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.

This study aims to analyze the Penta helix collaboration model for involving reserve component personnel in disaster resilience in Malang Regency. A qualitative approach was used with an in-depth interview method involving nine informants from various Penta helix actors, namely academia, business, the community, government, and the Media. The main findings indicate that the Penta helix collaboration model has the potential to enhance disaster resilience in Malang Regency. Its strengths lie in inclusive participation, transparency, clear leadership, and the commitment of stakeholders. However, there are still weaknesses, such as a lack of coordination, limited resources, and suboptimal role understanding that hinder the involvement of reserve component personnel. Each actor makes significant contributions: academics provide knowledge, businesses aid in logistics, communities engage in mitigation and emergency response, the government formulates policies, and the media disseminates information. Major challenges include a lack of coordination, limited resources, miscoordination, bureaucracy, insufficient training, and unclear legal frameworks. Improvement efforts include strengthening coordination, increasing resource capacity, clarifying roles, developing guidelines, and enhancing training. In conclusion, the Penta helix collaboration model in Malang Regency has great potential but requires improvements to enhance its effectiveness, providing insights for stakeholders to strengthen disaster resilience in the region.

Heavy metal contamination in soil poses a significant environmental challenge globally, affecting agricultural productivity and human health. Phytoremediation, using plants to extract and detoxify heavy metals, presents a promising solution. This study investigates the novel potential of Echium plantagineum, a metal-tolerant species, in phytostabilization and phytoremediation and explores the role of metallothioneins in heavy metal reduction. A comprehensive literature review identified known metallothioneins involved in heavy metal reduction across various plant species. Moreover, genome annotation and gene prediction of Echium plantagineum were performed, predicting a total of 39,520 proteins. This comprehensive protein list facilitates the identification of metallothioneins or other metal-related proteins with potential functional roles in heavy metal tolerance, suggesting new targets to improve the effectiveness of phytoremediation. The sequences of these proteins were utilized to construct a protein BLAST database, against which known metallothioneins protein sequences from other plant species were subjected to BLAST searches, resulting in 41 top hits. Subsequent 3D modeling, structural analysis, protein-metal virtual screening, and functional annotation of the proteins revealed novel high affinities of Ctr copper transporter, zinc/iron permease, and nicotianamine synthase proteins with nickel, zinc, and zinc ion, suggesting their unexplored roles in the uptake of aforementioned ligands. Notably, this study identifies novel metallothioneins proteins in Echium plantagineum, highlighting their role in metal tolerance and phytoremediation.

Natural disasters are natural activities that can disrupt various aspects. Natural disasters cannot be avoided, but the impact of natural disasters can be minimized through mitigation. This can be known through event history to determine an area’s vulnerability to natural disasters. This research aims to determine regional natural disaster vulnerability by calculating the natural disaster index. The data used in this research refers to data from the 2021 PODES data collection, which contains the intensity of natural disasters and casualties according to the type of natural disaster in Indonesia in 2020. The method used for the calculation is the weighted index method. The results of this research produced 5 clusters based on the level of natural disaster vulnerability according to sub-district/village. The top five provinces in Indonesia that have the highest natural disaster-prone areas are Aceh, North Sumatra, West Java, East Java, and Central Sulawesi. Research shows that sub-districts/villages in Indonesia are known according to their level of vulnerability to natural disasters. These results can be used as a reference for the government to carry out mitigation so that accelerated development in the local area can continue.

Soil erosion in the agricultural landscape of Assam has been impacting the livelihoods of millions. In administrative regions like districts, which are vulnerable to natural disasters like floods and bank erosion, GIS-based soil erosion estimating studies can help planners and policymakers identify areas of soil erosion to implement scientific conservation measures. The main purpose of this study is to estimate soil loss and to determine soil loss zones in the Morigaon district of Assam. The Revised Universal Soil Loss Equation (RUSLE) combined with GIS has been incorporated into the present study. The five parameters of RUSLE, namely, rainfall-runoff erosivity, soil erodibility, topographic factor, cover management, and conservation practices, are individually estimated from relevant and authentic data sources, and all these parameters are quantified in GIS. The research findings show that 46.89% of areas in the district are in moderate soil loss zone, eroding 0.78 ton/ha/year, 34.27% of areas are in low soil loss zone, 15.36% of areas are in high soil loss zone, eroding about 12.22 ton/ha/year and 3.47% of areas are in a very high soil loss zone, eroding 192.8 ton/ha/year. The high soil loss zones mainly cover the riverine areas and bare lands in the district. As per our estimation, there is an average of 205.85 tonnes of soil loss in the district per hectare per year.

Silver nanoparticles (AgNPs) were synthesized utilizing various methods, including bioreducing agents. The synthesis involved the use of silver nitrate (AgNO3) as the precursor and banana frond extract as the bioreducing agent, with different volume ratios being tested. Subsequently, the most optimal variant of AgNPs was immobilized onto activated carbon (AC) derived from soybean seeds. The AgNPs/SAC composite was subjected to thorough characterization using UV-Vis diffuse reflectance spectroscopy and X-ray diffraction (XRD). A series of degradation experiments were then conducted using methylene blue, with the reaction duration following a specific protocol. A comparison of methylene blue concentrations before and after the photodegradation process was made to assess the reaction’s efficacy. The findings revealed that the ideal ratio between the bioreducing agent and precursor was 9:30 (v/v). The AgNPs/SAC composite exhibited a peak absorption at a wavelength of 420-440 nm. The UV-DRS characterization of AgNPs/SAC unveiled a band gap energy of 1.52 eV. The AgNPs supported on AC displayed a peak absorption wavelength of 5,438.5 nm, showcasing a face-centered cubic (FCC) structure. The AgNPs/SAC effectively decreased the concentration of methylene blue through a combination of adsorption and photodegradation mechanisms, achieving efficiencies of 35.3813% and 81.1636%, respectively. The AgNPs/SAC composite demonstrates significant potential for efficient and sustainable water treatment.

The residual dye within the soil from the synthetic dye manufacturing and fabric industries is a global state of affairs. The discharge consists of an excessive content of pigments and other components, creating complicated structures. It leads to damage to the soil structure and its fertility. Amid existing amputation methods, microbial remediation takes significant consideration owing to its subordinate charge, sophisticated proficiency, and fewer influences on the milieu. The current study was premeditated for the seclusion and portrayal of azo dye- dye-decolorizing bacteria, which is a criterion for emerging a microorganism-facilitated treatment of adulterating dyes. In this present investigation, twenty sorts of bacteria that were talented to decolorize seven kinds of azo dyes (Crystal Violet, Methylene Blue, Safranine, Congo Red, Methyl Orange, Malachite Green, and Carbol Fuchsin) were isolated from dye-polluted soil from the dying industry near the railway station; in Calicut. Based on 16S rDNA scrutiny, the most resourceful decolourizing bacteria for these azo dyes was identified as Priestia megaterium strain NRBC 15308. After characterization, Priestia megaterium was found to be optimally nurtured at 35°C, on a pH of 7, with a 1.5% glucose concentration in a minimal salt medium. 100% decolorization of a 6% dye solution was found at optimal conditions by Priestia megaterium. Priestia megaterium can decolorize cotton and gauze suspended in the dye solution in 24 hours. Bioremediation studies with the isolate proved that the inhibition effect of the dye solution on seed germination could be removed by the application of Prestia megaterium. The isolation of Priestia megaterium strain NRBC 15308 as a dye-degrading bacterium holds immense promise for remediating dye-contaminated soil.

Microbially contaminated objects used in everyday life have been shown to impact human health by harboring infections through direct or indirect contact. For this reason, the development of alternative methods for bacterial elimination that do not lead to resistant microorganisms, large quantities of residues, or human cytotoxicity is warranted. Due to their proven bactericidal power, the use of electromagnetic waves lower than ultraviolet-C radiation would constitute a possible alternative. The main aim of this research was to determine the effect of 462 nm radiation emitted by light-emitting diodes (LEDs) on the most frequent bacteria contaminating everyday objects and surfaces in residential and hospital environments. The rationale behind the selection of this specific frequency within the blue light spectrum, in contrast to previous research exploring the application of higher frequencies, was its safety for individuals’ eyes and skin. The findings suggest that the use of low-frequency blue light can be effective in destroying environmental microorganisms stemming from the skin microbiome and mucous membranes, and even fecal bacteria, present in the surfaces of everyday objects such as inter alia, mobile phones, remote controls, credit cards, and of which some present high antibiotic resistance.