Due to the eutrophication of water bodies, large and intense phytoplankton blooms, most commonly associated to cyanobacteria, have been increasingly reported. Cyanobacteria blooms can cause negative socioeconomic effects or even pose a serious risk to human and animal health. To minimize possible harmful effects, the authorities need to monitor and report the bloom situations to the public. The standard laboratory methods for quantifying phytoplankton biomass are accurate, but have flaws in practical management use: specialized expertise is required, and they are time-consuming. We have tested practical solutions to monitor cyanobacteria nearly real-time by using probes measuring phycocyanin fluorescence, which can be used as a proxy for cyanobacteria biomass. In the basic application, a fluorescence probe is mounted in a datalogger monitoring station in the field and the results calibrated to cyanobacteria concentration are transferred via GSM to a web page available to the authorities and the public. To indicate the risk levels of cyanobacteria concentration to the public we have used a 3-scale ‘traffic light’ system. The automated monitoring station applications used in our studies, with nearly real-time web results, are an applicable and relatively low-cost method to monitor sensitive sites like water intakes, aquaculture and recreational areas.

INTRODUCTION

Due to the eutrophication of water bodies, large and intense phytoplankton blooms, most commonly associated to cyanobacteria, have been increasingly reported. Large and intensive cyanobacteria blooms can cause negative socioeconomic effects or even pose a serious risk to human and animal health as many of the bloom forming species are also potential producers of harmful cyanotoxins (Chorus & Bartram (eds.) 1999). Large biovolume of cyanobacteria can complicate water intake and aquaculture and decrease possibilities for recreational use of waters. Guidelines and thresholds have been set, e.g. for cyanobacteria cell concentration, biovolume and cyanotoxins concentration (Chorus & Bartram (eds.) 1999; WHO 2004).

To minimize the possible harmful effects, the municipality and governmental authorities need to monitor and report the bloom situations to the public. The standard laboratory method for quantifying phytoplankton biomass (Utermöhl-inverted microscopy technique) is accurate, but has flaws in practical management use: specialized expertise is required, and it is time-consuming. Because of the cost and time needed for laboratory analysis, the basic monitoring method since 1998 in Finland for up-to-date cyanobacteria bloom situation is based on trained observers who visually observe the amount of phytoplankton in >300 selected waterways in relative scale from 0 (no visible phytoplankton) to 3 (very high amount of phytoplankton). However being a rather subjective and inaccurate method, it is still a cost-effective tool for large spatial coverage. In offshore areas, cyanobacteria bloom intensity and distribution is also monitored using aerial surveys.

Phycocyanin (PC) is an accessory pigment found in cyanobacteria and the PC concentration has been proven to be a useful proxy for cyanobacteria cell density and biovolume (Brient et al. 2008). Currently there are several low cost PC probes commercially available from different manufacturers. The technical performance of the in situ PC probes has been evaluated good for quantifying cyanobacteria abundance (Bastien et al. 2011). The modern field probes can be used several ways: operated handheld, deployed to autonomous multiparameter monitoring stations and buoy platforms or attached to flow-through systems in moving research vessels and cargo and passenger ships for line transect and spatial survey studies (Seppälä et al. 2007; Lepistö et al. 2010; Anttila et al. 2012).

METHODS

Since 2006, we have used practical solutions to monitor cyanobacteria abundance nearly real-time by using autonomous PC probe monitoring stations in several locations in small freshwater lakes (lakes Littoistenjärvi, Kuralanjärvi and Kakskerranjärvi) and coastal brackish water areas in Baltic Sea in SW Finland. The main objective of the monitoring is to produce information about the up-to-date cyanobacteria bloom situation. In the basic application, one or several PC fluorescence probes and a temperature sensor are mounted in a stationary monitoring station in a location ,(e.g. beach) and the results are transferred via GSM network to a web page (available at http://www.luodedata.fi/littoinen) available for the authorities and the public. To indicate the risk levels to the public we have used a 3-scale ‘traffic light’ system based on cyanobacteria concentration (green light/low risk <3 mg L−1, yellow light/moderate risk 3–10 mg L−1 and red light/high risk >10 mg L−1). If needed, automatic alerts of risen concentrations can be programmed to send for example SMS to mobile phones.

The monitoring stations are powered with 12 VDC batteries. The measurement interval of the station is set to log the PC fluorescence and temperature every hour. One fluorescence measurement is based on average of 1,000 readings taken during a 10 second period. Fluorescence signal results are converted to wet weight biomass (mg L −1) with a standard calibration coefficient provided by the supplier (Luode Consulting Ltd.) in the datalogger. The datalogger stores and sends the results automatically via a GSM modem to a server in .csv format twice a day. The sampling interval and data transfer schedule are fully programmable. To avoid measurement errors caused by biofouling probes were manually cleaned during deployment periods once or twice a week. The current probe versions can be cleaned automatically and datalogger controlled with compressed air or brushes. In Lake Littoistenjärvi and Lake Kuralanjärvi, the PC fluorescence results were compared to cyanobacteria biomass of water samples analyzed using the standard inverted microscopy method.

RESULTS AND DISCUSSION

The automated monitoring stations equipped with PC fluorescence probe produced reliable and good quality up-to-date data on cyanobacteria abundance. In lakes Littoistenjärvi and Kuralanjärvi there was a significant relationship between the PC fluorescence and the water sample cyanobacteria biomass (R2 = 0.59, p < 0.001, N = 57 and Lake Kuralanjärvi R2 = 0.83, p < 0.001, N = 18). In these lakes, we were also able to apply site specific biomass calibrations. Examples of biomass calibrated fluorescence results compared to water samples from the both lakes are presented in Figures 1 and 2, the methods are described more detailed in Loisa et al. in press).
Figure 1

Cyanobacteria concentration (the raw and post calibrated fluorescence results (n = 4055) and the water sample biomass (n = 11)) and the air and water temperature from Lake Littoistenjärvi during the summer season 2011.

Figure 1

Cyanobacteria concentration (the raw and post calibrated fluorescence results (n = 4055) and the water sample biomass (n = 11)) and the air and water temperature from Lake Littoistenjärvi during the summer season 2011.

Figure 2

Cyanobacteria concentration (the biomass calibrated fluorescence results and the water sample biomass) from two monitoring stations at Lake Kuralanjärvi during the summer season 2008. Note that the maximum measuring range of the fluorescence probe (cyanobacteria wet weight biomass of 77.5 mg L−1) was exceeded in some days during July.

Figure 2

Cyanobacteria concentration (the biomass calibrated fluorescence results and the water sample biomass) from two monitoring stations at Lake Kuralanjärvi during the summer season 2008. Note that the maximum measuring range of the fluorescence probe (cyanobacteria wet weight biomass of 77.5 mg L−1) was exceeded in some days during July.

PC fluorescence probes have also been used in the cyanobacteria monitoring in drinking water sources, (e.g. McQuaid et al. 2011). Monitoring station data provides high temporal coverage and it can be used in planning of monitoring programs and as a quality control method of existing dataset (Anttila et al. 2012). In situ fluorescence data is also valuable for creating and validating models on spatial phytoplankton distribution based on remote sensing data (Lepistö et al. 2010). The equipment for online in situ monitoring has developed more reliable and easy to use and the cost of the device has decreased. If sensor technology is carefully tested and approved to be reliable, continuous monitoring of an environmental parameter in seconds to hours interval gives more accurate information on short time changes compared to normal monitoring programs were sample interval could be days to months. The water samples and inverted microscopy cell count results however are not in any case totally replaceable by the in situ fluorescence sensors as they can provide accurate quantitative and qualitative information on phytoplankton community structure on a taxonomical level.

For public dissemination purposes, we opened a web-based on-line service in July 2006. The actual cyanobacteria concentration and the water temperature are presented as figures (example from Lake Littoistenjärvi in Figure 3.) which are updated twice a day. The public feedback on the system has been almost entirely positive and the annual number of website visitors has been several thousands, e.g. 6,500 individual visitors in 2007. The temperature monitoring has also been important for the local recreational site users, especially swimmers. The results have been an important management tool for the authorities responsible of the sites and the on-line dissemination and interpretation of the results can be used as an easy platform to communicate science to the public and the media.
Figure 3

An example of cyanobacteria concentration data from Lake Littoistenjärvi (60 °27.2N 22 °23.5E, area 147 ha, average depth 2.0 m) monitoring station in June 2014 as presented online to the public. The background colors indicate the possible risk level based on cyanobacteria concentration. Available online at http://www.luodedata.fi/littoinen.

Figure 3

An example of cyanobacteria concentration data from Lake Littoistenjärvi (60 °27.2N 22 °23.5E, area 147 ha, average depth 2.0 m) monitoring station in June 2014 as presented online to the public. The background colors indicate the possible risk level based on cyanobacteria concentration. Available online at http://www.luodedata.fi/littoinen.

CONCLUSIONS

The automated monitoring station applications used in our studies, with nearly real-time web results, are an applicable and relatively low-cost method to monitor cyanobacteria abundance in sensitive locations, as in water intake facilities and public recreational sites. Website based dissemination of the results to the public is easy and it can be provided with understandable thresholds and recommendations. In situ phycocyanin fluorescence monitoring is a valuable tool for management purposes to minimize the effects of potentially toxic cyanobacteria blooms.

ACKNOWLEDGEMENTS

Study was supported by European Union, Southern Finland Objective 2 Programme, Central Baltic Interreg IVA programme 2007–2013, Turku University of Applied Sciences, Southwest Finland Regional Environment Centre and City of Kaarina. We like to thank Antti Lindfors, Mikko Kiirikki, Maiju Kyyhkynen, Teemu Koski, Teemu Lakka, Juha Lyytikäinen, GWM-engineering and Luode Consulting.

REFERENCES

REFERENCES
Anttila
S.
Ketola
M.
Vakkilainen
K.
Kairesalo
T.
2012
Assessing temporal representativeness of water quality monitoring data
.
J.Environ.Monit.
14
,
589
595
.
Bastien
C.
Cardin
R.
Veilleux
E.
Deplois
C.
Warren
A.
Laurion
I.
2011
Performance evaluation of phycocyanin probes for the monitoring of cyanobacteria
.
J.Environ.Monit.
13
,
110
118
.
Brient
L.
Lengronne
M.
Bertrand
E.
Rolland
D.
Sipel
A.
Steinmann
D.
Baudin
M.
Le Rouzic
B.
Bormans
M.
2008
A phycocyanin probe as a tool for monitoring cyanobacteria in freshwater bodies
.
J.Environ.Monit.
10
,
248
255
.
Chorus
I.
Bartram
J.
(eds.)
1999
Toxic cyanobacteria in Water: A guide to their public health consequences, monitoring and management
.
E&FN Spon
,
London
,
UK
.
Lepistö
A.
Huttula
T.
Koponen
S.
Kallio
K.
Lindfors
A.
Tarvainen
M.
Sarvala
J.
2010
Monitoring of spatial water quality in lakes by remote sensing and transect measurements
.
Aquatic ecosystem health & management
,
13
(
2
)
176
184
.
Loisa
O.
Kääriä
J.
Laaksonlaita
J.
Niemi
J.
Sarvala
J.
Saario
J.
2015
From phycocyanin fluorescence to absolute cyanobacteria biomass: An application using in-situ fluorometer probes in the monitoring of potentially harmful cyanobacteria blooms
.
Water Practice and Technology
10
(
4
),
695
698
.
doi: 10.2166/wpt.2015.083.
Seppälä
J.
Ylöstalo
P.
Kaitala
S.
Hällfors
S.
Raateoja
M.
Maunula
P.
2007
Ship-of-opportunity based phycocyanin fluorescence monitoring of the filamentous cyanobacteria bloom ynamics in the Baltic Sea
.
Estuarine, Coastal and Shelf Science
73
,
489
500
.
WHO
2004
Guidelines for drinking water quality 3rd edition
.
World Health Organization
,
Geneva
.