• Green H 2 is assessed as a diesel alternative for Lisbon waste collection trucks. • The study compares H 2 fuel cells and H 2 internal combustion engine trucks. • H 2 -FCT costs 13.7 €/t, while H 2 -ICET costs 17.8 €/t of waste collected. • H 2 -FCT cuts 1,510 t CO 2 /year; H 2 -ICET cuts 1,001 t CO 2 /year. • H 2 -FCT offers better economic and environmental performance than H 2 -ICET. Decarbonizing heavy road transport is critical for achieving sustainable cities and promoting clean energy use. This study presents a comprehensive techno-economic and environmental assessment of green hydrogen, produced via electrolysis, as an alternative to diesel for municipal solid waste collection trucks in Lisbon. This study investigates two hydrogen powertrain technologies: hydrogen fuel cell trucks (H 2 -FCT) and hydrogen internal combustion engine trucks (H 2 -ICET), compared to diesel internal combustion engine trucks (Diesel-ICET). The techno-economic analysis reveals costs reaching 13.7 €/ton of waste collected (1.88 M€/year) and 17.8 €/ton of waste (2.45 M€/year), respectively. Key infrastructure requirements include 2 MW electrolyzers for H 2 -FCT and 3.4 MW for H 2 -ICET, alongside hydrogen dispensing units and retrofitting trucks. Environmentally, H 2 -FCT demonstrate superior performance, avoiding 1,510 t of CO 2 annually (11 kg CO 2 /t waste collected) compared to H 2 -ICET’s 1,001 t of CO 2 (7.3 kg CO 2 /t waste), representing 64% and 42% reductions versus diesel trucks. While both hydrogen solutions offer convincing decarbonization pathways, the findings underscore H 2 -FCT’s combined economic and environmental advantages. The study highlights the role of green hydrogen in advancing sustainable urban transport and guiding supportive policy development.

Environmental conditions are increasingly recognized as key determinants of mental health. Waste and garbage, as ubiquitous elements of our environment, significantly influence our daily life and emotional well-being. However, research exploring the impact of waste-management policies, which are defined as institutional rules guiding the sorting, collection, and disposal of household waste, on mental health remains limited. Using data from the China Health and Retirement Longitudinal Study (CHARLS) and a difference-in-differences (DID) approach, we treat the mandatory, state-led waste sorting policy implemented in China since 2017 as a policy shock to investigate its effects on mental health. Our analyses indicate that mandatory waste sorting is associated with better mental health through two dimensions encompassing four specific mechanisms. First, through direct environmental improvements, including higher harmless waste treatment rates that correspond to reduced visible pollution, as well as lower PM2.5 concentrations. Second, through indirect social and behavioral changes, reflected in increased outdoor social interactions and greater participation in physical exercise. However, these benefits are not evenly distributed. Elderly individuals, those with lower education levels, and people with physical impairments do not experience significant mental health improvements following the policy implementation, suggesting potential adaptation challenges. These findings emphasize the need for more inclusive and user-friendly waste management systems to ensure mental health across all social groups. • The study uses nationally representative CHARLS panel data to examine China’s mandatory waste-sorting policy. • Policy implementation is associated with improved mental health among residents. • Four mechanisms are identified: improved waste treatment, reduced PM 2 . 5 , increased social interaction, and greater physical activity. • Vulnerable groups, including the elderly, low-education, and mobility-limited individuals, experience limited mental-health benefits. • Findings underscore the need for more inclusive and user-friendly waste- sorting systems.

This paper presents the design and implementation of a smart waste collection and automatic alert system aimed at improving waste management efficiency. Rapid urbanization has increased waste generation, making traditional collection methods inefficient. The proposed system uses ultrasonic sensors to detect garbage levels and load cells to measure weight. An Arduino UNO processes the data and transmits it using LoRa communication. At the receiver side, an ESP32 controller analyzes the data and triggers alerts through IoT platforms and buzzers. The system ensures timely waste collection, reduces overflow, and promotes a cleaner environment. This solution contributes to smart city development by integrating automation, wireless communication, and real-time monitoring

Electricity generation by burning by waste material, also known as thermal waste-to-energy, is a process that involves converting waste material into electricity by burning them in a combustion chamber. This process is a sustainable solution for waste management as it reduces the volume of waste sent to landfills while producing renewable energy. The methodology, for electricity generation by burning waste material typically involves waste collection, handling, and preparation, incineration, energy recovery, and ash management. The generated electricity can be used to power local communities or industries or fed back into the national grid. The process of electricity generation by burning waste materials provides a reliable source of electricity while reducing greenhouse gas emissions by avoiding the release of methane gas from landfill.

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management.

This study examined the nexus between green solid waste management and sustainable development among sanitation companies in Hargeisa, Somaliland. This study was anchored on the evolving theory of waste management and employed an explanatory research design. The study targeted 506 employees of sanitation firms working in street cleaning, waste collection, and waste disposal. Stratified and simple random sampling techniques were used to draw a sample of 218 employees. A conceptual model comprising four latent variables was tested using Covariance-based Structural Equation Modeling (CB-SEM). The results indicate that the three green practices of recycling (b=.413, p<.05) and reducing (b=.300, p<.05) are positive and significant predictors of sanitation companies in Hargeisa City. However, reuse (b = 0.180, p > 05) does not significantly predict the firms’ sustainability. These findings imply that sanitation companies in the city can protect the environment and human health by leveraging green waste management practices. Future studies should broaden the scope to include households, as they contribute significantly to waste generation and should be sensitive to green waste management practices.

Urban waste mismanagement remains a critical public health concern, particularly in densely populated market areas where delayed garbage collection can increase the risk of disease outbreaks such as cholera, especially during the rainy season. This issue is largely attributed to the absence of an efficient monitoring mechanism that informs municipal authorities about the real-time status of waste bins. To address this challenge, this project presents an intelligent waste monitoring and collection system that integrates Internet of Things (IoT) technology with Global Positioning System (GPS) capabilities. The proposed system continuously tracks the fill level of garbage bins using embedded sensors and transmits this information to a centralized IoT server. Alongside filllevel data, the system also provides precise geographic location details of each bin through GPS integration. This enables waste management personnel to identify which bins require immediate attention without the need for manual inspection. Automated alerts are generated when bins reach critical capacity, ensuring that collection activities are carried out in a timely manner. Furthermore, the availability of real-time data supports route optimization for garbage collection vehicles, thereby reducing fuel consumption, operational costs, and environmental impact. By enabling data-driven decision-making, the system enhances overall efficiency in municipal waste management operations. Ultimately, this solution not only minimizes the risk of overflow and associated health hazards but also contributes to cleaner and more sustainable urban environments through improved waste collection practices.

Manual sorting of household waste for composition analysis inevitably exposes samples to ambient air, but potential water losses and associated biases are not quantified in the existing literature or guidelines. This short communication provides preliminary insights on the occurrence and magnitude of moisture loss during waste characterization and aims to stimulate discussion on the relevance of these effects. Since food waste and residual waste account for most of the total water content in municipal household waste collection, these fractions were selected for the experiments. Six food waste and six residual waste samples were repeatedly weighed during 6-8h of exposure under typical indoor sorting conditions and subsequently oven-dried to determine total water content. Food waste samples lost approx. 2 mass-% of initial water in 8h, while residual waste showed greater variability (1.6-5 mass-%; up to14 mass-%). While these water losses seem to be relatively small, they affect not only the accuracy of compositional results but can translate to substantial overestimation of wet-basis performance indicators. The observed moisture losses corresponded to 9-13% higher estimates of lower heating values and biogas potential, which may exceed uncertainty levels commonly accepted in waste characterization and related assessments. Given the limited number of samples, the reported moisture losses demonstrate the existence of the phenomenon but should be interpreted as indicative for the documented indoor conditions (approx.20.6°C and 55.4% relative humidity). Under warmer or more ventilated conditions, moisture losses and the resulting bias may be even larger than observed here.

The present study aimed to assess the social and health conditions in the quilombola communities of Custaneira, Tronco, and Canabrava dos Amaros, located in the semi-arid region of Piauí. This is a quantitative and descriptive study conducted with 124 adult participants. Data were collected through a socioeconomic and health questionnaire, in addition to anthropometric measurements of weight and height to calculate Body Mass Index. The results revealed a profile marked by low educational attainment, predominance of agricultural work, and limited family income. Most participants considered themselves healthy (89.6%) and moderately active (88%). The prevalence of arterial hypertension was 52.5%, and 42% were overweight. The lack of waste collection and water supply were the main environmental complaints. The use of home remedies as a traditional therapeutic practice was also observed. It is concluded that the investigated communities present a scenario of socioeconomic and health inequalities that require greater attention from public policies, with emphasis on intersectoral actions aimed at health promotion, strengthening primary health care, and valuing the cultural and territorial identity of these populations.

Household waste issues, particularly organic waste, along with the suboptimal utilization of home gardens, remain significant challenges in supporting household food security in rural areas. This community service activity aimed to integrate the utilization of household waste into liquid organic fertilizer (LOF) and apply it to home-garden vegetable cultivation as an effort to support household food security and implement the zero-waste concept in Pontang Village. The activity employed a participatory approach through the collection of household waste over a 20-day period from partner households, waste segregation and weighing, processing organic waste into LOF, and applying the LOF to the cultivation of short-cycle vegetables such as water spinach (Ipomoea aquatica), spinach (Amaranthus spp.), and caisim (Brassica juncea) in home gardens. The results indicated that, quantitatively, approximately 44.58% of the total household waste generated had the potential to be processed into liquid organic fertilizer, thereby contributing to the reduction of household waste generation. However, home-garden management did not fully achieve the targeted harvest due to constraints related to seed quality, pest attacks, high rainfall, waterlogging, and limited knowledge of garden managers. Nevertheless, the successful harvest achieved in several home gardens demonstrates the potential effectiveness of LOF as an organic fertilizer when supported by appropriate cultivation practices. This activity highlights that the integration of waste management and home-garden utilization requires continuous assistance, the selection of adaptive seed varieties, and strengthening community capacity to sustainably support household food security.

Current EU textile policy efforts largely focus on post-consumer waste collection and end-of-life management while pre-consumer losses remain insufficiently addressed. In addition, many textile life cycle assessments (LCAs) and material flow studies apply single-lifecycle boundaries with conventional allocation approaches and consumer-country perspectives. This limits the understanding of the potential for fibre retention across consecutive lifecycles and global value chains. This study quantifies fibre flows and environmental performance of a cotton t-shirt across two consecutive lifecycles (loops) by combining material flow model with LCA for five impact categories (global warming, freshwater eutrophication and ecotoxicity, water consumption and land use). The model represents manufacturing in Bangladesh and use and end-of-life management in Norway as EU-relevant benchmark context. Results show that, under current conditions, a maximum of 17% of the initial fibre input could be mechanically recycled and reutilized into a new T-shirt. It is also revealed that fibre losses occur predominantly in upstream operations: an estimated 44% of material was lost during the manufacturing stages. Scenarios analysis demonstrated that recycling pre-consumer waste could increase material recovery to up to 44%, while improving process efficiency (reducing waste generation) in yarn, wet processing and apparel production could reduce the global warming by 10% and other impacts by 20-25%. These findings suggest that meeting EU circular textile ambitions requires prioritizing upstream material efficiency and pre-consumer waste management, supported by coordinated action across policymakers, brands and manufacturers along the value chain.

The degradation of coastal ecosystems due to waste accumulation and the decline of mangrove vegetation has become an increasingly significant environmental challenge in the coastal areas of Jayapura City. This community service program aimed to improve the environmental quality of Cibery Beach through an integrated action consisting of coastal clean-up activities, underwater waste collection through diving, and ecosystem rehabilitation by planting mangrove (bakau) seedlings, conducted in commemoration of the 28th Anniversary of the Faculty of Mathematics and Natural Sciences, Universitas Cenderawasih. The implementation employed a participatory approach based on Participatory Action Research (PAR) and service learning, involving lecturers, students, local communities, and environmental volunteers. The activity stages included site assessment, environmental education on coastal conservation, coastal and underwater waste removal, mangrove seedling planting, and initial post-planting monitoring. The results demonstrated a reduction in waste accumulation along the coastline and underwater areas, as well as the stable establishment of mangrove seedlings as an initial step toward ecosystem rehabilitation. Ecologically, the program has the potential to support shoreline stabilization and improve coastal habitat quality, while socially enhancing environmental awareness and community participation in conservation efforts. This integrated action model is recommended as a sustainable approach to collaborative coastal ecosystem management between universities and local communities.

The rapid growth of urban populations and waste generation has created significant challenges in waste management, with global municipal solid waste expected to exceed 3.4 billion tons annually by 2050 and inefficient collection systems increasing operational costs by nearly 20–30%, while smart waste management technologies are projected to grow at over 18% annually. Additionally, improper waste handling leads to environmental pollution, health hazards, and unhygienic urban conditions, emphasizing the need for intelligent monitoring systems. Traditional waste management systems rely on fixed collection schedules and manual inspection, which often result in overflowing bins, unnecessary fuel consumption, and increased labor costs. Furthermore, conventional systems lack real-time monitoring, waste classification, and route optimization capabilities, reducing their effectiveness in modern urban environments. To address these challenges, the proposed IoT Smart Trash Bin Management System utilizes the ESP32 microcontroller to develop an intelligent and automated waste monitoring solution. The system integrates ultrasonic sensors to measure dry and wet waste levels, along with a wet/dry sensor for accurate waste segregation. An IR sensor enables touch-free lid operation using a servo motor, enhancing hygiene. Real-time status is displayed on an LCD, while a buzzer alerts when the bin reaches capacity. IoT connectivity allows continuous data transmission to cloud platforms for remote monitoring, and a GPS module provides location tracking for optimized waste collection routing. This smart system improves efficiency, reduces environmental impact, minimizes operational costs, and supports the development of sustainable and intelligent urban waste management systems

Assessing the contributions of informal waste management sector and its economic potentials in Nigeria: Challenges and opportunities for macro-level circular economy implementation.

Efficient and sustainable municipal waste collection is a critical challenge in urban management, requiring data-driven strategies that align operational performance with environmental objectives. Despite the increasing use of clustering and routing techniques in waste management, current approaches often fail to fully integrate spatial clustering with optimization models that account for real-world constraints such as vehicle capacities and workload balancing. This paper addresses that gap by proposing a novel framework that combines clustering algorithms with mixed-integer linear programming (MILP) to optimize the division of a city into sustainable municipal solid waste collection sectors. Using real-world data from Tarnów, Poland, we apply the k-means algorithm to generate spatially and quantitatively balanced clusters of over 10,000 collection points. These clusters serve as input to a MILP model that solves the Sustainable Sectorization of Municipal Solid Waste Collection Problem (SSMSWCP), aiming to minimize disparities in either route lengths or collected waste volumes across a heterogeneous vehicle fleet, including diesel and electric trucks. The proposed method supports strategic decision-making by enabling planners to evaluate alternative sector layouts, fleet configurations, and environmental trade-offs. Computational experiments demonstrate the feasibility and flexibility of the approach and highlight how operational constraints and balancing criteria affect service equity and environmental outcomes. The entire methodology is implemented in open-source software and is transferable to other municipalities facing similar planning challenges.

Abstract Aquatic pollution is a pressing global challenge, with submerged plastics and organic debris posing serious threats to ecosystems and human health. Existing solutions—manual nets, diesel pumps, robotic skimmers, and solar boats—primarily target surface waste, leaving underwater debris untreated. This paper presents the design and fabrication of a solar-powered underwater waste collector that integrates a 12V DC pump, PVC framework, floatation drums, and a perforated basket. Powered by a 100W solar panel and battery backup, the system achieves stable floatation, a suction head of 1.8 m, and waste collection efficiency of 92%. CAD modeling in CATIA V5 validated the design, while fabrication trials confirmed affordability (~₹10,000) and sustainability. The project demonstrates a scalable, community-level solution aligned with UN Sustainable Development Goals (SDGs). Keywords: Solar energy, aquatic pollution, underwater waste collector, sustainable engineering, CAD modeling.

Urban waste management presents still major difficulties, especially in fast-growing cities like Kampala. This paper looks at how using digital technologies might help rural-urban communities better manage their waste. Data came from secondary sources, field observations, and questionnaires. With 60% of issues still unresolved, Kampala City struggles greatly with waste management. Just 28,000 tons of municipal garbage find their way to landfills every month, or only 40% of all created waste. The waste situation in Kampala has gotten rather bad. Previously managing 1,400 to 1,700 tons of waste daily, Kiteezi landfill, the main disposal site for the city, exceeded its capacity since 2009 and collapsed in 2024, with 64% efficiency, the waste collecting system lets most of the trash go unharmed. For locals, this poses serious health hazards as well as environmental ones. Given Africa's urban population is predicted to rise from 470 million in 2015 to 1.2 billion by 2025, the scenario could get more difficult. This study will explore how digital technologies might transform waste management in Kampala City. To address mounting urban waste issues, smart solutions including loT-enabled recycling bins and mobile apps now link homeowners with collectors. We will discuss how artificial intelligence can be used in recycling centers, how block chains provide waste handling transparency, and how data analytics might forecast waste trends to help to allocate resources more effectively. Results point to low waste collecting frequency, high organic waste composition, and inadequate recycling programs. Modern ideas come from digital technologies including smart bins, GIS tracking, and mobile waste collecting apps. Proposed to maximize operations is an integrated solid waste management model. The findings of this study end with suggestions for community involvement techniques and digital infrastructure acceptance.

Urban transportation systems are major sources of environmental contamination, generating complex mixtures of airborne particles, heavy metals, and organic pollutants that accumulate along roadways and in surrounding areas. These pollutants originate primarily from vehicular emissions, mechanical wear of vehicle components, and atmospheric deposition associated with urban industrial activities. Occupational groups working directly in these environments may therefore experience greater exposure to environmental contaminants than the general population. The purpose of this paper is to develop a theoretical model of the biological effects of environmental contaminants and health risks in street cleaners and road workers based on a detailed review of the existing literature. The results for occupational health outcomes show that exposure to particulate matter generated by sweeping and waste collection is an important determinant of respiratory risk among sanitation workers. Environmental monitoring studies have demonstrated elevated airborne particulate concentrations during manual sweeping operations, particularly under dry conditions when accumulated road dust is easily re-suspended into the breathing zone of workers. Furthermore, repeated exposure to these airborne particles may therefore contribute to chronic respiratory irritation and the development of respiratory disorders among street cleaners. Cardiopulmonary health effects have also been reported in association with exposure to particulate air pollution, while epidemiological and toxicological studies have demonstrated that inhalation of fine and ultrafine particulate matter can trigger systemic inflammatory responses and oxidative stress that extend beyond the respiratory tract. These biological responses may contribute to alterations in vascular function, increased blood pressure, and disturbances in autonomic regulation of the cardiovascular system. Street cleaners and road maintenance workers experience re

Waste management challenges in Tangerang have intensified due to the continuous increase in daily waste generation, which is not adequately supported by effective treatment systems, leading to waste accumulation and environmental degradation. This study aims to analyze the utilization of BSF maggots as a biological agent for organic waste decomposition at the Mustika Ikhlas Integrated Waste Management Facility (TPST), Tigaraksa. The research employed a descriptive qualitative approach conducted from November 2024 to June 2025. Data were collected through in-depth interviews with five key informants who were directly involved in organic waste management and BSF maggot cultivation activities. Data analysis was carried out using thematic analysis, encompassing data reduction, data display, and conclusion drawing. The findings reveal that the BSF maggot-based waste management process consists of several systematic stages, including organic waste sorting and collection, initial fermentation, controlled feeding, biodegradation by maggots, harvesting, utilization of harvested maggots as animal feed, and the reuse of residual media and larval exoskeletons as compost or soil enhancers. Based on field observations, 1 kg of adult BSF maggots can decompose approximately 10 kg of organic waste in 24 hours. Therefore, if the number of BSF maggots raised reaches 10 kg, the volume of organic waste that can be decomposed increases to approximately 100 kg in the same time period. These results indicate that BSF maggot utilization offers an effective, environmentally sustainable, and economically beneficial alternative for organic waste management. Furthermore, the study highlights the potential of BSF-based bioconversion as a scalable, integrated strategy for sustainable waste management in urban and peri-urban areas, particularly in regions with high organic waste generation. This research offers innovation in terms of research location (conducted at TPST Tigaraksa) and study focus (on the maggot cultivation process and its use in organic waste decomposition).

The use of performance indicators in urban solid waste collection management constitutes a strategic tool for technical planning, operational control, and improvement of municipal administrative efficiency. In a context of increasing urbanization and growing waste generation, the absence of standardized metrics compromises the rational allocation of public resources and the quality of services provided. This study analyzes the application of performance indicators in urban solid waste collection management in the municipality of Caucaia–CE, Brazil, focusing on operational efficiency, economic sustainability, and decision-making support in public administration. The adopted methodology is qualitative, descriptive, and technical-analytical, based on bibliographic review, regulatory analysis, and observation of municipal collection processes. Indicators evaluated include per capita waste generation, service coverage, operational productivity (ton per kilometer and ton per crew), cost per ton collected, and territorial efficiency. The results demonstrate that the systematization of these indicators enables the identification of logistical bottlenecks, route optimization, operational cost reduction, and enhanced transparency in contractual management. It is concluded that the structured incorporation of performance indicators strengthens urban planning, improves public service efficiency, and contributes to the environmental and fiscal sustainability of the municipality.