Effective Mitigation Measures Research Articles

Physics-based modeling of debris flows and assessing the performance of effective mitigation measures

BackgroundClimate change-induced geohazards are increasingly posing a critical threat to the sustained functionality and resilience of constructed infrastructures. Among these hazards, debris flows represent significant natural disasters, particularly in mountainous terrains, causing widespread property damage and loss of life globally. These phenomena involve complex interactions between solid and fluid forces, resulting in long run-out distances and high-speed flows. Predicting and monitoring debris flows is challenging due to the intricate interplay of these forces.ObjectiveThe objective of this study is to evaluate the structural damage caused by debris flows and assess the efficacy of rigid barrier structures with passages/apertures.MethodEmploying Smoothed Particle Hydrodynamics, a Lagrange-based mesh-free computational technique, the researchers simulated the impact of debris on a stiff structure. The focus was on the Rishiganga river valley in the Indian state of Uttarakhand, a region recently devastated by a debris flow that severely compromised essential infrastructure, including a hydroelectric power station. The research entailed modeling the debris flow in this specific locale and analyzing its effects on an assumed downstream rigid structure. Additionally, the study explored the outcomes of introducing a rigid barrier upstream.Results and ConclusionResults indicated a substantial reduction in the impact on the downstream structure with the presence of an upstream rigid structure. Moreover, as the number of apertures in the upstream barrier was increased, the impact of flow on the downstream structure further diminished, because of a more streamlined flow pattern.

The impact of changing exposure to PM2.5 on mortality for US diplomats with multiple international relocations: a modelling study.

Current evidence linking long-term exposure to fine particulate matter (PM2.5) exposure and mortality is primarily based on persons that live in the same residence, city and/or country throughout the study, with few residential moves or relocations. We propose a novel method to quantify the health impacts of PM2.5 for United States (US) diplomats who regularly relocate to international cities with different PM2.5 levels. Life table methods were applied at an individual-level to US mortality statistics using the World Health Organization's database of city-specific PM2.5 annual mean concentrations. Global Burden of Disease concentration-response (C-R) functions were used to estimate cause-specific mortality and days of life lost (DLL) for a range of illustrative 20-year diplomatic assignments for three age groups. Time lags between exposure and exposure-related mortality risks were applied. Sensitivity analysis of baseline mortality, exposure level, C-R functions and lags was conducted. The effect of mitigation measures, including the addition of air purifiers, was examined. DLL due to PM2.5 exposure for a standard 20-year assignment ranged from 0.3 days for diplomats' children to 84.1 days for older diplomats. DLL decreased when assignments in high PM2.5 cities were followed by assignments in low PM2.5 cities: 162.5 DLL when spending 20 years in high PM2.5 cities compared to 62.6 DLL when spending one of every four years (5 years total) in a high PM2.5 city for older male diplomats. Use of air purifiers and improved home tightness in polluted cities may halve DLL due to PM2.5 exposure. The results were highly sensitive to lag assumptions: DLL increased by 68% without inception lags and decreased by 59% without cessation lags for older male diplomats. We developed a model to quantify health impacts of changing PM2.5 exposure for a population with frequent relocations. Our model suggests that alternating assignments in high and low PM2.5 cities may help reduce PM2.5-related mortality burdens. Adding exposure mitigation at home may help reduce PM2.5 related mortality. Further research on outcome-specific lag structures is needed to improve the model.

In Search of Suitable Methods for Cost-Benefit Analysis of Cyber Risk Mitigation in Offshore Wind: A Survey

In recent years, notable incidents have highlighted the vulnerability of wind energy infrastructure, making cybersecurity crucial for the offshore wind industry. However, justifying the costs of cybersecurity measures is essential. A cost benefit analysis (CBA) is commonly utilised to support decision-making for risk mitigation. With a cost benefit analysis, risk mitigation strategies that strike an optimal balance between the costs of mitigation measures and the resulting risk reduction can be identified. This survey of literature was carried out to identify the existing proposed solutions for cost benefit analysis on cyber risk mitigation measures for offshore wind cyber physical systems. After narrowing the area scope, a systematic search across Scopus and Web of Science, yielded 18 articles, of which six met the selection criteria. It was found that the there was a lack of cost benefit analysis of cybersecurity solutions for, or set in, the area of offshore wind directly. From the analysis of the surveyed works, suggestions on future directions were given. The existing literature found lacks detailed cost modelling for offshore wind, beyond general breakdowns encompassing capital, maintenance, and labour/installation expenses, risk and scenario loss. Some of the literature used contextual factors such as compatibility and effectiveness of mitigation measures, effects on OT performance, geographical location, geopolitical context, and installed rated power which could be adapted to suit offshore wind. Since offshore operations contribute significantly to costs, cost modelling and consideration of other relevant factors pertaining to this area would be beneficial if explored. As an emerging area, in the future we expect this research to be a basis and a methodology that can be expanded with a larger data set from other publications in the field. Thus, it represents an opportunity to advance knowledge in offshore wind cyber-physical systems.

Land cover and drought risk assessment in Türkiye's mountain regions using neutrosophic decision support system.

Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000-2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye's mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.

Fingerprinting to trace sources of suspended solids in the transport of heavy metals in urban stormwater runoff

Suspended solids are an important pollutant in urban stormwater runoff. Past studies have mainly focused on a single transport stage of pollutants, constraining source identification of suspended solids at the catchment scale. Therefore, identifying the sources of suspended solids in stormwater runoff for the formulation of effective pollution mitigation measures is an effective way to manage suspended solids pollution in receiving waters. Sediment source fingerprinting is a widely used technique to trace the sources of river sediments, which can accurately identify the source of sediment through widely used tracers. This study used six heavy metals including Cd, Cr, Ni, Cu, Zn and Pb as tracers to quantify the sources of suspended solids in stormwater runoff from urban catchments. The spatial and temporal distribution characteristics of suspended solids during stormwater transport were investigated. The study results showed that the concentration of suspended solids was the highest in road runoff and sewer flow, especially particles <44 μm. In addition, relatively large rainfall depth, high rainfall intensity and long antecedent dry periods can lead to higher concentrations of suspended solids in roof and road runoff whereas longer rainfall duration can result in more suspended solids in sewer runoff. Sediment source fingerprinting and principal component analysis confirmed that coarse (>105 μm) particles primarily originate from road deposited sediments (63.80%), while fine (<105 μm) particles primarily originate from stormwater grate sediments and soil. The outcomes derived can help to comprehensively understand the sources of suspended solids and provide guidance for the management of urban stormwater particulate pollution, as well as being a technical reference for pollutant source traceability in urban stormwater runoff.

Factors shaping home ranges of Eurasian lynx (Lynx lynx) in the Western Carpathians

Understanding how large carnivores utilize space is crucial for management planning in human-dominated landscape and enhances the accuracy of population size estimates. However, Eurasian lynx display a large inter-population variation in the size of home ranges across their European range which makes extrapolation to broader areas of a species distribution problematic. This study evaluates variations in home range size for 35 Eurasian lynx in the Western Carpathians during 2011–2022 based on GPS telemetry and explains how intrinsic and environmental factors shape lynx spatial behaviour when facing anthropogenic pressure. The average annual home range size of lynx ranged from 283 (± 42 SE) to 360 (± 60 SE) km2 for males and from 148 (± 50 SE) to 190 (± 70 SE) km2 for females, depending on home range estimator (95% MCP, KDE and AKDE). Females with kittens had smaller annual and summer home ranges compared to non-reproducing females and subadults had smaller home ranges compared to adults. Lynx home range size was explained by availability of roe deer, except for summer, when alternative prey was likely available. We also found clear evidence of human-induced changes in lynx home range size, in particular, forest cover significantly decreased the home range size of male lynx during summer while road density led to an expansion of both annual and summer lynx home ranges. Lynx exhibited consistent fidelity to their home ranges throughout consecutive seasons, showing no seasonal variations. Strong territoriality was observed among competing males maintaining relatively low home range overlaps and considerable distances between centres of activity. The most pronounced tendency for association was observed between males and females, maintaining relatively close proximity year-round. The insights into lynx spatial requirements provided by our study will greatly enhance the accuracy of population size estimates and effectiveness of mitigation measures across the Western Carpathians.

Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability

To ensure food security in this age of production and supply disruption, the agricultural sectors of emerging economies are gradually adopting more smart technologies to achieve sustainability. However, literature on the challenges of adopting Agriculture 4.0-based smart farming technologies is still very limited. This research, therefore, explores the contextual interrelation among the challenges to adopting Agriculture 4.0-based smart technologies in the agricultural production system from a developing country's perspective and prioritizes the identified challenges. A case study was conducted in Bangladesh, an emerging economy, where data was collected through interviews and focus group discussion sessions. A total of 21 challenges were finalized as relevant to the country's context. The Interpretive Structural Modeling (ISM) technique was deployed to develop a hierarchical structure depicting the challenges' interrelations. The challenges were later ranked based on their relevant weight using the Best-Worst Method (BWM). This study finds technological complexity, lack of collaboration among different stakeholders, inadequate support from the government, and lack of action plans to have very high driving power. Challenges such as high initial investment and operational costs, lack of skilled workforce, and farmers' resistance were found to be dependent challenges. This study is expected to contribute by providing a deeper insight into the challenges of adopting Agriculture 4.0 in emerging economies so that practitioners can take effective mitigating measures to streamline the plant-based agricultural production systems to promote food security and sustainability.

Microplastics and nanoplastics in soil: Sources, impacts, and solutions for soil health and environmental sustainability.

The present review discusses the growing concern of microplastics (MPs) and nanoplastics (NPs) in soil, together with their sources, concentration, distribution, and impact on soil microorganisms, human health, and ecosystems. MPs and NPs can enter the soil through various pathways, such as agricultural activities, sewage sludge application, and atmospheric deposition. Once in the soil, they can accumulate in the upper layers and affect soil structure, water retention, and nutrient availability. The presence of MPs and NPs in soil can also have ecological consequences, acting as carriers for pollutants and contaminants, such as heavy metals and persistent organic pollutants. Additionally, the leaching of chemicals and additives from MPs and NPs can pose public health risks through the food web and groundwater contamination. The detection and analyses of MPs and NPs in soil can be challenging, and methods involve spectroscopic and microscopy techniques, such as Fourier-transform infrared spectroscopy and scanning electron microscopy. To mitigate the presence and effects of MPs and NPs in soil, it is essential to reduce plastic waste production, improve waste management practices, and adopt sustainable agricultural practices. Effective mitigation measures include implementing stricter regulations on plastic use, promoting biodegradable alternatives, and enhancing recycling infrastructure. Additionally, soil amendments, such as biochar and compost, can help immobilize MPs and NPs, reducing their mobility and bioavailability. This review article aims to provide a comprehensive understanding of these emerging environmental issues and identify potential solutions to alleviate their impact on soil health, ecosystem functioning, and community health.

Flood Modelling of Madi River Using HEC-RAS by Rain on Grid Approach

Indispensable for prognosticating flood impact in areas with limited data availability, reliable flood models are crucial for the analysis and mitigation of flood hazard. This study provides insight to accurately analyse flood scenarios by preparing a well calibrated 2D hydraulic model in HEC-RAS along with the Rain on Grid approach, which is subsequently used for hazard map preparation of Madi River. Hazard classification of flood depth indicated that for a 20-year return period, Moderate hazard levels covered 16.499%, High hazard levels covered 14.831%, and Very High hazard levels covered 68.670% of the total inundated area with reference to the depth hazard categories. Since, significant portion of the inundated area is classified as Very High Hazard; the finding of this research emphasizes the need of effective mitigation measure and provides essential insights crucial for flood risk assessment.

Potential use of Environmental Management Plan for the Sustainability of Coastal Area and Resources

The potential use of Environmental Management Plans (EMPs) for the sustainability of coastal areas has demonstrated significant benefits in enhancing environmental protection, regulatory compliance, and stakeholder engagement. However, challenges such as data collection, monitoring, and the effectiveness of mitigation measures must be addressed to fully realize the potential of EMPs. By investing in baseline data, implementing robust monitoring and adaptive management practices, enhancing stakeholder engagement, integrating EMPs with broader management frameworks, and allocating adequate resources, the effectiveness and sustainability of EMPs can be significantly improved.

Vulnerabilities and Threats to AIS Security Systems

This research explores vulnerabilities and threats to Accounting Information Systems (AIS) and evaluates effective mitigation measures to address these issues. Using both qualitative and quantitative approaches, this research involved literature studies, surveys, expert interviews, and case analysis to identify and analyze the different types of vulnerabilities and threats faced by AIS. The results showed that human error, software flaws, and system integration complexity are the main sources of vulnerabilities in AIS. The most common threats are cyberattacks, insider threats, and Distributed Denial of Service (DDoS) attacks. The impact of these threats includes financial loss, reputational damage, and significant legal implications.Mitigation strategies identified include the development of robust security policies, the use of cutting-edge security technologies, and the conduct of regular audits and monitoring. The findings emphasize the importance of a layered approach to improving AIS security and reliability. This research provides an in-depth insight into how organizations can identify, evaluate and manage vulnerabilities and threats to their AIS, which is essential for maintaining the integrity, confidentiality and availability of accounting data in an increasingly complex digital age.

Characterization and Quantification of Dam Seepage Based on Resistivity and Geological Information

Dam seepage significantly poses a serious threat to both the reservoir safety and the ecological health of the surrounding area. Characterizing and quantifying seepage zones is essential for effective risk mitigation and reinforcement measures. In this study, Electrical Resistivity Tomography (ERT) was applied to detect seepage on a reservoir dam. The ERT survey included three survey lines along the dam. The results indicated low resistivity in seepage zones, showing a distribution extending to the deep section in the middle of the dam and shallow section on both sides of the dam. The reservoir water came out to the ground surface in the downstream from seepage zones. Five seepage models were constructed to quantify seepage based on geological information. The models were further modeled based on the ERT results. Simulated results revealed the annual seepage of the reservoir is 78,880.16 m3. However, 75.5% of the total seepage is contributed by a region representing 50% of the dam. This concentrated seepage should draw the attention of future safety monitoring and reinforcement efforts. This study combines geophysics, geological, and numerical simulation to quantify dam seepage. This allows for the development of more scientifically sound solutions for preventing seepage and improving drainage ability in reservoir dams.

Implementation of Carbon Trading: Mechanisms of Execution and Legal Protection for Shareholders on the Indonesia Carbon Exchange

This research aims to find out how the trading mechanism on the carbon exchange and legal protection for company shareholders in carbon trading transactions on the Indonesian carbon exchange. Increased global awareness of climate change has prompted many countries, including Indonesia, to adopt more effective mitigation measures. A carbon exchange is a platform for buying and selling carbon credits, which can be obtained by companies that have taken decarbonisation measures. Through carbon trading, investors can be involved in reducing carbon emissions through a scheme that has been provided, namely emission trading or emission offsets. The Indonesian government issued carbon trading regulations and policies as an effort to reduce greenhouse gas emissions globally, and the presence of the Indonesian Carbon Exchange (IDX Carbon) provides positive sentiment for a number of issuers engaged in the environmentally friendly sector. This is because the presence of a carbon exchange opens up opportunities for environmentally friendly companies to increase their revenue by selling carbon credits. The method used in this research is normative juridical with statutory approach and conceptual approach. The results of this research show that shareholders of carbon exchange organisers can only be owned by sui generis institutions, Indonesian citizens, Indonesian legal entities, and/or foreign legal entities that have obtained permission or are under the supervision of the financial services regulator in their home country. In addition, members of the board of directors of the carbon exchange organiser must be domiciled in Indonesia.

Biomimetic multi-functional 3D fog collector: Independent of fog flow direction

The shortage of freshwater resources has a great impact on the daily life of human beings, and fog collection has become an effective mitigation measure. In this paper, inspired by the nanofabricated desert beetle, honeycomb, Euphorbia antiquorum L. and cactus, a 3D fog collector combining a conical array structural gradient and a wettability gradient was presented, with special three-dimensional array structure (TA-L/B (PPC)) that ensures that the fog-collecting efficiency of the samples is virtually independent of the direction of the fog flow. The fog collection efficiency of sample TA-L/B (PPC) is close to 4.3 g·h−1·cm−2, a value that is 1.77 times higher than the surface fog collection efficiency of the copper sheet (OS) sample. By rationally designing the surface structure of the sample, the fog circulation process is accelerated, and a sustainable and stable fog collection process is achieved.

Environmental Issues and Sustainable Development: The Global and Indian Perspective

The paper aims to discuss about the historic trend of global warming and greenhouse gas emission since the late 18th century due to widespread use of fossil fuels and industrial processes. This paper provides an overview of rise of greenhouse gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), nitrogen trifluoride (NF3), and other gases and their contribution in warming the global temperature in the prior and later period of industrial revolution. Highlighting the relationship between greenhouse gas emission and human activities that has resulted in significant challenges to the global ecosystems that includes severe and frequent heatwaves, altered precipitation patterns, melting of glaciers, rising of sea level, along with the backward economies facing disproportionate risks and challenges in water resources, food scarcity due to draughts and high temperature as well as health issues due to low productivity and slow development. Hence, considering the problems faced by the societies worldwide, it is essential to have cooperative strategies with local, national and international agencies to implement effective mitigation measures and adaptation schemes so that the societies could foster resilience, mitigate risks and work towards a sustainable future.

Exploring the driving factors of urban flood at the catchment Scale: A case study of multitype megacities in China

With the increasing frequency of extreme rainfall events worldwide, urban flood in densely populated and economically vibrant urban areas has become a pressing issue drawing global attention. Catchments are fundamental units for understanding urban hydrological processes and managing flood. Current research often lacks an investigation into the importance and spatial differences of driving factors for urban flood at the catchment scale, particularly for different types of cities. This study uses 15 megacities in China as case studies and establishes a comprehensive system of driving factors. It categorizes cities based on population levels, climate zones, and spatial locations. The study begins with hotspot analysis to identify areas with high occurrences of urban inundation. Subsequently, it employs Geographic Weighted Regression (GWR), a spatial analysis technique, to unveil the mechanisms driving the spatial distribution of inundation points in different types of cities and the spatial heterogeneity of driving factors at the urban catchment scale. The research findings, based on multiple cities, demonstrate that urban inundation points exhibit clear spatial clustering characteristics. While impervious surfaces and urban buildings are generally more important than factors such as landscape patterns and population density, the importance and explanatory power of driving factors for the spatial distribution of urban inundation vary across different cities and even within different catchments of the same city. The categorization of cities based on population levels and spatial locations demonstrates stronger regularities in driving mechanisms compared to categorization based on climate zones. The study’s results further underscore the complex driving mechanisms behind the spatial distribution of urban inundation. This highlights the need for flood management strategies that consider both universal patterns of inundation occurrence and the specific characteristics of local driving factors to develop targeted and effective mitigation measures.

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Long life spans can mitigate the genetic effects of strays from temporary conservation hatchery programs.

While conservation fish hatchery programs can be a valuable tool providing demographic support, they can also cause domestication, risking unintended fitness consequences to both target and connected populations. When conservation hatcheries are used over a fixed amount of time, the interaction between program duration and species life history might determine the scale of fitness effects and the effectiveness of mitigation measures. We develop a mathematical model to quantify the effectiveness of approaches to mitigate unintended fitness consequences in such temporary conservation hatchery programs. We parameterize our model to represent a conservation aquaculture-based recovery program for white sturgeon (Acipenser transmontatus) in the Nechako River (British Columbia), which might impose genetic risks on the adjacent populations within the Fraser River. We find that, over a period spanning 50-200 years of hatchery operation, the life history characteristics of white sturgeon, particularly late age of maturity and longevity, reduce the genetic risks of conservation hatcheries when compared to shorter-lived species. The genetic impacts of the hatchery accumulate slowly providing increasing the potential for adaptive management in this system.

Flood detection in the Upper Krishna Basin through integrated geospatial analysis: leveraging decision frameworks and statistical measures

ABSTRACT Floods threaten the environment and human settlements across river basins globally, including the Upper Krishna Basin in India. This research delves into evaluating flood hazard areas within the Upper Krishna Basin utilizing the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Statistical Index (SI). These methodologies prioritize and classify flood-prone regions by integrating spatial and non-spatial criteria. The findings reveal significant variations in flood risk classification across the Upper Krishna Basin based on the three models. The AHP model identifies 3.37% of the region as low flood risk, with 22.90% classified as moderate risk, and 68.27% as high risk. In contrast, the FR model designates 3.76% as low risk, 10.50% as moderate risk, and 42.21% as high risk. Meanwhile, the SI model identifies 1.04% of areas with low risk, 35.38% with under-high risk, and 57.87% with very high risk. Validation using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) values demonstrates the superior reliability of the SI model. These findings offer valuable insights for decision-makers to allocate resources and implement effective flood mitigation measures.

Assessment on the Impact of Mining and Industrial Activities in Groundwater Quality in Chandrapur, Maharashtra Through Remote Sensing and GIS Applications

This study evaluated the quality of groundwater in Chandrapur Taluka, Maharashtra, using geospatial techniques and data from 2014 and 2018. The study assessed various water quality parameters such as chloride, fluoride, pH, residual sodium carbonate (RSC), and sodium adsorption ratio (SAR). The findings showed that chloride concentrations were mostly within acceptable limits, but there was a slight increase in areas near mining and industrial sites, which was statistically significant (p &lt; 0.05). Fluoride levels were generally within permissible bounds, but there was a noticeable increase near industrial areas in 2018 compared to 2014, which was statistically significant (p &lt; 0.05) and raised environmental concerns. Variations in pH values were also observed, which could impact aquatic ecosystems, with a decrease noted from 2014 to 2018. RSC levels were higher near mining and industrial zones in 2018, exceeding recommended limits, indicating a potential threat to water quality. SAR levels in 2018 were higher, potentially impacting agriculture, but still within acceptable limits. Sulphate levels showed a reduction from 2014 to 2018. These comprehensive findings highlight the specific impacts of mining and industrial activities on groundwater quality and raise broader scientific questions. They can be used to develop evidence-based policies for effective mitigation measures and engage a wider readership.