Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.

Salinity stress is one of the primary abiotic stresses limiting crop growth and yield. Plants respond to salinity stress with several morphophysiological, molecular, and biochemical mechanisms, however, these mechanisms need to be improved further to cope with salt stress effectively. In this regard, the use of plant growth-promoting (PGP) and halotolerant bacteria is thought to be very efficient for enhancing growth and salinity tolerance in plants. The current study aims to assess Bacillus safensis PM22 for its ability to promote plant growth and resistance to salt. The PM22 produced substantial amounts of exopolysaccharides, indole-3-acetic acid, siderophore, and 1-aminocyclopropane-1-carboxylic acid deaminase (ACC-deaminase) under saline conditions. Additionally, inoculation of the halotolerant bacteria PM22 reduced the severity of salinity stress in plants and increased root and shoot length at various salt concentrations (0, 180, 240, and 300 mM). Furthermore, PM22-inoculated plants showed markedly enhanced photosynthetic pigment, carotenoid, leaf relative water content, 2,2-diphenyl-1-picrylhydrazyl (DPPH) activity, salt tolerance index, total soluble sugar, total protein, and ascorbic acid contents compared to non-inoculated control maize plants. PM22 substantially increased antioxidant (enzymatic and non-enzymatic) activities in maize plants, including ascorbate peroxidase, peroxidase, superoxide dismutase, catalase, total flavonoid, and phenol levels. Maize plants inoculated with PM22 also exhibited a significant reduction in electrolyte leakage, hydrogen peroxide, malondialdehyde, glycine betaine, and proline contents compared to non-inoculated control plants. These physiological appearances were further validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), which revealed the upregulation of expression in genes responsible for stress tolerance. In the current investigation, Bacillus safensis PM22 showed plant growth-promoting and salt tolerance attributes and can be utilized as a bio-inoculant to improve yield in salt stress affected areas.

Global waste generation is expanding rapidly in parallel with population expansion and urbanization, posing municipal solid waste management difficulties. This study examined the environmental impacts of domestic organic waste generated in the Dhaka North City Corporation in Bangladesh, a country with a fast-growing population and economy. No previous environmental life cycle assessment has been conducted in a decentralized waste-to-compost facility in Dhaka, nor has the environmental impact of manual informal labor be considered. Four major waste management scenarios were compared: conventional windrow composting (S1), proposed automated composting using an EP-1000 machine (S2), and existing practices of sanitary (S3) and unsanitary (S4) landfilling. The four scenarios' environmental impacts were assessed using OpenLCA software. Environmental Life cycle assessment (ELCA) of the four scenarios was conducted using OpenLCA software for their environmental impact. Results revealed that decentralized waste-to-compost process scenarios (S1 and S2) were lower than those of the conventional landfill scenarios (S3 and S4). The overall quantity of total yearly GWP100 from decentralized compost facility of S1 (1.14 million Mg CO₂-eq Mg^–1) and S2 (411 kg CO₂-eq Mg^–1) were multifold lower than emissions from conventional landfilling of S3 (~ 2.12 million Mg of CO₂-eq Mg^–1) and S4 (~ 3.87 million Mg of CO₂-eq Mg^–1) scenarios reflecting the environment-friendly outcome of the former than the latter scenarios. Similar trends of lesser quantities of FAETP, HTP, and TEP were noticed depicting the S1 and proposed S2 scenarios as better options than conventional landfill of S3 and S4 scenarios. In conclusion, the development of decentralized waste-to-compost facilities in Dhaka or other similar units across the globe can prove a better and more sustainable waste management strategy with a greater potential to mitigate adverse impacts of climate change and environmental pollution.

The rapid increase in the population, urbanization, increasing standard of living, indifference, and other factors can be used to explain the concerning rate of waste generation. Research suggests that most countries prefer to process and dispose of their waste via open dumping or landfilling due to its affordability and simple mechanism. However, leachate and landfill gas emissions present environmental risks associated with landfilling. To prevent landfilling’s detrimental impacts on the ecosystem, alternative methods of disposing of organic waste need to be investigated. An environmental impact analysis was undertaken utilizing the openLCA software to evaluate the ecological footprints of three distinct waste management strategies i.e., Landfilling, Composting, and composting using EP-50 assessing four key environmental indicators. Composting using EP-50 (EcoProbe 50) is a recent advancement towards waste management where organic waste is turned to compost artificially under high temperature and pressure along with constant mixing and churning of the waste for better nutrient value. The EP-50 composter, according to our research hypothesis, offers a greener option to conventional landfilling and composting. We evaluated the potential for global warming, acidification, eutrophication, and human toxicity of landfilling, conventional composting, and EP-50 composting utilizing Life Cycle Assessment (LCA). According to our findings, the EP-50 system minimizes the potential for global warming to around 420 kgCO2 eq in comparison to 1243 kgCO2 eq from conventional composting and 4653 kgCO2 eq for landfilling. In addition, EP-50 increases the amount of nutrients in the compost generated while reducing the effects of eutrophication and acidification by more than 15%. These results demonstrate EP-50 as a scalable and effective method for managing organic waste.