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Background: In response to the COVID-19 pandemic, most countries have introduced non-pharmaceutical interventions, such as stay-at-home orders, to reduce person-to-person contact and break trains of transmission. The aim of this systematic review was to assess the effect of different public health restrictions on mobility across different countries and cultures. The University of Bern COVID-19 Living Evidence database of COVID-19 and SARS-COV-2 publications was searched for retrospective or prospective studies evaluating the impact of COVID-19 public health restrictions on Google Mobility. Titles and abstracts were independently screened by two authors. Information from included studies was extracted by one researcher and double checked by another. Risk of bias of included articles was assessed using the Newcastle Ottowa Scale. Given the heterogeneous nature of the designs used, a narrative synthesis was undertaken. From the search, 1672 references were identified, of which 14 were included in the narrative synthesis. All studies reported data from the first wave of the pandemic, with Google Mobility Scores included from January to August 2020, with most studies analysing data during the first two months of the pandemic. Seven studies were assessed as having a moderate risk of bias and seven as a low risk of bias. Countries that introduced more stringent public health restrictions experienced greater reductions in mobility, through increased time at home and reductions in visits to shops, workplaces and use of public transport. Stay-at-home orders were the most effective of the individual strategies, whereas mask mandates had little effect of mobility., Conclusions: Public health restrictions, particularly stay-at-home orders have significantly impacted on transmission prevention behaviours. Further research is required to understand how to effectively address pandemic fatigue and to support the safe return back to normal day-to-day behaviours., Competing Interests: The authors have declared that no competing interests exist.

Ba0.5Sr0.5TiO3 (BST) thin-film capacitor structures with various thicknesses, (50–1200 nm) and different strain conditions (on lanthanum strontium cobalt oxide La0.5Sr0.5CoO3 and strontium ruthenate SrRuO3 buffer layers) were made using pulsed laser deposition, and characterized by x-ray diffraction. The out-of-plane lattice parameter was followed as a function of temperature within the 100–300 K temperature interval. The phase sequence (cubic-tetragonal-orthorhombic-rhombohedral) known to exist in the bulk analog is shown to be strongly affected by both the stress conditions imposed by the buffer layer and the thickness of the BST film itself. Thus, no phase transition was found for the in-plane compressed BST films. On the stress-free BST films, on the contrary, more phase transitions were observed. It appeared that the complexity of structural phase transitions increased as the film thickness in this system was reduced. [ABSTRACT FROM AUTHOR]

Thin film Au/Ba[sub 0.5]Sr[sub 0.5]TiO[sub 3]/SrRuO[sub 3] capacitor structures, with a thickness of dielectric varying between ∼70 and ∼950 nm, were deposited on {001} MgO single-crystal substrates using pulsed laser deposition. Low-field dielectric measurements were performed as a function of temperature and frequency. At all temperatures and frequencies, the dielectric response as a function of thickness was found to adhere reasonably well to the so-called “series capacitor model,” from which nominal “bulk” and “interfacial” capacitance components could be extracted. The bulk component showed weak frequency dependence but strong temperature dependence, with a peak in permittivity and dielectric loss around 250 K and 150 K, respectively. Well above 250 K, reasonable Curie–Weiss behavior was evident. Overall, the extracted bulk component behaved much as would be expected in real bulk ceramics or single crystals lending confidence as to the general applicability of the series capacitor model. The functional behavior of the extracted interfacial capacitance was rationalized as being due to a combination of a thermally independent background, and thermally activated space charge. The activation energy of the space charge (∼0.6 eV) is commensurate with the detrapping of electrons from shallow level traps associated with oxygen vacancies. Importantly, since this component acts in series with the bulk component, the functional analysis performed here implies that the oxygen vacancies lie in a plane parallel to the electrodes. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Thin film parallel-plate capacitor structures with Ba[sub 0.5]Sr[sub 0.5]TiO[sub 3] as the dielectric layer were fabricated using pulsed-laser deposition. The thickness of the dielectric was varied between 7.5 nm and 1.4 µm. Low-field measurements of the dielectric constant showed the system to possess a nonzero interfacial capacitance. Since there is considerable debate about the origin of interfacial capacitance, further functional analysis was performed: zero-field dielectric susceptibility information was extracted using Landau-Ginzburg-Devonshire fitting of saturated polarization loops. This analysis revealed that the zero-field dielectric susceptibility extracted from P-E loops does not possess a finite interfacial component. The origin of interfacial capacitance therefore disappears (albeit temporarily) after a significant field has been applied and then removed during the P-E loop measurement cycle. [ABSTRACT FROM AUTHOR]

Ba0.5Sr0.5TiO3 (BST) thin-films with various thicknesses and different strain conditions were made using pulsed laser deposition and characterized by x-ray diffraction. No phase-transition was found for the in-plane compressed BST films, on the contrary more phase transitions were observed on the stress-free BST films.

Thin film Ba0.5Sr0.5 TiO3 (BST) capacitors of thickness ∼75 nm to ∼1200 nm, with Au top electrodes and SrRuO3 (SRO) or (La, Sr)CoO3 (LSCO) bottom electrodes were fabricated using Pulsed Laser Deposition. Implementing the "series capacitor model," bulk and interfacial capacitance properties were extracted as a function of temperature and frequency. 'Bulk' properties demonstrated typical ceramic behaviour, displaying little frequency dependence and a permittivity and loss peak at 250 K and 150 K respectively. The interfacial component was found to be relatively temperature and frequency independent for the LSCO/BST capacitors, but for the SRO/BST configuration the interfacial capacitance demonstrated moderate frequency and little temperature dependence below T ∼ 300 K but a relatively strong frequency and temperature dependence above T ∼ 300 K. This was attributed to the thermal activation of a space charge component combined with a thermally independent background. The activation energy for the space charge was found to be EA ∼ 0.6 eV suggesting de-trapping of electrons from shallow level traps associated with a thin interfacial layer of oxygen vacancies, parallel to the electrodes. [ABSTRACT FROM AUTHOR]

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding. [ABSTRACT FROM AUTHOR]

Reliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity. Plain Language Summary: Onshore wind energy is expected to play a major role in the global energy transition. However, reliable and highly resolved information on the onshore wind energy potential (WEP) crucial for expansion planning is missing over southern Africa. This study evaluated high resolution 3.3 km ICON‐LAM atmospheric simulations and 31 km ERA5 reanalysis against 60 m wind speed (ws60m) observations and compared the corresponding derived WEPs. The results show that ERA5 underestimates ws60m by 27%, resulting in a 48% lower WEP assessment than ICON‐LAM, whose ws60m simulation results show a very small bias. Underestimation of wind energy yields may hinder further expansion of wind energy, as less economic performance is expected, which underlines the importance of highly resolved weather data. Key Points: Simulated ERA5 and km‐scale ICON‐LAM wind speeds are evaluated and corresponding southern Africa wind energy potentials are calculatedERA5 underestimates 60 m wind speed, whereas ICON‐LAM produces lower biases in the wind speed simulationsHigher wind energy potentials are revealed from wind speeds simulated by ICON‐LAM compared to ERA5, which is often used for such assessments [ABSTRACT FROM AUTHOR]

Understanding the mechanisms underlying a stable polarization at the surface of ferroelectric thin films is of particular importance both from a fundamental point of view and to achieve control of the surface polarization itself. In this study, we demonstrate that the X-ray standing wave technique allows the surface polarization profile of a ferroelectric thin film, as opposed to the average film polarity, to be probed directly. The X-ray standing wave technique provides the average Ti and Ba atomic positions, along the out-of-plane direction, near the surface of three differently strained thin films. This technique gives direct access to the local ferroelectric polarization at and below the surface. By employing X-ray photoelectron spectroscopy, a detailed overview of the oxygen-containing species adsorbed on the surface is obtained. The different amplitude and orientation of the local ferroelectric polarizations are associated with surface charges attributed to different type, amount and spatial distribution of the oxygen-containing adsorbates. [ABSTRACT FROM AUTHOR]

Heatwaves (HWs) pose a severe threat to human and ecological systems. Here we assess the projected changes in heatwaves over Latin America using bias corrected high-resolution regional climate simulations under two Representative Concentration Pathway scenarios (RCPs). Heatwaves are projected to be more frequent, long-lasting, and intense in the mid-century under both RCP2.6 and RCP8.5 scenarios, with severe increases under the RCP8.5 scenario. Even under the low emissions scenario of RCP2.6, the frequency of heatwaves doubles over most of the region. A three- to tenfold rise in population exposure to heatwave days is projected over Central and South America, with climate change playing a dominant role in driving these changes. Results show that following the low emission pathway would reduce 57% and 50% of heatwave exposure for Central and South American regions respectively, highlighting the need to control anthropogenic emissions and implement sustainable practices. [ABSTRACT FROM AUTHOR]

Urban pluvial floods can be produced or exacerbated by an insufficient density of inlets or by their poor hydraulic efficiency. Therefore, proper consideration of the hydraulic performance of inlets is essential to guarantee the correct functioning of urban drainage systems during heavy storm events. Recent advances in computational analysis in the field of hydrodynamics modelling allow the use of the well-known concept of dual drainage for design and planning purposes by simulating flow transfers between the surface layer (streets) and underground layers (sewers) through a proper hydraulic characterisation of inlet efficiency. Powerful commercial software packages allow simulations of flow transfer and use different approaches and formulas. Many of these approaches include the possibility of treating a sewer inlet as an orifice. In this context, this paper presents a methodology to obtain orifice discharge coefficients for three inlets previously tested at the hydraulics laboratory of the Technical University of Catalonia. Discharge coefficients of 0.18–0.58 were obtained for Froude numbers of 1.12–4.40, quite far from the usual recommended values. The proposed procedure can also be applied to non-tested grates. [ABSTRACT FROM AUTHOR]

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century. [ABSTRACT FROM AUTHOR]

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression‐based Bayesian Optimisation (G‐BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. Plain Language Summary: This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression‐based Bayesian Optimisation (G‐BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G‐BO, we identified more effective values that substantially improved the model's simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions. Key Points: Our study optimizes WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulationG‐BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidityResults suggest updating WRF model's default settings for better extreme heat event simulations [ABSTRACT FROM AUTHOR]

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions. [ABSTRACT FROM AUTHOR]

Cyclone Gabrielle passed along the northern coast of Aotearoa New Zealand in February 2023, producing historic rainfall accumulations and impacts. Gabrielle was an ex‐tropical cyclone that stalled and re‐energised off the north coast, resembling descriptions of worst case scenarios for the northeast of the country. Here we report on a comparison of the actual forecast of the storm against forecasts under conditions representative of a climate without anthropogenic interference and of a climate +2.0°C warmer than pre‐industrial (about 1.0°C cooler and warmer than present respectively). We find that regional total rainfall accumulations from a Gabrielle‐like storm are about 10% higher because of the historical anthropogenic warming, and will increase by a larger amount under similar future warming. These differences are driven by a 20% (relative to a non‐anthropogenic world) to 30% (relative to a +2.0°C world) rise in peak rainfall rates, which in turn is mainly driven by a more temporally concentrated column‐integrated moisture flux. The forecast model generates the larger increase for the +2.0°C world through greater precipitation efficiency, reflecting the importance of unresolved precipitation processes in the climate change response of rainfall extremes. Plain Language Summary: Tropical Cyclone Gabrielle formed in the Coral Sea in February 2023, then moved southeast and passed along the northern coast of Aotearoa New Zealand as an ex‐tropical cyclone. The storm's rainfall produced one of the worst natural disasters in the country's history. We compare the weather forecast of the storm against forecasts in which past anthropogenic warming is removed and in which future warming is added. We find that the storm would have dumped about 10% less total rainfall and 20% less peak hourly rainfall without human interference. A similar future amount of warming will result in a comparable total increase in storm rainfall but with about a 30% increase in the peak hourly rate. Key Points: Cyclone Gabrielle delivered large amounts of rain to northeastern Aotearoa New Zealand in February 2023Anthropogenic warming increases the total amount of rain delivered by a Gabrielle‐like storm by about 10%/°CAnthropogenic warming increases the peak amount of rain delivered by a Gabrielle‐like storm by about 20%–30%/°C [ABSTRACT FROM AUTHOR]

Coastal upwelling is of particular importance in the western Iberian Peninsula, considering its socioeconomic impact on the region. Therefore, it is of crucial interest to evaluate how climate change, by modifying wind patterns, might influence its intensity and seasonality. Given the limited spatial extension of the area, it is essential to use high-resolution data. Thus, the weather research and forecasting model was used to dynamically downscale data from a multi-model ensemble from the 6th phase of the Coupled Model Intercomparison Project, representing the latest climate projections. Two shared socioeconomic pathways, 2–4.5 and 5–8.5 scenarios, were considered. The results show that climate change will not modify the upwelling seasonality in the area, where the months from April to September represent the period of highest intensity. Conversely, this seasonality might be exacerbated throughout the 21st century, as upwelling is expected to strengthen during these months and decrease during others. Additionally, coastal upwelling shows the highest increase at the northerner locations of the western Iberian Peninsula, resulting in a homogenization of its intensity along this coast. These changes may result from the anticipated intensification and northward shift of the Azores High. [ABSTRACT FROM AUTHOR]

This study addressed the hypotheses that exposure to chronic hypoxia (CH) and chronic hypercapnia (CHC) would modify the acute hypercapnic ventilatory response in the cane toad (Rhinella marina; formerly Bufo marinus or Chaunus marinus) and its regulation by NMDA-mediated processes. Cane toads were exposed to 10 days of CH (10% O(2)) or CHC (3.5% CO(2)) followed by acute in vivo hypercapnic breathing trials, conducted before and after an injection of the NMDA-receptor channel blocker, MK801 into the dorsal lymph sac. CH, CHC and MK801 did not alter ventilation under acute normoxic normocapnic conditions. CH blunted the increase in breathing frequency during acute hypercapnia while CHC had no effect. The effect of CH on breathing frequency was mediated by a decrease in the number of breaths per breathing episode. Neither CH nor CHC altered breath area (volume). MK801 augmented breathing frequency (via an increase in breaths per episode) and total ventilation during acute hypercapnia in control toads and toads exposed to CH; there was no effect of MK801 on the increase in breathing frequency or total ventilation, during acute hypercapnia in toads exposed to CHC. The results indicate that CH and CHC differentially alter breathing pattern. Furthermore, they indicate an absence of NMDA-mediated glutamatergic tone during normoxic normocapnia but that NMDA-mediated processes attenuate the increase in breathing frequency during acute hypercapnia under control conditions and following CH but not following CHC. Given that MK801 was administered systemically, the effects could be acting anywhere in the reflex pathway from CO(2)-sensing to respiratory motor output.

This study examines the bushfire (wildland fire) risk to the built environment in Australia. The most salient result is that the annual probability of building destruction has remained almost constant over the last century despite large demographic and social changes as well as improvements in fire fighting technique and resources. Most historical losses have taken place in a few extreme fires which if repeated are likely to overwhelm even the most professional of fire services. We also calculate the average annual probability of a random home on the urban-bushland interface being destroyed by a bushfire to be of the order of 1 in 6500, a factor 6.5 times lower than the ignition probability of a structural house fire. Thus on average and if this risk was perceived rationally, the incentive for individual homeowners to mitigate and reduce the bushfire danger even further is low. This being the case and despite predictions of an increasing likelihood of conditions favouring bushfires under global climate change, we suspect that building losses due to bushfires are unlikely to alter materially in the near future.

Previous studies have shown that exposure to chronic hypoxia (CH) and chronic hypercapnia (CHC) alone have opposite effects on central respiratory-related pH/CO(2) chemosensitivity in the cane toad (Bufo marinus). This study examined the effects of chronic hypoxic hypercapnia (CHH) on central pH/CO(2) chemosensitivity. Cane toads were maintained at 10% O(2) and 3.5% CO(2) for 10 days. Changes in central pH/CO(2)-sensitive fictive breathing were measured using in vitro brainstem-spinal cord preparations. Whole animal experiments examined the effects of CHH on in vivo ventilatory responses. In vitro, CHH augmented fictive breathing frequency but attenuated the integrated fictive breath area such that total fictive breathing was not altered. The effects on frequency were mediated by changes in the number of episodes/min rather than breaths/episode. In vivo, CHH blunted the ventilatory response to severe hypoxia and moderate hypercapnia. The results indicate that CHH alters breathing pattern in response to central chemoreceptor stimulation in vitro and modifies in vivo ventilatory chemoreflexes.

This study examined the effects of chronic hypoxia (CH) and mid-brain transection on central respiratory-related pH/CO(2) chemosensitivity in cane toads (Bufo marinus). Toads were exposed to 10 days of CH (10% O(2)) following which in vitro brainstem-spinal cord preparations, with the mid-brain attached, were used to examine central pH/CO(2) chemosensitivity. A reduction in artificial cerebral spinal fluid (aCSF) pH increased fictive breathing frequency (fR) and total fictive ventilation. CH reduced fictive fR and total fictive ventilation, compared to controls. Mid-brain transection caused an increase in fictive fR, at the lower aCSF pH levels, in both control and CH preparations. In the CH preparations, mid-brain transection restored fictive breathing to control levels. In both groups, mid-brain transection eliminated fictive breath clustering. The data indicate that CH attenuates central pH/CO(2)-sensitive fictive breathing but a mid-brain transection in the middle of the optic lobes abolishes this attenuation. The results suggest that CH induces inhibition of central pH/CO(2) chemoreceptor function via descending inputs from the mid-brain region.

Objectives: In the face of escalating global aridification, this study examines the complex relationship between climate variability, air pollution, natural disasters, and the prevalence of cardiovascular disease (CVD) and diabetes mellitus (DM) in arid regions. Methods: The study conducted a scoping review of multiple databases using JBI guidelines and included 74 studies. Results: The results show that acute myocardial infarction (n = 20) and stroke (n = 13) are the primary CVDs affected by these factors, particularly affecting older adults (n = 34) and persons with hypertension (n = 3). Elevated air temperature and heat waves emerge as critical risk factors for CVD, exacerbating various cardiovascular mechanisms. Atmospheric pollutants and natural disasters increase this risk. Indirect effects of disasters amplify risk factors such as socioeconomic vulnerability (n = 4), inadequate medical care (n = 3), stress (n = 3), and poor diet (n = 2), increasing CVD and DM risk. Conclusion: The study underscores the need for nations to adhere to the Paris Agreement, advocating for reduced air pollutants, resilient environments, and collaborative, multidisciplinary research to develop targeted health interventions to mitigate the adverse effects of climate, pollution, and natural disasters. [ABSTRACT FROM AUTHOR]

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies. [ABSTRACT FROM AUTHOR]

Drawing on a case study from Ternate Island, a densely populated volcanic island in Eastern Indonesia, this research illustrates how multi-hazards and extreme weather events are likely to compound and cascade, with serious consequences for sustainable development in small island context. At the heart of Ternate Island sits the active Gamalama volcano, posing a constant eruption threat. Its location within the Ring of Fire further exposes the island to the risks of tsunamis and earthquakes. Additionally, the island's physical features make it highly susceptible to flooding, landslides, and windstorms. Rapid urbanization has led to significant coastal alterations, increasing exposure to hazards. Ternate's small-island characteristics include limited resources, few evacuation options, vulnerable infrastructure, and inadequate resilience planning. Combining GIS multi-hazard mapping with a structured survey in 60 villages in Ternate, this case study investigates the multi-hazard exposure faced by the local population and land coverage. The findings suggest significant gaps between village chiefs' perceptions of the types of hazards and the multi-hazard assessment in each village. Out of 60 village chiefs surveyed, 42 (70%) are aware of earthquake risks, 17 (28%) recognize tsunami threats, and 39 see volcanoes as a danger. GIS assessments show that earthquakes could impact all villages, tsunamis could affect 46 villages (77%), and volcanoes could threaten 39 villages. The hazard map indicates that 32 villages are at risk of flash floods and 37 are at risk of landslides, and extreme weather could affect all villages. Additionally, 42 coastal villages on Ternate Island face potential extreme wave and abrasion disasters, but only 18 chiefs acknowledge extreme weather as a threat. The paper argues that addressing the cognitive biases reflected in the perceptions of community leaders requires transdisciplinary dialogue and engagement. [ABSTRACT FROM AUTHOR]

Mesoscale weather systems cause spatiotemporal variability in offshore wind power, and insight into their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COnsortium for Small-scale MOdelling – CLimate Mode (COSMO-CLM) provided a wind and potential power fluctuation analysis in the Kattegat, a midlatitude sea strait with a width of 130 km and an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root-mean square difference of 1.35 m s -1. A comparison of 10 min wind speed at about 100 m with lidar data for a 2-year period reveals very good performance, with a slight model overestimation of 0.08 m s -1 and a high value for the Perkins skill score (0.97). From periodograms made using the Welch's method, it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin-up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers, the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17:00 UTC with a clear diurnal cycle. The winter-averaged mesoscale wind component is up to 10 % larger, with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability in power production. The root-mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation. [ABSTRACT FROM AUTHOR]

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. [ABSTRACT FROM AUTHOR]

Future changes in the mean, maximum and minimum temperature in continental Portugal were investigated using high-resolution future climate projections based on the latest IPCC AR6 CMIP6 climate scenarios. The results show that the mean, maximum and minimum temperatures are projected to substantially increase in all continental Portugal, particularly at the south-central inland regions. For the near-term future (2046–2065 period), SSP3-7.0 is the future climate scenario that projects higher increases, of around 1 °C, 1.5 °C and 2 °C for the daily mean, maximum and minimum temperatures, respectively. For the long-term future (2081–2100 period), the projected warming is higher, particularly under the SSP5-8.5 future climate scenario with projected warmings of 3 °C, 3.5 °C and 2.5 °C for the daily mean, maximum and minimum temperatures, respectively. Occurrences of hot days (maximum temperature above 30 °C), very hot days (maximum temperature above 40 °C) and tropical nights (minimum temperature above 20 °C) are all projected to increase up to 35–40, 12–15 and 50 more days per year, respectively, mainly in the interior areas of Portugal. Oppositely, the occurrence of frost days is projected to decrease in practically all mountainous areas in Portugal. These results show a clear tendency of a significant increase in the surface temperatures and frequency of occurrence of extreme temperature episodes in continental Portugal, which can have severe impacts on the population, environment, economy and vital human activities such as agriculture. [ABSTRACT FROM AUTHOR]

Economic development requires a constant supply of energy. The utilization of fossil fuels causes environmental pollution and greenhouse gas emissions. The effects of fossil fuel use have impacted global warming, which may affect the world. The problem of environmental degradation can be decreased by using renewable energy sources and nuclear energy. The role of nuclear energy is increasing. More than 10% of electric energy is now produced from nuclear energy worldwide. However, the share varies by country. For example, in France, it is 70%, in Slovakia, it is 55%, and in Ukraine, it is 53%. Many countries do not have nuclear energy at all. This study aims to investigate the development of gross nuclear electricity production both in the world and in the European Union (EU) in terms of stationarity and prognosis. To achieve the goal of this study, the authors utilized descriptive statistics. The time range included the period 1990–2022. This long time period enabled us to conduct the ADF (Augmented Dickey Fuller) test. According to our analysis, gross nuclear electricity production in the European Union (EU) was stationary. We also evaluated future prognosis using the ARIMA (Autoregressive Moving Average) model. We also used the Vector Autoregressive (VAR) model to evaluate changes within nuclear electricity production. Based on our research, we can conclude that the data were stationary. Finally, we concluded that gross nuclear electricity production in the European Union (EU) will increase in eight countries. In 2022, countries such as Belgium, Bulgaria, Czechia, Spain, France, Hungary, the Netherlands, Romania, Slovenia, Slovakia, and Finland increased their gross nuclear electricity production compared to 1990. Based on the ARIMA model prognosis, the following countries will increase their gross nuclear electricity production in the period 2023–2032: Belgium, Bulgaria, Czechia, Finland, Hungary, the Netherlands, Romania, Slovakia, and Slovenia. Based on the VAR model, we elaborated the prognosis, according to which countries such as France, Romania, Spain, and Sweden will increase their gross nuclear electricity production in the period 2023–2032. [ABSTRACT FROM AUTHOR]