Municipal Solid Waste Generation, Composition, and Management in the
Douala Municipality, Cameroon
Abstract
Increasing population levels, booming economy and rapid urbanization have greatly accelerated
municipal solid waste generation rate in Cameroon cities. Hence, reliable data on the
composition are key in determining and customizing suitable technologies for an overall
integrated solid waste management system. The study characterised municipal solid waste
generated in the Douala municipality at landfill level. Data was collected from scientific
literature, existing data bases, observations made during visits to landfill and urban areas. Load
count analysis of all collected samples was done on site. Descriptive and inferential statistic
methods were used to draw conclusions. The results show that, on average, municipal solid
waste are generated at a rate of 979.5 thousand tons per day giving an average per capita
generation rate of 0.496 ± 0.071 kg per person per day. While inert (7.4±0.8), metal (2.6 ± 0.8),
glass (3.5% ± 1.3), and paper (14.5% ± 0.9) wastes had higher proportions in the dry season,
plastic (16.1% ± 2.6), organic (49.8.3% ± 3.1) and special (2.0% ± 0.1) wastes had a
significantly higher proportion in rainy season. However, at α = 0.05, the results indicated that all
waste categories resulted in p > α, with extreme critical values for the test statistic t. It therefore
seem that waste composition do not significantly differ from season to season. Across the
different districts that make up the municipality, MSW per capita generation rates vary but this
does not differ in terms of their mean generation rates (x 2 (4,0.05) = 9.4877; p = 0.15).The paper
recommends that waste recycling and composting activities be encouraged since this approach is
considered to be the right measure in attaining sustainability in waste management.
Keywords: Municipal solid waste, Douala municipality, Load count analysis, Per capita
generation rate, landfill,
1. Introduction
Increasing population levels, booming economy, rapid urbanization and the rise in community
living standards have greatly accelerated the municipal solid waste (MSW) generation rate in
developing countries (Minghua et al., 2009). At the same time, improper handling and disposal
of solid waste constitutes a serious problem: it results in deterioration of the urban environment
in the form of air, water, and land pollution (Adekunle et al., 2011; Jalil, 2010) that pose risks to
human health, and cause serious environmental problems (Khajuria et al., 2008). The challenge,
therefore, for the municipal environmental engineers and planners is to employ green technology
approaches that are viable and sustainable. Reliable data on MSW composition are key in
determining and customizing suitable technologies for an overall integrated solid waste
management system (Magrinho et al., 2006). Waste composition indicates the components of the
waste stream in percentage of the mass or volume. A waste characterization study quantifies
waste components with respect to weight and composition fractions.
In the past decades, a large number of research studies have been undertaken to determine
influential factors affecting waste management. A variety of approaches have been adopted for
assembling detailed quantitative data on the amount, location, and characteristics of a waste
stream (Felder et al., 2001; Mason et al., 2003; Dahlen et al., 2007). Several other studies have
focused on waste characterization at the household level (e.g. Chang and Davila, 2008; Gomez et
al., 2009). A few other studies have attempted to develop either a systematic approach for MSW
at both the household and non-household level (Zotos et al. 2009), or identified factors
influencing the elements of the waste management systems (Akinci et al., 2012; Al-Jarallah and
Aleisa, 2013). Some researchers have documented how an adequate legal framework contributes
positively to the development of the integrated waste management system (Asase et al., 2009)
while the absence of satisfactory policies (Turan et al. 2009, Mrayyan and Hamdi, 2006) and
weak regulations (Seng et al., 2010) are detrimental to it.
Anjum and Deshazo (1996), report that in most cities, municipalities and towns in developing
countries, solid waste management costs consume between 20% and 50% of municipal revenues.
It has been reported that collection, transfer and transport practices are affected by improper bin
collection systems, poor route planning, lack of information about collection schedule (Hazra
and Goel, 2009), insufficient infrastructure (Moghadam et al., 2009), poor roads and number of
vehicles for waste collection (Henry et al., 2006). Some researchers that have investigated the
institutional factors that affect the system have come to the conclusion that local waste
management authorities have a lack of organizational capacities (leadership) and professional
knowledge. Besides they concluded that the information available is very scanty from the public
domain (Chung and Lo, 2008).
While numerous waste characterization studies have been conducted at the household level
(Chowdhury, 2009; Gomez et al., 2009) only a small number exist for the municipal sector
(Hristovski et al., 2007, Zhuang et al., 2008). In the same way that municipal waste
characterization studies provide local decision makers with a detailed understanding of a waste
stream and enable waste management programs to be tailored to local needs (Chang and Davila,
2008), waste characterization studies at municipal level identify urban specific and regionally
relevant opportunities for waste reduction and recycling, representing an essential step towards
greening the community. Such data is key to long term planning for the management of solid
waste in an efficient and economical manner (Gidarakos et al 2006), and for the identification of
waste components to target for source reduction, recycling, design of material recovery facilities
and waste-to-energy projects (del C Espinosa Llorens et al., 2008; Qu et al., 2009).
The anthology of MSW study throughout the world is scant.In developing economies the data on
MSW generation have a short history and insufficient national data or data of a large urban or
periurban population center (Shekdar, 2009). In Africa, waste characterization data specific to
African cities is generally not available (ADB, 2002), the composition of the waste varies
depending upon such diverse variables as urbanization, commercial enterprises, manufacturing,
and service sector activities; solid waste management in most countries is characterized by
inefficient collection methods, insufficient coverage of the collection system and improper
disposal of municipal solid waste. The extremely limited information is not complete or is
scattered around various agencies concerned, therefore, it is extremely difficult to gain an insight
into the complex problem of municipal solid waste management (Seng et al., 2010).
In Cameroon, MSW management including control, collection, processing, utilization, and
disposal, is the responsibility of the municipalities. Waste management policy is based on a
public-private partnership which ensures regular collection and processing service for domestic
waste in the major cities. However, data on MSW generation, composition and management
have a short history and insufficient national data or data of a large urban or periurban population
centre. Policy decisions that influence the components of MSW systems are not possible until
data of composition and quantity of solid waste are available. This study assessed the MSW
generation, composition and characteristics, and current practices of waste management in the
Douala municipality of Cameroon in 2013. Implications for resource conservation, waste
reduction and increased waste diversion are discussed. Key research questions are:
1. What are the trends in per capita yearly generation and the composition of municipal
solid wastes in the Douala municipality?
2. Which urban operational areas and material types should be targeted for waste reduction
and enhanced diversion (recycling and composting) efforts?
3. What technically and administratively feasible waste management improvements and
strategies should be adopted to advance the sustainability of the current system?
The approach consists in manually segregating the wastes on site. Load count analysis of all
collected samples was done on site. Mean values and standard deviations were calculated for
different components of MSW.
2. Materials and Methods
2.1. Study Area
The city of Douala (210 km2 /80 sq mi) is the capital of the Littoral region of Cameroon. It is
Cameroon’s economic capital, the richest city in the whole CEMAC region of six countries,
located on the banks of the Wouri River, at 4°02′53″ N Latitude 9°42′15″ E Longitude, situated
in the Wouri division at an average elevation of 13m above sea level. Five urban municipalities
(also known as districts) and one rural municipality form the urban community of Douala: the
town districts of Douala I whose headquarters is at Bonanjo, Douala II whose headquarters is
New Bell, Douala III whose headquarters is at Logbaba, Douala IV whose headquarters is at
Bonassama, Douala V whose headquarters is at Kotto, and Douala VI whose headquarters is at
Manoka (Fig.1).
Figure 1 Map of Africa and Cameroon showing the city of Douala with its municipalities; “a-f”: CEMAC countries
Douala is also an industrial city and one of the fastest developing urban areas in Africa and ranks
first at national level. According to the 2005 population estimate, Douala has a population of
1.907 million (UNdata, 2013) and according to estimates from the Douala urban council, the
average population growth rate is 1.8%. The present growth is attributable to various factors such
as industrialization, expanding educational institutes and information technology (IT) companies
and establishments of various government organizations. This growth reflects the complexity of
issues facing the development of the city.
The Hygiene and Sanitation Company of Cameroon (HYSACAM), established in 1969, is the
country’s leading private municipal solid waste management company. Based in Douala and
Yaoundé, HYSACAM operates across the entire municipal solid waste management chain, from
collection through to processing. It has 5,000 employees and a fleet of 400 vehicles. HYSACAM
operates a landfill at PK10, located 10 km away from Douala city center. The landfill is about 25
meters deep and covers a 63 ha area, of which more than 10 ha has been used already
(Biotecnogas, 2009). Since 2003 it has been used for disposal of domestic and commercial waste
collected in the city at an average rate of 287,000 tons per year. Approximately 1,500,000 tons of
wastes have been disposed in the landfill between 2003 and 2008 (Biotecnogas, 2009).
2.2.
Research design
The research began in January of 2013 and the following data-collection phases employed:

Review of existing literature;

Quantifying the waste stream through vehicle surveys; and

Determining the composition of the waste stream through sampling and sorting.
Review of existing literature began with an evaluation of internal policies and procedures related
to municipal sustainability and waste management, external documents including government
regulations and guidelines and various municipal waste composition studies. Through their
annual solid waste reports, HYSACAM office provided historic data on quantities of waste
generated daily, weekly, monthly and yearly. Commercial recyclers and end-users provided
information on the types and estimated quantities of wastes recycled in a voluntary survey.
Primary data was obtained through field work (January – December 2013) conducted to identify
the solid waste sources, generation and composition. The main tools used in data collection were
waste characterization forms, interviews sheets, and direct observation. In order to obtain a
higher accuracy in the generation rates, historic data from the facility and daily data on the waste
load weights collected by the trucks of the municipality were used.
2.3. Activities approach
The locations of waste generation centres were mapped and included Douala I - V. The load
count analysis, otherwise known as sort-count and/or-weigh, activities approach (Tchobanoglous
et al., 1993b; CCME, 1996). In this way, vehicles entering the landfill were surveyed using
survey forms. Information obtained from such surveys included, the dimensions of the car,
weight of the car when empty, weight when full of waste, and the source of the waste
(information obtained from drivers) it is transporting. To choose the waste collection trucks,
several truck drivers were asked from which route/district the waste was being collected.
Assessing waste quantity and composition in this way has been shown to capture the high spatial
variation of waste (Felder et al., 2001), thereby yielding more reliable and representative data.
2.4. Waste sampling
Sample collection and segregation sampling for the present work was done monthly from
January 2013 to December 2013 to account for seasonal variations and representative
characteristics of MSW. The sampling locations at the landfill site were identified in consultation
with HYSACAM authorities responsible for the operation of the site to obtain a representative
sample (Fig. 2).
Figure 2: Sampling stations of MSW at PK10 landfill
Loaded trucks carrying waste to the landfill are first weighed, their size and destinations
identified before off-loading takes place. Waste was sampled from numerous districts (strata):
Douala I, Douala II, Douala II, Douala IV and Douala V, to develop a waste composition profile
for each stratum. The strata were then “added together” in a way that reflects each stratum’s
relative contribution to the overall waste stream, thus producing overall waste composition
information.
2.5.
Waste characterization
A sorting & characterization form was used. Surveys were conducted on the same days that
waste was sampled. Sorting and processing of waste samples were manually done with the help
of landfill workers into seven categories, namely paper, plastic, electronic, household hazardous
wastes (HHW), other organics, inerts and others, and special waste (Table 1).
Table 1: Waste composition category
ID
Waste composition category
Waste components
1
Paper
Packaging paper, cardboard, wrapper, newsprint, magazines, office paper
2
Plastic
PETE Containers, HDPE Containers, Miscellaneous Plastic Containers
Plastic Trash Bags, Plastic Grocery and other Merchandise Bags,
Durable Plastic Items, Remainder/Composite Plastic
3
Glass
Clear, brown, green, other
Metal
Ferrous, non-ferrous, tin cans, metal foils, Other Non-Ferrous and ferrous
metals
Electronics
Computer-related Electronics, Other Small Consumer Electronics,
Video Display Devices, radio
Household Hazardous
Paint, Vehicle & Equipment Fluids , Used Oil, Batteries,
Remainder/Composite Household Hazardous
4
3
4

5
6
7
Other Organics
Food wastes, Leaves and Grass, Prunings and Trimmings, Branches and
Stumps, Manures, Carpet, Textiles (rubber, clothes, synthetic, cables,
leather), Remainder/Composite Organic
Inerts & other
Concrete, Asphalt Paving, Lumber, Gypsum Board, Rock, Soil, and Fines,
Remainder/Composite
Special Waste
Ash, Bulky Items, Tyres, Treated Medical Waste, Remainder/Composite
Special Waste
2.6. Limitations of the study
Despite the overall success of this project there are notable limitations that are worth mentioning.
In order to conserve limited time and financial resources, waste sampling and labelling at source
was never done. This exercise would have taken note of wastes uncollected from the so many
distant slums which are inaccessible all-round the year. Because sampling was done at the
landfill site, it was also difficult to account for waste diverted or sorted at source by primary
scavengers. Combined, data from these two sources represent a significant and distinct portion of
the municipality’s waste.
2.7.
Data Analyses
Completed data collection forms were checked for errors and placed into standard spreadsheet
data files (Excel, Microsoft). The weight-based percentage composition for each subcategory
(primary and secondary) was calculated.
2.7.1. Estimating the composition of MSW
For a given stratum (that is, for the samples belonging to the same waste sector within the same
subdivision), the composition estimate denoted by rj represents the ratio of the component’s
weight to the total weight of all the samples in the stratum. This estimate was derived by
summing each component’s weight across all of the selected samples belonging to a given
stratum and dividing by the sum of the total weight of waste for all of the samples in that
stratum, as shown in the following equation:
∑ c
rj = ∑i wij
i
(1)
i
where:
c = weight of particular component;
w = sum of all component weights;
for i = 1 to n, where n = number of selected samples; and
for j = 1 to m, where m = number of components.
The confidence interval for this estimate was derived in two steps. First, the variance around the
estimate was calculated, accounting for the fact that the ratio included two random variables (the
component and total sample weights). The variance of the ratio estimator equation follows
(William, 77):
1
1
∑i cij −rj wi 2
Var(rj ) ≈ (n) (w
)(
̅2
n−1
)
(2)
Where:
w
̅=
∑i wi
n
Second, precision levels at the 90 percent confidence level were calculated for a component’s
mean as follows:
rj ± (z√Var(rj )
Where z = value from z-statistics (1.645) corresponding to 90% confidence interval.
(3)
Composition results for strata were then combined using a weighted average method to estimate
the composition of larger portions of the waste stream. The relative tonnage associated with each
stratum served as the weighting factors. The calculation was performed as follows:
Oj = (P1 ∗ rj1 ) + (P2 ∗ rj2 ) + (P2 ∗ rj3 ) + ⋯
(4)
Where:

P = the proportion of tonnage contributed by the noted waste stratum (the weighting
factor),

r = the ratio of component weight to total waste weight in the noted waste stratum (the
composition percent for the given material component), and

For j = 1 to m, where m = the number of material components
The variance of the weighted average by taking the variance of equation (4):
V(Oj ) = [(p21 Var(rj1 ))] + [(p2 2 Var(rj2 ))] + [(p2 3 Var(rj3 ))] + ⋯
(5)
2.7.2. Estimating the quantities of MSW generated
Total waste generated on a daily base was estimated as:
Total waste generated = Disposed Waste + Recycled Waste + Diverted Waste
(6)
= Disposal + (Recycling + Reuse)
The proportion of waste diverted was obtained through field interviews and field observations.
The weight of waste for each truck entering the landfill equals, the weight of the truck when full
of waste minus weight of the truck when empty.
From [4], the per capita generation rate was calculated as (equation 5):
Per capita generation rate =
Total waste generated
Population
Kg
(day∗person)
(7)
Hence,
MSW Recycled
Percent of generation recycled = MSW disposed+MSW recycled ∗ 100%
(8)
To keep the waste composition tables and figures readable, estimated tonnages are rounded to
the nearest ton, and estimated percentages are rounded to the nearest tenth of a percent. Due to
this rounding, the tonnages presented, when added together, may not exactly match the subtotals
and totals shown. Similarly, the percentages, when added together, may not exactly match the
subtotals or totals shown. Percentages less than 0.05 percent are shown as 0.0 percent.
Hypothesis tests were conducted to compare the municipal waste composition among the
districts and seasons.
3. Results and Discussion
3.1.
Characterisation of solid waste
The mean composition of MSW in five districts was 49.8% of biodegradable wastes (food waste and yard
waste), 32.8% of combustibles (plastic, paper, Leather/Textile/Rubber), and 17.4% non-combustibles
(metal and aluminium, inerts, hazardous waste, nappies and sanitary napkins, and others). (Table 2).
Table 2: Overview of the Douala municipality’s overall disposed waste stream
Material
Paper and Cartons :
 Paper Bags
 Office Paper
 Magazines/catalogue
 Newspapers
 White ledger paper
 Mixed paper
Glass
 Clear glass bottles
 Composite Glass
 Windows breakings
Inert and other
 Rock/soil/concrete/fine
 Lumber/Wood
 Composite
Metals
 Tin/Steel cans
 Major Appliances
 Used oil Filters
 Aluminium cans
 Other Non-Ferrous
 Composite metal
Est.
percent
19.4%
1.2%
5.2%
1.9%
4.3%
0.3%
6.5%
2.6%
1.5%
0.7%
0.4%
9.4%
3.2%
0.4%
5.8%
3.6%
0.3%
0.1%
0.1%
1.3%
0.2%
1.6%
+/0.9%
0.9%
0.9%
0.9%
0.4%
0.9%
0.1%
0.1%
0.1%
0.1%
0.1%
2.2%
0.1%
0.1%
0.1%
0.1%
0.4%
0.1%
0.5%
Est.
tons
639646
39566
171451
62646
141777
9891
214314
85726
49457
23080
13189
309931
105509
13189
191234
118697
9891
3297
3297
42863
6594
52754
Material
Plastic
 PET containers
 HDPE containers
 Plastic trash bag
 Misc. containers
 Composite Plastic
Other Organics
 Food wastes
 Leaves & grass
 Composite organic
 Prunings/trimming
 Branches & stumps
 Manures
 Carpet
 Textiles
Special wastes
 Ash
 Bulky Items
 Tyres/Rubber
 Sewage Solids
 Industrial Sludge
 Composite
Est.
percent
13.4%
5.2%
2.4%
0.7%
4.3%
2.8%
49.8%
24.3%
3.8%
4.3%
2.7%
0.6%
0.1%
3.2%
10.4%
2.0%
0.2%
1.0%
0.2%
0.4%
0.1%
0.1%
+/0.1%
0.1%
0.1%
0.1%
0.7%
1.9%
0.7%
0.5%
1.5%
0.4%
0.1%
2.0%
0.2%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
Est. tons
441817
171451
79131
23080
141777
92320
1635382
807800
125291
141777
89023
19783
3297
105509
342903
65943
6594
32971
6594
13189
3297
3297
Confidence intervals calculated at the 90% confidence level. Percentages for material types may not total 100% due to rounding
Inert matter has increased from 15% in 2003 to 29.60% in 2013 (an increase of 15%). This can
be attributed to the practice of inclusion of the street sweepings, drain silt and construction and
demolition waste in MSW. High inert content increases the densities of MSW. There was small
variation in other organics, paper and plastic between 2003 and 2013. The percentage of glass
and metal has decreased from 6.3% to 3% and 8.6% to 4% respectively, from 2003 to 2013.
Waste categories differ across the different districts (Fig. 3)
Figure 3: Variation of MSW across the component zones of the Douala municipality
The average per capita generation rates were 0.46, 0.49, 0.57, 0.49 and 0.48 kg capita_1 day_1 for
Douala I, II, III, IV and V respectively, however, this variation was not significant(𝑥 2 (4,0.05) =
9.4877; 𝑝 = 0.15). The liberalization of the private sector in Cameroon and the growth in small
and medium size enterprises in the Douala municipality might have balanced standard of living,
population habits, income distribution and population density across the different districts that
make up the municipality.
3.2.
Waste Generation and Collection
Waste products from the Douala municipality originate from a variety of residential, commercial,
institutional, construction and demolition, municipal services, treatment plant sites, industrial and
agricultural activities. Regular collection and processing is carried out by HYSACAM. On
average, between 60 - 70% of wastes generated by the municipality are collected are collected
while about 30% remains uncollected (Fig. 4)
Figure 4: Trend in MSW collection rate in Douala (2003-2012)
One of the reasons for HYSACAM’s success is its ability to adapt to changing requirements. It
has recruited trained personnel and deployed modern, well-maintained equipment– key
requirements for managing waste in towns and cities with as many as several million. To reach
harder-to-access neighbourhoods, HYSACAM has developed pre-collection agreements with
community based organisations that gather the waste from the inaccessible areas and transfer it
to the company’s collection bins. As a result, it is able to achieve collection rates of 80-90%
(Parrot et al., 2009).
Generally, municipalities have failed to manage solid waste due to financial factors. The huge
expenditure needed to provide the service, the absence of financial support, limited resources, the
unwillingness of the users to pay for the service (Sujauddin et al., 2008) and lack of proper use of
economic instruments have hampered the delivery of proper waste management services.
Sharholy et al. (2008) indicated that the involvement of the private sector is a factor that could
improve the efficiency of the system.
The estimated per capita generation rate varies between 0.46 to 0.53 kg per person per day. The
results agree with the range estimated by other recent research findings from other similar cities
of the developing countries (Table 3)
Table 3: MSW generation in selected major cities from Africa
City-Country
Maputo-Mozambique
Kumasi-Ghana
Abuja-Nigeria
Yaounde-Cameroon
Bamako – Mali
Population
(Millions)
1.24
1.89
2.5
1.72
1.5
MSW generation rate
(Kg.capita-1.day-1)
0.23
0.48
0.44-0.66
0.52
0.33
Author (s) and year
Hunger and Stretz (2006)
Asase et al (2009)
Ogwueleka,2009
Parrot et al (2009)
Samake et al (2009)
The net per capita discard rate (after recycling, composting, and combustion) is estimated at 0.23
Kg per person per day. While recyclables have been at a steady decline over time, the
compostable, like the non-recyclables have been on a steady rise (Fig 5).
Figure 5: Variation of physical composition of MSW
Non-recyclables (inerts, special wastes and others) have increased from 15% in 2003 to about
40.60% in 2013 (an increase of 25.6%). This could be attributed to the practice of inclusion of
the street sweepings, drain silt and construction and demolition waste in MSW. On the other
hand, the decrease in recyclables can be attributed to the increase in recycling enterprises in the city over
time, which has in turn given rise to the practice of scavenging in the city in general, and at the landfill
area in particular, providing employment to hundreds of unskilled workers. The result is a large
proportion of compostable and non-recyclable material content both at collection and landfill sites.
Thus, if a recycling program for MSW is well conducted, it not only could potentially recover, reuse,
and/or regenerate useful resources, but also could reduce the amount of waste to be disposed. Solid
waste treatment at source can help to divert over 60% of the total waste and could lead to
enormous savings in the cost of waste collection, transport and disposal.
To achieve sustainable municipal waste management practices, the challenge will be to reduce
the amount of solid waste generated, while increasing the amount of waste diverted from
landfills through recycling and other initiatives in an economically feasible way. It is also
necessary to realise that economic growth cannot come at the expense of the environment. The
importance of understanding the implications for the diversion and recycling of these materials
merits an individual discussion on waste treatment in the municipality.
Solid waste management practices include: collection, recycling, solid waste disposal on land,
biological and other treatments as well as incineration and open burning of waste. The recycling
of materials (paper, plastics, metals, and glass) in the Douala municipality is practiced by several
small and medium size enterprises. These include SOCAVER for glass; SIPLAST,
POLYPLAST, SOFAMAC and SICA for plastic waste, African Recycling Industry, ICRAFON
and BOCOM Recycling. COMAGRI and SOCAVER are leaders of glass recycling in the city
with an estimated production of 110 tons of glass per day. Metals are sold to industry– mainly in
China and India. Hence, the recycling rate of MSW has risen to about 28% over the past decade
(Fig. 6).
Figure 6: MSW Recycling Rates, 2003–2013
In moving towards sustainable waste management, the Douala municipality must adopt multiple
strategies that target a range of materials and follow the principle waste management hierarchy:
first reducing waste at the source, re-using materials when possible and recycling what remains.
Should recycling matter go to the recycling industries, this will reduce the burden on landfill and
environmental effects as landfill will also be minimized. Although recycling is a definite step
towards waste reduction, processing materials for re-use still requires the use of energy and
resources (Finnveden and Ekvall, 1998) and recycling alone will not create an environmentally
sustainable waste management program (Armijo de Vega et al., 2003). It seems that by targeting
specific material categories, the municipality could achieve marked reductions in the amount of
waste generated and sent to landfill.
An examination of the paper recovered from the municipality waste stream indicates the
following sequence of material prevalence: mixed paper → Office papers → Newspapers →
Magazines &catalogues. Though targeting paper products in the order of their occurrence could
yield the highest rates of waste diversion and reduction, a number of technical and financial
complexities may prevent the implementation of this approach. For example, since office paper
represents 5.2% of the recyclable material found in the waste stream while magazines and
catalogues characterizes only 1.9%, it would be logical to aim source reduction efforts on office
papers. Currently there is no alternative for that. Determining the alternative that would be truly
more environmentally friendly choice would require a comprehensive life cycle analysis, which
may not be achievable based on available data.
One financially and technically feasible strategy to encourage the community to reduce paper
consumption is through information dissemination techniques (Amutenya et al., 2009) such as
education and awareness campaigns. In moving towards sustainable waste management, the
Douala municipality must adopt multiple strategies that target a range of materials and follow the
principle of waste management hierarchy: first reducing waste at the source, re-using materials
when possible and recycling what remains. Well cited campaigns for reducing the generation of
paper waste include double-sided copying, reutilization of unused side of paper for
memorandums and reports and the use of electronic mail as the main source of communication
(Armijo et al., 2008). The importance of understanding the implications for the diversion of these
materials merits an individual discussion on other methods of waste treatments in the
municipality.
Composting is a waste management practice that allows for the transformation of organic waste
into a stabilized product. Composting and anaerobic digestion of MSW are strategies that are
likely to be employed to reduce waste generation and to recycle nutrients. Organic wastes are
typically the heaviest component of a waste stream, thereby costing the most money to dispose
of, and have the highest potential to emit greenhouse gases, once buried in a landfill (Diaz et al.,
1993). The high financial and environmental costs of improperly disposed organic wastes make
this component especially important when considering opportunities for increased waste
reduction and diversion (Tammemagi, 1999).
Diverting organics from the waste stream has proven to be difficult, not only for municipalities
but also for the regions in which they are located. Currently, Cameroon lacks a nation-wide
strategy for managing compostable organics in the waste stream and as a result, policies for
dealing with this material vary significantly among municipalities. For example, composting of
green waste is being experimented at the landfill site for the production of organic fertilizers
(compost). Unfortunately, the market is dominated by competition from chemical fertilizers.
Furthermore, the city is not an agricultural area it is extremely difficult to find individuals who
practice composting at home.
Incineration is one of the waste treatment technologies that involve the combustion of organic
materials and other substances. Incinerator process converts the waste into bottom ash,
particulates and heat, which can be used to generate the electric power. In recent years, the
concepts of protection and preservation of the environment have gained a prominent place in
modern enterprises. Both BOCOM International (Waste Treatment and Incineration Company)
and BOCAM (Industrial Waste Management Company) - an ISO 14001 certified companies are
leading companies in waste incineration in Cameroon. In partnership with Mobil Oil Cameroon,
BOCAM collects used oil from Project work sites, processes the oil at a facility in Douala and
sells the treated oil to a nearby cement kiln for use as a fuel. Companies interested in ecoresponsible management solutions are signing memoranda of understandings with BOCAM for
the treatment of their waste products. If well managed, incineration can be a renewable source of
energy. However, the environmental pollution resulting from incineration is a call for concern.
Again, incineration significantly reduces, but does not eliminate, the volume of material to be
disposed.
3.3.
Variation of MSW characteristics with time
The monthly per capita variation of MSW in the municipality showed small fluctuations
throughout the year. There is for example, a noticeable decrease from January to February with a
minimum in February, then a sharp rise until the month of March (Fig. 7).
Figure 7: Monthly per capital generation of MSW
This corresponds to the peak of the dry season whereby, putrescibles which constitute over 50%
of the waste streams limited in supply. The high generation rates from February to April are
related additional wastes resulting from the consumption of drink products in plastic containers
(water, fruit juice, dairy products, artificially-flavored drinks, etc.). From April to June, average
per capita generation was constant. This corresponds to the rainy season when there is a general
drop in food supplies to the city as many food crops are not yet ready for harvest. From July to
August, food crops are ready, their supply to the city increase leading to an increase in
putrescibles oncemore and the cycle continues, beginning from October, the start of the next dry
season. The rise by year end could be explained by the numerous feasts (Christmas, New Year,
other cultural festivals) which traditionally characterize this period each year. On the whole,
while inert, metal and glass had higher proportions in the dry season, plastic, organic and special
wastes had a significantly higher proportion in rainy season.
Hypothesis tests were conducted to investigate differences in seasonal composition of MSW over
the past eleven years beginning 2003 and ending in 2013. For instance, for “Other organics”
category, the null hypothesis, using the independent sample t-test was H0: μ = 50.25 and as
alternate hypothesis was HA: μ ≠ 50.25. Similar hypotheses were created for the remaining
categories. Table 4 presents the hypothesis test results and confidence intervals.
Table 4: Statistical comparison of the seasonal composition of MSW from 2003-2013
Waste category
Mean (±SD)
Hypothesis
t
Dry
Rainy
Ho
Ha
Glass
3.42 ±1.26
3.33±1.28
μ =3.4
μ ≠3.4
.027
Inerts & Others
2.72 ±.877
3.04±1.5
μ =2.72
μ ≠2.72
Metal
2.90 ±.600
2.5±.945
μ =2.90
Paper
14.65 ±.98
14.47±.96
Putrescibles
45.0 ±4.0
Plastic
Special
p
Confidence Interval
Lower
Upper
0.735
-1.78
-1.78
-.382
0.172
-2.20
1.56
μ ≠2.90
.781
0.338
-.78
1.61
μ =14.65
μ ≠14.65
.294
0.730
-1.19
1.55
47.0±1.41
μ =45.0
μ ≠45.0
-.651
0.404
-11.78
7.78
16.42±3.1
16.15 ±2.6
μ =16.42
μ ≠16.42
.154
0.715
-3.67
4.20
1.27 ±1.17
2.90 ±2.0
μ =1.27
μ ≠1.27
-1.465
0.287
-4.13
.880
At α = 0.05, the results indicated that all categories resulted in P > α, with extreme critical values
for the test statistic t. We thus fail to reject the null hypothesis and conclude that, waste
composition do not significantly differ from season to season.
4. Conclusion and Recommendations
The study was to analyses municipal solid waste generation, composition, and management in
the Douala Municipality of Cameroon as a first step towards enhancing the sustainability of the
current waste management system. The results presented in this paper emphasize the potential
for municipalities to achieve higher rates of waste diversion as well as the challenges that
municipalities may face in the shift towards sustainable MSW management. Paper and paper
products, disposable drink containers and compostable organic material represented three of the
most significant material types for targeted waste reduction and recycling efforts. Hypothesis
tests were conducted to compare the municipal waste composition among seasons and districts
and indicated that these had a significant effect on composition. The rainy season had a higher
proportion of organic waste, whereas dry season produced higher proportions of, wood, metal,
concrete and glass waste. A statistical comparison with a previously published waste
characterization data obtained from HYSACAM indicated that the proportions of all waste
categories have not changed significantly. These insignificant changes in waste composition
indicate that over the past decade, there has been no new trend in population lifestyle, which
must be considered when planning future waste treatment scenarios. The following educational
and policy techniques are highly emphasized:

promulgation of the waste management bill, which will create an enabling environment
for enforcement and will provide a legal framework within which environmental impact
can be implemented; political motivation (waste management must be seen as a priority
at all levels of government);

education and awareness (waste management must be taken as a priority among
businesses and communities, to encourage waste minimization and recycling to enable
acceptance of instruments); development of capacity at all levels of government (for
administration, monitoring and enforcement of instruments and of illegal dumping,
billing for services to enable cost recovery); increased access to resources for waste
management departments (to allow development of capacity, recovery of costs, and
improved waste management services);

Although the data collection samples in percentile and tonnage are statistically reliable,
the idea of collecting data with a higher level of detail within subcategories of dominant
waste classes is inevitable. To design an efficient management system, that consider
the appropriate final treatment of MSW based on their physical and chemical
characteristics, it is important to consider not only information on the generation and
composition of MSW but also their physical and chemical characteristics. Therefore, it
became necessary for future research to include the physical (humidity, ashes, and
specific heat) and chemical characteristics (pH, organic matter and sulfur) of MSW. This
information could be very important when considering the design of biogas and other
energy plants with wastes as primary products; and

Finally, further analysis on waste trends with respect to household, commercial, and
industrial sectors is also necessary. Because organic waste treatment options depend on
waste origin, it is important to determine whether the organic waste is pre- or postconsumed and its moisture content. Such information will allow for comparisons between
various options, such as composting, biogas production, or utilization as animal feed.
However, whether the quantities produced are sufficient for a large-scale waste
management system requires further analyses in terms of economies of scale, operating
capacities, and break-even analysis. The data presented here will facilitate a thorough
compilation and evaluation of the inputs and outputs of such analyses
Acknowledgments
The authors would like to acknowledge the authorities of the Douala municipality and
management of the PK10 landfill for allowing us access to some of their in-kind support. We are
also very grateful to the Cameroon government for assisting research in higher education through
the modernisation allowance. Acknowledgements are due also to all anonymous reviewers for
their inspiring and constructive comments on the paper. We show our appreciation to colleagues,
friends, municipalities’ members and workers hat have contributed with valuable information.
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