Environ Monit Assess (2014) 186:5009–5026 DOI 10.1007/s10661-014-3755-0
Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications V. Markogianni & E. Dimitriou & I. Karaouzas
Received: 16 October 2013 / Accepted: 21 March 2014 / Published online: 5 April 2014 # Springer International Publishing Switzerland 2014
Abstract Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophylla concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R2) among point sampling results and the remotely V. Markogianni (*) : E. Dimitriou : I. Karaouzas Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, 46.7 km Athens–Sounio Av, 19013 Anavyssos, Greece e-mail: [email protected]
sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available. Keywords Water quality . Chlorophyll-a . Trophic status . Landsat . Lake
Introduction Freshwaters, worldwide, have been severely impacted by human activities during the last century (Arthington and Welcomme 1995). Eutrophication is one of the common stressor with obvious symptoms (e.g., cyanobacterial blooms) such as changes in biotic communities, food web disturbances, and degradation of water quality leading to biodiversity loss (MoustakaGouni et al. 2006). Niemi et al. (1990) reported that human activities mainly impact surface water quality through atmospheric pollution, effluent discharges, and the use of agricultural chemicals, in addition to the increased exploitation of water resources. These have generated great pressures on aquatic ecosystems, resulting in water quality deterioration and biodiversity decrease, loss of critical habitats, and an overall decrease in quality of life for local inhabitants (Herrera-Silveira and Morales-Ojeda 2009). Therefore, the implementation of monitoring and assessment programs for water pollution prevention and control is essential. However, a significant problem in developing water quality assessment techniques is the
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inherent noise and variability of field data caused by seasonality in hydrologic processes, patchiness, and other stochastic phenomena (Spatharis and Tsirtsisa 2010). The combination of different quality elements (physicochemical, hydromorphological, and biological) for the assessment of the quality status contributes in overcoming the aforementioned and other shortcomings that can be found when a single group of environmental parameters is used. Therefore, this multi-parametric approach, which is proposed by the Water Framework Directive 2000/60/EC, eliminates stochastic errors and facilitates the holistic water quality assessment based on the ecosystem functionality principle (Everard and Powell 2002). Remote sensing of lakes using satellite images offers good spatial and temporal coverage while some variables of water quality such as chlorophyll-a (Chl-a), total suspended sediment (TSS), suspended minerals (SM), turbidity, Secchi disk depth (SDD), particulate organic carbon, and colored dissolved organic matter (CDOM) can potentially be assessed up to several times per year (Allan et al. 2011). Imaging and non-imaging narrow-band sensors have been used to measure chlorophyll-a in surface waters, often with great precision (Dekker and Peters 1993; Goodin et al. 1993; Gitelson et al. 1994; Hamilton et al. 1993; Han et al. 1994; Millie et al. 1992; Vertucci and Likens 1989). Currently, the most popular operational spacecraft remote sensing system with a potential applicability to study water quality is Landsat Thematic Mapper (TM) (Gitelson et al. 1993; Ritchie et al. 1990; Tassan 1997; Zilioli and Brivio 1997). Multispectral Landsat 5 ΤΜ has seven spectral bands, spatial resolution of 30 m in the visible, near, and middle infrared bands and 120 m in the thermal band. Landsat TM data have been used by several groups for chlorophyll-a (Chl-a) estimations in inland waters due to their high spatial and temporal resolution (Dekker et al. 1992; Gitelson et al. 1986; Jacquet and Zang 1989; Reardon and McGarrigle 1989; Ritchie et al. 1990). In this study, the water quality status of a small urban lake situated in the outskirts of Athens was assessed by combining physicochemical and biological quality elements. In order to enhance the reliability of the sampling monitoring results, an effort has been made to assess chlorophyll-a concentration and determine the environmental quality of the Koumoundourou Lake through a remote sensing application and particularly Landsat 5 imagery of same dates as the sampling campaigns.
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This scientific effort focuses principally on the study of nutrient-dependent parameter fluctuations at a spatiotemporal basis, on examining the most widely applicable classification schemes for the trophic and chemical status assessment and subsequently on exploring whether Landsat 5 satellite data can efficiently estimate the chlorophyll-a concentrations in a brackish urban shallow lake.
Methodology Study area The Koumoundourou Lake is a brackish lake, which is located in the southeastern part of the Thriasian plain, in the northeastern part of Eleusis Bay. It is located at the 16th km of the National Road Athens–Korinthos within the administrative boundaries of Aspropirgos Municipality. The Koumoundourou Lake also expands to the southwestern edges of Mount Egaleo between hills Kapsalonas (273 m.a.s.l.) and Gikas (77 m.a.s.l.) (Fig. 1). The watershed of the Koumoundourou Lake passes through the southeastern edges of Mount Parnitha, while the basin’s area is 39.0 km2 and has a direction of northeast–southwest. Its southeastern part is open to the sea (Elefsis Bay), where Koumoundourou Lake is detected (Dimitriou et al. 2011). Koumoundourou Lake is shallow with a maximum depth that reaches 2.6 m and an average depth of 1 m (Fig. 2). The greatest depths are located northeast of the lake, where the main underwater, karstic springs are observed (Dimitriou et al. 2011). Sampling network Field measurements of physicochemical and chemical parameters have been conducted bimonthly from February 2011 to January 2012 (Fig. 2) at five sampling stations, while macroinvertebrates were collected seasonally. The sampling network was established in order to cover the lake spatially, taking into account anthropogenic pressures, the different habitats, and the hydromorphological conditions of the lake. Portable instruments were used to measure water temperature, conductivity, pH, turbidity, and dissolved oxygen concentration. Water samples were collected from the upper layer of the surface (approximately 20–30 cm) and transported to the laboratory of the Hellenic Centre for
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Fig. 1 Topographical map of the Koumoundourou Lake’s basin (M.G.S., Eleusis and Athens, scale 1:25,000)
Marine Research (HCMR) for analysis of major ions, nutrients, and chlorophyll-a concentrations. The results from this analysis were used to characterize the chemical quality (high, good, moderate, poor and bad) and trophic status (hypertrophic, eutrophic, mesotrophic, oligotrophic and hyper-oligotrophic) of the study area in five quality classes. Methodology of field chlorophyll-a measurement Water samples were collected from the sampling stations in February, April, June, July, September, and November 2011, and January 2012 in order to study the concentrations of chlorophyll-a, which is a very
good indicator of phytoplankton biomass (Eppley and Weiler 1979). Surface water samples were collected with NIO samplers of 1.5-l capacity. A specific quantity of water (usually 0.5–1 l) was filtered with Whatman GF/F filters. These filters were maintained in a dry and dark environment at −15 °C, and the chlorophyll-a concentration was determined by a TURNER 00-AU10U fluorometer (Holm-Hansen et al. 1965). HCMR had conducted another field campaign at Koumoundourou Lake in 15 August 2003 in order to measure the Chl-a concentration. These values have been used in establishing the remote sensing Chl-a algorithm and are discussed in the “Calibration of models relating Landsat and chlorophyll-a data” section.
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Fig. 2 Bathymetric map of the Koumoundourou Lake and the network of sampling stations
Macroinvertebrate assemblage sampling
Statistical analysis
For the purposes of this study, benthic invertebrates were used to assess the biological status of the lake. Benthic invertebrates have been used more than any other biological quality elements, and based on them, many methodologies and systems have been developed for the assessment of ecological quality (Rosenberg and Resh 1993; Metcalfe-Smith 2009). Benthic fauna was collected by an Ekman bottom grab sampler. Samples were sieved with 1-mm-diameter mesh, placed in plastic containers with 90 % ethanol as a preservative, and transported to the laboratory where they were sorted. Specimens were identified mainly at genus level and at species level where possible.
To identify groups of stations with similar environmental characteristics, a cluster analysis categorized with Euclidean distance was performed. The resulting clusters of objects should exhibit high internal (within cluster) homogeneity and high external (between clusters) heterogeneity. Hierarchical CA is the most common approach, which starts with each case in a separate cluster and joints the clusters together step by step until only one cluster remains and is typically illustrated by a dendrogram (tree diagram). To confirm site clustering, a non-metric multidimensional scaling (NMDS) analysis was performed (Kruskal 1964). The Euclidean distance usually provides the similarity between two samples,
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and a distance can be represented by the difference between analytical values from samples. Prior to the analysis, all environmental data (chemical–physicochemical data, Chl-a concentration) were logtransformed and normalised. Statistical analysis was performed with the statistical package PRIMER v.6 (Clarke and Gorley 2006). Satellite data Three Landsat 5 TM scenes of 25 August 2003, 28 June 2011, and 16 September 2011 were used for this study. Due to the small size of the Koumoundourou Lake (approximately 146,500 m2), the number of satellite data and sampling stations were considered to be adequate for monitoring variation of chlorophyll-a concentration. Based on meteorological data of the closest weather station to the lake, no significant alterations were observed in the meteorological conditions during the periods between the sampling data and acquisition of satellite data of all dates. Specifically, no precipitation occurred during the aforementioned periods while the temperature remained at relatively stable levels. The imageries were acquired from the USGS Data Center (United States Geological Survey). Previous studies indicated that the middle infrared bands (TM5 and TM7) showed low correlations and basically random relationships with water quality parameters, which could be due to the low water depth penetration of these wavelengths (Lathrop and Lillesand 1986), since they are absorbed in just a few centimeters of water. Accordingly, these bands were omitted from further consideration; thus, the analysis was restricted to TM bands 1 to 4 and their combinations for chlorophyll-a estimation. Digital data processing Data elaboration and analysis were conducted in ESRI’s ArcGIS 10.1 software, while for the analysis of the satellite imagery, the ENVI 5.0 software was used. After selecting the study area scenes and the appropriate dates (near simultaneous remote sensing data collection at the time of water samples), the digital data were submitted to the following procedures: 1. Geo-referencing of the imageries and geographical conversion from WGS’84 to EGSA’87 were performed using Beam 4.7 software.
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2. Radiometric correction which gives a scale to the pixel values, e.g., the monochromatic scale of 0 to 255, was converted to actual radiance values. The formula used in this process is as follows: Lλ = {(LMAXλ − LMINλ)/(QCALMAX − QCALMIN)} × (QCAL − QCALMIN) + LMINλ (YCEO 2010) where Lλ is the cell value as radiance, QCAL is the digital number, LMINλ is spectral radiance scales to QCALMIN, LMAXλ is spectral radiance scales to QCALMAX, QCALMIN is the minimum quantized calibrated pixel value (typically equal to 1), and QCALMAX is the maximum quantized calibrated pixel value (typically equal to 255). 3. Atmospheric correction Dark object subtraction technique has been used in the specific study through the relevant ENVI software tool. Although this method was used very often in the past, it still constitutes a simple and reliable manner to exclude the atmospheric bias from the image. This technique searches each band for the darkest pixel value. Assuming that dark objects reflect no light, any value greater than zero must result from atmospheric scattering. The scattering is removed by subtracting this value from every pixel in the band (Lathrop et al. 1991; Keiner and Yan 1998).
Calibration of models relating Landsat and chlorophyll-a data Landsat TM/ETM+ data in conjunction with in situ water sampling provide the means to establishing a relationship between satellite-derived reflectance values and chlorophyll-a concentration (Han and Jordan 2005; Tiedje et al. 2010; Matthews et al. 2012; Lesht et al. 2013). Initially, attempts were made to find combinations, transformations, or logarithmical transformations of Landsat TM bands which would provide more information about chlorophyll-a concentration in the lake than only one band. Such combinations and band transformations concern ratios of B1/B2, B2/B3, B1/B4, B4/ B1, B3/B2, B2/B4, B4/B2, B3/B4, and B4/B3; multiplications of B1*B4 and B2*B4; and the logarithmical transformations of log(B1/B2), log(B1/B3), log(B1/ B4), log(B2/B3), log(B2/B4), logB2/logB3, logB2/ logB4, and logB3/logB4. Subsequently, digital numbers (e.g., pixel values) of each transformed image were retrieved from those regions where the sampling stations
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are located. Linear regression models were developed between reflectivity values and in situ measurements for all transformed images and sampling stations. Dependent variables were chlorophyll-a concentration, and independent variables were image bands, band ratios, and other band combinations. This regression analysis resulted in models that predict chlorophyll-a concentration, and the selection of the best applicable model was based on the value of the correlation of determination between the reflectance and the values of chlorophyll-a. Predictive models with high coefficient of determination (R2), which were developed based on field sampling of 15 August 2003 and satellite image of 25 August 2003, were applied to the Landsat imageries of 28 June 2011 and 16 September 2011 in order to assess and validate their efficiency by comparing them with the in situ measurements of the validation data series.
Results Hydrochemical status of the Koumoundourou Lake In the present paper, parameters such as pH, temperature, transparency, dissolved oxygen, total phosphorous (TP), nitrates, nitrites, ammonium, and chlorophyll-a were selected in order to contribute to the description of the Koumoundourou Lake’s nutrient status. The selection of the parameters was based on their confluence as key variables for lake water quality. Based on pH measurements, the water of the lake is basic with values ranging from 7.7 (K3, in February 2011) to 10.2 (K1, in November 2011), with an average value of 8.7. Water temperature ranged from 6.5 °C (K1, in January 2012) to 31 °C (K2, in July 2011). Turbidity ranged from 0.78 NTU (K1, in September 2011) to 8.2 NTU (K1, in April 2011) with an average value of 2.5 NTU (Fig. 2). Low turbidity values during the summer could be attributed to the decreased wind intensity and to the dense vegetation, which prevents the re-suspension of the lake’s bed sediment. Dissolved oxygen (DO) concentrations ranged from 6.8 (at K2 station, in February 2011) to 18.7 mg/l (at K1 station, in June 2011), with an average of 11.5 mg/l. This specific average value is similar to average values of major Greek lakes (Zacharias et al. 2002), and despite the inflow from karstic springs near stations K4 and K5 that are located at the northern part of the lake, all
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sampling stations were almost equally enriched with dissolved oxygen (Table 1). Therefore, the relatively small area of the lake results in a relatively uniform ecosystem regarding its physicochemical parameters. Conductivity varied from 8,982 (at K5 station, in February 2011) to 14,490 μS/cm (K5, in September 2011), with an average value of 12,474 μS/cm, which can be characterized as slightly increased due to sea water intrusion. Conductivity was lower during February 2011, when both inflows from karstic springs and precipitation are higher. Subsequently, a gradual increase was observed during the spring due to the reduction of freshwater input. Nitrogen is recognized as a key variable determining eutrophication process in lakes (Scheffer et al. 1993). Nitrate concentrations varied from 0.009 (K2, in July 2011) to 9.8 mg/l (K4, in January 2012), with an average value of 2 mg/l. The highest average nitrate concentrations were detected in the vicinity of the karstic springs at stations K4 (2.8 mg/l) and K5 (2.7 mg/l, Fig. 2). Nitrate concentrations were in fact higher during the entire sampling period in these stations. The significant nitrate concentration increase, observed in June 2011, can be attributed to a specific pollution incident (e.g., disposal of urban waste), while the respective concentrations were significantly reduced in July and September. This reduction may be due to both great macrophyte and algae development that absorb nitrates from the water and to bacterial denitrification, which increases at higher temperatures (Bremner and Shaw 1958; Beauchamp et al. 1989). On the other hand, increased nitrate concentrations in November 2011 and January 2012 may be attributed to both urban sewage disposal and feces from numerous bird species, which are encountered in high numbers throughout the lake. The observed low seasonal temperatures also explain these high nitrate concentrations due to slower denitrification processes. Nitrite concentrations overall ranged from 0.001 (K1, in September 2011) to 0.26 mg/l (K5, in April 2011), with an average value of 0.08 mg/l. The highest nitrite concentrations were observed in April 2011, most possibly attributed to ammonia oxidation and high dissolved oxygen concentrations in the lake. Generally, the highest nitrite concentrations were observed in the northern part of the lake (stations K4 and K5, Fig. 2), with the exception of the last two sampling campaigns (in November 2011 and in January 2012), where the central part of the lake (K3, Fig. 2) presented the highest concentrations since it constitutes as a shelter for a large number of birds during this specific period.
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Ammonium varied from 0.0047 (K2, in September 2011) to 6.9 mg/l (K1, in February 2011), with mean value of 1.24 mg/l. The ammonium ion concentrations were relatively low, except for the samples taken in February and April 2011, which presented a tenfold value. Higher ammonium concentrations are usually observed during the dry season when the decomposition of organic matter and the production of NH4+ are stronger. Very high rates of ammonia during the winter may be attributed to bird feces. Regarding the sampling dates, N–NO3− ranged from 0.124 (in September 2011) to 4.062 mg/l (in February 2011), with a mean value that equals to 1.57 mg/l. According to Zacharias et al. (2002), the value of total inorganic nitrogen levels equal to 0.5 mg/l is taken to be the cut-off one for unpolluted lake water. Water samples with nitrogen concentrations greater than 0.5 mg/l N–NO3− (average values of February, April, and September 2011, and January 2012) are more likely to be characterized as polluted and of poor quality (Table 4). There is no doubt about the establishing of phosphorus as a key variable in lake ecology since it is the primary determining factor for numerous biological variables (Schindler 1978; OECD 1982; Wetzel 2001). Phosphate concentrations ranged from 0.003 (K1, in January 2012) to 0.019 mg/l (K5, in 2011) with an average value of 0.006 mg/l. On most sampling periods, station K5 presented generally higher values compared to the others, with the highest one observed in February 2011 (Fig. 2). Phosphate concentrations presented significant seasonal fluctuations—increased in spring (April to June), decreased in July, slightly increased in September–November, and decreased again in January. These fluctuations are attributed to macrophyte and algae blooming that absorb phosphate from the water during the summer season. In most major Greek lakes (Zacharias et al. 2002), total phosphorus concentration exceeds 20 μg/l, thereby indicating an anthropogenic influence on the lake catchments (Stanner and Bourdeau 1995). In Koumoundourou Lake, average P–PO43+ concentrations range from 1.476 μg/l (July 2011) to 6.93 μg/l (August 2003); values that are not considered to be high and reinforce the theory that the process of autochthonous calcite precipitation in hard-water lakes (due to enhanced photosynthesis) can result in significant phosphorus removal (Danen-Louwerse et al. 1995). Phytoplankton community biomass, as it is expressed by chlorophyll-a concentration, is closely related to the nutrient availability as a response variable, reflecting
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also the primary productivity of the lake (Wetzel 2001). Mean chlorophyll-a concentration of all sampling campaigns ranged from 1.4 μg/l (in July 2011) to 15.8 μg/l (in April 2011) with an average value of 6.8 μg/l (Table 2). Trophic status and lake water quality classification In Greece, as in many other EU member states, reference conditions for lakes have not been established and a nutrient classification system for determining lake water quality has not been developed yet. To overcome this issue, we used the three most utilized classification schemes for lake water quality: The OECD classification system According to this classification system, lakes are classified based on their phosphorus and chlorophyll concentrations into five categories, depending on their content of nutrients, water transparency, and algae concentration in the water body (OECD 1982). Based on phosphorus values of 2003, Koumoundourou Lake is characterized as oligotrophic and for 2011 as hyper-oligotrophic. Based on chlorophyll-a concentration, the lake is characterized for both 2003 and 2011 as mesotrophic. For the final classification of trophic status, the worst ranking of the relevant qualitative criteria is commonly used and therefore the lake can be characterized as mesotrophic (Table 2). The EPA classification system Another water quality classification system for temperate lakes is that proposed by the Environmental Protection Agency of USA (EPA 2000). According to this scheme, the classification of lakes into seven quality classes is based on the total phosphorus concentration, water transparency, and trophic index (Trophic State Index—TSI). Trophic index TSI is calculated for each classification quality parameter as follows (Carlson and Simpson 1996): TSIðSDÞ ¼ 60−14:41 lnðSDÞ; TSIðChl‐aÞ ¼ 9:81 lnðChl‐aÞ þ 30:6; TSIðTPÞ ¼ 14:42 lnðTPÞ þ 4:15;
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Table 1 Average concentration values (mg/l) of DO and certain nutrients during all sampling periods of the Koumoundourou Lake NO3− (mg/l)
NO2− (mg/l)
PO43− (mg/l)
NH4+ (mg/l)
Sampling stations
DO (mg/l)
K1
12.81
1.24
0.06
0.005
1.55
K2
11.47
1.29
0.06
0.006
1.27
K3
10.94
1.99
0.07
0.006
0.97
K4
11.23
2.79
0.10
0.006
1.04
K5
11.24
2.70
0.12
0.008
1.35
where SD is the Secchi disk (m) and Chl-a and TP (μg/l) are the concentrations of chlorophyll-a and total phosphorus, respectively. Taking into account both the past (2003) and the latest (2011-2012) concentrations of phosphorus in water, Koumoundourou Lake is characterized as oligotrophic. Regarding the past chlorophyll-a concentration, the lake is characterized as oligo-mesotrophic, and based on the latest chlorophyll-a values, it is characterized as eutrophic with anoxic conditions and intensive development of macrophytes. Based on the TSI index, Koumoundourou Lake appears to be oligotrophic in 2003 and oligo-mesotrophic in 2011–2012 (Table 3). The ECOFRAME classification system Based on the ECOFRAME scheme (Moss et al. 2003), which has been applied in order to determine the ecological status in 66 shallow European lakes and includes Table 2 Trophic status characterization based on OECD (1982) classification scheme Date
Phosphorus (μg/l P–PO4)
Chlorophyll-a (μg/l)
15 August 03
6.929d
3.555c
07 February 11
2.548e
4.749c
01 April 11 01 June 11 25 July 11
e
15.774b
e
2.107d
e
1.399d
e
2.894c
2.011
2.275 1.476
28 September 11
1.711
18 January 12
1.804e
Average a
Hypertrophic
b
Eutrophic
c
Mesotrophic
d
Oligotrophic
e
Hyper-oligotrophic
c
1.971
13.843b 6.794c
a typological classification for lakes, we attempted to classify the ecological status of the Koumoundourou Lake into five classes (high, good, moderate, poor, and bad). Taking into consideration that the average air temperature in the study area is 29.1 °C, the catchment area is 39 km2, consists of rocky formations and nonorganic soils at a rate greater than 50 %, and is a brackish lake, we categorized the lake into the 21st class (Moss et al. 2003). Hence, the ecological status of the lake concerning the water sampling of August 2003 was characterized as high based on pH, chlorophyll-a, and phosphorus values and as good based on nitrogen levels. For the rest of the sampling campaigns (February2011– January /2012), the ecological status was classified as high based on pH, phosphorus, and chlorophyll-a and as bad according to nitrogen concentration (Table 4). Statistics The results of the correlation analysis performed on environmental data are presented on Table 5. The strongest but negative correlation was observed between pH and NO3− (correlation coefficient or coefficient of linear regression, r=−0.99). NO3− concentrations were also strongly but negatively correlated with DO (r=−0.88) and NO2− (r=0.92) concentrations. Regarding the rest of the correlation analysis, pH was also strongly correlated with DO and moderately with the Chl-a concentrations (r=0.82 and r=−0.63, respectively). Temperature was negatively correlated with NH 4 + concentrations (r=−0.88). Chl-a concentrations were strongly and negatively correlated with DO concentrations (r=−0.95). According to Pillay and Kutty (2005), it is the indirect effects resulting from the interactions of pH with other variables that depend on the water acid–base equilibrium such as dissolved CO2 and the relationship between NH3–N and NH4+–N levels and NO2–N levels that an increase of their concentrations depresses the pH values in water. The higher toxicity levels of NH3–N and CO2
Environ Monit Assess (2014) 186:5009–5026 Table 3 Trophic status characterization based on Carlson (1996) classification scheme, proposed by EPA (2000)
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Date
Phosphorus (μg/l P–PO4)
Chlorophyll-a (μg/l)
TSI
15 August 03
6.929d
3.555c
38d
d
c
07 February 11
a
Hypertrophic
b
Eutrophic
c
Mesotrophic
d
Oligotrophic
e
Hyper-oligotrophic
2.548
01 April 11
2.011
15.774
36d
01 June 11
2.275d
2.107d
27d
25 July 11
d
d
22d
a
High
b
Good
c
Moderate
d
Poor
e
Bad
c
1.711
Average
26d
2.894
d
18 January 12
b
1.804
13.843
35d
d
b
30d
2.69
6.794
characteristics. Cluster analysis revealed three main groups of stations that were also confirmed by NMDS (Fig. 3). The main groups included sampling stations K1, K2–K3, and K4–K5. Non-metric multidimensional scaling (NMDS) of chemical and physicochemical data for the five sampling stations was consistent with the differences in their chemical status, and although no significant variations were observed among nutrient concentrations, NMDS has successfully managed to differentiate them. Benthic fauna Sampling of the benthic fauna of the lake was performed seasonally. However, only on one occasion (February 2011) have macroinvertebrates been successfully collected. On the other two sampling efforts (July and November 2011), collection of benthic organisms was not possible due to the complete coverage of the lake’s bottom by aquatic macrophytes. Overall, the fauna of the lake was very poor since only three taxa were recorded (Table 6).The most abundant were species of the Hydrobiidae family which were recorded in very high abundances. Most Hydrobiidae specimens were represented by Hydrobia ulvae; however, most were
Phosphorus (μg/l P–PO4)
Nitrogen (mg/l Ν–ΝΟ3)
Chl-a (μg/l)
7.7a
6.929a
0.754b
3.555a
a
a
d
4.749a
d
15.774b
a
Date
pH
15 August 03 01 April 11
1.399
d
28 September 11
07 February 11
b
1.476
in water depends on the water’s pH that controls acid– base equilibrium (Isla Molleda 2007). Therefore, such results of correlation analysis were expected especially among pH, DO, NO3−, and NO2− concentrations. Regarding the resulted negative and strong correlation between the Chl-a and DO concentrations, scientists have long recognized the ecological relationship between chlorophyll-a in surface waters and dissolved oxygen in bottom waters. These relationships were initially recognized in freshwater lakes where eutrophication problems developed because of the lakes’ proximity to anthropogenic nutrient sources (EPA 2007). Furthermore, a strong association was expected between the water temperature and NH4+ concentrations, since the temperature is one of the three factors (pH, temperature, and salinity) that influence the fraction of NH3− in a solution and consequently affect the toxicity of ammonia in the aquatic environment. More specifically, the concentration of unionized ammonia in the water is largely pH and temperature dependent, and to a lesser extent, salinity or ionic strength dependent (Starbuck 1998). Moreover, a cluster analysis categorized with Euclidean distance was conducted in order to identify groups of stations with similar environmental Table 4 Ecological status classification of the Koumoundourou Lake based on the ECOFRAME scheme
32d
4.749
d
7.8
a
7.8
a
2.011
3.008
8.8
2.275
0.274
2.107a
25 July 11
9.2a
1.476a
0.134a
1.399a
a
a
a
2.894a
d
13.843b
d
6.779a
18 January 12 Average
9.0
a
4.063
01 June 11 28 September 11
a
2.548
1.711
a
1.804
a
a
9.1
8.6
a
2.69
0.124 1.826 1.571
5018
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Table 5 Correlation matrix among all the under study environmental parameters, italic marked correlations are significant at p
Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications V. Markogianni & E. Dimitriou & I. Karaouzas
Received: 16 October 2013 / Accepted: 21 March 2014 / Published online: 5 April 2014 # Springer International Publishing Switzerland 2014
Abstract Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophylla concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R2) among point sampling results and the remotely V. Markogianni (*) : E. Dimitriou : I. Karaouzas Institute of Marine Biological Resources and Inland Waters, Hellenic Centre for Marine Research, 46.7 km Athens–Sounio Av, 19013 Anavyssos, Greece e-mail: [email protected]
sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available. Keywords Water quality . Chlorophyll-a . Trophic status . Landsat . Lake
Introduction Freshwaters, worldwide, have been severely impacted by human activities during the last century (Arthington and Welcomme 1995). Eutrophication is one of the common stressor with obvious symptoms (e.g., cyanobacterial blooms) such as changes in biotic communities, food web disturbances, and degradation of water quality leading to biodiversity loss (MoustakaGouni et al. 2006). Niemi et al. (1990) reported that human activities mainly impact surface water quality through atmospheric pollution, effluent discharges, and the use of agricultural chemicals, in addition to the increased exploitation of water resources. These have generated great pressures on aquatic ecosystems, resulting in water quality deterioration and biodiversity decrease, loss of critical habitats, and an overall decrease in quality of life for local inhabitants (Herrera-Silveira and Morales-Ojeda 2009). Therefore, the implementation of monitoring and assessment programs for water pollution prevention and control is essential. However, a significant problem in developing water quality assessment techniques is the
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inherent noise and variability of field data caused by seasonality in hydrologic processes, patchiness, and other stochastic phenomena (Spatharis and Tsirtsisa 2010). The combination of different quality elements (physicochemical, hydromorphological, and biological) for the assessment of the quality status contributes in overcoming the aforementioned and other shortcomings that can be found when a single group of environmental parameters is used. Therefore, this multi-parametric approach, which is proposed by the Water Framework Directive 2000/60/EC, eliminates stochastic errors and facilitates the holistic water quality assessment based on the ecosystem functionality principle (Everard and Powell 2002). Remote sensing of lakes using satellite images offers good spatial and temporal coverage while some variables of water quality such as chlorophyll-a (Chl-a), total suspended sediment (TSS), suspended minerals (SM), turbidity, Secchi disk depth (SDD), particulate organic carbon, and colored dissolved organic matter (CDOM) can potentially be assessed up to several times per year (Allan et al. 2011). Imaging and non-imaging narrow-band sensors have been used to measure chlorophyll-a in surface waters, often with great precision (Dekker and Peters 1993; Goodin et al. 1993; Gitelson et al. 1994; Hamilton et al. 1993; Han et al. 1994; Millie et al. 1992; Vertucci and Likens 1989). Currently, the most popular operational spacecraft remote sensing system with a potential applicability to study water quality is Landsat Thematic Mapper (TM) (Gitelson et al. 1993; Ritchie et al. 1990; Tassan 1997; Zilioli and Brivio 1997). Multispectral Landsat 5 ΤΜ has seven spectral bands, spatial resolution of 30 m in the visible, near, and middle infrared bands and 120 m in the thermal band. Landsat TM data have been used by several groups for chlorophyll-a (Chl-a) estimations in inland waters due to their high spatial and temporal resolution (Dekker et al. 1992; Gitelson et al. 1986; Jacquet and Zang 1989; Reardon and McGarrigle 1989; Ritchie et al. 1990). In this study, the water quality status of a small urban lake situated in the outskirts of Athens was assessed by combining physicochemical and biological quality elements. In order to enhance the reliability of the sampling monitoring results, an effort has been made to assess chlorophyll-a concentration and determine the environmental quality of the Koumoundourou Lake through a remote sensing application and particularly Landsat 5 imagery of same dates as the sampling campaigns.
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This scientific effort focuses principally on the study of nutrient-dependent parameter fluctuations at a spatiotemporal basis, on examining the most widely applicable classification schemes for the trophic and chemical status assessment and subsequently on exploring whether Landsat 5 satellite data can efficiently estimate the chlorophyll-a concentrations in a brackish urban shallow lake.
Methodology Study area The Koumoundourou Lake is a brackish lake, which is located in the southeastern part of the Thriasian plain, in the northeastern part of Eleusis Bay. It is located at the 16th km of the National Road Athens–Korinthos within the administrative boundaries of Aspropirgos Municipality. The Koumoundourou Lake also expands to the southwestern edges of Mount Egaleo between hills Kapsalonas (273 m.a.s.l.) and Gikas (77 m.a.s.l.) (Fig. 1). The watershed of the Koumoundourou Lake passes through the southeastern edges of Mount Parnitha, while the basin’s area is 39.0 km2 and has a direction of northeast–southwest. Its southeastern part is open to the sea (Elefsis Bay), where Koumoundourou Lake is detected (Dimitriou et al. 2011). Koumoundourou Lake is shallow with a maximum depth that reaches 2.6 m and an average depth of 1 m (Fig. 2). The greatest depths are located northeast of the lake, where the main underwater, karstic springs are observed (Dimitriou et al. 2011). Sampling network Field measurements of physicochemical and chemical parameters have been conducted bimonthly from February 2011 to January 2012 (Fig. 2) at five sampling stations, while macroinvertebrates were collected seasonally. The sampling network was established in order to cover the lake spatially, taking into account anthropogenic pressures, the different habitats, and the hydromorphological conditions of the lake. Portable instruments were used to measure water temperature, conductivity, pH, turbidity, and dissolved oxygen concentration. Water samples were collected from the upper layer of the surface (approximately 20–30 cm) and transported to the laboratory of the Hellenic Centre for
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Fig. 1 Topographical map of the Koumoundourou Lake’s basin (M.G.S., Eleusis and Athens, scale 1:25,000)
Marine Research (HCMR) for analysis of major ions, nutrients, and chlorophyll-a concentrations. The results from this analysis were used to characterize the chemical quality (high, good, moderate, poor and bad) and trophic status (hypertrophic, eutrophic, mesotrophic, oligotrophic and hyper-oligotrophic) of the study area in five quality classes. Methodology of field chlorophyll-a measurement Water samples were collected from the sampling stations in February, April, June, July, September, and November 2011, and January 2012 in order to study the concentrations of chlorophyll-a, which is a very
good indicator of phytoplankton biomass (Eppley and Weiler 1979). Surface water samples were collected with NIO samplers of 1.5-l capacity. A specific quantity of water (usually 0.5–1 l) was filtered with Whatman GF/F filters. These filters were maintained in a dry and dark environment at −15 °C, and the chlorophyll-a concentration was determined by a TURNER 00-AU10U fluorometer (Holm-Hansen et al. 1965). HCMR had conducted another field campaign at Koumoundourou Lake in 15 August 2003 in order to measure the Chl-a concentration. These values have been used in establishing the remote sensing Chl-a algorithm and are discussed in the “Calibration of models relating Landsat and chlorophyll-a data” section.
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Fig. 2 Bathymetric map of the Koumoundourou Lake and the network of sampling stations
Macroinvertebrate assemblage sampling
Statistical analysis
For the purposes of this study, benthic invertebrates were used to assess the biological status of the lake. Benthic invertebrates have been used more than any other biological quality elements, and based on them, many methodologies and systems have been developed for the assessment of ecological quality (Rosenberg and Resh 1993; Metcalfe-Smith 2009). Benthic fauna was collected by an Ekman bottom grab sampler. Samples were sieved with 1-mm-diameter mesh, placed in plastic containers with 90 % ethanol as a preservative, and transported to the laboratory where they were sorted. Specimens were identified mainly at genus level and at species level where possible.
To identify groups of stations with similar environmental characteristics, a cluster analysis categorized with Euclidean distance was performed. The resulting clusters of objects should exhibit high internal (within cluster) homogeneity and high external (between clusters) heterogeneity. Hierarchical CA is the most common approach, which starts with each case in a separate cluster and joints the clusters together step by step until only one cluster remains and is typically illustrated by a dendrogram (tree diagram). To confirm site clustering, a non-metric multidimensional scaling (NMDS) analysis was performed (Kruskal 1964). The Euclidean distance usually provides the similarity between two samples,
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and a distance can be represented by the difference between analytical values from samples. Prior to the analysis, all environmental data (chemical–physicochemical data, Chl-a concentration) were logtransformed and normalised. Statistical analysis was performed with the statistical package PRIMER v.6 (Clarke and Gorley 2006). Satellite data Three Landsat 5 TM scenes of 25 August 2003, 28 June 2011, and 16 September 2011 were used for this study. Due to the small size of the Koumoundourou Lake (approximately 146,500 m2), the number of satellite data and sampling stations were considered to be adequate for monitoring variation of chlorophyll-a concentration. Based on meteorological data of the closest weather station to the lake, no significant alterations were observed in the meteorological conditions during the periods between the sampling data and acquisition of satellite data of all dates. Specifically, no precipitation occurred during the aforementioned periods while the temperature remained at relatively stable levels. The imageries were acquired from the USGS Data Center (United States Geological Survey). Previous studies indicated that the middle infrared bands (TM5 and TM7) showed low correlations and basically random relationships with water quality parameters, which could be due to the low water depth penetration of these wavelengths (Lathrop and Lillesand 1986), since they are absorbed in just a few centimeters of water. Accordingly, these bands were omitted from further consideration; thus, the analysis was restricted to TM bands 1 to 4 and their combinations for chlorophyll-a estimation. Digital data processing Data elaboration and analysis were conducted in ESRI’s ArcGIS 10.1 software, while for the analysis of the satellite imagery, the ENVI 5.0 software was used. After selecting the study area scenes and the appropriate dates (near simultaneous remote sensing data collection at the time of water samples), the digital data were submitted to the following procedures: 1. Geo-referencing of the imageries and geographical conversion from WGS’84 to EGSA’87 were performed using Beam 4.7 software.
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2. Radiometric correction which gives a scale to the pixel values, e.g., the monochromatic scale of 0 to 255, was converted to actual radiance values. The formula used in this process is as follows: Lλ = {(LMAXλ − LMINλ)/(QCALMAX − QCALMIN)} × (QCAL − QCALMIN) + LMINλ (YCEO 2010) where Lλ is the cell value as radiance, QCAL is the digital number, LMINλ is spectral radiance scales to QCALMIN, LMAXλ is spectral radiance scales to QCALMAX, QCALMIN is the minimum quantized calibrated pixel value (typically equal to 1), and QCALMAX is the maximum quantized calibrated pixel value (typically equal to 255). 3. Atmospheric correction Dark object subtraction technique has been used in the specific study through the relevant ENVI software tool. Although this method was used very often in the past, it still constitutes a simple and reliable manner to exclude the atmospheric bias from the image. This technique searches each band for the darkest pixel value. Assuming that dark objects reflect no light, any value greater than zero must result from atmospheric scattering. The scattering is removed by subtracting this value from every pixel in the band (Lathrop et al. 1991; Keiner and Yan 1998).
Calibration of models relating Landsat and chlorophyll-a data Landsat TM/ETM+ data in conjunction with in situ water sampling provide the means to establishing a relationship between satellite-derived reflectance values and chlorophyll-a concentration (Han and Jordan 2005; Tiedje et al. 2010; Matthews et al. 2012; Lesht et al. 2013). Initially, attempts were made to find combinations, transformations, or logarithmical transformations of Landsat TM bands which would provide more information about chlorophyll-a concentration in the lake than only one band. Such combinations and band transformations concern ratios of B1/B2, B2/B3, B1/B4, B4/ B1, B3/B2, B2/B4, B4/B2, B3/B4, and B4/B3; multiplications of B1*B4 and B2*B4; and the logarithmical transformations of log(B1/B2), log(B1/B3), log(B1/ B4), log(B2/B3), log(B2/B4), logB2/logB3, logB2/ logB4, and logB3/logB4. Subsequently, digital numbers (e.g., pixel values) of each transformed image were retrieved from those regions where the sampling stations
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are located. Linear regression models were developed between reflectivity values and in situ measurements for all transformed images and sampling stations. Dependent variables were chlorophyll-a concentration, and independent variables were image bands, band ratios, and other band combinations. This regression analysis resulted in models that predict chlorophyll-a concentration, and the selection of the best applicable model was based on the value of the correlation of determination between the reflectance and the values of chlorophyll-a. Predictive models with high coefficient of determination (R2), which were developed based on field sampling of 15 August 2003 and satellite image of 25 August 2003, were applied to the Landsat imageries of 28 June 2011 and 16 September 2011 in order to assess and validate their efficiency by comparing them with the in situ measurements of the validation data series.
Results Hydrochemical status of the Koumoundourou Lake In the present paper, parameters such as pH, temperature, transparency, dissolved oxygen, total phosphorous (TP), nitrates, nitrites, ammonium, and chlorophyll-a were selected in order to contribute to the description of the Koumoundourou Lake’s nutrient status. The selection of the parameters was based on their confluence as key variables for lake water quality. Based on pH measurements, the water of the lake is basic with values ranging from 7.7 (K3, in February 2011) to 10.2 (K1, in November 2011), with an average value of 8.7. Water temperature ranged from 6.5 °C (K1, in January 2012) to 31 °C (K2, in July 2011). Turbidity ranged from 0.78 NTU (K1, in September 2011) to 8.2 NTU (K1, in April 2011) with an average value of 2.5 NTU (Fig. 2). Low turbidity values during the summer could be attributed to the decreased wind intensity and to the dense vegetation, which prevents the re-suspension of the lake’s bed sediment. Dissolved oxygen (DO) concentrations ranged from 6.8 (at K2 station, in February 2011) to 18.7 mg/l (at K1 station, in June 2011), with an average of 11.5 mg/l. This specific average value is similar to average values of major Greek lakes (Zacharias et al. 2002), and despite the inflow from karstic springs near stations K4 and K5 that are located at the northern part of the lake, all
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sampling stations were almost equally enriched with dissolved oxygen (Table 1). Therefore, the relatively small area of the lake results in a relatively uniform ecosystem regarding its physicochemical parameters. Conductivity varied from 8,982 (at K5 station, in February 2011) to 14,490 μS/cm (K5, in September 2011), with an average value of 12,474 μS/cm, which can be characterized as slightly increased due to sea water intrusion. Conductivity was lower during February 2011, when both inflows from karstic springs and precipitation are higher. Subsequently, a gradual increase was observed during the spring due to the reduction of freshwater input. Nitrogen is recognized as a key variable determining eutrophication process in lakes (Scheffer et al. 1993). Nitrate concentrations varied from 0.009 (K2, in July 2011) to 9.8 mg/l (K4, in January 2012), with an average value of 2 mg/l. The highest average nitrate concentrations were detected in the vicinity of the karstic springs at stations K4 (2.8 mg/l) and K5 (2.7 mg/l, Fig. 2). Nitrate concentrations were in fact higher during the entire sampling period in these stations. The significant nitrate concentration increase, observed in June 2011, can be attributed to a specific pollution incident (e.g., disposal of urban waste), while the respective concentrations were significantly reduced in July and September. This reduction may be due to both great macrophyte and algae development that absorb nitrates from the water and to bacterial denitrification, which increases at higher temperatures (Bremner and Shaw 1958; Beauchamp et al. 1989). On the other hand, increased nitrate concentrations in November 2011 and January 2012 may be attributed to both urban sewage disposal and feces from numerous bird species, which are encountered in high numbers throughout the lake. The observed low seasonal temperatures also explain these high nitrate concentrations due to slower denitrification processes. Nitrite concentrations overall ranged from 0.001 (K1, in September 2011) to 0.26 mg/l (K5, in April 2011), with an average value of 0.08 mg/l. The highest nitrite concentrations were observed in April 2011, most possibly attributed to ammonia oxidation and high dissolved oxygen concentrations in the lake. Generally, the highest nitrite concentrations were observed in the northern part of the lake (stations K4 and K5, Fig. 2), with the exception of the last two sampling campaigns (in November 2011 and in January 2012), where the central part of the lake (K3, Fig. 2) presented the highest concentrations since it constitutes as a shelter for a large number of birds during this specific period.
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Ammonium varied from 0.0047 (K2, in September 2011) to 6.9 mg/l (K1, in February 2011), with mean value of 1.24 mg/l. The ammonium ion concentrations were relatively low, except for the samples taken in February and April 2011, which presented a tenfold value. Higher ammonium concentrations are usually observed during the dry season when the decomposition of organic matter and the production of NH4+ are stronger. Very high rates of ammonia during the winter may be attributed to bird feces. Regarding the sampling dates, N–NO3− ranged from 0.124 (in September 2011) to 4.062 mg/l (in February 2011), with a mean value that equals to 1.57 mg/l. According to Zacharias et al. (2002), the value of total inorganic nitrogen levels equal to 0.5 mg/l is taken to be the cut-off one for unpolluted lake water. Water samples with nitrogen concentrations greater than 0.5 mg/l N–NO3− (average values of February, April, and September 2011, and January 2012) are more likely to be characterized as polluted and of poor quality (Table 4). There is no doubt about the establishing of phosphorus as a key variable in lake ecology since it is the primary determining factor for numerous biological variables (Schindler 1978; OECD 1982; Wetzel 2001). Phosphate concentrations ranged from 0.003 (K1, in January 2012) to 0.019 mg/l (K5, in 2011) with an average value of 0.006 mg/l. On most sampling periods, station K5 presented generally higher values compared to the others, with the highest one observed in February 2011 (Fig. 2). Phosphate concentrations presented significant seasonal fluctuations—increased in spring (April to June), decreased in July, slightly increased in September–November, and decreased again in January. These fluctuations are attributed to macrophyte and algae blooming that absorb phosphate from the water during the summer season. In most major Greek lakes (Zacharias et al. 2002), total phosphorus concentration exceeds 20 μg/l, thereby indicating an anthropogenic influence on the lake catchments (Stanner and Bourdeau 1995). In Koumoundourou Lake, average P–PO43+ concentrations range from 1.476 μg/l (July 2011) to 6.93 μg/l (August 2003); values that are not considered to be high and reinforce the theory that the process of autochthonous calcite precipitation in hard-water lakes (due to enhanced photosynthesis) can result in significant phosphorus removal (Danen-Louwerse et al. 1995). Phytoplankton community biomass, as it is expressed by chlorophyll-a concentration, is closely related to the nutrient availability as a response variable, reflecting
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also the primary productivity of the lake (Wetzel 2001). Mean chlorophyll-a concentration of all sampling campaigns ranged from 1.4 μg/l (in July 2011) to 15.8 μg/l (in April 2011) with an average value of 6.8 μg/l (Table 2). Trophic status and lake water quality classification In Greece, as in many other EU member states, reference conditions for lakes have not been established and a nutrient classification system for determining lake water quality has not been developed yet. To overcome this issue, we used the three most utilized classification schemes for lake water quality: The OECD classification system According to this classification system, lakes are classified based on their phosphorus and chlorophyll concentrations into five categories, depending on their content of nutrients, water transparency, and algae concentration in the water body (OECD 1982). Based on phosphorus values of 2003, Koumoundourou Lake is characterized as oligotrophic and for 2011 as hyper-oligotrophic. Based on chlorophyll-a concentration, the lake is characterized for both 2003 and 2011 as mesotrophic. For the final classification of trophic status, the worst ranking of the relevant qualitative criteria is commonly used and therefore the lake can be characterized as mesotrophic (Table 2). The EPA classification system Another water quality classification system for temperate lakes is that proposed by the Environmental Protection Agency of USA (EPA 2000). According to this scheme, the classification of lakes into seven quality classes is based on the total phosphorus concentration, water transparency, and trophic index (Trophic State Index—TSI). Trophic index TSI is calculated for each classification quality parameter as follows (Carlson and Simpson 1996): TSIðSDÞ ¼ 60−14:41 lnðSDÞ; TSIðChl‐aÞ ¼ 9:81 lnðChl‐aÞ þ 30:6; TSIðTPÞ ¼ 14:42 lnðTPÞ þ 4:15;
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Table 1 Average concentration values (mg/l) of DO and certain nutrients during all sampling periods of the Koumoundourou Lake NO3− (mg/l)
NO2− (mg/l)
PO43− (mg/l)
NH4+ (mg/l)
Sampling stations
DO (mg/l)
K1
12.81
1.24
0.06
0.005
1.55
K2
11.47
1.29
0.06
0.006
1.27
K3
10.94
1.99
0.07
0.006
0.97
K4
11.23
2.79
0.10
0.006
1.04
K5
11.24
2.70
0.12
0.008
1.35
where SD is the Secchi disk (m) and Chl-a and TP (μg/l) are the concentrations of chlorophyll-a and total phosphorus, respectively. Taking into account both the past (2003) and the latest (2011-2012) concentrations of phosphorus in water, Koumoundourou Lake is characterized as oligotrophic. Regarding the past chlorophyll-a concentration, the lake is characterized as oligo-mesotrophic, and based on the latest chlorophyll-a values, it is characterized as eutrophic with anoxic conditions and intensive development of macrophytes. Based on the TSI index, Koumoundourou Lake appears to be oligotrophic in 2003 and oligo-mesotrophic in 2011–2012 (Table 3). The ECOFRAME classification system Based on the ECOFRAME scheme (Moss et al. 2003), which has been applied in order to determine the ecological status in 66 shallow European lakes and includes Table 2 Trophic status characterization based on OECD (1982) classification scheme Date
Phosphorus (μg/l P–PO4)
Chlorophyll-a (μg/l)
15 August 03
6.929d
3.555c
07 February 11
2.548e
4.749c
01 April 11 01 June 11 25 July 11
e
15.774b
e
2.107d
e
1.399d
e
2.894c
2.011
2.275 1.476
28 September 11
1.711
18 January 12
1.804e
Average a
Hypertrophic
b
Eutrophic
c
Mesotrophic
d
Oligotrophic
e
Hyper-oligotrophic
c
1.971
13.843b 6.794c
a typological classification for lakes, we attempted to classify the ecological status of the Koumoundourou Lake into five classes (high, good, moderate, poor, and bad). Taking into consideration that the average air temperature in the study area is 29.1 °C, the catchment area is 39 km2, consists of rocky formations and nonorganic soils at a rate greater than 50 %, and is a brackish lake, we categorized the lake into the 21st class (Moss et al. 2003). Hence, the ecological status of the lake concerning the water sampling of August 2003 was characterized as high based on pH, chlorophyll-a, and phosphorus values and as good based on nitrogen levels. For the rest of the sampling campaigns (February2011– January /2012), the ecological status was classified as high based on pH, phosphorus, and chlorophyll-a and as bad according to nitrogen concentration (Table 4). Statistics The results of the correlation analysis performed on environmental data are presented on Table 5. The strongest but negative correlation was observed between pH and NO3− (correlation coefficient or coefficient of linear regression, r=−0.99). NO3− concentrations were also strongly but negatively correlated with DO (r=−0.88) and NO2− (r=0.92) concentrations. Regarding the rest of the correlation analysis, pH was also strongly correlated with DO and moderately with the Chl-a concentrations (r=0.82 and r=−0.63, respectively). Temperature was negatively correlated with NH 4 + concentrations (r=−0.88). Chl-a concentrations were strongly and negatively correlated with DO concentrations (r=−0.95). According to Pillay and Kutty (2005), it is the indirect effects resulting from the interactions of pH with other variables that depend on the water acid–base equilibrium such as dissolved CO2 and the relationship between NH3–N and NH4+–N levels and NO2–N levels that an increase of their concentrations depresses the pH values in water. The higher toxicity levels of NH3–N and CO2
Environ Monit Assess (2014) 186:5009–5026 Table 3 Trophic status characterization based on Carlson (1996) classification scheme, proposed by EPA (2000)
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Date
Phosphorus (μg/l P–PO4)
Chlorophyll-a (μg/l)
TSI
15 August 03
6.929d
3.555c
38d
d
c
07 February 11
a
Hypertrophic
b
Eutrophic
c
Mesotrophic
d
Oligotrophic
e
Hyper-oligotrophic
2.548
01 April 11
2.011
15.774
36d
01 June 11
2.275d
2.107d
27d
25 July 11
d
d
22d
a
High
b
Good
c
Moderate
d
Poor
e
Bad
c
1.711
Average
26d
2.894
d
18 January 12
b
1.804
13.843
35d
d
b
30d
2.69
6.794
characteristics. Cluster analysis revealed three main groups of stations that were also confirmed by NMDS (Fig. 3). The main groups included sampling stations K1, K2–K3, and K4–K5. Non-metric multidimensional scaling (NMDS) of chemical and physicochemical data for the five sampling stations was consistent with the differences in their chemical status, and although no significant variations were observed among nutrient concentrations, NMDS has successfully managed to differentiate them. Benthic fauna Sampling of the benthic fauna of the lake was performed seasonally. However, only on one occasion (February 2011) have macroinvertebrates been successfully collected. On the other two sampling efforts (July and November 2011), collection of benthic organisms was not possible due to the complete coverage of the lake’s bottom by aquatic macrophytes. Overall, the fauna of the lake was very poor since only three taxa were recorded (Table 6).The most abundant were species of the Hydrobiidae family which were recorded in very high abundances. Most Hydrobiidae specimens were represented by Hydrobia ulvae; however, most were
Phosphorus (μg/l P–PO4)
Nitrogen (mg/l Ν–ΝΟ3)
Chl-a (μg/l)
7.7a
6.929a
0.754b
3.555a
a
a
d
4.749a
d
15.774b
a
Date
pH
15 August 03 01 April 11
1.399
d
28 September 11
07 February 11
b
1.476
in water depends on the water’s pH that controls acid– base equilibrium (Isla Molleda 2007). Therefore, such results of correlation analysis were expected especially among pH, DO, NO3−, and NO2− concentrations. Regarding the resulted negative and strong correlation between the Chl-a and DO concentrations, scientists have long recognized the ecological relationship between chlorophyll-a in surface waters and dissolved oxygen in bottom waters. These relationships were initially recognized in freshwater lakes where eutrophication problems developed because of the lakes’ proximity to anthropogenic nutrient sources (EPA 2007). Furthermore, a strong association was expected between the water temperature and NH4+ concentrations, since the temperature is one of the three factors (pH, temperature, and salinity) that influence the fraction of NH3− in a solution and consequently affect the toxicity of ammonia in the aquatic environment. More specifically, the concentration of unionized ammonia in the water is largely pH and temperature dependent, and to a lesser extent, salinity or ionic strength dependent (Starbuck 1998). Moreover, a cluster analysis categorized with Euclidean distance was conducted in order to identify groups of stations with similar environmental Table 4 Ecological status classification of the Koumoundourou Lake based on the ECOFRAME scheme
32d
4.749
d
7.8
a
7.8
a
2.011
3.008
8.8
2.275
0.274
2.107a
25 July 11
9.2a
1.476a
0.134a
1.399a
a
a
a
2.894a
d
13.843b
d
6.779a
18 January 12 Average
9.0
a
4.063
01 June 11 28 September 11
a
2.548
1.711
a
1.804
a
a
9.1
8.6
a
2.69
0.124 1.826 1.571
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Table 5 Correlation matrix among all the under study environmental parameters, italic marked correlations are significant at p
