Default Values Research Articles

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. This practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m2 was calibrated to be 7.50 W/m2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

Thermal-hydraulic analysis of a 7-pin sodium-cooled fast reactor wire-wrapped fuel bundle

The thermal-hydraulic characteristics of 7-pin sodium-cooled fast reactor (SFR) wire-wrapped fuel bundle were numerically investigated by commercial computational fluid dynamics (CFD) code ANSYS Fluent. The sensitivity analyses of mesh size, geometric simplification and Reynolds-Averaged Navier–Stokes (RANS) model were conducted. The turbulent Prandtl number model was adopted. The flow field and heat transfer were analyzed. The results show that the geometric simplification has great influences on friction factor and Nusselt number. The simplification should be taken into consideration seriously. The results of the standard k-ε model, realizable k-ε model and Reynolds stress model (RSM) model are close. The results of the Shear Stress Transport (SST) k-ω model has the maximum error compared with experiment correlation. The strong secondary flows and eddies were observed. The flow velocity near the contact of wire and rod is low and causes high temperature. At the cross section of z=50mm, the difference between the highest and lowest temperatures of the central rod is 13K, and the highest and lowest local Nusselt Numbers are 2.29 and 4.07 respectively, which indicates that the inhomogeneity of heat transfer is considerable. The velocity distribution of the boundary layer agrees well with the law-of-the-wall while the temperature boundary layer is much thicker than that of velocity. At least at y+<100, heat transfer is dominated by molecular heat conduction. At high Peclet number, the default value 0.85 of turbulent Prandtl number overestimates the heat transfer performance and the results of Kays model are closer to the experiment correlation. At very low Peclet number, Kays model has a quite different from experiment correlation. This study provides a reference for SFR design.

A techno-economic assessment of landfill gas emissions and energy recovery potential of different landfill areas in Turkey

Landfills are a widespread application for the management of municipal solid waste and the production of energy. However, landfill gas estimations, analysis of its energy capacity, and economic analyses need to be performed properly for a landfill project area. Although there are many gas prediction models in the literature, the default values in the model such as the methane production capacity (Lo) and the methane production rate (k) need to be recalculated according to the climate and waste composition of the region in order to obtain a more accurate gas estimation. Furthermore, the energy project from waste is expected to evaluate the through lifetime with variables (landfill operation, gas collection efficiency, different combustion engines, etc.) for an optimum plant capacity. In this study, it is aimed to investigate the landfill gas and methane production potential that can be obtained in landfill areas in different provinces of Turkey and to determine an optimum plant capacity by performing energy production cost analysis. To achieve this, the LandGEM 3.02 version was used to estimate long-term landfill gas potential in this study. The Lo value was calculated by using the IPCC (Intergovernmental Panel on Climate Change) methodology, and the k value was determined by considering the meteorological data of the regions (precipitation amount, etc.). The future population of the selected regions was estimated using the arithmetic increase method. According to this estimation, the solid waste quantity to be generated in the future was calculated. Energy capacities of these areas were calculated using internal combustion reciprocating engines with six different capacities. The unit energy production cost was evaluated by employing the levelized cost method. The optimum plant capacity was found by evaluating the energy production costs obtained for each site and six different engines. As a result, it is observed that the energy production plant with the optimum capacity determined from waste is economically and ecologically feasible.

Route guidance ranking procedures with human perception consideration for personalized public transport service

The use of smartphone applications (apps) to acquire real time and readily available journey planning information is becoming instinctive behavior by public transport (PT) users. Through the apps, a passenger not only seeks a path from origin to destination, but a satisfactory path that caters to the passenger’s preferences at the desired time of travel. Essentially, apps attempt to provide a means of personalized PT service. As the implications of the Covid-19 pandemic take form and infiltrate human and environmental interactions, passenger preference personalization will likely include avoiding risks of infection or contagious contact. The personal preferences are enabled by multiple attributes associated with alternative PT routes. For instance, preferences can be connected to attributes of time, cost, and convenience.This work establishes a personalized PT service, as an adjustment to current design frameworks, by integrating user app experience with operators’ data sources and operations modeling. The work proceeds to focus on its key component: the personalized route guidance methodology. In addition to using the existing shortest path or k-weighted shortest path method, this study develops a novel, lexicographical shortest path method, considering a just noticeable difference (JND). The method adopts lexicographical ordering to capture passenger preferences for different PT attributes following Ernst Weber’s law of human perception threshold. However, a direct application of Weber’s law violates the axiom of transitivity required for an implementable algorithm, and thus, a revised method is developed with proven algorithms for ranking different paths. The differences between the three route-guidance methods and the effects of the JND perception threshold on the order of the alternative PT routes are demonstrated with an example.The developments were examined in a case study by simulation on the Copenhagen PT network. The results show that using the JND method reduces the value/cost of the most important attributes. Identical robust results are attained when JND parameters are not specified and default values are used. The latter may apply for the future with a mixture of specified and default preference input values. Finally, the computation time indicates a favorable potential for real-life applications. It is believed that the consideration of human threshold perception will encourage decision makers to establish new criteria to comply with this.

Nitrogen and phosphorus release from paper mill biosolids as affected by material source and soil type under controlled incubation

Combined paper mill biosolids (PB) are a well-known fertilizing source of nitrogen (N) and phosphorus (P). Many jurisdictions have established default values for estimating their agronomic N and P contribution, but nutrient release can vary with their chemical properties, and this has been little studied. The objective of this incubation study was to compare four PB of different sources along with a de-inking paper sludge (DPS) for their N and P release in three contrasting soils under controlled conditions (25 °C, 60% water-filled pore space). Treatments consisted of PB or DPS at 50 Mg fresh mass ha−1, PB at 30 Mg fresh mass ha−1 plus inorganic N to supply 100 kg available N ha−1, inorganic NP fertilizer, and an unfertilized control. Measurements of NO3 + NH4-N were made after 3 d and 1, 2, 3, 4, 6, 8, 12, and 16 wk of incubation. Mehlich-3 P was measured after 2 and 16 wk of incubation. Nitrogen mineralization generally declined a few days after PB addition and then gradually and constantly increased. The P mineralization of PB also increased with time. By the end of incubation, net N and P release averaged 30% (7%–49%) and 75% (16%–116%) of the NP fertilizer, respectively. The intensity of release, however, varied with material characteristics and soil type. Mineral N addition to the 30 Mg PB ha−1 promoted N release to a level comparable to NP fertilizer. Soil N immobilization occurred with the DPS application. Current default N value used for PB in Quebec and Ontario is appropriate but not P.

An analysis of the fees and pending time correlation in Ethereum

SummaryEthereum is a new blockchain‐based platform that is also capable of running smart contracts. Despite its increasing popularity, there is a lack of studies on characterizing this system, in special the fees paid by users and the respective delay to confirm the transactions, that is, the pending time. In this sense, we study the main features of Ethereum transactions and evaluate the common belief—for blockchain systems that rely on proof of work—that users who pay higher fees will have their transactions confirmed faster. Specifically, we collect information about 7.2 million of transactions in Ethereum and correlate their pending time to several fee‐related features. Moreover, we conduct our study evaluating different ranges of values for the features, such as default and unusual values adopted by users as well as clusters of users with similar behaviors. Our empirical analysis shows strong evidence that there is no clear correlation between fees‐related features and the pending time. Overall, we conclude from our investigation that transaction's features, including gas and gas price defined by users, cannot determine the pending time of transactions.

Uncertainty reduction in quantitative precipitation prediction by tuning of Kain–Fritch scheme input parameters in the WRF model using the simulated annealing optimization method

AbstractIn the study, using the simulated annealing (SA) optimization method, tried to reduce the uncertainty of the prediction of the Weather Research and Forecasting (WRF) numerical model in convective rainfall forecasts. To this end, three parameters Pd, Pe and Ph in Kain–Fritch convective scheme that's related to downdraft mass flux, entrainment mass flux and starting height of downdraft above updraft source layer, respectively, are optimized using SA algorithm to achieve better values. Two nested domain were used in the study with 30 and 10 km resolution which inner domain cover southern coasts of the Caspian Sea for the study area. Runtime of the model was 36 hr with the first 12 hr spin‐up time. Study period selected October 12, 2012 for training algorithm and October 8, 2015 for test run. After 100 iteration of the algorithm, 1, 1 and 50 was obtained for Pd, Pe and Ph respectively, while default values of these parameters was 0, 0, and 150. Results show that in both cases, model with default values underestimates rainfall amount and after optimization, model performance improves. Also, spatial distribution of the model precipitation forecast was less than observations and after optimization, spatial distribution improves. Statistical analysis of results indicated Mean Bias (MB) and root mean square error (RMSE) in training case were −4.6 and 17.1 in model with default values and became −3.7 and 14.3 in optimized one, respectively. Also, MB and RMSE for test case increased to −8.4 and 23.8, respectively, in model result with optimized parameters from −10.6 to 29.4 in model with default parameters.

Fault Diagnosis in Dynamic Systems Using Recurrent Neural Networks

The aim of this paper is to discuss the methodology of Fault Detection and Diagnosis (FDD) in dynamic systems. Fault diagnosis is characterized as a control system that controls the ability to adapt system component faults automatically. Fault detection is an implementation of using the error signals, where when error signal is zero or approximately zero. A coupled water tank system was used as a study case model for implementing and testing the proposed methodology. The developed system should generate a set of signals to notify the process operator about the faults that are occurring, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and structural faults of the real plant, the data set of the faults are computationally generated and the results will be collected from numerical simulations of the process model. A Recurrent Neural Networks (RNN) is employed in this paper for modeling the used system. This paper shows how to determine the structure and how to estimate the results using the gradient-based algorithm which it is allowed to obtain a Neural Network with relatively small modeling uncertainly. It describes how to increase the stability of dynamic systems using FDD based on RNN. Faults in the study case are faults in the sensors, faults in the actuators and the structural faults. The faults which are presented in the controlled system are reduced to 98% from the default value.

Calibration and Validation Indonesian Highway Capacity Manual 1997 Model

Population growth and uncontrolled use of vehicles result in changes in traffic behaviours. This has an impact on the validity of the IHCM 1997 model results when replicating traffic in the real conditions. This study aims to carry out calibration and validation IHCM 1997 signalised intersection model. Case studies are conducted at eleven signalized intersection locations in the Surakarta city. The results of study show that the IHCM 1997 method default values are not relevant to the current conditions. The traffic parameters that need to be changed in the IHCM 1997 model are passenger car equivalent (PCE) and base saturation flow (S0).

A Juxtaposition Between Integer Wavelet Transform and Discrete Wavelet Transform for Secure Image Steganography

Image steganography is a technique that is used to hide information. The information can be of various types like image, video, or audio. Steganography is done so that no one apart from the correct receiver can retrieve the information. This paper consists of all advantages and highlights of the wavelet transform but with the additional features like randomness and some default values that are already built-in it. Various algorithms can be used in steganography and they provide good hiding capacity and low detectability. Here we have hidden the image into the cover image using Integer Wavelet Transform (IWT) and also using Discrete Wavelet Transform (DWT) and compared which technique gives better results. It is very difficult to predict the presence of a hidden image inside the stego image since it looks exactly like the cover image. There is no loss in quality from the secret image to the extracted image since the PSNR (Peak Signal to noise ratio) is high for both of them. This process was done using both DWT and IWT and the results prove that that the IWT technique is not only simpler but also more efficient than the DWT technique since it gives higher PSNR values. Through the proposed algorithm, an increase in the strength and imperceptibility is noticed and it can also maintain various transformations such as scaling, translation, and rotation with algorithms that already exist. The final results, after comparing both the transforms prove that the algorithm which is being proposed in IWT is indeed effective

Laboratory characterization of fine-grained soils for Pavement ME Design implementation in Idaho

The subgrade layer often represents the weakest component of a pavement system, and can significantly affect pavement response and performance under loading. Adequate characterization of subgrade properties is critical to the design and construction of long-lasting, economical pavement systems. The Mechanistic-Empirical (M-E) pavement design protocol implemented through AASHTOWare Pavement ME Design requires resilient modulus of the subgrade soil as one of the primary input parameters. However, performing repeated load triaxial tests to establish the resilient modulus properties of soils is a cumbersome task, and is rarely undertaken by state transportation agencies on a regular basis. The Idaho Transportation Department (ITD) has recently invested significant amount of time and resources to facilitate state-wide implementation of M-E pavement design practices. This study was recently undertaken to characterize typical subgrade soils in Idaho. Extensive laboratory characterization of the soils was carried out including repeated load triaxial testing for determining the resilient modulus values. Several different pavement sections were analyzed by using currently used default values and laboratory-established soil properties as subgrade layer inputs during M-E design. Default soil modulus values that are currently used, were found to be significantly higher than laboratory-established values, and therefore, resulted in the design of pavement sections that would fail considerably early into their service lives. The effect of moisture content on subgrade soil properties was also studied, and the importance of restricting soil moisture contents to the dry side of optimum was highlighted.

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

The computation of the grass reference evapotranspiration with the FAO56 Penman-Monteith equation (PM-ETo) requires data on maximum and minimum air temperatures (Tmax, Tmin), actual vapour pressure (ea), shortwave solar radiation (Rs), and wind speed at 2 m height (u2). Nonetheless, related datasets are often not available, are incomplete, or have uncertain quality. To overcome these limitations, several alternatives were considered in FAO56, while many other procedures were tested and proposed in very numerous papers. The present study reviews the computational procedures relative to predicting the missing variables from temperature, i.e., the PM temperature approach (PMT), and estimating ETo with the Hargreaves-Samani (HS) equation. For the PMT approach, procedures refer to predicting: (a) the dew point temperature (Tdew) from the minimum or the mean air temperature; (b) shortwave solar radiation (Rs) from the air temperature difference (TD = Tmax-Tmin) combined with a calibrated radiation adjustment coefficient (kRs); and (c) wind speed (u2) using a default value or a regional or local average. The adequateness of computing Tdew from air temperature was reassessed and the preference for using an average u2 has been defined. To ease the estimation of Rs, for the PMT approach and the coefficient of the HS equation, multiple linear regression equations for predicting kRs were developed using local averages of the temperature difference (TD), relative humidity (RH) and wind speed as independent variables. All variables were obtained from the Mediterranean set of CLIMWAT climatic data. Two types of kRs equations were developed: climate-focused equations specific to four climate types - humid, sub-humid, semi-arid, and hyper-arid and arid -, and a global one, applicable to any type of climate. The usability of the kRs equations for the PMT and HS methods was assessed with independent data sets from Bolivia, Inner Mongolia, Iran, Portugal and Spain, covering a variety of climates, from hyper-arid to humid. With this purpose, ETo estimated with PMT and HS (ETo PMT and ETo HS) were compared with PM-ETo computed with full data sets to evaluate the usability of the kRs equations. Adopting the climate-focused kRs equations with ETo PMT, the RMSE averaged 0.59, 0.64, 0.65 and 0.72 mm d−1 for humid, sub-humid, semi-arid, and arid and hyper-arid climates, respectively, while the RMSE values relative to ETo HS when using the respective climate-focused kRs equations averaged 0.58, 0.60, 0.60 and 0.69 mm d−1 for the same climates. These results are similar to those obtained with the kRs global equation. The accuracy of the PMT approach when using the kRs equations was also evaluated when one, two, or all three Tdew, Rs and u2 variables are missing and the resulting goodness-of-fit indicators demonstrated the advantage of the combined use of observed and estimated weather variables. The usability of the kRs equations for an efficient parameterization of both the PMT approach and the HS equation is demonstrated with similar performance of PMT and HS procedures for a variety of climates. Because the ETo HS results depend almost linearly on temperature, the PMT approach, using estimates of the weather variables, is able to mitigate those temperature impacts, which trends may be contrary to those of other variables that determine ETo. The clear advantage of the PMT approach is that it allows using the available weather data in combination with estimates of the missing variables, which provides for more accurate ETo computations.

Influence of Unbound Materials Input Level on Pavement Performance using MEPDG

For material characterization, the new Mechanistic Empirical Pavement Design Guide (MEPDG) has 3 input levels. Level 1 input values must be obtained through direct laboratory testing. Level 2 inputs are determined by the application of correlations with other material properties. Finally, Level 3 inputs are simply typical default values. The level chosen for each design input parameter, however, may have a significant effect on project design, costs, and reliability. In this paper, the influence of the unbound granular base material characterization input level on pavement performance as predicted by MEPDG for a typical flexible pavement section was investigated. For this purpose, analyses were made for three weather stations representing different climatic regions in Egypt: Alexandria, Cairo and Aswan. For the typical pavement system, level 1 data for the resilient modulus based on measured laboratory values (k1, k2, and k3 elastic response coefficients) were used for the first set of MEPDG runs. The second set of computer simulation runs were conducted using correlation from California Bearing Ratio (CBR) values. The final set of runs, utilized default resilient modulus values of the unbound base material based on the AASHTO class. MEPDG predicted pavement distresses for the three input levels were compared and the results showed variation in performance due to the change in the input level.

Impact of Roughness Length on WRF Simulated Land‐Atmosphere Interactions Over a Hyper‐Arid Region

AbstractThe aerodynamic roughness length is a crucial parameter that controls surface variables including the horizontal wind, surface temperature, and heat fluxes. Despite its importance, in the Weather Research and Forecasting (WRF) model, this parameter is typically assigned a predefined value, mostly based on the dominant land‐use type. In this work, the roughness length is first estimated from eddy‐covariance measurements at Al Ain in the United Arab Emirates (UAE), a hyper‐arid region, and then ingested into WRF. The estimated roughness length is in the range 1.3–2.2 mm, one order smaller than the default value used in WRF. In line with previous studies, and from WRF model simulations during the warm and cold seasons, it is concluded that, when the roughness length is decreased by an order of magnitude, the horizontal wind speed increases by up to 1 m s−1, the surface temperature rises by up to 2.5°C, and the sensible heat flux decreases by as much as 10 W m−2. In comparison with in situ station and eddy covariance data, and when forced with the updated roughness length, WRF gives more accurate 2‐m air temperature and sensible heat flux predictions. For prevailing wind speeds &gt;6 m s−1, the model underestimates the strength of the near‐surface wind, a tendency that can be partially corrected, typically by 1–3 m s−1, when the updated roughness length is considered. For low wind speeds (&lt;4 m s−1), however, WRF generally overestimates the strength of the wind.

Design of a hybrid mechanistic/Gaussian process model to predict full-scale wastewater treatment plant effluent

This paper presents the design of a hybrid model of a wastewater treatment plant (WWTP), which is meant to improve the quality of effluent prediction. By combining mechanistic, i.e. activated sludge model, and data-driven model it is expected to retain physical transparency and achieve good prediction accuracy. For the data-driven model, a state-of-the-art machine learning approach based on Gaussian process (GP) model was applied. GP models systematically address model uncertainty when lacking identification data and are applicable also for small data-sets, which both are encountered in WWTP modelling. Serial and parallel hybrid structures were designed to address the challenges of missing input data, insufficient mechanistic model accuracy and demanding model parameter estimation. Results of full-scale effluent predictions show that, by applying hybrid models, the accuracy of the model is improved. Good results were obtained also for default values of activated sludge model parameters, which significantly simplifies the model design process.

Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon

Sensitive parameters of a numerical weather prediction model substantially influence the model prediction. Weather research and forecasting (WRF) model parameters are assigned default values based on theoretical and experimental analysis by the scheme developers. Calibrating the sensitive parameters of the model has the potential to improve model prediction. The objective of this study is to improve the prediction of the Indian summer monsoon by calibrating the WRF model parameters. A multiobjective adaptive surrogate model-based optimization (MO-ASMO) method is used to calibrate nine sensitive parameters from five physics parameterization schemes. Normalized root-mean-square error values corresponding to four meteorological variables precipitation, surface air temperature, surface air pressure, and wind speed are minimized by calibrating the WRF model sensitive parameters for high-intensity precipitation events of the Indian summer monsoon (ISM). Twelve high-intensity four-day precipitation events of ISM during the years 2015–2017 over the monsoon core region in India are considered to calibrate the model parameters. MO-ASMO method outputs a set of nondominated solutions for the model parameters that reduce the model prediction error. A decision analysis method is used to identify the best solution among the nondominated solutions, which contains the calibrated values of the parameters. A comparison of the default and calibrated parameter values across various precipitation events, driving data, and physical processes in the monsoon core region are carried out. Eighteen high-intensity four-day precipitation events of ISM during the years 2014–2018 are chosen to validate the robustness of the calibrated parameters. The WRF model is run with two different boundary data to verify the effectiveness of the calibrated parameters against the default parameters. The model calibrated parameters obtained using the MO-ASMO method are superior to the default parameters across various precipitation events and boundary data over the monsoon core region during the Indian summer monsoon.

Evaluation of N2O emission from rainfed wheat field in northwest agricultural land in China.

The net greenhouse gas (NGHG) emissions and net greenhouse gas intensity (NGHGI) were investigated via the determination of nitrous oxide (N2O) emission in loess soil under rainfed winter wheat monocropping system during 3years of field study in Northwest China. Five treatments were carried out: control (N0), conventional nitrogen (N) application (NCon), optimized N application with straw (SNOpt), optimized N application with straw and 5% of dicyanodiamide (SNOpt + DCD), and optimized N rate of slow release fertilizer with straw (SSRFOpt). Over a 3-year period, the NGHG emissions were achieved 953, 1322, 564, and 1162kg CO2-eq ha-1, simultaneously, and the NGHGI arrived 158, 223, 86, and 191kg CO2-eq t-1 grain in NCon, SNOpt, SNOpt + DCD, and SSROpt grain, respectively. Contrasted with conventional farming system, optimized farming methods reduced 32% of N fertilizer use without significant decrease in grain yield, but brought about 38% increase in N2O emissions, up to 28% gained in soil CH4 uptake. Thus, it was observed that the straw incorporation performs noticeable increased in N2O emissions in the winter wheat cropping season. Among the optimized N fertilizer rates compared with the SNOpt treatment, the SNOpt +DCD and SSROpt treatments decreased in N2O emissions by approximately 55% and 13%, respectively. Additionally, the N2O emission factor across over a 3-year period was 0.41 ± 0.08% derived from N fertilizer, and it was half of IPCC default values for upland corps. It is expected possibly due to low precipitation and soil moisture with the monocropping system. The 25% higher in the amount of rainfall (almost 300mm in 2013-2014) during a cropping season underwent into 1-2-fold increase in N2O emissions from N-fertilized plots. As the statistical differences among annual cumulative emissions coincided with that during winter wheat growing season, it can be concluded that crop growing season is a vital important period for the determination of N2O emissions from under rainfed monocropping system.

Pathways of nitrogen loss and optimized nitrogen management for a rice cropping system in arid irrigation region, northwest China

The reactive nitrogen (N) loss of the rice cropping system in the arid region shows a different pattern from that of subtropical humid region due to different climate types and crop management. However, little attention has been paid to this region. To fill this knowledge gap, a two-year (2009–2010) field observation was conducted in the Ningxia irrigation region, northwest China, to explore the major pathway of N loss following local farmers' optimal practice. Further, we determined the site-specific emission factors of ammonia and nitrous oxide, rate of surface runoff and subsurface (leaching and seepage) to improve the inventory resolution of arid irrigation region. Results showed that ammonia volatilization (45%–49% of total N loss), leaching and seepage (30%–33% of total N loss) were proved to be the primary factors of N loss in rice paddy fields. The emission factor of ammonia (21%) and N leaching rate (7.5%) following farmers' practice were 2.1 and 5.4 times higher than the country-specific default value in China. The country-specific N runoff rate and emission factor of N2O could be directly adopted in this region. A 20% reduction of N fertilizer to farmers’ practice (300 kg N ha−1) alongside the application of organic fertilizer (30% N in synthetic fertilizer was substituted by pig manure) were considered to be the optimal N rate in this region. Our study can narrow the gap between researches on N loss in arid regions and subtropical humid regions. Meanwhile, the results can provide specific advice on N loss mitigation for policy makers in arid irrigation regions.

Sustainability of Vehicle Fuel Biomethane Produced from Grass Silage in Finland

Increasing demand of fossil-free fuels in the transport sector drives towards using new biomass sources in fuel production. Municipal waste as a substrate is used in many countries in biomethane production, but the amount of waste can cover only a small portion of the fuel used. In Europe, the new renewable energy directive (RED II) was established December 2018 to ensure the sustainability of renewable fuels. The directive includes typical and default greenhouse gas (GHG) emissions for several potential substrates, such as biogas from manure or maize silage, which the biogas plants can use to verify their emissions directly or to calculate their emissions using the methods provided. However, such default value for grass silage as biogas substrate is lacking. We defined the conditions needed to fulfil the sustainability criteria of the directive when producing biomethane for vehicle fuel using grass silage as the feedstock in Finland. The emission reduction targets are not easy to achieve in Finland when using grass cultivated exclusively for energy production. The reduction targets can be achieved, however, if the grass is cultivated due to an improved crop rotation, where the grass is co-digested with manure and/or energy sources with zero emissions for the process can be applied.

Ethanol-Based Disinfectants Containing Urea May Reduce Soap Sensitivity.

The use of disinfectants is crucial to preventing the spread of nosocomial infections in health care workers. As many as 25 applications of hand disinfectants is a realistic default value during a working day. However, alcohol-based hand disinfectants may weaken skin barrier function and induce dryness and eczema, which decrease their acceptance. To evaluate the effect of ethanol-containing disinfectants with 5% urea on skin barrier function and on sensitivity to an irritant soap (sodium lauryl sulfate [SLS]). Twenty healthy volunteers treated one of their forearms twice daily for 17 days with an ethanol-containing gel with 5% urea. Two types of gels with urea were tested. Treatment was randomized to left or right forearm, and the contralateral forearm served as untreated control. Transepidermal water loss, skin capacitance (dryness), and sensitivity to SLS were evaluated. Twice-daily application of the urea-containing ethanol gels lowered transepidermal water loss, prevented dryness, and reduced sensitivity to SLS compared with the untreated control skin. Improved barrier function using this ethanol gel with urea may have relevance in daily disinfectant procedures.