Assessment of the Quality of Cause-of-Death Data in Serbia for 2005-2019 Vital Statistics Performance Index Estimation.

Background: We aimed to evaluate the quality of the cause of death (COD) concerning mortality patterns and completeness of death registration to identify areas for improvement in Serbia. Methods: COD data collected from the mortality register in Serbia from 2005 to 2019 (1540615 deaths) were analyzed with the software Analysis of National Causes of Death for Action. The Vital Statistics Performance Index for Quality (VSPI(Q)) is estimated for the overall COD data quality. Results: The completeness of death certification was higher than 98%. Usable underlying COD was registered in 57%, 24.1% with an unusable and 18.6% with insufficiently specified COD. The VSPI(Q) was 67.2%, denoting medium quality. The typical error was using intermediate COD (24.7% of all deaths), while 13.2% and 8.5% of all garbage codes (GC) belonged to the Very High and High Severity classes. The leading underlying COD is unspecified cardiomyopathy. The analysis revealed that 39.1% of GC has been redistributed to non-communicable diseases, 2.5% to external causes and 1.1% to communicable diseases. Conclusion: In the 15 years' worth of data analyzed, the true underlying COD, in many cases, was ill-defined, indicating that COD data at the national level could be distorted. The additional and continuous professional education of medical students as well as physicians is needed. It should focus on the most common GC among the leading COD and acquiring skills in certifying external causes of death.

A study on the effectiveness of narrative image types, message framing, and psychological distance in enhancing young people's self-efficacy in marine garbage recycling.

Intentional or unintentional littering, especially among young people, poses a threat to the marine environment. It is, therefore, necessary to enhance awareness of marine environmental protection among youth groups. This study explored the interaction between visual image types (photograph vs. cartoon), message framing (positive vs. negative), and psychological distance (human vs. turtle). A three-way analysis of variance (ANOVA) and regression analysis were used to verify the hypotheses. The key findings of the research were as follows: (1) Among the eight information construction methods, cartoons combined with turtles and presented with positive outcomes were the best way to communicate, i.e., cartoons showing animals in a friendly and positive way were the most effective form of marine garbage recycling for young people, and (2) Young people's preference for information will promote their self-efficacy in marine garbage recycling. The implications of these findings for developing marine protection information communication education for youth groups are also proposed.

The trend of the quality of cause-of-death data and its association with socio-economic indicators in Serbia in the period 2005-19.

This study aims to evaluate the temporal trend in the quality of cause-of-death data and garbage code profiles and to determine its association with socio-economic status in Serbia. A longitudinal study was assessed using data from mortality registers from 2005 to 2019. Computer application Analysis of Causes of National Deaths for Action (ANACONDA) calculates the distribution of garbage codes by severity and composite quality indicator: Vital Statistics Performance Index for Quality (VSPI(Q)). A relationship between VSPI(Q) and country development was estimated by analysing two socio-economic indicators: the Socio-demographic Index and the Human Development Index (HDI). Serbia indicates progress in strengthening cause-of-death statistics. The steady upward trend of the VSPI(Q) index has risen from 55.6 (medium quality) to 70.2 (high quality) over the examined years. Significant reduction of 'Insufficiently specified causes with limited impact' (Level 4) and an increase in the trend of 'High-impact garbage codes' (Levels 1 to 3) were evident. Decreased deaths of no policy value (annual percentage change of -1.41%) have manifested since 2014. A strong positive association between VSPI(Q) and socio-economic indicators was assessed, where the HDI has shown a stronger association with VSPI(Q). Improved socio-economic conditions on the national level are followed by enhanced cause-of-death data quality. Upcoming actions to improve quality should be directed at high-impact garbage codes. The study underlines the need to prioritise the education and training of physicians with a crucial role in death certification to overcome many cause-of-death quality issues identified in this assessment.

Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging.

Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.

Drinking water, sanitation, hygiene, and health conditions in India: Findings from the national sample survey.

Introduction: Uncertainty exists in the drinking water, sanitation, hygiene, and health conditions due to mosquitoes and flies across India. Therefore, this study aimed to assess the availability of drinking water, sanitation, hygiene, and health in India. Methods: We used secondary analysis data on 95,548 household members from the National Sample Survey, which included 113,822 homes and 555,351 persons across India between June 2017 and 2018. Results: About 36.4% of household members stated that tube wells or boreholes outside the property at a distance of less than 0.2 km were their main source of drinking water. More than 87% of respondents said that the water they drank was free of impurities, and 55.1% said that it had not been treated. About 45.7% of respondents stated that there were no bathrooms in the dwellings. More than 50% of the time, according to the respondents, garbage was not collected. A flea or mosquito problem was reported by about 97% of households. Around 97% of households reported fevers that were caused by a disease. Conclusion: In India, poor access to drinking water, sanitary facilities, good hygiene, and healthcare existed. The results will support the next visionary programs to increase living standards in the country.

Bi-objective optimization for last-mile routing of garbage trucks: Aesthetics and Public exposure.

In the area of Solid Waste Management, transportation of the collected waste is a critical aspect considering the substantial time spent by garbage trucks on public roads. Studies have reported that transporting garbage has challenges related to public exposure and aesthetics. This study presents a generalised bi-objective formulation for the optimal routing of garbage trucks from transfer stations to recycling sites/landfills considering the trade-off between public exposure and aesthetic loss and constraining the operating cost. The formulation uses the novel link capacity function to account for the delay at traffic signals and the mix of trucks and cars on link performance. The proposed formulation is solved using the weighted sum and ε-constraint methods and applied to a realistic case study of the City of Chicago, USA. The Pareto Front obtained for the bi-objective formulation offers diverse trade-off solutions catering to distinct performance metrics. The results highlight the disparity across the solutions; the solution (P0.95 on Pareto Front) for minimum operating cost (or travel time or distance travelled) is very different from the solution (P0.4 on Pareto Front) for aesthetic cost and public exposure. The parametric study indicated that a modest operating budget may suffice for achieving aesthetic benefits, but minimising public exposure requires a higher operating budget. Finally, the proposed framework is adaptable to address various challenges pertaining to waste transportation, thereby serving as a valuable tool for evaluating policies and practices geared towards sustainability objectives.

Do birds select the plastics debris used for nest construction? A case study in a Mediterranean agricultural landscape.

Plastic pollution is becoming a global problem due to its ubiquitous occurrence and the impacts detected for many species. However, the research about plastics in nests of terrestrial bird species has remained relatively overlooked in comparison to those devoted to marine ecosystems. Here we study the occurrence and patterns of use of anthropogenic material in nests of two passerine birds, the Eurasian magpie (Pica pica) and the European serin (Serinus serinus), breeding in an orange tree cultivation in Mediterranean Spain. Our results show that both species use extensively plastic debris as nest material; almost 71% of the European serin nests and 96% of nests of Eurasian magpies contained plastic debris. Furthermore, by analyzing the plastic debris availability in the agricultural landscape surveyed we confirmed a selection pattern in the two species. Thus, both species preferably select plastic filaments over other plastic debris. The Eurasian magpie does not select plastic based on size or color but the European serin avoid black plastics prefer smaller fragments in comparison to the average size available. Moreover, we suggest the apparent similarity of plastic filaments with the natural materials typically used by these species, as well as how they use the plastic in their nests could influence their selection behavior. More studies focused on terrestrial birds inhabiting human modified habitats could offer a deeper approach to how plastic debris interacts with wildlife in different ways.

Application research of image classification algorithm based on deep learning in household garbage sorting.

The classification of garbage types is an important issue in today's world, and its proper implementation can contribute to environmental conservation and improved efficiency of recycling processes. Unfortunately, the classification of garbage types is currently predominantly performed through human supervision, which leads to high errors and environmental risks. It is crucial to automate this procedure utilizing machine vision methods as a result. This research proposes a revolutionary deep learning-based strategy for classifying domestic waste. The suggested method uses deep learning methods to extract information from images. The Capuchin Search Algorithm (CapSA) is used to improve the hyperparameters of the convolutional neural network (CNN) used as the feature extraction model. Furthermore, for categorizing the retrieved features from the CNN model, a hybrid learning model based on Error-Correcting Output Codes (ECOC) and Artificial Neural Networks (ANN) is used. The classification accuracy may be successfully increased by using this hybrid model, and the benefit becomes more pronounced as the number of target categories rises. The TrashNet and HGCD databases were used to assess the suggested method's effectiveness, and its results in waste type detection were contrasted with those of earlier techniques. Based on the study findings, the suggested approach can identify trash types with an accuracy of 98.81 % and 99.01 % on the TrashNet and HGCD databases, respectively. This is at least a 1.46 % improvement over earlier approaches. The study's conclusions validate that the suggested strategy may be used in real-world scenarios and show how successful the approaches used in it are.

Uncovering and Expressing our Purpose.

Generative artificial intelligence (AI) is currently a source of angst, because of its ability to give us content that sounds uncannily like a real person, and because of concern that people will not stop at using it as a tool to generate and synthesize ideas, but instead will cede control over our words, and then our thoughts. This editorial details each article in Creative Nursing Vol. 30 Issue 2, highlighting the ways in which social media, different kinds of AI, and other tools for connectivity can be used for good: finding our purpose, uniting people over long distances, expediting knowledge implementation, managing large volumes of literature, advancing health equity, and enriching nursing education.

Interannual variability in marine debris accumulation on Hawaiian shores: The role of North Pacific Ocean basin-scale dynamics.

Community-based marine debris removal efforts on the Hawaiian Islands of Kaua'i and Hawai'i, spanning 2013-2022, provided large datasets and documented remarkable variations in annual amounts of debris, mainly from abandoned, lost and derelict fishing gear. To test the hypothesis that the influx of marine debris on Hawaiian shores is determined by the proximity of the North Pacific garbage patch, whose pattern changes under the control of large-scale ocean dynamics, we compared these observational data with the output of an oceanographic drift model. The high correlations between the total mass of debris collected and the model, ranging between r = 0.81 and r = 0.84, validate the attribution of the strong interannual signal to significant migrations of the garbage patch reproduced in the model experiments. Synchronous variations in marine debris fluxes on the two islands, separated by >500 km, confirm the large scale of the interannual changes in the North Pacific marine debris system.

Implementation of two-phase modeling of hydrogen sulfide in fresh market's combined sewers in Rat Burana, Bangkok.

Hydrogen sulfide (H2S) is one of the sewer gases commonly found in wastewater collection systems. This anaerobic degradation product causes issues, ranging from odor nuisances and health hazards to pipe corrosion. Several studies have provided an understanding of H2S formation mechanism, including simulations of H2S emissions in sewers, especially in pressurized systems. However, the present models necessitate a large amount of data due to the complexity of the H2S processes and common routine-monitoring water quality parameters may not fit the requirements. This study aims to simulate the fate and transport of H2S in both air and water phases in combined sewers, with a realization of practicableness of the application. The study case is centered around a fresh market in Bangkok, where the sewers are commonly plagued with garbage-related issues. These challenges pose difficulties for site monitoring across various aspects, necessitating the application of unconventional methods. On-site hydrodynamics, wastewater quality, and H2S gas concentration data were monitored on hourly and daily bases. It was found that the sulfides in the combined sewerage were correlated with sewage quality, e.g., COD, sulfate (SO42-), and pH concentrations in particular. The model results were in an acceptable range of accuracy (R2 = 0.63; NSE = 0.52; RMSE = 1.18) after being calibrated with the measured hydrogen sulfide gas concentration. The results lead to the conclusion that the simplified model is practical and remains effective even in sewers with untraditional conditions. This could hold promise as a fundamental tool in shaping effective H2S mitigation strategies.

Distribution and survival of pathogens from different waste components and bioaerosol traceability analysis in household garbage room.

Household garbage rooms release abundant bioaerosols and are an important source of pathogens; however, information on the distribution and survival patterns of pathogens in different waste components is limited. In this study, a culture method and 16S rRNA high-throughput sequencing were used to determine bacterial communities, culturable pathogens, and human bacterial pathogens (HBPs). The results showed that abundant culturable bacteria were detected in all waste types, and a large number of S. aureus was detected on the surface of recyclable wastes, whereas S. aureus, total coliforms, Salmonella, Enterococcus, and hemolytic bacteria were detected in food waste and other waste. The activities of these detected pathogenic bacteria decreased after 24 h of storage but re-activated within one week. Factors affecting the emergence of pathogens varied with different waste components. Sequencing results showed that Pseudomonas, Acinetobacter, and Burkholderia were abundant in the waste samples, whereas Achromobacter, Exiguobacteriums, Bordetella, and Corynebacterium were the primary pathogens in the bioaerosol and wall attachment. The results of traceability analysis showed that bioaerosol microbes were mainly derived from raw kitchen waste (5.98%) and plastic and paper contaminated with food waste (19.93%) in garbage rooms. In addition, bioaerosols were the main source of microflora in the wall attachment, which possessed high HBP diversity and required more attention. These findings will help in understanding the microbial hazards in different waste components and provide guidance for the control and risk reduction of bioaerosols during waste management and recycling.

A systematic review on garbage enzymes and their applications in environmental processes.

Understanding the new insight on conversion of organic waste into value-added products can improve the environmental activities driven by microorganisms and return the nutrients to environment and earth. Here, we comprehensively review the available knowledge on application of garbage enzyme (GE) for different environmental activities including waste activated sludge, composting process, landfill leachate treatment, soil remediation and wastewater treatment with special focus on their efficiency. To identify peer-reviewed studies published in English-language journals, a comprehensive search was performed across multiple electronic databases including Scopus, Web of Science, Pubmed, and Embase. The search was conducted systematically using relevant keywords. The eligible studies were analyzed to extract data and information pertaining to components of GE, fermentation process operational parameters, type of hydrolytic enzymes and improved environmental performance. The findings derived from this current review demonstrated that GE produced from the fruit and vegetable peels, molasses or brown sugar (carbon source), and water within fermentation process contain different hydrolytic enzymes in order to facilitate the organic waste degradation. Therefore, GE can be considered as a promising and efficient pathway in order to improve the environmental activities depended on microorganism including, composting, wastewater and leachate treatment and bioremediation process.

College students' knowledge, attitudes, and practices of garbage sorting and their associations: a cross-sectional study of several universities in Beijing, China.

Background: In recent years, the Chinese government has placed growing emphasis on environmental development. The implementation of effective waste separation practices in schools is crucial for establishing an ecological civilization in China. Objective: The present study aimed to assess the knowledge, attitude, and practice (KAP) of waste separation among Chinese university students and to understand the demographic factors influencing the KAP of the interviewed students. These sociodemographic factors include gender, age, education, and family environment. Methods: Based on the KAP theoretical model and the Lewin behavioral model (LBM), this study developed its questionnaire on college students' KAP of rubbish sorting. A survey was conducted on 1,282 college students from five colleges and universities in Beijing. A one-way ANOVA, Pearson's correlation analysis, and multiple linear stepwise regression analyzes were employed to explore the factors influencing college students' KAP scores on waste sorting. The questionnaire's reliability and validity were effectively verified through two rounds of Delphi expert consultation. Results: The scores for KAP dimensions were 55.64, 69.18, and 54.8%, respectively. The overall KAP score of university students in waste classification was 46.93 ± 9.93, with a percentage score of 62.57%. More than half of the college students lack a clear understanding of waste classification. Grade, gender, major, highest family education, and family economic status all influence college students' KAP scores on waste classification. There is a notable deficiency in school education regarding waste classification, with only 30.7% reporting having received such education. Conclusion: This study unveils the overall KAP score of waste separation among Chinese college students, which is marginally acceptable. The interviewed students exhibit a positive attitude and a willingness to participate in waste separation. However, there is room for improvement in both knowledge and practices. A lack of knowledge about waste sorting emerges as the primary influence on individual-level practices. Consideration should be given to enhancing education and management of waste separation among college students, emphasizing the cultivation of an eco-conscious culture, and guiding students to establish correct ecological values.

Water surface garbage detection based on lightweight YOLOv5.

With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.

Release characteristics of volatile organic compounds at residential garbage collection points: a case study of Hangzhou, China.

With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 µg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10-6-10-4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios.

Identifying high-vulnerable garbage accumulation areas in Coimbatore City, India: an AHP-GIS approach for effective waste management.

Urbanization has resulted in a surge in municipal solid waste (MSW) generation, posing critical waste management challenges in urban areas. To tackle this issue, we introduce a novel approach for mapping garbage vulnerability zones (GVZ) in Coimbatore City, India, combining the analytic hierarchy process (AHP) and Geographic Information System (GIS). Seven criteria, including per capita waste generation, open dumping, land use land cover, road/railway networks, and population, were integrated and analyzed in GIS. AHP pairwise comparison method assigned weights to each criterion and principal component analysis (PCA) further validated the interconnectedness of the criteria and their impact on the GVZs. The results indicated that open dumping locations and population density are the most influential factors contributing to the risk of garbage accumulation, making up 23.7% and 21.2% of the total weight, respectively. The GVZ map reveals that 94.6% of Coimbatore City is at risk of MSW accumulation, with 20.2% highly and 74.4% moderately vulnerable. Eleven high GVZ clusters were identified, with Saravanampatti, located in the northeastern part of Coimbatore City, being the most vulnerable area. The H3 hexagon format of the GVZ map enhances its usability for monitoring and mitigation capabilities. In conclusion, our comprehensive AHP-GIS approach facilitates effective waste management practices, sustainable resource utilization, and better environmental and public health outcomes in urban areas. The demonstrated methodology has the potential for application in similar developing urban areas in South Asia and the Global South, serving as a valuable tool to address the challenges posed by increasing MSW generation.

Tackling municipal solid waste crisis in India: Insights into cutting-edge technologies and risk assessment.

Municipal Solid Waste (MSW) management is a pressing global concern, with increasing interest in Waste-to-Energy Technologies (WTE-T) to divert waste from landfills. However, WTE-T adoption is hindered by financial uncertainties. The economic benefits of MSW treatment and energy generation must be balanced against environmental impact. Integrating cutting-edge technologies like Artificial Intelligence (AI) can enhance MSW management strategies and facilitate WTE-T adoption. This review paper explores waste classification, generation, and disposal methods, emphasizing public awareness to reduce waste. It discusses AI's role in waste management, including route optimization, waste composition forecasting, and process parameter optimization for energy generation. Various energy production techniques from MSW, such as high-solids anaerobic digestion, torrefaction, plasma pyrolysis, incineration, gasification, biodegradation, and hydrothermal carbonization, are examined for their advantages and challenges. The paper emphasizes risk assessment in MSW management, covering chemical, mechanical, biological, and health-related risks, aiming to identify and mitigate potential adverse effects. Electronic waste (E-waste) impact on human health and the environment is thoroughly discussed, highlighting the release of hazardous substances and their contribution to air, soil, and water pollution. The paper advocates for circular economy (CE) principles and waste-to-energy solutions to achieve sustainable waste management. It also addresses complexities and constraints faced by developing nations and proposes strategies to overcome them. In conclusion, this comprehensive review underscores the importance of risk assessment, the potential of AI and waste-to-energy solutions, and the need for sustainable waste management to safeguard public health and the environment.

Biomonitoring of arsenic, lead, manganese and mercury in hair from a presumably exposed Uruguayan child population.

Aim: To perform an exposure assessment of arsenic, manganese, mercury and lead levels in hair samples from children from poor neighborhoods. Materials & methods: A total of 38 Caucasian children were recruited with the consent of their parents or tutors. Determinations were performed by atomic absorption spectrometry. Results & conclusion: Results were 0.045-0.12 µg/g-1 (arsenic), 0.56-2.05 µg/g-1 (manganese) and 0.34-27.8 µg/g-1 (lead). Lead results did not correlate with those previously reported in blood from the same individuals, suggesting that hair is not useful for exposure assessment of this contaminant. Mercury was determined for the first time in Uruguayan children showing levels <0.083 µg/g-1. Results revealed low-to-moderate metal exposure, except for some high lead findings.

How does abstract and concrete garbage classification signage influence waste sorting behavior?

Despite the issuance of standardized garbage classification signage, the rate of garbage classification in China remains low. We conducted a pair of laboratory experiments to explore the cognitive processing differences between abstract (including recyclables, hazardous garbage, and food signs) and concrete (including paper, plastic, glass, metal, textiles, batteries, household chemicals, tubes, and food signs) classification signs. We tested a nudging strategy to enhance garbage classification behavior. In Experiment 1, we divided garbage classification signs into two conditions: an abstract condition (comprising abstract signs) and a concrete condition (comprising concrete signs). The Go/No Go task was used to simulate garbage classification behavior. Participants were instructed to press a key when the garbage stimulus matched the classification signs (Go condition) and to refrain from pressing the key when there was a mismatch (No Go condition). The results showed that responses under the concrete condition were expedited compared to those under the abstract condition. This suggests that concrete signage requires less cognitive exertion, thereby enhancing the efficiency of waste classification. In Experiment 2, we optimized the existing bin signage, which predominantly featured abstract signs (traditional condition), and transformed it into a bin signage that emphasized concrete classification signs. These concrete signs were strategically positioned on the upper part of the bins to draw attention (nudging condition). The results suggested that the nudging condition required fewer cognitive resources than the traditional condition, which in turn increased the efficiency of processing garbage classification. This study not only validates the effects of concreteness in garbage classification but also provides effective nudge strategies to complement existing garbage classification management policy tools in a realistic Chinese context.