Environmental Science Research Articles

Quantifying the association between PM2.5 air pollution and IQ loss in children: a systematic review and meta-analysis

Abstract Background A growing body of epidemiologic and toxicologic literature indicates that fine airborne particulate matter (PM2.5) pollution is neurotoxic and threatens children’s neurobehavioral development, resulting in reduced cognitive function. Understanding the magnitude of this effect is critical for establishing public health policies that will protect children’s health, preserve human capital, and support societal progress. Objective To quantify the association between ambient PM2.5 air pollution and loss of cognitive function in children, as measured by Intelligence Quotient (IQ) scores, through a systematic literature review and meta-analysis. Methods Following PRISMA guidelines, we conducted a systematic literature search across seven databases: Agricultural and Environmental Science, BIOSIS Citation Index, Embase, GreenFILE, PubMed, Scopus, and Web of Science to identify original scientific studies that investigated the impact of PM2.5 exposure during pre-and postnatal periods on IQ loss during childhood. Using data from studies included for final review, we conducted a meta-analysis, using a random effects model to compute a beta coefficient that quantifies the overall effect of PM2.5 exposure on Full-Scale IQ (FSIQ), Performance IQ (PIQ), and Verbal IQ (VIQ). Findings Of the 1,107 unique publications identified, six studies met the inclusion criteria for final review, representing 4,860 children across three continents (North America, Europe, and Asia). The mean PM2.5 concentration across all studies was 30.4 ± 24.4 µg/m3. Exposure timing ranged from the prenatal period to mid-childhood. Children were an average of 8.9 years old at the time of cognitive testing. We found that each 1 µg/m3 increase in PM2.5 concentration is associated with a -0.27 point change in FSIQ (p < 0.001), 0.39 point change in PIQ (p = 0.003), and -0.24 point change in VIQ (p = 0.021). Conclusion Through a systematic review and meta-analysis, we identified a statistically significant relationship between increased exposure to PM2.5 air pollution and reduced cognitive function in children, with the most pronounced impact on PIQ. This analysis will enable estimation of the burden of adverse neurobehavioral development attributable to PM2.5 in pediatric populations and will inform local and global strategies for exposure prevention.

Causal discovery from nonstationary time series

AbstractThis paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CD-NOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CD-NOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.

FAIR data management: a framework for fostering data literacy in biomedical sciences education.

Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022-2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master's thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.

INNOVATIVE AIRSHIP DESIGN FOR REAL-TIME AIR QUALITY MONITORING USING IOT TECHNOLOGY

The effect of poor air quality on human health has been linked to both short-term and long-term exposure to air pollutants. One of the most important steps in reducing emissions is accurate identification of these pollutants. This can be achieved by using Internet of Things equipped remotely operated airships that can survey large areas and gather pollution data. The ability of an airship to function at low altitudes for extended periods is the main design requirement of airships. This study describes a methodology for conceptualizing and building a helium-filled blimp for remote measurement of atmospheric pollutants (Carbon monoxide, Carbon dioxide, and Sulphur dioxide), temperature, and pressure. An onboard telemetry system measures the data while the airship is in flight and transmits them in real time to a ground-based station. The experiment showed that remotely operated airships are capable of gathering air data and their quality, enabling environmental scientists and regulatory agencies to better understand the behaviour of air pollutants and take steps to mitigate them.

Climate responsive innovations at Karachi steel

Learning outcomes The learning outcomes are as follows: to identify and analyze the importance of operations management in a situation demanding minimizing environmental impact and maintaining operational momentum; access the risks faced during project executions and apply project management concepts to facilitate Karachi Steel in implementing indigenous technological solutions; and evaluate the importance of adaptability, continuous improvement and innovation in creating sustainable solutions to address complex challenges. Case overview/synopsis Javaid Iqbal, CEO of Karachi Steel, was the case’s protagonist. With capacity expansion, Javaid relocated the steel facility from Rawalpindi to Islamabad, Pakistan. The company encountered several difficulties because of the air emissions’ inconvenience to nearby residents and the strict environmental regulations. To push the emissions into the air, the company first installed a locally fabricated chimney. Later, they hired a foreign Pakistani engineering firm to install air filters, but the project proved unsuccessful. To control emissions, the company developed a Wet Particulate Control (WPC) system based on a water-sprinkling mechanism. The endeavor was successful, but it resulted in water pollution. As a result, Karachi Steel signed a contract with a local engineering company that invented and effectively installed an air filtration system. Karachi Steel not only devised solutions for their predicaments but also made significant contributions toward achieving the Sustainable Development Goals (SDGs). However, the emissions reporting and monitoring mechanism continued to cause inconvenience for regulators. In addition, the filtration facility encountered a blocked duct conveying zinc sulfate from smoke, resulting in the periodic suspension of operations. As Karachi Steel seek long-term solutions to current challenges, it is critical to examine the relationship between internal circumstances and external forces and stimulate a holistic approach to resolving issues within the realms of operations management and project management. Complexity academic level The case study is suitable for students pursuing their undergraduate degree programs in business studies or management sciences. This case can be taught in specific subjects in the domain of management sciences, including project management and operations management. Furthermore, undergraduate students pursuing degrees in environmental sciences, specializing in environmental impact assessment and sustainable development, can also learn from this case study. These subjects have the potential to provide students with a detailed understanding of the dynamic relationship between environmental problems caused by business activities, and how to address these challenges using principles of project management and operations management. There is no pre-requisite for this case study, and the level of difficulty is moderate. The recommended teaching pedagogy for this multidisciplinary case study includes role-playing exercises, simulations to replicate real-world situations and the Socratic method, which encourages critical thinking. Supplementary material Teaching notes are available for educators only. Subject code CSS 7: Management Science.

Azo-Enhanced Raman Scattering Probing Proton Transfer between Water and Nanoscale Zero-valent Iron.

The interaction between a solid and water at their interface, especially proton transfer, impacts molecular-scale catalysis, macroscopic environmental science, and geoscience. Although being highly desired, directly probing proton transfer between a solid and water is a great challenge, given the subnanometer to nanometer scale of the interface. The fundamental challenge lies in the lack of a measurement tool to sensitively observe local proton concentration without introducing an exogenous electrode or nanoparticle with a minimum size of tens of nanometers. Here, we demonstrate an azo-enhanced Raman scattering strategy to design a 2 nm long small-molecule pH probe with a chelating group anchoring to the solid surface. Empowered by the intramolecular Raman enhancing sensitivity, the probe directly observes proton transfer between water and nanoscale zero-valent iron (nZVI), a famous environmental material for pollution control. This molecular-scale interfacial probing methodology offers a powerful tool to pave the way for advanced environmental and geochemical discernment and management.

Transcriptome Analysis of Aureobasidium pullulans YQ65 Grown on Yeast Extract Peptone Glucose and Potato Dextrose Agar Media and Quantification of Their Effects on Pullulan Production

Pullulan is a high-value polysaccharide produced through the fermentation of Aureobasidium pullulans. It has significant applications in the fields of food, medicine, environmental science, and packaging. However, the yield, molecular weight, and other characteristics of pullulan can vary depending on the fermentation substrate used. Therefore, it is crucial to analyze the underlying causes of these variations at the molecular level. In this study, we first investigated the morphological differences in A. pullulans YQ65 when cultured in YPD and PDA media. The results indicated that different culture media significantly influence the primary cell morphology of A. pullulans YQ65, which in turn affects the synthesis of secondary metabolites. Subsequently, we employed different culture media to ferment pullulan and examined the variations in pullulan yield, molecular weight, and biomass. Moreover, FTIR and thermodynamic stability tests were conducted to analyze the differences among pullulans across different culture media. Finally, transcriptome analysis revealed that A. pullulans YQ65, when cultured in YPD and PDA media, regulates its growth and metabolism through the expression of key genes that are involved in pathways such as the proteasome, oxidative phosphorylation, metabolism of various secondary metabolites, fatty acid anabolism, carbon metabolism, and amino acid metabolism. The transcriptome results were further validated by assessing the expression of specific genes. This study enhances the understanding of the fermentation differences observed with different substrates in A. pullulans and provides valuable insights for optimizing culture substrates. Additionally, it offers guidance for utilizing agricultural and forestry processing waste, as well as food processing by-products, to produce pullulan cost-effectively in the future.

Sharing of data archive of radiation exposure animal experiments in QST/NIRS and IES.

Institute for Radiological Science (NIRS), National Institutes for Quantum Science and Technology (QST), and Institute for Environmental Sciences (IES) have conducted large-scale animal experiments for radiation risk analyses in terms of life shortening and cancer prevalence. It is important to store data and biological samples from these large-scale experiments for sharing and future use since the economic and practical limitations, as well as the ethical considerations, make it difficult. QST/NIRS established an archive called the Japan Storehouse of Animal Radiobiology Experiments (J-SHARE) for the purpose of storing and sharing these historic collections. We plan to integrate the data and tissue specimen images obtained at the IES into J-SHARE by standardizing the archive format, with the aim of maximizing the results of radiation biology research. This integration is expected to contribute to the generation of new knowledge for radiation risk assessment and the provision of scientifically based information on radiation protection.

Spatiotemporal changes and influencing factors of ecosystem services in the Nanchang metropolitan area, China

Ecosystem services (ES) such as carbon storage (CS), soil conservation (SC), habitat quality (HQ), and water yield (WY) play a crucial role in maintaining ecological balance and supporting sustainable regional development. With increasing environmental changes, understanding the spatiotemporal dynamics of these services and their driving factors has become essential in environmental science. This study focuses on the Nanchang metropolitan area, quantifying CS, SC, HQ, and WY from 2000 to 2020. It explores the impacts of major factors, including climate, topography, and social aspects, on the spatial heterogeneity of ES. The results reveal that between 2000 and 2020, CS and HQ decreased by 0.1385×108 tons/ha and 0.0507, respectively, while SC and WY increased by 2.4754×109 tons/ha and 1.6668×1010 m3, respectively. Notable spatial heterogeneity exists in the correlation between driving factors and changes in ES. The spatial distribution of ESs is higher in mountainous regions compared to central plains. Among human factors, population (POP) and gross domestic product (GDP) predominantly influenced changes in CS and HQ, whereas climate and POP drove changes in SC. Changes in WY were primarily affected by climate and topography. These findings suggest a need to focus on key driving factors to formulate targeted land policies aimed at enhancing the ES value in the Nanchang metropolitan area.

Rapid Estimation of Soil Moisture Using Spectroscopic Sensor

ABSTRACT Soil moisture plays a significant role in the global hydrological cycle in the field of agriculture, environment science, urban planning and construction, forestry and many more. Soil moisture can be determined using various approaches, however some of the limitations in these approaches are longer time duration, labor intensive, require installation and maintenance, expensive, lower resolution, etc. This research work proposes a soil moisture detection method which is portable, economic and time efficient. Spectroscopic sensor enables rapid soil moisture estimation using photodiodes of varying sensitivities and wavelengths across a broad spectrum of light, allowing precise identification and analysis of various samples based on their unique spectral fingerprints. In this study, a relation between sensor output data and soil moisture percentage value obtained from gravimetric method has been established. Using various regression methods, the highest value of correlation coefficient was found in case of polynomial function of order 3. Residual values of all wavelengths were then calculated by comparing the calculated with the expected moisture values obtained using the polynomial function. Result showed that 705 nanometers (nm) wavelength has the least residual value, thus signifying that the polynomial equation of order 3 at 705 nm wavelength perfectly demonstrates the relation between soil moisture and sensor reading. Furthermore, the results were verified using optimization method which showed that 705 nm wavelength has the highest closeness coefficient, thus verifying this model and observations. This model can be helpful in determining the soil moisture instantaneously instead of hours as seen in traditional gravimetric methods.

Integrated Framework for Enhancing Liveability and Ecological Sustainability in UAE Communities

Urban growth is vast in the United Arab Emirates (UAE) due to economic development, and there is a need to consider liveability and sustainable ecosystems for future urban expansion. Promising strategies for sustainability focus on minimizing a building’s effects on the environment and improving residents’ quality of life, which is important in the desert and when confronting the issues of water and climate change. Sustainable practices that impact the livelihood of people in the UAE include factors such as walkable areas, open space, policing, healthcare, education, housing, and ensuring friendly transport that enhance the overall quality of life of residents in the region. Recognizing and appreciating the UAE’s cultural values is crucial when incorporating these aspects, allowing references to the nation’s character when creating communal areas. The primary research included quantitative surveys of three identified communities, composed of fifty participants each, where the findings indicate partial to full compliance, with 85.7% of the liveability indices being about public transport and green space. Through this analysis, liveability and sustainability principles need to be trialed and incorporated into future urban development to embrace the ecology as well as the inhabitants. To realize these targets, the proposed study adopted a four-part approach. Initially, an analysis of related studies concerning the UAE or the Gulf area was carried out to obtain important liveability and quality-of-life factors. A total of 6 dimensions and 51 indicators were extracted from the literature to inform the next stage. Subsequently, the authors identified and evaluated the design of three chosen communities in various cities in the UAE concerning liveability and sustainability indices. Consequently, a conceptual redesign of a typical community was made, illustrating the improved quality of life and sustainability. Lastly, a survey with respective facets from an urban planning architect and environmental scientist cum environmental economist was conducted to evaluate the practicality of the proposed design. This research gives a comprehensive picture of how liveability and sustainable ecosystem concepts need to be implemented in the UAE urban context and offers a direction to develop lively, context-specific, culturally attached, and sustainable urban environments for the present day and for the future.

MHD Casson Fluid Flow over a Stretching Surface with Suction, Double Stratification, and Heat and Mass Transfer Effects

Abstract In this article, we investigated the flow of a magnetohydrodynamic Casson fluid across an extending surface with exponential permeability in a double-stratified medium. Thermal radiation, suction, heat sources, viscous dissipation, and chemical reactions drive the stream field. We converted the flow describing partial differential equations into terms of ordinary differential equations by applying appropriate transformations. Next, we utilised the bvp4c package in Matlab to obtain numerical solutions for these equations. We explored and graphically showed the implications of different non-dimensional governing parameters for temperature, concentration, and velocity profiles. After analysis, we provide a tabular presentation of the friction factor, Nusselt, and Sherwood numbers. The Casson fluid temperature shows a rising trend for the solutal stratification parameter and a decreasing trend for the thermal stratification parameter, while the Casson fluid velocity shows a declining trend towards both of these parameters. The Casson fluid concentration also behaves differently depending on the stratification parameter; for example, it increases for thermal stratification and decreases for solutal stratification. We notice that an increase in the Casson parameter's value suppresses the velocity field. However, as the Casson parameter increases, both the temperature and the concentration improve. Furthermore, comparisons with previously published findings also support the current results. \sethlcolor{yellow}\hl{The study of MHD Casson fluid flow that includes suction, dual stratification, and the effects of heat and mass transfer is very useful in many areas, such as polymer processing, metallurgical engineering, biomedical applications, environmental sciences, and advanced cooling technologies.

Efficient Large-Scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks

Spatial processes observed in various fields, such as climate and environmental science, often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a Gaussian process with a nonstationary Matérn covariance is challenging, as it requires handling the complexity and computational demands associated with modeling the varying spatial dependencies over large and heterogeneous domains. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we use the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data by employing a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. We rely on the ExaGeoStat software for large-scale geospatial modeling to implement the nonstationary Matérn covariance for large scale exact computation of nonstationary Gaussian likelihood. We also assess the performance of the proposed method with synthetic and real datasets at large-scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches.

Bibliometric Insights into Microplastic Pollution in Freshwater Ecosystems

Microplastic pollution in freshwater ecosystems has emerged as a significant environmental concern, warranting comprehensive investigation, and understanding. This study employs bibliometric analysis to systematically review and synthesize the existing literature on microplastic pollution in freshwater environments from 2013 to 2023. The exponential growth in research output was uncovered by analyzing 885 documents sourced from the Web of Science database, with an average annual growth rate of 73.13% and an average document citation of 30.17. Our findings highlight the dominance of primary and secondary microplastics as pollutants, their ecological consequences, and the resultant socio-economic implications. Notably, the Science of the Total Environment and Environmental Pollution journals emerge as leading publication venues, while China, Germany, and the USA lead in research contributions, underlining the global nature of microplastic pollution research. The analysis further outlines the most commonly cited works, identifying pivotal studies that have shaped current understanding and future research directions. This bibliometric analysis provides a comprehensive overview of the research landscape on microplastic pollution in freshwater ecosystems, helping researchers to identify knowledge gaps and emerging trends. These insights can guide future research directions and inform policymakers and stakeholders on where scientific efforts should be concentrated to better understand and address the impacts of microplastic pollution.

Recurring Network Node Performance Evaluation Model for Enhancing the QOS Levels in 6G Enabled WSN

Among the many promising qualities of the Sixth Generation (6G) are its ultra-dense connection, integrated sensing and communication, secure communication, very high data rate, energy and spectrum efficiency, and ultra-reliable low latency communication. Novel approaches to environmental protection and resource monitoring have evolved with new forms of communication networks made possible by technological advancements. When it comes to environmental research and science, 6G enabled Wireless Sensor Networks (WSNs) plays a key role. There are several fields that can benefit from WSNs, including robotics, environmental monitoring, medicine, and the military. The Quality-of-Service (QoS) is a critical concern in wireless sensor applications because to the increasing demand for their use in various domains. Because the resources accessible from sensors and the many applications operating over these networks have varied limits in nature and requirements, ensuring QoS in WSNs is complex and increasingly demanding. Delays, throughput, jitter, and other network-level indicators used to be the main focus of QoS efforts. In this research, a model for providing suitable QoS measures for WSN is proposed, including service, reliability, and availability, all of which contribute to the preservation of high-quality service. Availability, Dependability, and Serviceability are the parameters considered for providing QoS. 6G wireless networks are largely focused on enhanced intelligence, synchronization of resources and topology from beginning to end, and the inherent support for high-bandwidth, low-latency communication. With these features, cyber physical user experiences that are rich in context are feasible. As a result, defining and identifying the function of quality of experience in 6G networks is crucial, particularly for network administration. To optimize application service satisfaction, a WSN should learn to govern the network's overall performance based on QoS characteristics, which guarantee the transfer of information. This research proposes a Recurring Network Node Performance Evaluation Model (RNN-PEM) for enhancing the QoS levels in 6G enabled WSN. The proposed model when compared with the traditional models, provided enhanced QoS levels.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

Performance evaluation of students’ hostel of the college of Agriculture and Environmental Science Kaduna State University, Nigeria

Performance evaluation of students’ hostel of the college of Agriculture and Environmental Science Kaduna State University, Nigeria

A new parametric quantile regression model based on an Owen distribution

Quantile regression is a versatile statistical tool for modeling conditional quantiles, offering a comprehensive view of the relationship between explanatory and response variables. This technique is widely used in fields like economics, finance, environmental science, and psychology. This paper introduces a new quantile regression model based on the Owen distribution, which provides flexibility in capturing quantile-specific relationships. The model parameters are estimated using maximum likelihood estimation, with a detailed discussion on the estimation process. Diagnostic measures, including a pseudo- R 2 statistic, quantile residuals, and generalized Cook’s distance, are proposed to assess model adequacy and identify influential data points. Monte Carlo simulations are employed to evaluate the estimators' performance. The model’s effectiveness is illustrated through an application to Chilean household income data, highlighting its advantages over conventional quantile regression models. Implemented in the R programming language, this model is accessible for practical use, offering a valuable addition to the quantile regression field and suggesting promising directions for further research and application across disciplines.

Diverse paths, stable differences – role of prior knowledge in learning biology over undergraduate years

ABSTRACT Undergraduate students enrol at university with diverse levels of prior knowledge and intuitive conceptions about essential biological topics. This longitudinal study explored the development of a conceptual understanding about three topics and how domain-specific prior knowledge affects this development. Undergraduate students (N = 50) of biological and environmental sciences participated in three measurement points – baseline (2019), follow-up 1 (2020), and follow-up 2 (2021) – where the same questionnaire with open-ended tasks was given. A mixed-methods approach was used for quantitative scoring of the answers and qualitative thematic analysis to describe the development of individual students’ conceptual understanding. The effect of prior knowledge was studied by dividing the students into two groups based on the baseline performance. The topic of evolution showed the most significant differences in prior knowledge and in knowledge gains. Students with lower prior knowledge experienced desirable shifts in their explanatory models about evolutionary processes, but an equal amount exhibited fragmented learning trajectories. Students with a more robust prior understanding of such an emergent phenomenon can more likely integrate scientific concepts in their knowledge framework. Implications for teaching in higher education are discussed.

INTEGRATING INTERTEXTUAL AND COMMUNITY RESPONSE ANALYTICAL LENSES TO TURN SYAIR CENDAWAN PUTIH’S A CHILDREN'S STORY “THE ADVENTURE OF CENDAWAN PUTIH” INTO A SCIENCE LEARNING

This study explores the transformation of the Cendawan Putih poem into prose suitable for children's science learning, examining the adaptation process and public perception. Using a qualitative approach, the study employs intertextual theory to analyze textual transformations such as expansion, conversion, modification, and citation and community response theory to capture stakeholders’ views. The research investigates how the Cendawan Putih poem has been creatively adapted into the children’s story The Adventure of Cendawan Putih to enhance science learning while maintaining cultural and educational value. The findings reveal that adapting Cendawan Putih for children’s literature not only preserves core narrative elements but also integrates science concepts relevant to young readers. The adaptation utilizes techniques such as expansion and modification, enriching the original storyline to highlight nature, ecosystems, and environmental science in an accessible format for children. Public feedback, collected through Focus Group Discussions (FGDs) with cultural figures, educators, academics, and youth, shows broad support for this transformation. Stakeholders emphasize the story’s potential to foster cultural awareness and introduce foundational science concepts, making it an innovative tool for learning. This study highlights how traditional literary forms can be reimagined for children’s science learning, merging cultural heritage with modern educational needs. By presenting Cendawan Putih in a format that supports both cultural preservation and science education, this work positions the story as an intergenerational bridge and a valuable addition to literature-based science learning. It underscores the importance of community involvement in literary adaptation, providing new insights for literature, cultural education, and science pedagogy.