A study on the waste generation rates and recycling potential for the construction and demolition waste in Dhaka, Bangladesh.

Construction and demolition waste has a high environmental and socio-economic impact due to its poor management infrastructure. In recent years, Dhaka, the capital city of Bangladesh, experienced significant growth in the real estate sector, which demands substantial construction and demolition activities within the city. Most of the construction and demolition waste finds its way to landfills, roadsides, and unapproved locations with a 2% recycling rate through local scrap vendors and unregulated recyclers. In an effort to assess the waste generation rates from ongoing construction and demolition activities, the current study employed the Site Visit method along with direct and indirect waste quantification methodologies for the investigated demolition and construction projects, respectively. The findings indicate that for per unit area (m2) of demolition and construction, the average WGR was found to be approximately 575.0 kg and 73.9 kg, respectively. Projection reveals that by 2025 and 2030, within Dhaka City, construction and demolition activities will generate roughly 1.15 MT and 1.69 MT of construction and demolition waste if no recycling actions are considered. Additionally, the results highlight the recycling potential of construction and demolition waste with respect to economic benefits through the maximum recycling rates for the relevant materials. Furthermore, to address the future of sustainable construction and demolition waste management infrastructure, this paper presents a detailed overview of the current onsite construction and demolition waste management practices as well as safety protocols for demolition and construction activities.

Modelling of demolition waste generation: Application to Greek residential buildings.

The construction sector in Europe is among the biggest waste generators, producing 370 million tonnes of construction and demolition waste (CDW) every year, which contain important secondary materials. Quantification of CDW is important from their circular management and environmental impact point of view. Thus, the overall objective of this study was to develop a modelling methodology for estimating demolition waste (DW) generation. The volumes (m3) of individual construction materials contained in 45 residential buildings in Greece were accurately estimated using computer-aided design (CAD) software and the materials were classified according to European List of Waste. These materials will become waste upon demolition, with a total estimated generation rate of 1590 kg m-2 of top view area and with concrete and bricks representing 74.5% of total. Linear regression models were developed to predict the total and individual amounts of 12 different building materials based on structural building characteristics. To test the accuracy of the models, the materials of two residential buildings were quantified and classified and the results were compared with the model predictions. Depending on the model used, the % differences between models' predictions and CAD estimates for total DW averaged 11.1% ± 7.4% for the first case study and 2.5% ± 1.5% for the second. The models can be used for accurate quantification of total and individual DW and their management within the framework of circular economy.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

An empirical study on waste generation rates at different stages of construction projects in China.

Estimation of construction waste generation is critical to construction waste management decisions. However, current construction waste estimation methods have various limitations (e.g. small samples). To address those limitations, this research conducts an empirical study to evaluate the waste generation rate of different types of waste at different construction stages. In this study, construction waste from 148 new-built residential construction sites in China were sorted and weighted on site and their waste generation rates were estimated separately. The results indicated that the amount of inorganic nonmetallic waste with a generation rate of 16.59 kg m-2 was the highest among the five types of waste (i.e. inorganic nonmetallic waste, organic waste, metallic waste, composite waste, hazardous waste), while the waste generation rate for the underground construction stage, which was 27.57 kg m-2, was the highest among the three stages (i.e. underground stage, superstructure stage, finishing stage). Compared with previous data, the new waste generation rate proposed in this research can better estimate the actual waste generation situation in construction sites, which provides reliable information for proper decision-making. Furthermore, based on the result of the empirical study, some recommendations for construction waste reduction are proposed.

Application of a methodology to design a municipal waste pre-collection network in real scenarios.

The design of efficient municipal solid waste (MSW) pre-collection networks can contribute to the global efficiency and sustainability of the reverse logistic chain of MSW in modern cities. With this aim, in this paper a comprehensive methodology that involves making decisions in several stages, from waste fraction classification to the final optimization of waste bins' location, was applied in two real cases of the city of Bahía Blanca, Argentina. This city, does not have much available data about waste generation and, therefore, an important fieldwork had to be performed for applying this methodology, involving estimating population density per block and waste generation rate per inhabitant, identifying the location of commercial and institutional buildings and also estimating its generation rate, as well as performing a characterization of the MSW from similar studies in the literature and surveys performed to make decisions. The modelling of the urban characteristics was performed in a geographic information system. In the bins' location problem, a mixed-integer optimization model was applied, seeking to minimize the investment costs, given the maximum area available and the capacity of the bins. Different scenarios were analysed, considering different collection frequencies and the maximum distance to be travelled by the user.

Investigation of construction wastes generated in the Malaysian residential sector.

The construction sector is among the fastest growing sectors in Malaysia; it consumes a vast amount of natural resources and produces a massive volume of construction and demolition waste. The waste is collected in a decentralised manner by sub-contracted companies. It is challenging to obtain reliable information on the amount of construction waste generated, because it is hard to determine its exact quantity and composition. Therefore, this study proposes a quantitative construction waste estimation model for residential buildings according to available data collected from the Construction Industry Development Board, Malaysia. In the development of this model, a theoretical investigation of the construction procedure and the construction waste generation process was conducted. The waste generated rate was determined as 25.79 kg m-2 for new residential constructions, which translates into about 553,406 t of anticipated waste annually.

Construction and demolition waste generation rates for high-rise buildings in Malaysia.

Construction and demolition waste continues to sharply increase in step with the economic growth of less developed countries. Though the construction industry is large, it is composed of small firms with individual waste management practices, often leading to the deleterious environmental outcomes. Quantifying construction and demolition waste generation allows policy makers and stakeholders to understand the true internal and external costs of construction, providing a necessary foundation for waste management planning that may overcome deleterious environmental outcomes and may be both economically and environmentally optimal. This study offers a theoretical method for estimating the construction and demolition project waste generation rate by utilising available data, including waste disposal truck size and number, and waste volume and composition. This method is proposed as a less burdensome and more broadly applicable alternative, in contrast to waste estimation by on-site hand sorting and weighing. The developed method is applied to 11 projects across Malaysia as the case study. This study quantifies waste generation rate and illustrates the construction method in influencing the waste generation rate, estimating that the conventional construction method has a waste generation rate of 9.88 t 100 m-2, the mixed-construction method has a waste generation rate of 3.29 t 100 m-2, and demolition projects have a waste generation rate of 104.28 t 100 m-2.

Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis.

Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA-k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA-k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m-2), 993.54 (kg·m-2) and 991.80 (kg·m-2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions.

Healthcare waste generation in hospitals per continent: a systematic review.

There are increasing worldwide concerns about the negative impacts of healthcare waste generated in hospitals, especially in low- and middle-income countries. Hazardous type of waste can contribute to adverse effects both in human populations and the environment because of its physical, chemical, and biological characteristics. A comprehensive view on increasing waste in the world has not been conducted to understand the breadth of the issue; thus, this paper sought to provide an analysis of hospitals' healthcare waste generation rate. Comparisons were made with Wilcoxon and Kruskal-Wallis tests for simple and multiple comparisons, to analyze nonparametric data, with post hoc by Nemenyi test. Median values indicated that hospital waste was the highest in North and South America (4.42, 1.64 kg/bed/day, respectively) and was almost nonexistent in Oceania (0.19 kg/bed/day), while the median rates for hazardous waste were the highest in Oceania (0.77 kg/bed/day). Africa was almost the lowest producer of waste in each category (0.19 and 0.39 kg/bed/day for hospital and hazardous waste, respectively). Over time, linear regression indicated that hazardous waste in Asia and Europe has increased, while in Oceania, the total waste also increased. Interestingly, in North America, it was observed a reduction in the generation for both total and hazardous waste. This information highlights the importance of understanding continent-specific characteristics and rates, which can be used to create a more individualized approach to addressing healthcare waste in the world.

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas.

Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.

Detailed municipal solid waste composition analysis for Nur-Sultan City, Kazakhstan with implications for sustainable waste management in Central Asia.

A detailed characterization of municipal solid waste (MSW) beyond a standard compositional analysis may offer insights useful for improving waste management systems. The present paper contributes to the scarce literature in the field by presenting new data from a rapidly developing Central Asian city, the capital of Kazakhstan, Nur-Sultan. Three sampling campaigns (each 1 week long) have been conducted at the city landfill over a 1-year period (2018-2019), and a detailed characterization for selected waste components and sub-components has been performed. The major fractions of MSW were organics (46.3%), plastics (15.2%), paper (12.8%), and diapers (5.9%). The detailed composition analysis showed high LDPE (low-density polyethylene) content (5.5%) mostly comprised of plastic bags (4.5%), transparent glass (3.2%), pharmaceuticals (0.4%), and fine (i.e., < 12 mm) organic fraction content (29%). The MSW generation rate of Nur-Sultan was estimated as 1.47 kg capita-1 day-1 based on the field collection as well as literature data. Among sustainable waste management recommendations addressed for Nur-Sultan and applicable to other cities in Central Asia, composting is recommended due to high organics fraction in MSW and its great potential to reduce the landfilled waste volume and to help valorizing the waste.

Landfill Site Selection Using GIS Based Multicriteria Evaluation Technique in Harar City, Eastern Ethiopia.

Solid waste disposal is one of the challenging components in integrated solid waste management. Particularly the problem is prominent in cities with rapid population growth and waste generation. Harar, a capital city of Harari regional state located in the eastern part of Ethiopia, covers an area of 19.5 km2 and has a total population of 270 000. Despite the fastest population growth of the city, it doesn't have a landfill site to accommodate the waste generated and open dumping is in full practice. As an integral part of a solid waste management plan, the construction of a landfill has been suggested by the city municipality. However, the multi-dimensional and conflicting aspect of landfill sitting, which involves environmental, social, technical, and economic considerations, challenges the location of a suitable landfill site. In the current study, we have applied geographic information system (GIS) and analytical hierarchy process (AHP) multi-criteria decision analysis to select a landfill site through minimizing conflicting interests. Environmental and socio-economic factors including well water, distance from residence, land use and land cover, elevation, slope, and wind direction were weighted to develop a suitability index for landfill siting. Experts' opinion was obtained to rank the aforementioned factors. The required landfill size was determined based on population growth, waste generation rate, and waste volume/year. Accordingly, the suitability index resulted in 3% of the area as highly suitable, and the rest 0.29%, 14.18%, 52.75%, and 29.8% classified as unsuitable, least suitable, moderately suitable, and suitable, respectively. Considering the future trend of waste generation, 16 ha of land located in the eastern part of the city was selected as a candidate landfill site with all the required suitability. The results of this study can be used as an input for decision making in siting landfill for Harar city.

Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China.

Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R2 = 86.87%) than the back-propagation neural network (R2 = 75.14%) and multiple linear regression (R2 = 61.93%).

Assessment of food waste generation and composition among Korean households using novel sampling and statistical approaches.

Food waste management in Korea has become increasingly important as the country continues to champion the transition into a circular economy among the OECD countries to achieve sustainable development target goals. However, reliable primary data on food waste quantity and composition to achieve its prevention and managementtargets by understanding food waste patterns among Korean households is poorly documented. This study investigates the quantity and composition of food waste generation rates among the sampled households by considering two important influencing factors of seasonality and housing types in the Buk-gu province of Daegu, South Korea. The food waste generation rates from three different housing types during four representative seasons were assessed, considering the availability of different food types at different seasons. The identified 46 food waste items from sampled data were statistically analyzed using the Kruskal-Wallis statistical test. The results showed that food waste generation rates were 0.88 ± 0.37 kg/household/day (0.26 ± 0.11 kg/capita/day), which varied seasonally. Significant seasonal variations (P < 0.002) in food waste generated from the selected housing types were shown by K-W mean rank analysis. The food waste generation rate followed the seasonal order of summer > autumn > winter > spring. The effect of housing type was also a pivotal factor affecting the food waste generation. This study adds to the ground-level insights of food waste generation trends in different seasons and housing types of Korea.

Waste generation, composition, and handling in building-related construction and demolition in Hanoi, Vietnam.

This study conducted a survey at 15 building construction and demolition sites in Hanoi, Vietnam in order to identify waste generation rates (WGR), composition, and current handling practices of construction and demolition waste (CDW). Waste quantification based on CDW layout, image analysis to identify CDW components, and face-to-face interviews with construction and demolition contractors to reveal CDW flows were performed. WGRs of 79.3 kg/m2 and 1,030 kg/m2 were determined in small- and large-scale construction sites, respectively, whilst WGRs at small and large demolition sites were 610 kg/m2 and 318 kg/m2. The composition analysis identified soil, concrete, and brick as the major CDW components, consistent with building structures in Vietnam. The interviews discovered that merely 10% of total CDW flows was from recycled and reused CDW. Reuse and recycling rates were most significant for metal and were lower (in descending order) for brick, concrete, and soil. These findings raise a need for aggressive and integrated strategies to promote more sustainable CDW management in the country, including the development of recycled CDW product standards, policies that facilitate recycling, and more importantly, a sustainable business model for CDW recycling, for which future evaluations of economic feasibility are of great importance.

Analysis of Waste Generation Characteristics during New Apartment Construction-Considering the Construction Phase.

The waste generation rate (WGR) is used to predict the generation of construction and demolition waste (C&DW) and has become a prevalent tool for efficient waste management systems. Many studies have focused on deriving the WGR, but most focused on demolition waste rather than construction waste (CW). Moreover, previous studies have used theoretical databases and thus were limited in showing changes in the generated CW during the construction period of actual sites. In this study, CW data were collected for recently completed apartment building sites through direct measurement, and the WGR was calculated by CW type for the construction period. The CW generation characteristics by type were analyzed, and the results were compared with those of previous studies. In this study, CW was classified into six types: Waste concrete, waste asphalt concrete, waste wood, waste synthetic resin, waste board, and mixed waste. The amount of CW generated was lowest at the beginning of the construction period. It slowly increased over time and then decreased again at the end. In particular, waste concrete and mixed waste were generated throughout the construction period, while other CWs were generated in the middle of the construction period or towards the end. The research method and results of this study are significant in that the construction period was considered, which has been neglected in previous studies on the WGR. These findings are expected to contribute to the development of efficient CW management systems.

Prediction of large-scale demolition waste generation during urban renewal: A hybrid trilogy method.

As a result of land resources constraining in China, demolition and reconstruction of existing buildings become an important means to meet the requirement of urban renewal, in which a large amount of demolition waste is generated. However, it is difficult to predict the generation of large-scale demolition waste with high efficiency due to the lack of basic data and technical support. This study aims to propose a hybrid trilogy method for predicting the generation of large-scale demolition waste during urban renewal based on two indicators of waste generation rate (WGR) and gross floor area (GFA). WGR was measured based on on-site measurement and existing industry standard data according to different building types and structure types. Composition and proportion of demolition waste were correspondingly analyzed. GFA was obtained based on image recognition technology and Google Earth. Two hundred buildings were selected as samples to verify GFA accuracy, whose error ranges were mostly controlled within 10%. Finally, prediction of large-scale demolition waste generation in Shenzhen was conducted as a case study during urban renewal for verification of the hybrid trilogy method proposed. Results show that 49.40 million tons of demolition waste will be generated. Findings of this study improve the accuracy and speed of existing prediction methods for large-scale demolition waste, indicating great potential for wide application. The current research provides guidance for demolition enterprises, transportation enterprises, recycling enterprises, and government departments to manage large-scale demolition waste precisely during urban renewal.

Assessment of Healthcare Waste Generation Rate and Its Management System in Health Centers of Bench Maji Zone.

BACKGROUND: It is known that the basic role of healthcare system is to preserve the health of patients and protect the public from diseases. However, in the process of performing these activities, health facilities generate hazardous waste that could be potentially harmful to healthcare workers, the public and the environment if there is insufficient handling, treatment and disposal of those wastes. Unfortunately, healthcare waste management is, in many regions, not yet carried out with a satisfactory degree of safety. Therefore, the aim of this study was to assess healthcare waste generation rate and its management system in health centers of Bench Maji Zone. METHODS: A cross-sectional study was conducted from February to August, 2016. Observational checklist, key informant interview guide and weight scale were used to assess healthcare waste generation rate and its management system in selected health centers. Training, pre-test, instrument calibration and daily meeting were used to improve data quality. The Data was entered, compiled and analyzed using EpiData version 3.1 and SPSS version 21. The results on waste management system were reported using different descriptive statistics. RESULTS: Out of the total HCW generated in health centers, more than half (57.9%) was general or non-risk HCW, and the remaining 42.1% was hazardous healthcare waste. The amount of HCW generated in the studied health centers was different from WHO's norm which may be attributed to different factors such as economy, patient flow, difference in services provided, poor waste segregation practice, available waste management system and seasonal factors. CONCLUSION: HCW was not adequately managed which is characterized by lack of HCW segregation at source of generation and inadequate facilities to manage HCW. Therefore, it is important to develop a HCW management plan for keeping human health as well environmental sustainability.

What gets measured gets managed: A new method of measuring household food waste.

The quantification of household food waste is an essential part of setting policies and waste reduction goals, but it is very difficult to estimate. Current methods include either direct measurements (physical waste surveys) or measurements based on self-reports (diaries, interviews, and questionnaires). The main limitation of the first method is that it cannot always trace the waste source, i.e., an individual household, whereas the second method lacks objectivity. This article presents a new measurement method that offers a solution to these challenges by measuring daily produced food waste at the household level. This method is based on four main principles: (1) capturing waste as it enters the stream, (2) collecting waste samples at the doorstep, (3) using the individual household as the sampling unit, and (4) collecting and sorting waste daily. We tested the feasibility of the new method with an empirical study of 192 households, measuring the actual amounts of food waste from households as well as its composition. Household food waste accounted for 45% of total waste (573 g/day per capita), of which 54% was identified as avoidable. Approximately two thirds of avoidable waste consisted of vegetables and fruit. These results are similar to previous findings from waste surveys, yet the new method showed a higher level of accuracy. The feasibility test suggests that the proposed method provides a practical tool for policy makers for setting policy based on reliable empirical data and monitoring the effectiveness of different policies over time.

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India.

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R2) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively.