Search Results (8)

This study aimed to compare the environmental performance of plastic bags made of three different polymers, considering two product functions: carrying goods and packing municipal solid waste. The three polymers studied were HDPE, LDPE, and thermoplastic starch (TPS). Life cycle assessment and emergy accounting were used to evaluate the environmental performance of each scenario in analysis. To develop this research, eight scenarios were created to represent the customs of use and consumption in the Brazilian population. The LCA results showed that, in general, the scenarios with HDPE plastic bags presented the best environmental performances, while those with TPS presented the worst. The processes that contributed most to these results, representing 70% or more of the environmental impact in each impact category, are related to the use of raw materials, electricity, and water for the manufacture of plastic bags and the treatment in landfills. In other words, the fact that TPS has a mass around six times greater than that of HDPE and two times greater than that of LDPE ends up leaving this type of polymer with the worst environmental performance. In the comparative analysis of scenarios for the same polymer, scenarios that involve the use and reuse of plastic bags present the lowest potential environmental impacts. In contrast, those related to the use and disposal in landfills present the highest possible environmental impacts. The results of emergy accounting showed that the HDPE scenarios had the lowest total emergy flow, ranging from 1.77 × 10¹³ seJ to 2.40 × 10¹³ seJ. In contrast, the LDPE scenarios had the highest total emergy flow, ranging from 1.15 × 10¹⁴ to 1.21 × 10¹⁴ seJ. Although LDPE had the highest total emergy flow values, these results are similar to those obtained by the fossil resource scarcity impact category, which focuses on resource consumption analysis. Thus, through a real approach to the use of plastic bags and solid waste management in the Brazilian context, this study brings essential insights to direct public policies related to the consumption of plastic bags. Full article

Addressing climate change necessitates reducing carbon emissions, with green behavior adoption being crucial. This study examines how green consumption awareness (GCA) and carbon emission disclosures influence consumer shopping channel choices, offering a practical approach to converting awareness into actionable behavior. Using stated preference (SP) data, we investigate the impact of green awareness and information disclosure on consumers’ choices between online and offline shopping channels. The key findings include the following: (1) GCA affects shopping channel choices in certain scenarios, though not always significantly. (2) Detailed carbon information disclosure steers consumers towards lower-emission channels, especially when specific carbon data are provided. (3) The type of goods significantly influences shopping channel decisions, serving as a variable across scenarios. (4) Effective scenarios, such as a 3 km shopping trip for categories like tissue and trash bags, where the difference in channel choice under varying levels of information disclosure is statistically significant, have been identified. These insights inform recommendations for information disclosure strategies that not only enhance GCA but also drive behavioral change, thereby fostering environmentally friendly consumption behaviors that contribute to a reduction in consumers’ carbon footprint. Full article

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird’s nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. Full article

Environmental persistence is one of the few shortcomings of plastic materials. As a consequence, alternative plastics labeled as compostable are replacing polyolefins in some commercial applications, such as food bags and trash bags. A rapid, high-throughput, and environmentally relevant method to assess the potential biodegradability in marine conditions is used to assess these materials already on the market, as well as novel bio-based polymers still in development. By fitting experimental data to a non-linear logistic model, ultimate biodegradability can be calculated without regard for incubation time. Whereas the commercial products show negligible or very low marine biodegradability, one of the novel materials exceeds the 20% biodegradation threshold relative to fully marine biodegradable PHB after 28 days. In addition, the sensitivity of the method can be enhanced and its duration reduced, at the expense of labor-demanding preconditioning of the microbial inoculum, by increasing the bacterial density in the incubation vessels. In contrast, pre-exposure of the inoculum to plastic, either in laboratory or field conditions, does not enhance the performance of the test. Full article

Ecosystem destruction is one of today’s significant challenges due to fast industrialisation and an increasing population. It takes several years for solid trash, such as plastic bottles and super-market bags, to decompose in nature. In addition, plastic disposal techniques such as landfilling, reuse, and incineration pose significant threats to human health and the environment. In this paper, we investigated whether the impact of mixing biodiesel with waste oil from recycled plastic on the resulting fuel mixture’s yields better physical and chemical properties. Consequently, pyrolysis is one of the most advantageous and practical waste disposal methods as it is both environmentally benign and efficient. Pyrolysis is the high-temperature thermal breakdown of solid waste to produce pyrolytic oil. The pyrolytic (plastic) oil produced is converted to a hydrocarbon-rich pyrolytic fuel. Similar to diesel and gasoline, pyrolytic fuel has the same calorific value. Internal combustion engines may operate on pyrolytic fuel without suffering a performance reduction. Researchers examined engine performance and exhaust pollutants. The research discovered that the engine could operate on plastic pyrolysis fuel at full load, enhance brake thermal efficiency by 6–8%, and lower UBHC and CO emissions; however, nitrous oxide (NOx) emissions were noticeably higher. The findings demonstrated the possibility of using plastic pyrolysis fuel as a diesel substitute. Full article

Economic and social progress in the Republic of Korea resulted in an increased standard of living, which subsequently produced more waste. The Korean government implemented a volume-based trash disposal system that may modify waste disposal characteristics to handle vast volumes of waste efficiently. However, the inconvenience of having to purchase standard garbage bags on one’s own led to passive participation by citizens and instances of illegally dumping waste in non-standard plastic bags. As a result, there is a need for the development of automatic detection and reporting of illegal acts of garbage dumping. To achieve this, we suggest a system for tracking unlawful rubbish disposal that is based on deep neural networks. The proposed monitoring approach obtains the articulation points (joints) of a dumper through OpenPose and identifies the type of garbage bag through the object detection model, You Only Look Once (YOLO), to determine the distance of the dumper’s wrist to the garbage bag and decide whether it is illegal dumping. Additionally, we introduced a method of tracking the IDs issued to the waste bags using the multi-object tracking (MOT) model to reduce the false detection of illegal dumping. To evaluate the efficacy of the proposed illegal dumping monitoring system, we compared it with the other systems based on behavior recognition. As a result, it was validated that the suggested approach had a higher degree of accuracy and a lower percentage of false alarms, making it useful for a variety of upcoming applications. Full article

This article presents research on how household waste is collected. An online survey, with 348 participants, from the Romanian region of Bacau, was conducted from October 2018 to May 2019. The online questionnaire included a set of over 40 questions, some with the aim of identifying the nature of the people participating, but most of the questions being designed to determine the collection methods for household waste. The major goal of the current study, as previously stated, was to determine the primary way of collecting household garbage from the public, while also learning various details about the participants, including their residence location, gender, age, and level of education. Referring to the means used for collecting household waste, the following items were noted: trash cans, cardboard boxes, dumpsters, and raffia bags. As a result of the study carried out, the following conclusions were drawn: it was noticed that 70 percent of those who participated in the survey came from urban areas; a larger percentage of female respondents took part in the survey (128 from 348); the majority of respondents were aged 18–29 (182); 178 respondents had a higher education level; collection of household waste in garbage bags represented 62.9 percent of the total collection methods. Following statistical processing of the data, and an overview of the main ways in which household waste was collected, a number of connections were found between the characteristics of the respondents and their household waste collection. What is noteworthy is that the characteristics of the respondents could be grouped into cumulative factors that played an important role in household waste collection: the first group formed by level of education and location of the respondents, and the second group formed by age and gender of the respondents. Full article

The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this problem by deploying more manpower, but considering the manpower and maintenance costs that impose a heavy burden on the local governments who are experiencing tight financial situations, it would not be feasible. Thus, an Internet of Things (IoT)-based Smart Trash Separation Bin model that can reduce the cost of trash separation work has been proposed in this paper. The three efficient designs that respectively use a sensor, image processing, or spectroscope technology are presented. These IoT-based designs can bring significant merit to reducing the manpower costs, as well as the administrative cost involved. Full article