Analyzing composition and generation rates of biomedical waste in selected hospitals of Uttarakhand,India

In India, a few studies have been conducted for analyzing the generation rates and composition of medical waste (MW). Inadequate information about the amount and composition of MW results in ineffective management practices. The present study seeks to evaluate healthcare waste (HCW) generation rates by healthcare facilities (HCFs) available in Uttarakhand, a northern state of India. Study also focuses on modeling the quantity of different types of MW generated at various HCFs and determining significant factors contributing towards MW generation. Seasonal variation in amount of MW generated from various HCFs has also been considered. To achieve these objectives, cross-sectional as well as longitudinal data have been collected from various HCFs in Uttarakhand, India. The survey revealed that around 36% of the total HCFs did not segregate their MW as per policy guidelines. Cross-sectional data for May 2015 were collected from 75 HCFs to analyze and model the composition and quantity of HCW generated. Multiple Linear Regression and Artificial Neural Network techniques were applied to model cross-sectional data. In the composition of the overall MW, ‘yellow waste’ carries the maximum share, followed by ‘red waste’ and then the ‘blue waste’. In addition, the ‘type of HCF’ and ‘bed occupancy’ have been modeled as the important factors, contributing towards the MW generations rates. Longitudinal data for 2 years (2013 and 2014) were collected to examine seasonal variation in HCW generation rates using polynomial regression analysis. Result shows that MW quantity also varies with the change in the season. Findings of the study will help hospitals and waste treatment facilities to predict amount of waste that may be generated, and plan resources towards efficient handling and disposal of MW.     

Mathematical modeling to predict residential solid waste generation

One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R(2) were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.     

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.     

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City,China

Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 – 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 – 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term.     

Investigating the length of data collection period with respect to rate constant in the first-order decay model

The first-order decay model is the only highly recommended method for estimating landfill gas emissions from solid waste disposal sites according to 2006 IPCC (Intergovernmental Panel on Climate Change) Guidelines. It is also encouraged to collect relevant activity data over the past 50 years to apply the first-order decay model. Even though it is beneficial to facilitate the accuracy of landfill gas emissions estimation, it may not be an easy task to collect reliable data for such a long period of time. It is discussed in this study that a data collection over a shorter period of time may yield a comparable accuracy for emissions estimation depending on methane generation rate or half-life of landfill wastes. Based on the analysis of mathematical properties of the first-order decay model, the estimation accuracy with respect to the length of data collection period has been investigated. Finally, it is also proposed how to estimate the amount of landfill gas emissions and analyze the level of estimation accuracy considering the length of time period since the deposition of wastes.     

Application of a simplified mathematical model to estimate the effect of forced aeration on composting in a closed system

The aeration rate is a key process control parameter in the forced aeration composting process because it greatly affects different physico-chemical parameters such as temperature and moisture content, and indirectly influences the biological degradation rate. In this study, the effect of a constant airflow rate on vertical temperature distribution and organic waste degradation in the composting mass is analyzed using a previously developed mathematical model of the composting process. The model was applied to analyze the effect of two different ambient conditions, namely, hot and cold ambient condition, and four different airflow rates such as 1.5, 3.0, 4.5, and 6.0m(3)m(-2)h(-1), respectively, on the temperature distribution and organic waste degradation in a given waste mixture. The typical waste mixture had 59% moisture content and 96% volatile solids, however, the proportion could be varied as required. The results suggested that the model could be efficiently used to analyze composting under variable ambient and operating conditions. A lower airflow rate around 1.5-3.0m(3)m(-2)h(-1) was found to be suitable for cold ambient condition while a higher airflow rate around 4.5-6.0m(3)m(-2)h(-1) was preferable for hot ambient condition. The engineered way of application of this model is flexible which allows the changes in any input parameters within the realistic range. It can be widely used for conceptual process design, studies on the effect of ambient conditions, optimization studies in existing composting plants, and process control.     

Path analysis of factors influencing household solid waste generation: a case study of Xiamen Island,China

Comprehensive study of the factors influencing household solid waste (HSW) generation is crucial and fundamental for exploring the generation mechanism and forecasting future dynamics of HSW. A case study of Xiamen Island, China was employed to reveal the direct and indirect effects of demographic/socioeconomic factors on solid waste generation at the urban household scale. Based on a face-to-face questionnaire and two-stage survey of solid waste generation, a path analysis model was built. Results showed that the proposed path model exhibited good fit indices. Family size and dinning-at-home rate (DR), whose coefficients were ?0.40 and 0.43, respectively, were the two major factors influencing HSW directly. Moreover, family size, education level, employment rate and age structure played different degrees of indirect effects on HSW generation through respective paths, which should not be ignored. In terms of total effects, coefficients of family size, DR and employment rate were ?0.46, 0.43 and ?0.37, respectively, which were three most dominant factors influencing HSW generation. As for waste composition, organic waste was the most representative of HSW dynamics, and was the most sensitive to impact by the factors studied. Quantitative results of this study have important policy implications for sustainable municipal solid waste management.     

A model for estimation of potential generation of waste electrical and electronic equipment in Brazil

Sales of electrical and electronic equipment are increasing dramatically in developing countries. Usually, there are no reliable data about quantities of the waste generated. A new law for solid waste management was enacted in Brazil in 2010, and the infrastructure to treat this waste must be planned, considering the volumes of the different types of electrical and electronic equipment generated.This paper reviews the literature regarding estimation of waste electrical and electronic equipment (WEEE), focusing on developing countries, particularly in Latin America. It briefly describes the current WEEE system in Brazil and presents an updated estimate of generation of WEEE. Considering the limited available data in Brazil, a model for WEEE generation estimation is proposed in which different methods are used for mature and non-mature market products.The results showed that the most important variable is the equipment lifetime, which requires a thorough understanding of consumer behavior to estimate. Since Brazil is a rapidly expanding market, the “boom” in waste generation is still to come. In the near future, better data will provide more reliable estimation of waste generation and a clearer interpretation of the lifetime variable throughout the years.     

Modelling leachate quality and quantity in municipal solid waste landfills.

The operational phase of landfills may last for 20 years or more. Significant changes in leachate quality and generation rate may occur during this operational period. A mathematical model has been developed to simulate the landfill leachate behaviour and distributions of moisture and leachate constituents through the landfill, taking into consideration the effects of time-dependent landfill development on the hydraulic characteristics of waste and composition of leachate. The model incorporates governing equations that describe processes influencing the leachate production and biochemical processes taking place during the stabilization of wastes, including leachate flow, dissolution, acidogenesis and methanogenesis. To model the hydraulic property changes occurring during the development stage of the landfills, a conceptual modelling approach was proposed. This approach considers the landfill to consist of cells or columns of cells, which are constructed at different times, and considers each cell in the landfill to consist of several layers. Each layer is assumed to be a completely mixed reactor containing uniformly distributed solid waste, moisture, gases and micro-organisms. The use of the proposed conceptual model enables the incorporation of the spatial changes in hydraulic properties of the landfill into the model and also makes it possible to predict the spatial and temporal distributions of moisture and leachate constituents. The model was calibrated and partially verified using leachate data from Keele Valley Landfill in Ontario, Canada and data obtained from the literature. Ranges of values were proposed for model parameters applicable for real landfill conditions.     

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R² were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R² confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.     

Overview of biomedical waste management in selected Governorates in Egypt: a pilot study

Inadequate management of biomedical waste can be associated with risks to healthcare workers, patients, communities and their environment. This study was conducted to assess the handling and treatment of biomedical waste in different healthcare settings in Egypt. Five hospitals and ten primary healthcare settings were surveyed using a modified survey questionnaire for waste management. This questionnaire was obtained from the World Health Organization (WHO), with the aim of assessing the processing systems for biomedical waste disposal. Researchers found that biomedical waste is inadequately processed in hospitals and primary healthcare settings due to the absence of written policies and protocols. Accordingly, healthcare staff, patients, the community and the environment may be negatively affected by exposure to the hazards of biomedical waste. The development of waste management policies, plans, and protocols are strongly recommended, in addition to establishing training programs on proper waste management for all healthcare workers.     

Assessment of factors affecting household solid waste generation and its composition in Gulberg Town,Lahore, Pakistan

This paper analyzes and compares the findings of the characterization study of collected solid waste from households of three different socioeconomic groups in Lahore, Pakistan, over the four seasons, i.e. Spring (March–April, 2008), Summer (May–June, 2008), Monsoon (August–September, 2008) and Winter (December 2008 and January 2009). The generation rate of waste was 0.96 kg/cap/day for high-income, 0.73 kg/cap/day for middle and 0.67 kg/cap/day for low-income group. The average of total household solid waste (HSW) generation is 0.79 kg/cap/day (including 0.75 kg/cap/day for spring, 0.77 kg/cap/day for summer, 0.86 kg/cap/day for monsoon and 0.76 kg/cap/day. The breakdown for the major physical components of the waste shows that organic waste accounts for the largest proportion (67.46 %). The relations between waste generation rates by physical category and subcategory, in addition to factors such as socioeconomic groups (population density levels, household income and household size), seasonal variation, and daily variation (difference of HSW generation among days of a week) were also analyzed. Statistical analysis shows that there was no significant difference in overall waste generation among days of a week. A significant difference between the seasons for food waste, cardboard, PET, HDPE, other hazardous waste, battery cells, and dust and stone (p < 0.001) was found. The generation rates were found to be higher when compared to other developing countries.     

ANALYSIS OF RESIDENTIAL SOLID WASTE AT GENERATION SITES

An essential preliminary step in municipal solid waste (MSW) management is the accurate determination of the quantities and composition of the wastes. The purpose of this study was to test a procedure for the determination of these parameters at the source of generation (houses), rather than getting such data at transfer stations or disposal sites, as usually done in most previous studies. The average generation rate in kg per capita day⁻¹and percentages of various components of residential solid waste in Abu Dhabi City were determined by carrying out a statistically designed sampling survey. This survey covered 40 houses with different socio-economic levels and totalled 840 samples. The study showed an average generation rate of 1.76 kg per capita day⁻¹. Linear regression analysis revealed that this rate was dependent on the income level with an increase of about 35% for the high income residents over the average rate. The waste contained approximately 50% food waste. Frequency distribution analysis of waste composition data indicated that the food waste component is normally distributed, whereas the other components do not show a normal distribution pattern.     

Development of numerical model for predicting heat generation and temperatures in MSW landfills

A numerical modeling approach has been developed for predicting temperatures in municipal solid waste landfills. Model formulation and details of boundary conditions are described. Model performance was evaluated using field data from a landfill in Michigan, USA. The numerical approach was based on finite element analysis incorporating transient conductive heat transfer. Heat generation functions representing decomposition of wastes were empirically developed and incorporated to the formulation. Thermal properties of materials were determined using experimental testing, field observations, and data reported in literature. The boundary conditions consisted of seasonal temperature cycles at the ground surface and constant temperatures at the far-field boundary. Heat generation functions were developed sequentially using varying degrees of conceptual complexity in modeling. First a step-function was developed to represent initial (aerobic) and residual (anaerobic) conditions. Second, an exponential growth-decay function was established. Third, the function was scaled for temperature dependency. Finally, an energy-expended function was developed to simulate heat generation with waste age as a function of temperature. Results are presented and compared to field data for the temperature-dependent growth-decay functions. The formulations developed can be used for prediction of temperatures within various components of landfill systems (liner, waste mass, cover, and surrounding subgrade), determination of frost depths, and determination of heat gain due to decomposition of wastes.     

A simple model for complex waste recycling scenarios in developing economies

Future uncertainties involved in the current waste management activities in the developing nations have been addressed through determining plastic waste recovery, recycling and landfilling scenarios in two case study countries — Bangladesh and India. In order to discern and comprehend the material in-flow and out-flow of such complex successive plastics recoveries and recyclings, within the closed-loop recycling systems present in these two countries, a simple mathematical model is developed. The model is based on limited published information, on extensive fieldwork in Dhaka, Calcutta and Delhi, and on experimental data. An environmental legislative factor has been included in the model which will allow balancing of the quality of recycled products and the amount of landfilling non-recyclable plastics. The model has the potential to create and predict a sound waste database for these countries. Bangladesh has been chosen as a model developing country for this study. The mathematical model can be used in future decision making processes within the plastics recycling arena of the countries concerned to achieve an environmentally sound and cost effective waste management option.     

Municipal solid waste generation in municipalities: quantifying impacts of household structure, commercial waste and domestic fuel

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).     

Multiple-scale dynamic leaching of a municipal solid waste incineration ash

Predicting the impact on the subsurface and groundwater of a pollutant source, such as municipal solid waste (MSW) incineration ash, requires a knowledge of the so-called "source term". The source term describes the manner in which concentrations in dissolved elements in water percolating through waste evolve over time, for a given percolation scenario (infiltration rate, waste source dimensions, etc.). If the source term is known, it can be coupled with a model that simulates the fate and transport of dissolved constituents in the environment of the waste (in particular in groundwater), in order to calculate potential exposures or impacts. The standardized laboratory upward-flow percolation test is generally considered a relevant test for helping to define the source term for granular waste. The LIMULE project (Multiple-Scale Leaching) examined to what extent this test, performed in very specific conditions, could help predict the behaviour of waste at other scales and for other conditions of percolation. Three distinct scales of percolation were tested: a laboratory upward-flow percolation column (30cm), lysimeter cells (1-2m) and a large column (5m) instrumented at different depths. Comparison of concentration data collected from the different experiments suggests that for some non-reactive constituents (Cl, Na, K, etc.), the liquid versus solid ratio (L/S) provides a reasonable means of extrapolating from one scale to another; if concentration data are plotted versus this ratio, the curves coincide quite well. On the other hand, for reactive elements such as chromium and aluminium, which are linked by redox reactions, the L/S ratio does not provide a means of extrapolation, due in particular to kinetic control on reactions. Hence extrapolation with the help of coupled chemistry-transport modelling is proposed.     

Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region

Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse ‘landscape’ of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) ‘model’ of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region, this method of waste prediction can have significant potential benefits if a universal method can be found to apply it effectively.     

Searching quality data for municipal solid waste planning

Effective waste reduction and recycling is predicated upon credible data on refuse generation and disposal. Despite improvements in the quality of data for municipal solid wastes (MSW) disposal, dependable generation and recycling statistics to support planning, regulation and administration are lacking. The available aggregates on national waste production from two sources do not conform to each other and fail to serve the requirements of local solid waste planning. As recycling estimates will be difficult to discern, the collection of generation data based on weighing waste samples at generator sites has been portrayed as the key for developing sustainable local databases. The coefficients developed from the databases for the various categories of residential, commercial, industrial and institutional wastes can be used as variables for waste generation models.