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Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented. [ABSTRACT FROM AUTHOR]

Stormwater green infrastructure (GI) has been applied as a method to address urban flooding for over forty years. However, GI has not yet been widely utilized across the US. Prior studies identified some challenges with technical, engineering, and socioeconomic aspects of GI, but detailed examination of non-scientific papers to identify and analyze real-world barriers that may hinder the implementation of GI has not yet been done. To achieve this goal, we conducted a meta-analysis of 351 public documents from federal, state, and 62 municipalities in Eastern Pennsylvania to systematically review the support of different levels of government for implementing GI. We summarized barriers in three categories: 1) governance and policy: failure to integrate GI into existing stormwater management policies due to unclearly defined responsibility and infrequent policy updates, 2) stormwater fees and credits: inequity in stormwater fee structures and debate of stormwater credits connected to GI, and 3) public education and outreach: most municipal governments provide little or no stormwater and GI-related information to their residents, but some municipalities with higher population density have tried to offer more. These barriers will restrain the original intention and vision of GI implementation and cause difficulties in effectively conveying GI's information from the federal and state levels to municipalities and residents. Our discussion highlights these difficulties of GI implementation at all governmental levels and shed light on potential solutions to address these barriers. • Barriers to Green Infrastructure (GI) adoption in the US context are identified. • Qualitative and quantitative methods are conducted to identify GI barriers. • Three categories of GI barriers are summarized at different levels of government. [ABSTRACT FROM AUTHOR]

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs. [ABSTRACT FROM AUTHOR]

• Aggregation and cooperation among peers will expand beyond prosumers in microgrids. • Growth in IoT and metering infrastructure is empowering end-use consumers. • Methodology allows aggregated consumers to schedule load and share energy. • Application demonstrates a significant cost reduction while assuring fairness. • Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology. [ABSTRACT FROM AUTHOR]

Although water and energy systems are interdependent, they are still designed individually due to their complexity. This paper proposes a multiple input multiple output (MIMO) model predictive control (MPC) for demand management of an integrated water distribution system (WDS) supplied by an islanded microgrid (MG) system, offering an additional opportunity for energy-saving at the WDS. This strategy benefits the island and remote communities, which have limited access to the centralized electric grid. The existing literature either used an open-loop approach for energy management of water-energy microgrids or ignored the interconnected nature and MIMO nature of these systems. Compared to that, the proposed closed-loop energy management system is developed to address the detailed dynamics of assets, the inherent interconnections between water and energy networks, and to provide resilience as well as energy saving and demand management capabilities. The integrated MPC algorithm is designed in discrete time, utilizing a state-space model and sequential quadratic programming (SQP) as the solution method. Considering the intensive use of electricity by the pumps in WDS, their energy consumption is accommodated by an islanded microgrid system that incorporates a diesel generator, renewable energy sources (wind and solar), and a battery bank that serves as an energy source and a storage device. The proposed approach is investigated using base water demand from a real-world community, Telford, in Pennsylvania. Several case studies, such as variations in renewable energy contributions and increments of the control actions, are carried out to demonstrate the feasibility and reliability of the integrated MPC to provide efficient water and energy demand management concurrently. • Model predictive control algorithm of the interconnected water-energy is developed. • The full dynamics of energy-consuming assets in the water system are considered, including quadratic pump relations. • Effect of the renewable energy variations on the 24-hr energy dispatch is studied. • Multiple optimal configurations result in up to $360k/year savings, reduced carbon footprint, and smoother control. • The developed framework is applied in a Telford, PA case study, showcasing its real-world implementation. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Copaifera reticulata oleoresin showed antigenotoxic effect on colon. • Pre-neoplastic lesions of the colon were reduced by treatment with Copaiba oleoresin. • The oleoresin reduced the progression of carcinogenesis. • Major chemical constituent of the oleoresin showed anti-inflammatory activity. • The anti-inflammatory activity of oleoresin is involved in its chemopreventive effect. Abstract In Brazilian folk medicine, copaiba oleoresin is widely known for its therapeutic activity, especially its wound healing and anti-inflammatory actions. Considering the relationship between inflammatory processes and carcinogenesis, this paper reports on the Copaifera reticulata Ducke oleoresin (CRO) chemopreventive potential in the colon carcinogenesis model in rats. To understand the mechanisms involved in this effect, the anti-inflammatory activity of CRO and its major chemical constituent, the diterpene ent -polyalthic acid (PA), were evaluated on the production of nitric oxide (NO) and prostaglandin E2 (PGE2) in mouse macrophages. For the chemoprevention assessment, the effect of CRO administered by gavage was investigated on DNA damage, pre-neoplastic lesions and mitotic frequencies induced by the 1,2-dimethylhydrazine (DMH; intraperitoneal injection) carcinogen by comet, aberrant crypt focus (ACF) and long-term assays, respectively. CRO reduced DNA damage (average 31.5%) and pre-neoplastic lesions (average 64.5%) induced by DMH, which revealed that CRO has antigenotoxic and anticarcinogenic effects. In the long-term assay, treatment with CRO significantly decreased mitoses in the tumor tissue, which suggested that CRO influenced carcinogenesis progression. PA reduced NO levels induced by lipopolysaccharides in macrophages. However, this diterpene showed no effect on PGE2. Taken together, our results suggest that PA exerts anti-inflammatory action via the NO pathway. The CRO chemopreventive effect may be partly due to the anti-inflammatory property of its major chemical constituent, PA. Our findings indicate that CRO is a promising agent to suppress colon carcinogenesis. [ABSTRACT FROM AUTHOR]

Dense phase pneumatic conveying of pulverized fuel particles under high pressure is one of the key techniques in large scale gasification of coal and petroleum coke. The real time and continuous measurement of dense phase gas–solid flow parameters such as particle velocity, concentration and mass flow rate has great significance for the in-depth understanding of particle flow dynamic behaviors and the optimized design of the conveying system. In this paper, an integrated electrostatic sensor is designed and used to explore the flow characteristics of anthracite and petroleum coke particles on a dense phase pneumatic conveyor. The ring electrodes in the integrated sensor are capable of measuring the “mean” velocity of solid particles over the whole cross-section of the pipeline, while the arc electrodes are employed to determine the local velocity of particles near them. The experimental results on a dense phase pneumatic conveyor under high pressure demonstrate that the flow characteristics depend on the physical properties of solid particles and carrying gas. Petroleum coke particles are much easier to be suspended in the gas flow than the anthracite particles, but its flow stability is worse. The local velocities from the arc electrode pairs mounted on the top of the pipeline are usually higher than those from the arc electrodes on the bottom for a stratified flow. The velocity from the ring electrode pair, indicating the mean velocity of particles over the whole cross-section, is usually higher than the local velocities from the arc electrode pairs for a suspension flow. In addition, the relationship between the mass flow rate and charge level of dense phase pneumatically conveyed solid particles is very complicated due to the complexity of the particle flow and the charge level cannot be used to measure particle concentration or mass flow rate. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

• The problem of historical steel truss bridges preservation is presented. • A novel hybrid deductive failure analysis approach (Analytical Fault Tree - AFT) is developed. • Numerical models investigate fault scenarios including fatigue and concurring causes. • AFT and fault scenarios are applied to a historical bridge located in the East of Spain. • Guidelines and conservation strategies are obtained for the investigated bridge. Historical steel bridges represent an important construction typology integrating the constructive heritage of the past century that needs to be preserved. Exposure to fatigue phenomenon, aging, improper design or execution, extreme events, and other aggressive environmental agents can seriously compromise the conservation and performance of these structures as some recent catastrophic collapses have shown (Mississippi River bridge, Minneapolis, Minnesota 2007; Kinzua Bridge State Park, Pennsylvania 2003). In this context, the diagnostic of historical steel bridges becomes of basic importance to identify and implement maintenance, monitoring and conservation strategies. Fault Trees are useful tools for practitioners that provide a complete overview of possible failures. In the related literature, this instrument is mainly applied with approaches that are based on previous experience or failures detected in similar structures, and so the Fault Tree can only provide qualitative support. This paper proposes a new method to achieve an "Analytical" Bridge Fault Tree linking the technician's experience and the numerical simulations of different fault scenarios. The latter are achieved by a numerical model able to consider fatigue failure and different concurring causes in the analysis (e.g., and aggressive phenomena, corrosion, defects, lack of maintenance). The proposed approach was applied to a historical railway steel truss bridge located in the East of Spain (Valencia Region) in order to show its applicability and potential. [ABSTRACT FROM AUTHOR]

Since the Pennsylvania oil rush of 1859, petroleum has quickly become the dominant fuel of industrial society. The "Peak Oil" debate focused on whether or not there was an impending production crunch of cheap oil, and whilst there have been no shortages across the globe, a shift from conventional to unconventional oil liquids has occurred. One aspect of this shift was not fully explored in previous discussions–although of some importance in a low-carbon energy transition context: the extent to which the net-energy supply of oil products is affected by the use of lower quality energy sources. To fill this gap, this paper incorporates standard EROI (energy-return-on-investment) estimates and dynamic decline functions in the GlobalShift all-liquids bottom-up model on a global scale. We determine the energy necessary for the production of oil liquids (including direct and indirect energy costs) to represent today 15.5% of the energy production of oil liquids, and growing at an exponential rate: by 2050, a proportion equivalent to half of the gross energy output will be engulfed in its own production. Our findings thus question the feasibility of a global and fast low-carbon energy transition. We therefore suggest an urgent return of the peak oil debate, but including net-energy issues and avoiding a narrow focus on 'peak supply' vs 'peak demand'. [Display omitted] • Global gross and net-energy of oil liquids production is determined from 1950 to 2050. • Energy required for production is estimated to be 15.5% of the actual gross energy. • Oil liquids become a limit to a rapid and global low-carbon energy transition. • The peak supply vs. peak demand dispute needs to be re-examined. • Focus should be put instead on net-energy transition and wise energy consumption. [ABSTRACT FROM AUTHOR]

This paper used farm income tax returns (Schedule F) data from 62 dairy farmers who milked 200 cows or fewer in western and central Maryland and south- western Pennsylvania (hereafter, the mid-Atlantic region) to assess the relative financial performance of management-intensive grazing (MIG) and confinement dairy operations over the 15-yr period from 1995 through 2009. Data were not available from all farmers in all years; on average, the sample analyzed contained 11 MIG farms and 26 confinement farms. Management-intensive grazing operators were more profitable on a per hundredweight, per cow, and per acre basis, and no less profitable on a whole-farm basis. Even though the confinement operators had higher gross income than MIG operators, their expenses exceeded those of MIG operators. Profits of MIG operations were less variable as well, so that MIG operators faced less income risk. Increased reliance on grazing has other benefits as well. Grazing seems to be a much healthier practice for dairy cows. Veterinary, breeding, and medicine costs per cow are much less for cows that are pastured than those raised in confinement systems. Because they are healthier, cows that are grazed can be milked longer (or culled less frequently). As a result, MIG operators have a larger number of higher quality animals for sale (e.g., bred heifers). Management-intensive operations are also less labor intensive. Reductions in crop production and in the time cows spend in the barn led to significant reductions in field work and cleaning operations in the barn. Costs of hired labor were thus substantially lower in MIG operations than in confinement operations. Land requirements likely impose the principal limitation on the size of intensive grazing operations. In the mid-Atlantic, for instance, grazing operations need 1.5 to 2.0 acres of pasture for every dairy cow/ calf equivalent to provide sufficient grass to support a dairy operation. Pasture land for MIG operators must be contiguous to the milking parlor and located no farther than a cow can walk to and from twice a day. That requirement likely limits the maximum size of an intensive grazing operation, especially in areas where land prices and rents are high, as they are in much of the mid-Atlantic. [ABSTRACT FROM AUTHOR]

Abstract: Price spikes are distinctive aspects of electricity price impacting its forecast accuracy. Electricity price spikes can also have serious economical effects on the market participants. However, prediction of electricity price spikes is a complex task and most of current electricity price forecast methods focus on prediction of normal prices. In this paper, a new forecast strategy for prediction of both occurrence and value of electricity price spikes is presented. The proposed strategy has a novel feature selection technique based on information theoretic criteria to select a minimum subset of the most informative features for the forecast process. Also, the strategy includes a new closed loop prediction mechanism composed of probabilistic neural network (PNN) and hybrid neuro-evolutionary system (HNES) forecast engines. The effectiveness of the proposed forecast strategy for the prediction of both price spike occurrence and value is extensively evaluated by the real-life data of PJM (Pennsylvania–New Jersey–Maryland) electricity market. The obtained results confirm the validity of the developed approach. [Copyright &y& Elsevier]

Most water management studies concentrate on the inter-temporal allocation problem or, more recently, spatial dynamics – but not both. While early spatial–temporal studies focused on the allocation of water quantity, this paper presents an approach to water quality analysis that incorporates both spatial and temporal dynamics in a watershed framework. The acid mine drainage (AMD) problem in the Cheat River watershed of West Virginia, USA, serves as a case study and provides an opportunity to test the modeling approach developed. The empirical models are written in General Algebraic Modeling System (GAMS) and solved using the CPLEX mixed integer programming package. The results suggest that available investments should be concentrated in heavily impaired stream segments. The model can be used to assess the economic implications of alternative watershed Total Maximum Daily Load (TMDL) implementation or other management strategies. [Copyright &y& Elsevier]

Abstract: Four decades ago, argued that spatial autocorrelation (i.e., local distance and configuration effects) and distance decay (i.e., global distance effects) intermingle in the estimation of spatial interaction model specifications. Today, massive computer power available with a desktop PC offers the necessary resources to account for these spatial autocorrelation effects within spatial interaction. Simple gravity model respecifications have been analyzed by and by . These results have been extended to a doubly-constrained gravity model respecification by . This paper summarizes results from a further refinement of this doubly-constrained respecification, namely differentiating within and between areal unit flows. In doing so, an eigenfunction-based spatial filter description of spatial autocorrelation is constructed, which lends itself to visualization of analytical spatial autocorrelation components. Results are illustrated with the 2000 journey-to-work dataset for the extended state of Pennsylvania, USA. [Copyright &y& Elsevier]

Abstract: Recent natural disasters indicate that modern technologies for environmental monitoring, modeling, and forecasting are not well integrated with cross-level social responses in many hazard-management systems. This research addresses this problem through a Java-based multi-agent prototype system, GeoAgent-based Knowledge System (GeoAgentKS). This system allows: (1) computer representation of institutional regulations and behavioral rules used by multiple social institutions and individuals in cross-level human–environment interactions, (2) integration of this representation with scientific modeling of dynamic hazard development, and (3) application of automated reasoning that suggests to users the appropriate actions for supporting cooperative social responses. This paper demonstrates the software architecture of GeoAgentKS and presents such an integrated approach by modeling the drought management processes in Central Pennsylvania, USA. The results show that it is possible to use GeoAgentKS to represent multilevel human–environment interactions and to use those interactions as input to decision making in hazard management. [Copyright &y& Elsevier]

• Probabilistic life-cycle cost analysis for concrete bridge decks under corrosion. • Agency and user cost quantification for decks with different reinforcement types. • Detailed traffic delay estimation based on vehicle queuing and traffic control procedures. • Analyze effect of concrete cover, bridge location, traffic volume, and number of lanes. • Optimum deck reinforcement selection based on bridge attributes and life-cycle cost. Reinforced concrete bridge decks with conventional reinforcement may experience deterioration due to deicing salts or marine environmental conditions. Routine maintenance, rehabilitation, and repair activities are implemented to maintain an acceptable performance level. In addition to the significant financial resources required to conduct these activities, traffic congestions at work zones can lead to an increase in delays and crash risk for road network users, as well as other environmental and social impacts. These user costs arising from traffic congestions can be significantly higher than the cost of maintenance, rehabilitation, and repair activities. Despite the higher initial unit cost, bridge decks constructed with corrosion resistant reinforcement can provide a longer service life and require less maintenance activities. Accordingly, from a life-cycle cost perspective, corrosion resistant reinforcement could be a better alternative compared to conventional reinforcement for bridge decks. This paper presents a probabilistic framework to estimate the life-cycle cost associated with bridge decks constructed with different reinforcement alternatives. The framework accounts for reinforcement deterioration to evaluate the maintenance activity timeline considering influential corrosion-related parameters (e.g., reinforcement type, concrete cover, and bridge location, among others). Key attributes affecting traffic delays, including traffic volume, number of lanes, and vehicular queuing, are also included in the framework. Monte Carlo simulation is used to predict the corrosion-induced cracking time, service life of the bridge deck, and the life-cycle cost under uncertainty. A bridge located in Pennsylvania is used as an illustrative example to demonstrate the application of the proposed framework. The results show that the concrete cover, chloride exposure condition, average daily traffic, and number of traffic lanes have a significant effect on the life-cycle cost of reinforced concrete bridge decks; especially for those constructed with conventional reinforcement. The presented framework can be beneficial in selecting the optimum bridge deck reinforcement type from a life-cycle cost perspective. [ABSTRACT FROM AUTHOR]

This paper presents chemical mass balance (CMB) analysis of organic molecular marker data to investigate the sources of organic aerosol and PM2.5 mass in Pittsburgh, Pennsylvania. The model accounts for emissions from eight primary source classes, including major anthropogenic sources such as motor vehicles, cooking, and biomass combustion as well as some primary biogenic emissions (leaf abrasion products). We consider uncertainty associated with selection of source profiles, selection of fitting species, sampling artifacts, photochemical aging, and unknown sources. In the context of the overall organic carbon (OC) mass balance, the contributions of diesel, wood-smoke, vegetative detritus, road dust, and coke-oven emissions are all small and well constrained; however, estimates for the contributions of gasoline-vehicle and cooking emissions can vary by an order of magnitude. A best-estimate solution is presented that represents the vast majority of our CMB results; it indicates that primary OC only contributes 27±8% and 50±14% (average±standard deviation of daily estimates) of the ambient OC in the summer and winter, respectively. Approximately two-thirds of the primary OC is transported into Pittsburgh as part of the regional air mass. The ambient OC that is not apportioned by the CMB model is well correlated with secondary organic aerosol (SOA) estimates based on the EC-tracer method and ambient concentrations of organic species associated with SOA. Therefore, SOA appears to be the major component of OC, not only in summer, but potentially in all seasons. Primary OC dominates the OC mass balance on a small number of nonsummer days with high OC concentrations; these events are associated with specific meteorological conditions such as local inversions. Primary particulate emissions only contribute a small fraction of the ambient fine-particle mass, especially in the summer. [Copyright &y& Elsevier]

Abstract: Results of nonlinear static pushover analyses of five bridges located in Pennsylvania are presented. The bridges, which vary from single column bents to multiple column bents, have reinforced concrete columns with ties at 12 in. spacing. The objective of the part of the study that is presented in the paper is to estimate displacement ductility and force reduction factors for the selected bridges. The procedures used to obtain moment–curvature relations and to perform nonlinear static pushover analysis using linear elastic analysis software are described. Calculated values for displacement ductility factors and response modification factors (-values) are presented. Observations made from the results of a literature review on experimental evidence for ductility of deficient columns are also discussed. [Copyright &y& Elsevier]

Daily ambient aerosol samples were taken in Pittsburgh, Pennsylvania from the summer 2001 to the winter 2002 as part of the Pittsburgh Air Quality Study (PAQS). The study measured PM2.5 mass by the Federal Reference Method (FRM) and the PM2.5 chemical composition by a variety of filter-based and continuous instruments. This paper examines the mass balance between the FRM-measured mass and the sum of the aerosol chemical components. For the 7-month study period, the average FRM-measured mass is 11% greater than the sum of the mass of the aerosol chemical components. This mass balance discrepancy varies seasonally, with the average FRM-measured mass 17% greater than the sum of the chemical components for the summer months, with discrepancies as large as 30% during certain periods. Meanwhile, the FRM-measured mass was at or slightly below the sum of the chemical components for the winter months.The mass balance discrepancy and its seasonal shift cannot be explained by measurement uncertainty; instead the discrepancy is due to combination of retained aerosol water on the conditioned FRM filters and volatilization losses. The relative importance of these different effects varies with aerosol composition and causes the observed seasonal variation in the mass balance. The contribution of the aerosol water to the FRM-measured mass is estimated using continuous measurements of aerosol water at the site; volatilization losses are estimated from other filter-based instruments. Water contributes 16% of the FRM mass in the summer, and 8% of the FRM mass in the winter; it also appears responsible for episodes where the FRM-measured mass is significantly greater than the sum of components. Retention of water is greatest during acidic conditions, which commonly occur during the summer months. Volatilization losses are estimated at 5% of the FRM mass during the summer, and 9% for the winter. Volatilization losses appear to be most significant on days dominated by organic aerosol, or winter days with relatively high nitrate concentration. Accounting for the effects of water and volatilization losses closes the mass balance between the FRM and the sum of the chemical components, providing insight into the FRM measurements. [Copyright &y& Elsevier]

• Background is provided about the need for tools to support regional energy baselining and efficiency planning. • 296 homes in Pennsylvania are simulated in EnergyPlus using a batch simulation tool and energy audit records. • Simulation results are scaled and compared to building level electricity use data and utility sales data. • Analysis reveals discrepancies between simulated and actual energy use that varies by utility. • The role of building simulation tools for energy baselining and efficiency planning is discussed. Building simulation tools are seeing growing use in the residential sector due to a combination of increased interest in building energy performance, more user-friendly software packages, and decreasing computing power requirements. These tools are now being applied in batch simulations, where large numbers of homes are simulated at once to understand existing building stock energy consumption and potential energy efficiency improvements to make to those stocks. As these types of applications grow, attention is needed to ensure that simulation inputs and outputs are handled with an appropriate understanding of the uncertainties involved. In this paper, we use a batch simulation tool to translate energy audit records of 296 existing single-family homes in Pennsylvania into EnergyPlus building simulation models. We compare the results of these simulations to actual metered electricity use data from these homes, representative device-level data, and local utility data to better understand the abilities and limits of such modeling practices. Results show that while batch simulations are capable of producing accurate estimates of the energy consumption of a large number of homes, many more homes had their energy consumption overestimated than underestimated, resulting in a significant net overestimate for the sample as a whole. When results are scaled up to the utility level, simulation accuracy varies by utility with net overestimates ranging from around 15% to 25%. Comparisons of individual simulation results to metered consumption identify electric space heating systems as the source of much of this error. This highlights the importance of gathering accurate, detailed information on these systems to generate reliable simulation results. NREL has recently released a batch simulation tool called ResStock that takes a more statistically robust approach to simulating representative archetype homes as opposed to actual existing homes. The results of this study suggest that the type of probabilistic treatment of building characteristics underlying that tool will provide more meaningful results than trying to simulate actual buildings. With additional validation measures similar to those used in this study, ResStock can likely be used to reliably estimate baseline energy consumption and quantify efficiency savings opportunities and can serve as a valuable decision-making tool for state, local, and utility efficiency program designers. [ABSTRACT FROM AUTHOR]

The article presents news and information related to dermatology. It states that the twelve dermatological residents will compete in December 1974 for the Fifth Henry W. Stelwagon Prize presented by the College of Physicians of Philadelphia to "the resident who shall be adjudged to give the most outstanding paper" at the annual meeting of the American Academy of Dermatology. The Stelwagon Prize stems from an endowment bequeathed to the College of Physicians of Philadelphia by Henry Weightman Stelwagon, who was the first teacher of Dermatology at Jefferson Medical College, Philadelphia, Pennsylvania.