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Table 8. SWOT analysis for MSW. Social Register (NSR) compiled by the National Social Safety Nets Coordinating Office (NASSCO) under the Federal Ministry of Humanitarian Affairs, Disaster Management, and _ Social Development has enumerated 12,828,135 Poor and Vulnerable Households (PVHHs), encompassing 52,838,729 individuals residing across 128,249 villages in 737 local government areas (LGAs) in Nigeria, as of September 30th, 2022. A_ strategically designed and _ well- maintained waste management program, with an emphasis on waste-to-wealth initiatives, could uplift these PVHHs from poverty. Engaging these households in waste sorting and recycling could potentially generate approximately N10,000 per week, translating to N40,000 monthly income for a single household—surpassing Nigeria's current minimum wage (Figure 10 for an illustration of this pyrolysis and gasification stand as valuable conversion options to maximize energy yields while minimizing pollution-related consequences (lonescu et al., 2013; Venderbosch et al., 2010). It is crucial to establish coordination among government policies, individual behavioral changes, and concerted efforts by waste managers to drive and implement a robust waste management system. unsorted MoOW exacerbates garbdage-related air pollution, contributing to climate change (Emodi et al., 2015). A waste management model was devised to synthesize the gaps, strengths, opportunities, weaknesses, and threats hindering effective MSW management. Figure 8 illustrates the separation of waste at its source into biodegradable and non-biodegradable components, emphasizing the importance of recycling recoverable waste and_ effectively managing non-combustible waste to avoid exposure to potentially hazardous contaminants. Furthermore, the potential for using food waste and feces to generate biogas and employing the residuals as soil conditioners for agriculture is recognized (Ajieh et al., 2020; Ihoeghian et al., 2023; Isagba et al., 2023; Ren et al., 2018). However, in the presence of hazardous materials,

Table 8 SWOT analysis for MSW. Social Register (NSR) compiled by the National Social Safety Nets Coordinating Office (NASSCO) under the Federal Ministry of Humanitarian Affairs, Disaster Management, and _ Social Development has enumerated 12,828,135 Poor and Vulnerable Households (PVHHs), encompassing 52,838,729 individuals residing across 128,249 villages in 737 local government areas (LGAs) in Nigeria, as of September 30th, 2022. A_ strategically designed and _ well- maintained waste management program, with an emphasis on waste-to-wealth initiatives, could uplift these PVHHs from poverty. Engaging these households in waste sorting and recycling could potentially generate approximately N10,000 per week, translating to N40,000 monthly income for a single household—surpassing Nigeria's current minimum wage (Figure 10 for an illustration of this pyrolysis and gasification stand as valuable conversion options to maximize energy yields while minimizing pollution-related consequences (lonescu et al., 2013; Venderbosch et al., 2010). It is crucial to establish coordination among government policies, individual behavioral changes, and concerted efforts by waste managers to drive and implement a robust waste management system. unsorted MoOW exacerbates garbdage-related air pollution, contributing to climate change (Emodi et al., 2015). A waste management model was devised to synthesize the gaps, strengths, opportunities, weaknesses, and threats hindering effective MSW management. Figure 8 illustrates the separation of waste at its source into biodegradable and non-biodegradable components, emphasizing the importance of recycling recoverable waste and_ effectively managing non-combustible waste to avoid exposure to potentially hazardous contaminants. Furthermore, the potential for using food waste and feces to generate biogas and employing the residuals as soil conditioners for agriculture is recognized (Ajieh et al., 2020; Ihoeghian et al., 2023; Isagba et al., 2023; Ren et al., 2018). However, in the presence of hazardous materials,

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The point of view of the respondents was examined using factor analysis, a Microsoft Excel (2016) add-on program (Liu et al., 2003; Wang and Xiao, 2004). Factor analysis is a technique that assesses if various variables, such as waste management economic significance, attitude toward waste, policy enforcement, and energy value, are linearly related. Each variable is regressed against the factors to test their loadings (Liu et al., 2003). Following the methodology outlined by Liu et al. (2003), factors were identified using principal component analysis (PCA), and data adequacy was evaluated using the Kaiser-Meyer-Olkin (KMO) normalization test. An unrotated factor matrix typically suggests the need for additional iterations, potentially under the theory that the best result is more likely achieved through further iterations under the same conditions. Mixed waste is sometimes collected at individual levels using unclassified containers before being hauled away by waste managers. Issues like poor protective covering of the buckets o1 breakdowns during collection contribute to problems with trucking- related littering. Plastics, batteries, cans, and glass bottles are ofter salvaged for recycling at dumpsites, while other waste is frequently burned directly, further harming the environment and _ posing significant health risks (Albanna, 2012; Ferronato and Torretta., 2019). Data matrices consisting of 61 columns by 46 rows of dependent and independent variables were created from the responses, as shown in Equation 3.
The point of view of the respondents was examined using factor analysis, a Microsoft Excel (2016) add-on program (Liu et al., 2003; Wang and Xiao, 2004). Factor analysis is a technique that assesses if various variables, such as waste management economic significance, attitude toward waste, policy enforcement, and energy value, are linearly related. Each variable is regressed against the factors to test their loadings (Liu et al., 2003). Following the methodology outlined by Liu et al. (2003), factors were identified using principal component analysis (PCA), and data adequacy was evaluated using the Kaiser-Meyer-Olkin (KMO) normalization test. An unrotated factor matrix typically suggests the need for additional iterations, potentially under the theory that the best result is more likely achieved through further iterations under the same conditions. Mixed waste is sometimes collected at individual levels using unclassified containers before being hauled away by waste managers. Issues like poor protective covering of the buckets o1 breakdowns during collection contribute to problems with trucking- related littering. Plastics, batteries, cans, and glass bottles are ofter salvaged for recycling at dumpsites, while other waste is frequently burned directly, further harming the environment and _ posing significant health risks (Albanna, 2012; Ferronato and Torretta., 2019). Data matrices consisting of 61 columns by 46 rows of dependent and independent variables were created from the responses, as shown in Equation 3.
Figure 1. Map of Oleh municipal area.
Figure 1. Map of Oleh municipal area.
Figure 3. SWOT analysis framework. respondents recognize the significant potential value of waste, particularly the value o material (0.6909) and as a soil digesta conditioner. Digestates, e waste as a known for their rich bacterial consortiums as highlighted by Isagba et al. (2023), can ef ectively multiplication, thereby supporting Interestingly, the analysis revea among respondents regarding t led a lac he poten oster microbial plant growth. k of awareness ial of waste for energy production for heating and power generation. Analyses of attitudinal perception The results of factor analysis after rotation and extraction are shown in Tables 1, 2, 3, 4, 5, 6 and 7. In examining Factor 1 with a loading of 0.7168, it is evident that
Figure 3. SWOT analysis framework. respondents recognize the significant potential value of waste, particularly the value o material (0.6909) and as a soil digesta conditioner. Digestates, e waste as a known for their rich bacterial consortiums as highlighted by Isagba et al. (2023), can ef ectively multiplication, thereby supporting Interestingly, the analysis revea among respondents regarding t led a lac he poten oster microbial plant growth. k of awareness ial of waste for energy production for heating and power generation. Analyses of attitudinal perception The results of factor analysis after rotation and extraction are shown in Tables 1, 2, 3, 4, 5, 6 and 7. In examining Factor 1 with a loading of 0.7168, it is evident that
adherence to best practices in waste management, respectively. Notably, a respondent scored 0.7601, acknowledging the adverse effects of indiscriminate waste disposal on health, as corroborated by studies (Ezechi et al., 2017; Igoni et al., 2007; Ugwu et al., 2020). The data presented in Table 3 underscores the evident lack of awareness among respondents regarding the significance of waste managers in ensuring efficient Moreover, some respondents expressed disagreement with conforming to established norms and regulations governing waste disposal (loading factor of 0.5011) and exhibited lower awareness of relevant policies (loading factor of 0.5980)
adherence to best practices in waste management, respectively. Notably, a respondent scored 0.7601, acknowledging the adverse effects of indiscriminate waste disposal on health, as corroborated by studies (Ezechi et al., 2017; Igoni et al., 2007; Ugwu et al., 2020). The data presented in Table 3 underscores the evident lack of awareness among respondents regarding the significance of waste managers in ensuring efficient Moreover, some respondents expressed disagreement with conforming to established norms and regulations governing waste disposal (loading factor of 0.5011) and exhibited lower awareness of relevant policies (loading factor of 0.5980)
Table 1. Factor 1.
Table 1. Factor 1.
Figure 4. Composition of municipal solid waste.
Figure 4. Composition of municipal solid waste.
Table 3. Factor 3. Table 4. Factor 4.
Table 3. Factor 3. Table 4. Factor 4.
Table 6. Factor 6. Table 7. Factor 7.
Table 6. Factor 6. Table 7. Factor 7.
Table 5. Factor 5.
Table 5. Factor 5.
indicating respondents’ lack of understanding about the roles of waste and environmental enforcement agencies. There was also discordance concerning respondents' limited awareness of waste management regulations. Moreover, respondents disagreed about the feasibility of recycling garbage into valuable products, echoing findings from previous studies (Babaei et al., 2015; Kofoworola, 2007). waste management, a critical aspect for maintaining environmental cleanliness as highlighted in previous research (Ezechi et al., 2017).
indicating respondents’ lack of understanding about the roles of waste and environmental enforcement agencies. There was also discordance concerning respondents' limited awareness of waste management regulations. Moreover, respondents disagreed about the feasibility of recycling garbage into valuable products, echoing findings from previous studies (Babaei et al., 2015; Kofoworola, 2007). waste management, a critical aspect for maintaining environmental cleanliness as highlighted in previous research (Ezechi et al., 2017).
Figure 5. Eigenvalues.
Figure 5. Eigenvalues.
Figure 6. (a) Factor pattern. (b) Correlations between variables and factors.
Figure 6. (a) Factor pattern. (b) Correlations between variables and factors.
Figure 7. Factor loadings after varimax rotation.
Figure 7. Factor loadings after varimax rotation.
Challenges encompass improper waste disposal and non-compliance with regulations, which pose health risks (Ferronato and Torretta, 2019), inadequate staffing and training for waste managers, and insufficient funding for waste management facilities. Uncontrolled burning of as job creation, economic opportunities, and alignment with policies. However, the lack of understanding regarding waste management causes deficiencies, leading to environmental concerns (Ezechi et al., 2017; Igoni et al., 2007; Uwadiegwu and Chukwu, 2013).
Challenges encompass improper waste disposal and non-compliance with regulations, which pose health risks (Ferronato and Torretta, 2019), inadequate staffing and training for waste managers, and insufficient funding for waste management facilities. Uncontrolled burning of as job creation, economic opportunities, and alignment with policies. However, the lack of understanding regarding waste management causes deficiencies, leading to environmental concerns (Ezechi et al., 2017; Igoni et al., 2007; Uwadiegwu and Chukwu, 2013).
Figure 8. Summary of observations.
Figure 8. Summary of observations.
Figure 9. Flow chart of proposed waste management model.
Figure 9. Flow chart of proposed waste management model.
Figure 10. Sustainable waste recovery.
Figure 10. Sustainable waste recovery.
the pyrolysis of cellulosic materials include carbon dioxide (COz), carbon monoxide (CO), ammonia (NHs), methane (CHa), hydrogen (Hz), hydrogen cyanide (HCN), and water vapor (H2O). Additionally, the hydrocarbon fraction may contain methane, polycyclic aromatic hydrocarbons (PAHs), and a tar mixture composed of oxygenated species such as phenols, acids, light aromatics, and heavy tars (C1-C36 components) like pyrene and anthracene (Ayomoh et al., 2008). During cellulose degradation, the process may involve cleavage of the glucosidic bond or dehydration, typically occurring below 300°C. This results in gradual charring and depolymerization of the macromolecules. Higher temperatures lead to rapid glucosidic bond cleavage and evaporation of products. Cellulosic pyrolysis follows two pathways: one involves depolymerization forming levoglucosan, which further decomposes into flammable volatiles, and the other leads to complete dehydration to produce water and char.
the pyrolysis of cellulosic materials include carbon dioxide (COz), carbon monoxide (CO), ammonia (NHs), methane (CHa), hydrogen (Hz), hydrogen cyanide (HCN), and water vapor (H2O). Additionally, the hydrocarbon fraction may contain methane, polycyclic aromatic hydrocarbons (PAHs), and a tar mixture composed of oxygenated species such as phenols, acids, light aromatics, and heavy tars (C1-C36 components) like pyrene and anthracene (Ayomoh et al., 2008). During cellulose degradation, the process may involve cleavage of the glucosidic bond or dehydration, typically occurring below 300°C. This results in gradual charring and depolymerization of the macromolecules. Higher temperatures lead to rapid glucosidic bond cleavage and evaporation of products. Cellulosic pyrolysis follows two pathways: one involves depolymerization forming levoglucosan, which further decomposes into flammable volatiles, and the other leads to complete dehydration to produce water and char.
Figure 13. Energy values of MSW. Figure 12. Thermo-chemical treatment methods.
Figure 13. Energy values of MSW. Figure 12. Thermo-chemical treatment methods.
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