Modelling and evaluation of nitrogen removal performance in subsurface flow and free water surface constructed wetlands


Tuncsiper B., Ayaz S. C., Akca L.

WATER SCIENCE AND TECHNOLOGY, vol.53, no.12, pp.111-120, 2006 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 12
  • Publication Date: 2006
  • Doi Number: 10.2166/wst.2006.412
  • Journal Name: WATER SCIENCE AND TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Agricultural & Environmental Science Database, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chimica, Compendex, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Public Affairs Index, Veterinary Science Database
  • Page Numbers: pp.111-120
  • Istanbul Technical University Affiliated: No

Abstract

With the aim of protecting drinking water sources in rural regions, pilot-scale subsurface water flow (SSF) and free water surface flow (FWS) constructed wetland systems were evaluated for removal efficiencies of nitrogenous pollutants in tertiary stage treated wastewaters (effluent from the Pasakoy biological nutrient removal plant). Five different hydraulic application rates and emergent (Canna, Cyperus, Typhia sp., Phragmites sp., Juncus, Poaceae, Paspalum and Iris) and floating (Pistia, Salvina and Lemna) plant species were assayed. The average annual NH4-N, NO3-N and organic-N treatment efficiencies were 81, 40 and 74% in SSFs and 76, 59 and 75% in FWSs, respectively. Two types of the models (first-order plug flow and multiple regression) were tried to estimate the system performances. Nitrification, denitrification and ammonification rate constants (k(20)) values in SSF and FWS systems were 0.898 d(-1) and 0.541 d(-1), 0.486 d(-1) and 0.502 d(-1), 0.986 d(-1) and 0.908, respectively. Results show that the first-order plug flow model clearly estimates slightly higher or lower values than observed,when compared with the other model.