Search Results (98)

Municipal solid waste management (MSWM) remains a critical environmental governance challenge in rural and peri-urban regions of the Global South, where service delivery gaps exacerbate illegal dumping and public health risks. Geographic Information Systems (GIS) are increasingly promoted as decision-support tools to improve waste collection efficiency and environmental monitoring; however, their adoption in resource-constrained municipalities remains limited. This study investigates the institutional and policy barriers shaping GIS readiness in four rural municipalities within South Africa’s Vhembe District. Using a qualitative case-study design, semi-structured interviews were conducted with 29 municipal officials across managerial and operational levels, complemented by 399 community responses to an open-ended survey question. Thematic analysis, guided by Institutional Theory and the Technology Acceptance Model (TAM), identified five interrelated themes: waste production and disposal behaviours, collection and infrastructure constraints, institutional and operational challenges, policy and standardisation gaps, and technology readiness. The findings reveal that weak service reliability, fragmented governance structures, limited human and financial capacity, and inconsistent policy enforcement collectively undermine GIS adoption, despite its high perceived usefulness among officials. The study demonstrates that the effectiveness of GIS as an environmental management tool is contingent on institutional readiness rather than technological availability alone and highlights the need for integrated reforms in service delivery, institutional capacity, and policy implementation to enable GIS-supported sustainable waste management. Full article

Heavy metals are widespread environmental contaminants from natural and anthropogenic sources, posing risks to human health and ecosystems. In urban areas, levels are elevated due to industrial activity, traffic emissions, and building materials. Camden, New Jersey, a city with a history of industry and illegal dumping, faces increased risk due to aging sewer and stormwater systems. These systems frequently flood neighborhoods and parks, heightening residents’ exposure to heavy metals. Despite this, few studies have examined metal distribution in Camden, particularly during storm events. This study analyzes stormwater metal concentrations across residential and commercial areas to assess contamination levels, potential sources, and land use associations. Stormwater samples were collected from 33 flooded street locations after four storm events in summer 2023, along with samples from a flooded residential basement during three storms. All were analyzed for total lead, cadmium, and arsenic using inductively coupled plasma–mass spectrometry (ICP-MS, (Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ, USA)). Concentration data were visualized using geographic information system (GIS)-based mapping in relation to land use, socioeconomic, and public health factors. In Camden’s stormwater, lead levels (1–1164 µg L⁻¹) were notably higher than those of cadmium (0.1–3.3 µg L⁻¹) and arsenic (0.2–8.6 µg L⁻¹), which were relatively low. Concentrations varied citywide, with localized hot spots shaped by environmental and socio-economic factors. Principal component analysis indicates lead and cadmium likely originate from shared sources, mainly industries and illegal dumping. Notably, indoor stormwater samples showed higher heavy metal concentrations than outdoor street samples, indicating greater exposure risks in flooded homes. These findings highlight the spatial variability and complex sources of heavy metal contamination in stormwater, underscoring the need for targeted interventions in vulnerable communities. Full article

Solid waste management remains a critical challenge in rapidly urbanizing regions of the Global South, where limited infrastructure and informal disposal practices compromise environmental and public health. This study addresses the issue of illegal waste dumping in Dakar, Senegal, by integrating remote sensing, geographic information systems, and citizen science into a multi-criteria framework to identify areas most susceptible to dumping. Using Landsat 8 and Sentinel-2 imagery, indicators such as land surface temperature, vegetation, soil, and water indices were combined with demographic and infrastructural data. A citizen survey involving local university students provided social perception scores and criterion weights through the Analytic Hierarchy Process. The resulting susceptibility maps revealed that high and very high dumping probabilities are concentrated around the Mbeubeuss landfill and densely populated areas of Keur Massar, while Malika showed lower susceptibility. Sensitivity analysis confirmed the model’s robustness but highlighted the influence of thermal and social perception variables. The results show that 28–35% of the study area falls under high or very high susceptibility, with hotspots concentrated near wetlands, informal settlements, and poorly serviced road networks. The weighted model demonstrates stronger spatial coherence compared to the unweighted version, offering improved interpretability for waste monitoring. These findings provide actionable insights for the Société Nationale de Gestion Intégrée des Déchets (SONAGED) and for municipal planners to prioritize interventions in high-susceptibility zones. Rather than being entirely novel, this study builds on existing remote sensing, geographic information systems and citizen science approaches by integrating them within a multi-criteria framework specifically adapted to a West African context. Full article

Annual oyster production in southern Korea reaches about 300,000 tons, generating an equivalent amount of waste oyster shells. Most are illegally dumped or stockpiled along coastlines, causing serious environmental issues. This study utilized machine learning to predict the compressive strength of oyster shell cementitious composites. A total of 336 datasets were used, including 189 experimental results and 147 from published literature. Input variables were water-to-cement ratio (W/C), silica fume, blast furnace slag, superplasticizer content, and curing conditions. Algorithm selection compared the performance of Ridge Regression, Support Vector Regression, Artificial Neural Network, and Random Forest (RF), with RF exhibiting the highest predictive performance (R² = 0.8411). Ensemble algorithms including XGBoost, AdaBoost, Extra Trees, and LightGBM were optimized using GridSearchCV. Among these, LightGBM showed the best predictive capability with a mean absolute error of 3.1671, mean squared error of 17.8054, root mean square error of 4.2196, and R² of 0.9042. SHAP analysis revealed that W/C and superplasticizer were the most influential variables. Oyster shells showed a negative correlation with sand, indicating the role of oyster shells as a substitute material. Thus, cementitious composites can maintain compressive strength and serve as sustainable construction materials when waste oyster shells are incorporated with appropriate admixtures. Full article

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation. Full article

The escalating environmental crisis and the threat posed by environmental crime demand more effective prevention strategies. The predictive mapping of environmental crimes can address this challenge by improving monitoring and response. This study proposes an analysis and modelling of the occurrence of environmental crimes in the Canary Islands, a territory of exceptional ecological value and strong tourism and urban sprawl pressures. Four types of illegal activity were examined: buildings and constructions, mining and tilling, solid waste dumping, and liquid waste discharging. A predictive modelling framework based on Random Forest (RF) machine learning algorithms was applied to identify spatial patterns and environmental crime potential. A colour-based environmental crime potential map was generated for each island, showing the likelihood of 0, 1, 2, 3, or all 4 types of environmental crime. Findings reveal that 43.2% of the surface area of the islands could potentially be affected by at least one crime type. Potential occurrences are lower in protected natural areas, in islands with lower population densities and in inland areas compared to coastal regions. The methodology provides a foundation for future research which could assist policymakers and environmental protectors in combating and preventing environmental crimes more effectively and contribute to the preservation of their ecosystems. Full article

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