Search Results (289)

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

Water vapor plays a crucial role in maintaining global energy balance and water cycle, and it is closely linked to various meteorological disasters. Precipitable water vapor (PWV), as an indicator of variations in atmospheric water vapor content, has become a key parameter for meteorological and climate monitoring. However, due to limitations in observation costs and technology, traditional atmospheric monitoring techniques often struggle to accurately capture the distribution and variations in space–time water vapor. With the continuous advancement of Global Navigation Satellite System (GNSS) technology, ground-based GNSS monitoring technology has shown rapid development momentum in the field of meteorology and is considered an emerging monitoring tool with great potential. Hence, based on the GNSS observation data from July 2023, this study retrieves PWV using the Global Pressure and Temperature 3 (GPT3) model and evaluates its application performance in the “7·31” extremely torrential rain event in Beijing in 2023. Research has found the following: (1) Tropospheric parameters, including the PWV, zenith tropospheric delay (ZTD), and zenith wet delay (ZWD), exhibit high consistency and are significantly affected by weather conditions, particularly exhibiting an increasing-then-decreasing trend during rainfall events. (2) Through comparisons with the PWV values through the integration based on fifth-generation European Centre for Medium-Range Weather Forecasts (ERA-5) reanalysis data, it was found that results obtained using the GPT3 model exhibit high accuracy, with GNSS PWV achieving a standard deviation (STD) of 0.795 mm and a root mean square error (RMSE) of 3.886 mm. (3) During the rainfall period, GNSS PWV remains at a high level (>50 mm), and a strong correlation exists between GNSS PWV and peak hourly precipitation. Furthermore, PWV demonstrates the highest relative contribution in predicting extreme precipitation, highlighting its potential value for monitoring and predicting rainfall events. Full article

The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. Full article

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m⁻² under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography. Full article

Block-level urban land use classification (BLULUC), like residential and commercial classification, is highly useful for urban planners. It can be achieved in the form of high-frequency full coverage without biases based on the data of high-spatial-resolution remote sensing images (HSRRSIs), which social sensing data like POI data or mobile phone data cannot provide. However, at present, the extraction of quantitative features from HSRRSIs for BLULUC primarily relies on computer vision or deep learning methods based on image signal characteristics rather than land cover patterns, like vegetation, water, or buildings, thus disconnecting existing knowledge between the landscape patterns and their functions as well as greatly hindering BLULUC by HSRRSIs. Well-known landscape metrics could play an important connecting role, but these also encounter the scale selection issue; i.e., the optimal spatial unit size is an image pixel or a segmented image object. Here, we use the task of BLULUC with 2 m satellite images in Beijing as a case study. The results show the following: (1) pixel-based classification can achieve higher accuracy than segmented object-based classification, with an average of 3% in overall aspects, while some land use types could reach 10%, such as commercial land. (2) At the pixel scale, if the quantity metrics at the class level, such as the number of patches, and the proportion metrics at the landscape level, such as vegetation proportion, are removed, the accuracy can be greatly reduced. Moreover, removing landscape-level metrics can lead to a more significant reduction in accuracy than removing class-level metrics. This indicates that in order to achieve a higher accuracy in BLULUC from HSRRSIs, landscape-level land cover metrics, including patch numbers and proportions at the pixel scale, can be used instead of object-scale metrics. Full article

The construction of subways exacerbates the non-uniformity of surface deformation, which in turn poses a potential threat to the safe construction and stable operation of urban rail transit systems. Beijing, the city with the most extensive subway network in China, has long been affected by land subsidence. Utilizing data from Envisat ASAR, Radarsat-2, and Sentinel-1 satellites, this study employs PS-InSAR technology to monitor and analyze land subsidence within a 2 km buffer zone along Beijing Metro Line 22 over a span of 20 years (from January 2004 to November 2024). The results indicate that land subsidence at Guanzhuang Station and Yanjiao Station along Metro Line 22 is particularly pronounced, forming two distinct subsidence zones. After 2016, the overall rate of subsidence along the subway line began to stabilize, with noticeable ground rebound emerging around 2020. This study further reveals a strong correlation between land subsidence and confined groundwater levels, while geological structures and building construction also exert a significant influence on subsidence development. These findings provide a crucial scientific foundation for the formulation of effective prevention and mitigation strategies for land subsidence along urban rail transit lines. Full article

Satellite retrievals can capture the spatiotemporal variation of O₃ over a large area near the surface. However, due to the unstable functional relationships between variables across spatiotemporal scales, the outlier predictions will reduce the accuracy of the prediction model. Therefore, a validated residual constrained random forest model (RF-RVC) is proposed to estimate the monthly and annual O₃ concentration datasets of 0.1° in China from 2005 to 2020 using O₃ precursor remote-sensing data and other auxiliary data. The temporal and spatial variations of O₃ concentrations in China and the four urban agglomerations (Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Sichuan–Chongqing (SC)) were calculated. The results show that the annual R² and RMSE of the RF-RVC model are 0.72~0.89 and 8.4~13.06 μg/m³. Among them, the RF-RVC model with the temporal residuals constraint has the greatest performance improvement, with the annual R² increasing from 0.59 to 0.8, and the RMSE decreasing from 17.24 μg/m³ to 10.74 μg/m³, which is significantly better than that of the RF model. The North China Plain is the focus of ozone pollution. Summer is the season of a high incidence of ozone pollution in China, YRD, PYD, and SC, while pollution in the PRD is delayed to October due to the monsoon. In addition, the trend of the O₃ and its excess proportion in China and the four urban agglomerations is not satisfactory; targeted measures should be taken to reduce the risk of environmental ozone. The research findings confirm the effectiveness of the residual constraint approach in long-term time-series modeling. In the future, it can be further extended to the modeling of other pollutants, providing more accurate data support for health risk assessments. Full article

The Directional Polarimetric Camera (DPC) aboard the Chinese GaoFen-5 02 satellite is designed to monitor aerosols and particulate matter (PM). In this study, we retrieved the aerosol optical depth (AOD) over the Jing–Jin–Ji (JJJ) region using multi-angle data from the DPC, employing a combination of dark dense vegetation (DDV) and multi-angle retrieval methods. The added value of our method included novel hybrid methodology and good practical performance. The retrieval process involves three main steps: (1) deriving AOD from DPC data collected at the nadir angle using linear parameters of land surface reflectance between the blue and red bands from the MOD09 surface product; (2) after performing atmospheric correction with the retrieved AOD, calculating the variance of the normalized reflectance at all observation angles; and (3) leveraging the calculated variance to obtain the final AOD values. AOD images over the JJJ region were successfully retrieved from DPC data collected between January and June 2022. To validate the retrieval method, we compared our results with aerosol products from the AErosol RObotic NETwork (AERONET) Beijing-RADI site, as well as aerosol data from MODerate-resolution Imaging Spectroradiometer (MODIS) and the generalized retrieval of atmosphere and surface properties (GRASP)/models over the same site. In terms of validation metrics, the correlation coefficient (R²) and root mean square error (RMSE) indicated that our method achieved high accuracy, with an R² value greater than 0.9 and an RMSE below 0.1, closely aligning with the performance of GRASP. Full article

A gale occurred in Beijing on 30 May 2024, which led to fallen trees and damaged infrastructure. This event was primarily driven by surface divergent winds induced by strong convective downdrafts. During the occurrence and development of this gale, solar shortwave radiation and cloud-related variables played a crucial role in triggering, sustaining, and organizing convection. This study proposes a new diagnostic analysis approach for this gale focusing on shortwave radiation and cloud-related variables involved in the physical processes of gale development, based on the FY-4B L2 products and simulations from the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO). The diagnostic analysis results of this case show that before cloud formation, the CMA-MESO simulates stronger shortwave radiation heating in the initial stages, leading to an overestimation of surface temperature rise. Additionally, the simulated cloud formation occurs slightly later than observed, with reduced cloud coverage, shorter cloud duration, and lower cloud top heights, resulting in a weaker convective intensity compared to observations. Furthermore, the CMA-MESO underestimates the temperature gradient between the middle and lower troposphere and predicts lower convective instability, which leads to weaker forecasts of convection organization. Ultimately, this study provides a theoretical basis and technical support for enhancing the ability of the CMA-MESO to simulate this gale by using the FY-4B L2 data products for diagnostic analysis. Full article

Understanding the characteristics of wildfires in the Beijing–Tianjin–Hebei (BTH) region is crucial for improving the monitoring of local wildfire danger. Our investigation first establishes the spatial distributions of fire weather index (FWI) distributions and satellite-observed wildfire occurrences. The FWI provides a reasonably accurate representation of wildfire danger in the BTH region. Through Self-Organizing Maps (SOM) clustering analysis, we identify nine distinct spatial patterns in FWI composites. Notably, the annual frequency of SOM modes 2 and 7 has shown a significant increasing trend over the past 40 years. The spatial distribution of the highest FWI values in these two modes is in the southern and central BTH regions, respectively. Subsequently, we examine the relationship between FWI variations and atmospheric circulation patterns. A synoptic analysis indicates that the increased fuel availability index observed in SOM modes 2 and 7 can be primarily attributed to two key factors. One is a post-trough system, which is marked by a decrease in water vapor transport. The other is a high-pressure system, which is associated with higher temperatures and drought conditions. Finally, the relative contributions of the fuel available index and the wildfire spread rate index to the FWI are quantified using a partial differential approach. The variations in the fuel available index are the primary drivers of the high FWI values in these two SOM patterns. This study underscores the importance of analyzing the synergistic effects of multiple atmospheric circulation patterns on the fuel availability index, which is critical for improving wildfire danger prediction at different timescales in the BTH region. Full article

Surface ozone has emerged as a concerning pollutant in Beijing, China. This study assessed ozone pollution and its health impacts in Beijing using ground (35 stations) and satellite data (2014–2023). Temporal trends were analyzed across various temporal scales, while spatial variability was evaluated using integrated ground and satellite-derived continuous data. Health impacts were quantified via a log-linear concentration–response model. Results show that for ozone concentrations during the post-pandemic period (2019–2023, covering the onset of COVID-19 in 2019 and the period following), daytime concentrations decreased by 6.8 μg/m³, but nighttime concentrations increased by 5.4 μg/m³. Spatially, ozone concentrations were higher in urban areas than in suburban areas in summer, but the reverse occurred in other seasons. Satellite data revealed broader Grade II (160 μg/m³) exceedance variability (3.0–20.3%) compared to station estimates (15.3–18.7%). Health impact assessments indicated that achieving the Grade I standard (100 μg/m³) could prevent approximately 576 (95% CI: 317–827) all-cause deaths and 294 (95% CI: 111–467) cardiovascular deaths per year, which is 3.5 times more than the reductions from meeting the Grade II standard (160 μg/m³). These findings underscore the need for adaptive ozone controls and tiered mitigation strategies to reduce health risks in Beijing. Full article

Accurately assessing the impact of anthropogenic carbon dioxide (CO₂) emissions on CO₂ concentrations is essential for understanding regional climate change, particularly in high-emission countries like China. This study employed the GEOS-Chem chemical transport model to simulate and compare the spatiotemporal distributions of XCO₂ of three anthropogenic CO₂ emission inventories in mainland China for the 2018–2020 period and analyzed the effects of emission variations on atmospheric CO₂ concentrations. In eastern China, particularly in the Yangtze River Delta (YRD) and Beijing-Tianjin-Hebei (BTH) regions, column-averaged dry air mole fractions of CO₂ (XCO₂) can exceed 420 ppm during peak periods, with emissions from these areas contributing significantly to the national total. The simulation results were validated by comparing them with OCO-2 satellite observations and ground-based monitoring data, showing that more than 70% of the monitoring stations exhibited a correlation coefficient greater than 0.7 between simulated and observed data. The average bias relative to satellite observations was less than 1 ppm, with the Emissions Database for Global Atmospheric Research (EDGAR) showing the highest degree of agreement with both satellite and ground-based observations. During the study period, anthropogenic CO₂ emissions resulted in an increase in XCO₂ exceeding 10 ppm, particularly in the North China Plain and the YRD. In scenarios where emissions from either the BTH or YRD regions were reduced by 50%, a corresponding decrease of 1 ppm in XCO₂ was observed in the study area and its surrounding regions. These findings underscore the critical role of emission control policies in mitigating the rise in atmospheric CO₂ concentrations in densely populated and industrialized areas. This research elucidates the impacts of variations in anthropogenic emissions on the spatiotemporal distribution of atmospheric CO₂ and emphasizes the need for improved accuracy of CO₂ emission inventories. Full article

Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m⁻³, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m⁻³, 7 μg·m⁻³, 8 μg·m⁻³, and 15 μg·m⁻³ for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies. Full article