Search Results (396)

Our previous study examined the stratosphere-troposphere exchange (STE) of air mass and ozone using ERA5 and MERRA2 reanalysis data and observations for 2007–2010. Their analysis applied a lower stratosphere mass budget approach, with the 380 K isentropic surface serving as the upper boundary of the lowermost stratosphere. This study employs a dynamic isentropic surface fitted to the tropical tropopause, providing an update to the results using the static 380 K boundary. Additionally, we improve the numerical scheme for deriving the mass of the lowermost stratosphere. Under this new framework, the air mass upward flux at the isentropic surface in the tropics increases from 19.3 × 10⁹, 19.3 × 10⁹, and 22.0 × 10⁹ kg s⁻¹ in our previous study to 21.9 × 10⁹, 20.9 × 10⁹, and 26.3 × 10⁹ kg s⁻¹ in the present study for ERA5, MERRA2, and observations, respectively. The global ozone fluxes across the fitted isentrope become −347.6, −362.5 and −368.4 Tg yr⁻¹ as compared to −345.7, −359.5 and −335.6 Tg yr⁻¹ at the 380 K level from our previous study for ERA5, MERRA2 and observations, respectively. The corresponding extratropical ozone fluxes are −539.3, −541.3 and −565.5 Tg yr⁻¹ versus previous estimates of −538.1, −542.5 and −527.8 Tg yr⁻¹. The increased role of tropical cirrus clouds near the tropopause is also highlighted under the updated framework in observations. The contribution of cloud heating to tropical air mass flux increases from 2.0% in our previous study to 8.2% in the present analysis, while for ozone, the corresponding contribution increases from 1.8% to 8.1%. We further show that the improved estimate of the change rate of mass in the lowermost stratosphere has an impact on seasonal ozone STE results from chemistry climate models presented in another of our previous studies. These findings provide new insights into the processes governing stratosphere-troposphere exchange. Full article

Accurate estimation of path loss is essential for evaluating the impact of the propagation medium, determining transmission power requirements, and optimizing cell layouts for effective 5G millimetre wave coverage. At 28 GHz, rain attenuation is a critical factor, with its impact varying significantly based on environmental and regional characteristics. This study quantifies the degradation of 5G millimetre wave link budgets due to rainfall in South Africa and assesses the maximum coverage ranges for urban micro and urban macro deployments under varying rain intensities. The analysis focuses on Pretoria, a city characterized by diverse urban landscapes and seasonal thunderstorms. Urban micro cells are deployed on streetlights and building facades in dense zones such as Hatfield and Sunnyside to deliver high-capacity coverage. In contrast, urban macro cells target broader coverage from elevated structures, such as those in the Pretoria CBD. Using the Close-In path loss model for both line-of-sight and non-line-of-sight conditions, this study examines the relationships between link budget parameters, maximum path loss, and 5G millimetre wave link distances under rain-affected and clear-sky scenarios. The results highlight the significant influence of rainfall, particularly in non-line-of-sight conditions, and provide insights for designing efficient 5G networks tailored to South Africa’s unique climate. Full article

The research focuses on the National Rural Development Programme (NRDP) during the transition period, assessing the absorption level of sub-measure 4.1, “Investments in agricultural holdings”, which impacts rural development in the agricultural sector in western Romania. A quantitative and qualitative analysis of all selection reports associated with sub-measure 4.1 submitted during the transition period (2021–22) was conducted to investigate a potentially relevant link between the number of beneficiaries identified in the analysed region and their location. Fisher’s exact tests indicate that the null hypothesis, which postulates independence between county and measure in the observed dataset, cannot be rejected. Further empirical analysis was conducted using panel data analysis to identify any relevant regression traits. Tests indicate that funding allocation, the spatial dimension and the temporal dimension are all statistically and substantively significant. Larger budget allocations are associated with a higher volume of proposals. Two out of the four analysed counties systematically outperformed the predicted values in the model by submitting more proposals than would be expected given their budgets. Later application stages yielded a greater number of successful proposals, which is consistent with residual demand capture in sequential competitive calls. Full article

Increasing debt, natural disasters, and extreme weather events claim an ever-larger part of national budgets across the globe, undermining global stability and the capacity of our societies to transition to sustainability. The dominant crisis response policy paradigm treats the economy and the environment as separate domains and is based on a ‘fix-the-economy-first’ principle, i.e., fiscal consolidation and debt sustainability need to be achieved first before addressing other socio-environmental policy goals. This paper demonstrates that this approach entraps countries and the global economy in a vicious cycle. In the absence of an integrated policy framework for addressing these intersecting challenges, our responses to financial stress often exacerbate the environmental crisis and its consequences, adding further financial strain on an already fragile socio-environmental system. Breaking out from this conundrum requires a new crisis response policy paradigm. To this end, this study develops the Unsustainable Growth Vicious Cycle (UGVC) as an analytical framework that exemplifies the incentive structure that governs the dominant crisis response model, and the negative feedback loops that sustain it. Our analysis unfolds in four stages. We analyse how financial stress triggers multidimensional poverty traps and how these impact on the environment. We use the concept of poverty-environment trap 2.0 to capture the emergence of the environmental crisis as a global poverty and inequality trap in its own right. We explicate the limits of the dominant economic policy paradigm through the lens of unsustainable economic growth. We finally discuss the need of transforming ‘economic adjustment programmes’ into ‘sustainability adjustment programmes’, as part of a new global settlement for sustainability transition. Full article

Massive wildfires release enormous amounts of biomass-burning (BB) aerosols into the atmosphere, which might have a major impact on its thermal and radiative budget, as well as the environment and human health. This work presents the results of a study and characterization of a long-range transport episode of smoke aerosols from Canadian forest fires towards the entirety of Europe, as observed over Sofia, Bulgaria, in early October 2023. This study makes use of data from combined lidar, ceilometer, and sun-photometer measurements, supported by model and forecast data, meteorological radiosonde profiling, and (re)analyses, together with tracking and mapping of the aerosol air transport. A distinctive feature of the considered episode over Europe is the downward movement of the air masses, entraining smoke aerosols from the continental mid-troposphere down to the near-surface layers. The driving mechanism of the long-range transport of BB aerosols and their spread over Europe is revealed. Optical parameters of the registered aerosols are determined and vertically profiled with a high range resolution by lidar data analysis. A wide set of columnar optical and microphysical aerosol characteristics is also provided by sun-photometer measurements. The results show a dominance of relatively fine modes of dry smoke particles in the submicron size range, with a predominantly low degree of non-sphericity, indicating minimal up-size aging during the BB aerosol transport from Canada to the Sofia region. The average daily aerosol radiative forcing is determined by sun-photometer measurements and briefly discussed. Full article

Coastal erosion is a growing concern in the Mediterranean region, where the combined effects of anthropogenic pressure, reduced fluvial sediment supply, and climate change-driven sea level rise and extreme storm events threaten the stability of sandy shorelines. This study examines the geomorphological impacts of the exceptional storm surge of 3 November 2023, associated with Storm Ciaran, which affected a vulnerable coastal segment north of the Morto Nuovo River in northern Tuscany (Italy). Using UAV-based photogrammetric surveys and high-resolution morphological analysis, we quantified shoreline retreat, dune toe regression, beach slope changes, and sediment volume loss. The storm induced an average shoreline retreat of over 5 m, with local peaks reaching 30 m, and a dune toe setback of up to 7 m. A net sediment budget deficit of approximately 1800 m³ was recorded, over 50% of the total volume added during soft nourishment interventions performed in the previous decade. Our findings highlight how a single high-energy event can match or exceed the annual average erosion rate, emphasizing the limitations of traditional shoreline-based monitoring and hard defense structures. This study highlights the importance of frequent, high-resolution monitoring focused on individual storm events, which is crucial to better understand their specific geomorphological impacts. Such detailed analyses help clarify whether long-term erosion trends are primarily driven by the cumulative effect of high-energy events. This knowledge is essential for identifying the most effective coastal protection strategies and for improving the design of defense structures. This approach is particularly relevant in the context of climate change, which is expected to increase the frequency and intensity of extreme events, making it imperative to base future planning on accurate, event-driven data. Full article

Older adults, aged 65 years and older, develop and experience frailty at different rates. Yet, this heterogeneity is not well understood, nor are the factors, such as geographical residence, that influence different frailty trajectories and subsequent healthcare outcomes. We aim to identify factors that impact older adult frailty trajectories, skilled nursing facility (SNF) placement, and death. Medicare beneficiaries ≥ 65 years from the National Health and Aging Trend Study (2011–2021) with complete data using Fried’s frailty phenotype on ≥ 2 occasions (n = 6082) were included in the analysis. Rural/urban residence was defined using Office of Management and Budget criteria. Latent class growth analysis (LCGA) helped identify four frailty trajectories: improving, stable, mildly worsening, and drastically worsening. Cox proportional hazard analysis and logistic regression determined the association of social determinants of health (sex, race/ethnicity, education and income level, healthcare and transportation access, and social support) on death and SNF admission, respectively. The mean age was 75.12 years (SE 0.10); 56.4% female, 18.6% (n = 1133) rural residence. In the overall sample, 1094 (23.0%) older adults were classified as robust, 3242 (53.0%) as pre-frail, and 1746 (24.0%) as frail. Urban residence did not modify the relationship between frailty trajectories and SNF placement, nor did geographic residence on death. Higher income was associated with lower odds of a worse frailty trajectory, SNF admission, and a lower hazard of death, all reaching statistical significance. Future work should examine the factors that influence older adult participation in research and the impact of standardizing the definition of geographic rurality on older adult frailty and health outcomes. Full article

The Earth’s Radiation Budget (ERB) is a critical component for understanding the planet’s climate system, as it governs the balance between incoming solar energy and outgoing thermal radiation. Accurate monitoring of the ERB, combined with Ocean Heat Content (OHC) measurements, is essential to assess Earth’s Energy Imbalance (EEI) and its implications for global warming. This paper presents new results on the ERB based on data from the Uvsq-Sat and Inspire-Sat nanosatellite missions, which operated from 2021 to 2024. These satellites constitute the first European constellation demonstrator designed for broadband, Wide Field-Of-View (WFOV) measurements of the ERB. While WFOV instruments provide enhanced temporal and spatial coverage, they do not replace the need for Narrow Field-Of-View (NFOV) measurements, such as those provided by the established Clouds and the Earth’s Radiant Energy System (CERES) instruments. Instead, they are designed to complement them. By using data from both the WFOV constellation and CERES instruments to measure Reflected Solar Radiation (RSR) and Outgoing Longwave Radiation (OLR), we estimate the EEI and monitor its evolution. Our analysis reveals a generally good agreement between Uvsq-Sat and CERES data for EEI from 2021 through the end of 2024. Over this period, EEI derived from Uvsq-Sat averaged +0.87 ± 0.23 ${\mathrm{Wm}}^{-2}$, closely matching the recent CERES trend. Both datasets indicate a peak in EEI in mid-2023, followed by a decline throughout 2024, likely reflecting stabilizing feedbacks triggered by the 2023 El Niño event. Importantly, this short-term decline occurred within a sustained upward trend in EEI since 2013, as shown by CERES observations, with solar activity having a negligible impact. Comparisons with OHC measurements confirm ongoing ocean heat accumulation, consistent with the rising decadal trend in EEI. These insights underscore the importance of continuous, high-frequency observations to capture the complex and rapidly evolving processes influencing Earth’s energy balance. Demonstrations using nanosatellites at different local times illustrate the advantages of small satellite constellations for improved monitoring frequency and coverage, particularly for variables that change over short time scales, such as RSR, also known as Outgoing Shortwave Radiation (OSR). Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS^® version 23, to identify, assess, and prioritize key risks. Surveys and interviews revealed seven management factors (budget, schedule, safety, productivity, contracting, quality, and environment) and 18 critical risks, including design errors and procurement delays. The model quantifies risk impacts, provides a regression equation for risk classification, and supports effective mitigation strategies. Based on this model, decision-making can be facilitated for the implementation of effective mitigation strategies. It also promotes continuous improvement, optimizing economic resources and minimizing environmental impacts, addressing a research gap in Colombia’s petrochemical sector and paving the way for broader industrial applications. Full article

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions. Full article

The maritime sector faces urgent decarbonisation pressures due to regulatory instruments, such as the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII), which mandates reductions in greenhouse gas emissions per transport work. This paper investigates the challenge of identifying CII-compliant strategies that are also financially viable for UK shipowners. To address this, operational and technical data from UK-flagged vessels over 5000 GT are analysed using a capital budgeting framework. This includes scenario-based evaluation of speed reduction, payload limitation, and retrofitting with dual-fuel LNG and methanol engines. The analysis integrates carbon taxation, and pilot fuel use to assess impacts on emissions and profitability. The findings reveal that while the short-term operational measures examined offer modest gains, long-term compliance and financial performance are best achieved through targeted retrofitting supported by carbon taxes and favourable market conditions. The study provides actionable insights for shipowners and policymakers seeking to align commercial viability with regulatory obligations under the evolving CII framework. Full article

Climate change continues to pose significant challenges to global water security, with dams being particularly vulnerable to hydrological cycle alterations. This study investigated the climate-based impact on the hydrology of the Doğancı dam, located in Bursa, Turkey, using feed-forward neural networks (FNNs). The modeling used meteorological parameters as inputs. The employed FNN comprised one input, hidden, and output layer. The efficacy of the models was evaluated by comparing the correlation coefficients (R), mean squared errors (MSE), and mean absolute percentage errors (MAPE). Furthermore, two training algorithms, namely Levenberg-Marquardt and resilient backpropagation, were employed to determine the algorithm that yields more accurate output predictions. The findings of the study showed that the model using air temperature, solar radiation, solar intensity, evaporation, and evapotranspiration as predictors for the water budget and water level of the Doğancı dam exhibited the lowest MSE (0.59) and MAPE (1.31%) and the highest R (0.99) compared to other models under LM training. The statistical analysis determined no significant difference (p > 0.05) between the Levenberg and Marquardt and resilient backpropagation training algorithms. However, a visual interpretation revealed that the Levenberg-Marquardt algorithm outperformed the resilient backpropagation, yielding lower errors, higher correlation values, and faster convergence for the models tested in this study. The novelty of this study lies in the use of certain meteorological inputs, particularly snow depth, for dam inflow forecasting, which has seldom been explored. Moreover, this study compared two widely used ANN training algorithms and applied the modeling framework to a region of strategic importance for Turkey’s water security. This study highlights the effectiveness of ANN-based modeling for hydrological forecasting and determining climate-induced impacts on water bodies such as dams and reservoirs. Full article

Background/Objectives: Reducing waiting times for elective surgeries remains a critical global healthcare challenge that negatively impacts patient outcomes and economic productivity. This study develops an adaptable cost-utility modeling framework for assessing the cost-effectiveness (CE) of reducing waiting time for elective surgeries in data-limited environments. Methods: We evaluated the economic and health impacts of Egypt’s recent initiative aimed at decreasing surgical waiting lists. The study conducts a CE analysis of the initiative by estimating incremental costs (expressed in Egyptian Pounds—EGP) and outcomes (expressed in quality-adjusted life years—QALYs) before and after its implementation, performs a benefit–cost analysis to quantify the initiative’s return on investment, and employs a budget share method to evaluate catastrophic health expenditure (CHE). The analysis included five elective surgical interventions: open-heart surgery, cardiac catheterization, cochlear implantation, ophthalmic surgery, and orthopedic (joint replacement) surgery. Results: The main research outcomes of the study are as follows. The initiative resulted in incremental cost-effectiveness ratios of EGP 46,795 (societal perspective) and EGP 56,094 (payer perspective) per QALY, both within acceptable CE thresholds. Most of the evaluated interventions demonstrated substantial returns on the investment. Without public funding, more than 90% of patients faced CHE, indicating considerable financial barriers to elective surgeries. Conclusions: Egypt’s initiative to reduce waiting times was deemed cost-effective. Our adaptable modeling framework could be practical for similar evaluations in low/middle-income countries, especially where data is limited. Scaling up the initiative to include additional curative and preventive services and integrating it with broader health system reforms in Egypt is strongly recommended. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article