title: Universal temperature model for shallow algal ponds provides improved accuracy.
abstract: While temperature is fundamental to the design and optimal operation of shallow algal ponds, there is currently no temperature model universally applicable to these systems. This paper presents a model valid for any opaque water body of uniform temperature profile. This new universal model was tested against 1 year of experimental data collected from a wastewater treatment high rate algal pond. On the basis of 1 year of data collected every 15 min, the average errors of the predicted afternoon peak and predawn minimum were both only 1.3 °C and the average error between these extremes was just 1.2 °C. In order to demonstrate the improvement in accuracy gained, the expressions for heat fluxes used in nine prior temperature models were systematically substituted into the new universal model and evaluated against the experimental data. Errors in the peak and minimum temperatures increased by up to 2.1 and 3.2 °C, respectively, while the error between these extremes increased by up to 2.9 °C. In practical applications, these levels of inaccuracies could lead to an under/overestimation of the algal productivity and the evaporative water loss by approximately 40% and 300%, respectively.
authors: Center for Environmental Technology and Engineering, School of Engineering and Advanced Technology, Massey University , Private Bag 11 222, Palmerston North 4442, New Zealand.
authors: Quentin
authors: Q
authors: Béchet
authors: Andy
authors: A
authors: Shilton
authors: Jason B K
authors: JB
authors: Park
authors: Rupert J
authors: RJ
authors: Craggs
authors: Benoit
authors: B
authors: Guieysse
mesh_terms: D001272:Atmosphere / Q000737:chemistry; D018481:Convection; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000032:analysis / Q000737:chemistry*; D058086:Microalgae / Q000254:growth & development*; D008954:Models, Biological; D013472:Sunlight; D013696:Temperature; D014865:Waste Disposal, Fluid / Q000379:methods / Q000706:statistics & numerical data