Search Results (37)

Urban growth has intensified the generation of solid waste, particularly in densely populated and vulnerable neighborhoods, leading to environmental degradation and public health risks. This study presents a multidisciplinary methodology to estimate the mass of macroplastic litter mobilized from urban surfaces into nearby watercourses during storm events. Focusing on the Villa Páez neighborhood in Cordoba, Argentina—a data-scarce and flood-prone urban basin—the approach integrates socio-environmental surveys, field observations, Google Street View analysis, and hydrologic modeling using EPA SWMM 5.2. Macroplastic accumulation on streets was estimated based on observed waste density, and its transport under varying garbage collection intervals and rainfall intensities was simulated using a conceptual pollutant model. Results indicate that plastic mobilization increases substantially with storm intensity and accumulation duration, with the majority of macroplastic mass transported during high-return-period rainfall events. The study highlights the need for frequent waste collection, improved monitoring in vulnerable urban areas, and scenario-based modeling tools to support more effective waste and stormwater management. Full article

Garbage sweepers are crucial to municipal cleaning; however, they frequently encounter two significant challenges during their operations: overflowing rubbish storage compartments and vacuum duct clogging. The conventional method of monitoring overflowing refuse and clogging vacuum ducts is based on manual labour, which is inefficient and susceptible to error. Consequently, this investigation suggests an automated monitoring approach that is predicated on the integration of wind speed sensors and ultrasonic sensors. The ultrasonic sensors assess the blockage status by monitoring the height of the accumulation of rubbish in real time, while the wind speed sensors monitor the change in wind speed. The experimental results indicate that the system is an effective solution for monitoring the refuse intake status of intelligent sweepers, significantly reducing manual intervention and improving the operational efficiency of the equipment. Additionally, the system is accurate and reliable. Full article

Rural villages function as relatively self-sustained production and living units with well-developed infrastructure. In this setting, investigating the transmission pathways of airborne biological particles, including pathogenic microorganisms, is pivotal for ensuring the health of residents. This study investigated the sources and dispersion of biogenic particulate matter in rural ambient air and factors influencing their behavior. Potential bioaerosol sources including livestock farming areas, composting sites, garbage dumps, and sewage treatment facilities were investigated using a calibrated portable bioaerosol detector to collect and analyze the dispersion of bioaerosol particles. The dispersal characteristics of Enterobacteriaceae were explored using an Andersen six-stage sampler. Livestock farming areas were the primary source of bioparticles. The distribution of the bioparticles varied significantly with environmental conditions. Key factors influencing their distribution included the dispersal capabilities due to wind speed and the processes of aggregation and coagulation of particles. The dispersal pathway of Enterobacteriaceae indicated that the inhabitants of residences near the dispersion source might be exposed to health risks from pathogenic bacteria present in bioparticles indoors. Understanding such characteristics and transmission patterns of bioparticles in rural environments provides a scientific basis for risk assessment and management strategies, with important implications for improving air-quality monitoring, public health policies, and environmental management in rural areas. Full article

With urbanization and population growth, waste management has become a pressing issue. Intelligent detection systems using deep learning algorithms to monitor garbage bin overflow in real time have emerged as a key solution. However, these systems often face challenges such as lack of dataset diversity and high energy consumption due to the extensive use of IoT devices. To address these challenges, we developed the Garbage Bin Status (GBS) dataset, which includes 16,771 images. Among them, 8408 images were generated using the Stable Diffusion model, depicting garbage bins under diverse weather and lighting scenarios. This enriched dataset enhances the generalization of garbage bin overflow detection models across various environmental conditions. We also created an energy-efficient model called HERD-YOLO based on Spiking Neural Networks. HERD-YOLO reduces energy consumption by 89.2% compared to artificial neural networks and outperforms the state-of-the-art EMS-YOLO in both energy efficiency and detection performance. This makes HERD-YOLO a promising solution for sustainable and efficient urban waste management, contributing to a better urban environment. Full article

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh Full article

Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and traffic accidents, present significant risks to public safety and infrastructure, necessitating real-time monitoring and early warning systems. This study develops Urban Road Anomaly Visual Large Language Models (URA-VLMs), a generative AI-based framework designed for the monitoring of diverse urban road anomalies. The InternVL was selected as a foundational model due to its adaptability for this monitoring purpose. The URA-VLMs framework features dedicated modules for anomaly detection, flood depth estimation, and safety level assessment, utilizing multi-step prompting and retrieval-augmented generation (RAG) for precise and adaptive analysis. A comprehensive dataset of 3034 annotated images depicting various urban road scenarios was developed to evaluate the models. Experimental results demonstrate the system’s effectiveness, achieving an overall anomaly detection accuracy of 93.20%, outperforming state-of-the-art models such as InternVL2.5 and ResNet34. By facilitating early detection and real-time decision-making, this generative AI approach offers a scalable and robust solution that contributes to a smarter, safer road environment. Full article

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system. Full article

Under the background of increasingly serious global environmental pollution, ensuring the safety of drinking water has become one of the focuses of global attention. In this study, Meihu Reservoir, a drinking water source, was selected as the research object, and the main pollution problems and their sources were revealed through conventional water quality analysis, suitability evaluation of the drinking water source and eutrophication evaluation of the reservoir. Using modern water quality monitoring technology and methods, the paper monitors and analyzes various water quality parameters of the Meihu Reservoir. The results showed that the water quality indexes, except total nitrogen, met the class II–III standard of drinking water, and the comprehensive nutrient state index method (TLI) evaluated the reservoir, and its index met $30\le \mathrm{TLI}(\sum )\le 50$, indicating that the reservoir belongs to the medium nutrition category. Therefore, the water quality of the reservoir has been affected by different degrees of agricultural, domestic and livestock pollution, mainly reflected in the serious excess of the total nitrogen index (the peak has reached 2.99 mg/L). The results of the on-site investigation showed that the main sources of nitrogen in the reservoir included agricultural non-point-source pollution, domestic sewage pollution, domestic garbage pollution and livestock and poultry pollution, accounting for 50.09%, 23.99%, 14.13% and 11.80% of the total load, respectively. On this basis, this paper puts forward some countermeasures for pollution control in order to provide a scientific basis and practical path for water quality protection and improvement of the Meihu Reservoir and other similar reservoirs. Full article

This paper presents an assessment of X-band radar’s detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases at sea of SGI modules assembled in the laboratory. One campaign was carried out with a calm sea and almost no wind in order to determine the X-band radar system’s detection capabilities in an ideal scenario, while the other campaign took place with rough seas and wind. An analysis of the data acquired during the campaigns confirmed that X-band radar can detect small aggregations of litter floating on the sea surface. To demonstrate the radar’s ability to detect SGIs, a statistical analysis was carried out to calculate the probability of false alarm and the probability of detection for two releases at two different distances from the radar. For greater readability of this work, all of the results obtained are presented both in terms of radar intensity and in terms of the radar cross-section relating to both the targets and the clutter. Another interesting study that is presented in this article concerns the measurement of the speed of movement (drift) of the SGIs compared with the measurement of the speed of the surface currents provided at the same time by the radar. The study also identified the radar detection limits depending on the sea state and the target distance from the antenna. Full article

Methane fermentation, utilizing food waste (FW), is viewed as a sustainable strategy that leverages garbage and agricultural waste to conserve the environment. However, FW compost encounters growth inhibition issues, which we examine in this study. The aim of this study was to assess the influence of various compost mixtures on seed germination growth and the yield of Komatsuna (Brassica rapa). The experiment employed FW compost mixtures with biochar (BC), clay (CL), weeds (WD), and a control group in a completely randomized design with three replications to monitor germination. The experimental pots, arranged in a complete factorial design, involved three treatment factors: compost type (FW or HM), biochar presence or absence (WB or NB), and three input rates (25 g pot⁻¹, 50 g pot⁻¹, and 100 g pot⁻¹), each in triplicate. The combination of FW and BC exhibited an enhanced germination rate compared to FW alone. Moreover, the inclusion of biochar significantly amplified this effect, particularly at the input rate of 50 g pot⁻¹ and had a substantial impact on the interaction between input rate, compost type, and biochar on variables such as nitrogen (N) uptake, % N, soil carbon, and yield. Homemade BC demonstrates an increasing fertilizer cost performance (FCP) as the input rate rises across all fertilizer combinations, while commercially priced BC exhibits a reverse relationship with FCP. These findings suggest that the addition of biochar enhances the performance of methane fermentation residue compost, thereby promoting plant growth through the processing of environmentally sustainable waste. Full article

Leptospirosis, also known as Weil’s disease, is a febrile tropical disease of humans and diverse animals. The maintenance hosts of the infectious pathogen, Leptospira spp., are primarily rodents, while other warm-blooded animals and some reptiles are secondary or transient hosts of this pathogen. African giant pouched rats (Cricetomys spp.) have been identified to be important maintenance hosts of pathogenic leptospires in the tropical and subtropical regions of the world. This study assessed the seroprevalence of Leptospira spp. in the African giant pouched rats of the Ngorongoro Conservation Area (NCA), Tanzania, where there is close human, domestic animal, and wildlife interaction. A total of 50 African giant pouched rats were sampled between July 2020 and December 2021. Blood sera were screened for specific leptospiral antibodies using a microscopic agglutination test (MAT), while urine and kidney tissues were examined for the pathogen and pathogen-specific genes using cultures and polymerase chain reactions (PCR), respectively. The pathogen detection varied from 0% in cultures to 6% via the MAT and 20% via PCR. The Fisher exact test was applied to compare positive cases detected through the diagnostic tests, and showed a significant difference in the indirect and direct detection of Leptospira serovars via the MAT and PCR. We conclude that pathogenic Leptospira serovar are found in the NCA and recommend that the NCA authority raises awareness of the existence of the Leptospira serovar in giant African pouched rats, and possibly other rodents. The NCA should initiate appropriate management strategies, including the guided disposal of household garbage, which is the major attractant of rodents to residential areas. Where necessary, the NCA should carry out limited rodent control and periodic monitoring of the pathogen carrier (rodent) populations. Full article

Effective waste management is of paramount importance as it contributes significantly to environmental preservation, mitigates health hazards, and aids in the preservation of precious resources. Conversely, mishandling waste not only presents severe environmental risks but can also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression of global warming and climate change, consequently giving rise to atmospheric pollution. This pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of cardiovascular disorders, and negatively impact overall public health. Hence, efficient management of trash is extremely crucial in any society. It requires integrating technology and innovative solutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary communication paradigm with significant contributions to remote monitoring and control. IoT-based trash management aids remote garbage level monitoring but entails drawbacks like high installation and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes the need for global commitment by policymakers, stakeholders, and civil society, working together to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and time, our proposed method leverages quick response (QR) codes to enable the remote monitoring of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any technology is its ability to interact and collaborate with humans. Within our framework, citizens will employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these teams will plan for optimized garbage collection procedures, considering parameters such as garbage volume and the most efficient collection routes aimed at minimizing both time and fuel consumption. Full article

IoT-based smart e-waste management is an emerging field that combines technology and environmental sustainability. E-waste is a growing problem worldwide, as discarded electronics can have negative impacts on the environment and public health. In this paper, we have proposed a smart e-waste management system. This system uses IoT devices and sensors to monitor and manage the collection, sorting, and disposal of e-waste. The IoT devices in this system are typically embedded with sensors that can detect and monitor the amount of e-waste in a given area. These sensors can provide real-time data on e-waste, which can then be used to optimize collection and disposal processes. E-waste is like an asset to us in most cases; as it is recyclable, using it in an efficient manner would be a perk. By employing machine learning to distinguish e-waste, we can contribute to separating metallic and plastic components, the utilization of pyrolysis to transform plastic waste into bio-fuel, coupled with the generation of bio-char as a by-product, and the repurposing of metallic portions for the development of solar batteries. We can optimize its use and also minimize its environmental impact; it presents a promising avenue for sustainable waste management and resource recovery. Our proposed system also uses cloud-based platforms to help analyze patterns and trends in the data. The Autoregressive Integrated Moving Average, a statistical method used in the cloud, can provide insights into future garbage levels, which can be useful for optimizing waste collection schedules and improving the overall process. Full article

Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter $V$ and auxiliary parameter $P$ are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability. Full article

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the environment. To address this problem, a Smart Waste Management System is introduced in this paper, employing machine learning techniques for air quality level classification. Furthermore, this system safeguards garbage collectors from severe health issues caused by inhaling harmful gases emitted from the waste. The proposed system not only proves cost-effective but also enhances waste management productivity by categorizing waste into three types: wet, dry, and metallic. Ultimately, by leveraging machine learning techniques, we can classify air quality levels and garbage weight into distinct categories. This system is beneficial for improving the well-being of individuals residing in close proximity to dustbins, as it enables constant monitoring and reporting of air quality to relevant city authorities. Full article