Search Results (206)

We present coordinated observations from ozone Differential Absorption lidar (DIAL), aerosol lidar, and Doppler wind lidar at the City College of New York (CCNY) in northern Manhattan during the summer 2023 AGES+ campaigns across the New York City (NYC) region and Long Island Sound (LIS) areas. The results highlight significant ozone formation within the planetary boundary layer (PBL) and the concurrent transport of ozone/aerosol plumes aloft and mixing into the PBL during 26–28 July 2023. Especially, 26 July experienced the highest ozone concentration within the PBL during the three-day ozone episode despite having a lower temperature than the following two days. In addition, the onset of the afternoon sea breeze contributed to increased ozone levels in the PBL. A mobile ozone DIAL was also deployed at Columbia University’s Lamont–Doherty Earth Observatory (LDEO) in Palisades, NY, 29 km north of NYC, from 11 August to 8 September 2023. A notable high-ozone episode was observed by both ozone DIALs at the CCNY and the LDEO site during an unusual heatwave event in early September. On 7 September, the peak ozone concentration at the LDEO reached 120 ppb, exceeding the ozone levels observed in NYC. This enhancement was associated with urban plume transport, as indicated by wind lidar measurements, the HRRR (High-Resolution Rapid Refresh) model, and the Copernicus Sentinel-5 TROPOMI (TROPOspheric Monitoring Instrument) tropospheric column NO₂ product. The results also show that, during both heatwave events, those days with slow southeast to southwest winds experienced significantly higher ozone pollution. Full article

Stratospheric ozone shields life on Earth from harmful ultraviolet radiation and plays a crucial role in climate formation, while tropospheric ozone is a pollutant and greenhouse gas. Satellite methods based on measurements of outgoing thermal radiation are the only methods that provide information on global ozone distribution, independent of solar illumination. Since about 90% of atmospheric ozone is concentrated in the stratosphere, ozone total column measurements can be used as stratospheric ozone measurements. We present techniques for deriving information on total ozone columns (TOCs) and tropospheric ozone columns (TrOCs) from spectra of outgoing thermal radiation measured by the IKFS-2 instrument aboard the Meteor-M No. 2 satellite. The techniques are based on principal component analysis and the artificial neural network approach, providing high accuracy in TOC (less than 3%) and TrOC (within 2–4 DU) retrieval in accordance with the WMO requirements for the quality of satellite measurements. Full article

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines geostatistical interpolation with nonlinear modeling by integrating RK with ML models—specifically comparing gradient boosting regression (GBR), random forest (RF), and K-nearest neighbors (KNN)—to determine the most suitable auxiliary predictor. This structure enables the framework to capture both spatial autocorrelation and complex relationships between NO₂ concentrations and environmental drivers. Model performance was evaluated using the coefficient of determination (r²), computed against observed TROPOMI NO₂ column values filtered by quality assurance criteria. GBR achieved the highest validation r² values of 0.83 for January and February, while RF yielded 0.82 and 0.79 in January and December, respectively. These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. In contrast, models using only static land cover inputs performed poorly (r² < 0.58), emphasizing the limited predictive capacity of such variables in isolation. Interpretability analysis using the SHapley Additive exPlanations (SHAP) method revealed temperature as the most influential meteorological driver of NO₂ variation, particularly during winter, while forest cover consistently emerged as a key land-use factor mitigating NO₂ levels through dry deposition. By integrating dynamic meteorological variables and static land cover features, the hybrid RK–ML framework enhances the spatial and temporal completeness of satellite-derived air quality datasets. As the first RK–ML application for TROPOMI data in Taiwan, this study establishes a regional benchmark and offers a transferable methodology for satellite data imputation. Future research should explore ensemble-based RK variants, incorporate real-time auxiliary data, and assess transferability across diverse geographic and climatological contexts. Full article

Atmospheric parameter acquisition along the vertical profile of the troposphere across different locations on the Earth is of primary importance in gaining knowledge of the evolution of large-scale meteorological systems and the relative movements of air masses. Normally, this happens thanks to the launch, into the atmosphere, of radiosondes connected to balloons filled with helium gas. However, on a small scale, and in particular geomorphological contexts, different and peculiar meteorological situations may arise, in which the air column in the lower layers can behave differently from normal, giving rise to the so-called thermal inversions. In this work, in a particular sinkhole in the Apulia region, the use of a multi-rotor UAV (Unmanned Aerial Vehicle) equipped with a temperature data logger was tested. The flight along the vertical, starting from the lowest point of the sinkhole, made it possible to archive the temperature data of the air column in the first 80 m of altitude. The data validation confirmed the goodness of the UAV acquisitions and their subsequent processing made it possible to extrapolate the vertical temperature profile of the sinkhole during the winter thermal inversion phenomenon. In addition to confirming the predisposition of this sinkhole to strong thermal inversions, the preliminary results of this work have highlighted the efficiency of this new methodology. It has proved to be useful in assessing small-scale vertical profiles of atmospheric variables in a relatively low altitude range. Furthermore, this methodology can represent a strong scientific and technological innovation applicable in the meteorological field and in that of environmental monitoring. Full article

The 2020 wildfire season in the Western U.S. was historic in its intensity and impact on the land and atmosphere. This study aims to characterize satellite retrievals of carbon monoxide (CO), a tracer of combustion and signature of those fires, from two key satellite instruments: the Cross-track Infrared Sounder (CrIS) and the Tropospheric Monitoring Instrument (TROPOMI). We evaluate them during this event and assess their synergies. These two retrievals are matched temporally, as the host satellites are in tandem orbit and spatially by aggregating TROPOMI to the CrIS resolution. Both instruments show that the Western U.S. displayed significantly higher daily average CO columns compared to the Central and Eastern U.S. during the wildfires. TROPOMI showed up to a factor of two larger daily averages than CrIS during the most intense fire period, likely due to differences in the vertical sensitivity of the two instruments and representative of near-surface CO abundance near the fires. On the other hand, there was excellent agreement between the instruments in downwind free tropospheric plumes (scatter plot slopes of 0.96–0.99), consistent with their vertical sensitivities and indicative of mostly lofted smoke. Temporally, TROPOMI CO column peaks were delayed relative to the Fire Radiative Power (FRP), and CrIS peaks were delayed with respect to TROPOMI, particularly during the intense initial weeks of September, suggesting boundary layer buildup and ventilation. Satellite retrievals were evaluated using ground-based CO column estimates from the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), showing Normalized Mean Errors (NMEs) for CrIS and TROPOMI below 32% and 24%, respectively, when compared to all stations studied. While Normalized Mean Bias (NMB) was typically low (absolute value below 15%), there were larger negative biases at Pasadena, likely associated with sharp spatial gradients due to topography and proximity to a large city, which is consistent with previous research. In situ CO profiles from AirCore showed an elevated smoke plume for 15 September 2020, highlighted consistency between TROPOMI and CrIS CO columns for lofted plumes. This study demonstrates that both CrIS and TROPOMI provide complementary information on CO distribution. CrIS’s sensitivity in the middle and lower free troposphere, coupled with TROPOMI’s effectiveness at capturing total columns, offers a more comprehensive view of CO distribution during the wildfires than either retrieval alone. By combining data from both satellites as a ratio, more detailed information about the vertical location of the plumes can potentially be extracted. This approach can enhance air quality models, improve vertical estimation accuracy, and establish a new method for assessing lower tropospheric CO concentrations during significant wildfire events. Full article

The long-term nature of and heterogeneity in industrialization has led to high formaldehyde (HCHO) concentrations with seasonal and regional variation in North China, and this is highly influenced by changes in meteorological and population conditions. Here, we analyzed the spatial and temporal distribution characteristics of tropospheric HCHO VCD (vertical column density) and their key drivers in North China from 2019 to 2023 based on the HCHO daily dataset from TROPOMI. The results showed that the spatial distribution of tropospheric HCHO VCD in North China presented similar variation characteristics in the past 5 years, with the highest in the center, followed by the east and the lowest in the west. Seasonal variations were characterized, with the highest tropospheric HCHO VCD concentrations in summer and the lowest ones in spring. In addition, the effects of meteorological elements on HCHO VCD were analyzed based on the ERA5 dataset, and the correlation of HCHO VCD with temperature and wind was strong. In contrast, the correlation with precipitation and surface solar radiation was low, and the effects were different between the growing and non-growing seasons (the growing season, i.e., March–November, is defined as the period when the plant or a part of it actually grows and produces new tissues, while the non-growing season refers to December–the following February). Population density is directly proportional to tropospheric HCHO VCD. In this study, a higher-resolution spatial and temporal distribution model of tropospheric HCHO VCD in North China is obtained based on TROPOMI, which effectively characterizes the driving factors of HCHO VCD. Our study provides an important reference for developing of air pollution control measures in North China. Full article

This work presents a comprehensive investigation of tropospheric NO₂ measurements using a portable ground-based Pandora spectrometer, incorporating an independently designed and implemented calibration and retrieval process (P-CAR v1.0). We designed and optimized a region-specific algorithm for retrieving tropospheric NO₂ column densities in China. The measurement process began with establishing a spectral calibration system for processing the Pandora’s raw observations, followed by enhancing the differential optical absorption spectroscopy (DOAS) algorithm to retrieve both the slant column densities (SCDs) and tropospheric vertical column densities (VCDs) of NO₂. To validate our retrieval products, comparative analyses were conducted against co-located MAX-DOAS measurements. The results demonstrate excellent agreement between Pandora-retrieved tropospheric NO₂ and MAX-DOAS observations, with correlation coefficients exceeding 0.96 for both hourly and daily mean VCDs and fitting slopes greater than 0.90. Furthermore, the validation extended to multi-satellite observations from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), exhibiting pronounced consistency, as evidenced by the correlation coefficients all surpassing 0.90 for the hourly mean values. These findings confirm the high accuracy and reliability of NO₂ retrievals from the portable Pandora instrument, significantly boosting its potential for atmospheric monitoring and application. Full article

Nitrogen oxides (NO_x) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NO_x cycling across the Tibetan Plateau (TP), which plays a crucial role in regional and global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data to examine the tropospheric NO₂ vertical column density (Ω_NO2) spatiotemporal variability over TP, a pristine environment marked with natural sources. GEMS observations revealed that the Ω_NO2 over TP is generally low compared with surrounding regions with significant surface emissions, such as India and the Sichuan basin. A spatial decreasing trend of Ω_NO2 is observed from the south and center to the north over Tibet. Unlike the surrounding regions, the TP exhibits opposing seasonal patterns and a negative correlation between the surface NO₂ and Ω_NO2. In the Lhasa and Nam Co areas within Xizang, the highest Ω_NO2 in spring contrasts with the lowest surface concentration. Diurnally, a midday increase in Ω_NO2 in the warm season reflects some external sources affecting the remote area. Trajectory analysis suggests strong convection lifted air mass from India and Southeast Asia into the upper troposphere over the TP. These findings highlight the mixing interplay of nonlocal and local NO_x sources in shaping NO₂ variability in a high-altitude environment. Future research should explore these transport mechanisms and their implications for atmospheric chemistry and climate dynamics over the TP. Full article

The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to enhance the efficiency and effectiveness of data processing in the GST. This paper develops a method for detecting carbon dioxide (CO₂) emission hotspots using a convolutional neural network (CNN) with short-lived and co-emitted nitrogen dioxide (NO₂) as a proxy. To address the data gaps in model parameter training, we constructed a dataset comprising over 210,000 samples of NO₂ plumes and emissions based on atmospheric dispersion models. The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. The trained model was also applied to the NO₂ column data from the TROPOspheric Monitoring Instrument (TROPOMI) for hotspot detection, and the detections were compared with the MEIC inventory. The results demonstrate that in high-emission areas, the proposed method successfully identifies emission hotspots with an average accuracy of over 80%, showing a high degree of consistency with the emission inventory. In areas with multiple observations from TROPOMI, we observed a high degree of consistency between high NO₂ emission areas and high CO₂ emission areas from the Global Open-Source Data Inventory for Anthropogenic CO₂ (ODIAC), indicating that high NO₂ emission hotspots can also indicate CO₂ emission hotspots. In the future, as hyperspectral and high spatial resolution remote sensing data for CO₂ and NO₂ continue to grow, our methods will play an increasingly important role in global data preprocessing and global emission estimation. Full article

Pollutants information can be retrieved from visible (VIS) and ultraviolet (UV) diffuse solar spectra exploiting Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. In May 2021, the Italian research institute CNR-ISAC acquired and deployed a MAX-DOAS system SkySpec-2D. It is located in the “Giorgio Fea” observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, where it has constantly acquired zenith and off-axis diffuse solar spectra since the 1st October 2021. This work presents the retrieved tropospheric NO₂ and aerosol extinction profiles (and their columns) derived from the MAX-DOAS measurements using the newly developed DEAP retrieval code. The code has been validated both using synthetic differential Slant Column Densities (dSCDs) from the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project and real measured data. For this purpose, DEAP results are compared with the ones obtained with three state-of-the-art retrieval codes. In addition, an inter-comparison with satellite products from Sentinel-5P TROPOMI, for the tropospheric NO₂ Vertical Column Densities (VCDs), and MODIS-MAIAC for the tropospheric Aerosol Optical Depth (AOD), is performed. We find a bias of −0.6 × 10¹⁵ molec/cm² with a standard deviation of 1.8 × 10¹⁵ molec/cm² with respect to Sentinel-5P TROPOMI for NO₂ tropospheric VCDs and of 0.04 ± 0.08 for AOD with respect to MODIS-MAIAC data. The retrieved data show that the SPC measurement site is representative of the background pollution conditions of the Po Valley. For this reason, it is a good candidate for satellite validation and scientific studies over the Po Valley. Full article

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product. Full article

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article

Diagnosing ozone (O₃) formation sensitivity using tropospheric observations of O₃ and its precursors is important for formulating O₃ pollution control strategies. Photochemical reactions producing O₃ occur at the earth’s surface and in the elevated layers, indicating the importance of diagnosing O₃ formation sensitivity at different layers. Synchronous measurements of tropospheric O₃ and its precursors nitrogen dioxide (NO₂) and formaldehyde (HCHO) were performed in urban Hefei to diagnose O₃ formation sensitivity at different atmospheric layers using multi-axis differential optical absorption spectroscopy observations. The retrieved surface NO₂ and O₃ were validated with in situ measurements (correlation coefficients (R) = 0.81 and 0.80), and the retrieved NO₂ and HCHO vertical column densities (VCDs) were consistent with TROPOMI results (R = 0.81 and 0.77). The regime transitions of O₃ formation sensitivity at different layers were derived using HCHO/NO₂ ratios and O₃ profiles, with contributions of VOC-limited, VOC-NO_x-limited, and NO_x-limited regimes of 74.19%, 7.33%, and 18.48%, respectively. In addition, the surface O₃ formation sensitivity between HCHO/NO₂ ratios and O₃ (or increased O₃, ΔO₃) had similar regime transitions of 2.21–2.46 and 2.39–2.71, respectively. Moreover, the O₃ formation sensitivity of the lower planetary boundary layer on polluted and non-polluted days was analyzed. On non-polluted days, the contributions of the VOC-limited regime were predominant in the lower planetary boundary layer, whereas those of the NO_x-limited regime were predominant in the elevated layers during polluted days. These results will help us understand the evolution of O₃ formation sensitivity and formulate O₃ mitigation strategies in the Yangtze River Delta region. Full article

The central Mediterranean and nearby regions were affected by extreme wildfires during the summer of 2021. During the crisis, Türkiye, Greece, Italy, and other countries faced numerous challenges ranging from the near-complete destruction of landscapes to human losses. The crisis also resulted in reduced air quality levels due to increased emissions of pollutants linked to biomass-burning processes. In the Mediterranean Basin, observation sites perform continuous measurements of chemical and meteorological parameters meant to track and evaluate greenhouse gas and pollutant emissions in the area. In the case of wildfires, CO (carbon monoxide) and formaldehyde (HCHO) are effective tracers of this phenomenon, and the integration of satellite data on tropospheric column densities with surface measurements can provide additional insights on the transport of air masses originating from wildfires. At the Lamezia Terme (code: LMT) World Meteorological Organization–Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy, a new multiparameter approach combining different methodologies has been used to further evaluate the effects of the 2021 wildfires on atmospheric measurements. A previous study focused on wildfires that affected the Aspromonte Massif area in Calabria; in this study, the integration of surface data, tropospheric columns, and backtrajectories has allowed pinpointing additional contributions from other southern Italian regions, as well as North Africa and Greece. CO data were available for both surface and column assessments, while continuous HCHO data at the site were only available through satellite. In order to correlate the observed peaks with wildfires, surface BC (black carbon) was also analyzed. The analysis, which focused on July and August 2021, has allowed the definition of three case studies, each highlighting distinct sources of emission in the Mediterranean; the case studies were further evaluated using HYSPLIT backtrajectories and CAMS products. The LMT site and its peculiar local wind patterns have been demonstrated to play a significant role in the detection of wildfire outputs in the context of the Mediterranean Basin. The findings of this study further stress the importance of assessing the effects of wildfire emissions over wide areas. Full article

Ozone pollution in Hangzhou Bay, one of the seven petrochemical clusters in China, is severe. Early ozone pollution has been detected recently, such as the maximum daily 8 h average (MDA8) ozone concentration in Jiaxing achieving 171.0 μg/m³ on 7 March 2023. Satellites have observed tropospheric column concentrations of ozone precursors formaldehyde (HCHO) and nitrogen dioxide (NO_x), and quantitative models are proposed to reveal the causes of the early onset of ozone pollution. VOC-limited and transitional regimes dominate most areas in Hangzhou Bay, and NO_x-limited regimes dominate the region around Hangzhou Bay, such as northeastern Jiangsu Province. Results show that HCHO column concentrations are increasing in VOC-limited regions, and NO_x column concentrations are increasing more rapidly than HCHO in NO_x-limited regions. According to multivariate linear regression (MLR), early spring ozone pollution in Hangzhou Bay is mainly caused by meteorological drivers. Hangzhou Bay has formed an atmospheric meteorological environment with high temperature and low humidity. The richer solar radiation intensifies the photochemical reactions associated with tropospheric ozone formation, producing more tropospheric ozone. Based on the Shapley Additive Explanation (SHAP) algorithm, ozone pollution increases when solar radiation exceeds 12 million J/m² and is accompanied by high temperatures. Overall, reducing VOC emissions helps to mitigate ozone growth in Shanghai and northern Hangzhou Bay, while reducing NO_x emissions is more effective in northeastern Jiangsu Province. Full article