A survey was sent out to 98 DWTPs. 77 of the DWTPs (79%) participated. 62 of them completely lacked monitoring program or had any other type of surveillance for cyanobacteria and/or cyanotoxins. 3 of them performed ocular inspections for the presence of algal bloom or cyanobacteria in the surface water and/or at the raw water intake. 6 of them monitored the concentration of the cyanotoxin microcystin at the raw water intake and lastly, 6 of them were monitoring the presence of cyanobacteria and/or cyanotoxins occasionally. On indication of cyanobacteria in raw water, 3 of them followed up with analysis of microcystins also in the drinking water. Such indications were, most commonly, identification of potentially toxin producing cyanobacteria by microscope. The sense of urgency concerning problems with blooms was low.
High occurrence of cyanobacteria (i.e. median value > 0.2 mm3 L− 1) present in 9 lakes (8% of SwAM trend lakes) mainly located in south of Sweden (Fig. 1). Lillsön has the highest median value and more than 60% of the cases, cyanobacteria are dominating (> 50%). Krageholmssjön has the largest variance. All 108 lakes cyanobacterial biomass values with minimum, maximum and median refer to Tab.S1. Land use and ecosystem statues of those 9 lakes are summarized in Tab. S2, which indicates that those 9 lakes are categorized as having bad or unsatisfied ecosystem, high eutrophication statues and intensive land use.
How eutrophication states impact the diversity of cyanobacteria species
Figure 3 illustrates the diversity of cyanobacteria species in the 9 lakes plus Lake V. Rännöbodsjön as a reference lake. The figure shows that the more eutrophic the water is, the more diverse the cyanobacteria taxa are. Significant correlation (0.54) between TP and the number of cyanobacterial taxa was found. The reference lake with the lowest level of phosphorus has only one specie while the lake with highest level of phosphorus, Krageholmssjön, has more than 7 taxa.
Which parameters are playing important roles in cyanobacterial blooms?
Investigation of primary water information and important factors were done by Pearson correlation and PCA analysis. Pearson correlation matrix in Fig. S2 shows the correlations between cyanobacteria biomass and other water quality parameters, where cyanobacterial biomass is positively (in order) correlated with TP, chlorophyll-a, TOC, alkalinity condition and TN; and strongly negatively correlated with transparency. This is also confirmed by PCA analysis in Fig. S3; and variables that significantly correlated to each component are highlighted in Tab. S5. The first main component which accounts for 42% variance of original data reflects prior information in those lakes are nutrients condition and total plankton biomass and they are positively connected, particularly in lakes surround by agriculture land. The second main component explains 27% of the variance of the original data and reflects the cyanobacterial condition, which is significantly positively connected to the total organic matter and Fe content, particularly in lakes surrounded by forestry lakes, and are negatively correlated with transparency, pH and conductivity. Thus, it might indicate that the increase of cyanobacteria would increase together with TOC and Fe in water, causing water deterioration.
Figure S3 also illustrates clearly that lakes surrounded by more forest form a distinct cluster on the top left, and lakes that are surrounded by more agricultural land, are clustered in the bottom right. Forestry lakes are closer to high cyanobacterial biomass and cyanobacteria dominating situation, total organic matter and Fe. Lakes surrounded by agriculture land are closer to nutrient condition. Besides, two lakes are very distinct from others, one is a lake with mixed land use (Ymsen), located in-between the two clusters and a reference lake (V. Rännöbodsjön) with comparatively clearer water is located far from all others.
Evaluation transparency and chlorophyll-a as indicator for cyanobacterial presence
Transparency and chlorophyll-a are listed as important factors for assessing the potential presence of cyanobacterial biomass in water among other indicators such as total phosphorus, water residence time, temperature and pH (Kreakie et al., 2015; Lee & Lee, 2018; World Health Organization, 2015). If transparency is low (less than 1–2 m) and accompanied by a blue and green water colour, high cyanobacterial biomass is likely. A chlorophyll-a concentration of 1 µg L− 1 in raw water triggers Alert Level 1 in WHO management system for cyanobacterial blooms in the perspective of drinking water (Ingrid Chorus & Bartram, 1999). Taste and odour problems begin occurring once chlorophyll-a values reach 10 µg L− 1(KDHE (Kansas Department of Health and Environment Bureau of Water), 2011).
A general overview of transparency and chlorophyll-a concentration with cyanobacterial biomass in the 9 lakes are presented in boxplot (Fig. S4 and S5) with drinking water alert levels and health thresholds. In general, when transparency decreases, it is likely to trigger higher alert levels and health thresholds; and the more chlorophyll-a content in water, the more likely higher cyanobacterial biomass present. In the selected 9 lakes, an average transparency as 1.2 m and chlorophyll-a median value as of 32 µg L− 1 correspond in general to their high cyanobacteria occurrence. From specific lake study, high correlation between transparency and cyanobacterial risk are only found in Lillsjön (-0.75) and Ymsen (-0.70). A high correlation between chlorophyll-a and cyanobacterial risk are only found in Krankesjön (0.85) and Tångerdasjön (0.79).
Significant indicators for the variance of cyanobacterial biomass might be different in different lakes, for example, Iron and pH for Edasjön, conductivity for Krageholmssjön and pH condition for Rundbosjön.
Phosphorus control for maximizing drinking water safety and minimizing health risks from cyanobacterial blooms.
Quantile regression curves (0.05–0.99) using a fitted 3-parameter sigmoid nonlinear model are displayed in Fig. 4 by using all 108 lakes’ data. All quantiles, including lower quantile (0.05), show significant relationship between cyanobacteria biomass and TP (Table 1). This relationship differs from Carvalho and his colleagues’ study, involving more than 800 European lakes (Carvalho et al., 2013), in which a significant relationship was only present in higher quantiles (0.25 and above). Figure 4 shows that the higher the quantile, the earlier the take-off of cyanobacteria response to TP. For example, the 95th quantile and 75th quantile have a take-off of cyanobacteria response at a threshold around 10 µg L− 1, while for the 50th and 25th quantile, cyanobacteria start to response at TP around 30 µg L− 1. All quantiles have a flattening of response at a threshold about 100 µg L− 1. It means that the increase of TP after this point have low impact on additional cyanobacteria growth.
Table 1
correlation matrix of quantile estimates and phosphorus concentrations.
Pearson's product-moment correlation
|
Quantiles
|
0.05
|
0.25
|
0.5
|
0.75
|
0.95
|
0.99
|
Correlation with TP concentration
|
0.81
|
0.63
|
0.68
|
0.77
|
0.87
|
0.96
|
P value
|
2.2e-16
|
2.2e-16
|
2.2e-16
|
2.2e-16
|
2.2e-16
|
2.2e-16
|
Application of quantile responses for predicting bloom capacity
Quantile regression curves in Fig. 4 can be used to predict the bloom capacity at present temperature regimes. For example, in Table 2, the 95th quantile (i.e. 5% exceeding rate) is used to estimate the potential maximum capacity of cyanobacteria in response to the increase of TP. In the table, the results show that the capacity for cyanobacteria growth increases with increasing TP, until TP concentrations > 150 µg L− 1; 5% of samples would exceed drinking water alert level 1 at TP of 10 µg L− 1; and exceed low health risk threshold for recreational water at TP of 27 µg L− 1 and exceed drinking water alert level 2 and medium risk threshold at TP of 53 µg L− 1.
Table 2
95% quantile fitted values showing the changing cyanobacterial biovolume (mm3 L− 1) with change in total phosphorous (µg L− 1). The fitted quantile reaches an asymptote at 38 mm3 L− 1 of cyanobacteria biovolume
Total Phosphorus (µg L− 1)
|
0
|
10
|
27
|
53
|
100
|
150
|
200
|
250
|
300
|
500
|
Cyanobacteria capacity (95%) (mm3 L− 1)
|
0
|
0.2
|
2
|
10
|
25
|
32
|
35
|
36
|
37
|
38
|
Nutrient targets in relation to alert levels for drinking water and health risk for recreational water
By applying quantiles algorithms with quantiles > 0.5 in Table 3, nutrients targets are calculated in relation to alert levels for drinking water and health risk thresholds. The results in Table 4 demonstrates that at a TP of about 14 µg L− 1, 10% of samples exceeded the WHO drinking water alert level 1, if TP increases to 35 µg L− 1, the percentage of exceedance will be 50%. Similarly, 10% of water samples would surpass low health risk for recreational water (2 mm3 L− 1), at TP level of 35 µg L− 1, at a level of around 60 µgL− 1, the percentage of exceedance will be 50%. In the same way, it shows at a TP concentration level of 72 µg L− 1, 10% of samples would surpass the drinking water alert level 2 and medium health risk.
Table 3
Parameter estimates derived using nonlinear quantile regression for 108 Swedish trend lakes together with the estimates for nonlinear mean response. The rest P < 0.01, * p < 0.05.
Model
|
Asym
|
mid
|
Scal
|
0.05
|
0.09 ±0.04
|
1.77 ±0.15
|
0.19 ±0.02
|
0.25
|
0.36 ±0.15 *
|
1.86 ±0.12
|
0.09 ±0.03
|
0.5
|
0.67 ±0.09
|
1.70 ±0.04
|
0.07 ±0.01
|
Mean (Non-linear)
|
0.71 ± 0.03
|
1.70 ± 0.02
|
0.14 ± 0.01
|
0.75
|
1.23 ±0.19
|
1.79 ±0.06
|
0.17 ±0.01
|
0.95
|
1.61 ±0.23
|
1.62 ±0.09
|
0.21 ±0.02
|
0.99
|
1.71 ±0.22
|
1.41 ±0.11
|
0.26 ±0.04
|
Table 4
Total phosphorus (TP) concentrations for a given likelihood (quantile) of being below low and medium risk World Health Organisation (WHO 1999) threshold levels for cyanobacteria volume. TP concentrations are obtained from the fitted quantile regression models
Quantile
|
50th
|
60th
|
70th
|
80th
|
90th
|
95th
|
98th
|
% exceeded
|
50%
|
40%
|
30%
|
20%
|
10%
|
5%
|
2%
|
Drinking water alert 1 0.2 mm3 L− 1
|
TP µgL− 1
|
35.34
|
30.31
|
23.90
|
19.39
|
13.42
|
10.25
|
5.87
|
Low health risk 2 mm3 L− 1
|
TP µgL− 1
|
57.88
|
56.15
|
53.58
|
48.69
|
35.44
|
27.84
|
19.33
|
Drinking water alert 2/Medium health risk 10 mm3 L− 1
|
TP µgL− 1
|
|
|
|
117.30
|
72.15
|
56.11
|
43.99
|
By applying results from Table 4 to our selected 9 lakes, of which all median value of TP above 33 µg L− 1, that least 40% exceedance rate above the drinking water alert level 1 and 10% exceedance rate above the low health threshold. Lake Krageholmssjön with the highest median value of TP, around 129 µg L− 1, it is more than 20% likely to above the medium health risk and drinking water alert level 2. TP target for low health risk in SwAM trend lakes allow 10% exceedance is 35 µgL− 1.