Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

A principled approach to conductivity uncertainty analysis in electric field calculations

Uncertainty surrounding ohmic tissue conductivity impedes accurate calculation of the electric fields generated by non-invasive brain stimulation. We present an efficient and generic technique for uncertainty and sensitivity analyses, which quantifies the reliability of field estimates and identifies the most influential parameters. For this purpose, we employ a non-intrusive generalized polynomial chaos expansion to compactly approximate the multidimensional dependency of the field on the conductivities. We demonstrate that the proposed pipeline yields detailed insight into the uncertainty of field estimates for transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), identifies the most relevant tissue conductivities, and highlights characteristic differences between stimulation methods. Specifically, we test the influence of conductivity variations on (i) the magnitude of the electric field generated at each gray matter location, (ii) its normal component relative to the cortical sheet, (iii) its overall magnitude (indexed by the 98th percentile), and (iv) its overall spatial distribution. We show that TMS fields are generally less affected by conductivity variations than tDCS fields. For both TMS and tDCS, conductivity uncertainty causes much higher uncertainty in the magnitude as compared to the direction and overall spatial distribution of the electric field. Whereas the TMS fields were predominantly influenced by gray and white matter conductivity, the tDCS fields were additionally dependent on skull and scalp conductivities. Comprehensive uncertainty analyses of complex systems achieved by the proposed technique are not possible with classical methods, such as Monte Carlo sampling, without extreme computational effort. In addition, our method has the advantages of directly yielding interpretable and intuitive output metrics and of being easily adaptable to new problems.

Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability

Although hydrological models play an essential role in managing water resources, quantifying different sources of uncertainties is a challenging task. In this study, the application of two parameter uncertainty quantification methods and their performances for predicting runoff was investigated. Sequential Uncertainty Fitting version 2 (SUFI-2) and DiffeRential Evolution Adaptive Metropolis (DREAM-ZS) algorithms were employed to explore the output uncertainty of Soil and Water Assessment Tool (SWAT) at a multisite flow gauging station. In order to optimize the model and quantify the parameter uncertainty, S1 and S2 strategies, which belong to the SUFI-2 and DREAM-ZS algorithms, were defined. The prior ranges of the S1 were adopted from SWAT manual, and the prior ranges of the S2 were selected using a compromising approach between the prior and posterior ranges extracted from S1. P-factor, d-factor, Nash-Sutcliffe coefficient (NS), the dimensionless variant of average deviation amplitude (S), and the average relative deviation amplitude (T), as performance criteria, were assessed. The NS, S, and T for total uncertainty ranged 0.60-0.71, 0.46-0.51, and 0.94-1.01 under S1 strategy and 0.64-0.78, 0.07-0.22, and 0.39-0.64 under S2, respectively. In parameter uncertainty analysis, S and T indices ranged from 1.51 to 1.88 and 2.20 to 2.60, correspondingly. The results showed that the DREAM-ZS algorithm improved model calibration efficiency and led to more realistic values of the parameters for runoff simulation in SWAT model. However, the S2 strategy, which implicitly takes advantage of both formal and informal Bayesian approaches simultaneously, will be able to outperform the S1 for reducing the prediction uncertainties.

EANM practical guidance on uncertainty analysis for molecular radiotherapy absorbed dose calculations

A framework is proposed for modelling the uncertainty in the measurement processes constituting the dosimetry chain that are involved in internal absorbed dose calculations. The starting point is the basic model for absorbed dose in a site of interest as the product of the cumulated activity and a dose factor. In turn, the cumulated activity is given by the area under a time-activity curve derived from a time sequence of activity values. Each activity value is obtained in terms of a count rate, a calibration factor and a recovery coefficient (a correction for partial volume effects). The method to determine the recovery coefficient and the dose factor, both of which are dependent on the size of the volume of interest (VOI), are described. Consideration is given to propagating estimates of the quantities concerned and their associated uncertainties through the dosimetry chain to obtain an estimate of mean absorbed dose in the VOI and its associated uncertainty. This approach is demonstrated in a clinical example.

Uncertainty analysis of industrial energy conservation management in China's iron and steel industry

There are remarkable uncertainty factors in the industrial sector that enhance the difficulties of setting energy conservation strategies, such as the macro economy, industrial structures, and technical uncertainties. However, current studies simply predict the possible trends or conduct scenario analyses, and neglect uncertainty factors in the management of industrial energy conservation. In response, this article considers China's iron and steel industry as an example and builds the Industrial Energy Conservation Uncertainty Analysis (IECUA) model to recognize and analyze the uncertainty factors via a 200-thousand-time Latin hypercube sampling. Then, we propose some management measures, including setting energy conservation targets and energy conservation strategies. The results show that energy conservation targets should be more flexible than just the predicted values, to enhance the feasibility of their realization. In addition, energy conservation strategies are set at industrial and technique levels. On the one hand, such key parameters as production output, the coke/steel ratio, and pig iron/steel ratio, should be strictly controlled to avoid non-compliance risks. On the other hand, energy conservation technologies can be considered under four quadrants depending on their sensitivity to energy conservation and economic efficiency. Finally, some differentiated technologies promotion suggestions are made, such as economic stimulation, market entry standards and technical application guidelines.

A comprehensive probabilistic approach for integrating natural variability and parametric uncertainty in the prediction of trace metals speciation in surface waters

The main objectives of this study were to evaluate global uncertainty in the prediction of Distribution coefficients (Kds) for several Trace Metals (TM) (Cd, Cu, Pb, Zn) through the probabilistic use of a geochemical speciation model, and to conduct sensitivity analysis in speciation modeling in order to identify the main sources of uncertainty in Kd prediction. As a case study, data from the Loire river (France) were considered. The geochemical speciation model takes into account complexation of TM with inorganic ligands, sorption of TM with hydrous ferric oxides, complexation of TM with dissolved and particulate organic matter (i.e. dissolved and particulate humic acids and fulvic acids) and sorption and/or co-precipitation of TM to carbonates. Probability Density Functions (PDFs) were derived for physico-chemical conditions of the Loire river from a comprehensive collection of monitoring data. PDFs for model parameters were derived from literature review. Once all the parameters were assigned PDFs that describe natural variability and/or knowledge uncertainty, a stepwise structured sensitivity analysis (SA) was performed, by starting from computationally 'inexpensive' Morris method to most costly variance-based EFAST method. The most sensitive parameters on Kd predictions were thus ranked and their contribution to Kd variance was quantified. Uncertainty analysis was finally performed, allowing quantifying Kd ranges that can be expected when considering all the sensitive parameters together.

Apportionment and uncertainty analysis of nitrate sources based on the dual isotope approach and a Bayesian isotope mixing model at the watershed scale

Identifying and eliminating pollutant sources of water bodies is critical for drinking water safety. In this research, river water, reservoir water and groundwater samples (n = 259) were collected from November 2015 to January 2017. Spatial Analysis was made of the isotopic compositions of potential nitrate sources (i.e., manure, sewage, chemical nitrogen fertilizer, soil organic nitrogen and rainfall) so as to obtain the site source isotopic signatures. Different sources pools and fractionation factors were loaded to a Bayesian isotope mixing model to ensure posterior estimates with less uncertainty. Results showed that the total nitrogen (TN) concentrations in Hexi Reservoir watershed were higher than the Environmental Quality Standards for Surface Water of China (GB 3838-2002), and NO₃^--N was the dominant form of TN (accounting for 68.63% on average). There are significant spatio-temporal variations in the isotope data (δ¹⁵N-NO₃^- and δ¹⁸O-NO₃^-) and the dominant nitrate sources, which were related to the land use types. Loading the site source isotopic signatures to the Bayesian isotope mixing model effectively improved the accuracy and precision of nitrate source apportionment. Chemical nitrogen fertilizer (NF) was the foremost largest contributor of NO₃^--N (38.82%), especially for Hexi North Stream (34.19%) and Yangmei Stream (44.39%), while atmospheric deposition (AD) contributed the least to NO₃^--N (0.47%) of river water in the watershed; soil organic nitrogen (NS) contributed more to NO₃^--N in the dry season than in the wet season; and manure and sewage (M&S) contributed approximately 30.22% in the whole study period, 53.60% in September 2016 and 41.33% in Hexi South Stream. This research suggests that combination of Spatial Analysis and the Bayesian isotope mixing model with the measured isotopic signatures of potential nitrate sources accurately apportion the nitrate source contributions.

Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD

PURPOSE

Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI.

METHODS

Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified.

RESULTS

The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients.

CONCLUSION

The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.

Occupational exposure assessment of phthalate esters in indoor and outdoor microenvironments

Phthalate esters (PAEs) are widely used as plasticizers in consumer products. PAEs are a group of environmental hormone which disrupts human and animals' endocrine systems. Different occupational groups are exposed to various levels of PAEs. In the present study, four typical occupational groups were chosen, including doctors, college teachers, college students, and drivers who worked in public traffic system. In order to understand the exposure levels to PAEs via inhalation, air samples were collected from multiple microenvironments including indoor and outdoor in Hangzhou to measure the gas and particle concentrations of six PAEs, together with time spent in different microenvironments of these four groups. A comprehensive PAEs exposure model was built to estimate the daily PAEs exposure through inhalation, oral and dermal pathways. The Monte Carlo simulation results show that doctors were exposed to the highest level of PAEs, and consequently had the highest health risk among these four occupational groups. In contrast, college students had the lowest health risk. By setting the exposure level of staying in residences as the baseline, doctors and drivers were two occupations exposed to high PAEs health risk. Di-(2-ethylhexyl) phthalate (DEHP) was the largest contributor among the six phthalates, posing moderate health risk (10^-5-10^-6) to every occupation. For traffic microenvironments alone, the total exposure levels for different transportation modes were in the descending order of busses, cars, cabs, tubes, motor bikes, and walking.

A revised dosimetric characterization of 60Co BEBIG source: From single-source data to clinical dose distribution

PURPOSE

Although the dosimetric characterization of ⁶⁰Co BEBIG source can be found in several literature studies, the data sets show major discrepancies and the lack of uncertainty analyses. This study tried to determine an accurate dosimetric data set for this source using Monte Carlo (MC) simulations along with detailed uncertainty analysis. To explore how different dosimetric data sets can make changes in practical situations, clinical dose distributions based on our results were compared with the dose distributions derived from Granero et al. and consensus data sets.

METHODS AND MATERIALS

The MC simulations were performed with Monte Carlo N-Particle eXtended code (MCNPX) version 2.6.0 and the TG-43 parameters were estimated adhering to the American Association of Physicists in Medicine (AAPM) and European SocieTy for Radiotherapy and Oncology (ESTRO) 229 report. The dose rate distributions for single-source and two typical clinical cases, including one intracavitary and one interstitial, were calculated using an in-house code on the basis of the TG-43 formalism.

RESULTS

The total uncertainties for water dose rate on source transverse axis at 1 cm and 5 cm, air kerma strength, and dose rate constant were evaluated to be 0.10%, 0.09%, 0.04%, and 0.11%, respectively. Meaningful differences were found for the interstitial case in which 22% of clinical target volume (CTV) showed differences from ±1% to ±10% or even larger.

CONCLUSIONS

The MC uncertainty was derived about 16 times smaller than the typical MC component stated in TG-138, partly because of large number of histories and partly because the spectra of ⁶⁰Co and also its photons' attenuation coefficients are adequately accurate. The results showed that in the clinical situations, the applicator geometry and the superposition of single-source dose distributions can reduce the differences observed between several data sets.

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

Developing a non-point source P loss indicator in R and its parameter uncertainty assessment using GLUE: a case study in northern China

Uncertainty analysis is an important prerequisite for model application. However, the existing phosphorus (P) loss indexes or indicators were rarely evaluated. This study applied generalized likelihood uncertainty estimation (GLUE) method to assess the uncertainty of parameters and modeling outputs of a non-point source (NPS) P indicator constructed in R language. And the influences of subjective choices of likelihood formulation and acceptability threshold of GLUE on model outputs were also detected. The results indicated the following. (1) Parameters RegR₂, RegSDR₂, PlossDP_fer, PlossDP_man, DPDR, and DPR were highly sensitive to overall TP simulation and their value ranges could be reduced by GLUE. (2) Nash efficiency likelihood (L₁) seemed to present better ability in accentuating high likelihood value simulations than the exponential function (L₂) did. (3) The combined likelihood integrating the criteria of multiple outputs acted better than single likelihood in model uncertainty assessment in terms of reducing the uncertainty band widths and assuring the fitting goodness of whole model outputs. (4) A value of 0.55 appeared to be a modest choice of threshold value to balance the interests between high modeling efficiency and high bracketing efficiency. Results of this study could provide (1) an option to conduct NPS modeling under one single computer platform, (2) important references to the parameter setting for NPS model development in similar regions, (3) useful suggestions for the application of GLUE method in studies with different emphases according to research interests, and (4) important insights into the watershed P management in similar regions.

Analyzing the uncertainty of diffusive gel-based passive samplers as tools for evaluating the averaged contamination of surface water by organic pollutants

Agarose gel-based passive samplers are nowadays one of the most effective sampling devices able to provide a quantitative evaluation of water contamination level for a broad range of organic contaminants. These devices show significant improvements in comparison to the previous passive samplers dedicated to hydrophilic compounds because they tend to reduce the effect of hydrodynamic flow conditions on the uptake rate of compounds and thus subsequently to improve their accuracy. However, albeit their effects minimized, hydrodynamic water flow and temperature are reported as variables likely to change the uptake rate of compounds that may lead to some inaccuracy if they are not adequately taken into account. This work aims to investigate the magnitude of effects of such uncorrected variables on the bias of the deduced water contamination level. The analysis of the error structure shows that the uncertainty on the diffusivity of contaminants in agarose gel and its dependency on temperature are the most inferring factors. At 8 °C, these factors are, respectively, responsible of 34 and 33% of the squared uncertainty on the final deduced contamination level. The overall uncertainty for a single exposed passive sampler is in the order of 39% and drops to 23% if threesamplers are co-exposed (at 8 °C). Despite this uncertainty, we present results for a set of pesticides and personal care products throughout a field monitoring conducted over a 4-month period, which show the potential of passive samplers to allow assessing the temporal trend of water contamination.

Integrating uncertainties to the combined environmental and economic assessment of algal biorefineries: A Monte Carlo approach

The economic and environmental performance of microalgal processes has been widely analyzed in recent years. However, few studies propose an integrated process-based approach to evaluate economic and environmental indicators simultaneously. Biodiesel is usually the single product and the effect of environmental benefits of co-products obtained in the process is rarely discussed. In addition, there is wide variation of the results due to inherent variability of some parameters as well as different assumptions in the models and limited knowledge about the processes. In this study, two standardized models were combined to provide an integrated simulation tool allowing the simultaneous estimation of economic and environmental indicators from a unique set of input parameters. First, a harmonized scenario was assessed to validate the joint environmental and techno-economic model. The findings were consistent with previous assessments. In a second stage, a Monte Carlo simulation was applied to evaluate the influence of variable and uncertain parameters in the model output, as well as the correlations between the different outputs. The simulation showed a high probability of achieving favorable environmental performance for the evaluated categories and a minimum selling price ranging from $11gal^-1 to $106gal^-1. Greenhouse gas emissions and minimum selling price were found to have the strongest positive linear relationship, whereas eutrophication showed weak correlations with the other indicators (namely greenhouse gas emissions, cumulative energy demand and minimum selling price). Process parameters (especially biomass productivity and lipid content) were the main source of variation, whereas uncertainties linked to the characterization methods and economic parameters had limited effect on the results.

Optimizing accuracy of protocols for measuring dry matter and nutrient yield of forage crops

Farmers around the world must precisely manage nutrients applied to and removed from crop fields to maintain production and without causing nutrient pollution. This study is the first to quantify the baseline accuracy of current industry measurement protocols and achievable accuracy from intensifying protocols for measuring dry matter (DM), nitrogen (N), potassium (K), and phosphorus (P) yields from forage crops harvested for silage. The 'true' DM and nutrient yields of three fields each of corn, sorghum, and small grain were intensively measured by weighing and sampling every truckload of harvested forage. Simulations quantified the accuracy of practical sampling protocols by repeatedly subsampling the complete dataset for each field to measure average truckload weight and average DM and nutrient concentrations. Then uncertainty was propagated to DM, N, P, and K yield calculations using standard error equations. Yields measured using current industry protocols diverged from the true yields of some fields by more than ±40%, emphasizing the need for improved protocols. This study shows that improving average DM and nutrient concentration measurements is unlikely to improve accuracy of yield measurements if average load weight is not precisely measured. Accuracy did not come within 27% of true yields without weighing all truckloads on some fields even when DM and nutrient concentration measurements were perfectly accurate. Once all truckloads were weighed, the timing of forage sample collection to measure average DM concentration had the greatest impact on accuracy; precision improved by an average of 6.2% when >3 samples were evenly spaced throughout the harvest compared to the same number of consecutive samples. All crop fields are affected by within field variation in growing conditions that results in heterogeneity in DM and nutrient yield. Globally, this study provides foundational methodology to quantitatively evaluate and improve yield measurement protocols that ultimately support sustainable crop production.

Hourly disaggregation of industrial CO2 emissions from Shenzhen, China

Shenzhen's total industrial CO₂ emission was calculated using the IPCC recommended bottom-up approach and data obtained from the China High Resolution Emission Gridded Data (CHRED). Monthly product yield was then used as the proxy to disaggregate a facility's total emission into monthly emissions. Since a thermal power unit's emission changes with daily and hourly power loads, typical power load curves were used as the proxy to disaggregate the monthly emissions on a daily and hourly basis. The daily and hourly emissions of other facilities were calculated according to two specially designed models: the "weekdays + Spring Festival holidays" model for February and the "weekdays + weekends" model for non-February months. The uncertainty ranges associated with the process of the total amount calculation, monthly disaggregation, daily disaggregation and hourly disaggregation were quantitatively estimated. The total combined uncertainty of the hourly disaggregation of "weekdays + weekends" mode was ±26.19%, and that of the "weekdays + Spring Festival holidays" mode was ±33.06%. These temporal-disaggregation methods and uncertainty estimate approaches could also be used for the industrial air pollutant emission inventory and easily reproduced in the whole country.

The relevance of shear, sedimentation and diffusion during spin freezing, as potential first step of a continuous freeze-drying process for unit doses

Recently, a continuous freeze-drying process for the production of unit doses was presented and evaluated. In this concept, the freezing step is modified compared to traditional batch freeze-drying, as glass vials filled with a liquid formulation, are rotated around their longitudinal axis while cooled and frozen with a cold, sterile and inert gas (i.e. spin freezing). Finally, a thin frozen product layer spread over the entire vial wall is achieved. The aim of this paper is twofold: firstly, the relation between the rotation velocity and the relative difference between top and bottom of the frozen product layer thickness was determined for different vial types. Secondly, the impact of shear and centrifugal forces generated during spinning was examined, to find out whether they might cause pharmaceutical instability and sedimentation, respectively. Mechanistic and experimental evaluation showed that shear has no effect on proteins. Calculations showed that the sedimentation and diffusion velocity is too low to cause inhomogeneity in the product layer. In addition, Global Sensitivity Analysis (GSA) and Uncertainty Analysis (UA) were performed in order to account for the uncertainty of the used mechanistic model.

A framework for uncertainty and risk analysis in Total Maximum Daily Load applications

In the United States, the computation of Total Maximum Daily Loads (TMDL) must include a Margin of Safety (MOS) to account for different sources of uncertainty. In practice however, TMDL studies rarely include an explicit uncertainty analysis and the estimation of the MOS is often subjective and even arbitrary. Such approaches are difficult to replicate and preclude the comparison of results between studies. To overcome these limitations, a Bayesian framework to compute TMDLs and MOSs including an explicit evaluation of uncertainty and risk is proposed in this investigation. The proposed framework uses the concept of Predictive Uncertainty to calculate a TMDL from an equation of allowable risk of non-compliance of a target water quality standard. The framework is illustrated in a synthetic example and in a real TMDL study for nutrients in Sawgrass Lake, Florida.

Monte Carlo uncertainty analysis of dose estimates in radiochromic film dosimetry with single-channel and multichannel algorithms

PURPOSE

To provide a multi-stage model to calculate uncertainty in radiochromic film dosimetry with Monte-Carlo techniques. This new approach is applied to single-channel and multichannel algorithms.

MATERIAL AND METHODS

Two lots of Gafchromic EBT3 are exposed in two different Varian linacs. They are read with an EPSON V800 flatbed scanner. The Monte-Carlo techniques in uncertainty analysis provide a numerical representation of the probability density functions of the output magnitudes. From this numerical representation, traditional parameters of uncertainty analysis as the standard deviations and bias are calculated. Moreover, these numerical representations are used to investigate the shape of the probability density functions of the output magnitudes. Also, another calibration film is read in four EPSON scanners (two V800 and two 10000XL) and the uncertainty analysis is carried out with the four images.

RESULTS

The dose estimates of single-channel and multichannel algorithms show a Gaussian behavior and low bias. The multichannel algorithms lead to less uncertainty in the final dose estimates when the EPSON V800 is employed as reading device. In the case of the EPSON 10000XL, the single-channel algorithms provide less uncertainty in the dose estimates for doses higher than four Gy.

CONCLUSION

A multi-stage model has been presented. With the aid of this model and the use of the Monte-Carlo techniques, the uncertainty of dose estimates for single-channel and multichannel algorithms are estimated. The application of the model together with Monte-Carlo techniques leads to a complete characterization of the uncertainties in radiochromic film dosimetry.

Distributions of emissions intensity for individual beef cattle reared on pasture-based production systems

Life Cycle Assessment (LCA) of livestock production systems is often based on inventory data for farms typical of a study region. As information on individual animals is often unavailable, livestock data may already be aggregated at the time of inventory analysis, both across individual animals and across seasons. Even though various computational tools exist to consider the effect of genetic and seasonal variabilities in livestock-originated emissions intensity, the degree to which these methods can address the bias suffered by representative animal approaches is not well-understood. Using detailed on-farm data collected on the North Wyke Farm Platform (NWFP) in Devon, UK, this paper proposes a novel approach of life cycle impact assessment that complements the existing LCA methodology. Field data, such as forage quality and animal performance, were measured at high spatial and temporal resolutions and directly transferred into LCA processes. This approach has enabled derivation of emissions intensity for each individual animal and, by extension, its intra-farm distribution, providing a step towards reducing uncertainty related to agricultural production inherent in LCA studies for food. Depending on pasture management strategies, the total emissions intensity estimated by the proposed method was higher than the equivalent value recalculated using a representative animal approach by 0.9-1.7 kg CO₂-eq/kg liveweight gain, or up to 10% of system-wide emissions. This finding suggests that emissions intensity values derived by the latter technique may be underestimated due to insufficient consideration given to poorly performing animals, whose emissions becomes exponentially greater as average daily gain decreases. Strategies to mitigate life-cycle environmental impacts of pasture-based beef productions systems are also discussed.

Assessment and uncertainty analysis of groundwater risk

Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment.

Cost-benefit analysis of copper recovery in remediation projects: A case study from Sweden

Contamination resulting from past industrial activity is a problem throughout the world and many sites are severely contaminated by metals. Advances in research in recent years have resulted in the development of technologies for recovering metal from metal-rich materials within the framework of remediation projects. Using cost-benefit analysis (CBA), and explicitly taking uncertainties into account, this paper evaluates the potential social profitability of copper recovery as part of four remediation alternatives at a Swedish site. One alternative involves delivery of copper-rich ash to a metal production company for refining. The other three alternatives involve metal leaching from materials and sale of the resulting metal sludge for its further processing at a metal production company using metallurgical methods. All the alternatives are evaluated relative to the conventional excavation and disposal method. Metal recovery from the ash, metal sludge sale, and disposal of the contaminated soil and the ash residue at the local landfill site, was found to be the best remediation alternative. However, given the present conditions, its economic potential is low relative to the conventional excavation and disposal method but higher than direct disposal of the copper-rich ash for refining. Volatile copper prices, the high cost of processing equipment, the highly uncertain cost of the metal leaching and washing process, coupled with the substantial project risks, contribute most to the uncertainties in the CBA results for the alternatives involving metal leaching prior to refining. However, investment in processing equipment within the framework of a long-term investment project, production of safe, reusable soil residue, and higher copper prices on the metal market, can make metal recovery technology socially profitable.

Performance evaluation of a smart buffer control at a wastewater treatment plant

Real time control (RTC) is increasingly seen as a viable method to optimise the functioning of wastewater systems. Model exercises and case studies reported in literature claim a positive impact of RTC based on results without uncertainty analysis and flawed evaluation periods. This paper describes two integrated RTC strategies at the wastewater treatment plant (WWTP) Eindhoven, the Netherlands, that aim to improve the use of the available tanks at the WWTP and storage in the contributing catchments to reduce the impact on the receiving water. For the first time it is demonstrated that a significant improvement can be achieved through the application of RTC in practice. The Storm Tank Control is evaluated based on measurements and reduces the number of storm water settling tank discharges by 44% and the discharged volume by an estimated 33%, decreasing dissolved oxygen depletion in the river. The Primary Clarifier Control is evaluated based on model simulations. The maximum event NH4 concentration in the effluent reduced on average 19% for large events, while the load reduced 20%. For all 31 events the reductions are 11 and 4% respectively. Reductions are significant taking uncertainties into account, while using representative evaluation periods.

Coupled Monte Carlo simulation and Copula theory for uncertainty analysis of multiphase flow simulation models

Simulation-optimization techniques are effective in identifying an optimal remediation strategy. Simulation models with uncertainty, primarily in the form of parameter uncertainty with different degrees of correlation, influence the reliability of the optimal remediation strategy. In this study, a coupled Monte Carlo simulation and Copula theory is proposed for uncertainty analysis of a simulation model when parameters are correlated. Using the self-adaptive weight particle swarm optimization Kriging method, a surrogate model was constructed to replace the simulation model and reduce the computational burden and time consumption resulting from repeated and multiple Monte Carlo simulations. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were employed to identify whether the t Copula function or the Gaussian Copula is the optimal Copula function to match the relevant structure of the parameters. The results show that both the AIC and BIC values of the t Copula function are less than those of the Gaussian Copula function. This indicates that the t Copula function is the optimal function for matching the relevant structure of the parameters. The outputs of the simulation model when parameter correlation was considered and when it was ignored were compared. The results show that the amplitude of the fluctuation interval when parameter correlation was considered is less than the corresponding amplitude when parameter estimation was ignored. Moreover, it was demonstrated that considering the correlation among parameters is essential for uncertainty analysis of a simulation model, and the results of uncertainty analysis should be incorporated into the remediation strategy optimization process.

Design of waste stabilization pond systems: A review

A better design instruction for waste stabilization ponds is needed due to their growing application for wastewater purification, increasingly strict environmental regulations, and the fact that most of previous design manuals are outdated. To critically review model-based designs of typical pond treatment systems, this paper analyzed more than 150 articles, books, and reports from 1956 to 2016. The models developed in these publications ranged from simple rules and equations to more complex first-order and mechanistic models. From a case study on all four approaches, it appeared that rules of thumb is no longer a proper tool for pond designs due to its low design specification and very high output variability and uncertainty. On the other hand, at the beginning phase of design process or in case of low pressure over land and moderate water quality required, regression equations can be useful to form an idea for pond dimensions. More importantly, mechanistic models proved their capacity of generating more precise and comprehensive designs but still need to overcome their lack of calibration and validation, and overparameterization. In another case study, an essential but often overlooked role of uncertainty analysis in pond designs was investigated via a comparison between deterministic and uncertainty-based approaches. Unlike applying a safety factor representing all uncertainty sources, probabilistic designs quantify the uncertainty of model outputs by including prior uncertainty of inputs and parameters, which generates more scientifically reliable outcomes for decision makers. Based on these findings, we advise engineers and designers to shift from the conventional approaches to more innovative and economic tools which are suitable for dealing with large variations of natural biological systems.

Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed

Quantitative identification of nitrate (NO₃^--N) sources is critical to the control of nonpoint source nitrogen pollution in an agricultural watershed. Combined with water quality monitoring, we adopted the environmental isotope (δD-H₂O, δ¹⁸O-H₂O, δ¹⁵N-NO₃^-, and δ¹⁸O-NO₃^-) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO₃^--N inputs from four potential NO₃^--N sources, namely, atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S), in the ChangLe River watershed of eastern China. Results showed that NO₃^--N was the main form of nitrogen in this watershed, accounting for approximately 74% of the total nitrogen concentration. A strong hydraulic interaction existed between the surface and groundwater for NO₃^--N pollution. The variations of the isotopic composition in NO₃^--N suggested that microbial nitrification was the dominant nitrogen transformation process in surface water, whereas significant denitrification was observed in groundwater. MCMC mixing model outputs revealed that M&S was the predominant contributor to riverine NO₃^--N pollution (contributing 41.8% on average), followed by SN (34.0%), NF (21.9%), and AD (2.3%) sources. Finally, we constructed an uncertainty index, UI₉₀, to quantitatively characterize the uncertainties inherent in NO₃^--N source apportionment and discussed the reasons behind the uncertainties.

Modeling pesticide diuron loading from the San Joaquin watershed into the Sacramento-San Joaquin Delta using SWAT

Quantifying pesticide loading into the Sacramento-San Joaquin Delta of northern California is critical for water quality management in the region, and potentially useful for biological weed control planning. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model streamflow, sediment, and pesticide diuron loading in the San Joaquin watershed, a major contributing area to the elevated pesticide levels in the downstream Delta. The Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm was employed to perform calibration and uncertainty analysis. A combination of performance measures (PMs) and standardized performance evaluation criteria (PEC) was applied to evaluate model performance, while prediction uncertainty was quantified by 95% prediction uncertainty band (95PPU). Results showed that streamflow simulation was at least "satisfactory" at most stations, with more than 50% of the observed data bracketed by the 95PPU. Sediment simulation was rated as at least "satisfactory" based on two PMs, and diuron simulation was judged as "good" by all PMs. The 95PPU of sediment and diuron bracketed about 40% and 30% of the observed data, respectively. Significant correlations were observed between the diuron loads, and precipitation, streamflow, and the current and antecedent pesticide use. Results also showed that the majority (>70%) of agricultural diuron was transported during winter months, when direct exposure of biocontrol agents to diuron runoff is limited. However, exposure in the dry season could be a concern because diuron is relatively persistent in aquatic system. This study not only provides valuable information for the development of biological weed control plan in the Delta, but also serves as a foundation for the continued research on calibration, evaluation, and uncertainty analysis of spatially distributed, physically based hydrologic models.

The approaches to measuring the potential spatial access to urban health services revisited: distance types and aggregation-error issues

BACKGROUND

The potential spatial access to urban health services is an important issue in health geography, spatial epidemiology and public health. Computing geographical accessibility measures for residential areas (e.g. census tracts) depends on a type of distance, a method of aggregation, and a measure of accessibility. The aim of this paper is to compare discrepancies in results for the geographical accessibility of health services computed using six distance types (Euclidean and Manhattan distances; shortest network time on foot, by bicycle, by public transit, and by car), four aggregation methods, and fourteen accessibility measures.

METHODS

To explore variations in results according to the six types of distance and the aggregation methods, correlation analyses are performed. To measure how the assessment of potential spatial access varies according to three parameters (type of distance, aggregation method, and accessibility measure), sensitivity analysis (SA) and uncertainty analysis (UA) are conducted.

RESULTS

First, independently of the type of distance used except for shortest network time by public transit, the results are globally similar (correlation >0.90). However, important local variations in correlation between Cartesian and the four shortest network time distances are observed, notably in suburban areas where Cartesian distances are less precise. Second, the choice of the aggregation method is also important: compared with the most accurate aggregation method, accessibility measures computed from census tract centroids, though not inaccurate, yield important measurement errors for 10% of census tracts. Third, the SA results show that the evaluation of potential geographic access may vary a great deal depending on the accessibility measure and, to a lesser degree, the type of distance and aggregation method. Fourth, the UA results clearly indicate areas of strong uncertainty in suburban areas, whereas central neighbourhoods show lower levels of uncertainty.

CONCLUSION

In order to accurately assess potential geographic access to health services in urban areas, it is particularly important to choose a precise type of distance and aggregation method. Then, depending on the research objectives, the choices of the type of network distance (according to the mode of transportation) and of a number of accessibility measures should be carefully considered and adequately justified.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future.

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO₂ and NO_x. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C₀ = 35 μg/m³) to 0.9% (VSL, WHO guideline: C₀ = 10 μg/m³) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C₀). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

Roles of steady-state and dynamic models for regulation of hydrophobic chemicals in aquatic systems: A case study of decamethylcyclopentasiloxane (D5) and PCB-180 in three diverse ecosystems

We seek to contribute to the improved regulatory use of mass balance models to complement environmental monitoring data by applying the steady-state Quantitative Water Air Sediment Interactive model (QWASI) and a novel unsteady-state QWASI model. A steady-state model can yield not only a useful simulation of chemical fate under near steady-state conditions, but it can provide insights into the likely influences of increasing or decreasing emission rates, temperature changes, and unexpectedly high sensitivities to model parameters that may require additional investigation. We compared the consistency of insights from both types of model, in the expectation that while the dynamic model provides a closer simulation of actual conditions, for many purposes a simple, less computationally demanding, more transparent and less expensive model may be adequate for many regulatory purposes. We investigated the response times of decamethylcyclopentasiloxane (D5) and PCB-180 concentrations in water and sediment under three emission scenarios in three different aquatic systems, namely Lake Ontario, Oslofjord, and Lake Pepin. D5 was predicted to be removed largely by hydrolysis and volatilization in Lake Ontario and Oslofjord whereas it is subject to removal by advective loss in Lake Pepin. The half-times of D5 water concentration to a stepwise reduction in emission were <60 days in all three water bodies. In contrast, the predicted half-times were 0.53, 1.4, and 2.9 years in Lake Pepin, Oslofjord, and Lake Ontario, respectively. We also explored how uncertainties in input parameters propagate into uncertainties of concentrations in water and sediments possibly necessitating more accurate values.

Uncertainty estimation by Bayesian approach in thermochemical conversion of walnut hull and lignite coal blends

The main purpose of the present study was to incorporate the uncertainties in the thermal behavior of walnut hull (WH), lignite coal, and their various blends using Bayesian approach. First of all, thermal behavior of related materials were investigated under different temperatures, blend ratios, and heating rates. Results of ultimate and proximate analyses showed the main steps of oxidation mechanism of (co-)combustion process. Thermal degradation started with the (hemi-)cellulosic compounds and finished with lignin. Finally, a partial sensitivity analysis based on Bayesian approach (Markov Chain Monte Carlo simulations) were applied to data driven regression model (the best fit). The main purpose of uncertainty analysis was to point out the importance of operating conditions (explanatory variables). The other important aspect of the present work was the first performance evaluation study on various uncertainty estimation techniques in (co-)combustion literature.

Validating proposed migration equation and parameters' values as a tool to reproduce and predict 137Cs vertical migration activity in Spanish soils

This paper shows the procedure performed to validate the migration equation and the migration parameters' values presented in a previous paper (Legarda et al., 2011) regarding the migration of ¹³⁷Cs in Spanish mainland soils. In this paper, this model validation has been carried out checking experimentally obtained activity concentration values against those predicted by the model. This experimental data come from the measured vertical activity profiles of 8 new sampling points which are located in northern Spain. Before testing predicted values of the model, the uncertainty of those values has been assessed with the appropriate uncertainty analysis. Once establishing the uncertainty of the model, both activity concentration values, experimental versus model predicted ones, have been compared. Model validation has been performed analyzing its accuracy, studying it as a whole and also at different depth intervals. As a result, this model has been validated as a tool to predict ¹³⁷Cs behaviour in a Mediterranean environment.

Characterization, quantification and management of China's municipal solid waste in spatiotemporal distributions: A review

Municipal Solid Waste (MSW) is a heterogeneous waste stream, which is harmful for human health and the ecological environment if it is not well managed. Based on results from different authors by analyzing the generation, physical components and management of MSW from different cities, this paper presents an overview of the temporal trends and spatial variation characterization of MSW generation and its physical components in China. Total MSW generation has increased from 31,320 thousand tons in 1980 to 178,602 thousand tons in 2014, and MSW generation per capita has also increased from 448.3g to 653.2g. The distribution of MSW generation is mostly concentrated in the coastal southeastern region, as well as large point sources of more than 200 thousand tons per year are mostly distributed in Jiangsu, Zhejiang, Shandong, Hebei and Guangdong provinces. The review shows that the largest proportion of food waste, plastics and paper is 61.2% (54.2-65.9%, 95% CI), 9.8% (7.2-14.0%, 95% CI), 9.6% (6.7-12.3%, 95% CI), respectively, in 2014; the best estimates of other waste were as follows: 3.1% textile, 2.1% glass, 1.1% metal, 1.8% wood and grass, 1.3% rubber and leather, 1.8% ceramic, 2.5% ash, 1.2% hazardous waste, and 4.5% miscellaneous. To better manage China's MSW, several possible and appropriate solutions (e.g., concentrating on key regions, intensifying source separation, promoting green lifestyle, and establishing specialized regulations and policies) should be adopted, which might facilitate the application of China's 13th Five, and identify gaps in our knowledge of MSW management subject.

Uncertainties in mapping forest carbon in urban ecosystems

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m², aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Modeling the key factors that could influence the diffusion of CO2 from a wellbore blowout in the Ordos Basin, China

Carbon dioxide (CO₂) blowout from a wellbore is regarded as a potential environment risk of a CO₂ capture and storage (CCS) project. In this paper, an assumed blowout of a wellbore was examined for China's Shenhua CCS demonstration project. The significant factors that influenced the diffusion of CO₂ were identified by using a response surface method with the Box-Behnken experiment design. The numerical simulations showed that the mass emission rate of CO₂ from the source and the ambient wind speed have significant influence on the area of interest (the area of high CO₂ concentration above 30,000 ppm). There is a strong positive correlation between the mass emission rate and the area of interest, but there is a strong negative correlation between the ambient wind speed and the area of interest. Several other variables have very little influence on the area of interest, e.g., the temperature of CO₂, ambient temperature, relative humidity, and stability class values. Due to the weather conditions at the Shenhua CCS demonstration site at the time of the modeled CO₂ blowout, the largest diffusion distance of CO₂ in the downwind direction did not exceed 200 m along the centerline. When the ambient wind speed is in the range of 0.1-2.0 m/s and the mass emission rate is in the range of 60-120 kg/s, the range of the diffusion of CO₂ is at the most dangerous level (i.e., almost all Grade Four marks in the risk matrix). Therefore, if the injection of CO₂ takes place in a region that has relatively low perennial wind speed, special attention should be paid to the formulation of pre-planned, emergency measures in case there is a leakage accident. The proposed risk matrix that classifies and grades blowout risks can be used as a reference for the development of appropriate regulations. This work may offer some indicators in developing risk profiles and emergency responses for CO₂ blowouts.

A Bayesian approach of high impaired river reaches identification and total nitrogen load estimation in a sparsely monitored basin

In this study, a modeling framework based on the theory of SPAtially Referenced Regression On Watershed attributes (SPARROW) model was developed to identify impaired river reaches with respect to total nitrogen (TN) and estimate the TN sources in the Xin'anjiang River basin, which had limited monitoring sites. A Bayesian approach was applied to estimate the mean values and uncertainties of parameters, including land use export coefficients and in-stream attention rates. Based on the parameters, the midranges (25-75 %) of annual TN concentrations were assessed by the model and 4.5 % of river reaches in the basin were found to be with higher impaired probabilities (namely [TN] > 1.5 mg/l) than other reaches. The amount and yields of TN discharged from diffuse sources were estimated for each county in the basin. The results suggested that Tunxi City had the highest TN yields from farm land and population, while the highest TN yields in Huangshan City were from tea plantations. The outcomes of this study will guide the implementation of practical management measures to reduce TN loads.

Multi-SNP Haplotype Analysis Methods for Association Analysis

Haplotype analysis forms the basis of much of genetic association analysis using both related and unrelated individuals (we concentrate on unrelated). For example, haplotype analysis indirectly underlies the SNP imputation methods that are used for testing trait associations with known but unmeasured variants and for performing collaborative post-GWAS meta-analysis. This chapter is focused on the direct use of haplotypes in association testing. It reviews the rationale for haplotype-based association testing, discusses statistical issues related to haplotype uncertainty that affect the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons, first they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature.This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes, (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters, and (3) a simplified approximation to full ML for case-control data.Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and argue that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of haplotype risk estimation genome-wide and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.

Reviewing the anaerobic digestion and co-digestion process of food waste from the perspectives on biogas production performance and environmental impacts

In this paper, factors that affect biogas production in the anaerobic digestion (AD) and anaerobic co-digestion (coAD) processes of food waste are reviewed with the aim to improve biogas production performance. These factors include the composition of substrates in food waste coAD as well as pre-treatment methods and anaerobic reactor system designs in both food waste AD and coAD. Due to the characteristics of the substrates used, the biogas production performance varies as different effects are exhibited on nutrient balance, inhibitory substance dilution, and trace metal element supplement. Various types of pre-treatment methods such as mechanical, chemical, thermal, and biological methods are discussed to improve the rate-limiting hydrolytic step in the digestion processes. The operation parameters of a reactor system are also reviewed with consideration of the characteristics of the substrates. Since the environmental awareness and concerns for waste management systems have been increasing, this paper also addresses possible environmental impacts of AD and coAD in food waste treatment and recommends feasible methods to reduce the impacts. In addition, uncertainties in the life cycle assessment (LCA) studies are also discussed.

Modelling ecological and human exposure to POPs in Venice lagoon - Part II: Quantitative uncertainty and sensitivity analysis in coupled exposure models

The study is focused on applying uncertainty and sensitivity analysis to support the application and evaluation of large exposure models where a significant number of parameters and complex exposure scenarios might be involved. The recently developed MERLIN-Expo exposure modelling tool was applied to probabilistically assess the ecological and human exposure to PCB 126 and 2,3,7,8-TCDD in the Venice lagoon (Italy). The 'Phytoplankton', 'Aquatic Invertebrate', 'Fish', 'Human intake' and PBPK models available in MERLIN-Expo library were integrated to create a specific food web to dynamically simulate bioaccumulation in various aquatic species and in the human body over individual lifetimes from 1932 until 1998. MERLIN-Expo is a high tier exposure modelling tool allowing propagation of uncertainty on the model predictions through Monte Carlo simulation. Uncertainty in model output can be further apportioned between parameters by applying built-in sensitivity analysis tools. In this study, uncertainty has been extensively addressed in the distribution functions to describe the data input and the effect on model results by applying sensitivity analysis techniques (screening Morris method, regression analysis, and variance-based method EFAST). In the exposure scenario developed for the Lagoon of Venice, the concentrations of 2,3,7,8-TCDD and PCB 126 in human blood turned out to be mainly influenced by a combination of parameters (half-lives of the chemicals, body weight variability, lipid fraction, food assimilation efficiency), physiological processes (uptake/elimination rates), environmental exposure concentrations (sediment, water, food) and eating behaviours (amount of food eaten). In conclusion, this case study demonstrated feasibility of MERLIN-Expo to be successfully employed in integrated, high tier exposure assessment.

Probabilistic assessment of the influence of lake properties in long-term radiation doses to humans

The assessment processes concerning the safety of nuclear waste repositories include the modelling of radionuclide transport in biosphere and the evaluation of the doses to the most affected humans. In this paper, a scenario, in which a contaminated lake is the water source for drinking water, irrigation water and watering of livestock, is presented. The objective of the paper is to probabilistically study the influence of lake properties as parameters in the assessment scenario. The properties of the lake are a result of previously conducted probabilistic studies, where the land uplift of the terrain surrounding the repositories and the formation of water bodies were studied in a 10,000-year time span using Monte Carlo simulation. The lake is formed at 3000 years from present day and the changing properties of the lake have been used in the study. The studied radionuclides ³⁶Cl, ¹³⁵Cs, ¹²⁹I, ²³⁷Np, ⁹⁰Sr, ⁹⁹Tc and ²³⁸U enter the lake with a rate of 1 Bq/year. The transport process from the lake water to humans is described and the doses (dose conversion factors) to adult humans are evaluated based on a study on average food consumption. Sensitivity analysis is used for identifying the parameters having the most influence on the outcome of the dose. Based on the results from the sensitivity analysis, the volumetric outflow rate of the lake and the volume of the lake were taken into closer consideration. The results show the influence of probabilistically derived geomorphic lake input parameters on the dose.

Uncertainty analysis of displacement measurement with Imetrum Video Gauge

Imetrum Video Gauge is a commercial image-based two-dimensional displacement measurement system which has been widely used. Its uncertainty analysis is presented in this paper. First, the procedure to use Video Gauge is introduced. Then, based on the measurement model, two major sources of uncertainty are identified: (1) the uncertainty associated with the calibration procedure u(C) which is composed of the uncertainty of the known length used in calibration u(L) and the uncertainty of the projection of the known length in the image u(D) and (2) the uncertainty associated with the measurement system itself u(P). Following the Guide to the Expression of Uncertainty in Measurement, the uncertainties can be quantified. In the end, 60 experiments are performed to analyze the relationship between the measurement uncertainty and the working parameters, which are working distance, acquisition frequency and focal length of the lens. In order to ensure the validity of the calculation, two calculation methods are used. The main results of this paper are as follows: (1) the displacement measurement uncertainty increases along with working distance and decreases with the increase of focal length. At the same time, the results indicate that using a longer known length in calibration can also reduce the measurement uncertainty. (2) u(C) is greatly influenced by the known length used in the calibration procedure. It can be reduced when u(L) is reduced. (3) Under the laboratory circumstances, reducing u(C) can greatly reduce the total measurement uncertainty. (4) The displacement measurement uncertainty is more sensitive to the measurement uncertainty of the known length used in calibration than the projection of the known length in the image. (5) As the working distance grows, the sensitivity to the known length is getting weaker and the sensitivity to the projection of the known length in the image is getting stronger. (6) When a longer focal length lens is used, the influence of the working distance to the sensitivity gets weaker. The reported results can help people better understand the characteristics of this system and better use the system for their own purposes.

Predicted and measured concentrations of pharmaceuticals in hospital effluents. Examination of the strengths and weaknesses of the two approaches through the analysis of a case study

This study deals with the chemical characterization of hospital effluents in terms of the predicted and measured concentrations of 38 pharmaceuticals belonging to 11 different therapeutic classes. The paper outlines the strengths and weaknesses of the two approaches through an analysis of a case study referring to a large hospital. It highlights the observed (and expected) ranges of variability for the parameters of the adopted model, presents the results of an uncertainty analysis of direct measurements (due to sampling mode and frequency and chemical analysis) and a sensitivity analysis of predicted concentrations (based on the annual consumption of pharmaceuticals, their excretion rate and annual wastewater volume generated by the hospital). Measured concentrations refer to two sampling campaigns carried out in summer and winter in order to investigate seasonal variability of the selected compounds. Predicted concentrations are compared to measured ones in the three scenarios: summer, winter and the whole year. It was found that predicted and measured concentrations are in agreement for a limited number of compounds (namely atenolol, atorvastatin and hydrochlorothiazide), and for most compounds the adoption of the model leads to a large overestimation in all three periods. Uncertainties in predictions are mainly due to the wastewater volume and excretion factor, whereas for measured concentrations, uncertainties are mainly due to sampling mode.

Uncertainty analysis of daily potable water demand on the performance evaluation of rainwater harvesting systems in residential buildings

The objective of this paper is to perform a sensitivity analysis of design variables and an uncertainty analysis of daily potable water demand to evaluate the performance of rainwater harvesting systems in residential buildings. Eight cities in Brazil with different rainfall patterns were analysed. A numeric experiment was performed by means of computer simulation of rainwater harvesting. A sensitivity analysis was performed using variance-based indices for identifying the most important design parameters for rainwater harvesting systems when assessing the potential for potable water savings and underground tank capacity sizing. The uncertainty analysis was performed for different scenarios of potable water demand with stochastic variations in a normal distribution with different coefficients of variation throughout the simulated period. The results have shown that different design variables, such as potable water demand, number of occupants, rainwater demand, and roof area are important for obtaining the ideal underground tank capacity and estimating the potential for potable water savings. The stochastic variations on the potable water demand caused amplitudes of up to 4.8% on the potential for potable water savings and 9.4% on the ideal underground tank capacity. Average amplitudes were quite low for all cities. However, some combinations of parameters resulted in large amplitude of uncertainty and difference from uniform distribution for tank capacities and potential for potable water savings. Stochastic potable water demand generated low uncertainties in the performance evaluation of rainwater harvesting systems; therefore, uniform distribution could be used in computer simulation.

Systematic influences of gamma-ray spectrometry data near the decision threshold for radioactivity measurements in the environment

Several methods for reporting outcomes of gamma-ray spectrometric measurements of environmental samples for dose calculations are presented and discussed. The measurement outcomes can be reported as primary measurement results, primary measurement results modified according to the quantification limit, best estimates obtained by the Bayesian posterior (ISO 11929), best estimates obtained by the probability density distribution resembling shifting, and the procedure recommended by the European Commission (EC). The annual dose is calculated from the arithmetic average using any of these five procedures. It was shown that the primary measurement results modified according to the quantification limit could lead to an underestimation of the annual dose. On the other hand the best estimates lead to an overestimation of the annual dose. The annual doses calculated from the measurement outcomes obtained according to the EC's recommended procedure, which does not cope with the uncertainties, fluctuate between an under- and overestimation, depending on the frequency of the measurement results that are larger than the limit of detection. In the extreme case, when no measurement results above the detection limit occur, the average over primary measurement results modified according to the quantification limit underestimates the average over primary measurement results for about 80%. The average over best estimates calculated according the procedure resembling shifting overestimates the average over primary measurement results for 35%, the average obtained by the Bayesian posterior for 85% and the treatment according to the EC recommendation for 89%.

SWAT-CS(enm): Enhancing SWAT nitrate module for a Canadian Shield catchment

Nonpoint source modeling using hydrological models has been extensively studied at agriculture and urban watersheds; however, this has not been well addressed in forested ones where agricultural sources are comparatively minimal and nitrogen deposition exerts remarkable impacts on the nutrient cycles of a catchment. Thus it is critically important for hydrological models to incorporate the dynamics of nitrogen deposition and its transport processes, for reasonable nitrogen modeling. This is especially so for the Canadian Shield, which is characterized by a cold climate and special physiographic features. A revision of Soil and Water Assessment Tool for Canadian Shield (SWAT-CS) was proposed by Fu et al. (2014) to better characterize the hydrological features. In this study, more revisions were added to better simulate processes of nitrate by: 1) incorporating the dynamics of nitrogen deposition; and 2) allowing the deposition to distribute along with rapid-moving macropore flows. The newly revised model, SWAT-CS(enm) (SWAT-CS with an Enhanced Nitrate Module), and SWAT-CS were calibrated and tested with data of a subbasin of Harp Lake in south-central Ontario for 1990 to 2007. Modeling performance of nitrate flux rate in the stream for SWAT-CS(enm) was nearly acceptable with maximum daily Nash-Sutcliffe efficiencies (ENSs) for calibration and validation periods of 0.66 and 0.43, respectively; whereas the result of SWAT-CS was generally unsatisfied with maximum daily ENSs of 0.16 and 0.07, respectively. An uncertainty analysis using GLUE (generalized likelihood uncertainty estimation) showed a modest performance as about 50% of observations can be incorporated by the 95% prediction range deriving from the behavioral solutions (ENS≥0.5) for both daily and monthly simulations. It is concluded that the enhanced nitrate module improved the model performance of SWAT-CS on nitrate modeling, since the previous SWAT-CS failed to consider the effect of dynamics of nitrogen deposition and its sequential processes at the investigated site.

Uncertainty analysis of primary water pollutant control in China's pulp and paper industry

The total emission control target of water pollutants (e.g., COD and NH4-N) for a certain industrial sector can be predicted and analysed using the popular technology-based bottom-up modelling. However, this methodology has obvious uncertainty regarding the attainment of mitigation targets. The primary uncertainty comes from macro-production, pollutant reduction roadmap, and technical parameters. This research takes the paper and pulp industry in China as an example, and builds 5 mitigation scenarios via different combinations of raw material structure, scale structure, procedure mitigation technology, and end-of-pipe treatment technology. Using the methodology of uncertainty analysis via Monte Carlo, random sampling was conducted over a hundred thousand times. According to key parameters, sensitive parameters that impact total emission control targets such as industrial output, technique structure, cleaner production technology, and end-of-pipe treatment technology are discussed in this article. It appears that scenario uncertainty has a larger influence on COD emission than NH4-N, hence it is recommended that a looser total emission control target for COD is necessary to increase its feasibility and availability while maintaining the status quo of NH4-N. Consequently, from uncertainty analysis, this research recognizes the sensitive products, techniques, and technologies affecting industrial water pollution.

Sensitivity and uncertainty analysis for Abreu & Johnson numerical vapor intrusion model

This study conducted one-at-a-time (OAT) sensitivity and uncertainty analysis for a numerical vapor intrusion model for nine input parameters, including soil porosity, soil moisture, soil air permeability, aerobic biodegradation rate, building depressurization, crack width, floor thickness, building volume, and indoor air exchange rate. Simulations were performed for three soil types (clay, silt, and sand), two source depths (3 and 8m), and two source concentrations (1 and 400 g/m(3)). Model sensitivity and uncertainty for shallow and high-concentration vapor sources (3m and 400 g/m(3)) are much smaller than for deep and low-concentration sources (8m and 1g/m(3)). For high-concentration sources, soil air permeability, indoor air exchange rate, and building depressurization (for high permeable soil like sand) are key contributors to model output uncertainty. For low-concentration sources, soil porosity, soil moisture, aerobic biodegradation rate and soil gas permeability are key contributors to model output uncertainty. Another important finding is that impacts of aerobic biodegradation on vapor intrusion potential of petroleum hydrocarbons are negligible when vapor source concentration is high, because of insufficient oxygen supply that limits aerobic biodegradation activities.

Practical identifiability and uncertainty analysis of the one-dimensional hindered-compression continuous settling model

The practical application of the one-dimension hindered-compression settling models remains a challenge, since the model calibration strongly depends on experimental observations with limited information. In this study, the identifiability of parameter subsets of the hindered-compression models is evaluated for various experimental layouts. Global sensitivity analysis is used to preliminarily select the influential parameters which can be reasonably estimated, while the identifiability analysis of parameter subsets is conducted based on the local sensitivity functions and collinearity measures. The batch settling curve observations are informative for calibrating hindered parameters, and to determine the compression parameters, the concentration profile observations may need to be collected. For different experimental layouts, at least three parameters are identifiable, and the number of identifiable parameters can potentially increase to five, if both batch settling curve and concentration observations are available. The parameter subset identifiability is sensitive to the choice of initial parameter values, and determining the initial values of hindered parameters and gel concentration by measuring the hindered settling velocities and the top concentration of the static sediment respectively allows efficient reduction of the sensitivity. Parameter subset estimates are sensitive to the values of fixed parameters, and reliable estimation of identifiable parameter subsets is possible to significantly decrease model prediction uncertainties.

Environmental risk assessment of acid rock drainage under uncertainty: The probability bounds and PHREEQC approach

Acid rock drainage (ARD) is a major environmental problem that poses significant environmental risks during and after mining activities. A new methodology for environmental risk assessment based on probability bounds and a geochemical speciation model (PHREEQC) is presented. The methodology provides conservative and non-conservative ways of estimating risk of heavy metals posed to selected endpoints probabilistically, while propagating data and parameter uncertainties throughout the risk assessment steps. The methodology is demonstrated at a minesite located in British Columbia, Canada. The result of the methodology for the case study minesite shows the fate-and-transport of heavy metals is well simulated in the mine environment. In addition, the results of risk characterization for the case study show that there is risk due to transport of heavy metals into the environment.