Assessment of seasonal and spatial variations of ...

Environ Monit Assess (2017) 189: 585 https://doi.org/10.1007/s10661-017-6309-4

Assessment of seasonal and spatial variations of physicochemical parameters and trace elements along a heavily polluted effluent-dominated stream Gülşah Tulger Kara & Melik Kara & Abdurrahman Bayram & Orhan Gündüz

Received: 24 July 2017 / Accepted: 12 October 2017 / Published online: 28 October 2017 # Springer International Publishing AG 2017

Abstract This study focuses on a heavily polluted effluent-dominated stream that passes through an industrialized region near Izmir, Turkey. The intermittent creek receives domestic and industrial discharges of Kemalpaşa District Center and its neighborhoods and more than 180 factories of the organized industrial zone. A monitoring campaign was conducted on the creek and samples were taken in two different seasons with distinct hydrological characteristics from 20 stations along the creek to quantify the quality status of water and sediment columns. A number of physicochemical parameters, heavy metals, and trace elements were measured by field and laboratory techniques to assess the status of creek’s water and sediment quality. The spatial and temporal variations were determined, and statistical tools were used to conduct an environmental forensic overview along the creek. A geo-accumulation index and a modified heavy metal pollution index were calculated to cumulatively assess the quality of sediment and water columns, respectively. The results revealed that the creek was under significant pollution load from the industrial zone where metal processing, food and beverage production, marble and

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10661-017-6309-4) contains supplementary material, which is available to authorized users. G. T. Kara : M. Kara : A. Bayram : O. Gündüz (*) Department of Environmental Engineering, Dokuz Eylül University, Izmir, Turkey e-mail: [email protected]

natural stone manufacturing, and paper production are made. In particular, elements such as Co, Cu, Cd, Mn, Ni, Pb, Zn, and Zr were found to be above the surface water quality standard values. Similarly, B, Cr, Ni, Cu, Zn, and Sn were determined to be in extreme levels in the sediment column with values exceeding the probable effect concentrations. Keywords Heavy metals and trace elements . Industrial discharges . Water and sediment quality . Nif Creek

Introduction Heavy metals and trace elements are among the most crucial water quality parameters that define the overall status of a water resource. Their persistence in the environment and characteristic toxicity for many organisms are the main reasons for the necessity to routinely monitor these contaminants in water and sediment columns. As a consequence, their corresponding levels need to be determined accurately for effective and sustainable water quality management. Treated or untreated domestic and industrial discharges can cause significant amounts of heavy metal and trace element releases into water systems such as rivers, lakes, and estuaries (Tang et al. 2014). Increasing amount of heavy metals can accumulate in water and sediment compartments as well as in biological systems, and can reveal a serious threat to aquatic and terrestrial ecosystems (He et al. 2001;

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Rai 2008). While dissolved contaminants in the water column pose a short-term risk, contaminants fixed to the sediment matrix typically create a prolonged hazard for the ecosystem. Certain contaminants such as heavy metals, trace elements, and micro-pollutants bind to suspended solids and deposit onto sediments (Koukina et al. 2016), and convert the sediment layer to a major pollutant sink (Kara et al. 2015; Yu et al. 2016). Although the water column is quite dynamic and contaminants in this layer are quickly transported along the flow direction, the sediment layer provides an immobile and continuous pollution source due to fairly slow physical re-suspension and biochemical dissolution processes that release contaminants back into the water column (Mulligan et al. 2001; Yu et al. 2016). Heavy metal pollution in water and sediments are commonly evaluated by pollution indices such as pollution load index, ecological risk index and geoaccumulation index (Telci and Aral 2011; Dede et al. 2013; Islam et al. 2015; Ali et al. 2016; Bian et al. 2016; El Azhari et al. 2016). These index techniques provide valuable tools to comparatively assess the extent of metal pollution at particular points of interest. Furthermore, they can assist the decision maker on the relative priorities of choices on where to act first. This procedure is extremely useful for streams that receive multiple discharges along the river and, hence, is suitable for conducting an environmental forensics overview on the water resource (Telci et al. 2009; Lim et al. 2013; Miller 2013). Based on these fundamentals, this study focuses on the Nif Creek that pass through a heavily industrialized region near Izmir, Turkey. The intermittent creek receives domestic discharges of Kemalpaşa District Center and its neighborhoods as well as industrial waste loads of more than 180 factories of the organized industrial zone. The creek is considered to be an effluent-dominated stream particularly during summer and autumn months when natural flow ceases. Previous researchers have shown that Gediz River, to which Nif Creek confluences, and the Nif Creek carry large amounts of heavy metals and trace elements (Akcay et al. 2003; Oner and Celik 2011). Thus, a water quality assessment study was conducted on Nif Creek and samples were taken in two different seasons with distinct hydrological characteristics to quantify and evaluate the quality status of water and sediment columns. The spatial and

Environ Monit Assess (2017) 189: 585

temporal variations were determined and statistical tools were used to assess the status of creek’s water and sediment quality and to conduct an environmental forensic overview along the stream channel.

Materials and methods Study area The city of Izmir located in western Anatolia is the third largest city and is one of the heavily industrialized regions in Turkey. The District of Kemalpaşa is situated to the east of İzmir and has an organized industrial zone where metal processing, food and beverage production, marble and natural stone production, paper production and chemical manufacturing are the main sectors (Fig. 1). The Gediz River is the second largest river in western Aegean Region and drains about 17,000 km2 area. This study focused on the Nif Creek, which is a major tributary to Gediz River. Nif Creek is a 63.5-km long hydrologically intermittent stream that drains a watershed of 1,071 km2 (Fig. 1). The creek passes through the Kemalpaşa Organized Industrial Zone (KOIZ) and receives discharge from numerous industries and domestic wastes of residential areas. While 165 factories discharge their wastewaters to the main collector channel that drains into KOIZ industrial wastewater treatment plant, 18 factories discharge their wastewaters directly to Nif Creek after being treated in their own wastewater treatment plants. The total flowrate of industrial discharges made to the creek is around 13,650 m3/day. Sample collection and analysis A water and sediment quality sampling campaign was carried out in two different hydrological periods representing wet (May 2016) and dry (August 2016) seasons in the study area. While water quality sampling was done in two periods, sediment quality sampling was only conducted in wet season since sediment quality does not show major changes in a short span of time. Overall, a number of physicochemical parameters were measured in situ and heavy metals and trace elements were analyzed by laboratory techniques. The physical parameters (temperature, dissolved oxygen, pH, electrical


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Fig. 1 The study area and locations of sampling stations

conductivity, oxidation-reduction potential) in water column were measured by multi-parameter probes in the field. The water samples were collected manually at a depth of 0.1–0.3 m below the water surface with a polyethylene plastic beaker after rinsing at each site. Samples were then placed in 100-mL polyethylene bottles and HNO3 was added to samples for reducing pH level to below 2. Concurrently, sediment samples were collected manually from the same sampling site by a Van Veen grab sampler. Sediment samples were placed in polyethylene bags after separating small pebbles and plant parts. All samples were then transported to the laboratory in a temperature controlled cooler and were stocked at 4 °C until analysis.

Prior to laboratory analysis, water samples were brought back to room temperature and filtered through 0.45 μm pore-sized cellulose acetate filters before analysis. Sediment samples were mixed, dried, crushed, and sieved before acid digestion procedure. Later, 0.5 g of the sieved sediment sample was digested using HNO3, HF, and HCl mixture with a microwave digestion system. Digested samples were then diluted to 100 mL with deionized water and filtered through 0.45 μm pore-sized cellulose acetate filters before analysis. Analysis for 33 heavy metals and trace elements (Ag, Al, As, B, Ba, Be, Ca, Cd, Ce, Co, Cr, Cu, Fe, Ga, K, La, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Se, Si, Sn, Sr, Ti, V, Zn, Zr) was carried out using inductively coupled

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plasma-mass spectrometry (ICP-MS) (Agilent 7700x). All quality control/quality assurance procedures were applied during sample preparation and analysis. Aliquots of reference material sandy loam soil (CRM03050) from RTC (RT Corp.) were digested and analyzed along with the samples to determine recovery efficiencies of the sediment extraction procedure. The recovery of elements was in the range of 80–110% of the certified elemental concentrations. The continuing check verification standard solution was used to check the validity of the calibration curve during analysis. The limit of detection (LOD) values for all elements was defined as the average plus three standard deviations of the amounts in blanks.

Data analysis and assessment All the data was gathered in a spatial database. This database was later imported to a geographical information system platform (ArcGIS v10.3.1), and spatial distribution maps were created for data assessment. Statistical analysis of the data was conducted by SPSS software (v.22). Apart from general statistics of the raw data, quality indices were calculated for sediment and water columns in order to obtain a cumulative overview of a large data set consisting 33 parameters. Accordingly, a modified heavy metal pollution Index (mod-HPI) was calculated for the water column; and, a geoaccumulation index (Igeo) was calculated for the sediment column. The original heavy metal pollution index (HPI) is a well-known technique to evaluate the overall quality of drinking water with reference to heavy metals. The index was developed by Mohan et al. (1996) based on the weighted arithmetic mean method. HPI values are calculated as follows: HPI ¼

∑ni¼1 Qi W i ∑ni¼1 W i

ð1Þ

where Qi is the sub index and Wi is the unit weight of the ith parameter. Wi and Qi values of the parameter are calculated by the following equations: Wi ¼

1 Si

ð2Þ

Qi ¼

8 < :

∑ni¼1

M i −I i *100 ðS i −I i Þ 0

when M i ≥ I i otherwise ð3Þ

where Mi is the measured concentration, Ii is the ideal value, and Si is the standard value of the ith parameter. The HPI method was intended for drinking water (Mohan et al. 1996; Prasad and Bose 2001) but is modified in this study to assess the ambient surface water quality. The mod-HPI method is, thus, a modified version of the original technique where the standard value, S, is taken to be the maximum allowable concentration value and the ideal value, I, is taken to be annual average concentration value applicable to rivers and lakes as depicted in the list of specific and priority pollutants in Turkish regulation on surface water quality (YSKKY 2016). The standard and ideal values for the heavy metal and trace elements are presented in Table SM-1. For elements with equal standard and ideal values, the ideal value was taken to be equal to zero to avoid a mathematically undefined condition. When the overall mod-HPI value is above 100, it is concluded that an overall elemental contamination is present in the water sample. The Igeo, proposed by Muller (1969), is a common method for the assessment of pollution level in sediments (Islam et al. 2015; Ali et al. 2016; El Azhari et al. 2016; Yu et al. 2016). Igeo is calculated by the following equation: I geo ¼ log2

Cn 1:5Bn

ð4Þ

where Cn is the measured and Bn is the background concentrations of the examined element n. The factor 1.5 is the background matrix correction factor for the anthropogenic effects. In the method, there are 7 classes for the pollution level of the trace elements due to calculated Igeo values: Igeo = 0; unpolluted; 0 < Igeo ≤1 unpolluted to moderately polluted; 1 < Igeo ≤2 moderately polluted; 2 < Igeo ≤3 moderately to strongly polluted; 3 < Igeo ≤4 strongly polluted; 4 < Igeo ≤5 strongly to extremely polluted; and Igeo > 5 extremely polluted. Measured concentrations at sampling point N-20 (the most upstream pristine point along the creek) was used as background the concentrations in this study.


Finally, a source identification study was conducted by multivariate data analysis techniques to identify the possible sources of trace elements in the sediment samples. Source apportionment is based on the principle of assessing the relative contributions of inorganic or organic contaminants that are emitted from natural and anthropogenic sources. In order to identify the sources, data analysis tools (receptor modeling approaches) are applied to derive information on the sources of species from the measured concentrations. In principal component analysis (PCA) technique, source apportionment of a data set is performed under orthogonal constraints for both source contributions and source profiles (loadings). Additionally, loadings are also normalized and forced to be in the direction of explaining maximum variance. Under such restrictions, PCA provides a unique solution (Tauler et al. 2008). As a common unsupervised pattern recognition procedure that can group different objects and variables based on their similarities or dissimilarities, a PCA was implemented in this study using SPSS Statistics Software v.22. The variables for PCA were mean centered, and only the principle components with eigenvalues larger than 1 were retained. Varimax rotation and Kaiser normalization were also used to describe possible factors associated with elemental pollution sources in the study area.

Results and discussions Water column The statistical summaries of the results of two sampling periods are presented in Table SM-2 and SM-3 for field parameters and heavy metals and trace elements, respectively. In general, temperature, pH, and electrical conductivity (EC) values increased from wet season to dry season whereas oxidationreduction potential (ORP) and dissolved oxygen (DO) decreased from wet season to dry season. While the pH values ranged between 7.05 and 8.14 in the wet season, it increased to a range of 7.36 to 8.57 in the dry season. Similarly, the EC values ranged between 399 and 3740 μS/cm in the wet season, and between 827 and 8280 μS/cm in the dry season. The spatial distribution of EC in water column revealed that the majority of pollution

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originated from the Kemalpaşa Organized Industrial Zone (Fig. SM-1). The figure clearly demonstrated the fact that EC values were strongly influenced from the natural dilution capacity of Nif Creek and had a twofold difference between the two periods with regards to average conductivity values. It can be stated that both pH and EC values followed a similar trend and were mainly attributed to the decreased natural dilution capacity of the creek during dry season. The Nif Creek and its sub-tributaries run dry during the summer months, and the substantial part of the flow in the channel was from the treated and untreated domestic and industrial wastewater discharges. Thus, the creek can be considered to be an effluent-dominated stream that mainly serves as a natural waste conduit. Being an indicator for oxidation state of natural waters, ORP values ranged between −236 to 128.5 mV in the wet season and between −320 and 115 mV in the dry season. The arithmetic averages decreased from 28.71 to −9.89 mV during these two periods. The dissolved oxygen levels, on the other hand, ranged between 0.16 and 9.84 mg/L in the wet season and between 0.06 and 9.30 mg/L in the dry season. ORP and dissolved oxygen values showed that the creek had fairly anaerobic conditions in some reaches as a result of extensive organic loads, particularly from the food processing industries (Wang et al. 2006). These organic loads created a sudden decline in water column dissolved oxygen levels and the system suddenly became a reductive environment. Later along the river, discharges with high DO levels from the two wastewater treatment plants and side tributaries created a gradual increase in DO levels. The results also revealed that the standard deviations of all field parameters were comparably higher in the dry season (see Table SM-2). This finding was associated with the discharge-dominated character of the stream where dry season flows were almost entirely composed of wastewater effluents. When the dilution capacity of the creek was at its minimum level and sampling stations are relatively close to each other as in this study, discharges with distinct characteristics result in widespread variability and larger deviations from the mean values in the measured water quality parameters. When the results of heavy metals and trace elements are analyzed, one can clearly see that the

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creek was heavily polluted with heavy metals and trace elements (see Table SM-3). As the creek receives numerous industrial discharges originating from different industrial plants, the elemental concentrations measured in the water column represented the quality of these waste loads, and were considered to be marker elements for these industrial processes. The statistical summary of elemental results indicated that apart from the major elements such as Na, Ca, Mg, K, and P, Nif Creek also contained elevated levels of Fe, Mn, Co, Ni, Cu, Zn, Si, and Pb when compared to the standard levels given in Table SM-1. The spatial distributions of levels of Cu, Fe, and Co are given as Figs. SM-2, SM-3. and SM-4, respectively, and clearly demonstrated the influence of the industrial discharges made from the organized industrial zone. For some parameters, the enrichment from the industrial discharges continued along the flow direction, whereas for some others, they were reduced as a result of the diluting effect of other discharges and side tributaries. The discrete discharge points and some elemental concentrations in both sediment and water columns are jointly shown in Fig. 2. The figure demonstrated the fact that elemental enrichment in water and sediment compartments coincided with the industrial zone boundaries (i.e., between sampling stations N-13 and N-19) and the associated discharges originating from paint industry, building materials industry, chemical industry, tobacco industry, plating and molding industry, ceramic industry, food industry, and packing industry. In essence, total elemental concentrations of As, B, Cd, Cr, Cu, Li, Mo, Ni, Pb, P, Sn, and Zn were measured to be high in sampling station N-17 where as other elements such as Ag, B, Ba, Cd, Li, Sn, Ga, Co, Si, P, Pb, V, Zn, and Zr have reached peak values at the sampling station N-14. In particular, the discharge B (Fig. 2), which contained high strength chemical and paint industry wastes, was made prior to sampling station N-17. The average elemental concentrations in water column were compared with those reported form other industrial and non-industrial regions around the world (Table 1). Accordingly, concentrations of some anthropogenic elements (Ag, Co, Cr, Cu, Mn, Ni, and Zn) were much higher in Nif Creek waters than other rivers around the world, and


concentrations of other elements such as Al, Fe, Li, Mg, Pb, and Sr were found to be similar to the results obtained from these studies. Some elements deviated significantly from global values such as Na, which was measured to be extremely high, or Sb and Mo, which were found to be considerably lower than other studies. The high Na values were mostly associated with the discharges made from vinegar and pickle processing plant where as lower Sb and Mo values were associated with lower level of interactions with traffic sources (Gomez et al. 2005), which are considered to be main anthropogenic sources of these elements in water. Overall, Nif Creek waters were found to be comparably more polluted than other rivers presented in Table 1, with regards to nine elements (Al, Ba, Co, Cr, Cu, Li, Mn, Ni, Zn). In order to jointly assess the results of 33 elements, the mod-HPI values were evaluated at all sampling stations and the spatial statistics were calculated. The results revealed that the mod-HPI values ranged between 18.3 and 1575.4 with average and standard deviation values of 279.4 and 365.1, respectively. The high standard deviation value is an indicator for wide variability of index values at the sampling points. In the study of Mohan et al. (1996), the average HPI value (calculated for Cd, Pb, Cu, and Zn) was 3.26 in 12 stations which were lower than the mod-HPI values of all Nif sampling points. Prasad et al. calculated HPI values for the surface waters of Giri and Tons Rivers, which were near a limestone mining area, using four sampling points for seven heavy metals (Prasad and Bose 2001). The calculated HPI values were 10.32 and 8.98 for Giri and Tons, respectively. These values were also lower than the mod-HPI values calculated for Nif Creek. In Nif Creek, the majority of the stations had modHPI values higher than the limit of 100, which represented a significant cumulative elemental contamination in the water column. As shown in the spatial distribution of mod-HPI values (Fig. 3), the highest values were observed between N-14 and N-17 stations where the majority of industrial discharges were made. This finding further supported the negative influence of the discharges originating from the organized industrial zone on the creek water quality, since the creek water was found to be significantly contaminated for numerous elements on a temporally averaged basis.


Fig. 2 The variation of selected elements along the stream in water and sediments columns

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Table 1 Elemental concentrations (μg/L) in water column reported in previous studies around the world Parameter Nif Creek Nif Creek Yangtze River, study area (except (hot spot) China (Muller hot spots) (N13–N17) et al. 2008)

Ag Al As B Ba Cd Co Cr Cu Fe Li Mg Mn Mo Ni Pb Sb Se Sr V Zn

0.11 1261 18.3 183.6 264.8 0.16 3.87 10 13.31 1600 21.69 22,980 635.3 1.33 32.11 5.51 0.84 3.01 515 6.11 197

0.11 4688 5.89 485.2 416.7 0.17 9.72 84.4 83.8 2319 62.22 19,413 284.6 1.89 168.8 17.3 0.76 9.12 577 3.99 2372

1779 4.45 71 73.3 0.12 0.54 5.38 4.99 1023 6.89 8953 24.11

Yangtze River, China (Wu et al. 2009)

20.9 10.7 240 14.1 5.4 11.7 13.4 55.1 65.3 114.3 210 10.5 9.4

17.81

Parameter Yangtze River, Yarlung Changjiang study area China (Huang Tsangpo, China River, China et al. 2008) (Huang et al. (Wang et al. 2008) 2011) Ag

0.01

0.01

Al

433

428

As Ba 0.02

87.2 2.34 3 12.2 15.9 36.8

7.72 68.5

31.15

30.14

1.71 11.21 43.1 14.28 235 70.1

1.83

9.44

Andhra Pradesh, India (Krishna et al. 2009)

Northern Greece (Simeonov et al. 2003)

Salween, China (Huang et al. 2008)

Mekong, China (Huang et al. 2008)

0.01 256

0.06 200

0.02 1.43 0.57 0.58 240 19.34 30,160 22.28 9.85 5.95 220.2

0.02 0.68 0.68 1.35 97.5 24.3 45,975 32.73 11.9 2.2 9.49

5.95

2.2

Bangpakong River, Thailand (Bordalo et al. 2001)

Akhuryan River, Armenia (Arpine and Gayane 2016)

1.1 7.04

0.02

55.3

974

B Cd

189 16.99

13.2 37.2 37.4 4.7

2.19 2.67 1.41 0.57 221

Upper Han Yeongsan River, China (Li River, Korea and Zhang 2010) (Kang et al. 2010)

514 29.2

< 0.1

0.49

669.8

52.3

132

77.6

47.2

0.28

0.26

Co

0.85

2.64

1.55

2.8

Cr

0.73

0.61

8.9

16.8

Cu

2.5

3.4

8.4

Fe

624

761

1660

Li

5.26

32.78

Mg

26,060

19,236

Mn

45.07

39.68

0.01

6.5

0.85

4.2

53.05

2.7

161

326

2710

210

72.9

155.4

3800 25.18

Mo

8.41

10.8

Ni

19.28

8.74

3.69

26.7

4.1

1.59

Pb

4.15

16.57

6.4

2.1

3.4

0.33

< 0.1

0.23

Sb Se

0.73

Sr

762

V Zn

1.35 2.46

3.4

18.75

98.6

57.2

881

3.35


Sediment column The statistical summary of heavy metals and trace elements in the sediment column are presented in Table SM-4. When these results are compared with the crustal average values and the threshold and probable effect concentrations, elements such as B, Cr, Ni, Cu, Sn, and Zn were found to be extremely high and were associated with the enrichment resulting from particular industrial discharges. Only boron was associated with mostly geogenic sources. In addition, elements such as Co, Ga, As, Se, Zr, Ag, Cd, Sn, Ba, and Pb were also found to be moderately high. These results were an indication for the polluted characteristic of Nif Creek sediments. The spatial distribution of As, Cu, Ga, and Se presented in Fig. SM-5 also revealed that the major enrichment of these river sediments occurred in and around the industrial zone. Other local high values were observed around side tributaries to Nif

Fig. 3 The spatial distribution of mod-HPI values along Nif Creek

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Creek, which potentially carried the particular contaminant. Overall, 17 out of 33 elements presented in Table SM-4 had average values in Nif Creek sediments higher than that of crustal averages. These 17 elements were mostly in the heavy metal group and were associated with metal processing or coating industries. In almost all elements, highest level of enrichment was observed within the industrial zone. Later, the concentrations decreased with tributary contributions and sporadically increased again from discrete sources along the flow path. The high levels of Cd, Ni, P, and Pb measured at N-17 (Fig. 2) were linked to the wastes originating from industries where solid-mixed metal compounds Ca/Zn and Pb in various forms and liquid-mixed metal stabilizers Ca/Zn, Ba, Ba/Zn, and Sn were produced. Continuous discharges of these elements have resulted in the accumulation of these metals in N-17 sediments. On the other hand, the concentrations of Ni, Cr, and Cu

1.86 8,385 37.5 1,744 1.21 37.31 554.3 487.52 13,837 323.8 2.42 424.29 149.19 1.48 59.82 154.15 1,169 42.6 5715 1027

Ag Al As Ba Cd Co Cr Cu Fe Mn Mo Ni Pb Sb Sn Sr Ti V Zn Zr 83.8 108 987 84 11,275 49,903 568.5 5.9 45 1423 8.3 2,544.8 145.5 3924

54 30.3 6,798 79 21,728 45,548 5,153 6.1 69.4 2,391.4 17.9 1,145.4 143.6 414.6

2.1

6.4

45

125

12,300

Cd

Co

Cr

Cu

Fe

Ba

74,100

29

92.6

13.8

0.28

17.8

68,700

21.9

52

8.62

0.18

19.4

62,300

26.7

99.7

12.6

0.22

13.2

64,232

65

207

23

0.75

10.95

25

As

64,400

4 75,800

96,120

83,700

84.5 467.3

33,000

20

77.1

79,000

27,000

17

57

61,000

Arrozal Channel, Brazil (Oliveira et al. 2016)

33.87 54.54

689.7

15

9.63 45.12 13.48 18,400 303.1

424.5

45,500

Northern Gulf of California, USA (Daessle et al. 2004)

Murucupi River, Brazil (Oliveira et al. 2016)

44,604 29 481 1.7 12.7 145 97.6 36,904 557.7 2.4 70.7 80.3 4.4

Kim Nguu River, Hanoi, Vietnam (Marcussen et al. 2008)

Brisbane River, Australia (Duodu et al. 2016)

5,100

Das-Mestas River Galician Rias Estuarine (Álvarez-Vazquez et al. 2016)

121 860

54,845 50.3 797.7 163 19.4 221 116.7 64,927 895.7 3.8 91 131.8 5.7

To Lich River, Hanoi, Vietnam (Marcussen et al. 2008)

Al

Mandeo River Galician Rias Estuarine (ÁlvarezVazquez et al. 2016)

6.4

Lubumbashi River Democratic Republic of the Congo (Atibu et al. 2016)

1.7

Tshamilemba Canal Democratic Republic of the Congo (Atibu et al. 2016)

Eume River Galician Rias Estuarine (Álvarez-Vazquez et al. 2016)

4.1 87,000 15.2 259 0.1 7.7 61.5 36.8 31,000 400 2.6 33.1 55.2 1.4 4.5 172 3,000 73.5 85.6 63.5

Cai River estuary, Vietnam (Koukina et al. 2016)

Ag

Parameter Nerbioi Baizabal study area River, Bilbao (FdezOrtiz de Vallejuelo et al. 2010)

0.15 11,337 19.86 326.8 0.25 8.1 74.07 49.02 18,477 442 0.5 47.31 27.1 1.28 3.45 117.96 1,484 51.12 300.5 46.08

Nif Creek (hot spot) (N13– N17)

Parameter Nif study area Creek (except hot spots)

Table 2 Elemental concentrations (mg/kg) in sediments reported for different rivers around the world

41,000

22

72.8

67,000

Para River, Brazil (Oliveira et al. 2016)

10.1 170

4.7

16.4 40.9

0.37 5.8 34.7 113 10,400 449

13,300 11.7

Cavado Estuary, Portugal (Gredilla et al. 2015)

1882

30

51.55

0.17

9.67

Bortala River, China (Zhang et al. 2016)

54.7 388

82.9 54.7

0.78 11.4 99.7 108

8.85

Shima River, China (Gao et al. 2016)

585 Page 10 of 16 Environ Monit Assess (2017) 189: 585

99.19

31.98

73 60.3 84 Zr

181

199.5 80.2

91.4 75.6

92.2 Zn

56.4 12

570

V

Ti

20 Sr

210 Pb

129

24 Sn

270

0.95 Sb

22.32 27.2

27.1 25.1

19.9 22.3

28.3 75.5

27 47

17.5 25.1

27.6 22 Ni

23.4

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30.7

411.3 304.1 305.3 731 328

2.66 1.2

375 354

1.94

250 Mn

Mo

Eume River Galician Rias Estuarine (Álvarez-Vazquez et al. 2016) Parameter Nerbioi Baizabal study area River, Bilbao (FdezOrtiz de Vallejuelo et al. 2010)

Table 2 (continued)

Mandeo River Galician Rias Estuarine (ÁlvarezVazquez et al. 2016)

Das-Mestas River Galician Rias Estuarine (Álvarez-Vazquez et al. 2016)

Brisbane River, Australia (Duodu et al. 2016)

Murucupi River, Brazil (Oliveira et al. 2016)

Arrozal Channel, Brazil (Oliveira et al. 2016)

Para River, Brazil (Oliveira et al. 2016)

Bortala River, China (Zhang et al. 2016)


were found to be high in sampling station N-15 due to discharge D (Fig. 2), which originated from a plant where Cr/Ni plating and plastics molding was made. The sampling station N-14 was also found to be another hot spot that had highest element concentrations. This point is situated immediately downstream the discharge E (Fig. 2) which contained the wastes of brazing alloys and ceramic industries. Particularly, the alloy brazing industry produces silver brazing alloys with cadmium, copper-phosphorus brazing alloys, and nickel and cobalt brazing powders. The water column results at this point were also found to be strongly correlated with the wastes of the brazing industry. Similarly, high Zr and Si levels found in this point were also considered to be marker elements for the ceramic industry wastes that were discharged from the same point (Fig. 2). Finally, the high levels of Cd and Pb at sampling points N2 and N-3 were found to be associated with traffic sources (i.e., downstream the drainage point from the main highway). The average elemental concentrations in Nif Creek sediments were then compared to those reported from other sites around the world as shown in Table 2. The concentrations of Al, As, Cd, Fe, Mn, and V were similar to corresponding values from all around the world. On the other hand, some anthropogenic elements such as Cu, Ni, Pb, Sn, Zn, and Zr were measured higher, especially in hot spot region of Nif Creek, in comparison to other rivers. On the other hand, levels of Ag, Cd, Co, Sb, and Mo were considerably lower than other regions. Besides, the measured concentrations for some elements in the study were generally lower than those measured in contaminated sediments taken from the industrial ports of rivers/estuaries and around abandoned and active mine areas. A combined overview of results was obtained by the Igeo values evaluated in sediment column at all sampling stations. The spatial statistics of index values are shown in Table SM-5. The maximum index values were observed for elements such as Ag, B, Cr, Cu, Ni, Sn, Zn, and Zr. The average index values, on the other hand, were calculated to be relatively high for elements such as Ag, Zn, and B. Based on the scale presented previously; a total of ten elements had average index values higher than 1 that corresponded from moderately

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Fig. 4 The spatial distribution of elementally averaged Igeo index values along Nif Creek

contaminated to extremely contaminated conditions (see Table SM-5). The elemental average of Igeo values at each sampling station was calculated and presented in Fig. 4. Accordingly, it can be seen that the vicinity of the organized industrial zone was the hot spot for contaminated sediments. When compared with the average Igeo values of the surface sediments of Bortala River, As, Cd, and Pb were found to be higher than Nif Creek whereas Cr, Cu, Ni, and Zn were lower in Bortala River (Zhang et al. 2016). Similarly, Ma et al. (2016) studied Yellow River sediments and found maximum values of Igeo for Cr, Cd, Cu, and Ni to be lower than the corresponding maximums of Nif sediments even along the industrialized reaches of Yellow River. Finally, a PCA was conducted on the more stable sediment compartment and the results are shown in Table 3 for factor loadings that are higher than 0.5. The analysis revealed four major factors accounting for 89.5% of the cumulative variance. The first factor

(PC1) accounted for 37.6% of the variance and was comprised of Ga, Ba, B, Ag, Zn, Co, Li, Cd, and Pb with high loadings, and Si and Se with relatively low loadings. The strong correlation among Ag, Zn, Co, Cd, and Pb was attributed to anthropogenic activities (Sun et al. 2014). The second factor (PC2) accounted for 25.7% of the variance and was associated positively with V, Al, Mn, Fe, Se, Mg, K, Si, and rare earth elements (La and Ce). This factor was, hence, assigned to lithogenic contribution which was evident by the high positive loadings of the lithogenic elements (Al, Fe, Mn, Mg, Si) (Brady et al. 2014; Kara et al. 2015; Saleem et al. 2015). The third factor (PC3) covered 19.6% of the variance and contained high loadings of Ni, Cu, Cr, Sn, As, Mo, and Pb. This factor was associated with a second group of anthropogenic activities (Christophoridis et al. 2009; Kucuksezgin et al. 2011). The last factor (PC4), which accounted for 6.6% of variance, was attributed to lithogenic elements due to its high correlation with Ca and Sr.


Page 13 of 16 585

Table 3 Factor loadings of elemental composition in sediment samples Element

PC1

Ga

0.987

Ba

0.987

Zr

0.982

B

0.980

Ag

0.976

Zn

0.976

Co

0.973

Li

0.967

Cd

0.871

Na

0.821

Pb

0.752

Be

0.727

Si

0.669

PC2

0.519 0.923

La

0.891

V

0.870

Al

0.843

Mn

0.830

Fe

0.822 0.542

PC4

0.514

Ce

Se

PC3

0.799

Mg

0.783

K

0.776

Ni

0.988

P

0.981

Cu

0.980

Cr

0.978

Sn

0.854

As

0.847

Mo

0.786

Sb

0.585

Ca

0.897

Sr

0.676 − 0.524

Ti % of variance

37.612

25.736

19.572

6.614

Conclusions The water and sediment quality can serve as clear indicators for industrial pollution in surface water bodies. While water column represents temporarily more dynamic conditions that can change dramatically in a short period of time, sediment column typically demonstrates fairly stable conditions that can characterize the long-term history

of contamination. For effluent-dominated streams in arid and semi-arid climates, water column conditions essentially describe a mixture of the quality of industrial discharges, particularly for elements which can hardly undergo biochemical degradation reactions. A clear example for an effluent-dominated stream was studied in this research and the water and sediment quality were assessed to quantify the influence of the industrial zone and to exhibit an environmental forensics overview. The results revealed significant contamination of water and sediment columns. The element distribution in sediment matrix was particularly above the recommended sediment quality limits. The water column results, on the other hand, showed a spatially and temporally variable quality pattern. The sampling stations near industrial establishments were strongly influenced from the quality of the industrial discharges and the associated chemical processes implemented in these industries. In particular, elements such as Co, Cu, Cd, Mn, Ni, Pb, Zn, and Zr were found to be above the surface water quality standard values. Similarly, B, Cr, Ni, Cu, Zn, and Sn were determined to be in extreme levels in the sediment column with values exceeding the probable effect concentrations. On the other hand, the PCA outcomes clearly indicated that anthropogenic sources (paint industry, building materials industry, chemical industry, tobacco industry, plating and molding industry as well as traffic sources originating from the road network in the study area) were the important sources of contamination for the sediment compartment. Other sources with crustal origin were also found to contribute to the high level of contamination recorded in the sediment samples of the studied system. In general, the Nif Creek was characterized to be under significant contamination threat in both water and sediment columns. This was a clear indication that the existing wastewater treatment plants of the discrete industries either do not operate properly and cannot reduce the element loads reachingthe creek, or were not designed to treat these pollution parameters. As the creek is a tributary to the Gediz River that finally meets the Bay of Izmir, highly contaminated stream water can potentially influence probable uses of Gediz River waters such as agricultural irrigation. Overall, it can be concluded that the influences of waste loads made to effluent-dominated streams are more profoundly felt by the environment, and it is more difficult to mitigate the quality of these water resources due to their limited natural carrying capacity. In essence, strict managerial decisions are necessary when discharge permits are granted in such systems.

585 Page 14 of 16

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Environ Monit Assess (2017) 189: 585 Zhang, Z., Juying, L., Mamat, Z., & QingFu, Y. (2016). Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River, Northwest China. Ecotoxicology and Environmental Safety, 126, 94–101. https://doi.org/10.1016/j.ecoenv.2015.12.025.