Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China.

Urban areas' performance in water, energy, infrastructure, and socio-economic sectors is intertwined and measurable through Sustainable Development Goals (SDGs) 6-13. Effective synergy among these is critical for sustainability. This study constructs an indicator framework that reflects progress towards these urban SDGs in China. Findings indicate underperformance in SDGs 8-11, suggesting the need for transformative actions. Through network analysis, the research reveals complementarities among these SDGs. Notably, the SDG space divides into socio-economic and ecological clusters, with SDG 6 (Clean Water and Sanitation) central to both. Additionally, SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) act as bridges, while greater synergies exist between SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). An in-depth view at the indicator-level shows a core-periphery structure, emphasizing indicators like SDG 6.2 (Wastewater Treatment Rate) and SDG 6.6 (Recycled Water Production Capacity per capita) as pivotal. This study confirms the urban SDG space's stability and predictiveness, underscoring its value in steering well-aligned policy decisions for sustainable growth.

Mitigating thermal stratification in lakes/reservoirs through wind-powered air diffusers.

Thermal stratification can cause various water quality issues in large water bodies. To address this, a new wind-powered artificial mixing system is designed and experimentally tested for various Savonius rotor combinations (three-stage and four-stage rotors). These turbines directly utilize wind energy to draw air into the water column for aeration, bypassing the need for electrical conversion. The rotor performances were tested in terms of power and torque coefficients. Additionally, these rotors were tested for artificial mixing efficiencies in a specially designed water tank that can mimic thermal stratification typically observed in an actual water supply reservoir. Among the rotors, the three-stage rotor with a 60° phase shift was found to exhibit superior power and torque coefficients, achieving a power efficiency value of 0.14. As for the mixing efficiency, the four-stage rotor with a 45° phase shift excelled in mixing efficiency, reaching 95%. PRACTITIONER POINTS: A new wind-powered artificial mixing system is designed and tested for various Savonius rotor combinations. While keeping the total rotor height constant, the three-stage Savonius rotor class shows superior performance against the four-stage Savonius rotor class in terms of power and torque efficiency. Apart from the rotor performance results, the four-stage Savonius rotors show greater artificial mixing efficiency than the three-stage Savonius rotors. Single-pump/diffuser artificial destratification system exhibits better mixing efficiency than multiple-pump/diffuser systems.

Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM).

Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reservoir design and management assistance. This study aims to generalize the machine learning model using the support vector machine (SVM), which is support vector regression (SVR), to predict the discharges of the Euphrates River upstream of the Haditha Dam reservoir in Anbar province West of Iraq. Time series data were collected for the period (1986-2024) for the river's daily, monthly, and seasonal flow. Different kernel functions of SVR were applied in this study. The kernels are linear, Quadratic, and Gaussian (RBF). The results showed that the daily time scale is better than the monthly and seasonal performance. In contrast, the linear kernel outperformed the other SVR kernel with a time delay of one day based on the value of the coefficient of determination (R2 = 0.95) and the root mean square error (RMSE = 53.29) m3/sec for predicting daily river flow. The results showed that the proposed machine learning model performed well in predicting the daily flow of the Euphrates River upstream of the Haditha Dam reservoir; this indicates that the model might effectively forecast flows, which helps improve water resource management and dam operations.

Impact of a school-based water and hygiene intervention on child health and school attendance in Addis Ababa, Ethiopia: a cluster-randomised controlled trial.

BACKGROUND: School-based water, sanitation and hygiene (WASH) may improve the health and attendance of schoolchildren, particularly post-menarcheal girls, but existing evidence is mixed. We examined the impact of an urban school-based WASH programme (Project WISE) on child health and attendance. METHODS: The WISE cluster-randomised trial, conducted in 60 public primary schools in Addis Ababa, Ethiopia over one academic year, enrolled 2-4 randomly selected classes per school (~ 100 pupils) from grades 2 to 8 (aged 7-16) in an 'open cohort'. Schools were assigned 1:1 by stratified randomisation to receive the intervention during the 2021/2022 or the 2022/2023 academic year (waitlist control). The intervention included improvements to drinking water storage, filtration and access, handwashing stations and behaviour change promotion. Planned sanitation improvements were not realised. At four unannounced classroom visits post-intervention (March-June 2022), enumerators recorded primary outcomes of roll-call absence, and pupil-reported respiratory illness and diarrhoea in the past 7 days among pupils present. Analysis was by intention-to-treat. RESULTS: Of 83 eligible schools, 60 were randomly selected and assigned. In total, 6229 eligible pupils were enrolled (median per school 101.5; IQR 94-112), 5987 enrolled at study initiation (23rd November-22nd December 2021) and the remaining 242 during follow-up. Data were available on roll-call absence for 6166 pupils (99.0%), and pupil-reported illness for 6145 pupils (98.6%). We observed a 16% relative reduction in odds of pupil-reported respiratory illness in the past 7 days during follow-up in intervention vs. control schools (aOR 0.84; 95% CI 0.71-1.00; p = 0.046). There was no evidence of effect on pupil-reported diarrhoea in the past 7 days (aOR 1.15; 95% CI 0.84-1.59; p = 0.39) nor roll-call absence (aOR 1.07; 95% 0.83-1.38; p = 0.59). There was a small increase in menstrual care self-efficacy (aMD 3.32 on 0-100 scale; 95% CI 0.05-6.59), and no evidence of effects on other secondary outcomes. CONCLUSIONS: This large-scale intervention to improve school WASH conditions city-wide had a borderline impact on pupil-reported respiratory illness but no effect on diarrhoeal disease nor pupil absence. Future research should establish relationships between WASH-related illness, absence and other educational outcomes. TRIAL REGISTRATION: ClinicalTrials.gov, number NCT05024890.

Research on mine water source classifications based on BO-CatBoost.

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process Bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Study on water resources carrying capacity based on Pressure-State-Response modeling: An empirical study of the urban agglomeration in Central Yunnan, China.

Water resources carrying capacity (WRCC) is the basis for sustainable regional development and an important indicator of core competitiveness, and its quantitative assessment and comparison is a key link in clarifying the development capacity of the region. The study comprehensively considers economic, social, resource, environmental and ecological factors, constructs a WRCC evaluation index system based on the Pressure-State-Response (PSR) model, adopts the entropy value method to assign weights to each index, and utilizes the set-pair analysis method and the obstacle diagnostic model to evaluate WRCC of the urban agglomerations in central Yunnan (UACY) in the period from 2008 to 2020. The results show that the comprehensive development of WRCC of UACY is characterized by stage-by-stage evolution in the time dimension, with a decreasing trend in the carrying capacity from 2008 to 2012, and an overall fluctuating upward development trend from 2013 to 2020. In the spatial dimension, Kunming's WRCC is generally poor, and Honghe and Yuxi have the greatest advantages in water resources storage and conservation capacity. The stress of water use in Kunming is higher, but Kunming has advantages in industrial structure and water resources development and utilization rate. Through the diagnosis of obstacle degree, the main obstacle factors of WRCC have large differences among cities (states), but the main subsystems constraining WRCC are all pressure subsystems. The results of the study can provide data support for water resources related policies and rational water resources dispatching in the UACY.

Menstrual hygiene management practice and associated factors among secondary school girls in eastern Ethiopia: The influence of water, sanitation and hygiene facilities.

BACKGROUND: Inadequate water supply, poor sanitation and hygiene (WASH) facilities in schools, insufficient puberty education and a lack of hygienic menstrual hygiene management (MHM) items (absorbents) cause girls in developing countries like Ethiopia to view menstruation as shameful and uncomfortable. However, there was little evidence of female MHM practice and associated factors among secondary school girls in eastern Ethiopia. OBJECTIVE: To assess MHM practice and associated factors among secondary school girls in eastern Ethiopia. DESIGN: Institutional-based cross-sectional study design was conducted. METHODS: A total of 473 secondary schoolgirls were selected using a stratified sampling technique. Data were collected using a self-administered structured questionnaire, double-entered to Epidata version 3.1 and exported to SPSS version 26 for analysis. Descriptive analysis was computed using frequency, percentage, mean and standard deviations. Multivariable logistic regression was applied to assess the association of the school water, sanitation and hygiene facilities with MHM. Adjusted odd ratio (AOR) with its 95% confidence interval was computed to show the strength of the association between dependent and independent variables. The goodness of fit of the model was tested by the Hosmer-Lemeshow goodness of fit test. RESULTS: Out of 473 study participants, 72.93% of them (95% CI: 68.74-76.76) reported good MHM practices. The study also found that availability of continuous water supply (AOR = 2.40, 95% CI: (1.42-4.01)); types of toilet (AOR = 2.01, 95% CI: (1.20-3.40)), confined space (AOR = 3.02, 95% CI: (1.49-4.76)) and having females' toilets alone inside in the school (AOR = 2.70, 95% CI: (1.20-4.40)) were significantly associated with female students' good MHM practice. CONCLUSION: The survey revealed that some of the secondary schoolgirls practiced poor menstrual hygiene management (MHM), which needs further improvement. The study also found that the availability of continuous water supply in the school, types of toilet facility in the school, availability of private space to manage periods at school, learning about MHM in schools and availability of female toilets kept locked inside were factors significantly associated with MHM practice of students, which require integration of Zonal Health and education bureau to jointly work towards the improvement of school WASH facilities.

Entamoeba histolytica in Different Water Sources of Nigde Province of Turkey.

PURPOSE: This study was carried out to determine the presence of Entamoeba histolytica in water sources of Nigde province in Turkey, between June and November 2021. METHODS: A total of 90 water samples were taken from 15 different water sources (drinking water, well water, spring water, wastewater and dam water) every month and the presence of E. histolytica antigens in the samples was examined by ELISA. RESULTS: The positivity for E. histolytica was determined in 7 (7.7%) of 90 samples. While no antigens were found in any of the samples in June and September, E. histolytica was positive for three samples (20%) in July, one sample (6.6%) in August and October and two samples in November (13.3%). One of 24 dam samples (4.1%), 1 of 12 wastewater samples (8.3%), 1 of 12 well samples (8.3%), and 4 of 24 fountain samples (16.6%) that examined by ELISA were found positive. On the other hand, none of the examined 18 spring samples were positive. In addition, 4 (8.8%) of 45 samples that examined in summer and 3 (6.6%) of 45 samples that examined in autumn were detected positive by using ELISA. Entamoeba histolytica positivity in samples was statistically insignificant in terms of months, water resources and seasons (P > 0.05). CONCLUSION: As a result, the presence of E. histolytica, which is an important public health problem in water sources, was determined for the first time in Nigde province of Türkiye with this study.

Waterborne Cryptosporidium species and Giardia duodenalis in resources of MENA: A systematic review and meta-analysis.

This review explores our understanding of Cryptosporidium species and Giardia duodenalis distribution in Middle East and North African (MENA) water resources. Results emphasize that Cryptosporidium species (sp.) and G. duodenalis (oo)cysts are present in distinct categories of water in ten MENA countries. Cryptosporidium sp. proportional prevalence in the MENA region was 24.5% (95% CI 16.3-33.8), while G. duodenalis prevalence was 37.7% (95% CI 21.9-55.1). Raw wastewater and surface water were the water categories most significantly impacted. Both parasites were reported in the various types of MENA drinking waters. The most frequent species/genotypes reported were C. hominis, C. parvum, and G. duodenalis assemblage A. Despite the high prevalence of (oo)cysts reported, we should consider the absence of waterborne outbreaks. This indicates significant underestimation and underreporting of both parasites in MENA. Stakeholders should apply water contamination legislation to eradicate Cryptosporidium sp. and G. duodenalis (oo)cysts from water resources/categories.

The Contribution of Legionella anisa to Legionella Contamination of Water in the Built Environment.

Legionella bacteria can proliferate in poorly maintained water systems, posing risks to users. All Legionella species are potentially pathogenic, but Legionella pneumophila (L. pneumophila) is usually the primary focus of testing. However, Legionella anisa (L. anisa) also colonizes water distribution systems, is frequently found with L. pneumophila, and could be a good indicator for increased risk of nosocomial infection. Anonymized data from three commercial Legionella testing laboratories afforded an analysis of 565,750 water samples. The data covered July 2019 to August 2021, including the COVID-19 pandemic. The results confirmed that L. anisa commonly colonizes water distribution systems, being the most frequently identified non-L. pneumophila species. The proportions of L. anisa and L. pneumophila generally remained similar, but increases in L. pneumophila during COVID-19 lockdown suggest static water supplies might favor its growth. Disinfection of hospital water systems was effective, but re-colonization did occur, appearing to favor L. pneumophila; however, L. anisa colony numbers also increased as a proportion of the total. While L. pneumophila remains the main species of concern as a risk to human health, L. anisa's role should not be underestimated, either as a potential infection risk or as an indicator of the need to intervene to control Legionella's colonization of water supplies.

Integrated simulation-surrogate-optimization modeling framework for multiple tradeoffs among socioeconomic and ecological targets in reservoir operations.

Integrated reservoir water quantity and quality management is significant for water supply security and river ecosystem health. However, the spatiotemporal heterogeneity of water quality and the nonuniform response of multiple indicators to operation changes make it difficult to determine optimal operation schedules. This study proposes a coupled simulation-surrogate-optimization modeling approach for compromising multiple water quantity and quality targets in reservoir operations. The Environmental Fluid Dynamics Code (EFDC) was used to simulate spatiotemporal reservoir water quality dynamics. Subsequently, an ecological damage assessment method was established, accounting for the spatiotemporal heterogeneity of multiple water quality indicators and the nonlinear relationship between the water quality deterioration and ecological damage. To quickly simulate the ecological damage, a surrogate model was developed using the nonlinear autoregressive network with exogenous inputs (NARX). Finally, the surrogate model was integrated into a reservoir operation optimization model for compromising socioeconomic and ecological targets. By applying the methods to China's Danjiangkou Reservoir as a case, it was shown that more even nutrient distribution in the reservoir increased water eutrophication area while reducing concentration peak values, which helped decrease the ecological damage. Operation changes could lead to opposite effects on in-reservoir and downstream ecological targets, increasing operation optimization complexity. Both ecological and socioeconomic benefits significantly increased (by 9.4%-16.4%) during dry years under the optimized operation scheme, implying that synergies were obtained. This study offers implications and a management tool for reservoir operations to address the multiple tradeoffs among socioeconomic and ecological benefits.

Unlocking synergies of drawdown operation: Multi-objective optimization of reservoir emergency storage capacity.

Optimizing reservoir drawdown operations holds significant implications for hydropower generation, water supply, and drought mitigation strategies. However, achieving multi-objective optimization in reservoir drawdown operations poses fundamental challenges, particularly considering emergency storage capacity and seasonal drought patterns. This study introduces a novel multi-objective optimization framework tailored for a mega reservoir, focusing on drawdown operations to enhance hydropower generation and water supply reliability. A drawdown operation model leveraging a multi-objective ant lion optimizer is developed to simultaneously maximize reservoir hydropower output and minimize water shortage rates. China's Three Gorges Reservoir (TGR), situated over the upper reaches of the Yangtze River, constitutes the case study, with the standard operation policy (SOP) serving as a benchmark. Results showcase the efficacy of the proposed method, with substantial improvements observed: a 10.6% increase in hydropower output, a 6.0% reduction in water shortage days, and a 9.5% decrease in minimal reservoir water release compared to SOP. This study provides robust technical and scientific bolster to optimize reservoir ESC and enhance the synergy between hydropower generation, water supply, and drought resilience. Additionally, it offers decision-makers actionable strategies that account for emergency water supply capacities. These strategies aim to support mega reservoir's resilience against extreme drought events facilitating the collaboration between modelers and policy-makers, by means of intelligent optimization and decision-making technologies.

Dynamic marginal cost curves to support water resources management.

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

Unveiling a water-resilient service economy: A model-based approach for enhanced service excellence in GVCs.

Urbanization, climate change, and irresponsible resource management exacerbate the global water crisis. The necessity for water resilience, the capacity of systems and communities to adjust and flourish in the face of water shocks and pressures, has been brought to light by these critical issues. Water resilience enables Global Value Chains (GVCs) to survive scarcity, pollution, and flooding, ensuring sustainability and service delivery. Current service excellence models focus on stakeholder satisfaction, punctuality, and reliability over water resilience. This oversight may limit GVC growth and flexibility, reducing sector services. As recommended service excellence models focus on satisfaction, punctuality and reliability among stakeholders but water resilience is not considered. This can create issues for the growth and flexibility of GVCs which could cut back on services sector. This research is examined a complex relationship between service quality and water resilience to improve the GVCs in China regions especially Guangdong Province, Shanghai Municipality, and Beijing Municipality. By using multiple regression, GVCs service quality and water resilience is analyzed in the existence of Service Excellence Model. The study used 15 years (2009-2023) secondary data to measure how water resilience and GVCs services quality affect each other in Chinese regions. The results show that water resilience strategies can strengthen global production networks, optimize resource usage, and enhance service excellence. Chinese GVCs can produce a water-resilient service economy, enlightening service quality and preserving competitiveness in rapidly changing global markets.

Seeking a pathway towards a more sustainable human-water relationship by coupled model - From a perspective of socio-hydrology.

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010-2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Long-term trends in human-induced water storage changes for China detected from GRACE data.

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024-2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

Political Economy of Maternal Child Malnutrition: Experiences about Water, Food, and Nutrition Policies in Pakistan.

This study examined access to water, food, and nutrition programs among marginalized communities in Southern Punjab, Pakistan, and their effects on nutrition. Both qualitative and quantitative data were used in this study. We held two focus group discussions (one with 10 males and one with 10 females) and conducted in-depth interviews with 15 key stakeholders, including 20 mothers and 10 healthcare providers. A survey of 235 households was carried out to evaluate water and food insecurity, with the data analyzed using Wilcoxon's rank-sum test, t-test, and Pearson's chi-square test. The results revealed that 90% of households experienced moderate-to-severe water insecurity, and 73% faced moderate-to-severe food insecurity. Household water and food insecurity were positively correlated with each other (correlation coefficient = 0.205; p = 0.004). Greater household water (p = 0.028) and food insecurity (p < 0.001) were both associated with higher perceived stress. Furthermore, lower socioeconomic status was strongly related to higher levels of water (p < 0.001) and food insecurity (p < 0.001). Qualitative findings highlight the impact of colonial and post-colonial policies, which have resulted in water injustice, supply issues, and corruption in water administration. Women face significant challenges in fetching water, including stigma, harassment, and gender vulnerabilities, leading to conflicts and injuries. Water scarcity and poor quality adversely affect sanitation, hygiene, and breastfeeding practices among lactating mothers. Structural adjustment policies have exacerbated inflation and reduced purchasing power. Respondents reported a widespread lack of dietary diversity and food quality. Nutrition programs face obstacles such as the exclusion of people with low social and cultural capital, underfunding, weak monitoring, health sector corruption, and the influence of formula milk companies allied with the medical community and bureaucracy. This study concludes that addressing the macro-political and economic causes of undernutrition should be prioritized to improve nutrition security in Pakistan.