The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the \"i\" and \"P\" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the \"i\" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of \"G,\" \"L,\" \"D,\" and \"i\" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Tuberculosis (TB) remains a significant global health concern, necessitating effective control strategies. This article presents a mathematical model to evaluate the comparative effectiveness of medical mask usage and case detection in TB control. The model is constructed as a system of ordinary differential equations and incorporates crucial aspects of TB dynamics, including slow-fast progression, medical mask use, case detection, treatment interventions and differentiation between symptomatic and asymptomatic cases. A key objective of TB control is to ensure that the reproduction number, R c , remains below unity to achieve TB elimination or persistence if R c exceeds 1. Our mathematical analysis reveals the presence of a transcritical bifurcation when the R c = 1 signifies a critical juncture in TB control strategies. These results confirm that the effectiveness of case detection in diminishing the endemic population of symptomatic individuals within a TB-endemic equilibrium depends on exceeding a critical threshold value. Furthermore, our model is calibrated using TB yearly case incidence data per 100 000 population from Indonesia, India, Lesotho and Angola. We employed the bootstrap resampling residual approach to assess the uncertainty inherent in our parameter estimates which provides a comprehensive distribution of the parameter values. Despite a declining trend in new incidence, these four countries exhibit a reproduction number greater than 1, indicating persistent TB cases in the presence of ongoing TB control programmes. We employ the partial rank correlation coefficient in conjunction with the Latin hypercube sampling method to conduct a global sensitivity analysis of the R c parameter for each fitted parameter in every country. We find that the medical mask use is more sensitive to reduce R c compared with the case detection implementation. To further gain insight into the necessary control strategy, we formulated an optimal control and studied the cost-effectiveness analysis of our model to investigate the impact of case detection and medical mask use as control measures in TB spread. Cost-effectiveness analysis demonstrates that combining these interventions emerges as the most cost-effective strategy for TB control. Our findings highlight the critical importance of medical masks and their efficacy coupled with case detection in shaping TB control dynamics, elucidating the primary parameter of concern for managing the control reproduction number. We envisage our findings to have implications and be vital for TB control if implemented by policymakers and healthcare practitioners involved in TB control efforts.

The temperature stratification in reservoirs post-construction carries substantial environmental implications. A MIKE11-2DV model was developed to simulate yearly water temperature changes in the newly established Qincun Reservoir. Sensitivity analysis was performed to assess the influence of eight key parameters on the model\'s performance. Linear and Boltzmann curves were employed to fit water temperature profiles and extract four features for quantitative sensitivity analyses. In addition, a comparative analysis was conducted considering two different withdrawal scenarios. The SSI were employed for the assessment of thermal stratification. The MIKE11-2DV model demonstrated a high accuracy in simulating hydrodynamics and water temperature in the monomictic reservoir. A sensitivity analysis showed varying levels of sensitivity among model parameters. Specifically, the hypolimnetic water temperature was highly sensitive to the vertical viscosity factor but almost insensitive to the horizontal factor. Additionally, the light attenuation coefficient and constant in Beer\'s law significantly influenced the thermocline. Radiation and evaporation parameters affected overall the water temperature, maintaining a stable \"shape\". Furthermore, the initial scenarios simulation results showed that initial water temperature significantly affected the hypolimnion. The analysis of the thermal structure based on two water intake scenarios revealed that multi-level withdrawal had a higher Schmidt\'s Stability Index (SSI) and a longer stratified period compared to single-level withdrawal. During the fish breeding season, using multi-level water intake resulted in higher outlet temperatures than single-level water intake. The SSI reflected both temporal and spatial heterogeneity. Hysteresis behavior was observed between SSI and air temperature, with the hysteresis loops\' direction being influenced by different water intake levels. Enhanced hysteresis was observed in deeper reservoir segments during multi-level water intake scenario. These findings provide novel insights for interpreting thermal stratification in reservoirs.

The present paper applies a simplified Green-Ampt (GA) infiltration model for the numerical modelling of seepage processes within the body of the Maga earth dam, located in the Far-North Cameroon, under rainfall conditions. The parametrization approach makes possible the predictions of infiltrations parameters for various textures that constitute the body of earth dam and its vicinities. The effects of soil texture, permeability coefficient, rain intensity and initial moisture on infiltration rate and cumulative depth are investigated using numerical simulations carried out by running an Excel VBA code. A proposed optimization procedure of the literature is then used to improve the GA model parameters and accuracy of predictions. A case sensitivity analysis of the infiltration model parameters has also been made. Initial and saturated water contents, rain intensity and permeability coefficient at saturation have been identified as the most influents parameters for the model predictions, with obvious interactions between inputs. The contribution of main effects and interactions of above inputs ranges between 68.8 % and 90.03 % of the total variance. Comparisons of numerical results with the experimental ones obtained from the literature on real cases of simulated rainfall on main sub-Saharan soils have been done. The results show that numerical simulations underestimated 60 % and 56.25 % of the hydrodynamics parameters respectively for one and four simulated rain-sequence with relative errors (RE) ranging between -76.22 % and 300.25 %. The most representative results were obtained for 53.13 % of the predicted hydrodynamics parameters with RE value ranging between -23.90 % and 12.75 %. The obtained results proved that, the simplified GA model exhibited an acceptable accuracy in predicting seepage processes for ferruginous and degraded vertic soils. Therefore, the present study contributes to understand the role of numerical simulations tools in modelling and predicting subsurface phenomenon as seepage, for areas with high floods risks in a context of significant climate changes.

Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.

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.
UNASSIGNED: The online version contains supplementary material available at 10.1186/s40068-024-00341-y.

The present investigation delineates groundwater potential zones (GPZ) in the Jhargram district through an integrated approach employing analytical hierarchical process (AHP), remote sensing, and geographical information systems (GIS). Twelve parameters were utilized for GPZ analysis based on the Groundwater Potential Index, subsequent to multicollinearity testing. Classification of GPZ yielded five distinct categories: very poor, poor, moderate, good, and very good. Validation through receiver operating characteristics (ROC) and cross-validation with borewell yield data affirmed prediction accuracies of 78.4% and 84%, respectively. Spatial distribution analysis revealed that 30.39%, 30.86%, and 13.19% of the surveyed area fell within the poor, moderate, and good potentiality zones, respectively, whereas 15.86% and 9.69% were categorized as very poor and very good GPZs. Sensitivity analysis highlighted the significance of geology, elevation, geomorphology, slope, and lineament density as influencing parameters; elimination of any single parameter engendered significant alterations in the GPZ classification. The investigation culminated in the formulation of a block-wise sustainable groundwater management blueprint designed to inform policy initiatives.

In this paper, we present a structured approach based on portfolio decision analysis to support the consideration of interdependencies between actions (i.e. interactions) in the selection of an efficient portfolio. One of the main challenges in modelling interactions is that the possible number of them between the pairs of actions increases exponentially with the number of actions. In environmental management, the problems can include tens of possible actions potentially leading to hundreds of pairwise interactions between them. For example, a strategy for mitigating climate change can consist of various actions in different sectors for improving technology, reducing emissions and the sequestration of carbon. Our approach aims to reduce the burden of assessing interactions by initially selecting a shortlist of actions based on specific heuristics and focusing on modelling interactions exclusively within this chosen set of actions. Another feature of the approach is the use of holistic evaluation of interactions to further reduce the cognitive load of stakeholders making the assessment. As a possible disadvantage, these features may increase the imprecision related to the results of the model. To analyse the impacts of this imprecision, we propose a way to carry out sensitivity analysis on the basis of how intensively the interactions would be taken into account in the modelling. The applicability of the approach was tested in a case related to the roadmap to a carbon neutral North Savo region in Finland by the year 2035. The approach helped to better understand synergies and trade-offs when putting the actions of the roadmap into practice, which is expected to lead to better results in terms of preparedness and adaptation to climate change.

BACKGROUND: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking.
OBJECTIVE: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS.
METHODS: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case-control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid-spina bifida and ondansetron-cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over- (or under-) representation to produce the crude OR (cOR) in NBDPS. For the smoking-hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR.
RESULTS: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid-exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron-exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking-exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking-hypospadias OR = 2.02).
CONCLUSIONS: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research.

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.