Journal of Environmental & Earth Sciences

Studying, Analyzing, and Interpreting the Gut Microbiome of the Earthworm M. peguana (Rosa, 1890) Using Next-Generation Sequencing

Wed, 02 Jul 2025 00:00:00 +0800

This study investigates the diversity of gut microbiota in Metaphire peguana, an earthworm species commonly found in agricultural areas of Thailand. Earthworms play a critical role in soil ecosystems by supporting nutrient cycling and breaking down organic matter. Understanding the microbial diversity in their gut is essential for exploring their ecological contributions. Using Next Generation Sequencing (NGS), we analyzed the mycobiome in the gut of M. peguana. Our findings revealed a high diversity of fungal species, primarily belonging to two major phyla: Ascomycota and Basidiomycota. Ascomycota was the most abundant phylum, comprising 40.1% of the total fungal species identified. A total of 33 distinct fungal species were identified, which underscores the richness of microbial life within the earthworm gut. This study successfully created the first genetic database of the microbial community in M. peguana, providing a foundation for future research in agricultural applications. The microbial species identified, particularly siderophore-producing fungi, could have significant implications for improving soil fertility and promoting sustainable agricultural practices. The use of NGS technology has enabled comprehensive profiling of microbial communities, allowing for precise identification of fungi that may play essential roles in soil health. Furthermore, the study paves the way for future studies on the potential applications of earthworm gut microbiomes in biotechnology, especially in enhancing soil nutrient availability and plant growth. The findings of this research contribute to the broader understanding of the ecological roles of earthworms and their microbiomes in soil ecosystems.

Assessing the Convergence of Cropland Ecological Balance: A Panel Data Analysis of 13 Major Agricultural Countries

Orhan Şimşek, İlkay Güler, Sefa Özbek, Mustafa Naimoğlu, Zafer Adalı — Tue, 24 Jun 2025 00:00:00 +0800

This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022. The study's concentrated samples are Russia, the United States, the Netherlands, Brazil, Germany, China, France, Spain, Italy, Canada, Belgium, Indonesia, and India. The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests (Panel CIPS, CADF, Panel-LM, Panel-KPSS, and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test). The findings highlight significant improvements in Canada, contrasting with declines in the Netherlands, France, Germany, and the United States. The results indicate convergence in ecological balance among these countries, suggesting that agricultural practices are progressively aligning with sustainability objectives. The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability. However, the univariate outcome also shows that the cropland ecological balance of Germany, China, France, Indonesia, and India does contain a unit root and stationary which means the presence of the constant-mean. The univariate actions from the mentioned governments will not promote persistent impact. Therefore, joint actions determined by the countries considered are recommended for the mentioned countries. However, the rest of the countries also enact local policies. The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts. This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.

Quantifying Sedimentation and Capacity Loss in Mwimba Reservoir, Malawi: A Baseline Assessment Using Bathymetric and GIS Analysis

Mon, 30 Jun 2025 00:00:00 +0800

Sedimentation in reservoirs is a significant challenge that affects water storage capacity and operational efficiency. This study establishes a baseline sedimentation status for Mwimba Reservoir in Kasungu, Malawi, five years after its commissioning in 2017, using an integrated bathymetric survey and Geographic Information System (GIS) analysis. A bathymetric survey conducted in March 2022 collected depth measurements at 507 points along 23 transects, which were used to construct a Triangulated Irregular Network (TIN) model in ArcGIS for accurate volume calculations. Sediment concentration was determined from seven water samples using the filtration method. The original design volume of 89,200 m³ was compared to the current volume of 72,966 m³, indicating an 18.2% loss in capacity over the five-year period. Statistical analysis using a one-sample T-test confirmed that this reduction is significant (p = 0.013). The annual sedimentation rate was estimated at 1.25 tonnes per year, and the reservoir’s projected operational life is 27.5 years if no intervention is undertaken. Despite a relatively low sedimentation rate compared to other regional reservoirs, targeted sediment management and further catchment analysis are essential. This study provides critical baseline data for future sediment monitoring, management, and conservation planning for Mwimba Reservoir and similar small water bodies in Sub-Saharan Africa.

Eco-Friendly Amylase Production and Immobilization on Macadamia Based Carbon Using Aspergillus niger

Mon, 30 Jun 2025 00:00:00 +0800

This study demonstrates the valorization of macadamia nutshells, a lignocellulosic agricultural waste, as both a carbon source for amylase production and a support matrix for enzyme immobilization. Under optimized solid-state fermentation conditions, Aspergillus niger ICP2 synthesized amylase with a peak activity of 0.312 U/mL after 72 hours. A four-step purification process of the crude enzyme extract resulted in a 188.54-fold increase in specific activity, albeit with a final recovery yield of 0.0031%. In parallel, nutshells were carbonized at 600 °C, 700 °C, and 800 °C, then chemically activated with ZnCl₂. The carbon derived at 700°C exhibited superior physicochemical characteristics, including enhanced porosity and increased availability of functional groups, which enabled effective enzyme adsorption, improved catalytic performance, and enhanced reusability. Immobilized amylase on this support retained approximately 30% of its initial activity after five hydrolysis cycles, demonstrating moderate operational reusability and potential for repeated use in bioprocesses. In contrast, carbon materials from 600 °C and 800 °C showed lower stability and enzyme performance. These findings highlight the critical role of carbonization conditions in designing effective immobilization matrices and underscore the potential of macadamia nutshells as a renewable and sustainable resource for biocatalyst development. This "biowaste-to-biocatalyst" strategy exemplifies a circular bioeconomy model with implications for green chemistry, industrial biocatalysis, and environmental sustainability.

Improving the Combustion Process of Biofuels for Diesel Engines to Reduce Environmental Pollution

Tuan Duc Ho, Nghia Duc Mai, Trung Dinh Pham — Fri, 04 Jul 2025 00:00:00 +0800

Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures, including encouraging biofuel use because biofuel is environmentally friendly and renewable. A mixture of diesel fuel and vegetable oil is a form of biofuel. However, some properties of the mixed fuel, such as viscosity and density, are higher than those of traditional diesel fuel, affecting the injection and combustion process and reducing power and non-optimal toxic emissions, especially soot emissions. This study uses Kiva-3V software to simulate the combustion process of a diesel-vegetable oil mixture in the combustion chamber of a fishing vessel diesel engine with changes in fuel injection timing. The results show that when increasing the fuel injection timing of a diesel-vegetable oil mixture about 1–2 degrees of crankshaft rotation angle before the top dead center compared to diesel fuel injection timing, the engine power increases, and soot emissions decrease compared to no adjustment. The above simulation research results will help orient the experiments conveniently and reduce costs in the future experimental research process to quantify the fuel system adjustment of fishing vessels' diesel engines when using biofuels, including diesel-vegetable oil mixtures. Thus, the engine's economic indicators will improve, and emissions that pollute the environment will be limited.

Bioremediation of Wastewater of Osh City of Kyrgyzstan with Lemna minor and Azolla caroliniana

Mon, 30 Jun 2025 00:00:00 +0800

Under conditions of increased pollution of water resources, the search for effective and environmentally safe methods of wastewater treatment becomes an urgent task. As noted by many researchers Lemna minor L. and Azolla caroliniana have great potential for biological treatment of wastewater. The present study is devoted to the biological treatment of municipal wastewater of Osh city (Kyrgyzstan) using aquatic plants Lemna minor L. and Azolla caroliniana Willd. Experiments were conducted in concrete basins of treatment facilities. The area of each pool was 1 m², depth 30–45 cm. The temperature of nutrient media ranged from 20 to 35 ℃, pH from 6.1 to 8.7, and light intensity from 284 to 360 W/m²FAR. The results showed that the cultivation of Lemna minor resulted in the oxygenation of wastewater and, a significant reduction of pollutants and pathogens. At the same time, the use of Azolla caroliniana provided a significant improvement in the physicochemical characteristics of water - reduction of BOD₅ to 4.3 mgO₂/L, disappearance of all forms of nitrogen, as well as improvement of transparency and elimination of unpleasant odor. Thus, both plants showed high potential as biological treatment agents. The obtained data confirm the prospect of their application for a sustainable water treatment system in the conditions of southern regions of Kyrgyzstan

Environmental Assessment of Wastewater Treatment Plants in Developing Countries Using LCA: A Case Study in Perú

Daniela Castro, Hebert Ccalloapaza, Isaac Yanqui, Luis Rodriguez — Wed, 02 Jul 2025 00:00:00 +0800

Treating municipal wastewater is essential to safeguarding both ecosystem integrity and public health. Although wastewater treatment plants (WWTPs) significantly improve effluent quality, they also incur collateral environmental burdens. In this investigation, a “gate to gate" Life Cycle Assessment (LCA) was conducted to analyze the environmental performance of two major WWTPs in Arequipa: La Escalerilla (Plant A, activated sludge) and La Enlozada (Plant B, trickling filters). The analysis was conducted using OpenLCA and the ReCiPe Midpoint (H) 2016 impact assessment method, with a functional unit defined as 1 m³ of treated effluent. Energy consumption emerges as the primary driver for the climate change (GWP100), fossil depletion (FDP), and human toxicity (HTPinf) impact categories, accounting for approximately 75% to 85% of the total effects. Plant A, which requires 0.59 kWh/m³ of electricity, achieves superior nutrient removal reflected in a freshwater eutrophication potential of 1.92 × 10⁻⁶ kg P-eq/m³, and exhibits marginally higher CO₂-eq emissions (GWP100) (1.17 × 10⁻¹ kg CO₂-eq/m³). Conversely, Plant B consumes only 0.34 kWh/m³, resulting in a slightly lower GWP100 (1.14 × 10⁻¹ kg CO₂-eq/m³) and a significantly greater reduction in fossil depletion potential (FDP) (2.56 × 10⁻² kg oil-eq/m³ vs. Plant A's 4.75 × 10⁻² kg oil-eq/m³), although it exhibits an elevated eutrophication potential of 4.10 × 10⁻⁶ kg P-eq/m³. Both plants meet discharge standards. This study shows that treatment technologies must balance efficiency and sustainability, with energy use being critical. As Peruvian LCA research is scarce, these results offer key insights for future policies.

Assessment of the Promotional Effects of New Energy Fitness Equipment on Sports Economics and Management

Shaoai Wu, Dan Du — Fri, 27 Jun 2025 00:00:00 +0800

The Internet of Things (IoT) technology offers significant advancements in fitness trackers and AI-driven health management systems and presents practical applications for monitoring health and performance. New energy composites, which improve the performance of traditional metals, have been widely used in automotive manufacturing but have limited application in the sports industry. To bridge this gap, the study proposes integrating these advanced composites into sports equipment and facilities, utilizing IoT technology as the foundation for intelligent health monitoring. The research explores how IoT technology can enhance the promotional impact of fitness equipment within the sports industry. Additionally, the communication process for data assessment is conducted using the Priority-based Congestion-avoidance Routing Protocol (PCRP) to ensure efficient data transmission. The analysis of sports activities is performed by utilizing the data transferred through PCRP. Experimental results show that the proposed mechanism outperforms conventional models, achieving an energy efficiency of 0.502 joules (J), a delay of 0.407 seconds (s), and a throughput of 0.620. These results demonstrate the potential of combining IoT technology and new energy composites to revolutionize sports equipment and enhance fitness monitoring systems. These findings highlight the potential of combining IoT and advanced composite materials to revolutionize sports equipment, improve fitness monitoring, and contribute to the growth of the sports industry through enhanced data management and energy-efficient technologies.

Urban Vertical Greening Optimization Supported by Deep Learning and Remote Sensing Technology and Its Application in Smart Ecological Cities

Jian Sun, Peng Li — Tue, 01 Jul 2025 00:00:00 +0800

This research systematically investigates urban three-dimensional greening layout optimization and smart eco-city construction using deep learning and remote sensing technology. An improved U-Net++ architecture combined with multi-source remote sensing data achieved high-precision recognition of urban three-dimensional greening with 92.8% overall accuracy. Analysis of spatiotemporal evolution patterns in Shanghai, Hangzhou, and Nanjing revealed that three-dimensional greening shows a development trend from demonstration to popularization, with 16.5% annual growth rate.The study quantitatively assessed ecological benefits of various three-dimensional greening types. Results indicate that modular vertical greening and intensive roof gardens yield highest ecological benefits, while climbing-type vertical greening and extensive roof gardens offer optimal benefit-cost ratios. Integration of multiple forms generates 15–22% synergistic enhancement.Compared with traditional planning, the multi-objective optimization-based layout achieved 27.5% increase in carbon sequestration, 32.6% improvement in temperature regulation, 35.8% enhancement in stormwater management, and 42.3% rise in biodiversity index. Three pilot projects validated that actual ecological benefits reached 90.3–102.3% of predicted values.Multi-scenario simulations indicate optimized layouts can reduce urban heat island intensity by 15.2–18.7%, increase carbon neutrality contribution to 8.6–10.2%, and decrease stormwater runoff peaks by 25.3–32.6%. The findings provide technical methods for urban three-dimensional greening optimization and smart eco-city construction, promoting sustainable urban development.

A New Method to Calculate Soil Water Content by Imaging and Testing the Color of the Soil Surface

Thu, 26 Jun 2025 00:00:00 +0800

Soil color changes with water content due to chemical and physical reactions, making it a potential indicator for moisture estimation. By analyzing soil surface images and comparing color variations against laboratory-measured water content, a rapid and cost-effective method for moisture determination can be developed. Traditional moisture measurement techniques are time-consuming, so an imaging-based approach would be highly beneficial for quick decision-making. Soil color is also influenced by factors such as particle coarseness, which creates shadows and alters perceived darkness. This research introduces a novel method to isolate true soil color by analyzing the maximum color response in image pixels, minimizing shadow effects. Several equations were derived to correlate color changes with moisture content and were validated against lab measurements to ensure accuracy and simplicity. The most effective equation can be further adapted for satellite imagery by accounting for atmospheric light scattering differences between ground and satellite sensors, enabling large-scale moisture monitoring. The derived equations can be programmed into a software tool, allowing moisture estimation from simple soil surface images. The study involved controlled experiments where soil samples at varying moisture levels were imaged to establish an empirical color-moisture relationship. This method provides a fast, economical, and practical alternative to conventional techniques. However, the approach requires further refinement to account for different soil types globally. Future work should focus on adjusting the model with variables that adapt the color-moisture relationship for diverse soils, ensuring broader applicability. Once optimized, this could significantly improve moisture assessment in agriculture, environmental monitoring, and land management.

Optimization Method of Teaching Program under the Concept of Sustainable Environmental Development of Renewable Energy Based on Artificial Intelligence Internet

Tue, 01 Jul 2025 00:00:00 +0800

The increasing global demand for energy, coupled with concerns about environmental sustainability, has underscored the need for a transition toward renewable energy sources. A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information, abilities, and critical thinking needed to solve energy-related problems sustainably. This research proposes AI-powered personalized learning, innovative real-time integration of diverse data, and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts. The sheep flock-optimized innovative recurrent neural network (SFO-IRNN) will recommend relevant topics and resources based on students' performance. Renewable energy teaching data from assessments are combined with real-time IoT-based renewable energy data. This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning. The data was preprocessed by handling missing values and min-max scaling. The data features were extracted using Fourier Transform (FT). Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy, F1-score, recall, and precision on different subsets of student data, which improves its robustness and prevents overfitting. The findings showed that the proposed method is significantly better, which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies. In addition, integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning. The research shows that AI provides context-aware guidance on sustainable energy infrastructure, enhancing interactive and personalized learning.

Home Gardens into Climate Resilience Strategies: Insights from Tribal Communities in Keonjhar, Odisha

Mon, 23 Jun 2025 00:00:00 +0800

Home gardens serve as a crucial adaptation strategy for tribal communities in Keonjhar district, Odisha, enhancing food security, income generation, and climate resilience. This study examines the role of home gardens in sustaining tribal livelihoods while addressing challenges such as climate variability, soil degradation, and market constraints. Adopting a mixed-methods approach, it integrates the Sustainable Livelihoods Framework (SLF) and socio-ecological resilience theory. Data from 120 tribal households across four villages in Banspal block were collected through structured interviews and focus group discussions. A key innovation lies in integrating indigenous knowledge systems with the Problem Facing Index (PFI) methodology to generate quantified, community-driven insights on climate adaptation barriers. Findings reveal that home gardens contribute around ₹10,000 per season to household income, reducing market dependency by over 70% during crises such as the COVID-19 lockdown. Over 75% of households face high to medium challenges due to climate variability (PFI = 252), soil degradation (PFI = 251), and wildlife intrusions (PFI = 250). Other notable constraints include pests (75%) and poor market access (61.7%). Although 82.5% reported no issue with agricultural inputs, water scarcity remains a seasonal concern for 23%. Despite constraints, home gardens were found to enhance all five SLF capitals: natural, human, social, financial, and physical. The study recommends context-sensitive interventions, including seed distribution, organic inputs, weather-based advisories, and strengthened institutional support. It highlights the potential for scaling home garden models across other marginalized agroecological regions in India and Asia, aligning them with national rural development and climate adaptation programs.

A Mixed-Methods Analysis of Systemic Factors Affecting the Integration into Rice Straw Supply Chains in Thailand

Adisai Watanaputi, Thammanoon Hengsadeekul — Mon, 30 Jun 2025 00:00:00 +0800

Rice straw, a by-product of rice cultivation, is commonly disposed of through open-field burning, which contributes to air pollution and environmental degradation. This study aims to identify the key factors influencing farmers’ decisions on rice straw management and to develop policy recommendations that encourage the sustainable utilization of rice straw within the supply chain. A mixed-methods approach was adopted, combining qualitative interviews with nine key informants and a quantitative survey of 585 rice farmers across Thailand. Multinomial Logit Regression (MLR) was employed to analyze farmers’ preferences among four management options: burning, composting, animal feeding, and selling. The results reveal that membership in farmer groups, ownership of livestock, access to baling machinery, knowledge, and skills related to straw utilization, ease of field access, availability of storage facilities, engagement in integrated farming, and year-round access to baling services significantly increased the likelihood of choosing sustainable alternatives over the burning straw. These findings underscore the importance of both capacity-building and infrastructure in enabling sustainable practices. Based on these insights, the study proposes a multi-level policy framework to enhance the value creation of rice straw. National policies should focus on expanding access to machinery and supporting innovation, while local governments should facilitate farmer training and improve straw logistics. Strengthening farmer organizations and market connections is also crucial for scaling adoption. Overall, structural integration and stakeholder coordination are key to reducing straw burning and promoting sustainable resource use in rice-producing regions.

Assessment of the Pollution Load by Raw Waste Waters from Mechraa BelKsiri (Morocco)

Mon, 30 Jun 2025 00:00:00 +0800

Population growth and the industrial production modernization have generated considerable water needs. This consumption has led to a disproportionate discharge of untreated wastewater, directly into receiving environments (Oceans, Rivers, Lakes, Surfaces waters, etc.) and leads to thus promote the spread of waters-borne diseases. The main objective of our present work is the description of the pollutant load of wastewater from the city of Mechraa Belksiri (Kénitra - Morocco), to classify them and identify their composition and their intensity to seek an adequate treatment allowing their subsequent reuse and reducing their dangerousness on their receiving environment Oued Sebou. The analysis of temperature, pH, redox potential, electrical conductivity, dissolved oxygen and salinity are recorded in the field using a portable multiparametric analyzer. The wastewater samples intended for analysis in the laboratory were preserved following the general conservation protocol and handling of samples according to ISO 5667/3. Total Suspended Solids (TSS) are measured by filtration. The analysis of the BOD5 is determined using a BODmeter and the COD, according to DIN 38409-H52. Wastewater from Belksiri town has a pH of 7.2 and an average temperature of 20.5°C and an electrical conductivity of 3145 µs/cm. The average concentration of oxygen is very weak at 0.35 mg/L. Wastewater is loaded with organic matter estimated by Suspended Solid matter (average 446 mg/L), BOD5 (280 mg/L) and COD (557 mg/L). The overall pollutant load of wastewater presents satisfactory biodegradability and is well prepared for suitable biological treatment with activated sludge plant.

Balancing Tradition and Innovation: Ecological Impacts of Intensive Farming on Soil Health in Telangana, India

Mon, 30 Jun 2025 00:00:00 +0800

Intensive farming practices, aimed at maximizing crop yields through substantial inputs of labour, technology, and chemical fertilizers, have significantly transformed modern agriculture. However, these methods have raised serious concerns regarding soil health, environmental sustainability, and long-term agricultural viability. This study examines the ecological impact of intensive farming on soil health in the KB Asifabad District of Telangana, India, where traditional and modern farming techniques coexist. The objectives include analysing socio-economic factors influencing farming methods, evaluating the impact of tilling techniques and fertilizer use on soil health, and promoting sustainable practices through education and policy recommendations. Findings reveal a strong reliance on chemical fertilizers, with 98.3% of farmers using them exclusively due to their perceived efficiency and rapid results. However, this overdependence has led to soil degradation, reduced microbial diversity, and environmental pollution. Conversely, despite its ecological benefits, natural manure remains underutilized due to scepticism and economic constraints. Mechanical tilling methods, while effective, have negatively impacted soil structure and fertility. The study highlights the necessity of transitioning to sustainable practices, integrating organic inputs, and adopting conservation techniques to restore soil health and ecosystem balance. This research provides practical pathways for achieving sustainable agriculture by integrating traditional knowledge with modern practices. It is particularly relevant for policymakers, agricultural extension services, and farming communities as it highlights the need for educational initiatives, financial incentives, and regulatory measures to ensure long-term soil fertility, environmental stewardship, and improved farmer livelihoods.

Sources and Control Strategies of Arsenic in the Atmosphere

Qingyang Liu — Thu, 03 Jul 2025 00:00:00 +0800

Arsenic (As), classified as a Group I carcinogen by the International Agency for Research on Cancer (IARC), poses severe risks to ecosystems and human health through atmospheric exposure. This review synthesizes current knowledge on the sources, health impacts, and control strategies of atmospheric arsenic, with an emphasis on its global transport and toxicity. Natural sources, such as volcanic eruptions and soil erosion, contribute approximately 2.1 Gg/year; however, anthropogenic activities, notably metal smelting and coal combustion, dominate emissions, with global anthropogenic releases reaching approximately 28.6 Gg/year. Atmospheric arsenic primarily exists in two forms: particulate matter (PM₂.₅-bound As(V)/As(III) and methylated species) and gaseous forms (e.g., AsH₃, As₂O₃), facilitating long-range transport and cross-continental pollution, as evidenced by Asian emissions contributing 39% of Arctic deposition. Advanced techniques, such as inductively coupled plasma mass spectrometry (ICP-MS) and models like GEOS-Chem, enhance emission tracking; however, gaps persist in monitoring gaseous arsenic and refining emission inventories. Health risks include lung cancer, neurotoxicity, and cardiovascular diseases, exacerbated by inhalation and dietary exposure via contaminated crops. Control technologies, including calcium- and iron-based adsorbents and industrial scrubbers, show promise but face challenges related to efficiency and cost. Regional strategies, such as China’s tightened emission limits (0.5 mg/m³) and the EU’s Best Available Techniques (BAT), highlight progress, yet global cooperation remains vital for transboundary mitigation. Future research should prioritize low-cost sensors, elucidating speciation-toxicity relationships, and AI-driven emission management to address data gaps and optimize policies. Integrating multidisciplinary approaches—advanced science, stringent regulations, and international collaboration—is crucial to mitigate the environmental and public health impacts of arsenic amid growing industrialization and climate change.