Search Results (92)

Efficient transportation management is essential for the sustainability and safety of modern urban infrastructure. Traditional road inspection and transport management methods are often labor-intensive, time-consuming, and prone to inaccuracies, limiting their effectiveness. This study presents a UAV-based transport management system that leverages machine learning techniques to enhance road anomaly detection and severity assessment. The proposed approach employs a structured three-tier model architecture: A unified obstacle detection model identifies six critical road hazards—road cracks, potholes, animals, illegal dumping, construction sites, and accidents. In the second stage, six dedicated severity classification models assess the impact of each detected hazard by categorizing its severity as low, medium, or high. Finally, an aggregation model integrates the results to provide comprehensive insights for transportation authorities. The systematic approach seamlessly integrates real-time data into an interactive dashboard, facilitating data-driven decision-making for proactive maintenance, improved road safety, and optimized resource allocation. By combining accuracy, scalability, and computational efficiency, this approach offers a robust and scalable solution for smart city infrastructure management and transportation planning. Full article

Illegal waste dumping poses a severe challenge to sustainable urban and regional development, undermining environmental integrity, public health, and the efficient use of resources. This study contributes to sustainability science by proposing a circular data feedback loop that enables dynamic, scalable, and cost-efficient monitoring and prevention of illegal dumping, aligned with the goals of sustainable waste governance. Historical data from the Slovenian illegal dumping register, UAV-based surveys and a newly developed application were used to update, monitor, and validate waste site locations. A comprehensive risk model, developed using machine learning methods, was created for the Municipality of Maribor (Slovenia). The modelling approach combined unsupervised and semi-supervised learning techniques, suitable for a positive-unlabeled (PU) dataset structure, where only confirmed illegal waste dumping sites were labeled. The approach demonstrates the feasibility of a circular data feedback loop integrating updated field data and predictive analytics to support waste management authorities and illegal waste dumping prevention. The fundamental characteristic of the stated approach is that each iteration of the loop improves the prediction of risk areas, providing a high-quality database for conducting targeted UAV overflights and consequently detecting locations of illegally dumped waste (LNOP) risk areas. At the same time, information on risk areas serves as the primary basis for each field detection of new LNOPs. The proposed model outperforms earlier approaches by addressing smaller and less conspicuous dumping events and by enabling systematic, technology-supported detection and prevention planning. Full article

Background: Kigali, Rwanda’s rapidly growing capital, faces major challenges in household-level Fecal Sludge Management (FSM), with over 89% of households using pit latrines and only 48% accessing unshared sanitation. FSM services are limited, costly, and poorly executed, leading to frequent illegal dumping. Objective: This review analyzes the literature on sanitation in Kigali to identify key gaps, synergies, and recommendations for improvement. Methods: Following PRISMA 2020 guidelines, 73 relevant publications were selected from various scientific and governmental sources. Publications were included only if they were published from 2013 to 2024 and had information on sanitation in Kigali. NOS and JBI tools were utilized to assess the quality of included publications. Results: Data were categorized into four themes, (1) access to sanitation, (2) FSM services, (3) public health, and (4) sanitation governance, and analyzed using thematic, narrative, and descriptive methods. Findings reveal a dysfunctional FSM service chain, weak policy enforcement due to overlapping responsibilities, underfunding, and limited private sector participation. These issues contribute to poor sanitation, inadequate hygiene, and prevalence of diarrheal diseases and Tropical Neglected Diseases, especially among young children. Conclusions: The review recommends strengthening governance and clarifying roles, enforcing adaptable regulations, promoting public–private partnerships, and managing the full FSM service chain more effectively. Future research should focus on developing context-specific technologies and financing strategies to support sustainable FSM solutions in Kigali. Full article

The aim of the article is to highlight the increasing environmental burden on aquatic ecosystems in Slovakia due to continuous pollution from municipal, industrial and agricultural sources. Laboratory analyses have shown alarming exceedance of the limit values of contaminants, with nitrate nitrogen (NO₃⁻) reaching 5.8 mg/L compared to the set limit of 2.5 mg/L and phosphorus concentrations exceeding the permissible values by a factor of five, thereby escalating the risk of eutrophication and loss of ecological stability of the aquatic ecosystem. The accumulation of heavy metals is also a problem—lead (Pb) concentrations reach up to 9.7 μg/L, which exceeds the safe limit by a factor of ten. Despite the measures implemented, such as scum barriers, there is continuous contamination of the aquatic environment, with illegal waste dumps and uncontrolled runoff of agrochemicals playing a significant role. The research results underline the critical need for a more effective environmental policy and more rigorous monitoring of toxic substances in real time. These findings highlight not only the urgency of more effective environmental policy and stricter real-time monitoring of toxic substances, but also the necessity of integrating environmental logistics into the design of sustainable solutions. Logistical approaches including the optimization of waste collection, coordination of stakeholders and creation of infrastructural conditions can significantly contribute to reducing environmental burdens and ensure the continuity of environmental management in ecologically sensitive areas. Full article

Malawi’s Blantyre City faces escalating waste management challenges due to increased urbanization and inadequate waste collection services. This research utilized remote sensing (RS) and geographic information system (GIS) techniques to map potential illegal dump sites (PIDSs). MODIS and Sentinel-5P satellite imagery and GPS locations of dumpsites were used to extract environmental and spatial variables, including land surface temperature (LST), the enhanced vegetation index (EVI), Formaldehyde (HCHO), and distances from highways, rivers, and official dumps. An analytical hierarchical process (AHP) pairwise comparison matrix was used to assign weights for the six-factor variables. Further, fuzzy logic was applied, and weighted overlay analysis was used to generate the PIDS map. The results indicated that 10.27% of the study area has a “very high” probability of illegal dumping, while only 2% exhibited a “very low” probability. Validation with field data showed that the GIS and RS were effective, as about 89% of the illegal dumping sites were identified. Zonal statistics identified rivers as the most significant contributor to PIDS identification. The findings of this study underscore the significance of mapping PIDS in low-resource regions like Blantyre, Malawi, where inadequate waste management and illegal dumping are prevalent. Future studies should consider additional factors and account for seasonal variations. Full article

This study examines the assessment and management strategies for odor risks during emergency cleanup of VOC-contaminated waste. By analyzing illegally dumped VOC waste, the impact on odor intensity levels and exceedance probabilities in nearby residential areas was evaluated. Utilizing a VOC source emission model, a Gaussian plume dispersion model, and Monte Carlo simulations under various meteorological conditions, the effectiveness of the control measures was assessed. Key pollutants included ethylbenzene, toluene, styrene, and m/p-xylene, which, despite posing minimal short-term health risks (PHI: 0.17–0.64), exhibited significant odor risks (Odor PHI: 127–1156). At 20 m from the source, the probability of the odor intensity exceeding Level 2.5 approached 100%, decreasing to 85% at 50 m and further declining with distance. Atmospheric stability shifts—from very unstable (Class A) to stable (Class F)—increased the odor intensity from 0.5 to 2.5. Under moderately stable conditions (Class E), m/p-xylene had a 44.2% probability of exceeding an odor intensity level of 2.5. Even at 250 m, the odor intensity levels ranged between 1.2 and 1.7, remaining perceptible. Effective mitigation strategies include establishing appropriate buffer distances and using adsorption materials like activated carbon. Full article

The occurrence of illegal waste activities is a worldwide problem, due to improper actions and inadequate services across many territories. Geographical Information Systems (GISs) software plays a crucial role in optimizing waste management and determining the shortest route paths for waste transportation. This work focuses on the development of a GIS-based workflow for the detection of Illegal Abandoned Waste Sites (IAWSs) and waste management planning. The integration of remote/ground sensing activities, geospatial data, and models within a GIS framework is a useful practice for conducting cost analysis and supporting the development of efficient waste management plans. Firstly, available satellite images are employed in a baseline assessment, combining ancillary and remote sensing data. As a result of satellite monitoring, a ground-piloted survey is carried out by checking the potential-IAWSs density map retrieved from the satellite pre-recognition phase. Hence, a total of 171 ground points are geo-localized and spatialized, according to qualitative on-site products and 2.5D volume analysis. Consequently, distances from illegal dumping sites to proper disposal plants are calculated, achieving the shortest route paths as geospatial information. From these data, a Functional Unit (FU) of 1 ton of mixed waste plus 381.6 kg of inert material is determined, a fundamental stage for comparing different cost analysis processes in similar contexts. By using a GIS-based workflow, a cost analysis assessment is provided, aiming to support principal activities such as waste transportation and disposal to the proper plant (e.g., landfill or incineration). In conclusion, spatial data analysis results are fundamental in managing illegal abandoned waste sites, helping to establish a cost analysis assessment. Full article

Illegal waste dumping is a significant global issue, particularly in low- and middle-income countries such as South Africa. This study aimed to investigate the risk factors for acute respiratory symptoms among residents living near illegal domestic waste dumping sites. The study also explored community perceptions regarding illegal dumping and its health effects. This cross-sectional study was conducted in Rocklands, Free State, South Africa, with 200 participants: 117 living within 0–5 km of a dumping site and 83 residing ≥5–10 km away. Data were collected using a structured questionnaire. Generalized linear models were employed to explore the relationship between proximity to illegal dumping sites and respiratory symptoms, adjusting progressively for confounders in successive models. Respiratory symptoms were more prevalent among those within 5 km of illegal dumping sites: cough (48.0% vs. 27.0%), shortness of breath (23.0% vs. 18.0%), wheezing (20.0% vs. 7.0%), and chest tightness (20.0% vs. 7.0%). Proximity was significantly associated with wheezing (PR: 2.77; 95% CI: 1.10–6.98) and chest tightness (PR: 2.86; 95% CI: 1.19–6.84). Community-driven initiatives, such as awareness campaigns and recycling, were strongly supported as solutions. These findings highlight the need for education on waste management. Collaborative efforts are essential to reduce illegal dumping and improve waste management. Full article

E-waste, or electronic waste, refers to discarded electronic devices and components, and the management of e-waste has become a newly arising and challenging issue both in India and globally. Due to the increase in population, urbanization, global demand, and expansion of the digital infrastructure, generation of electronic waste is increasing annually. This study provides a comprehensive and thoroughly reviewed qualitative study on electronic waste management practice. This study highlights an outline of the amount of electronic waste generation in India and the world and examines prevailing approaches in the treatment and management of electronic waste, including unsafe informal recycling and inadequate inventory control. This article focuses on major problems such as child labor, illegal dumping, poor infrastructure, limited knowledge and awareness among the public inadequate legal regulation, and spillage of various toxic heavy metals such as arsenic (As), mercury (Hg), and barium (Ba) via electronic waste. This study analyzes the harmful effects of toxic heavy metals, such as arsenic and mercury, on environmental quality and human well-being. To address these issues, this study outlines various sustainable recommendations, such as technology improvement proper collection; handling, management, and eradication of waste generated by electrical equipment in formal recycling practices; the 3Rs (reduce, reuse, and recycle) following circular economy practice, including collaboration between governmental, non-governmental, business, industries, and civil society; better legislative measures such as extended producer responsibility (EPR) and a single approach method, where collecting, sorting, and dismantling electronic waste is handled by the informal sector, while the formal sector manages extraction of metal, disposal, and recycling. Full article

The SARS-CoV-2 pandemic has increased healthcare waste (HCW) across the globe, giving rise to new challenges such as illegal dumping of medical waste, and an increased risk to hazardous waste exposure such as blood and body fluids that could cause diseases. The study aimed to determine factors associated with SARS-CoV-2 infection among employees handling medical waste in selected healthcare risk waste (HCRW) facilities in Johannesburg, South Africa. The pilot study followed a cross-sectional design, where self-administered questionnaires were used to collect data on occupation-related risk factors for SARS-CoV-2 among HCW handlers working in waste generation, transportation, and final disposal. A total of 33 participants selected from eight HCRW facilities participated in the study. The analysis showed that 21.9% (n = 7) of the HCW handlers who participated in the study reported a positive test result for COVID-19, while 78.1% (n = 26) reported a negative test result for COVID-19. The logistic regression analysis showed that repeated handling of HCW (COR: 1.50, 95% CI: 1.00–2.25) and not having sufficient hand washing facilities (COR: 1.13: 95% CI: 1.04–1.24) increased the odds of SARS-CoV-2 infection; however, these factors were not significant as risks for SARS-CoV-2. In the multivariable analysis, not being trained on personal protective equipment (PPE) use (AOR: 1.25, 95% CI: 1.00–1.58) increased the odds of SARS-CoV-2 infection, while having 3-6 years of experience in medical waste handling significantly lowered the odds of occupation-related SARS-CoV-2 infection by 33% (AOR: 0.67, 95% CI: 0.48–0.95). These preliminary findings show that factors such as the accessibility of handwashing facilities, training on the use of PPE, years of work experience, and repeated contact with medical waste may play a role in modifying the odds of SARS-CoV-2 infection among HCW handlers. A study with a larger sample size is needed to comprehensively quantify occupation related risk factors associated with communicable disease infections among HCW handlers. Full article

Poor solid waste management in Kisumu, Kenya, contributes to adverse health, social, and environmental outcomes as a result of open burning, illegal dumping, and reliance on landfills. Taking Kisumu as a case study, we use behavioural systems mapping (BSM) for the purpose of understanding the role of behaviour in this complex problem. We qualitatively analysed transcripts from focus groups and interviews with 45 stakeholders in Kisumu to construct a BSM of the perceived actors, behaviours, and behavioural influences affecting waste management, as well as causal links. Influences were analysed using the capability, opportunity, and motivation model of behaviour (COM-B). The resulting BSM connects 24 behaviours by 12 different actors and 49 unique influences (30 related to opportunity, 16 to motivation, and 3 to capability). It reflects three sub-systems: policy-making, public waste management, and the policy–public interface. Six key feedback loops are described, which suggest that cycles of underfunding are interlinked with problematic practices around the build-up, handling, and segregation of waste and conflicting public and political views around responsibility. We demonstrate how the BSM method can be used with transcript data and provide steps that others can follow to inform the design of systemic behaviour change interventions. Further research to validate and adapt this approach may extend the learnings to other countries and health and sustainability challenges. Full article

In this paper, we describe a collaborative community-based research project that centered on community members’ lived experiences, which led to the identification of key community issues that resulted in a representative art project in the form of a public mural. Eleven mothers who were long-time residents of the community were the drivers of the issue identification and mural creation. The issues identified, and subsequently depicted in the mural, revealed the importance of the environment in neighborhoods, with residents dealing with encampments, illegal dumping, prostitution, eviction, and gentrification. In the mural Mosaicos de la Comunidad (Mosaics of the Community), a group of madres (mothers) sought to emphasize their shared admiration of art as a form of remembrance of ample food, clean air, and beautiful spaces to live and raise their children. Drawing from the madres’ reflections and written testimonials, this paper describes the collective mural-making process; moving from research-based issues identified by the madres into the mural design stage, including the identification of symbols and their meanings, to the creation and painting, and the culmination with a mural-unveiling celebration. The paper ends with a description of the value of community-based art as a form of resistance and as a reminder to concretize the environmental justice issues and values that are central to community members. Full article

Illegal solid waste dumping is a significant factor contributing to environmental damage. In this study, 16S rRNA gene sequencing technology was used for the identification and assessment of environmental damage in an illegal dumping area in China, with the aim of confirming environmental damage through analyzing changes in the soil bacterial communities across slag, sewage sludge, and non-contaminated areas. The results indicate that the diversity of soil bacteria decreases with an increase in the degree of pollution. The illegal dumping of slag resulted in an increase in the relative abundance of Firmicutes and a decrease in the relative abundance of Acidobacteriota. Additionally, illegal dumping of sewage sludge resulted in an increase in the relative abundance of Proteobacteria and a decrease in the relative abundance of Acidobacteriota. The contents of Ni and Be in slag and Cu, Pb, and Cd in sewage sludge were key factors affecting bacterial community composition. The results reveal the effects of heavy metal pollution on the soil bacterial community structure and its environmental driving factors, thus expanding understanding in the context of management of the environmental damage caused by illegal dumping, as well as providing a perspective on the changes in the soil bacterial community, allowing for environmental damage confirmation. Full article

Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development. Full article

This study was aimed at monitoring the occurrence and potential sources of emerging contaminants in water sources within the Modder River catchment. Selected water quality indicators were analysed by Hanna multi-parameter meters. Emerging contaminants such as acetaminophen, carbamazepine, ibuprofen, atrazine, simazine, metolachlor, terbuthylazine, 17-alpha-ethinyl-estradiol, estradiol, progesterone, and testosterone were analysed by high performance liquid chromatography mass spectrometry. The sources of emerging contaminants were determined by statistical methods such as Pearson correlation and hierarchical cluster analysis. Results showed that all the sampled water sources have some level of questionable drinking water quality and necessitate some amount of treatment to reduce the contamination before consumption, especially DO, EC, and pH. The 17-alpha-ethinyl-estradiol mean values in rivers (7.79 and 31.55 µg/L), dams (1.83 and 6.90 µg/L), and treated drinking water (0.2 and 0.73 µg/L) were the highest in summer and autumn seasons, respectively. Wastewater effluents, domestic sewage, urban surface runoff, agricultural runoff, and illegal dumping were identified as the possible sources of emerging contaminants pollution. Waste management education, proper application of herbicides, and advance wastewater treatment methods were some of the suggested mitigation strategies. The outcomes may be relevant for environmental protection and water sustainability in the catchment. Full article