Search Results (223)

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO₂/HCHO, and surface observations within machine learning models, achieving cross-validated R² of 0.94 and RMSE of 12.05 μg/m³ for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.91, RMSE = 10.83 μg/m³) and reproduce seasonal gradients, with summer concentrations (131 μg/m³) more than double winter levels (61 μg/m³). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is potentially applicable to other regions and sensors under similar conditions, providing a case study for integrating national satellite products into multi-source surface ozone estimation. Full article

Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH₄ product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m⁻² (0.12% relative) and the mean RMSE is 39.24 mol m⁻² (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing. Full article

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

This research presents a comprehensive multi-domain environmental assessment of Delos Island, a UNESCO World Heritage Site, through integration of long-term atmospheric and satellite remote sensing datasets. A significant methodological contribution of this research is the development of a cross-mission harmonization approach that enables the reconstruction of a continuous, multi-decadal atmospheric record. By implementing a hierarchical calibration pipeline to harmonise Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) observations, the study effectively eliminated a 6.61-fold systematic instrument offset, producing a 21-year time series (2004–2025) of tropospheric NO₂ concentrations. Simultaneously, a 24-year analysis (2000–2024) of coastline dynamics was conducted using the Landsat archive to quantify land area changes across the island and within a 1.03 km² Archaeological Area of Interest (AOI). Results indicate that atmospheric NO₂ concentrations stabilised following a 2015 peak, while coastal erosion represents a measurable risk to structural integrity. Net land loss of 18,400 m² was documented within the AOI, driven by localised geomorphological factors and exposure to Meltemi winds. The results indicate that these environmental processes are physically independent yet collectively require a multilayered conservation strategy to protect vulnerable archaeological heritage from atmospheric pollution and coastal retreat. Furthermore, the research highlights the value of long-term satellite datasets spanning more than two decades for supporting heritage monitoring and management, especially in remote or hard-to-reach locations. Through the analysis of the spatial and temporal characteristics of these sensors, the research enables the identification of hazard proxies that can inform risk-aware decision-making. 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

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region. 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

The Italian peninsula is, as shown by satellite and ground-based measurements, a pollution hotspot. In recent years, ground-based MAX-DOAS commercial systems have been installed in the Po Valley and the area surrounding Rome to monitor NO₂ tropospheric column densities and validate coincident satellite (e.g., TROPOMI) products. Three of the instruments are located in the Po Valley at San Pietro Capofiume (Bologna), Bologna city, and Mount Cimone (Modena), and one is located in Tor Vergata (Rome). The chosen system is the SkySpec-2D from Airyx. All the recorded spectra are saved in the FRM4DOAS format and processed with QDOAS software to obtain slant column densities (SCDs) of NO₂, O₄, and other trace gases. The MAX-DOAS SCD sequences are then analysed with the DEAP code to retrieve tropospheric profiles of NO₂ and aerosol extinction, while zenith-sky SCDs are used to retrieve NO₂ total columns. A dedicated campaign, involving the network instruments, has been conducted in the Po Valley to compare the performance of the individual instruments in the network with respect to the one that participated in the CINDI-3 campaign (Cabauw, The Netherlands). The results of the intercomparison campaign indicated that all instruments showed comparable performance. As an example of obtainable products, one year (from September 2024 to August 2025) of NO₂ tropospheric columns, as well as their comparison with TROPOMI measurements, is presented, highlighting the potential of this network for both air quality studies and satellite validation. Due to Italy’s location in the highly complex Mediterranean hotspot region, these data represent an important contribution to satellite validation efforts, particularly in view of upcoming missions such as Copernicus Sentinel-4, Sentinel-5, and the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) constellation. We found a negative TROPOMI bias relative to SkySpec-2D for NO₂ tropospheric columns ranging from −13% in San Pietro Capofiume, to −25% in Bologna and −44% in Rome Tor Vergata. The comparison between NO₂ total columns from TROPOMI and SkySpec-2D at Mount Cimone shows generally good agreement, with TROPOMI being 15% higher. 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

High-accuracy spatiotemporal monitoring of surface nitrogen dioxide (NO₂) concentrations is essential for air quality management. This study evaluates machine learning-based estimates of near-surface NO₂ concentrations using data from the geostationary GEMS instrument and the polar-orbiting TROPOMI over China in 2022. Four tree-based models—Random Forest, XGBoost, CatBoost, and LightGBM—were trained by integrating satellite vertical-column densities with multi-source meteorological and ancillary data. Results show that CatBoost achieved the highest accuracy, with an R² of 0.842 for GEMS and 0.765 for TROPOMI, alongside the lowest RMSE and MAE. Models trained on GEMS data consistently outperformed TROPOMI-based models across all metrics. This advantage is primarily attributed to the substantially larger training sample size enabled by GEMS’s high temporal resolution, as confirmed through a controlled experiment with consistent sample sizes which isolated the effect of data volume. Spatially, GEMS estimates captured sharper concentration gradients and localized emission hotspots, while TROPOMI produced smoother fields. Temporally, only GEMS allowed the reconstruction of detailed diurnal patterns and near-real-time pollution episode tracking. This study confirms the significant added value of geostationary satellite data for high-frequency air quality monitoring and analysis when combined with machine learning. 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

This study investigates the spatiotemporal variability of ambient methane (CH₄) using a drone-deployable Aeris Technologies MIRA Strato LDS midwave-infrared analyzer. Laboratory calibration with NOAA-certified gas standards and Standard Reference Material (SRM) for CH₄ demonstrated high measurement precision across a range of concentrations (R² = 0.9986, slope = 0.9678). Field validation conducted during a two-week intercomparison with a Picarro G2301 in September 2023 confirmed the MIRA Strato’s reliability under ambient conditions (R² = 0.9845; slope = 0.9438), indicating strong agreement with the reference analyzer. Diurnal patterns revealed peak CH₄ concentrations (~2.2 ppm) between 04:00–08:00 LT and minima (~2.1 ppm) between 13:00–17:00 LT, consistent with nocturnal boundary-layer stability and daytime convective mixing. Across 14 midday UAV flights from October 2023 to September 2024, CH₄–altitude slopes ranged from −3.05 × 10⁻⁴ to +1.41 × 10⁻⁴ ppm/m, reflecting variable stratification and uplift regimes. The highest flight concentration (2.23 ppm) was observed on 19 October under stable conditions, while the lowest (2.03 ppm) was observed on 14 August under elevated vertical mixing. These extremes reflect seasonal background accumulation and convective transport effects. Temperature was the most consistent predictor, with regression coefficients ranging from −0.021 to +0.008 ppm/°C, while ethane (C₂H₆) coefficients were significant but confounded due to measurements below detection limits. The analyzer maintained strong signal stability throughout (mean CV ≈ 0.0066; max = 0.0114), and remote sensing validation with TROPOMI supported observed seasonal accumulation trends. These results demonstrate the MIRA Strato’s capability to resolve near-surface CH₄ dynamics and characterize convective transport in complex atmospheric environments. Full article

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article