The Yellow River Basin in Inner Mongolia (YRBIM) is a typical arid—semiarid ecological transition zone highly sensitive to climate change. Using long-term Normalized Difference Vegetation Index (NDVI) data together with meteorological and land cover data, this study applied the Sen+Mann–Kendall method and path coefficient analysis to quantify the direct and indirect effects of climatic factors on vegetation coverage. The YRBIM experienced a non-significant warm–wet trend from 1998 to 2019, characterized by slight increases in precipitation and temperature with asynchronous spatial patterns. Vegetation coverage generally improved, with high coverage areas expanding by 12.66% and low coverage areas decreasing by 10.04%. Improvement occurred mainly in eastern croplands and grasslands, while degradation in the northwest coincided with urban expansion and mining. Precipitation showed a highly significant positive correlation with the NDVI at 0.7510. The direct effect of precipitation was dominant at 0.7515, while the indirect effect was negligible at 0.0005. Temperature showed a weak inhibitory effect with a comprehensive effect of 0.0302, where the indirect inhibitory effect at 0.0400 slightly exceeded the direct promotional effect at 0.0098. These response patterns were consistent across most land cover types, except in rural settlements and unused land where temperature showed a weak positive influence. This study provides a scientific basis for ecological conservation and sustainable management in arid—semiarid transition zones.

This study investigates subauroral phenomena during the main phase of the 10 May 2024 geomagnetic storm using a combination of ground-based observations from the WD IZMIRAN observatory (magnetometer, ionosonde, and all-sky imager) and Global Self-consistent Model of the Thermosphere, Ionosphere, Protonosphere (GSM TIP) simulations. During 18:00–20:00 UT, we identified the simultaneous occurrence of ionospheric signatures of Polarization Jets (PJ)/Sub-Auroral Ion Drifts (SAID) and Strong Thermal Emission Velocity Enhancement (STEVE) over Kaliningrad, consistent with previously reported PJ/SAID identification from DMSP drift velocity measurements. This identification is supported by: (1) characteristic purple emissions (clearly visible in all three channels) moving rapidly westward; (2) U-shaped structures in ionogram sequences; (3) the reproduction of supersonic westward plasma drifts within a narrow latitudinal band by the first-principles model; and (4) observed and simulated significant Ne depletion. The estimated ion drift velocity from all-sky imaging (assuming an emission altitude of 200 km) is consistent with GSM TIP simulations, which predicted PJ/SAID velocities of ~750 m/s driven by a latitudinally narrow (~3°) but longitudinally extended (>50°) poleward electric field (40 mV/m). Simulations reveal that this PJ/SAID phenomenon causes a reversal of the zonal thermospheric wind at 250 km and induces Ne disturbances across the 200–700 km altitude range. The electron temperature enhancement (up to 1500 K) exhibits a “falling drop” shape, peaking at 350 km, while ion heating exceeds 150 K. The neutral temperature shows a dual response: frictional heating at 120–160 km and localized cooling at 175–250 km due to drop in electron density. Additionally, an increase in atomic oxygen concentration was predicted within the 90–200 km range across the PJ/SAID longitudinal sector.

Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH4 emissions at the facility-level. Near-field quantification of methane (CH4) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH4 h−1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved R2∈(0.53,0.75) across PM2.5, PM10, NO2, and O3, representing from 40% (for O3) to 408% (for PM2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates (α) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM10 and O3, p<0.05). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived α parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design.

Decarbonizing energy production is critical to slowing the effects of climate change and furthering global sustainability. Progress is often gauged via carbon dioxide (CO2) emissions; however, sources of CO2 vary beyond combustion, presenting a significant challenge to accurate tracking due to these various sources and sinks and the ubiquitous nature of CO2 in the atmosphere. Nitrogen oxide (NOX) emissions have previously been proposed as a surrogate for tracking sustainability, as they are primarily released from combustion processes. Facility-level data from Canada’s National Pollutant Release Inventory and Greenhouse Gas Reporting Program over a six-year period is used to assess the correlation between NOX and CO2 emissions from integrated facilities across Canada. Combustion-related CO2 emissions accounting for approximately 94% of Canadian industrial emissions are examined, targeting eleven industries which together encompass over 90% of combustion emissions. Multiple linear regressions (MLRs) on each industry correlating NOX, CO2, and the inventory methods used (i.e., emission factors (EFs), source monitoring, mass balance, engineering estimates, and speciation) show R2 values ranging from 0.81 to 0.96 for all but one industry. Several industries indicate that the methods used to calculate emissions influence the correlation of CO2 to NOX, highlighting issues in the current inventory techniques. The NOX-to-CO2 ratios calculated for the integrated facilities are similar to the ratios of the published main process-level EFs for NOX to CO2 (where available). These MLR models on NOX could be used to predict CO2 emissions with relative ease and accuracy in other jurisdictions, thereby simplifying large-scale emission inventory compilation while tracking sustainability.

Variations in terrestrial carbon flux influence atmospheric CO2 exchange and related climate feedback, with Net ecosystem productivity (NEP) serving as a key metric for assessing ecosystem carbon source–sink dynamics. Given the vital ecological barrier function of the Tibetan Plateau (TP), understanding the spatiotemporal variability of NEP and its climatic controls is essential for elucidating carbon sink and climate interactions under ongoing climate change. The spatiotemporal dynamics of NEP across the TP from 1979 to 2018 are investigated using the process-based Community Land Model version 5.0 (CLM5.0). And climate sensitivity experiments are conducted to quantify the relative contributions of different climate factors to NEP variability. Furthermore, future changes in NEP for the period 2025–2100 under multiple Shared Socioeconomic Pathway (SSP) scenarios are projected. The results indicate that the TP functioned predominantly as a net carbon sink during the historical period, with a multi-year mean NEP of 23.96 g C m2 yr−1. Spatially, NEP showed a significantly increasing gradient from the northwest to the southeast. During 1979–2018, NEP exhibited an overall decreasing trend across most regions of the TP. Air temperature was identified as the dominant controlling factor, accounting for approximately 68% of the interannual NEP variability, followed by solar radiation (21%) and precipitation (11%). The dominant climatic drivers of NEP variation differ among regions: air temperature predominates in the southwestern and southeastern regions, radiation dominates in the northwestern and central areas, and precipitation exerts a controlling effect in the northern and western regions. Future projections suggest that NEP remains positive under all SSP scenarios, indicating that the TP is likely to persist as a carbon sink throughout the 21st century. This study provides important reference for the development of ecological protection, restoration planning, and regional carbon neutrality strategies.

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas.

This study evaluates the applicability of ERA5 wind speed (WS) and wind direction (WD) in the East China Sea, using high-resolution vertical wind profiles measured by a floating LiDAR at the Shanghai Nanhui Offshore Wind Farm from 15 January 2022 to 15 January 2023. Key findings are as follows: (1) Strong positive correlations exist between LiDAR-measured and ERA5 WS across all evaluated heights, with correlation coefficients of 0.76 (ground level), 0.86 (50 m), 0.88 (100 m), and 0.90 (200 m), respectively, and corresponding root mean square errors (RMSEs) of 2.33 m/s, 1.78 m/s, 1.73 m/s, and 1.77 m/s. This systematic improvement in correlation and modest reduction in RMSE with increasing height indicate that ERA5 captures vertical wind structure with progressively higher fidelity above the surface layer. (2) Both the ERA5 dataset and LiDAR measurements consistently show dominant wind frequencies in the NNE and SSE directions, with peaks at approximately 1000 occurrences. The minimal differences in the two datasets demonstrate the ERA5’s robust representation of near-surface offshore WD climatology. (3) The ERA5 reanalysis data of typhoon Muifa can better illustrate the increase in the initial WS and its subsequent decreases. However, the peak WS lags behind measurements by 2 h, and the extreme WS is significantly lower than that measured. Evaluations of the multi-year return period WS demonstrate an underestimation of extreme WS by 16.06–16.51% for the ERA5 data. Regarding the WD, the measured direction is clockwise, while that of the ERA5 is counterclockwise, revealing a fundamental deficiency in its representation of mesoscale cyclonic wind structure. Therefore, ERA5 reanalysis data provides reliable characterization of typical offshore WS and WD within the operational wind turbine hub-height range (100–200 m). For typhoon-related wind engineering assessments, the applicability of ERA5 data necessitates caution and potentially bias correction.

Accurate prediction of extreme rainfall events during the Indian Summer Monsoon (ISM, June to September) is critical for disaster preparedness and mitigation. This study evaluates the performance of two operational numerical weather prediction models, a high-resolution version of Global Forecast System (GFS T1534) and the control member of the Met Office Global and Regional Ensemble Prediction System-Global (MOGREPS-G), in forecasting such events during the ISM from 2020 to 2023. The results demonstrate that, with respect to observations, both models tend to underestimate the mean and variability of rainfall; GFS-T1534 represents the mean and correlation better while MOGREPS-G represents the variability better over the Indian landmass. To assess the models’ performance for extreme rainfall prediction, we fix a rainfall threshold of 50 mm day−1, and the skill scores are computed including Probability of Detection, False Alarm Rate, Bias score and F1 score. Together, these scores indicate that both models show potential in short-range forecasting of extreme rainfall events, particularly within 24 h, but their skills remain limited at longer lead times. Specifically, the model biases vary over different geographical locations, often showing contrasting features. This underscores the need for model-specific post-processing and calibration techniques if these forecasts are to be used effectively for operational decision-making.

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management.