Chemical Analysis and Evaluation of Fe and Ni Spatio-temporal Variations along Tundzha River Southeastern Downstream,

Nedyalka Georgieva*1, Zvezdelina Yaneva1 Chemistry Unit, Department of Pharmacology, Animal Physiology and Physiological Chemistry, Faculty of Veterinary Medicine, Trakia University, Students Campus, 6000 , Bulgaria E-mail: [email protected]; [email protected] *Corresponding author: Assoc. Prof. N. V. Georgieva PhD, Tel.: (+359) 42 699 642; Fax: (+359) 42 672 009; Е-mail: [email protected]

Abstract The urge for sustainable development of natural water resources, especially in the developing countires and these with economies in transition implements the establishment and/or further development of national monitoring networks and water resources databases, as well as the development of relevant national indicators. In this context, the present study evaluated the current status of Fe and Ni levels, their origin and spatio-temporal distribution in the surface waters from the southeastern downstream of Tundzha River, Bulgaria, including five of its tributaries in Stara Zagora Region. To accomplish the latter goal, Ni concentrations in real surface water samples were determined by atomic absorption spectrophotometry (AAS) and Fe contents - spectrophotometrically, during the period December 2009 - November 2010. The statistical significance of the experimental data was tested. The ecological monitoring revealed sharp increases of Ni concentrations, surpassing the Ist category standard for surface water quality (0.05 mg/L Ni) from 3 to183 times, in the six studied rivers during July, 2010. Tundzha River and Radova River characterized with the maximum Ni contents predominantly during the spring and summer months. Accidental industrial wastewater discharge from the machine-building and textile enterprises in Municipality could be the probable reason for the registered elevated Fe contents (from 0.24 to 2.30 mg/L) in Leshnitza River and Tundzha River surpassing the IInd category standard for surface water quality (1.50 mg/L Fe) with approximately 1.5 times during April, 2010. The high measured Fe concentrations in the samples from Radova River (1.20 mg/L) and Lazova River (1.70 mg/L), Gurkovo Municipality, during May 2010, could be attributed to reverse infiltration of Fe-enriched mine leachate from the nearby situated abandoned mines. The significance of the research was expressed by the fact that it provided a database necessary for the development of pollution control strategies on national and international basis considering that Tundzha River is the biggest tributary of Maritza River, emptying into it on Turkish territory near . Keywords: Fe, Ni, monitoring, rivers, Bulgaria

Introduction Heavy metals are a serious pollutant of the environment due to their toxicity, persistence and bioaccumulation ability. Their occurrence in waters and biota indicate the presence of natural or anthropogenic sources. Trace metals tend to be trapped in the aquatic environment and accumulate in sediments and may be (i) directly available to benthic fauna or (ii) released to the water column through sediment re-suspension, adsorption/desorption reactions, reduction/oxidation reactions and the degradation of organisms (Pekey et al., 2004). Industrial and household heavy metals-rich effluents discharged, directly or indirectly, through leakages in the sewage systems and consequently into natural water sources cause excessive pollution of surface and groundwaters. Consequently, water quality and irrigation value are lost. It is necessary to know the mechanisms of the heavy metals transportation and their complexes in rivers to understand their chemical cycles in nature. In natural

BALWOIS 2012 - Ohrid, Republic of Macedonia - 27 May, 2 June 2012 1 media, trace metals undergo numerous changes during their transport due to dissolution, precipitation and sorption phenomena. Trace element concentrations of river basins depend on not only industrial and household waste inputs but also on the geochemical composition of the area (Akcay et al., 2003). Some trace metals like iron (Fe) and copper (Cu) are important both as nutrients and potential toxicants for algal growth in natural water bodies. It is not yet clearly established if the relatively high dissolved Fe concentrations (< 0.45 µm) observed in many organic rich freshwater systems are attributed to Fe-organic (humic) complexes and/or small Fe-oxyhydroxide particles. Recently, both types of colloids, Fe-oxyhydroxides and Fe-C colloidal matter, have been identified as important carriers of dissolved Fe in boreal river systems (Ingri et al., 2006; Benedetti et al., 2003). The direct and indirect effects of Fe on the structure and function of lotic ecosystems have been reviewed. In addition to the mining of Fe-enriched ores, intensified forestry, peat production, and agricultural water runoff have increased the load of iron in many river ecosystems. The effects of iron on aquatic animals and their habitats are mainly indirect, although the direct toxic effects of Fe2+ are also important in some lotic habitats that receive Fe-enriched effluents particularly during cold seasons. Ferric hydroxide and Fe-humus precipitates, on both biological and other surfaces, indirectly affect lotic organisms by disturbing the normal metabolism and osmoregulation, and by changing the structure and quality of benthic habitats and food resources. The combined direct and indirect effects of Fe contamination decrease the species diversity and abundance of periphyton, benthic invertebrates, and fishes (Eugen et al., 2003). The bioaccumulation of Fe in the organs and tissues of the freshwater crab, Potamonautes warreni Calman, from three metal-polluted aquatic ecosystems was examined by Steenkamp et al. (1993). Differences in Fe concentrations in water and sediment were related to environmental variables. The highest Fe concentrations in the crab occurred in the gills, suggesting this organ to be the prime site for the absorption and/or loss of Fe to and from the aquatic environment. Despite the absence of a seasonal or gender-related tendency in the heavy metal concentrations in the various organs and tissues, an inverse relationship between the size and the capacity of the crab to bioaccumulate Fe was established (Steenkamp et al., 1993). In addition, Fe is vital for almost all living organisms, participating in a wide variety of metabolic processes, including oxygen transport, DNA synthesis, and electron transport. Iron concentrations in body tissues must be tightly regulated because excessive Fe leads to tissue damage, as a result of the formation of free radicals. Disorders of Fe metabolism are among the most common diseases of humans, encompassing a broad spectrum of diseases with diverse clinical manifestations ranging from anemia to iron overload and, possibly, to neurodegenerative conditions (Eugen et al., 2003). Iron is found in surface and ground waters at varying concentration levels, usually up to 3 - 4 mg/L, but in some cases even up to 15 mg/L. When present, even at low concentrations it can be linked to aesthetic and operational problems such as bad taste and color, staining, as well as deposition in the water distribution system leading to incidence of high turbidity. Besides, Fe promotes the growth of certain types of chlorine-tolerant microorganisms in water distribution systems, thus causing increased costs for cleaning and sterilizing systems in addition to odor and taste problems. The state of iron in water depends above all on the pH and the redox potential. By increasing the pH, dissolved iron (Fe2+ or Fe3+) hydrolyzes to form precipitates. The ferrous ion hydrolyzes to produce the array of + 2- 3+ mononuclear species FeOH to Fe(OH)4 between pH 7 and 14. The ferric ion (Fe ) hydrolyzes much more readily than the ferrous ion, beginning at about pH 1. There are several genera of bacteria that oxidize dissolved iron with different mechanisms, including Gallionella sp., Leptothrix ochracea, Crenothrix polyspora, Leptothrix sp., Crenothrix sp., Clonothrix sp., Sphaerotilus sp., Toxothrix thrichogenes and Siderocapsaceae (Tekerlekopoulou et al., 2006). Nickel (Ni) is a non-biodegradable toxic heavy metal. According to the World Health Organization guidelines (WHO), the maximum permissible concentration of Ni in drinking water should be less than 0.1 mg/L (WHO, 2004). It has been reported that Ni is ubiquitous in the biosphere and is a common component of natural fresh waters due to erosion and weathering. The high consumption of nickel- containing products inevitably leads to environmental pollution by Ni and its by-products at all stages of production, recycling and disposal. It has also been reported that fresh water levels of Ni are typically about 1–10 µg/L in unimpacted areas and anthropogenic loading of Ni into aquatic and terrestrial ecosystems in industrial areas occurs by mining, smelting, refining, alloy processing, scrap metal reprocessing, fossil fuel combustion and waste incineration (Kandah et al., 2007). Ni concentrations in highly contaminated fresh waters may reach as high as several hundred to 1000 µg/L.

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It is known that exposure to nickel compounds can have adverse effects on human health (Can et al., 2006). Nickel allergy in the form of contact dermatitis is the most common and well-known reaction. Although the accumulation of the heavy metal in the body through chronic exposure can lead to lung fibrosis, cardiovascular and kidney diseases, the most serious concerns relate to its carcinogenic activity (Garg et al., 2008). Epidemiological studies have clearly implicated Ni compounds as human carcinogens based upon a higher incidence of lung and nasal cancer among nickel mining, smelting and refinery workers. Additionally, insoluble nickel compounds like nickel subsulfide (Ni3S2) efficiently transform rodent and human cells in vitro. Based on these observations the International Agency for Research on Cancer (IARC) evaluated the carcinogenicity of nickel in 1990 (IARC, 1990). All nickel compounds except for metallic nickel were classified as carcinogenic to humans. Direct leaching from rocks and sediments can produce significant concentrations of nickel in water where it is present in dissolved forms as well as suspended insoluble particles. Nickel concentration in deep-sea water usually range from 0.1 to 0.5 ppb Ni, whereas surface water contains 15 - 20 ppb Ni. Divalent nickel is the predominate form of nickel in aquatic sources (Denkhausa et al., 2002). In Europe, Ni is considered a priority substance in the Water Framework Directive 2000/60/EC, implying that environmental quality standards are required for the whole European Union (European Commission, 2000). In this context, the present study evaluated the current status of Fe and Ni levels, their origin and spatio-temporal distribution in the surface waters from the southeastern downstream of Tundzha River, Bulgaria, including five of its tributaries flowing through Stara Zagora Region. Case study description The catchment area of Tundza River (Fig. 1) is determined by the GPS coordinates N41°55' E24°55' - N42°45' E27°00'. It is the largest left tributary of River, which flows into it on Turkish territory near Edirne. Due to the large size of its catchment area (7884 km2) and to the fact that it flows as a single river to the national boundary, Tundzha River is considered as a separate valley. The number of tributaries mouthing into it is about 50. Tundzha River basin within the territory of Bulgaria stretches from Botev peak (Yumrukchal) in the Central to the state boundary with . Two large dams - Koprinka (Kazanlak Municipality) and Zhrebchevo (Gurkovo Municipality), were built along the river. In the region before Buzovgrad Village the valley narrows considerably: from 0.5 km before Koprinka Reservoir to several tens of meters, and then widens again up to 1 km before Kazanlak City. The river bed near Koprinka Reservoir is 28-30 m wide, and the flow rate - 1 m/s. The river bottom is composed mainly of sand and coarse gravel. From Buzovgrad Village Tundzha River valley broadens up to to 5-7 km. The transverse profile remains trapezoidal.

Figure 1 Catchment areas of Maritsa and Tundzha Rivers (---- southeastern downstream of Tundzha River) (Roelevink et al., 2010).

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The present research is a part of a large scale project subjected to assessment, reduction and prevention of air, water and soil pollution in Stara Zagora Region. In this context, considering that Tundzha River is one of the major water resources for drinking, domestic purposes and irrigation in Stara Zagora Region, the analyses of the experimental database related to heavy metals content including evaluation of Tundzha River quality, as well as assessment of the influence exerted by its tributaries flowing through the studied area, is essential and imperative.

Methodology applied Sample collection and preservation A total of 59 surface water samples were collected from 6 rivers situated in three major municipalities: Stara Zagora, Kazanlak, and Gurkovo, Stara Zagora Region, Bulgaria (Table 1). Global positioning system (GPS) was used to determine the coordinates of the sampling points. Table 1 List of the sampling points along the downstream of Tundzha River and its tributaries.

Municipality Sampling Point (s.p.) Code GPS coordinates Stara Zagora Tundzha River (bridge Yagoda Village) 211 N 42.327400 E 25.338050 Kazanlak Tundzha River (before Koprinka Reservoir) 206 N 42.412150 E 25.188630 Gabrovnitsa River (Dolno Sahrane Village) 207 N 42.389830 E 25.165540 Leshnitsa River (after Village) 208 N 42.389600 E 25.164790 Eninska River (after Enina Village) 209 N 42.400040 E 25.245380 Tundzha River (after Buzovgrad Village) 210 N 42.344840 E 25.249200 Gurkovo Radova River 212 N 42.393920 E 25.463810 Lazova River 213 N 42.417090 E 25.470400 Tundzha River (Zhrebchevo Reservoir mouth) 214 N 42.383330 E 25.493500

All water samples were collected and preserved according to the standard methods for the examination of water and wastewater (American Public Health Association, 2008). The concentrations of Fe were determined on UV/VIS Spectrophotometer DR 5000 (Hach Lange, Germany) using FerroVer Iron Reagent Powder Pillows (0.02 - 3.00 mg/L). Before analysis, the pH of the samples was adjusted to 3 - 5 with 5.0 N NaOH standard solution. FerroVer Iron Reagent converts all soluble iron and most insoluble forms of iron in the sample to soluble ferrous iron. The ferrous iron reacts with the 1,10-phenanthroline indicator in the reagent to form an orange color in proportion to the iron concentration. Test results are measured at λ 510 nm. The concentrations of Ni were determined by atomic absorption spectrometry (AAS) ISO 8288 on AAnalyst 800 (Perkin Elmer) Atomic Absorption Spectrometer. All investigations were performed in triplicate. The ecological assessment of the rivers water quality was done on the basis of national standards for surface water quality (Regulation No. 7/8.08.1986); surface water intended for drinking, water abstraction and household supply (Regulation No. 12/18.06.2002), and water intended for irrigation (Regulation No. 18/27.05.2009) (Table 2).

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Table 2 National surface water quality standards according to indices Fe and Ni (Regulation No.7/8.08.1986; Regulation No.12/18.06.2002; Regulation No.18/27.05.2009).

Index Fe, mg/L Ni, mg/L Surface water I category 0.50 0.05 II category 1.50 0.20 III category 5.00 0.50 Water intended for irrigation Allowable Limit Concentration (ALC) 5.00 0.20 Surface water intended for drinking water abstraction and household supply 0.10 (G)* Category A1 0.02 (G)* 0.30 (M)* 1.00 (G)* Category A2 - 2.00 (M)* Category A3 1.00 (G)* -

*If there is no danger to public health, deviations are allowed in the case of surface water from lakes or shallow non-flowing water bodies with depth of 20 m, with less than one year proven period of water exchange and in which no wastewater is discharged; G - guide requirements; M - mandatory requirements.

Statistical analyses The statistical significance of the results was tested on the basis of the Standard Deviation (SD) values calculated by the Student’s t-test. The application of different multivariate statistical techniques allows the identification of the possible sources that influence water systems and offer a valuable tool for reliable management of water resources as well as rapid solution to pollution problems (Kazi et al., 2009; Wang et al., 2011). In this paper, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to the experimental data to assess relationship between variables and possible distribution patterns. The multivariate statistical analyses were carried out by XLStat Pro.

Results obtained Spatio-temporal variations of Fe content The experimentally determined average monthly Fe concentrations in the surface waters of Tundzha River and its five feeders are presented in Figure 2.

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Figure 2 Average monthly Fe concentrations in the surface waters of Tundzha River along its southeastern downstream and its tributaries during the period December 2009 - November 2010. It was established that Fe content in Tundzha River after Buzovgrad Village (s.p. 210) was 1.5 times above the IInd category quality standard for surface water (1.5 mg/L) and surpassed the category A2 mandatory limit for surface water intended for drinking water abstraction and household supply (2.00 mg/L) with 15 % in April 2010. Consequently, to transform these waters for drinking purposes they should be subjected to proper treatment methods - normal physical treatment, chemical treatment and disinfection, e.g. pre-chlorination, coagulation, flocculation, decantation, filtration, disinfection (final chlorination). The observed sharp increase in Fe content during the spring could be attributed to accidental discharge of industrial wastewaters from the machine-building and textile enterprises situated in Kazanlak Municipality. In addition, the surface waters of Tundzha River tributaries in Gurkovo Municipality, namely s.p. 212 and 213, also characterized with high Fe contents: the average monthly concentration in s.p. 212 was 1.20 mg/L in May 2010, while Fe content in s.p. 213 was 1.70 mg/L in April 2010, thus surpassing the II category limit (1.5 mg/L) with 13 %. Considering that a few abandoned mines are situated in the southern part of this municipality, it could be assumed that the high groundwaters in spring provoked intensive inverse penetration of Fe-enriched mining infiltrates upwards the water layers, thus serving as a probable source of the heavy metal in these natural surface waters. According to the investigations of Inaba et al. (1997), the amounts of Fe supplied from rivers could be affected by stream conditions, such as flow, flux and contamination by suspended matter, and those from sediments will depend on the reduction-oxidation potential. Once supplied to the main river, Fe could form hydrolyzed polymer species - reactions controlled by chemical conditions such as oxidation- reduction potential and pH. Mechanical forces such as stirring and lifting lake sediments by wind or rainfall would also affect the supply and the oxidation of iron species, and are probably another reason for the variations (Inaba et al., 1997). The comparative analyses of the experimental data proved that the surface waters of s.p. 207, 206 and 214 characterized with the lowest Fe contents during the entire monitoring period, thus corresponding to the national quality requirements for Ist category surface water (0.5 mg/L), the ACL for water intended for irrigation (5.00 mg/L), as well as to the mandatory category A1 limit for drinking water abstraction and household supply (0.30 mg/L) (Figure 2, Table 2).

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Spatio-temporal variations of Ni content The experimental data for Ni contents in the investigated natural flowing surface water bodies during the period December 2009 - November 2010 is presented in Figure 3.

Figure 3 Average monthly Ni concentrations in the surface waters of Tundzha River along its southeastern downstream and its tributaries during the period December 2009 - November 2010. According to the determined in the present study average monthly Ni concentrations, it was observed that the heavy metal levels in all analyzed water bodies in November 2010 were the lowest and below the Ist category standard for surface water quality (0.05 mg/L). However, the guide category A1 standard for surface water intended for drinking water abstraction and household supply (0.02 mg/L) was surpassed in 86 % of the surface water samples examined. The analyses of the experimental results outlined s.p. 210, 211 and 212 (Table 1) as the surface water bodies with the highest Ni loading - 0.148 mg/L, 0.144 mg/L and 0.183 mg/L, respectively, in May (s.p. 210) and July (s.p. 211, 212) 2010. Considering that s.p. 210 and 211 are located in the part of Tundzha River flowing in close proximity to the industrial area of Kazanlak City, the possibility of direct and uncontrolled discharge of industrial effluents into the river from the machine-building, textile and plastic processing plants. Although s.p. 207, 206, 208, 212 and 213 characterize with remoteness from large industrial regions and cities, the registered comparatively high contents of the heavy metal could be due to agricultural activities such as the excessive application of Ni-containing phosphate fertilizers. Nickel compounds may also be found in sludge, and in slags and fly ashes from waste incinerators. Besides, the mode of Ni concentration curves characterizing Ni spatial distribution in the surface waters of Tundzha River and its five feeders in June and July 2010 displayed nearly constantly increased heavy metal concentrations in all investigated sampling points during the entire monitoring period (Figure 3). The observed general tendency of elevated Ni contents predominantly during May, June, July and partially September in most of the rivers investigated could be attributed to the specific meteorological conditions: sharp temperature increase, decreased rainfall and intensified evapouration.

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In general, related to index Ni the flowing surface waters of Tundzha River and its feeders could be applied for irrigation of the surrounding agricultural lands, however they are not suitable for drinking water abstraction and household supply. Statistical Analyses To establish a comprehensive quality assessment of Tundzha River and its five ajacent feeders surface waters related to indices Fe and Ni during the monitoring period the experimental data was also analyzed from the viewpoint of Fe and Ni average annual (C*), minimum (Cmin) and maximum (Cmax) concentrations presented in Table 3. The SD values for all investigated sampling points were also calculated and displayed in Table 3. The range of their variation, from 0.001 to 0.06, proved the statistical significance of the experimental results.

Table 3 Average annual (C*), minimum (Cmin) and maximum (Cmax) Fe and Ni concentrations in the river waters and SD values. Index Fe Ni

s.p. n C*, mg/L SD Cmax, mg/L Cmin, mg/L n C*, mg/L SD Cmax, mg/L Cmin, mg/L 207 4 0.03 0.01 0.05 0.001 4 0.08 0.02 0.12 0.01 206 4 0.05 0.02 0.09 0.01 4 0.09 0.03 0.13 0.01 208 8 0.08 0.03 0.24 0.02 8 0.06 0.01 0.11 0.01 209 8 0.24 0.16 0.12 0.01 8 0.06 0.01 0.11 0.03 210 8 0.35 0.28 2.30 0.02 8 0.06 0.01 0.148 0.02 211 7 0.12 0.03 0.27 0.06 7 0.08 0.02 0.144 0.004 213 8 0.26 0.21 1.70 0.02 8 0.07 0.01 0.12 0.004 212 8 0.22 0.14 1.20 0.03 8 0.10 0.02 0.18 0.04 214 4 0.11 0.03 0.18 0.06 4 0.06 0.02 0.09 0.002 n - number of samples examined

• Principal component analysis (PCA) PCA of the surface water quality data, based on the average annual concentrations, developed two principle components explaining the total variability. However, only the eigenvalue of F1 was greater than 1 (1.272), which accounted for 63.598 % of the total variability. The scatterplot based on the case-wise F1 and F2 factor scores for the surface water sampling points is presented in Figure 4. Obviously, the surface water bodies could be classified into three major groups. Sampling points 212 did not correspond to any of them, as its waters characterized with the maximum measured heavy metals concentrations.

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Figure 4 Scatterplot of the PCA case-wise factor scores for the surface water samples. • Hierarchical cluster analysis (HCA) HCA was performed to identify the analogous behavior between the different sampling stations or between the measured variables. It is an unsupervised classification procedure that involves measuring either the distance or the similarity between the objects to be clustered. The resulting clusters of objects should then exhibit high internal (within cluster) homogeneity and high external (between clusters) heterogeneity (Kazi et al. 2009). The information of HCA results reflected in a two- dimensional plot called dendrogram which can be used to provide information on chemical behavior and verify the results obtained by PCA (Wang et al. 2011). To understand the relationship among sampling points and the municipalities they are situated in, in this study HCA was performed for station-wise average annual values by means of the Ward’s method, using squared Euclidean distances as a measure of similarity. The results obtained with the HCA analysis regarding surface water quality were displayed in the dendrogram presented in Figure 5.

Figure 5 Dendrogram of the dissimilarity clusters formed by the surface water samples taken from various sampling points along the downstream of Tundzha River.

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According to the dendrogram the examined surface water bodies were divided into four clusters. The first one included s.p. 206, 207 and 211. The waters from these natural bodies characterized with comparatively high Ni content. The second cluster, comprising of s.p. 212 was featured with the highest complex Fe/Ni loading attributed as to intensive agricultural activities so to the influence of infiltrates from the nearby abundant mines. Cluster 3 embedding s.p. 209, 210 and 213 covered surface water bodies with elevated Fe contents and the most pronounced anthropogenic impact. Direct discharge of industrial wastewater effluents and domestic sewage from the nearby cities (Kazanlak, Gurkovo) could be outlined as the common pollution sources for this cluster. Cluster 4 included s.p. 208 and 214 and characterizing with relatively the lowest complex Fe/Ni loading of the flowing surface waters.

Conclusions The conducted chemical analyses and assessment of Tundzha River and its ajacent feeders (Stara Zagora Region) surface water quality revealed that: • Tundzha River after Buzovgrad Village (Kazanlak Municipality) and Lazova River (Gurkovo Municipality) characterized with the highest average monthly Fe concentrations (2.30 mg/L and 1.70 mg/L, respectively) measured in spring, surpassing the IInd category standard for surface water quality (1.50 mg/L), category A1 (0.30 mg/L) and A2 (2.00 mg/L) mandatory limits for surface water intended for drinking water abstraction and household supply. • Related to Ni, the guide category A1 standard for surface water intended for drinking water abstraction and household supply (0.02 mg/L) was surpassed in 86 % of the surface water samples examined. The experimental data outlined Tundzha River after Buzovgrad Village and at Yagoda Village bridge, as well as Radova River as the surface water bodies with the highest Ni loading - 0.148 mg/L, 0.144 mg/L and 0.183 mg/L, respectively, in May and July 2010. • The PCA and HCA delineated four clusters featured individually with: increased Ni content; highest complex Fe/Ni loading attributed both to intensive agricultural activities and to the influence of infiltrates from abundant mines; elevated Fe contents and pronounced anthropogenic impact - direct discharge of industrial wastewater effluents and domestic sewage from the nearby cities (Kazanlak, Gurkovo); lowest complex heavy metals loading.

Recommendations and future perspectives • The generated experimental data base could be used to form an integrated Fe/Ni management strategy of the surface water bodies investigated and of the adjacent agricultural lands, as recommended by the most up to date trends of environmental research in the field of agricultural land use planning aimed at water pollution control. • Although the experimental data presented in this paper allowed some significant conclusions to be drawn, they are still scarce. Therefore, further monitoring is necessary, to corroborate the results presented, to confirm the hypotheses made and the trends envisaged.

Acknowledgement This work was supported financially by the Norwegian Collaboration Program, NORWAY GRANTS, Project: “Assessment, reduction and prevention of air, water and soil pollution in Stara Zagora Region” Ref. No. 2008/115236, Trakia University, AF.

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