Carbon footprint of anaerobic digestion combined with ultrasonic post-treatment of agro-industrial organic residues

Anaerobic digestion (AD) of organic waste, although widely practiced, may require suitable accompanying treatments to enhance the degradability of complex materials. Since these may require significant efforts in terms of energy and chemical demand, careful assessment of their overall environmental sustainability is mandatory to evaluate their full-scale feasibility. The study aims to represent the environmental profile of ultrasonication (US) applied as a post-treatment of anaerobic digestion of agro-industrial organic residues. There is an interest in the US treatment for the processing of complex organic materials prior to AD in order to enhance the hydrolysis of complex organic substrates and increase the biogas yield of the biological process. An attributional, process-based life cycle assessment (LCA) study was applied to quantify and compare the potential environmental impacts of an AD plant, the biogas utilization options as well as the different digestate processing alternatives grouped into a set of 16 scenarios. Based on the results, upgrading of biogas and bio-methane use as vehicle fuel instead of energy generation from CHP or fuel cell was recommended due to the lower impact on GWP. Similarly, composting was a suitable option to reduce environmental impacts compared to belt drying. From the uncertainty analysis, AD without US as post-treatment proves to be more sustainable in terms of GWP compared to when US is used, showing net savings in GHG emissions especially when upgrading of biogas is applied. The analysis provides useful indications to policy makers to define sustainable management alternatives for organic residues as well as identify the environmental advantages associated with biogas utilization and digestate treatment and disposal alternatives.

Experimental and stochastic analysis of lyophilisation

The development of the freeze-drying processes through the use of a combination of targeted experiments and the application of multidimensional computational models is applied increasingly in pharmaceutical practice, especially for scale-up purposes. This study deals with the analysis of uncertainties in the data on material properties and model parameters, and their influence on the results delivered by advanced computational models of lyophilisation. As a means of uncertainty analysis, the Stochastic Collocation Method is applied, allowing the use of existing reliable deterministic models as black boxes in the stochastic computations. As a deterministic model, the lyophilisation model is used, based on the axisymmetric approximation of a vial, and the Boundary Element Method as a solver. Five parameters, covering material properties, process conditions and model constants, are selected for the sensitivity analysis simulation of the lyophilisation of an aqueous mannitol solution. The results show that during the initial stage of the primary drying heat transfer from the shelf is crucial, and that the uncertainties in the contact surface area and material properties of the vial play a more important role than the thermal properties of the drying material. When the temperature of the material reaches its distinct primary drying stage level the mass transfer through the porous cake becomes the most important, with great sensitivity to the Knudsen diffusivity in the porous cake. We observed uncertainties in the results for the primary drying time in the order of ±6%, and uncertainties in the results for temperatures of ±0.6°C in the frozen material and ±3°C in the porous cake. The uncertainty analysis proved to be a great help in determining the critical parameters in the heat and mass transfer during the important primary drying step, which led to a better definition of the numerical model for use in the context of design space determination.

"This Is What We Don't Know": Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment

Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2021;17:221-232. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A comprehensive probabilistic approach for integrating and separating natural variability and parametric uncertainty in the prediction of distribution coefficient of radionuclides in rivers

A geochemical speciation model was developed to predict Distribution coefficients (K_ds) of radionuclides (RNs) in rivers. The model takes into account complexation of RNs with inorganic ligands, sorption of RNs with hydrous ferric oxides, complexation of RNs with dissolved and particulate organic carbon (DOC and POC) and sorption and/or co-precipitation of RNs to carbonates. A sorption model of Cs onto clay was also integrated. The tool is also designed to conduct uncertainty and sensitivity analysis. Sensitivity analysis follows a stepwise structured approach, starting from computationally 'inexpensive' Morris method to most costly variance-based EFAST method. A nested Monte Carlo approach was also implemented to separate natural variability and lack of knowledge in global uncertainty assessment. As case studies, K_d distributions were estimated for Co, Mn, Ag and Cs in seven French rivers. Uncertainty analysis allowed to quantify K_d ranges that can be expected when considering all the sensitive parameters together.

Application of a liquid scintillation system with 100-ml counting vials for environmental tritium determination: Procedure optimization, performance test, and uncertainty analysis

For more efficient and accurate determination of airborne tritium in the environment, the procedure optimization, performance test and uncertainty analysis of a commercially available low-background liquid scintillation counting (LSC) system with 100-ml counting vials were studied in this work. The results showed that 50 ml water sample mixed with 50 ml scintillation cocktail (Ultima Gold uLLT, PE) could achieve the optimal counting condition after a dark adaption time longer than 1440 min. The minimum detectable activity (MDA) of the 100-ml vial system was estimated to be 0.18 Bq·L^-1 in a continuous counting time of 3600 min, which was approximately 3.5 times lower than that of 20-ml vial system, and its determination uncertainty was also generally lower provided the collected samples was more than 15 ml. It indicates that the LSC system with 100-ml counting vials is preferable for environmental tritium determination. However, for more accurate determination, the electrolytic enrichment is still needed for the sample with the specific activity lower than 0.4 Bq·L^-1. On the other hand, considering the cost and potential environmental impact of present available cocktails, the system with 20-ml vials is recommended for determining the sample with the specific activity higher than 2 Bq·L^-1.

Impact of soil erosion potential uncertainties on numerical simulations of the environmental fate of radiocesium in the Abukuma River basin

The Fukushima Dai-ichi Nuclear Power Plant accident in March 2011 resulted in the deposition of significant quantities of radionuclides, including radiocesium (¹³⁷Cs), over a wide area. Most of the deposited ¹³⁷Cs is strongly adsorbed on fine soil particles such as clay and silt near the ground surface. Therefore, to estimate the environmental fate of ¹³⁷Cs, it is necessary to predict its transport with eroded sediment in rainfall-runoff processes. In this study, a distributed radiocesium prediction model was applied to simulations of ¹³⁷Cs transport associated with hydrological processes in the Abukuma River Basin, the largest river system in Fukushima, over the period of 2011-2012. The soil erosion potential, which is a key input to the distributed radiocesium prediction model, was estimated using the Universal Soil Loss Equation (USLE). This study focused on the uncertainty in estimating the environmental fate of ¹³⁷Cs associated with the USLE factors. The USLE has five physically meaningful factors: the rainfall and runoff factor (R), soil erodibility factor (K), topographic factor (LS), cover and management factor (C), and support practice factor (P). Because the USLE factors were determined using various methods, R, LS, and the product of C and P (C×P) were divided into two, three, and five cases, respectively, based on previous studies. Therefore, we conducted 30 different simulations. The average total ¹³⁷Cs outflow during the computational period in the simulation cases using the same USLE factors was 13.3 and 11.7 TBq for R (two cases), 12.6, 13.9 and 10.9 TBq for LS (three cases), and 26.5, 8.64, 0.47, 22.8 and 4.03 TBq for C×P (five cases). For the total outflow, C and P had the highest uncertainty of all the USLE factors. The outflow rates of the average total ¹³⁷Cs in the simulation cases using the same C and P from the croplands and forest areas and from the undisturbed croplands and paddy fields were 62-91% and 18-34%, respectively. These results were due to the high erodibility of the croplands, the large forest areas in grids with high ¹³⁷Cs deposition density, and the high concentration of ¹³⁷Cs in the soil of the undisturbed croplands and paddy fields. This study indicates that land use, especially forest areas, croplands, and undisturbed paddy fields, has a significant impact on the environmental fate of ¹³⁷Cs.

Characterizing the anthropogenic-induced trace elements in an urban aquatic environment: A source apportionment and risk assessment with uncertainty consideration

The spatial distribution of water quality status, especially in water bodies near intensively urbanized areas, is tightly associated with patterns of human activities. For establishing a robust assessment of the sediment quality in an urban aquatic environment, the source apportionment and risk assessment of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb in sediments from an anthropogenic-influenced lake were carried out with considering uncertainties from the analysis methods, random errors in the sample population and the spatial sediment heterogeneity. The distribution analysis of the trace metals with inverse distance weighting-determined method showed that the pollutants were concentrated in the middle and southern areas of the lake. According to the self-organizing map and constrained positive matrix factorization receptor model, agricultural sources (24.8%), industrial and vehicular sources (42.5%), and geogenic natural sources (32.7%) were the primary contributors to the given metals. The geogenic natural had the largest random errors, but the overall result was reliable according to the uncertainty analysis. Furthermore, the stochastic contamination and ecological risk models identified a moderate/considerable contamination level and a moderate ecological risk to the urban aquatic ecosystem. With consideration of uncertainties from the spatial heterogeneity, the contamination level of Hg, and the ecological risk of Cd in had a 20-30% probability of the increase.

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1-0.5 Mg C ha^-1 y^-1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5-1.5 Mg C ha^-1 y^-1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.

Uncertainty analysis of tumour absorbed dose calculations in molecular radiotherapy

BACKGROUND

Internal dosimetry evaluation consists of a multi-step process ranging from imaging acquisition to absorbed dose calculations. Assessment of uncertainty is complicated and, for that reason, it is commonly ignored in clinical routine. However, it is essential for adequate interpretation of the results. Recently, the EANM published a practical guidance on uncertainty analysis for molecular radiotherapy based on the application of the law of propagation of uncertainty. In this study, we investigated the overall uncertainty on a sample of a patient following the EANM guidelines. The aim of this study was to provide an indication of the typical uncertainties that may be expected from performing dosimetry, to determine parameters that have the greatest effect on the accuracy of calculations and to consider the potential improvements that could be made if these effects were reduced.

RESULTS

Absorbed doses and the relative uncertainties were calculated for a sample of 49 patients and a total of 154 tumours. A wide range of relative absorbed dose uncertainty values was observed (14-102%). Uncertainties associated with each quantity along the absorbed dose calculation chain (i.e. volume, recovery coefficient, calibration factor, activity, time-activity curve fitting, time-integrated activity and absorbed dose) were estimated. An equation was derived to describe the relationship between the uncertainty in the absorbed dose and the volume. The largest source of error was the VOI delineation. By postulating different values of FWHM, the impact of the imaging system spatial resolution on the uncertainties was investigated.

DISCUSSION

To the best of our knowledge, this is the first analysis of uncertainty in molecular radiotherapy based on a cohort of clinical cases. Wide inter-lesion variability of absorbed dose uncertainty was observed. Hence, a proper assessment of the uncertainties associated with the calculations should be considered as a basic scientific standard. A model for a quick estimate of uncertainty without implementing the entire error propagation schema, which may be useful in clinical practice, was presented. Ameliorating spatial resolution may be in future the key factor for accurate absorbed dose assessment.

How to balance ecosystem services and economic benefits? - A case study in the Pearl River Delta, China

There is a significant challenge in resource management: the perceived trade-off between economic growth and ecosystem conservation. In this study, we integrate a variety of quantitative research methods and models, such as the ecosystem service value (ESV), interval parameter planning (IPP), Dyna-CLUE, and Monte Carlo methods, in an attempt to balance the ESV and economic benefits. The highest system benefits can be obtained, and uncertainty in the ecosystem assessment is considered. Taking the Pearl River Delta as the study area, the results show that when the GDP growth rate is less than 6%, the ESV in 2025 will be higher than the ESV in 2017. An interval approach (upper and lower bounds) is used. For a scenario with a 5% GDP growth rate, the ESV is RMB¥ [1.85, 20.79] × 10⁹, which is more than the ESV of the scenario with a 9% GDP growth rate. When the GDP growth rates are 5% and 9%, the proportions of forestland are [61.5%, 61.7%] and [58%, 58.2%], respectively. Furthermore, spatialization was performed using the Dyna-CLUE model. In 2025, the simulated area of farmland is larger in some small regions with 9% GDP growth rate than it is in regions with 5% GDP growth rate, thus achieving a balance between occupation and compensation of regional farmland. By comparing ecosystem planning under different GDP growth rates, an optimized land-use allocation method can help decision makers balance system benefits and ecological risks, which can provide multiple options and specific locations for decision.

Analysis of the PM2.5 emission inventory and source apportionment in Jilin City, Northeast of China

This study collected and compiled statistical data on atmospheric pollution in Jilin City, China during 2013-2014, using models and methods to calculate the source proportion of PM_2.5 emitted by various sources. The statistical activity levels and emission factors of various pollution sources were found to be key parameters for obtaining the total amount of PM_2.5 in the exhaust gas emitted from all types of pollution sources using an emissions model. In this study, relevant data were collected by the top-down method, and pollutant emission was calculated by the emission factor method to establish the PM_2.5 pollution emission inventory of Jilin City. The source apportionment was calculated using the Chemical Mass Balance (CMB) model. Industrial process source and fixed combustion source are the largest sources of PM_2.5 emission from all sources, respectively. Among the two calculation results, the results of pollution emission inventory are more accurate. The PM_2.5 emission inventory in Jilin was established and countermeasures were proposed focused on the coordinated control of air pollution and the prevention and control of industrial dust pollution sources, as well as environmental management and impact assessment.

Life cycle assessment and Monte Carlo simulation to evaluate the environmental impact of promoting LNG vehicles

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As a novel and alternative type of fuel for heavy-duty trucks, it is very important to assess a broad array of environmental impacts of liquefied natural gas (LNG). However, few studies have evaluated comprehensively the environmental impact of LNG as an alternative fuel on human health, ecosystems and resources from a life cycle perspective. In particular, the environmental benefit of promoting LNG vehicles is often complicated and uncertain due to many variable factors, which are also often not given enough attention. This method article describes the use of a combination of life cycle assessment (LCA) and Monte Carlo simulation to evaluate the potential environmental benefits of promoting LNG heavy-duty diesel vehicles in Saguenay, a city in Canada. It not only conducts a full-range analysis of environmental impacts, but also considers the impact of joint changes in uncertain factors such as methane emission rates, energy efficiency of engine and the project promotion prospects on the environmental benefits of LNG, making life cycle environmental impact assessment more systematic and comprehensive. The paper provides the details of all the steps used in the method and can be replicated and applied to other similar studies and research settings.

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This combined approach provides a comprehensive assessment of the environmental impacts incurred by the promotion of LNG vehicles. Besides, it also provides a certain degree of risk assessment for LNG projects.

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This method takes into account the complexity of the joint change of multiple uncertainties, which makes up for the deficiencies of previous studies that only analyze one uncertainty in isolation.

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This method takes the development prospect of LNG promoting project as an uncertain factor for environmental benefit assessment.

Least cost energy system pathways towards 100% renewable energy in Ireland by 2050

Studies focusing on 100% renewable energy systems have emerged in recent years; however, existing studies tend to focus only on the power sector using exploratory approaches. This paper therefore undertakes a whole-system approach and explores optimal pathways towards 100% renewable energy by 2050. The analysis is carried out for Ireland, which currently has the highest share of variable renewable electricity on a synchronous power system. Large numbers of scenarios are developed using the Irish TIMES model to address uncertainties. Results show that compared to decarbonization targets, focusing on renewable penetration without considering carbon capture options is significantly less cost effective in carbon mitigation. Alternative assumptions on bioenergy imports and maximum variability in power generation lead to very different energy mixes in bioenergy and electrification levels. All pathways suggest that indigenous bioenergy needs to be fully exploited and the current annual deployment rate of renewable electricity needs a boost. Pathways relying on international bioenergy imports are slightly cheaper and faces less economic and technical challenges. However, given the large future uncertainties, it is recommended that further policy considerations be given to pathways with high electrification levels as they are more robust towards uncertainties.

Uncertainty analysis for precipitation and sea-level rise of a variable-density groundwater simulation model based on surrogate models

Effective coastal aquifer management typically relies on numerical models to analyze the seawater intrusion (SI) process. Before using groundwater simulation models to predict the extent of SI in the future, preparing input data is an extremely necessary and important step. For precipitation and sea-level rise (SLR), which are two of the most influential factors for SI, it is difficult to precisely forecast their variations. Current studies of using numerical models to predict future SI often overlook the uncertainty of these two factors. This can result in compromised predictions of SI. In this study, a three-dimensional variable-density groundwater simulation model was established for a coastal area in Longkou, China. Then, the Monte Carlo method was applied to perform uncertainty analysis for the input data of precipitation and SLR of the SI model. In order to reduce the huge computational load brought by repeated invocation of the SI model during the process of Monte Carlo simulation, a surrogate model based on a multi-gene genetic programming (MGGP) method was developed to replace the SI simulation model for calculation. A comparison between the MGGP surrogate model and the Kriging surrogate model was carried out, and the results show that the MGGP surrogate model has a distinct advantage over the Kriging surrogate model in approximating the excitation-response relationship of the variable-density groundwater simulation model. Through statistical analysis of Monte Carlo simulation results, an object and reasonable risk assessment of SI for the study area was obtained. This study suggests that it is essential to take the uncertainty of precipitation and SLR into account when modeling and predicting the extent of SI.

Riverine nitrate source apportionment using dual stable isotopes in a drinking water source watershed of southeast China

It is crucial to quantitatively track riverine nitrate (NO₃^-) sources and transformations in drinking water source watersheds for preventing current and future NO₃^- pollution, and ensuring a safe drinking water supply. This study identified the significant contributors to riverine NO₃^- in Zhaoshandu reservoir watershed of Zhejiang province, southeast China. To achieve this goal, we used hydrochemistry parameters and stable isotopes of NO₃^- (δ¹⁵N-NO₃^- and δ¹⁸O-NO₃^-) accompanied with a Markov Chain Monte Carlo mixing model to estimate the proportional contributions of riverine NO₃^- inputs from atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S). Results indicated that the main form of riverine nitrogen in this region was NO₃^-, constituting ~60% of the total nitrogen mass on average (total organic nitrogen ~37% & ammonium ~3%). Variations in the isotopic signatures of NO₃^- demonstrated that microbial nitrification of NF, SN and M&S was the primary nitrogen transformation process within the Zhaoshandu reservoir watershed, whereas denitrification was minimal. A classical dual isotope bi-plot incorporating chloride concentrations suggested NF, SN and M&S were the major contributors of NO₃^- to the river. Riverine NO₃^- source apportionment results were further refined using the Markov Chain Monte Carlo mixing model, which revealed that AD, NF, SN and M&S contributed 7.6 ± 4.1%, 22.5 ± 12.8%, 27.4 ± 14.5% and 42.5 ± 11.3% of riverine NO₃^- at the watershed outlet, respectively. Finally, uncertainties associated with NO₃^- source apportionment were quantitatively characterized as: SN > NF > M&S > AD. This work provides a comprehensive approach to distinguish riverine NO₃^- sources in drinking water source watersheds, which helps guide implementation of management strategies to effectively control NO₃^- contamination and protect drinking water quality. SUMMARY OF THE MAIN FINDING FROM THIS WORKS (CAPSULE): We utilized NO₃^- stable isotope analysis and a Markov Chain Monte Carlo mixing model to quantify riverine nitrate pollution sources in a drinking water source watershed in Zhejiang province, southeast China. Markov Chain Monte Carlo mixing model output showed that NF, SN and M&S were the dominant sources of riverine NO₃^- during the sampling period in Zhaoshandu watershed. Uncertainty analysis characterized the variation strength associated with contributions of individual nitrate sources and indicated the greatest uncertainty for SN, followed by NF, M&S and AD.

Bioenergy in China: Evaluation of domestic biomass resources and the associated greenhouse gas mitigation potentials

As bioenergy produces neutral or even negative carbon emissions, the assessment of biomass resources and associated emissions mitigation is a key step toward a low carbon future. However, relevant comprehensive estimates lack in China. Here, we measure the energy potential of China's domestic biomass resources (including crop residues, forest residues, animal manure, municipal solid waste and sewage sludge) from 2000 to 2016 and draw the spatial-temporal variation trajectories at provincial resolution. Scenario analysis and life cycle assessment are also applied to discuss the greenhouse gas mitigation potentials. Results show that the collectable potential of domestic biomass resources increased from 18.31 EJ in 2000 to 22.67 EJ in 2016 with overall uncertainties fluctuating between (-26.6%, 39.7%) and (-27.6%, 39.5%). Taking energy crops into account, the total potential in 2016 (32.69 EJ) was equivalent to 27.6% of China's energy consumption. If this potential can be realized in a planned way to displace fossil fuels during the period 2020-2050, cumulative greenhouse gas emissions mitigation would be in the range of 1652.73-5859.56 Mt CO₂-equivalent, in which the negative greenhouse gas emissions due to the introduction of bioenergy with carbon capture and storage would account for 923.78-1344.13 Mt CO₂-equivalent. Contrary to increasing bioenergy potentials in most provinces, there are declining trends in Tibet, Beijing, Shanghai and Zhejiang. In addition, Yunnan, Sichuan and Inner Mongolia would have the highest associated greenhouse gas mitigation potentials. This study can provide valuable guidance on the exploitation of China's untapped biomass resources for the mitigation of global climate change.

Spatial characteristics of total phosphorus loads from different sources in the Lancang River Basin

Lancang River, the upstream reach of Mekong River, is a hotspot region in the sustainable management of water resources and environment as it is currently facing the deterioration of aquatic ecosystems. Nutrient balance (i.e., Phosphorus) in the Lancang-Mekong River Basin has become a highly disputed issue in recent years due to the construction of cascade hydropower stations. However, the estimation of the total phosphorus (TP) load faces great difficulties and challenges due to the absent measured water quality data. This study estimates the TP load based on the social economic data, analyzes the spatial distribution of TP and the contribution of different TP sources in the Lancang River basin under the level of social-economic development in 2014. Results show that the annual average TP load in the Lancang River Basin is 1.6 × 10⁴-3.9 × 10⁴ tons, which is at a very low level compared with other large-scale basins in China. The TP load from natural soil erosion dominates all other sources, accounting for 69%, followed by agricultural production and fertilization. In general, the TP load increases from upstream to downstream, but heterogeneity also exits in different regions under the influence of various factors, such as rainfall intensity, soil properties and human activities. The results reveal a holistic picture of TP load in the Lancang River Basin, which could provide a new perspective on the trans-border international river management.

Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty

Heavy metal(loid)s are natural constituents of the Earth's crust, and apportionment of their sources in surface soils is a challenging task. This study evaluated the application of positive matrix factorization (PMF) model, assisted with regression modeling and geospatial mapping, in the quantitative source apportionment of heavy metal(loid)s in the agricultural soils of Handan, a region covering >12,000 km². Obvious enrichment of As, Cd, Cu, Pb, and Zn was found in the surface soils, with Cd alone accounted for 73 % of the overall potential ecological risk. PMF model revealed that Cd (56.9 %) and Pb (47.8 %) in the region's agricultural soils were predominantly contributed by industrial sources, Fe (71.8 %), Cr (60.0 %), V (52.9 %), Cu (50.7 %), Ni (42.2 %), and Mn (41.4 %) were primarily of lithogenic origin, while Co (54.1 %), As (42.9 %), and Zn (40.0 %) mainly came from the mixed sources of natural background, agricultural sources, and vehicle emissions. Uncertainty analysis showed that the contributions of pollution sources to the soil heavy metal(loid)s estimated by PMF model had considerable variations. While quantitative source apportionment of heavy metal(loid)s in soils could be achieved with PMF based on their spatial distributions, combination with emission inventory and reactive transport are probably necessary to obtain more accurate results.

Data on the comprehensive first-principles diffusion study of the aluminum-magnesium system

First-principles calculation of diffusion coefficients between Mg and Al is investigated comprehensively using density functional theory (DFT). The effect of different uncertainty sources arising from first principles calculations has been investigated systematically. These sources include the diffusion model, energetic, entropic and attempt frequency calculations. Variation in self and impurity diffusion coefficients of Mg and Al in stable phases are quantified using different DFT settings and compared with the experiments. Using the optimal DFT settings, diffusion coefficients in metastable phases of Al and Mg are predicted. The dataset refers to "An integrated experimental and computational study of diffusion and atomic mobility of the aluminum-magnesium system" [1].

Leveraging spatial uncertainty for online error compensation in EMT

Purpose

Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmically. Compensation algorithms for EMT in guidewire procedures are only practical in an online setting.

Methods

We collect positional data and train a symmetric artificial neural network (ANN) architecture for compensating navigation error. The results are evaluated in both online and offline scenarios and are compared to polynomial fits. We assess spatial uncertainty of the compensation proposed by the ANN. Simulations based on real data show how this uncertainty measure can be utilized to improve accuracy and limit radiation exposure in hybrid navigation.

Results

ANNs compensate unseen distortions by more than 70%, outperforming polynomial regression. Working on known distortions, ANNs outperform polynomials as well. We empirically demonstrate a linear relationship between tracking accuracy and model uncertainty. The effectiveness of hybrid tracking is shown in a simulation experiment.

Conclusion

ANNs are suitable for EMT error compensation and can generalize across unseen distortions. Model uncertainty needs to be assessed when spatial error compensation algorithms are developed, so that training data collection can be optimized. Finally, we find that error compensation in EMT reduces the need for X-ray images in hybrid navigation.

A new approach for uncertainty analysis of ETW Rider Head Injury Reconstruction via Coupling Response Surface and Monte Carlo methodologies

Traditional vehicle accident reconstructions do not take into account all existing uncertainties and may over- or under-estimate the injury risk. The objective of this study was to introduce a new uncertainty analysis method by applying Response Surface-Monte Carlo Methods (RS-MCM) to predict head injury risk in real electric two wheelers (ETW) to vehicle accidents. Vehicle impact velocity ranges in three detailed ETWs accidents (including video records and injury reports) were estimated using direct linear transformation (DLT) or video frame (VF) methods. A response surface methodology (RSM) was used to obtain an approximate model of the each real ETW accident, and a vehicle impact velocity distribution was estimated by applying the Monte Carlo Method (MCM) to the resulting model. If the velocity distribution was in agreement with the initial estimated velocity, the reconstruction quality was deemed acceptable. The injury severity was then assessed using the initial conditions resulting from the range of potential head impact conditions identified in the reconstruction activities. The identified head linear and angular impact velocities were input to finite element analyses to the THUMS Ver4.02 pedestrian head model and resulting in head injury criteria (HIC). The HIC values were further explored using the same RSM method used earlier to establish impact conditions. The distribution of reconstructed AIS levels show good agreement with the injury results from forensic reports. The results illustrated that the RS-MCM enriches the information for head trauma injury mechanisms caused by the vehicle collisions or ground impact.

A cyberGIS approach to spatiotemporally explicit uncertainty and global sensitivity analysis for agent-based modeling of vector-borne disease transmission

While agent-based models (ABMs) provide an effective means for investigating complex interactions between heterogeneous agents and their environment, they may hinder an improved understanding of phenomena being modeled due to inherent challenges associated with uncertainty in model parameters. This study uses uncertainty analysis and global sensitivity analysis (UA-GSA) to examine the effects of such uncertainty on model outputs. The statistics used in UA-GSA, however, are likely to be affected by the modifiable areal unit problem (MAUP). Therefore, to examine the scale varying-effects of model inputs, UA-GSA needs to be performed at multiple spatiotemporal scales. Unfortunately, performing comprehensive UA-GSA comes with considerable computational cost. In this paper, our cyberGIS-enabled spatiotemporally explicit UA-GSA approach helps to not only resolve the computational burden, but also to measure dynamic associations between model inputs and outputs. A set of computational and modeling experiments shows that input factors have scale-dependent impacts on modeling output variability. In other words, most of the input factors have relatively large impacts in a certain region, but may not influence outcomes in other regions. Furthermore, our spatiotemporally explicit UA-GSA approach sheds light on the effects of input factors on modeling outcomes that are particularly spatially and temporally clustered, such as the occurrence of communicable disease transmission.

Measurement, model prediction and uncertainty quantification of plasma clearance of cerium citrate in humans

Double tracer studies in healthy human volunteers with stable isotopes of cerium citrate were performed with the aim of investigating the gastro-intestinal absorption of cerium (Ce), its plasma clearance and urinary excretion. In the present work, results of the clearance of Ce in blood plasma are shown after simultaneous intravenous and oral administration of a Ce tracer. Inductively coupled plasma mass spectrometry was used to determine the tracer concentrations in plasma. The results show that about 80% of the injected Ce citrate cleared from the plasma within the 5 mins post-administration. The data obtained are compared to a revised biokinetic model of Ce, which was initially developed by the International Commission on Radiological Protection (ICRP). The measured plasma clearance of Ce citrate was mostly consistent with that predicted by the ICRP biokinetic model. Furthermore, in an effort to quantify the uncertainty of the model prediction, the laboratory animal data on which the ICRP biokinetic Ce model is based, was analyzed. The measured plasma clearance and its uncertainty was also compared to the plasma clearance uncertainty predicted by the model. It was found that the measured plasma clearance during the first 15 min after administration is in a good agreement with the modelled plasma clearance. In general, the measured clearance falls inside the 95% confidence interval predicted by the biokinetic model.

Optimizing water resources allocation and soil salinity control for supporting agricultural and environmental sustainable development in Central Asia

In this study, a stochastic-fuzzy-based fractional programming (SFFP) method is advanced for optimizing water-resources allocation and soil-salinity control under uncertainty. The developed method can address ratio objective optimization problems of complex system in association with stochastic and fuzzy uncertainties, which can help gain in-depth analysis of the interrelationships between marginal effectiveness and system reliability. Then, SFFP is applied to an irrigation region in the lower reach of Amu Darya River basin, where linear crop yield-salinity functions and salt-leaching functions are introduced into the modeling formulation for reflecting the complicated interactions among water resources, soil salinity, arable land, and electricity supply. Solutions under 96 scenarios related to different irrigation efficiencies, water availabilities, and electricity supplies have been obtained. Our findings are: i) increased water availability, electricity supply, and irrigation efficiency result in high marginal benefit; ii) irrigation efficiency is the key factor influencing water allocation patterns for crop irrigation and salt-leaching, promotion of which can facilitate mitigating economic and environmental losses in the water-deficit and soil-salinized region; iii) leaching water allocation patterns for soil-salinity washing is related to salinity characters of crops and regions, and boosting drought- and salt-tolerance crop can be effective in adaption to risks of water scarcity and land salinization. Compared to the conventional approaches, SFFP can generate more flexible alternatives and achieve higher marginal effectiveness. These findings can provide effective decision support to identify desired water management strategies under multiple uncertainties for supporting agricultural sustainability in arid regions.

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression

A key feature of living tissues is their capacity to remodel and grow in response to environmental cues. Within continuum mechanics, this process can be captured with the multiplicative split of the deformation gradient into growth and elastic contributions. The mechanical and biological response during tissue adaptation is characterized by inherent variability. Accounting for this uncertainty is critical to better understand tissue mechanobiology, and, moreover, it is of practical importance if we aim to develop predictive models for clinical use. However, the current gold standard in computational models of growth and remodeling remains the use of deterministic finite element (FE) simulations. Here we focus on tissue expansion, a popular technique in which skin is stretched by a balloon-like device inducing its growth. We construct FE models of tissue expansion with various levels of detail, and show that a sufficiently broad set of FE simulations from these models can be used to train an accurate and efficient multi-fidelity Gaussian process (GP) surrogate. The approach is not limited to simulation data, rather, it can fuse different kinds of data, including from experiments. The main appeal of the framework relies on the common experience that highly detailed models (or experiments) are more accurate but also more costly, while simpler models (or experiments) can be easily evaluated but are bound to have some error. In these situations, doing uncertainty analysis tasks with the high fidelity models alone is not feasible and, conversely, relying solely on low fidelity approximations is also undesirable. We show that a multi-fidelity GP outperforms the high fidelity GP and low fidelity GP when tested against the most detailed FE model. In turn, having trained the multi-fidelity GP model, we showcase the propagation of uncertainty from the mechanical and biological response parameters to the spatio-temporal growth outcomes. We expect that the methods and applications in this paper will enable future research in parameter calibration under uncertainty and uncertainty propagation in real clinical scenarios involving tissue growth and remodeling.