Search Results (17)

Illegal dumping poses serious risks to ecosystems and human health, requiring effective and timely monitoring strategies. Advances in uncrewed aerial vehicles (UAVs), photogrammetry, and deep learning (DL) have created new opportunities for detecting and characterizing waste objects over large areas. Within the framework of the EMERITUS Project, an EU Horizon Europe initiative supporting the fight against environmental crimes, this study evaluates the performance of pre-trained prompt-based multimodal (PBM) DL models integrated into ArcGIS Pro for object detection and segmentation. To test such models, UAV surveys were specially conducted at a semi-controlled test site in northern Italy, producing very high-resolution orthoimages and video frames populated with simulated waste objects such as tyres, barrels, and sand piles. Three PBM models (CLIPSeg, GroundingDINO, and TextSAM) were tested under varying hyperparameters and input conditions, including orthophotos at multiple resolutions and frames extracted from UAV-acquired videos. Results show that model performance is highly dependent on object type and imagery resolution. In contrast, within the limited ranges tested, hyperparameter tuning rarely produced significant improvements. The evaluation of the models was performed using low IoU to generalize across different types of detection models and to focus on the ability of detecting object. When evaluating the models with orthoimagery, CLIPSeg achieved the highest accuracy with F1 scores up to 0.88 for tyres, whereas barrels and ambiguous classes consistently underperformed. Video-derived (oblique) frames generally outperformed orthophotos, reflecting a closer match to model training perspectives. Despite the current limitations in performances highlighted by the tests, PBM models demonstrate strong potential for democratizing GeoAI (Geospatial Artificial Intelligence). These tools effectively enable non-expert users to employ zero-shot classification in UAV-based monitoring workflows targeting environmental crime. Full article

Urbanicity is a recognized determinant of mental health, yet conventional measures such as population density or the rural–urban divide often fail to capture the complex realities of informal settlements in low- and middle-income countries. This paper conceptualizes neighborhood effects through the lived experiences of young women in Kampala, Uganda, drawing on participatory research from the NIH-funded TOPOWA study. Using community mapping and Photovoice, participants identified neighborhood features that shape wellbeing, including sanitation facilities, drainage systems, alcohol outlets, health centers, schools, boda boda stages (motorcycle taxis), lodges, religious institutions, water sources, markets, and recreational spaces. These methods revealed both stressors—poor waste management, flooding, violence, gendered harassment, crime, and alcohol-related harms—and protective resources, including education, places of worship, health centers, social networks, identity, and sports activities. We argue that urbanicity in slum contexts should be understood as a multidimensional construct encompassing deprivation, fragmentation, exclusion, and resilience. This reconceptualization advances conceptual clarity, strengthens the validity of mental health research in low-resource settings, and informs interventions that simultaneously address structural risks and promote community assets. The case of Kampala demonstrates how participatory evidence can reshape the understanding of neighborhood effects with implications, for global mental health research and practice. Full article

In recent years, awareness of environmental issues has grown significantly. This awareness is evident in the increase in academic research on this topic, social initiatives that focus on environmental issues, and programs promoting environmental literacy. At the same time, there is growing recognition of the accumulating environmental damage caused by individuals’ choices and habits and the threat this poses to ecosystems and sustainability. Numerous studies have explored various facets of environmental literacy to promote pro-environmental behavior, which is essential for tackling the environmental challenges of the 21st century. Over the past few decades, research in the social sciences has investigated the influence of war on crime and delinquency rates. Additionally, studies have measured the impacts of war, terrorism, and stress on mental health and social relationships. Ecological research has also examined the relationship between war and environmental degradation, highlighting the impact on ecosystems, infrastructure destruction, and the long-term environmental consequences. To the best of our knowledge, however, no research has examined how stress and war influence individuals’ sense of environmental responsibility. The aim of this qualitative research was to examine the influence of stress and war on both environmentally engaged individuals and those indifferent to the environment, focusing on their sense of environmental responsibility and its practical expressions. Twenty-four participants were categorized into two groups: environmentally engaged and environmentally indifferent. The findings indicate that the commitment of environmentally engaged individuals to environmental protection decreased during periods of stress and war. This decline can be attributed to more immediate concerns for survival. The participants reported that this change was temporary; as calm returned, their level of commitment reverted to its previous state. Among the environmentally indifferent, no change occurred in their level of commitment to environmental protection, which was consistently low before, during, and after situations of stress and war. Additional findings indicate that the social environment plays a crucial role in fostering environmental responsibility and protection. A supportive social environment has a positive influence on individuals, while indifference or opposition can harm their sense of environmental responsibility. Our research findings also suggest that both environmentally oriented and environmentally indifferent individuals primarily focus on recycling and waste separation when they refer to pro-environmental behavior. 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

The emphasis on collaborative governance and effective leadership to bring any social change is gaining prominence among researchers. This study investigates how leadership and collaborative governance can drive social change in waste management in Campania, using a qualitative, multi-site case study approach. Interviews of relevant politicians, public managers, citizens, waste management organizations and academics were conducted (2023–2024). This research uses the lens of multiple theoretical frameworks such as collaborative governance, network agency, public value framework and transformational leadership to explore different dynamics of the research such as stakeholder engagement, public value creation and leadership effectiveness in waste management. Moreover, several obstacles in the way of achieving social change in Campania concerning waste management and environmental sustainability are identified. The findings of this study added to the theoretical understanding of collaborative governance and social change through the demonstration of how transformational leadership, stakeholder participation and trust-based relationships drive good waste management while identifying structural limitations such as corruption and organized crime that hindered governance. Practically, it highlights five interconnected themes from interviews of leadership that civic leaders and managers must establish to promote positive social change: Difficulties in leadership effectiveness, civic potential unused, media mobilization, hope for a miracle and stakeholder engagement diversification. The public value framework is used to analyze the potential role of leadership in bringing change in society. The research findings are replicable and can be applied to a similar set of circumstances. This study advances existing research by addressing the gap in understanding how collaborative governance and transformational leadership can be integrated to tackle waste management and environmental sustainability challenges. The findings can be applied to other regions facing similar governance challenges, making it relevant for global sustainability efforts. Italy’s government has green-lighted projects to make these activities more visible while downplaying their negative impacts on the environment and public health. Full article

This paper critically examines Brazil’s legislative framework for pesticide waste management, highlighting recent advancements and persistent challenges. The introduction of Law 14785/2023 marks a pivotal shift in pesticide regulation, streamlining registration processes and enhancing safety measures. This law consolidates various aspects of pesticide management, from research to disposal, aiming for more efficient oversight. Complementary to this, foundational laws such as the Federal Constitution (1988), Law 6938/1981 (National Environmental Policy), Law 12651/2012 (New Forest Code), and Law 13123/2015 (Biodiversity Law) establish broad guidelines for environmental protection and the sustainable use of resources. Specific regulations, including the Environmental Crimes Act (Law 9605/1998) and CONAMA Resolutions No. 465/2014 and No. 420/2009, address critical aspects of pesticide waste, focusing on proper disposal and soil quality management. Despite these advancements, challenges persist in the effective implementation of these laws. Inadequate monitoring systems and limited stakeholder awareness hinder regulatory enforcement. Strategic recommendations to address these issues include enhancing monitoring technologies, strengthening educational initiatives, fostering stakeholder collaboration, and adapting legislation to keep pace with technological advancements. A robust pesticide waste management system is essential for ensuring long-term sustainability in agricultural production. Inefficient disposal practices can contaminate soil and water, posing risks to biodiversity and public health. By reinforcing regulatory enforcement and integrating sustainable waste management strategies, Brazil can mitigate the environmental impact of pesticide use while promoting safer agricultural practices. The ongoing evolution of legislation reflects a commitment to sustainability; however, continuous efforts are necessary to fully realize these goals and align agricultural development with environmental conservation. Full article

This study focuses on the analysis, implementation and integration of techniques and methods, also based on mathematical algorithms and artificial intelligence (AI), to acquire knowledge of some phenomena that produce pollution with an impact on environmental health, and which start from illicit practices that occur in urban areas. In many urban areas (or agroecosystems), the practice of illegal waste disposing by commercial activities, by abandoning it in the countryside rather than spending economic resources to ensure correct disposal, is widespread. This causes an accumulation of waste in these areas (which can also be protected natural areas), which are then also set on fire to reduce their volume. Obviously, the repercussions of such actions are many. The burning of waste releases contaminants into the environment such as dioxins, polychlorinated biphenyls and furans, and deposits other elements on the soil, such as heavy metals, which, by leaching and percolating, contaminate water resources such as rivers and aquifers. The main objective is the design and implementation of monitoring programs against specific illicit activities that take into account territorial peculiarities. This advanced approach leverages AI and GIS environments to interpret environmental states, providing an understanding of ongoing phenomena. The methodology used is based on the implementation of mathematical and AI algorithms, integrated into a GIS environment to address even large-scale environmental issues, improving the spatial and temporal precision of the analyses and allowing the customization of monitoring programs in urban and peri-urban environments based on territorial characteristics. The results of the application of the methodology show the percentages of the different types of waste found in the agroecosystems of the study area and the degree of concentration, allowing the identification of similar areas with greater criticality. Subsequently, through network and nearest neighbour analysis, it is possible to start targeted checks. Full article

In recent years, the application of biocompatible and non-toxic nanomaterials for the detection of fingerprints has become the major interest in the forensic sector and crime investigation. In this study, waste chickpea seeds, as a natural resource, were bioprocessed and utilized for the synthesis of non-toxic graphene nano-sheets (GNSs) with high fluorescence. The graphene GNS were synthesized via pyrolysis at high temperatures and were characterized by TEM, XPS, fluorescence and UV-Vis spectroscopy, and FTIR analysis. The GNS exhibited excitation-independent emission at about 620 nm with a quantum yield of over 10% and showed more distinct blue light under a UV lamp. Biocompatibility of the synthesized GNS in terms of cell viability (88.28% and 74.19%) was observed even at high concentrations (50 and 100 mg/mL), respectively. In addition, the antimicrobial properties of the synthesized GNS-based coatings were tested with the pathogenic strain of Bacillus cereus via live/dead cell counts and a plate counting method confirming their biocompatible and antimicrobial nature for their potential use in safe fingerprint detection. The developed chickpea-originated fluorescent GNS-based spray coatings were tested on different surfaces, including plastic, glass, silicon, steel, and soft plastic for the detection of crime scene fingerprints. Results confirmed that GNS can be used for the detection of latent fingerprints on multiple non-porous surfaces and were easy to detect under a UV lamp at 395 nm. These findings reinforce the suggestion that the developed fluorescent GNS spray coating has a high potential to increase sensitive and stable crime traces for forensic latent fingerprint detection on nonporous surface material. Capitalizing on their color-tunable behavior, the developed chickpea-originated fluorescent GNS-based spray coating is ideal for the visual enhancement of latent fingerprints. Full article

The Bumbu River Watershed is an essential source for the drinking and sanitation needs of settlement communities within Lae, Papua New Guinea. However, poor sanitation and waste management practices have led to concerns over the safety and integrity of the watershed’s resources. In this study, we explored the effect of these factors on water quality in the Bumbu river and its tributaries using water quality (22 sampling stations), geospatial (degree of urbanisation), and community survey (sanitation and hygiene practices) data. Water Quality Index (WQI) was calculated based on the Canadian Council of Ministers of Environment (CCME) template using pH, Total Dissolved Solids (TDS), conductivity, turbidity, alkalinity, calcium, magnesium, total hardness, mercury, manganese, iron, and Escherichia coli. Using geospatial techniques, principal component analysis, and forward regression analysis, we found that better water quality outcomes coincided with better community health conditions of Crime and Pollution, and better household health outcomes. Land-use itself was not significantly correlated with water quality, but distressingly, we found 19 of 22 water samples to be of “poor” quality, indicating a need for better community water regulation. The methodology and results presented can be used to inform policy decisions at the provincial/national level, and to aid future research activities in other watersheds. Full article

The Science, Research, and Innovation Plan 2023–2027 (SRI Plan) is a national policy that will support researchers to steer Thailand towards a more sustainable path. In contrast to most existing policies, which focus on poverty reduction through economic growth schemes, and national security, Thailand Science, Research, and Innovation (TSRI) aims at balancing the social, economic, and environmental aspects of the SRI Plan. While the TSRI has been incorporating the Sustainable Development Goals (SDGs) into its policies, it realized that there is the need to cater for diverse lives across Thailand. This research aims to support the TSRI in drafting a more inclusive SRI Plan, especially during the COVID-19 outbreak. Through the SDGs’ lens, the researchers collaborated with regional investigators to examine the area-specific needs across six regions of Thailand. Given the COVID-19 constraints, the horizon scanning and modified Delphi technique were used to collect data from April 2020 to April 2021. The results obtained from the participants unveiled that, while several issues are shared, each region ranked mostly different issues as their development concerns. The TSRI stands to benefit from these insights by drafting a region-specific national research plan. Regarding the shared concerns, the economic slowdown was ranked as one of the most grievous matters concerning the regions. Interestingly, through the interlinkage approach highlighted by the SDGs, the participants associated this economic problem with other issues unique to their regions, such as multidimensional poverty, loss of tourists due to the global pandemic, and drug-related crimes. Unfortunately, the relevant authorities usually treat these problems as being separate issues. A silos-dominant administrative system, combined with rigid regulations, prevents efficient cooperation. In addition to the economic issues, waste management and water-related problems due to climate change were ranked as significant concerns. Lastly, the participants also viewed injustice and the lack of good governance as drawbacks to sustainable development. From these results, it is evident that TSRI can draft a more inclusive SRI Plan to support research for sustainable development through the SDGs’ lens. Indeed, the Plan would reflect a more balanced view of regional needs than those formulated through the usual technocratic techniques. Full article

Surveillance cameras have been increasingly used in many public and private spaces in recent years to increase the security of those areas. Although many companies still recruit someone to monitor the cameras, the person recruited is more likely to miss some abnormal events in the camera feeds due to human error. Therefore, monitoring surveillance cameras could be a waste of time and energy. On the other hand, many researchers worked on surveillance data and proposed several methods to detect abnormal events automatically. As a result, if any anomalous happens in front of the surveillance cameras, it can be detected immediately. Therefore, we introduced a model for detecting abnormal events in the surveillance camera feed. In this work, we designed a model by implementing a well-known convolutional neural network (ResNet50) for extracting essential features of each frame of our input stream followed by a particular schema of recurrent neural networks (ConvLSTM) for detecting abnormal events in our time-series dataset. Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. Therefore, we categorized our data into three main and essential categories: the first groups mainly need firefighting service, while the second and third categories are about thefts and violent behaviour. We implemented the proposed method on the UCF-Crime dataset and achieved 81.71% in AUC, higher than other models like C3D on the same dataset. Our future work focuses on adding an attention layer to the existing model to detect more abnormal events. Full article

This paper reveals the linear economy contribution to resource degradation and environmental pollution in eastern Romania that could further feed environmental crimes and conflicts, such as in the Pungesti shale gas case. Preservation of material and water resources in the region is required through various circular mechanisms under a cross-sectoral approach including solid waste as a material resource for industry and agriculture; wastewater treatment and water reuse; composting and organic agriculture; and using renewables. Six non-conflictual sustainable alternative routes related to circular economy mechanisms, water preservation, and to the clean energy transition are proposed in this paper, which are further examined through key statistics and indicators, current best practices, and local development pathways in both urban and rural communities. Full article

A smart city is an urbanization region that collects data using several digital and physical devices. The information collected from such devices is used efficiently to manage revenues, resources, and assets, etc., while the information obtained from such devices is utilized to boost performance throughout the city. Cloud-based Internet of Things (IoT) applications could help smart cities that contain information gathered from citizens, devices, homes, and other things. This information is processed and analyzed to monitor and manage transportation networks, electric utilities, resources management, water supply systems, waste management, crime detection, security mechanisms, proficiency, digital library, healthcare facilities, and other opportunities. A cloud service provider offers public cloud services that can update the IoT environment, enabling third-party activities to embed IoT data within electronic devices executing on the IoT. In this paper, the author explored cloud-based IoT applications and their roles in smart cities. Full article

Due to increasing urbanization, many people find themselves living in slums that expose them to several health risks. We explored urban health risks that fall short of the planetary boundaries in an urban slum in Kampala, Uganda using photovoice. We selected, trained, and assigned ten youth (five females and five males) to take photos on urban health risks. The photographs were discussed, and transcripts were analyzed based on the doughnut economics model using content analysis in NVivo 12. Environments and actions of slum dwellers expose them to health risks, and cause them to live at the edge of planetary boundaries. Environmental sanitation challenges, including solid and liquid waste management, excreta management, and food hygiene and safety expose slum dwellers to risks at the edge of the lower boundary of the planet. Urban conditions expose slum dwellers to poor physical infrastructure, undesirable work conditions, pollution, and health and safety challenges. Crime, violence, and substance use were also viewed as vices that make slum environments dangerous habitats. On the other hand, practices like inhabiting wetlands and using biomass fuels in addition to traffic fumes expose slum dwellers to effects associated with living above the planetary boundaries. Urban youth reflected on health risks that have immediate effects on their health and day-to-day living. Urbanization, especially in low resource settings, needs to be cognizant of the ensuing risks to health and thus ensure sustainable growth. Full article

As a result of tightened waste regulation across Europe, reports of waste crime have been on the rise. Significant stockpiles of tyres and plastic materials have been identified as a threat to both human and environmental health, leading to water and livestock contamination, providing substantial fuel for fires, and cultivating a variety of disease vectors. Traditional methods of identifying illegal stockpiles usually involve laborious field surveys, which are unsuitable for national scale management. Remotely-sensed investigations to tackle waste have been less explored due to the spectrally variable and complex nature of tyres and plastics, as well as their similarity to other land covers such as water and shadow. Therefore, the overall objective of this study was to develop an accurate classification method for both tyre and plastic waste to provide a viable platform for repeatable, cost-effective, and large-scale monitoring. An augmented land cover classification is presented that combines Copernicus Sentinel-2 optical imagery with thematic indices and Copernicus Sentinel-1 microwave data, and two random forests land cover classification algorithms were trained for the detection of tyres and plastics across Scotland. Testing of the method identified 211 confirmed tyre and plastic stockpiles, with overall classification accuracies calculated above 90%. Full article