High rates of nitrogen removal in aerated VFCWs treating sewage through C-N-S cycle.

The efficiency of deep aerated vertical flow constructed wetlands (DA-VFCWs) being operated in Hyderabad, India, was evaluated herein using physicochemical analysis and 16S rRNA amplicon sequencing. The results showed 2-4-fold higher removal rate coefficients for Biochemical oxygen demand (1.32---3.53 m/d) and nitrogen (0.88--1.36 m/d) in DA-VFCWs than those of passive VFCWs. Elevated sulfate concentration in the DA-VFCWs effluent (84-113 mg/L) indicated possibility of sulfur-driven autotrophic denitrification (SDAD) as a major pathway operating in these wetlands besides the classical nitrogen removal pathways. The presence of nitrifiers (3.09-10.02 %), heterotrophic and aerobic denitrifiers (0.79-0.83 %), anammox bacteria (1.31-2.22 %) and SDAD bacteria (0.08-0.73 %) in the biofilm samples collected from the DA-VFCWs exemplify an interplay of Carbon-Nitrogen-Sulfur cycles in these systems. If proven, the presence of an operational SDAD pathway in DA-VFCWs can help reduce surface area requirement in VFCWs substantially besides alleviating biological clogging of the wetland substrate.

Assessment of removal rate coefficient in vertical flow constructed wetland employing machine learning for low organic loaded systems.

Secondary datasets of 42 low organic loading Vertical flow constructed wetlands (LOLVFCWs) were assessed to optimize their area requirements for N and P (nutrients) removal. Significant variations in removal rate coefficients (k20) (0.002-0.464 md-1) indicated scope for optimization. Data classification based on nitrogen loading rate, temperature and depth could reduce the relative standard deviations of the k20 values only in some cases. As an alternative method of deriving k20 values, the effluent concentrations of the targeted pollutants were predicted using two machine learning approaches, MLR and SVR. The latter was found to perform better (R2 = 0.87-0.9; RMSE = 0.08-3.64) as validated using primary data of a lab-scale VFCW. The generated model equations for predicting effluent parameters and computing corresponding k20 values can assist in a customized design for nutrient removal employing minimal surface area for such systems for attaining the desired standards.