Journal of Environmental & Earth Sciences

The Role of Pigs in the Carbon Footprint of Red Meat in Canada

2024-11-25T15:49:24+08:00

Global livestock production is a major driver of climate change. Lumping beef and pork together as red meat masks important differences in their carbon footprints, land uses, and social status. These two red meat choices in Canada were compared by using a meta-model of the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). ULICEES calculated fossil CO₂, N₂O and CH₄ emissions for beef, dairy, pork, poultry, and sheep production in Canada, based on both the livestock and their supporting land base in 2001. The dynamic drivers of the meta-model were crop yields, breeding female populations, tillage practices, nitrogen fertilizer use, and the crop complex of each livestock industry. When the potential carbon sequestration in the land growing harvested perennial forage is credited to beef production, the CO₂e emissions offset does not reduce the carbon footprint of beef enough to match the lower carbon footprint of pork. Most of the land required to grow hay for beef would not be needed to feed a protein-equivalent pig population. In a hypothetical conversion of all beef production to pork production for 2021, 4.5 Mha of land under perennial forage was freed and 10.0 MtCO₂e per year was mitigated when that area was re-cultivated for annual crops—a GHG mitigation equal to 12% of the GHG emissions budget of Canadian agriculture. Leaving that area under a perennial ground cover mitigated 19.8 MtCO₂e per year, the equivalent of 23% of the sector’s GHG emissions budget.

Effect of Heavy Metals on the Morphological and Physiological Responses of the Torro Plus Variant of Zea mays

2025-01-10T15:17:07+08:00

This study evaluates the impact of heavy metals (zinc, copper and cadmium) on the development and metabolic responses of the maize (Zea mays) variety “Torro Plus”. Seeds were cultivated on MS medium enriched with progressively higher concentrations of heavy metals (50, 100 and 150 μM), and plants were analyzed after 21 days. The results show a significant reduction in morphological parameters, notably an 87.28% decrease in the fresh weight of aerial parts and a 69.93% decrease in the fresh weight of roots under 150 μM of Cd. Chlorophyll a, b and total content also decreased drastically, reaching a maximum reduction of 74.31% under Cd (150 μM). In contrast, secondary metabolites such as proline and flavonoids increased, with a maximum proline accumulation of 0.71 mg/g under Cu (150 μM) and a flavonoid concentration reaching 176.33 mg/g under Cu (100 μM). These results show mechanisms of adaptation to stress, notably the accumulation of flavonoids and proline, while highlighting the increased toxicity of cadmium at high doses. These data are promising for applications in phytoremediation and sustainable agriculture. This study provides important data on the physiological and biochemical responses of plants to heavy metals and opens up prospects for phytoremediation applications.

Enhancing Environmental Sustainability through Machine Learning: Predicting Drug Solubility (LogS) for Ecotoxicity Assessment and Green Pharmaceutical Design

2025-03-10T11:45:38+08:00

Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down, and the drug products are accumulated in living organisms. The ability of a drug to dissolve in water (i.e., LogS) is an important parameter for assessing a drug's environmental fate, biovailability, and toxicity. LogS is typically measured in a laboratory setting, which can be costly and time-consuming, and does not provide the opportunity to conduct large-scale analyses. This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants. We used a dataset from the ChEMBL database that contained 8832 molecular compounds. Various data preprocessing and cleaning techniques were applied (i.e., removing the missing values), we then recorded chemical properties by normalizing and, even, using some feature selection techniques. We evaluated logS with a total of several machine learning and deep learning models, including; linear regression, random forests (RF), support vector machines (SVM), gradient boosting (GBM), and artificial neural networks (ANNs). We assessed model performance using a series of metrics, including root mean square error (RMSE) and mean absolute error (MAE), as well as the coefficient of determination (R²). The findings show that the Least Angle Regression (LAR) model performed the best with an R² value close to 1.0000, confirming high predictive accuracy. The OMP model performed well with good accuracy (R² = 0.8727) while remaining computationally cheap, while other models (e.g., neural networks, random forests) performed well but were too computationally expensive. Finally, to assess the robustness of the results, an error analysis indicated that residuals were evenly distributed around zero, confirming the results from the LAR model. The current research illustrates the potential of AI in anticipating drug solubility, providing support for green pharmaceutical design and environmental risk assessment. Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.

Resource Retrieval from End-of-Life Passenger Cars in the Informal Sector of Morocco

2024-12-05T10:41:57+08:00

Sustainably managing vehicles at their end-of-life stage (ELVs) presents significant potential for resource recovery, effectively addressing resource scarcity through the closure of the material loop. While ELVs in countries like Morocco have traditionally been treated as waste rather than secondary resource material (SRM), they have the potential to reduce reliance on primary materials when used judiciously. Despite policymakers aiming for increased resource efficiency in the automobile sector, there is limited research exploring the role of the informal sector in recovering materials and parts from ELVs. This study investigates the ELV processing scenario at Salmia scrap market, recognized as one of Africa’s largest informal markets for ELVs. Using a mass-balance approach, the disposal of sedan cars is examined, and a conceptual framework illustrating the process flow and interactions among multiple stakeholders is developed. From sampled sedan cars, approximately 7% of aluminum and 76% of iron, by weight, are recovered. These findings contribute to estimating the potential for recycling and recovering materials from ELVs processed by the informal sector in Morocco. In a standard operational context, estimations suggest that the sector holds substantial potential to recover aluminum and iron by 2030. This underscores the importance of formalizing operations and integrating informal players into the value chain to effectively address resource scarcity within a circular economy.

The Bugis Tribe Community Behavior in Developing Vernacular House Models in the Coastal Area of South Sulawesi Province

2024-12-26T17:28:08+08:00

This study aims to understand the behavior of the Bugis community in developing vernacular house models in the coastal areas of South Sulawesi Province, along with the factors influencing this behavior, such as knowledge, motivation, attitude, locus of control, commitment, concern, and income level. Bugis vernacular houses reflect local wisdom that is adapted to environmental, socio-cultural conditions, and the challenges of modernization. This correlational research was conducted in the regencies of Pangkajene and Kepulauan, Barru, and Bone, with a sample of 300 household heads. The variables analyzed include knowledge, motivation, attitude, locus of control, commitment, concern, and income level. Data were collected through knowledge tests and questionnaires and were analyzed using simple and multiple regression techniques. The results show that the Bugis community's behavior in developing vernacular houses is moderate. Individually, motivation, locus of control, commitment, and concern significantly influence behavior, while knowledge, attitude, and income do not have a significant impact. However, collectively, all independent variables (knowledge, motivation, attitude, locus of control, commitment, concern, and income) affect the behavior of the Bugis community in developing vernacular houses in coastal areas. This study provides valuable insights into the environmental and social adaptations of the Bugis coastal community, as well as efforts to preserve culture through the sustainable development of vernacular houses.

Development of Computer Model for Specification of Irrigation Service: A Case Study of Irrigation System in Northern Vietnam

2025-02-18T13:23:37+08:00

Irrigation service defines the responsibilities and rights of irrigation system management agencies, water users, and other parties involved in the irrigation service contract. As a result, the irrigation service must be clearly specified and updated by crop seasons and by all partners. Given the inherent complexity of the service, this article presents and discusses the development and application of a computer model designed to support the specification of public service levels in rice-based irrigation systems. Applied to the Tu Mai irrigation system, the model has enabled all involved parties to define irrigation service levels through systematic analysis and a thorough consideration of constraints such as water resource characteristics, hydraulic structures, and the operational plans of the irrigation system. The research findings have also helped relevant agencies reach agreements on irrigation service levels for the particular irrigation season of spring 2023, which included one irrigation period for land preparation and five subsequent irrigation periods for rice crops corresponding with a specific schedule for operating the system (discharges and duration) that met the farmers’ requests for their farming practices and reduced the loss due to rice crop yield decline at the irrigation system as a whole. Additionally, recommendations for improving irrigation services in the Tu Mai system have been made, including upgrading the head pumping station to accommodate lower water levels in the Cau River, aligning the irrigation schedules of the Water User Associations (WUAs) more flexibly, and strictly supervising water deliveries to ensure safety and fairness.

Climatic Variables and Food Security of Villagers during the COVID-19 Pandemic in the Districts of Huancayo, Peru

2024-11-03T18:50:11+08:00

The study investigated the relationship between climatic variables and food security in households in the districts of Huancayo (Chongos Alto, Viques, Pucará, and Huancayo) during the COVID-19 pandemic. A cross-sectional observational study was conducted with a sample of 272 households out of 36,453. Food security data were collected through questionnaires, and climatic variables (temperature, humidity, and precipitation) were obtained from CEPREANDES weather stations between September 2020 and February 2021. The results showed that 44.49% of households experienced mild food insecurity, while 55.5% experienced moderate food insecurity. Recorded climatic conditions included maximum temperatures of 28°C in Pucará and 27°C in Huancayo, and a minimum of -8°C in Chongos Alto. Relative humidity reached 89% in Pucará and 87% in Chongos Alto and Huancayo, while maximum rainfall was 28 mm in Chongos Alto and 23 mm in Huancayo. Multivariate analysis revealed that relative humidity had a significant association with moderate food insecurity (B=16.406; 95% CI: -64735 to 64768), increasing the risk 16 times under high humidity conditions. No significant relationships were found with temperature (B=-7.107; 95% CI: -77320 to 77306) or precipitation (B=-7.831; 95% CI: -25690 to 25674). It was concluded that relative humidity is a key factor in food security, particularly during the pandemic, while other climatic variables showed no significant impacts. These findings highlight the need for urgent adaptations to climatic challenges in vulnerable contexts.

Encouraging Environmental Sensitivity and Earth Science Performance: Perceptions from First-Year Teacher Education Students

2025-01-04T17:13:11+08:00

This study explores the environmental sensitivity of first-year teacher education stu dents, focusing on the relationship between their Earth Science performance, demographic factors, and their cognitive and emotional responses to environmental challenges. Using a descriptive correlational design within a mixed-methods framework, the research incorporates tools such as the Environmental Sensitivity Test (EST), focus group discussions (FGDs), and eco-mapping to comprehensively collect and analyze data. The findings reveal that while students exhibit a general awareness of environmental issues, this awareness does not consistently translate into sustainable practices, particularly in areas such as water conservation and waste management. A weak and statistically insignificant correlation was identified between Earth Science performance and environmental sensitivity, indicating that academic achievement in the subject does not necessarily lead to environmentally responsible behaviors. The results underscore the importance of teacher education programs integrating principles of behavioral psychology, experiential learning, and focused environmental education. Specifically, secondary science teachers should be equipped with practical strategies, such as implementing project-based learning, organizing community-centered environmental initiatives, and fostering interdisciplinary approaches to sustainability. These interventions address the gap in preparing future educators to effectively advocate for and implement sustainable practices. Strengthening teacher preparation programs with these components ensures that science educators are better equipped to cultivate a new generation of environmentally responsible citizens.

Alternating Environmental Teaching through AI: Potential Benefits and Limitations

2025-01-13T17:14:54+08:00

Environmental education is essential for developing awareness, critical thinking, and problem-solving skills needed to address pressing global challenges such as climate change, biodiversity loss, and resource depletion. Artificial intelligence (AI) can expand access to environmental learning by providing scalable, personalized educational tools that overcome geographical and logistical barriers. This paper explored the perceptions of science teaching about the potential application of AI in environmental teaching. A purposive sampling method was employed to select 25 science teachers, who were selected through an online screening process and subsequently interviewed individually. Findings indicated that AI enabled personalized learning pathways, allowing students to engage with designed content and tasks suited to their individual levels, which enhanced academic growth and interest. AI-powered simulations allowed students to experiment with environmental changes in immersive, risk-free environments, while teachers used AI to simplify complex concepts and create diverse materials, enhancing instructional strategies like flipped classrooms. Individualistic nature of AI-based learning could reduce collaboration, limiting students’ understanding of environmental science and social dimensions. Overreliance on AI also hindered hands-on fieldwork, essential for practical skills and adaptability, while causing strong trust in AI-generated results, weakening critical evaluation and data collection abilities. These findings highlight the need for an optimized integration of AI with collaborative activities, field experiences, and critical thinking to ensure a comprehensive environmental science education.

Copula Method and Neural Networks for X-Band Polarimetric Radar Rainfall Retrieval in West Africa

2024-12-02T11:36:34+08:00

In the context of climate change, countries in West Africa are faced with recurrent flooding with catastrophic consequences, that makes it imperative to have access to rainfall information on fine spatial and temporal scales for better monitoring and prediction of these phenomena, as could be provided by weather radars. Based on an extensive archive of data from the X-band polarimetric radar and rain gauges observations gathered during the intensive AMMA campaigns in 2006–2007 and the Megha-Tropiques satellite measurement validation programme in 2010 in West Africa, we (i) simulated jointly realistic data for polarimetric radar variables and rain intensity using copula, and (ii) assessed rain rate estimation methods based on neural network (NN) inversion techniques and non-linearly calibrated parametric algorithms. The assessment of rainfall rate retrieval by these estimators is carried out using the part of the observations database not employed for calibration steps. The multiparametric algorithms R(Z_H,K_DP) and R(Z_DR,K_DP) perform better than R(Z_H,Z_DR) and R(Z_H,Z_DR,K_DP), especially since they are calibrated using copulas with upper tail dependencies, with KGE ranging in 0.68-0.75 and 0.79-0.82, respectively versus ranges of 0.40-0.64 and 0.20–0.51, for the two latter estimators. The neural network-based estimators R_NN(Z_DR,K_DP) and R_NN(Z_H,K_DP), show KGE score characteristics comparable to those obtained from the best parametric relations, specifically optimized for the synthetic copula-based dataset. However, the neural network-based estimators were shown to be more robust when applied to a specific rainfall event. More specifically, neural network-based estimators trained on synthetic data are sensitive to the copulas' ability to capture the dependence between the variables of interest over the entire distribution of joint values. This leads to a near-cancellation of sensitivity to variability in the raindrop size distribution, as shown the coefficients of correlation near 1, especially for R_NN(Z_DR,K_DP), and for less extent R_NN(Z_H,K_DP).

Training to Improve Community’s Knowledge, Attitude, Motivation and Behavior Related to the Building of Family Toilets in the Coastal Area

2025-01-07T13:47:40+08:00

The purpose of this study was to examine the knowledge, attitude, motivation and behavior of the community before and after the experiment, and also to determine the effect of the experiment on increasing knowledge, attitude, motivation, and behavior related to the construction of family toilets in coastal areas. The study was conducted in Pangkep and Maros Regencies. A total of 50 heads of families were selected as participants using the purposive sampling method. 25 participants became the experimental group and 25 people became the control group. The research variables included knowledge, attitudes, motivation, and behavior of the community in building family toilets before and after the experiment. Data collection through tests, questionnaires, and observations to each participant. The research instruments were knowledge tests, questionnaires, and observations. Data analysis used descriptive and inferential statistical analysis, with the t-test. The results of the study showed that based on the experiment, knowledge had a significant effect with a correlation coefficient of 0.94, attitudes had an effect of 0.91, motivation was 0.756, and behavior was 0.865. It can be concluded that the construction of family toilets in the coastal areas of Pangkep and Maros Regencies, before the experiment, the knowledge, attitudes, motivation, and behavior of the community were in the low category, and after the experiment increased significantly to the high category. In addition, the results of the analysis showed that the experiment had a significant effect on increasing the knowledge, attitudes, motivation, and behavior of the community towards the construction of family toilets in coastal areas.

Development of a Two-Dimensional Hydrodynamic Model to Simulate Rip Currents in the Bai Dai-Cam Ranh Coast, Vietnam

2025-02-13T16:22:07+08:00

Rip currents are a significant threat to swimmers worldwide, responsible for numerous drowning incidents each year. In Vietnam, Bai Dai Beach in Cam Ranh Bay, Khanh Hoa Province, has experienced an increase in drowning events due to rip currents in recent years. To address this issue, a comprehensive study was conducted based on developing a depth-averaged 2D hydrodynamic model to simulate rip currents in the Bai Dai-Cam Ranh coast. The HYDIST-2D numerical model was applied to simulate the rip current evolution in space and time for the study area. The results showed that the HYDIST-2D numerical model can accurately predict the location, magnitude, and microstructure of rip currents, including rip current speed, width, and length. The simulation results revealed that the rip current speed is greater during the low tide phase, with an average speed of 0.5 m s^–¹, while during high tide, the rip current speed is lower, around 0.1–0.8 m s^–¹. The width and length of the rip current also vary with the tide phase, with a wider and longer rip current observed during the low tide phase. The results also showed that the rip current speed and microstructure are influenced by the wave features, tide current, and bathymetry of the study area. The present study provides valuable insights into the dynamics of rip currents in the Bai Dai-Cam Ranh coast. The findings can be used to support the management of bathing activities and provide early warnings for potential risks associated with rip currents.

Origins of Organic Matter in Tuo River Sediments

2024-11-03T20:03:26+08:00

Ultraviolet-visible (UV-Vis) and three-dimensional excitation emission matrix fluorescence (3D-EEM) spectroscopies were conducted to investigate the structure and origin of dissolved organic matter (DOM) from soils around the Tuo river in Suzhou city in different season. The results showed that the characteristics of all samples, UV-Visible spectra were similar and the relative concentrations of DOM showed an overall increasing trend in the middle and upper reaches of the Tuo River and reached a maximum in the middle reaches of the river. In particular, the aromaticity (A₂₅₀/A₃₆₅) of DOM in sediments at the midstream point of the Tuo River and the degree of humification degree (SUVA₂₅₄) were higher than those in other river sections. The 3D-EEM fluorescence spectra showed that fulvic acid-like peaks in the visible region, fulvic acid-like peaks in the UV-visible region, and two humic acid-like peaks were reflected in the dissolved organic matter of the Tuo River sediments. Combining the three-dimensional fluorescence spectrum with the fluorescence index (fluorescence index, FI) and autochthonous index (autoch-thonous index, BIX) of DOM in the sediments of the Tuo River in different seasons, it shows that the exogenous input of DOM in the sediments of each section of the Tuo River is extremely obvious and less bioavailable. The aromaticity of DOM molecules is enhanced after the Tuo River flows through urban areas. The present study can provide a reference for the future management of the water environment of related rivers.

Instructional Modules for Constructivist Environmental Learning in Science, Technology and Society (STS) Subject

2025-01-05T00:15:33+08:00

Modules enable students to engage with content at their own pace, fostering autonomy and deeper understanding. The modular approach ensures clarity in presenting objectives, instructions, and concepts, while having illustrations, activities, and assessments could enhance comprehension and retention. This paper was a developmental study on STS module for college students using the ADDIE Model (Analysis, Design, Development, Implementation, and Evaluation). Sampled 673 first-year students from Northwest Samar State University participated in the study, with 299 participating in a test try-out and 374 in the students’ performance evaluation. Three expert evaluators with backgrounds in science, English, and psychology, each with over four years of experience, assessed the modules to ensure alignment with the study’s constructivist learning goals and instructional integrity. The findings revealed that both students and experts had rated the instructional module positively, indicating its effectiveness in facilitating learning and completing lessons. Key aspects such as the style of illustrations and written expressions, the usefulness of learning activities, and the guidance provided by illustrations and captions were especially well-received. The module was praised for its clear objectives, understandable instructions, and engaging tasks like trivia and puzzles. Expert evaluations highlighted relevance, simplicity, and balanced emphasis on topics in the module content. Furthermore, students in test group demonstrated significant improvement in performance, with post-test scores notably higher than pre-test scores, confirming the module’s effectiveness in enhancing learning outcomes. Consequently, this paper provides an opportunity to integrate science learning with initiatives aimed at promoting environmental preservation and driving social change.

Environmental-Based Supply Chain Integration for the Development of Carrageenan Production Centers in Laikang Village Takalar Regency, South Sulawesi Province

2025-01-20T09:40:58+08:00

The rapidly growing seaweed farming contributes to food, feed, biofuel, and biochemicals, with Indonesia being the largest exporter of raw seaweed (53.35%) but low in carrageenan derivative products (4.49%). Sustainable farming supports SDG 13 and SDG 14 through emission reduction, marine ecosystem conservation, and increased biodiversity. However, its supply chain faces challenges in raw material availability, such as fluctuating harvests, inconsistent quality, limited logistics infrastructure, and market price volatility. Therefore, good environmental management is needed to optimize raw materials in carrageenan production through resource efficiency, environmentally friendly products, environmental regulations, and market and stakeholder awareness. This study identifies factors in the environment-based supply chain that influence the development of carrageenan production centers and analyzes their ecological impacts using quantitative SEM analysis. SEM analysis reveals that resource efficiency, waste management, environmental regulations, and stakeholder awareness and commitment are the main influencing factors. shows significant factors including raw material efficiency, waste management, and environmental regulations. Environmentally based supply chain integration for the development of carrageenan production center areas can be done by; focusing on sustainability and minimal environmental impact, support for achieving SDG 12, 13, and 14, strategies for sustainable raw material management, circular economy, sustainability certification, renewable energy and environmentally friendly technology, and stakeholder collaboration and awareness.