Search

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

• The California mandate for solar PV systems on residential rooftops will have a negative occupational safety impact. • Results indicate that 16.6 recordable non-fatal injuries are expected to occur between 2020 and 2029 as a result of this policy. • The impact could be reduced by 18% by eliminating work on roofs. Introduction: A 2018 change to the California building code mandates that new residential construction in the state include rooftop solar photovoltaic power systems beginning in 2020. As residential construction (especially work on rooftops) is among the more dangerous occupations in the United States, this paper seeks to quantify the increased risks to workers as a result of this mandate. Method: An analysis of the trends by occupation of nonfatal safety incident rates in the United States combined with a Monte Carlo simulation provide an estimate of the uncertain impact of this new mandate. Results: Recordable safety incidents are anticipated to increase by a total of 16.6 incidents (standard deviation = 1.0 incidents) over the 2020–2029 time period as a result of this policy change. However, lessons from Germany and other industries offer potential avenues to reduce the negative social impact of this mandate. Conclusions: While it is not possible to increase employment in any sector without increasing the expected number of occupational injuries to some degree, these results indicate that risks could be considerably reduced by making solar PV system design decisions that increase worker productivity and reduce roof exposure time. Practical Applications: Changes such as eliminating work on roofs could decrease the expected number of recordable injuries over the 10-year period by 0.30 incidents per year (a reduction of 18%). [ABSTRACT FROM AUTHOR]

The quality of traffic services depends on the accuracy and completeness of the collected traffic data. However,the existing traffic data imputation methods usually only rely on the predefined road network structure to capture the spatio-temporal features and only consider the imputation effect from a single perspective, which are very limited for imputation of different missing patterns of road traffic data. In this paper, we propose a novel deep learning framework called Hierarchical Spatio-temporal Graph Convolutional Neural Networks(HSTGCN) to impute traffic data,through the macro layer and the road layer. The model constructs macro graph of the road network based on the data temporal correlation clustering, which can mine the temporal dependencies of road traffic data from a hierarchical perspective. Besides, a temporal attention mechanism and adaptive adjacency matrix are introduced in the road layer to better extract the spatio-temporal information of the road traffic data. Finally, we use graph convolution neural networks to learn the spatio-temporal feature representations of the road layer and macro layer, which are then fused to achieve data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle. The proposed model performs better than the comparison model for traffic data imputation. • A full dynamic graph is built based on the urban road structure. • Extracting spatio-temporal features from multiple dimensions using hierarchical thinking. • Gate-GCN perform effective feature selection. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

Machine learning methods are increasingly being used in the field of air quality research to investigate the relationship between air pollutant levels, emissions, and meteorological changes over time. This research is used for both scientific investigation, and policy assessment and development. However, there is a lack of studies that have compared the performance of different machine learning methods. To address this gap, this paper employed various machine learning techniques, including decision tree, random forest (RF), support vector machine (SVM), support vector regression (SVR), k-nearest neighbor, neural network, and Gaussian process regression, to predict daily average PM 2.5 levels and the number of days with PM 2.5 exceedance in the South Coast Air Basin of California from 2000 to 2019. The models were trained using meteorological factors, estimated emissions, and large-scale climate indices as inputs. The SVR model demonstrated the highest predictive accuracy for PM 2.5 levels and the SVM model gave the most accurate results for predicting the number of days with PM 2.5 exceedances. Conversely, the decision tree model performed the least accurately. The results also showed that emissions have a greater impact on PM 2.5 levels over time compared to meteorological factors, though meteorology is responsible for daily variability. The most important meteorological factors were identified as surface relative humidity and relative humidity at 850 mbars, which are related to partitioning, cloud cover and wet deposition. We conducted sensitivity tests on the model's response to emissions and meteorological factors. The predicted PM 2.5 from RF and SVR showed large correlations with emissions at the early period (2000–2010). However, the changes were minimal in more recent years (2011–2019), implying that there are biases in machine learning models, in which the models consistently predict the minimum PM 2.5 levels at a baseline. [Display omitted] • SVR is more accurate on daily PM 2.5 , particularly PM 2.5 exceedances predictions. • Surface RH was the most important meteorological factor for PM 2.5 prediction. • The impact of emissions on PM 2.5 was significant before 2010 but reduced thereafter. • ML models predict past better than future; all the ML models are limited at extremes. [ABSTRACT FROM AUTHOR]

How much should electric utilities pay to maintain a reliable electricity system? This paper describes an open-ended stated-preference experiment that generates estimates for how advanced notification impacts household willingness-to-pay (WTP) to avoid outages. We find positive and statistically significant WTP to avoid power outages of $10/kWh, consistent with the expectation that outages are costly to the residential sector. We find notification reduces the WTP, but the effects are not statistically significant. There is limited evidence that these results vary by income and wealth levels. Back-up power ownership is positively correlated with respondents' WTP to avoid outages. • Stated preference survey estimates WTP to avoid power outages. • Notifications reduce WTP but result is not statistically significant. • Back-up power ownership is positively correlated with WTP. • Results inform deployment of California PSPS and EPSS programs. [ABSTRACT FROM AUTHOR]

Inequities in the built environment have long persisted in communities around the world. Prior studies have exposed that energy retrofit programs invest inequitably in residential buildings, leaving racial minorities with disproportionately less energy inefficient homes, regardless of income level. This inequity in energy performance and retrofit investments has yet to be studied for commercial and institutional buildings. Further research has shown that tree canopy coverage and vegetation is less dense in historically disadvantaged communities, contributing to a greater urban heat island effect and corresponding higher energy consumption for cooling compared to historically advantaged communities. Moreover, there is limited albeit emerging research that utilizes granular socioeconomic data with fine temporal scale energy consumption data to help identify energy inequalities among race and income groups. This paper examines the differential impact of energy efficiency retrofit installation on energy performance between advantaged vs. disadvantaged groups using data from U.S. public school buildings. Additionally, it studies how the effect of retrofits is compounded by the presence of vegetation surrounding the building. Utilizing hourly interval smart meter data from primary and secondary school buildings in California, the impact of retrofits are quantified using three energy metrics. Then the disparities in retrofit impact for advantaged vs. disadvantaged groups (as defined by three equity metrics) are evaluated using school-level demographic data. Lastly, regression on the percent change in monthly energy consumption from the pre-retrofit to post-retrofit period is used to quantify the incremental benefits of retrofits compounded by vegetation. Findings show that energy efficiency retrofits can improve energy performance and reduce energy inequities. Specifically, retrofits significantly increase energy efficiency and reduce the energy efficiency equity gap by up to 198%. Evidence shows that vegetation surrounding a building has a synergistic effect with mechanical retrofits for reducing energy consumption in respect to reduced cooling loads, even in cases where mechanical retrofits alone resulted in significant increases in energy consumption. Dense vegetation provides around 6% energy savings, similar in magnitude to savings from mechanical retrofit installation. This validates the effectiveness of retrofits for reducing energy consumption and improving energy equity, and demonstrates the ability of vegetation to provide additional energy savings beyond mechanical interventions. [ABSTRACT FROM AUTHOR]

Nitrate pollution of surface water from farms and urban runoff is widespread – impairing drinking water supplies, recreation, and wildlife habitat. The scale of the problem in rivers has overwhelmed most watershed-based best management practices. A new paradigm is needed. In this paper we highlight the 119 ha (294 acres) Prado Wetlands in southern California, USA as an example of a gravity-enabled, free surface flow vegetated wetlands to treat a river – the nitrate-rich, effluent-dominated Santa Anna River. The wetland treats ∼1–2 m3/s of the 7.1 m3/s (250 cfs) flow of the river in 28 wetland cells in two parallel trains (residence time 2–4 d, hydraulic loading rate 10–20 cm/d). The cleaner water is returned to the river and infiltrated downstream into a managed groundwater basin supplying 2.4 million customers. Since 1998, ∼1200 t of nitrate-N have been permanently removed from the river and released as N 2 -gas into the air. Over 24 years, in the warm summers an average of 6.1 mg-N/L entered the wetland and 3.7 mg-N/L flowed out, a removal efficiency of 57%. In the cooler winters, an average of 6.4 mg NO 3 -N/L entered and 2.6 mg NO 3 -N/L flowed out, a removal efficiency of 45%. Individual months varied widely in concentration removal efficiency from 7% (winter) to 98% (high summer). Areal summer removal averaged 137 mg NO 3 -N/m2/d and the apparent "k" was around 46 m/y. All-year rates were 129 mg NO 3 -N/m2/d or 47 g/m2/y, a reasonable performance considering the size of the system and its configuration for both nitrate removal and duck hunting (i.e., more open water). Annual average nitrate removal efficiency increased substantially over time (45 to 72% for 1998–2004 versus 2016–22) and is attributed to an increase in hydraulic residence time, upgraded internal plumbing, and increased winter temperatures (+3C°). Prado Wetlands also removes phosphorus, though not as well as nitrate; average warm season removal efficiency was 23% (inflow 1.20 mg PO 4 -P/L, outflow 0.93 mg PO 4 -P/L). The aerial removal rate was high at 23 mg P/m2/d or 8.5 g/y, probably due to the high inflowing P-concentrations and the hard water. Wildlife was also enhanced. Least Bell's Vireo (Vireo belli pusillus) , an endangered bird in the Prado Basin, increased from 19 pairs (1998) to a recent average of 444 pairs due to habitat improvement and reduction of cowbird parasitism. Results from the Prado Wetlands show that cleaning up rivers via diversion through wetlands benefits both water quality and wildlife. [ABSTRACT FROM AUTHOR]

This study characterized nitrogen oxides (NOx) and ammonia (NH 3) emissions from five in-use goods movement vehicles, including one diesel vehicle without selective catalytic reduction (SCR), two diesel vehicles with SCR, and two ultra-low NOx natural gas vehicles equipped with three-way catalysts (TWCs). Emissions testing was performed under real-world driving conditions in typical goods movement routes in the South Coast Air Basin (SCAB) of California. NOx emissions varied depending on the vehicle and the route. The no-SCR diesel vehicle showed the highest NOx emissions over all the routes. One of the SCR-equipped vehicles showed NOx emission rates that were two times higher than the other SCR-equipped vehicle with similar engine model year and mileage, which may be attributed to catalyst deterioration. The SCR-equipped diesel vehicles were within two to three times 0.2 g/bhp-hr certification standard over the different routes. The natural gas vehicles showed average NOx emissions around or below the optional Low NOx emissions standard of 0.02 g/bhp-hr. The three-bin moving average window (MAW) method was utilized to show the NOx emissions across various modes of operation, including idle, low load, and medium/high load. The highest fraction of vehicle operation was found in either the low load or medium/high load bins, depending on the route. NOx emissions for each of the bins varied between different vehicles, and are further analyzed in this paper. NH 3 emissions formation was favored for the TWC-equipped natural gas vehicles, which produced about 20 times more NH 3 emissions than the SCR-equipped diesel vehicles. [Display omitted] • Significant NOx reductions for the natural gas vehicles compared to the SCR-equipped diesel vehicles. • NH 3 formation favored in the TWC for the natural gas vehicles and produced during acceleration events. • Low load and medium/high load conditions dominated vehicle operation and NOx emissions. [ABSTRACT FROM AUTHOR]

With many countries planning to significantly increase grid renewable energy penetration levels, we consider the role of wave energy in supply–demand matching. We investigate how incorporating wave power into an offshore wind farm affects farm power predictability, smoothness, required energy storage capacity, and cost. In this paper, we do a first-order cost analysis of an offshore farm comprised of floating wind turbines and wave energy converters that are both standalone and combined and onshore compressed air energy storage. Then, we do a parameter sweep investigation of an isolated power network supplied by varied grid renewable energy penetration levels supplemented by natural gas, varied distribution of renewable energy between wind and wave power, and varied power capacity of a compressed air energy storage system supplying power to a shoreline community. For each parameter set, we consider the historical hourly electricity demand and wind-sea data of a coastal California community over a year, and optimize the energy storage schedule to reduce curtailed power, stored energy, and base gas plant operational cost. We show that a co-located wind-wave farm has smoother power supply, less energy curtailment, and higher farm-to-grid efficiency than a solely wind farm. That is, a 50%–50% wind-wave farm has a 15% smaller coefficient of variation in the power supply, 6% less curtailed power, and 2% higher farm-grid efficiency than a 100% wind farm when the grid is 100% renewable energy. These benefits of wave power potentially decrease the need for interconnecting regional transmission lines to match power supply with demand. The intent of this paper is to provide baseline system technical results to help future researchers and policy makers make decisions about offshore hybrid wind-wave-storage farms. • We do a joint parameter sweep and energy storage schedule optimization on the farm. • A co-located wind-wave farm has 15% smoother power than a wind farm. • A co-located wind-wave farm has 6% less energy curtailment than a wind farm. • A co-located wind-wave farm has 2% higher power efficiency than a wind farm. • Attaching a wave energy converter (WEC) to a wind turbine reduces WEC cost by 43%. [ABSTRACT FROM AUTHOR]

• Numerically represented a diagonal SMA-wire based bracing system. • Developed probabilistic bridge models for various uncertainties. • Proposed refined definition for failure for SMA-retrofitted bridges. • Exemplified risk-based design of SMA wire-based brace for bridges in California. In this paper, a risk-based seismic design method for diagonal self-centering braces (SCB) with shape memory alloy (SMA) wires implemented in multi-column bents is proposed to enhance the performance of older concrete bridge classes. An existing SMA-SCB system that improves the performance of older reinforced concrete building frames was adopted to numerically examine further applications to nonductile bridges. To this end, risk-based seismic design of the SMA-SCB to minimize the probability of collapse and demolition of bridge classes was developed by adopting a sophisticated finite element model that reflects the recentering behavior of an actual SMA-SCB; probabilistic bridge models considering uncertainties associated with the material and geometrical properties of bridges; refined failure condition; and the total probability theorem. A comparison of the results of the traditional seismic fragility-based and proposed designs indicated that the amount of SMA-SCB required by the former is nearly half of that required by the proposed approach because the traditional approach overemphasizes the demand at larger ground motion intensities. [ABSTRACT FROM AUTHOR]

• Assess the seismic reliability analysis of a bridge network using BC approach. • Propose a new computationally efficient BC concept to rank importance order of bridges. • Suggest the seismic retrofit plan of a bridge network based on the BC importance of bridges. • Present a BC-based approach that improves the network performance using new constructions. Although bridges are integral parts of transportation networks, they are vulnerable to natural hazards. The damage incurred during these natural hazards events leads to network disruptions and hinders post-event recovery. Therefore, improving the seismic performance of transportation networks is critical. This paper proposes an origin–destination pair betweenness centrality (BC)-based methodology to rank bridges according to their importance in a complex bridge network. The proposed methodology is an easy-to-use approach that considers user equilibrium and topology and significantly shorten the computational time. In addition, this study proposes two strategies including the retrofit of bridges and construction of new bridges based on the BC importance of bridges to mitigate the seismic risk of bride networks. A well-studied bridge network in California was used to demonstrate the application and the effect of different strategies was compared in this study. Bridges located at the shortest path significantly affect the seismic performance of bridge networks, indicating there is no need to rehabilitate or replace all bridges under budget constraints. Also, constructing five new bridges based on BC importance improved seismic performance of the bridge network much more than retrofitting 21 bridges. Moreover, the proposed methodology presents a significant reduction in computational time (only 1/30 compared with an existing study) in resolving the importance of bridges in a network and can be applied to provide useful information for disaster management teams to mitigate the seismic risk of bridge networks. [ABSTRACT FROM AUTHOR]

Globally, decentralized energy systems are gaining popularity due to their potential for energy accessibility, energy resilience, and sustainability benefits. Existing research on an energy system decentralization approach, community choice aggregation (CCA), shows its ability to lower energy costs and increase renewable electricity consumption for U.S. communities. Nevertheless, research on the relationship between CCA and distributed electricity generation development is lacking. This paper fills this gap by investigating if the CCA approach associates with distributed generation capacity interconnection in California municipalities. The finding shows that although the average capacity has increased for all municipalities throughout the study period, contrary to proponents' arguments, the CCA approach has insignificantly decreased the capacity interconnected for municipalities. It is unclear if the result is due to a lack of higher-level support for the full CCA implementation or substitution by community-owned distributed generation. Future research is necessary to determine the CCA effect comprehensively in California. With this understanding, the research could be expanded to explore how community energy approaches work towards distributed generation across the U.S. and the globe. • Statistical evaluation of the CCA-distributed generation relationship. • The finding is contrary to CCA proponents' expectations. • Results show that CCA does not increase residential distributed generation capacity. • Global, national, and state legislative and financial supports are necessary. • Need community- and household-level distributed generation tradeoff research. [ABSTRACT FROM AUTHOR]

Ever since California experienced its energy crisis two decades ago, dynamic pricing of electricity has been the topic of discussion in numerous conferences and papers. Scores of pilots involving some 400 treatments of time-varying rates have been done to assess customer response to dynamic pricing around the globe, beginning with California's Statewide Pricing Pilot that ran from 2003 to 04. In the past few years, we are witnessing large scale deployment of static time-varying rates in states such as California and Michigan. Colorado is about to embark on that journey. However, other than OG&E's deployment of dynamic pricing, we have not seen much deployment of dynamic pricing in North America. California's power outages in August 2002 have rekindled interest in the topic. But almost all of the discussion about dynamic pricing has focused on summer peaking utilities. In this paper, we discuss the experience of Hydro Quebec, a winter peaking utility in Canada. Hydro Quebec has tested both critical-peak pricing and peak-time rebates. The results are very encouraging and quite consistent with results from summer peaking utilities. The article is based on a question and answer format with Frederic Pelletier, who advises Hydro Quebec on tariff strategy. He had posted some results from their dynamic pricing deployment on LinkedIn in response to some results I had posted from three-pilots with TOU rates that had been carried out in Maryland. I put a few questions to Frederic and this article evolved out of that conversation. In the article that follows, the questions are mine and the answers are exclusively his. [ABSTRACT FROM AUTHOR]

As low-cost air quality sensors become more widely utilized, more tools and methods are needed to help users access/process sensor data, identify poorly performing sensors, and analyze/visualize sensor data. Free and open-source software (FOSS) packages developed for use on FOSS data science platforms are well-suited to support this need by offering replicable and shareable tools that can be adapted to meet a user or project's specific needs. This paper describes enhancements to the FOSS AirSensor R package (version 1.0) and the DataViewer web application (version 1.0.1) that have been developed to support data access, processing, analysis, and visualization for the PurpleAir PA-II sensor. This paper also demonstrates how these enhancements may be used to track and assess the health of air sensors in real-time or for large historical datasets. The dataset used for this analysis was collected during a multi-year project (with sensors deployed from October 2017 to October 2020) involving the distribution of approximately 400 PA-II sensors across 14 communities in southern, central, and northern California. Applying the tools in the AirSensor package revealed a dramatic variability in sensor performance, mainly driven by seasonal trends or particulate matter source type. These results also indicate that this sensor can provide useful data for at least three years with little evidence of substantial or consistent drift. Further, high agreement was observed between co-located sensors deployed at different times, indicating that it may be reasonable to compare data from old and new PA-II sensors. In addition to assessing the long-term performance and reliability of the PA-II sensor, this analysis serves as a model for how data from large sensor networks may be effectively processed, evaluated, interpreted, and communicated. • An open-source R package has been enhanced to better support sensor data analysis. • Package is used to assess multiple years of data from multiple sensor networks. • Analysis reveals seasonal variability in the performance of air quality sensors. • Though there is little indication of substantial or consistent drift over time. • Example illustrates how the package can be used with real-time or historical data. [ABSTRACT FROM AUTHOR]

Applying synthetic fibers is one of the most effective physical methods for soil improvement and stabilization. In this paper, we determine the effects of polypropylene (PP) and poly vinyl alcohol (PVA) fibers contents (0.2, 0.5, 1.0 and 1.5%) at environment temperature (25 °C) and various freezing soil temperatures (0, −5, −10 and − 15 °C) on stabilization of the clayey sand soil (SC) samples. The results show that for both types of fibers, by simultaneous increasing of fiber content and lowering the temperature, the values of the unconfined compressive strength (UCS), elastic modulus (EM), ductility and California bearing ratio (CBR) increase, and they all reach their maximum values for 1.5% fiber content at −15 °C. Moreover, we show that PVA fibers perform better compared to PP fibers in increasing of UCS, EM, ductility and CBR values for different fiber contents and temperatures. This can be attributed to the fact that PVA has higher tensile and flexural strength compared to PP fiber. • The effect of freezing soil temperatures (FSTs) on strength parameters of a clayey sand (SC) soil stabilized by poly propylene (PP) and poly vinyl alcohol (PVA) fibers is investigated. • For both types of PP and PVA fibers, by simultaneous increasing of fiber content and lowering the temperature (FSTs effect), the strength parameters of SC soil samples are increased. • The strength parameters of SC soil samples reinforced by PP and PVA fibers reach their maximum values for 1.5% fiber content at −15 °C. • The PVA fibers perform better compared to PP fibers in increasing of strength parameters for different fiber contents at environment temperature and FSTs. [ABSTRACT FROM AUTHOR]

• Propose a novel deep learning model based on Runge-Kutta recurrent neural network (RKRNN) with prior knowledge to realize structural system identification and seismic response prediction. • Formulate a partition training strategy to train the proposed neural network to improve the efficiency of training. • Utilize three numerical examples to valid the feasibility of RKRNN model including a linear three degrees of freedom system, a nonlinear single degree of freedom system with Bouc-Wen hysteresis and a simply supported bridge. • Site monitoring data from a bridge located in California has been used to further validate the proposed approach. In the seismic analysis of structural systems, dynamic response prediction is an essential problem and is significant in every stage during the structural life cycle. Conventionally, response analysis is carried out by numerical analysis. However, when the structural parameter is unknown, the establishment of a numerical model will be difficult. Enlightened by the Runge-Kutta (RK) numerical algorithm, this paper proposes a novel recurrent neural network named Runge-Kutta recurrent neural network (RKRNN) to realize the seismic response prediction. A partition training strategy is formulated to train the proposed neural network and to improve the efficiency of training. The proposed model can be trained by using a limited number of samples. Three numerical examples are utilized to validate the feasibility of RKRNN model including a linear three degrees of freedom (DOFs) system, a nonlinear single DOF system with Bouc-Wen hysteresis, and a numerical reinforced concrete bridge model. Additionally, the site monitoring data from a real-world bridge is utilized to further validate the proposed network. The results show that the proposed RKRNN model can effectively and efficiently predict the structural response under seismic load and exhibits robustness to noise, with good potential for applications in engineering practice. [ABSTRACT FROM AUTHOR]

• Dispensed cost of hydrogen projected to reach cost parity by mid 2020s creating condition for self-sustainability. • Biomass-to-hydrogen assessed to be least-cost in the 2020s at roughly $2/kg but electrolytic ultimately becomes least cost. • Renewable hydrogen has the potential to be a cost-effective approach to decarbonization in multiple sectors. • Massive construction program needed to meet potential demand. • Significant government support needed to ensure sector scaling. This paper describes a multi-element analysis to assess least-cost approaches to scaling the renewable hydrogen sector to support deep decarbonization of applications requiring energy dense, transportable, and storable fuel. The analysis uses California as a reference market. The focus of the analysis is understanding the resource potential and cost evolution of renewable hydrogen dispensed as vehicle fuel (the anchor source of demand) and determining the feasibility and timing of achieving fuel cost parity with conventional fuels adjusting for fuel-economy. The analysis is novel in that is fully cross-sectoral on the demand side and multi-technology and spatially resolved at the facility level on the supply side. Production technology shares are assessed based on relative cost of produced hydrogen including feedstock constraints. Technology costs are benchmarked and projected using both secondary sources and primary analysis. The sectoral demand forecast is based on a combination of quantitative and qualitative assessment of cost competitiveness with alternative decarbonization strategies. The overall conclusion is that, with appropriate policy support, the RH2 sector can reach self-sustainability (price point at parity with conventional fuel on a fuel-economy adjusted basis) by the mid to late 2020s. The analysis also brings into focus the massive construction program needed to scale the renewable hydrogen sector at a regional scale. [ABSTRACT FROM AUTHOR]

Microbially induced calcium carbonate precipitation (MICP) technology has a great potential for improving the engineering properties of recycled aggregates, but the major limitation is the inhomogeneous distribution of calcium carbonate precipitation resulting in the nonuniform modification of recycled aggregates. This paper proposed a spraying method to equally distribute the calcium carbonate, and discussed the influence of the new treatment on the physical and mechanical properties of the MICP modified recycled aggregates. The results showed that the water absorption of the coarse, medium and fine aggregates after seven cycles of treatment was greatly reduced by 58%, 26% and 46%, respectively. The shear strength, crushing value and California bearing ratio (CBR) were increased by the treatments, while the improvement was limited by the strength of the generated calcium carbonate precipitation. A significant decrease in the porosity and total pore volume was found after treatments, and the cubic and spherical calcium carbonate crystals were generated on the surface of the recycled aggregates. This indicated that the improvement of the physical and mechanical properties of the recycled aggregates was mainly attributed to the generated calcium carbonate filling the pores and reinforcing the surface. The MICP technology facilitates the high-value utilization of the recycled aggregates in pavement engineering. [ABSTRACT FROM AUTHOR]

• Propose a method for embedding demand flexibility into building benchmarking. • Utilizes emerging building characteristics and time-series energy data streams. • Apply method to case study of 306 buildings in Southern California, USA. • Demonstrates ability to cluster buildings by attributes of demand flexibility. • Can inform decarbonization performance-specific policy targeting for buildings. The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. The interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures. [ABSTRACT FROM AUTHOR]

This paper argues that important improvements in the Genuine Progress Indicator (GPI) can be made by directly calculating the loss of natural resources, the benefits of leisure, and adjusting for inequality using a global norm, rather than using local, historical benchmarks. Local benchmarking is an obstacle to the standardisation and comparability of the GPI. We provide alternative methods for the five components that have used benchmarking in the standard GPI. We present empirical estimates for the GPI of the United States and California over the period 1995 through 2017, calculated with and without the alternative methods. Using our alternative methods, we show that some differences between the GPI of CA and the U.S. are artefacts of the benchmark methods. Implementing the alternative methods narrows the gap between the CA and the U.S. GPI as it reduces the U.S. environmental costs, and removes the artificial differential between CA and the U.S. in the cost of inequality. However, we find that the GPI provides insight into net welfare not reflected in GDP, both with and without the benchmarking methods. Overall, we suggest that the GPI can be significantly improved with these high priority revisions without changing the fundamental approach or theoretical framework. • Genuine Progress Indicator is sensitive to the benchmarking methods in 5 components • We recalculate without benchmarking for better comparability across regions • We report the GPI for the US and CA with and without benchmarking • Removing benchmarking affects the US GPI more than CA, mainly from the wetlands update [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic not only poses an unprecedented threat to global health but also severely disrupts the natural environment and ecosystems. Mitigating the adverse impacts of plastic-based personal protective equipment (PPE) waste requires the cooperation of professionals from various fields. This paper discusses a novel, cleaner approach to soil stabilisation by repurposing the nitrile gloves into a sustainable road material to improve the mechanical properties of expansive clay soil as pavement subgrade. For the first time, extensive geotechnical testings, including standard compaction, unconfined compressive strength (UCS), unsoaked California bearing ratio (CBR), repeated load triaxial (RLT), and swelling-shrinkage tests, were carried out to investigate the engineering performance of different proportions of the shredded nitrile gloves (SNG) (e.g., 1%, 1.5%, 2%) were blended with expansive clay (EC). In addition, surface roughness, scanning electron microscopy (SEM), and X-ray micro-CT analyses were conducted, and images were obtained to study the microstructural modification of the EC-SNG mixtures. The experimental results indicated that the blend of expansive clay with SNG helped in increasing the compressive strength, resilient modulus, and CBR and assisted in reducing the swelling and shrinkage of the soil. SEM and surface roughness analyses indicated the interaction between the soil matrix interface and the rough surface of the SNG. The main reasons for increasing the strength and stability of clay soil could be attributed to the high tensile strength of the SNG and the formation of the three-dimensional grid, and friction between the soil particles and SNG. According to the X-ray micro-CT test results, the incorporation of SNG led to an increase in closed porosity. [Display omitted] • An innovative approach to mitigate the problem of medical waste generation associated with COVID-19 is presented. • A cleaner and sustainable subgrade was developed by using discarded nitrile gloves. • The addition of 1.5% SNG to the soil provided the highest strength properties. • SNG reduced the swelling and shrinkage of the expansive clay subgrade. [ABSTRACT FROM AUTHOR]

Expansive clays are problematic soils as they contain minerals that swell when wetted and shrink during drying. Highway construction on expansive soils requires some form of chemical stabilisation or other treatments to improve pavement performance. Many commercial and waste by-products, such as cement, lime, fly ash and slag, are used as chemical stabilisers to treat expansive clays. Municipal solid waste incineration (MSWI) fly ash is a product obtained from waste-to-energy plants which have attracted increasing attention to prevent land contamination and to reduce landfill costs. This paper investigates the stabilisation mechanism and hydro-mechanical performance of MSWI fly ash-stabilised high plasticity expansive clays. In this study, compressive strength, California bearing ratio (CBR), dynamic cone penetration, shrinkage and swelling, X-ray diffraction (XRD), scanning electron microscopy (SEM) and X-ray micro-computed tomography (micro-CT) tests were conducted to understand the performance of MSWI fly ash for the treatment of high-plasticity expansive clay. The study found that MSWI fly ash reduces swelling potential and increases the ten-day soaked CBR to about 80%. Microlevel analysis showed that hydration reaction, cationic exchange, flocculation, and agglomeration between clay sheets are the key phases in MSWI fly ash stabilisation. In addition, the porosity of the clay reduced from 3.43% to 0.18% after stabilisation with 20% MSWI fly ash. The outcomes from the study provide guidance on using MSWI ash for improving problematic soils while enabling an efficient way to manage municipal solid wastes. [Display omitted] • Application of municipal solid waste incineration ash (MSWI fly ash) to stabilise high plasticity clay was investigated. • MSWI fly ash stabilised samples exhibit negligible swelling and shrinkage. • Even for extreme rainfall cases, MSWI fly ash stabilised samples perform well. • Cationic exchange and hydration are the main stabilisation mechanisms. • Porosity of clay reduced from 3.43% to 0.18% after stabilisation with 20% MSWI fly ash. [ABSTRACT FROM AUTHOR]

• Zero net energy is feasible when clean fuel is combined with electrification. • Building energy consumption valuation method impacts the component sizing. • Time dependent valuation of energy in California reflects its cost and emissions. • Solid oxide fuel cell provides efficient electricity and heat at the building. • Building can go off-grid with optimized operation of battery and fuel cell. California is seeking to mandate Zero Net Energy (ZNE) buildings by 2030 as part of its efforts to combat climate change. A ZNE building is required to compensate its annual energy consumption from the grid (electricity or gas) by injecting clean electricity back to the grid. So far, the most common approach for a ZNE building is utilizing solar photovoltaic (PV) or a PV and battery energy storage system. One key problem is the feasibility of installing PV if the surface area requirement is too large to achieve ZNE; another issue is grid congestion during the off-peak hours of midday when a large number of houses export their excess solar power to the grid. Hence, other ZNE home scenarios need to be evaluated to tackle these restrictions in achieving California's climate goals. In this work, we proposed and evaluated different system arrangements to reduce the required PV installation that is commonly installed as power generation source for ZNE residential units in California. The aim of this technical analysis is to provide a societal level policy guidance to accelerate fulfilling the ZNE home mandate. Due to the availability of the gas grid and potential for having more renewable gas in the grid, we investigated using gas-based heat generation versus only electric-based heat appliances. In addition, we incorporated a residential solid oxide fuel cell (SOFC) on the building site as a micro combined heat and power (mCHP) to facilitate achieving ZNE. The impact and contributions of different components, including energy storage, on grid-dependence, and the overall energy demand are also studied. With analysis of different mix of technologies that could be used for a ZNE home, we propose the potential solutions and discuss their strengths and weakness. In this paper, energy performance and optimal PV capacity are assessed for a ZNE home in all 16 climate zones of California established by California Energy Commission (CEC). The ZNE measurements for all four scenarios are based on three metrics of energy evaluation: (1) site energy (energy measured by the meter used at the building site); (2) source energy (raw fuel consumption at the source of energy generation to account for energy loss in production and distribution); and (3) time dependent valuation (TDV) of energy, which has been developed by the CEC to comply with its building energy efficiency standards. The TDV of each energy type depends on the time of use, climate, grid constraints, and other factors that reflect its societal value at any given hour of the year. The PV size required by each ZNE metric is calculated and compared. The results proved that relying on some gas in addition to electricity improves the practicality of required PV for ZNE homes particularly in regions with high heating demands. It was found that depending on the energy metric (site, source, and TDV), a different arrangement is required for the most efficient ZNE performance. Unlike the site metric, using source and TDV metrics where electricity is more valuable than gas shows that having both gas and electricity significantly reduces the PV size. This encourages investment in introduction of more renewable gas in the grid as one possible solution to achieving ZNE in individual homes. The great impact of an mCHP SOFC on a ZNE home in terms of reliability and feasibility points to the need for research, development, and commercialization to reduce the cost of mCHP SOFCs in residential application. Also, the addition of a battery does not reduce the PV size with site and source metrics; it only reduces the electric grid reliance. However, with TDV, where the time of energy consumption affects its value, not only does it reduce grid reliance during peak hours, but also less PV is required to achieve ZNE. [ABSTRACT FROM AUTHOR]

Traditional energy and demand savings metrics for distributed energy resources (DER) do not accurately describe the value of DERs to meet future energy needs, minimize grid investments, maintain reliability, and reduce greenhouse gasses. This problem is exacerbated in an increasingly renewable grid in which DER impact varies significantly with time and location. Moreover, different DERs are also valued using disparate metrics; this fragmented DER valuation and procurement creates process and economic inefficiencies. A new path forward is the Total System Benefits metric (TSB); the TSB aggregates all electric system benefits, and relevant environmental externalities that accrue to DERs. The TSB is the only metric that comprehensively values DER's to meet future electric system needs and environmental policy goals. This common metric will enable electricity planners, regulators, utilities, and implementers to best deploy and track DER to meet electric grid and environmental policy needs. This paper explains why the TSB is the right metric to value all DER, data requirements to develop the TSB, how to express DER in terms of the TSB, and lessons learned so far from California's implementation of the TSB. [ABSTRACT FROM AUTHOR]

Geopolymer based soil stabilization is an emerging research field as shown by recent research studies which indicate strong potential of their wide use for sustainable ground treatment. This paper comprehensively reviews the literature on expansive soil stabilization using alkali activated binders, focusing on the stabilization mechanism and geotechnical characteristics of treated soil inclusive of mechanical strength and durability characteristics. The stabilization mechanism is governed by the cation exchange process and the geopolymer/hydration products formation. The reported highest unconfined compressive strength (UCS) and California bearing ratio (CBR) of the geopolymer treated expansive soil cured at room temperature are 15 MPa and 81%, respectively and these values satisfy the strength criteria of most of the standards used for road base materials. The findings of the review revealed that incorporation of precursors in the range of 15–20%, alkaline molarities between 5 and 10 M, along with strong alkalis (NaOH or KOH), Ca rich additives, and discrete fibers could enhance the stabilization performance with respect to strength, swell, and durability. Alkaline activation treatment is expensive compared to the conventional Portland cement based stabilization methods, and yet the cost can be significantly reduced by employing cost effective silicates for alkaline activation. Overall, alkali activated binders (AAB) can be sustainable stabilizers to treat expansive soils for satisfactory geotechnical performance (compaction, strength and durability), however, as recommended in this review, further research should be advanced to realize the utmost viability of this promising approach in successful field application. • Precursor contents between 15 and 20% and alkaline molarities between 5 and 10 M are endorsed for better strength gains in treated soil. • Activator/Precursor ratios between 0.7 and 0.85 reduce the swelling and permeability characteristics of treated soil. • Fiber reinforced AAB-soil mixed with slag, effectively enhances soil stiffness, and tensile and flexural resistance. • AAB treatment is endorsed for tropical climates, but not for freezing weathers. • Liquid alkaline activator contributes to a major portion of the greenhouse gas emissions and costs of AAB-expansive soil treatment. [ABSTRACT FROM AUTHOR]

One of the reasons for the emerging adoption of behind-the-meter distributed energy resources (DERs), particularly photovoltaic (PV) and storage, is the impressive decrease in technology costs over the last years together with favorable policy and regulatory environments that created early-stage incentives to the proliferation of these assets. However, when looking at the next decade, the evolution of the regulatory framework and the trajectory of technology costs are difficult to predict. This uncertainty poses a new challenge to prosumers and microgrid owners who are trying to find the best moment to invest in these DERs. In particular, unpredictable changes in the conditions of the investments may translate into economic risks to potential DER adopters, which raises the need for new risk-mitigation methods to support their investment decisions. To address this issue, this paper proposes a multi-period DER investment model with economic risk constraints, considering technology costs and regulatory uncertainties. A case study, involving a large building in California, is used to show that different types of economic risk constraints can affect not only the size, but also the optimal timing of these investments in a multi-year planning horizon with significant DER technology costs and regulatory uncertainties. • Adds technology and regulatory uncertainty to the DER planning. • Optimizes multi-year DER investments strategies with risk constraints. • Presents DER planning strategies in a "cost-risk" decision-support framework. • Analyzes how different risk criteria impact DER investment solutions. [ABSTRACT FROM AUTHOR]

• Ownership structure of small commercial buildings makes energy retrofits challenging. • Retrofit package for Zero Net Energy small offices in Mediterranean climate. • Energy use and occupant comfort evaluated experimentally, side-by-side, in real time. • 65%-68% energy savings for cooling season; 22%-25% for heating season. • Energy savings are achieved while maintaining visual and thermal comfort. Making buildings zero-net energy (ZNE) is one of the major strategies for achieving carbon emission reduction goals. For this strategy to be successful, it entails a very significant reduction in energy use – 50% or more. In small commercial buildings, however, owners and building management teams usually have limited resources for identifying, analyzing, and procuring appropriate retrofit measures for reducing such use. An approach to overcome this limitation is the development of bundles of energy efficiency measures that can be presented to building owners/operators as a comprehensive package. The research presented in this paper focuses on an experimental evaluation of the impacts of a retrofit package developed for small office buildings in California. Performing this evaluation in a full-scale whole-building integrated systems test facility allowed a side-by-side evaluation in real time, against a reference case, of the impact of the retrofit package not only on energy use but also on visual and thermal comfort. The retrofit package evaluated is comprised of a combination of HVAC, lighting (including daylighting), and plug load measures. The evaluation occurred at different times of the year in order to account for seasonal variations in environmental conditions, including solar angles and weather. The experimental facility allowed testing for two different façade orientations: south and west. Results show that the proposed ZNE retrofit package can save significant amounts of energy for small commercial buildings. During cooling-prevalent periods, total energy savings were 65% for south orientation and 68% for west orientation; during heating-prevalent periods total energy savings were 22% for south orientation and 25% for west orientation. Measurements indicate that the ZNE retrofit package resulted in small but not very significant changes in comfort levels for building occupants. [ABSTRACT FROM AUTHOR]

In this paper, a novel finite volume model with unstructured mesh and implicit dual time stepping is proposed for simulating unsaturated infiltration flows based on Richards's equation (RE), which is one of the most challenging problems in earth science. In time, a matrix-free fully implicit dual time stepping algorithm is developed to discrete the equation, and in space, the equation is discretized by the finite volume model with unstructured grid. The new method is assessed and evaluated by benchmark and engineering water infiltration cases. Both numerical solutions in the literature and solutions of a commercial package called GEO-Studio are used to verify the accuracy of the proposed algorithm. An unsteady water infiltration simulation case is carried out as the application of the current model by using the topographic dataset of the Monterey Bay in California. The results demonstrate that the new algorithm is robust and practical, with fast convergence and high accuracy. [ABSTRACT FROM AUTHOR]

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers' charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group's sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers' battery capacities, behavior, and segment access we present scenarios for California's charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Each was calculated in under 45 s on a laptop computer. • Novel data-driven, graphical model framework for large-scale electric vehicle charging. • Hierarchical clustering of electric vehicle drivers by charging behavior histories. • Driver clusters highlight patterns for mixed use of multiple charging segments. • Distributions over driver groups and behaviors determine future charging demand. • Scenarios presented for charging of 8 million electric vehicles in California in 2030. [ABSTRACT FROM AUTHOR]

This paper presents a new random field modeling to assess the site-scale spatial variability of soil properties applied in liquefaction mapping. In this procedure, the initial random fields are primarily generated, based on in-situ measured data. These initial fields are considered as the virtual known values to construct interior random fields, section-by-section and around a central axis. Assembling these sections results in a three-dimensional random field model. Through this approach, more accurate vertical semivariogram models can be available, in addition to a higher number of measured data involved in the generation of random fields. Based on a verification procedure, computational cost reduction and a more appropriate prediction of the layering characteristics are the two most important beneficial points of changing the modeling strategy from planar to sectional. The proposed approach is implemented in a case study in Oceano, California and the spatial distribution of liquefaction probability is estimated. It is concluded that the sectional random field can efficiently locate the liquefiable zone through the soil volume, based on liquefaction evidence. In addition to performing the liquefaction severity assessment, it is also illustrated that the hazard assessments based on liquefaction severity alone cannot provide the comprehensive prediction of liquefaction potential and may lead to unconservative engineering judgments. • A new modeling strategy is introduced to generate 3D random fields implemented in liquefaction potential assessment. • Through sectional modeling of random fields, more accurate vertical semivariograms are obtained. • The approach is beneficial for site-scale liquefaction problems that are accompanied by limitations of data measurement. • The advantages of sectional random field over the conventional approaches are discussed. • It is noticed that the engineering judgments based only on liquefaction severity mapping may lead to unconserving designs. [ABSTRACT FROM AUTHOR]

Employing existing traffic data for the accurate classification of roads can provide significant references for the planning and construction of urban traffic infrastructure. This paper devises a novel deep learning framework for traffic station classification (DeepTSC) which classifies the traffic monitoring stations to in turn classify the road where these stations are located. The monitoring data of these traffic stations are interdependent with regards to both their spatial and temporal dimensions. We design a one-dimensional convolution layer for preliminary feature extraction and feature fusion. Following this, we employ a hybrid combination of the multilayer dilated convolution, long short-term memory network (LSTM) and attention mechanism, to extract the spatio-temporal features of the traffic data. Experiments on a public California freeway dataset show that the DeepTSC model's classification results outperform other existing state-of-the-art methods, with its accuracy being at least 1.4% higher than other models. [ABSTRACT FROM AUTHOR]

Machine-learning has recently gained considerable attention in the earthquake engineering community, as it can map the complex relationship between the expected damage and the input parameters. It is often necessary to understand the reasons for the behavior and predictions of the machine-learning model. This paper addresses this issue through interpretable machine-learning approaches such as partial dependence plots, accumulated local effects, and Shapely additive explanations. The evaluation of these approaches is carried out (1) at a component level by analyzing the shear strength predictions by a machine-learning model and (2) at a regional level through the machine-learning model for the regional damage assessment of bridges in California. The comparison helps to identify (1) the proper implementation of these approaches for the efficient use of machine-learning models and (2) key influential variables and thresholds that govern the prediction of the machine-learning models. [ABSTRACT FROM AUTHOR]

Californian strawberry growers have been relying on methyl bromide for several decades. Notwithstanding the substantial dependence on this broad-spectrum biocide, a phase out was planned due to its ozone layer depleting character. With the aim to meet sustainability issues facing the emerging organic strawberry producers and reinforce the interaction among local actors, a research network was developed through 2004–2020. In this paper we investigated the factors affecting the creation and dissemination of knowledge within the above-mentioned multi-year network. Embeddedness in agro-food networks is the framework that we chose, while from an empirical point of view we analyzed a case study with many available datasets. We observed a progressive build-up of trust among people throughout the sixteen years. The shared activities and the already established relationship provided the base of this strong networking. Furthermore, innovative methods emerging from network activities provided critical information and management approaches for expanding organic production in the region. Relational embeddedness explains the observed performance, whereas a complementary role seems to be played by the structural aspect of the same framework at the regional level. The facilitation function by a university within a participatory method, the unique role of advisors along with the connection to the industry contributed to the reinforcement of the network allowing new ways of stable multi-year co-operation. [ABSTRACT FROM AUTHOR]

Pluvial lake deposits in the Basin and Range province have long been exploited for records of hydrologic and climatic change. To obtain the most accurate reconstructions, a thorough understanding of the geomorphic evolution of such lake basins must be grounded in field stratigraphy and mapping, which are often lacking or are not well integrated with core studies. We conducted extensive stratigraphic investigation, mapping, and dating of exposed strata in the Lake Manix basin, former terminus of the Mojave River, California, during the Pleistocene to provide such a background and to supplement interpretations from a 45-m-long core. In this paper, we emphasize the geomorphic and tectonic processes that shaped the evolution of the Manix basin after the arrival of the Mojave River in the early-middle Pleistocene. The main processes include (1) sedimentary infilling of the lake basin, (2) interaction among the subbasins as controlled by internal sills and sill failures, (3) tectonics, and (4) climate change. From about 500 to 190 ka, Lake Manix was confined to its western subbasins, and fluctuated in response to climate change and perhaps to brief diversions of the river into the upstream Harper Lake basin. During this time, about 24 m of sediment accumulated near the confluence of the Mojave River and Manix Wash, and a 15-km-long clastic wedge built into the western basins, gradually filling the accommodation space. Two basin-integration events subsequently occurred, both caused by failure of sills that lay on sheared deposits along the Manix fault zone. When Lake Manix rose at the onset of glacial marine isotope stage 6, the sill on the south flank of Buwalda Ridge failed and triggered a catastrophic flood that entered the new, lower elevation, previously endorheic Afton subbasin. Subsequent lake-level changes affected both subbasins, but the thickest deposits after the flood are preserved in the Afton subbasin. The area upstream of Buwalda Ridge was only submerged during relatively high lake levels. Thus, sediment records interpreted from the core site and nearby outcrops in part reflect a geomorphic event (basin integration) rather than climatic conditions. The sill of Lake Manix after the integration event lay at the northeast end of the Afton subbasin, athwart strands of the Manix fault zone. At about 25 ka, this eastern sill was rapidly incised, and the river advanced to terminate in Lake Mojave in the Soda and Silver Lake basins. Recognition and dating of integration events, common for lake-river interactions in active tectonic settings, provide important context for interpreting paleoclimate records from sediments and provide temporal and spatial constraints on the biogeography of aquatic species. • Thorough understanding of geomorphic evolution required to understand lake records • Basin integration by overflow can strongly affect sediment records of climate change. • Active tectonic regimes set the stage for lake overflow events. • Catastrophic flood events may be more frequent than previously recognized. [ABSTRACT FROM AUTHOR]

• Renewable hydrogen can compete with lithium-ion batteries for seasonal storage. • Hydrogen's competitiveness depends on heat rate of plant it is replacing. • Net present value of hydrogen-fired gas turbine varies based on operation of plant. • Lack of infrastructure will impede demand growth for hydrogen in the United States. Non-emitting variable renewable energy (VRE) resources are needed on the power grid if the United States is to "deeply decarbonize" the power sector. The intermittent nature of these resources makes them difficult to integrate into the power system. Existing energy storage technologies, such as lithium-ion (LI) batteries, could be used to aid the integration of these resources, but these technologies are sized to produce power for hours at a time before needing to be charged again. While these energy storage technologies could address daily imbalances between supply and demand for electric power, they cannot address the seasonal nature of power production of VREs. This paper details a methodology to estimate the levelized cost of energy (LCOE) of meeting this seasonal imbalance with either a hydrogen-fired gas turbine (HFGT) or lithium-ion battery system (LI) as a measure of economic efficiency of the technologies. Applying our model, we find the average LCOE associated with meeting this seasonal imbalance is $2400/MWh using a HFGT fueled with green hydrogen and $3000/MWh using a LI. If we allow the model to operate the HFGT with blue hydrogen the average LCOE decreases to $1560/MWh. However, we find the power prices required to justify investment in an HFGT to replace a natural gas-fired gas turbine are considerably higher than those seen in the market today. [ABSTRACT FROM AUTHOR]

• New self-centering precast concrete frames with increasing post-decompression stiffness. • Seismic design method considers the changing stiffness of variable friction dampers. • A method to mitigate the concentration of story drifts was proposed. • A method to mitigate detrimental higher mode effects was proposed. This paper presents the numerical investigation of the seismic performance of post-tensioned self-centering precast concrete (SCPC) frames with variable friction dampers (VFDs) and hidden corbels (HCs). The VFDs are characterized by the combination of flat and grooved plates, where the friction forces change as the connection deformation increases, which enables designs to meet target seismic performance objectives at different seismic hazard levels. Theoretical and numerical models for the connection are proposed based on connection geometry, and a performance-based seismic design approach for the SCPC frames is outlined to meet target performance objectives at two common seismic hazard levels, the design basis earthquake (DBE) and the maximum considered earthquake (MCE). SCPC frames for a six-story prototype building located in a high seismic zone in California were designed using this procedure. Three frames with HC-VFD-SPSC connections were designed using various methods to mitigate concentration of drifts in the first story, and one frame was designed with conventional constant friction devices (CFDs) for comparison. Nonlinear time-history analyses were performed for these different designs using two sets of ground motions representing the DBE and MCE seismic hazard levels. The analysis results indicate that the frames with the HC-VFD-SCPC connections achieved the target performance objectives. The proposed approaches of modifying post-tensioning and VFD design parameters or column stiffness in the first story were effective in mitigating the concentration of drifts. Additionally, through comparison with the results of the frame with CFDs, it was found that the VFDs are effective in decreasing overall drift demands at the MCE level, as well as mitigating detrimental higher mode effects observed in SCPC frames with CFDs. [ABSTRACT FROM AUTHOR]

Last-mile deliveries usually occur within urban areas where logistics operations increase congestion and emissions, as well as associated roadway-related health impacts. This paper evaluates the role of incentives for zero and near-zero emission vehicle (ZEV) technologies in last-mile delivery operations. Specifically, the study investigates the total cost of ownership and life cycle environmental impacts associated with last-mile deliveries, and compares the private and public costs of different vehicle powertrain and fuel pathways, under existing incentives in the State of California. The study analyzes driving patterns for several last-mile delivery vocations, including beverage, warehouse, parcel, grocery, food and local deliveries based on real-world truck travel data. Statistical analyses are conducted to compare driving patterns across vocations, vehicle classes, and powertrains, as well as across parcel carrier fleets. To better assess the role of the incentives, the study also provides a comparative estimate of the marginal health damages from life cycle emissions of criteria pollutants (e.g. $/ton). Our results suggest that parcel deliveries are characterized by short trips (95% less than 100 miles), a high number of stops, and lower average driving speeds compared to other delivery vocations. Total cost of ownership for conventional diesel Class 3 to 6 vehicles used in last mile applications were estimated to be approximately between $282 and $305 thousand dollars, with the cost of negative health impacts ranging from $38 to $64 thousand dollars over the vehicle lifetime. The results show that battery electric freight vehicles are cost competitive with conventional diesel vehicles, particularly for smaller (Class 3) light commercial vehicles. Available financial incentives were sufficient to offset additional purchase and fuel expenses, leading to lower total costs of ownership. • The study evaluates the use of electric zero-emission vehicles for parcel deliveries. • Evaluates total cost of ownership (TCO) for various powertrains and fuels. • Estimates environmental (health-damages) impacts of last-mile deliveries. • Conducts sensitivity analyses for factors affecting the TCO. • Evaluates the role of financial incentives with respect to pollution abatement. [ABSTRACT FROM AUTHOR]

• Development of a multiclass spiking neural classifier with time-varying weights. • Meta neuron-based learning algorithm for a spiking neural classifier. • Improve computational power of a compact spiking neural classifier. • A spiking neural classifier for high imbalance classification problems. This paper presents a meta-neuron learning-based spiking neural classifier with a time-varying weight model (MeST). MeST is developed to handle the class imbalance in classification problems without any data preprocessing methods. Meta-neuron based learning algorithm in MeST uses normalized postsynaptic potentials (global information) and weight of the connection (local information) to determine the sensitivity modulation factor. This modulation factor determines the proportion of the weight update for a given set of presynaptic spikes. The weight update is then embedded in a Gaussian function to determine the time-varying weight update. The centre of the time-varying Gaussian function is determined by the presynaptic spike times. MeST is demonstrated on 10 benchmark datasets from the University of California, Irvine California machine learning repository and then applied to solve credit scoring using three real-world datasets. Performance studies show that the generalization ability of MeST is better than other spiking neural networks with constant weight model, despite having a simple architecture. Furthermore, compared to other non-spiking shallow machine learning classifiers, MeST is a slightly better model for classification using highly imbalanced datasets. This indicates the learnability of a stand-alone classifier on an imbalanced dataset can be increased by using time-varying weights. [ABSTRACT FROM AUTHOR]

The wildland-urban interface (WUI) is defined as a geographic area where human developments and flammable vegetation merge in a wildfire-prone environment. Losses due to wildfire have been rising in the past decade, attributed to changes in vegetation growth, fuel availability, and increased land developments in WUI. This paper studies the process of wildfire spread inside WUI communities. The fire spread rate within WUI communities is determined for nine wildfires that were ranked among the most destructive wildfires in North America. An improved quasi-empirical model that considers radiation and fire spotting as modes of fire spread inside a community is proposed. The new model is validated using the documented spread rates during the 2007 Witch and Guejito fires and the 2017 Tubbs fire. The proposed model is computationally efficient and can be used to quantify fire spread rate and the number of affected structures inside a community during a wildfire event. • Communities are being exposed to faster spreading wildfires in recent years. • A streamlined integrative urban fire spread model is proposed. • The proposed model is applied to two case studies in California. • The proposed model is capable of capturing fire progression in WUI communities when compared to real scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an integrative methodology for managing and stabilizing the output of a wind/solar farm using storage devices in a cost effective and real-time manner. We consider the problem where a renewable farm should decide the amount of energy charged into, or withdrawn from, the battery given the stochastic and time-varying nature in the renewable energy power output. Our methodology features a seamless integration of a non-myopic decision framework and a sequential non-parametric predictive model based on functional principal component analysis. A key feature of our algorithm is that it quantifies costs over a rolling horizon where both predictions and decisions are updated on the fly as new data is acquired. Our technology is tested on the California ISO dataset. The case study provides a proof-of-concept that highlights both the benefits and ease of implementation of our forward looking framework. • We present a methodology for managing renewable farm outputs using storage devices. • A key feature of the algorithm is its lookahead decision framework where: costs are quantified over a rolling horizon; both predictions and decisions are updated on the fly as new data is acquired. • The algorithm is able to incorporate any optimization or forecasting approach given limitations on the time between real-time decision updates. • The presented approach is easily implementable in real-time settings where storage decisions may be updated regularly. [ABSTRACT FROM AUTHOR]