Comprehensive assessment of water environmental carrying capacity for sustainable watershed development

Due to insufficient understanding of human-water interaction, many water-related problems arise in watersheds, posing severe threats to the sustainability of watershed development. Although water environmental carrying capacity (WECC) is a powerful tool to support sustainable development of watersheds, few studies considered aquatic ecological factors and uncertainty in indicator values, leading to losses of sample information in the evaluation of WECC. This paper developed a systematic framework for comprehensive WECC assessment that included the indicator system and a novel variable fuzzy pattern recognition (VFPR) approach. The WECC index system incorporated aquatic ecological factors, and addressed uncertainties associated with the indicator values. The proposed VFPR-based assessment model could realize successive evaluation to retain more original information of the sample and distinguish similar result values by treating the sample as having a continuous degree of membership instead of the traditional point form. In addition, it could be more adaptable to various circumstances including extreme cases, and closely reflect the impacts of indicator changes on the results. The established evaluation framework has been applied to Dongjiang River Basin in Guangdong Province. The spatial differences and main influencing factors of WECC in the study area were analyzed. Results show that 50% and 16.7% of the sub-regions in the study area would be subject to a poor level of WECC under pessimistic and optimistic circumstances, respectively. WECCs in the upper and lower reaches are the best and worst, respectively, which is in line with the levels of economic development in the Dongjiang River Basin. The proposed method can also be applicable to many other problems involving numerous indicators.

Resource recovery in life cycle assessment of sludge treatment: Contribution, sensitivity, and uncertainty

This study focused on the resource recovery of sludge treatment by quantifying the environmental contributions, identifying the influential factors, and comparing different scenarios. Life cycle assessment (LCA) of sewage sludge treatment was carried out to estimate the environmental impacts of six scenarios: (1) co-digestion of sludge and food waste; (2) co-gasification of sludge and woody waste; (3) co-incineration of sludge and used oil; (4) landfilling; (5) incineration; and (6) anaerobic digestion combined with incineration. Results demonstrate that the resource recovery had a substantial contribution to the environmental performance of the sludge treatment, while the degree of contribution was largely affected by various treatment scenarios and diverse impact categories. To gain deep insight into the parameters related to resource recovery, sensitivity analysis was performed to investigate the influence of the parameters on the LCA results, including the organic content, conversion efficiency of organic matter to methane, and other energy conversion efficiencies. After integrating the inventory variation of those parameters into the decision process via the Monte Carlo simulation, results indicate that no obviously superior scenario could be identified. Conversely, when parameter uncertainty was not considered, co-gasification of sludge and woody waste exhibited the most preferable environmental performance. Overall, this study demonstrates that considering the parameter uncertainty of resource recovery will contribute to a more transparent evaluation process, but will inevitably increase the complexity of the decision-making process based on LCA results because it is difficult to determine a sludge treatment scenario that decisively outperforms the others.

Uncertainty and sensitivity analysis of spatial distributed roughness to a hydrodynamic water quality model: a case study on Lake Taihu, China

Roughness is an important parameter in hydrodynamic and water quality modelling; it has direct effects on bottom shear stress which relied on sediment and vegetation. The varied roughness caused by spatial heterogeneity of sediment and vegetation may lead to uncertain simulation results. To investigate the effect of roughness uncertainty on the performance of hydrodynamic water quality models, a typical large shallow lake in China (Lake Taihu) was divided into eight areas for illustrating the effect of spatial variation of roughness on hydrodynamics and water quality. Total nitrogen (TN) was selected as the variable to calculate the uncertainty interval, and sensitive positions greatly affected by roughness as well as the appropriate range of roughness were explored by means of regional sensitive analysis (RSA). The results showed that roughness had the most significant effect on the bottom velocity. The uncertainty for water quality caused by roughness presented a striking spatial difference; the uncertainty interval for TN could be up to 1.3 mg/L. The posterior distribution of roughness was given to further narrowed the range of roughness, and the updated roughness range manifested that roughness value should be set higher in the area with thick sediment and abundant vegetation. It is of utmost importance to consider the comprehensive effects of sediment and vegetation in the determination of roughness. For certain lake areas with great water quality simulation error, the error could be effectively reduced by setting spatial distributed roughness. The optimization scheme was provided for the reasonable determination of roughness, so that the dynamic characteristic at the sediment-water interface could be represented synthetically. In this paper, the uncertainty and sensitivity of roughness in hydrodynamic water quality model are analyzed to provide reference for parameter setting of large shallow water lake model. For large scale lakes, parameters need to be modified according to the actual condition due to the spatial difference of friction coefficient at the bottom.

Exploring a variance decomposition approach integrated with the Monte Carlo method to evaluate groundwater fluoride exposure on the residents of a typical fluorosis endemic semi-arid tract of India

This study appraised the groundwater fluoride (F^-) endemicity and the exposure levels under the Central Tendency Exposure (CTE) condition and the Reasonable Maximum Exposure (RME) condition on the residents of the semi-arid parts of the Birbhum district of Peninsular India using a Variance Decomposition (Sobol Sensitivity Indices) approach combined with Monte Carlo Simulations. The study finds the national scale drinking water standard limit for F^- (1.5 mg L^-1) is inappropriate for the present survey area where F^- concentration in groundwater varied between 0.26 and 11.82 mg L^-1 and ~54.5% of the samples (N = 400) exceeded this limit. Therefore, estimated the optimum F^- concentration of 0.733 mg L^-1 for the region using the method recommended by the World Health Organization (WHO) to calculate the optimum F^- limit at a regional scale. The average value of F^- concentrations for this region (1.71 mg L^-1) is considerably higher than the estimated optimum concentration or even the maximum permissible limits recommended for the subtropical regions (0.5-0.7 mg L^-1). The exposure analysis revealed the infants and children as potentially vulnerable populations compared to adolescents and adults of the study area for CTE and RME scenarios. The multi-exposure pathways indicated oral intake as the main exposure pathway whereas exposure through dermal contact was insignificant for the residents of all age groups of this region. Based on the first, second and total order Sobol Sensitivity Indices, F^- concentration (C) in groundwater, the groundwater ingestion rate and their combined interaction are the greatest significant parameters for the oral exposure model whereas C and its interaction effects with the proportion of the skin surface area in contact with groundwater as the utmost sensitive variables for the dermal health risks assessment model. The present study insists the inhabitants to intake defluoridated groundwater.

Human health risk assessment using Monte Carlo simulations for groundwater with uranium in southern India

Uranium naturally occurs in groundwater and its concentration is mostly controlled by the geology of an area. The regular human consumption of groundwater with uranium causes health effects and hence the assessment of radiological and chemical toxicity effects on humans is essential. Hence, the present study was carried out to assess the general hydrochemistry of groundwater in different geological formations of southern India and its relation to uranium as well as to estimate the health risks posed to humans due to consumption of groundwater with uranium using both deterministic and probabilistic approaches. Four river basins representing the major geological formations of southern India were chosen for this study, from where a total of 141 groundwater samples were collected in the year 2016 and analyzed for the concentration of major ions and uranium. The groundwater occurring in granites had high concentration of uranium followed by gneiss and charnockites. Radiological risks to humans were higher in granitic terrain of Bhima basin, where about 1 in 10,000 may get affected due to cancer. The chemical toxicity risks were higher for the people in granite and gneissic terrain of Bhima basin followed by the people in charnockite terrain of Vaniyar basin. The deterministic method has overestimated the actual risk in comparison to the probabilistic risk assessment. The sensitivity analysis indicates that increase of exposure frequency and ingestion rates increases the chemical risks, whereas decrease of body weight increases the chemical risk. Therefore, the probabilistic approach is much superior to deterministic method since it exhibits variability in the values. The current study highlights the risks to humans by consuming groundwater with uranium, emphasizing on the urgent need for supplying treated water to the community.

Examining the carbon footprint of rice production and consumption in Hubei, China: A life cycle assessment and uncertainty analysis approach

This study aimed to quantify greenhouse gas emissions derived from the production-consumption of rice in Hubei-a major rice-producing province in central China. This research employed primary and secondary data collection methods. Primary data sources included interviews and experimental observations from seven counties in Hubei collected from June 2016 to December 2016. Secondary data sources-including national datasets, inter-governmental reports, and peer-reviewed articles-were used to extract relevant data, such as emission factors, and national and provincial rice output. Life Cycle Assessment was employed to build a comprehensive inventory and map of the rice carbon footprint, including the following five stages: production inputs, farm management, growth period, processing and sale, and consumption. Uncertainty analysis was performed to validate the reliability of carbon footprint estimations. Results showed that the carbon footprint for every 1 ton of polished rice in Hubei ranged between 4.19-6.81 t CO₂e/t and was 5.39 t CO₂e/t on average. Greenhouse gas emissions were primarily produced from rice fields during the growth stage (over 60% of greenhouse gas emissions of the whole life cycle of rice), followed by the consumption stage, and the production and transportation of agricultural inputs. Uncertainty analysis estimations indicated acceptable levels of reliability. This study's results indicate that the production and consumption of rice is a significant contributor to agricultural carbon emissions in Hubei-consistent with national estimates that place China as the largest carbon dioxide emitter globally. This research provides further insight into future policies and targeted initiatives for the efficient use of low-carbon agricultural inputs for rice production and consumption stages in China.

Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR

The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS.

A Human-Bovine Schistosomiasis Mathematical Model with Treatment and Mollusciciding

To mitigate the spread of schistosomiasis, a deterministic human-bovine mathematical model of its transmission dynamics accounting for contaminated water reservoirs, including treatment of bovines and humans and mollusciciding is formulated and theoretically analyzed. The disease-free equilibrium is locally and globally asymptotically stable whenever the basic reproduction number [Formula: see text], while global stability of the endemic equilibrium is investigated by constructing a suitable Lyapunov function. To support the analytical results, parameter values from published literature are used for numerical simulations and where applicable, uncertainty analysis on the non-dimensional system parameters is performed using the Latin Hypercube Sampling and Partial Rank Correlation Coefficient techniques. Sensitivity analysis to determine the relative importance of model parameters to disease transmission shows that the environment-related parameters namely, [Formula: see text] (snails shedding rate of cercariae), [Formula: see text] (probability that cercariae shed by snails survive), c (fraction of the contaminated environment sprayed by molluscicides) and [Formula: see text] (mortality rate of cercariae) are the most significant to mitigate the spread of schistosomiasis. Mollusciciding, which directly impacts the contaminated environment as a single control strategy is more effective compared to treatment. However, concurrently applying mollusciciding and treatment will yield a better outcome.

Practical kidney dosimetry in peptide receptor radionuclide therapy using [177Lu]Lu-DOTATOC and [177Lu]Lu-DOTATATE with focus on uncertainty estimates

Background

Kidney dosimetry after peptide receptor radionuclide therapy using ¹⁷⁷Lu-labelled somatostatin analogues is a procedure with multiple steps. We present the SPECT/CT-based implementation at Aarhus University Hospital and evaluate the uncertainty of the various steps in order to estimate the total uncertainty and to identify the major sources of uncertainty. Absorbed dose data from 115 treatment fractions are reported.

Results

The total absorbed dose with uncertainty is presented for 59 treatments with [¹⁷⁷Lu]Lu-DOTATOC and 56 treatments with [¹⁷⁷Lu]Lu-DOTATATE. For [¹⁷⁷Lu]Lu-DOTATOC the mean and median specific absorbed dose (dose per injected activity) is 0.37 Gy/GBq and 0.38 Gy/GBq, respectively, while for [¹⁷⁷Lu]Lu-DOTATATE the median and mean are 0.47 Gy/GBq and 0.46 Gy/GBq, respectively. The uncertainty of the procedure is estimated to be about 13% for a single treatment fraction, where the absorbed dose calculation is based on three SPECT/CT scans 1, 4 and 7 days post-injection, while it increases to about 19% if only a single SPECT/CT scan is performed 1 day post-injection.

Conclusions

The specific absorbed dose values obtained with the described procedure are comparable to those from other treatment sites for both [¹⁷⁷Lu]Lu-DOTATOC and [¹⁷⁷Lu]Lu-DOTATATE, but towards the lower end of the range of reported values. The estimated uncertainty is also comparable to that from other reports and judged acceptable for clinical and research use, thus proving the kidney dosimetry procedure a useful tool. The greatest reduction in uncertainty can be obtained by improved activity determination, partial volume correction and additional SPECT/CT scans.

A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products

Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products - face cream containing 0.1% coumarin and deodorant containing 1% coumarin - demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.

Uncertainty of nitrogen budget in China

The accuracy of the nitrogen (N) budget is of great importance for evidence-based decision-making to address both food security and environmental protection challenges. This study attempts to advance understanding of uncertainties in China's N budget using the Coupled Human And Natural Systems (CHANS) model and Monte Carlo simulation from 1980 to 2018. Results show that the spatial and temporal variations in agricultural and industrial activities and insufficient knowledge on N cycling parameterization are the two dominant causes of uncertainties in the N budget in China. Uncertainties of N inputs generally are <10%, while they are <30% for N outputs and >30% for N accumulations. Uncertainty of nitrogen oxides emission is more sensitive to energy consumption due to the large contributions from industry and transportation. While the uncertainty of ammonia emission is predominantly affected by agricultural activity. Combining surface measurements, satellite observations, and atmospheric simulation models enables cross-check of N fluxes in multiple systems and reduces uncertainties of N budget.

Spatial prediction of soil depth using environmental covariates by quantile regression forest model

Prediction of soil depth for larger areas provides primary information on soil depth and its spatial distribution that becomes vital for land resource management, crop, nutrient, and ecosystem modeling. The present study assessed the spatial distribution of soil depth over 160,205 km² of Andhra Pradesh, India, using 20 covariables by quantile regression forest (QRF). An aggregate of 2854 soil datasets compiled from various physiographic units were randomly partitioned into 80:20 ratio for calibration (2283 samples) and validation (571 samples). Landsat imagery, terrain datasets (8), and bioclimatic factors (11) were utilized as covariates. The QRF model outputs signified that precipitation, multi-resolution index of valley bottom flatness (MrVBF), mean diurnal range, isothermality, and elevation were the most important variables influencing soil depth variability across the landscape. Spatial prediction of soil depth by QRF model yielded a ME of - 1.81 cm, RMSE of 34 cm, PICP of 90.2, and a R² value of 42% as compared to ordinary kriging which results in a ME of - 0.14 cm, a RMSE of 37 cm, and a R² value of 32%. As soil depth is spatially dynamic and has significant correlation with terrain and environmental covariates, better prediction was possible by the QRF model. However, high-density bioclimatic variables could be utilized along with high-resolution terrain variables to improve the predictive accuracy.

Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion-A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model

Over the past few decades, the La Paz aquifer system in Baja California Sur, Mexico, has been under severe pressure due to overexploitation for urban water supply and agriculture; this has caused seawater intrusion and deterioration in groundwater quality. Previous studies on the La Paz aquifer have focused mainly on seawater intrusion, resulting in limited information on nitrate and sulfate pollution. Therefore, pollution sources have not yet been identified sufficiently. In this study, an approach combining hydrochemical tools, multi-isotopes (δ²H_H2O, δ¹⁸O_H2O, δ¹⁵N_NO3, δ¹⁸O_NO3, δ³⁴S_SO4, δ¹⁸O_SO4), and a Bayesian isotope mixing model was used to estimate the contribution of different nitrate and sulfate sources to groundwater. Results from the MixSIAR model revealed that seawater intrusion and soil-derived sulfates were the predominant sources of groundwater sulfate, with contributions of ~43.0% (UI₉₀ = 0.29) and ~42.0% (UI₉₀ = 0.38), respectively. Similarly, soil organic nitrogen (~81.5%, UI₉₀ = 0.41) and urban sewage (~12.1%, UI₉₀ = 0.25) were the primary contributors of nitrate pollution in groundwater. The dominant biogeochemical transformation for NO₃^- was nitrification. Denitrification and sulfate reduction were discarded due to the aerobic conditions in the study area. These results indicate that dual-isotope sulfate analysis combined with MixSIAR models is a powerful tool for estimating the contributions of sulfate sources (including seawater-derived sulfate) in the groundwater of coastal aquifer systems affected by seawater intrusion.

Near-road air quality modelling that incorporates input variability and model uncertainty

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM_2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM_2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM_2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM_2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM_2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM_2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM_2.5 concentrations at twelve out of the eighteen locations.

A high-resolution typical pollution source emission inventory and pollution source changes during the COVID-19 lockdown in a megacity, China

To control the spread of COVID-19, China has imposed national lockdown policies to restrict the movement of its population since the Chinese New Year of January 2020. In this study, we quantitatively analyzed the changes of pollution sources in Shanghai during the COVID-19 lockdown; a high-resolution emission inventory of typical pollution sources including stationary source, mobile source, and oil and gas storage and transportation source was established based on pollution source data from January to February 2020. The results show that the total emissions of sulfur dioxide (SO₂), nitrogen oxides (NO_x), particulate matter (PM), and volatile organic compounds (VOCs) were 9520.2, 37,978.6, 2796.7, and 7236.9 tons, respectively, during the study period. Affected by the COVID-19 lockdown, the mobile source experienced the largest decline. The car mileage and oil sales decreased by about 80% during the COVID-19 lockdown (P3) when compared with those during the pre-Spring Festival (P1). The number of aircraft activity decreased by approximately 50%. The impact of the COVID-19 epidemic on industries such as iron and steel and petrochemicals was less significant, while the greater impact was on coatings, chemicals, rubber, and plastic. The emissions of SO₂, NO_x, PM_2.5, and VOCs decreased by 11%, 39%, 37%, and 47%, respectively, during P3 when compared with those during P1. The results show that the measures to control the spread of the COVID-19 epidemic made a significant contribution to emission reductions. This study may provide a reference for other countries to assess the impact of the COVID-19 epidemic on emissions and help establish regulatory actions to improve air quality.

Systematic and statistical uncertainties of the hilbert-transform based high-precision FID frequency extraction method

Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is one of the methods that have been used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described, and it is briefly compared with other commonly used frequency extraction methods. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the χ² used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum χ²-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed.

Using a deep convolutional network to predict the longitudinal dispersion coefficient

Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D_x) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of D_x predictions, this paper proposes the use of a Deep Convolutional Network (DCN), a sub-field of machine learning. The proposed deep neural network architecture consists of two parts; first, a one-dimensional convolutional neural network (CNN) to build informative feature maps, and second, a stack of deep, fully connected layers to estimate pollution dispersion (as D_x) in streams. To accurately predict D_x the developed model draws upon a large and diverse array of datasets in the form of three dimensionless parameters: Width/Depth (W/H), Velocity/Shear Velocity (U/u*), and Longitudinal Dispersion Coefficient/(Depth * Shear Velocity) (D_x /Hu*). The model's accuracy is compared to that of several empirical models using a number of statistical measures. In addition, the DCN model results are compared with artificial neural network (ANN) and support vector machine (SVM) models implemented in this research and also similar studies applying various machine learning models (ML) towards D_x prediction. The statistical evaluation indicates that the DCN model outperforms the tested empirical, ANN, SVM and ML models with a significant difference. Additionally, five-fold cross-validation is performed to analyze the sensitivity and dependency of the DCN model's results on dataset selection, which shows that the dataset selection process does not significantly affect the model's accuracy. Since both ML and empirical models are, in general, poor predictors of the upper and lower ranges of D_x values, the DCN model's predictions of D_x in six different extreme-value ranges are assessed. The DCN model shows excellent accuracy in estimating D_x over the full possible range of data. In comparison with the empirical and ML models mentioned above, the DCN model more accurately predicts D_x values from river geometry and hydraulic datasets, with low errors across all ranges of D_x. The most significant advantage of DCN is that it tries to learn high-level features from data in an incremental manner.

An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during multiple water pollutant modeling

An automatic calibration framework of water quality parameters for surface runoff during modeling with InfoWorks ICM was constructed. The framework is based on a genetic algorithm (GA) and fully considers the calibration sequence for multiple water pollutants, namely, total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP). Meanwhile, four different objective functions including the Nash-Sutcliff efficiency coefficient (NSE), coefficient of determination (R²), percentage error in the peak (PEP), and percentage bias (PBIAS) were selected as fitness evaluators for the GA. The framework was applied successfully to a specific area of Fuzhou in China, and the multi-objective results were compared with the single-objective results. The comprehensive indexes of TSS, COD, TN, and TP by multi-objective calibration were lower than that of the single-objective calibration in both scenarios. Compared with single-objective calibration, the iterations to reach the optimal value were shortened 9, 5, 13, and 15 iterations by multi-objective calibration. Therefore, the findings showed that the multi-objective function GA was more balanced and more efficient than the single-objective function GA. Then, the uncertainty of the model was evaluated by using the samples generated by automatic calibration, which provided a reliable basis for the subsequent application of the model. This framework can be applied to other programs through adjustments of the number and weight of objective functions according to the specific situation, which will make the modeling more efficient and accurate.

Spatial distribution of heavy metal contamination and uncertainty-based human health risk in the aquatic environment using multivariate statistical method

Heavy metal contamination in the aquatic environment is one of the most serious health issues worldwide. In this study, an evaluation framework is developed to identify the sources and health risk of heavy metals (i.e., As, Hg, Cr, Cu, Zn, Pb, and Cd) contamination in the North Canal of Fengtai District, China, which is based on multiple approaches, including multivariate statistical method, health risk assessment, and uncertainty analysis. Spatial distribution of these heavy metals could exhibit their impact on the aquatic environment. Pearson's correlation analysis shows that a majority of the correlations between different heavy metals are not significant due to the differences in sources of heavy metals. Principal component analysis indicates that there are four principal components to explain 91.381% of the total variance. Moreover, health risk reveals that hazard quotient values are in low levels, ranging from 0.48 to 0.74, relative higher quotient levels could be observed in the northern section. The carcinogenic risk of Cd has exceeded the acceptable level in S1, S3, and S7. Sensitivity analysis ensures the reliability of health risk assessments. Furthermore, some specific recommendations are given to help decision-makers develop more comprehensive strategies for improving water environment quality.

Temporal variations of levels and sources of health risk associated with heavy metals in road dust in Beijing from May 2016 to April 2018

To analyze the temporal variations of heavy metals, health risk, and source-specific health risk, 24 road dust samples were collected from Beijing in each month in two years. The temporal variations of Hg, Pb, and Ni were higher than other heavy metals. Most heavy metals reached their highest concentrations either in winter or in spring, then the concentrations decreased and reached the lowest values in autumn. Human health risk assessment (HHRA) model showed that As, Cr, and Ni might pose cautionary carcinogenic risk (CR) to children (CR > 10^-6). CR for adults were only 0.15 to 0.19 times of that for children. Four sources were identified based on positive matrix factorization model and HHRA model, they were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Influenced by the difference of carcinogenicity of heavy metals, traffic exhaust contributed the largest to heavy metals (36.02%, over 42.24% higher than other sources), while contributions of fuel combustion to CR (36.95%) was similar to traffic exhaust (37.17%). Monte-Carlo simulation showed that the 95th percentile of probability density functions of CR posed by Cr and Ni from each source were 9.90 × 10^-5 to 2.64 × 10^-4, posing cautionary carcinogenic risk to children. The seasonal change of CR varied among different sources. CR from use of pesticides and fertilizers in spring was 35.06 times of that in winter, and that from fuel combustion in winter was 1.15-2.40 times of that in other seasons. CR from each source was sensitive to ingestion rate and skin adherence factor.

Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China

Due to the rapid pace of urbanization in China, there has been a significant increase in construction work, which has resulted in the generation of more waste. Reducing the waste at source is the most efficient way to reduce its negative impacts, and prefabrication is a construction method that does exactly that. Since prefabricated construction generates less waste compared to conventional cast-in-situ construction, it is being promoted by the Chinese government. This study investigates the benefits of prefabrication and quantifies the percentage of construction waste reduction through its application in China. It does so by using a 26-storey concrete-brick residential building as a case study, and by conducting uncertainty analysis with Oracle Crystal Ball simulation software to assess the reduction of waste when using prefabricated components in place of cast-in-situ elements. Simulation results demonstrated that the waste generation rate for in-situ timber formwork and masonry work was 10.52 and 4.77 kg/m² respectively, and that the use of prefabricated components reduced those figures by 36.04% and 25.53% respectively. This study quantifies the benefits of prefabrication as a method for reducing the generation of construction waste in China. Not only would extensive use of prefabrication decrease the cost related to construction waste management in China, but it could also mitigate the environmental and social impacts of construction waste globally.

Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model

Accuracy analysis of a process-based model is important for evaluating the reliability of model estimates of crop growth. Uncertainties in projections of crop growth may derive from different sources in modelling. The parameter-induced uncertainty is one of the important aspects. Here we calibrated the parameters for rice, wheat and maize combined with observed data of aboveground biomass (AGB) and leaf area index (LAI) at 16 Chinese Ecosystem Research Network (CERN) sites under different rotation systems and subsequently validated the model at these sites using the data independent of calibration. The results showed that the simulated AGB and LAI exhibited good agreement with the observations. The model performance for rice and maize was better than that for wheat. The statistical analysis of model performance showed that the RMSE (root mean square error), RMD (relative mean deviation) and EF (model efficiency) were 32.52%, - 0.95% and 0.87 of the means, respectively. The three components of the modelling uncertainty, bias of mean (U_M), bias of slope (U_R) and random residue (U_E) accounted 0.1%, 0.9% and 99% of the total errors, respectively. The main contributor to the error was the random disturbances, indicating that the parameters calibration in this study had reached relatively reasonable conditions on the whole. Although the model displayed an overall good prediction in crops AGBs and LAI, there were still notable bias at some sites due to non-random errors (U_M and U_R). This indicated that there were still uncertainties in the modelling procedure, e.g. the model mechanism or parameterization. The uncertainty of the simulated results may greatly restrict the application of a model. To effectively and reasonably apply a model, it is necessary to evaluate and analyse the main sources of uncertainty in the simulated results. The parameter-induced uncertainty analysis in this study showed that, at the site scale, the range of uncertainty brought by the changes in three parameters (SLA, PL and α) to the modelling results (95% CI) of Agro-C covered more than 90% of the observations and brought approximately 21% uncertainty to the simulated AGBs of the three major crops.

An integral approach to address socio-ecological systems sustainability and their uncertainties

The analysis of the sustainability should be addressed with a holistic approach that facilitates an integral analysis of the social, economic, institutional and environmental factors and their interactions characterizing complex socio-ecological systems (SES). Nevertheless, despite the increasing acknowledgment about the need for such systemic approaches, their application in real SES are less frequent than desirable. Among the difficulties behind this, the need for a new conceptual perspective concerning the relationships between science and the management of real SES, as well as the lack of tools to manage the inherent complexity of such systems should be emphasized. In this work, we further discuss these difficulties and propose an integral methodological framework for the assessment of SES sustainability, with the following key components: i) The hierarchical definition of sustainability goals and indicators. ii) A dynamic system model taking into account the key socio-economic and environmental factors and their interactions, in which the most representative indicators and their sustainability thresholds are integrated. iii) The analysis of vulnerabilities to exogenous drivers (scenario analysis) and the exploration of available management and planning options (policy assessment). iv) An uncertainty assessment concerning system behavior and model outcomes to guide decisions for an improved sustainability in complex SES. The whole framework highlights the need to integrate a participative approach, above all at the initial and final steps. In this work, these components are exemplified by means of their application to a real socio-ecological system: Fuerteventura island (The Canary Islands, Spain).

Robust Likelihood-Based Approach for Automated Optimization and Uncertainty Analysis of Toxicokinetic-Toxicodynamic Models

Toxicokinetic-toxicodynamic (TKTD) models offer a mechanistic understanding of individual-level toxicity over time and allow for meaningful extrapolations from laboratory tests to exposure conditions in the field. Thereby, they hold great potential for ecotoxicological studies, both in a regulatory context as well as for basic research. In contrast to mechanistic effect models at higher levels of biological organization, TKTD models can be, and generally are, parameterized by fitting them to data (results from toxicity tests). Fitting models comes with a range of statistical and numerical challenges, which may hamper the application of TKTD models in a practical setting. Especially in the context of environmental risk assessment, there is a need for robust and user-friendly software tools to automatically extract the best-fitting model parameters and quantify their uncertainty from any data set. The study presents a general outline for TKTD model analysis, rooted in likelihood-based ("frequentist") inference. The general outline is followed by a presentation of the specific algorithm that has been implemented into software for the robust and automated analysis of toxicity data for survival. However, the presented approach is more broadly applicable to low-dimensional problems. Integr Environ Assess Manag 2021;17:388-397. © 2020 SETAC.

COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling

Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.