Influent Total Phosphorus Concentrations Research Articles

Efficiency and plant indication of nitrogen and phosphorus removal in constructed wetlands: A field-scale study in a frost-free area

Frost-free areas have suitable climate for wetland plant growth and constructed wetlands (CW) technology. Information on the quantification of plant biomass and uptake efficiency in field-scale CWs is limited in these climates. The removal efficiency of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and total suspended solids (TSS) in wastewater from sewage plants, domestic sewage, and an industrial park in 15 rural and urban CWs in Guangdong Province, China, with an average temperature of 30 °C was evaluated. The effects of influent concentration, hydraulic load, the wastewater's physicochemical properties, operating conditions, and plant uptake were analysed. The mean removal rates were 40.0%, 45.2%, 41.1%, and 71.7% for TN, TP, COD, and TSS, respectively, which were higher than the removal load of the field-scale CWs in temperate regions. Removal loads of TN, TP, COD, and TSS were highest in CWs that have been operating for 5–6 years, treating wastewater volumes of over 1 m3/m2·d. The removal efficiency was mainly related to the inflow concentration and less affected by the type of CWs. Nutrient accumulation trends were primarily linked to influent concentrations (TN: r2 = 0.89, P = 0.007; TP: r2 = 0.96, P = 0.001) and plant biomass (TN: r2 = 0.96, P = 0.001; TP: r2 = 0.92, P = 0.004). Plant biomass contributed 2%–29% and 2%–70%, respectively, to removing N and P in CWs. The average uptake concentration of N and P in aboveground plant organs (15.66 ± 4.44 mg N/g, 2.15 ± 1.18 mg P/g) was generally higher than that of other temperate plants. A strong relationship between TN and TP in the biomass was also observed; however, the relationship is only restricted by the influent TP concentration. Arundo donax is well-adapted for nutrient accumulation and adaptation and is an ideal wetland plant to purify wastewater in frost-free climates.

Ensuring sustainability of conventional aerobic wastewater treatment system via bio-augmentation of aerobic bacterial consortium: An enhanced biological phosphorus removal approach

Enhanced biological phosphorus removal (EBPR) is an inevitable need of sustainable waste water treatment systems (WWTS). In this regard, this study aimed to investigate the potential of an aerobic continuously stirred tank reactor (CSTR), enriched with selected bacterial consortium, for enhanced phosphorus removal. The CSTR biomass was bioaugmented through addition of 12 distinct phosphorus accumulating and solubilizing strains. The medium and high strength synthetic waste water, having the phosphorus concentration in the range of 1.4–40 mg/L, was fed to the CSTR throughout this study. With respect to medium strength feed, total phosphorus (TP) removal efficiency reached up to 90% at an influent concentration of 20 mg/L, followed by a declining trend of removal efficiency as 62 and 30%, at the TP concentration of 30 and 40 mg/L, respectively. Contrary, the EBPR activity was observed to be inhibiting (lesser accumulation of phosphorus in biomass) at lower influent TP value of 10 mg/L, when the CSTR was operated with high strength waste water. Further, ammonical nitrogen and chemical oxygen demand removal performance of the system was observed to be independent of influent TP concentration. Detailed experimental investigations revealed that the inoculated bacterial consortium favored the transformation and storage of influent TP into intracellular storage of polyphosphate (poly-P). Overall, the results indicated that the sustainability of existing WWTS can be ensured through bioaugmentation of non-EBPR sludge to aerobically cultivated EBPR performing sludge.

Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective.

The environmental-economic focus of wastewater treatment and management attracts growing attentions in recent years. The static efficiencies and their dynamic changes are helpful to systematically assess the environmental performance of the water agencies and wastewater treatment plants (WWTPs). Additionally, identifying key factors of efficiencies is critical to improve the operation of WWTPs. In this study, the window method of data envelopment analysis (DEA) was applied to estimate the annual efficiency for four Canadian WWTPs and to explore the variations of annual efficiency under different window lengths. Meanwhile, the Tobit regression analysis was developed to determine the driving forces for WWTPs' efficiency. The empirical results showed that: (i) the selected DEA window length remarkably affected both the average efficiency and the variations; however, it had no impact on the ranking of plants' efficiency; (ii) lower efficiencies were observed in plants with larger capacities due to higher infrastructure and operation investments involved; (iii) both the influent total phosphorus concentrations and influent flow rates had significant effects on the WWTPs' performance. Moreover, the staff and utility expenditures should be reduced to generate greater potential cost savings and efficiency improvement given the treatment technologies employed.

Enhanced biological phosphorus removal in aerated stirred tank reactor using aerobic bacterial consortium

Recently, some studies in the field of enhanced biological phosphorus removal (EBPR) have reported that anaerobic zone is not mandatory and EBPR could be accomplished under fully aerobic conditions. This study investigated the EBPR performance of a bacterial consortium in a fully aerobic continuous stirred tank reactor (CSTR) fed with synthetic wastewater containing molasses as the sole carbon source. Total phosphorus (TP) concentration in the feed was varied between 1.4 and 40mg/L. The results showed that TP removal efficiency was over 90% up to 20mg/L of influent TP concentration but decreased to 62% and 30% at 30 and 40mg/L of influent TP concentrations, respectively. TP removal efficiency was affected more by its own concentration in the influent. COD and NH4+-N removal efficiencies largely remained unaffected by different TP concentrations. Further investigations showed that TP was transformed to intracellular storage of polyphosphate (poly-P) with single stage aerobic process. The primarily reason for TP removal was the synergistic activity among the individual strains of phosphate solubilizing and accumulating microbes. Based on tracer studies, dead space in CSTR was found to range from 1.5–2.5%. The flow pattern in the CSTR was classified as completely mixed.

Modeling total phosphorus removal in an aquatic environment restoring horizontal subsurface flow constructed wetland based on artificial neural networks.

A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were used to model the removal of total phosphorus (TP). Four variables were selected as the input parameters based on the principal component analysis: the influent TP concentration, water temperature, flow rate, and porosity. In order to improve model accuracy, alternative ANNs were developed by incorporating meteorological variables, including precipitation, air humidity, evapotranspiration, solar heat flux, and barometric pressure. A genetic algorithm and cross-validation were used to find the optimal network architectures for the ANNs. Comparison of the observed data and the model predictions indicated that, with careful variable selection, ANNs appeared to be an efficient and robust tool for predicting TP removal in the HSSF-CW. Comparison of the accuracy and efficiency of MLP and RBF for predicting TP removal showed that the RBF with additional meteorological variables produced the most accurate results, indicating a high potentiality for modeling TP removal in the HSSF-CW.

Statistical Modeling of Phosphorus Removal in Horizontal Subsurface Constructed Wetland

A horizontal subsurface flow constructed wetland (HSSF-CW) was constructed to improve the water quality of an artificial lake in Beijing wildlife rescue and rehabilitation center, Beijing, China. Multiple Regression Analysis (MRA) and Artificial Neural Networks (ANNs) including Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were used to model the treatment performance of total phosphorus (TP). In order to increase the model efficiency, input parameters were selected as influent TP concentration, hydraulic retention time, wastewater temperature, month of the year, porosity, area, precipitation and evapotranspiration based on the methods of principal component analysis (PCA) and redundancy analysis (RDA). Genetic algorithm and cross-validation were utilized to find the optimal network architecture and parameters for ANNs. The overall performance of the models was validated using different datasets from the case study spanning 3 years. The results implied that modeling using adequate but crucial parameters can provide an efficient and robust tool for predicting performance. By comparing the three models in terms of model fitness when applied to the prediction, ANNs seemed to be more efficient than MRA in modeling of the areal TP removal and RBF (R2: 0.829, p = 0.000) produced the most accuracy and efficiency indicating strong potential for modeling the TP treatment processes in HSSF-CW systems.