1 Climate Change and Bioenergy Development, Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia

2 Chemical, Environmental and Process Engineering, Institute of Technology, Bahir Dar University, Ethiopia

3 GIS-Remote Sensing and Environmental Management, Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia

4 Water Supply and Environmental Engineering, Institute of Technology, Hawassa University, Ethiopia


BACKGROUND AND OBJECTIVES: The amount of solid waste produced and its impact on communities and the environment are becoming a global concern. This study aims to assess the amount, composition, and prediction models of solid waste generation in the study area.
METHODS: Solid waste data were collected from both residential and non-residential areas using stratified and systematic sampling approaches. Interviews and field measurements were used to obtain socioeconomic and solid waste data from 90 households and 69 samples from non-residential areas.
FINDINGS: The research area's mean household solid waste generation rate is 0.39kilograms per capita per day. Organic waste accounted for the majority of the waste generated in the study area (71.28 percent), followed by other waste (9.77 percent), paper (6.71 percent), and plastic waste (6.41 percent). The solid waste generation rate demonstrated a positive relationship (p<0.05) with monthly household income and educational level. However, there was a negative association between family size and age (p > 0.05). Based on a high regression coefficient determination value (0.72), low mean absolute error (0.094), sum square error (1.28), and standard error of the estimate (0.908), Model 4 was chosen as the best-fit model among the proposed models.
CONCLUSION: The developed models met multiple linear regression assumptions and could be used to estimate the rate of household solid waste generation. This study generated large amounts of organic waste present in municipal solid waste sources that can contaminate the environment and have an impact on human health while also having a massive energy recovery capability.

Graphical Abstract

Municipal solid wastes quantification and model forecasting


  • Solid waste generation varied along with variations in socioeconomic income levels and seasons;
  • A large amount of organic waste was generated compared to other wastes;
  • Monthly households’ income and educational levels were related to solid waste generation positively, while household family size and age were related negatively;
  • Solid waste generation prediction models were developed using a combination of monthly income, educational level, household family size, and age.


Main Subjects


The population of the universe has rapidly expanded, from 3.1 billion in 1960 to almost 7 billion in 2010. By 2050, 9.3 billion people are projected to exist on Earth (Malavet al., 2020). Municipal solid waste (MSW) production worldwide is reportedly between 1.7 and 1.9 billion metric tons per year (Wilson et al., 2016). Also, solid waste generation will increase from 1.3 billion to 2.5 billion metric tons per year by 2025, with developing countries accounting for the majority of the growth (Pandeyet al., 2015). The amount of solid waste produced has increased over time due to population growth and urbanization worldwide. However, there are fewer rooms accessible to keep waste (Ebohet al., 2016). Owing to the differences in population growth, geography, climate, and living standards, solid waste generation trends fluctuate from area to area, country to country, and city to city (Noufalet al., 2020).Developed countries can produce more solid waste than developing countries, but because of institutional competency, access to technology, and sufficient costs for sustainable solid waste treatment, most developed countries are effective in regulating waste (Shahzad et al., 2013). Since solid waste management has an impact on both the environment and human health, as well as having the potential to considerably increase resource conservation, it is becoming a concern for both national and municipal governments (Ghineaet al., 2016). An effort is being made in Africa for a range of waste streams to develop and put into effect rules, regulations, and policies that facilitate the management and collection of urban solid waste, including recycling, recovery, and environmentally sound disposal (Mukwanaet al., 2014). To manage waste effectively, it is important to gather a lot of data from several sources, including accurate estimates of the quantity of waste that will be produced in the future as well as data on the factors that will affect that generation of waste (Grazhdani, 2016). The development of current waste management infrastructures as well as their continued sustainable development and optimization are based on future projections of the generation of MSW (Abasi and El Hanandeh, 2016). For proper decision-making about the management of solid waste in urban areas, it is crucial to know the amountand kind of waste produced (Intharathiratet al., 2015). MSW is diverse in both quantity and composition. The changes in the seasons and household income levels affect it differently (Monavariet al., 2012). Investigators have conducted studies on the factors that influence the rate of waste formation. The studies’ findings demonstrate that factors such as educational level, age, family size, and income have a substantial impact on the amount of household waste generated (Zulkifliet al., 2019; Noufalet al., 2020). The categorization and measurement of waste quantity and composition are made more challengingdue tothis fluctuation. Domestic solid waste  generation and composition in various regions of the world have been evaluated by severalstudies (Noufalet al., 2020). The studies revealed that analyzing the characteristics of MSW is critical for a variety of reasons, including determining the potential of waste resources for recycling, reuse, and recovery processes; estimating solid waste generation sources; and designing simple treatment facilities. However, solid waste generated in households varies greatly and is largely dependent on socioeconomic status (Amayaet al., 2019). Forecasts of solid waste from mathematical prediction models are regarded as a crucial tool for decision-makers, policy-makers, and stakeholders in creating the best and most comprehensive solid waste management policies (Abbasi and El Hanandeh, 2016). To estimate the solid waste generation rate, several multiple regression models have been built for various cities around the world (Vermaet al., 2019). Unfortunately, the social, economic, and geographic heterogeneity of the various regions of the world makes it difficult to draw conclusions or make projections with the suggested models. It is necessary to adapt models and their variables to the circumstances in other places, often with varying degrees of success. Some of the difficulties associated with adapting these models, according to Kumar and Samandder (2017), are related withinadequate or unavailable information in databases from other countries. The majority of the work put into creating models for estimating the generation of solid waste is based on the data that is only available for one country, which makes it unrepresentative of the elements of Ethiopian MSW. There is littlecurrent, trustworthy data on the composition and quantity of solid waste in Ethiopia, including thestudy location. Because there are so few solid wastecharacteristic data points, the Yirgalem town Administration appears to struggle to create effective site-specific SWM programs and initiatives. Similar to other Ethiopian towns, Yirgalem rarely has access to accurate waste statistics about the rates and types of solid waste that are generated, the effectiveness of solid waste collection, and the quantity of recycled and disposed solid waste. Due to the absence of accurate waste statistics, the rate of generation of solid waste must be anticipated by using predictive techniques based onthe limited amount of available data. When modeling genuine MSW, it iscritical that you employ the right preparation method. Therefore, the study aims to identify the quantity and composition of solid waste, as well as correlate waste quantity with relevant socioeconomic parameters of households, and develop a model for forecasting solid waste generation. The study was carried out in Yirgalem town, in Ethiopia, using information from the two seasons' variations in 2021.


Study site description

This study was carried out in Yirgalemtown, Sidama Regional State, Ethiopia. It is located at 6º44´- 6º46´ N latitude and 38º24´ - 38 °26´ E longitudes (Fig. 1). The study area has an elevation of 1600–1960 meter (m). In addition, it is the biggest settlement in the Daleworeda (Yusufet al., 2018). It is situated 311 kilometers (km) south of Addis Ababa and 47 km from Hawassa, the capital of the Southern Nations, Nationalities, and Peoples’and Sidama regions. The total population of Yirgalem town is 64,507, of whom 31,737 are male and 32,770 are female (Yusufet al., 2018). Yirgalem town has a moderate climate, with minimum and maximum annual temperatures of 14 °C and 30 oC, respectively. The study area experienced bimodal rainfall with peaks in April, June, and August, with an annual rainfall of 1138-1690 millimeters (mm) (Yusufet al., 2018).

Fig. 1. Geographic location of the study area in Yirgalem town, Sidama Regional State, EthiopiaHypotheses

The hypotheses are drawnfrom the study's goal. The rates of solid waste generation inhouseholds in Yirgalem town are constant throughout the wet and dry seasons;there is no significant difference between the solid waste generation rate and socioeconomic income levels; the quantity of solid waste produced and socioeconomic characteristics do not significantly correlate with one another.

Sampling design and techniques

Solid waste data were collected in a longitudinal study.Information on MSW was gathered from both residential and non-residential locations. A stratified sampling technique was used because of the variety of sources used to generate MSW. The municipality was classifiedinto five categories based on the sources of the production of solid waste; residential, commercial areas, institutions, healthcare facilities, and street sweepings (Okeyet al., 2013). For each municipal solid waste source, representative samples were gathered using a systematic sampling technique. Household samples were selected based on income, housing types, and the presence of fundamental social services, which serve to divide socioeconomic status into low-, middle-, and high-income categories (Nyanksonet al., 2015). The residential zones were divided into three housing types: low-cost landed (low-cost houses), middle-cost landed (living in flats and medium-cost), and high-cost landed (living in high-cost homes) (Yahyaet al., 2013). In the study, questionnaires were utilized to collect data on a variety of topics, including the personal and socioeconomic background of the residentials and the overall amount of waste produced.

Sample size determination

According to waste management recommendations, a total of thirty household samples were taken fromeach of the three social-economic groups (low, middle, and high) for a MSW survey (Yahyaet al., 2013; Mucyo, 2013). A total of 90 household samples were collected for this study, representing all socioeconomic levels.A previous study (Yahyaet al., 2013) that looked intothe generation of solid waste from diverse sources, such as commercial areas, institutions, healthcare facilities, and street sweeping, was the basis for the determination of total samples for non-residential locations. Each waste source was given five sample recommendations. For this investigation, 44 samples from commercial areas, 12 samples from institutions, 9 samples from healthcare facilities, and 7 km of street sweeping were collected twice during the dry and wet seasons.

Solid waste data collection methods

Depending on the amount and type of material generated in the area, MSW was measured at each source using plastic bags with one or more of their daily waste collections. Data on MSW were collected over seven consecutive days (Sachi and Mensah, 2020). To determine the weight of the waste for each solid waste collection location, the collected waste was weighed first. All samples were manually classified into eight waste categories (paper and paper products, plastics, organic (compostable) materials, glass, metals, textiles, wood, and others) at each collectionstationas indicated in Table 1 (Osei-Mensahet al., 2014). To account for seasonal variation, data on solid waste were gathered in two seasons (dry and wet). Dry season data were collected from December 2020 to February 2021, and wet season data were collected from June to August 2021.

Waste categories Waste description
Organic materials All biodegradable materials like food waste, yard trimming, grass including Khat, agricultural crop residues, manures, and other organic
Paper and paper products Office paper, computer paper, magazines, glossy paper, waxed paper, and newsprint
Plastics All plastic materials like polyethylene terephthalate (PET) bottles, high-density polyethylene (HDPE), film plastic, plastic bag,
Glass All glass materials like windows and mirror glass as well as broken bottles and other containers
Metal The waste originating from Ferrous (Iron, steel, tin cans, and bi-metal cans), aluminum, and non-ferrous non-aluminum metals
textiles Waste of clothes, carpets, pillows
wood The waste which includes sawn timber, wooden boards, furniture
others Dust, ash, e-wastes, stone
Table 1: Waste categories of MSW

Solidwaste generation and composition calculations

Solid waste generation rate

Household solid waste generation (HSWG) kilogram per capita per day (Kg/c/day) was determined as per the mixed or total waste collected in a day and the separated fractions using Eq. 1 (Miezahet al., 2015).

The total amount of household solid waste (HSW) produced by all houses in a town was calculated using Eq. 2 (Miezahet al., 2015).

Composition of solid waste

The total weight of all constituents in the sample was combined to compute the weight of the entire sample. The percentage composition of each componentis calculated using Eq. 3 (Miezahet al., 2015).

Methods of model development

A solid waste generation forecasting model was built based on socioeconomic characteristics, such as household size, monthly income, age of the household head, gender, job status,marital status,andeducational level. All these most common traits have an impact on HSWG rates integrated with other variables (Popliet al., 2021). Multiple linear regression was used to develop solid waste generation models. Multiple linear regression assumptions, such as linear relationshipsbetween dependent and independent variables, normality of the tested data, multicollinearity test, and homoscedasticity, were evaluated before the data were analyzed (Tabachnicket al., 2019). Bivariate Pearson correlation coefficient (r) and a statistical significance test were used to ensure that the dependent and independent variables had a linearrelationship. To make sure the data was normal, a graphic representation of the P-P plot, histograms, and the Kolmogorov-Smirnov test were also utilized. Additionally, the multicollinearity of independent variables was examined using the variance inflation factor (VIF) to identify multivariate correlations and the Pearson correlation coefficient (r) to identify bivariate associations.An illustration of a scatter plot was used to study the homoscedasticity of the standardized residual and predictive values.Four fundamental criteria—the mean absolute error (MAE), the sum of square error (SSE), standard error of the estimate (SEE), and coefficient of multiple determination—were used to select the best-fit model (R2) (Kulisz and Kujawska, 2020). The average absolute error is expressed using Eq. 4 (Chhayet al., 2018).

Where, SWG and SWGp denote the actual solid waste generation data and the predicted values, respectively. where n represents the number of observations.

The sum of square error can be given using Eq. 5 (Wanget al., 2021).

The standard error of the estimate can be shown as Eq. 6 (Wanget al., 2021).

Where, pis the number of parameters in the regression model.

The coefficient of multiple determinations can be expressed using Eq. 7 (Chhayet al., 2018).

Where,is the arithmetic mean of the observed data.

Statistical analysis

The association between the amount of waste produced and socioeconomic factors such as household size, monthly income, age of the household head, gender, employment status, marital status, and educational attainment was assessed using correlation analysis. One-way analysis of variance (ANOVA) was used to examine the statistically significant variations in waste generation rates based on income class, and the Student's t-test was used to examine seasonal change.Version 25.0 of SPSS statistics for Windows was used to conduct all statistical analyses. The Tukey test was applied to compare statistical differences and means.There is a p-value &lt; 0.05 for each analysis presented in this study.


Solidwaste generation rate

Table 2 displays the average solid waste generation for the three income levels and two seasonal variations. Based on a statistical analysis of variance, it was discovered that there was a significant difference (p < 0.05) between the socioeconomic income level and the rate of HSWG. A multiple comparison analyses of the solid generation rate (kg/c/day) between low- and middle-income groups showed a significant difference (p = 0.000). The rate of solid waste generation between the low and high socioeconomic income levels also showed a statistically significant difference (p = 0.000). Between the middle- and high-income categories, there was no discernible difference in the rate of solid waste generation (p = 0.222). According to this finding, the high- and middle-income socioeconomic categories generated more solid waste than the low-income group. This is because the activities of higher-income families consume more resources than those of lower-income families. According to Amayaet al. (2019) households generate more solid waste as their socioeconomic status improves. This outcome is consistent with the findings reported by other researchers (Heriantoet al., 2019). Yirgalemtown's mean HSWG rate is 0.39 kg /c/day, with low-income (0.28), middle-income (0.42), and high-income groups (0.47). The result of the predicted solid waste generation rate aids in the development of effective solid waste management strategies. A similar study was reported in Addis Ababa, Ethiopia (Tassieet al., 2019), Shire-Endasilasie, Ethiopia (Zewdu and Mohammedbirhan, 2014), Dhanbad, India (Khanet al., 2016), Ghana cities (Miezahet al., 2015), Thika Municipality, Kenya (Kinyua and Njogu, 2015), and Laga Dadi town, Ethiopia (Assefa and Muktar, 2017). The current solid wastegeneration rate is higher than elsewhere reported for Bahir Dar city (Asmare, 2019), Robetown (Erasuet al., 2018), Chiro town (Umeret al., 2019), and Debre Berhan town, Ethiopia (Abera, 2017). Thisstudy is less than the report made in Jima town (Getahunet al., 2012) and Sawla town, Ethiopia (Haileet al., 2020). The result found fromthis study is within the range of 0.2-0.8 kg/c/day of solid waste generation for most of Sub-Saharan African countries (Miezahet al., 2015) and developing countries within the range of 0.3 to 0.9 kg/c/day (Nadeem and Farhan, 2016). Location, climate, lifestyle, urbanization, and economic development of cities contribute to differences in solid waste generation rates. At p<0.05, there was also significant variation in the solid waste generation between the wet and dry seasons. The wet season (0.43) had a higher per capita HSWG rate than did the dry season (0.35 kg/c/day). This is because the wet season produces more vegetables, fruits, Khat, grass, and other resources than the dry season does. Several studies (Kamranet al., 2015; Mshelia, 2015; Ziaet al., 2017), have found that the rate of solid waste generation decreases from the wet season to the dry season. Households generated almost 80% of the solid waste, followed by commercial areas (12.13%) and institutions (4.59%). Previous studies have shown that solid waste generation comes from a variety of sources, including residential areas (50–80%), commercial areas (10–30%), street sweeping, and institutions, all of which have varying proportions (Sachiand Mensah, 2020), which is consistent with the results of this study.

Income level n solid waste generation (kg/c/day) f-value p-value
Mean* S.E.**
Low income 60 0.28a 0.017 19.77 0.000
Middle-income level 60 0.42b 0.024
High-income Level 60 0.47b 0.023
Total 180 0.39 0.014
Wet 90 0.43a 0.020 10.741 0.001
Dry 90 0.35b 0.018
Total 180 0.39 0.014
*Means with different superscript letters are significantly different at (α< 0.05)
**Standard error
Table 2: Analysis of variance of solid waste generation under the three income levels and seasons

Solid waste composition

The majority of organic wastewas generated by street sweeping (78.79%), followed by institutions (71.26%), commercial areas (69.98%), and residential areas (68.91%) as shown in Table 3. Institutions (22.55%) and commercial areas (5.89%) produced larger amounts of paper waste. As shown in Fig. 2, Yirgalemtown generated a high amount of organic waste (71.28%), followed by miscellaneous waste (9.77%), paper (6.71%), and plastic waste (6.41%). The overall results of the present study indicated that organic (compostable) waste had the highest percentage. Comparable studies were found in Laga Dadi town, Ethiopia (Assefa and Muktar, 2017), Guayaquil, Ecuador (Amaya et al., 2020), Homs City, Syria (Noufalet al., 2020), Thu Dau Mot, Vietnam (Tranget al., 2017), and Sulaimanyah, Iraq (Hamza, 2020). Lower organic waste was found in this study compared to work done by Umeret al. (2019) for Chiro town, Ethiopia.

Types of solid waste Sources of solid waste (%)
Residential Commercial Institutions Street sweeping
Organic 68.91 69.98 71.26 78.79
paper 2.15 5.89 22.55 4.84
plastic 5.85 5.57 6.19 7.12
Glass 1.09 1.13 0.00 1.24
Metal 0.59 7.98 0.00 0.00
Textile 5.07 2.01 0.00 1.09
woods 0.21 0.00 0.00 0.00
others 16.14 7.45 0.00 6.92
total 100.00 100.00 100.00 100.00
Table 3: Types of solid wastes under different generated sources

Fig. 2. Overallsolid waste composition in Yirgalem town

The relation between HSWGrate and socioeconomic factors

Table 4 shows the relationship between the rate of HSWG and socioeconomic factors. Household monthly income (r = 0.476, p = 0.000), educational level (r = 0.327, p = 0.002), and solid waste generation rate all showed positive correlations.Solid waste production increases in direct proportion to household prosperity. This is due to the different home consumption habits, which is consistent with a study conducted by Batuet al. (2016). Severalstudiesobtained a negative correlation between household monthly income and the solid generation rate per capita each day (Monavariet al., 2012; Tranget al., 2017). It means that those with a greater income generated a lower rate of solid waste production per capita than lower-income households. There was a negative relationship between household family size and the solid waste generation rate (r =-0.436, p =0.000).In comparison to large families, more people in their homes live together with shared common resources and consume more items, resulting in fewer waste disposals. This study is consistent with reports from other sources (Ogwueleka, 2013). Numerous studies have discovered a positivecorrelation between the size of a household's family and the rate of solid waste produced per capita (Noufalet al., 2020). Households with a large number of people generate more solid waste than those with small families. The differences in the outcomes of various studies are related to differences in economic and cultural standing as well as techniques. A higher level of education in the household results in a higher rate of solid waste generation due to increased household income and work prospects. Several academics endorse this study (Getahunet al., 2012). Other socioeconomic factors such as job status, marital status, home ownership, and gender had no significant impact on the solid waste generation rate in this study. Similar studies were carried out by earlier researchers (Batuet al., 2016).

Socioeconomic factors Pearson correlation (r) P-value
Solid waste generation rate (kg/c/day) Gender 0.181 0.087
Age -0.053 0.620
Marital status 0.183 0.084
Education level 0.327 0.002
household size -0.436 0.000
House ownership -0.058 0.587
Job-status -0.007 0.950
Monthly income 0.606 0.000
Table 4: Relation between HSWG rate and socioeconomic factors

Model development

The rate of HSWG was not normally distributed when examined using the Kolmogorov–Smirnov method at a significance level of 0.05. To match the data normality, the logarithm data transformation approach for the solid waste generation rate (response variable) was used. For independent variables, Pearson correlation (r) less than 0.3 and VIF less than 5 revealed no multicollinearity issues (Ghineaet al., 2016), which meets the current study as shown in Table 6. Normality in terms of error was assessed using normal probability plots and histograms, as shown in Figs. 3 and 4. The homoscedasticity assumption was further tested using a graphical depiction of the scatter plot between the standardized residual and expected response variables, as illustrated in Fig. 5. Using independent variables such as household size, educational level, monthly income, and age of the household head, four types of models were proposed at a significance level of ANOVA analysis (Table 5). Because these independent variables had the greatest impact on the rate of solid waste production at the study site, which is used in the development model. Model 4 (Eq. 9) is the best-fit model, followed by Model 3 (Eq. 8), as described below, based on a high R2 and low values of mean absolute error (MAE), the sum of square error (SSE), and standard error of the estimate (SEE), as shown in Table 7.

Model Sum of squares df Mean square F Sig.
1 Regression 1.677 1 1.677 51.167 .000a
Residual 2.885 88 .033
Total 4.562 89
2 Regression 2.970 2 1.485 81.183 .000b
Residual 1.592 87 .018
Total 4.562 89
3 Regression 3.211 3 1.070 68.159 .000c
Residual 1.351 86 .016
Total 4.562 89
4 Regression 3.281 4 .820 54.445 .000d
Residual 1.281 85 .015
Total 4.562 89
Table 5: Analysis of variance for solid waste prediction model development
Model Unstandardized coefficients t Sig. Collinearitystatistics
B S.E. Tolerance VIF
1 (Constant) .392 .035 11.227 .000
Monthly income .006 .001 7.153 .000 1.000 1.000
2 (Constant) .669 .042 15.916 .000
Monthly income .007 .001 10.720 .000 .978 1.023
Household size -.150 .018 -8.407 .000 .978 1.023
3 (Constant) .600 .043 14.042 .000
Monthly income .007 .001 10.576 .000 .940 1.064
Household size -.152 .017 -9.226 .000 .976 1.024
Educational level .034 .009 3.917 .000 .957 1.045
4 (Constant) .687 .058 11.822 .000
Monthly income .007 .001 11.010 .000 .908 1.101
Household size -.153 .016 -9.432 .000 .976 1.024
Educational level .031 .009 3.628 .000 .935 1.069
Age -.026 .012 -2.155 .034 .953 1.050
Table 6: Estimated regression coefficient of independent variables
Model Regression equation R2 MAE SSE SEE Sum rank Total rank
1 logY=0.392+0.006MI 0.37(4) 0.140(4) 2.902(4) 1.338(4) 16 4
2 logY =0.669+0.007MI - 0.150HS 0.65(3) 0.107(3) 1.597(3) 1.025(3) 12 3
3 logY=0.600+0.007MI+0.034 Edu -0.152HS 0.70(2) 0.097(2) 1.366(2) 0.937(2) 8 2
4 logY=0.687+0.007MI+0.031 Edu -0.153HS-0.026Age 0.72(1) 0.094(1) 1.28(1) 0.908(1) 4 1
Table 7: Selection of best-fitted multiple linear regression model

Fig. 3. Residual histogram plots for normality assumption

Fig. 4. Residual P-P plots for normality assumption

Fig. 5. residual scaterplots for homoscedasticity assumption

Where, logY is the log-transformed solid waste generation rate (kg/c/day), MI is household monthly income (US dollars), Edu is the educational level of the household head, and Hs is the household size.In the final model, variables (monthly income, family size, educational level, and age) of the household head explained 72% of the solid waste generation rate. This research was similar to that of Lebersorger and Beigl (2011), who reported an R2 of 74.3 percent. The R2 rarely exceeded 50%, except in studies with a large number of predictors and a small sample size (Lebersorger and Beigl, 2011), which supports the current study. When compared to the findings of the earlier studies (Beitezet al., 2008), the developed models produced lower solid waste per capita. These discrepancies have occurred as a result of differences in the influencing factors of independent variables in various locations, nations, cities, and climates. The multiple regression coefficient determination value (R2 = 0.51) estimated by Beitezet al. (2008), which is a minor relative measure of fit compared to this study model (R2 = 0.72), was also used to explain variances in model prediction values. The different independent variables utilized during the model development caused this difference in coefficient determination (R2). This way, the study was conductedto close the information gaps that the nation and the study area were encountering.The model established in this study can be used to estimate solid waste generation rates in Yirgalem and other similar towns.

Validation of developed models

The residual errors' behavior, notably their normal distribution, independence, and homoscedasticity—the gap between the dependent variable's observed and predicted values—determines the validity of the MLR models (Kumar and Samandder, 2017). To ensure the validity and correctness of the findings, the values of R2 (a relative measure of fit) and performance indicators (an absolute measure of fit), such as mean absolute error (MAE), sum of square error (SSE), and standard error of the estimate (SEE), were computed (Table 7). Using a pair-wise t-test of the anticipated and actual values of a response variable (solid waste generation rate), the superior model was further validated.In Models 3 and 4, there was not a statistically significant difference (p > 0.05) between the dependent variable's observed and predicted values. Due to the higher value of p = 0.878, model 4 is more precise and accurate in this investigation, as shown in Table 8.

Models Paired differences t df Sig.
Model No. S.D.* S.E. 95% confidence Interval of the difference
Lower Upper
Model 3 .12341 .01301 -.01445 .03724 .876 89 .383
Model 4 .12028 .01268 -.02324 .02715 .154 89 .878
*Standard deviation (S.D.)
Table 8: Comparisons of the observed and predicted value of solid waste generation rate


The characterization of solid waste is crucial for long-term sustainable planning and development. The analysis of the solid waste in Yirgalem town and its characteristics, along with the Pearson correlation results, show the significant impact of socioeconomic factors on waste generation. The most important aspect in identifying the best alternatives for solid waste treatment and investment is the composition of the waste. Low-income groups generated less solid waste per capita than high- and middle-class groups. In contrast to the dry season, the wet season showed a higher per capita generation rate of household solid waste. The majority of MSW was generated by households, followed by commercial areas and institutions in this study area. The overall results of the current study revealed that organic (compostable) waste received the highest percentage of coverage. The rate of solid waste generation was positively correlated with the monthly household income and educational level. While household size and age of the household headwere negatively associated with the rate of solid waste generation, theresult indicated that households with a large number of people generate more solid waste than those with small families. Other socioeconomic factors such as job status, marital status, home ownership, and genderhad no significant impact on the solid waste generation rate. Four models were developed using the most influential socioeconomic factors such as household monthly income, household size, age, and level of education as predictors and solid waste generation as a response variable. Based on the high regression coefficient determination, least mean absolute error, sum square error, and standard error of the estimate, the last equation (model 4) was selected as the best-fit model among these models. The model developed in this study can be used to estimate solid waste generation rates in the study area and other towns of comparable size. Although, the models generated in this study only consider socioeconomic factors, other researchers should integrate other environmental factors to improve model prediction accuracy.Large amounts of biodegradable (organic) waste present in municipal solid waste sources can contaminate the environment and have an impact on human health, while also having a massive energy recovery capability. MSW composition should be segregated further into sub-categories of solid waste, which is crucial for in-depth analysis. The town administration of Yirgalem should utilise this organic waste as compost for urban agriculture and the manufacture of biogas fuel to decrease the amount of solid waste and energy consumption. This study can serve as a basis for more research in the field as it provides a solid foundation for comparison.


Y.M. Teshome performed the data collection, experimental design, sampling campaigns, solid waste analysis, and prepared the manuscript text. N.G. Habtu performed the literature review and the model configuration and simulations, analyzedand interpreted the data and results, and edited the manuscript. M.B. Mollaorganized the methodology, analyzed, and interpreted the data and results, prepared the manuscript text, and edited the manuscript. M.D. Ulsido organized the methodology, analyzed and interpreted the data and results, prepared the manuscript text, and edited the manuscript.


Hawassa University, Wondo Genet College of Forestry and Natural Resources, University of Gondar, and the German Academic Exchange Service (DAAD) provided financial support [1712874] and educational opportunities. The authors appreciated the invaluable feedback from anonymous reviewers.


The authors declare no potential conflict of interest regarding the publication of this work. In addition, the ethical issues including plagiarism, informed consent, misconduct, data fabrication and, or falsification, double publication and, or submission, and redundancy have been completely witnessed by the authors.


©2023 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third-party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit:


GJESM Publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


% Percent
o Degree
oC Degree celsius
ANOVA Analysis of variance
DAAD German Academic Exchange Service
df Degree of freedom
E East
Edu Educational level
Eq. Equation
Fig. Figure
HDPE High-density polyethylene
Hs Household size
HSW Household solid waste
HSWG Household solid waste generation
Kg/c/day Kilogram per capita per Day
kg Kilogram
m Meter
Km Kilometer
logY Logarithm of solid waste generation
MAE Mean absolute error
MI Household monthly income
mm millimeters
MSW Municipal solid waste
n Number of samples
N North
p Number of regression parameters
PET Polyethylene terephthalate
r Pearson correlation coefficient
R2 Coefficient of multiple determinations
S.D. Standard deviation
S.E. Standard error
SEE Standard error of the estimate
Sig. Significance value
SPSS Statistical package for the social sciences
SSE Sum of square error
Student's t-test Parametric tests based on the Student's or t-distribution
SWG Solid waste generation
SWGp Solid waste generation prediction value
VIF Variance inflation factor


  1. Abbasi, M.; El Hanandeh, A., (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manage., 56: 13–22 (10 pages).
  2. Abera, K.A., (2017). Household solid waste generation rate and onsite handling practices in Debre Berhan Town, Ethiopia. Sci. J. Public Health. 1(5): 1-9 (9 pages).
  3. Amaya, J.L.; Hidalgo, J.; Jervis, F.; Moreira, C., (2019). Influence of socio-economic factors on household solid waste generation of the city of Guayaquil, Ecuador. in: Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation, and Infrastructure for Sustainable Cities and Communities”. Latin American and Caribbean Consortium of Engineering Institutions.
  4. Asmare, M., (2019). Bahir Dar City municipal solid waste potential assessment for clean Energy. Am. J. Energy Eng., 7(1): 1-8 (8 pages).
  5. Assefa, M.; Muktar, M., (2017). Solid waste generation rate and characterization study for LagaTafo Laga Dadi Town, Oromia, Ethiopia. Int. J. Environ. Prot. Policy. 5(6): 84-93 (10 pages).
  6. Batu, M.M.; Admasu, E.; Tolosa, F., (2016). Determinants of households’ willingness to pay for improved solid waste management in Ethiopia: The case study of Jimma Town. J Environ. Earth Sci., 6(7): 1–14 (14 pages).
  7. Benitez, S.O.; Lozano-Olvera, G.; Morelos, R.A.; Vega, C.A., (2008). Mathematical modeling to predict residential solid waste generation. Waste Manage., 28: 7–13 (7 pages).
  8. Chhay, L.; Reyad, M.A.; Suy, R.; Islam, M.R.; Mian, M.M., (2018). Municipal solid waste generation in China: Influncing factors analysis and multi-model forecasting. J. Mater. Cycles Waste Manage., 20(3): 1761-1770 (10 pages).
  9. Eboh, F.C.; Ahlstrom, P.; Richards, T., (2016). Estimating the specific chemical exergy of municipal solid waste. Energy Sci. Eng., 4(3): 217–231 (15 pages).
  10. Erasu, D.; Feye, T.; Kiros, A.; Balew, A., (2018). Municipal solid waste generation and disposal in Robe town, Ethiopia. J. Air Waste Manage. Assoc., 68(12): 1391–1397 (7 pages).
  11. Getahun, T.; Mengistie, E.; Haddis, A.; Wasie, F.; Alemayehu, E.; Dadi, D.; Van Gerven, T.; Van der Bruggen, B., (2012). Municipal solid waste generation in growing urban areas in Africa: Current practices and relation to socioeconomic factors in Jimma, Ethiopia. Environ. Monit. Assess., 184(10): 6337–6345 (9 pages).
  12. Ghinea, C.; Dragoi, E.N.; Comanita, E.D.; Gavrilescu, M.; Campean, T.; Curteanu, S.; Gavrilescu, M., (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. J. Environ. Manage., 182: 80–93 (14 pages).
  13. Grazhdani, D., (2016). Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park. Waste Manage., 48: 3–13 (11 pages).
  14. Haile, M.Z.; Mohammed, E.T.; Gebretsadik, F.D., (2020). Physicochemical characterization of municipal solid waste in Sawla town, Gofa Zone, Ethiopia. J. Appl. Sci. Environ. Manage., 23(11): 2023-2029 (7 pages).
  15. Hamza, A.A., (2020). Municipal solid waste quantity, ingredients, and site disposal problems in Pshdar District in Sulaimanyah: Iraqi Kurdistan Region, Iraq. Kufa J. Eng., 11(4): 1–18 (18 pages).
  16. Herianto, H.; Maryono, M.; Budihardjo, M.A., (2019). Factors affecting waste generation: household study in Palangka Raya City, Central Kalimantan. E3S Web of Conference 125: 07007 (5 pages).
  17. Intharathirat, R.; Abdul Salam, P.; Kumar, S.; Untong, A., (2015). Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Manage., 39: 3–14 (12 pages).
  18. Kamran, A.; Chaudhry, M.N.; Batool, S.A., (2015). Effects of socio-economic status and seasonal variation on municipal solid waste composition: a baseline study for future planning and development. Environ. Sci. Eur., 27(1): 1-16 (16 pages).
  19. Khan, D.; Kumar, A.; Samadder, S.R., (2016). Impact of socioeconomic status on municipal solid waste generation rate. Waste Manage., 49: 15–25 (11 pages).
  20. Kinyua, R.; Njogu, Paul., (2015). An analysis of solid waste generation and characterization in Thika Municipality of Kiambu County, Kenya. J. Environ. Sci. Eng., 4(4): 210-215 (6 pages).
  21. Kulisz, M.; Kujawska, J., (2020). Prediction of municipal waste generation in Poland using neural network modeling. Sustainability. 12(23): 10088 (16 pages).
  22. Kumar, A.; Samadder, S.R., (2017). An empirical model for prediction of household solid waste generation rate – A case study of Dhanbad, India. Waste Manage., 68: 3–15 (13 pages).
  23. Lebersorger, S.; Beigl, P., (2011). Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel. Waste Manage., 31(9): 1907–1915 (9 pages).
  24. Malav, C.L.; Yadav, K.K.; Gupta, N.; Kumar, S.; Sharma, G.K.; Krishnan, S.; Rezania, S.; Kamyab, H.; Pham, Q.B.; Yadav, S.; Bhattacharyya, S.; Yadav, V.K.; Bach, Q.V., (2020). A review on municipal solid waste as a renewable source for waste-to-energy project in India: Current practices, challenges, and future opportunities. J. Clean. Prod., 277: 123227 (80 pages).
  25. Miezah, K.; Obiri-Danso, K.; Kadar, Z.; Fei-Baffoe, B.; Mensah, M.Y., (2015). Municipal solid waste characterization and quantification as a measure towards effective waste management in Ghana. Waste Manage., 46: 15–27 (13 pages).
  26. Monavari, S.M.; Omrani, G.A.; Karbassi, A.; Raof, F.F., (2012). The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries: A case study Ahvaz, Iran. Environ. Monit. Assess., 184(4): 1841–1846 (6 pages).
  27. Mshelia, A.D., (2015). Seasonal variations of household solid waste generation in Mubi, Nigeria. Int. J. Innovations Educ. Res., 3(5): 115–124 (10 pages).
  28. Mucyo, S., (2013). Analysis of key requirements for effective implementation of biogas technology for municipal solid waste management in Sub-Saharan Africa. A Case Study of Kigali City, Rwanda. Abertay University, Rwanda.
  29. Mukwana, K.C.; Samo, S.R.; Tunio, M.M.; Jakhrani, A.Q.; Luhur, M.R., (2014). Study of energy potential from municipal solid waste of Mirpurkhas city. J. Eng. Sci. Technol., 13(2): 3 (3 pages).
  30. Nadeem, K.; Farhan, K., (2016). Waste amount survey and physio-chemical analysis of municipal solid waste generated in Gujranwala-Pakistan. Int. J. Waste Resour., 6(1): 1-8 (8 pages).
  31. Noufal, M.; Yuanyuan, L.; Maalla, Z.; Adipah, S., (2020). Determinants of household solid waste generation and composition in Homs City, Syria. J. Environ. Public Health. 2020: 1–15 (15 pages).
  32. Nyankson, E.A.; Fei-Baffoe, B.; Gorkeh-Miah, J., (2015). Household solid waste generation rate and physical composition analysis: Case of Sekondi-Takoradi Metropolis in the Western Region, Ghana. Int. J. Environ., 4 (2): 1-15 (15 pages).
  33. Ogwueleka, T.C., (2013). Survey of household waste composition and quantities in Abuja, Nigeria. Resour. Conserv. Recycl., 77: 52–60 (9 pages).
  34. Okey, E.N.; Umana, E.J.; Markson, A.A.; Okey, P.A., (2013). Municipal solid waste characterization and management in Uyo, AkwaIbom State, Nigeria. WIT Trans. Ecol. Environ., 173: 639–648 (10 pages).
  35. Osei-Mensah, P.; Adjaottor, A.A.; Owusu-Boateng, G., (2014). Characterization of solid waste in the Atwima-Nwabiagya District of the Ashanti Region, Kumasi-Ghana. Int. J. Waste Manage. Technol., 2: 1–14 (14 pages).
  36. Pandey, B.K.; Vyas, S.; Pandey, M.; Gaur, A., (2016). Municipal solid waste to energy conversion methodology as physical, thermal, and biological methods. Curr. Sci. Perspect. 2: 1–6 (6 pages).
  37. Popli, K.; Park, C.; Han, S.M.; Kim, S., (2021). Prediction of solid waste generation rates in urban region of laos using socio-demographic and economic parameters with a multi linear regression approach. Sustainability. 13(6): 3038 (15 pages).
  38. Sachi, P.J.; Mensah, E.A., (2020). Household characteristics and waste generation paradox: What influences solid waste generation in Bolgatanga.Int. J. Environ. Waste Manage., 26(2): 212–233 (22 pages).
  39. Shahzad, S.; Butt, A.; Anwar, S.; Ahmad, S.; Sarwar, T.; Asghar, N., (2017). Municipal solid waste as a renewable source of energy: an overview from the Lahore District in Punjab, Pakistan. Pol. J. Environ. Stud., 26(6): 2721–2729 (9 pages).
  40. Tabachnick, B.G.; Fidell, L.S.; Ullman, J.B., (2019). Using multivariate statistics, Seventh edition. ed. Pearson, N.Y.
  41. Tassie, K.; Endalew, B.; Mulugeta, A., (2019). Composition, generation and management method of municipal solid waste in Addis Ababa City, Central Ethiopia: A Review. Asian J. Environ. Ecol., 9(2): 1–19 (19 pages).
  42. Trang, P.T.; Dong, H.Q.; Toan, D.Q.; Hanh, N.T.; Thu, N.T., (2017). The effects of socio-economic factors on household solid waste generation and composition: A case study in Thu Dau Mot, Vietnam. Energy Procedia., 107: 253–258 (7 pages).
  43. Umer, N.; Shimelis, G.; Ahmed, M.; Sema, T., (2019). Solid waste generation rate and management practices in the case of Chiro Town, West Hararghe Zone, Ethiopia. Am. J. Environ. Prot., 8(4): 87-93 (7 pages).
  44. Verma, A.; Kumar, A.; Singh, N.B., (2019). Application of multi linear model for forecasting municipal solid waste generation in Lucknow City: A case study. Curr. World Environ., 14(3): 421–432 (12 pages).
  45. Wang, D.; Tang, Y.T.; He, J.; Yang, F.; Robinson, D., (2021). Generalized models to predict the lower heating value of municipal solid waste. Energy. 21: 1-29 (29 pages).
  46. Wilson, D.C.; Rodic, L.; Modak, P.; Soos, R.; Rogero, A.C.; Velis, C.; Simonett, O., (2015). Global waste management outlook. United Nations Environment Programme.
  47. Yahya, N.; Mohd, A.B.; Abdullah, P.F.; Hj, N.B., (2013). Survey on solid waste composition, characteristics and existing practice of solid waste recycling in Malaysia.Perumahan Dan Kerajaan Tempatan, Malaysia.
  48. Yusuf, E.; Fiseha, F.; Dulla, D.; Kassahun, G., (2018). Utilization of kangaroo mother care and influencing factors among mothers and care takers of low birth weight babies in Yirgalem Town, Southern, Ethiopia. Divers. Equal. Health Care. 15(2): 87-92 (6 pages).
  49. Zewdu, A.; Mohammedbirhan, M., (2014). Municipal solid waste management and characterization in Aksum and Shire-Endaslassie Towns, North Ethiopia. J. Environ. Earth Sci., 4(13): 1-8 (8 pages).
  50. Zia, A.; Batool, S.; Chauhdry, M.; Munir, S., (2017). Influence of income level and seasons on quantity and composition of municipal solid waste: A case study of the capital City of Pakistan. Sustainability. 9(9): 1568 (13 pages).
  51. Zulkifli, A.A.; MohdYusoff, M.Z.; AbdManaf, L.; Zakaria, M.R.; Roslan, A.M.; Ariffin, H.; Shirai, Y.; Hassan, M.A., (2019). Assessment of municipal solid waste generation in Universiti Putra Malaysia and its potential for green energy production. Sustainability. 11(14): 3909 (15 pages).


©2023 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit:


GJESM Publisher remains neutral concerning jurisdictional claims in published maps and institutional affliations.


Google Scholar Scopus Web of Science PlumX Metrics Altmetrics Mendeley |


GJESM Publisher

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.