Search Results (123)

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

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

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

Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of >2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m⁻³ is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM₁₀ values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m⁻³ in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons. Full article

Sulfur dioxide (SO₂) can be of natural and anthropogenic origin and is one of the sulfur compounds present in the atmosphere. Among natural sources, volcanoes contribute with relevant annual outputs, and major eruptions lead to spikes in these outputs. In the case of anthropogenic pollution, SO₂ emissions are mostly correlated with the sulfur content of fuels, which has been the focus of specific emission mitigation policies for decades. Following other examples of cyclic and multi-year evaluations, an analysis of SO₂ at the Lamezia Terme (code: LMT) WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) station in Calabria, Southern Italy, was performed. The coastal site is characterized by wind circulation patterns that result in the detection of air masses with low or enhanced anthropic influences. The presence of the Aeolian Arc of active, quiescent, and extinct volcanoes, as well as Mount Etna in Sicily, may influence LMT observations with diffused SO₂ emissions. For the first time in the history of the LMT, a multi-year analysis of a parameter has been integrated with TROPOMI data gathered by Sentinel-5P and used to test total tropospheric column densities at the LMT itself and select coordinates in the Tyrrhenian and Ionian seas. Surface and satellite data indicate that SO₂ peaks at the LMT are generally linked to winds from the western–seaside wind corridor, a pattern that is compatible with active volcanism in the Tyrrhenian Sea and maritime shipping to and from the Gioia Tauro port located in the same region. The findings of this research provide the basis for enhanced source apportionment, which could further differentiate anthropogenic sources in the area from natural outputs. Full article

Nitrogen dioxide (NO₂) is a critical air pollutant that has significant health and environmental impacts. Tropospheric NO₂ refers specifically to the vertical column density of NO₂, which is measured by satellites and serves as an indicator of anthropogenic NO₂ sources. This pollutant is frequently assessed using satellite data owing to limitations in local monitoring. This investigation employs the Spectral Angle Mapper (SAM), a geometric machine-learning model, given its advantages in simplicity and computational efficiency, and OMI satellite measurements to carry out spatially supervised classification of tropospheric NO₂ global patterns from 2005 to 2021. This study identifies four typical trends across developed urban centers, examining correlations with population growth, economic factors, and air quality policies. The results demonstrated regional variations, with a general downward trend in North America, Europe, and parts of Asia, underscoring the efficacy of stricter emission controls. However, upward trends persist in some Asian regions, reflecting varying policy implementations. This study revealed a pivotal inflection point around 2013, marking a shift in global NO₂ dynamics. Although policies have led to improved air quality in some regions, achieving absolute decoupling of economic growth from NO₂ emissions remains challenging. The COVID-19 pandemic has also exerted a significant influence, temporarily reducing emissions due to economic slowdowns. Overall, the SAM model effectively delineated NO₂ patterns and provided insights for future policy and emission control strategies. Full article

This study explores the connection between tropospheric nitrogen dioxide (NO₂) vertical column density levels and asthma hospitalization cases in Louisiana from 2005 to 2020. Utilizing NO₂ data from NASA’s Ozone Measurement Instrument (OMI) aboard the Aura satellite, the research integrates these atmospheric measurements with socioeconomic data at the census tract level. This study employs a generalized linear mixed model (GLIMMIX) with a logit link and Beta distribution to analyze the relationship between seasonal NO₂ levels and asthma hospitalization cases during winter, fall, spring, and summer. By analyzing OMI data, this research quantifies seasonal variations in NO₂ levels and their corresponding impact on asthma hospitalizations. The findings reveal a relationship between NO₂ levels and asthma hospitalizations, particularly in communities with high Black and/or low-income populations, with the strongest effects observed during winter. Specifically, the analysis shows that, for each unit increase in NO₂ levels, the odds of asthma-related hospitalizations increase by approximately 26.3% (p < 0.0001), with a 95% confidence interval ranging from 23.3% to 29.5%. Assuming a causal link between NO₂ and asthma, these findings suggest that reducing NO₂ emissions could alleviate healthcare burdens associated with respiratory diseases such as asthma. Full article

Formaldehyde (HCHO) is harmful to human health and an adequate assessment of its concentrations, both in outdoor and indoor environments, is necessary in the context of sustainable policies designed to mitigate health risks. In this research, ground indoor and outdoor HCHO measurements are integrated with the analysis of tropospheric total columns obtained by satellite surveys to assess the concentrations of HCHO in a number of environments, exploiting the proximity of a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy to a National Institute for Insurance against Accidents at Work (INAIL) department in the municipality of Lamezia Terme. The meteorological parameters used by the WMO station are also used to provide additional data and test new correlations. Using statistical significance tests, this study demonstrates the presence of a correlation between indoor and outdoor HCHO concentrations, thus showing that an exchange between indoor and outdoor formaldehyde does occur. Rooms located in the local INAIL building where indoor measurements took place also demonstrate degrees of susceptibility to HCHO exposure, which are correlated with the orientation of prevailing wind corridors in the area. The new findings constitute an unprecedented characterization of HCHO hazards in Calabria and provide regulators with new tools with which to mitigate formaldehyde-related risks. Full article

Satellite remote sensing is the best way to derive sea surface skin temperature (SST_skin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SST_skin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SST_skin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SST_skin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SST_skin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SST_skin data to study surface ocean processes and climate change in the Arctic. Full article

The Environmental Trace Gases Monitoring Instrument (EMI-II) onboard the Chinese GaoFen-5B (GF5B) and DaQi-1 (DQ1) satellites is the successor of the previous EMI onboard the Chinese GaoFen-5 (GF5) satellite, and has a higher spatial resolution and a better signal-to-noise ratio. The GF5B and DQ1 were launched in September 2021 and April 2022, respectively. As part of China’s ultraviolet-visible hyperspectral satellite instrument series, the EMI-II aims to conduct network observations of pollution gases globally in the morning and early afternoon. In this study, NO₂ data were retrieved from the EMI-II payloads on the GF5B and DQ1 satellites using the Differential Optical Absorption Spectroscopy (DOAS) algorithm. The two satellites were consistently compared, and the results showed strong consistency on various spatial and temporal scales (R² > 0.8). In four representative regions worldwide, NO₂ data from the EMI-II exhibited good spatial consistency with those from the TROPOMI. The correlation coefficient (R²) of the total vertical column density (VCD) between the EMI-II and TROPOMI exceeded 0.85, and that of the tropospheric NO₂ VCD exceeded 0.57. Compared with single-satellite observations, the dual-satellite network of the GF5B and DQ1 can effectively increase the observation frequency. On a daily scale, dual-satellite observations can reduce the impact of cloud coverage by 6–8% compared to single-satellite observations, and there are two valid observations of nearly 50% of the world’s regions. Additionally, the differences between the two satellites can reflect the NO₂ diurnal variations, which demonstrates the potential for studying pollutant gas diurnal variations. Full article