to 2017–19. A yearly reduction of ~ 41%, ~ 6%, ~ 10% and ~ 39% was observed in NSUPII (NO2), NSUPII (PM2.5), AUPII (AOD) and AUPII (NO2), respectively. The environmental regime of the urban purlieu was revitalized, and the ecological sensitiveness was restored by vegetation growth and reduced anthropogenic activities; however, the scenario was quickly restored in 2021 showing increased pollution levels. [ABSTRACT FROM AUTHOR]

Air quality has been one of the main concerns among Tehran residents for at least two decades. In this study, we investigated air quality in the metropolitan area of Tehran utilizing TROPOMI and OMI data based on the Google Earth Engine platform. Long-term analysis indicated slight negative trends in NO2, SO2, and HCHO over the last two decades, which are due to the development of urban transportation systems. Air quality parameters were classified based on spatiotemporal similarities. Urban pollutants include CO1, NO2, and HCHO, which were concentrated over the eastern part of Tehran and decreased radially towards the city border. Among the pollutants, CO1 showed a dependency with altitude. SO2 was controlled by both urban vehicles and nonurban industrial activities. SO2 was thus classified as an urban-industrial pollutant. The exterior parameters almost entirely controlled by external factors include O3, aerosols, and clouds. The spatial variations of the pollutants highly differed from a fraction to several times. All pollutants exhibited seasonality associated with fuel consumption and air conditions. However, the seasonality in the exterior parameters was associated with regional air masses. The Iranian New Year holiday significantly impacts air pollution. NO2, CO1, and SO2 experienced their annual minimum levels during this holiday period. COVID-19–related closures also led to negative trends in NO2, CO1, and SO2 after March 2020. However, the exterior parameters were not affected by these events. The results agreed with in situ measurements. As the final objective, we aimed to support urban management to reduce atmospheric pollution in Tehran. [ABSTRACT FROM AUTHOR]

Non‐methane volatile organic compounds (NMVOCs) have a significant impact on air quality in urban areas. Detecting NMVOCs emission with its proxy HCHO on urban scales from space, however, has been limited by the lack of discernible enhancement. Here we show clear urban HCHO plumes from 16 cities over the globe by rotating TROPOspheric Monitoring Instrument HCHO pixels according to wind directions. We fit the downwind structure of the plumes with the exponentially modified Gaussian approach to quantify urban HCHO effective production rates between 7.0 and 88.5 mol s−1. Our results are in line with total NMVOC emissions from the EDGAR inventory (r = 0.76). Our work offers a new measure of total NMVOC emissions from urban areas and highlights the potential of satellite HCHO data to provide new information for monitoring urban air quality. Plain Language Summary: Non‐methane volatile organic compounds (NMVOCs) play an important role in urban air quality. Formaldehyde (HCHO) satellite observations have been shown to be able to reliably track and quantify NMVOC emissions at global and regional scales. Here, we use state‐of‐the‐art satellite sensors to quantify effective HCHO production rates in 16 global cities and further constrain total NMVOC emissions. Our results are broadly consistent with current emissions inventories, implying that satellites may be able to provide new information for urban air studies. Key Points: We show clear urban HCHO plumes from 16 cities over the globe by relating satellite pixels with wind fieldsWe obtain urban effective HCHO production rates by fitting the downwind structure of HCHO plumesSatellite‐based effective HCHO production rates provide potential measures of total non‐methane volatile organic compound emissions [ABSTRACT FROM AUTHOR]

Lightning is one of the main sources of NOx in the Earth's atmosphere. However, there is a large variability in NOx production during the lifetime of thunderstorms. In this study, we used the TROPOspheric Monitoring Instrument (TROPOMI) cloud and NO2 research products along with Lightning Mapping Array (LMA) measurements to investigate the possible relation between the amount of NOx produced per lightning flash and flash channel length in the Ebro Valley. We found that there is a positive relationship between both variables. In turn, the vertical structure of the analyzed lightning flashes indicates that longer flashes could release more LNOx at lower altitudes than shorter flashes, while higher flash rates produce less LNOx per flash. Plain Language Summary: Lightning produces significant amounts of NOx in the Earth's atmosphere. However, the quantity of NOx generated during thunderstorms exhibits significant variation. In this study, we used a space‐based instrument called TROPOMI to look at clouds and measure NO2, and we also used a network of antennas called Lightning Mapping Array to map the spatial structure of lightning strikes. Our main goal was investigating if there is a connection between the amount of NOx produced by lightning and how long the lightning flashes were in the Ebro Valley. We found that there is a positive relationship between the two variables. We also looked at the structure of the lightning flashes and found that longer flashes release more NOx at lower altitudes compared to shorter flashes. Additionally, when there are more frequent lightning flashes, each flash produces less NOx. Key Points: Lightning Mapping Array data reveals a positive correlation between lightning NOx production efficiency and the lightning flash lengthsThe investigation of space‐based data demonstrates a negative correlation between lightning NOx production efficiency and flash frequencyMean NOx per flash length obtained in this work vary between 1.9 × 1021 and 3.8 × 1021 molec NOx/m [ABSTRACT FROM AUTHOR]

Bromine monoxide (BrO) plays an important role in tropospheric chemistry. The state‐of‐the‐science TROPOspheric Monitoring Instrument (TROPOMI) offers the potential to monitor atmospheric composition with a fine spatial resolution of up to 5.5 × 3.5 km2. We present here the retrieval of tropospheric BrO columns from TROPOMI. We implement a stratospheric correction scheme using a climatological approach based on the latest GEOS‐Chem High Performance chemical transport model, and improve the tropospheric air mass factor calculation with TROPOMI surface albedo data accounting for the geometrical dependency. Our product presents a good level of consistency in comparison with measurements from ground‐based zenith‐sky differential optical absorption spectroscopy (r = 0.67), aircrafts (r = 0.46), and satellites (similar spatial distributions of BrO columns). Furthermore, our retrieval captures BrO enhancements in the polar springtime with values up to 7.8 × 1013 molecules cm−2 and identifies small‐scale emission sources such as volcanoes and salt marshes. Based on TROPOMI data, we probe a blowing snow aerosol bromine mechanism in which the snow salinity is reduced to better match simulation and observation. Our TROPOMI tropospheric BrO product contributes high‐resolution global information to studies investigating atmospheric bromine chemistry. Plain Language Summary: Bromine monoxide (BrO) is an important species that affects the global chemistry of the troposphere. However, global observations of tropospheric BrO remain challenging and limited due to the short lifetime and low abundance. In this study, we present a global high‐spatial‐resolution tropospheric BrO column product from the TROPOspheric Monitoring Instrument. We describe the retrieval algorithm and present a comprehensive verification and evaluation. In addition, we use the data set to investigate sources and sinks on a daily scale for measurement scenarios of BrO enhancements, such as polar sea ice, volcanic plumes, and salt marshes. We additionally optimize salinity, the key parameter in modeling blow snow aerosol bromine emissions, by comparing simulation and observation. Our work provides unique information to studies exploring atmospheric bromine chemistry. Key Points: We present the retrieval and evaluation of tropospheric BrO columns from TROPOspheric Monitoring Instrument (TROPOMI)Our high‐resolution BrO product identifies small‐scale emission sources on a daily scaleA blowing snow aerosol bromine scheme with reduced snow salinity improves agreement between the model and TROPOMI [ABSTRACT FROM AUTHOR]

The paper analyzes the trends in the average monthly concentrations of nitrogen dioxide in the period from 2005 to 2021 in 18 large cities in different geographical areas: St. Petersburg, Moscow, Kyiv, Donetsk, Helsinki, Warsaw, Istanbul, Athens, Rome, Milan, Barcelona, Madrid, Paris, London, and Amsterdam. It was found that, in all cities, the concentration of this gas decreases, but at different rates. The highest rate of change was recorded in cities with the highest gas content at the time of the initial stage—2005. A linear dependence of the average nitrogen dioxide concentration on the population and anthropogenic emissions was revealed according to HTAPv3 data. Approximately every 1 million people of the population of large cities is responsible for the formation of a gas concentration of ∼200 × 1015 molecules/cm2, regardless of the geographical location of the city. Every ∼10 000 t/month of nitrogen dioxide emissions based on HTAPv3 generate gas concentrations of ∼500 × 1015 molecules/cm2. Using these dependences, it is possible to estimate anthropogenic emissions of NO2 sources throughout the country, based on remote sensing data. The results of the study also indicate the possibility of verifying and correcting inventory databases of gas emissions based on satellite observations of the nitrogen dioxide content in the atmosphere. [ABSTRACT FROM AUTHOR]

Abstract The Outer Continental Shelf of the Gulf of Mexico (GOM) is populated with numerous oil and natural gas (ONG) platforms which produce NOx (NOx = NO + NO2), a major component of air pollution. The Bureau of Ocean Energy Management (BOEM) is mandated to ensure that the air quality of coastal states is not degraded by these emissions. As part of a NASA‐BOEM collaboration, we conducted a satellite data‐based analysis of nitrogen dioxide (NO2) patterns and trends in the GOM. Data from the OMI and TROPOMI sensors were used to obtain 18+ year records of tropospheric column (TrC) NO2 in three GOM regions: (a) Houston urban area, (b) near shore area off the Louisiana coast, and a (c) deepwater area off the Louisiana coast. The 2004–2022 time series show a decreasing trend for the urban (−0.027 DU/decade) and near shore (−0.0022 DU/decade) areas, and an increasing trend (0.0019 DU/decade) for the deepwater area. MERRA‐2 wind and TROPOMI NO2 data were used to reveal several NO2 hotspots (up to 25% above background values) under calm wind conditions near individual platforms. The NO2 signals from these deepwater platforms and the high density of shallow water platforms closer to shore were confirmed by TrC NO2 anomalies of up to 10%, taking into account the monthly TrC NO2 climatology over the GOM. The results presented in this study establish a baseline for future estimates of emissions from the ONG hotspots and provide a methodology for analyzing NO2 measurements from the new geostationary TEMPO instrument.

Atmospheric methane is one of the major greenhouse gases and has a great impact on climate change. To obtain the polluted levels of atmospheric methane in the ground-level range, this study used satellite observations and vertical profile features derived by atmospheric chemistry model to estimate the ground methane concentrations in first. Then, the improved daily ground-level atmospheric methane concentration dataset with full spatial coverage (100%) and 5-km resolution in mainland China from 2019 to 2021 were retrieved by station-based observations and gaussian regression model. The overall estimated deviation between the estimated ground methane concentrations and the WDCGG station-based measurements is less than 10 ppbv. The R by ten-fold cross-validation is 0.93, and the R2 is 0.87. The distribution of the ground-level methane concentrations in the Chinese region is characterized by high in the east and south, and low in the west and north. On the time scale, ground-level methane concentration in the Chinese region is higher in winter and lower in summer. Meanwhile, the spatial and temporal distribution and changes of ground-level methane in local areas have been analyzed using Shandong Province as an example. The results have a potential to detect changes in the distribution of methane concentration.

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH4) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH4 than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH4 difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH4 difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH4 (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH4 differences comparable to or smaller than those from ground-based remote sensing.

Atmospheric methane is one of the major greenhouse gases with a drastic impact on climate change. This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.

In this study, the changes in nitrogen dioxide (NO2), carbon monoxide (CO) and sulfur dioxide (SO2) pollutant gases were examined between June 2019 and June 2021 during the COVID-19 pandemic period. For this purpose, monthly and annual averages of Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) values were calculated on the Google Earth Engine (GEE) platform. According to the results obtained using the GEE platform, the average column density values of NO2, CO, and SO2 in the Marmara Region between the selected dates were calculated as 8.40E-05 mol/m2, 3.23E-02 mol/m2, and 3.75E-04 mol/m2, respectively. During the lockdown, these values decreased to 7.84E-05 mol/m2, 3.05E-02 mol/m2 and 2.75E-04 mol/m2 respectively. According to TROPOMI data, these three gas column density values showed a decreasing trend during the COVID-19 pandemic lockdown period. However, in a 25-month examination in general, these three gas values showed an increasing trend due to population growth, industrialization, and increasing traffic density.

The variation of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO2 concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China’s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO2 variations. Firstly, NO2 tropospheric vertical column densities (NO2 TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO2 TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China’s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO2 concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO2 concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO2 of China.

Abstract Non‐methane volatile organic compounds (NMVOCs) have a significant impact on air quality in urban areas. Detecting NMVOCs emission with its proxy HCHO on urban scales from space, however, has been limited by the lack of discernible enhancement. Here we show clear urban HCHO plumes from 16 cities over the globe by rotating TROPOspheric Monitoring Instrument HCHO pixels according to wind directions. We fit the downwind structure of the plumes with the exponentially modified Gaussian approach to quantify urban HCHO effective production rates between 7.0 and 88.5 mol s−1. Our results are in line with total NMVOC emissions from the EDGAR inventory (r = 0.76). Our work offers a new measure of total NMVOC emissions from urban areas and highlights the potential of satellite HCHO data to provide new information for monitoring urban air quality.