In 2023, the waste management situation in Sukoharjo showed a combination of achievements and difficulties. Out of the 12 Temporary Waste Disposal Sites with 3R (Reduce, Reuse, Recycle) facilities, only 4, including Temporary Waste Disposal Sites with 3R (Temporary Waste Disposal Sites 3R) Anugrah Palur, were functioning at their best. This study examines the factors that impact the establishment and long-term viability of these facilities, employing a combination of research methods that incorporates RAP-Temporary Waste Disposal Sites 3R analysis, partial least squares (SEM-PLS), observations, and interviews. The results emphasize that attitude is the most influential component in supporting the growth of Temporary Waste Disposal Sites with 3R, as indicated by a p-value of 0.000. On the other hand, knowledge (0.052) and behavior (0.279) are identified as the least important aspects that hinder development. The Temporary Waste Disposal Sites with 3R have an overall sustainability rating of 72.79, which classifies them as ‘very sustainable.’ The environmental component achieved a score of 79.54, the social dimension scored 72.88, the management and infrastructure dimension scored 71.30, and the economic dimension scored 65.44. These findings emphasize the crucial importance of community attitudes in promoting sustainable waste management practices. They also highlight specific areas that can be improved to enhance the effectiveness and sustainability of Temporary Waste Disposal Sites with 3R facilities.

This study aims to demonstrate the importance of the Social Enterprise Model Canvas (SEMC) as an alternative to addressing social and ecological system challenges that describe important aspects of obtaining economic benefits from forest conservation in remote areas such as Papua. The method is carried out through Systematic Reviews and MetaAnalyses (PRISMA) and qualitative content analysis process of social services implemented in Indonesia and formulated into the required SEMC using 216 documents sourced from the Scopus Core Collection database, which consists of three types of documents: articles, reviews, and book reviews. The results are: First, content analysis of environmental service payment business models in Indonesia provides insight for the government and environmental service providers. Second, the benefits scheme as part of SEMC is an important component in determining successful outcomes. Third, in special regions such as Papua which have special autonomy status, where traditional community regulations play an important role, SEMC must cover aspects of government and regional regulations. The implications of this research can be used as recommendations in determining policies related to payment for forest environmental services.

Air pollution is a required environmental and public health issue in India, with multiple municipalities repeatedly ranking among the most polluted in the world. This study leverages large datasets to construct a predictive model for forecasting air quality trends using a novel approach that integrates the Recency Frequency Monetary (RFM) model with deep learning. The research aims to efficiently quantify pollution events frequency and assess the impact of air quality variations on public health, offering a more flexible and adaptive system for air quality monitoring. As a result, a large volume of air quality data provided by RFM (Recency, Frequency, and Monetary) will be flexible and frequently handled and analyzed. In this research, the performance of the integrated RFM technology is examined using Python and Google Colab, and the simulation results are compared to air pollution information from neural networks for structures in additional data using existing air quality monitoring systems in India. Performance examination of both regression and classification techniques in RFM. The execution of RFM can be one of the models and its potential to enhance air quality monitoring and urban sustainability

Landscapes are vital for ecological protection and cultural heritage, facing challenges from urbanization, agricultural modernization, and climate change. By setting legal boundaries, land use can be regulated to prevent unreasonable development and ensure the sustainable use of landscapes. This paper assesses the forest, geological, aquatic, cultural, and religious relic landscape resources of Xinchang County, Zhejiang Province, using the Analytic Hierarchy Process (AHP) and fuzzy evaluation methods to quantify their protection needs. The study finds that establishing nature reserves, ecological protection red lines, and historical and cultural villages can effectively maintain ecosystem stability and biodiversity, and protect cultural heritage. Legal protection has significantly improved forest coverage and water quality in Tianmu Mountain National Forest Park and Wozhou Lake Scenic Area, while Meizhu Ancient Village and Waipo Keng Village have excelled in cultural landscape protection. However, challenges such as inadequate law enforcement, low public participation, and insufficient funding hinder the execution of legal boundaries. Recommendations include strengthening law enforcement, raising public environmental awareness, and expanding funding sources. This paper provides a scientific basis and practical guidance for the formulation and implementation of landscape protection policies, contributing to the sustainable utilization and long-term protection of landscape resources in Xinchang County and other regions.

In India’s economy, farming is crucial, making early detection of plant diseases an important task. This helps in reducing crop damage and preventing the diseases from spreading further. Numerous plants, such as corn, tomatoes, and potatoes, display evident symptoms of diseases on their leaves. These noticeable patterns can be employed to accurately predict the diseases and facilitate prompt intervention to reduce their impact. The customary method involves farmers or plant pathologists visually inspecting plant leaves and identifying the specific disease. This project involves a deep learning model designed for classifying plant diseases, utilizing CNNs for their proficiency in image classification. The model, which utilizes architectures like MobileNet, InceptionNet, ResNet, and ResNeXt, delivers faster and more accurate predictions than traditional manual methods. Notably, ResNeXt, with its added dimension of cardinality that aids in learning more complex features, achieved the highest accuracy, reaching 98.2%.

The swift process of industrialization and urbanization in our society has resulted in a growing issue of wastewater production, which presents a substantial danger to ecosystems and human well-being. This study examines the efficacy of aquatic plants in wastewater treatment by using their innate ability to remove pollutants. Water hyacinth (Eichhornia crassipes), water lettuce (Pistia stratiotes), and duckweeds (Lemnaceae) are types of aquatic plants that have been thoroughly researched due to their capacity to cleanse domestic, industrial, agricultural, and wastewater. This study encompasses a range of studies completed from 2014 to 2024, which investigate the efficacy of different aquatic plants in eliminating contaminants and provide insights into the specific mechanisms employed by these plants. Research has revealed remarkable findings, indicating that specialist plants can eliminate pollutants, including nitrogen, phosphate, and heavy metals, with an efficiency of up to 100%. Furthermore, the incorporation of these plants into wetlands and natural purification systems has been demonstrated to enhance the purification process by stimulating increased biomass production and the absorption of noxious gases. Future research should give priority to genetically modifying plants to enhance their capacity for absorbing contaminants and to develop integrated systems for treating wastewater. In summary, this study showcases the capacity of aquatic plants to serve as a highly effective and eco-friendly substitute for wastewater treatment. Implementing phytoremediation techniques can enhance the sustainability of water management practices and aid in safeguarding our ecosystems and the health of society

Diabetes mellitus is a prevalent chronic disease with significant implications for public health, including an expanded chance of coronary heart malady, stroke, persistent kidney illness, misery, and useful inability. In India, the predominance of diabetes among grownups matured 20 a long time and more seasoned rose from 5.5% in 1990 to 7.7% in 2016. Traditionally, diabetes management involves costly consultations and diagnostic tests, presenting challenges for timely diagnosis and treatment. Additionally, a comprehensive study was conducted to investigate the relationship between the incidence of type 2 diabetes mellitus (T2DM) and environmental exposure to arsenic in the form of air, water, and food pathways. The majority of the analyzed studies examined the levels of arsenic in water samples, with analyses of urine, blood, serum, and plasma samples coming next. Groundwater supplies may get contaminated by arsenic, especially in regions where arsenic deposits are naturally occurring or as a result of industrial activity. Additionally, various meals contain it, particularly rice, seafood, and poultry. Besides, it might be released into the environment by industrial processes such as coal combustion, smelting, and mining, which could lead to occupational exposure. There may be a genetic component to the association between arsenic exposure and the onset of diabetes. Ultimately, diabetes mellitus is enhanced by arsenic pollution through air, food, and drinking water. Advances in machine learning and telemedicine offer innovative solutions to address these challenges. Data mining, a crucial aspect of machine learning, facilitates the extraction of valuable insights from extensive datasets, enabling more efficient and effective diabetes management. This study explores a telemedicine-based system utilizing five classification techniques Tree, Naive Bayes, Support Vector Machine, and others to predict Type-2 diabetes. By leveraging real-time data analysis, the system aims to enhance early diagnosis and management of Type-2 diabetes, potentially preventing progression to critical conditions. The results evaluate the effectiveness of these models in a telemedicine context, identifying the bestperforming model to assist healthcare professionals in making informed decisions for early intervention and improved patient outcomes.

Silicon fertilizer combined with biochar improved the utilization of phosphorus fertilization applications. The experiment was carried out with eight treatment combinations with varying proportions of rice husk biochar, silicon, and phosphorus in a completely randomized design with 75 days of growth in the greenhouse. To identify the optimum rate of phosphorus combined with rice husk biochar and Si for maximizing maize yield and soil chemical properties. This experiment showed that the application of biochar combined with silicon has the potential to reduce the amount of phosphorus fertilizer requirement. The application of 5 t ha-1 RHB + 100% Si + 25% TSP showed the highest pH compared to other treatments. While application of 2.5 t ha-1 RHB + 100% Si + 100% TSP showed the highest exchangeable K, Ca and Mg. Moreover, the application of 5 t ha-1 RHB + 100% Si + 100% TSP recorded the highest dry biomass compared to other treatments. Lastly, the application of 5 t ha-1 RHB + 100% Si + 50% TSP Showed the highest cob length(cm), cob weight(g), no of grain per cob, and grain yield (t.ha-1) compared to other treatments. The combined application of biochar and silicon, along with 50% phosphorus, is recommended for improving maize yield and soil health in greenhouse conditions.

Accurately classifying land use and land cover (LULC) is crucial for understanding Earth’s dynamics under human influence. This study proposes a novel approach to assess LULC classification accuracy using Sentinel-2 data. Authors have compared traditional and Principal Component Analysis (PCA)-based approaches for Maximum Likelihood Classification, Random Forest, and Support Vector Machine (SVM) classifiers. Four key classes (agricultural land, water bodies, built-up areas, and wastelands) are classified using supervised learning. Accuracy is evaluated using producer, user, overall accuracy, and kappa coefficient. Our findings reveal superior accuracy with PCA-SVM compared to other methods. PCA effectively reduces data redundancy, extracting essential spectral information. This study highlights the value of combining PCA with SVM for LULC classification, empowering policymakers with enhanced decision-making tools and fostering informed policy development.

Using algae to break down or detoxify dangerous environmental pollutants, thereby changing them into a non-hazardous condition, is known as bioremediation. Investigating the ability of the green algae Scenedesmus quadricauda (Turpin) Brébisson to break a particular polycyclic aromatic hydrocarbon (PAH) known as Benzo[a]pyrene (BaP), Under regulated laboratory circumstances and on BG11 media, the alga was cultivated and exposed to different BaP dosages (0.5, 1, and 1.5 mM). High-performance liquid chromatography (HPLC) study helped to ascertain the BaP concentration. Involving the growth curve, doubling time, photosynthetic pigments, total protein, carbohydrates, and Lipid peroxidation (Malondialdehyde MDA) levels, the research investigated various physiological and biochemical aspects. Furthermore, measured were the levels of catalase (CAT), superoxide dismutase (SOD), and reactive oxygen species (ROS). Whereas the lowest growth rate was 0.00047 on the 15th day at a concentration of 1.5 mM, the maximum growth rate (k) recorded was 0.391 on the 7th day at a concentration of 0.5 mM. Doubling time also varied from 0.00014 throughout the 15th day with 1.5 mM and from 0.1179 throughout the 7th day with 0.5 mM BaP. The results showed a definite influence of the different quantities of BaP degradation by S. quadricauda; the greatest magnitude was 40.13 throughout the 15th with 0.5 mg.L-1, while the lowest magnitude was 0 throughout the 1st day with 0.5 Mm. While the min magnitude was 0.41µg.mL-1 in 0.5 mM throughout 1st day, the max magnitude of chlorophyll-a was 18.71 (µg.mL-1) in 1.5 mM throughout the 15th day. Whereas the greatest magnitude was 9.19 µg.mL-1 in 1.5 mM throughout the 15th day, the lowest magnitude of chlorophyll b was 0.36 µg.mL-1 in 1.5 mM throughout the 1st day. While the min was 0.013 on 1st day with 1 mM, the max magnitude of ROS was 0.28 until the 15th day with 1.5 mM. With 1 mM over 1st day, the carbohydrate showed a max magnitude of 35.13 µm.mL-1, and with 1.5 mM over the 15th day, the min magnitude was 12.25(µm.mL-1). While the min protein content was 1.83 µg.mL-1 in 1.5 Mm throughout the 8th day, the max protein content was 2.14 µg.mL-1 in 1 mM throughout the 8th day; moreover, SOD fluctuated between 22.22 µg.mL-1 in 0.5 mM throughout 1st day, and 60 µg.mL-1 in as the min magnitude throughout 8th day with 1.5 mM. The results show that magnitudes of CAT fluctuated between 13.33 µg.mL-1 in the 8th and 15th mM throughout the 15th day and 73.33 µg.mL-1 in 1 mM throughout the 15th day. MDA showed the largest magnitude 59.92 µmoL.L-1 in 1.5 mM over the 1st day, while the lowest magnitude, 36.58 µmoL.L in 1 mM over the 15th day.