development of a mobile phone application for the...

© 2016 E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany www.schweizerbart.deDOI: 10.1127/fal/2016/0722 1863 - 9135/16/0722 $ 4.25

Development of a mobile phone application for the prediction of harmful algal blooms in inland lakes

J. P. Gotthold 1, A. Deshmukh 2, V. Nighojkar 2, J. Skalbeck 3, D. Riley 2, * and H. Sander 1

With 9 figures and 4 tables

Abstract: 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 tem-perature at the surface to predict algae blooms.

Key words: cyanobacteria; cyanotoxins; algal growth; monitoring; eutrophied shallow lakes; App Inventor

Fundam. Appl. Limnol. Vol. 188/1 (2016), 1–17 Articlepublished online 11 March 2016, published in print May 2016

Authors’ addresses:1 Faculty of Supply Engineering, Inst. of Bio- and Environmental Sciences, Ostfalia University of Applied Sciences,

Wolfenbüttel, Germany2 Dept. of Computer Science, University of Wisconsin at Parkside, Kenosha, USA3 Dept. of Geosciences, University of Wisconsin at Parkside, Kenosha, USA* Corresponding author; [email protected]

E

2 J. P. Gotthold et al.

Introduction

Cyanobacteria in freshwater ecosystems are often the cause of harmful algal blooms within eutrophied shallow lakes due to cyanotoxins, which are released into the water mostly during the final stage of an al-gal bloom. Cyanotoxins are associated with immedi-ate health risks for humans and pets such as itching, rashes, numbness, and paralysis. Permanent exposure to cyanotoxins can lead to either neuro- or hepatotoxic effects (Elder et al. 1993; Falconer 1999; EPA Factsheet 2012). Water treatment methods like chlorination, UV disinfection or granular activated carbon filters do not eliminate cyanotoxins from surface waters completely (Westrich et al. 2010), thus efforts should be directed towards steps to predict and prevent cyanobacterial blooms once such blooms are predicted. Recommen-dations for critical limits for peptide toxins are 1 μg L–1 for drinking water (Microcystins, Nodularin) and 1 mg L–1 for bathing water as the sum of Microcyst-ins LR, RR and YR (guidelines of WHO 2003a). In addition, German government guidelines recommend restrictions for bathing activities once cyanobacterial concentrations reach 40 µg L–1 Chl-a (Umweltbundes-amt 2003).

There is a wide range of options to achieve pre-diction of algal growth, and most approaches involve either the Verhulst Model (Verhulst 1838) considering the influence of limiting factors on birth and death rates of a population or the Monod Model within a chemostat (Monod 1950; Grimm 1977). Several accu-rate models incorporate bioreactor growth light inten-sity expressed as photosynthetic photon flux density (Kurano & Miyachi 2005); however, these parameters are not easily available in the field and not applica-ble to lakes where environmental dynamics can af-fect biomass behavior. Other models are designed for continuous industrial algal production in a controlled bioreactor environment and account for nutrient up-take and availability (Batel & Paul 1972). Modelling algal growth within a lake environment has to consider aspects of nutrient distribution and environmental im-pact, which are not taken into account in the industrial algal models. Within lakes, however, behavior of flow and transport phenomena influence biomass develop-ment as the output factor as well as light incidence, water temperature, fluid velocity, and nutrient concen-tration. A fuzzy logic algal model approach classified these input parameters as medium, high or low for de-cision criteria (Krol et al. 2010). Most of these existing models rely on input parameters that are not readily available for smaller lakes, so development of a differ-

ent model is necessary to make a harmful algal bloom alert tool available to the public, especially since there are many more smaller lakes than larger lakes.

We present a study to establish a combination of several easy-to-measure indicator parameters re-trieved from a data set on a lake in Northern Germany (Wolfenbüttel, Lake Stadtgraben) for a simple algal growth prediction model. We have made this model widely available to users by implementing the model within an Android mobile phone application. We used App Inventor, a visual online programming language to implement our tool (Wolber 2011). Our application is designed to make it easy for a member of the gen-eral public to be able to take simple measurements in a body of water near them and make a prediction of the likelihood of an algal bloom. The app also saves user measurements so the user can recall measured data or compare it at different times. We also present valida-tion data that confirms that our model closely resem-bles the behavior of algae growth.

Material and methods

Lake characteristics

Sampling to establish lake characteristics and algal content for app development and validation was carried out on an artifi-cial shallow lake (Lake Stadtgraben, Wolfenbüttel) located in Northern Germany (latitude: 52.164, longitude: 10.5408) within a city recreational area (city population 51,546). The lake cov-ers an area of 24,941 m2 (approx. 2.5 ha) with a length of 550 m and width of 25 –70 m. The lake receives water partially from ground water und partially from the Oker river within the near vicinity with an adjustable influx (200 mm tube diameter and Vmax = 90 m3 h–1). Water exits the lake into the Oker river via an unadjusted efflux (tube diameter 150 mm and Vmax = 34.6 m3 h–1).

First validation attempts of app performance were initi-ated in addition in Eagle Spring Lake, Mukwonago, Wisconsin, USA, and Lake Russo, Pleasant Prairie, Wisconsin, USA. Both lakes are shallow lakes with lake characteristics similar to Lake Stadgraben, Wolfenbüttel, Germany. Eagle Spring Lake is an impounded 1,129,074 m2 through-flow or drainage lake, located on the Mukwonago River within U.S. Public Land Survey Sec-tions 25, 26, 35 and 36, Township 5 North, Range 17 East, in the Town of Eagle, Waukesha County. The length is approximately 1000 m and the width is approximately 1750 m. The Lake is fed and drained by the Mukwonago River (influx 6830 m3 h–1, efflux 632 – 856.3 m3 h–1 or at maximum 1936.8 m3 h–1 under flood conditions) as well as some springs. The tributary water-shed area is 54.4 km2.

Lake Russo is a shallow artificial lake located within the village of Pleasant Prairie, Wisconsin, USA (latitude 42° 33″ 10′ N, longitude 87° 56″ 40′ W). The lake has a surface area of 89,031 m2 with a length of 592 m (northwest–southeast) and a width of 238 m (northeast–southwest). The lake is fed by groundwater springs and surface runoff, water is discharged by

3Mobile phone application for the prediction of harmful algal blooms in inland lakes

a fixed culvert at southwest bay, with an in- and outflow at ap-proximately 8 m3 h–1.

Field conditions: Sampling strategy and data analysis

Lake Stadtgraben

Eleven equidistant sampling sites were established throughout the length of the lake, with 40 m distance between sites starting 30 m from bank to avoid bias created by shallow areas. Loca-tions were sampled weekly by boat from July 26th to October 23rd in 2013. To gain a more complete dataset, depth profiling (Ruttner device) was done in addition at the sampling sites 1, 3, 7, 10 within the depths of 0, 1, 2 and 3 m (Fig. 1).

General measurements were taken on site including water temperature, pH values, dissolved oxygen (DO), redox poten-tial, Secchi depth, and conductivity. Measurements related to algal development were taken on site including turbidity [FTU], chlorophyll-a concentration (Chl-a) as total and cyanobacterial Chl-a using an Algal Torch (bbe moldaenke), for fluorescence measurements (Beutler et al. 2002). Water samples were also taken for cell count using a Thoma chamber under a light mi-croscope (400× magnification, Zaiß Axiostar). In addition, nu-trient status of the lake was monitored within the water samples by analyzing ammonia (NH4-N), nitrate (NO3-N), nitrite (NO2-N), phosphate (PO4-P) (analyzed according to DIN Methods, DIN ISO 15923-1, 2012).

Descriptive statistics of analyzed data (complete data set see: Gotthold 2014) were performed using Excel. In addition

Pearson correlation coefficients of the parameters measured and their transformations (inverse, squared) were compared using the program Virtual Beach (Version 3.0), a program widely used to predict pathogen indicator levels at recreational beaches. Chl-a (mg L–1) was chosen as the dependent variable in order to find easy-to-measure parameters, replacing the cost intensive and therefore often inaccessible measurement of chlo-rophyll fluorescence data.

Eagle Spring Lake and Lake Russo

Water sampling was done at five (Eagle Spring Lake) or six (Lake Russo) points evenly spaced throughout lake area at two times within a time span of 14 days at the end of August 2015. Temperature, conductivity, and dissolved oxygen were meas-ured with multiparameter instruments on site at four depths: 0 m (surface), 1 m, 2 m and lake bottom, also ammonia (NH4-N), nitrate (NO3-N), nitrite (NO2-N), phosphate (PO4-P) were analyzed (Strip quick tests, JBL Test Kit) at the first sampling date. Water transparency (Secchi depth) and local lake depth were also measured at that time. Water samples did serve to check for chlorophyll-a concentrations at the 1rst and 2nd sam-pling time.

Controlled conditions: bioreactor study

Growth parameters for our model were derived from an ad-ditional experiment in a twofold run with Chlorella vulgaris, an endemic species in Lake Stadtgraben, Northern Germany, under controlled conditions within a bioreactor (Infors, vol-

0 m

1 m2 m3 m

134.4 µg L-1

331.5 µg L-1

731.9 µg L-1

1028.3 µg L-1

233.8 µg L-1

1129.8 µg L-1

930.1 µg L-1

830.6 µg L-1

632.2 µg L-1

532.7 µg L-1

433.5 µg L-1

X

*

Fig. 1. Lake Stadtgraben Wolfenbüttel sampling strategy (numbers = sampling sites, bold type numbers = depth profiling 0 – 3 m, X = affluent site, * = effluent site). Also shown are chlorophyll-a concentrations as an example for on site measurements. Also shown are depth profiles (0, 1, 2, 3 m) at sampling sites 1, 3, 7 and 10.


ume 3.5 L) using media with (> 0.1 mg L–1 PO4-P) or without (< 0.02 mg L–1 PO4-P) phosphate (twofold concentrated ES Me-dia, EPSAG Göttingen) in otherwise optimal growth conditions allowing for no other limitations of algal development (pH 7 adjusted via CO2, 28 °C, 8000 lux, stirrer 150 rpm, inoculation 1 mio cells mL–1). Growth was followed over 10 days by daily measurements of pH, cell count (using a Thoma chamber under light microscope, Axiostar), optical density and Chl-a as well as start and end assessment of phosphate values. Growth rates were calculated from changes in cell count over time for both conditions (media with and without phosphate).

Results

Basic parameters: temperature, conductivity, pH and DO

Marked temperature differences (Table 1, Fig. 2b) could be observed between the surface (0 m) and bot-tom layer (3 m) June and July (range 5.4 –11.6 °C). Gradual temperature adjustment between the upper three layers (0 –1– 2 m, partial circulation) followed, while the lower layer temperatures remained almost unchanged, resulting in diminished overall tempera-ture differences (range 3 – 4.4 °C) in mid-August (Ta-ble 1). Temperatures reached equilibrium between all four layers (0 – 3 m, temperature differences less than

1.6 °C) at the beginning of September indicating on-set of fall full circulation stage of the lake (Table 1). Surface temperatures (Fig. 3a) for the lake ranged between 24.8 (06/19/2013) and 10.4 °C (10/16/2013) throughout the season.

Bottom conductivity values (µS cm–1) followed surface temperature changes in a less marked manner (Fig. 2b). In the beginning bottom conductivity levels were higher compared to slightly lower conductivity values of the upper three layers, reaching an equilib-rium at full circulation point (09/07/2013; Gotthold 2014), when bottom conductivity started to decline. This evidence suggests an equal distribution of ions within the water layers as temperature differences even out (Table 1, Fig. 2b), with the lower layer serv-ing as a trap for nutrient ions at lower temperature lev-els before turnover.

DO (% saturation, Fig. 3b) is saturated due to al-gal biomass production in the upper layer between the onset of sampling period in mid-June to the end of August. In contrast, the third and fourth water layer show low DO values (well below 80 % within the third and below 30 % saturation in the fourth layer) indicating eutrophication, i.e. biomass decomposition under consumption of oxygen (Gotthold 2014; values

Table 1. Changes in temperature differences between surface and bottom layer over time (weekly sampling, on site measurement, 06/19 –10/23) during summer season 2013 in Lake Stadtgraben, Wolfenbüttel (mean values and standard deviation (SD)). Note the onset of partial circulation at 08/14/2013 with temperature differences approaching 4 °C as well as the onset of full circulation at 09/04/2013 with temperature differences of less than 1.5 °C.

Temperature differences calculated as surface (0 m) – bottom (3 m) temperatures (°C)Date Site 1 Site 3 Site 7 Site 10 Mean [°C] SD [°C]

06/26/2013 7.2 6.4 5.4 7 6.5 0.707/03/2013 7.2 8.4 8.2 8 7.95 0.4507/10/2013 8.4 8.4 10 9.4 9.05 0.6807/17/2013 6 5.2 6.6 6.8 6.15 0.6207/24/2013 7.8 9.4 11.6 8 9.2 1.5107/31/2013 7.8 8.2 8.4 9.6 8.5 0.6708/07/2013 8.8 8.4 8 7.8 8.25 0.3808/14/2013 4 4.4 4.2 3.8 4.1 0.2208/28/2013 2.8 3 3.2 3 3 0.1409/04/2013 1.2 1.6 1.6 1.2 1.4 0.209/11/2013 0.4 0 0 0.2 0.15 0.1609/17/2013 0.6 0.6 0.4 0.4 0.5 0.109/25/2013 0.2 0.4 0.4 0.2 0.3 0.110/01/2013 0.4 0.8 0.2 0.4 0.45 0.2110/08/2013 1.8 1.4 1.6 1 1.45 0.2910/16/2013 0.2 0 0 0 0.05 0.0810/23/2013 1.4 1.8 1.6 1.6 1.6 0.14


Fig. 2. (a) Inverse Secchi depth and total and cyanobacterial Chl-a development, (b) temperature differences (0 – 3 m depth) and bottom conductivity changes, as well as (c) bottom (3 m) nutrient concentrations (NO3-N, NH4-N, PO4-P) over time (weekly sam-pling, on site measurement) during summer season 2013 in lake Stadtgraben, Wolfenbüttel. The lines and areas represent mean val-ues taken at all sampling sites 1–10 (mean out of n = 11 single values/point) for Secchi depth and Chl-a and values taken at all depth profiling sampling sites 1, 3, 7, 10 (mean out of n = 4 single values/point) for conductivity at bottom (3 m), temperature differences (between 0 and 3 m) and nutrient concentrations (out of 3 m depth) within the middle of the lake. Dotted lines indicate starting (1st line) and full harmful algal bloom (2nd line).


Fig. 3. (a) Surface temperatures (°C, 0 m), (b) dissolved oxygen (% DO at 0 m) and (c) total and cyanobacterial Chl-a develop-ment (µg L–1) over time (weekly sampling, on site measurement) during summer season 2013 in Lake Stadtgraben, Wolfenbüttel. The lines represent mean values taken at all sampling sites 1–10 (mean out of n = 11 single values/point) for Chl-a values and mean values from sites 1, 3, 7, 10 (depth profile sampling sites) for DO and temperature at the surface (0 m) within the middle of the lake. Dotted lines indicate starting (1st line) and full harmful algal bloom (2nd line). The missing DO values at 08/21 are an artifact (DO probe breakdown).


not depicted here). Finally, the onset of full circulation is resulting in slightly lower DO values at the surface starting in mid-August (Fig. 3b) as a more or less sta-ble overall equilibrium is reached throughout all lay-ers monitored.

Indicator parameters: algal growth

The indicator parameters monitored were Sec-chi depth (m), total Chl-a and cyanobacterial Chl-a (Fig. 2a and 3c; Table 2), green algae Chl-a was deducted from there (Fig. 2a). Secchi depth values (Fig. 2a) showed a depression (Secchi depth values below 0.6 m) from the beginning of August to mid-September reaching a minimum of 0.4 m at Septem-ber 4th, when full fall circulation was reached within the lake (Table 1). This coincided with onset and full harmful algal bloom as well as total Chl-a peak val-ues. Afterwards, Secchi depth values recovered and started to rise again up to a peak of 1 m Secchi depth seen in the second week of October (Fig 2a), when algal growth visibly declined.

Total Chl-a (µg L–1, Table 2, Fig. 3c, range over time 19.8 – 64.8 µg L–1) and cyanobacterial Chl-a (µg L–1, Table 2, Fig. 2a and Fig. 3c, range over time

2.2 – 39.2 µg L–1) followed a pattern inverse to Secchi depth values (Fig. 2a). Peak values up to and above 60 µg L–1 total Chl-a occurred in early August and September coinciding with partial and later on full cir-culation stages of the lake (09/04/2013, Table 1). Green algae (Chlorophyta) Chl-a concentrations reached their peak early in August (08/07/2013: Chl-a concen-trations 44.8 µg L–1 as difference from total and cyano-bacterial chlorophyll values, Fig. 2a), while blue green algae (cyanobacteria) Chl-a reached the seasonal peak as expected almost a month later (09/04/2013: 39.2 µg L–1), close to a value defined as the onset of an harmful algal bloom (40 µg L–1 Chl-a, James et al. 2007).

Influence of wind and current were not noticeable within this comparatively small lake as can be de-ducted from the rather small standard deviations (range 1.7– 6.9 µg L–1 for total Chl-a and 0.6 – 5.1 µg L–1 for cyanobacterial Chl-a, Table 2). However, much larger values of cyanobacterial Chl-a could be observed at the embankment (09/04/2013: values up to 117 µg L–1) at randomly taken additional samples, where depth was below 1 m and temperature distribution different from the sampling sites in the middle of the lake. Light microscope investigation of those samples showed a

Table 2. Changes in Chl-a concentration (total and cyanobacteria only) over time (weekly sampling, on site measurement, 06/26 –10/23) during summer season 2013 in Lake Stadtgraben, Wolfenbüttel (mean values and standard deviation (SD)). Note the comparatively even standard deviations (range 1.7– 6.9 for total Chl-a and 0.6 – 5.1 for cyanobacterial Chl-a).

Date Chl-a total Mean[µg L–1]

Chl-a total SD[µg L–1]

Chl-a cyanobacteria Mean [µg L–1]

Chl-a cyanobacteria SD [µg L–1]

06/19/2013 16.3 1.5 5.8 0.706/26/2013 34.8 1.9 15.4 1.207/03/2013 42.8 5.7 12.4 3.107/10/2013 19.8 4.0 2.2 0.907/17/2013 22.9 2.9 4.7 1.107/24/2013 20.6 2.7 6.8 1.607/31/2013 37.5 2.1 9.8 1.808/07/2013 55.0 3.7 11.2 1.608/14/2013 51.2 5.5 27.9 5.108/21/2013 34.9 5.9 17.2 3.308/28/2013 38.1 6.9 17.9 4.509/04/2013 64.8 5.9 39.2 3.909/11/2013 39.6 2.5 22.4 1.909/17/2013 32.1 3.6 17.2 1.909/25/2013 28.6 2.3 10.4 2.310/01/2013 21.8 2.3 8.8 0.610/08/2013 31.7 3.0 4.4 0.810/16/2013 31.6 1.7 16.3 0.910/23/2013 20.5 2.5 8.6 1.8


mix of several Chlorophyta species (mainly Chlorella vulgaris, Scenedesmus quadricauda, Botryococcus braunii and Chlamydomonas reinhartii), whereas among the blue-green algae the Microcystin producer species Microcystis was abundant.

Nutrient parameter: NO2-N, NO3-N, NH4-N and PO4-P

Of the nutrient parameters (Fig. 2c) observed, nitrate levels (Fig. 2c, bottom layer values) stayed at approxi-mately 0.2 mg L–1 with a mostly even depth distribu-tion throughout the observation period until the end of September. Values then rose up to 0.7 mg L–1 in Octo-ber (10/02 –10/23/2013) concurrent with the decrease in Chl-a levels (total and cyanobacterial) below the 20 µg L–1 level and increase in Secchi depth shown in Fig. 2a, suggesting release of nitrate after cell death as well as diminished nitrate uptake out of the water.

Ammonia concentrations stayed well below detec-tion limit in the upper 3 layers of the lake (0 – 2 m). However, ammonia concentrations within the bottom layer (3 m, Fig. 2c) were distinctly elevated (range from > 3 – 6 mg L–1 in July and August), then sharply dropped below detection limit from early September to the end of the observation period. Interestingly,

temperature differences between surface and bottom layer diminish from a range between 3 to 9.05 °C to 1.4 ± 0.2 °C at the same time (Table 1) indicating onset of full circulation. The drop in ammonia concentra-tions also coincides well with peak total Chl-a values and onset of harmful algal bloom (04/09/2013: peak cyanobacterial Chl-a, Table 2, Fig. 2a and 2c), indicat-ing nutrient uptake by algal biomass.

Phosphate values follow a quite similar pattern to the ammonia concentrations. Overall phosphate con-centrations remain below detection limit within the upper 3 layers of the lake (0 – 2 m), while phosphate concentrations within the bottom layer (3 m, Fig. 2c) are much more elevated (range 0.5 – almost 1 mg L–1 in July), then gradually start to decline in August and drop below the detection limit starting in September for the rest of the season. The drop parallels the previ-ously mentioned diminished temperature differences between water layers (Fig. 2b, Table 1) indicating on-set of full circulation. The sharp decrease in phosphate concentrations also coincides with the drop in am-monia concentrations and again with peak total Chl-a values and beginning of harmful algal bloom (peak cyanobacterial Chl-a 04/09/2013, Table 2), again indi-cating nutrient uptake by algal biomass.

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19.06.201326.06.201303.07.201310.07.201317.07.201324.07.201331.07.201307.08.201314.08.201321.08.201328.08.20 1304.09.201311.09.201317.09.201325.09.201301.10.201308.10.201316.10.201323.10.2013

Time (Months)

Chl-a

(µg

L-1)

Total real

a

b

Fig. 4. Real (black line) and estimated (grey line) values (Virtual Beach 3.0, PLS Model Equations) for cyanobacterial Chl-a (a: Estimate Equation for cyanobacterial Chl-a (µg L–1) C2 = 0.409 + 17.6979/S – 0.7486*T ; r2 = 0.76) and total Chl-a concentra-tions (b: Estimate Equation for total Chl-a (µg L–1) C1 = – 6.4775 + 21.6396/S +0.0006*D²]; r2 = 0.69) over time (real values weekly sampling, on site measurement at the 4 depth profiling sites) during summer season 2013 in Lake Stadtgraben, Wolfenbüttel. The horizontal dotted line represents WHO guideline threshold value of 40 µg L–1 Chl-a concentrations.


Data analysis: total and cyanobacterial Chl-a values

In order to detect some easy-to-measure indicator pa-rameters, which could serve as a replacement for not al-ways available Chl-a measurement options in the field, an analysis of the collected data was done using the software Virtual Beach 3.0. A comparison of Pearson correlation coefficients (Table 3 a,b) with total Chl-a as the dependent variable revealed notable Pearson Coefficient values with cyanobacterial Chl-a (Pearson coefficient 0.79, Correlation p-value 0.0001), Secchi depth (Pearson coefficient – 0.71, correlation p-value 0.0006), and inverse Secchi depth (Pearson coefficient 0.77, correlation p-value 0.0001, Fig. 2a), as these val-ues are the direct consequence of algal growth.

For cyanobacterial Chl-a as the dependent vari-able, the parameters with the most promising Pearson correlation coefficients for an estimation of harmful algal boom values are inverse Secchi depth (Pearson coefficient 0.8041, correlation p-value 0.0000) and

surface temperature (°C) (poly transformation, Pear-son coefficient 0.4895, correlation p-value 0.0334).

Multiple Linear Regression (MLR) and Partial Least Square (PLS) analysis using the Virtual Beach software generated two equations involving Secchi depth as an easy to handle indicator parameter for Chl-a estimation (Fig. 4a,b), which were implemented in our model for prediction of algal growth. We define Chl-a total (µg L–1) as C1, cyanobacterial Chl-a (µg L–1) as C2, Secchi depth (m) as S, DO percent as D, and surface temperature (°C) as T for the following equations to describe the total Chl-a and cyanobacte-rial Chl-a.

The parameters with high correlation coefficients mentioned above were followed in correlation by the parameters conductivity (log10 transformation, Pearson coefficient 0.32, correlation p-value 0.1725), DO (square transformation, Pearson coefficient 0.33, correlation p-value 0.1673), phosphate bottom layer (quadroot transformation, Pearson coefficient 0.27,

Table 3. Pearson coefficient results matrix with (a) total Chl-a (µg L–1) and (b) cyanobacterial Chl-a (µg L–1) as the dependent variable, untransformed and selected transformation data.

a. Dependent variable: total Chl-a Variable Transformation Pearson coefficient Correlation p-valueSurface temperature (°C) None 0.0795 0.7464Chl-a (µg L–1) (cyanobacteria) None 0.7932 0.0001Secchi depth (m) None – 0.7168 0.0006Inverse Secchi depth (m) Inverse 0.7759 0.0001Conductivity (µS cm–1) None 0.3001 0.2118Conductivity (µS cm–1) Log 10 0.3265 0.1725DO (%) None 0.2869 0.1673DO (%) Square 0.3303 0.1673Phosphate bottom layer (mg L–1) none 0.0778 0.7516Phosphate bottom layer (mg L–1) Quadroot 0.2746 0.2553Temperature differences top to bottom layer (°C) None – 0.1948 0.4242Temperature differences top to bottom layer (°C) Poly 0.4005 0.0893

b. Dependent variable: cyanobacterial Chl-a Variable Transformation Pearson coefficient Correlation p-valueSurface temperature (°C) Poly 0.4895 0.0334Chl-a (µg L–1) (total) Square 0.7977 0.0000Secchi depth (m) None – 0.7034 0.0008Inverse Secchi depth (m) Inverse 0.8041 0.0000Conductivity (µS cm–1) Inverse – 0.2124 0.3826DO (%) Poly 0.0894 0.7158Phosphate bottom layer (mg L–1) Square – 0.2478 0.3064Temperature differences top to bottom layer (°C) Square – 0.4997 0.0294


correlation p-value 0.2553) and temperature differ-ences between the top and bottom layer (poly transfor-mation, Pearson coefficient 0.40, correlation p-value 0.0.0893) to a much lesser extent. Temperature differ-ences seem to represent the cause behind the changes in algal growth together with nutrient availability (PO4-P) throughout the season, so they are used for prediction of algal growth in our model.

Model description

There are several requirements to be met by our model for prediction of algal growth in the field for wide us-ability. Simplicity is needed, which excludes models like Monod growth models relying on parameters such as enzyme turnover rates or photosynthetic photon flux density, as they are not easily accessed under field conditions. The basic Verhulst model requires a start value, growth rates and considerations as to carrying capacity parameters, and thus seems to be most appro-priate for our goals.

Our data set allows us to establish a few relation-ships between parameters observed. Examples include the dependency of algal growth on temperature dif-ferences from the lake surface to the bottom, which in turn influence nutrient distribution throughout the water body and availability at the surface (Fig. 2a– 2c). It has to be taken in consideration that these conditions may differ from shallow embankment areas (below 1 m depth with almost no temperature differences) to conditions in the middle of a lake (several m depths with temperature differences occurring within water

layers). Among nutrients, phosphate may well be the limiting parameter for carrying capacity as shown in the bioreactor studies, as nitrogen is mostly available via either nitrate or ammonia during the summer sea-son within the lake.

Several additional assumptions for our model must be defined in advance, and these are drawn from lit-erature or previous experiments. We assume that algal blooms do not readily occur with a surface temperature below 15 °C, and algal growth for production relies on temperatures between 20 – 30 °C (Chisti 2007), a find-ing backed by the data in this study (Fig. 3a and 3c). Growth parameters also needed for a Verhulst model were derived from our additional bioreactor study un-der controlled conditions in a twofold run with Chlo-rella vulgaris, an endemic species in Lake Stadtgra-ben, Northern Germany, using media with (> 0.1 mg L–1 PO4-P) or without (< 0.02 mg L–1 PO4-P) phosphate in otherwise optimized growth conditions. A marked difference in growth was noted due to presence or ab-sence of phosphate (Table 4), from which maximum growth rates µ were calculated (P-free: µ = 0.034, full medium: µ = 0.065, Table 4). Subsequently, this infor-mation is used for choices in model predictions under different phosphate concentration conditions within the environment (Fig. 5).

The choice of input parameters for biomass start values suited for an adequate prediction has to com-promise between parameter availability in the field and contribution to accuracy of the model. In the first place, the input parameters should be easy to access, including substitute parameters for Chl-a start values,

Table 4. Optical density (OD), cell count (CC), Chl-a values and growth rates (µ) of two Chlorella vulgaris cultures over time (10 days) under controlled conditions (3.5 L Biorecator Infors) in ES media with and without phosphate. Note different maximum growth rates with (µ = 0.065) and without (µ = 0.034) phosphate.

Full ES medium Phosphate free ES mediumIncubation

(days)OD (λ) CC

(cells mL–1)Chl-a

(mg L–1)µ OD (λ) CC

(cells mL–1)Chl-a

(mg L–1)µ

1 0.06 5.549 E +05 0.006 0.25 1.399 E +06 0.005 2 0.10 1.140 E +06 0.012 0.029 0.24 1.113 E +06 0.006 – 0.011 3 0.28 2.071 E +06 0.015 0.026 4 2.71 2.459 E +07 0.223 0.065 0.48 0.006 0.034 5 3.17 2.697 E +07 0.283 0.004 0.56 0.005 0.001 6 4.59 2.706 E +07 0.287 0.000 7 5.15 2.611 E +07 0.280 – 0.002 0.67 7.103 E +06 0.002 0.001 8 5.08 2.880 E +07 0.175 0.003 0.71 8.218 E +06 0.001 – 0.001 9 5.27 2.832 E +07 0.178 – 0.001 0.72 7.500 E +06 0.000 – 0.00210 0.70 8.438 E +06 0.001 – 0.00111 5.45 2.750 E +07 0.137 – 0.002 0.72 7.844 E +06 0.002 0.002


such as Secchi depth in combination with tempera-tures or DO at the surface (Chl-a estimation param-eters drawn from equation [1] and [2]). Therefore, a choice option has to be implemented to make optional adjustments for available parameters possible (Fig. 5). It can be seen in the figure that a user of the model can input four different types of data to predict Chl-a start values in our model.

Fig. 6 demonstrates the different prediction out-comes under different environmental conditions for cyanobacteria. A PO4-P concentration of 7 mg L–1, lake depth of 4 m and 20 °C were selected as starting condi-tions with a variation in temperature difference (1 °C or 5 °C) and varying start values of Chl-a (20 µg L–1 or 5 µg L–1) for the trial runs depicted there.

A low Chl-a start concentration (5 µg L–1 as pre-sented in Fig. 6) will extend the lag phase of the growth curve resulting in a belated rise in Chl-a con-centrations as compared to higher Chl-a concentra-tions (20 µg L–1, Fig 6). On the other hand, a small

temperature difference between surface and bottom water layers (1 °C, Fig. 6) will trigger algal growth much more pronounced than a high temperature dif-ference (5 °C example shown in Fig. 6), algal bloom is therefore not likely to occur soon.

An estimation when harmful algal bloom values (cyanobacterial Chl-a concentrations above 40 µg L–1) will be reached is given for a difference of surface to bottom temperatures of 1 °C at a starting value of 20 µg L–1 Chl-a with approximately 24 hours, while a temperature difference of 5 °C results in more than the triple time needed, since a small temperature dif-ference is responsible for onset of full circulation and ends the entrapment of phosphate, the limiting factor for algal growth, within the bottom layer. If the start value is lowered to 5 µg L–1 Chl-a, the threshold value of 40 µg L–1 Chl-a is reached within approximately 72 hours at a difference of surface to bottom temperatures of 1 °C, while at a temperature difference of 5 °C more than triple time will be needed to even reach thresh-

Declaration of variables: Assumptions: Light intensity and nutrients other than PO4-P are not rate limiting parameters Carrying capacity 100 µg L–1 Chl-a for cyanobacteria only and 150 µg L –1 for total Chl-a Growth rates: µ = 0.065 (PO4-P > 0.1 mg L–1) or µ = 0.034 (PO4-P > 0.02 and < 0.1 mg L–1) or µ = 0.01 (PO4-P < 0.02 mg L–1) Choice options (Input cases): Starting value (Chl-a) Input variables: Secchi depth (m) DO (%) PO4-P (bottom) (mg L–1) Depth of lake (m) Surface temperature (°C) Bottom temperature (°C) Computation: Assumption: Surface temperature > 15 °C & PO4-P concentration > 0.02 mg L–1 & temperature difference < 4 °C

Yes Temperature difference > 1

No Display: End

Yes r = µ = 0.034

Nt = N0 + (k – N0)*exp(–r0*t) Display & plot

Nor = µ = 0.065

Nt = N0 + (k – N0)*exp(–r0*t) Display & plot

1st Option (Direct data input): Total Chl-a (µg L–1) real value

2nd Option (Direct data input): Cyanobacterial Chl-a (µg L– 1) real value

3rd Option (Indirect estimate): Cyanobacterial Chl-a (µg L– 1) estimated value: C2 = 0.409 + 17.6979/S – 0.7486 *T

4th Option (Indirect estimate): Total Chl-a (µg L–1) estimated value: C1 = –6.4775 + 21.6396/S + 0.0006*D²

PO4-P concentration (mg L–1) at circulation

Temperature difference surface – bottom

Fig. 5. Flow chart of the prediction model for harmful algal blooms.


old values reflecting the much prolonged lag phase of a low density culture under unfavorable conditions (Fig. 6).

Model validation

To demonstrate the correctness of our model of algal growth, we have compared the model predictions to the outcome of our bioreactor studies as well as to

sample data from a second lake, Lake Russo in Pleas-ant Prairie, WI, USA, in a preliminary study.

Lab data

Bioreactor data were used for a first verification of model performance. The Verhulst equation Nt = N0 + (k – N0)*exp(–r0*t) under the assumptions for growth rates derived from the bioreactor experiments

a) 1°C Tempdiff 20 µg L-1 Chl-a

020406080

100120140160

Chl-a

(µg

L-1)

b) 1°C Tempdiff 5 µg L-1 Chl-a

020406080

100120140160

Chl-a

(µg

L-1)

c) 5°C Tempdiff 20 µg L-1 Chl-a

020406080

100120140160

Chl-a

(µg

L-1)

d) 5°C Tempdiff 5 µg L-1 Chl-a

020406080

100120140160

0 24 48 72 96 120 144 168 192

Time (hours)

Chl-a

(µg

L-1)

Algal development prediction

Time at WHO limit surpass

WHO limit

Fig. 6. a, b, c, d.Output of the prediction model for harmful algal bloom (Chl-a content (µg L–1) under dif-fering initial conditions (starting value either 5 or 20 µg L–1)) and temperature difference between lake surface and bottom (either 1 or 5 °C), while other lake condi-tions stay the same (lake depth 4 m, lake bottom PO4-P 7 mg L–1). Black line indicates the threshold value of 40 µg L–1 for cyanobacteria, dotted line the time when threshold value is being reached.


(P-free: r0: µ = 0.034, carrying capacity k 7.1 mio cells mL–1, full medium: r0: µ = 0.065, carrying capacity k 28.8 mio cells mL–1) yielded close simulation results with a mean deviation of 12.2 % from the real bioreac-tor data set mainly in the phase of exponential growth, whereas the mean deviation for phosphate free envi-ronment was 16.2 % (Fig. 7). Later the prediction was even more precise than that.

Field data

Lake Russo in Wisconsin (USA) served for a first vali-dation of model performance using data taken in Au-

gust 2015. Eagle Spring Lake data were not included as chlorophyll data could not be analyzed a second time.

Depth measurements at the 6 locations of Lake Russo were evenly 3 m with the exception of loca-tion 6 (2 m) throughout the shallow lake, Secchi depth was also evenly distributed throughout the 6 sampling locations of the lake (range 0.5 – 0.6 m). Tempera-tures ranged between 24.0 and 24.6 °C never exceed-ing a small surface bottom temperature difference of 0.4 °C. Nitrite values were low (range 0.017– 0.02 mg L–1), while ammonia concentrations ranged between

0.05.0

10.015.020.025.030.035.0

0 24 48 72 96 120 144 168 192 216 240Cell

Coun

t (m

Cel

ls/m

l)

Time (hours)

Datenreihen1 Datenreihen2

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Coun

t (m

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l)

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a.

b.

Cell

coun

t (m

io c

ells

ml-1

)

Cel

l cou

nt (m

io c

ells

ml-1

) High PO4

Low PO4

Deviation of prediction: Low (grey) and high (black) PO4

Comparison: Real (black) vs predicted (grey) values

Fig. 7. Comparison of real and estimated algal culture development (cells m L–1) from bioreactor studies. (a) Comparison of real bioreactor data set (cells mL–1) of two Chlorella vulgaris cultures in ES media with and without phosphate (P-free) under controlled conditions (3.5 L Infors Bioreactor, pH 7 adjusted via CO2, 28 °C, 8000 lux, stirrer 150 rpm, inoculation 1 mio cells mL–1) over 10 days and simulated data using the Verhulst equation Nt = N0 + (k – N0)*exp(–r0*t) (assumptions: P-free: r0: µ = 0.034 carrying ca-pacity k 7.1 mio cells m L–1 with a start value of N0 = 0.56 mio cells m L–1; full medium: r0: µ = 0.065, carrying capacity k 28.8 mio cells m L–1 with a start value of N0 = 1.4 mio cells m L–1). Black lines represent real data, grey lines estimated values. (b) Deviation of real and estimated values (cells m L–1) over time under either high PO4 (black line) or low PO4 (grey line) conditions.


0.28 – 0.32 mg L–1 in all depths (0, 1, 2, and 3 m) and locations (1– 6) measured. Bottom PO4 values were be-low 0.02 mg L–1, except for location 2 and 5, where PO4 values were locally higher (1.2 and 3 mg L–1, respec-tively). Conductivity was at 700 µS cm–1 in all depth at location 2,3 and 6, between 600 – 650 µS cm–1 in loca-tion 5 and locally reduced at location 4 (450 – 480 µS cm–1). Cell counts were well below 1 mio cells mL–1 (range 50.000 – 250.000 cells ml–1) and dissolved oxy-gen values ranged between 67.7 and 89.1 % DO at all sampling sites and depths, except for location 3 with 22.5 % DO at 3 m depth locally. These values did hint to evenly oligotrophic lake conditions within the lake with no apparent stratification of water layers.

Water samples taken from location 1, 3, 4 and 5 14 days later accordingly showed chlorophyll-a val-ues between 1.06 to 3.75 µg L–1, locations 2 and 6 were not taken. These low values did not suggest any algal bloom nor did the visual assessment of lake surface appearance.

Estimated values from the data input derived from the first sampling did not show any algal growth for data from locations 1, 3, 4 and 6 (below 5 µg L–1 Chl-a) at 384 or even 900 hrs thus closely resembling the real data situation in a reliable forecast.

However, estimated values of locations 2 and 5 with higher bottom PO4 values (1.2 and 3 mg L–1, re-

spectively) were high, reaching algal boom values above 40 µg L–1 Chl-a after 96 hrs and reaching 150 µg L–1 Chl-a in an imaginary stationary phase at 144 hrs. These falsely positive results reinforce the importance to measure PO4 several times and if possible in several locations in order to reach reliable predictions, as in-dividual nutrient values (PO4) may reach exceptional concentrations locally due to local imbalances within bottom lake soil, a recommendation that should be in-cluded in the help section of the application.

Mobile phone application

Our model was implemented for Android mobile phones to allow a member of the general public to use the application to predict whether an algal bloom would be likely to occur in a body of water. We im-plemented the application using App Inventor, a web-based visual programming tool for Android (Wolber 2011). The application allows a user to choose an op-tion according to which type of data they have col-lected, and the app adjusts the model to accommodate their data set according to the flow chart in Fig. 6.

A screenshot of the data entry screen of the appli-cation can be seen in Fig. 8 on the left. In this screen, the user enters their choice of input mode (direct or indirect), and based on the input mode, the user is pre-sented with boxes to enter input parameters such as

Fig. 8. Screenshots of the mobile phone application (Version 1.0). The screenshot on the left shows the input screen and the screen-shot on the right shows the results screen.


surface temperature, phosphate concentration, Secchi depth, etc. While the user is entering data into the ap-plication, thresholds set on each input issue a warning if the values entered are outside the prediction range of the underlying model.

After pressing the ‘calculate’ button, the user is shown the estimated development of algal popula-tion for six days in the future, shown in Fig. 8 on the right. The user is also shown the parameters used in the model calculation as well as other information collected by the application such as GPS and weather information. Weather information is collected using a public weather API tool and will be used in the future to improve the accuracy of the model.

Users can press the “graph” button on the results screen, and it will open a window that displays an inter-active graph of the results, shown in Fig. 9. The graph depicts the population trend over time to help users understand how the population of algae will develop. The graph also depicts a horizontal limit at 40 µg L–1 Chl-a, which is the WHO limit for safe drinking water (guidelines of WHO 2003b). The text below the graph explains the limit and graph results for the user.

Novice users of the application are not likely to know what types of measurements they need to make and also may lack information on the proper sampling methods, so the application provides a help menu for

new users. The menu explains the purpose of the ap-plication, the types of inputs allowed, the model itself, sampling methods, and how to interpret the results. The information is included to not only help people use the application, but also to offer educational infor-mation on algae blooms and the activities that often lead to harmful algae blooms.

Users of the application will probably want to use the tool repeatedly over time, so it is likely that they will want to recall previously-entered samples for comparison. Once data are entered into the application and calculated, the input data set is saved in a database on the phone. The user can access the saved data by selecting the ‛data log’ option from the input menu. Users are then presented with a list of the various sam-pling dates and times, and they can pick one to display.

The application has been posted on the Google Play marketplace, so we can collect data on the usage of the application. Data on the usage of the application will be published in a future work.

Discussion

The model presented here does allow for a prediction of algal development from simple and easy to access parameters, as they can replace chlorophyll-a concen-tration values, which are hard to obtain without appro-priate equipment. These parameters have been derived from a data set in a small freshwater lake in Germany (Lake Stadtgraben) that include temperature difference between lake bottom and surface, phosphate concen-trations, dissolved oxygen and Secchi depth values.

Temperature adjustment between top and bot-tom layers of Lake Stadtgraben as an example of a small polymictic lake in a temperate climate seems to emerge as one of the foremost causes for nutrient distribution within the lake: Change of conductivity values over time as parameters for nutrient distribu-tion within the water layers of the lake are indicating nutrient availability at the surface at the time of tem-perature equilibrium and onset of full circulation.

The decline in ammonia and phosphate concentra-tions within the bottom layer at the onset of full circu-lation is due to this nutrient availability for biomass at the surface as nutrients – especially phosphate as the limiting factor for algal growth – seem to be made available for photosynthetically active biomass within the surface layer by full circulation. Nitrate concen-trations, however, are evenly available within all lay-ers throughout the season until the end of September within the lake investigated, then rose at a time period

Fig. 9. Screenshot of the results graph in the mobile phone ap-plication (Version 1.0).


when algal bloom conditions started to decline and cell death occurred. Since similar nitrate levels were also found within the river Oker feeding into the sur-face water of the lake, agricultural activity alongside the rather rural river areas may have contributed to stable nitrate values over time offering a continuous unlimited nitrogen supply for biomass growth.

Therefore, phosphate distribution within the lake water body seems to play a major role for the occur-rence of algal blooms. In another study in Southern Germany investigating 155 recreational lakes, the authors observed a high correlation (r2 = 0.803) be-tween total phosphate concentrations and Chl-a val-ues within the monitored lakes, although sampling frequency was low (1 to > 2 times/season from July to September; Wolf & Frank 2002). The major role of phosphate concentrations may be attributed to the fact that they are vitally important for keeping up cel-lular adenylate energy charge [(ATP)+1/2 (ADP)]/[(ATP)+(ADP)+(AMP)] between values of 0.5 – 0.8 to maintain viability (Chapman et al. 1971). Cell death will occur at values below 0.5 due to an inverse rela-tionship between ATP consuming regulatory enzymes, which show little activity at low levels of energy charge, and ATP regenerating Enzymes, which are ac-tive at low levels of energy charge only resulting in an intersect at an energy charge level of about 0.85 (Chapman et al. 1971).

These findings coincide well with application rou-tines used in the German guidelines for evaluation of trophic levels within stagnant waters, which rely on Secchi depth, Chl-a and phosphate concentrations for classification of different trophic levels (Länderarbe-itsgemeinschaft Wasser (LAWA) 1999). They also tie in well with the findings of (James & Havens 2007) for a subtropical lake, that algal bloom can be predicted with a probability > 95 % once total phosphorus ex-ceeds concentrations of 0.10 mg L–1 and total nitrogen 2.5 mg L–1. In our bioreactor experiments, phosphate values > 0.1 mg L–1 led to maximum growth rates of µ = 0.065, and in the field, nitrate background concen-trations around 1– 3 mg L–1 were available for biomass consumption all seasons in addition to ammonia avail-ability once onset of full circulation was reached and an occurrence of harmful algal bloom could be regis-tered.

Another useful indirect parameter is DO (% satu-ration), as it is associated with algal growth and pho-tosynthetic activity. The onset of partial circulation results in slight decline in surface DO values starting in mid-August until they reach a slightly lower value around 100 % saturation in mid-September, when full

circulation is reached due to even distribution through-out the water body (Gotthold 2014). The parameter itself thus also depends on algal growth as well as temperature changes within the lake and is included in prediction equations as well as Secchi depth, which is high in situations with low algal development. In addition the surface temperature was taken into con-sideration as an indicator parameter as well as no algal bloom is taking place below temperatures of 15 °C.

Conclusions and future work

In summary, the prediction tool developed here into a smartphone application for everyday use does provide a simple prediction option from easily accessible data by taking the relationships discussed into account. However, the provided prediction remains a snapshot picture since environmental changes influencing pa-rameter development like weather conditions and in-formation on wind and currents as well as are nutrient influx from point sources (agriculture, urbanization, industry) not yet taken into account. This information would still need to be included in further development steps.

Also, further studies from other lakes out of com-parable regions are needed to validate predictions from this simple and generally applicable prediction tool for the onset of a possible harmful algal bloom. Making this tool available to a general public via mo-bile phone application may help to collect a reliable database in this respect.

In the future the app will be improved to increase usability and add new features. We plan to add a cen-tral database where information can be collected from all the individual users for datamining and further re-search to improve the model. Further, we plan to add features to export collected phone data to a .csv file. The app will also be modified to incorporate photos as well as further weather information and other features that users suggest. Lastly, we are in the process to re-develop the app using a native programming language instead of App Inventor to allow for the incorporation of additional features.

Acknowledgements

The authors would like to thank Dr. Julie Kinzelman, Racine City Health Departement, Racine, WI, USA, for support and advice provided for the use of the Software Virtual Beach. We would also like to acknowledge our thanks to Mr. Manfred Dicks, Amt für Stadtentwicklung, Planen und Bauen / 660 – Straßen, Stadtgrün und Geoinformation, in Wolfenbüttel, Ger-many, for his support on the Lake Stadtgraben investigations.


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Submitted: 10 October 2014; accepted: 19 February 2016.

development of a mobile phone application for the...

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