Proceedings of the 23rd WasteCon Conference 17-21 October 2016,Emperors Palace, Johannesburg,

The Role of Socio-Economic Factors, Seasonality and Geographic Differences on Household Waste Generation and Composition in the City of Tshwane

Author: K. Komen, (City of Tshwane, South Africa, [email protected]), N. Mtembu ( Infrastructure Financing Agency, South Africa, [email protected]), and M.A. van Niekerk (Golder Associates Africa (Pty) Ltd, South Africa, [email protected]) and E.J. Perry (Golder Associates Africa (Pty) Ltd., South Africa, [email protected])

ABSTRACT This paper looks at the effect of distribution of the current population, and projected population per socio- economic level, on waste generation rates and composition.

The household waste from three spatially different areas of each low, medium and high socio-economic income level in the City of Tshwane (CoT) were assessed. The household waste was evaluated in terms of the quantity and composition. The waste was sorted into 17 categories to determine the percentage contribution of each category by weight, as well as moisture content and calorific value (CV).

The results showed that waste composition and generation from the households in CoT is significantly affected by the socio-economic context and spatial distribution of the household. A better understanding of the social context of waste generation together with the waste composition has a major influence on what alternative waste management facilities can be developed in the CoT in the future.

1. INTRODUCTION

The landfill sites in the most densely populated areas of the CoT are close to capacity and alternatives to landfilling the waste are being sought in order for the waste to become a resource through recycling, compositing or energy recovery, in addition to prolonging the lifespan of the landfills. In response to this, the City of Tshwane (CoT) and Gauteng Infrastructure Financing Agency (GIFA) commissioned Golder Associates Africa (Pty) Ltd to undertake a feasibility study on alternative waste treatment technologies (AWTT). This paper addresses one aspect of this study, which is how household waste generation and composition in the CoT are influenced by socio-economic factors, seasonality and geographic differences. There is no previous study of composition of municipal waste in the CoT.

2. BACKGROUND

The CoT is home to the capital city of South Africa and is the largest municipality in terms of land mass. It houses approximately 3.15 million residents and is currently growing at a rate of 3.1% per annum (IHS Global Insight, 2014). The CoT was the fastest growing municipality in terms of economic output between 2003 and 2012. It is also the second wealthiest municipality in terms of GDP per capita contribution (StatsSA, 2011).

3. METHODOLOGY

In the majority of waste compositional analysis undertaken in Europe and North America have found that socio- economic differences in households are the primary factor in waste compositional differences. The households were therefore split into low, middle and high income levels based on the categories used by Bureau of Market Research of the University of South Africa (Masemola et al., 2012). The three socio-economic groups were split into three different geographic areas so that the analysis whether geographic differences were a major factor in the waste composition. The sampling was undertaken in accordance with the European Commission (2004), Methodology for the Analysis of Solid Waste (SWA-Tool) with waste collected from 63 houses in each of the 9 sampling areas (see Figure 1). Two round of sampling were undertaken, one for summer (February) and one for winter (May).

The weight of the waste was determined for each household. The waste was then sorted into 17 categories to determine the percentage contribution of each category by weight, as well as moisture content and calorific value (CV).

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Figure 1. Distribution of Low, Middle and High Income Sample Areas in the CoT

4. RESULTS

The following section presents key results of the Waste Characterisation Study (WCS).

4.1 Waste Generation

In total, 19 769 kg of waste was collected and sorted from the low, middle and high income sample areas, with waste collected from the high income areas accounting 38 % of the total waste sorted, followed by waste from the middle (37 %) and low income sample areas (25 %) – see Figure 2.

8 000 7 718 7 783

6 000 5 152

4 000

Weight (kg) 2 000

0 Low Middle High Income Level of Sample Areas

Figure 2. Quantity of Waste Sorted from Low, Middle and High Income Sample Areas

The quantity of waste collected varied between the income areas, as well as between the seasons. The quantity of waste sorted from the middle and high income sample areas is generally greater than waste from

422 Institute of Waste Management of Southern Africa Proceedings of the 23rd WasteCon Conference 17-21 October 2016,Emperors Palace, Johannesburg, South Africa the low income sample areas. Further to this, the quantity of waste sampled in summer (shown as dotted) is generally greater in most sample areas than in winter (shown by diagonal stripes).

1 600 1 557 1 534 1 546 1 334 1 400 1 281 1 320 1 257 1 214 1 200 1 068 1 054 1 047 1 002 1 000 942 844 781 821 800 559 609 600 Weight (Kg) 400

200

-

Low Income Sample Areas

Figure 3. Comparison of Quantity of Waste Sorted per Income Area in Summer (dotted) and Winter (diagonal stripes)

4.2 Waste Composition

The composition of the waste was determined through the sorting and weighing of the waste collected from the nine sample areas into the 17 categories. Figure 4 below presents the composition of the low, middle and high income sample areas for both winter and summer together.

Figure 4. Combined Composition of Waste Sorted from Low, Middle and High Income Areas in Summer and Winter

423 Institute of Waste Management of Southern Africa Proceedings of the 23rd WasteCon Conference 17-21 October 2016,Emperors Palace, Johannesburg, South Africa

Organic waste accounts for the majority of waste by weight (60 %), comprising garden refuse (37 %), kitchen waste (17 %), and fines (6 %). Paper accounts for 8 % of the waste, followed by glass (6%), plastic film and nappies / sanitary waste (5% each), and dense plastics and cardboard (4% each).

The comparison of waste composition from the low income sample areas in summer and winter showed the percentage of kitchen waste increases by 14% from 23.9% in summer to 37.5% in winter. Similarly, the percentage of fines increases by 4.2% from 11.8% in summer to 16% in winter. In contrast, the percentage of garden refuse decreases by 13% from 18.7% in summer to 5.7% in winter.

The comparison of waste composition from the middle income sample areas in summer and winter showed the percentage of garden refuse decreases by 13 % from 52 % in summer to 38.8 % in winter. In contrast, the percentage of kitchen waste increases by 5.5 % from 9.7 % in summer to 15.2 % in winter. Similarly, percentage of glass increases by 3.2% from 6.1% in summer to 9.3% in winter.

The comparison of waste composition from the high income sample areas in summer and winter showed the percentage of garden refuse increases by 6.1 % from 41.6 % in summer to 47.7 % in winter.

Figure 5 below shows the comparison of the different waste categories collected in summer versus winter for all of the sample areas combined.

45% 40,0% 40% 33,6% 35% 30% 19,5% 25% 14,4% 20% 4,6% 6,1% 1,5% 6,9% 4,1% 6,2% 15% 4,4% 5,7% 1,7% 0,2% 8,5% 1,7% 7,3% 0,2% 1,7% 0,3% 10% 5,0% 5,7% 6,2% 0,2% 3,5% 4,0% 5% 0,4% 1,2% 0,3% 1,4% 0,3% 1,6% 0,3% 1,3%

Percentage of Total Waste(%) 0%

Waste Categories

Figure 5. Comparison of Waste from All Sample Areas in Summer (dashed) versus Winter (stripes).

5. DISCUSSION

5.1 Socio-Economic Factors

There are a number of socio-economic factors that influence the quantity and composition of waste, with population and income being two of the main drivers. In order to better understand the relationship between waste volumes landfilled annually and annual population growth, a regression analysis was performed for 2001 – 2011 (see Figure 6). The results of the regression analysis showed a moderate coefficient of determination or r-squared (R²) value of 0.66. This indicates that there is a relationship between the collected data and the predicted data (i.e. trend data), but that volumes of waste landfilled may be dependent on other factors, such as household income.

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Waste / Population Linear (Waste / Population)

3 000 000

2 500 000 R² = 0,6634 2 000 000

1 500 000

1 000 000

Waste Volumes (m3) 500 000

- 2 000 000 2 200 000 2 400 000 2 600 000 2 800 000 3 000 000 Population

Figure 6. Regression Analysis of the Total Volume of Waste Landfilled and CoT’s Total Population For 2001 to 2011.

Similarly, a regression analysis was performed for waste volumes landfilled annually and household income. The results of the regression analysis showed a moderate coefficient of determination or r-squared (R²) value of 0.59. This again indicates that there is a relationship between the collected data and the predicted data (i.e. trend data) however, this relationship is not as strong as that with population. The volumes of waste landfilled are therefore likely to be due to population and household income.

Waste / Household Income Linear (Waste / Household Income) 3 000 000 2 800 000 2 600 000 R2 = 0.5855 2 400 000 2 200 000 2 000 000 1 800 000 1 600 000 1 400 000 Waste Volumes (m3) 1 200 000 1 000 000 50 000 70 000 90 000 110 000 130 000 150 000 170 000 190 000 210 000 230 000

Annual HH Income (R)

Figure 7. Regression Analysis of the Total Volume of Waste Landfilled and CoT’s Annual Household Income for 2001 to 2011.

In order to determine if there a statistical difference in the total tonnage and composition of the waste between the low, middle and high income areas, the Mann-Whitney U Test was used. This test allows two independent data sets to be compared (i.e. summer and winter) without making the assumption that the values are normally distributed. There is only a statistical difference at a 95% confidence level between the two data sets if the p- value is less or equal 0.05. If there is no statistically significant difference it means that the differences are just due to the variability of the data and are not due to the socio-economic difference in the samples taken.

Table 1 below presents the p-values (the level of statistical significance) comparing the weight of bins collected from low, middle and high income areas in summer and winter. In both summer and winter, there was statistically no difference in the weight of household bins collected from middle and high income areas. In contrast, there is a statistical difference in the weight of bins collected from low income areas compared to middle and high income areas.

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Table 1. Significant difference in quantity of waste collected in different income areas

Season Income Areas P-Value Description Summer Low Middle 0 Significant Middle High 0.33706 No significant High Low 0 Significant Winter Low Middle 0.00008 Significant Middle High 0.43540 No significant High Low 0 Significant

The significant difference would appear to be due to the distribution of household bins in the middle and high income areas being very similar compared to the distribution of household bins in the low income areas.

0,040

0,035

0,030

0,025

0,020

0,015

0,010

0,005

0,000 0 20 40 60 80 100 120 -0,005

Low Income Mid Income High Income

0,045

0,040

0,035

0,030

0,025

0,020

0,015

0,010

0,005

0,000 0 20 40 60 80 100 120 -0,005

Low Income Mid Income High Income

Figure 8. Distribution of Household Bin Weights in Low, Middle and High Income Areas in Summer and Winter

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The range of household bins weights collected from low income areas was found to be much narrower than the middle and high income areas ranging between 0.5 kg and 59.5 kg in summer (standard deviation of 11.49 kg) and 1 kg and 63 kg in winter (standard deviation of 9.33 kg). In comparison, the range of household bin weights collected from middle income areas is much wider ranging between 2 kg and 110 kg in summer (standard deviation of 18.75 kg) and 1 kg and 107 kg in winter (standard deviation of 15.85 kg). The range of household bin weights collected from high income areas is similar to that of middle income areas ranging between 2 kg and 80 kg in summer (standard deviation of 15.83 kg) and 3 kg and 96 kg in winter (standard deviation of 16.19 kg).

5.2 Seasonality

The Mann-Whitney U Test was also used to determine if there a statistical difference in the total tonnage and composition of the waste between the seasons. Table 2 below presents the p-values (the level of statistical significance) comparing the weight of household bins collected from the nine sample areas in summer compared to winter. Of the nine sample areas, only the household bins collected from Shoshanguve and Chantelle were found to be statistically different in summer and winter. This indicates that in general, there is no statistical difference in household bin weights between the summer and winter.

Table 2. Significant Difference in Quantity of Waste Collected in Summer Versus Winter for Each Sample Area

Area Season P-Value Description Low Shoshanguve Summer Winter 0.004 Significant Summer Winter 0.849 Not significant Summer Winter 0.660 Not significant Middle Meyerspark Summer Winter 0.646 Not significant Chantelle Summer Winter 0.001 Significant Lyttleton Manor Summer Winter 0.298 Not significant High Summer Winter 0.134 Not significant Woodhill Summer Winter 0.561 Not significant Ridge Summer Winter 0.535 Not significant

5.3 Geographic Differences

The Mann-Whitney U Test was also used to determine if there is a statistical difference in the total tonnage and composition of the waste between the different geographies.

5.3.1 Low Income Areas

Table 3 below presents the p-values (the level of statistical significance) comparing the weight of household bins collected from the 3 different low income sample areas. There is a significant difference in household bin weights collected from Ekangala compared to Shoshanguve and Mamelodi, in both summer and winter. In contrast, there is no significant difference in the household bin weights collected from Mamelodi and Shoshanguve.

Table 3. Significant Difference in Quantity of Waste Collected in Different Low Income Sample Areas

Season Sample Areas P-Value Description Summer Shoshanguve Ekangala 0 Significant Ekangala Mamelodi 0 Significant Mamelodi Shoshanguve 0.849 No significant Winter Shoshanguve Ekangala 0.032 Significant Ekangala Mamelodi 0 Significant Mamelodi Shoshanguve 0.020 Significant

One of the reasons why the low income areas are significantly different from each other is that the bin weights are less variable for each area and therefore a small change in bin weights can be seen as statistically significant.

427 Institute of Waste Management of Southern Africa Proceedings of the 23rd WasteCon Conference 17-21 October 2016,Emperors Palace, Johannesburg, South Africa

4.3.2 Middle Income Areas

Table 4 below presents the p-values (the level of statistical significance) comparing the weight of household bins collected from the 3 different middle income sample areas. With the exception of the winter sample collected from Meyerspark and Chantelle, there is no statistical difference in the household bin weights collected from Meyerspark, Chantelle and Lyttleton Manor. The difference in bin weights collected in Meyerspark and Chantelle in the winter WCS can be attributed to the significant difference in bin weights collected from Chantelle in the summer sample.

Table 4. Significant difference in quantity of waste collected in different middle income sample areas

Season Sample Areas P-Value Description Summer Meyerspark Chantelle 0.384 No significant Chantelle Lyttleton Manor 0.313 No significant Lyttleton Manor Meyerspark 0.937 No significant Winter Meyerspark Chantelle 0.039 Significant Chantelle Lyttleton Manor 0.080 No significant Lyttleton Manor Meyerspark 0.741 No significant

The bin weights for the middle income area are extremely variable and therefore a large difference in weight would be required for it to be significantly different. Chantelle was the one area where there was a large decrease in bin weights with a total decrease in weight of 47% from summer to winter giving rise to the significant difference in the table above.

4.3.3 High Income Areas

Table 5 below presents the p-values (the level of statistical significance) comparing the weight of household bins collected from the 3 different high income sample areas. The statistically significant differences in bin weights would appear to show that the household bins collected from Rooihuiskraal differ significantly in terms of weight from bins collected in Woodhill and .

Table 5. Significant difference in quantity of waste collected in different high income sample areas

Season Sample Areas P-Value Description Summer Rooihuiskraal Woodhill 0 Significant Woodhill Waterkloof Ridge 0.171 No significant Waterkloof Ridge Rooihuiskraal 0.009 Significant Winter Rooihuiskraal Woodhill 0.022 Significant Woodhill Waterkloof Ridge 0.133 No significant Waterkloof Ridge Rooihuiskraal 0.465 No significant

CONCLUSION

Waste characterisation studies are extremely complex and although statistical results can be obtained regarding the economic, seasonal and geographic differences it is not always possible to explain why these differences occur. The main findings from this study are listed below.

• The low income households produce less waste than the middle and high income households. This is supported by the findings of similar study undertaken by the Council for Scientific and Industrial Research (2009).

• Putrescible or ‘organic’ waste, which comprises food waste, garden refuse and fines, accounts for 60 % of total waste sampled, 55 % of waste collected from low income sample areas, 65 % of waste collected from middle income sample areas, and 63 % of waste collected from high income sample areas.

• The low income households produce a greater amount of food waste than the middle and high income households. This is supported by two studies undertaken in South Africa; waste characterisation study by Jarrod Ball and Associates (2001) for the City of Johannesburg and a case study of Mamelodi in 2014 (Ramukhwatho et al. 2014).

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• The low income households produce a greater amount of nappies than the middle and high income households. StatsSA Census 2011 data shows that 10.9 % of the total population in the low income wards are below the age of four, compared to 7.8 % in middle income wards and 7.9 % in high income wards.

• It was noted that there was no significant seasonal difference in the total tonnage and composition of waste in summer and winter, which was confirmed by statistical analysis of the two samples. This could be attributed to the summer being unusually dry and rainfall events occurring in close proximity to the winter sample.

6. REFERENCES

A Case Study of Mamelodi Township In South Africa, Proceedings of the 20th WasteCon Conference 6-10 October 2014. Somerset West, Cape Town.

CSIR (2009), The State of Domestic Waste Management in South Africa, Briefing Note 2009/1.

IHS Global Insight Regional explorer version 759 - ReX, 2014.

Ramukhwatho F.R., du Plessis F., and Oelofse S. (2014), Household Food Wastage in a Developing Country:

Masemola M.E., van Aardt C.J., and Coetzee M.S. (2012), Income and Expenditure of Households in South Africa, 2011, Research Report No. 429, Bureau of Market Research of the University of South Africa,

Statistics South Africa (StatsSA) (2011), City of Tshwane, http://www.statssa.gov.za/?page_id=1021&id=city- of-tshwane-municipality, accessed on 22 December 20155.

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