# Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Sites Description

^{2}, a volume of 153 × 106 m

^{3}, and a theoretical renewal time of 1.7–1.9 years [37,38]. Its catchment, with a surface area of 115.5 km

^{2}, hosts an average population density of 700 inhabitant/km

^{2}and is associated with many industrial and commercial activities. The lake has two tributaries: The Brabbia channel and the Tinella stream, with annual average discharges of 23 × 10

^{6}and 10 × 10

^{6}m

^{3}yr

^{−1}, respectively, and one effluent, the Bardello stream, with annual average discharge of 80.4 × 10

^{6}m

^{3}yr

^{−1}[30]. Information related to watershed characteristics of Lake Varese can be retrieved at the following link: https://www.regione.lombardia.it/wps/portal/istituzionale/HP/aqst-lago-di-varese/documenti-e-atti-istitutivi.

#### 2.2. Sampling and Analysis

#### Phytoplakton Analysis

^{3}/m

^{3}), measured as integrated samples from the surface to 2.5 times the SD (considered as the limit of the euphotic zone). The total cyanobacteria cell density (CyanoD) and biovolume (CyanoBV) was calculated as the sum of all reported genera/species for each sampling date. Due to the large range of measured total biovolumes, spanning over several order of magnitudes, we applied the log based 10 transformation to predict the cyanobacteria biovolume (LOG CyanoBV). Variability associated with multiple cell counters could be present, however, a Phytoplankton Proficiency Test organized by EQAT Phytoplankton (External Quality Assessment Trials) was successfully completed in 2013 and 2016.

#### 2.3. Meteorological Data

#### 2.4. Statistical Analysis

^{2}), adjusted coefficient of determination (R

^{2}adj), and root mean square error (RMSE). The best relationship was finally validated using an independent dataset from the European Commission Joint Research Centre (JRC) (see Section 3.2.).

## 3. Results

#### 3.1. Occurrence, Magnitude, and Timing of Cyanobacteria

^{3}/m

^{3}, with maximum of 126 million of cells/L and 39,000 mm

^{3}/m

^{3}, respectively (Table 1). As expected in eutrophic environments, cyanobacteria were over represented in the lake phytoplanktonic community, accounting for more than 50% of the total cell abundance, in average. Over those 10 years, Oscillatoriales and Chroococcales accounted for 50% of the total CyanoBV, while among Nostocales (43%), Aphanizomenon accounted for 38% (data not shown).

#### 3.2. Relationships between Cyanobacteria and Environmental Parameters

^{2}, because of the relationship of CD with cyanobacteria, the outbreak may be more difficult to interpret from the biological and physical–chemical point of view. Model No.2 was therefore selected among the two-variable models as the most suitable for LOG CyanoBV prediction in the Lake of Varese among the two-variable models.

## 4. Discussion

^{3}/L (LOG CyanoBV = 3.34 mm

^{3}/m

^{3}) and 0.5 mm

^{3}/L (LOG CyanoBV = 2.7 mm

^{3}/m

^{3}), respectively. An alert is set above these thresholds requiring weekly monitoring and issue warning to the public. Above 15 mm

^{3}/L (LOG CyanoBV = 4.18 mm

^{3}/m

^{3}) and 10 mm

^{3}/L (LOG CyanoBV = 4 mm

^{3}/m

^{3}) The Netherlands and New Zealand authorities set an action level, continue the monitoring, notify the public of a potential risk to health, and if potentially toxic taxa are present, consider testing samples for cyanotoxins. Germany defined a single threshold for surveillance and alert level at 1 mm

^{3}/L (LOG CyanoBV = 3 mm

^{3}/m

^{3}), above which local authorities must publish warnings, discourage bathing, and consider temporary closure.

## 5. Conclusions

- In Lake Varese, a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was observed;
- This change may be related to changes in the nutrients, as well as precipitation patterns, as suggested by other studies, but more frequent samplings combined with the microscopy analysis and the metagenomics technique (microbiome) would allow a more conclusive analysis;
- Air temperature can be used as a good proxy of the lake surface water temperature and of the lake stratification;
- The 14 days mean air temperature showed the highest correlation with lake stratification strength derived by vertical water temperature profiles. At surface, this parameter is easily computable from weather forecast data, and together with total phosphorus continuous measured in situ, can be used as an early warning tool to anticipate by two weeks the beginning of cyanobacteria blooms in Lake Varese.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**ERA-Interim reanalysis of daily mean data at Lake Varese (black) compared to ground observations in the area of Lake Varese for the period 2004–2014: Surface temperature; photosyntetically active radiation (PAR); wind speed; total precipitation.

**Table A1.**Total phytoplankton community expressed as cell density (cells/L) and biovolume (mm

^{3}/m

^{3}) for the period 2004–2014.

Year | Cell Density (Cells/L) | Biovolume (mm^{3}/m^{3}) |
---|---|---|

2004 | 2.11 × 10^{7} | 1.43 × 10^{4} |

2005 | 2.54 × 10^{7} | 1.22 × 10^{5} |

2006 | 2.65 × 10^{7} | 6.01 × 10^{4} |

2007 | 1.32 × 10^{8} | 2.84 × 10^{4} |

2008 | 1.60 × 10^{8} | 1.80 × 10^{4} |

2009 | 4.54 × 10^{7} | 1.27 × 10^{4} |

2010 | 7.85 × 10^{7} | 2.73 × 10^{4} |

2011 | 2.17 × 10^{8} | 3.12 × 10^{4} |

2012 | 2.04 × 10^{8} | 2.16 × 10^{4} |

2013 | 1.31 × 10^{8} | 2.31 × 10^{4} |

2014 | 8.48 × 10^{7} | 2.27 × 10^{4} |

## Validation of T14

^{2}) of 0.99, the T14 value with forecasted temperature is on average 0.7 °C larger than the T14 calculated using the reanalysis. Thus, we can assume that the 14 days average of forecasted atmospheric temperatures could be reliable enough for an early warning of cyanobacteria algal bloom outbreak, despite the forecast uncertainty increases with time.

**Figure A3.**Relationship between the thermocline slope and the average atmospheric surface temperature of the current and the preceding 1–28 days. The maximum correlation is found for the preceding 14 days.

## Two-Variable MLR Models

^{2}) is ranged from 0.28 to 0.33, and a bit lower when taking into account the sample size (73) and the number of predictors (2), R

^{2}adj ranged from 0.26 to 0.31. The coefficients estimated for the atmospheric temperature (T14) are always statistically significant at the 1% or 0.1% level, while only the coefficient of CD in model No.1 was statistically significant at the 5% level. However, all the models listed in Table 1, are statistically significant according to the analysis of variance with F value higher than 3.12, which is the critical value for a p-value of 0.05, two predictor variables, and 73 samples.

**Table A2.**Two-variable multiple linear regression (MLR) models of cyanobacteria biovolume (LOG CyanoBV) using the 14-days mean atmospheric temperature (T14) and one of the physical–chemical variables (AN: Ammonium nitrogen; CD: Conductivity; TP: Total phosphorous; SD: Secchi disk depth; RS: Reactive silicates; DO: Dissolved oxygen; OS: Saturation oxygen percentage; and pH). The statistical significance of each coefficient is indicated when below 0.05 (p-values: ***: 0.001; **: 0.01; *: 0.05). For each model the coefficient of determination (R

^{2}), the adjusted R

^{2}(R

^{2}adj), and the F statistic are reported. LOG means 10-based logarithm.

No. | Linear Model | R^{2} | R^{2} adj | F |
---|---|---|---|---|

1 | LOG CyanoBV = 15.353 * + 0.055 T14 ** − 5.645 LOG CD * | 0.33 | 0.31 | 17.16 |

2 | LOG CyanoBV = 0.718 + 0.095 T14 *** + 0.463 LOG TP | 0.31 | 0.29 | 15.41 |

3 | LOG CyanoBV = 2.886 ** + 0.074 T14 *** − 1.324 LOG DO | 0.3 | 0.28 | 14.95 |

4 | LOG CyanoBV = 1.869 *** + 0.070 T14 *** − 0.065 SD | 0.3 | 0.28 | 14.79 |

5 | LOG CyanoBV = 1.561 *** + 0.079 T14 *** + 0.230 LOG RS | 0.3 | 0.28 | 14.68 |

6 | LOG CyanoBV = 1.759 *** + 0.079 T14 *** − 0.002 OS | 0.29 | 0.27 | 14.01 |

7 | LOG CyanoBV = −1.015 + 0.063 T14 ** + 0.329 pH | 0.29 | 0.27 | 14.49 |

8 | LOG CyanoBV = 1.616 *** + 0.077 T14 *** + 0.055 LOG AN | 0.28 | 0.26 | 13.92 |

**Table A3.**List of water surface physical–chemical variables selected for the final MLR analysis. For each physical–chemical variable, the mean, median, range, and number of measurements above/below the level of quantification (LOQ) are reported.

Mean | Median | Range | Above LOQ | Below LOQ | ||
---|---|---|---|---|---|---|

Total phosphorous (µg/L) | TP | 40.4 | 25.5 | 2.5–110 | 69 | 9 |

Ammonium nitrogen (mg/L) | AN | 0.11 | 0.048 | 0.0075–0.6 | 64 | 14 |

pH | pH | 8.2 | 8.2 | 7.5–9.6 | 78 | 0 |

Conductivity (µS/cm 20 °C) | CD | 256 | 257 | 175–310 | 78 | 0 |

Secchi disk depth (m) | SD | 4 | 3.5 | 1.1–9.6 | 78 | 0 |

**Figure A4.**Validation of model No.2 in Table 1: Measured and predicted cyanobacteria biovolumes at Lake Varese using an independent dataset (not included in the MLR analysis) of water samples collected at Lake Varese during summer 2017. The T14 was calculated using forecasted temperatures at Lake Varese by the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP, http://www.emc.ncep.noaa.gov/).

## Three-Variable MLR Models

**Table A4.**Three-variable multiple linear regression (MLR) models of cyanobacteria biovolume (LOG CyanoBV) using the 14-days mean atmospheric temperature (T14) and two of the physical–chemical variables (AN: Ammonium nitrogen; CD: Conductivity; TP: Total phosphorous; SD: Secchi disk depth; and pH). The statistical significance of each coefficient is indicated when below 0.1 (p-values: ***: 0.001; **: 0.01; *: 0.05; °, 0.1). For each model the coefficient of determination (R

^{2}), the adjusted R

^{2}(R

^{2}adj), and the F statistic are reported. LOG means 10-based logarithm.

No. | Linear Model | R^{2} | R^{2} adj | F |
---|---|---|---|---|

1 | LOG CyanoBV = 3.391 * + 0.074 T14 *** − 0.0097 CD * + 0.504 LOG TP ° | 0.33 | 0.31 | 12.41 |

2 | LOG CyanoBV = 4.859 *** + 0.046 T14 ** − 0.0105 CD * − 0.067 SD | 0.32 | 0.3 | 11.75 |

3 | LOG CyanoBV = 4.401 *** + 0.052 T14 * + 0.0218 LOG AN − 0.010 CD * | 0.31 | 0.28 | 74 |

4 | LOG CyanoBV = 15.201 * + 0.053 T14 ** - 0.0236 pH − 5.481 LOG CD * | 0.31 | 0.28 | 10.91 |

5 | LOG CyanoBV = 0.918 + 0.091 T14 *** + 0.5077 LOG TP °− 0.048 SD | 0.3 | 0.27 | 10.53 |

6 | LOG CyanoBV = −1.369 + 0.088 T14 *** + 0.2519 pH + 0.554 LOG TP ° | 0.3 | 0.27 | 10.42 |

7 | LOG CyanoBV = 0.742 + 0.102 T14 *** + 0.1191 LOG AN + 0.532 LOG TP ° | 0.29 | 0.27 | 10.29 |

8 | LOG CyanoBV = 2.070 *** + 0.076 T14 *** + 0.1702 LOG AN − 0.065 SD | 0.28 | 0.25 | 9.49 |

9 | LOG CyanoBV = 0.931 + 0.064 T14 *** + 0.1207 pH − 0.051 SD | 0.27 | 0.24 | 9.3 |

10 | LOG CyanoBV = 0.415 + 0.071 T14 ** + 0.1659 pH + 0.099 LOG AN | 0.27 | 0.24 | 9 |

## Testing an Improvement with Additional Meteorological Parameters

^{2}/s

^{2}), the average total precipitation (mm/day), and the PAR (W/m

^{2}) were calculated (as for the average surface temperature). Adding wind speed (iteratively from 1 to 28 days) to the predictors of model No.2 (Table 3), the corresponding R

^{2}ranged from 0.29 to 0.31, thus no relevant improvement was detected. This is probably due to the generally weak winds at Lake Varese, not strong enough to break the lake stratification. Indeed, ERA-Interim data showed that annual mean wind speed is ranged between 1.82 and 2.02 m/s, i.e., light breeze for the period 2003–2015. Stronger winds, such as gentle breeze, between 3.3 and 5.2 m/s, were observed for 17–37 days/year, and only up to three days/year of moderate breeze, between 5.2 and 7.4 m/s. Similarly, for the average total precipitation, R

^{2}ranged from 0.29 to 0.30. On the opposite, an improvement was detected for PAR, where R

^{2}ranged from 0.37 to 0.40. Since PAR, calculated for 14 days, was correlated with T14 to a high degree (Pearson correlation coefficient is 0.82), including it in the model would be deemed as an overfitting.

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**Figure 1.**Location of Lake Varese (red). The positions of the available meteorological stations in the region are indicated by the light blue dots (Regional Environmental Protection Agency Lombardia, ARPA), and the dark blue dot (the Joint Research Center). The red polygon in the lower right corner map indicates the location of the model grid-cell from the ERA-Interim atmospheric reanalysis, where Lake Varese is located.

**Figure 2.**Distribution of the total phytoplankton community expressed in percentage (Y-axis) of the average of the annual campaign from 2004 to 2014. (

**a**) On the top plot, the distribution is reported as percentage of cell density (cells/L); (

**b**) on the bottom the distribution is shown as percentage of biovolume (mm

^{3}/m

^{3}). The total cell density and biovolume values for each year are reported in Table A1 in Appendix A.

**Figure 3.**(

**a**) The top graph shows a representation of total cyanobacteria community distribution as percentage, based on biovolume (mm

^{3}/m

^{3}). Community distribution is presented as the average of the annual campaigns data, grouped as Summer-Spring (ss) and Autumn-Winter (aw) seasons, and by order. The red line reports the total cyanobacteria number per data set; (

**b**) bottom graph represents the Secchi disk depth (continuous line in orange) at each sampling campaign and the six-month cumulative precipitations, Autumn-Winter (aw, blue circles) and Spring-Summer (ss, blue squares).

**Figure 4.**Relationship between the thermocline slopes and the average air temperature of current plus the preceding 14 days. Every dot is from a probe sampling campaign (from 2003 to 2015 included). The linear relationship is strong (R

^{2}= 0.92) and without any relevant bias in the plot, thus supporting the use of the average air temperature of the current and the last 14 days as a proxy of water stratification.

**Figure 5.**Measured and predicted cyanobacteria biovolumes at Lake Varese for all samples collected during the period 2004–2014. Predicted values are calculated using model No.2 of Table 3. RMSE indicates the root mean square error of predicted vs. measured values.

**Figure 6.**Validation of the model: Measured and predicted cyanobacteria biovolumes at Lake Varese using an independent dataset (not included in the MLR analysis) of water samples collected at Lake Varese during the period 2008–2010 by JRC. RMSE indicates the root mean square error of predicted vs. measured values.

**Figure 7.**Estimated cyanobacteria biovolume calculated for Lake Varese using model No.2 of Table 3. The colored field represents LOG CyanoBV at different levels of the two predictors, T14 ranging between 5 and 30 °C, and TP ranging between 2.5 and 110 μg/L. The colored bars above the plot and the black lines refers to examples of threshold levels for cyanobacteria biovolume (surveillance in green, alert in orange, and action in red) of defined by legislation in The Netherlands, New Zealand, and Germany.

**Table 1.**List of water surface physical–chemical variables and total cyanobacteria biovolume/density measured at Lake Varese during the period 2004–2014. The mean, median, and range (minimum–maximum) values are reported for each variable. The number of samples above and below the level of quantification (LOQ), and missing values are reported. The number of cyanobacteria (present/absent) over the total sampling campaigns is shown.

Parameter and Unit of Measures | Mean | Median | Range | Above LOQ | Below LOQ | Missing | |
---|---|---|---|---|---|---|---|

Total phosphorous (µg/L) | (TP) | 42.3 | 28 | 2.5–110 | 80 | 9 | 1 |

Ammonium nitrogen (mg/L) | (AN) | 0.12 | 0.048 | 0.0075–0.69 | 75 | 14 | 1 |

Reactive silicates (SiO2) (mg/L) | (RS) | 1.16 | 0.92 | 0.05–3.9 | 82 | 6 | 2 |

pH | (pH) | 8.18 | 8.2 | 7.5–9.6 | 90 | 0 | 0 |

Conductivity (µS/cm 20 °C) | (CD) | 257 | 258 | 175–310 | 90 | 0 | 0 |

Dissolved oxygen (mg/L) | (DO) | 9.64 | 9.8 | 3.7–14.8 | 87 | 0 | 3 |

Oxygen saturation (%) | (OS) | 101 | 100 | 50–173 | 88 | 0 | 2 |

Water temperature (°C) | (WT) | 16.3 | 18 | 3.6–30 | 89 | 0 | 1 |

Secchi disk depth (m) | (SD) | 4.1 | 3.6 | 1.1–9.6 | 90 | 0 | 0 |

Mean | Median | Range | Present | Absent | |||

Cyanobacteria biovolume (mm^{3}/m^{3})
| (CyanoBV) | 1.62 × 10^{3} | 3.05 × 10^{2} | 0–3.94 × 10^{4} | 83 | 7 | |

Cyanobacteria density (cells/L) | (CyanoD) | 9.69 × 10^{6} | 3.62 × 10^{5} | 0–1.26 × 10^{8} | 84 | 6 |

**Table 2.**List of meteorological variables extracted from the grid cell of the ERA-Interim reanalysis where Lake Varese is located. The mean, median, and range (minimum and maximum value) calculated over the period 2004–2014 are reported for each variable.

Variable and Unit of Measures | Mean | Median | Range |
---|---|---|---|

Surface air temperature (°C) | 9 | 8.97 | −11.6–24.1 |

Photosynthetically active radiation (W/m^{2}) | 126 | 122 | 2.17–273 |

Wind speed (m/s) | 1.96 | 1.76 | 0.46–6.37 |

Total precipitation (mm/day) | 6.07 | 0.84 | 0–163 |

**Table 3.**Multiple linear regression (MLR) models of cyanobacteria biovolume (LOG CyanoBV) using the 14-days mean air temperature (T14) and one of the physical–chemical variables listed in Table S1 of SI (AN: Ammonium nitrogen; CD: Conductivity; TP: Total phosphorous; SD: Secchi disk depth; and pH). The statistical significance of each coefficient is indicated when below 0.1 (p-values: ***: 0.001; **: 0.01; *: 0.05; °, 0.1). For each model the coefficient of determination (R

^{2}), the adjusted R

^{2}(R

^{2}adj), and the F statistic are reported. LOG means ten-based logarithm.

No. | Linear Model | R^{2} | R^{2} adj | F |
---|---|---|---|---|

1 | LOG CyanoBV = 14.894 * + 0.052 T14 ** − 5.431 CD * | 0.31 | 0.29 | 16.58 |

2 | LOG CyanoBV = 0.658 + 0.096 T14 *** + 0.527 LOG TP ° | 0.29 | 0.27 | 15.45 |

3 | LOG CyanoBV = 0.244 + 0.065 T14 *** + 0.178 pH | 0.27 | 0.26 | 13.56 |

4 | LOG CyanoBV = 1.895 *** + 0.068 T14 *** − 0.055 SD | 0.27 | 0.25 | 14.05 |

5 | LOG CyanoBV = 1.724 *** + 0.077 T14 *** + 0.109 LOG AN | 0.27 | 0.25 | 13.53 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Chirico, N.; António, D.C.; Pozzoli, L.; Marinov, D.; Malagó, A.; Sanseverino, I.; Beghi, A.; Genoni, P.; Dobricic, S.; Lettieri, T. Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations. *Water* **2020**, *12*, 675.
https://doi.org/10.3390/w12030675

**AMA Style**

Chirico N, António DC, Pozzoli L, Marinov D, Malagó A, Sanseverino I, Beghi A, Genoni P, Dobricic S, Lettieri T. Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations. *Water*. 2020; 12(3):675.
https://doi.org/10.3390/w12030675

**Chicago/Turabian Style**

Chirico, Nicola, Diana C. António, Luca Pozzoli, Dimitar Marinov, Anna Malagó, Isabella Sanseverino, Andrea Beghi, Pietro Genoni, Srdan Dobricic, and Teresa Lettieri. 2020. "Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations" *Water* 12, no. 3: 675.
https://doi.org/10.3390/w12030675