Search Results (191)

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency. 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

Monitoring the horizontal distribution of PM_2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM_2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM_2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O₄) differential slant column densities (O₄ dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM_2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM_2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM_2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O₄ dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM_2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM_2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM_2.5 pollution transport event was captured: PM_2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM_2.5 distributions. Full article

Accurate quantification of carbon dioxide (CO₂) sources and sinks is becoming a key aspect in recent carbon flux research; yet our understanding of satellite performance on regional scales remains insufficient. In this work, the column-averaged dry-air mole fraction of CO₂ retrieved from OCO-2 v11.1r and GOSAT v03.05 was evaluated against CarbonTracker (CT) using data from March 2022 to August 2023. Also, the satellite data were validated against those from the Total Carbon Column Observing Network (TCCON) for March 2022 to February 2024. Comparison with CT revealed that both satellites had a general negative bias over land and the best performance in spring. In Southern Hemisphere land regions, the satellites captured monthly variability reliably, with OCO-2 obtaining the most accurate monthly concentrations. In Northern Hemisphere land regions, CT demonstrated the best performance, although both satellites accurately quantified monthly variations in some regions. In tropical land regions, none of the satellites showed superior performance. OCO-2 data showed bias features in sub-regional areas such as East and South Asia. For ocean regions, the bias was the largest in spring. Phase offset, slight underestimation of concentrations, and seasonal biases were found over several ocean regions in OCO-2 time series, whereas GOSAT was unable to provide reasonable results. When comparing TCCON with OCO-2 and GOSAT data, we found systematic errors of −0.12 and −0.56 ppm and root mean square errors of 1.08 and 1.70 ppm, respectively, mainly contributed by topographic variation and aerosol load. The errors were the smallest in spring and larger in summer and winter. Both CT- and TCCON-based analyses indicated that current satellite products may have better performance in desert surfaces. Clouds, aerosols, and surface pressure still challenged OCO-2 retrieval, while the bias-correction process can be emphasized for GOSAT. Full article

The Directional Polarimetric Camera (DPC) onboard the Gaofen-5 (02) satellite includes gas absorption bands that are crucial for the quantitative retrieval of clouds, atmospheric aerosols, and surface parameters. However, in-flight radiometric calibration of these bands remains challenging due to strong absorption features and the lack of onboard calibration devices. In this study, a calibration method that exploits functional relationships between the reflectance ratios of gas absorption and adjacent reference bands and key surface–atmosphere parameters over sunglint were presented. Radiative transfer simulations were combined with polynomial fitting to establish these relationships, and prior knowledge of surface pressure and water vapor column concentration was incorporated to achieve high-precision calibration. Results show that the calibration uncertainty of the oxygen absorption band is mainly driven by surface pressure, with a total uncertainty of 3.01%. For the water vapor absorption band, uncertainties are primarily associated with water vapor column concentration and surface reflectance, yielding total uncertainties of 3.45%. Validation demonstrates the robustness of the proposed method: (1) cross-calibration using desert samples confirms the stability of the results, and (2) the retrieved surface pressure agrees with the DEM-derived estimates, and the retrieved total column water vapor agrees with the MODIS products, confirming the calibration. Overall, the method provides reliable in-flight calibration of DPC gas absorption bands on Gaofen-5 (02) and can be adapted to similar sensors with comparable spectral configurations. Full article

Atmospheric aerosols are known to alter the Earth’s radiative balance and influence climate. However, accurately quantifying the magnitude of aerosol-induced radiative forcing remains challenging. We characterize optical properties of biomass-burning (BB) and non-biomass-burning (NB) aerosols and quantify BB aerosol radiative forcing at two AERONET (AErosol RObotic NETwork) sites in Huancayo (Peru) and La Paz (Bolivia) during 2015–2021. From AERONET data, we derive aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo (SSA), and asymmetry parameter (ASY). We then employ the SBDART model to calculate aerosol radiative forcing (ARF) on monthly and multiannual timescales. BB aerosols peak in September (AOD: 0.230 at Huancayo; 0.235 at La Paz), while NB aerosols reach maxima in September at Huancayo (0.109) and November at La Paz (0.104). AE values exceeding unity for BB aerosols indicate fine-mode dominance. Huancayo exhibited the highest BB ARF in November: +16.4 W m⁻² at the top of the atmosphere (TOA), –18.6 W m⁻² at the surface (BOA), and +35.1 W m⁻² within the atmospheric column (ATM). This was driven by elevated AOD and high scattering efficiency. At La Paz, where SSA data was only available for September, BBARF values were also significant (+15.16 at TOA, –17.52 at BOA, and +32.73 W m⁻² within the ATM). This result underscores the importance of quantifying the ARF, particularly over South America where data is scarce. Full article

This single high-impact case study investigates the impact of planetary boundary layer (PBL) representation on long-range transport of Amazon fire smoke that reached the Metropolitan Area of São Paulo (MASP) from 15 to 20 August 2019, using the WRF model to compare three PBL schemes (MYNN 2.5, YSU, and BouLac) and three source-tagged tracers. The simulations are evaluated against MODIS-derived aerosol optical depth (AOD), the Light Detection and Ranging (LiDAR) time–height curtain over MASP, and HYSPLIT forward trajectories. Transport is diagnosed along the source-to-MASP pathway using six-hourly cross-sections and two integrative metrics: the projected mean wind in the 700–600 hPa layer and the vertical moment of tracer mass above the boundary layer. Outflow and downwind impact are strongest when a persistent reservoir between 2 and 4 km coexists with projected winds for several hours. In this episode, MYNN maintains an elevated 2–5 km transport layer and matches the observed arrival time and altitude, YSU yields a denser but delayed column, and BouLac produces discontinuous pulses with reduced coherence over the city. A negatively tilted trough, jet coupling, and a nearly stationary front establish a northwest-to-southeast corridor consistent across model fields, trajectories, and satellite signal. Seasonal robustness should be assessed with multi-event, multi-model analyses. Full article

Aromatics and their key oxidation intermediate such as formaldehyde and dicarbonyl compounds (glyoxal and methyglyoxal) are crucial precursors for ozone (O₃) and secondary organic aerosols (SOA). However, the spatial–temporal variation in aromatics’ contribution to these intermediate species and O₃/SOA over Eastern China during the past decades remains insufficiently quantified. This study combines satellite observations of formaldehyde and glyoxal column densities (2008–2014) with an innovative tracer method implemented in the Community Multiscale Air Quality (CMAQ) modeling system to quantify aromatic-driven dicarbonyl chemistry. Simulations of summer and autumn in 2010, 2012, 2014, and 2016 are conducted to demonstrate the change in aromatics and its impact through the years. Estimated primary and intermediate VOCs show good consistency with measurements at a supersite; and the simulated vertical column density of formaldehyde and glyoxal agree with satellite observations in spatial distributions. The contribution of aromatic hydrocarbons to the columnar concentration of glyoxal has seen a significant increase since 2010, which can, to some extent, explain the interannual trend of glyoxal column concentrations in key regions of Beijing–Tianjin–Heibei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). A cross-comparison reveals a good consistency between the observed glyoxal columnar concentrations to formaldehyde columnar concentration ratio (R_GF) from satellite measurements and the high contribution areas of aromatics to glyoxal: pronounced values are observed in the above three key regions in Eastern China. Additionally, the applicability of R_GF and its indicative nature in Eastern China was discussed, revealing notable seasonal and regional variations in R_GF. Revised R_GF thresholds ([0.015–0.03] for models vs. [0.04–0.06] for satellites) improve summer precursor classification, while a threshold of >0.04 could distinguish the areas with high anthropogenic impacts during autumn. These findings advance understanding of VOC oxidation pathways in polluted regions, providing critical insights for ozone and secondary organic aerosol mitigation strategies. The integrated satellite model approach demonstrates the growing atmospheric influence of aromatics amid changing emission patterns in Eastern China. Full article

Wildfires are a complex and underexplored aspect of the Earth system, significantly affecting climate, as they emit greenhouse gases and aerosols that alter the Earth’s radiative balance. This study utilizes the EC-Earth3 Earth System Model to investigate how interannual variability in biomass burning emissions influences variability in total aerosol optical depth (AOD), as well as carbon monoxide (CO) and ozone (O₃) tropospheric columns. We demonstrate that fluctuations in biomass burning emissions impact AOD, CO, and O₃ variability at regional and global scales, emphasizing the need for improved understanding of wildfires and their climate effects. Full article

Fine particulate matter (PM_2.5) and ozone (O₃) are the main pollutants affecting the air quality in China, yet their common influencing factors and spatial patterns remain unclear. Focusing on the year 2020, this study adopted the least absolute shrinkage and selection operator algorithm to construct land use regression models with 34 environmental variables for the O₃ concentration at the air quality monitoring stations in the Shenyang Metropolitan Area. For comparison, PM_2.5 models had been developed in our previous work using the same approach. Model performance was satisfactory (cross-validated R² = 0.49–0.81 for O₃; 0.56–0.65 for PM_2.5 in our previous study), confirming the robustness of the approach. The results showed that: (1) Tree cover and grassland exerted synergistic, co-directional mitigation on both pollutants, whereas built-up areas and permanent water bodies were positively associated with their concentrations; (2) Longitude, elevation, and population, as well as atmospheric components such as nitrous dioxide column density and aerosol optical depth, displayed opposite effects on both pollutants, indicating trade-offs; (3) Spatially, PM_2.5 played the dominant role in shaping the pattern of combined pollution, with higher PM_2.5 levels than O₃ in nearly half of the area (46.97%), while O₃-dominant regions were rare (4.27%) and mostly confined to localized zones. This study contributes to a deeper understanding of the synergies and trade-offs driving PM_2.5 and O₃ pollution as well as providing a scientific basis for formulating policies on integrated control measures against combined pollution. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Volatile phenols in the atmosphere are important precursors of ozone and secondary organic aerosols (SOAs). Despite their importance, the lack of effective observation and analysis methods has led to less attention paid to them, leading to gaps in our understanding of their behavior and effects on atmospheric chemistry. This study aimed to evaluate the concentration levels, sources, and environmental impacts of volatile phenols in ambient air, focusing on the urban area of Beijing and the suburban district of Heze in the North China Plain during winter. Samples were collected using an XAD-7 column and analyzed by high-performance liquid chromatography with ultraviolet detection (UPLC-UV). Results indicated that a higher concentration of 11 detected phenols was found in Beijing than that in Heze, with the average concentration of 23.60 ± 8.99 ppbv and 18.38 ± 2.34 ppbv. Phenol and cresol with strong photochemical activity were the predominant species, accounting for about 52% (Heze) and 66% (Beijing) of the total phenols, which indicates that more attention should be paid to volatile phenols in urban areas. Higher levels of L_OH in Beijing (36.86 s⁻¹) and Heze (22.06 s⁻¹) compared to other studies about PAMS and carbonyls indicated that these volatile phenols play an undeniable role in atmospheric oxidation reactions. Positive Matrix Factorization (PMF) identified major sources as pesticide usage (15.6%), organic chemicals (31.9%), and combustion or secondary conversion (52.5%). These findings underscore the multifaceted impact of phenols, influencing both gaseous pollutant concentrations and particulate matter formation, with potential implications for environmental and public health. Full article

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

Biomass burning (BB) aerosols are the largest source of absorbing aerosols on Earth. Coupled with marine stratocumulus clouds (MSC), their radiative effects are enhanced and can cause cloud property changes (first indirect effect) or cloud burn-off and warm up the atmospheric column (semi-direct effect). Nevertheless, the derivation of their quantity and optical properties in the presence of MSC clouds is confounded by the uncertainties in the retrieval of the underlying cloud properties. Therefore, a robust methodology is needed for the coupled retrievals of absorbing aerosol above clouds. Here, we present a new retrieval approach implemented for a Spectro radiometric multi-angle polarimetric airborne platform, the research scanning polarimeter (RSP), during the ORACLES campaign over the Southeast Atlantic Ocean. Our approach transforms the 1D measurements over multiple angles and wavelengths into a 3D image-like input, which is then processed using various deep learning (DL) schemes to yield aerosol single scattering albedos (SSAs), aerosol optical depths (AODs), aerosol effective radii, and aerosol complex refractive indices, together with cloud optical depths (CODs), cloud effective radii and variances. We present a comparison between the different DL approaches, as well as their comparison to existing algorithms. We discover that the Vision Transformer (ViT) scheme, traditionally used by natural language models, is superior to the ResNet convolutional Neural-Network (CNN) approach. We show good validation statistics on synthetic and real airborne data and discuss paths forward for making this approach flexible and readily applicable over multiple platforms. Full article

In this study, a quantitative profiling method for detecting free fatty acids in crude lanolin based on the Quality by Design (QbD) concept was developed. High-performance liquid chromatography (HPLC) equipped with a charged aerosol detector (CAD) and a Proshell 120 EC C18 column was employed for the separation of crude lanolin components. Initially, the analytical target profile and critical method attributes were defined. Potential critical method parameters, including column temperature, flow rate, isocratic run time, gradient end organic phase ratio, and gradient time, were identified using fishbone diagrams and single-factor experiments. The definitive screening design (DSD) was then utilized to screen and optimize these parameters. Stepwise regression was applied to establish quantitative models between the critical method attributes and the method parameters. Subsequently, the method operable design region (MODR) was calculated and was successfully verified. The analytical conditions established were configured with 0.1% formic acid in water and 0.1% formic acid in acetonitrile serving as the mobile phases. The flow rate was set at 0.8 mL/min, and the column temperature was maintained at 35 °C with the evaporation tube temperature also set at 35 °C. An injection volume of 10 μL was used for each analysis. The gradient elution conditions were as follows: from 0 to 30 min, 75% of solvent B was used, and from 30 to 60 min, the proportion of solvent B was increased from 75% to 79%. Ten components, including 12-hydroxystearic acid, 2-hexyldecanoic acid, and palmitic acid, were identified by mass spectrometry, and seven common peaks were found in the fingerprints. The contents of palmitic acid, oleic acid, and stearic acid in the crude lanolin were quantitatively determined. Both the fingerprint and quantitative analysis methods were validated. The method was applied to analyze 15 batches of crude lanolin from different sources. The new established quantitative profiling method for free fatty acids can be potentially used for industrial applications to enhance the quality control of crude lanolin. Full article