Identifying air pollutant sources and quantifying related emissions provides crucial information towards improving global air quality and public health. However, emission inventories for oil and gas (OG) activities inadequately represent nitrogen oxide (NOx) emissions-a key precursor to tropospheric ozone and secondary aerosols-with notable discrepancies identified. Satellite remote sensing provides a unique vantage point to map and quantify these emissions consistently on a global scale. Here, we quantify annual NOx emissions from 44 major OG basins distributed globally, utilizing TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) observations with the divergence flux method. In addition, we use the spaceborne Visible Infrared Imaging Radiometer Suite (VIIRS) natural gas flaring detections to further constrain satellite-derived NOx emissions. The divergence flux method, which addresses 3D topography corrections and chemical loss of NOx while accounting for wind-induced flux smearing, provides a robust approach for estimating NOx emissions. This top-down approach resolves emissions at the facility and basin scale (0.01 degrees x 0.01 degrees), enabling direct quantification of a major gap in current bottom-up inventories. Our findings reveal significant differences between satellite observations and established inventories, which systematically underestimate OG sector emissions. A direct comparison of our TROPOMI-derived NOx emissions against the existing inventories suggests that EDGARv6.1 underestimates onshore emissions by 61 % and offshore emissions by 26 %. The discrepancy is even more pronounced for the CAMS-GLOB-ANT_v5.3 inventory, which underestimates onshore and offshore emissions by 78 % and 92 %, respectively. These findings hold significance for global emission assessments, demonstrating that current inventories are missing a substantial source of NOx pollution, particularly from OG fields. Furthermore, our approach provides detailed spatial emission maps that enhance the granularity in depicting NOx distribution with error analysis (uncertainties: 32 %-54 %) supporting that inventory discrepancies are statistically significant, representing structural deficiencies rather than measurement error. We further explore the variations in the correlation between NOx emissions with OG production volumes and CH4 concentrations across different OG basins in North America. The observed co-locations and correlations provide important insights into co-pollution emission characteristics of methane and NOx, that are respectively a potent greenhouse gas and a reactive air quality pollutant, linked to be originating from oil and gas activity. These findings have important implications for regulatory monitoring and verification, particularly for addressing emissions transparency requirements under international climate frameworks.

The precise retrieval of aerosol properties from satellite data is pivotal for advancing our understanding of their impacts on climate and air quality. The RemoTAP (Remote Sensing of Trace Gas and Aerosol Products) algorithm represents a significant leap forward, leveraging data from multi-angle polarimeters (MAPs), such as the past PARASOL-POLDER instrument, the current PACE-SPEXone and the future Metop-SG-3MI and CO2M-MAP instruments. A unique ability of these instruments to measure both the intensity and polarization of sunlight across multiple wavelengths and viewing angles offers an unparalleled dataset for aerosol characterization, including number concentrations, size distributions, and refractive indices. We have substantially enhanced the RemoTAP results by integrating improved cloud fraction values derived from MAPs using a neural network approach, ensuring more accurate aerosol retrievals through better cloud filtering techniques. To further elevate data quality, advanced quality filters utilizing multiple key metrics were developed, effectively enhancing data integrity, resulting in a more refined aerosol dataset essential for precise atmospheric analysis. The validation of these enhancements involved comparisons with ground-based AERONET (Aerosol Robotic Network) observations over 284 sites, demonstrating the reliability of RemoTAP-derived aerosol properties. Furthermore, a pixel-level cross-comparison was carried out with GRASP-derived PARASOL-based aerosol data, as RemoTAP and GRASP are similar kind of algorithms for polarimetric measurements. The scientific implications of these advancements are profound, as the improved retrieval of aerosol size and composition using advanced polarimetric observations directly refines the estimation of cloud condensation nuclei (CCN) (proxy) concentrations and consequently the global CCN-Nd (cloud droplet number concentration) relationship. This refined relationship is crucial for understanding aerosol-cloud interactions, allowing for more accurate quantification of aerosol-induced cloud albedo changes, thereby reducing uncertainties in radiative forcing estimates due to aerosol-cloud interactions (RFaci). Such improvements contribute to a more precise representation of aerosol impacts in climate models, ultimately enhancing predictions of climate sensitivity and future warming scenarios. By advancing the RemoTAP algorithm, our findings underscore the transformative potential of these methodologies in delivering accurate and reliable aerosol climatology, driving forward the frontier of atmospheric science and climate research.

Aerosol-cloud interaction is the subject of considerable scientific research, due to the importance of clouds in controlling climate. In the present study, three years (2011–2013) satellite observations are used to investigate the aerosol indirect effect (AIE) over Dehradun. The low values of Angstrom exponent (α) during March–July are attributed to the loading of dust-like coarse particles in the atmosphere, whereas the analysis of aerosol type and Spectral Radiation Transport Model for Aerosol species (SPRINTARS) simulated aerosol species variation supports the fact. Moderate Resolution Imaging Spectroradiometer (MODIS) derived AOD data are associated to the cloud product to examine the dust impact on properties of liquid cloud and ice cloud. The positive values of aerosol cloud interaction effect (ACI) for ice cloud during pre-monsoon (March–May) and monsoon (June–August) seasons reveal the significant impact of dust on ice clouds over Dehradun, which is maximum during May (~0.24 ± 0.05). The present study shows that ice cloud effective radius (ICER) decrease with AOD during dust period. The increase in ice water path (IWP) and ice cloud optical depth (ICOD) reveals the impact of dust on heterogeneous ice generation in low level clouds. However, there is no relation between dust and liquid water cloud during dust period. It is difficult to provide definite conclusions that the dust and cloud changes are driven by the same meteorological conditions. Cloud and the Earth's Radiant Energy System (CERES) derived flux data are used to examine the associated changes in TOA cloud radiative forcing. The diminution in effective size of ice crystal due to aerosol first indirect effect traps more longwave radiation and reflects more solar radiation. Both first and second indirect effects enhance cloud cooling, whereas the dust induced cloud warming is mainly the result of the semi-direct effect.

Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote-sensing-based algorithm that quantifies the vertically resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the lookup tables developed in this study, based on the observations from the Aerosol Robotic Network, to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar – Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) – measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management.

Dust storm, a natural hazard, has a direct impact on daily life for a short period. Dust storms are periodic events over India, especially in northern regions. This study has been carried out to investigate the dust impacts on the aerosol characteristics over Dehradun (DDN) during pre-monsoon (March–June), 2012 using ground measurements, satellite observations and model simulations. The measurements illustrate the distinct monthly impact on the aerosol properties with maximum dust loading during May (aerosol optical depth at 500 nm (AOD500) = 0.72 ± 0.18) over DDN, which is confirmed with the Terra-MODIS (AOD550 = 0.70 ± 0.19) measurements. The major dust loading was recorded in aerosol measurements during May at the station, which permitting to examine the influence of dust transports on the aerosol characteristics. Spectral variation of AOD and Angstrom exponent (α) values displayed day to day variation of aerosol during dust episodes. Analysis of aerosol types and seven-day back-trajectories reveal the transportation of desert dust during May over DDN. The Optical Properties of Aerosols and Clouds (OPAC) model was used to compute the aerosol optical properties (e.g., Single scattering albedo (SSA) and asymmetry parameter (g)) and size distribution. The high values of SSA and g are indicating the dust loading in the atmosphere during May. Aerosol volume concentration at the coarse mode (geometric mean radii (RV) = 2.89 ± 0.027 µm) is found to be increased in the May, whereas decrement has been observed in the finer mode (RV = 0.16 ± 0.006 µm). The aerosol direct radiative forcing (ARF) was computed using Santa Barbara Discrete Ordinate Atmospheric Radiative Transfer (SBDART) model in the shortwave (SW) region (0.25–4.00 µm). The mean top of the atmosphere (TOA) and surface forcing come out to be –14.49 W m–2 and –53.29 W m–2 respectively in May. The mean net atmospheric radiative forcing (38.79 W m–2 maximum during May) corresponds to heating rate of ~1.06° K d–1 in the atmosphere.

Extreme smog in India widely impacts air quality in late autumn and winter months. While the links between emissions and air quality are well-recognized, the association of smog and its intensification with climatic trends in the lower troposphere, where aerosol pollution and its radiative effects manifest, are not understood well. Here we use long-term satellite data to show a significant increase in aerosol exceedances over northern India, resulting in sustained atmospheric warming and surface cooling over the last two decades. We find several lines of evidence suggesting these aerosol radiative effects have induced a multidecadal (1980-2019) strengthening of lower tropospheric stability and an increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol-radiation-meteorological feedback driving the smog intensification, we anticipate results from this study will help inform mitigation strategies supporting stronger region-wide measures, which are critical for solving the smog challenge in India.

The wildfires over the central Indian Himalayan region have attracted the significant attention of environmental scientists. Despite their major and disastrous effects on the environment and air quality, studies on the forest fires’ impacts from a renewable energy point of view are lacking for this region. Therefore, for the first time, we examine the impact of massive forest fires on the reduction in solar energy production over the Indian subcontinent via remote sensing techniques. For this purpose, we used data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO), the Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) in conjunction with radiative transfer model (RTM) simulation, in addition to 1-day aerosol forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The energy production during the first quarter of 2021 was found to reach 650 kWh/m2 and the revenue generated was about INR (Indian rupee) 79.5 million. During the study period, the total attenuation due to aerosols and clouds was estimated to be 116 and 63 kWh/m2 for global and beam horizontal irradiance (GHI and BHI), respectively. The financial loss due to the presence of aerosols was found to be INR 8 million, with the corresponding loss due to clouds reaching INR 14 million for the total Indian solar plant’s capacity potential (40 GW). This analysis of daily energy and financial losses can help the grid operators in planning and scheduling power generation and supply during the period of fires. The findings of the present study will drastically increase the awareness among the decision makers in India about the indirect effects of forest fires on renewable energy production, and help promote the reduction in carbon emissions and greenhouse gases in the air, along with the increase in mitigation processes and policies.

Over the past two decades, smoke aerosols from crop residue burning have increasingly degraded postmonsoon (October‐November) air quality in north India. We use satellite data and atmospheric modeling to investigate whether cascading delays in monsoon rice growth and postmonsoon fires over 2003–2019 have exacerbated the already poor urban air quality downwind of the fires. Beginning in 2008, a government effort to combat groundwater depletion in Punjab mandated rice sowing until closer to the arrival of monsoon rains. We find evidence of district‐level delays in the timing of both monsoon rice growth and postmonsoon fires, which vary from 1 to 4 weeks with largely an east‐west gradient. These delays are correlated spatially (r = 0.51–0.77), with northern and western districts in Punjab, which rely less on groundwater for irrigation, tending to have the greatest delays. Had the delays in fire activity not occurred, we estimate that cities downwind and near the fire source would have consistently seen less smoke‐related fine particulate matter (PM2.5), on average ranging from 11% to 21% for New Delhi, Bathinda, and Jind during 2008–2019. This net benefit of earlier postmonsoon burning could have been even larger given that (a) a longer rice‐to‐wheat transition could incentivize farmers to find alternatives to burning crop residues; and (b) background PM2.5 is less abundant earlier in the season, decreasing the likelihood of extreme pollution episodes. Strategies aiming to mitigate air pollution while conserving groundwater may be more effective by promoting an earlier monsoon growing season in districts with less groundwater depletion.

Abstract. Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions (ACI), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote sensing-based algorithm that quantifies the vertically-resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the look-up tables developed in this study, based on the observations from the Aerosol Robotic Network to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors to lidar measurements. The retrieved optically equivalent particle size distributions and aerosol type dependent particle composition are utilized to calculate critical diameter using the κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ORACLES campaign and validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar - Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) - measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which help to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

Atmospheric aerosols play essential roles in regional energy balance, hydrological cycle, and air quality, thus greatly influencing the global climate and public health. Rapid economic expansion, industrialization, urbanization, and energy demand have significantly enhanced anthropogenic emissions over the Middle East (ME) that received the utmost scientific attention. Therefore, we present the temporal variability of atmospheric aerosols over the ME for a period of 15 years (2005–2019). Here, the long-term measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Clouds and the Earth’s Radiant Energy System (CERES) on Aqua are analyzed in order to understand the spatio–temporal variability of aerosols and their impacts on radiation budget over the ME. On average, a significant increase in aerosol optical depth (AOD) trend is observed by ~0.01 per year over ME. The peak aerosol loading was observed in summer (March–September) followed by the winter (October–February). A similar trend was observed in the CALIOP-derived extinct aerosol coefficients over ME. In addition, MODIS retrievals are validated against the AErosol RObotic NETwork (AERONET)’s ground-based sun photometers. Overall, MODIS AOD showed good agreement against AERONET AOD, with ~70% of the retrievals falling within the expected error and high correlation coefficient (R > 0.8). Furthermore, the associated changes in clear-sky Shortwave (SW) radiative flux indicates the enhanced aerosol loading over ME further increases the surface cooling (~1.2 W m−2 per year) and atmospheric warming (~1.8 W m−2 per year). Overall, the results suggest that enhanced aerosol emissions have significantly impacted the regional energy budget over ME during 2005–2019. The assessment also demonstrates the potential of synergetic use of multi-platform measurements for climate system studies.

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (NCCN) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy’s Southern Great Plains (SGP) site to illustrate the dependence of NCCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (Naccu; median value 805 cm−3) and relatively high aerosol hygroscopicity (κ; median value ∼0.25) values that result in the higher CCN activation and relatively high NCCN (median value 258–1578 cm−3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher Naccu (∼35%), and NCCN (230–1721 cm−3 at a SS of 0.1%–1.0%) with the lowest κ (∼0.17). In contrast, relatively high nucleation mode particles (Nnucl; ∼20%) and low NCCN (128–1553 cm−3 at a SS of 0.1%–1.0%) with higher κ (∼0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better NCCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the NCCN prediction uncertainty at lower SS.

To date, a large number of existing applications in India have used multi-band observations from airborne and spaceborne platforms.New sensors are providing additional capabilities thanks to special aerial missions with the compact airborne spectrographic imager (CASI), the short-wave infrared (SWIR) full spectrum imager (SFSI) and the National Aeronautics and Space Administration's (NASA's) Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG).Opportunities to exploit quantitative spectroscopic signatures and high spatial resolution have garnered great interest among the scientific community, and the success of these missions will rely on accurate calibration.Here we focus on a vicarious calibration experiment conducted for the AVIRIS-NG India campaign.We discuss initial validation results, with descriptions of in situ and remote calibration and measurement protocols, geometric processing with precise position and attitude data, and atmospheric simulations used to validate the remote measurement.A partnership between Indian Space Research Organisation (ISRO) and NASA investigators proved a unique opportunity to assess the empirical variability in results, indicating their sensitivity to modelling choices and assumptions.The vicarious calibration exercise uses multiple radiative transfer models, including MODTRAN 6.0 and a new version of the 6S radiative transfer code, viz.6SV2.1, which is capable of accounting for polarization.

Abstract A growing body of research has underscored the radiative impact of mineral dust in influencing Indian summer monsoon rainfall variability. However, the various aerosol‐cloud‐precipitation interaction mechanisms remain poorly understood. Here we analyze multisatellite observations to examine dust‐induced modification in ice clouds and precipitation susceptibility. We show contrasting dust‐induced changes in ice cloud regimes wherein despite a 25% reduction in ice particle radius in thin ice clouds, we find ~40% increase in ice particle radius and ice water path in thick ice clouds resulting in the cloud deepening and subsequently strengthened precipitation susceptibility, under strong updraft regimes. The observed dust‐ice cloud‐precipitation interactions are supported by a strong correlation between the interannual monsoon rainfall variability and dust frequency. This microphysical‐dynamical coupling appears to provide negative feedback to aerosol‐cloud interactions, which acts to buffer enhanced aerosol wet scavenging. Our results underscore the importance of incorporating meteorological regime‐dependent dust‐ice cloud‐precipitation interactions in climate simulations.

This study describes the post-launch calibration for visible (VIS) and shortwave infrared (SWIR) bands of Indian National Satellite System (INSAT)-3DR imager over Great Rann of Kutch (GROK) on Day-1 (15th September 2016), when the first time INSAT-3DR Imager camera was switched on. In order to account the characterization of errors and undetermined post-launch changes in sensor spectral response, this calibration activity was performed and extended for its monitoring to Day-56 (since the Day-1; 09th November 2016). A reflectance based technique is used in the present study. The surface reflectance and atmospheric variables were measured over the site as per solar and viewing geometry of the INSAT-3D scan. Top of atmosphere (TOA) spectral radiances were computed using 6SV (second simulation of the satellite signal in the solar spectrum) radiative transfer code with the in situ measurements as well as spectral response function of each channel. Preliminary results of the Day-1 vicarious calibration yield gain coefficients of 0.974 and 0.820 for VIS and SWIR channels respectively despite the inhomogeneity of the ground target caused by sufficient sub-surface soil moisture. In extension of the present study, the obtained gain coefficients were 1.001 and 0.9887 for VIS and SWIR, respectively, during Day-56 which indicates the performance of sensor is within the range of pre-launch laboratory calibration.

9 In a previous, study of Quaas et al., (2008) the radiative forcing by anthropogenic aerosol due 10 to aerosol-cloud interactions, RFaci, was obtained by a statistical analysis of satellite retrievals 11 using a multilinear regression. Here we employ a new statistical approach to obtain the fitting 12 parameters, determined using a non-linear least square statistical approach, for the relationship 13 between planetary albedo and cloud properties and, further, the relationship between cloud 14 properties and aerosol optical depth. In order to verify the performance, the results from both 15 statistical approaches (previous and present) were compared to the results from radiative 16 transfer simulations over three regions for different seasons. We find that the results of the new 17 statistical approach agree well with the simulated results both over land and ocean. The new 18 statistical approach increases the correlation by 21%-23% and reduce the error, compared to 19 the previous approach. 20

In a previous study of Quaas et al. (2008) the radiative forcing by anthropogenic aerosol due to aerosol–cloud interactions, RFaci, was obtained by a statistical analysis of satellite retrievals using a multilinear regression. Here we employ a new statistical approach to obtain the fitting parameters, determined using a nonlinear least square statistical approach for the relationship between planetary albedo and cloud properties and, further, for the relationship between cloud properties and aerosol optical depth. In order to verify the performance, the results from both statistical approaches (previous and present) were compared to the results from radiative transfer simulations over three regions for different seasons. We find that the results of the new statistical approach agree well with the simulated results both over land and ocean. The new statistical approach increases the correlation by 21–23 % and reduces the error compared to the previous approach.