Search Results (11,030)

Ionospheric tomography, an effective method for reconstructing 3-D electron density, is traditionally pictured by 3-D IED (ionospheric electron density) slices to express ionospheric disturbances, which may overlook the critical information in 3-D spherical manifold space. Here, we develop a novel visualization framework that integrates tomography reconstruction with a spherical latitude–longitude grid system, enabling the comprehensive characterization of 3-D IED dynamic evolution in 3-D manifold spherical space. Through this method, we visualized two cases: the Hualien earthquake on 2 April 2024 and the geomagnetic storm on 24 April 2023. The results demonstrate the evolution of the electron density during earthquake and geomagnetic storms in the real 3-D space, showing that seismic events induce bottom-up IED negative anomalies localized near epicenters, while geomagnetic storms trigger top-down depletion processes, with IED propagating from higher altitudes in the real 3-D manifold space. Compared to the conventional slice, our visualization model can visualize the characteristics, with the coverage area being observed to increase with the altitude within the same geospatial coordinates. This framework can advance the identification of ionosphere anomalies by enabling the precise differentiation of anomaly sources. This work bridges gaps in geospatial modeling by harmonizing ionospheric tomography with Earth system grids, offering a feasible solution for analyzing multi-scale ionospheric phenomena. Full article

The broadband spectrum of solar radiation is commonly characterized by indices such as the average photon energy (APE) and the blue fraction (BF). This work explores the effectiveness of the two indices in a narrower spectral band, namely the ultraviolet (UV). The analysis is carried out from two perspectives: sensitivity to the changes in the UV spectrum and the uniqueness (each index value uniquely characterizes a single UV spectrum). The evaluation is performed in relation to the changes in spectrum induced by the main atmospheric attenuators in the UV band: ozone and aerosols. Synthetic UV spectra are generated in different atmospheric conditions using the SMARTS2 spectral solar irradiance model. The closing result is a new index for the signature of the solar UV radiation spectrum. The index is conceptually just like the BF, but it captures the specificity of the UV spectrum, being defined as the fraction of the energy of solar UV radiation held by the UV-B band. Therefore, this study gives a new meaning and a new utility to the common UV-B/UV ratio. Full article

Lightning is associated with severe thunderstorm events and causes hundreds of deaths annually in Brazil. Additionally, it is responsible for losses amounting to millions in Brazil’s electricity and telecommunication sectors. Between November 2011 and March 2012, the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign was conducted in the Vale do Paraíba region and the Metropolitan Area of São Paulo (MASP), located in southeastern São Paulo state, Brazil, to enhance the understanding of cloud processes, including lightning. During the campaign, several instruments were available: a meteorological radar, lightning location systems, rain gauges, a vertical-pointing radar, a surface tower, and others. In this context, the main goal of this study was to evaluate the temporal evolution of lightning properties, such as frequency, type (cloud-to-ground (CG) and intracloud (IC) lightning), peak current, length, and duration, in the MASP between November 2011 and March 2012. To achieve this objective, lightning data from the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) were utilized. The maximum amount of lightning for the BrasilDAT (322,598 events/month) occurred in January, while for the SPLMA (150,566 events/month), it occurred in February, suggesting that thunderstorms displayed typical summer behavior in the studied region. Most of lightning registered by the BrasilDAT were concentrated between 2:00 and 5:00 pm local time, with a maximum of 5.0 × 10⁴, 6.2 × 10³, and 95 events/month.hour for IC, −CG, and +CG lightning, respectively. These results are associated with the favorable conditions of diurnal atmospheric instability caused by surface heating. Regarding the lightning properties from the SPLMA, longer-duration lightning (up to 0.4 s) and larger spatial extension (up to 14 km) occurred during the nighttime period (0–6:00 am local time), while the highest lightning frequency (up to 9 × 10⁴ events month⁻¹ h⁻¹) was observed in the afternoon (3–4:00 pm local time). Full article

Although the benefits of trees in cities are of great variety and value, attention must also be paid to the consequences for public health of the presence of pollen aeroallergens in the atmosphere, which are likely to interact with air pollutants, influencing the alteration of the immune system, facilitating allergic reactions, and enhancing the symptoms of asthmatic patients. This study analyses (using multiple linear regression calculations performed with the data analysis tool Statgraphics Centurion 19) the interaction of the concentration of six types of tree pollen (Cupressaceae, Olea, Platanus, Pinus, Ulmus, and Populus) and six atmospheric pollutants (O₃, PM₁₀ and PM_2.5, NO₂, CO, and SO₂), on asthma care episodes in the Community of Madrid (CAM). In most of the calculated equations, the adjusted R² value is higher than 30%, and in all cases, the P-value of the models obtained is lower than 0.0001. Therefore, almost all models obtained in the study period for asthma are statistically significant. Olea is the pollen type most frequently associated with asthma (followed by Pinus and Populus), in all the years studied. In the same period, O₃ is the most common air pollutant in the equations obtained for asthma. Stronger interrelations with asthma are generally found in more urban municipalities. Full article

High-spatiotemporal-resolution surface temperature data play a crucial role in monitoring urban heat island effects. Compared with Landsat 8, MODIS surface temperature products offer high temporal resolution but suffer from low spatial resolution. To address this limitation, a two-step downscaling model (TSDM) was developed in this study for MODIS surface temperature by leveraging random forest (RF) algorithms. The model integrates remote sensing data, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI), alongside the land cover type, digital elevation model (DEM), slope, and aspect. Additionally, a water surface temperature fitting model (RF-WST) was established to mitigate the issue of missing data over water bodies. Validation using Landsat 8 data reveals that the average out-of-bag (OOB) error for the RF-250 m model is 0.81, that for the RF-WST model is 0.73, and that for the RF-30 m model is 0.76. The root mean square error (RMSE) for all three models is below 1.3 K. The construction of the RF-WST model successfully supplements missing water body data in MODIS outputs, enhancing spatial detail. The downscaling model demonstrates strong performance in grassland areas and shows robust applicability during winter, spring, and autumn. However, due to a half-hour temporal discrepancy in the validation data during the summer, the model exhibits reduced accuracy in that season. Full article

Fires drive global ecosystem change, impacting carbon dynamics, atmospheric composition, biodiversity, and human well-being. Biomass burning, a major outcome of fires, significantly contributes to greenhouse gas and aerosol emissions. Among these, fine particulate matter (PM_2.5) is particularly concerning due to its adverse effects on air quality and health, and its substantial yet uncertain role in Earth’s energy balance. Variability in emission factors (EFs) remains a key source of uncertainty in emission estimates. This study evaluates PM_2.5 emission sensitivity to EFs variability in Brazil’s Amazon and Cerrado biomes over 2002–2023 using the 3BEM_FRP model implemented in the PREP-CHEM-SRC tool. We updated the EFs with values and uncertainty ranges from Andreae (2019), which reflect a more comprehensive literature review than earlier datasets. The results reveal that the annual average PM_2.5 emissions varied by up to 162% in the Amazon (1213 Gg yr⁻¹ to 3172 Gg yr⁻¹) and 184% in the Cerrado (601 Gg yr⁻¹ to 1709 Gg yr⁻¹). The Average peak emissions at the grid-cell level reached 5688 Mg yr⁻¹ in the “Arc of Deforestation” region under the High-end EF scenario. Notably, the PM_2.5 emissions from Amazon forest areas increased over time despite shrinking forest cover, indicating that Amazonian forests are becoming more vulnerable to fire. In the Cerrado, savannas are the primary land cover contributing to the total PM_2.5 emissions, accounting for 64% to 80%. These findings underscore the importance of accurate, region-specific EFs for improving emission models and reducing uncertainties. Full article

High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction. Full article

Calibration is crucial in quantitative analysis, ensuring the traceability of standards for an accurate comparison of results. In mercury determinations, a gas calibrator unit containing liquid mercury is used for calibration by injecting headspace volumes via syringe. The Dumarey equation has been used for over 35 years to calculate mercury headspace concentration, aligning closely with saturated vapor pressure equations. However, the 2006 Huber equation yields different values, creating discrepancies. This paper compares calibrations using the Dumarey equations against NIST 3133 certified reference material, with detection by a cold vapor atomic fluorescence spectrophotometer (CV-AFS). The gas standard was injected directly, while Hg^II in NIST 3133 was reduced to Hg⁰ and captured on gold traps. Across 10–24 °C, the Hg⁰ concentration was determined, with uncertainties ranging from 2.9% to 8.4% for a coverage factor of two. No significant differences were found between calibrations using NIST 3133 and the Dumarey equation. These findings provide crucial insights into the traceability and accuracy of mercury calibration methods, ensuring the reliability of measurements used for environmental monitoring and regulatory compliance. Full article

Flood disasters are one of the major natural hazards threatening agricultural production. To reduce agricultural disaster losses, accurately identifying agricultural flood-affected areas is crucial. Taking Shengzhou City as a case study, we proposed a refined method for identifying agricultural flood-affected areas by integrating microwave and optical remote sensing data with deep learning techniques, GIS, and the pixel-based direct differencing method. Complementary advantages of microwave and optical remote sensing data can effectively solve the problem of difficulty in accurately detecting floods due to thick clouds before and after flood disasters. Deep learning technology can effectively identify farmland areas, and the pixel direct difference method can accurately analyze agricultural flood disasters. Analyzing three typical rainfall events along with the topographical and geomorphological characteristics of Shengzhou City, the results indicate that agricultural flood disaster areas exhibit significant spatial heterogeneity. The primary influencing factors include rainfall intensity, topography, and drainage infrastructure. The northern, eastern, and southwestern regions of Shengzhou City, particularly the peripheral areas adjacent to mountainous and hilly terrains, contain most of the flood-affected farmland. These areas, characterized by low-lying topography, are highly susceptible to flood disasters. Therefore, optimizing the drainage systems of farmland in low-lying areas near mountainous and hilly regions of Shengzhou City is essential to enhance flood resilience. Full article

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter tuning. Key findings reveal spatiotemporal prediction patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R²: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking at SPEI-12 where CPSO-XGBoost attains optimal performance (R²: 0.85–0.90, RMSE: 0.33–0.43) with 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak accuracy at SPEI-12 (R² ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) show dramatic improvement from SPEI-1 (R² < 0.35, RMSE > 1.0) to SPEI-12 (R² > 0.85, RMSE reduction > 52%). Multivariate probability density analysis confirms the model’s robustness through enhanced capture of nonlinear atmospheric-land interactions and reduced parameterization uncertainties via swarm intelligence optimization. The CPSO-XGBoost’s superiority stems from synergistic optimization: binary particle swarm feature selection enhances input relevance while adaptive parameter tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These findings establish an advanced computational framework for drought early warning systems, providing critical support for climate-resilient water management and agricultural risk mitigation through spatiotemporally adaptive predictions. Full article

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. In this paper, nanoparticle number emissions are estimated using nanoparticle number emission factors (NPNEF) and road traffic characteristics. Traffic flow and fleet composition were estimated using the Leeds Transport Model, which showed that the road traffic in Leeds consisted of 41% petrol cars, 43% diesel cars, 9% LGV, 2% HGV, and 4.5% buses and coaches. Two approaches were used for emission estimation: (a) a detailed model, which required detailed information on traffic flow and fleet composition and NPNEFs of various vehicle types; and (b) a simple model, which used total traffic flow and a single NPNEF of mixed fleet. The estimations of both models demonstrated a strong correlation with each other using the values of R, RMSE, FAC2, and MB, which were 1, 2.77 × 10¹⁷, 0.95, and −1.92 × 10¹⁷, respectively. Eastern and southern parts of the city experienced higher levels of emissions. Future work will include fine-tuning the road traffic emission inventory and quantifying other emission sources. Full article

With more than half of the world already living in urban spaces—a number set to increase in the coming decades—the need is clear to understand urban microclimates and extremes. This study placed twenty MX2302a HOBOmobile weather microsensors placed in aerated housings across the ~4 km² of the campus of the University of South Alabama from September to November 2022 and recorded temperature, relative humidity, and dewpoint every minute during the study period. These sensors were placed in situ, which allowed for the diversity in land cover, canopy cover, and aspect—large microclimatic drivers—to be captured. Sensors were compared to a campus mesonet station, part of the South Alabama Mesonet, a member of the National Mesonet Program. During the study period, temperatures were found to vary as much as 13 °C at the same minute across campus, with substantial changes in humidities and dewpoints also found. For example, the campus mesonet may have read 32 °C, yet the sensors could read as low as 29 °C and as high as 42 °C at the same moment. This study shows that the world is far more complex than what is seen at the mesoscale under idealized conditions, and the implications for society are considered. Full article

This study aimed to assess the long-term trends in PM₁₀, PM_2.5, and hazardous trace elements in Makkah from 2020 to 2024, evaluating seasonal variations, health risks, and potential mitigation strategies. The results indicated that the PM₁₀ concentrations ranged from a minimum of 127.7 ± 14.2 µg/m³ (2020) to a maximum of 138.3 ± 15.7 µg/m³ (2024), while PM_2.5 levels varied between 100.7 ± 18.7 µg/m³ and 109.8 ± 21.3 µg/m³. A seasonal analysis showed the highest PM₁₀ and PM_2.5 levels during winter (147.8 ± 16.4 µg/m³ and 119.5 ± 21.7 µg/m³ in 2024, respectively), coinciding with lower wind speeds and reduced dispersion. Among the nine trace elements analyzed, Cr VI exhibited the highest increase from 0.008 ± 0.001 µg/m³ (2020) to 0.012 ± 0.001 µg/m³ (2024), while Cd and Ni also rose significantly. The excess cancer risk (ECR) associated with these pollutants exceeded the recommended threshold, with a strong correlation between PM₁₀ and ECR (r = 0.85–0.93, p < 0.01). These findings highlight the need for enhanced air quality monitoring and sustainable urban planning. Future research should focus on identifying the dominant pollution sources and assessing the long-term health impacts to support evidence-based air quality management in Makkah. Full article

This study explores rainfall variability and trends in the Enkangala Escarpment of South Africa using station data from 1972 to 2022 (51 years). The coefficient of variation (CV) is indicative of pronounced inter-annual variability in seasonal rainfall totals across the region. The trend-free pre-whitening Mann–Kendall (TFPWMK) test and innovative trend analysis (ITA) were used to determine the presence of monotonic trends in the station records, despite the pronounced inter-annual variability in the time series. Sen’s slope estimator was used to quantify the magnitude of the trends. For a given season, the ITA test, in general, allocates local statistical significance to the time series for more stations compared to the TFPWMK test. For winter, spring and summer, there is spatial coherency of decreasing rainfall trends across the Enkangala Escarpment. These trends also exhibit local significance for spring at most stations, and are indicative of less favorable growing conditions for crops during this season. Reduced spring rainfall is likely to also translate to later planting dates (a shorter growing season) and a longer burning season. Trends for autumn are generally weak and lack in local statistical significance or spatial coherency. Full article