J.D. Wood1, R.J. Gordon2, G.W. Stratton3, and A. Madani2. (1) Dept of Process Eng. and Applied Sciences, Dalhousie Univ, 1360 Barrington St, Halifax, NS B3J 1Z1, Canada, (2) Dept of Engineering, Nova Scotia Agricultural College, PO Box 550, Truro, NS B2N 5E3, Canada, (3) Dept of Environmental Sciences, Nova Scotia Agricultural College, Truro, NS B2N 5E3, Canada
A pilot scale surface flow constructed wetland (~100 m2) in Truro Nova Scotia, Canada was loaded with dairy wastewater over a continuous 6 year period (November, 2000 through April, 2005). The wetland included both deep and shallow zones, and was planted with cattails. A long-term assessment of the phosphorous (P) treatment efficiency was performed. Mean daily loading rates for total phosphorus (TP) and soluble reactive phosphorus (SRP) were 1.5 ± 1.0 and 1.0 ± 0.9 kg P ha-1 d-1, respectively. The mean daily wastewater hydraulic loading rate was 2.5 ± 1.5 mm d-1. Treatment efficiencies for both TP and SRP were strongly influenced by both mass and hydraulic loading. The majority of wetland outflow and effluent loads occurred during the spring and fall due to increased precipitation as well as ice and snowmelt. As the wetland aged the sensitivity to higher loading rates increased, while overall P treatment performance decreased. In the first year of operation monthly mass removals routinely exceeded 90% for both TP and SRP. In subsequent years mass and hydraulic loads were higher and a decrease in treatment performance coupled with an increase in treatment variability was observed. During the winter of 2005 wetland outflows were extremely high and mass reductions were negative indicating that the wetland was acting as a source of P. Linear regression analyses of monthly TP and SRP mass reductions and effluent hydraulic load (qout) found mass reductions decreased as qout increased. Mass reductions for both parameters were >50% when qout was <100 mm month-1. When qout increased beyond 100 mm month-1, mass reductions became much more variable making it difficult to predict system performance.