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Author: search.filters.author.Ogunrinde, Akinwale T.Has files: No

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    The Effect of Retention Time and Seasonal Variation on the Characterization of Phyto-Remediated Aquaculture Wastewater in a Constructed Wetland
    (2025-10-01)
    Akadiri, Shadrach A.
    ;
    Dada, Pius O.O.
    ;
    Badejo, Adekunle A.
    ;
    Adeosun, Olayemi J.
    ;
    Ogunrinde, Akinwale T.
    The insufficient availability of safe water has emerged as a prevalent issue severely impacting public health in developing nations. Moreover, studies reporting the efficacy of treatment plants (TPs)—specifically Phragmites karka and Typha latifolia—in removing toxic elements in aquaculture wastewater are scanty. Therefore, this study is aimed at investigating the effects of hydraulic retention time (HRT), seasonal variations, and TPs on the removal efficiency of pollutants from a vertical subsurface flow constructed wetland (VSSF-CW) in Nigeria. The experiments spanned three seasons (November–December–January—NDJ; March–April–May—MAM; and July–August–September—JAS) of the year, with samples collected from the CW at 7 day intervals for analysis. The aquaculture wastewater was analyzed in the laboratory to determine its chemical and toxic compositions before and after the introduction of treatment plants. Three-way ANOVA was used to analyze the main and interactive effects between HRT, seasons, and TPs on the physicochemical properties of the CW’s effluents. The removal efficiency was determined to evaluate the performance of the constructed wetland in comparison to the treatment plants. Results showed that these constructed wetlands effectively removed contaminants, with significant differences (p < 0.05) mostly observed in the effects of treatment plant types and seasons on the chemical and heavy metal concentrations. This was further confirmed by the main effects of HRT, seasons, and treatment plant choice, which significantly (p < 0.05) influenced treatment efficiency. Removal efficiencies increased with longer HRTs, reaching peak removal efficiencies of approximately 69, 67, and 61% for Na, K, and Ca, respectively. The BOD and COD reached 85 and 90% removal efficiency, while removal efficiency of 100% was achieved for most heavy metals at 21 day retention time. In summary, the study found that TPs (Phragmites karka and Typha latifolia), HRT, and seasonal variation are important for treating integrated poultry and aquaculture wastewater in a VSSF CWs.
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    Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria
    (2025-01-01)
    Faloye, Oluwaseun Temitope
    ;
    Kamchoom, Viroon
    ;
    Ogunrinde, Akinwale T.
    ;
    Ajayi, Ayodele Ebenezer
    ;
    Oguntunde, Philip Gbenro
    Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.