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Increasing specificity in water quality regulations for the discharge of stormwater to the environment has increased the requirement to more accurately characterize the performance of filtration interventions. This work presents a statistical performance analysis for the Ecosol Litter Basket, an at source filtration device, based on an extensive field study. The field evaluation of the Ecosol Litter Basket, a primary stormwater filtration device, was performed over a three-year period in an urban catchment in Queensland, Australia. A total of 29 rainfall events were recorded, of which between 13 to 16 events were evaluated as qualifying for the purposes of characterizing the removal efficiency. A variety of pollutant removal evaluation metrics, including concentration-based and total load-based metrics, were utilized in this study to characterize the efficacy of the device for removing a range of pollutants. Two approaches are proposed to facilitate the analysis: a nonlinear regression approach to more effectively deal with nonlinear patterns in the influent and effluent data; and the regression of concentrations (ROC), which is an additional concentration-based metric. A statistical analysis of the results demonstrated that the differences between influent and effluent streams for TSS are significantly different in their mean and median, and the removal efficiency of the Ecosol Litter Basket was evaluated to be 57–65% for TSS with the influent event mean concentration (EMC) up to 142 mg/L.
Fereydoon Pooya Nejad; Aaron Zecchin. Statistical Analysis of Field-Based Stormwater Filtration Performance for the Ecosol Litter Basket. Sustainability 2021, 13, 6493 .
AMA StyleFereydoon Pooya Nejad, Aaron Zecchin. Statistical Analysis of Field-Based Stormwater Filtration Performance for the Ecosol Litter Basket. Sustainability. 2021; 13 (11):6493.
Chicago/Turabian StyleFereydoon Pooya Nejad; Aaron Zecchin. 2021. "Statistical Analysis of Field-Based Stormwater Filtration Performance for the Ecosol Litter Basket." Sustainability 13, no. 11: 6493.
An independent field performance evaluation for a secondary stormwater filtration device, named the Ecosol Strom Pit (Class 2), was performed between May 2017 and July 2019 in an urban catchment in Queensland, Australia. During the testing period, a total of 37 rainfall events were recorded, of which between 15 and 21 events were evaluated as qualifying for the purposes of characterizing the removal efficiency performance of the device. A statistical analysis of the event mean concentrations (EMCs) of the flow streams through the device indicate a statistically significant difference between the influent and effluent streams. A variety of pollutant removal evaluation metrics, including concentration-based and total load-based metrics, were utilized in this study to characterise the efficacy of the device. Two new approaches are proposed for facilitation the analysis: a nonlinear regression approach to more effectively deal with nonlinear patterns in the influent and effluent data and the regression of concentration (ROC), which is an added concentration-based metrics. In summary, the removal efficiencies of the Ecosol Storm Pit (Class 2) were evaluated to be 72–74% for total suspended solids (TSS), 45–50% for total phosphorus (TP), 41–45% for total nitrogen (TN), 27–32% for total heavy metals (THM), 79–85% for total petroleum hydrocarbons (TPH), and 80–88% for total recoverable hydrocarbons (TRH).
Fereydoon Pooya Nejad; Aaron C. Zecchin. Stormwater Filtration Performance for the Ecosol Storm Pit (Class 2): Statistical Analysis of Field Data. Water 2020, 12, 2723 .
AMA StyleFereydoon Pooya Nejad, Aaron C. Zecchin. Stormwater Filtration Performance for the Ecosol Storm Pit (Class 2): Statistical Analysis of Field Data. Water. 2020; 12 (10):2723.
Chicago/Turabian StyleFereydoon Pooya Nejad; Aaron C. Zecchin. 2020. "Stormwater Filtration Performance for the Ecosol Storm Pit (Class 2): Statistical Analysis of Field Data." Water 12, no. 10: 2723.
Rolling dynamic compaction (RDC), which employs non-circular module towed behind a tractor, is an innovative soil compaction method that has proven to be successful in many ground improvement applications. RDC involves repeatedly delivering high-energy impact blows onto the ground surface, which improves soil density and thus soil strength and stiffness. However, there exists a lack of methods to predict the effectiveness of RDC in different ground conditions, which has become a major obstacle to its adoption. For this, in this context, a prediction model is developed based on linear genetic programming (LGP), which is one of the common approaches in application of artificial intelligence for nonlinear forecasting. The models are based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided, 8-t impact roller (BH-1300). It is shown that the model is accurate and reliable over a range of soil types. Furthermore, a series of parametric studies confirms its robustness in generalizing data. In addition, the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.
R.A.T.M. Ranasinghe; M.B. Jaksa; F. Pooya Nejad; Y.L. Kuo. Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results. Journal of Rock Mechanics and Geotechnical Engineering 2019, 11, 815 -823.
AMA StyleR.A.T.M. Ranasinghe, M.B. Jaksa, F. Pooya Nejad, Y.L. Kuo. Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results. Journal of Rock Mechanics and Geotechnical Engineering. 2019; 11 (4):815-823.
Chicago/Turabian StyleR.A.T.M. Ranasinghe; M.B. Jaksa; F. Pooya Nejad; Y.L. Kuo. 2019. "Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results." Journal of Rock Mechanics and Geotechnical Engineering 11, no. 4: 815-823.