Influencing factors on knowledge of aquatic pollution and waste management practices among Nigerian youths

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Due to these developments, the quantity of waste and the variety of contaminants that potentially enter the aquatic environment have significantly increased. Hence, this study not only investigated the specific influencing factors of aquatic pollution and waste management practices among Nigerian youth, but further investigated how geographical variations influence waste management practices. The result shows that employment status, settlement types and geopolitical zones influence the knowledge of aquatic pollution in Nigeria. Additionally, the results showed that the highest quantity of wastes generated was in the northwestern part of Nigeria, although, this could have been influenced by the lack of data on the overall quantity of waste generated from some regions. Also, there is a positive correlation between formal education and environmental awareness and knowledge of aquatic pollution and waste. This study further shows that those generating more waste were more likely to be highly educated likely due to “Planned Behaviour”. Findings from this study have provided information that will be helpful for policymakers to formulate interventions to promote the conservation sustainability of aquatic environments. Aquatic pollution waste production pollution awareness Nigerian youths Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Globally, the negative impacts of waste on human health and local environments, including air, land, and water, are increasingly studied [ 51 ]. Effective waste management, given its inherent complexities, costs, and coordination demands, necessitates multi-stakeholder involvement throughout its entire process. In developing countries, waste management often presents significant challenges to authorities [ 2 ]. Approximately half of municipal budgets are allocated to waste management, and up to 90% of these funds primarily goes to waste collection. Despite these efforts, only about 50% of the population is served, and 40–70% of generated waste is collected, leaving a substantial gap in proper waste management [ 51 ]. In low-income countries, the situation is more pronounced, with only about 10% of waste generated in suburban areas being collected [ 51 ]. This indicates a high risk to public health and the environment in these communities, particularly affecting vulnerable populations such as children and older people living near dumping sites [ 53 ]. As a developing nation, Nigeria faces substantial challenges in waste management within both its urban and rural communities [ 46 ]. Nigeria is recognized as one of Africa's largest producers of solid waste [ 11 ] with projections indicating an exponential increase in waste generation by the end of 2025. With a projected collection rate of 24,263 \(\:\times\:\) 10 3 t/year, a significant portion of this waste is expected to be uncollected, often ending up in the environment, particularly water bodies ([ 51 ]). Ineffective waste management is often exacerbated by a lack of robust policies and regulations, insufficient public awareness, inadequate waste management technologies, improper and unavailable financial resources, and poor governance [ 51 ]; [ 5 ]; [ 22 ]; [ 46 ]. Aquatic environments are critical ecosystems, providing numerous benefits such as transportation, fisheries, irrigation, ecological diversity preservation, environmental balance, and water supply for human consumption. Despite their significance, water resources worldwide are often poorly managed [ 15 ]. Large amounts of garbage are being discharged into the surrounding environment, including rivers and coastal lagoons, as a result of various anthropogenic activities, including industrial, pharmaceutical, agricultural, residential, and municipal sources [ 45 ]. The close proximity of aquatic environments to human settlements frequently impairs ecosystem services, rendering these environments unsuitable, hazardous, and unfit for primary and secondary uses [ 14 ]. Nigeria, like many other countries, is significantly confronted by environmental pollution driven by growing industrialization, increasing urbanization, and inadequate waste management [ 29 ]. Different studies have investigated the knowledge and awareness of Nigerian youth regarding aquatic pollution and waste management across various regions of Nigeria ([ 44 ]; [ 6 ]; [ 16 ]; [ 35 ]; [ 34 ]; [ 43 ]; [ 40 ]; [ 39 ]; [ 25 ]; [ 24 ]). However, these studies often have limitations in their analysis of awareness and knowledge concerning aquatic pollution and waste management across Nigeria's diverse geopolitical zones. Furthermore, there is limited research on how geographical variations influence waste management practices and the correlation between youth understanding of aquatic pollution and their waste generation habits. To move beyond merely documenting the problem, it is therefore crucial to understand the factors that influence knowledge and practices related to waste management and aquatic pollution. While awareness campaigns are important, their effectiveness is often limited without a nuanced understanding of the underlying drivers of behaviour. This study, therefore, goes beyond assessing awareness to investigate the specific factors that influence the knowledge and waste management practices of Nigerian youth. 2 Materials and Methods 2.1 Study area Nigeria, located in the Western part of Africa, spans a land area of approximately 923,768 square kilometres, making it the 14th largest country in Africa. The country is bordered by Gulf of Guinea in the Atlantic Ocean in the south, Cameroon in the east, Niger in the north and Benin in the west and has six geopolitical zones – North-east, North-west, North-central, South-west, South-east and South-south. Its geographical position within the tropics results in a diverse climate, transitioning from semi-arid conditions in the North to humid in the South. The country experiences significant temporal variations in annual rainfall, ranging from over 4,000mm in the South-East to below 250mm in the extreme North-East. Nigeria possesses substantial water resources. The surface water potential is estimated at 267.3 billion cubic meters, while groundwater resources are approximately 51.9 billion cubic meters [ 33 ]. The total renewable water resources (TRWR) per capita is estimated to be 2514 m 3 /year [ 19 ]. Since Nigeria independence in 1960, the urban population has increased sporadically from an estimated 31.8 percent in 1985 to 55 percent of the total population while percentage of rural population decrease from 68.16 percent in 1985 to 64.40 percent in 1995 to 54.25 percent in 2005 and 50.38 percent in 2011 to 45.72 percent in 2023 ([ 55 ]). This has resulted to increase in cities expansion with growth in manufacturing sectors, government centres, large office apartments and small business enterprises, many in the informal sector. At present the Nigeria population of more than 230 million is skewed toward youth. The median age is 18.1 years while 58% of the total population is under age 30 ([ 57 ]; [ 56 ]). 2.2 Pilot study To determine the questionnaire's clarity, dependability, and efficacy in evaluating Nigerian youths' awareness and knowledge of aquatic pollution and waste management practices, a pilot study was carried out. Key findings from the pilot study were (1) Small sample size: The small sample size limited the statistical power and thus affected results of the study. (2) Questionnaire limitation: The survey questions used in the questionnaire did not capture the research full research question, thus this hindered the different statistical and data analysis that should be done. These limitations affected the robustness of the results and highlighted the need for adjustments in the main study. In response: (1) The sample size was increased in order to improve statistical reliability and results. (2) The survey questions were refined to capture the research questions, and collect comprehensive data on research topic. Consequently, this pilot study therefore served as a critical step that ensured the general viability of the primary study, helped improve the questionnaire, and identified possible obstacles to data collection. 2.3 Data collection Primary data was obtained using a web-based questionnaire. A semi-structured questionnaire was created with Google Forms (Google LLC, CA, USA; https://docs.google.com/forms/ ). Administration of questionnaire was done using different online social media platforms and physical engagement from November 1st 2024 to January 31st 2025 (three months). To facilitate extensive distribution of the online questionnaire across numerous social media platforms, dissemination of questionnaire was carried out by Aquaworld Community Development Initiative (CDI) volunteers present in the six geopolitical zones of Nigeria. A physical survey was carried out in rural areas that do not have access to internet as well as people with limited access to education. The privacy of each respondent was respected as they were kept anonymous. 2.4 Structure of the questionnaire To reduce biased response of participants, the questionnaire used terminologies that were well-defined and neutral. The questionnaire was structured into four sections: Section A - Demographic information section provided details of the respondents gender, age, marital status, education level, employment status, settlement type, place of residence, geopolitical zone and residential status. Section B - Knowledge of aquatic pollution and improper waste disposal section inquired questions to know how well participants understand aquatic pollution, improper waste disposal and their impacts in aquatic environments. Section C - The waste management section focused on questions regarding knowledge of waste management, and knowledge of waste disposal type. Section D - The policy recommendation section provided the respondents with the opportunity to evaluate the existing laws, collection systems, sensitization programs and other institutions or facilities that are in place to address aquatic pollution and to also provide recommendations to ensure proper waste management. 2.5 Data analysis The results of the questionnaire were organized and compiled in Microsoft Excel 365 (Microsoft Corporation, WA, USA). This involved removing duplicate and response falling out of the age range. The data was further divided based on the structure of the questionnaire as highlighted above for proper exploration and understanding of the responses. QGIS version 3.40 (QGIS Team, Gossau ZH, Switzerland) was employed to visualize the distribution of the number of responses from each state. The demographic data was subjected to Pearson's Chi-squared test to determine association with the knowledge of aquatic pollution. When significant association were observed, Post-Hoc test with Bonferroni p -value adjustment was used to determine where the difference lies. Principal component analysis (PCA) was used to identify the underlying demographic factors influencing the knowledge of aquatic pollution among the Nigerian youths. Prior to PCA the data was numerically transformed. To aid management priority, decision tree was used to identify categories of respondents/regions where waste management and aquatic pollution awareness should be prioritized in Nigeria. The tree was prune with Bonferroni test type and minimum split and minimum criterion were set to 50%. The significant difference in the mean waste generated per bag were determined using One-way Analysis of Variance. When necessary, Spearman’s rank correlation coefficient was used to determine relationship among the responses. All p -values were set to 0.05. All statistical analyses and visualizations were conducted using R (version 4.4.0) and RStudio (version 2024.04.1.748) (R Core Team, Vienna, Australia). 3 Results 3.1 Distribution of the responses across the states A total of 265 responses were obtained from participants across the country. Southwestern regions (Lagos, Ogun, Oyo) had highest number of respondents between 32 to 53. Two states from the North, i.e. Kano (53 responses) and Jigawa (11 responses), recorded the highest responses in the region. Eleven states, Sokoto, Adamawa, Yobe, Abia, Anambra, Cross River, Ebonyi, Ekiti, Enugu, and Imo recorded no response (states in white), and the rest of the states showed respondents between 1 and 11 (Fig. 2 ) 3.2 Influencing factors of aquatic pollution awareness From the survey, 109 respondents were identified as female, and 156 were male. Significant portion of the respondents were within the age bracket of 18–40 years while very few respondents were above 40 and below 18. Significant associations were observed between employment status χ²(3, 265) = 10.62, p -value = 0.01, settlement χ²(2, 265) = 16.62, p -value < 0.001, geopolitical zone χ²(4, 265) = 33.28, p -value < 0.001, and the knowledge of aquatic pollution (Table 1 ). Post-hoc pairwise comparisons of proportions with Bonferroni correction indicated that employed respondents were significantly more likely to have knowledge of aquatic pollution than those that are unemployed ( p -value < 0.05). Urban settlement respondents were more likely to be aware of the knowledge of aquatic pollution compared to rural settlers ( p -value < 0.05). Similarly, respondents from the Southwest were more likely to express knowledge than those from the Northwest ( p < .001). Additionally, respondents from the North-central region have significant knowledge than those from the Northwest ( p = .019). Table 1 Number and percentage of respondents across the demographic characteristics with χ2 and p-value. Characteristic Aquatic pollution No 1 N = 47 Yes 1 N = 218 Χ 2 -value p-value 2 Gender 1.57 0.2 Female 15 (32%) 94 (43%) Male 32 (68%) 124 (57%) Age 4.64 0.3 18–24 14 (30%) 82 (38%) 25–30 17 (36%) 89 (41%) 31–40 14 (30%) 36 (17%) above 40 1 (2.1%) 7 (3.2%) Under 18 1 (2.1%) 4 (1.8%) Marital status 1.64 0.4 Divorced 0 (0%) 1 (0.5%) Married 12 (26%) 39 (18%) Single 35 (74%) 178 (82%) Education level 5.22 0.2 Graduate 27 (57%) 132 (61%) Others 0 (0%) 2 (0.9%) Primary school 1 (2.1%) 1 (0.5%) Secondary school 6 (13%) 12 (5.5%) Undergraduate 13 (28%) 71 (33%) Employment status 10.62 0.014 Employed a 5 (11%) 48 (22%) Self-employed ab 12 (26%) 70 (32%) Student ab 16 (34%) 73 (33%) Unemployed b 14 (30%) 27 (12%) Settlement 16.62 < 0.001 Rural a 21 (45%) 38 (17%) Semi-urban b 6 (13%) 45 (21%) Urban b 20 (43%) 135 (62%) Geopolitical zone 33.28 < 0.001 North-central b 0 (0%) 21 (9.6%) Northeast ab 3 (6.4%) 11 (5.0%) Northwest a 29 (62%) 47 (22%) South-south ab 2 (4.3%) 13 (6.0%) Southwest bc 13 (28%) 126 (58%) Residential status 2.21 0.3 Owned apartment 18 (38%) 61 (28%) Rented apartment (living alone) 15 (32%) 73 (33%) Rented apartment (not living alone) 14 (30%) 84 (39%) 1 n (%) 2 Pearson's Chi-squared test; p-adjust method = Bonferroni a,b,c showed significant differences across the response ( p -value < 0.005) 3.2.1 Distribution of responses and influencing factors To understand the distribution of our respondents based on the knowledge of aquatic pollution and important variables influencing the distribution, the PCA results (Fig. 3 below) identified dimension 1 to explain 29.3% of the total variation in our response, while dimension 2 showed about 18.2%. Dimension 1 showed positive correlations with sociodemographic characteristics, with high correlation with employment status and high values for age. Dimension 2 showed a strong positive correlation with settlement and a negative correlation with geopolitical zone and gender. 3.2.2 Prioritization category on aquatic pollution awareness The decision tree highlighted geopolitical zone and settlement to be important factors to be considered ( p -value < 0.05). Result highlighted that 70% of respondents from the Northwest with generation of average waste more than 2.21 bag/day likely have no knowledge of aquatic pollution. While those who generate waste lower than 2.21 bag/day within the age of 18–30 and above 40 have 70% chance to be knowledgeable about aquatic pollution. In contrast, those within the age of 31–40 also have high chance of no knowledge of aquatic pollution (Fig. 4 ). Meanwhile, those from zones other than Northwest have a high chance of aquatic pollution knowledge. High chance of aquatic pollution knowledge (> 80%) falls into respondents who settled in semi-urban and urban areas of the country while about 70% are likely to have knowledge of aquatic pollution if they are living in rural areas of North-central, Northeast, South-south and southwest (Fig. 4 ). 3.3 Aquatic pollution knowledge and waste production High number of participants highlighted a weekly waste production 1–10 bags/week while very few participants generated above 30 bags/week. Pearson’s Chi-square value showed significant association between waste production and knowledge of aquatic pollution (χ²(3, 265) = 12.83, p -value < 0.005, Fig. 5 A). When Post-Hoc pairwise comparison was conducted with Bonferroni adjustment, it was observed that the proportion of respondents with ‘yes’ to knowledge of aquatic pollution were not significant across the waste production category ( p -value > 0.05) but with wide confidence intervals. The differences are not large enough between individual pairs to be significant after Bonferroni correction, even though the overall Chi-square was significant (Fig. 5 B). 3.3.1 Waste frequency across the country Average quantity of waste generated per day across the country shows statistically significant differences (p-value < 0.05). North west region recorded highest mean waste value 1.63 ± 1.21*** which is significant from the mean waste generated from South west (0.94 ± 0.51***) and South-south (0.78 ± 0.00***). No record from South east (white) (Fig. 6 ). 3.3.2 Policies and regulation effectiveness The perception of the respondents on the effectiveness of the existing laws, policies and management of waste in Nigeria indicates that more than 30% (n = 97) agree the policies and other measures are effective. Similarly, more than 20% (n = 57) strongly agree with this notion (Fig. 7 ). In contrast, a total of 41% of the respondents indicated that the policies, laws and regulations on waste management are not effective. From the suggestions, three main recommendations were identified including enforcement through penalties, monitoring of waste management laws, regulations, proper disposal site and awareness of people of the danger, impacts, and consequences of mismanagement and improper disposal of waste (Fig. 7 ). Out of these recommendations, penalties were highly highlighted by the respondents (n = 21, 30%), followed by awareness (n = 15, 22%) and monitoring (n = 14, 20%). Only 4% (n = 3) indicated the three recommendations. 4 Discussion Human deliberate actions to minimize harmful impacts on the environment is essential [ 50 ]. Various factors such as socio-demographic – age, gender, education, social class, marital status, and household income; external factors – institutional and economic and internal factors – self-efficacy, self-esteem, motivation, environmental knowledge, awareness, values, attitudes, and emotion are pivotal in environmental awareness [ 31 ]. In this study, we showed that socio-demographic factors that influence aquatic pollution awareness among Nigerian youths are employment status, settlement types and geopolitical zone. This is in line with the findings of [ 26 ] that highlighted income and region as the most influencing factors of pro-environmental behaviour in Nigeria. [ 42 ] also showed that occupation and education both had marked effects on environmental knowledge and attitudes. In accordance, we found that regions with high dependence on the natural environment, mostly rural communities, have low knowledge of aquatic pollution. This might be influenced by several factors such as inadequate education level and limited environmental awareness by the managing bodies. It is also possible due to the disparity in the number of respondents from such a location. This study identifies a complex and, at times, counterintuitive relationship between aquatic pollution awareness and waste production behaviour among Nigerian youths. While statistical analysis revealed a significant association between knowledge levels and waste generation (p < 0.05), a paradox emerged: extreme waste producers, those generating more than ten bags daily were more likely to be highly educated, with 67% holding graduate degrees (Table S1 ). Such findings challenge the assumption that environmental knowledge translates directly into sustainable behaviour, corroborating the knowledge-practice gap reported in environmental psychology and waste management literature ([ 36 ]; [ 12 ]). The Theory of “Planned Behaviour” offers one explanatory lens, positing that attitudes, intentions, and actual behaviours are mediated by critical external and internal factors, including social norms, infrastructure, and perceived behavioural control ([ 28 ]; [ 58 ]; [ 8 ]; [ 37 ]; [ 17 ]; [ 27 ]). Demographic trends add further distinction. Despite presuming higher waste generations in urban settings, 50% of extreme waste producers were rural residents and 83% were male. Furthermore, education did not uniformly predict pro-environmental behaviour. High waste producers were disproportionately undergraduates or held secondary-level education, with only 20% being graduates. These results suggest that contextual and socioeconomic variables, such as property ownership, income, disposal infrastructure, and local regulatory environments exert powerful influence, often overriding the effects of schooling ([ 23 ]; [ 54 ]; [ 47 ]). These discrepancies reinforce the importance of moving beyond knowledge-based interventions to more holistic strategies that account for social, structural, and economic constraints. Cognitive dissonance, lifestyle differences, and gaps in service provision, especially in rural areas, further complicate the knowledge-action link ([ 38 ]; [ 60 ]; [ 7 ]; [ 41 ]). Although there is no available data for the Southeast, which limits a comprehensive comparison of the six geopolitical zones in Nigeria. Studies have shown that economic development, high population size, and urbanisation are the significant contributors to waste generation in Nigeria ([ 4 ]; [ 13 ]; [ 52 ]). This is especially true of states in northwestern Nigeria, such as Kano and Kaduna, where waste generation is the highest, second only to Lagos State [ 4 ], which corroborates the results of this present study (Fig. 6 ). The high level of waste in northern Nigeria can be attributed to inadequate infrastructure and a lack of plans for a proper recycling process and sustainable waste management ([ 1 ]). The southwest recorded lower waste generation than the northern region, despite having urbanized cities with high populations, such as Lagos and Ibadan, which are characterised by commercial hubs ([ 3 ]; [ 4 ]). This may be due to the improved waste management strategies, including public and private partnerships, that have been put in place in these cities, hence helping to reduce the per capita waste production in the southwestern states ([ 3 ]; [ 9 ]). In contrast to the assertion by [ 18 ] that the South-South region faces significant waste management challenges due to the high waste generated in the area, primarily resulting from notable urbanization and industrial activities in states such as Bayelsa and Rivers, our findings reveal that the South-South region records significantly lower levels of waste generation. Moreover, oil exploration and exploitation in the region is believed to significantly contribute to solid waste pollution, especially plastics, through the improper management of materials used for packaging, logistics, pipeline maintenance, and other purposes [ 59 ]. Furthermore, plastics are used for cleanup activities after an oil spill ([ 49 ]; [ 48 ]; [ 32 ]; [ 21 ]), which may contribute to the waste generated. The anomaly recorded in the result could, however, be due to the very low response recorded in the region, resulting in low data. The general findings in this study show clear trends: undergraduates and graduates are significantly more aware of aquatic pollution, compared to primary, secondary school and other respondents. This agrees with the study of [ 30 ] who mentioned that environmental education at school level enhances water-pollution knowledge among youth in Nigeria. Furthermore, these results align with global findings that highlight a positive correlation between formal education and environmental awareness. According to [ 10 ], environmental education enhances cognitive understanding of environmental issues, making individuals more likely to recognize and respond to ecological problems. However, it is notable that 27 graduates (57% of the “No” respondents) still indicated a lack of awareness. This illuminates the importance to observe that while awareness increases with education, it does not necessarily equate to environmentally responsible behavior, thereby creating a gap known as the knowledge-behavior gap, as identified in environmental psychology ([ 31 ]). While education plays a critical role in increasing awareness, curricular content and environmental literacy initiatives must be specific and integrated into formal instruction, particularly in Nigeria where environmental education may not be a significant focus across all disciplines. Conclusion Findings from this study highlighted employment status, settlement types and geopolitical zones as influencing factors of aquatic pollution awareness and waste management. Participants from rural settlements and also in the Northern part of Nigeria, generated the highest waste. More so, the study revealed that, although education plays an important role in environmental awareness and knowledge of aquatic pollution and waste management, those highly educated generate the highest quantity of waste. For policy development and formulation, these findings provide the need for both specific-context and inclusive interventions. Policies should integrate environmental literacy initiatives to curriculums not to just promote environmental awareness but to also encourage a conscious attitude towards protecting the environment. Furthermore, policies should also include strategic interventions for the rural settlement and Northern part of Nigeria in order to promote good environmental behaviour and infrastructures to ensure proper waste management. Conclusively, to promote aquatic pollution awareness and good management practices, does not require just technical interventions but also the need for a socio-behavioural approach that empowers communities, fortifies institutional accountability, and encourages sustainable waste management techniques. Promoting the conservation and sustainability of aquatic environments requires matching policy tools and instruments with sociocultural realities of regions and the population. Declarations CRediT authorship contribution statement Sarah Tosin Fashagba: Conceptualization, Questionnaire design, Data curation, Methodology, Writing original draft, Writing-review & editing. Azeez Olalekan Baki: Conceptualization, Questionnaire design, Data curation, Methodology, Data Analysis & Visualization, Writing original draft, Writing-review & editing. Gift Samuel David: Conceptualization, Questionnaire review, Writing original draft, Writing-review & editing. Paul Oluwatimileyin Olatunji : Conceptualization, Questionnaire review, Writing original draft, Writing-review & editing. Sherifdeen Olamilekan Babalola: Conceptualization, Questionnaire review, Writing original draft, Writing-review & editing. Authors statement All authors have seen and approved the final version of the submitted manuscript and warrant that the article is the author’s original work, hasn’t received prior publication and isn’t under consideration for publication elsewhere. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Data will be made available on request. Funding This study was conducted without specific financial assistance from public, private, or non-profit organisations. Ethics approval and accordance Ethical approval for this study was obtained from the Research Ethics Committee of Aquaworld Community Development Initiative, Nigeria; reference: (AQW/CDI/POL/26/00004), date of approval: (13 September 2024). The ethical statement of this research was included in the online questionnaire, and for physical participation the aims, procedures, and anonymous treatment of the data were clearly explained to all potential participants before they took part. The study was conducted in accordance with the ethical principles for research involving human participants set out in the Nigerian National Code of Health Research Ethics and the guidelines of Aquaworld Community Development Initiative Research Ethics Committee. Consent to participate Participation in both the online and physical components was entirely voluntary, informed consent was obtained, and no personally identifying information was collected, ensuring that all responses were treated confidentially and used solely for research purposes. Clinical trial number Not applicable. Consent to publish Not applicable References Abd’Razack NT, Medayese SO, Shaibu SI, Adeleye BM. Habits and benefits of recycling solid waste among households in Kaduna, North West Nigeria. Sustainable cities Soc. 2017;28:297–306. Abdel-Shafy HI, Mansour MSM. (2018). Solid waste issue: Sources, composition, disposal, recycling, and valorization. In Egyptian Journal of Petroleum (Vol. 27, Issue 4, pp. 1275–1290). Egyptian Petroleum Research Institute. https://doi.org/10.1016/j.ejpe.2018.07.003 Abila B, Kantola J. (2013). Municipal solid waste management problems in Nigeria: Evolving knowledge management solution. 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Biomonitoring, physico-chemical, and biomarker evaluations of abattoir effluent discharges into the Ogun River from Kara Market, Ogun State, Nigeria using Clarias gariepinus . Environ Monit Assess. 2019;191(1):44. https://doi.org/10.1007/s10661-018-7168-3 . Olukanni DO, Pius-Imue FB, Joseph SO. Public perception of solid waste management practices in Nigeria: Ogun State experience. Recycling. 2020;5(2):8. https://doi.org/10.3390/recycling5020008 . Oyegoke B, Nkomo D, Dube B. Critical Interconnectedness of Awareness, Waste Management and Infrastructure in Environmental Discourse and Practice. Int J Res Innov Social Sci (IJRISS). 2024;8(3):1541–53. https://doi.org/10.47772/IJRISS.2024.803114 . Saleem J, Moghal ZKB, Shakoor RA, McKay G. Sustainable solution for plastic pollution: upcycling waste polypropylene masks for effective oil-spill management. Int J Mol Sci. 2023;24(15):12368. Saleem J, Ning C, Barford J, McKay G. Combating oil spill problem using plastic waste. Waste Manag. 2015;44:34–8. Sara A, Nurit C. Pro-environmental behaviour and its antecedents as a case of social and temporal dilemmas. Br J Educ Soc Behav Sci. 2014;4(4):508–26. Scarlat N, Motola V, Dallemand JF, Monforti-Ferrario F, Mofor L. Evaluation of energy potential of Municipal Solid Waste from African urban areas. Renewable and Sustainable Energy Reviews. Volume 50. Elsevier Ltd; 2015. pp. 1269–86. https://doi.org/10.1016/j.rser.2015.05.067 . Umar Y, Yakubu RO, Abdulazeez AA, Ijeoma MW. Exploring Nigeria’s waste-to-energy potential: a sustainable solution for electricity generation. Clean Energy. 2024;8(6):82–95. United Nations. (2010). Solid Waste Management in The World’s Cities: Water and Sanitation in the World’s Cities 2010. United Nations Human Settlements Programme ; Earthscan: London, UK, 2010. Vahmanetta A. (2025). The impact of economic factors on annual waste generation in Indonesia. IOP Conference Series: Earth and Environmental Science , 1438 (1), 012031. https://doi.org/10.1088/1755-1315/1438/1/012031 World Bank. (1960–2024). GDP per capita, Purchasing Power Parity. Retrieved May 28, 2025, from TheGlobalEconomy.com website: https://www.theglobaleconomy.com/ World Bank. (2023). Nigeria. Data. Worldometer. (2025). Nigeria population (live). Wu L, Zhu Y, Zhai J. (2022). Understanding Waste Management Behavior Among University Students in China: Environmental Knowledge, Personal Norms, and the Theory of Planned Behavior. Frontiers in Psychology , 12 . https://doi.org/10.3389/fpsyg.2021.771723 Yahaya M, Enakireru EE, Dennar OT. Quantitative assessment and environmental implications of hazardous crude oil waste in the Niger Delta region of Nigeria. Global J Environ Sci Technol. 2024;12(10):138–43. https://doi.org/10.54978. Yalçın AB, Güneş-Durak S. Investigation of society’s sensitivity to environmental pollution and electronic waste in Turkey. Environ Prog Sustain Energy. 2024;43(6):e14474. https://doi.org/10.1002/ep.14474 . Additional Declarations No competing interests reported. Supplementary Files AquaPollDiscSustSupplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8408696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585102354,"identity":"fea22665-c694-41cf-8e1f-3697225e9ced","order_by":0,"name":"Sarah Tosin Fashagba","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"Tosin","lastName":"Fashagba","suffix":""},{"id":585102357,"identity":"e8e8be2b-60be-4301-b076-ad695dec7e73","order_by":1,"name":"Azeez Olalekan Baki","email":"","orcid":"","institution":"University of the Basque Country","correspondingAuthor":false,"prefix":"","firstName":"Azeez","middleName":"Olalekan","lastName":"Baki","suffix":""},{"id":585102358,"identity":"48aeba38-98db-4adf-965e-e635bd59cc9d","order_by":2,"name":"Gift Samuel David","email":"data:image/png;base64,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","orcid":"","institution":"University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Gift","middleName":"Samuel","lastName":"David","suffix":""},{"id":585102359,"identity":"d84c9429-5e55-4a5b-8100-fcbe2bf320ae","order_by":3,"name":"Paul Oluwatimileyin Olatunji","email":"","orcid":"","institution":"University of Liège","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"Oluwatimileyin","lastName":"Olatunji","suffix":""},{"id":585102360,"identity":"becf37f0-3326-433a-8fc6-aa97beb3fc8f","order_by":4,"name":"Sherifdeen Olamilekan Babalola","email":"","orcid":"","institution":"TU Dresden","correspondingAuthor":false,"prefix":"","firstName":"Sherifdeen","middleName":"Olamilekan","lastName":"Babalola","suffix":""}],"badges":[],"createdAt":"2025-12-20 00:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8408696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8408696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101948875,"identity":"3c750161-e4f7-4b6d-9802-48afb1522001","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":936615,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Nigeria showing the 36 states and the major water bodies\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/e512849d5971e82a6f6dba37.png"},{"id":101948874,"identity":"03d39d2d-53ce-4b08-8530-ac2b235a816b","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152631,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Nigeria showing the 36 states, including the Federal Capital Territory (FCT), with a colour gradient depicting the number of respondents. The darker the colour, the higher the number of respondents from the state. The states in white show no response.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/0f800868ab1655eaecf61370.png"},{"id":101948876,"identity":"d522dabb-768a-4f71-a680-e3b2f3292a2b","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":207749,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis, PCA (A) and Contribution of each variable to the PCA (B)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/d163ebc420777cbaa3ab240f.png"},{"id":101948881,"identity":"63e4eed3-11de-4435-bc85-5c99fca523e9","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119736,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree plot showing the likelihood proportion of the respondents to the knowledge of aquatic pollution\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/522586354fa9720b17aec9f9.png"},{"id":102295442,"identity":"894d76c5-75e8-424b-ac8b-72416ab727eb","added_by":"auto","created_at":"2026-02-10 10:11:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115379,"visible":true,"origin":"","legend":"\u003cp\u003eA- Number of participants and the waste production, B- Association between knowledge of aquatic pollution and the waste production.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/9d765bad8d78e84fb3410ad1.png"},{"id":101948880,"identity":"a31b1d19-499c-4411-a0d2-d7945cbb588c","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120081,"visible":true,"origin":"","legend":"\u003cp\u003eRelative frequency of waste generated across the states\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/03d418f5f35b16c5b81864cf.png"},{"id":101948879,"identity":"49129f45-6a80-40f0-92ea-b9527cb08b12","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":153974,"visible":true,"origin":"","legend":"\u003cp\u003eEffectiveness of the existing waste management laws, policies and regulations\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/4aff8e3cdf67121cabfdb811.png"},{"id":109168351,"identity":"049988bd-0240-479b-9e04-d7c78a3db626","added_by":"auto","created_at":"2026-05-13 08:33:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/672b4b35-d426-46eb-8932-ba59f08baae5.pdf"},{"id":101948878,"identity":"5e3dc711-ed86-4ce6-b5ad-611a4d398617","added_by":"auto","created_at":"2026-02-05 10:20:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17969,"visible":true,"origin":"","legend":"","description":"","filename":"AquaPollDiscSustSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8408696/v1/2473ea4ccde0da6d36066948.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing factors on knowledge of aquatic pollution and waste management practices among Nigerian youths","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlobally, the negative impacts of waste on human health and local environments, including air, land, and water, are increasingly studied [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Effective waste management, given its inherent complexities, costs, and coordination demands, necessitates multi-stakeholder involvement throughout its entire process. In developing countries, waste management often presents significant challenges to authorities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately half of municipal budgets are allocated to waste management, and up to 90% of these funds primarily goes to waste collection. Despite these efforts, only about 50% of the population is served, and 40\u0026ndash;70% of generated waste is collected, leaving a substantial gap in proper waste management [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In low-income countries, the situation is more pronounced, with only about 10% of waste generated in suburban areas being collected [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This indicates a high risk to public health and the environment in these communities, particularly affecting vulnerable populations such as children and older people living near dumping sites [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a developing nation, Nigeria faces substantial challenges in waste management within both its urban and rural communities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Nigeria is recognized as one of Africa's largest producers of solid waste [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] with projections indicating an exponential increase in waste generation by the end of 2025. With a projected collection rate of 24,263 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e3\u003c/sup\u003e t/year, a significant portion of this waste is expected to be uncollected, often ending up in the environment, particularly water bodies ([\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]). Ineffective waste management is often exacerbated by a lack of robust policies and regulations, insufficient public awareness, inadequate waste management technologies, improper and unavailable financial resources, and poor governance [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAquatic environments are critical ecosystems, providing numerous benefits such as transportation, fisheries, irrigation, ecological diversity preservation, environmental balance, and water supply for human consumption. Despite their significance, water resources worldwide are often poorly managed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Large amounts of garbage are being discharged into the surrounding environment, including rivers and coastal lagoons, as a result of various anthropogenic activities, including industrial, pharmaceutical, agricultural, residential, and municipal sources [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The close proximity of aquatic environments to human settlements frequently impairs ecosystem services, rendering these environments unsuitable, hazardous, and unfit for primary and secondary uses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nigeria, like many other countries, is significantly confronted by environmental pollution driven by growing industrialization, increasing urbanization, and inadequate waste management [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferent studies have investigated the knowledge and awareness of Nigerian youth regarding aquatic pollution and waste management across various regions of Nigeria ([\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]; [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]). However, these studies often have limitations in their analysis of awareness and knowledge concerning aquatic pollution and waste management across Nigeria's diverse geopolitical zones. Furthermore, there is limited research on how geographical variations influence waste management practices and the correlation between youth understanding of aquatic pollution and their waste generation habits. To move beyond merely documenting the problem, it is therefore crucial to understand the factors that influence knowledge and practices related to waste management and aquatic pollution. While awareness campaigns are important, their effectiveness is often limited without a nuanced understanding of the underlying drivers of behaviour. This study, therefore, goes beyond assessing awareness to investigate the specific factors that influence the knowledge and waste management practices of Nigerian youth.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eNigeria, located in the Western part of Africa, spans a land area of approximately 923,768 square kilometres, making it the 14th largest country in Africa. The country is bordered by Gulf of Guinea in the Atlantic Ocean in the south, Cameroon in the east, Niger in the north and Benin in the west and has six geopolitical zones \u0026ndash; North-east, North-west, North-central, South-west, South-east and South-south. Its geographical position within the tropics results in a diverse climate, transitioning from semi-arid conditions in the North to humid in the South. The country experiences significant temporal variations in annual rainfall, ranging from over 4,000mm in the South-East to below 250mm in the extreme North-East. Nigeria possesses substantial water resources. The surface water potential is estimated at 267.3\u0026nbsp;billion cubic meters, while groundwater resources are approximately 51.9\u0026nbsp;billion cubic meters [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The total renewable water resources (TRWR) per capita is estimated to be 2514 m\u003csup\u003e3\u003c/sup\u003e/year [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Since Nigeria independence in 1960, the urban population has increased sporadically from an estimated 31.8 percent in 1985 to 55 percent of the total population while percentage of rural population decrease from 68.16 percent in 1985 to 64.40 percent in 1995 to 54.25 percent in 2005 and 50.38 percent in 2011 to 45.72 percent in 2023 ([\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]). This has resulted to increase in cities expansion with growth in manufacturing sectors, government centres, large office apartments and small business enterprises, many in the informal sector. At present the Nigeria population of more than 230\u0026nbsp;million is skewed toward youth. The median age is 18.1 years while 58% of the total population is under age 30 ([\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]; [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Pilot study\u003c/h2\u003e \u003cp\u003eTo determine the questionnaire's clarity, dependability, and efficacy in evaluating Nigerian youths' awareness and knowledge of aquatic pollution and waste management practices, a pilot study was carried out. Key findings from the pilot study were (1) Small sample size: The small sample size limited the statistical power and thus affected results of the study. (2) Questionnaire limitation: The survey questions used in the questionnaire did not capture the research full research question, thus this hindered the different statistical and data analysis that should be done. These limitations affected the robustness of the results and highlighted the need for adjustments in the main study. In response: (1) The sample size was increased in order to improve statistical reliability and results. (2) The survey questions were refined to capture the research questions, and collect comprehensive data on research topic. Consequently, this pilot study therefore served as a critical step that ensured the general viability of the primary study, helped improve the questionnaire, and identified possible obstacles to data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003ePrimary data was obtained using a web-based questionnaire. A semi-structured questionnaire was created with Google Forms (Google LLC, CA, USA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.google.com/forms/\u003c/span\u003e\u003cspan address=\"https://docs.google.com/forms/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Administration of questionnaire was done using different online social media platforms and physical engagement from November 1st 2024 to January 31st 2025 (three months). To facilitate extensive distribution of the online questionnaire across numerous social media platforms, dissemination of questionnaire was carried out by Aquaworld Community Development Initiative (CDI) volunteers present in the six geopolitical zones of Nigeria. A physical survey was carried out in rural areas that do not have access to internet as well as people with limited access to education. The privacy of each respondent was respected as they were kept anonymous.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Structure of the questionnaire\u003c/h2\u003e \u003cp\u003eTo reduce biased response of participants, the questionnaire used terminologies that were well-defined and neutral. The questionnaire was structured into four sections:\u003c/p\u003e \u003cp\u003eSection A - Demographic information section provided details of the respondents gender, age, marital status, education level, employment status, settlement type, place of residence, geopolitical zone and residential status.\u003c/p\u003e \u003cp\u003eSection B - Knowledge of aquatic pollution and improper waste disposal section inquired questions to know how well participants understand aquatic pollution, improper waste disposal and their impacts in aquatic environments.\u003c/p\u003e \u003cp\u003eSection C - The waste management section focused on questions regarding knowledge of waste management, and knowledge of waste disposal type.\u003c/p\u003e \u003cp\u003eSection D - The policy recommendation section provided the respondents with the opportunity to evaluate the existing laws, collection systems, sensitization programs and other institutions or facilities that are in place to address aquatic pollution and to also provide recommendations to ensure proper waste management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003eThe results of the questionnaire were organized and compiled in Microsoft Excel 365 (Microsoft Corporation, WA, USA). This involved removing duplicate and response falling out of the age range. The data was further divided based on the structure of the questionnaire as highlighted above for proper exploration and understanding of the responses. QGIS version 3.40 (QGIS Team, Gossau ZH, Switzerland) was employed to visualize the distribution of the number of responses from each state. The demographic data was subjected to Pearson's Chi-squared test to determine association with the knowledge of aquatic pollution. When significant association were observed, Post-Hoc test with Bonferroni \u003cem\u003ep\u003c/em\u003e-value adjustment was used to determine where the difference lies. Principal component analysis (PCA) was used to identify the underlying demographic factors influencing the knowledge of aquatic pollution among the Nigerian youths. Prior to PCA the data was numerically transformed. To aid management priority, decision tree was used to identify categories of respondents/regions where waste management and aquatic pollution awareness should be prioritized in Nigeria. The tree was prune with Bonferroni test type and minimum split and minimum criterion were set to 50%. The significant difference in the mean waste generated per bag were determined using One-way Analysis of Variance. When necessary, Spearman\u0026rsquo;s rank correlation coefficient was used to determine relationship among the responses. All \u003cem\u003ep\u003c/em\u003e-values were set to 0.05. All statistical analyses and visualizations were conducted using R (version 4.4.0) and RStudio (version 2024.04.1.748) (R Core Team, Vienna, Australia).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of the responses across the states\u003c/h2\u003e \u003cp\u003eA total of 265 responses were obtained from participants across the country. Southwestern regions (Lagos, Ogun, Oyo) had highest number of respondents between 32 to 53. Two states from the North, i.e. Kano (53 responses) and Jigawa (11 responses), recorded the highest responses in the region. Eleven states, Sokoto, Adamawa, Yobe, Abia, Anambra, Cross River, Ebonyi, Ekiti, Enugu, and Imo recorded no response (states in white), and the rest of the states showed respondents between 1 and 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Influencing factors of aquatic pollution awareness\u003c/h2\u003e \u003cp\u003eFrom the survey, 109 respondents were identified as female, and 156 were male. Significant portion of the respondents were within the age bracket of 18\u0026ndash;40 years while very few respondents were above 40 and below 18. Significant associations were observed between employment status χ\u0026sup2;(3, 265)\u0026thinsp;=\u0026thinsp;10.62, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.01, settlement χ\u0026sup2;(2, 265)\u0026thinsp;=\u0026thinsp;16.62, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, geopolitical zone χ\u0026sup2;(4, 265)\u0026thinsp;=\u0026thinsp;33.28, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and the knowledge of aquatic pollution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Post-hoc pairwise comparisons of proportions with Bonferroni correction indicated that employed respondents were significantly more likely to have knowledge of aquatic pollution than those that are unemployed (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Urban settlement respondents were more likely to be aware of the knowledge of aquatic pollution compared to rural settlers (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, respondents from the Southwest were more likely to express knowledge than those from the Northwest (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, respondents from the North-central region have significant knowledge than those from the Northwest (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.019).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber and percentage of respondents across the demographic characteristics with χ2 and p-value.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAquatic pollution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;47\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;218\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΧ\u003csup\u003e2\u003c/sup\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eabove 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSettlement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-urban\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeopolitical zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-central\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwest\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-south\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwest\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwned apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRented apartment (living alone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRented apartment (not living alone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;n (%)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Pearson's Chi-squared test; p-adjust method\u0026thinsp;=\u0026thinsp;Bonferroni\u003c/p\u003e \u003cp\u003e\u003csup\u003ea,b,c\u003c/sup\u003e showed significant differences across the response (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Distribution of responses and influencing factors\u003c/h2\u003e \u003cp\u003eTo understand the distribution of our respondents based on the knowledge of aquatic pollution and important variables influencing the distribution, the PCA results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below) identified dimension 1 to explain 29.3% of the total variation in our response, while dimension 2 showed about 18.2%. Dimension 1 showed positive correlations with sociodemographic characteristics, with high correlation with employment status and high values for age. Dimension 2 showed a strong positive correlation with settlement and a negative correlation with geopolitical zone and gender.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Prioritization category on aquatic pollution awareness\u003c/h2\u003e \u003cp\u003eThe decision tree highlighted geopolitical zone and settlement to be important factors to be considered (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Result highlighted that 70% of respondents from the Northwest with generation of average waste more than 2.21 bag/day likely have no knowledge of aquatic pollution. While those who generate waste lower than 2.21 bag/day within the age of 18\u0026ndash;30 and above 40 have 70% chance to be knowledgeable about aquatic pollution. In contrast, those within the age of 31\u0026ndash;40 also have high chance of no knowledge of aquatic pollution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Meanwhile, those from zones other than Northwest have a high chance of aquatic pollution knowledge. High chance of aquatic pollution knowledge (\u0026gt;\u0026thinsp;80%) falls into respondents who settled in semi-urban and urban areas of the country while about 70% are likely to have knowledge of aquatic pollution if they are living in rural areas of North-central, Northeast, South-south and southwest (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Aquatic pollution knowledge and waste production\u003c/h2\u003e \u003cp\u003eHigh number of participants highlighted a weekly waste production 1\u0026ndash;10 bags/week while very few participants generated above 30 bags/week. Pearson\u0026rsquo;s Chi-square value showed significant association between waste production and knowledge of aquatic pollution (χ\u0026sup2;(3, 265)\u0026thinsp;=\u0026thinsp;12.83, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). When Post-Hoc pairwise comparison was conducted with Bonferroni adjustment, it was observed that the proportion of respondents with \u0026lsquo;yes\u0026rsquo; to knowledge of aquatic pollution were not significant across the waste production category (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05) but with wide confidence intervals. The differences are not large enough between individual pairs to be significant after Bonferroni correction, even though the overall Chi-square was significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Waste frequency across the country\u003c/h2\u003e \u003cp\u003eAverage quantity of waste generated per day across the country shows statistically significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). North west region recorded highest mean waste value 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21*** which is significant from the mean waste generated from South west (0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51***) and South-south (0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00***). No record from South east (white) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Policies and regulation effectiveness\u003c/h2\u003e \u003cp\u003eThe perception of the respondents on the effectiveness of the existing laws, policies and management of waste in Nigeria indicates that more than 30% (n\u0026thinsp;=\u0026thinsp;97) agree the policies and other measures are effective. Similarly, more than 20% (n\u0026thinsp;=\u0026thinsp;57) strongly agree with this notion (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In contrast, a total of 41% of the respondents indicated that the policies, laws and regulations on waste management are not effective. From the suggestions, three main recommendations were identified including enforcement through penalties, monitoring of waste management laws, regulations, proper disposal site and awareness of people of the danger, impacts, and consequences of mismanagement and improper disposal of waste (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Out of these recommendations, penalties were highly highlighted by the respondents (n\u0026thinsp;=\u0026thinsp;21, 30%), followed by awareness (n\u0026thinsp;=\u0026thinsp;15, 22%) and monitoring (n\u0026thinsp;=\u0026thinsp;14, 20%). Only 4% (n\u0026thinsp;=\u0026thinsp;3) indicated the three recommendations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eHuman deliberate actions to minimize harmful impacts on the environment is essential [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Various factors such as socio-demographic \u0026ndash; age, gender, education, social class, marital status, and household income; external factors \u0026ndash; institutional and economic and internal factors \u0026ndash; self-efficacy, self-esteem, motivation, environmental knowledge, awareness, values, attitudes, and emotion are pivotal in environmental awareness [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we showed that socio-demographic factors that influence aquatic pollution awareness among Nigerian youths are employment status, settlement types and geopolitical zone. This is in line with the findings of [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] that highlighted income and region as the most influencing factors of pro-environmental behaviour in Nigeria. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] also showed that occupation and education both had marked effects on environmental knowledge and attitudes. In accordance, we found that regions with high dependence on the natural environment, mostly rural communities, have low knowledge of aquatic pollution. This might be influenced by several factors such as inadequate education level and limited environmental awareness by the managing bodies. It is also possible due to the disparity in the number of respondents from such a location.\u003c/p\u003e \u003cp\u003eThis study identifies a complex and, at times, counterintuitive relationship between aquatic pollution awareness and waste production behaviour among Nigerian youths. While statistical analysis revealed a significant association between knowledge levels and waste generation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a paradox emerged: extreme waste producers, those generating more than ten bags daily were more likely to be highly educated, with 67% holding graduate degrees (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Such findings challenge the assumption that environmental knowledge translates directly into sustainable behaviour, corroborating the knowledge-practice gap reported in environmental psychology and waste management literature ([\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]). The Theory of \u0026ldquo;Planned Behaviour\u0026rdquo; offers one explanatory lens, positing that attitudes, intentions, and actual behaviours are mediated by critical external and internal factors, including social norms, infrastructure, and perceived behavioural control ([\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]; [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eDemographic trends add further distinction. Despite presuming higher waste generations in urban settings, 50% of extreme waste producers were rural residents and 83% were male. Furthermore, education did not uniformly predict pro-environmental behaviour. High waste producers were disproportionately undergraduates or held secondary-level education, with only 20% being graduates. These results suggest that contextual and socioeconomic variables, such as property ownership, income, disposal infrastructure, and local regulatory environments exert powerful influence, often overriding the effects of schooling ([\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eThese discrepancies reinforce the importance of moving beyond knowledge-based interventions to more holistic strategies that account for social, structural, and economic constraints. Cognitive dissonance, lifestyle differences, and gaps in service provision, especially in rural areas, further complicate the knowledge-action link ([\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]; [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eAlthough there is no available data for the Southeast, which limits a comprehensive comparison of the six geopolitical zones in Nigeria. Studies have shown that economic development, high population size, and urbanisation are the significant contributors to waste generation in Nigeria ([\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]). This is especially true of states in northwestern Nigeria, such as Kano and Kaduna, where waste generation is the highest, second only to Lagos State [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which corroborates the results of this present study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The high level of waste in northern Nigeria can be attributed to inadequate infrastructure and a lack of plans for a proper recycling process and sustainable waste management ([\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]). The southwest recorded lower waste generation than the northern region, despite having urbanized cities with high populations, such as Lagos and Ibadan, which are characterised by commercial hubs ([\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]). This may be due to the improved waste management strategies, including public and private partnerships, that have been put in place in these cities, hence helping to reduce the per capita waste production in the southwestern states ([\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eIn contrast to the assertion by [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] that the South-South region faces significant waste management challenges due to the high waste generated in the area, primarily resulting from notable urbanization and industrial activities in states such as Bayelsa and Rivers, our findings reveal that the South-South region records significantly lower levels of waste generation. Moreover, oil exploration and exploitation in the region is believed to significantly contribute to solid waste pollution, especially plastics, through the improper management of materials used for packaging, logistics, pipeline maintenance, and other purposes [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Furthermore, plastics are used for cleanup activities after an oil spill ([\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]), which may contribute to the waste generated. The anomaly recorded in the result could, however, be due to the very low response recorded in the region, resulting in low data.\u003c/p\u003e \u003cp\u003eThe general findings in this study show clear trends: undergraduates and graduates are significantly more aware of aquatic pollution, compared to primary, secondary school and other respondents. This agrees with the study of [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] who mentioned that environmental education at school level enhances water-pollution knowledge among youth in Nigeria. Furthermore, these results align with global findings that highlight a positive correlation between formal education and environmental awareness. According to [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], environmental education enhances cognitive understanding of environmental issues, making individuals more likely to recognize and respond to ecological problems. However, it is notable that 27 graduates (57% of the \u0026ldquo;No\u0026rdquo; respondents) still indicated a lack of awareness. This illuminates the importance to observe that while awareness increases with education, it does not necessarily equate to environmentally responsible behavior, thereby creating a gap known as the knowledge-behavior gap, as identified in environmental psychology ([\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]). While education plays a critical role in increasing awareness, curricular content and environmental literacy initiatives must be specific and integrated into formal instruction, particularly in Nigeria where environmental education may not be a significant focus across all disciplines.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFindings from this study highlighted employment status, settlement types and geopolitical zones as influencing factors of aquatic pollution awareness and waste management. Participants from rural settlements and also in the Northern part of Nigeria, generated the highest waste. More so, the study revealed that, although education plays an important role in environmental awareness and knowledge of aquatic pollution and waste management, those highly educated generate the highest quantity of waste. For policy development and formulation, these findings provide the need for both specific-context and inclusive interventions. Policies should integrate environmental literacy initiatives to curriculums not to just promote environmental awareness but to also encourage a conscious attitude towards protecting the environment. Furthermore, policies should also include strategic interventions for the rural settlement and Northern part of Nigeria in order to promote good environmental behaviour and infrastructures to ensure proper waste management. Conclusively, to promote aquatic pollution awareness and good management practices, does not require just technical interventions but also the need for a socio-behavioural approach that empowers communities, fortifies institutional accountability, and encourages sustainable waste management techniques. Promoting the conservation and sustainability of aquatic environments requires matching policy tools and instruments with sociocultural realities of regions and the population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSarah Tosin Fashagba:\u003c/strong\u003e Conceptualization, Questionnaire design, Data curation, Methodology, Writing original draft, Writing-review \u0026amp; editing. \u003cstrong\u003eAzeez Olalekan Baki:\u0026nbsp;\u003c/strong\u003eConceptualization, Questionnaire design, Data curation, Methodology, Data Analysis \u0026amp; Visualization, Writing original draft, Writing-review \u0026amp; editing. \u003cstrong\u003eGift Samuel David:\u003c/strong\u003e Conceptualization, Questionnaire review, Writing original draft, Writing-review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003ePaul Oluwatimileyin Olatunji\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eConceptualization, Questionnaire review, Writing original draft, Writing-review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eSherifdeen Olamilekan Babalola:\u003c/strong\u003e Conceptualization, Questionnaire review, Writing original draft, Writing-review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors statement\u003c/p\u003e\n\u003cp\u003eAll authors have seen and approved the final version of the submitted manuscript and warrant that the article is the author\u0026rsquo;s original work, hasn\u0026rsquo;t received prior publication and isn\u0026rsquo;t under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted without specific financial assistance from public, private, or non-profit organisations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Research Ethics Committee of Aquaworld Community Development Initiative, Nigeria; reference: (AQW/CDI/POL/26/00004), date of approval: (13 September 2024). The ethical statement of this research was included in the online questionnaire, and for physical participation the aims, procedures, and anonymous treatment of the data were clearly explained to all potential participants before they took part. The study was conducted in accordance with the ethical principles for research involving human participants set out in the Nigerian National Code of Health Research Ethics and the guidelines of Aquaworld Community Development Initiative Research Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipation in both the online and physical components was entirely voluntary, informed consent was obtained, and no personally identifying information was collected, ensuring that all responses were treated confidentially and used solely for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbd\u0026rsquo;Razack NT, Medayese SO, Shaibu SI, Adeleye BM. 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Investigation of society\u0026rsquo;s sensitivity to environmental pollution and electronic waste in Turkey. Environ Prog Sustain Energy. 2024;43(6):e14474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ep.14474\u003c/span\u003e\u003cspan address=\"10.1002/ep.14474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aquatic pollution, waste production, pollution awareness, Nigerian youths, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-8408696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8408696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNigeria has had a notable surge in population, resulting in increased industrialisation, intensive urbanisation, increased resource exploitation, and waste generation, all of which have had a negative effect on the aquatic environment. Due to these developments, the quantity of waste and the variety of contaminants that potentially enter the aquatic environment have significantly increased. Hence, this study not only investigated the specific influencing factors of aquatic pollution and waste management practices among Nigerian youth, but further investigated how geographical variations influence waste management practices. The result shows that employment status, settlement types and geopolitical zones influence the knowledge of aquatic pollution in Nigeria. Additionally, the results showed that the highest quantity of wastes generated was in the northwestern part of Nigeria, although, this could have been influenced by the lack of data on the overall quantity of waste generated from some regions. Also, there is a positive correlation between formal education and environmental awareness and knowledge of aquatic pollution and waste. This study further shows that those generating more waste were more likely to be highly educated likely due to \u0026ldquo;Planned Behaviour\u0026rdquo;. Findings from this study have provided information that will be helpful for policymakers to formulate interventions to promote the conservation sustainability of aquatic environments.\u003c/p\u003e","manuscriptTitle":"Influencing factors on knowledge of aquatic pollution and waste management practices among Nigerian youths","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-05 10:19:53","doi":"10.21203/rs.3.rs-8408696/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed8b8a6f-c70c-4f0f-92ef-b5c767d91af1","owner":[],"postedDate":"February 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T08:31:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-05 10:19:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8408696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8408696","identity":"rs-8408696","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}