Comprehensive assessment of exposure and environmental risk of potentially toxic elements in surface water and sediment across China: A synthesis study

China faces a serious challenge with water pollution posed by potentially toxic elements (PTEs). Comprehensive and reliable environmental risk assessment is paramount for precise pollution prevention and control. Previous studies generally focused on a single environmental compartment within small regions, and the uncertainty in risk calculation is not fully considered. This study revealed the current exposure status of 11 PTEs in surface water and sediment across China using previously reported concentration data in 301 well-screened articles. Ecological and human health risks were evaluated and the uncertainty related to calculation parameters and exposure dataset were quantified. PTEs of high concern were further identified. Results showed Mn and Zn had the highest concentration levels, while Hg and Cd had the lowest concentrations in both surface water and sediment. Risk assessment of individual PTE showed that high-risk PTEs varied by risk receptors and environmental compartments. Nationwide, the probability of aquatic organisms being affected by Mn, Zn, Cu, and As in surface water exceeded 10 %. In sediment, Cd and Hg exhibited high and considerable risk, respectively. As was identified as the major PTE threatening human health as its carcinogenic risk was 1.45 × 10-4 through direct ingestion. Combined risk assessment showed the PTE mixture in surface water and sediment posed medium and high ecological risk with the risk quotient and potential ecological risk index of 1.76 and 558.36, respectively. Adverse health effects through incidental ingestion and dermal contact during swimming were negligible. This study provides a nationwide risk assessment of PTEs in China's aquatic environment and the robustness is verified, which can serve as a practical basis for policymakers to guide the early warning and precise management of water pollution.

Quantification of NOx sources contribution to ambient nitrate aerosol, uncertainty analysis and sensitivity analysis in a megacity

Dual isotopes of nitrogen and oxygen of NO3- are crucial tools for quantifying the formation pathways and precursor NOx sources contributing to atmospheric nitrate. However, further research is needed to reduce the uncertainty associated with NOx proportional contributions. The acquisition of nitrogen isotopic composition from NOx emission sources lacks regulation, and its impact on the accuracy of contribution results remains unexplored. This study identifies key influencing factors of source isotopic composition through statistical methods, based on a detailed summary of δ15N-NOx values from various sources. NOx emission sources are classified considering these factors, and representative means, standard deviations, and 95 % confidence intervals are determined using the bootstrap method. During the sampling period in Tianjin in 2022, the proportional nitrate formation pathways varied between sites. For suburban and coastal sites, the ranking was [Formula: see text] (NO2 + OH radical) > [Formula: see text] (N2O5 + H2O) > [Formula: see text] (NO3 + DMS/HC), while the rural site exhibited similar fractional contributions from all three formation pathways. Fossil fuel NOx sources consistently contributed more than non-fossil NOx sources in each season among three sites. The uncertainties in proportional contributions varied among different sources, with coal combustion and biogenic soil emission showing lower uncertainties, suggesting more stable proportional contributions than other sources. The sensitivity analysis clearly identifies that the isotopic composition of 15N-enriched and 15N-reduced sources significantly influences source contribution results, emphasizing the importance of accurately characterizing the localized and time-efficient nitrogen isotopic composition of NOx emission sources. In conclusion, this research sheds light on the importance of addressing uncertainties in NOx proportional contributions and emphasizes the need for further exploration of nitrogen isotopic composition from NOx emission sources for accurate atmospheric nitrate studies.

Uncertainty analysis of greenhouse gas emissions of monorail transit during the construction

This paper examines the uncertainty of greenhouse gas (GHG) emissions during monorail construction. Firstly, a deterministic analysis is conducted. Subsequently, the obtained data are evaluated using the data quality indicator (DQI), and a Markov chain Monte Carlo (MCMC) simulation method is employed to assume different parameter distributions. The results of the deterministic calculation indicate that the calculated emissions per unit area of the station amount to 1.97 ton CO2e/m2, while the calculated emissions per unit section length reach 7.55 ton CO2e/m2. To simulate parameter distribution, we utilize a Beta distribution with good shape applicability. Furthermore, we establish scenarios involving system boundary reduction, low-emission factors, and reduced material and energy inputs in order to analyze scenario uncertainties. Regarding model uncertainty, this paper assumes that the material and energy quantity data conform to the normal, log-normal, uniform, and triangular distributions, respectively, subsequently analyzing the uncertainty distributions. This paper analyzes the GHG emission uncertainty evaluation of 16 monorail stations and sections during the construction period, which is divided into parameter, scenario, and model uncertainty. We provide a concrete framework for studying uncertainties related to GHG emissions at stations and sections during the monorail construction period. The scenario analysis results will help to make decisions about the choice of parameters, system boundaries, and other settings. It provides new guidance for emission reduction policies, such as reducing the use of steel-related products or using alternative environmentally friendly materials, considering emission reduction factors more comprehensively and setting emission reduction factors according to uniform distribution principle as far as possible.

High spatiotemporal resolution ammonia emission inventory from typical industrial and agricultural province of China from 2000 to 2020

As a typical industrial and agricultural province, Shandong is one of China's most seriously air-polluted regions. One comprehensive ammonia emission inventory with a high spatial resolution (1 km × 1 km) for 136 county-level administrative divisions in Shandong from 2000 to 2020 is developed based on county-level activity data with the corrected and updated emission factors of seventy-seven subcategories. Annual ammonia emissions decrease from 1003.3 Gg in 2000 to 795.9 Gg in 2020, with an annual decrease rate of 1.2 %. Therein, the ammonia emissions associated with livestock and farmland ecosystems in 2020 account for 50.8 % and 32.9 % of the provincial total ammonia emission, respectively. Laying hen and wheat are the livestock and crop with the highest ammonia emissions, accounting for 23.3 % and 36.3 % of ammonia emissions from livestock and the application of synthetic fertilizers, respectively. Furthermore, waste treatment, humans and vehicles are the top three ammonia emission sources in urban areas, accounting for 5.0 %, 4.7 % and 1.3 % of total ammonia emissions, respectively. The spatial distribution of grids with high ammonia emissions is consistent with the distribution of intensive farms. Significant emission intensity areas mainly concentrate in western Shandong (e.g., Caoxian of Heze, Qihe of Dezhou, Yanggu of Liaocheng, Liangshan of Jining) due to the large area of arable land and the high levels of agricultural activity. Overall, prominent seasonal variability characteristics of ammonia emission are observed. Ammonia emissions tend to be high in summer and low in winter, and the August to January-emission ratio is 5.6. The high temperature and fertilization for maize are primarily responsible for Shandong's increase in ammonia emissions in summer. Finally, the validity of the estimates is further evaluated using uncertainty analysis and comparison with previous studies. This study can provide information to determine preferentially effective PM2.5 control strategies.

Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil

Given the global prevalence of soil heavy metal contamination, knowledge concerning of soil environmental quality assessment, pollution area identification and source apportionment is critical for implementation of soil pollution prevention and safe utilization strategies. In this study, soil static environmental capacity (QI) for heavy metals was selected to evaluate pollution risks in agricultural soils of Wenzhou, southeast China. Combined with geostatistical methods, the pollution area was identified along with uncertainty analysis. Potential sources were quantitatively apportioned using a positive matrix factorization model (PMF). Results showed that agricultural soils in this study were mainly contaminated by Cd and Pb based on both Nemerow and QI indices. The environmental capacity assessment found more than 90% areas were identified as polluted soils for Qi-Zn, Qi-Cd and Qi-Pb, with minor uncertain areas. Cu was identified as having a high proportion of uncertain pollution area status, which was similar to the results of the integrated environmental capacity for all metals. PMF results indicated that industrial discharge, agrochemicals and parent material accounted for 32.1%, 32.2% and 35.7% of heavy metal accumulation in soils, respectively. Implementation of strict policies to reduce anthropogenic source emissions and remediate soil pollution are crucial to minimize metal pollution inputs, improve agricultural soil quality and enhance food safety.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Poisson rectangular pulse (PRP) model establishment based on uncertainty analysis of urban residential water consumption patterns

The commonly used Poisson rectangular pulse (PRP) model, employed for simulating high-resolution residential water consumption patterns (RWCPs), relies on calibration via medium-resolution RWCPs obtained from practical measurements. This introduces inevitable uncertainty stemming from the measured RWCPs, which consequently impacts the precision of model simulations. Here we enhance the accuracy of the PRP model by addressing the uncertainty of RWCPs. We established a critical sampling size of 2000 household water consumption patterns (HWCPs) with a data logging interval (DLI) of 15 min to attain dependable RWCPs. Through Genetic Algorithm calibration, the optimal values of the PRP model's parameters were determined: pulse frequency λ = 91 d-1, mean of pulse intensity E(I) = 0.346 m3 h-1, standard deviation of pulse intensity STD(I) = 0.292 m3 h-1, mean of pulse duration E(D) = 40 s, and standard deviation of pulse duration STD(D) = 55 s. Furthermore, validation was conducted at both HWCP and RWCP levels. We recommend a sampling size of ≥2000 HWCPs and a DLI of ≤30 min for PRP model calibration to balance simulation precision and practical implementation. This study significantly advances the theoretical foundation and real-world application of the PRP model, enhancing its role in urban water supply system management.

Analysis of Uncertainty and Sensitivity in Tailings Dam Breach-Runout Numerical Modelling

Tailings dam breaches (TDBs) and subsequent flows can pose significant risk to public safety, the environment, and the economy. Numerical runout models are used to simulate potential tailings flows and understand their downstream impacts. Due to the complex nature of the breach-runout processes, the mobility and downstream impacts of these types of failures are highly uncertain. We applied the first-order second-moment (FOSM) methodology to a database of 11 back-analyzed historical tailings flows to evaluate uncertainties in TDB runout modelling and conducted a sensitivity analysis to identify key factors contributing to the variability of the HEC-RAS model output, including at different locations along the runout path. The results indicate that prioritizing resources toward advancements in estimating the values of primary contributors to the sensitivity of the selected model outputs is necessary for more reliable model results. We found that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. However, the primary contributors to the sensitivity of the model outputs varied depending on the case study; therefore, the selection of appropriate rheological models and consideration of site-specific conditions are crucial for accurate predictions. The study proposes and demonstrates the FOSM methodology as an approximate probabilistic approach to model-based tailings flow runout prediction, which can help improve the accuracy of risk assessments and emergency response plans.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10230-024-00970-w.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors

With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.

Mathematical optimization of waste management systems: Methodological review and perspectives for application

The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders.

Analysis of the uncertainty of the AIS-based bottom-up approach for estimating ship emissions

Although the AIS-based bottom-up approach has become the dominant method for estimating ship emissions, there are still inherent uncertainties due to the numerous complex factors involved. This paper aims to investigate the development process of the AIS-based bottom-up approach and identify the primary sources of uncertainty by conducting a systematic review of 29 articles published since 2015. The result shows three sources of uncertainty for estimating ship emissions, i.e., the acquisition and processing of AIS data, ship characteristic information and engine load calculation, and the determination of emission factors. This paper suggests that the accuracy of ship emission inventories can be improved by enhancing the reliability of datasets, uniformly defining engine load calculation formulas, and making more extensive measurements of local emissions to provide substantial support for ship emissions management and facilitate the development of more effective emission reduction strategies.

Human health risk assessment and uncertainty analysis of silver nanoparticles in water

Despite frequent detection in environmental waters, literature which quantifies the health risk of silver nanoparticles (Ag NPs) through oral ingestion is scarce. This study compiled literature data to find the removal of Ag NPs from different treatment schemes (i.e., natural, engineered, or hybrid). Ag NP concentrations were found either in surface water or in groundwater based on where the effluent of treatment schemes was discharged, i.e., either in surface water or in groundwater. Monte-Carlo simulation was carried out for probabilistic assessment of health risks for children for two hypothetical exposure scenarios: (a) ingesting river water while swimming and (b) drinking groundwater. Bio-accessible fraction, dietary metal adsorption factor, and concentrations of silver ions were incorporated to simulate realistic situations. Different treatment schemes were ranked for their nanoparticles' removal efficiency with respect to (i) exceedance probability from guideline value and (ii) health risk to children. Hybrid treatment combinations, i.e., conventional primary and secondary treatment units followed by nature-based units (constructed wetlands and soil aquifer treatment), were ranked the best. The health risk value was found to be less than 1, with the 99th percentile value less than 10-3 in all cases. The maximum allowable concentration of Ag NPs was found to be as low as 1.43 mg/L for groundwater, suggesting probable potential for risk. Uncertainty analysis revealed that the uncertainty of the influent NPs concentration in raw wastewater contributes > 99% to the variance of the hazard index. The results of this work indicate that the use of natural treatment technologies with existing engineered treatments provides higher nanoparticle removal from wastewater without the requirement of any tertiary treatment unit.

Environmental assessment of Rhodosporidium toruloides-1588 based oil production using wood hydrolysate and crude glycerol

Rhodosporidium toruloides, an oleaginous yeast, is a potential feedstock for biodiesel production due to its ability to utilize lignocellulosic biomass-derived hydrolysate with a considerably high lipid titer of 50-70 % w/w. Hence, for the first-time environmental assessment of large-scale R. toruloides-based biodiesel production from wood hydrolysate and crude glycerol was conducted. The global warming potential was observed to be 0.67 kg CO2 eq./MJ along with terrestrial ecotoxicity of 1.37 kg 1,4-DCB eq./MJ and fossil depletion of 0.13 kg oil eq./MJ. The highest impacts for global warming (∼45 %) and fossil depletion (∼37 %) are attributed to the use of chloroform for lipid extraction while fuel consumption for transportation contributed more than 50 % to terrestrial ecotoxicity. Further, sensitivity analysis revealed that maximizing biodiesel yield by increasing lipid yield and solid loading could contribute to reduced environmental impacts. In nutshell, this investigation reveals that environmental impact varies with the type of chemical utilized.

Source characteristics of polycyclic aromatic hydrocarbons and polychlorinated biphenyls in surface soils of Shenyang, China: A comparison of two receptor models combined with Monte Carlo simulation

The surface soil concentrations of 16 PAHs and 15 PCBs were simultaneously determined by gas chromatography-tandem mass spectrometry in 21 locations of urban areas of Shenyang. The average concentrations of PAHs and PCBs were 26.40 ± 34.68 mg/kg and 48.03 ± 27.47 μg/kg, respectively. Factor analysis with nonnegative constraints (FA-NNC) and absolute principal component score with multiple linear regression (APCS-MLR) model were used to explore and evaluate the sources of PAHs and PCBs in the study area. The results of FA-NNC showed that PAHs in soils were mainly from traffic emissions (49.64%), coal combustion (46.88%) and petrogenic source (3.49%). The PCBs in soils were mainly from commercial and high temperature combustion mixed sources (20.3%), combustion and industry emission mixed sources (21.1%), electrical equipment sources (22.2%) and traffic emission sources (36.4%). The results of APCS-MLR were consistent with those of FA-NNC. The uncertainty of FA-NNC and APCS-MLR model was analyzed by Monte Carlo simulation method. The results revealed the reliability of the two receptor models on source apportionment. The estimated carcinogenic risks indicated that the risks of PAHs in soils exceed the acceptable range (10-6-10-4), while the risks of PCBs were below the acceptable risk level of 10-6.

Bayesian model averaging by combining deep learning models to improve lake water level prediction

Water level (WL) is an essential indicator of lakes and sensitive to climate change. Fluctuations of lake WL may significantly affect water supply security and ecosystem stability. Accurate prediction of lake WL is, therefore, crucial for water resource management and eco-environmental protection. In this study, three deep learning (DL) models, including long short-term memory (LSTM), the gated recurrent unit (GRU), and the temporal convolutional network (TCN), were used to predict WLs at five stations of Poyang Lake for different forecast periods (1-day ahead, 3-day ahead, and 7-day ahead). The forecast results of the three DL models were synthesized through Bayesian model averaging (BMA) to improve prediction accuracy, and Monte Carlo sampling method was used to calculated the 90 % confidence intervals to analyze the model uncertainty. All the three DL models achieved satisfactory prediction accuracy. GRU performed best in most forecast scenarios, followed by TCN and LSTM. None of the models, however, consistently provided the optimal results in all forecast scenarios. Lake WL prediction accuracy of BMA had a further improvement in metrics of NSE and R2 in 80 % of the forecast scenarios and ranked at least top two in all forecast scenarios. The uncertainty analysis showed that the containing ration (CR) values were above 84 % while the relative bandwidth (RB) maintained reliable performance over the 7-day ahead prediction. The proposed framework in the present study can realize satisfactory WL forecast accuracy while avoiding complex comparison and selection of DL models, and it can also be easily applied to the prediction of other hydrological variables.

Investigating key drivers of N2O emissions in heterogeneous riparian sediments: Reactive transport modeling and statistical analysis

Nitrous oxide (N2O) is a potent greenhouse gas that also contributes to ozone depletion. Recent studies have identified river corridors as significant sources of N2O emissions. Surface water-groundwater (hyporheic) interactions along river corridors induce flow and reactive nitrogen transport through riparian sediments, thereby generating N2O. Despite the prevalence of these processes, the controlling influence of physical and geochemical parameters on N2O emissions from coupled aerobic and anaerobic reactive transport processes in heterogeneous riparian sediments is not yet fully understood. This study presents an integrated framework that combines a flow and multi-component reactive transport model (RTM) with an uncertainty quantification and sensitivity analysis tool to determine which physical and geochemical parameters have the greatest impact on N2O emissions from riparian sediments. The framework involves the development of thousands of RTMs, followed by global sensitivity and responsive surface analyses. Results indicate that characterizing the denitrification reaction rate constant and permeability of intermediate-permeability sediments (e.g., sandy gravel) are crucial in describing coupled nitrification-denitrification reactions and the magnitude of N2O emissions. This study provides valuable insights into the factors that influence N2O emissions from riparian sediments and can help in developing strategies to control N2O emissions from river corridors.

Establishing an emission inventory for ammonia, a key driver of haze formation in the southern North China plain during the COVID-19 pandemic

Despite the significant reduction in atmospheric pollutant levels during the COVID-19 lockdown, the presence of haze in the North China Plain remained a frequent occurrence owing to the enhanced formation of secondary inorganic aerosols under ammonia-rich conditions. Quantifying the increase or decrease in atmospheric ammonia (NH3) emissions is a key step in exploring the causes of the COVID-19 haze. Historic activity levels of anthropogenic NH3 emissions were collected through various yearbooks and studies, an anthropogenic NH3 emission inventory for Henan Province for 2020 was established, and the variations in NH3 emissions from different sources between COVID-19 and non-COVID-19 years were investigated. The validity of the NH3 emission inventory was further evaluated through comparison with previous studies and uncertainty analysis from Monte Carlo simulations. Results showed that the total NH3 emissions gradually increased from north-west to south-east, totalling 751.80 kt in 2020. Compared to the non-COVID-19 year of 2019, the total NH3 emissions were reduced by approximately 4 %, with traffic sources, waste disposal and biomass burning serving as the sources with the top three largest reductions, approximately 33 %, 9.97 % and 6.19 %, respectively. Emissions from humans and fuel combustion slightly increased. Meanwhile, livestock waste emissions decreased by only 3.72 %, and other agricultural emissions experienced insignificant change. Non-agricultural sources were more severely influenced by the COVID-19 lockdown than agricultural sources; nevertheless, agricultural activities contributed 84.35 % of the total NH3 emissions in 2020. These results show that haze treatment should be focused on reducing NH3, particularly controlling agricultural NH3 emissions.

Ecological risk evaluation of ammonia nitrogen pollution in China based on the ecological grey water footprint model

The biosafety criteria of ammonia nitrogen (NH3-N) exhibit uncertainties, posing challenges to the assessment of the hazard of social NH3-N load to aquatic ecosystem. To evaluate this ecological hazard in China, an ecological grey water footprint (E-GWF) model is designed based on the uncertainty analysis theory. In the E-GWF model, the acute toxicity is quantified via short-term E-GWF (E-GWFs) and acute risk (AR), while its chronic toxicity is quantified via long-term E-GWF (E-GWFl) and chronic risk (CR). Results show the following. (i) Compared with the conventional GWF, the E-GWF performs better in the uncertainty analysis of the biosafety threshold, and it is more effective in ecological risk evaluation and environment planning. (ii) The E-GWFs and E-GWFl of NH3-N in China are 309.4 and 2382.5 billion m3, respectively. Regions with large E-GWF are concentrated in the east and south, while regions with small E-GWF are concentrated in the north and west. (iii) The ecological risks of NH3-N in Shanghai City, Tianjin City, Ningxia Province, Hebei Province, Jiangsu Province, Shanxi Province, and Shandong Province belong to the "High" grade. The ecological risks of NH3-N in Tibet and Qinghai Province belong to the "Negligible" grade. (iv) The ecological risk of NH3-N in China is mostly determined by industrial and domestic pollution. (v) To control the risk within allowable grades, the social NH3-N pollution load of China should be decreased to 988.7 kilotons.

Performance of a gross pollutant trap-biofilter and sand filter treatment train for the removal of organic micropollutants from highway stormwater (field study)

This field study assessed the occurrence, event mean concentrations (EMCs), and removal of selected organic micro-pollutants (OMPs), namely, polycyclic aromatic hydrocarbons (PAHs), petroleum hydrocarbons (PHCs), nonylphenol (NP), 4-t-octylphenol (OP), and bisphenol A (BPA), in a gross pollutant trap (GPT)-biofilter/sand filter stormwater treatment train in Sundsvall, Sweden. The effects of design features of each treatment unit, including pre-sedimentation (GPT), sand filter medium, vegetation, and chalk amendment, were investigated by comparing the units' removal performances. Overall, the treatment train removed most OMPs from highway runoff effectively. The results showed that although the sand filter provided moderate (<50 % for phenolic substances) to high (50-80 % for PAHs and PHCs) removal of OMPs, adding a vegetated soil layer on top of the sand filter considerably improved the removal performance (by at least 30 %), especially for BPA, OP, and suspended solids. Moreover, GTP did not contribute to the treatment significantly. Uncertainties in the removal efficiencies of PAHs and PHCs by the filter cells increased substantially when the ratio of the influent concentration to the limit of quantification decreased. Thus, accounting for such uncertainties due to the low OMP concentrations should be considered when evaluating the removal performance of biofilters.

Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning

Understanding of runoff response changes (RRC) is essential for water resource management decisions. However, there is a limited understanding of the effects of climate and landscape properties on RRC behavior. This study explored RRC behavior across controls and predictability in 1003 catchments in the contiguous United States (CONUS) using catchment classification and machine learning. Over 1000+ catchments are grouped into ten classes with similar hydrological behavior across CONUS. Indices quantifying RRC were constructed and then predicted within each class of the 10 classes and over the entire1000+ catchments using two machine learning models (random forest and CUBIST) based on 56 indicators of catchment attributes (CA) and 16 flow signatures (FS). This enabled the ranking of the important influential factors on RRC. We found that (i) CA/FS-based clusters followed the ecoregions over CONUS, and the impact of climate on RRC seemed to overlap with physiographic attributes; (ii) CUBIST outperforms the random forest model both within the cluster and over the whole domain, with a mean improvement of 39 % (depending on clusters) within clusters. Runoff sensitivity was better predicted than runoff changes; (iii) FS related to runoff ratio, average, and high flow are the most important for RRC, whereas climate (evaporation and aridity) is a secondary factor; and (iv) RRC patterns are substantial in the dominant factor space. High total changes and catchment characteristic-induced changes occurred mainly at 100°west longitude. The elasticity of climate and catchment characteristics was found to be high in spaces with high evaporation and low runoff ratios and low in spaces with low evaporation and high runoff ratios. Uncertainties existed in the number of catchments between clusters which was verified using a fuzzy clustering algorithm. We recommend that future research that clarifies the impact of uncertainty on hydrological or catchment behavior should be conducted.

Numerical analysis of surface hydrogeological water budget to estimate unconfined aquifers recharge

Under changing climate, groundwater resources are the main drivers of socioeconomic development and ecosystem sustainability. This study assessed the contribution of two adjacent watersheds, Muse Street (MS) and West Wood (WW), with low and high urban development, to the Memphis aquifer recharge process in central Jackson, Tennessee, USA. The numerical MODFLOW model was created using data from 2017 to 2019 and calibrated using reported water budget components derived from in-situ data. The calibrated MODFLOW model was then used to investigate the impact of high and low urban developments on the recharge rate. The hydraulic parameters and recharge rates were optimized by adjusting the groundwater level based on the observed water level using PEST. The stochastic modeling was also carried out using the Latin Hypercube approach to reduce the uncertainty. The calibration results were satisfactory, with RMSE of 0.124 and 0.63 obtained in the WW and MS watersheds, respectively, indicating accurate estimation of the input parameters, precisely the hydrodynamic coefficients. The study results indicate that, per unit area, the MS watershed contributes 119% more to recharge and 186% more to riverbed leakage compared to the WW watershed. However, regarding total recharge and riverbed leakage, the WW watershed contributed more than the MS watershed. The results of this study have enhanced the knowledge of the impact of urbanization on hydrology and the recharge process in watersheds with diverse land uses.

An operational approach for large-scale mapping of water clarity levels in inland lakes using landsat images based on optical classification

Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the ad(443)/ap(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the Rrs(443)/Rrs(655) ratio was used for water type 1 SDD estimation, and the band combination of (Rrs(443)/Rrs(655) - Rrs(443)/Rrs(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R2 of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R2 = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.

Production of volatile fatty acids by anaerobic digestion of biowastes: Techno-economic and life cycle assessments

Production of volatile fatty acids from food waste and lignocellulosic materials has potential to avoid emissions from their production from petrochemicals and provide valuable feedstocks. Techno-economic and life cycle assessments of using food waste and grass to produce volatile fatty acids through anaerobic digestion have been conducted. Uncertainty and sensitivity analysis for both assessments were done to enable a robust forecast of key-aspects of the technology deployment at industrial scale. Results show low environmental impact of volatile fatty acid with food wastes being the most beneficial feedstock with global warming potential varying from -0.21 to 0.01 CO2 eq./kg of product. Food wastes had the greatest economic benefit with a breakeven selling price of 1.11-1.94 GBP/kg (1.22-2.33 USD) of volatile fatty acids in the product solution determined through sensitivity analysis. Anaerobic digestion of wastes is therefore a promising alternative to traditional volatile fatty acid production routes, providing economic and environmental benefits.

A comparative environmental analysis of conventional and organic rice farming in Thailand in a life cycle perspective using a stochastic modeling approach

System stochasticity is an inherent characteristic of agricultural systems. Many studies have been conducted in Thailand to analyze the rice production systems. However, most of the prior work just focused on deterministic approach to investigate the rice production systems while disregarding the system variability. In this study, the conventional and organic rice farming systems in Thailand were compared considering the uncertainties associated with parameters. The system variability was taken into account by employing a stochastic modeling approach. The considered impact categories include global warming, ozone formation (human health), freshwater ecotoxicity, terrestrial acidification, fine particulate matter formation, freshwater eutrophication, and fossil resource scarcity. The results showed that yield had considerable influence on the environmental profiles of the two systems; organic and conventional farming showed similar results in terms of global warming on a per hectare basis, but the considerable difference was observed on a per tonne basis. The field emissions due to farm inputs were the most significant contributor to most of the impact categories. The fuel used for irrigation, land preparation, and harvesting also contributed significantly to several impact categories. On the other hand, the impacts of inputs production and material transportation were modest. Uncertainty analysis outcomes indicated that there was a noticeable deviation from the deterministic results in terms of global warming and freshwater ecotoxicity. However, when considering the associated uncertainties, no significant difference was observed between the environmental profiles of the two systems.

Integrated evaluation of the performance of phosphogypsum recycling technologies in China

The Chinese government is implementing policies, such as the "Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period", to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings.

Simple solutions for improving thermal comfort in huts in the highlands of Peru

In the Peruvian mountains, hundreds of thousands of rural households living in poverty live in cold indoor environments, close to 0 °C. Indoor cold causes thousands of respiratory diseases and excess of winter deaths. In this study, we numerically calculated the impact of simple low-cost refurbishments on discomfort time during a year. Using EnergyPlus and Python, we modelled a typical one-room hut used as bedroom built with a metal-sheet roof, adobe walls, dirt floors, and high infiltration rates. Then, 9 individual solutions were studied, and their combination resulted in 215 different hut designs. The model was calibrated with field measurements to estimate the infiltration. All the numerical calculations included an uncertainty analysis based on Monte Carlo method, and a sensitivity analysis to assess the impact of reducing infiltration on discomfort time. The base case had a discomfort time of 44% of time. The calibration of infiltration resulted in a mean hourly air exchange rate equal to 29.1 h-1 (SD = 17.0 h-1). Five different designs formed the Pareto front that optimized discomfort time and costs. The solution with the lowest discomfort time during a year, 37% of the time, was adding insulation to the roof (U = 0.83 W/m2•K) and the door (U = 1.00 W/m2•K); and its cost was 286USD. In this solution, when infiltrations were reduced to 4.1 h-1 (SD = 4.1 h-1) discomfort time decreased until 16%. These results benefit those households that nowadays invest their limited resources to improve their living conditions but without technical guidance.

Toward understanding the interaction of shale gas-water-carbon nexus in Sichuan-Chongqing region based on county-level water security evaluation

This study develops a comprehensive framework for understanding the interaction of shale gas-water-carbon nexus in Sichuan-Chongqing region. Within this framework, a county-level water security index (WSI) evaluation system is structured. Spatial autocorrelation model and spatial matching degree model are integrated to illustrate the spatial agglomeration characteristics of water security and the water-carbon relationship, respectively. The impacts of shale gas development on water security and carbon emissions are evaluated based on identification of shale well productivity. Results show that about 25.17% of counties with WSI < 0.4 (unsafe), especially in the eastern region. The central cities (such as Chengdu and Neijiang) should take active steps to reach a safety threshold (WSI ≥ 0.6). Population growth can accelerate water security deterioration through uncertainty analysis. Moreover, the spatial matching degree between WSI and carbon emissions in most cities is extremely poor (< 0.5), implying that these cities should optimize their energy structure and promote green transformation. Water used for shale gas extraction can hardly be ignored from a county-scale perspective, especially in Tongliang, Tongnan, and Jianyang. The future shale gas development would pose a threat to the regional climate.

Scale-invariant sensitivity for multi-purpose water reservoirs management with temporal scale-dependent modeling

High-resolution temporal data (e.g., daily) is valuable for the decision-making of water resources management because it more accurately captures fine-scale processes and extremes than the coarse temporal data (e.g., weekly or monthly). However, many studies rarely consider this superior suitability for water resource modeling and management; instead, they often use whichever data is more readily available. So far, no comparative investigations have been conducted to determine if access to different time-scale data would change decision-maker perceptions or the rationality of decision making. This study proposes a framework for assessing the impact of different temporal scales on water resource management and the performance objective's sensitivity to uncertainties. We built the multi-objective operation models and operating rules of a water reservoir system based on daily, weekly, and monthly scales, respectively, using an evolution multi-objective direct policy search. The temporal scales of the input variables (i.e., streamflow) affect both the model structures and the output variables. In exploring these effects, we reevaluated the temporal scale-dependent operating rules under uncertain streamflow sets generated from synthetic hydrology. Finally, we obtained the output variable's sensitivities to the uncertain factors at different temporal scales using the distribution-based sensitivity analysis method. Our results show that water management based on too coarse resolution might give decision makers the wrong perception because the effect of actual extreme streamflow process on the performance objectives is ignored. The streamflow uncertainty is more influential than the uncertainty associated with operating rules. However, the sensitivities are characterized by temporal scale invariance, as the differences of the sensitivity between different temporal scales are not obvious over the uncertainties in streamflow and thresholds. These results show that water management should consider the resolution-dependent effect of temporal scales for balancing modeling complexity and computational cost.

Occurrence and concentrations of organic micropollutants (OMPs) in highway stormwater: a comparative field study in Sweden

This study details the occurrence and concentrations of organic micropollutants (OMPs) in stormwater collected from a highway bridge catchment in Sweden. The prioritized OMPs were bisphenol-A (BPA), eight alkylphenols, sixteen polycyclic aromatic hydrocarbons (PAHs), and four fractions of petroleum hydrocarbons (PHCs), along with other global parameters, namely, total organic carbon (TOC), total suspended solids (TSS), turbidity, and conductivity (EC). A Monte Carlo (MC) simulation was applied to estimate the event mean concentrations (EMC) of OMPs based on intra-event subsamples during eight rain events, and analyze the associated uncertainties. Assessing the occurrence of all OMPs in the catchment and comparing the EMC values with corresponding environmental quality standards (EQSs) revealed that BPA, octylphenol (OP), nonylphenol (NP), five carcinogenic and four non-carcinogenic PAHs, and C16-C40 fractions of PHCs can be problematic for freshwater. On the other hand, alkylphenol ethoxylates (OPnEO and NPnEO), six low molecule weight PAHs, and lighter fractions of PHCs (C10-C16) do not occur at levels that are expected to pose an environmental risk. Our data analysis revealed that turbidity has a strong correlation with PAHs, PHCs, and TSS; and TOC and EC highly associated with BPA concentrations. Furthermore, the EMC error analysis showed that high uncertainty in OMP data can influence the final interpretation of EMC values. As such, some of the challenges that were experienced in the presented research yielded suggestions for future monitoring programs to obtain more reliable data acquisition and analysis.

Long-lead streamflow forecasting using computational intelligence methods while considering uncertainty issue

While some robust artificial intelligence (AI) techniques such as Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS) have been frequently employed in the field of water resources, documents aimed to explore their uncertainty levels are few and far between. Meanwhile, uncertainty determination of these AI models in practical applications is highly important especially when we aimed to use the AI models for streamflow forecast due to the repercussions of poorly managed water resources. With the aid of a global daily streamflow dataset, understanding the uncertainty of GEP, MT, and MARS for forecasting streamflow of natural rivers was studied. The efficiency of uncertainty analysis was quantified by two statistical indicators: 95% Percent Prediction Uncertainty (95%PPU) and R-factor. The results demonstrated that MT had lower uncertainty (95%PPU=0.59 and R-factor=1.67) in comparison with MARS (95%PPU=0.61 and R-factor=1.92) and GEP (95%PPU=0.64 and R-factor=2.03). Overall, although the confidence interval bands of uncertainty for the AI models almost captured the mean streamflow measurements, wide bands of uncertainty were obtained and consequently remarkable uncertainty in the calculation of monthly streamflow values was met.

Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data

Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs.

The effects of leaflet material properties on the simulated function of regurgitant mitral valves

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated (1) the influence of tissue extensibility and (2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

Phylourny: efficiently calculating elimination tournament win probabilities via phylogenetic methods

The prediction of knockout tournaments represents an area of large public interest and active academic as well as industrial research. Here, we show how one can leverage the computational analogies between calculating the phylogenetic likelihood score used in the area of molecular evolution to efficiently calculate, instead of approximate via simulations, the exact per-team tournament win probabilities, given a pairwise win probability matrix between all teams. We implement and make available our method as open-source code and show that it is two orders of magnitude faster than simulations and two or more orders of magnitude faster than calculating the exact per-team win probabilities naïvely, without taking into account the substantial computational savings induced by the tournament tree structure. Furthermore, we showcase novel prediction approaches that now become feasible due to this order of magnitude improvement in calculating tournament win probabilities. We demonstrate how to quantify prediction uncertainty by calculating 100,000 distinct tournament win probabilities for a tournament with 16 teams under slight variations of a reasonable pairwise win probability matrix within one minute on a standard laptop. We also conduct an analogous analysis for a tournament with 64 teams.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11222-023-10246-y.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km²) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Modeling Opioid Abuse: A Case Study of the Opioid Crisis in New England

For the past two decades, the USA has been embroiled in a growing prescription drug epidemic. The ripples of this epidemic have been especially apparent in the state of Maine, which has fought hard to mitigate the damage caused by addiction to pharmaceutical and illicit opioids. In this study, we construct a mathematical model of the opioid epidemic incorporating novel features important to better understanding opioid abuse dynamics. These features include demographic differences in population susceptibility, general transmission expressions, and combined consideration of pharmaceutical opioid and heroin abuse. We demonstrate the usefulness of this model by calibrating it with data for the state of Maine. Model calibration is accompanied by sensitivity and uncertainty analysis to quantify potential error in parameter estimates and forecasts. The model is analyzed to determine the mechanisms most influential to the number of opioid abusers and to find effective ways of controlling opioid abuse prevalence. We found that the mechanisms most influential to the overall number of abusers in Maine are those involved in illicit pharmaceutical opioid abuse transmission. Consequently, preventative strategies that controlled for illicit transmission were more effective over alternative approaches, such as treatment. These results are presented with the hope of helping to inform public policy as to the most effective means of intervention.

Safe CO2 threshold limits for indoor long-range airborne transmission control of COVID-19

CO₂-based infection risk monitoring is highly recommended during the current COVID-19 pandemic. However, the CO₂ monitoring thresholds proposed in the literature are mainly for spaces with fixed occupants. Determining CO₂ threshold is challenging in spaces with changing occupancy due to the co-existence of quanta and CO₂ remaining from previous occupants. Here, we propose a new calculation framework for deriving safe excess CO₂ thresholds (above outdoor level), C _t, for various spaces with fixed/changing occupancy and analyze the uncertainty involved. We categorized common indoor spaces into three scenarios based on their occupancy conditions, e.g., fixed or varying infection ratios (infectors/occupants). We proved that the rebreathed fraction-based model can be applied directly for deriving C _t in the case of a fixed infection ratio (Scenario 1 and Scenario 2). In the case of varying infection ratios (Scenario 3), C _t derivation must follow the general calculation framework due to the existence of initial quanta/excess CO₂. Otherwise, C _t can be significantly biased (e.g., 260 ppm) when the infection ratio varies greatly. C _t can vary significantly based on specific space factors such as occupant number, physical activity, and community prevalence, e.g., 7 ppm for gym and 890 ppm for lecture hall, indicating C _t must be determined on a case-by-case basis. An uncertainty of up to 6 orders of magnitude for C _t was found for all cases due to uncertainty in emissions of quanta and CO₂, thus emphasizing the role of accurate emissions data in determining C _t.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that E_NS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

COVID-19 public transit precautions: Trade-offs between risk reduction and costs

Public transit has received scrutiny as a vector for spreading COVID-19 with much of the literature finding correlations between transit ridership and COVID-19 rates by assessing the role that transportation plays as a vector for human mobility in COVID-19 spread. However, most studies do not directly measure the risk of contracting COVID-19 inside the public transit vehicle. We fill a gap in the literature by comparing the risk and social costs across several modes of transportation. We develop a framework to estimate the spread of COVID-19 on transit using the bus system in Pittsburgh. We find that some trips have demand that exceed their COVID-19 passenger limit, where the driver must decide between: (1) leaving a passenger without a ride or (2) allowing them on the bus and increasing COVID-19 risk. We consider five alternatives for alleviating overcapacity: allow crowding, additional buses, longer buses as substitutes, Transportation Network Company (TNC) rides, or Autonomous Vehicles (AVs) for passed-by passengers. We use transit ridership and COVID-19 data from the spring of 2020 by combining transportation data and an epidemiological model of COVID-19 stochastically in a Monte Carlo Analysis. Our results show that 4% of county cases were contracted on the bus or from a bus rider, and a disproportionate amount (52%) were from overcapacity trips. The risk of contracting COVID-19 on the bus was low but worth mitigating. A cost-benefit analysis reveals that dispatching AVs or longer buses yield the lowest societal costs of $45 and $46 million, respectively compared to allowing crowding ($59 million).

Stochastic simulation of seawater intrusion in the Longkou area of China based on the Monte Carlo method

Seawater intrusion is a common groundwater pollution problem, which has a great impact on ecological environment and economic development. In this paper, a numerical simulation model of variable density groundwater was constructed to simulate and predict the future seawater intrusion in Longkou city, Shandong Province of China. The influence of the sensitive parameter uncertainty of the model on the simulation results was evaluated by using the Monte Carlo method. In order to reduce the computational load from repeatedly calling the simulation model, the surrogate model was established by using the support vector regression (SVR) method. After training, the correlation coefficient R² of the input-output relationship between the SVR surrogate model and the seawater intrusion simulation model reached 0.9957, with an average relative error of 0.2%, indicating that the surrogate model has a high fitting accuracy. Stochastic simulations of seawater intrusion showed that the seawater intrusion in the Longkou area will gradually aggravate at a slow rate, and the increase of seawater intrusion in the study area after 30 years was expected to range from - 6.03% to 7.37% at the 80% confidence level.

Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and probabilistic frequency analysis of stability factors

The detention rockfill dams are of promising importance in flood control projects, due to their minimal technical requirement, low cost, minimal environmental side effects, and self-automotive operation process. However, due to the complexity of Non-Darcian flow interactions with stability and uncertainties of dam, the reliable design is a challenging topic. This study aimed to examine the effects of uncertainties in probabilistic design of these dams. We proposed a reliable design framework for detention rockfill dams with a focus on the importance of stability analysis. The effects of design uncertainty sources on the stability of dam, safety factors of overturning, sliding and bearing, along with the hydraulic performance of the dam were examined. The results of the model revealed that the uncertainties in input parameters can effectively regenerate uncertainties in the hydraulic performance ranges from - 53.54 to + 110.11%. The safety factor against the sliding (SFS) has maximum dependencies with the uncertainties ranging - 32.63 to + 87.81%. The Monte-Carlo Simulation (MCS) and fitting probability distribution functions to the safety factor histograms, and uncertainty quantifications results in 88.3%in increasing the safety factors as a reliable methodology for stability design of detention rockfill dams. Thus, the study calls for reliable, certain, and safe design of flood protection rockfill ponds. The ecological evaluation and applying more advanced uncertainty assessment methods remains a future research direction of the current study. The developed framework can be used to acquire future detention rockfill dam design/modeling requirements for reliability-based design optimization as a simulation-optimization model coupled whit MCS.

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.

Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities

In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

Uncertainty analysis of vehicle-pedestrian accident reconstruction based on unscented transformation

In order to investigate the sensitivity of parameters and analyze the uncertainty of reconstructed results in traffic accident, the impact of correlations between parameters on accident reconstruction results was taken into account using uncertainty analysis. Based on unscented transformation (UT), a parameter sensitivity analysis method and an efficient uncertainty analysis method in accident reconstruction were proposed. Sensitivity analysis was performed through the sigma point sets generated by the UT method. A first-order response surface model was constructed to analyze the sensitivity of accident reconstruction parameters combined with regression analysis, which is more flexible and controllable than the general experimental design. For the uncertainty analysis of the reconstructed results, the other methods have been used to demonstrate the validity of the proposed method, including the first second-order method of moments (FOSM), the uncertainty theory, and the Monte Carlo (MC) methods, through analyzing the numerical and real-world cases. The results show that the presented method has high accuracy, significantly reduces the computational burden, and does not depend on the distribution type of variables. When considering the effect of the correlation between parameters of the vehicle-pedestrian crash on accident reconstruction results, the results show that the correlation coefficient between random variables had a much more significant impact on the standard deviation of vehicle speed than on the mean value of vehicle speed. Regardless of negative or positive correlations, the relative error of standard deviation of vehicle speed increased continuously as the correlation increased, reaching 52%. The proposed method is effective and reliable for vehicle collision accident reconstruction under uncertainty and correlation, which can provide more comprehensive information in accident reconstruction.

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.