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Biodiversity and Soil Characterization of Ancestral Domain of the Tagbanua Tribe in Aborlan, Palawan, Philippines

This study was conducted to determine strategies to enhance the sustainable forest management practices of the Tagbanua tribe. Specifically to describe the biodiversity and soil characteristics of the ancestral domain. The modified belt-transect method for biodiversity assessment developed by B+WISER (2014), further modified by the Department of Environment and Natural Resources (DENR) in the assessment, was used in this study. Results of soil chemical analysis showed significant variations among various land uses. The ancestral domain had at least 73 plant species belonging to 34 families and 59 genera. Four (4) taxa whose SN/families were still undetermined and another three (3) genera under families Annonaceae, Meliaceae, and Sapindaceae were unidentified. It had 12 plant species that are threatened with one critically endangered based on the list of threatened Philippine plants of the DENR. On the other hand, a total of 372 birds representing 61 species from 29 families were recorded. The high Shannon-Weiner Diversity Index (H’=3.69) and Shannon’s Evenness (HE=0.90) values indicate high avifaunal diversity and equitable distribution among the detected species. Most of the conservation priority species recorded in the area are Palawan endemic species. The survival of these birds is threatened by extinction due to habitat loss. This observation emphasized the importance of the ancestral domain as a refuge for these endemic species and conservation priority areas.

Reynald M. Quilang

Dynamic Impact-Based Heavy Rainfall Warning with Multi-classification Machine Learning Approaches

The majority of flood assessment and warning systems primarily focus on the occurrence of floods caused by river overflow, taking into account factors such as intense precipitation. Improving flood resilience, on the other hand, requires a deeper understanding of how these factors affect each other and how specific local conditions can have an impact. This study offers impartial tools for estimating the severity of the effects brought on by heavy rainfall to facilitate the prompt communication of effective measures, such as the evacuation of livestock and human settlements and the provision of medical assistance. These tools take into account the cascading effects of various causative factors contributing to heavy rainfall. This article aims to assess the various factors that contribute to the impacts of heavy rainfall, including the timestamp (indicating soil saturation and moisture levels), river gauges (determining water congestion in canal systems), average aerial precipitation (indicating runoff), and the rainfall itself, taking into account both in situ and ex-situ impacts. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. This analysis is conducted by leveraging historical observed datasets. The results demonstrate the feasibility of mitigating the issue of excessive and insufficient flood warnings resulting from the cumulative effects of intense precipitation. By implementing a categorization system that divides the affected areas into various portions, or districts, according to the main factors contributing to flooding, namely rainfall, river discharge, and runoff, The suggested model presents novel insights into the sequential consequences of intense precipitation in the regularly inundated regions of North Bihar, India. Innovative tools can serve as valuable resources for flood forecasters and catastrophe managers to comprehend the extent of flooding and the consequential effects of intense precipitation.

Anand Shankar

Heavy Metals in Water and Sediments and Their Impact on Water Quality in Andean Micro-watersheds: A Study of the Colorado and Alajua Rivers in the Ambato River Watershed, Tungurahua, Ecuador

The present study aims to characterize the water and sediment quality of the Colorado and Alajua rivers within Ecuador’s Ambato River watershed, with a specific focus on the presence of heavy metals. Measurements were conducted at five sampling points along the upper and lower zones of each river, where both physicochemical and microbiological parameters, as well as concentrations of heavy metals in water and sediments, were analyzed. Most parameters exhibited statistically significant differences, as determined by the analysis of variance (ANOVA), between the values observed in the upper and lower zones of the micro-watersheds. Water quality in the mentioned rivers was assessed using specific water quality indices, WQI, namely the NSF-WQI and Dinius WQI. Additionally, the impact of heavy metal presence in the water and sediments was evaluated using the Heavy Metal Evaluation Index (HEI). While most parameters met the Ecuadorian quality standards for water sources intended for human consumption, concerns emerged regarding elevated levels of total and fecal coliforms along both rivers, which could limit the suitability of these rivers as a water source for human use and consumption. At various sampling points, water quality criteria for the preservation of aquatic life were not met for several heavy metals. For example, the Colorado River exhibited elevated levels of zinc (59-76 ?g.L-1), copper (12-47 ?g.L-1), lead (1.2-3.9 ?g.L-1 ), iron (0.33-0.37 mg.L-1 ), and manganese (0.37-0.47 mg.L-1), while the Alajua River showed excess copper (11 ?g.L-1), iron (0.61-0.72 mg.L-1), and manganese (0.62-0.98 mg.L-1). Geological factors likely contribute to the concentration of heavy metals in the upper segments of the rivers, while agricultural runoff may contribute to concentrations in the lower segments. Sediments exhibited higher average values of the Heavy Metal Evaluation Index (HEI) (20.6-26.7) compared to water samples (13.9-15.4), indicating a potential accumulation of heavy metals in the river sediments. Overall, both rivers exhibited contamination levels ranging from regular to moderate, as indicated by the calculated average Water Quality Indices (WQI), with certain areas showing slight contamination or meeting acceptable standards. These results highlight the influence of anthropogenic activities on water quality, emphasizing the necessity of continuous monitoring to assess and control their impact.

Rodny Peñafiel, Fabián Rodrigo Morales-Fiallos, Bolivar Paredes-Beltran, Dilon Moya, Adriana Jacqueline Frias Carrion and Belén Moreano

Hepatotoxic Effects of Gaseous Sulfur Dioxide (SO?), Nitrogen Dioxide (NO?), and Their Mixture on Sea Bass (Centropristis striata): Hematological, Biochemical and Genotoxic Studies

This study meticulously explores the intricate hepatotoxic effects stemming from acute exposure to gaseous sulfur dioxide (SO2), nitrogen dioxide (NO2), and their amalgamation on sea bass (Centropristis striata). Employing a comprehensive approach involving hematological, cytotoxic, and histochemical analyses, the research provides crucial insights into the potential adverse impacts of these pollutants on fish health. The examination specifically focuses on the effects of SO2+NO2 on hematological, histochemical, and serum biochemical parameters in Centropristis striata. Treatment groups, subjected to LC30, LC50, and LC90 acute exposure of gaseous SO2, NO2, and SO2+NO2, alongside a control group, underwent evaluation of parameters such as red and white blood cells, hemoglobin, hematocrit, mean corpuscular hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin concentration, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, acid phosphatase, lactate dehydrogenase, total protein, albumin, serum creatinine, and blood urea. At the 96th hour, RBC values decreased, and WBC values increased in all experimental conditions compared to the control group (p>0.05). MCV and MCH increased with the concentration of gaseous pollutants and exposure time (p>0.05). Hematological parameter variations underscore disruptions in blood composition and immune responses. Simultaneously, alterations in serum biochemical parameters suggest potential impairments in liver and kidney functions, along with disturbances in lipid metabolism. Significant declines in albumin levels, indicating potential liver dysfunction or inflammation due to SO2 and NO2 exposures, were observed at all experimental conditions, while decreased globulin levels suggest immunosuppressive effects from combined pollutants. A substantial increase in the albumin/globulin ratio further signals an imbalance indicative of potential liver dysfunction or inflammation. Varied responses in liver enzyme levels (SGPT/ALT, SGOT/AST, ALP) underscore potential liver damage or injury (p< 0.05). These findings deepen our understanding of environmental impacts on aquatic ecosystems, emphasizing the need for ongoing efforts to ensure the health and sustainability of fish populations in polluted environments.

N. Gandhi, Y. Rama Govinda Reddy and Ch. Vijaya

Analysis of the Lebanese Society’s Behavior Regarding Electronic Waste Management

This paper examines electronic waste and cycling in Lebanon. It describes the current situation regarding e-waste among government agencies and non-governmental organizations. It addresses two research questions: The first one asks if the Lebanese society and government are aware of the dangers posed by electronic waste and whether any action has been taken to prevent an environmental catastrophe. The second question asks about Lebanese attitudes toward e-waste and whether they are willing to fight against it. Interviews provided the first question’s responses. The authors have visited Organization A and NGO B. The first is worried about gathering waste in more prominent Beirut, while the second targets spreading attention to e-waste’s risks on legislative and social levels the same. Question two was discussed through surveys filled out by arbitrary people from Lebanese society. The answers to both research questions came in a manner that demonstrates the two hypotheses expected toward the start of the study, specifically that e-waste represents an incredible danger to the Lebanese climate. Hypothesis two, if climate neighborliness and proclivity to right e-garbage removal rely upon the instructive level of some random resident, has been confirmed while analyzing the answers in the survey.

M. Trad and A. Harb

Transforming Soil Stability: A Review on Harnessing Plant Cell Compounds and Microbial Products for Modifying Cation Exchange Capacity

Soil stabilization is a very important method of science and engineering for improving the properties of soil. This paper aims to stabilize expansive black cotton soil through a biological approach involving plant extracts, plant waste materials, and microorganisms. While chemical methods exist, i.e., lime stabilization, geotextiles, etc., they are not economically feasible for large-scale applications. The primary issue with black cotton soil is due to the presence of montmorillonite clay mineral, which makes it unsuitable for the construction of roads and airfields. The cation exchange capacity (CEC) can be defined as the ability of soil to absorb and exchange positively charged ions; thus, if free positively charged ions are not available, the soil will not exchange them with others. The CEC of the soil is diminished, and ultimately, the soil is stabilized to some extent. This paper explores the preparation of plant extract, which contains a high number of anions, and directly inoculates it with soil, which nullifies the positive charge of the soil and diminishes the CEC. The use of cellulose and lignin-degrading microorganisms as an energy source and other minerals that are needed for their growth will be utilized from the soil to reduce CEC, i.e., Mg required for DNA replication and Ca required for their growth and maintenance. Another approach to diminishing the CEC is to use the microorganisms that produce EPS, which require Ca and Mg as adhesions for the formation of biofilm, i.e., Pseudomonas aeruginosa, Bacillus subtilis, and Escherichia coli. The use of microorganisms that have specific enzymes is also used in the diminishing soil CEC, i.e., by using ureolytic enzyme-producing bacteria like Sporosarcina pasteurii, Bacillus paramycoides, Citrobacter sedlakii, and Enterobacter bugadensis.

M. V. Shah, N. M. Rathod, D. N. Prajapati, P. J. Mehta, R. R. Panchal and Vijay Upadhye

Landslide Susceptibility Zonation Mapping Using Machine Learning Algorithms and Statistical Prediction at Hunza Watershed Basin, Pakistan

The mountainous region of the Hunza River watershed basin, especially along the Karakorum highway, and also known as a third pole for the high accumulation of glaciers, which leads to huge devastating landslides occurring every year. Landslide susceptibility mapping was carried out using two deep machine learning techniques (DeeplabV3+ & universal network U-Net) and two statistical models (Intuitionistic Fuzzy divergence IF-D & Frequency ratio FR). The landslide susceptibility mapping is conducted using landslide inventory data and twelve conditional factors. The landslide susceptibility maps obtained from the two statistical models were compared with those generated by two deep machine learning models based on prediction accuracy measures, such as the Area Under the Curve (AUC) and Seed Cell Area Index (SCAI). The Success Rate Curve (SRC) was obtained using the training dataset, and the AUC values for the four models were as follows: 76.9% for IF-D, 76.9% for FR, 80.4% for DeeplabV3+, and 76.3% for U-Net. In terms of the Prediction Rate Curve (PRC) obtained from the validation dataset, the AUC values were found to be 80.8% for IF-D, 80.8% for FR, 81% for DeeplabV3+, and 77.8% for U-Net. To assess the classification ability of the models, the Seed Cell Area Index (SCAI) test was conducted. The results indicated that the SCAI (D-value) was 7.3 for U-Net, 10 for DeeplabV3+, 7.6 for IF-D, and 9.1 for FR. Overall, the findings revealed that DeeplabV3+ exhibited the highest prediction accuracy and classification ability, making it the most suitable choice for landslide susceptibility mapping in the relevant study area.

A. Khan, G. Khan, M. Minhas, S. A. Hussain Gardezi, J. Ahmed and N. Abbas

Optimization, Characterisation and Evaluation of Biochar Obtained from Biomass of Invasive Weed Crotalaria burhia

Invasive weed plants are unwanted and hazardous waste biomass; and have extraordinary potential to serve as raw materials for biochar production. To evaluate the potentiality of invasive weed for bioenergy production in the form of biochar, Crotolaria burhia was investigated. The response surface modeling and optimization of the biochar parameters were conducted using the experimental design expert 13.0. The optimum value of the desirability function was obtained at a pyrolysis temperature of 450°C and a particle size of 50-100 mm. The model represents a p-value less than 0.0500 and a high F value, which denotes its reliable and accurate prediction of experimental data. A strong correlation was observed between actual and predicted values for biochar composites fixed carbon, carbon, surface area, pore size, and pore volume. In the present study, C. burhia biochar production was carried out by slow pyrolysis at 450°C under vacuum conditions. Biochar was found to be alkaline, with a 33.23% yield. Proximate analysis of C. burhia revealed 3.35% moisture content, 8.48% volatile matter, 81.24% fixed carbon and 6.94% ash content. The elemental analysis shows major concentrations of carbon, hydrogen, and oxygen as 57.77%, 6.123%, and 27.60%, respectively. Low H/C and O/C molar ratios were quantified as 0.10% and 0.47%, respectively. It possesses a honeycomb structure having mesoporous surface porosity with a surface area of 155.19m²/g and the presence of a remarkable concentration of mineral elements calcium and potassium. Biochar rich in hydroxyl, carboxylic, and alkene functional groups enhances its applicability areas. These findings make C. burhia a potential feedstock for the production of good-quality biochar.

Loveena Gaur and Poonam Poonia

The Waste Management System in the Parking and Traders Arrangement in the Borobudur Temple Area, Central Java, Indonesia

The Indonesian government continues to accelerate the resolution of all problems related to the planning, infrastructure development, and arrangement of tourist visits, including the arrangement of parking spaces and commercial areas in the Borobudur temple area. The purpose of this study is to develop a waste management system in the parking and commercial areas of Kujon as an alternative to structuring the Borobudur temple area. The research method is a descriptive-qualitative observational approach. Surface water and groundwater examinations are carried out in laboratories and compared with quality criteria determined by the Indonesian government. Toxic and hazardous waste is stored in temporary facilities until it is collected by a company licensed by the Indonesian environmental ministry. The Shannon-Wiener Plankton and Benthos Diversity Index measures the diversity of organisms in a community. The study’s findings highlight the need to establish a waste processing facility based on the reduction, reuse, and recycling principles. Waste will be collected at a certain site and stored temporarily in line with the technical instructions for the Minister of Environment and Forestry’s Regulation. The findings of surface water and groundwater studies demonstrate that all measured parameters continue to meet the Indonesian government’s quality thresholds. Plankton Bioindicator Measurements: Plankton diversity index values range from 1.040 to 1.943, indicating moderate pollution, while benthos values range from 0.811 to 0.918, indicating weakly to moderately contaminated conditions. Sustainable environmental management is critical and should serve as a baseline for environmental quality in the activity area.

S. Isworo, E. Jasmiene and P. S. Oetari

Integrating Satellite Data and In-situ Observations for Trophic State Assessment of Renuka Lake, Himachal Pradesh, India

The present study focuses on estimating the Trophic State Index (TSI) of Renuka Lake, the smallest Ramsar site in India, utilizing in-situ observed Secchi disk transparency (SDT) and satellite data. Site-specific algorithms were developed by establishing the relationship between the spectral band ratio of Landsat 8 OLI and LISS-III with that of in-situ measured SDT data. Notably, the exponential regression model outperformed other regression models (linear, logarithmic, polynomial, and power), achieving a better model output (R2=0.94). Additionally, water quality parameters, namely pH and dissolved oxygen (DO), were measured using the TROLL 9500 multi-parameter instrument. Various interpolation methods were applied to the in-situ data, with the exponential regression model yielding the most accurate results.This method was subsequently selected to generate two-dimensional water-quality images of Renuka Lake. The combined analysis of in-situ and satellite-derived trophic status indicates the eutrophic to hypereutrophic condition of the lake’s eastern and western parts. Satellite imagery spanning 2010-2019 consistently reveals a eutrophic state in the lake, with fluctuations in intensity over the period. The sustained eutrophic condition is attributed to escalating human-induced activities surrounding the lake, particularly in the western region.

Sujit Kumar Jally, Rakesh Kumar and Sibabrata Das

Economic Feasibility of On-Grid Photovoltaic Solar Power Plants at Private Universities in Indonesia

Campus 2 of the National Institute of Technology (ITN) Malang shows its commitment to utilizing solar energy by adopting a 500 kWp photovoltaic solar power plant (PV), making it the largest in Indonesia for a private university. This research aims to evaluate the economic feasibility of photovoltaic solar power plants (PV) at Campus 2 of the National Institute of Technology Malang. The implementation of renewable energy, particularly photovoltaic solar power, is gaining attention due to its contribution to reducing greenhouse gas emissions and economic growth. However, the development of renewable energy sources faces several challenges, including the limitations of economic feasibility studies in Indonesia. A mixed-methods research approach is used, combining qualitative and quantitative data. Qualitative data are obtained from interviews with PV management staff, while quantitative data include net present value (NPV) calculations and payback periods (PBP). The research findings indicate that the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology (ITN) Malang has a capacity of 500 kWp, with a peak load reaching 380 kVA. The total project cost is Rp. 4,084,498,826, with annual operational and maintenance costs of Rp. 81,595,607. The price of electricity from the on-grid photovoltaic solar power plant is Rp. 930 per kWh. An NPV value of Rp. 7,789,395,602 indicates future profitability, while a PBP of 8.55 years demonstrates feasibility in terms of return on investment. In conclusion, the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology Malang has good economic feasibility due to factors such as controlled costs, competitive prices, a positive NPV, and a short PBP. Regular evaluations are necessary to ensure efficient operation and maximum benefits.

Rijal Asnawi, Antariksa, Sukir Maryanto and Aminudin Afandhi

Impact of Urban Xenobiotics on Mycorrhizal Associations in Urban Plants

Urban xenobiotics are a vital contamination phenomenon of urban plants in the overall country. They are a result of human activity due to growing urbanization and population growth. There are extensive sources of both natural (soil or rock erosion, fires, biodegradation, and volcanic eruptions) and anthropogenic (soil pollution, air, and herbicides). Currently, the demand for pharmaceuticals, compared to the growing population, has placed a risk on the urban plant. Additionally, the production of illegal drugs has caused the release of dangerous carcinogens into fungal activities, which will have an impact on plant health, microbial structure, and fungal interaction. Because of the harsh environment, higher temperatures, heavy metals, and higher N deposition, most urban trees suffer from stress conditions, and mycorrhiza is negatively impacted by plant conditions. Some mycorrhiza fungi are unable to sporulate and hyphal at higher xenobiotic concentrations in urban areas. This chapter takes a look at the sources and compounds of xenobiotics and their harmful impact on mycorrhiza; and its association with the urban plants.

Aashutosh Kumar Mandwa, Atul Kumar Bhardwaj, Rajesh Kumar, K.K. Chandra, Chanchal Kumari and S. K. Padey

Cost Assessment of Emission Mitigation Technology for the Palm Oil Sector in Indonesia

Indonesia must establish a policy on the application of technology for mitigating greenhouse gas emissions because it is the nation that produces the most palm oil. When evaluating different technologies, policymakers should consider how much the technology will cost compared to the potential emissions abated, in terms of marginal abatement cost (MAC), which reflects priorities in the form of marginal abatement cost curves (MACC). The objective of this research is to evaluate and estimate the ranking of MAC from eight mitigation technologies used in Indonesia’s palm oil sector between 2020 and 2030. The least MAC is given as technology ranked first, namely the high-capacity boiler, with a value of $-19.61/tonne CO2eq followed by the high-efficiency steam turbine with $-7.2/tonne CO2eq, and the POME-to-biogas technology with $-0.1/tonne CO2eq. Additionally, the MAC of five additional technologies is positive, suggesting that implementation expenses were incurred. Subsequently, a sensitivity analysis is performed to see which technology ranks are impacted by interest rate fluctuations. Biogas upgrading technology is therefore liable to changes in the discount rate, which occur at different values. Other mitigation technologies, however, are also increasing their parameters, although less significantly than biogas upgrading, therefore this has no bearing on mitigation technology ranking.

A. S. Nur Chairat, L. Abdullah, M. N. Maslan , M. S. M. Aras, M. H. F. Md Fauadi, R. A. Hamid and H. Batih

Advancements in Machine Learning and Deep Learning Techniques for Crop Yield Prediction: A Comprehensive Review

Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.

V. Ramesh and P. Kumaresan

Anaerobic Co-digestion of Palm Oil Sludge, Cassava Peels, Cow Dung and Ground Eggshells: Process Optimization and Biogas Generation

Indiscriminate disposal of crop and animal wastes has grown in acceptance across the globe as an environmentally hazardous practice. This study used a 225L polyethylene digester that was specially made to produce biogas from anaerobic co-digestion of palm oil sludge, cassava peels, and cow dung using ground eggshells for pH stabilization and a greenhouse for temperature control. Cassava peels, palm oil sludge, cow dung, and water were combined in a ratio of 1:1:2:5.3, respectively, and 1.3 kilograms of crushed eggshells were added. The bio-digestion system generated 650.60 L of cumulative biogas throughout the 30-day sludge retention period. The pH averaged 6.0, and the slurry temperature averaged 34.76oC during digestion, which is favorable for the production of biogas since microbial populations thrive under hospitable conditions. The biogas produced after a hydraulic retention time (HRT) of over 20 days had the highest methane concentration of 60%, while days under 10 HRT had the lowest methane content of 45.5%. On the 13th day of anaerobic digestion, biogas output peaked at 34.90L, and pH and temperature were maintained at 6.5 and 35.0°C, respectively, the ideal ranges for a healthy process. An efficient technique for producing energy in the form of biogas was shown by optimized anaerobic co-digestion of animal and crop waste utilizing ground eggshells and a greenhouse for pH and temperature control. Future research should focus on developing more efficient, cheaper microbial agents, such as enzymes for biological pre-treatment of palm oil sludge to reduce lignin, which negatively impacts biogas generation.

D. O. Olukanni, M. J. Kamlenga, C. N. Ojukwu and T. Mkandawire

Plant Leaf Disease Detection Using Integrated Color and Texture Features

In the realm of precision agriculture, a pivotal challenge lies in the detection, identification, and grading of crop diseases. This multifaceted task necessitates the involvement of expert human resources and time-sensitive actions aimed at mitigating the risks of production losses and the rapid spread of diseases. The effectiveness of the majority of developed systems in this domain hinges on the quality of image features and disease segmentation accuracy. This paper presents a comprehensive research endeavor in the domain of Content-Based Image Retrieval (CBIR), specifically tailored to detect and classify leaf diseases. The proposed system integrates both color and texture features to underpin its functionality, providing a robust framework for accurate disease detection. By leveraging advanced image processing techniques, the system enhances the precision of disease identification, which is crucial for timely and effective intervention in agricultural practices. To evaluate the system’s performance, maize leaves afflicted by rust and blight serve as prime candidates for testing. These diseases were chosen due to their prevalence and significant impact on crop yield. The experimental results demonstrate that the developed system consistently excels in its disease detection and identification tasks, boasting an impressive efficiency rate of 98.33%. This high level of accuracy underscores the potential of the system to be a valuable tool in precision agriculture, aiding farmers and agricultural experts in maintaining healthy crops and optimizing production. The integration of color and texture features not only improves the detection accuracy but also provides a comprehensive understanding of the disease characteristics. This dual-feature approach ensures that the system can distinguish between different types of diseases with high precision, making it a versatile solution for various agricultural applications. The findings of this research highlight the importance of advanced image analysis techniques in enhancing the capabilities of disease detection systems, paving the way for more efficient and effective agricultural practices.

Jayamala Kumar Patil and Vinay Sampatrao Mandlik

Optimization and Thermodynamic Analysis of CO2 Refrigeration Cycle for Energy Efficiency and Environmental Control

Supermarket applications are significant contributors to greenhouse gas emissions, necessitating efforts to reduce carbon footprints in the food retail sector. Carbon dioxide (R744) is recognized as a viable long-term refrigerant choice due to its favorable properties, including low Global Warming Potential, non-toxicity, non-flammability, affordability, and widespread availability. However, enhancing the energy efficiency of pure CO2 systems in basic architecture units, particularly in warm regions like India, remains a challenge. To address this, modern refrigeration systems must prioritize low energy consumption and high coefficient of performance (COP) while meeting environmental standards. This study investigates different operating conditions to determine the optimal parameter range for maximizing COP and improving the efficiency of conventional CO2 refrigeration configurations. It examines both subcritical and transcritical refrigeration cycles under varying parameters, emphasizing the importance of understanding COP’s relationship with factors such as subcooling, superheating, ambient temperature, and evaporator temperature. The study advises against superheating in CO2 systems but highlights the substantial COP increase with higher degrees of subcooling, leading to enhanced system performance. Additionally, it provides a comprehensive theoretical comparison between advanced pure CO2 supermarket applications and commonly used hydrofluorocarbons-based systems, offering insights into energy efficiency and environmental impacts for informed decision-making in the industry.

Manish Hassani and Kamlesh Purohit

Enhancing Smart Grids for Sustainable Energy Transition and Emission Reduction with Advanced Forecasting Techniques

Smart grids are modernized, intelligent electricity distribution systems that integrate information and communication technologies to improve the efficiency, reliability, and sustainability of the electricity network. However, existing smart grids only integrate renewable energies when it comes to active demand management without taking into consideration the reduction of greenhouse gas emissions. This paper addresses this problem by forecasting CO2 emissions based on electricity consumption, making it possible to transition to renewable energies and thereby reduce CO2 emissions generated by fossil fuels. This approach contributes to the mitigation of climate change and the preservation of air quality, both of which are essential for a healthy and sustainable environment. To achieve this goal, we propose a transformer-based encoder architecture for load forecasting by modifying the transformer workflow and designing a novel technique for handling contextual features. The proposed solution is tested on real electricity consumption data over a long period. Results show that the proposed approach successfully handles time series data to detect future CO2 emissions excess and outperforms state-of-the-art techniques.

Farah Rania, Farou Brahim, Kouahla Zineddine and Seridi Hamid

Microplastics in Agricultural Soil and Their Impact: A Review

The rapid global plastic production of 348 million tonnes in 2018 has led to widespread environmental pollution, especially in terrestrial ecosystems. This study examines microplastics in agricultural soils, coming alarmingly. Particles ?5 mm, which are defined as microplastics, have detrimental effects on the earth’s environment. Because of its ecological importance, soil acts as an important microplastic sink, affecting soil and plant health and microbial activity. A variety of factors contribute to microplastic pollution in agricultural soils, including plastic mulching, manure, agricultural products (silage nets, twine), sewage sludge, weathering, and other indirect processes. These microplastics migrate, threatening soil integrity and biodiversity. Soil microplastics are analyzed for size, volume fraction, and polymer. Common materials include polyethylene, polypropylene, polyamide, polystyrene, polyvinyl chloride, and polyesters. Techniques, including optical microscopy and spectroscopy, extract and analyze microplastics. This comprehensive review calls for increased concern about the ecological effects of microplastics in agricultural soils. It emphasizes the importance of managing plastics to solve environmental challenges. The integrated environmental assessment highlights the complex relationship between microplastics and soil ecosystems, providing insights into potential risks and suggesting strategies to combat this looming environmental threat.

P. Solanki, S. Jain, R. Mehrotra, P. Mago and S. Dagar

Numerical Modeling of Instantaneous Spills in One-dimensional River Systems

Modeling the fate and transport of spills in rivers is critical for risk assessment and instantaneous spill response. In this research, a one-dimensional model for instantaneous spills in river systems was built by solving the advection-dispersion equation (ADE) numerically along with the shallow water equations (SWEs) within the MATLAB environment. To run the model, the Ohio River’s well-known accidental spill in 1988 was used as a field case study. The verification process revealed the model’s robustness with very low statistic errors. The mean absolute error (MAE) and root mean squared error (RMSE) relative to the absorbed record were 0.0626 ppm and 0.2255 ppm, respectively. Results showed the spill mass distribution is a function of the longitudinal dispersion coefficient and the mass decay rate. Increasing the longitudinal dispersion coefficient reduces the spill impact widely, for instance after four days from the mass spill the maximum concentration decreased from 0.846789 to 0.486623 ppm, and after five days it decreased from 0.332485 to 0.186094 ppm by increasing the coefficient from 15 to 175 m2/sec. A similar reduction was achieved by increasing the decay rate from 0.8 to 1.2 day-1 (from 0.846789 to 0.254274 ppm and from 0.332485 to 0.0662202 ppm after four and five days, respectively). Thus, field measurements of these two factors must be taken into account to know the spill fate in river systems.

Fatima M. A. Al-khafaji and Hussein A. M. Al-Zubaidi

Assessment of Water Poverty Index (WPI) Under Changing Land Use/Land Cover in a Riverine Ecosystem of Central India

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Girish Kumar, M. M. Singh, Dheeraj Kumar Singh, Bal Krishan Choudhary , Vijay Kumar Singh Rathore and Pramod Kumar

Assessment of Physicochemical Properties of Water and Public Perceptions of Water Quality in Tasik Chini, Pahang, Malaysia

The study was conducted to evaluate the physicochemical parameters of water and assess the public perception of the water quality status in the Tasik Chini watershed based on a community survey. The water sample was analyzed based on standard methods and categorized according to WQI (Water Quality Index). Multivariate statistical analysis was adopted to find spatial variations in water quality, determining the pollution level and sources of contamination. The study results were compared with NWQS (National Water Quality Standard for Malaysia). The results showed that the value of dissolved oxygen (DO) was low (4.68 mg.L-1), while the level of biological oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS) was found to be high, 2.92 mg.L-1, 26.10 mg.L-1 and 22.93 mg.L-1 respectively. High turbidity was recorded in a mining area in the rainy season (35.76 NTU). The DOE-WQI value categorized the lake under class II and class III. The Principal Component Analysis (PCA) revealed that the major sources of contamination were due to anthropogenic activities, especially settlement, mining, agriculture, and illegal activities. Overall, Tasik Chini’s water quality status was classified as slightly polluted to highly polluted based on hierarchical cluster analysis (CA) results. The survey showed that 55% of the local community reported that the water quality was poor. The knowledge and attitude level of the local people was medium category, while community practice was low. The Pearson correlation coefficient test showed a strong significant relationship at 0.01 level between knowledge and attitude and knowledge and practices. The scientific findings with public perceptions might be useful for policymakers and the general public to improve the management system for a desirable future.

M. S. Islam, T. M. Ekhwan, F. N. Rasli and C. T. Goh

Environmental Monitoring and Assessment for Sustainable Construction Projects: Leveraging Lean Techniques

To increase productivity and avoid waste, the construction industry has started implementing Lean ideas and methodologies in construction projects. Due to a lack of awareness of lean practices in the preparation, design, and execution of building and infrastructure projects, lean practices are not very familiar among construction projects, which are most commonly used in the manufacturing industry. Hence, an effort has been made in this paper to provide a comprehensive review of the literature and case studies to analyze the suitability of lean practice in sustainable waste management, increased productivity, and on-time project delivery. It aims to explore the effect of improving communication and fostering collaboration among stakeholders on time, costs, and resource management. The review identified the most commonly applied lean practices, Just in Time (JIT) and Last Planner System (LPS), and linked the adoption of lean techniques within the construction sector to a total of sixteen distinct benefits for the economy, society, and the environment. According to this study, lean techniques have a strong chance of boosting productivity in the construction industry and developing a sustainable built environment, but they also need to be used widely and continuously to achieve these goals.

Ardra Suseelan and Senthil Vadivel. T.

Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (?²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, ?², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

Al Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul Islam

An Assessment of Land Use Land Cover Using Machine Learning Technique

This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.

V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

Evaluating the Association Between Ambient Pollutants and Climate Conditions in Chiangmai, Thailand

The most significant air pollutant is particulate matter of less than 10 microns (PM10), followed by ozone (O3) during the monitoring period from 2006 to 2022 in Chiangmai. The association between ambient pollutants and climate conditions in Chiangmai was assessed using regression analysis and analysis of variance (ANOVA). The ANOVA analysis indicated that the average temperature was associated significantly with the nitrogen dioxide (NO2) concentration in the ambient, but the average rainfall volume was associated significantly with most pollutants except only sulfur dioxide (SO2). From the prediction models, the rise in average temperature affected to increase in the concentrations of PM10 and O3. Interestingly, the increase in rainfall will be advantageous to compromise the severity of all pollutants. Meanwhile, on hotter days should be careful of the rise of PM10 and O3 concentrations. Therefore, the vital meteorological variables associated with air pollution are very useful for forecasting the harmful and severity level of each air pollutant.

S. Piyavadee, R. Chumaporn and V. Patipat

Environmental Awareness Toward Issues and Challenges of Sustainable Consumerism in the Indian Apparel Industry

This confirmatory study focused on studying the attitude and behavior as well as environmental awareness towards sustainable consumerism. The study also aimed to check if accountability on the part of brands and the government could enhance sustainability in the apparel industry. An empirical inquiry was conducted with 396 respondents, considering they are consumers with purchasing power. The collected data were analyzed using correlation and descriptive analysis. Based on the findings, consumers’ apparel use and brand accountability are positively associated. At the same time, it was found that the attitude and behavior of consumers are the least essential determinants for sustainable apparel consumption. This might imply that their optimistic outlook may not always translate into real purchase behavior, which is consistent with earlier studies. The results of this research provide a foundation for a better comprehension of the many factors, including the sustainability of a clothing brand or product, which may affect consumer behavior. This approach could help the fashion industry develop practical strategies and alter how people think about and utilize apparel in the future.

Shivani Jadhav and Asha Verma

Evaluation of the Drought Situation Using Remote Sensing Technology, an Applied Study on a Part of North Wasit Governorate in Iraq

Drought presents a substantial threat to both ecological and agricultural systems. Agriculture in Iraq is predicated on precipitation, which is a major contributor to the likelihood of drought resulting from even marginal fluctuations in precipitation. Furthermore, research suggests that Iraq suffers an approximate annual loss of 100,000 acres of arable land due to drought. NDVI and VCI, two significant indices, were utilized in this research to assess and monitor the severity of the drought in the northern region of Wasit province in Iraq. For the period from 1993 to 2023, drought intensity maps were generated utilizing NDVI-based VCI and the Geographic Information System (GIS), an extremely effective spatial data management instrument. NDVI results evidenced that the vegetation cover area was the highest in 1993 and 1998 and declined until it reached the lowest levels in 2023. The vegetation area was concentrated in the southwest parts. In contrast, VCI results demonstrated the extreme drought through the years from 2003 to 2023, which can be attributed to higher temperatures, evaporation, and lower amounts of rainfall. Throughout the thirty-year analysis period, extreme drought conditions were prevalent, especially in the last two decades. Furthermore, this drought should prompt the government to implement preventative measures to avert it. Implementing soil and water conservation measures, such as the establishment of percolation basins, contour bunds, and check dams, can also enhance drought management.

A. J. Dakhil, E. K. Hussain and F. F. Aziz

Testing the Validity of Environmental Kuznets Curve for Carbon Emission: A Cross-Section Analysis

Global warming and its consequences have heightened the urgency of reducing emissions of carbon dioxide globally. The concern arises from countries’ relentless efforts to achieve economic development at the expense of the environment. In this context, the paper examines the Environmental Kuznets Curve (EKC) hypothesis at the world level using carbon emission as an indicator of environmental degradation. The EKC hypothesis postulates an inverted U-shaped curve between economic development and environmental degradation; degrading environmental quality at the initial stages of development and, after a threshold level, environmental degradation lowers. The study investigates the validity of the EKC hypothesis for carbon emission with an analysis of 158 countries in the world, with population, urbanization, forest cover, and tourist inflow as the control variables. The study is based on secondary data collected from the World Bank. A regression analysis is used for the study. To ensure environmental sustainability, it is important to identify the determinants of carbon emissions across countries with varying levels of economic development. The findings of the study support the hypothesized inverse U-shaped association between Gross Domestic Product per capita and carbon emission per capita at the world level. Out of the four control variables, urbanization and tourist inflow were found statistically significant. Urbanization was positively correlated with carbon emission per capita while forest area was negatively correlated. Carbon emission per capita initially increases with rising GDP per capita and declines after GDP per capita reaches a certain level. The estimated turning point of GDP per capita occurs at a high level and therefore, most of the countries are anticipated to emit carbon dioxide.

Punam Chanda, Pintu Majhi and Salina Akther

Environmental Education Model Based on Local Wisdom of the Dayak Paramasan Tribe Indonesia

The indigenous knowledge of the Dayak Paramasan in Indonesia holds the potential for environmental sustainability. This study aims to assess an environmental education framework grounded in the local wisdom of the Paramasan Dayak tribe. A survey was conducted among 300 individuals, including traditional leaders and members of indigenous communities residing in the Paramasan Subdistrict, Indonesia. Data collection occurred from May 2023 to July 2023 and was analyzed using Structural Equation Modelling (SEM). The findings indicate a significant association between indigenous values, local expertise, and community cohesion concerning environmental education. Local wisdom includes local skills, values, and community solidarity, which are crucial for environmental education. Local skills, like farming and hunting, have a significant impact on environmental protection. Passing down knowledge to younger generations needs improvement. Limited local resources create a gap between generations, but some believe traditional leaders can safeguard nature without formal education. Further exploration of implementing environmental education models within school settings will offer valuable insights for Indigenous communities and society, fostering environmentally conscious behaviors.

D. F. Wardhani, D. Arisanty, A. Nugroho and U. B. L. Utami

Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works

Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.

Dnyaneshwar V. Wadkar, Ganesh C. Chikute, Pravin S. Patil, Pallavi D. Wadkar and Manasi G. Chikute

A Comparative Study on India’s Green Tax Policies Vis-a-Vis China with Reference to Environmental Justice in the Automobile Industry

As part of green economics, taxes are imposed on emissions of pollutants that adversely impact the environment and public health to reward more innovative, environmentally sustainable, and low-carbon resource use. There are still many nation-states testing the concept of green taxation. Many environmental performance indicators place India low on the list of countries with the worst pollution. One of the main sources of pollution is vehicle exhaust. Green taxes will be imposed on older motor vehicles under guidelines released by the Indian government in 2021. The United Nations Framework Convention on Climate Change received the Indian Nationally Determined Contribution Report in 2022. Taxonomies and low-carbon transport systems were prioritized in India, and incentives and tax breaks were offered to encourage the manufacture and use of vehicles that consume more ethanol. Academic discussions and literature on the subject are still lacking among the masses. Researchers intend to analyze the legal and economic measures taken by the Indian Government to curb vehicular pollution against this background. Due to its significant contribution to air and water pollution, as well as greenhouse gas emissions, the automobile industry has come under increasing scrutiny in recent years. India and China, for instance, have implemented green tax policies to reduce the automotive sector’s environmental footprint and promote environmental sustainability. These policies are effective, but not all of them address the disproportionate impact of environmental injustice on vulnerable populations. Specifically, this study examines the impact of Indian green tax policies on environmental justice in the automobile industry as compared to those in China. A key aim of this study is to provide insights into the strengths and weaknesses of the green taxation policies adopted by each country in the automotive sector, as well as their implications for achieving environmental justice, by analyzing the scope, enforcement, impact on vulnerable communities, industry implications, and alignment with international commitments.

Naresh Anguralia and Shamsher Singh

Investigations on Photodegradation and Antibacterial Activity of Mixed Oxide Nanocrystalline Materials

In this study, we synthesized cobalt-doped molybdenum supported on silica (Co/MS) nanocomposites with varying concentrations of cobalt (1, 5, 10, 15, and 20 wt%) using the sol-gel method. We investigated their physico-chemical properties, photocatalytic activity, and antimicrobial efficacy. The synthesized nanocomposites were characterized using a range of techniques, including X-ray powder diffraction (XRD) to determine crystal structure, UV-vis spectroscopy for optical properties, Fourier transform infrared spectroscopy (FT-IR) for functional group analysis, and scanning electron microscopy coupled with energy-dispersive X-ray microanalysis (SEM-EDX) for morphological and elemental composition analysis. The photocatalytic performance of these catalysts was assessed by their ability to degrade organic dyes, specifically methyl orange and methylene blue, under visible light irradiation. Our results demonstrated that the photocatalytic efficiency increased with higher cobalt content, with the 20 wt% Co/MS nanocomposite showing the highest degradation rates. Additionally, we evaluated the antibacterial activity of the nanocomposites against a range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as fungal species. The 20 wt% Co/MS nanocomposite exhibited superior antimicrobial activity compared to the other samples, indicating its potential for applications in environmental remediation and antimicrobial treatments.

P. P. Shinde, R. J. Sayyad, S. S. Shukla, S. A. Waghmode and S. R. Gadale

Potential Low-cost Treatment of Tannery Effluents from Industry by Adsorption on Activated Charcoal Derived from Olive Pomace

Tannery wastewater contains a significant amount of chemical compounds, including toxic substances. Due to the toxicity and negative environmental effects of these tannery effluents, mandatory treatment is necessary. The main objective of this study was to treat effluent from an artisanal tannery in the city of Fez (Morocco) using the adsorption process with activated charcoal derived from olive pomace. The physicochemical characterization of tanning water included several parameters, such as chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), suspended solids (SS), sulfate ions (SO42-), nitrate, and chromium Cr(VI). The analyses show that the adsorption process reduced nitrate by 57.54%, sulfate by 94.08%, TKN by 74.84%, COD by 68.18%, Cr by 91.27%, and Cr (VI) by 89.78%. The activated charcoal was characterized before and after tannery effluent treatment using various techniques, including FT-IR, SEM, and EDX. From the above, it can be inferred that using activated carbon made from olive pomace has the potential to reduce tannery effluent pollution parameters. This innovative approach demonstrates that competitive results can be achieved without sacrificing economic viability, thereby promoting sustainable practices in the treatment of industrial liquid waste and wastewater treatment plants.

I. Alouiz, M. Benhadj, D. Elmontassir, M. Sennoune, M.Y. Amarouch and D. Mazouzi

Contribution of Organic Carbon, Moisture Content, Microbial Biomass-Carbon, and Basal Soil Respiration Affecting Microbial Population in Chronosequence Manganese Mine Spoil

The research was carried out to determine the potential effect of microbiota, organic carbon, percentage of moisture content, and microbial biomass concentration as an evaluator of variation in basal soil respiration rate. Relative distribution and composition of the microbial population were estimated from six different chronosequence manganese mine spoil (MBO0, MBO2, MBO4, MBO6, MBO8, MBO10) and forest soil (FS). The variation was seen in moisture content (6.494±0.210-11.535±0.072)%, organic carbon (0.126±0.001- 3.469± 0.099)%, MB-C (5.519±1.371- 646.969± 11.428) ?g.g-1 of soil. A positive correlation was shown between OC with MB-C (r = 0.938; p< 0.01) and moisture content (MC) (r = 0.962; p< 0.01). Variation in the basal soil respiration (BSR) and microbial metabolic quotients (MMQ) was shown to range between 0.352 ± 0.007- 0.958 ±0.014?g CO2-C.g-1 and 6.5× 10-3 - 1.481×10-3 ?g CO2-C.g-1 microbial-C.h-1 with BSR: OC from (2.793-0.276)% respectively. This result shows that there is a gradual increase in OC, MC, MB-C, and BSR across seven different sites due to progressive enhancement in soil fertility that leads to the initialization of succession. Stepwise multiple regression analysis further confirms the degree of variability added by microbial biomass C, moisture content, organic carbon, and microbial population on basal soil respiration in microbes. Principal component analysis enables the differentiation of seven different soil profiles into independent clusters based on cumulative variance given by physico-chemical and microbial attributes that indicate the level of degradation of land and act as an index to restore soil fertility.

S. Dash and M. Kujur

Community Perception on the Effect of Cultural Livelihoods on the Environment in Kogi State, Nigeria

This study examines the cultural livelihood of Kogi State and its effects on the environment. The study describes some of the cultural livelihood practices found in Kogi State, considering the contemporary condition of cultural livelihood and its effects on the environment. Secondary and primary data were employed, which include archives and internet search engines. Using a 4-stage sampling procedure, data were collected from a 120-person sample through an interview, field observation, a focus group discussion, and a questionnaire. Descriptive statistics using frequencies, percentages, and charts were used for the analyses. The results were compiled using the Statistical Package for Social Sciences (SPSS). Findings show that about 85% of the participants discovered crop farming, arable farming, weaving, blacksmithing, fishing, and festivals of harvest, such as the New Yam Festival, among others, as the predominant cultural livelihoods. The local farming implements were made of local materials, like stones and wood. They have indigenous crop production, protection, and harvest techniques. The farming tools were economical in terms of labor, affordability, and time savings in the subsistence farming system. The study discovered that cultural livelihoods are 4% very efficient and 56% on the verge of extinction. Analyses of the effect of cultural livelihood show that 78% have a high negative effect on the economic environment, 57% have a moderate negative effect on the social environment, 51% hurt the political environment, and 22% have a low negative effect on the political environment. The intervention of the various tiers of government with the cooperation of the various communities is needed for the provision of a conducive environment for the practice of cultural livelihood, particularly in the aspect of insecurity. Adequate provision of modern equipment, funding, and social welfare services is also recommended to enhance cultural livelihoods.

G. O. Chukwurah, N. M. Aguome, M. O. Isimah, E. C. Enoguanbhor, N. E. Obi-Aso, N. U. Azani and O. C. Nnamani

Process Optimization for Madhuca indica Seed Kernel Oil Extraction and Evaluation of its Potential for Biodiesel Production

The current research aims to optimize the solvent-based oil extraction process from Mahua (Madhuca indica) seed using response surface methodology and biodiesel production using heterogeneous catalysts. The oil extraction was varied through the levels of process parameters including extraction temperature (60 to 80°C), solvent-to-seed ratio (3 to 9 wt/wt), and time (2 to 4 h). The experiments were designed following the Central Composite model. The regression model provided optimal values for the selected process parameters based on the extraction yield percentage. To ensure the model’s reliability, it was experimentally validated. Maximum experimental oil yields of 50.9% were obtained at an optimized extraction scenario of 70 °C extraction temperature, solvent-to-seed ratio of 6 wt/wt, and time 4 h. The extracted oil’s physicochemical properties and fatty acid composition were tested. Also, using copper-coated dolomite as a catalyst, the extracted oil was transformed into biodiesel via transesterification. The FAME (94.31%) content of the prepared biodiesel was determined via gas chromatography. As a result, the findings of this study will be useful in further research into the use of Madhuca indica as a potential feedstock for biodiesel production.

S. Sudalai, S. Prabakaran, M. G. Devanesan and A. Arumugam

Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology

Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.

R. Krishna Kumari

An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.

Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

A Comprehensive Genetic Analysis of Mycotoxin-Producing Penicillium expansum Isolated from River Water Using Molecular Profiling, DNA Barcoding, and Secondary Structure Prediction

This study marks the first report on the genetic characterization of Penicillium expansum strain capable of mycotoxin production isolated from river water. Situated in Ganagalawanipeta village, Srikakulam, Andhra Pradesh, India, where river water serves as a vital resource, our investigation probed the presence of pathogenic opportunistic fungi adept at mycotoxin synthesis. Over six months, 30 samples were collected to assess their occurrence. This article revolves around the use of morphological traits for Penicillium genus identification. Precise species determination involved PCR analysis using universal primers ITS1 and ITS4, followed by sequence analysis through NCBI-BLASTn and the ITS2 database. The analysis indicated a striking 99.49% genetic similarity to Penicillium expansum isolate MW559596 from CSIR-National Institute of Oceanography, Goa, an Indian isolate, with a resultant 600-base pair fragment. This sequence was officially cataloged as OR536221 in the NCBI GenBank database. Sequence and phylogenetic assessments were conducted to pinpoint the strain and geographical origin. Notably, the ribosomal nuclear ITS region displayed significant inter- and intra-specific divergence, manifested in DNA barcodes and secondary structures established via minimum free energy calculations. These findings provide crucial insights into the genetic diversity and potential mycotoxin production of P. expansum isolates, shedding light on the environmental repercussions and health risks associated with river water contamination from agricultural and aquaculture effluents. This pioneering research advances our understanding of mycotoxin-producing fungi in aquatic environments and underscores the imperative need for water quality monitoring in regions reliant on such water sources for their sustenance and livelihoods.

R. Ravikiran, G. Raghu and B. Praveen

Quantitative Impact of Monthly Precipitation on Urban Vegetation, Surface Water and Potential Evapotranspiration in Baghdad Under Wet and Dry Conditions

Precipitation is a fundamental variable that is widely used in the organization of water resources and has a great influence on hydrological processes and ecological assessment. This study investigated the quantitative effect of monthly precipitation on surface water area (denoted by the Modified Normalized Difference Water Index, MNDWI), vegetation area (denoted by Normalized Difference Vegetation Index, NDVI), and potential evapotranspiration (PET) during two years (2018 and 2021) in the city of Baghdad, Iraq. Using the Thornthwaite aridity index, the annual aridity was first assessed to quantify the climate category of these years. The result shows that they were semi-arid and very arid, respectively. The empirical relationships between precipitation and areas of MNDWI and NDVI, and between rainfall and PET, were also examined. Due to less precipitation in 2021, no relationship was found in arid climates, while in 2018 for semi-arid climates, precipitation had a positive non-linear correlation with MNDWI and NDVI areas and a negative correlation with PET.

Jamal S. Abd Al Rukabie, Salwa S. Naif and Monim H. Al-Jiboori

Dolomite as A Potential Source of Heterogenous Catalyst for Biodiesel Production from Pongamia pinnata

Biodiesel production from Pongamia pinnata, a tree-based oil using healthcare industrial waste dolomite as a catalyst, was studied. The studies aimed to establish the ideal parameters for producing biodiesel, such as temperature, the ratio of methanol to oil, and the weight percentage of the catalyst. The healthcare industrial waste was procured and characterized. With the operating conditions, temperature maintained at 75°C, methanol to oil molar ratio of about 20:1, and a catalyst weight of 5%, the optimum yield of 92.3% was obtained. The tree-based nonedible oil source for biodiesel production was suggested widely due to its ability to achieve sustainable development goals (SDGs). The Pongamia Pinnata cultivation on barren land supports the afforestation projects with economic and environmental values; further biodiesel from renewable bioresources reduces emissions, and livelihood development to eradicate unemployment are the primary objectives for achieving the SDGs. The tree-based biodiesel production and adaptation of dolomite as a heterogeneous catalyst have proven to be a recent attraction among scientists. The present study is the first report on Pongamia pinnata for biodiesel production catalyzed by dolomite.

S. Sudalai, M. G. Devanesan and A. Arumugam

Aquatic Macroinvertebrate Diversity and Water Quality, La Gallega-Morropón Creek, Piura, Peru

Freshwater systems are one of the most important natural resources for life. Despite their value, these ecosystems have suffered great impacts caused by human activities, which directly affect the aquatic biota and the quality of water sources. Considering the value of aquatic macroinvertebrates as bioindicators of water quality, the richness, composition, and water quality of La Gallega-Morropón stream, Piura-Peru, were compared. Two field trips were conducted between November 2018 and May 2019 (contemplated wet and dry periods, respectively), performing 4 sampling stations. A total of 1772 individuals of macroinvertebrates were recorded, distributed in 22 families. Psychodidae had an abundance of 670 individuals, followed by morphospecies (Gasteropoda) with 379 individuals, Chironomidae with 275 individuals, and Elmidae with 136 individuals (all indicators of water quality). Finally, the water quality index method: 1) BMWP/Col, presented one station with good (HB1), acceptable (HB2), and critical (HB3 and HB4) quality, while 2) EPT exhibited two stations with good quality (HB3 and HB4), HB1 regular quality and HB2 poor (HB3 and HB4), HB1 regular quality and HB2 poor quality.

Mónica Santa María Paredes-Agurto, Armando Fortunato Ugaz Cherre,, José Manuel Marchena Dioses, and Robert Barrionuevo Garcia

Bisphenol A in Indian Take-Out Soups: Compliance, Implications and Sustainable Solutions

This research investigates the migration of Bisphenol A (BPA) from packaging containers into take-out vegetable soups and premixed tomato soups through three replicate studies. The samples underwent extraction using solid-phase extraction (SPE) cartridges, followed by separation on a C18 column. BPA concentrations in the soups were assessed at 15, 30, and 45-minute intervals, consistently revealing undetectable levels (<LOQ). Plastic packaging samples, known for BPA utilization in production, remained below the Specific Migration Limit (SML) set at 0.5 mg.kg-1, irrespective of material type or contact conditions. These results, conforming to EC regulations, suggest that food-contact materials (FCMs) in the Indian market pose no apparent health hazards during initial use. The absence of detectable BPA levels is attributed to the limited time-temperature relationship during the study. However, caution is warranted as BPA migration can occur with repeated use, emphasizing the importance of considering material quality and intended use of FCMs. The study underscores the significance of understanding BPA leaching under varied conditions, necessitating further research to explore long-term implications. Overall, the findings provide valuable insights for regulators, manufacturers, and consumers, contributing to the ongoing discourse on food safety and using plastic materials in food packaging.

Sugata Datta, , Abhishek Chauhan, Anuj Ranjan, Abul Hasan Sardar, Hardeep Singh Tuli, Ammar Abdulrahman Jairoun, , Moyad Shahwan, , Ujjawal Sharma and Tanu Jindal

The Impact of Socio-Economic and Climate Change on Poverty in Indonesia

Climate change can impact farmers’ incomes as agricultural production still depends on the weather. Currently, the majority of the impoverished rely primarily on agriculture for their income. The connection between poverty and climate change has been extensively studied, but further research is needed in this area. This research was conducted to provide empirical evidence regarding the impact of climate change on poverty using time series data, which has never been done. This research wants to examine the impact of socio-economics (economic growth, agricultural sector growth, inequality, inflation) and climate change on poverty. This research uses time series data from 2007 to 2022. The Central Bureau of Statistics and Climate Change Performance Index (CCPI) reports are the sources of research data. The study results suggest that the government’s performance index in combating inflation, agricultural sector growth, and climate change has a positive impact on poverty. Poverty is negatively affected by the Gini index and economic growth. Government efforts to adaptively address climate change are necessary to prevent worsening impacts on poverty rates. To reduce the risk of crop failure, farmers must also practice practical agricultural management.

Watemin, Slamet Rosyadi and Lilis Siti Badriah

The Influence of Gibberellins and Smoke Water as a Stimulant for Germination and Vegetative Growth of Syzygium aromaticum (L.) Merr. & L. M. Perry

Clove or cengkeh (Syzygium aromaticum) is one of Indonesia’s commodities with high domestic and international potential, considering that this plant is used as raw material for the cigarette industry. Therefore, it is necessary to optimize the production of Indonesian cloves, one of which is by using growth stimulators such as plant growth regulators (PGR). This study uses gibberellic acid (GA3) and smoke water as exogenous growth triggers. The treatment given was soaking S. aromaticum seeds in gibberellic acid (GA3) and liquid smoke for 24 h. The GA3 concentrations used were 100 ppm, 75 ppm, 50 ppm, and 25 ppm. Smoke water was obtained from the pyrolysis of coconut shells, and the concentrations used were 0.5%, 1%, 2%, and 3%. Observations were conducted for 11 weeks and divided into two phases, namely the germination phase and the vegetative growth phase. Parameters measured included germination percentage, radicle, and plumula length in the first phase, root length, plant height, and number and area of leaves in the second phase. The best results were achieved with the soaking treatment using 0.5% smoke water, which showed a significant increase in all observed growth parameters. This is due to the content of karrikin in smoke water, which acts like a growth hormone and triggers the performance of other growth hormones. In addition, karrikin plays an active role in the germination process by changing the morphology of the seeds.

W. Muslihatin, R. P. D. Wahyudi, M. Iqbal, T. B. Saputro and T. Nurhidayati

Characterization of the Liquid Fuel Produced from Catalytic Depolymerization of Polymeric Waste Using Batch Reactor

The high rate of generation of plastic waste in the country and the fact that all other means of Municipal Plastic Waste (MPW) management techniques had failed leading to the requirement of efficient and alternative disposal technique-depolymerization. The technique involves heating the polymeric waste at an elevated temperature in an inert environment to produce condensable, non-condensable, hydrocarbon and biochar. The plastic waste was collected at the Ilokun dumpsite in Ado-Ekiti, southwest Nigeria. Each component of the waste samples was depolymerized in a batch reactor without the use of a catalyst and with the addition of 10 g of activated carbon (AC) and calcium oxide (CaO) as catalysts. The liquid fuels which were produced between the temperature range of 219 and 232 were blended with standard fuel. Fuel samples with conventional diesel and depolymerized plastic diesel were characterized based on ASTM standards. The results of the proximate and ultimate analysis indicated that percentage moisture content ranges from 0.00-0.18%, volatile matter ranges between 96.66-99.75% and percentage ash content ranges from 0.13-3.03%. Fixed carbon ranges from 0.004-0.31% while the Gross Heating Value (GHV) ranges from 42.66-45.87 MJ/kg. The CHONS analyzer indicated the percentage of carbon, hydrogen, oxygen, nitrogen, and sulfur content range 81.64-85.51%, 12-31-18.04%, 0.00-1.51%, 0.00-0.73%, and 0.10- 0.97% respectively. The results of the physiochemical properties of the samples show that the density, API gravity, Kinematic viscosity and Flash point vary from 0.76-0.83 (g/cm3), 38.98-54.68, 17-2.80 (cm2/s) and 50.0-70.0 (°C) respectively while Cloud point, Pour point, Fire point and Cetane index range from -20-15.0 (°C), -23-7 (°C), 61.0-79.0 (°C) and 38.50-47.0. The pH values of the liquid fuel samples vary from 6.60-3.30. The overall results of the characterization indicated the fuel samples have proximity to the properties of the conventional diesel following the ASTM D975, ASTM D4737, ASTM D1298, ASTM D445, ASTM D2709, and ASTM D482 standards. The depolymerized polymeric waste is sustainable, with a low cost of production. Hence a good substitute as an alternative fuel and means of wealth creation from waste.

O. L. Rominiyi, M. A. Akintunde, E. I Bello, L. Lajide, O. M. Ikumapayi, O. T. Laseinde and B. A. Adaramola

Climate Change Effects on Crop Area Dynamics in the Cachar District of Assam, India: An Empirical Study

Climate change is a worldwide phenomenon that significantly impacts the area, production, and yield of crops. Changes in climate conditions have diverse effects on farming globally. For instance, an increase in temperature can make specific crops more vulnerable to pests. Similarly, a decrease in rainfall reduces water availability, affecting both irrigated and rainfed farming practices. This study aims to investigate climate change effects on crop area dynamics in the Cachar district of Assam, India, for a period spanning from 1981 to 2017. The time series ARDL (Autoregressive Distributed Lag) model is employed to analyze the relationship between climate factors and areas under different crops. As a pre-requisite condition for ARDL, the Augmented Dickey-Fuller (ADF) test is employed to check the order of integration of area under selected crops. The research reveals that the annual average temperature negatively affects the area dedicated to chickpeas, while annual average rainfall negatively impacts the areas allocated to rice and chickpeas. Conversely, annual average relative humidity has a significant positive impact on the area of these crops in the study region. Policymakers may consider strategies and policies for agriculture by encouraging the cultivation of crop varieties that are more resilient to climate change.

Mashud Ahmed, Md Kamrul Islam and Samar Das

Laser Induced Spectroscopy (LIBS) Technology and Environmental Risk Index (RI) to Detect Microplastics in Drinking Water in Baghdad, Iraq

Drinking water contamination by microplastic particles is a global concern that is becoming increasingly common due to consumer abuse, and we use laser fractionation spectroscopy to examine what microplastic particles in water packaging can do. Several types of bottled water were sampled at several manufacturing facilities in Baghdad. The presence of the measured micropolymer species in water was immediately classified and detected using a laser production resolution spectrometer as well as signal and plasma scattering spectra, various MP polymers “polyethylene terephthalate, polystyrene, polypropylene, polyethylene, and polyvinyl chloride” are five polymers that were successfully detected in drinking water to validate the ability to identify health risk factors based on potential environmental risk index (RI) and potential environmental risk factors (Tin), the results are calculated to show that risk predicates have evolved over a decade depending on the risk factors. To do. The smallest particle was 20 microns and the largest particle was 63.4 microns. Microplastics were detected in 5 out of 10 samples, PET in 4 samples, PS and PP in 2 samples, and PVC in sample 1, the most common polymer in bottled water is polyethylene. The average C/H ratios of the five samples were PE (1.76), PET (1.21), PS (1.52), PP (1.23), and PVC (0.99), on average, the measured trends of C/H values were [PE greater than PS], [PP greater than PET], and [PVC greater than PET]. According to our results, the integration of LIBS technology provides a fast and efficient way to detect microplastics. It has a high resolution of fine particles, allowing the detection of very small particles associated with various adverse effects on human health. The feasibility study for water bottling was approved, and the WHO water quality criteria were confirmed. As a result, we will undertake a thorough analysis of the best water bottling quality. In this study, the initial LIBS signals of several samples were used to completely detect microplastics. Microplastics in bottled water samples have been detected and quantified using LIBS spectroscopy techniques with Ecological Potential Ecological Risk. Analytical technology is used to investigate sources, perform research, and collect relevant data, worldwide reports, and permitted statistics to deliver crucial insights and recommendations.Water samples were obtained from several locations throughout Baghdad. At the source, 2 liters of water were obtained in plastic bottles for each sample, for a total of 10 samples. Each sample is owned by the factories that supplied it.

Estabraq Mohammed Ati, Shahla Hussien Hano, Rana Fadhil abbas, Reyam Naji Ajmi and Abdalkader Saeed Latif

Technogenically Disturbed Lands of Coal Mines: Restoration Methods

The issues of human impact on the environment are evident and pose a threat to the health and well-being of future generations. Technogenic disturbances in coal mining sites, such as open pits, excavations, and industrial waste, pose risks to both human health and the environment. Open-pit coal mines not only frequently cause the destruction of natural ecosystems, including landscapes, vegetation, and biodiversity, but they also significantly contribute to greenhouse gas emissions into the atmosphere. Addressing the carbon footprint necessitates not only the use of renewable energy but also the restoration of disturbed landscapes and vegetation, including trees and shrubs. All of this is achieved by implementing biological remediation within technogenically disturbed territories. This process fosters a return of biological balance and establishes favorable conditions for plant and animal life, while at the same time reducing carbon footprint indicators. The biological remediation of areas affected by the mining activities of coal mines can create new economic opportunities. The reclaimed land can be utilized for various purposes such as agriculture, forestry, park development, and tourism, thereby contributing to local economic growth and job creation. When planning measures for land bioremediation, it is essential to analyze all quality indicators of the land. In this case, the selection of technologies such as plants, fertilizers, and microorganisms can effectively restore territories.

S. Ivanova,, A. Vesnina, N. Fotina and A. Prosekov

Fitting Probability Distributions and Statistical Trend Analysis of Rainfall of Agro-climatic Zone of West Bengal

This research aimed to identify the most appropriate probability distribution for modeling average monthly rainfall in the agro-climatic zones of West Bengal and to detect any trends in this data. The study utilized historical rainfall data spanning 51 years (1970-2020) obtained from the IMD in Pune. To determine the best-fitting distribution and assess trends, 23 different probability distributions were employed, with the Mann-Kendall test and Sen’s slope estimator used for trend analysis. Goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Chi-square tests, were employed to determine the most suitable distribution. The findings indicated that the Generalized Extreme Value, Gamma, and Lognormal (3-parameter) distributions were the best fits for two specific districts. The monthly rainfall distributions can be effectively used for predicting future monthly rainfall events in the region. The Mann-Kendall test revealed an increasing trend in rainfall for Kalimpong and Nadia Districts and a decreasing trend for Malda District.

Bhawishya Pradhan, Banjul Bhattacharyya, N. Elakkiya and T. Gowthaman

Enhancing Driving Safety and Environmental Consciousness through Automated Road Sign Recognition Using Convolutional Neural Networks

Traffic accidents remain a pressing public safety concern, with a substantial number of incidents resulting from drivers' lack of attentiveness to road signs. Automated road sign recognition has emerged as a promising technology for enhancing driving assistance systems. This study explores the application of Convolutional Neural Networks (CNNs) in automatically recognizing road signs. CNNs, as deep learning algorithms, possess the ability to process and classify visual data, making them well-suited for image-based tasks such as road sign recognition. The research focuses on the data collection process for training the CNN, incorporating a diverse dataset of road sign images to improve recognition accuracy across various scenarios. A mobile application was developed as the user interface, with the output of the system displayed on the app. The results show that the system is capable of recognizing signs in real time, with average accuracy for sign recognition from a distance of 10 meters: i) daytime = 89.8%, ii) nighttime = 75.6%, and iii) rainy conditions = 76.4%. In conclusion, the integration of CNNs in automated road sign recognition, as demonstrated in this study, presents a promising avenue for enhancing driving safety by addressing drivers' attentiveness to road signs in real-time scenarios.

M. H. F. Md Fauadi, M. F. H. Mohd Zan, M. A. M Ali, L. Abdullah, S. N. Yaakop and A. Z. M. Noor

Evaluating Sustainability: A Comparison of Carbon Footprint Metrics Evaluation Criteria

The two biggest environmental issues the world is currently dealing with are global warming and climate change. Minimizing energy consumption will help to cut down on greenhouse gas emissions, which is our responsibility. Companies choose ‘Carbon Footprint’ as a tool to calculate greenhouse gas emissions to show the impact of their activities on the environment. The techniques and procedures used in the analysis of carbon footprints are the primary focus of this study. Several criteria for evaluating carbon footprints were compared to one another to uncover parallels, variances, and deficiencies. Carbon footprints of companies and items were analyzed, and their objectives, ideas, topics of inquiry, calculation techniques, data choices, and additional elements were investigated. Standards for both organizations (ISO14064 and the GHG protocol) and products were compared and contrasted to arrive at accurate carbon footprint estimates. The most important aspects of a carbon footprint and assessment criterion are the research of GHG, system settings, measurement and carbon footprint, date, and treatment of individual emissions. Especially true for commercial enterprises and consumer goods. Guidelines have been produced for these challenges based on valuation criteria that have been used up to this point; nonetheless, they should be enhanced. This study highlights the need to formulate policies to reduce greenhouse gas emissions.

Mahima Chaurasia, Sanjeev Kumar Srivastava and Suraj Prakash Yadav

A New Approach to Assessing the Accuracy of Forecasting of Emergencies with Environmental Consequences Based on the Theory of Fuzzy Logic

Prevention of the occurrence and development of emergencies of a natural and man-made nature is one of the basic fundamental foundations of ensuring the national security of any state. The most important mechanism for preventing emergencies is an effective system of monitoring and forecasting emergencies established at the state level. In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. In this format, special attention is currently being paid worldwide to a comprehensive assessment of the adverse consequences of emergency situations, primarily related to the safety of the population, environmental conservation, and environmental safety. From the standpoint of solving this significant scientific and practical problem, the purpose of this work was to develop and justify a more advanced method for calculating the feasibility of forecasts of emergencies with environmental consequences as a tool for a reasonable detailed assessment of the quality, optimality of emergency forecasting processes and the reliability of the forecasts themselves.

Eduard Tshovrebov, Vladimir Moshkov, Irina Oltyan and Filyuz Niyazgulov

Sustainability and Environmental Impact of Mining and Maintaining Cryptocurrencies: A Review

Cryptocurrency has seen an increased popularity with the introduction of Bitcoins. It has been adapted in several countries and has become an alternate solution to conventional currency. Despite its benefits, some controversies surround the manufacturing of bitcoins. While all the countries are moving to sustainability development and global warming control, Bitcoin production has raised several concerns about environmental pollution and sustainability. The increased carbon emissions and high electrical consumption have accompanied the popularity of cryptocurrency. Hence, there is an immediate need to reduce the carbon footprint and electricity consumption caused by human cryptocurrency for a sustainable future. This study presents the current scenario and trends of worldwide cryptocurrency growth and discusses the environmental impact of cryptocurrency mining. It explores crypto mining worldwide and provides a qualitative review. Further, this article highlights the need to take necessary measures to control cryptocurrency circulation.

D. Srinivasa Rao, Ch. Rajasekhar, P. M. K. Prasad and G. B. S. R. Naidu

Community-Based Plastic Waste Management Model in Bangun Village, Mojokerto Regency, Indonesia

This study aims to design a community-based plastic waste management model specifically for Bangun Village, Mojokerto. Using a qualitative approach through a detailed case study, we gathered rich data from observations, interviews, and document reviews. Our findings reveal that the plastic waste management situation in Bangun Village is fraught with significant social, economic, and environmental challenges. These include inadequate waste segregation, limited recycling facilities, and a general lack of community awareness and participation. The proposed model seeks to address these issues by implementing several key components: community-based plastic waste collection and processing, educational programs to raise awareness and promote sustainable practices, partnerships with external stakeholders such as local government bodies, NGOs, and private sector entities, and institutional restructuring to support and sustain these initiatives. Central to this model is the belief that community education and awareness are crucial foundations for fostering sustainable behavior. By actively involving the community in the waste management process, the model not only aims to mitigate the plastic waste problem but also seeks to provide economic and social benefits to the residents of Bangun Village. This includes creating job opportunities, improving public health, and enhancing the overall quality of life. The strength of this model lies in its ability to integrate community participation, policy support, and external partnerships, making it a robust and effective solution for sustainable plastic waste management. By fostering a collaborative and inclusive approach, the model aims to create a sustainable and resilient community that can effectively tackle the plastic waste challenge while reaping economic and social benefits. In conclusion, the community-based plastic waste management model proposed for Bangun Village has the potential to bring about significant positive changes in the way plastic waste is managed. Through this model, we hope to empower the community to contribute to solving the plastic waste problem while also benefiting economically and socially.

A. S. Ulum, M. S. Djati, Susilo and A. I. Rozuli

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