Assessment of River Water Quality in Northwestern Greece

Water Resources Management (2005) 19: 77Ð94 C Springer 2005

MELINA E. KOTTI, ATHANASIOS G. VLESSIDIS∗, NICHOLAOS C. THANASOULIAS and NICHOLAOS P. EVMIRIDIS Laboratory of Analytical Chemistry, Department of Chemistry, University of Ioannina, 451 10 Ioannina, Greece (∗author for correspondence, e-mail: [email protected]; Fax: +30-2651044831)

(Received: 1 September 2003; in ﬁnal form: 3 May 2004) Abstract. The effect of land use patterns on river water quality was studied in three different river basins located in Epirus, Northwestern Greece. Studies were conducted from October 2000 to September 2001. During this period, the parameters chemical oxygen demand (COD), biological − − + 3− oxygen demand (BOD), NO2 ,NO3 ,NH4 and PO4 were measured, employing standard methods of analysis. The results were subjected to principal component analysis (PCA) for the estimation of the underlying variable correlations and were further explored by means of cluster analysis. The values of the above parameters were also compared with those awkward in the Fresh Water Fisheries Directive (78/659/EEC). It was found that the phosphate content was much higher than the upper limiting criteria for eutrophication for salmonid waters, whereas nitrate levels were lower than the permissible criteria according to the Nitrates Directive 91/676/EEC for drinking water. The inorganic nutrient load was mostly attributed to sites that drain agricultural areas, especially during winter and spring. The organic matter was due to urban activities during autumn.

Keywords: cluster analysis, nutrients, organic matter, principal component analysis, water quality

1. Introduction Deterioration of river water quality is caused by various point and non-point sources (Berankova and Ungerman, 1996; Carpenter et al., 1998). The impact of the point source pollution can be localized and well-established by measurement of the organic matter. High concentrations of organic matter in surface waters can result in depletion of dissolved oxygen with subsequent adverse effects upon human life. The organic matter may occur naturally or may be introduced from municipal and/or industrial effluents (Malcolm, 1985) or runoff as in the case of pesticides. The concentration and quality of natural organic matter vary widely with climate, geographical zone and a number of other environmental factors (e.g., soils). Humic substances generally comprise one-third to one-half (Thurman, 1985) and sometimes more of the dissolved organic matter in many surface waters, and are primarily responsible for the colour of the waters (Aiken, 1985). The dissolved organic matter according to Peverly (1982) was highest in autumn. Other researchers (Eatherall et al., 2000) found that high concentrations of dissolved organic matter in catchments are related 78 M. E. KOTTI ET AL. with sewage point sources during low flows and with diffuse (nonpoint) sources during high flows. River water quality has been classified by Dunette and O’Brien (1992) and Petts and Eduljee (1994) into four classes. Class 1 is regarded as ‘good quality’ water, with a BOD value below 3 mg L−1, suitable for potable supply and game fisheries, and of high amenity value. Class 2 is regarded as ‘fair quality’ water needing improvement and known to receive toxic and turbid discharges. Class 3 is ‘poor quality’ water with a dissolved oxygen saturation below 50% and urgently needing improvement. Class 4 is defined as ‘bad quality’ water, considered to be grossly polluted, having a BOD value of 12 mg L−1or more, unable to support fish life and offensive with respect to odour and appearance. River water usually is of the highest quality in its headwater reaches, becoming dirtier along its length as it passes through different land uses and used for a variety of purposes. River pollution is created by human activities. Pollutants include solids, nutrients (e.g., phosphorous and nitrogen), toxic substances (e.g., heavy metals and pesticides) and other substances (e.g., chloride and salts) that come from a variety of sources. Surface runoff from urban and agricultural areas deteriorates water quality of water bodies and this diffuse pollution is difficult to control. Since various land uses in a river catchment contribute to water pollution, it is important to look at the catchment as a whole when protecting river quality. The pollution of river waters is of most concern of the communities involved and the management of it needs studies based on multivariable systems of the quality parameter data obtained. Such studies were conducted for other river systems in Greece. The application of PCA on data from five river systems in Macedonia, North Greece showed four PCs to account for 67% of the variance in the whole data set (Voutsa et al., 2001). The first PC, accounting for 21.7% of the total variance was correlated primarily with COD, BOD and TON. The second PC accounting for 20.8% of the variance was correlated with soluble nitrogen and phosphorus species. The other two PCs were strongly related with DO and pH and finally with conductivity and TSS. Individually, the three rivers Aliakmon, Loudias and Axios were mostly influenced by nutrients (nitrogen and phosphorus) and the other two Strymon and Gallikos from organic-type components. Pinios river, located in Central Greece, showed (Fytianos et al., 2002) low concentrations of phosphate (below the upper acceptable limit value) and relatively high nitrate concentrations than the rivers under study. The phosphate and nitrate concentrations were higher during winter and spring and relatively low during summer. In this work, water quality is examined in three different river basins for a time interval of one year (from October 2000 to September 2001). The rivers under study were the Kalamas, Louros and Arachthos, which drain the mountainous landscape of the region of Epirus, northwest Greece. All these rivers are also used for irrigation purposes. COD, BOD, nitrite, nitrate, ammonium and orthophosphate are determined at different sampling sites from each river basin. The results were ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 79 subjected to PCA to estimate the correlations between these parameters. Cluster analysis (CA) was also employed to explore the similarity of the samples. The results were, also, compared with maximum permitted values in the Fresh Water Fisheries Directive 78/659/EEC for ammonium, ammonia, nitrite and phosphate set at 1, 0.025, 0.03 and 0.2 mg L−1, respectively, and the Nitrates Directive 91/676/EEC for nitrate set at 50 mg L−1.

2. Methods and Materials

2.1. STUDY AREA DESCRIPTION Epirus is situated in the northwestern part of Greece. It is a mountainous region with an average elevation of 600 m above sea level with a large area of high land over 1500 m. The climate is Mediterranean along the coast and becomes continental inland in the mountainous areas. The geology of Epirus includes mostly karstic limestones. The Epirus Water District is one of the fourteen national water districts that comprise the country’s administrative units for water resources management. Many rivers drain this region of Greece. The most important are rivers Arachthos, Louros and Kalamas. River Arachthos (length = 110 km) is the longest river of Epirus and drains into the closed Amvrakikos gulf. Amvrakikos is a very sensitive area of great ecological interest. It is a habitat for endangered species (especially birds) and is protected by the Ramsar Convention (Directive 70/409/EEC). It has many tributaries and a mean annual discharge of 61 m3 s−1 (Albanis et al., 1995). Its catchment area is about 2009 km2.Acatchment area of 80.4 km2 has mainly agricultural land use. Four sampling sites were chosen, as follows: 1. Site A1 lies near the springs of the river on high grounds. 2. Site A2 is situated near the large hydro-electricity generation station of Arachthos between forested and agricultural areas. 3. Site A3 is in the city of Arta with many orange groves located in this area. 4. Site A4 lies two kilometers upstream of the estuary of the river (Amvrakikos gulf). Louros (length = 75 km) has a catchment area of 983 km2 and a mean annual discharge of 19 m3 s−1.Italso drains into the Amvrakikos gulf. Louros receives treated domestic and industrial efﬂuents and has high recreational value (swim- ming, canoeing, kayaking). Water is taken for drinking water supply, for irrigation (133.6 km2) and industrial uses. Five sampling sites were chosen as, follows: • Site L1A is situated in the spring of Saint George village. • Site L1 is situated near the village of Saint George. Both Site L1A and Site L1 are transitional between forested and agricultural areas. Many ﬁsh farms are located along this part of the river. The use of chemicals to control diseases and waste production contribute to river pollution. 80 M. E. KOTTI ET AL.

• Site L2 and • Site L3 lie near agricultural areas and areas with other activities like pig farms. • Site L4 lies in the estuary of the river in the Amvrakikos gulf. Kalamas (length = 99 km) has a catchment area of 1831 km2 (43.6 km2 irrigated) and a mean annual flow of 54 m3 s−1.Itdrains into the Ionian sea and has fewer tributaries than the other two rivers. Kalamas river is used for irrigation and is famous for its salmon. In 1960 a man made tunnel and ditch was constructed to drain the overflow of the eutrophic lake ‘Pamvotis’ (Romero et al., 2002) to Kalamas. In 1992, a wastewater treatment plant was operated that treated municipal wastewater to a 2nd degree, before its discharge to Kalamas river, through this lake ditch. Agricultural land use covers 43 km2 of its catchment. Cereals like corn and wheat are the principal crops grown. From the five sampling sites that were chosen (K1A, K1, K2, K3 and K4) only Site K3 is upstream of a village wastewater discharge, and is draining more or less virgin mountainous. The river sampling sites of the three river basins are shown in Figure 1. Gauging stations that could provide information on hydrological conditions governing the flow of the three rivers are not available in this area of Epirus. During the sampling period, no rainfall occurred from mid-June to mid-August, and very high temperatures were recorded. Rainy periods were observed especially in September, November, December and January. Detailed precipitation data are not available for each site, but the average rainfall in the area was 86.9 mm for the year 2000, 93.2 mm for the year 2001 and 58.8 mm for the first semester of the year 2002. During the winter, snowfall occurred.

3. Sampling and Analytical Procedures One sample was collected from each sampling site once or twice a month, filling a wide-mouthed container from just under the water surface. The samples were kept in refrigerator at 4◦C and were then analyzed for COD, BOD, nitrite, nitrate, ammonium and orthophosphate (Rump and Krist, 1992; Dean, 1995). COD was determined titrimetrically with FAS (ferrous ammonium sulfate) after oxidation with the dichromate reflux method (% rsd = 12.5, limit of detection/LOD = 6mgL−1). BOD was determined manometrically (% rsd = 12, LOD = 0.6 mg L−1) before and after five days of incubation at 20◦Cindarkness. Ammonium, which is the predominant form of ammonia at the pH of the samples (pH = 6.5Ð 8.5), was determined spectrophotometrically using the Nessler method (% rsd = 10, LOD = 5.5 µgL−1). Nitrite was determined spectrophotometrically after diazotation (% rsd = 2.4, LOD = 1 µgL−1). Nitrate was determined spectrophotometrically after reduction with Cd to nitrite followed by the application of the diazotation method and condensation with salicylate (% rsd = 10, LOD = 8 µg L−1). Orthophosphate was determined spectrophotometrically using the molybde- num blue method (% rsd = 0.6, LOD = 4.5 µgL−1). All results were expressed in mg L−1. ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 81

Figure 1. Map of Greece and magniﬁed prefectures of Epirus (a: Thesprotia, b: Ioannina, c: Preveza, d: Arta) showing the three river basins (Louros, Kalamas and Arachthos) and relevant sampling sites. (: meat processing enterprises, •: dairy enterprises, : wine enterprises, : poultry processing enterprises). 82 M. E. KOTTI ET AL.

The percentage rsd data were obtained by analyzing standard samples or materials whose contents had been determined with standard methods of analysis (elemen- tal analysis). The standard deviation of the standard samples when compared with the standard deviation of the real samples was found to be statistically insigniﬁcant.

4. Multivariate Statistical Procedures The application of multivariate techniques has proved to be a very useful tool for the interpretation of large sets of environmental data. PCA, a pattern recognition technique, has a wide use for identification of pollution sources in air and sediments, butalimited application on water quality data (Reisenhofer et al., 1998; Voutsa et al., 2001). Also, cluster analysis, an unsupervised pattern recognition technique, has been used for the interpretation of environmental data (Jordan et al., 1998; Vidal and Melgar, 2000). In this work, PCA was used mainly as a visualization technique for the projection of data points on a plane, thus observing similarities and differences between samples. Therefore, strict criteria usually employed in this analysis (e.g., Kaiser criterion, scree plot, etc.) were not taken into account in deciding how many components to extract and rotate as only two components were necessary to achieve the aforementioned projection of data points. For each river, the results obtained throughout the sampling period should ideally be checked for normality before being subjected to PCA. However, as each value represented the level of each parameter at a certain point in time, these levels were considered to belong to different populations due to the fluctuation of the values with time. This prevented the application of meaningful normality tests, which are known to work well for large samples only. The results were therefore log10 transformed to ensure normality of the data prior to the PCA (Thanasoulias et al., 2002). Samples from each basin were coded according to the sampling site and the days passed since the initiation of sampling (e.g., sample L1 0 corresponds to site L1 for Louros and − the first day of sampling). The log10 transformed parameters COD, BOD, NO2 , − + 3− NO3 ,NH4 and PO4 were chosen as the dimensions describing the position of each sample in a six-dimensional space. The input matrix was fed into a desktop personal computer running the STATISTICA for Windows v4.3 program by StatSoft, Inc., (Thanasoulias et al., 2003) and the first two principal components were extracted. Component loadings and their statistical significance were calculated after varimax rotation. Cluster analysis was also applied to the transformed data, using complete linkage and Euclidean distances, and the corresponding hierarchical trees were drawn.

5. Results

5.1. LOUROS BASIN COD values (Figure 2) were below 80 mg L−1 with the exception of those on 1/11/00 at sites L1A, L1, L2 and L4, which reached the concentrations of 562.4, 514.9, 514.9 ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 83

− − 3− Figure 2. COD, BOD, NO2 ,NO3 and PO4 concentrations at sites L1A, L1, L2, L3 and L4 in the Louros basin. and 372.3 mg L−1, respectively. Higher values in COD were observed at site L1A. This is probably due to humic substances. The BOD values (Figure 2) were lower than 7 mgL−1 and, during the summer sampling months, concentrations were below limits of detection. Ammonium was detected only in summer (30/6/01) at sites L1A (0.12 mg L−1) and L3 (1.67 mg L−1), probably due to sewage pollution. Nitrite (Figure 2) was detected at every site only on 1/11/00 (the same month when high COD values appeared) at sites L3 (16/10/00) and L2 (22/11/01). Nitrate showed very low values in most samples (Figure 2). Higher nitrate values were observed on 1/11/00 at every sampling site. Phosphate tended to increase in winter and spring (Figure 2). 84 M. E. KOTTI ET AL.

Table I. Component loadings and their statistical signiﬁcance for the Louros, Kalamas and Arachthos rivers

Variable PC1 loading p-value (PC1)PC2 loading p-value (PC2) Louros River BOD 0.7214 <0.001 −0.3634 0.008 COD 0.5623 <0.001 0.0388 0.785 + − − < NH4 0.0794 0.576 0.8351 0.001 − < NO2 0.7663 0.001 0.2074 0.140 − − < NO3 0.2918 0.036 0.5738 0.001 3− − PO4 0.4252 0.002 0.0368 0.796 Eigenvalue 1.7661 1.1347 %explained variance 29.44 18.91 Kalamas River BOD 0.6085 <0.001 0.6181 <0.001 COD −0.2259 0.127 0.6778 <0.001 + NH4 0.4470 0.002 0.1900 0.201 − − < NO2 0.0737 0.623 0.7218 0.001 − < − NO3 0.7693 0.001 0.1211 0.417 3− − < PO4 0.8164 0.001 0.0693 0.643 Eigenvalue 1.9073 1.3954 %explained variance 31.79 23.26 Arachthos River BOD 0.6943 <0.001 0.5878 0.001 COD 0.0105 0.958 0.9458 <0.001 − < NO2 0.6886 0.001 0.0644 0.745 − − NO3 0.4151 0.028 0.1249 0.527 3− < − PO4 0.6237 0.001 0.4068 0.032 Eigenvalue 1.6350 1.3080 %explained variance 32.70 26.16

When PCA was applied to the results, approximately 48% of the total variance + wasexplained by both principal components. With the exception of NH4 , all other transformed variables were found to load signiﬁcantly on PC1 at the p = 0.05 level. Phosphate was the only variable with a negative loading on this component. − ≈ > > 3− > − The absolute loadings followed the order NO2 BOD COD PO4 NO3 + > − > (Table I). The order of absolute signiﬁcant loadings on PC2 was NH4 NO3 BOD, with all three variables exhibiting negative correlation with this component. Inspection of the hierarchical tree for Louros (Figure 3) showed that samples at the beginning of the sampling period (mid and late autumn) demonstrated little resemblance to each other (samples LX 0 and LX 15; X varying according to sampling site). In contrast, LX 31 samples formed a somewhat uniform cluster, and ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 85

Figure 3. Cluster analysis hierarchical tree for the Louros basin. the same was true for most of LX 173 samples. Towards the end of the sampling period, most samples (e.g., LX 207, LX 242) formed a cluster that lay at the other end of the graph. All in all, two main clusters formed: one comprising samples mainly at the end of the sampling period (left side of the tree) and another one containing samples that were collected at the beginning and middle of the sampling period (right side of the tree). This is probably due to pollution sources from ﬁsh farms or weather conditions. This gives evidence of the change of water quality of the river with rainfall levels between springÐsummer and autumnÐwinter.

5.2. KALAMAS BASIN COD concentrations were lower than 40 mg L−1 at all sites during the sampling period, except for site 4 on 22/12/00 (376.9 mg L−1). The BOD concentrations were also lower than 6 mg L−1 (Figure 4), except for 16/10/00 at sites 1 and 3 and on 23/3/01 at all sites that were slightly higher. Ammonium was found high only on 24/7/01 at sites 3 (1.64 mg L−1) and 4 (0.89 mg L−1). Nitrite was high only at site 1A on 31/5/01 (0.32 mg L−1) and on 30/6/01 (0.16 mg L−1). Nitrate was very low in most cases (<4mgL−1), and only a slight increase appeared from 30/6/01 to the end of the sampling period (Figure 4). Phosphate concentrations were also high during the winter and spring months, with maximum values of ca. 12 mg L−1 (Figure 4). PCA explained 55% of the total variance of the data with both principal components. The absolute signiﬁcant loadings on PC1 (p = 0.05) followed the order 86 M. E. KOTTI ET AL.

− − 3− Figure 4. COD, BOD, NO2 ,NO3 and PO4 concentrations at sites K1A, K1, K2, K3 and K4 in the Kalamas basin.

3− > − > > + 3− PO4 NO3 BOD NH4 , with PO4 exhibiting a negative correlation with − > ≈ this component (Table I). In the case of PC2 the order was NO2 COD BOD. All three variables had a positive correlation with the second component. The homogeneity of Kalamas water along its length was found rather good from data obtained during certain periods of sampling (Figure 5, samples KX 272, KX 346, KX 207). Samples taken at the beginning and at the end of the sampling period formed a large cluster (right side of the graph, samples KX 0, KX 15, KX 82, KX 296, KX 346 corresponding to mid and late autumn, early winter, mid-summer and ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 87

Figure 5. Cluster analysis hierarchical tree for the Kalamas basin. early autumn. Mid-period samples formed a smaller cluster that exhibited different chemical composition from the other samples due to increased phosphate content and in some cases BOD-parameter (left side of the graph, samples KX 173, KX 207, KX 242 corresponding to spring months. This comes from the smaller values of COD parameter found during spring season, which was the result of either the increased rainfall and/or lower pollution from slaughtering houses in the area.

5.3. ARACHTHOS BASIN COD values were very low (below 60 mg L−1). Only on 1/10/00 at sites 3 and 4 and especially on 1/11/00 at the same sites, were the values extremely high (Figure 6). At those sites, which drain agricultural areas, and at that period of time, the producers had ploughed the fields. For the same months, the nitrite values were high (Figure 6). Most BOD concentrations were lower than 4 mg L−1 except on 22/1/01 (Figure 6). Neither ammonium nor nitrate was detected in Arachthos waters. Phosphate was found high especially during the winter and did not vary along the river length (Figure 6). When PCA was applied to the log10 transformed data, 59% of the total variance + wasexplained by the first two principal components (PC1 and PC2). The NH4 variable was not taken into account since no variance was observed for it. The order of absolute loadings on PC1 for the statistically significant variables (p = ≈ − > 3− > − 0.05) was BOD NO2 PO4 NO3 (Table I). The last variable was the only one > > 3− that correlated negatively with this component. The order COD BOD PO4 88 M. E. KOTTI ET AL.

− 3− Figure 6. COD, BOD, NO2 and PO4 concentrations at sites A1, A2, A3 and A4 in the Arachthos basin.

was followed by the absolute signiﬁcant loadings on PC2. Phosphates correlated negatively with this component. From the hierarchical tree (Figure 7) it was evident that in most cases a reasonable homogeneity of water composition was observed along the river length throughout the sampling period (e.g., samples AX 0, AX 15, AX 113).

6. Discussion The traditional methods of obtaining information on the bulk organic matter of water are chemical oxygen demand (COD) and biological oxygen demand (BOD). COD is a measure of biological oxygen demand and the oxygen demand of decomposable ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 89

Figure 7. Cluster analysis hierarchical tree for the Arachthos basin. organic and inorganic compounds. BOD is a measure of the amount of oxygen for the decomposition of both organic and inorganic substances (e.g., iron (II), sulfite). BOD may also measure the oxygen consumption by reduced forms of nitrogen (e.g., nitrite). Humic substances cannot be biodegraded in five days and therefore their influence is not included in this parameter. In cases that COD appears high and BOD values are low, the organic compounds do not biodegrade or heavy metals exist that destroy the microorganisms. On the other hand, non-point pollution, cannot be controlled easily because it is diffuse, it comes from a variety of sources and varies significantly with time due to effects of weather. This type of pollution is mainly estimated by high concentration of nutrients. Of course this is not strict, because point sources may also provide rivers with high concentrations of nutrients. Nutrients are forms of biologically available N and P, known to play a key role in determining the ecological status of aquatic systems. The forms that are of greater interest in waters are the inorganic ones: ammonium, nitrate, nitrite, for N and orthophosphate for P. Also, it has been suggested that the ratio N to P influences the types of organism, which occur and whether or not individual species or whole communities are likely to be N- or P-limited. High levels of nitrate in surface waters, indicate pollution seeping in from sep- tic tanks, livestock wastes, fertilizers, municipal landfills and nonpoint sources of pollution, such as runoff from agricultural areas. In addition to these sources, at- mospheric deposition has become a major source of nitrate in surface waters in recent years (Smith et al., 1987). Nitrite arises, usually, from anthropogenic activities (e.g., it is used as a corrosion inhibitor in industrial process waters). Nitrite is 90 M. E. KOTTI ET AL. formed at an intermediate stage in the microbiological decomposition of nitrogen- containing compounds. Nitrate and nitrite are destroyed or generated by reactions during storms (Verhoff et al., 1982). According to Peverly (1982) levels of nitrate/nitrite are the highest during autumn and winter flows, (periods of high flow) and the lowest during the summer (period of low flow). Other researchers found that nitrate concentrations increase with increasing flow and the main changes occur at low to intermediate flow (Neal et al., 2000a, 2000b). This can be explained because nitrate is not absorbed by soils, so it is washed from the land during storm events. Ammonium is a product of microbiological decay of plant and animal protein and is applied directly to land as fertilizer. The presence of ammonia in surface waters usually reflects recent sewage pollution and is widely used to characterize water quality, since it is toxic to fish, and at high levels threatens aquatic life. Generally, + NH4 -N is less than 10% of the total inorganic N, and, during the summer, it appears to have low concentrations (Peverly, 1982). In cases that the fertilizer (NH4)2SO4, is applied to croplands, high levels of NH+-N are detected and low levels of NO−-N − 4 3 and NO2 -N (Hirose and Kuramoto, 1981). The entry of P into a river may be classified, as either a runoff, point source, (surface, subsurface, groundwater) or as direct wet or dry deposition (Tiessen, 1988). Also, the leaching and weathering of igneous and sedimentary rocks such as calcium hydroxyapatite, fluorapatite, strengite, whitlochite and berlinite is another source of P (Robards et al., 1994). Point sources of P such as sewage effluents are of greater importance during periods of low flow and in dry years. In wetter years and during storm events diffuse inputs are the dominant nutrient source. The total P concentration increases with increasing river flow rate during storms (Verhoff et al., 1982) and at downstream river stations it comes from runoff water from the land area of the catchment. According to Keup (1968), T-P comes from the river bottom, banks and flood plains during storm events. A high correlation of orthophosphate with the area of settlement due to municipal sewage effluents has been found (Hirose et al., 1981). Studies (Glandon et al., 1981) that were conducted for a watershed on the storm-related nutrient loading from urban, wetland and agricultural sources showed that rain related discharge was 0.578 kg ha−1 for total P and 3.688 kg ha−1 for total N (urban areas), 0.180 kg ha−1 for total P and 5.965 kg ha−1 for total N (agricultural land) and 0.023 kg ha−1 for total P and 0.585 kg ha−1 for total N (marsh). According to Dorioz and Ferhi (1994), one half of total P was transferred as dissolved P and 73% of total N as nitrate in an agricultural watershed. Pwas essentially transferred during storm flows, while N losses depended upon the season. Upstream pollutant sources from the three river systems studied in this work are given in Table II. According to our study, most COD extremes were observed in the autumn. This is due to certain circumstances: at sites A3 and A4 (Arachthos), the values were extremely high because untreated effluents from a fruit juice company were discharged into the river. At site K4 (Kalamas), COD was high since this ASSESSMENT OF RIVER WATER QUALITY IN NORTHWESTERN GREECE 91

Table II. Description of the sampling sites of the three river systems

Site Upstream pollution sources Louros River L1A Natural springs L1 Trout farms, along the national road L2 Cereal crops, pig farms, slaughterhouses, dairies L3 Tributary, pig farms, slaughterhouses L4 Meat industry, estuary Kalamas River K1A Agricultural land, cereal crops K1 K2 Effluents from industrial zone activities (slaughterhouses) and biological treatment plant of Ioannina municipality K3 Sewage discharges from a village K4 Agricultural land Arachthos River A1 Springs, mountainous area A2 Hydroelectric dam A3 Orange crops, fruit juice company A4 Orange crops, estuary site is downstream from wastewater discharges and also receives runoff water from agricultural areas. BOD concentrations were found to be low even in samples with high COD values, thus indicating the presence of organic compounds difficult to biodegrade. BOD values did not reach 12 mg L−1,sothe quality of the three rivers was classified as ‘fair’ to ‘poor’. As far as the inorganic load is concerned, ammonium was detected only in specific cases probably due to sewage contamination. Ammonium was detected mostly in summer months (low flow periods). Nitrate concentrations were low at all sampling sites, sometimes falling below detection limits. Nitrite was relatively high at the sites that showed COD extremes. Generally, the sum of nitrite and nitrate was found to be below the permissible criteria in the Drinking Water Directive 80/778/EEC. Generally, orthophosphate were found above the permissible levels mentioned in the Fresh Water Fisheries Directive 78/659/EEC. Highest values were observed for the three rivers during the winter and spring months. This is expectable because this is fertilization period. The application of PCA has allowed an analysis of the possible correlations between the parameters measured. Cluster analysis helped to realize the relation- ship between sampling sites and sampling time as far as the homogeneity of water 92 M. E. KOTTI ET AL. composition is concerned. For Louros, the patterns observed suggest that the composition of water varied greatly along the river length during mid and late autumn probably because of different activities performed near the river banks at that time of the year. A more uniform water composition was observed during the low activity winter months. It is worth mentioning that LX 346 samples (early autumn) showed a scattered pattern similar to that of LX 0 and LX 15 samples. This might suggest that water uniformity along the river length is a periodic phenomenon that depends on activities performed near the river banks during the year. Similar observations were made for Kalamas, but in the case of Arachthos, a consistent pattern of homogeneity was not observed as far as different sampling times were concerned. For example, samples AX 0 were more similar to AX 82 and AX 346 samples than to AX 15 and AX 31 samples.

7. Conclusions From the studies conducted, the water quality of the three rivers is classified between the two intermediate classes ‘fair’ and ‘poor’. The nitrate and nitrite concentrations −1 − were found below the levels established for potable water (50 mg L NO3 ). Phosphate concentrations exceeded eutrophication trigger levels. Excessively high levels in COD were determined in some cases. The BOD values were low, showing the presence of organic compounds difficult to biodegrade (e.g., lignin, cellulose). According to the Bathing Water Directive 76/160/EEC, the rivers are considered as sensitive areas that are subject to eutrophication. Phosphate concentrations were significantly higher during winter and spring due to rainfall and the melting of snow. From the three rivers, the most polluted is the Louros (higher COD values). This is expected since many agricultural and other activities take place along this river. Special care should be taken because water is abstracted from Louros for potable use. Kalamas is at risk along its entire length and Arachthos at the sites that drain the plains of Arta.

Acknowledgments This study is part of the project ‘Development of a network for the quality control of surface waters and groundwater in Epirus’, supported by the Centre for Hydrobiological Research of the University of Ioannina, Greece.

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