Scholarly record
THE USE OF A FUZZY MODEL OF PHOSPHORUS CONCENTRATION DISTRIBUTION IN A STRATIFIED LAKE WATER FOR PREDICTION OF WATER QUALITY.
Abstract
The research object was the D?ugie Lake in Olsztyn (north-eastern Poland) (area 26.8 ha, maximum depth 17.3 m). It is a stratified, bradimictic urban lake - a depth index of 0.3 indicates a deep recess of the lake's bowl in the ground. In the past, the lake was almost completely degraded as a result of the discharge of raw domestic sewage. After applying two methods of restoration (artificial aeration with destratification and the phosphorus inactivation), the quality of lake water was improved. The data of multiannual phosphorus concentration in lake water after completion of restoration treatments were used to build a fuzzy model of phosphorus concentration in particular layers of lake waters. The data used to build the model of phosphorus concentration in lake water was: the oxygen content, thermic systems (in different lacustrine year seasons), the amount of atmospheric precipitation and estimated loads of phosphorus flowing from the lake's basin. Created fuzzy model allows to determine the concentration of phosphorus in individual layers for different seasons of the lake year. This allows the assessment of lake water quality in the context of forecasting the risk of algal blooms. The assessment of this research can be used for quick alert of lake users (tourists, swimmers) from the possibility of exposure to cyanotoxins.
Publication Impact Profile
Publication details
References8
Bukowska A., Kaliński J; Koper M., et al. 2017. Predicting blooms of toxic cyanobacteria in eutrophic lakes with diverse cyanobacterial communities. Scientific Reports: 8342 DOI: 10.1038/s41598-017-08701-8, 2017.
Grochowska J., Augustyniak R., Łopata M., How durable is the improvement of environmental conditions in a lake after the termination of restoration treatments. Ecological Engineering, 104(A), pp. 23-29, 2017.
Xie L., Xie P., Li S., Tang H., Liu H., The low TN:TP ratio, a cause or a result of Microcystis blooms? Water Research, vol. 30(6), pp. 2073 – 2080, 2003.
Sen Z., Fuzzy Logic and Hydrological Modeling, CRC Press Taylor & Francis Group, 2010, ISBN 978-1-4398-0939-6 (Hardback)
Nayak P.C.,, K.P Sudheer K.P.,, D.M Rangan, K.S Ramasastri, A neuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology, Volume 291, Issues 1–2, 2004, Pages 52-66, ISSN 0022-1694, DOI: 10.1016/j.jhydrol.2003.12.010.
Chang F.-J., Ya-Ting Chang Y.-T., Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Advances in Water Resources, Volume 29, Issue 1, pp.1-10, 2006, ISSN 0309-1708, DOI: 10.1016/j.advwatres.2005.04.015.
Khadr M., Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models. In:. The Handbook of Environmental Chemistry. Springer, Berlin, Heidelberg, 2017, DOI: 10.1007/698_2017_110
Bruder, S., Babbar-Sebens, M., Tedesco, L., Soyeux E., Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies, Environ Monit Assess, 186, 1525, 2014, DOI: 10.1007/s10661-013-3471-1
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
