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The exposure conditions experienced by copper-coated high-level nuclear waste containers in a deep geologic repository will evolve with time. An early exposure period involving the gamma irradiation of aerated humid vapor could lead to the formation of nitric acid condensed in limited volumes of water on the container surface. The evolution of the corrosion processes under these conditions have been studied using pH measurements in limited volumes of water containing various concentrations of nitric acid. The extent and morphology of corrosion was examined using scanning electron microscopy on surfaces and on focused ion beam cut cross sections. The composition of corrosion products was determined by energy dispersive x-ray analyses and Raman spectroscopy. In the absence of dissolved oxygen only minor corrosion was observed with the reduction of nitric acid inhibited by the formation of either chemisorbed nitrate and nitrite species or the formation of a thin cuprite (Cu2O) layer. When the solution was aerated, both oxygen and nitric acid acted as cathodic reagents. After extensive exposure periods corrosion was stifled by the formation of corrosion product deposits of Cu2O, CuO (tenorite), and Cu2NO3(OH)3 (rouaite).

Abstract Garbage classification is more and more significant recent years. In order to promote the implementation of garbage classification and the improvement of intellectualization of urban sanitation work, a new type of intelligent garbage bin system is designed, which includes the bottom control part, android app and IOT cloud platform. Firstly, the system controls the inlets of intelligent garbage bin through interaction with android app, which realizes the function of ordinary garbage can. On top of that, the bottom control part collects all kinds of data of garbage bin by using some external sensors. And then the data will be reported to Android and Android will report that to cloud platform. Cloud platform server then transmits data to monitoring platform, which facilitates data statistics and the removing of garbage. By deploying the system, garbage classification can be promoted and the status of garbage can be effectively monitored, which will significantly improve garbage recovery efficiency and sanitation work level.

Abstract Flies as mechanical vectors of disease spread in humans. This study aimed to detect helminth eggs on the flies body surface in markets in Makassar City Market. Flies collected in sale places such as vegetables, chicken and beef, cakes, fish, and trash cans at the market. Then flies and helminth eggs were idemtificated examined in the laboratory for identification of flies and helminth eggs. Flies found throughout markets in Makassar City were Musca domestica , Calliphora spp., and Sarcophaga haemorroidalis . The highest flies number of was Calliphora spp. (n=27) at the fish sale place in Tamalate market. The greatest number of egg helminth was Taenia sp. (n=17) found on the body surface of Chrysomia sp. This study showed that most helminth eggs found on the body surface of Chrysomia sp. compared to other flies.

The purpose of this study is to identify the effect of high-tech bins on people's interest in disposing trash. The method used in this study is a quantitative descriptive method and literature study to analyses community perceptions of smart trash bin. The results of this study state that the mind-set of the community in disposing trash increased after the existence of smart trash bin project. These results were obtained because the community became more interested in disposing trash in the presence of unique designs and diverse functions of these high-tech bins. This study concluded that smart trash bin can increase people's interest in disposing garbage in its place.

This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time

Healthcare waste management is very important due to its hazardous nature that can cause risk to human health and the environment. In developing countries, healthcare waste has not received much attention and has been disposed of together with municipal waste. The aim of the study was to assess the healthcare waste management practices in Addis Ababa City Administration Public Health Facilities. An institutional-based cross-sectional design was used for the study at Addis Ababa city 15 Public health centres and 3 hospitals. Data were collected using self-administered questionnaires distributed to 636 randomly selected healthcare waste handlers and managers. Observational check list also used. The data were entered into the EPI- INFO version TM 7 and exported to IBM SPSS 20 for analysis. Both descriptive and analytic statistics were employed. Among the respondents, 358 (90.86%) from health centres and 96.38% (133) from hospitals indicated that their facilities had separate containers for hazardous and non-hazardous waste however, 61 (15.48%) from health centres and 29 (21.01%) from hospitals indicated that healthcare waste containers were not clearly marked or labelled. The study found that the main forms of on-site treatment of healthcare waste for health centres and hospitals before disposal were burning. Manager respondents from the health centres 65 (92.86%), 64 (91.43%) and from hospitals 31 (91.18%), 30 (88.24%) indicated that healthcare waste handlers were used protective clothing when handling waste and were provided with protective clothing when handling healthcare waste respectively. In this study healthcare waste management among healthcare waste handlers and healthcare facility managers were not getting full attention. Collection of healthcare waste were not done regularly, containers were not clearly marked and were not located in appropriate areas where they might be needed. Support healthcare waste handlers by training help to improve their knowledge, attitude and practice.

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.

Although pharmaceuticals treat illnesses and prevent diseases in humans and animals, ironically, they are now among the emerging pollutants in the environment. As individuals continue to consume medicines, households can become a primary source of pharmaceutical pollutants. This paper explains relevant Malaysian law and policy on the issue. Using a questionnaire survey, it also assesses public perception concerning the environmental impact of pharmaceutical waste and the current methods of disposal that are practiced in Selangor. The survey used a convenience sampling and the data collected were analysed using statistical descriptive analysis. Most of the participants were aware that pharmaceutical waste can have an adverse impact on the environment and public health. Half of the participants disposed of their pharmaceutical waste in trash bins. About 2.9% of the respondents poured their household pharmaceutical waste (HPW) directly down the drain, while 8.8% poured them down the sink in the kitchen or toilet. The study also discovered that while 73.8% of the respondents felt that HPW should be separated from other household solid waste, only 25.2% returned their medicinal waste through the medicine return-back programme. The majority of the respondents (82.5%) agreed that information concerning the proper disposal of HPW is insufficient. The study concludes that while the respondents were aware of the adverse impact of HPW, their practices to ensure proper disposal is discouraging. There is a need for effective unwanted medicines return-back programme as a more prudent disposal method of HPW to avoid any risk to the environment or human health.

The purpose of this work is to assess the environmental impacts of the collection of mixed municipal solid waste (MSW) in a selected neighbourhood of the Portuguese city of Aveiro. To this purpose, the main elements necessary for the collection process (carrier bags, dustbins, street containers and vehicles) were analysed applying the life cycle assessment methodology, making use of locally gathered data. The main impacts associated with this activity are mostly related to the use of polyethylene plastic bags to carry the waste from the household to the waste container, and to the fuel consumption of collection vehicle that picks MSW from street containers and transports it to the treatment facility. The impacts associated with the plastic bags were primarily due to their disposal in a sanitary landfill after use and secondarily to the consumption of fossil raw materials required for their production. Given the relative high impact of the plastic bags, alternative scenarios were tested: using bags entirely produced with recycled polyethylene and bags produced with bio-based plastics derived from starch (TPS) and from wastewater (PHA). PHA bio-based bags were found to perform slightly better than conventional high-density polyethylene (HDPE) bags, but HDPE bags with 100% recycled content remained as the environmentally best option. A sensitivity analysis was performed to check the influence of bag size. Regarding the fuel consumption by collection vehicles, a comparison was performed to check how site-specific conditions can influence the impact of this activity, resulting in remarkably higher consumptions when local data were used instead of reference databases.