Forests are essential to the terrestrial ecosystem, which supports a sustainable way of life and economy for people. As a result of their ability to capture atmospheric carbon dioxide and mitigate its worldwide consequences, forests are crucial for halting climate change. In light of this context, the current study’s objective is to assess the changes in Land Use & Land Cover (LULC) and forest cover across the North Eastern Ghat Zone (NEGZ) of Odisha, India, from 1990 to 2020. Firstly, multi-year preprocessed Landsat data at ten-year intervals (1990, 2000, 2010 and 2020) were collected using cloud computing Google Earth Engine (GEE) platform, and the entire region was divided into five separate classes based on the Normalized Difference Vegetation Index (NDVI) thresholds viz., Very Dense Forest (VDF), Moderately Dense Forest (MDF), Open Forest (OF), and Non- Forest Land (NFL). Through the use of Supervised & Unsupervised techniques of classification, five main LULC categories were also established, viz., Agriculture, Barren Lands, Forest, Settlements, and Water Bodies. The results infer that the forest cover was reduced by 20%, wherein a gradual decrease in the VDF area by 14.21% of the NEGZ was significant during the study period. Unlike the VDF dynamics, the OF coverage showed a slight increase of 4.56% of NEGZ. On the contrary, the settlement area increased by about 130%. However, this study could infer that the expansion of settlements due to population hike is the primary driver of deforestation and forest fragmentation (because the population growth and increased settlements accounted for 97% and 93% of the variability in forest cover). Additionally, it was found that the variation in the forest cover could explain 45% variability of the mean air temperature as indicated by the coefficient of determination. Therefore, by placing special focus on the aforementioned findings and conclusions, we may conclude that the current study may contribute to research on forest management, climate change mitigation, and sustainable development.

Rivers are highly dynamic systems. Rapid population growth and the development of industrial areas around rivers significantly influence water quality. This research aims to analyze the heavy metal content in both water and catfish in the lower reaches of the Citarum River. Five locations were purposively selected: Station 1 (26.87 m asl), Station 2 (18.35 m asl), Station 3 (22.5 m asl), Station 4 (22.5 m asl), and Station 5 (22.5 m asl). At each site, physical and chemical water parameters (temperature, pH, depth, brightness, current speed, and DO), as well as concentrations of heavy metals (Hg, Pb, and Cd), were measured. Catfish were collected using stocking nets and fishing rods from the five stations, with assistance from local fishermen. Sampling was conducted over 14 consecutive days, from June 20 to July 4, 2022. The bioaccumulation of metals in both water and catfish was analyzed and calculated using the bioaccumulation factor (BAF) formula. The results showed that the accumulation of Hg in catfish was higher than that of Pb and Cd. Spatially, Pb accumulation was highest at Station 5 (4 times that of the water concentration) and lowest at Station 2 (2 times that of the water). Cd accumulation was highest at Station 5 (5 times that of the water) and lowest at Station 2 (3 times that of the water). Hg accumulation was highest at Station 1 (6 times that of the water) and lowest at Station 4 (2 times that of the water).

The effluent produced by the electroplating industry contains hazardous and toxic chemicals that pose a threat to living organisms and ecosystems. Consequently, it is essential to employ advanced treatment technologies to remove the toxicants from the wastewater. Over the past two decades, the concept of Electro Fenton has been developed and demonstrated as an effective method for significantly alleviating pollutants in wastewater, making it a promising solution for treating wastewater. In the present investigation, the efficiency of the Electro Fenton (EF) process in removing Chemical oxygen demand (COD) from electroplating wastewater using stainless steel as the sacrificial electrode was examined. The influence of various operating parameters, including pH, hydrogen peroxide concentration, reaction time, and Fe2+ concentration, was investigated with the help of Box-Behnken design (BDD) in Response surface methodology (RSM). Notably, EF treatability studies demonstrated that optimal conditions of pH 2, Fe2+ concentration of 0.005M, H2O2 concentration of 0.5M, and RPM of 450 resulted in more than 75% COD removal. Hence, the sacrificial electrodes can be effective in removing COD from the wastewater.

A polybutylene succinate (PBS) composite reinforced with natural jute (Corchorus olitorius) fibers (50-80 μm) was investigated for its mechanical properties and biodegradability. This study aims to investigate the effects of fiber additions and size variations on composite performance and environmental sustainability. The PBS80/JF20 composite with 80 μm jute particles demonstrated the lowest MFI at 26 g.10 min-1, significantly lower than that of pure PBS (p<0.05), indicating reduced flowability. Tensile strength decreased with the addition of jute fiber, reaching 21.4 MPa with 50 μm particles. Density was also reduced, with the lowest recorded at 1.27 g.cm³- in PBS95/Jute5 (p<0.05). The composites with 80 μm fibers exhibited a slightly higher weight loss (9.5%) compared to those with 50 μm fibers (6.8%), likely due to insufficient interfacial adhesion in larger fibers, making them more susceptible to microbial degradation. Results indicate that adding natural jute fibers into the PBS matrix leads to significant decreases in melt flow index, tensile strength, and impact energy, while significantly enhancing density, water absorption, and biodegradability with respect to neat PBS. Further analysis of fiber sizes revealed that increasing fiber size (from 50 to 80 μm) results in a non-significant decrease in melt flow index, tensile strength, density, impact energy, water absorption, and biodegradation rates. These findings suggest that while the addition of natural fibers compromises mechanical properties, it significantly improves the environmental attributes of the composites, like water absorption and biodegradation (p<0.05). Fibers with a smaller diameter are preferable for maintaining mechanical integrity, while fibers with a larger diameter enhance biodegradability. The paper provides valuable insights into the development of a biocomposite material that balances mechanical performance with environmental sustainability.

This study investigates the relationship between green marketing practices and the sustainability performance of manufacturing firms in emerging markets. A self-administered questionnaire was used to collect data from 270 respondents, and the analysis was conducted using Smart PLS-SEM (version 4). The results demonstrate a significant positive relationship between green internal marketing and the overall sustainability performance of the firms. Specifically, green marketing communication was found to positively influence both environmental and social performance, although it did not have a significant effect on financial performance. Likewise, the adoption of green products substantially improved environmental performance but did not significantly impact financial or social performance. Additionally, the study supports a positive association between green strategy implementation and sustainability performance. These findings underscore the critical role of integrating green marketing practices into sustainability initiatives. The research provides valuable insights for managers and policymakers, emphasizing the need for a holistic approach to green marketing to enhance environmental and social outcomes, even if financial benefits are not immediately apparent. This study contributes to the growing body of knowledge on sustainable business practices and offers practical implications for achieving long-term sustainability in manufacturing firms.

Forward Osmosis is a suitable pretreatment process for reverse osmosis for secondary-treated sewage reuse and secondary-treated industrial effluents. In this study, the FO process is investigated for concentrating synthetic secondary treated tannery effluents using 24 g.L-1 and 38 g.L-1 of NaCl solution as draw solution. Results showed that 38 g.L-1 NaCl solution when used, provided higher flux and lower flux decline ratio as compared to 24 g.L-1 NaCl solution. The solute rejection by FO membrane was more in FO experiments using 38 g.L-1 NaCl solution as DS as compared to 24 g.L-1 NaCl solution. Contact angle, Fourier transform infrared spectroscopy, and scanning electronic microscopy tests on pristine and chemically cleaned membranes indicated the change in membrane structure and the presence of foulants on the membrane surface, indicating insufficient chemical cleaning. Findings signify implications on the concentration of DS and the cleaning method adopted for concentrating treated tannery effluent efficaciously using the FO process.

Landslides are significant natural hazards that cause damage to the environment, life, and properties, mainly in hilly terrain. This research was mostly focused on generating a landslide susceptibility zone map of Papumpare District, Arunachal Pradesh, and classifying the region from high susceptibility to least susceptibility using AHP modeling techniques considering the landslide causative factors. The Analytical Hierarchy Process (AHP) is a multicriteria decision-making model (MCDM) in which each parameter is compared based on its role in triggering a landslide. A total of eight parameters were selected based on the factors that could affect the most, like Slope, Rainfall, Drainage Density, Lineament Density, Geomorphology, Soil, Geology, and Land use/Land cover. These layers were prepared using ArcGIS 10.8 software and ERDAS IMAGINE 2014. Based on the output, the region was classified into five zones of landslide susceptibility classes. Of these, the high-very-high landslides are mostly amassed near the steep and disturbed slopes due to earth-cutting, especially for building or construction of roads. Validation was done using the ROC curve (73.2%) suggesting good performance of the model. The outcome of this work will provide information for proper landslide hazard management and will help in formulating suitable mitigation strategies in the future.

The rapid increase in heavy metal accumulation within soil ecosystems has become a significant concern due to various anthropogenic activities such as industrial processes, agricultural practices, and urbanization. These activities have led to elevated levels of heavy metals like lead, cadmium, mercury, and arsenic in the soil, which, when surpassing permissible limits, pose severe toxicological risks to both human health and plant life. Once heavy metals are introduced into the soil, they can be readily absorbed by plants, subsequently entering the food chain and affecting the metabolic activities of humans and animals consuming these contaminated plants. Although trace amounts of heavy metals are naturally present in the soil, their concentration beyond safe thresholds can lead to deleterious effects, including disruption of enzymatic functions, damage to cellular structures, and interference with essential biological processes. Studies have highlighted that children living in urban and industrial areas are particularly vulnerable to heavy metal exposure, which can result in cognitive impairments, developmental delays, and various other health issues. Furthermore, long-term exposure to these metals can lead to chronic diseases such as cancer, kidney dysfunction, and cardiovascular disorders. Given the escalating threat posed by soil metal contamination, it is imperative to implement stringent management practices aimed at maintaining soil chemistry within safe limits. These practices may include the remediation of contaminated sites, the adoption of sustainable agricultural methods, regular monitoring of soil quality, and the use of phytoremediation techniques to mitigate the impact of heavy metals. Ensuring the safe production of food requires a comprehensive understanding of soil dynamics and the integration of innovative strategies to prevent and control heavy metal pollution. Consequently, addressing this environmental challenge is crucial for safeguarding public health, preserving ecological balance, and promoting sustainable development.

With traditional knowledge passing through generations and habits of indigenous people, the local communities perform a crucial role in managing the environment and development. It should be the Local communities who should be involved in the conservation and management of the wetland resources, however, increasing government controls and prohibitions are harming wetland conservation, which potentially promotes responsible use habits in the region. This literature review investigates the role of traditional knowledge systems (TKS) in wetland conservation, focusing on four key domains: agriculture, fishing practices, stormwater management, and traditional knowledge of wetland plants and produce harvesting. This review methodologically synthesizes current research to provide a thorough understanding of the contribution of traditional knowledge to wetland conservation efforts. It does this by using a total selection of 68 papers within a range of five to ten articles per category. Using the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methods of literature review as a guide, this study identifies, evaluates, and synthesizes peer-reviewed and localized publications that examine the application of Traditional knowledge systems to various wetland management contexts, drawing from scholarly databases and pertinent literature sources. By delving into diverse disciplines such as environmental engineering, ecology, and environmental science, the review elucidates the multifaceted ways in which indigenous wisdom informs conservation practices, fosters sustainable resource utilization, and enhances community resilience in wetland ecosystems. Moreover, it examines the challenges and opportunities associated with integrating traditional and scientific knowledge paradigms, emphasizing the need for inclusive and participatory approaches to conservation that respect cultural diversity and local knowledge systems. The results of the literature study have been compiled to highlight several traditional systems for wetland conservation. These include traditional stormwater management in wetland watersheds, resource management by local communities, the use of wetland plants in conservation, traditional fishing practices, traditional agricultural practices, and religious and cultural practices. The findings of this review contribute valuable insights to academia, policy development, and on-the-ground conservation efforts, serving as a foundation for future research and practice aimed at promoting the holistic and equitable stewardship of wetland ecosystems. This paper concludes with suggestions on using traditional knowledge systems in the conservation of wetlands in India, along with the different traditional methods that could be part and parcel of the decision-making system in this field. The results of this paper are highly significant, as they demonstrate the integration of traditional knowledge systems as a method for environmental conservation and management, specifically targeting wetland ecosystems and their biota.

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.