Reconfiguration of garbage collection system based on Voronoi graph theory: a simulation case of Beijing region

Author(s):  
Chun-lin Xin ◽  
Shuo Liang ◽  
Feng-wu Shen
Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.


Author(s):  
Nancy Mazur ◽  
Peter Ross ◽  
Gerda Janssens ◽  
Maurice Bruynooghe

Author(s):  
Md. Junayed Siddique ◽  
Mohammad Aynul Islam ◽  
Fernaz Narin Nur ◽  
Nazmun Nessa Moon ◽  
Mohd. Saifuzzaman

Author(s):  
Tatsuki OHNO ◽  
Hironari TANIGUCHI ◽  
Yusuke INOUE ◽  
Kazunori HOSOTANI

Author(s):  
Damon M. K. Taam

Abstract “Alternative Revenue Sources” are tipping fee revenues received from the combustion of non-traditional waste. Such alternative solid wastes are not hazardous wastes and are not typically delivered by the normal garbage collection system. Alternative Revenue Wastes (ARW) need a due diligence review showing that disposal of the waste will not violate any laws, ordinances and/or permit conditions. Disposal of ARW will need coordination and additional special handling for final disposal ARW generators do pay a tipping fee greater than the solid waste tipping fee in order to compensate the owner/operator for the extra effort. ARW wastes are derived from the following special considerations: 1) Liability concerns from disposal of such waste. 2) Sensitive security. 3) Legal/Regulatory compliance. 4) Environmental concerns. 5) Resource recovery. 6) Infectious Wastes.


2021 ◽  
Vol 37 (6) ◽  
Author(s):  
Antonio Fernando Boing ◽  
Alexandra Crispim Boing ◽  
S. V. Subramanian

Abstract: This study aims (1) to test the association between access to basic sanitation/hygiene services in Brazilian households with their householders’ socioeconomic and demographic characteristics; (2) to analyze the distribution of urban health-relevant elements in the census tracts according to their income, education and race/color composition. The information come from the 2010 Brazilian Demographic Census, which collected data regarding both household conditions and urban structure of the census tracts. Prevalence ratios were calculated using crude and adjusted Poisson regression models. The proportional distribution of the census-tract urban structure was performed, according to the deciles of the exploratory variables, and the ratios and the absolute differences between the extreme deciles were calculated. Around 4.8% of the households had no piped water, 34.7% had no sewage collection system, 9.8% had no garbage collection and 39% were considered inadequate. Families whose householders were black, indigenous or brown had lower income and educational level, and lived in the North, Northeast, and Central West regions. They were more likely to be considered inappropriate for not having piped water, sewage collection system, and garbage collection. Moreover, sectors where the majority of the population was black, had lower educational levels and lower income had significantly poor paving, street lighting, afforestation, storm drain, sidewalk and wheelchair ramp. This study analyzed national data from 2010 and provides a baseline for future studies and government planning. The relevant social inequalities reported in this study need to be addressed by effective public policies.


1972 ◽  
Vol 65 (4) ◽  
pp. 307-309
Author(s):  
Walter Meyer

Is there an area of mathematics that deals with garbage collection, Sunday strolls, and soldering problems all at once? Indeed there is, and it is called graph theory, a subject that considers the properties of configurations consisting of points and connecting lines such as the configuration shown in figure 2. (There is another meaning for the word graph, as in bar graph or graph of a function, which is not meant here.) The practical applications of graph theory are so widespread that this theory has become one of the most important and rapidly growing areas of applied mathematics in recent years. What is especially unique about it, however, is the extreme simplicity of the basic ideas. Because of this dual nature of practicality and simplicity, graphs have been creeping into the high school curriculum lately, often in the form of optional topics.


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