Municipal solid waste management in Kinshasa, the capital of the Democratic Republic of the Congo (DRC), represents a major urban challenge with health, environmental, and economic consequences. Kinshasa has an estimated population of over 17 million inhabitants, generating approximately 7000 tons of waste per day (
World Population Review, 2024
), which exerts intense pressure on outdated equipment and an unsustainable management plan for urbanization. This scenario has concerning consequences; among others, there is an increase in diseases associated with poor sanitation, pollution of rivers and groundwater, and degradation of the urban environment (
Pembi et al., 2022
). Organisms faced with non-ecological forms of waste disposal, such as throwing waste into ravines, burying it in plots, open-air burning, or dumping it in the street and gutters, maintain the living environment in a state of advanced fragility and precariousness (
Balet, 2016
). These practices, coupled with the lack of adequate systems to drain wastewater and rainwater, are the source of erosion phenomena, landslides, and floods, and maintain water stagnation in the streets, creating conditions conducive to the spread of disease vectors such as malaria and typhoid fever.
The Kinshasa Sanitation and Public Works Agency (RATPK), currently RASKIN, which is a technical service of the Ministry of Environment (
Raymond, 2021
), is among the service providers contributing to waste management in Kinshasa. While this agency has taken over instruments bequeathed by the Kinshasa Urban Rehabilitation and Sanitation Support Project (PARAU) funded by the European Union, its activity is severely hampered by limited financial resources and lack of equipment. Municipalities struggle to meet the challenges related to waste collection, transport, treatment, and disposal (
Raymond, 2021
). This situation is exacerbated by the absence or lack of follow-up of a clear regulatory framework and ineffective coordination between the various actors involved in waste management.
Moreover, the rapid population growth and unplanned urban expansion of Kinshasa (
Diabagate & Konan, 2019
) exert pressure on available infrastructure, making the task even more arduous. Waste treatment is hardly better in the Lukunga health district. The district produces far more waste than it can normally manage, leading to anarchic waste aggregates, degrading the living environment, threatening ecological resources, and endangering the population’s health. This highlights significant structural and cultural problems, such as low levels of environmental awareness among residents.
Furthermore, many citizens are unaware of the implications associated with their non-ecological practices on public health and the local ecosystem (
Ali et al., 2015
), due to the effects, for example, of pollution on soil and waterways. The absence of a true waste management culture results in few or no community initiatives aimed at encouraging more responsible behaviors. Moreover, the inconsistency of political will and coordination are challenges for developing and executing such waste management policies (
Kulkarni, 2020
). Disorganized waste collection and treatment services, as well as deficient and aging infrastructure, exacerbate the crisis.
Research in different sectors of the Congolese capital (DRC) highlights poor management of waste disposal that is not only non-ecological (
Kang et al., 2023
) but also constitutes a danger to residents’ health. However, household waste in Kinshasa has a major characteristic in its composition: organic matter (
Kaushika et al., 2016
). Such a characteristic presents significant potential for establishing composting and recycling channels (
Holenu Mangenda et al., 2020
), but this potential is still largely untapped due to the lack of adequate infrastructure and incentive policies. Such valorization of organic waste (
Dieng et al., 2018
) would not only minimize the volume of waste to be disposed of but would also provide a resource opportunity, particularly for urban and peri-urban agriculture.
In this complex context, an essential research question identifies its object: “What is the current state of solid household waste management in the Lukunga health district, and what context-specific strategies could improve its effectiveness?” The empirical analyses conducted in this research aim to provide useful recommendations to decision-makers, urban managers, and civil society actors. By addressing these solutions or recommendations comprehensively, it could also offer an opportunity to transform current challenges into economic development solutions, thus improving the environmental and social resilience of the inhabitants of the Lukunga district.
2. Material and Method2.1. Study Area
This study was conducted in the Lukunga health district (
Figure 1
), in the
Figure 1. Lukunga health district location.
province of Kinshasa, the capital of the DRC. This district is part of the decentralized health system of this city. Lukunga was one of the urban health districts established in Kinshasa in the context of Congolese health system reforms that were launched with the intention of decentralizing health services at the local level.
2.2. Population and Sampling
The study population included all residents of the Lukunga health district, including people of all socio-economic and demographic statuses, estimated at over 10,000, without any distinction. The survey unit was the residential plot, while the statistical unit was a household. We used a three-stage stratified random sampling (
Voxco, 2022
), combining purposive and random choice approaches to ensure representativeness of the studied population.
Stage 1: The study area was stratified into five geographical sectors according to urban and socio-economic criteria. Such stratification was designed to ensure adequate spatial coverage based on the characteristics of the assessed health district. Stage II involved the selection of twenty (20) avenues in these sectors proportionally to their size as a Probability Proportional to Size sampling method (
Gavin, 2021
). This approach ensures that some of the more densely populated areas have better chances of being selected, representing a more homogeneous distribution of the population. Sites were selected based on criteria such as population density, socio-economic level, accessibility, and variety of habitat types. This selection was validated by a panel of local urban planning experts to ensure its relevance and representativeness.
In the third stage, we applied Cochran’s formula (
Rahi, 2017
) to identify the optimal sample size for an unknown population of over 10,000 inhabitants. The applied formula was: n = Z2*p*(1 − p)/e2 with a confidence level of 95% and a margin of error of 5% where: n = 1.962 × 0.5 × 0.5/0.052 = 384. To compensate for refusals/incomplete questionnaires, we increased the sample by 10%, bringing the number of households to contact to 422.
In each avenue, households were selected using a systematic sampling step with a random start. From there, the sampling step was estimated by dividing the estimated number of households per avenue by the number of households that needed to be drawn from it. We conducted in-depth analyses of non-respondent demographics, including follow-up interviews with a subsample of non-respondents for apparent biases. Data quality control was performed through interviewer training, field verification, and consistency analysis of the obtained data. Thus, a pilot study with a small sample (10% of the final size) was conducted to test and improve the adequacy and usefulness of the data collection instruments and sampling procedures employed.
Participants were informed of the study objectives, protocol implementation modalities, and their right to withdraw from the study at any time. All participants provided informed consent before inclusion in the study in accordance with ethical standards.
2.3. Measurement
In terms of measurement, average estimates of household solid waste quantities generated per day in the 384 surveyed households were made. For this, each house was provided with a plastic bag for waste. All they had to do was deposit all solid waste generated by the household during one day in this bag. Quantification was carried out for three consecutive days using a “Salter” brand scale (1000 mg). The study was conducted from September 25 to October 25, 2024.
2.4. Study Limitations
We wish to acknowledge certain limitations of this study. In fact, the study was limited to the Lukunga health district, which would tend to limit the generalization of results to the entire city of Kinshasa. Although the sample of 384 individuals is statistically significant, more information could have been obtained if the size had been larger. Interviews or focus groups, which could have included mapping, would have provided a greater richness of results.
3. Results
The results of this
Table 1
indicate that the 384 subjects were diverse in terms of sociodemographic characteristics. The sample shows a slight male predominance (54.7% men versus 45.3% women) and an age distribution evenly spread across the twenties, thirties, and beyond (each group representing about a quarter of the sample). 36.5% had a low level of education. Monthly incomes were mainly low to moderate, with 41.7% of respondents earning less than $100 and 37.5% earning between $100 and $300. The largest socio-professional category was the general population (residents/households) (46.9%), followed by merchants (15.6%). Regarding housing, most (62.5%) were tenants, 31.2% owners. This gives a balanced overview of the studied population, which is mainly composed of low-income people and tenants, which can be beneficial for understanding behaviors or opinions based on these variable sociodemographic attributes. Household size was mainly composed of 6 or more people, 70.3%. These characteristics could be useful for guiding targeted interventions or policies towards this population.
As shown in
Table 2
, food waste, packaging, and papers had the largest share with 58.9%. For optimized waste management, this means that reducing or recycling this type of waste should be a priority. The second largest category, food waste and packaging, accounted for 15.1%. Papers came next with a contribution of 11.5%.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 1. Distribution according to participant profile.
Variable
Categories
Number. n = 384
Percentage (%)
Sex
Male
210
54.7
Female
174
45.3
Age
Less than 25
96
25.0
25 to 30
88
22.9
31 to 36
98
25.5
Over 36
102
26.6
Education Level
Low (primary or illiterate)
140
36.5
Secondary
124
32.3
University or Higher
120
31.2
Monthly income
Less than 100$
160
41.7
Between 100$ and 300$
144
37.5
More than 300$
80
20.8
Socioprofessional categories
General population (resident/household)
180
46.9
Merchant
60
15.6
Work at à NGO
40
10.4
State agent
36
9.4
Health worker
28
7.3
Waste picker (informal sector)
40
10.4
Type of housing
Tenant
240
62.5
Landlord
120
31.2
Other
24
6.3
Household size
Less than 3 persons
26
6.8
3 to 5 persons
88
22.9
6 and more
270
70.3
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 2. Distribution of waste types.
Variable
Categories
Number
n = 384
Percentage
Management method
Food waste and packaging and paper
226
58.9
Food waste and packaging
58
15.1
Paper
44
11.5
Food or organic waste
20
5.2
Packaging (plastic. cardboard. etc.)
20
5.2
Glass
6
1.6
Metal
6
1.6
Others
4
1.0
Table 3
shows a diverse distribution of waste management methods. It reveals that the most used method was “Dumping garbage in nature”, representing 62.2% of cases, which constitutes a serious environmental problem. A less widespread practice, reuse, was practiced by only 22.1% of respondents, indicating some adoption of acceptable behaviors. Other options, such as sorting and recycling (1.3%), composting (0.5%), and energy recovery (1.0%) were barely mentioned.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 3. Distribution according to waste management methods.
Variable
Categories
Number
n = 384
Percentage
Management method
Sorting and Recycling
5
1.3
Dumping in nature
239
62.2
Composting
2
0.5
Landfilling
16
4.2
Sorting and incineration
5
1.3
Sanitary burial
7
1.8
Energy recovery
4
1.0
Reuse
85
22.1
Source reduction
19
4.9
All these methods
2
0.5
Statistics confirmed that management methods are used very unevenly: the average is 38.4 people per method (about 9.98%), but a significant standard deviation means that some practices are massively dominant. The median showed that only 6 people or fewer used 50% of the methods. This data shows a strong emphasis on a limited number of behaviors, particularly waste disposal in the natural environment. This is a manifestation of a lack of awareness or accessibility to sustainably conscious practices.
The results of
Table 4
can help assess the complexity (i.e., degree of connection, set of possible actions, associated resources, and their availability) of overall household waste management problems. The major issue raised was that there are no waste treatment facilities (16.4%). The most common challenges were inefficiency of municipal services in waste management (15.4%), presence of illegal dumps (15.1%), followed by an inefficient waste disposal system (14.4%). Infrastructure failures, administrative gaps, and citizen behaviors are interconnected, and these findings highlight this interdependence. Significant challenges also included waste collection problems, such as insufficient dumpsters and collection points (9.6%), inadequate collection services (9.4%), and poor landfill management (9.4%). Notably, the relatively low mention of challenges regarding correct waste sorting and recycling could suggest that these practices are not yet priorities in the studied community. The fragmentation of difficulties specific to each area reinforces the need for comprehensive improvements in household waste management, including better infrastructure investments, improved municipal services, communication campaigns for awareness, and gradual implementation of sorting and treatment practices.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 4. Distribution according to the main challenges encountered in household waste management.
Variable
Categories
Number
n = 384
Percent
Challenges encountered
Lack of information on waste sorting
6
1.6
Poor landfill management
36
9.4
Some of these challenges
2
0.5
Insufficient recycling bins
6
1.6
Irregular collection
24
6.3
Poor waste management by municipal services
59
15.4
Lack of awareness or education
57
14.8
Lack of waste treatment infrastructure
63
16.4
Presence of illegal dumps
58
15.1
Lack of adequate collection services
36
9.4
Lack of garbage bins and collection points
37
9.6
The results of
Table 5
showed that most respondents (45.8%) suggested improving public awareness and education, emphasizing behavior change in favor of health and the environment. 38.0% of respondents recommended stricter sanctions for illegal dumping and aimed to reduce incentives for negligent actions. An improvement in waste collection frequency was highlighted by 9.9% of respondents as an area of dissatisfaction with current services.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 5. Distribution according to suggestions.
Variable
Categories
Number
n = 384
Percent
Suggestions
More collection points for recyclables
6
1.6
Better public awareness and education
176
45.8
Improved waste collection frequency
38
9.9
Installation of community composters
14
3.6
Strengthening sanctions against illegal dumping
146
38.0
Other (to specify)...
4
1.0
The results show that respondents tend to prefer options aimed at changing individual and collective behaviors (education and sanctions) rather than approaches limited to infrastructure (which can be circumvented). This perception indicates that the cornerstones of challenges associated with waste management are not rooted in the lack of infrastructure or services but rather in human practices.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 6. Estimation of waste production.
Household size
Frequency
Percent (%)
Average production (kg/jour)
Total production (kg/jour)
Less than 3
26
6.8
16.64
16.64
3 to 5
88
22.9
56.32
56.32
6 and more
270
70.3
172.80
172.80
Total
384
100.0
245.76
245.76
For
Table 6
:
1) Household distribution:
2) Waste production:
3) Average by household size:
These results reflect a relationship between household size and waste production, with larger households producing more waste.
Estimation of waste production per household:
The average waste production per household was approximately 3.60 (kilograms/ day). This number is calculated by obtaining the total waste production divided by the total number of households.
Total population estimate:
Using the following assumptions for household sizes:
The average waste production per person is 0.64 kg/day. This is done by dividing the total waste generated (1381.76 kg/day) by the estimated total population of 2160 people. Total waste production (
Figure 2
) provides more information below.
Figure 2. Total waste production by type.
Figure 2
shows that food waste, packaging, and papers are the largest category with 750 kg/day and the largest share of production. This includes food waste and packaging: the second has about 300 kg/day. Papers rank third with about 200 kg/day of waste produced.
The graph above highlights the importance of food waste combined with packaging and papers in waste management. Efforts to reduce or recycle this category should be prioritized to maximize environmental impact.
Logistic Regression
The results of this
Table 7
show that the variables sex, age, income, socio-professional category, and type of housing have a p-value < 0.05. Statistically, there is evidence of a significant influence on the development of large-scale municipal waste management problems. These findings highlight their importance as determinants requiring special attention in strategy formulation.
<xref ref-type="bibr" rid="scirp.141431-"></xref>Table 7. Logistic regression between sociodemographic categories and challenges.
Variable
Coefficient (βbeta)
p-value
Odds Ratio (OR)
Constant
−1.30
0.05
0.27
Sex (male)
0.75
0.03
2.12
Age (>36 years)
0.68
0.01
1.98
Education level (low)
−0.45
0.10
0.64
Income (<100$)
1.25
0.001
3.52
SP category (population)
0.85
0.02
2.34
SP category (population)
1.05
0.001
2.85
Regarding the OR (Odds Ratio), the analysis results indicated that certain sociodemographic factors notably increase the likelihood of facing significant waste management difficulties:
Figure 3. Data projection after principal component analysis (PCA).
Therefore, these results highlight the relevance of sociodemographic specificities in strategies and the significant role that education and awareness play in improving waste management practices. To complete the analysis comprehension (
Figure 3
), we tried to sort the principal components.
Principal Component Analysis (PCA)
Through
Figure 3
, the PCA allowed for dimensionality reduction to two principal components, providing insights that could help improve waste management:
Waste management methods (blue curve)
Improvement suggestions (green curve)
These two approaches (education and legislation) are highly complementary and serve to guide the population to change behaviors. Such integrated strategies that combine awareness, incentives, and penalties could help evolve current practices towards more environmentally friendly alternatives. The underlying logic of this approach is to address some of the main waste management challenges resulting from the PCA results, including improvements in waste production and management, while tackling behavioral changes at the individual level.
4. Discussion
This discussion of results highlights the complex realities and challenges associated with waste management in low-income country contexts. It draws on an analysis that integrates structural, behavioral, and socio-economic dimensions, as well as strategic recommendations for rectification towards sustainable management.
4.1. Sociodemographic Profile and Behaviors
As previously mentioned, the studied sample, composed mainly of low-income households and tenants, reflects trends introduced in the literature, notably by Wilson et al. and Guerrero et al. Due to their socio-economic vulnerability, these population groups are often more exposed to unregulated waste management practices (
Guerrero et al., 2013
). Furthermore, Kulkarni et al. confirm that income-related groups do not focus on waste sorting and demonstrate how economic status plays a vital role (
Kulkarni, 2020
). For Abarca Guerrero et al., these studies confirm that low-income households are the largest waste generators on average, as they lack the resources to reduce waste and tend to use more disposable items (
Guerrero et al., 2013
).
4.2. Waste Management Methods
Waste disposal in nature (62.2%) also constitutes a major environmental problem in developing countries. This high rate confirms the findings of Guerrero et al. and Zurbrugg et al. on the environmental effects of informal waste disposal practices (
Zurbrügg et al., 2012
). Today, uncontrolled waste disposal is still common, occurring mainly due to a lack of collection services and awareness. This is particularly concerning in densely populated urban centers, where the amount of waste generated often exceeds waste management capacity. In Ghana, each day, out of a daily generation of 810 tons, only 27% is collected, resulting in 594 tons still untreated, thus becoming a significant environmental and public health risk (
Zurbrügg et al., 2012
).
Nie and Wadehra prove that an integrated approach encompassing not only access to adequate infrastructure but also awareness campaigns can radically change practices, even in low-income contexts like Bangladesh (
Wadehra & Nie, s.d.
). Their study revealed that with curbside collection of inorganic waste and information campaigns, household-level waste sorting could be clearly improved. Community members can also play a significant role in better waste management solutions through environmental education.
4.3. Waste Management Challenges
A major challenge in solid waste management lies in a lack of infrastructure and awareness. Socio-economic aspects further complicate this problem, as observed in the study by Tsheleza et al., conducted in South Africa. Their research reveals that socio-economic status and household structure determine solid waste generation and management systems (
Tsheleza et al., 2022
).
These findings underscore the need for a systemic vision of the waste problem, where it is necessary to find a balance between these interconnected elements. In fact, daily waste-related problems still concern low-income communities, although the South African government has attempted to implement better waste management practices. This makes the pro-poor orientation of the solid waste management system critical. Moreover, Nie and Wadehra recently showed, through a study in Nepal, that a combination of information campaigns and construction of adequate infrastructure (e.g., curbside collection of inorganic waste) works. This approach has been particularly successful in promoting waste sorting at the household level, even in low-income contexts (
Wadehra & Nie, s.d.
).
Such observations justify the need for a holistic approach aimed at simultaneously addressing infrastructure, education, and socio-economic issues. This is why the development and extension of waste management infrastructure accessible to all, educational campaigns, and policies taking into account the socio-economic realities of local contexts become crucial to unleash our potential for sustainable waste management in urban and peri-urban communities.
4.4. Measurement
The relationship between household size and waste production found in this study is consistent with general trends reported by other studies. However, the scale of this relationship seems to be more pronounced. For example, the 2011 EPIC survey mentioned in the research results indicated that waste generation increases with the number of people in a household, but generally at a decreasing rate. However, the current study showed a more linear increase per household, with those in the 6 people and above range producing an average of 4.16 kg/day, followed by households with 3 to 5 people with an average of 2.56 kg/day, and households with less than 3 people producing an average of 1.28 kg/day. The per person waste production estimated at 0.64 kg per day in this study is within the normal range observed on average in other urban contexts of developing countries. For instance, the specific production of household waste in Yaoundé (Cameroon), according to Ngambi, varies between 0.5 - 0.8 kg/person/day depending on housing standards and an average of 0.62 kg/person/day. However, Dieng et al. found that the average production of household solid waste was 3.60 kg per day/household, which, considering a similar household size in this study, would have given a comparable per person production (
Dieng et al., 2018
).
Since a large part of what we throw away consists of organic waste and packaging, there is much value in recycling or composting. Mbue et al. further affirm this observation with general recommendations to help improve waste management in African cities in their studies in Yaoundé (
Zhang et al., 2024
).
4.5. Statistical Analysis and Impact of Sociodemographic Factors
The data show that education has a strong influence in decreasing existing waste management issues (OR = 0.64). This correlation is confirmed by research conducted by Tsheleza et al., emphasizing that awareness and community participation in recycling practices are the greatest contributors to reducing waste generation rates (
Tsheleza et al., 2022
). These results highlight the vital need for environmental education in general, especially in low-income settings. Three steps progressively increase interest and understanding of waste-related issues (interest, understanding, action), and indeed, environmental education builds an understanding of waste through these three levels. This gradual method encourages the establishment of safe waste disposal routines such as sorting and recycling. Moreover, young people are particularly supported in this change when this sustainable lifestyle is taught at school, allowing this message to be part of not only their lives but also enabling children to share the message with their parents.
4.6. Improvement Suggestions and Perceived Priorities
The principal component analysis (PCA) shows that awareness, education, and more severe sanctions for illegal dumping are the most important factors in waste management. This ranking attempts to find the right balance between pedagogy and deterrence. The work presented by Nie and Wadehra in Nepal offers valuable evidence on the effectiveness of combined interventions (
Wadehra & Nie, s.d.
). Their research shows that the separation of inorganic waste at the household level is positively stimulated by the deployment of an information campaign aimed at targeting such behavior. Indeed, this pragmatic approach that combines awareness and logistical facilitation has proven particularly useful in the Nepalese context.
Furthermore, providing tangible solutions by providing appropriate bins and maintaining regular collection services produces a sustainable and measurable result. These types of interventions are more effective than isolated information campaigns, which underscores the need for a holistic approach to waste management. Zaman also supports these findings, reporting that environmental education and incentive policies are critical to achieving more sustainable waste management. His research shows that the mixture of these elements creates an environment that favors the adoption of environmentally friendly behaviors. This integrated effort, combining education, infrastructure, and incentives, is more effective than isolated interventions. It provides a framework for capturing the various aspects of waste management, from awareness to action through implementation through support systems. A comprehensive approach, as described above, is necessary to encourage a sustainable transformation of community waste management behaviors.
4.7. Economic Opportunities and Social Inclusion
The successful development of waste management systems can bring considerable economic opportunities. Recycling and waste-to-energy initiatives can create jobs, boost local industries, and contribute to fostering a green economy. As demonstrated by the example of SWaCH in India, integrating the informal sector with an existing formal waste management system can improve working conditions and income for marginalized workers while increasing the efficiency of waste collection (
Saifi & Jha, 2024
).
4.8. Strategic Recommendations
To address these challenges, a multidimensional approach is essential, combining awareness, infrastructure, inclusion, and innovation:
4.9. Future Perspectives
A multidimensional approach would encompass education, adaptive infrastructure, and policies to reflect local socio-economic conditions. Future interventions should consider other practical, feasible, and culturally sensitive solutions, as well as strengthen environmental education through awareness and community engagement. Integrated and inclusive approaches can also foster progress towards sustainable waste management in low-income contexts.
5. Conclusion
Focused on the Lukunga health district, this analysis provides a detailed and multifaceted exploration of solid waste management challenges in low-income urban settings in Kinshasa. It highlights the structural, behavioral, and socio-economic gaps that constitute barriers to effective waste management, while suggesting strategic pathways for a sustainable and versatile model.
The results are alarming, indicating a critical lack of infrastructure, immense deficits in awareness and sensitization, and the persistence of informal and dangerous forms of management. On the other hand, the study also shows that integrated strategies combining education, community inclusion, and investments in accessible infrastructure can transform these challenges into opportunities.
Based on solid empirical data and citizen feedback, the study provides concrete and localized recommendations, with the aim of extending them to other districts of Kinshasa. Thus, this strategic framework targets policymakers, urban management, and civil society actors and equips them with the necessary tools and innovative solutions to effectively address solid waste management challenges in such contexts. This study paves the way for a sustainable transformation of public health, a progression of urban environmental protection, and a decent quality of life for Kinshasa residents through a holistic, inclusive, and adaptable approach.
The implementation of the recommendations could make waste management in the Lukunga health district a showcase of waste management not only for other districts of Kinshasa but also for other African cities facing similar challenges. We believe that by linking education, adapted infrastructure, and community engagement, this approach can create sustainable change in waste management and ultimately guide the region towards the Sustainable Development Goals (SDGs).
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