Visualization for better understanding global sensitivity analysis results: The case of safety assessment of radioactive waste disposal.

Sensitivity analysis answers questions about the influence of parameters on the simulation results and plays a significant role in the development of environmental models by helping to understand the relations within the model and test its adequacy. Comparison of various sensitivity analysis approaches is often also quite useful because different methods employ different measures for ranking model parameters and their unconformities and disagreements provide additional information on model behavior. The visual representation of numerical results is crucial for their correct interpretation, and at first sight, the visualizations for the sensitivity analysis should be quite universal because in most cases an outcome of sensitivity analysis is the same: a set of indices measuring the significance of model inputs for the selected output. Surprisingly, it is not so straightforward. This paper compares visualization types suitable for the graphical representation of the sensitivity indices and demonstrates their benefits and caveats in different cases.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

An integrated groundwater vulnerability and artificial recharge site suitability assessment using GIS multi-criteria decision making approach in Kayseri region, Turkey.

Groundwater resources worldwide face significant challenges that require urgent implementation of sustainable measures for effective long-term management. Managed aquifer recharge (MAR) is regarded as one of the most promising management technologies to address the degradation of groundwater resources. However, in urban aquifers, locating suitable areas that are least vulnerable to contamination for MAR implementation is complex and challenging. Hence, the present study proposes a framework encapsulating the combined assessment of groundwater vulnerability and MAR site suitability analysis to pinpoint the most featured areas for installing drywells in Kayseri, Turkey. To extrapolate the vulnerable zones, not only the original DRASTIC but also its multi-criteria decision-making (MCDA)-based modified variants were evaluated with regard to different hydrochemical parameters using the area under the receiver operating characteristic (ROC) curve (AUC). Besides, the fuzzy analytical hierarchy process (FAHP) rationale was adopted to signify the importance level of criteria and the robustness of the framework was highlighted with sensitivity analysis. In addition, the decision layers and the attained vulnerability layer were combined using the weighted overlay (WOA). The findings indicate that the DRASTIC-SWARA correlates well with the arsenic (AUC = 0.856) and chloride (AUC = 0.648) and was adopted as the vulnerability model. Groundwater quality parameters such as chloride and sodium adsorption ratio, as well as the vadose zone thickness, were found to be the most significant decision parameters with importance levels of 16.75%, 14.51%, and 15.73%, respectively. Overall, 28.24% of the study area was unsuitable for recharge activities with high to very high vulnerability, while the remaining part was further prioritized into low to high suitability classes for MAR application. The proposed framework offers valuable tool to decision-makers for the delineation of favorable MAR sites with minimized susceptibility to contamination.

Enhancing environmental performance in biogas production from wastewater-grown microalgae: A life cycle assessment perspective.

The production of biogas from microalgae has gained attention due to their rapid growth, CO2 sequestration, and minimal land use. This study uses life cycle assessment to assess the environmental impacts of biogas production from wastewater-grown microalgae through anaerobic digestion within an optimized microalgae-based system. Using SimaPro® 9 software, 3 scenarios were modeled considering the ReCiPe v1.13 midpoint and endpoint methods for environmental impact assessment in different categories. In the baseline scenario (S1), a hypothetical system for biogas production was considered, consisting of a high rate algal pond (HRAP), a settling, an anaerobic digester, and a biogas upgrading unit. The second scenario (S2) included strategies to enhance biogas yield, namely co-digestion and thermal pre-treatment. The third scenario (S3), besides considering the strategies of S2, proposed the biogas upgrading in the HRAP and the digestate recovery as a biofertilizer. After normalization, human carcinogenic toxicity was the most positively affected category due to water use in the cultivation step, accounted as avoided product. However, this category was also the most negatively affected by the impacts of the digester heating energy. Anaerobic digestion was the most impactful step, constituting on average 60.37% of total impacts. Scenario S3 performed better environmentally, primarily due to the integration of biogas upgrading within the cultivation reactor and digestate use as a biofertilizer. Sensitivity analysis highlighted methane yield's importance, showing potential for an 11.28% reduction in ionizing radiation impacts with a 10% increase. Comparing S3 biogas with natural gas, the resource scarcity impact was reduced sixfold, but the human health impact was 23 times higher in S3.

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques.

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl-, Fe++, Ca++, Mg++, Na+, SO4--, Mn++, HCO3-, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

Mathematical Assessment of the Role of Intervention Programs for Malaria Control.

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.

Techno-economic and environmental analysis of renewable energy integration in irrigation systems: A comparative study of standalone and grid-connected PV/diesel generator systems in Khyber Pakhtunkhwa.

Water is an essential requirement for agricultural productivity. In the agriculture sector, electricity generated by conventional sources contributes to a substantial amount of carbon footprints for pumping water through tube wells. Over the past few decades, a transitional shift towards renewable resources has increased leading to decarbonizing the environment and is considered as a viable solution for electricity production. To assist and provide a road map for this paradigm shift, the proposed study presents a techno-economic and environmental analysis of irrigation systems by carrying comparative analysis of both standalone and grid-connected systems based on four independent sites in a developing country. PV system integrated with grid enabling both energy purchase and sale (PV + G(P+S)), proved to be the most optimal configuration with cost of energy (COE) of $0.056/kWh, $0.059/kWh, $0.061/kWh, and $0.068/kWh while having net present cost (NPC) of $7,908, $20,186, $25,826, and $34,487 for Peshawar, Khyber Agency, Mardan, and Charsadda respectively, over a useful life span of 25 years. Furthermore, sensitivity analysis has been carried out based on uncertain variables such as Grid power purchase (GPP) and average solar radiation (GHI) to check the optimality behavior of the system. Results from environmental analysis revealed that (PV+ G(P+S)) system has a relatively low carbon impact as compared with conventional sources. This configuration also has the ability to prevent excess water extraction by selling any excessive solar PV energy to the grid. This study provides a policy framework insight for the entities for future optimization.

Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels.

Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors - PM2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.

Decisional tool development and application for techno-economic analysis of fungal laccase production.

Textile and medical effluents causing bioaccumulation and biomagnification have been successfully biodegraded by fungal laccases. Here, a decision-making tool was developed and applied to evaluate 45 different laccase production strategies which determined the best potential source from a techno-economical perspective. Laccase production cost was calculated with a fixed output of 109 enzymatic units per batch (USD$per109U) and a sensitivity analysis was performed. Results indicate that optimization of enzymatic kinetics for each organism is essential to avoid exceeding the fermentation time point at which production titer reaches its peak and, therefore, higher production costs. Overall, the most cost-effective laccase-producing strategy was obtained when using Pseudolagarobasidium acaciicola with base production cost of USD $42.46 per 109 U. This works serves as platform for decision-making to find the optimal laccase production strategy based on techno-economic parameters.

Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD.

OBJECTIVES: Health economic (HE) models are often considered as "black boxes" because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making. This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes. METHODS: The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist. Functionalities of PACBOARD are (1) exploring and validating model parameters and outcomes using standardized validation tests and interactive plots, (2) visualizing and investigating the relationship between model parameters and outcomes using metamodeling, and (3) predicting HE outcomes using the fitted metamodel. To test PACBOARD, 2 mock HE models were developed, and errors were introduced in these models, eg, negative costs inputs, utility values exceeding 1. PACBOARD metamodeling predictions of incremental net monetary benefit were validated against the original model's outcomes. RESULTS: PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared with the original model outcomes. CONCLUSIONS: PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodeling based on HE models' parameters and outcomes.

Human health risks of heavy metal(loid)s mediated through crop ingestion in a coal mining area in Eastern China.

The heavy metal(loid)s (HMs) in soils can be accumulated by crops grown, which is accompanied by crop ingestion into the human body and then causes harm to human health. Hence, the health risks posed by HMs in three crops for different populations were assessed using Health risk assessment (HRA) model coupled with Monte Carlo simulation. Results revealed that Zn had the highest concentration among three crops; while Ni was the main polluting element in maize and soybean, and As in rice. Non-carcinogenic risk for all populations through rice ingestion was at an "unacceptable" level, and teenagers suffered higher risk than adults and children. All populations through ingestion of three crops might suffer Carcinogenic risk, with the similar order of Total carcinogenic risk (TCR): TCRAdults > TCRTeenagers > TCRChildren. As and Ni were identified as priority control HMs in this study area due to their high contribution rates to health risks. According to the HRA results, the human health risk was associated with crop varieties, HM species, and age groups. Our findings suggest that only limiting the Maximum allowable intake rate is not sufficient to prevent health risks caused by crop HMs, thus more risk precautions are needed.

From biomass-derived fructose to γ-valerolactone: Process design and techno-economic assessment.

This work proposes a process design and techno-economic assessment for the production of γ-valerolactone from lignocellulosic derived fructose at industrial scale, with the aim of exploring its feasibility, identifying potential obstacles, and suggesting improvements in the context of France. First, the conceptual process design is developed, the process modelled and optimized. Second, different potential scenarios for the energy supply to the process are analyzed by means of a set of economic key performance indicators, aimed at highlighting the best potential profitability scenario for the sustainable exploitation of waste biomass in the context analyzed. The lowest Minimum Selling Price for GVL is obtained at 10 kt/y plant fueled by biomass, i.e. 1.89 €/kg, along with the highest end-of-live revenue, i.e. 113 M€. Finally, a sensitivity and uncertainties analysis, based on Monte Carlo simulations, are carried out on the results in order to test their robustness with respect to key input parameters.

Sensitivity analysis for a dynamic macroeconomic policy game in a monetary union.

In this paper, we examine the sensitivity of the results of an earlier paper which presented and analyzed a dynamic game model of a monetary union with coalitions between governments (fiscal policy makers) and a common central bank (monetary policy maker). Here we examine alternative values of the parameters of the underlying model to show how the earlier results depend on the numerical parameter values chosen, which were obtained by calibration instead of econometric estimation. We demonstrate that the main results are qualitatively the same as in the original model for plausible smaller and larger values of the parameters. For the few cases where they differ, we interpret the deviations in economic terms and illustrate the policies and their macroeconomic effects resulting from the change to the parameter under consideration for one of these cases.

Assessment of urban greenhouse gas emissions towards reduction planning and low-carbon city: a case study of Montreal, Canada.

Greenhouse gases (GHGs) can be produced from a broad range of anthropogenic activities at different spatial and temporal scales. In particular, emissions from urban area are an import source of GHGs. City is a complicated system consisting of various component and processes. Efforts have been made to reduce urban GHG emissions. However, there is a lack of available methods for effective assessment of such emissions. Many urban sources and factors which can influence the emissions are still unknown. In the present study, the GHG emissions from municipal activities was assessed. A model for the assessment of urban GHG emissions was developed. Based on the collected data, a case study was conducted to evaluate urban GHG emissions. The comprehensive assessment included the emissions from transportation, electricity consumption, natural gas, waste disposal, and wastewater treatment. There was a variation for GHG emissions from these sectors in different years. This study provided a new approach for comprehensive evaluation of urban GHG emissions. The results can help better understand the emission process and identify the major emission sources. Supplementary Information: The online version contains supplementary material available at 10.1186/s40068-024-00341-y.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China.

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas.

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Life cycle assessment and technoeconomic analysis of biofuels produced from polyculture microalgae cultivated in greywater.

This study evaluated the environmental and economic impacts of substituting synthetic media with greywater for cultivating microalgae in the biofuel production process. Life cycle assessment (LCA) and technoeconomic assessment (TEA) were employed to compare the impacts of two scenarios - one containing bold's basal (BB) media and another containing greywater as growth mediums for microalgae cultivation. Scenarios 1 and 2 mitigated 1.74 and 2.14 kg CO2 per kg of biofuel production, respectively. Substituting BB media with greywater resulted in a 16.3% reduction in energy requirements, leading to a 79.3% increase in net energy recovered. LCA findings demonstrate a reduction in all seven environmental categories. TEA reveals that, despite a 21.7% higher capital investment, scenario 2 proves more economically viable due to a 39.8% lower operating cost and additional revenue from wastewater treatment and carbon credits. The minimum selling price of biofuel dropped from Rs 73.5/kg to Rs 36.5/kg, highlighting the economic and environmental advantages of substituting BB media with greywater in microalgal biofuel production.

Entropy-weighted water quality index, hydrogeochemistry, and Monte Carlo simulation of source-specific health risks of groundwater in the Morava River plain (Serbia).

Population growth, urbanization, industry, floods, and agriculture globally degrade groundwater in river plains, necessitating action for its quality assessment and management. Hence, a comprehensive methodology, including hydrogeochemical facies (Piper, Gibbs), irrigation indices (SAR, Wilcox), entropy-weighted water quality index (EWQI), positive matrix factorization (PMF), and Monte Carlo simulation of source-specific health risks was used in this study to analyze groundwater in the Morava river plain (Serbia). The results revealed a prevalent Ca-Mg-HCO3 groundwater type, influenced by water-rock interactions. Although groundwater was found suitable for irrigation, only 66.7 % of the samples were considered drinkable. Agricultural activities, natural processes, and municipal wastewater were identified as primary pollution sources. The incremental lifetime cancer risk (ILCR) and hazard index (HI) threshold exceedance for adults and children ranged from 8.5 % to 39 % of the samples, with arsenic identified as the most risk-contributing contaminant. These findings provide valuable insights for researchers studying groundwater vulnerability in river plains.

[Research advance on quantitative assessment methods of ecosystem water conservation service functions]. / ç”Ÿæ€ç³»ç»Ÿæ°´æºæ¶µå »æœåŠ¡åŠŸèƒ½å®šé‡è¯„ä¼°æ–¹æ³•ç ”ç©¶è¿›å±•.

The water conservation service function, which is one of the most important ecological service function in the regional system, directly reflects the regulation role of a region in precipitation, the redistribution function of precipitation, and the ecohydrological value. With the development of the comprehensive evaluation method and the deepening of research on water conservation service function, relevant evaluation calculation process has changed significantly. Nowadays, in the assessment of the water conservation service function, it is necessary not only to calculate and evaluate relevant indicators, but also to localize specific parameters in the model and analyze the effectiveness of the overall model for specific study areas. However, the current literature review lacks systematic summaries of model evaluation methods. Meanwhile, the review is also insufficient on model validity verification and significance analysis methods, the result verification and applicability analysis methods such as parameter localization in water conservation studies. We reviewed the research advance on typical ecosystem water conservation ser-vice assessment methods with a specific focus on the model assessment methods that have developed rapidly in recent years. At the same time, we summarized methods commonly used for parameter localization, as well as validity testing and sensitivity analysis of simulation results, and discussed existing problems and future directions in this field.