Modelling of chlorophyll-a content in running waters

• Autorzy: Neverova-Dziopak E. , Kowalewski Z.

Identyfikatory

DOI

10.2429/proc.2016.10(1)047

Warianty tytułu

PL

Modelowanie zawartości chlorofilu-a w wodach płynących

• Konferencja: ECOpole’15 Conference (14-16.10.2015, Jarnoltowek, Poland)
• Języki publikacji: EN

Abstrakty

EN

Chlorophyll-a is one of the key parameters for assessment of trophic status of surface waters. However, Polish standard environmental monitoring procedures assume a low frequency of chlorophyll measurements in running waters, which does not provide the possibility of permanent control of eutrophication process and taking the appropriate preventive and protective measures sufficiently in advance. The article is focusing on constructing of predicting model of chlorophyll-a content based on data obtained within monitoring realized by Regional Inspectorates for Environmental Protection. Multivariate linear regression (MLR) model for chlorophyll-a content prediction was formulated on the base of chosen parameters like: pH, oxygen saturation, different forms of nitrogen and phosphorus. Formulation of the model was followed by a test of the applicability of each of the individual components of the regression equation. The main purpose was to develop an algorithm allowing for quick adaptation of model to local conditions in the rivers in order to make a reliable prediction of chlorophyll content.

PL

Chlorofil-a jest jednym z kluczowych parametrów służących do oceny stanu troficznego wód. W Polsce w ramach standardowego monitoringu rzek jest jednak badany rzadko. Artykuł skupia się na skonstruowaniu modelu predykcji zawartości chlorofilu-a w oparciu o dane pochodzące z rutynowego monitoringu realizowanego przez Wojewódzkie Inspektoraty Ochrony Środowiska. W tym celu na podstawie parametrów jakości wód, takich jak pH, nasycenie tlenem oraz różne formy azotu i fosforu, został sformułowany model regresyjny, a następnie przeprowadzono test zasadności zastosowania w nim poszczególnych składników równania regresji. Ostatnim etapem było opracowanie algorytmu pozwalającego na szybkie dostosowywanie modelu do lokalnych warunków w rzekach w celu dokonania wiarygodnej prognozy zawartości chlorofilu.

Słowa kluczowe

EN

eutrophication; chlorophyll-a; running waters; prediction models

PL

eutrofizacja; chlorofil a; wody płynące; modele predykcyjne

• Wydawca: Towarzystwo Chemii i Inżynierii Ekologicznej
• Czasopismo: Proceedings of ECOpole
• Rocznik: 2016
• Tom: Vol. 10, No. 2
• Strony: 455--462
• Opis fizyczny: Bibliogr. 19 poz., rys., wykr., tab.

Twórcy

autor

Neverova-Dziopak E.

• Department of Environmental Management and Protection, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland, phone [phone]

autor

Kowalewski Z.

• Department of Environmental Management and Protection, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland, phone [phone]

Bibliografia

• [1] Biggs B. Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae. J North Am Benthol Soc. 2000;19:17-31.
• [2] Neverova-Dziopak E. Podstawy zarządzania procesem eutrofizacji antropogenicznej (Fundumentals of anthropogenic eutrophication management) Kraków: Wydawnictwa AGH; 2010.
• [3] Bezy JL, Delwart S, Rast M. A new generation of ocean-colour sensor onboard Envisat. Esa Bull Sp Agency. 2000;103:48-56.
• [4] Yacobi Z, Wesley J, Kaganovsky S, Sulimani B, Leavitt B, Gitelson AA. NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study. Water Res. 2011;45(7):2428-2436. DOI: 10.1016/j.watres.2011.02.002.
• [5] Mishra S, Mishra DR. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sens Environ. 2012;117:394-406. DOI: 10.1016/j.rse.2011.10.016.
• [6] Reboreda R, Cordeiro GF, Nolasco R. Modeling the seasonal and interannual variability (2001-2010) of chlorophyll-a in the Iberian margin. J Sea Res. 2014;93:133-149. DOI: 10.1016/j.seares.2014.04.003.
• [7] Saitoh S, Iida T, Sasaoka K. A description of temporal and spatial variability in the Bering Sea spring phytoplankton blooms (1997-1999) using satelite multi-sensor remote sensing. Prog Oceanogr. 2002;55(1-2):131-146. DOI: 10.1016/S0079-6611(02)00074-5.
• [8] Jeong K-S, Kim D-K, Joo G-J. River phytoplankton prediction model by Artificial Neural Network: Model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system. Ecol Inform. 2006;1(3):235-245. DOI: 10.1016/j.ecoinf.2006.04.001.
• [9] Zhang X, Recknagel F, Chen Q, Cao H, Li R. Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation. Ecol Modell. 2015;306:216-225. DOI: 10.1016/j.ecolmodel.2014.05.013.
• [10] Malek S, Syed A, Sharifah M, Singh S, Milow P, Salleh A. Assessment of predictive models for chlorophylla concentration of a tropical lake. BMC Bioinformatics. 2011;12 Suppl 1(Suppl 13):S12. DOI: 10.1186/1471-2105-12-S13-S12.
• [11] Heiskary S, Bouchard W. Development of eutrophication criteria for Minnesota streams and rivers using multiple lines of evidence. Freshw Sci. 2015;34(2):574-592. DOI: 10.1086/680662.
• [12] Rocha RRA, Thomaz SM, Carvalho P, Gomes LC. Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River , Brazil). Brazilian J Biol. 2009;69(2):491-500. DOI: 10.1590/S1519-69842009000300005.
• [13] Cho Kyung H, Kang J-H, Ki Seo J, Park Y, Cha Sung M, Kim Joon H. Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan Reservoir, Korea. Sci Total Environ. 2009;407(8):2536-2545. DOI: 10.1016/j.scitotenv.2009.01.017.
• [14] Håkanson L, Malmaeus JM, Bodemer U, Gerhardt V. Coefficients of variation for chlorophyll, green algae, diatoms, cryptophytes and blue-greens in rivers as a basis for predictive modelling and aquatic management. Ecol Modell. 2003;169(1):179-196. DOI: 10.1016/S0304-3800(03)00269-2.
• [15] Niu L, Van Gelder P, Guan Y, Zhang C, Vrijling JK. Probabilistic analysis of phytoplankton biomass at the Frisian Inlet (NL). Estuar Coast Shelf Sci. 2015;155:29-37. DOI: 10.1016/j.ecss.2014.12.049.
• [16] Wu N, Huang J, Schmalz B, Fohrer N. Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches. Limnology. 2013;15(1):47-56. DOI: 10.1007/s10201-013-0412-1.
• [17] Zhao X, Zhang H, Tao X. Predicting the short-time-scale variability of chlorophyll a in the Elbe River using a Lagrangian-based multi-criterion analog model. Ecol Modell. 2013;250:279-286. DOI: 10.1016/j.ecolmodel.2012.11.018.
• [18] Mulia Iyan E, Tay H, Roopsekhar K, Tkalich P. Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentrations. J Hydro-Environ Res. 2013;7(4):279-299. DOI: 10.1016/j.jher.2013.04.003.
• [19] Qiuhua I, Lihai S, Tingjing G. Use of principal component scores in multiple linear regression models for simulation of chlorophyll-a and phytoplankton abundance at a karst deep reservoir, southwest of China. Acta Ecol Sin. 2014;34(1):72-78. DOI: 10.1016/j.chnaes.2013.11.009.