The waste recycling industry plays a vital role in reducing environmental pollution and promoting sustainable urban development. This study examines the economic and environmental benefits of waste sorting and recycling in the Baghdad Governorate, noting its role in conserving resources, reducing pollution, and creating jobs. The research also classifies solid waste sources and evaluates recycling processes that return materials to their raw form for industrial reuse. In addition, the study assesses the challenges that hinder recycling initiatives, including limited government support, lack of public awareness, and infrastructure constraints. A case study of a recycling plant in Mahmoudiya provides insights into the potential of modern waste management technologies. The findings suggest that enhancing recycling infrastructure and policy interventions could significantly improve waste management in Baghdad. Future research should focus on incorporating advanced recycling technologies and public engagement strategies to improve waste reduction efforts.

This review explores eco-printing as a sustainable alternative to conventional textile dyeing, emphasizing techniques, innovations, and limitations. A Systematic Literature Review (SLR) following PRISMA guidelines was conducted using databases like ScienceDirect, Scopus, Web of Science, and Google Scholar from 1993 to 2025. Eco-printing offers significant environmental benefits, such as reduced water and chemical use. However, challenges include weak color fastness and scalability limitations. Innovative mordants and hybrid digital approaches are potential solutions. This comprehensive study supports further research and industrial adoption of eco-printing methods in sustainable fashion.

For centuries, traditional medicines have been an essential part of healthcare practices around the world. Despite significant variations in their usage across countries, their value in terms of clinical effectiveness, pharmaceutical properties, and economic benefits is widely acknowledged. Therefore, it is imperative to regulate the exploitation and export of these medicines, and promote global cooperation for their conservation to ensure their availability for future generations. Legislative framework in respect of medicinal plants in Himalayan region has not evolved due to various reasons, although Himalayas are treasures of medicinal plants. The present review highlights the status of medicinal plants in the Himalayan region and its legal perspectives to regulate their use for sustainable approach.

Health systems throughout the world are becoming core focus of scientists due to the kaleidoscopic pandemics from time to time, and wastewater-based epidemiology (WBE) is emerging epidemiological tool far superior to conventional methods with the potential to complement existing infectious disease surveillance systems and serve as an early warning system for disease outbreaks. It utilizes analysis of infectious agents, resistance patterns, and population-pooled wastewater to monitor and detect the emergence of new disease outbreaks at the community level in real time. Biosensors are rapid, sensitive, inexpensive, portable and carry no risk of exposure in their detection schemes. They offer a cost-effective means of monitoring disease progression through WBE, thereby aiding in the containment of local outbreaks. Biosensors, owing to their affordability, ease of use, and potential for field testing, have emerged as highly selective and sensitive techniques for studying sewage biomarkers. The focus of this review is to find out WBE-specific applications of biosensors, compare biosensors with traditional analytic methods, provide information on the detection of bacteria and viruses in wastewater and pay insight to the new detection methods aimed at screening for SARS-CoV-2. Emphasis to provide insights into the future application of biosensor technology within WBE, facilitating rapid on-site sewage monitoring that can provide near real-time data for public health assessment and effective intervention.

The present study explores the removal of Cr (VI) from contaminated water using green-synthesized zero-valent iron nanoparticles derived from Aegle marmelos leaf extract. Batch adsorption experiments were conducted to investigate the effect of key variables—solution pH, zero-valent iron nanoparticles dose, contact time, and initial Cr (VI) concentration—on removal efficiency. The analysis revealed that pH, nanoparticle dosage, and contact time significantly influenced Cr (VI) removal, with optimal removal efficiency (∼97.6%) observed at pH 2.3, nanoparticle dosage of 2.63 g/L, and 30 min of contact time. Regression analysis and ANOVA confirmed the statistical significance and robustness of the model (R 2 = 0.9987). The study demonstrates that zero-valent iron nanoparticles are a highly effective, eco-friendly adsorbent for Cr (VI) removal, offering potential for industrial wastewater treatment applications.

India aims to achieve net-zero emissions by 2070, emphasizing hydropower as a key renewable source. The Northeastern region, with abundant rainfall and favorable topography, holds significant hydropower potential, contributing 4309 MW with 3157 MW under construction. States like Arunachal Pradesh and Sikkim lead in capacity development. Despite challenges such as flooding, water deficits, and project delays, the region’s landscape supports efficient, sustainable energy generation. Hydropower also offers solutions for water scarcity in Peninsular India. Ensuring inclusive, environmentally sound development is essential to maximize the region’s role in India’s clean energy transition.

Urban waste management poses significant challenges due to rising population density and the increasing complexity of waste types. This study proposes an optimized Faster R-CNN model enhanced by Golden Eagle Optimization (GEO) to improve garbage detection and classification. The model addresses overfitting, class imbalance, and visual similarity between waste categories. It is trained on a self-collected, labeled dataset of 3,030 images across 10 waste types, including plastics, masks, and cans. Data augmentation is applied to enhance robustness. Experimental results demonstrate superior performance with 99.04% accuracy, 98.96% precision, and a low error rate of 0.96% compared to existing deep learning approaches.

Pesticide application is pivotal in modern agriculture for enhancing crop yield and minimizing pest-related damage. However, their widespread use has raised concerns about potential adverse effects on human health, groundwater, soil, and non-target organisms. Among all pesticides, ​chlorpyrifos has the highest share in cumulative consumption at the global level. Therefore, an assessment of chlorpyrifos consumption, its exposure, toxicity, and remedial approaches is required. Contamination (up to concentration) of chlorpyrifos in drinking water has been observed in India (2.73 µg/L), Iran (3.163 µg/L), Vietnam (0.0119 µg/L), Argentina (0.408 µg/L), Pakistan (1.85 µg/L), Sri Lanka (0.63 µg/L), Bangladesh (0.895 µg/L), Malaysia (0.0929 µg/L), Ethiopia (4.31 µg/L), Thailand (0.069 µg/L), Egypt (2.74 µg/L), Italy (0.0148 µg/L), Portugal (0.159 µg/L), New Zealand (0.056 µg/L), and Ghana (0.69 µg/L). Consuming contaminated drinking water poses significant health risks, therefore, monitoring and remediation are the only options. Chemical treatment approaches often face economic challenges, particularly in developing countries, which often prefer low-cost, sustainable treatment methods over expensive ones. Microbial degradation, sorption through natural materials, metal-organic frameworks, and constructed wetlands can be used to remove chlorpyrifos. Nature-based approaches, such as microalgae treatment, have shownremoval potentials up to 99.85% from wastewater, supporting the achievement of sustainable development goal 6 (Clean Water and Sanitation). In addition, the development of smart, portable and efficient on-site biosensors can play a significant role in monitoring. Developing awareness and understanding of pesticide contamination and appropriate remediation strategies can contribute to protecting both natural ecosystems and human health.

This study evaluates natural radionuclides and radiological hazards in 10 cement samples from Rajasthan, India, using a p-type HPGe detector. Specific activities of 226Ra,2³2Th, and 40K were 29.60, 36.80, and 160.58 Bq kg−1, all below global averages. Radiological indices, including radium equivalent activity (94.58 Bq kg−1), hazard indices, and annual effective doses, were mostly within safe limits. However, AEDEin for CMT-5 and CMT-6 and AEDEout for CMT-5 slightly exceeded global averages. Uncertainty and correlation analysis confirmed data reliability. The activity utilization index was 0.8496 (<2), indicating overall radiological safety despite minor localized risks.

One of the main economic sectors in the world is agriculture. The need to increase productivity, ensure food security and reduce waste has led to the application of modern technologies in agriculture. Increasing productivity, lessening the impact on the environment, and improving livelihoods are the objectives of revolutionizing agriculture through the use of innovative technologies, environmentally friendly methods, and efficient systems. Machine learning (ML) and Deep learning (DL) have demonstrated significant promise in recent years for precision agriculture. This paper discusses Advancements in Precision Agriculture: A Literature Review of Machine Learning Applications for Crop Monitoring and Yield Prediction. In precision agriculture system, to predict crop health monitoring and yield prediction includes soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection, species identification, and nitrogen status estimation are reviewed. Furthermore, the review explores the integration of ML/DL models with IoT-enabled farm machinery to enhance livestock production by predicting fertility patterns, diagnosing eating disorders, and monitoring cattle behavior using collar sensors. Additionally, the review discusses the implementation of intelligent irrigation and harvesting techniques, which reduce human labor while optimizing resource utilization. By analyzing the literature, this review demonstrates the potential of knowledge-based agriculture to improve sustainable productivity and product quality. Moreover, it identifies challenges and opportunities for future research, highlighting the need for interdisciplinary collaborations and ethical considerations in deploying ML- based solutions in agricultural settings. Overall, this review contributes to the understanding of how ML and DL technologies are revolutionizing precision agriculture, paving the way for informed decision-making, resource optimization, and enhanced sustainability in food production systems. This paper provides a comprehensive survey of existing papers and applications of ML in agriculture. In order to demonstrate the numerous uses of ML, the author reviewed 100 publications that were published between 2020 and 2024, among them 53 ML/DL-based algorithms were used in revolutionizing precision agriculture, paving the way for informed decision-making, resource optimization, and enhanced sustainability in food production systems.