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<p>Despite significant advances in Decentralized Wastewater Treatment Systems (DEWATS) technology, critical knowledge gaps persist in predictive modelling approaches that can accurately forecast treatment efficiency under varying operational conditions, limiting practitioners' ability to optimize system design and ensure consistent compliance with discharge standards. This study employed a comprehensive case study methodology at Kigamboni DEWATS facility, utilizing systematic batch sampling over an extended period with 100 samples collected at 6-h intervals to analyse seven key water quality parameters (Biological oxygen demand BOD<sub>5</sub>, Chemical Oxygen Demand COD, Total Suspended Solids TSS, Total Dissolved Solids TDS, Colour, Temperature, and pH).Multiple linear regression analysis was applied to develop predictive models, with rigorous statistical assumption testing including normality, linearity, independence, homoscedasticity, and multicollinearity assessments. The DEWATS system demonstrated exceptional organic pollutant removal with BOD<sub>5</sub>, COD, and TSS removal efficiencies of 95.30%, 79.50%, and 87.40% respectively.The developed comprehensive model equation: Efficiency (%) = 82.19 + 0.0652(BOD<sub>5</sub><sub>in</sub>) + 0.1432(COD<sub>in</sub>) + 0.1547(TSS<sub>in</sub>) − 0.0048(TDS<sub>in</sub>) − 2.625(pH<sub>in</sub>) − 1.452(Temp) + 0.0387(Colour<sub>in</sub>) achieved excellent predictive capability with R<sup>2</sup> = 0.892, RMSE = 3.45%, and MAE = 2.78%. The study revealed that TSS input showed the strongest positive influence while pH demonstrated the most substantial negative impact on treatment efficiency. implementing pH and temperature control systems, developing multicollinearity-adjusted models, and establishing comprehensive monitoring protocols recommended. The validated model provides a replicable framework for DEWATS performance optimization that supports international development priorities including sustainable development goals 6.2 and 3.9.2, enabling evidence-based decision-making for sustainable decentralized sanitation solutions across diverse geographical and operational contexts.</p>

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Multivariate regression modelling for predicting decentralized wastewater treatment system efficiency at Kigamboni

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • R. J. Mduma,
  • D. B. Mmasi,
  • T. B. Mwamila

摘要

Despite significant advances in Decentralized Wastewater Treatment Systems (DEWATS) technology, critical knowledge gaps persist in predictive modelling approaches that can accurately forecast treatment efficiency under varying operational conditions, limiting practitioners' ability to optimize system design and ensure consistent compliance with discharge standards. This study employed a comprehensive case study methodology at Kigamboni DEWATS facility, utilizing systematic batch sampling over an extended period with 100 samples collected at 6-h intervals to analyse seven key water quality parameters (Biological oxygen demand BOD5, Chemical Oxygen Demand COD, Total Suspended Solids TSS, Total Dissolved Solids TDS, Colour, Temperature, and pH).Multiple linear regression analysis was applied to develop predictive models, with rigorous statistical assumption testing including normality, linearity, independence, homoscedasticity, and multicollinearity assessments. The DEWATS system demonstrated exceptional organic pollutant removal with BOD5, COD, and TSS removal efficiencies of 95.30%, 79.50%, and 87.40% respectively.The developed comprehensive model equation: Efficiency (%) = 82.19 + 0.0652(BOD5in) + 0.1432(CODin) + 0.1547(TSSin) − 0.0048(TDSin) − 2.625(pHin) − 1.452(Temp) + 0.0387(Colourin) achieved excellent predictive capability with R2 = 0.892, RMSE = 3.45%, and MAE = 2.78%. The study revealed that TSS input showed the strongest positive influence while pH demonstrated the most substantial negative impact on treatment efficiency. implementing pH and temperature control systems, developing multicollinearity-adjusted models, and establishing comprehensive monitoring protocols recommended. The validated model provides a replicable framework for DEWATS performance optimization that supports international development priorities including sustainable development goals 6.2 and 3.9.2, enabling evidence-based decision-making for sustainable decentralized sanitation solutions across diverse geographical and operational contexts.