Search Results (90)

Aerosol optical depth (AOD), fine-mode fraction (FMF), and absorption aerosol optical depth (AAOD) are essential for quantifying aerosol extinction and related climate and air-quality effects. Yet, most satellite retrievals target a single wavelength or parameter. In this study, a deep neural network (DNN) framework was developed to synergistically retrieve AOD, FMF, and AAOD from Sentinel-5P/TROPOMI at seven wavelengths across 380–772 nm. Parameter-specific feature engineering was designed by incorporating physical linkages among aerosol optical properties. Bayesian optimization was employed to tune hyperparameters, and SHAP (Shapley additive explanations) was used to interpret feature contributions. The proposed model demonstrated high accuracy and robustness on an independent test set. The retrieved AOD showed excellent agreement with AERONET (R = 0.960, MAE = 0.034, RMSE = 0.070), and similarly strong performance was achieved for FMF (R = 0.955, MAE = 0.027, RMSE = 0.039). For AAOD, an overall correlation of 0.86 was obtained (MAE = 0.005, RMSE = 0.008). Comparisons with existing satellite products indicated globally consistent spatial patterns and improved spatial continuity under high aerosol loading. Overall, the proposed data-driven approach enhances the efficiency and coverage of multi-parameter aerosol retrieval while maintaining high accuracy, supporting absorbing aerosol monitoring, aerosol-type discrimination, and climate-effect assessment. Full article

Satellite observations of methane are frequently compromised by extensive data gaps caused by cloud cover and aerosol contamination, limiting their utility for continuous regional monitoring. To reconstruct these spatiotemporal discontinuities, this study developed the Stacked Ensemble Reconstruction Framework for Methane (SERF-XCH₄). By integrating Sentinel-5P TROPOMI retrievals with 25 multi-source environmental covariates, we generated a spatiotemporally continuous, high-resolution (0.1°) monthly dataset (SERF-XCH₄-IM) for Inner Mongolia spanning 2019 to 2023. Comprehensive validation demonstrates that the framework achieves exceptional predictive fidelity with a Coefficient of Determination ($R^2$) of 0.93 and a Root Mean Square Error (RMSE) of 7.89 ppb, significantly surpassing the performance of individual base learners and traditional interpolation methods. Furthermore, spatial block cross-validation confirmed robust generalization capabilities ($R^2=0.90$) in data-void regions. To unravel the “black box” of the model, SHapley Additive exPlanations (SHAP) analysis was employed, revealing that temporal factors (contributing 63.9%), air temperature, and elevation are the dominant drivers governing XCH₄ variability. Spatiotemporal analysis further identified the Hulunbuir region as a significant growth “hotspot” with an annual increase rate exceeding 18.5 ppb/yr, a trend primarily driven by intensified emissions during the autumn and winter seasons. Consequently, this framework establishes a high-precision, interpretable paradigm for regional methane monitoring and geo-information reconstruction. Full article

Air pollution remains a major public health and environmental challenge in China, driven by complex non-linear interactions among anthropogenic activities, meteorological conditions, and topographic features that go beyond simple linear relationships. This study presents a comprehensive spatio-temporal assessment of key air pollutants (CO, NO₂, SO₂, and PM_2.5) and their relationships with Total Column Ozone (TCO) across China’s provinces from 2019 to 2023. Multi-source high-resolution satellite data from Sentinel-5P/TROPOMI, the China High PM_2.5 dataset, MODIS, and ERA5-Land reanalysis were integrated. A tiered analytical framework was applied, combining linear Pearson correlations, non-linear Spearman rank correlations, and interpretable XGBoost machine learning with SHAP values. Results reveal a distinct seasonal “seesaw” pattern, with primary pollutants peaking during winter stagnation and TCO reaching maximum levels in late winter and spring. Non-linear analyses uncover critical threshold effects, including exponential increases in PM_2.5 and SO₂ when surface temperatures drop below 0 °C, very strong SO₂-TCO coupling (ρ = 0.93), and significant pollutant trapping in low-elevation regions (CO-elevation ρ = −0.82). These findings support the development of precision environmental policies with dynamic, temperature-threshold-based emission controls and topography-specific strategies to effectively mitigate air pollution in China. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

Coal mining is a major source of methane emissions globally, and monitoring these emissions has become a sustained area of interest in both scientific research and policy-making. Coal mine methane emissions typically manifest as discrete point sources, such as individual mines or ventilation shafts, and spatially concentrated area sources, such as mining clusters. In recent years, satellite remote sensing technology has become a key tool for monitoring and assessing methane emissions from coal mines. Notable progress has been made in quantifying emissions through point-source inversion using high-resolution satellite data, such as GF-5B/AHSI, and in estimating regional-scale area-source emissions using wide-swath instruments, such as S5P/TROPOMI. However, there remains a lack of systematic comparison between inversion results derived from these two types of satellite data with differing spatial resolutions. This study comprehensively analyzes the strengths and limitations of the GF-5B/AHSI and S5P/TROPOMI sensors for quantifying methane emissions. It conducts a spatiotemporal comparative analysis of point-source and area-source methane emission datasets from the coal-mining regions of Shanxi Province. The research aims to clarify the intrinsic relationship between remote-sensing data at different observational scales and to systematically evaluate how prior information on emission-source locations influences emission quantification results. The comparative analysis between TROPOMI grid-level emissions and GF-5B/AHSI point-source emissions indicates that TROPOMI-gridded emission data, owing to its longer time series, can more effectively characterize the annual-average methane emission levels in mining areas. Meanwhile, high-resolution observations from GF-5B/AHSI show distinct advantages in detecting small-scale plumes and attributing emissions to specific facilities. Although the regional-average emissions derived from TROPOMI are significantly higher than point-source emission rate estimates, their data ranges overlap within their uncertainty intervals, demonstrating substantial consistency between the monitoring results of the two methods. Furthermore, the study reveals that when key emission facilities, such as ventilation shafts, are located far from the core operational areas of mines, relying solely on point-source observations may not fully capture the spatial distribution pattern of methane emissions at the mine scale. Full article

Fine particulate matter (PM_2.5) remains one of the most relevant pollutants affecting air quality and human health worldwide. While satellite-derived aerosol optical depth (AOD) is commonly used to estimate surface PM_2.5 concentrations, most existing approaches rely heavily on auxiliary meteorological model data. This study presents a novel approach that derives PM_2.5 for Germany and neighboring countries for the year 2022 based on TROPOMI satellite observations by applying a Random Forest (RF) algorithm. In addition to AOD, various TROPOMI products related to atmospheric composition are included to assess their added value for improving model performance. A comparison with CAMS forecasts is performed to demonstrate that the satellite-based model can more realistically reproduce both spatial patterns and temporal dynamics of PM_2.5. Furthermore, with a case study for March 2022 the model’s ability to capture pollution peaks during high-pollution events, which are particularly relevant for public health assessments, is illustrated. The TROPOMI-based RF model achieves high accuracy despite the absence of meteorological input and successfully captures the spatiotemporal variability of PM_2.5 concentrations. The results of the study highlight the potential of TROPOMI data for near-real-time PM_2.5 monitoring and underline its value as an independent, observation-based alternative to chemical transport model forecasts. As part of the DLR project INPULS, the proposed approach provides an important step toward the development of an operational daily satellite-based PM_2.5 product from the atmospheric Copernicus Sentinel missions and contributes to improving air quality surveillance, both under common and extreme pollution conditions. Full article

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions. Full article

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article

Accurate nitrogen dioxide (NO₂) forecasting is crucial for proactive emission control and issuing public health warnings. This study provides the first evaluation of the China Meteorological Administration’s (CMA) operational CUACE/Haze-Fog V3.0 numerical prediction system, assessing its daily NO₂ forecast accuracy against independent satellite measurements and in situ observations. We compare model forecasts with TROPOspheric Monitoring Instrument (TROPOMI) satellite column data and observations from 1677 Chinese ground monitoring stations, focusing on four key regions: the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Urumqi. An optimal spatial resolution of 0.15° × 0.15° was determined for TROPOMI data processing. The results indicate a strong seasonal dependency in model performance. The model systematically underestimates NO₂ concentrations in winter but performs significantly better in summer. This systematic bias is confirmed by a Normalized Mean Bias (NMB) consistently below −20% in northern regions during the winter. In the Beijing–Tianjin–Hebei region, the Root Mean Square Error (RMSE) reached 3.57 × 10¹⁵ molec/cm² (vs. TROPOMI) and 1.09 × 10¹⁵ molec/cm³ (vs. ground stations) in winter, decreasing to 0.95 and 0.91, respectively, in summer. Critically, this winter bias pertains to pollution magnitude rather than temporal correlation; the model captures pollution trends but underestimates peak severity. Our study reveals a ‘vertical decoupling’ in the operational forecasting system. While the model utilizes surface data assimilation to correct surface pollutants, this study demonstrates that these corrections fail to propagate vertically to the total NO₂ column during winter stable boundary layer conditions. This finding has broader implications for chemical transport models (CTMs): relying solely on surface data assimilation is insufficient for constraining column burdens in regions with complex vertical stratification. We propose that future operational systems integrate satellite-based vertical constraints to resolve the systematic winter bias identified here. Full article

Monitoring air pollution remains a significant challenge for both environmental policy and public health, particularly in parts of Eastern Europe where industrial structures are undergoing transition. In this paper, we examine long-term air quality trends in Poland between 1990 and 2023, drawing on multiple sources: satellite observations (from 2019 to 2025), ground-based stations, and official national emission inventories. The analysis focused on sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM₁₀, PM_2.5). Data were obtained from the Sentinel-5P TROPOMI sensor, processed through Google Earth Engine, and monitored by the Chief Inspectorate of Environmental Protection (GIOŚ, Warsaw, Poland) and the National Inventory Report (NIR, Warsaw, Poland), compiled by KOBiZE (The National Centre for Emissions Management, Warsaw, Poland). The results show a decline in emissions. SO₂, for instance, dropped from about 2700 kilotons in 1990 to under 400 kilotons in 2023. Ground-based measurements matched well with inventory data (correlations around 0.75–0.85), but the agreement was noticeably weaker when satellite estimates were compared with surface monitoring. In addition to analyzing emission trends, this study examined the relationship between pollution levels and meteorological conditions across major Polish cities from 2019 to mid-2024. Pearson’s correlation analysis revealed strong negative correlations between temperature and pollutant concentrations, especially for SO₂, reflecting the seasonal nature of pollution peaks during colder months. Wind speed exhibited ambiguous relationships, with daily data indicating a dilution effect (negative correlations), whereas monthly averages revealed positive associations, likely due to seasonal confounding. Higher humidity was consistently linked to higher pollution levels, and precipitation showed weak negative correlations, likely influenced by seasonal weather patterns rather than direct atmospheric processes. These findings suggest that combining different monitoring methods, despite their quirks and mismatches, provides a fuller picture of atmospheric pollution. They also point to a practical challenge. Further improvements will depend less on sweeping industrial reform and more on shifting everyday practices, like how homes are heated and how people move around cities. Full article

Nitrogen dioxide (NO₂) poses severe risks to human health and the environment, especially in densely populated megacities. Ground-based air quality monitoring stations provide high-temporal-resolution data but are spatially limited, while satellite observations offer broad coverage but measure column densities rather than surface concentrations. To overcome these limitations, this study integrates ground-based observations with satellite-derived NO₂ from Sentinel-5P TROPOMI and GEOS-CF products to estimate ground-level NO₂ in Istanbul using machine learning (ML) approaches. Three ML algorithms (RF, XGB, and CB) were tested on two datasets spanning 2019–2024 at ~1 km resolution, incorporating 20 features, including topographic, meteorological, environmental, and demographic variables. Among models, CB achieved the best performance (R: 0.686, RMSE: 16.23 µg/m³, and MAE: 11.75 µg/m³ in the test dataset) with the Sentinel-5P dataset, successfully capturing spatial and seasonal variations in ground-level NO₂ both quantitatively and qualitatively. SHAP analysis revealed that regarding satellite-derived NO₂, anthropogenic indicators such as population density, road length, and digital elevation model were the most influential features, while meteorological factors contributed secondarily. Despite the lower spatial resolution of GEOS-CF data, both Sentinel-5P and GEOS-CF datasets supported reliable model outputs. This study provides the first ML-based ground-level NO₂ estimation framework for the Istanbul Metropolitan City. Full article

Volcanic gas emissions, particularly sulfur dioxide (SO₂), are crucial for volcano monitoring. SO₂ has a significant impact on air quality, the climate, and human health, making it a critical component of volcano monitoring programs. Additionally, SO₂ can be used to assess the state of a volcano and the progression of an individual eruption and can serve as a proxy for volcanic ash. The Tajogaite La Palma (Spain) eruption in 2021 emitted large amounts of SO₂ over 85 days, with the plume reaching Central Europe. In this study, we present the results achieved by monitoring Tajogaite SO₂ emissions from 19 September to 31 October 2021 at different acquisition times (i.e., 10:00 UTC, 12:00 UTC, 14:00 UTC, and 16:00 UTC). An optimized configuration of the Robust Satellite Technique (RST) approach, tailored to volcanic SO₂ detection and exploiting the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) channel at an 8.7 µm wavelength, was used. The results, assessed by means of a performance evaluation compared with masks drawn from the EUMETSAT Volcanic Ash RGB, show that the RST product identified volcanic SO₂ plumes on approximately 81% of eruption days, with a very low false-positive rate (2% and 0.3% for the mid/low and high-confidence-level RST products, respectively), a weighted precision of ~79%, and an F1-score of ~54%. In addition, the comparison with the Tropospheric Monitoring Instrument (TROPOMI) S5P Product Algorithm Laboratory (S5P-PAL) L3 grid Daily SO₂ CBR product shows that RST-SEVIRI detections were mostly associated with SO₂ plumes having a column density greater than 0.4 Dobson Units (DU). This study gives rise to some interesting scenarios regarding the near-real-time monitoring of volcanic SO₂ by means of the Flexible Combined Imager (FCI) aboard the Meteosat Third-Generation (MTG) satellites, offering improved instrumental features compared with the SEVIRI. Full article

Air pollution critically impacts global health, climate change, and ecosystem balance. In Türkiye, rapid population growth, urban expansion, and industrial activities lead to significant land use and cover changes, negatively affecting air quality. This study examined the relationship between land use and land cover changes and six key pollutants (sulfur dioxide, ozone, aerosol index, carbon dioxide, nitrogen dioxide, and formaldehyde) using TROPOMI/Sentinel-5P and European Space Agency Climate Change Initiative data between 2018 and 2024. Satellite-based remote sensing techniques, MODIS data, land surface temperature, and Normalized Vegetation Index analyses were employed. The findings revealed that nitrogen dioxide and carbon dioxide emissions increase with urban expansion and traffic density in metropolitan areas (Istanbul, Ankara, Izmir), while agriculture and deforestation increase aerosol index levels in inland areas. Additionally, photochemical reactions increased surface ozone in the Mediterranean and Aegean regions. At the same time, sulfur dioxide and formaldehyde concentrations reached high levels in highly industrialized and metropolitan cities such as Istanbul, Ankara, and Izmir. This study highlights the role of green infrastructure in improving air quality and provides data-based recommendations for sustainable land management and urban planning policies. Full article

This study presents a comprehensive validation of total ozone columns from ozone profile (O₃P) and total ozone column (O₃T) products measured by the Geostationary Environment Monitoring Spectrometer (GEMS), through comparisons with Pandora, Ozone Mapping and Profiler Suite (OMPS) and TROPOspheric Monitoring Instrument (TROPOMI). O₃P version 3.0 demonstrates reduced dependence on viewing geometry compared to version 2.0, whereas the O₃T product shows a consistent offset between versions (v2.0 vs. v2.1). In comparison with Pandora, O₃P exhibits seasonal bias patterns similar to those seen in TROPOMI and OMPS, ranging from −2% in summer to +5% in winter. However, O₃T maintains abnormally persistent negative biases across seasons and times of day, along with a long-term degradation of 2–3% from 2021 to 2024. These findings suggest that O₃T biases likely result from uncorrected radiometric biases rather than algorithmic limitations. Validation metrics further highlight inconsistencies in O₃T, including a lower regression slope (~0.95) in the mid-latitude and higher root mean square errors in the low-latitude (~5%), compared to the other products (near 1.0 and 1–3%, respectively). Overall, O₃P outperforms TROPOMI and OMPS across most validation metrics in mid-latitudes and performs similarly at low latitudes. Full article