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The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy. [ABSTRACT FROM AUTHOR]

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California's Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. [ABSTRACT FROM AUTHOR]

A decisive aspect of site evaluation for construction is the presence of anthropogenic materials occurring in the geological environment. The geotechnical properties of blast-furnace slag were investigated as a potential substitute for aggregates in the construction industry. The basic geotechnical parameters of the slag were determined, which are critical for evaluating its stability, environmental impact, and usability in geotechnical construction. The research focused on monitoring the physical and mechanical properties of the two samples, and also included mineralogical analysis. The obtained results demonstrated that the slag belongs to the category of poorly graded gravel, G2/GP, and gravel with an admixture of fine-grained soil, G3/G-F. In addition, other important parameters, such as the water disintegration of the slag aggregate, the minimum and maximum bulk densities, the California bearing ratio (CBR), the oedometric modulus (Eoed), and shear tests (the angle of internal friction φ and cohesion c), were determined. The results from this paper provide important information for the proper management of blast-furnace slag so to minimize its environmental impact and achieve sustainability in the mining industry. At the same time, it enables a better understanding of the use of slag as a substitute for aggregates in geotechnical tasks. Despite its local importance in relation to the investigated case, the presented study has significant educational and scientific value for the construction sector, where it is necessary to evaluate anthropogenic activities and materials. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

This paper presents a new direct maximum power point tracking (MPPT) with a reference voltage ( V r e f ) based on the metaheuristic earthquake algorithm (EA) where the optimization variable is the V r e f for hard-switching converters. The efficiency and performance of EA-MPPT- V r e f is compared with the perturb-and-observe (P&O) counterpart technique due to the fact that it is widely used for commercial products. Static and dynamic responses for both MPPT strategies are evaluated, which correspond to steady-state oscillations when they are near the maximum power point (MPP), and the tracking-speed, respectively. The efficiency was evaluated with the EN 50530 standard. The results show that the new MPPT proposed is a competitive method using the EA to obtain the optimal voltage reference. From static results, EA-MPPT VP presented a better efficiency of 5.13% and 3.23% for European and California energy commission (CEC) efficiency, respectively. Whereas, from dynamic results, MPPT- V r e f techniques presented an efficiency from 95.13% to 99.91%, and 99.01% to 99.91% of the total power of the PV system for P&O and EA strategies, respectively. [ABSTRACT FROM AUTHOR]

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area. [ABSTRACT FROM AUTHOR]

Fuel break effectiveness in wildland-urban interface (WUI) is not well understood during downslope wind-driven fires even though various fuel treatments are conducted across the western United States. The aim of this paper is to examine the efficacy of WUI fuel breaks under the influence of strong winds and dry fuels, using the 2018 Camp Fire as a case study. The operational fire growth model Prometheus was used to show: (1) downstream impacts of 200 m and 400 m wide WUI fuel breaks on fire behavior and evacuation time gain; (2) how the downstream fire behavior was affected by the width and fuel conditions of the WUI fuel breaks; and (3) the impacts of background wind speeds on the efficacy of WUI fuel breaks. Our results indicate that WUI fuel breaks may slow wildfire spread rates by dispersing the primary advancing fire front into multiple fronts of lower intensity on the downstream edge of the fuel break. However, fuel break width mattered. We found that the lateral fire spread and burned area were reduced downstream of the 400 m wide WUI fuel break more effectively than the 200 m fuel break. Further sensitivity tests showed that wind speed at the time of ignition influenced fire behavior and efficacy of management interventions. [ABSTRACT FROM AUTHOR]

The length–weight relationships (LWR) of the common seadragon Phyllopteryx taeniolatus (Lacepède, 1804) and the leafy seadragon Phycodurus eques (Günther, 1865), Syngnathidae, are presented in this paper, based on specimens raised in the Birch Aquarium at Scripps, La Jolla, California. Furthermore, we used the length at known age of 40 specimens of common and 15 leafy seadragons to estimate the parameter of the von Bertalanffy growth function (VBGF) for these species. Some of the pros and cons of the newly proposed length type used, 'scalene length' are presented. The parameters of the LWR and the VBGF parameters are discussed, with an emphasis on the rearing conditions, the peculiar anatomy of seadragons, especially of P. eques, and on the Gill-Oxygen Limitation Theory (GOLT). [ABSTRACT FROM AUTHOR]

The tribe Loteae of the family Leguminosae has major centers of diversity in the Mediterranean Region and California. It is used here to illustrate different traditions of taxonomic interpretation of infraspecific variation among researchers in different continents. Recent accounts of European and Mediterranean taxa commonly make an emphasis on recognizing subspecies. In contrast, accounts of North American Loteae recognize all infraspecific taxa as varieties. Harmonization of taxonomic concepts across continents is essential for a more accurate representation of data in global biodiversity databases. It is reasonable to use the rank of subspecies for geographically defined infraspecific units with localized zones of intergradation. Such subspecies may be at least partially compatible across continents and taxonomic groups. In the present paper, we discuss taxa traditionally included in or associated with Acmispon glaber (Vogel) Brouillet (=Syrmatium glabrum Vogel) and demonstrate a need for recognizing three species, of which one has three geographically defined subspecies. Required nomenclatural combinations are proposed. It is proposed that subspecies rather than varieties should be recognized in a number of other species of North American Loteae (and other plant groups). Inland subspecies of Acmispon glaber and A. maritimus offer an opportunity for comparative analysis in an eco-evo-devo framework. [ABSTRACT FROM AUTHOR]

This paper evaluates the energy performance of shallow ground source heat pumps using the state-of-art whole building energy simulation tool. In particular, the paper presents a systematic and easy to implement approach to model the energy performance of shallow and helical ground heat exchangers and assess their energy efficiency benefits to heat and cool buildings. The modeling approach is based on the implementation of G-functions, generated using a validated numerical model, in a state-of-art whole building energy simulation tool. Both the numerical model and the simulation tool are applied to assess the energy performance of various shallow geothermal systems designed to meet heating and cooling needs for detached single-family homes in California. Specifically, a series of sensitivity analyses is conducted to determine the energy performance of the shallow geothermal systems in 16 locations representing all California climate zones. It is found that the suitability and the efficiency of the shallow geothermal systems vary widely and depend on several factors including their design specifications as well as the climate conditions. Compared with conventional air-to-air heat pumps, the shallow ground source heat pumps can be more energy efficient in most climate zones in California except those locations with extreme weather conditions resulting in either heating or cooling only operation. Moreover, configurations of shallow ground source heat pumps with 16 boreholes with 6.7 m (22 ft) depth are found to be cost-effective in several California climate zones. [ABSTRACT FROM AUTHOR]

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository. [ABSTRACT FROM AUTHOR]

Lithium is the principal component of high-energy-density batteries and is a critical material necessary for the economy and security of the United States. Brines from geothermal power production have been identified as a potential domestic source of lithium; however, lithium-rich geothermal brines are characterized by complex chemistry, high salinity, and high temperatures, which pose unique challenges for economic lithium extraction. The purpose of this paper is to examine and analyze direct lithium extraction technology in the context of developing sustainable lithium production from geothermal brines. In this paper, we are focused on the challenges of applying direct lithium extraction technology to geothermal brines; however, applications to other brines (such as coproduced brines from oil wells) are considered. The most technologically advanced approach for direct lithium extraction from geothermal brines is adsorption of lithium using inorganic sorbents. Other separation processes include extraction using solvents, sorption on organic resin and polymer materials, chemical precipitation, and membrane-dependent processes. The Salton Sea geothermal field in California has been identified as the most significant lithium brine resource in the US and past and present efforts to extract lithium and other minerals from Salton Sea brines were evaluated. Extraction of lithium with inorganic molecular sieve ion-exchange sorbents appears to offer the most immediate pathway for the development of economic lithium extraction and recovery from Salton Sea brines. Other promising technologies are still in early development, but may one day offer a second generation of methods for direct, selective lithium extraction. Initial studies have demonstrated that lithium extraction and recovery from geothermal brines are technically feasible, but challenges still remain in developing an economically and environmentally sustainable process at scale. [ABSTRACT FROM AUTHOR]

Operation of distribution networks involves a series of criteria that should be met, aiming for the correct and optimal behavior of such systems. Some of the major drawbacks found when studying these networks is the real losses related to them. To overcome this problem, distribution network reconfiguration (DNR) is an efficient tool due to the low costs involved in its implementation. The majority of studies regarding this subject treat the problem by considering networks only as three-phase balanced, modeled as single-phase grids with fixed power demand, which is far from representing the characteristics of real networks (e.g., unbalanced loads, variable power and unbalance indexes). Due to the combinatorial nature of the problem, metaheuristic techniques are powerful tools for the inclusion of such characteristics. In this sense, this paper proposes a study of DNR considering balanced and unbalanced systems with variable power demand. An analysis of the direct influence of voltage unbalance index (VUI) and current unbalance index (CUI) is carried out for unbalanced cases. To solve the DNR problem, a selective bio-inspired metaheuristic based on micro bats' behavior named the selective bat algorithm (SBAT) is used together with the EPRI-OpenDSS software (California, US, EPRI). Tests are initially conducted on balanced systems, aiming to validate the technique proposed for both demands and state their differences, and then they are conducted on unbalanced systems to study the influence of VUI and CUI in the DNR solution. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of personal responsibility, futility, and faith in visual representations of nuclear apocalypse. In two films produced during the late Cold War, Testament (1983) and When the Wind Blows (1986), the protagonists attempt to follow public guidance, maintain daily routines as their health and communities break down, and make muddled connections to religious faith. In Testament, a mother is left to care for her children in suburban California for months after an unexplained nuclear attack isolates and contaminates the town. In When the Wind Blows, a retired couple living in the British countryside diligently follow government instructions to "protect and survive", while quickly succumbing to radiation poisoning. In a contrasting post-Cold War visual representation, the speculative artwork of the artists Erich Berger and Mari Keto imagine the storage of nuclear waste as a personal responsibility. In OpenCare (2016), waste is encased in steel pellets mounted on a bronze disc, and a series of artifacts and instructions assist in determining continued toxicity. While Testament and When the Wind Blows project the futility of personal responsibility and faith in nuclear survival, Berger and Keto's system envisions a deep nuclear future requiring continued personal management and care. [ABSTRACT FROM AUTHOR]

This paper aims to study the feasibility of low cement content foamed concrete using waste lime mud (LM) and fly ash (FA) as mineral additives. The LM/FA ratio was first optimized based on the compressive strength. Isothermal calorimetry test, ESEM, and XRD were used to investigate the role of LM during hydration. Afterward, the optimized LM/FA ratio (1/5) was used to design foamed concrete with various wet densities (600, 700, 800 and 900 kg/m3) and LM–FA dosages (0%, 50%, 60%, 70% and 80%). Flowability measurements and mechanical measurements including compressive strength, flexural strength, splitting strength, elastic modulus, and California bearing ratio were conducted. The results show that the foamed concretes have excellent workability and stability with flowability within 170 and 190 mm. The high alkalinity of LM accelerated the hydration of FA, thereby increasing the early strength. The significant power functions were fitted for the relationships between flexural/splitting and compressive strength with all correlation coefficients (R2) larger with 0.95. The mechanical properties of the foamed concrete increased with the density increasing or LM–FA dosage decreasing. The compressive strength, tensile strength, CBR of all prepared foamed concretes were higher than the minimum requirements of 0.8 and 0.15 MPa and 8%, respectively in the standard. [ABSTRACT FROM AUTHOR]

Earthquakes are known to generate disturbances in the ionosphere. Such disturbances, referred to as co-seismic ionospheric disturbances, or ionoquakes, were previously reported for large earthquakes with magnitudes Mw ≥ 6.6. This paper reports ionoquakes associated with the Ridgecrest earthquakes of magnitude (Mw = 6.4) , that occurred on 4 July 2019 in California, USA. The ionoquakes manifested in total electron content (TEC) in the form of traveling ionospheric disturbances (TIDs) within 1 h from the mainshock onset. These seismic-origin TIDs have unique wave characteristics that distinguish them from TIDs of non-seismic origin arising from a moderate geomagnetic activity on the same day. Moreover, in the space-time domain of the detection of seismic-origin TIDs, TIDs are absent on the day before and day after the earthquake day. Their spectral characteristics relate them to the Earth's normal modes and atmospheric resonance modes. We found the ground velocity associated with the mainshock, rather than the ground displacement, satisfies the threshold criteria for detectable ionoquakes in TEC measurements. Numerical simulation suggested that the coupled seismo–atmosphere–ionosphere (SAI) dynamics energized by the atmospheric waves are responsible for the generation of ionoquakes. This study's findings demonstrate the potential of using TEC measurement to detect the ionospheric counterparts of moderate earthquakes. [ABSTRACT FROM AUTHOR]

In the American West, wildfires and earthquakes are increasingly threatening the archaeological, historical, and tribal resources that define the collective identity and connection with the past for millions of Americans. The loss of said resources diminishes societal understanding of the role cultural heritage plays in shaping our present and future. This paper examines the viability of employing stationary and SLAM-based terrestrial laser scanning, close-range photogrammetry, automated surface change detection, GIS, and WebGL visualization techniques to enhance the preservation of cultural resources in California. Our datafication approach combines multi-temporal remote sensing monitoring of historic features with legacy data and collaborative visualization to document and evaluate how environmental threats affect built heritage. We tested our methodology in response to recent environmental threats from wildfire and earthquakes at Bodie, an iconic Gold Rush-era boom town located on the California and Nevada border. Our multi-scale results show that the proposed approach effectively integrates highly accurate 3D snapshots of Bodie's historic buildings before/after disturbance, or post-restoration, with surface change detection and online collaborative visualization of 3D geospatial data to monitor and preserve important cultural resources at the site. This study concludes that the proposed workflow enhances the monitoring of at-risk California's cultural heritage and makes a call to action to employ remote sensing as a pathway to advanced planning. [ABSTRACT FROM AUTHOR]

Buildings with solar photovoltaic (PV) generation and a stationary battery energy storage system (BESS) may self-sustain an uninterrupted full-level electricity supply during power outages. The duration of off-grid operation is dependent on the time of the power fault and the capabilities of the home energy management system (HEMS). In this paper, building resilience is quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year. An evaluation method is proposed and exercised on a reference house in California climate zone 9 for which the detailed electricity usage is simulated using the EnergyPlus software. The influence of factors such as energy use behavioral patterns, energy storage capacity from the BESS, and an electric vehicle (EV) battery on the building resilience is evaluated. Varying combinations of energy storage and controllable loads are studied for optimally improved resilience based on user preferences. It is shown that for the target home and region with a solar PV system of 7.2 kW, a BESS with a capacity of 11 kWh, and an EV with a battery of 80 kWh permanently connected to the home, off-grid self-sustained full operation is guaranteed for at least 72 h. [ABSTRACT FROM AUTHOR]

This paper presents a generalized second-order hydrodynamic traffic model. Its central piece is the expression for the relative velocity of the congestion (compression wave) propagation. We show that the well-known second-order models of Payne–Whitham, Aw–Rascal and Zhang are all special cases of the featured generalized model, and their properties are fully defined by how the relative velocity of the congestion is expressed. The proposed model is verified with traffic data from a segment of the Interstate 580 freeway in California, USA, collected by the California DOT's Performance Measurement System (PeMS). [ABSTRACT FROM AUTHOR]

California has set two ambitious targets aimed at achieving a high level of decarbonization in the coming decades, namely (i) to generate 60% and 100% of its electricity using renewable energy (RE) technologies, respectively, by 2030 and by 2045, and (ii) introducing at least 5 million zero emission vehicles (ZEVs) by 2030, as a first step towards all new vehicles being ZEVs by 2035. In addition, in California, photovoltaics (PVs) coupled with lithium-ion battery (LIB) storage and battery electric vehicles (BEVs) are, respectively, the most promising candidates for new RE installations and new ZEVs, respectively. However, concerns have been voiced about how meeting both targets at the same time could potentially negatively affect the electricity grid's stability, and hence also its overall energy and carbon performance. This paper addresses those concerns by presenting a thorough life-cycle carbon emission and energy analysis based on an original grid balancing model that uses a combination of historical hourly dispatch and demand data and future projections of hourly demand for BEV charging. Five different scenarios are assessed, and the results unequivocally indicate that a future 80% RE grid mix in California is not only able to cope with the increased demand caused by BEVs, but it can do so with low carbon emissions (<110 g CO2-eq/kWh) and satisfactory net energy returns (EROIPE-eq = 12–16). [ABSTRACT FROM AUTHOR]

Studying the temporal and spatial evolution trends in earthquakes in an area is beneficial for determining the earthquake risk of the area so that local governments can make the correct decisions for disaster prevention and reduction. In this paper, we propose a new method for analyzing the temporal and spatial evolution trends in earthquakes based on earthquakes of magnitude 3.0 or above from 1980 to 2019 in California and Nevada. The experiment's results show that (1) the frequency of earthquake events of magnitude 4.5 or above present a relatively regular change trend of decreasing–rising in this area; (2) by using the weighted average center method to analyze the spatial concentration of earthquake events of magnitude 3.0 or above in this region, we find that the weighted average center of the earthquake events in this area shows a conch-type movement law, where it moves closer to the center from all sides; (3) the direction of the spatial distribution of earthquake events in this area shows a NW–SE pattern when the standard deviational ellipse (SDE) method is used, which is basically consistent with the direction of the San Andreas Fault Zone across the north and south of California; and (4) the spatial distribution pattern of the earthquake events in this region is found to be clustered using the global spatial autocorrelation analysis method. This study provides a new perspective for the exploration of the temporal and spatial evolution trends in earthquakes and understanding the earthquake risk in an area. [ABSTRACT FROM AUTHOR]