Environmental Management Applications Research Articles

Advancements in the Impact of Insect Gut Microbiota on Host Feeding Behaviors

With the application and development of high-throughput sequencing technology, the structure and function of insect gut microbiota have been analysed, which lays a foundation for further exploring the intricate relationships between gut microbiota and host feeding behaviour. The microbial community in the insect gut, as an important ecological factor, affects the host’s food selection and nutritional metabolic processes through various mechanisms, which play a key role in population dynamics and ecosystems. The implications of these interactions are profound, affecting agricultural practices, biodiversity, and the broader environment, such as pollination and pest control. In-depth exploration of the molecular mechanism of the interaction between gut microbiota and hosts contributes to the grasp of insect biology and evolution and offers novel avenues for manipulating insect behaviour for practical applications in agriculture and environmental management. This paper focuses on the possible mechanisms of insect gut microbiota regulating host feeding behaviour. It inspires further research on the interaction between gut microbiota and insects affecting host behaviour.

A novel BiVO4/vermiculite Z-scheme heterojunction with enhanced removal rate of dyes through adsorption photodegradation synergistic mechanism

Water pollution, exacerbated by industrialization, poses severe threats to aquatic ecosystems and human health, particularly due to persistent organic pollutants like dyes. To mitigate this issue, the development of effective and sustainable water treatment technologies is essential. This study presents the synthesis of a novel bismuth vanadate (BVO)/vermiculite (Vt) adsorption-photocatalytic self-cycling composite using a simple hydrothermal method. The composite's Z-scheme heterojunction structure, along with the introduction of surface oxygen vacancies, significantly reduces electron-hole recombination and enhances charge separation. These modifications lead to a 1.64-fold increase in adsorption capacity compared to Vt and a 6.4-fold improvement in photocatalytic efficiency over BVO. The material maintained a 76 % removal efficiency after five usage cycles and demonstrated high efficacy in methylene blue (MB) solutions, even at concentrations up to 300 mg/L. Moreover, the composite displayed stable performance under repeated use and in high-pollutant environments, highlighting its durability and practicality. These characteristics underscore its strong potential for widespread application in environmental remediation and sustainable water resource management, offering an innovative solution to the global challenge of water pollution.

Interpretable machine learning for predicting heavy metal removal efficiency in electrokinetic soil remediation

Electrokinetic remediation (EKR) presents a promising approach for polluted soil remediation, leveraging electric fields to mobilize contaminants and facilitate their removal. Accurate efficiency prediction is crucial for optimizing EKR processes, reducing costs, and minimizing environmental impact. However, the EKR efficiency is influenced by numerous complex factors, making it challenging to predict outcomes reliably. This study introduces a novel approach for developing interpretable machine learning (ML) models tailored for efficient and unbiased EKR efficiency prediction based on material properties and experimental conditions. A large experimental dataset comprising 185 tests was compiled, encompassing various EKR parameters, heavy metal concentration, soil characteristics. A structured ML workflow was devised, evaluating diverse ML algorithms and employing model-agnostic interpretation methodologies to uncover intrinsic correlations between removal efficiency and pertinent attributes. Among the investigated ML models, Extreme Gradient Boosting (XGB) exhibited the best performance. Bayesian optimization was applied to further enhance its capabilities. The optimized XGB model demonstrated superior predictive accuracy on the test set, with an RMSE of 0.255, an MAE of 0.158, and an R² of 0.82. Moreover, SHapley Additive exPlanations analysis was employed to interpret the contributions of the input variables. Among inputs, electrode distance emerged as the most significant factor influencing EKR efficiency, followed by area, electrolyte species, and remediation duration. This research not only advances the predictive capabilities for EKR efficiency but also provides crucial insights into the underlying factors influencing remediation success, paving the way for more efficient and scalable applications in environmental management.

Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management.

Compaction Characteristics of Fly Ash and Pond Ash: A Comparative Analysis

This study investigates the compaction characteristics of Fly Ash and Pond Ash, which are by-products of coal combustion in thermal power plants. Thermal power plants generate three types of ash: (a) Fly Ash, (b) Pond Ash, and (c) Bottom Ash. Fly Ash consists of fine particles carried out of the boiler with flue gases, while Bottom Ash is the residue that settles at the bottom of the boiler. In most coal plants, Fly Ash is captured using electrostatic precipitators and other filtration equipment before the flue gases are released through the chimney. Pond Ash, on the other hand, is a by-product of thermoelectric power plants, often regarded as waste material, posing significant environmental disposal challenges. The compaction characteristics of Fly Ash and Pond Ash are influenced by various factors, including specific gravity, moisture content, compaction energy, layer thickness, and mold area. In this study, Fly Ash collected from NTPC Sipat , Chhattisgarh exhibited an Optimum Moisture Content (OMC) ranging from 18% to 27% and a Maximum Dry Density (MDD) ranging from 0.90 to 1.59 gm/cc under standard Proctor compaction energy. Similarly, Pond Ash demonstrated an OMC ranging from 33% to 46% and an MDD ranging from 0.856 to 1.248 gm/cc under the same conditions. The findings indicate that variations in these factors significantly affect the dry density of both Fly Ash and Pond Ash. The study also aims to determine the geotechnical properties of these materials, providing insights into their potential applications in engineering and environmental management

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.

Advances in Extremophile Research: Biotechnological Applications through Isolation and Identification Techniques.

Extremophiles, organisms thriving in extreme environments such as hot springs, deep-sea hydrothermal vents, and hypersaline ecosystems, have garnered significant attention due to their remarkable adaptability and biotechnological potential. This review presents recent advancements in isolating and characterizing extremophiles, highlighting their applications in enzyme production, bioplastics, environmental management, and space exploration. The unique biological mechanisms of extremophiles offer valuable insights into life's resilience and potential uses in industry and astrobiology.

Prune and Quantize Semantic Segmentation Network for Aerial Objects Recognition

Semantic segmentation of aerial and satellite images is crucial for applications in environmental management, urban planning, and traffic safety. While deep learning techniques with convolutional neural networks (CNNs) and attention mechanisms have achieved superior accuracy compared to traditional methods, they often struggle with model complexity and resource constraints. This paper introduces two novel techniques - pruning and quantization - to enhance both the performance and efficiency of semantic segmentation models for remote sensing images (RSIs). Pruning reduces model complexity by eliminating less significant weights, while quantization decreases memory usage by converting weights into a more compact format. We applied these techniques to the DeepLabV3+ model with ResNet18 and ResNet50 backbones and assessed their performance across multiple RSI datasets. Our results show that pruning and quantization effectively balance accuracy and computational efficiency, achieving a mean IoU of 81.24% with a memory footprint of 135.19 MB for pruning, and 81.04% mean IoU with a memory footprint of 33.79 MB for quantization on the ISPRS Vaihingen dataset. These methods offer a viable solution for deploying semantic segmentation models on resource-constrained hardware.

Multi-metal-doped transition metal compounds for high-performance asymmetric capacitive deionization

To address the limited ion adsorption capacity of carbon-based capacitive deionization (CDI) which primarily relies on double-layer mechanism, recent efforts have focused on the rational design and development of efficient Faradaic materials for CDI applications. In this study, we synthesized and applied Co- and Mn-doped transition metal compounds (Cu₂O@MCF) as the electrodes for asymmetric CDI using a simple hydrothermal method. The synthesized electrodes were analyzed using various characterization techniques, to confirm the successful doping and structural integrity. Electrochemical performance assessments revealed significant improvements in ion-capture capacity and overall desalination efficiency. Specifically, the optimized asymmetric CDI cell configuration, consisting of Cu₂O@MCF(Cl) as an anode and Cu₂O@MCF(Na) as a cathode, led to a significant increase in salt adsorption capacity (27.42 mg/g) over conventional symmetric carbon electrode (11.22 mg/g), an increase of over 2.4 times. Furthermore, the synthesized electrodes demonstrated effective phosphate ion removal, highlighting their potential applicability in wastewater treatment. The findings suggest that multi-metal doping significantly enhances electrode performance by synergistically combining electric double-layer capacitance and pseudocapacitance effects, due to their unique morphological and electrochemical properties. These results contribute to sustainable water treatment and resource recovery efforts, showcasing the effectiveness of the materials in improving CDI performance and opening new avenues for their application in environmental cleanup and resource management.

Anti-quorum Sensing Activity and Bioactive Components of Marine-derived Bacteria

Understanding and harnessing quorum sensing activity and identifying bioactive substances produced by marine-derived bacteria are essential for exploring their potential applications in various fields, including biotechnology, pharmaceuticals, and environmental management. This research aims to investigate the quorum-sensing activities employed by these bacteria and to characterize the bioactive compounds they produce, to unlock their therapeutic, industrial, and ecological potentials. This study focuses on screening, isolation and characterization of marine bacteria from Thoothukudi Harbour Beach, India, and potential antibacterial and anti-quorum sensing activities of their respective spent media against biofilm forming pathogens. Three soil samples were collected and processed for bacterial isolation. Seventeen different bacterial isolates were obtained and identified after prior culture. Antibacterial activity was evaluated against four pathogenic bacteria, with some isolates demonstrating significant inhibition. Additionally, biofilm inhibition assays were conducted, revealing the ability of certain isolates to inhibit the formation of biofilms. The secondary metabolites present in the ethyl acetate fraction of I.B 6 isolate exhibiting relatively high antibacterial and antibiofilm properties were identified by GC-MS. Anti-quorum sensing activity was also investigated using swarming assay and the MIC was determined accordingly for the ethyl acetate fraction. Hence, these marine bacteria hold for producing bioactive compounds with potential pharmaceutical and industrial applications. Finally, the positive organism is subjected to 16S rRNA sequencing for identification and was found to be Bacillus thuringiensis.

The Potential of Talisay-Dagat (Terminalia catappa L.) for Phytoremediation in Langihan Lagoon, Butuan City, Agusan del Norte, Philippines

The study aims to determine the potential of Talisay-dagat (Terminalia catappa L.) for phytoremediation and to examine its influence on reducing concentrations of heavy metals in the soil of Langihan Lagoon, Butuan City. Soil, roots, and leaves were collected and brought to the Regional Soils Laboratory using microwave-assisted aqua-regia digestion and determination through Inductively Coupled Plasma Optical Emission Spectrometer. The study made use of mathematical computations such as translocation factor (TF), bioconcentration factor (BCF), and enrichment factor (EF) to determine whether the tree is a hyperaccumulator, excluder, or indicator. In the TF results, T. catappa was a hyperaccumulator for Ni and Cu, considering that the concentration exceeds one (1) while demonstrating as a possible excluder for Cr. There was also an emphasis on limited absorption of heavy metals, as evidenced by the BCF and EF value of less than 1. The results show that based on TF, BCF, and EF values, only TF shows the effectivity of restricting the root-shoot ratio translocation of Ni and Cu (TF > 1). Regression analysis found that the absorption of T. catappa was not influenced by the amount of heavy metal in the soil within the studied condition. This insight was crucial in understanding the plant’s absorption and could guide further research or practical applications in environmental management and phytoremediation. Keywords: bioconcentration factor, hyperaccumulator, phytoremediation, Terminalia catappa, translocation factor

Pelatihan penerapan pembelajaran sainstivik dan penataan lingkungan untuk menfasilitasi merdeka belajar pada guru-guru KB TK Bantul

This service is motivated by the problems faced by family planning teachers at Cluster 7 Kindergarten, Kasihan Bantul District, Yogyakarta regarding: 1). Teachers still lack understanding of the implementation of a scientific approach in learning in schools. 2). Teachers in implementing scientific learning such as observing, asking, collecting, associating and communicating information in class activities are still confused/don't understand the whole thing. This service aims to provide training and assistance in the application of scientific learning and environmental management to facilitate independent learning for early childhood. The method in this research uses: lectures, questions and answers, and supervisory actions carried out over 2 cycles. There are four stages in service activities and each cycle consists of: planning, implementation, observation and evaluation (reflection). The results of the training implementation show that PAUD teachers at KB TK Gugus 7, Kasihan Bantul District, Yogyakarta, have improved in implementing scientific learning and environmental arrangements to facilitate independent learning for early childhood children after participating in training and mentoring activities.

Water‐controlled ecosystems as complex networks: Evaluation of network‐based approaches to quantify patterns of connectivity

AbstractThis study provides a new perspective on understanding the intricacies of water‐mediated connectivity in ecosystems, bridging landscape ecology and geomorphology through network science. We highlight dryland and river‐floodplain ecosystems as distinct examples of contrasting water‐controlled systems. We (1) discuss central considerations in developing structural connectivity and functional connectivity networks of water‐mediated connectivity; (2) quantify the emergent patterns in these networks; and (3) evaluate the capacity of network science tools for investigating connectivity characteristics. With a focus on strength (weights) and direction, connectivity is quantified using seven parameters at both network and node levels. We find that link density, betweenness centrality and page rank centrality are highly sensitive to directionality; global efficiency and degree centrality are particularly sensitive to weights; and relative node efficiency remains unaffected by weights and directions. Our study underscores how network science approaches can transform how we quantify and understand water‐mediated connectivity, especially in consideration of the role(s) of weights and directionality. This interdisciplinary perspective, linking ecology, hydrology and geomorphology, has implications for both theoretical insights and practical applications in environmental management and conservation efforts.

Annual Review of Ecology, Evolution, and Systematics, Volume 54

The Annual Review of Ecology, Evolution, and Systematics covers significant developments in the fields of ecology, evolutionary biology, and systematics, as they apply to all life on Earth. Essay reviews cover topics ranging from phylogeny, speciation, and molecular evolution through behavior and evolutionary physiology to population dynamics, ecosystems processes, and applications in invasion biology, conservation, and environmental management. There are 22 articles.

Enhanced Cadmium Sensing in Fertilizer Samples using Zeolite-modified Graphite Electrode

The presence of heavy metals in fertilizers poses significant environmental and health risks, necessitating robust detection methods to ensure agricultural sustainability and food safety. This study focuses on synthesizing Na-P1 zeolite from coal fly ash and fumed silica wastes, employing it as a potent modifier in graphite electrodes to enhance the electrochemical detection of cadmium (II) in fertilizer samples. Through meticulous assessment using cyclic voltammetry (CV) in a potential window of −1.2 V to 0.5 V, square wave voltammetry (SWV) in a potential region of −1 V to 0.5 V, and electrochemical impedance spectroscopy (EIS) in a frequency ranging from 100 kHz to 10 mHz under open circuit potential polarization, the electrochemical performance of the zeolite-modified graphite electrode (ZGE) was evaluated. The optimization process involved fine-tuning various parameters such as paste composition, solution pH, scan rate, and analyte concentration. Real sample analysis confirmed the applicability of the method in fertilizer samples. Notably, in an HCl medium (pH = 1.0), the SWV oxidation peak of Cd(II) was observed at −0.56 V (vs SCE) with a deposition time of 20 s and a scan rate of 75 mV∙s−1. The ZGE exhibited a remarkable detection limit of 1 μM, a quantification limit of 5 μM, and a high sensitivity of 6.581 μA/μM.cm2 within a linear detection range of 10−3–10−6 M. These findings highlight the potential utility of Na-P1 zeolite in handheld analytic device manufacturing, offering promising applications in environmental monitoring, resource management, and water and soil resource remediation.

Preface

The 5th International Conference of Geography and Disaster Management 2023 (ICGDM 2023) was successfully held by the Faculty of Geography, Universitas Muhammadiyah Surakarta. The conference was held via online Zoom meeting on 5-6th December 2023. ICGDM 2023 had a relevant theme for the tropical regions which is “Exploring Sustainable Solutions for Regional Development in Tropical Geographies: Unveiling the Potential, Embracing the Challenges”. It aimed to be a scientific platform for research dissemination, ideas sharing, and collaboration related to topics of environmental studies, socio-economic, regional development, disaster management, remote sensing, and GIS applications. ICGDM 2023 invited speakers from different backgrounds who are experts in the field of either physical or socio-economic geography. The keynote speakers were Dr. Nawaz M Mian (National University of Singapore, Singapore), Prof. Christopher Gomez (Kobe University, Japan), Muhammad Aufaristama, Ph.D. (University of Twente, Netherlands), Asaba Joyfred, M.A. (Kyambogo University, Uganda), Dr. Mukhamad Ngainul Malawani, M.Sc. (Universitas Gadjah Mada, Indonesia), Dr. Edy Trihatmoko (Impact Team 2050 Member, State Atomic Energy Corporation Rosatom (ROSATOM), Russia). This ICGDM 2023 was attended by 123 participants and presenters. The committee has received 123 paper submissions, and after the review process, 60 papers were selected for publication in the IOP Conference Series: Earth and Environmental Science. We would like to thank all the authors who have actively participated in the conference. We also highly appreciate the full support from Universitas Muhammadiyah Surakarta. List of Committee is available in this pdf.

Detection of Water bodies using Satellite Imagery based on Deep Learning

Abstract: Satellite images are important for monitoring and managing natural resources. Water bodies such as lakes, rivers, and oceans are of great importance to the environment and human populations. The identification of water body pixels in satellite images is therefore an important task for environmental management and planning. In recent years, deep learning has shown great promise in image processing, including object detection and segmentation. This study employs CNNs and RNNs to identify water body pixels in satellite images, aiming to create a model that can accurately distinguish water and non-water pixels. This model has practical applications in environmental monitoring and management

Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry

In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.

Matrix-free laser desorption/ionization mass spectrometry imaging for rapid evaluation of wood biomass conversion.

This study overcomes traditional biomass analysis limitations by introducing a pioneering matrix-free laser desorption/ionization (LDI) approach in mass spectrometry imaging (MSI) for efficient lignin evaluation in wood. The innovative acetic acid-peracetic acid (APA) treatment significantly enhances lignin detection, enabling high-throughput, on-site analysis. Wood slices, softwood from a conifer tree (Japanese cypress) and hardwood from a broadleaf tree (Japanese beech), were analyzed using MSI with a Fourier transform ion cyclotron resonance mass spectrometer. The developed APA treatment demonstrated effectiveness for MSI analysis of biomass. Our imaging technique successfully distinguishes between earlywood and latewood and enables the distinct visualization of lignin in these and other wood tissues, such as the radial parenchyma. This approach reveals significant contrasts in MSI. It has identified intense ions from β-O-4-type lignin, specifically in the radial parenchyma of hardwood, highlighting the method's precision and utility in wood tissue analysis. The benefits of matrix-free LDI include reduced peak overlap, consistent sample quality, preservation of natural sample properties, enhanced analytical accuracy, and reduced operational costs. This innovative approach is poised to become a standard method for rapid and precise biomass evaluation and has important applications in environmental research and sustainable resource management and is crucial for the effective management of diverse biomass, paving the way towards a sustainable, circular society.

Signalling: Growth Promoters and Inhibitors

Plant growth and development are intricately regulated by a multitude of signaling pathways that respond to internal and external cues. Central to these regulatory mechanisms are the signaling pathways of growth promoters and inhibitors. Various growth-promoting hormones, including auxins, cytokinins, gibberellins, and brassinosteroids, stimulate cell division, elongation, and differentiation, playing vital roles in shaping plant architecture and influencing various developmental processes. The pathways of these growth promoters involve specific receptors and transcription factors that orchestrate growth in response to dynamic signals. In contrast, growth inhibitors, represented by hormones like abscisic acid, ethylene, jasmonic acid, and salicylic acid, act as crucial checkpoints that restrict growth during stress, pathogen attacks, or other adverse conditions. These signaling pathways trigger growth inhibition and activate defense mechanisms, enabling plants to adapt to challenging environments. Balancing the interplay between growth promoters and inhibitors allows plants to allocate resources efficiently and ensure their survival under changing circumstances. Understanding these signaling pathways is not only fundamental to plant biology but also essential for applications in agriculture, horticulture, and environmental management. This abstract provides a concise overview of these essential processes. This knowledge enables us to manipulate plant growth and development for improved crop yields, disease resistance, and stress tolerance. Ongoing research continues to unravel the complexities of these pathways, offering valuable insights into how plants delicately balance growth promotion and inhibition to thrive in an ever-evolving world.