Development of a mobile phone application for the prediction of harmful algal blooms in inland lakes
Gotthold, J.P.; Deshmukh, A.; Nighojkar, V.; Skalbeck, J.; Riley, D.; Sander, H.
published: May 1, 2016
ArtNo. ESP141018801000, Price: 29.00 €
Harmful algal blooms mainly caused by cyanobacteria in freshwater ecosystems often present a health risk to the public within eutrophied shallow lakes due to algal toxins released into the water during the final stage of an algal bloom. Thus, algal growth should be carefully monitored during the summer season, especially in fre- quented recreational areas. Traditionally, water samples must be sent to a lab to analyze the data to predict algal blooms, costing time and money. Models on a smartphone predicting harmful algal blooms from easily measurable parameters could help individuals to take precautionary measures in order to prevent health risks from drinking and bathing in water and help to raise public awareness. In this work we present a mobile smartphone application that generates a prediction of the likelihood of an algal bloom from a variety of easily-measured input parameters that could be obtained by an informed smartphone user with simple instruments. Our model was implemented in an Android mobile phone application using App Inventor. The model we use is based on the Verhulst equation and allows users to enter any of the following measurements to predict and algal bloom: surface temperature, inverse Secchi depth, dissolved oxygen (DO) at the surface, and chlorophyll fluorescence (Chl-a). Our model was developed using a data set by weekly sampling of eutrophication parameters (temperature, con- ductivity, DO, phosphate, ammonia, nitrite, nitrate, Chl-a, Secchi depth) during the summer season of 2013 (June– October) from a shallow lake situated in a recreational area within the town of Wolfenbüttel, Germany. Temperature differences within water depth layers were observed in mid-June, then partial circulation of the upper three water layers was reached in mid-August until temperatures gradually reached equilibrium at the beginning of August (full circulation). This coincided with full development of algal bloom (cyanobacterial Chl-a values reaching 40 μg L –1), Secchi depth values below 0.6 m and a sharp drop in phosphate and ammonia levels within the bottom water layer. Thus, phosphate concentration at lake bottom, temperature difference between water layers, and surface tempera- ture were recognized as valuable parameters for a simple prediction model of harmful algal growth based on the Verhulst equation Nt = N0 + (k – N0)*exp(–r0 *t). A partial least square analysis revealed parameters for estimation of chlorophyll fluorescence (total Chl-a (μg L–1) = –6.4775 + 21.6396 * inverse Secchi depth (m) + 0.0006 * square (DO surface (%); r2 = 0.69) as well as cyanobacterial chlorophyll fluorescence (cyanobacterial Chl -a (μg L–1 ) = 0.409 – 0.7486 * surface temperature (°C) + 17.6979 * inverse Secchi depth (m); r2 = 0.76) from this data set. From these datasets and models, we created a single model that uses Secchi depth in combination with either DO or temperature at the surface to predict algae blooms.