Intra- and inter-operator variability in MRI-based manual segmentation of HCC lesions and its impact on dosimetry

PURPOSE

The aim was to quantify inter- and intra-observer variability in manually delineated hepatocellular carcinoma (HCC) lesion contours and the resulting impact on radioembolization (RE) dosimetry.

METHODS

Ten patients with HCC lesions treated with Y-90 RE and imaged with post-therapy Y-90 PET/CT were selected for retrospective analysis. Three radiologists contoured 20 lesions manually on baseline multiphase contrast-enhanced MRIs, and two of the radiologists re-contoured at two additional sessions. Contours were transferred to co-registered PET/CT-based Y-90 dose maps. Volume-dependent recovery coefficients were applied for partial volume correction (PVC) when reporting mean absorbed dose. To understand how uncertainty varies with tumor size, we fit power models regressing relative uncertainty in volume and in mean absorbed dose on contour volume. Finally, we determined effects of segmentation uncertainty on tumor control probability (TCP), as calculated using logistic models developed in a previous RE study.

RESULTS

The average lesion volume ranged from 1.8 to 194.5 mL, and the mean absorbed dose ranged from 23.4 to 1629.0 Gy. The mean inter-observer Dice coefficient for lesion contours was significantly less than the mean intra-observer Dice coefficient (0.79 vs. 0.85, p < 0.001). Uncertainty in segmented volume, as measured by the Coefficient of Variation (CV), ranged from 4.2 to 34.7% with an average of 17.2%. The CV in mean absorbed dose had an average value of 5.4% (range 1.2-13.1%) without PVC while it was 15.1% (range 1.5-55.2%) with PVC. Using the fitted models for uncertainty as a function of volume on our prior data, the mean change in TCP due to segmentation uncertainty alone was estimated as 16.2% (maximum 48.5%).

CONCLUSIONS

Though we find relatively high inter- and intra-observer reliability overall, uncertainty in tumor contouring propagates into non-negligible uncertainty in dose metrics and outcome prediction for individual cases that should be considered in dosimetry-guided treatment.

Uncertainty and sensitivity analysis of radionuclide migration through fractured granite aquifer

The radionuclide migration in the high-level radioactive waste (HLW) disposal is usually predicted by numerical simulations for risk analysis of radionuclide contamination in a large scale of time and space. However, the uncertainties in radionuclide migration models and their associated parameters significantly affect the simulation results. In the present study, we first selected certain parameters and output data as independent parameters and risk metrics and performed a series of radionuclide transport models at a research site in Northwestern China. The models considered radionuclide migration in the equivalent porous medium with the mechanism of nuclide decay in an arbitrary-length decay chain, adsorption, advection, diffusion, and dispersion. Then 3000 Monte Carlo (MC) simulations were performed to carry out a set of uncertainty and global sensitivity analysis by coupling an uncertainty quantification tool with a radionuclide migration simulator. The results indicated that both hydraulic gradient and hydraulic conductivity significantly influenced the risk metrics. Thus, it is critical to obtain hydraulic gradient and hydraulic conductivity data under the same economic conditions. We applied the multivariate adaptive regression spline (MARS) method to generate response surface models representing the relationships among independent parameters and risk metrics. Calculations of the risk metric distribution ranges revealed that the peak release doses would appear at 0.40 and 0.79 million years, and their values will be in the range of 4.7 × 10-7-1.93 × 10-6 Sv/a. Uncertainty and sensitivity analysis results of radionuclide contamination in the fractured granite upon which HLW is disposed can improve simulation and prediction accuracy for radionuclide migration.

Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty

Activated sludge models are widely used to simulate, optimize and control performance of wastewater treatment plants (WWTP). For simulation of nutrient removal and energy consumption, kinetic parameters would need to be estimated, which requires an extensive measurement campaign. In this study, a novel methodology is proposed for modeling the performance and energy consumption of a biological nutrient removal activated sludge system under sensitivity and uncertainty. The actual data from the wastewater treatment plant in Slupsk (northern Poland) were used for the analysis. Global sensitivity analysis methods accounting for interactions between kinetic parameters were compared with the local sensitivity approach. An extensive procedure for estimation of kinetic parameters allowed to reduce the computational effort in the uncertainty analysis and improve the reliability of the computational results. Due to high costs of measurement campaigns for model calibration, a modification of the Generalized Likelihood Uncertainty method was applied considering the location of measurement points. The inclusion of nutrient measurements in the aerobic compartment in the uncertainty analysis resulted in percentages of ammonium, nitrate, ortho-phosphate measurements of 81%, 90%, 78%, respectively, in the 95% confidence interval. The additional inclusion of measurements in the anaerobic compartment resulted in an increase in the percentage of ortho-phosphate measurements in the aerobic compartment by 5% in the confidence interval. The developed procedure reduces computational and measurement efforts, while maintaining a high compatibility of the observed data and model predictions. This enables to implement activated sludge models also for the facilities with a limited availability of data.

Rainfall prediction using multiple inclusive models and large climate indices

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables.

A high-precision prediction for spatiotemporal distribution and risk assessment of antibiotics in an urban watershed using a hydrodynamic model

A methodology for the high-precision prediction and risk assessment of antibiotics at the watershed scale was established. Antibiotic emission inventory and attenuation processes were integrated into the MIKE 11 model to predict the spatiotemporal distribution of norfloxacin, ofloxacin, enrofloxacin, erythromycin, roxithromycin, and sulfamethoxazole in the Nanfei River watershed, China. Considering the variations in antibiotic removal in sewage treatment plants, manure composting, and lagoon systems, the high, medium, and low removal efficiencies of selected antibiotics across China were obtained and used as the best, expected, and worst scenarios, respectively, to evaluate the uncertainty of antibiotic emissions. The predicted concentrations were comparable to antibiotic measurements after flow calibration. The prediction results showed that the highest concentration exposures were mainly concentrated in urban areas with a dense population. Flow variations controlled the temporal distribution characteristics of antibiotics via the dilution effect, and the concentrations of antibiotics in the dry season were 3.1 times higher than those in the wet season. The median concentrations of norfloxacin and erythromycin ranged from 111.36 ng/L to 592.33 ng/L and 106.63 ng/L to 563.01 ng/L, respectively, which both posed a high risk to cyanobacteria and a medium risk to spreading antibiotic resistance. Scenario analysis further demonstrated that high removal efficiencies of these antibiotics can considerably reduce the potential ecotoxicity risks and bacterial resistance selection. The developed methodology for predicting the distribution and risk of antibiotics was suitable for the risk assessment and control strategy of human- and livestock-sourced pollutants.

Uncertainty evaluation and performance assessment of water quality model for mariculture management

Ecosystem based water quality models are important tools for prognostic site assessment and evaluation of ecosystem-performance of marine fish farms. We present the development and application of a comprehensive Fish Culture Zone Water Quality Model using continuous bi-weekly field data over a six-year period (2012-2017). The model simulates five interacting subsystems: phytoplankton, phosphorus and nitrogen cycles, and the dissolved oxygen (DO) and particulate organic carbon balance. The application of the model to two fish culture zones in Hong Kong shows the model captures the trends of nutrient and DO variation and the performance in quantitative prediction of algal biomass is challenging. The effect of errors in the specification of primary model inputs are evaluated using dimensionless sensitivity coefficients based on First Order Variance Analysis reveals the relative importance of fish stock (loading), physical size (volume), tidal flushing rate and boundary conditions in the prediction of key water quality variables.

Spatio-temporal variation of net anthropogenic nitrogen inputs (NANI) from 1991 to 2019 and its impacts analysis from parameters in Northwest China

At present, excessive nutrient inputs caused by human activities have resulted in environmental problems such as agricultural non-point source pollution and water eutrophication. The Net Anthropogenic Nitrogen Inputs (NANI) model can be used to estimate the nitrogen (N) inputs to a region that are related to human activities. To explore the net nitrogen input of human activities in the main grain-producing areas of Northwestern China, the county-level statistical data for the Ningxia province and NANI model parameters were collected, the spatio-temporal distribution characteristics of NANI were analyzed and the uncertainty and sensitivity of the parameters for each component of NANI were quantitatively studied. The results showed that: (1) The average value of NANI in Ningxia from 1991 to 2019 was 7752 kg N km^-2 yr^-1. Over the study period, the inputs first showed an overall increase, followed by a decrease, and then tended to stabilize. Fertilizer N application was the main contributing factor, accounting for 55.6%. The high value of NANI in Ningxia was mainly concentrated in the Yellow River Diversion Irrigation Area. (2) The 95% confidence interval of NANI obtained by the Monte Carlo approach was compared with the results from common parameters in existing literature. The simulation results varied from -6.4% to 27.4% under the influence of the changing parameters. Net food and animal feed imports were the most uncertain input components affected by parameters, the variation range was -20.7%-77%. (3) The parameters of inputs that accounted for higher proportions of the NANI were more sensitive than the inputs with lower contributions. The sensitivity indexes of the parameters contained in the fertilizer N applications were higher than those of net food and animal feed imports and agricultural N-fixation. This study quantified the uncertainty and sensitivity of parameters in the process of NANI simulation and provides a reference for global peers in the application and selection of parameters to obtain more accurate simulation results.

Prognostication of scour around twin and three piers using efficient outlier robust extreme learning machine

One of the most essential difficulties in the design and management of bridge piers is the estimation and modeling of scouring around the piers. The scour depth downstream of twin and three piers were simulated using a new outlier robust extreme learning machine (ORELM) model in this study. Furthermore, k-fold cross-validation with k = 4 was employed to validate the outcomes of numerical models. Four ORELM models with effective scouring parameters were first created to simulate scour depth. After then, the number of hidden layer neurons increased from two to thirty. The number of ideal hidden neurons was determined by examining the modeling results. The sigmoid activation function was also introduced as the best function. Furthermore, a sensitivity analysis was used to identify the superior model. The best model predicted scour depth as a function of the Froude number (Fr), the pier diameter to flow depth ratio (D/h), and the distance between the piers to flow depth ratio (d/h). The values of the objective function were accurately approximated by this model. As a result, using the ORELM model, the R², scatter index, and Nash-Sutcliffe efficiency coefficient were calculated to be 0.953, 0.146, and 0.949, respectively. The most efficient parameters for simulating the scour depth were Fr and D/h, according to the modeling results. It is worth noting that nearly half of the superior model's simulated outputs had an inaccuracy of less than 10%. The superior model's performance has been underestimated, according to uncertainty analysis. After that, a simple and practical equation for calculating the scour depth was established for the superior model. Additionally, the influence of each input parameter on the objective function was assessed using a partial derivative sensitivity analysis.

Nitrate pollution source apportionment, uncertainty and sensitivity analysis across a rural-urban river network based on δ15N/δ18O-NO3- isotopes and SIAR modeling

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ¹⁵N/δ¹⁸O-NO₃^- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ¹⁵N/δ¹⁸O-NO₃^-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD, <0.1-7.9%) was a minor source. Uncertainties associated with NF (UI₉₀ = 0.32) and SN (UI₉₀ = 0.30) were high, whereas those associated with MS (UI₉₀ = 0.14) were moderate and AD low (UI₉₀ = 0.0087). A sensitivity analysis was performed for the SIAR modeling and indicated that the isotopic composition of the predominant source (i.e., MS in this study) had the strongest effect on the overall riverine nitrate source apportionment results.

20% of uncertainty in yield estimates could be caused by the radiation source

Solar radiation is the energy for all biological, physical, and chemical processes of the earth's surface system, and affects the growth and development of crops at all stages. But the diverse data sources and fusion algorithms lead to large differences in the radiation values in various climate datasets. Accurate estimates of the radiation data is not an easy task, the uncertainty of which and the impact on crop yield simulation remains unknown. In this study, the total solar radiation amounts from four independent global radiation datasets were shown considerable heterogeneity across regions and cropping seasons. Forcing the dynamic crop models with the four radiation inputs produced similarly great uncertainties of simulated yield in most regions, with the greatest uncertainty up to 30% of average yield for wheat in Europe. The global-scale uncertainty of simulated yield is increasing during the past three decades and would reach up to 20% of its averages in the future, equivalent to 300 million tons when converting to the global crop production. The results of this study suggest that the previously projected crop yield changes with climate change have large uncertainties propagated from solar radiation data sources used for projections. These uncertainties may mislead the assessment of future food security.

Decision making in next generation risk assessment for skin allergy: Using historical clinical experience to benchmark risk

Our aim is to develop and apply next generation approaches to skin allergy risk assessment that do not require new animal test data and better quantify uncertainties. Quantitative risk assessment for skin sensitisation uses safety assessment factors to extrapolate from the point-of-departure to an acceptable human exposure level. It is currently unclear whether these safety assessment factors are appropriate when using non-animal test data to derive a point-of departure. Our skin allergy risk assessment model Defined Approach uses Bayesian statistics to infer a human-relevant metric of sensitiser potency with explicit quantification of uncertainty, using any combination of human repeat insult patch test, local lymph node assay, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we describe the incorporation of benchmark exposures pertaining to use of consumer products with clinical data supporting a high/low risk categorisation for skin sensitisation. Margins-of-exposure (potency estimate to consumer exposure level ratio) are regressed against the benchmark risk classifications, enabling derivation of a risk metric defined as the probability that an exposure is low risk. This approach circumvents the use of safety assessment factors and provides a simple and transparent mechanism whereby clinical experience can directly feed-back into risk assessment decisions.

Multi-criteria coastal environmental vulnerability assessment using analytic hierarchy process based uncertainty analysis integrated into GIS

Changes in environmental conditions influence vulnerability due to interacting stresses and pressures across the nations and regions. Coastal resources are under severe stress due to climate change, growing trade and commerce, and the human population depends on them. The coastal vulnerability to changing climatic variables has created a major concern at regional, national and global scales. The present model study assessed the coastal vulnerability of the densely populated districts in South India, which are prone to extreme climatic events at a higher frequency. The seven crucial influencing variables that have been selected for the study were sea-level rise, coastal elevation, coastal slope, extreme rainy days, historical shoreline change, tidal range, and geomorphology. The identified variables were ranked by relative importance and linked by weightage using analytical hierarchy process-based uncertainty analysis. Mapped and reclassified variables have been integrated to derive the overall vulnerability using geospatial techniques. The study shows that the coast has experienced high vulnerability to SLR impact, extreme rainfall, geomorphology, and elevation; medium vulnerability to the shoreline change and least vulnerable to coastal slope and tidal range. Of the coastal regions studied, 29% and 14.3% had high vulnerability; 70.5% and 85.7% had medium vulnerability in the two selected densely populated districts (Kancheepuram and Tiruvallur District). Applying geospatial techniques to assess the environmental vulnerability resulted in reliable and informative maps which will serve as a model to determine the critical coastal regions to plan for the conservation and adaptation measures.

The influence of white matter lesions on the electric field in transcranial electric stimulation

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Sensitivity analysis allows the simulation of tDCS with uncertain conductivities.

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White matter lesions (WML) have no global influence on the electric field in tDCS.

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In subjects with a high lesion load, a local influence can be observed.

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In low to medium lesion load subjects, explicit modeling of WML is not required.

Background

Transcranial direct current stimulation (tDCS) is a promising tool to enhance therapeutic efforts, for instance, after a stroke. The achieved stimulation effects exhibit high inter-subject variability, primarily driven by perturbations of the induced electric field (EF). Differences are further elevated in the aging brain due to anatomical changes such as atrophy or lesions. Informing tDCS protocols by computer-based, individualized EF simulations is a suggested measure to mitigate this variability.

Objective

While brain anatomy in general and specifically atrophy as well as stroke lesions are deemed influential on the EF in simulation studies, the influence of the uncertainty in the change of the electrical properties of the white matter due to white matter lesions (WMLs) has not been quantified yet.

Methods

A group simulation study with 88 subjects assigned into four groups of increasing lesion load was conducted. Due to the lack of information about the electrical conductivity of WMLs, an uncertainty analysis was employed to quantify the variability in the simulation when choosing an arbitrary conductivity value for the lesioned tissue.

Results

The contribution of WMLs to the EF variance was on average only one tenth to one thousandth of the contribution of the other modeled tissues. While the contribution of the WMLs significantly increased (p≪.01) in subjects exhibiting a high lesion load compared to low lesion load subjects, typically by a factor of 10 and above, the total variance of the EF didnot change with the lesion load.

Conclusion

Our results suggest that WMLs do not perturb the EF globally and can thus be omitted when modeling subjects with low to medium lesion load. However, for high lesion load subjects, the omission of WMLs may yield less robust local EF estimations in the vicinity of the lesioned tissue. Our results contribute to the efforts of accurate modeling of tDCS for treatment planning.

Modeling the migration of chemicals from food contact materials to food: The MERLIN-expo/VERMEER toolbox

Evaluating the migration of chemicals from food contact materials (FCM) into food is a key step in the safety assessment of such materials. In this paper, a simple mechanistic model describing the migration of chemicals from FCM to food was combined with quantitative property-property relationships (QPPRs) for the prediction of diffusion coefficients and FCM-Food partition coefficients. The aim of the present study was to evaluate the performance of these operational models in the prediction of a chemical's concentration in food in contact with a plastic monolayer FCM. A comparison to experimental migration values reported in literature was conducted. Deterministic simulations showed a good match between predicted and experimental values. The tested models can be used to provide insights in the amount and the type of toxicological data that are needed for the safety evaluation of the FCM substance. Uncertainty in QPPRs used for describing the processes of both diffusion in FCM and partition at the FCM-Food interface was included in the analysis. Combining uncertainty in QPPR predictions, it was shown that the third quartile (75th percentile) derived from probabilistic calculations can be used as a conservative value in the prediction of chemical concentration in food, with reasonable safety factors.

Multi-scenario simulation of ecological risk assessment based on ecosystem service values in the Beijing-Tianjin-Hebei region

In this study, a framework for ecological risk assessment based on ecosystem service values and risk probability was established. Remote sensing was used to estimate the value of ecosystem services at the regional scale. Considering the natural and anthropogenic factors and using the entropy weight method to assign weights, probability index was constructed. In addition, multiple scenarios based on the ordered weighted averaging (OWA) method were simulated to reduce subjective uncertainty in the assessment. The results showed that the ecosystem service values generated by the gas regulation value accounted for the largest proportion, with a ratio of 46% in the Beijing-Tianjin-Hebei region. From 2005 to 2015, the value of ecosystem services decreased, falling by 2.5 × 10⁷ Yuan. The level of ecological risk was relatively high, with a corresponding area ratio of 32.89%. Spatially, the areas with high risk were concentrated in the southeastern areas, and areas with relatively low risk were distributed in the western and northern areas. This high risk was probably caused by urbanization which was characterized by reduction of farmland and increase in impervious surface. Multi-scenario simulation showed that the areas of unstable ecological risk zones covered 30% and were mainly concentrated in the surroundings of developing cities. In areas of unstable risk distribution, the relationship between development and protection should be considered. This framework increases the reliability and practicability of ecological risk assessment results and has potential application value for regional risk control in the context of urbanization.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R² are 0.940 and 0.976) than the published NN-LFER model (R² are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C_e, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Industrial-environmental management in China's iron and steel industry under multiple objectives and uncertainties

Industrial-environmental management is a multi-objective optimization problem plagued with multiple uncertainties. Most studies only optimize few objectives and often neglect these uncertainties. This study builds a 6-objective optimization problem to quantify energy conservation and emission reduction (ECER) potentials in China's iron and steel industry. First, uncertainties are simulated through 100,000-time random sampling, NSGA-II and the mean-effective objective mechanism are applied to calculate optimal solutions. Finally, a global sensitivity analysis is performed to classify uncertainty parameters based on their impacts on objectives' performance. Results show: (1) There exist significant discrepancies between objectives' performance under certainty and uncertainty. For example, the deterministic CO₂ intensity is 1148 kg/t, which is 11.93% lower than its value under uncertainty. Therefore, neglecting uncertainty increases the risk of noncompliance with policy targets as they might be too strict; (2) Two critical uncertainty parameters (steel ratios and technology penetration rates) have the most severe impacts on objectives' performance, hence, reducing their fluctuation can minimize uncertainties when estimating ECER potentials; (3) By-product recycling and energy efficiency measures have good performance in all objectives, thus, should be prioritized. Moreover, from 77-strategies assessed, 11 are identified as key-strategies due to their large ECER effects, hence, should be strongly promoted.

City-wide model-based analysis of heat recovery from wastewater using an uncertainty-based approach

Around 90% of the energy requirement for urban water systems management is for heating domestic tap water. In addition, the energy content of wastewater is mainly in the form of heat (85%). Hence, there is an obvious interest in recovering a large portion of this heat. However, city-wide scenario analyses that evaluate heat recovery at various locations while considering impacts on wastewater treatment plant (WWTP) performance are currently very limited. This study presents a comprehensive model-based city-wide evaluation considering four different heat recovery locations (appliance, household, precinct and WWTP effluent) for a Swedish city with varying degrees of implementation using an uncertainty-based approach. Results show that heat recovery at the appliance level, with heat exchangers installed at 77% of the showers at domestic households, leads to a mean energy recovery of 127 MWh/day with a 0.25 °C reduction in mean WWTP inlet temperature compared to the default case without heat recovery. The highest mean temperature reduction compared to the default case is 1.5 °C when heat is recovered at the precinct level for 77% of the domestic wastewater flow rate. Finally, the impact on WWTP nitrification capacity is negligible in this case due to its large existing capacity and design.

Establishment of watershed ecological water requirements framework: A case study of the Lower Yellow River, China

It is of great practical significance to ensure ecological water requirements (EWRs) for the maintenance of river health and the sustainable development of human socioeconomics. How to scientifically determine the comprehensive EWRs and estimate the uncertainty of hydro-ecological tools performed in the process of conducting remains one of the most important yet most complicated issues. In this study, the ecological water requirements framework (EWRsF) of the Lower Yellow River (LYR), which considers instream ecological base flow, survival and reproduction of indicator fish species, equilibrium of erosion and siltation and ecological function of the estuary, was constructed by integrating hydrological, hydraulic and ecological habitat methods. The framework contains three crucial components - determination of instream EWRs and estuarine EWRs, uncertainty analysis of hydro-ecological tools. For instream ecological base flow, we proposed an improved Tennant method, which took into account both seasonality and sediment transport characteristics of the LYR, and could better reflect the actual hydrological regime. For the hydrological ecological response relationship of indicator fish species, we estimated the uncertainty of the model output of River2D to improve its credibility of the simulation results. The results demonstrated that: 1) Two-grade intra-annual monthly EWRs process of suitable and minimum for four instream sections and estuary area were obtained. The flood season (June-October) is the period with the largest proportion of intra-annual instream EWRs, whereas in estuary area, is the spawning period (April-July) of dominant species. 2) The uncertainty of HSI curves directly leads to the uncertainty of model output. Although the shape and position of the WUA curve can be uncertain, it does not affect the judgment of EWRs threshold. 3) The research results can provide scientific basis for water resource management decision-making in the LYR. Additionally, the ideas also have reference significance for similar basins.

Optimal design of two-dimensional water trading based on risk aversion for sustainable development of Daguhe watershed, China

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.

Identifying the source and transformation of riverine nitrates in a karst watershed, North China: Comprehensive use of major ions, multiple isotopes and a Bayesian model

Nitrate (NO₃^-) contamination of surface water is a globally concern, especially in karstic regions affected by intensive agricultural activities. This study combines hydrochemistry, and environmental isotopes (δ²H_H2O, δ¹⁸O_H2O, δ¹⁵N_NO3, and δ¹⁸O_NO3) with a Bayesian isotope mixing model (Simmr) to reduce the uncertainty in estimating the contributions of different pollution sources. Samples were collected from 32 surface water sites in the Yufu River (YFR) watershed, North China, in September and December 2019. The results revealed that NO₃^--N was the predominant form of inorganic nitrogen that caused the deterioration of water quality in the watershed, accounting for approximately 58% of the total nitrogen (TN). The hydrochemical compositions and nitrate isotopes indicated that NO₃^- mainly originated from soil nitrogen (SN), ammonium fertilizer (AF), but nitrate fertilizer (NF), manure and sewage (M&S) and atmospheric precipitation (AP) were limited. The isotopic composition of nitrate in the upper reaches of the watershed was mainly affected by microbial nitrification, while the mixture of multiple sources was the dominant nitrogen transformation process in the mid-lower reaches of the watershed. Simmr model outputs revealed that SN (56.5%) and AF (29.5%) were the primary contributor to riverine NO₃^- pollution, followed by NF (7.1%), MS (3.6%), and AP (3.4%) sources. Moreover, an uncertainty index (UI₉₀) of the isotope mixing showed that SN (0.73) and AF (0.67) had the highest values, followed by NF (0.22), M&S (0.22) and AP (0.10). Chemical fertilizer and SN collectively contributed >50% of nitrate during the two sampling campaigns. These results indicated that reducing the application of nitrogen fertilizers and rational irrigation are the keys to alleviate of NO₃^- pollution. The study is helpful in understanding the source and transformation of riverine NO₃^- and effectively reducing NO₃^- pollution in karst agricultural rivers or watersheds.

Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Assessing the externalities of a waste management system via life cycle costing: The case study of the Emilia-Romagna Region (Italy)

Effective and efficient urban waste management systems (WMSs) are a cornerstone for a sustainable society. Life cycle costing (LCC) provides a useful framework for the joint analysis of economic and environmental impacts of a WMS, by considering both financial and external costs. The present study applies the methodology of societal LCC to the WMS of the Italian region of Emilia-Romagna to provide a case study on how the available information on waste flows and budget costs of a real WMS can be used to obtain an estimate of the total cost of waste management, including externalities. The results evidence that the main source of negative externality in the analyzed WMS is the transportation of waste, with only a minor role of external burdens due to incinerators and landfills. However, the positive externality resulting from recycling more than compensates those impacts, leading to a net external benefit associated to the WMS. The contribution of both uncertain unit external costs and environmental benefits imputable to recycled materials to the overall uncertainty of the result is systematically investigated by parametric uncertainty analysis. The most critical parameters in determining the sensitivity of the result are the monetary values attributed to primary energy consumption and CO2 emissions, together with assumptions on energy savings related to recycling. Eventually, it is shown how the developed LCC model can be used as decision-support tool to preliminarily investigate the implications of alternative management options on the financial and external costs of the WMS.

Global sensitivity and uncertainty analysis of a microalgae model for wastewater treatment

The results of a global sensitivity and uncertainty analysis of a microalgae model applied to a Membrane Photobioreactor (MPBR) pilot plant were assessed. The main goals of this study were: (I) to identify the sensitivity factors of the model through the Morris screening method, i.e. the most influential factors; (II) to calibrate the influential factors online or offline; and (III) to assess the model's uncertainty. Four experimental periods were evaluated, which encompassed a wide range of environmental and operational conditions. Eleven influential factors (e.g. maximum specific growth rate, light intensity and maximum temperature) were identified in the model from a set of 34 kinetic parameters (input factors). These influential factors were preferably calibrated offline and alternatively online. Offline/online calibration provided a unique set of model factor values that were used to match the model results with experimental data for the four experimental periods. A dynamic optimization of these influential factors was conducted, resulting in an enhanced set of values for each period. Model uncertainty was assessed using the uncertainty bands and three uncertainty indices: p-factor, r-factor and ARIL. Uncertainty was dependent on both the number of influential factors identified in each period and the model output analyzed (i.e. biomass, ammonium and phosphate concentration). The uncertainty results revealed a need to apply offline calibration methods to improve model performance.