The use of constructed wetland (CW) is a natural way of treating wastewater sustainably and economically. However, the implementation of these systems in freezing conditions is still a matter of research and development. The treatment capacity of CWs relies largely on the biological and biochemical processes which further depends on physical conditions such as temperature, solar radiations, etc. Application of wetland systems for treating wastewater faces many challenges in regions with cold climates, resolving which this review has been made. This paper presents a thorough understanding of the components of CWs and their role in contaminant removal. A comprehensive review of the different types of CWs has been done describing the treatment efficiency achieved by its implementation in the cold climate. Furthermore, various technologies which can be clubbed with CWs have also been listed along with the treatment efficiencies obtained. Literature survey indicates that the extent of removing organics (COD and BOD5) and total phosphorous (TP) are not likely to be affected, but total nitrogen (TN) removal appears to slow down at low temperatures. Despite several advantages of CW technology, further research is required to select suitable macrophytes and optimum design parameters to compensate for frigid conditions.

This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were used for this study. The growth of all the plants was inhibited in rice mill wastewater due to low pH, high chemical oxygen demand, high conductivity, and high phosphorus concentration. Subsequently, a 1:1 ratio of mill water to tap water was used. A control was maintained to assess the aquatic plant technology. In this study, the aquatic plants reduced the total phosphorus content up to 80 % within 15 days. A comparison between three modeling techniques e.g. Artificial neural network (ANN), Random forest (RF) and M5P has been done considering the reduction rate of total phosphorus as predicted variable. In this paper, the data set has been divided in two parts, 70 % is used to train the model and residual 30 % is used for testing of the model. Artificial neural network shows promising results as compared to random forest and M5P tree modelling. The root mean square error (RMSE) for all the three models is observed as 0.0162, 0.0204 and 0.0492 for ANN, RF and M5P tree, respectively.