This review investigates the pivotal challenge of recycling material waste in the context of additive manufacturing. We place an emphasis on decentralized 3D printing, shedding light on its environmental and economic implications. As additive manufacturing experiences exponential growth, the environment impact of waste generation during 3D printing processes has become increasingly significant. This paper explores various recycled materials commonly used in 3D printing, including polymers like polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PETG), evaluating their characteristics and usability. General recycling methodologies, encompassing mechanical and chemical processes, are examined, with attention paid to challenges such as polymer sorting, additives, coatings, contamination, and thermoset reprocessing. The economic, societal, and environmental impacts of integrating recycled materials into 3D printing are examined. By identifying research gaps and proposing future trends, this review contributes to the development of a deeper understanding of how recycling can play a pivotal role in achieving environmental sustainability and economic viability within the decentralized 3D printing landscape.

In a highly rugged terrain, shielded by hard crystalline rocks like that of Kerala, locating potential zones of groundwater is found to be an unenviable task. Remote sensing and Geographical information system technologies have been attempted widely to delineate the potential regions in such terrain. Geographical information system tool has been used for delineation of groundwater prospect zones in midland physiographic zone (30-200m) of Vamanapuram river basin. The terrain variables are generated using satellite imageries, SRTM DEM data of 30m resolution and SOI toposheets. The groundwater prospect zones were delineated through the integration of the reclassified raster map layers of geomorphology, slope percent, geology, land use / land cover and soil texture using the weighted overlay analysis in the GIS platform. The groundwater prospects in the study area were grouped into five classes and their distribution are; ‘very high/high’ (8.79%), ‘moderate’ (39.08%), and ‘very low / low’ (52.01%). The study result of the area has been validated with water level data of dug wells and bore wells of the area. The spatial distribution map of the water level of the region is overlaid on groundwater prospect map and shows a positive correlation i.e., the water level at shallow depth in higher prospect zones and at deeper depth in poor to very poor zones. The Groundwater prospect map of midland region of Vamanapuram river basin can be used as base level information which can be further investigated with geophysical methods to locate potential well sites for the execution of water supply schemes.

This study comprises of an analysis of various Machine Learning (ML) algorithms for municipal solid waste management to enhance waste management procedures and reduce the adverse environmental effects. The increasing population has resulted in substantial environmental hazards due to increased waste generation. Therefore, an effective waste management system with much more efficient and innovative waste management techniques is required to reduce the adverse effects that would occur due to the generation of massive waste. This study reviews various ML algorithms to automate and optimize garbage generation, collection, transportation, treatment, and disposal. To deliver and predict effective and precise waste generation, segregation, and collection forecasts, the system integrates multiple ML methods including decision trees (DT), k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and clustering algorithms.