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Assessment of spatio-temporal variations of selected water quality parameters of Lake Ziway, Ethiopia using multivariate techniques

Assessment of spatio-temporal variations of selected water quality parameters of Lake Ziway,... Excess agrochemicals input from agricultural activities and industrial effluent around Lake Ziway catchment can pose a serious threat on the lake ecosystem. Lake Ziway is a shallow freshwater lake found in the northern part of the Ethiopian Rift Valley. It is characterized as semi-arid to sub-humid type of climate. Expansions of the flower industry, widespread fisheries, intensive agricultural activities, fast population growth lead to deterioration of water quality and depletion of aquatic biota. The spatial and temporal variations of selected water quality parameters were evaluated using multivariate techniques. The data were collected from nine sampling stations during dry and wet seasonal basis for analysis of fifteen water quality parameters. The physicochemical parameters were measured in-situ with portable multimeter and nutrients were determined by following the standard procedures outlined in the American Public Health Association using UV/Visible spectrophotometer. Mean nutrient concentrations showed increasing trend in all seasons. These sites were also characterized by high electrical conductivity and total dissolved solid ( TDS). All the nine sampling sites were categorized into three pollution levels according to their water quality features using cluster analysis (CA). Accordingly, sampling sites Fb and Ketar River (Kb) are highly and moderately polluted in both seasons, respectively. On the other hand, sampling sites at the center (C), Meki river mouth (Ma), Ketar river mouth (Ka), Meki River (Mb), Korekonch (K ) and Fa in dry season and Ka, C, Ma, Ko, Bulbula river mouth (B) and Fa during wet season were less polluted. Principal component analysis (PCA) analysis also showed the pollutant sources were mainly from Fb during dry season Mb and Kb during wet season. The values of comprehensive pollution index illustrated the lake is moderately and slightly polluted in dry and wet seasons, respectively. Comparatively, the pollution status of the lake is high around floriculture effluent discharge site and at the two feeding rivers (Kb and Mb) due to increasing trends in agrochemical loads. In order to stop further deterioration of the lake water quality and to eventually restore the beneficial uses of the lake, management of agrochemicals in the lake catchments should be given urgent priority. Keywords: Cluster analysis, Comprehensive pollution index, Factor analysis, Principal component analysis development. They are essential for agriculture, industry Introduction and human existence in general. The health of aquatic Water pollution is one of the critical issues in environ- ecosystems is dependent on the presence of the right mental conservation. Freshwater resources have been of proportions of nutrients and requires succession of major great importance to both natural ecosystems and human nutrients in the water and sediment [1]. Appropriate assemblage of the nutrients ensures the status of water quality in any ecosystem and provides significant infor - *Correspondence: dessie.1977@gmail.com Department of Chemistry, College of Natural and Computational mation about the available resources for supporting life Sciences, University of Gondar, P. O. box 196, Gondar, Ethiopia [2]. Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Tibebe et al. BMC Chemistry (2022) 16:11 Page 2 of 18 Nitrogen and phosphorus (and silicon too for diatom Moreover, pollution of freshwater with potential con- species) are typical limiting nutrients influencing primary taminants due to natural phenomena and anthropogenic production. Nutrients occur in many different forms and activities are of great concern worldwide [9]. The fresh - only bio-available forms such as nitrate, nitrite, ammo- water lakes are susceptible to chemical contaminations nia, orthophosphate and soluble reactive silica can be as they are stagnant in nature [9]. Systematic studies on utilized directly by phytoplankton. Other forms of nutri- geochemical variability’s and inference of natural and ents, however, can become bio-available through desorp- anthropogenic factors are crucial to explain and protect tion, dissolution and biomass turnover. Nutrients in the the water quality in the lake ecosystem. However, stud- water body may originate from weathering of bedrock, ies on the comprehensive spatio-temporal variations and atmospheric precipitation, terrestrial input, storm water the systematic identification of the potential pollution runoff, sewage effluent and agricultural discharge [3, 4]. sources of Lake Ziway water qualities were very limited. Nutrient enrichment of lakes is considered to be one u Th s, reliable information on water quality and pollution of the major environmental problems in many countries sources is important for effective lake water manage - especially in developing ones [5]. In recent decades, pop- ment. Therefore, the objective of this study is to assess ulation growth, agricultural practices and sewage runoff the spatio-temporal variations of selected water quality from urban areas have increased nutrient inputs many parameters of Lake Ziway using Multivariate Techniques. folds to the level of their natural occurrence, resulting in accelerated eutrophication [5, 6]. Many urban and rural Materials and methods lakes have vanished under this pressure with worldwide Description of the study area environmental concerns [7]. The evaluation of water Lake Ziway is shallow freshwater located in the most quality in freshwater lakes is indispensable due to its northern section of the Ethiopian Rift Valley. The region immense significance in terms of ecological services and is characterized as semi-arid to sub-humid type of cli- livelihood perspectives. Anthropic pressures, however, in mate and has mean annual precipitation varying between the form of rapid urbanization, excessive use of pesticides 650 and 1200 mm and mean annual temperature between and fertilizers, land use and climate change are diminish- 15 and 25  °C [11]. During the last few decades, Lake ing the water quality, which necessitates better insights Ziway has begun to show reduction in its water level into pollution variability and its controlling measures [8, because of some climatic factors and excessive water 9]. abstraction for irrigation, municipals and industrial pur- Multivariate statistical techniques have been widely poses [12]. The lake is fed primarily by Meki and Ketar adopted to analyze and evaluate surface and freshwater Rivers and drained by the Bulbula River. The lake’s catch - water quality, and are useful to verify temporal and spa- ment has an area of 7025 k m with the town of Ziway tial variations caused by natural and anthropogenic fac- lying on the lake’s western shore [12]. Lake Ziway is situ- tors linked to seasonality [7, 10]. Although the numerous ated at 1636 m above sea level and at 08º01’N and 38º47’E management challenges, the multivariate techniques (Fig. 1 and Table 1) in a complex geological arrangement have a limited usage in the assessment of water quality in of sedimentary deposits. many lakes in developing countries including Lake Ziway. The lake provides water for domestic use and shares Despite its ecological and economic importance, both the same water table with key groundwater aquifers that locally and globally, Lake Ziway has been facing alarming provide borehole water supply for the rapidly-expanding environmental degradation and loss of biodiversity due human population in Ziway town and surrounding areas. to the pressure of human land-use and climate change. Currently, the population of Lake Ziway catchment is Substantial increases in water pollution, largely from the about 2 million and about 1.9 million livestock [13, 14]. discharge of untreated municipal and industrial waste The fishery of the lake is also an important source of live - and high sediment load from agricultural fields caused lihood to scores of fishermen and their families and pro - by unchecked erosion in upper catchments, are the major vides the sources of food to many families within the lake causes. The main significances of the study will address basin and beyond. Tourism is also a major activity in the for discouraging farming activities along the lakeshore; area due to the presence of hipotanuse, scenery Islands to set a standardized buffer zone around the lake shore; with monasteries, bird sanctuaries and the presence of all the free access policy (no ownership scenario) to water rich tropical related biodiversity [15]. Other socio-eco- bodies will have had to change for Lake Ziway by giving nomic activities conducted along the lake’s shore include concession rights to users with the appropriate environ- livestock production and small-scale farming [16]. mental regulatory protocols and it also helps to develop Agriculture is the most dominant land use system con- mitigation and restoration strategies for the lake and tributing to the livelihoods of the majority of the catch- aquatic ecosystems in Ethiopia. ment population. The agricultural sector is characterized T ibebe et al. BMC Chemistry (2022) 16:11 Page 3 of 18 Figure1 Location and bathymetric Map of Lake Ziway and its tributaries with the sampling sites by small-scale subsistence-based farming and rising of Table 1 Geographic coordinates of the sample points livestock. About 74.3% of the total land-use types within Sampling site Abr North East Elevation (m) the catchment are agricultural lands. Lake Ziway water description demands have massively increased, along with increased Floriculture effluent Fb 07º54.715’ 038º44.020’ 1642 population and intensification of agriculture since the Floriculture after mixing Fa 07º54.79’ 038º144.111’ 1639 end of the last decade [17]. Bulbula River mouth B 07º53.943’ 038º44.134’ 1641 Ketar River mouth Ka 07º55.398’ 038º52.086’ 1640 Chemicals, reagents and standards Ketar River at Abura Kb 08º02.019’ 038º49.340’ 1646 Analytical reagent grade sodium hydroxide, concen- Town trated hydrochloric acid, concentrated sulfuric acid, con- Meki River at Meki Mb 08º03.019’ 039º01.144’ 1673 centrated phosphoric acid, anhydrous sodium sulfate, Town ammonium persulfate, potassium persulfate, Phenol, Meki River mouth Ma 08º03.379’ 038º56.459’ 1633 sodium nitroprusside, sulfanilamide, N-(1-naphthyl)- Korekonch Kt 07º55.494’ 038º43.697’ 1637 ethylenediamine dihydrochloride, Potassium chloride, Central station C 07º55. 49’ 038º52.934 1635 sodium salicylate, potassium sodium tartarate, boric acid, Tibebe et al. BMC Chemistry (2022) 16:11 Page 4 of 18 potassium antimony tartrate, ammonium molybdate, through 0.45-μm poly tetrafluoroethylene (PTFE) disk ascorbic acid ethanol (99.99%), phenolphthalein, methyl syringe filter, filled in high-density polyethylene (HDPE) orange. All chemical and reagents are products of Sigma- bottles, sealed with Parafilm were collected in the field Aldrich, Germany. and kept in the same environment with other water sam- ples. The results of these field blank samples showed neg - Apparatus and equipment ligible contamination during the sampling, filtering, and UV–Visible Spectrophotometer (Jenway 6405, UK); Kjel- storage processes, as the values of most hydrochemical dahl apparatus (Gallenhamp, USA); Oven dry (Binder, variables were below the detection limit [19–21].. The Germany); Turbidimeter (T-100, Singapore); portable average analytical precision for nutrients was better than multi meter (HACH MM150, China) were used in the 2%. The alkalinity as HCO was estimated by charge bal- experiments. ance [19]. To interpret the data and develop a conclusive understanding of the geochemistry of Lake Ziway water In‑situ measurements quality, a series of statistical tests were performed using All field equipments were calibrated according to the an IBM SPSS 22.0 [20]. These tests include a normal - manufacturer’s specifications. Temperature, pH, elec - ity test, descriptive statistics (mean, max, min, SD etc.), trical conductivity, total dissolved solids, and dissolved Spearman correlation, and principal component analysis oxygen (DO) were measured with a portable ion meter and factor analysis (PCA/FA) [19, 20]. The normality test (HACH model150 made in Spain. Secchi depth (SD) and correlation analysis were performed by considering was measured with a standard Secchi disk of 20  cm all of the parameters to predict the degree of dependent diameter. of one variable on others with a correlation significance level of 0.01. PCA/FA was applied to group the changing Sampling and laboratory analysis patterns of physico-chemicals parameters and nutrients The collection of data for analyses of major nutrients in order to explain the fluctuation in dataset with mini - and physico-chemical water quality parameters in water mum loss of original information. PCA/FA is attained by samples in different depths at the selected sampling sites analyzing the correlation matrix and transforming the taken at selected seasons for two years. Nine representa- original variables to uncorrelated ones, commonly called tive sampling sites were selected purposefully based on varifactors (VFs) [19]. Additionally, the eigen values in access, safety, waste disposal activities, lake inflow and PCA/ FA define how much variance is present in associ - outflow and geographical proximity. These sites were ated VFs. The VF that holds the maximum eigenvalue is evenly distributed along the course of Lake Ziway. found to have the most co-variability [22, 23]. Suitability Water samples were collected with a Van Dorn water of the dataset for PCA/FA was tested by using the Kai- sampler from different depths of the entire water column ser–Meyer–Olkin (KMO) and Bartlett’s sphericity meth- at 1  m intervals and mixed in equal proportions to pro- ods which is run prior to PCA/FA. duce composite samples. The collected water samples were kept in precleaned polyethylene plastic bottles for Chemical analysis nutrient analysis following the standard guideline values Concentrations of inorganic nutrients (NO-N, NO -N, 2 3 [18]. All water samples were stored in insulated dark ice PO-P, NH -N, total phosphorus (TP), total nitrogen 4 3 boxes and taken on the same day to the laboratory. (TN), total inorganic nitrogen (TIN) and soluble reactive For quality control and quality assurance, the stand-silica (SiO -Si) were determined for all samples following ard operating procedures were strictly followed during the standard procedures outlined in [18]. Table 2 summa- sampling and laboratory analysis as directed by [18]. rizes the analytical methods for surface water samples. In order to avoid contamination, powder-free nitrile exam gloves and mask was used during the sample col- Multivariate statistical methods lection and testing. At each sampling site, three water Lake water quality data sets were subjected to three mul- samples, i.e., at left bank, middle, and right bank of the tivariate techniques: cluster analysis (CA), principal com- lake, were taken and mixed before a composite sample ponent analysis (PCA) and factor analysis (FA) [24]. All was prepared. The sample bottles were prerinsed three statistical analyses were performed using the SPSS statis- times with the same water before the final sample was tical software (Version 20) and PAST statistical software acquired. Before the in  situ measurements, the instru- (Version 1.93) [24]. ments were properly calibrated. Triplicate samples were run, and the average recovery of quality control analysis Cluster analysis was 99 ± 4%, indicating the good quality of the data. In CA classifies objects, so that each object is similar to the addition, four blank samples of deionized water filtered others in the cluster with respect to a predetermined T ibebe et al. BMC Chemistry (2022) 16:11 Page 5 of 18 Table 2 Summary of analytical methods used for surface water sample (APHA [18]) Parameter Method Description Total alkalinity APHA 2320 B Titrimetric pH Membrane Electrode Portable HACH model 150 EC Membrane Electrode Portable HACH model 150 TDS Membrane Electrode Portable HACH model 150 Temperature Membrane Electrode Portable HACH model 150 Ammonia APHA4500-NH C Spectrophotometric, Phenate Nitrate Yang et al., 1998 Spectrophotometric, sodium salicylate NitriteAPHA4500- NO A Spectrophotometric, Colorimetric TN APHA4500- N C Spectrophotometric, Kjeldahl method Phosphate APHA4500-P C Spectrophotometric, Ascorbic Acid TP APHA4500-P C Spectrophotometric, Persulfate digestion method, then Ascorbic acid method Secchi depth Lind Field equipment Dissolved oxygen Membrane Electrode probe method (YSI model 58) Silica APHA4500-SiO Spectrophotometric, Molybdosilicate Method selection criterion. Hierarchical agglomerative clus- P = 1/n (Ci/Si) tering is the most common approach, which provides n=1 intuitive similarity relationships between any one sam- ple and the entire data set and is typically illustrated by where P is comprehensive pollution index, C is the a dendrogram (tree diagram). The dendrogram provides −1 measured concentration of the pollutant (mg L ), S a visual summary of the clustering processes, presenting represents the limits allowed by the State Environmen- a picture of the groups and their proximity with a dra- tal Protection Administration (SEPA) in the particular matic reduction in dimensionality of the original data [7, country for water quality standard, and n is the number 25, 26]. In this study, hierarchical agglomerative CA was of selected pollutants [24, 28, 29]. Ultimately, the values carried out on the normalized data by means of Ward’s determined for P could be used to classify the water qual- method, using squared Euclidean distances as a measure ity level of the lake (Table 3). of similarity. Principal component analysis (PCA)/factor analysis (FA) Statistical analysis In this research, PCA was applied to summarize the Different procedures of statistical analyses were used statistical correlation among water quality parameters. to analyze the data. Analysis of variance (ANOVA) The concentrations of physico-chemical parameters and was conducted to test the differences between, and nutrients tend to differ greatly; as such, the statistical within, sampling sites at 95% confidence interval using results should be highly biased by any parameter having SPSS (version 20) software (Chicago, USA). The differ - a high concentration. Thus, each water quality parameter ences between sites were examined to determine the was standardized before PCA the analysis was performed spatial variation while the differences within seasons in order to minimize the influence of different variables addressed the temporal variation for water samples. and their respective units of measurements. The calcula - Table 3 Standard of surface water quality classification (WHO, tions were performed based on the correlation matrix of 1996) chemical components, and the PCA scores were obtained from the standardized analytical data [25, 27]. Comprehensive pollution index (P) Water quality level ≤< 0.20 I cleanness Comprehensive evaluation of water quality in the lake 0.20 to 0.40 II sub-cleanness A comprehensive pollution index method has been 0.41to1.00 III slight pollution applied to evaluate water quality qualitatively in many 1.01 to 2.00 IV moderate pollution existing studies. The comprehensive pollution index can ≥ 2.01 V sever pollution be calculated as follows [28]: Tibebe et al. BMC Chemistry (2022) 16:11 Page 6 of 18 Table 4 Mean, mean standard error and range of the physicochemical parameters in dry season (Temp for temperature in C; DO for −1 + −1 −1 dissolved oxygen in mg L ; pH for H concentration; EC for electrical conductivity in μS cm ; TDS for total dissolved solids in mg L ; −1 SD for secchi depth in cm; TA for total alkalinity, mg L ; Turbidity in NTU) Site Temp DO pH EC TDS SD TA B x̄ ± Std. Err 24.8 ± 1.3 7.3 ± 1.8 8.3 ± 0. 3 385 ± 38 248 ± 24 24.6 ± 0.5 314 ± 41 Range 21–28 4.8–12.4 8–9 289–521 189–335 23–26 216–425 C x̄ ± Std. Err 21.2 ± 1.3 5.2 ± 1.1 8.1 ± 0.2 408 ± 43 268 ± 33.7 27.7 ± 1.3 225 ± 22 Range 18–25 4–8.4 8–9 337–558 215–393 23–30 184–300 Fa x̄ ± Std. Err 23.8 ± 1.0 6.8 ± 1.7 8.13 ± 0.2 639.73 ± 114 423.8 ± 83 26 ± 0.7 320 ± 50 Range 21–26 4.2–11.2 8–9 376–1028 249–720 24–28 200–425 Fb x̄ ± Std. Err 23. 5 ± 1.4 6.2 ± 1.4 7.56 ± 0.1 1233.6 ± 107 789.6 ± 68.3 25.8 ± 0.6 463 ± 71.7 Range 19–27 2.6–9.3 7–8 1050–1650 672–1056 24–27 200–625 Ka x̄ ± Std. Err 21.3 ± 0.5 4.4 ± 1.5 8.1 ± 0.16 382.6 ± 39.4 237.8 ± 18.6 24.4 ± 1.8 24 ± 29 Range 20–23 2.5–9.2 7–8 307–543 196–307 19–30 156–325 Kb x̄ ± Std. Err 20.15 ± 0.2 5.5 ± 0.6 7.4 ± 0.1 170.7 ± 14.3 107.7 ± 9 21 ± .7 187 ± 44 Range 20–21 4.0–7.0 7–8 134–204 86–130 19–23 104–275 Ko x̄ ± Std. Err 24.2 ± 1.3 5.4 ± 1.3 7.4 ± 0.8 399 ± 14.4 254 ± 10.5 25.6 ± 1.1 330 ± 38 Range 21–28 2.4–9.0 7–9 362–435 219–278 22–28 200–425 Ma x̄ ± Std. Err 22.2 ± 0.7 4.4 ± 0.6 7.98 ± 0.2 404.1 ± 43 291.9 ± 37.7 27.8 ± 1.8 227.6 ± 26 Range 21–25 3.3–6.5 8–9 333–576 213–403 22–31 160–300 Mb x̄ ± Std. Err 22.9 ± 0.8 7.6 ± 1.6 7.95 ± 0.2 424 ± 625 267 ± 38 21 ± 0.71 263 ± 48 Range 20–24 3.8–10 7–9 203–584 130–365 19.00 100–375 Table 5 Mean, mean standard error and range of the physicochemical parameters in wet season season ( Temp for temperature in C; −1 + −1 DO for dissolved oxygen in mg L ; pH for H concentration; EC for electrical conductivity in μS cm ; TDS for total dissolved solids in −1 −1 mg L ; SD for secchi depth in cm; TA for total alkalinity, mg L ; Turbidity in NTU) Site Temp DO pH EC TDS SD TA B x̄ ± Std. Err 22 ± 0.5 4.72 ± 0.4 8.59 ± 0.1 274.5 ± 9 175.7 ± 58.2 15.3 ± 0.3 207 ± 6.3 Range 21.5–23 4.27–5.4 8.4—8.7 176—456 113–292 15–16 200–220 C x̄ ± Std. Err 21.2 ± 0.3 4.75 ± 28 8.57 ± 0.1 218 ± 44 147.8 ± 37 17.3 ± 0.7 168 ± 6.1 Range 20.7–22 4.4–5.3 8.4–8.7 173–306 110–222 16–18 160–180 Fa x̄ ± Std. Err 22 ± 0.6 4.4 ± 0.3 8.78 ± 0.5 353 ± 89 226 ± 56 18.6 ± 0.7 172 ± 14 Range 20.8–23 3.95–4.8 7.9–9.7 187–488 120–312 18–20 148- 196 Fb x̄ ± Std. Err 21 ± 1.6 6.2 ± 0.7 8.53 ± 0.1 370 ± 98 216 ± 84 19.6 ± 0.7 250 ± 111 Range 18–24 4.9–7.2 8.3–8.7 175–478 49–306 19–21 120–472 Ka x̄ ± Std. Err 22 ± 0.3 3.1 ± 0.3 8.4 ± 0.1 230 ± 41.4 180 ± 32 16–1.2 117–35 Range 20—27 2.4 -3.5 8.3–8.5 183–312 117–221 14–18 80–188 Kb x̄ ± Std. Err 20 ± 0.2 2.6 ± 0.6 7.78 ± 0.2 101 ± 17.9 65 ± 11.7 17.3 ± 0.9 72.3 ± .3 Range 20–21 1.4–3.6 7.5–8.3 65–120 42–78.3 16–19 72–73 Ko x̄ ± Std. Err 22.5 ± 0.9 4.8 ± 0.3 8.7 ± 0.1 381 ± 101 234 ± 61 18.3 ± 0.9 175 ± 14.6 Range 21–24 4.35–5.3 8.5–8.8 179–496 116–318 17.-20 146–190 Ma x̄ ± Std. Err 23 ± 0.1 3.9 ± 0.1 8.6 ± 0.1 273 ± 49 153 ± 21.3 17 ± 1.2 130 ± 24.9 Range 22–23 3.7–4.1 8.6–8.61 176–337 113–185 15–19 100–180 Mb x̄ ± Std. Err 20.4 ± 0.7 4.8 ± 0.8 7.4 ± 0.5 120 ± 3 77 ± 1.9 18 ± 1.7 100 ± 20 Range 19.5–22 3.4–5.8 6.5–8.3 115–126 73.7–80.4 15–21.0 60–120 T ibebe et al. BMC Chemistry (2022) 16:11 Page 7 of 18 −1 All correlations were considered statistically significant showed the lowest mean EC (134 μScm ) in dry and 65 −1 when the significance level was p < 0.05. μS cm in wet seasons while site Fb recorded the high- −1 est mean values of 1650 μS cm in dry and Fa mean val- −1 Results ues 488 μS cm in wet seasons, respectively (Tables  4 Spatial and temporal variations in Physico‑chemical water and 5). Total dissolved solid (TDS) ranged from 119.77 −l quality parameters to 746.80  mg L with the lowest value was recorded in The average spatio-temporal values of physico-chem - sampling site Kb and the highest value was recorded in ical water quality parameters in dry and wet seasons sampling site Fb while in the wet season, it ranged from −1 are given separately in Tables  4 and 5, respectively. 129.5 to 547.76  mg L at sites Mb and Fb, respectively The surface-water temperature measured in the study (Tables 4 and 5). The mean SD values ranged from 0.20 to site ranged from 19.0 to 27.0  °C and 18.0 to 27.0  °C 0.22 m, with mean values of 0.21 m. in dry and wet seasons, respectively. The highest val - Total alkalinity (TA) in the study sites were ranged −1 ues were measured at B and Ka and the lowest values from 100 to 625 and 60 to 472 mg CaCO L in dry and were measured at C and Fb during dry and wet seasons, wet seasons, respectively (Tables  4 and 5). Mb showed −1 respectively. the lowest mean TA 100 mg CaCO L in dry and 60 mg −1 The level of dissolved oxygen (DO) ranged from 2.42 CaCO L in wet seasons while the highest mean values −1 −1 −1 to 12.4 mg L and 1.4 to 7.2 mg L in dry and wet sea- of TA was 625 and 472  mg CaCO L in dry and wet sons, respectively (Tables  4 and 5). The lowest values seasons, respectively in sampling site Fb. −1 in both seasons were at K (2.4  mg L ) in dry and K o b −1 (1.4 mg L ) in wet seasons where as the highest values Nutrients analyses −1 −1 were at B (12.4  mg L ) in dry and F (7.2  mg L ) in The spatial and temporal variations of nutrients are sum - wet seasons, respectively. Whilst the pH values ranged marized in Tables  6 and 7. The mean NO -N concentra- −1 from 7.0 to 9.0 and 6.5 to 9.7 in dry and wet seasons tion ranged from 0.1 to 5.26 mg L and 0.01 to 0.86 mg −1 respectively (Tables 4 and 5). L in dry and wet seasons, respectively. The highest The mean electrical conductivity (EC) values in the mean NO -N was recorded at Fb in dry and Kb in wet −1 study sites ranged from 134.0 to 1650 μS c m in the dry season while the lowest values were in Ma in both dry −1 −1 season and 65.0 to 488 μS c m in the wet season. Kb and wet seasons. N O -N ranged from 0.06 to 2.89 mg L −1 Table 6 Mean, mean standard error and range of nutrient concentrations (mg L ) measured in sampling sites at Lake Ziway in dry −1 −1 −1 season ( TP for total phosphorus in mg L ; SRP for soluble reactive phosphorus in mg L ; NO -N for nitrite-nitrogen in mg L ; NO -N 2 3 −1 −1 −1 for nitrate-nitrogen in mg L ; NH4-N for ammonia–nitrogen in mg L ; TIN for total inorganic nitrogen in mg L ; TN for total nitrogen −1 −1 in mg L; SiO -Si for soluble silica in mg L ) Site TP PO ‑P NO ‑N NO ‑N NH ‑N TIN TN SiO ‑Si 4 2 3 3 2 B x̄ ± Std. Err 0.12 ± 0.02 0.06 ± 0.01 0.48 ± 0.20 0.17 ± 0.04 0.21 ± 0.05 0.85 ± 0.25 5.7 ± .25 46.2 ± 6.2 Range 0.06–0.15 0.04–0.08 0.188–1.3 0.06–0.25 0.1–0.35 0.34–1.8 4.9–6.4 32–68 C x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.29 ± 0.05 0.26 ± 0.17 0.17 ± 0.03 0.72 ± 0.22 9.1 ± 0.65 46.8 ± 2.4 Range 0.1–0.185 0.03–0.07 0.18–0.41 0.01–0.91 0.09–0.26 0.29–1.5 7.34–11.20 39.5–54 Fa x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.96 ± 0.22 0.38 ± 0.13 0.24 ± 0.04 1.6 ± 0.36 6.1 ± .6 46.8 ± 4.9 Range 0.105–0.23 0.03–0.1 0.6–1.8 0.1–0.75 0.15–0.35 0.9–2.9 4.5–8.3 35–60 Fb x̄ ± Std. Err 0.19 ± 0.05 0.08 ± 0.03 1.7 ± 0.44 0.58 ± 0.19 0.29 ± 0.05 2.6 ± 0.6 8.1 ± .56 91.39.8 Range 0.05–0.32 0.04–0.16 0.72–2.89 0.08–0.97 0.15–0.42 1.0–4.2 6.5–9.8 56.9–114 Ka x̄ ± Std. Err 0.13 ± 0.02 0.05 ± 0.01 0.35 ± 0.08 0.12 ± 0.02 0.22 ± 0.05 0.68 ± 0.10 7.1 ± 1.1 50.5 ± 6.7 Minimum 0.09–0.19 0.04–0.07 0.155–0.64 0.08–0.18 0.10–0.4 0.40–0.95 5.0–11 40.1–77 Kb x̄ ± Std. Err 0.17 ± 0.03 0.05 ± 0.01 0.34 ± 0.03 0.22 ± 0.05 0.34 ± 0.03 0.89 ± 0.08 9.2 ± 1.3 68 ± 7.8 Range 0.08–0.24 0.04–0.09 0.24–0.42 0.07–0.35 0.24–0.42 0.76–1.2 14-Jul 43–88.7 Ko x̄ ± Std. Err 0.12 ± 0.02 0.05 ± 0.01 0.32 ± 0.09 0.10 ± 0.03 0.2 ± 0.04 0.62 ± 0.14 9.7 ± .42 39 ± 3.4 Range 0.07–0.19 0.04–0.07 0.05 ± 0.188 0.01–0.18 0.09–0.3 0.3–1.2 8.5–11 30–47 Ma x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.23 ± 0.08 0.10 ± 0.02 0.17 ± 0.03 0.55 ± 0.08 6.3 ± .49 49 ± 3.8 Range 0.09–0.2 0.04–0.06 0.1–0.53 0.04–0.14 0.114–0.3 0.35–0.8 4.8–7.6 38–62 Mb x̄ ± Std. Err 0.97 ± 0.75 0.06 ± 0.01 0.41 ± 0.17 0.28 ± 0.19 0.41 ± 0.17 1.3 ± 0.53 9.4 ± 1.2 61.3 ± 3.9 Range 0.2–3.95 0.04–0.08 0.06–1.1 0.03–1.1 0.1–1.1 0.21–3.2 5.6–13 50.8–73 Tibebe et al. BMC Chemistry (2022) 16:11 Page 8 of 18 −1 Table 7 Mean, mean standard error and range of nutrient concentrations (mg L ) measured in sampling sites at Lake Ziway in wet −1 −1 −1 season( TP for total phosphorus in mg L ; SRP for soluble reactive phosphorus in mg L ; NO -N for nitrite-nitrogen in mg L ; NO -N 2 3 −1 −1 −1 for nitrate-nitrogen in mg L ; NH4-N for ammonia–nitrogen in mg L ; TIN for total inorganic nitrogen in mg L ; TN for total nitrogen −1 −1 in mg L; SiO -Si for soluble silica in mg L ) Site TP(mg/L) PO ‑P NO ‑N NO ‑N NH ‑N TIN TN SiO ‑Si 4 2 3 3 2 B x̄ ± Std. Err 0.35 ± 0.1 0.046 ± 0.01 0.47 ± 0.06 0.15 ± 0.03 0.11 ± 0.02 0.73 ± 0.08 5.13 ± 1.20 35.5 ± 11.60 Range 0.21–0.42 0.04–0.06 0.35–0.53 0.08–0.20 0.07–0.13 0.57–0.86 2.8–7.00 12.37–47.30 C x̄ ± Std. Err 0.38 ± 0.023 0.05 ± 0.01 0.33 ± 0.12 0.17 ± 0.12 0.09 ± 0.01 0.59 ± 0.24 6.02 ± 0.26 43.6 ± .72 Range 0.34–0.41 0.03–0.08 0.21–0.57 0.05–0.41 0.08–0.10 0.352–1.1 5.60–6.5 42.9–45.1 Fa x̄ ± Std. Err 0.29 ± 0.12 0.05 ± 0.01 0.74 ± 0.22 0.26 ± 0.12 0.09 ± 0.03 1.1 ± 0.33 7.0 ± 0.81 36 ± 7.5 Range 0.18–0.52 0.04–0.07 0.43–1.2 0.03–0.39 0.03–0.14 0.48–1.62 5.6–8.4 22.2–47.7 Fb x̄ ± Std. Err 0.42 ± 0.175 0.11 ± 0.04 0.89 ± 0.44 0.44 ± 0.19 0.09 ± 0.04 1.42 ± 0.62 6.66 ± 0.53 39.2 ± 26 Range 0.17–0.75 0.04–0.16 0.34–1.8 0.17–0.80 0.02–0.15 0.75–2.7 5.6–7.4 5.8–90.5 Ka x̄ ± Std. Err 0.23 ± 0.02 0.05 ± 0.01 0.84 ± 0.16 0.47 ± 0.04 0.09 ± 0.03 1.39 ± .18 7.6 ± 0.43 37.9 ± 4.68 Range 0.20–0.27 0.04–0.07 0.5—1.1 0.4–0.54 0.03–0.12 1.1 ± 1.7 7–8.4 30.3–46.45 Kb x̄ ± Std. Err 0.73 ± 0.27 0.06 ± 0.01 1.2 ± 0.20 0.86 ± 0.22 0.15 ± 0.10 2.2 ± .50 8.1 ± 0.86 38.15 ± 17.10 Range 0.24–1.2 0.05–0.08 0.89–1.6 0.52–1.3 0.05–0.3 1.5–3.1 7–9.8 11.24–70 Ko x̄ ± Std. Err 0.27 ± 0.08 0.05 ± 0.01 0.42 ± 0.12 0.20 ± 0.04 0.08 ± 0.02 0.7 ± 0.12 12 ± 1.9 34.3 ± 11 Range 0.12–0.376 0.04–0.07 0.2–0.6 0.1–0.3 0.05–0.1 0.5–0.9 8.4–14 12.6–48.2 Ma x̄ ± Std. Err 0.24 ± 0.04 0.06 ± 0.02 0.76 ± 0.06 0.23 ± 0.11 0.08 ± 0.02 1.06 ± 0.14 6.1 ± .55 42.62 ± 1.9 Range 0.19–0.33 0.04–0.082 0.66–0.86 0.01–0.369 0.03–0.11 0.8–1.3 4.97–7 38.96–45.42 Mb x̄ ± Std. Err 1.0 ± 0.30 0.09 ± 0.02 1.02 ± .2 0.50 ± 0.24 0.16 ± 0.06 1.66 ± .48 13.5 ± 4.5 39.1 ± 14.72 Range 0.47–1.5 0.06–0.12 0.7–1.42 0.02–0.75 0.05–0.23 0.76–2.4 5.6–21 11.2–61.2 −1 and 7). Mean TP concentration was highest at M in both in dry season and 0.20 to 1.8 mg L in wet season during seasons whereas the lowest concentrations were at B and the study period (Tables 6 and 7). Low concentrations of K in dry and K in wet seasons. NO -N were at M in both dry and wet seasons whereas o a 2 a The concentration of SiO -Si ranged from 39.4 to high concentrations were at F and K in dry and wet sea- b b −1 91.3 and 35.5 to 42.6  mg L with mean values of 55.4 sons, respectively (Tables 6 and 7). −1 and 38.5  mg L in dry and wet seasons, respectively Ammonia- nitrogen (NH -N) concentrations ranged −l −1 (Tables 6 and 7) whereas the highest and lowest concen- from 0.17 to 0.29 mg L in dry and 0.08 to 0.15 mg L trations were noticed during the dry and wet seasons, in wet seasons with the lowest concentrations at C and respectively (Tables  6 and 7). Significant fluctuations in M in dry and K and M in wet seasons while the highest a o a the mean SiO -Si concentrations were observed in both values in F and K in dry and wet seasons respectively b b seasons showed that the fluctuations in it over the differ - (Tables  6 and 7). The mean total nitrogen (TN) concen - −1 ent seasons and across the different sampling sites were trations ranged from 5.69 to 12.21 mg L in dry and 4.98 −1 significant in the lake. to 12.0  mg L in wet seasons. The highest concentra - tions were at K in dry and at F in wet season whereas o b Multivariate analysis the lowest concentrations were at B in both seasons Principal component analysis (PCA) (Tables 6 and 7). Four components of PCA analysis showed 88.10% of the Soluble reactive phosphorus (SRP) ranged from 0.05 to −1 variance in the data set of the wet season, as the eigen- 0.08 mg L and showed similar concentrations for lower vectors classified the 15 physico-chemical parameters values for most of the sampling sites and high values at F into four groups. P C (38.93% of the total variance in in the dry season, while in the wet season it ranged from −1 the data set) has strong positive loadings on TP, NH -N, 0.05 to 0.12  mg L (Tables  6 and 7). Most sites have NO-N, NO -N, TIN, pH and SD (Table  8). The sec - also similar concentrations in the wet season and only 2 3 ond component (P C ) accounted for 24.02% of the total Site F had highest values. Similarly, the mean TP con- −1 variance measured, demonstrated strong positive load- centrations ranged from 0.12 to 0.97 mg L and 0.23 to −1 ings for TN, EC, TDS and TA and the third component 1.02 mg L in dry and wet seasons respectively (Tables 6 T ibebe et al. BMC Chemistry (2022) 16:11 Page 9 of 18 Table 8 The Factor loadings values and explained variance of water quality in two seasons (positive and negative strong correlations are marked bold) Dry season Wet season Parameters PC1 PC 2 PC3 PC 4 Parameters PC1 PC2 PC3 PC 4 TP 0.065 − 0.69 0.48 0.10 TP 0.92 − 0.18 0.21 0.23 PO4 0.93 − 0.16 0.13 − 0.03 PO4 0.15 − 0.18 0.85 0.37 NH3 0.907 − 0.04 0.27 − 0.11 NH3 0.85 − 0.38 0.32 0.05 NO2 0.966 0.10 − 0.07 − 0.09 NO2 0.88 0.09 − 0.33 − 0.15 NO3 0.963 − 0.04 − 0.21 0.10 NO3 0.90 − 0.005 0.03 − 0.34 TIN 0.98 − 0.01 − 0.17 0.06 TIN 0.94 0.03 − 0.14 − 0.235 TN − 0.097 − 0.32 0.18 0.88 TN 0.56 0.68 − 0.28 0.20 SiO2 0.791 − 0.52 − 0.29 − 0.05 SiO2 0.11 − 0.10 − 0.62 0.49 Temp 0.283 0.79 0.43 − 0.09 Temp − 0.11 0.19 0.63 − 0.62 DO 0.349 − 0.42 0.76 − 0.09 DO 0.08 0.55 0.64 0.40 PH − 0.373 0.76 0.49 0.08 pH − 0.85 0.13 0.01 − 0.17 EC 0.963 0.16 − 0.02 0.09 EC − 0.25 0.96 − 0.07 0.03 TDS 0.955 0.17 − 0.05 0.08 TDS − 0.26 0.95 − 0.087 0.03 SD 0.035 0.66 − 0.34 0.32 SD 0.61 0.58 − 0.21 − 0.16 TA 0.801 0.54 0.20 0.11 TA 0.44 0.63 0.43 0.03 Eigen value 7.97 3.05 1.66 0.96 Eigen value 5.84 3.60 2.52 1.26 % variance 53.133 20.34 11.09 6.41 % variance 38.93 24.02 16.76 8.39 % Cumulative variance 53.133 73.47 84.56 90.97 % cumulative variance 38.93 62.95 79.71 88.10 (PC ) demonstrated 16.76% of the total variance and have of the following parameters: nutrients (NH-N, NO -N, 3 3 2 strong positive loadings on SiO-Si, PO -P, DO and tem- NO -N, TIN, PO-P, SiO -Si), TDS, EC, TA. The second 2 4 3 4 2 perature, while, the fourth component (P C ) accounts PC accounted for 20.34% of the total variance and had only 8.39% of the total variance in the season (Table 8). strong positive loading with temperature, pH, TP and The dry season PCA analysis showed that four principal SD as the associated parameters. The third PC explained components (PCs) represented about 90.97% of the total 11.09% of the total variations between sites comprising variation in the entire dataset. The first PC accounted for only DO. Scree plot showed the eigenvalues sorted from 53.4% of the total variations between sites and comprised large to small as a function of the principal components 28 42 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 Component Component Fig. 2 a Wet season Scree plot of the eigenvalues. b Dry season Scree plot of the eigenvalues Eigenvalue% Eigenvalue % Tibebe et al. BMC Chemistry (2022) 16:11 Page 10 of 18 Fig. 3 Results of the bi-plot of the correlation between for various water quality parameters with respect to studied sites using PCA in the dry season number after the fourth PC. After the fourth PC (Fig. 2a, Cluster analysis (CA) b), starting in the downward curve, other components A dendrogram of sampling sites obtained by Ward’s can be omitted. method is shown in Fig.  5. Nine sampling sites were The bi-plot of PCs associated with nutrients (NH -N, divided into three groups. Cluster 1 corresponded to site NO-N, NO-N, SiO -Si and PO -P), EC and TDS char- Fb, which was located in the western part of the lake. 3 2 2 4 acterizing Fb sampling site from axis 1 (Fig. 3) and Fa dis- Cluster 2 included site Kb, which were located in the tinctiveness was attributed to temperature, SD and TA. eastern portion of the lake. Cluster 3 contained sites Fa, The parameter influencing the distinction in the B site K and B the western part of the lake, C which was in the from axis 2 was mainly pH while Mb site from axis 2 was lake central station; site Mb and Ma in northern part of influenced by DO, TN and TP in the dry season. the lake and Ka was in the eastern part of the lake. The bio-plot of PCs associated with nutrients (NH -N, The wet season classification performed by the use of NO-N, NO-N, SiO -Si, TIN, PO -P and TP), which cluster analysis grouped in all the nine sampling sites 3 2 2 4 were the key parameters characterizing the Mb and Kb of the basin into three statistically significant clusters sampling sites (Fig.  4) and Fa distinctiveness was attrib- (Fig. 6). uted to temperature, TDS, EC and DO. The parameter influencing the distinction in the K site was mainly pH Comprehensive evaluation of Lake Ziway water quality while Fb site was influenced by DO, TN, TA and SD in analysis the wet season. The values of the comprehensive pollution index were For the two temporal clusters, 90.97% and 88.10% of 1.8, 1.0, 1.01 and 1.08 for sites Fb, Fa, B and Mb, respec- the variances in dry and wet seasons were explained by tively (Table  9), which demonstrated moderate pollution the four main factors, respectively. in dry season while sampling sites of Ka, Ma, C, and Kb T ibebe et al. BMC Chemistry (2022) 16:11 Page 11 of 18 Fig. 4 Results of the bi-plot of the correlation between for various water quality parameters with respect to studied sites using PCA in the wet season Fig. 5 Dendrogram based for agglomerative hierarchical clustering Fig. 6 Dendrogram based for agglomerative hierarchical clustering (wards method) based on the PCA scores in dry season (wards method) based on the PCA scores in the wet season Tibebe et al. BMC Chemistry (2022) 16:11 Page 12 of 18 Table 9 Paired samples test for dry and wet seasons Paired samples test Paired differences Sig. (2‑tailed) Mean Std. deviation Std. error mean Pair 1 TP in dry—TP in Wet − 0.20 0.15 0.05 0.00 Pair 2 PO -P in dry—PO4-P in wet − 0.01 0.02 0.01 0.27 Pair 3 NH -N in Dry—NH3-N wet 0.11 0.05 0.02 0.00 Pair 4 NO -N in Dry—NO2-N in wet − 0.171 0.502 0.17 0.34 Pair 5 NO -N in Dry—NO3-N in wet 0.48 1.68 0.56 0.42 Pair 6 TIN in dry—TIN in wet 0.41 2.14 0.71 0.58 Pair 7 TN in dry—TN in wet 0.57 2.79 0.93 0.56 Pair 8 SiO -Si in dry—SiO2- Si in wet 16.88 15.68 5.23 0.01 Pair 9 Temp in dry—Temp in wet 1.68 1.22 0.416 0.00 Pair 10 DO in dry—DO in wet 0.58 0.60 0.20 0.02 Pair 11 PH in dry—DO in wet 2.97 0.97 0.32 0.00 Pair 12 EC in dry—EC in wet 109.04 112.82 37.61 0.02 Pair 13 TDS in dry—TDS in wet 73.10 71.67 23.89 0.02 Pair 14 SD in dry—SD in wet 7.73 2.11 0.70 0.00 Pair 15 TA in dry—TA in wet 93.65 85.24 28.41 0.01 −1 Table 10 Comparison of the physico-chemical parameters and nutrients of Lake Ziway with other tropical lakes (mg L for nutrients Lakes Temp( c) DO P pH EC SRP TP NO ‑N SiO ‑Si SD References 3 2 Hawasa 23.5 5–7 8.66 846 0.015 0.034 0.025 37.6 0.85 [30] Chamo 26.3 5–9 8.84 1910 0.118 0.182 0.033 1.0 0.18 [30] Hayq 18.2 1–8.4 9 910 0.022 0.058 0.042 3.7 2.7 [46] Tana 20–27 5.9–7 7.3–8.5 115–148 1.8 0.1–1 0.51–1.82 [58] Abaya – – 8.9 623 0.04 – – 40 – [59] Langano – – 9.4 1810 0.09 – – 48 – [59] Bishoftu – – 9.2 1830 0.005 to 0.1 – – 38 – [59] Abijata – – 10.2 15,800 0.05 – – 128 – [59] Shala – – 9.9 19,200 0.76 – – 112 – [59] Chitu – – 9.8 28,600 1.7 – – 320 – [59] Ziway 23 5 8.1 404 0.06 0.311 0.21 40.7 0.2 Present study have pollution index of 0.71, 0.69, 0.81, 0.79 and 0.84, Discussion respectively, which demonstrated slight pollution in the Spatial and temporal variations Physico‑chemical water same season. However, in the wet season, the values of quality of Lake Ziway the comprehensive pollution index ranged from 0.38 to The spatial and temporal variation of mean water tem - 0.68 which demonstrated slight pollution of the whole perature in Lake Ziway was not significant (p > 0.05) sampling sites. during the study period. The mean temperature of the lake water was 23.0 °C in both seasons, which is almost similar to the previously reports in [30] but lower than Temporal variation of water quality the value reported by [31]. Lake Ziway has narrow sea- Significant temporal variations were observed in physico- sonal fluctuations in water temperature due to the lake chemical parameters and nutrients of Lake Ziway water is shallow tropical lake. quality where most of the physicochemical parameters The lowest DO values in dry season at K was attrib- have significantly higher values in the dry season as com - uted to human impacts like fishing, car and human pared to wet season (P < 0.05) (Table 10). washing while the low DO level at Kb in the wet season T ibebe et al. BMC Chemistry (2022) 16:11 Page 13 of 18 was attributed to its muddy water with agricultural sites were well below the WHO guideline values pre- −1 runoff. The highest values of DO at sampling sites B in scribed for drinking water purpose (1500 μS c m ) [36]. dry season might be attributed to the presence of mac- Accordingly, the value of EC in different water samples rophytes and phytoplankton with higher biomass and could not be water quality problem of the study area. abundance than other sites [31]. The high values of DO TDS also followed the same trend as that of EC as EC is at sampling site Fb in the wet season could be probably sensitive to variations in dissolved solids, mostly mineral due to high dilution. The overall mean DO concentra - salts, and there were significantly lower value of EC and −1 tion in this study (5.00 mg L ) is much lower than the TDS during the main rainy season which may be because −1 value reported by [32], (8.72  mg L ). Reference [33] of dilution. −1 has also reported the DO concentration of 1.4  mg L Similar result of mean SD values with this study (0.21 m) around the floriculture effluent which is smaller than was reported by [39] which was 0.19 m. however, the range −1 the present study. Concentrations below 4.0  mg L values of this study (0.20 to 0.22 m) was smaller than the val- adversely affect aquatic life [34]. The value of DO in ues which were ranged from 0.20 to 0.35 m and 0.4 to 1.06 m this study is within the [35] and [36] permissible limits. reported by [40, 41], respectively (Table  11). Moreover, [40] According to [35] and [36], the standard for DO value also reported that the mean SD value was 0.29  m in Lake for fisheries and aquatic life is between 5.0 to 9.0  mg Ziway. The declining trend in SD reading is one of the indica - −1 L (Table 10). tions which suggest the increasing trend in turbidity of the The overall mean pH value of the lake water was 8.10 lake, which can be mainly attributed to catchment degrada- which is in a close agreement with previous data reported tion and siltation. by [32] (8.39), [30] (8.65) and [31] (8.44), respectively. Reference [40] reported that the mean value of TA in −1 However, significant temporal variation was noted during the Lake was 247.5  mg C aCO L which was similar −1 the study as significantly lower value was measured dur - value in this study in dry season (239.3  mg C aCO L ) ing the rainy season than dry season. The pH value could where as the value in the wet season (154.6  mg CaCO −1 mainly be controlled by freshwater swamp exudates that L ) was very low. In the lake, TA was solely due to bicar- regulate the acidity of the water body. A pH range of 6 to bonates and carbonate alkalinity that could be traced at 8.5 is normal according to the [18]. In general, the pH of any station during the entire period of study. According Ziway Lake water is within the acceptable range accord- to [7] nutrient status classifications using TA, Lake Ziway ing to [36] (Table 10). can be considered nutrient rich. During all the seasons, −1 The overall mean value of EC (404.30 μS cm ) was fluctuations in TA across the sites were significant. TA comparable with previous report of [30, 31, 37] with EC has generally decreased in the wet seasons probably due −1 values of 410, 478, 419.14 μS cm , respectively. Higher to the dilution effect of the rains and fresh incoming run - conductivity values were measured at the floriculture offs [42, 43]. farming sites than other sampling sites could be attrib- uted to the use of high amount of dissolved agrochemi- Nutrients analyses cals from effluents of floriculture industry [33, 38]. For All the nutrient species analyzed in the surface water the present study, the EC values of different sampling of the lake showed increased trend. The mean nitrate Table 11 Single pollution index and comprehensive pollution index of nine sampling sites in some selected water quality parameters in dry and wet seasons Site Dry season Wet season P P P P P P P P P P P P PO4 NH3 NO2 NO3 DO PO4 NH3 NO2 NO3 DO Fb 0.83 0.20 1.91 0.53 1.20 1.80 0.60 0.06 0.99 0.53 1.21 0.68 Fa 0.48 0.16 1.06 0.04 1.09 1.0 0.50 0.06 0.93 0.04 1.02 0.51 B 0.55 0.14 0.53 0.02 1.33 1.01 1.10 0.07 0.37 0.02 1.18 0.55 Ka 0.50 0.14 0.39 0.01 0.89 0.71 0.50 0.06 0.52 0.01 0.78 0.38 Ma 0.51 0.11 0.31 0.01 0.88 0.69 0.57 0.05 0.84 0.01 0.84 0.46 Ko 0.51 0.13 0.36 0.01 1.07 0.81 0.50 0.05 0.47 0.01 1.03 0.41 C 0.46 0.11 0.32 0.03 1.04 0.79 0.51 0.06 0.82 0.03 0.97 0.48 Kb 0.52 0.12 0.37 0.02 1.10 0.84 0.59 0.10 1.32 0.02 0.70 0.55 Mb 0.62 0.15 0.45 0.03 1.43 1.08 0.85 0.10 1.13 0.03 1.25 0.67 Tibebe et al. BMC Chemistry (2022) 16:11 Page 14 of 18 −1 highly agricultural activities around the lake watershed nitrogen values found in this study (0.21  mg L ) −1 [17]. was higher than those values 0.17, 0.003, 0.06  mg L Higher concentration of SiO -Si was found in dry sea- reported by [30, 31, 44], respectively. The increasing son compared to wet season, this might be because of trend in nitrate concentration in the lake is probably dilution in the wet season. Similar results were reported because of nutrient enrichment of the littoral zone of by [48]. Significant fluctuations in mean SiO -Si concen- the lake from anthropogenic sources from the catch- trations were observed over the different seasons and ment area. The mean nitrite nitrogen values found in −1 across the different sampling sites in the Lake. The range this study (0.5  mg L ) was also higher than the values and mean concentration of SiO -Si in this study (39.36 to reported by previous studies on the lake. For instance, −1 −1 91.29 and 35.53 to 42.62 mg L with mean values of 55.4 [45] and [31] has reported 0.06 and 0.01  mg L nitrite −1 and 38.5  mg L in dry and wet seasons, respectively) is nitrogen respectively. Relatively higher nitrite concentra- higher than that of the reports by previous studies. [37, tions were measured near effluent of floriculture indus - 59] reported that SiO -Si concentrations of Lake Ziway try which could be due to the application of high amount −1 ranged 13.4 to 31 and 14.7 to 37.5  mg L with mean of agrochemicals (Tadele, 2012). Comparatively, higher −1 values of 19.0 and 22.9  mg L in dry and wet seasons, concentration of nitrite nitrogen value also measured in respectively. Lake Ziway than some other Ethiopian lakes for instance, The overall mean concentration of SiO -Si (40.68  mg Lake Hayq [46]. The mean concentration of nitrite nitro - −1 L ) in this study was higher than the previous reported gen in this study is beyond the concentration limit of the values in the same lake and other Ethiopian rift val- EU guide lines for drinking water (0.1  mg nitrite nitro- −1 ley Lakes, Awasa and Chamo by [30, 58, 59] which was gen L ) [35]. Consequently, it might cause environmen- −1 23.8, 37.6 and 1.00  mg L in Lake Ziway, Awasa and tal concern due to its toxicity to aquatic biota as well as Chamo, respectively. In view of the high silica concentra- because of human health effects. −1 −1 tions (> 10 mg SiO L ) commonly encountered in Afri- The mean concentration of NH -N (0.121  mg L ) can lakes [49], the lake might encounter some ecological in this study is closely similar with relatively recent −1 changes especially towards higher Diatom productivity reports; by [39] (0.111  mg L ), and [31] (0.143) but (Table 10). higher than that of earlier reports; for example, by [44] −1 Generally, a pattern of low mean concentrations (0.036 mg L ) indicating increasing trend. The mean of NH-N, NO -N, TN, TIN, SiO -Si in dry season TN concentration in both dry and wet season in this 3 3 2 have higher mean concentrations in wet season. This study is higher than the standard limit value even for strongly indicated point source pollution for this param- eutrophic waters [18] (Table 10). eter, which might be associated with industrial effluents, In addition, the mean SRP concentration (0.06  mg −1 human interference, municipal discharge and animal L ) was higher than that of the pervious reported waste [14]. During dry season both decreased precipita- data of [31, 37, 39, 44] which was 0.016, 0.01, 0.059 −1 tion and increased agricultural withdraws for irrigation and 0.029  mg L , respectively. The measured concen - contributed to lower flows of those nutrients, however, tration is also beyond the range of its threshold (0.05 −1 TP, PO -P and NO -N were observed in a higher con- to 0.1mgL ) as a nutrient for natural waters [47, 58]. 4 2 centration during wet season. Similarly [50, 58], noted This is because in recent times Lake Ziway is exposed that nutrients that have a high concentration during dry to anthropogenic activities due to over usage of agro- season than wet season tend to come from point sources chemicals like fertilizers, pesticides in which organic whose supply is constant, whereas the inverse pattern and inorganic pollutants releases and discharge of can be attributed to non-point sources that are mobilized water from domestic sources, agricultural runoff, and by high run-off during wet periods. horticulture including floriculture activities around the lake. Besides, the mean TP value of the lake water −1 Multivariate analysis (0.311 mg L ) is higher than the previous reported Principal component analysis (PCA) data of [44], and [30], which was 0.069 and 0.219  mg −1 As indicated in the PCA analysis, P C has strong positive L , respectively. Higher TP concentration was also loadings on N H-N, NO-N, NO-N, PO -P, TP, SiO -Si, measured in this lake in this study as compared to that 3 2 3 4 2 TIN, EC, TDS, TA and SD associated sampling sites Mb of other Ethiopian rift valley lakes like Lake Awasa and and Kb during wet season. The presence of nutrients in Chamo [30]. The increasing trend in TP is also probably PC demonstrated the intense of agricultural activities in because of nutrient enrichment of the lake from the 1 T ibebe et al. BMC Chemistry (2022) 16:11 Page 15 of 18 the environment of the lake ecosystem and this resulted In the present study, a scree plot also showed the eigen in pollution with nutrients coming from fertilizers and values sorted from large to small as a function of the prin- pesticides [14]. cipal components number. After the fourth PC (Fig.  2a, One of the main sources of TP in runoff is soils with b), starting in the downward curve, other components high phosphorus levels. In other words, the nutrient can be omitted. The scree plot was used to identify the parameters, pH and SD account for similar patterns seen number of PCs to be retained in order to comprehend in lake water samples. This group of nutrient param - the underlying data structure [53]. Thus, a new set of data eters also reflected the degree of eutrophication of the is obtained. This may explain the variation of data set lake, suggesting that the anthropogenic pollution mainly with fewer variables. Scree plots in PCA/FA to visually from the discharge of domestic and agricultural wastes, assess which components or factors explain most of the industrial sewage and agricultural runoff [14]. Moreo - variability in the data. ver, it might be due to farmers use ammonium fertilizers The bi-plot of PCs associated with nutrients (NH -N, and phosphate pesticides, and the lake receive ammo- NO-N, NO-N, SiO -Si and P O -P), EC and TDS which 3 2 2 4 nium via surface runoff and irrigation waters [17]. Nitrate were the key parameters characterizing the Fb sampling nitrogen source is due to numerous sources, such as, site (Fig.  3), which were due to the floriculture effluents geologic deposits, natural organic matter decomposi- [31, 33] and Fa distinctiveness was attributed to tempera- tion and agricultural runoff [51]. The second component ture, SD and TA. The parameter influencing the distinc - (PC ) demonstrated strong positive loadings for TN, EC, tion in sampling site B was mainly pH while Mb site was TDS and TA. The third components (PC ) demonstrated influenced by DO, TN and TP in the dry season [31]. strong positive loadings for SiO-Si, PO -P, DO and tem- The bio-plot of PCs associated with nutrients (NH -N, 2 4 3 perature. This factor indicates that PO -P source is from NO-N, NO-N, SiO -Si, TIN, PO -P and TP), which 4 3 2 2 4 domestic and agricultural wastes, detergents from indus- were the key parameters characterizing the Mb and Kb tries whereas SiO -Si is from bed rock materials and sampling sites (Fig.  4), can suggest an influence of agri - compounds containing silica from floriculture industry cultural activities in the catchment of the two rivers [33], while, the fourth component (PC ) had no strong feeding the lake (Meki and Ketar Rivers) and Fa distinc- loadings in any measured parameters. tiveness was attributed to temperature, TDS, EC and DO. In the dry season the PCA performed on the cor- The parameter influencing the distinction in the K site relation matrix of means of the analyzed water quality was mainly pH while Fb site was influenced by DO, TN, parameters by sites showed that four principal compo- TA and SD in the wet season. nents (PCs) represented about 90.97% of the total vari- The results from temporal PCA/FA suggested that ation in the entire dataset. The first PC accounted agrochemicals pollution were potential pollution sources nutrients (NH-N, NO-N, NO -N, TIN, PO-P, SiO -Si), for both temporal clusters but that the influence of each 3 2 3 4 2 TDS, EC, TA associated with Fa and Fb sampling sites. was different. The results of the present study showed The high values in these sampling sites were attributed to the existence of the contamination of Lake Ziway in both the point pollution sources from floriculture industry in inorganic and organic agrochemicals mainly in the lake dry season. catchment in particular to Fb, Fa, Mb and Kb sampling The second PC had strong positive loading with tem - sites. The major pollutant sources to the lake might be perature, pH, TP and SD as the associated parameters. mainly from agricultural activities, human interference TP demonstrating that intense agricultural activity had for different purposes, domestic wastes, industrial efflu - occurred at the sampling site Fa and B, causing pollu- ents and urban origin [14]. tion due to fertilizers and pesticides [14, 42] interpreted as nutrient pollution from anthropogenic sources, such Cluster analysis (CA) as eutrophication from domestic wastewater, indus- The three groups obtained by cluster analysis vary trial effluents and agricultural activities. The third PC according to natural backgrounds features, land use explained the total variations between sites compris- and land cover, industrial structure and anthropogenic ing only DO in sampling site Mb. The inverse relation - sources of pollution [14]. The cluster analysis revealed ship between temperature and DO is a natural process different properties at each site with respect to physical because it can hold less dissolved oxygen [42]. The fourth and chemical variables. PC explained site variations with TN only. [52] classified Sites mainly located at middle reach of the lake (Sta- the factor loadings as “strong,” “moderate,” and “weak,” tion C, M, K, K and F ) were grouped under Cluster III, a a o a corresponding to absolute loading values of > 0.75, 0.75 to which were basically at the center of the lake and shore 0.50, and 0.50 to 0.30, respectively. water. In addition, Station Ma and Ka located upstream Tibebe et al. BMC Chemistry (2022) 16:11 Page 16 of 18 of the lake, showed the similar water environment qual- of pollution sources based on parameter association. ity characteristics with these stations. Urbanization and Their findings agree with the present study particu- industrialization level is relatively low at these sites. larly in the association of nutrients with catchment Direct discharged domestic wastewater contaminated runoff in the wet season and point sources during the the water; the cluster III correspond to relatively less pol- dry season. Reference [55] used PCA and CA in 33 luted (LP), because the inclusion of the sampling location sampling sites for 13 physico-chemical and biological suggests the anthropogenic sources of pollution is less in water quality parameters which helped them in iden- the study period. tifying the underlying processes responsible for the Mb and Kb sampling sites were grouped under cluster heterogeneity in different parts of Lake Neusiedler in II; the two stations are the tributaries of the lake; one is Hungary. Their study also showed that the river input Meki River that drains part of the western high land and region was significantly different. Moreover, [56], 16 the second is the Ketar River which can drains the Arsi water quality parameters; in their finding also agreed Mountains to the eastern part of the lake. These two riv - with the present study that most parameters increase ers transported many agrochemicals from western high their concentrations in the dry season due to evapora- land and Arsi Mountains ([14, 17]. Therefore, these sam - tive effects, whereas lower values are observed in the pling stations received pollutants mostly from agricul- wet season as the lake water is diluted by rain water. tural runoff, domestic waste and industrial effluent from Furthermore, [50] also applied PCA and CA to iden- the local people and Meki and Abura towns ([14, 17]. tify the factors influencing the water quality in dif- Cluster II corresponds to moderately pollution. ferent seasons in Hyderabad lakes in India. The study Sampling site Fb is grouped under Cluster I; this revealed that water pollution was more significant cluster site is the effluents of the floriculture indus- during the dry season as compared to the rainy sea- tries which is directly enter to the lake and polluted son because of precipitation and tidal influence which the lake water. Cluster I correspond to relatively highly cause dilution. [57], applied PCA and CA in order to polluted (HP) site, because the inclusion of floricul- provide an insight on water quality in Lake Naivasha, ture industry, due to the untreated sewage of flori- Kenya showed the usefulness of such multivariate culture effluent at this site [33]. Accordingly, spatial analysis in establishing the characteristics of differ- variations of water quality in Lake Ziway showed that ent regions in aquatic ecosystems based on numerous water quality was better in center and some portions water quality parameters. of the shore water than in western and eastern areas in the lake. At the same time these results showed that Comprehensive evaluation of Lake Ziway water quality for a rapid assessment of water quality, only one site analysis in each cluster presents a useful spatial assessment of According to the comprehensive pollution index val- the water quality for the entire network in different ues sites Fb, Fa, B and Mb showed moderate pollution seasons. This implies that, the results indicate the CA in dry season. The low water qualities parameters in technique is useful in offering reliable classification of these sites might be the influences of floriculture indus - surface water in the whole region and make it possi- try and domestic wastes from Ziway and Meki Towns. ble to design a future spatial sampling strategy in an However, the wet season, the values of the comprehen- optimal method, which can reduce the number of sam- sive pollution index ranged from (0.38 to 0.68) dem- pling sites and associated costs. Similar reports have onstrated slight pollution of the whole sampling sites. been dispatched by different authors [7, 25]. The water quality of the lake was determined to have This implies that, for a rapid assessment of water been influenced by different major source of pollution quality, only one site in each cluster presents a useful such as agricultural activities, domestic wastes, fish - spatial assessment of the water quality for the entire ing industries, swimming and car washing. Similarly network in different seasons. In this study we found [24], applied comprehensive pollution index model to the PCA and CA analysis techniques are useful in explain the pollution status of Lake Baiyangdian, china. apportionment of pollution sources based on param- Their result revealed that Lake Baiyangdian has a pollu - eter association. Similar findings has been reported in tion status ranging from less polluted to sever polluted. the study of [50, 54–56]. Other water quality studies that applied PCA and CA analysis found the techniques helpful in the inter- Conclusion pretation of large datasets. [54] used PCA and CA in Lake Ziway has shown some undesirable changes the analysis of water quality in Manchar Lake in Paki- in terms of hydrology and lake water quality due to stan and found the techniques useful in apportionment uncontrolled agrochemicals use in the lake watershed. T ibebe et al. BMC Chemistry (2022) 16:11 Page 17 of 18 Received: 22 November 2021 Accepted: 24 February 2022 The concentrations of most physicochemical param - eters and nutrients showed high values in dry season and then decreased in wet season. All the nutrient spe- cies analyzed in the surface water of the lake showed References increased trend and these variables might be primarily 1. Venkatesharaju K, Ravikumar P, Somashekar R, Prakash K. Physico- due to different environmental factors associated with chemical and Bacteriological Investigation on the river Cauvery of Kollegal Stretch in Karnataka. J sci Eng and techno. 2010;6:50–63. intensive anthropogenic activities in the lake catchment 2. 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Assessment of spatio-temporal variations of selected water quality parameters of Lake Ziway, Ethiopia using multivariate techniques

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Abstract

Excess agrochemicals input from agricultural activities and industrial effluent around Lake Ziway catchment can pose a serious threat on the lake ecosystem. Lake Ziway is a shallow freshwater lake found in the northern part of the Ethiopian Rift Valley. It is characterized as semi-arid to sub-humid type of climate. Expansions of the flower industry, widespread fisheries, intensive agricultural activities, fast population growth lead to deterioration of water quality and depletion of aquatic biota. The spatial and temporal variations of selected water quality parameters were evaluated using multivariate techniques. The data were collected from nine sampling stations during dry and wet seasonal basis for analysis of fifteen water quality parameters. The physicochemical parameters were measured in-situ with portable multimeter and nutrients were determined by following the standard procedures outlined in the American Public Health Association using UV/Visible spectrophotometer. Mean nutrient concentrations showed increasing trend in all seasons. These sites were also characterized by high electrical conductivity and total dissolved solid ( TDS). All the nine sampling sites were categorized into three pollution levels according to their water quality features using cluster analysis (CA). Accordingly, sampling sites Fb and Ketar River (Kb) are highly and moderately polluted in both seasons, respectively. On the other hand, sampling sites at the center (C), Meki river mouth (Ma), Ketar river mouth (Ka), Meki River (Mb), Korekonch (K ) and Fa in dry season and Ka, C, Ma, Ko, Bulbula river mouth (B) and Fa during wet season were less polluted. Principal component analysis (PCA) analysis also showed the pollutant sources were mainly from Fb during dry season Mb and Kb during wet season. The values of comprehensive pollution index illustrated the lake is moderately and slightly polluted in dry and wet seasons, respectively. Comparatively, the pollution status of the lake is high around floriculture effluent discharge site and at the two feeding rivers (Kb and Mb) due to increasing trends in agrochemical loads. In order to stop further deterioration of the lake water quality and to eventually restore the beneficial uses of the lake, management of agrochemicals in the lake catchments should be given urgent priority. Keywords: Cluster analysis, Comprehensive pollution index, Factor analysis, Principal component analysis development. They are essential for agriculture, industry Introduction and human existence in general. The health of aquatic Water pollution is one of the critical issues in environ- ecosystems is dependent on the presence of the right mental conservation. Freshwater resources have been of proportions of nutrients and requires succession of major great importance to both natural ecosystems and human nutrients in the water and sediment [1]. Appropriate assemblage of the nutrients ensures the status of water quality in any ecosystem and provides significant infor - *Correspondence: dessie.1977@gmail.com Department of Chemistry, College of Natural and Computational mation about the available resources for supporting life Sciences, University of Gondar, P. O. box 196, Gondar, Ethiopia [2]. Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Tibebe et al. BMC Chemistry (2022) 16:11 Page 2 of 18 Nitrogen and phosphorus (and silicon too for diatom Moreover, pollution of freshwater with potential con- species) are typical limiting nutrients influencing primary taminants due to natural phenomena and anthropogenic production. Nutrients occur in many different forms and activities are of great concern worldwide [9]. The fresh - only bio-available forms such as nitrate, nitrite, ammo- water lakes are susceptible to chemical contaminations nia, orthophosphate and soluble reactive silica can be as they are stagnant in nature [9]. Systematic studies on utilized directly by phytoplankton. Other forms of nutri- geochemical variability’s and inference of natural and ents, however, can become bio-available through desorp- anthropogenic factors are crucial to explain and protect tion, dissolution and biomass turnover. Nutrients in the the water quality in the lake ecosystem. However, stud- water body may originate from weathering of bedrock, ies on the comprehensive spatio-temporal variations and atmospheric precipitation, terrestrial input, storm water the systematic identification of the potential pollution runoff, sewage effluent and agricultural discharge [3, 4]. sources of Lake Ziway water qualities were very limited. Nutrient enrichment of lakes is considered to be one u Th s, reliable information on water quality and pollution of the major environmental problems in many countries sources is important for effective lake water manage - especially in developing ones [5]. In recent decades, pop- ment. Therefore, the objective of this study is to assess ulation growth, agricultural practices and sewage runoff the spatio-temporal variations of selected water quality from urban areas have increased nutrient inputs many parameters of Lake Ziway using Multivariate Techniques. folds to the level of their natural occurrence, resulting in accelerated eutrophication [5, 6]. Many urban and rural Materials and methods lakes have vanished under this pressure with worldwide Description of the study area environmental concerns [7]. The evaluation of water Lake Ziway is shallow freshwater located in the most quality in freshwater lakes is indispensable due to its northern section of the Ethiopian Rift Valley. The region immense significance in terms of ecological services and is characterized as semi-arid to sub-humid type of cli- livelihood perspectives. Anthropic pressures, however, in mate and has mean annual precipitation varying between the form of rapid urbanization, excessive use of pesticides 650 and 1200 mm and mean annual temperature between and fertilizers, land use and climate change are diminish- 15 and 25  °C [11]. During the last few decades, Lake ing the water quality, which necessitates better insights Ziway has begun to show reduction in its water level into pollution variability and its controlling measures [8, because of some climatic factors and excessive water 9]. abstraction for irrigation, municipals and industrial pur- Multivariate statistical techniques have been widely poses [12]. The lake is fed primarily by Meki and Ketar adopted to analyze and evaluate surface and freshwater Rivers and drained by the Bulbula River. The lake’s catch - water quality, and are useful to verify temporal and spa- ment has an area of 7025 k m with the town of Ziway tial variations caused by natural and anthropogenic fac- lying on the lake’s western shore [12]. Lake Ziway is situ- tors linked to seasonality [7, 10]. Although the numerous ated at 1636 m above sea level and at 08º01’N and 38º47’E management challenges, the multivariate techniques (Fig. 1 and Table 1) in a complex geological arrangement have a limited usage in the assessment of water quality in of sedimentary deposits. many lakes in developing countries including Lake Ziway. The lake provides water for domestic use and shares Despite its ecological and economic importance, both the same water table with key groundwater aquifers that locally and globally, Lake Ziway has been facing alarming provide borehole water supply for the rapidly-expanding environmental degradation and loss of biodiversity due human population in Ziway town and surrounding areas. to the pressure of human land-use and climate change. Currently, the population of Lake Ziway catchment is Substantial increases in water pollution, largely from the about 2 million and about 1.9 million livestock [13, 14]. discharge of untreated municipal and industrial waste The fishery of the lake is also an important source of live - and high sediment load from agricultural fields caused lihood to scores of fishermen and their families and pro - by unchecked erosion in upper catchments, are the major vides the sources of food to many families within the lake causes. The main significances of the study will address basin and beyond. Tourism is also a major activity in the for discouraging farming activities along the lakeshore; area due to the presence of hipotanuse, scenery Islands to set a standardized buffer zone around the lake shore; with monasteries, bird sanctuaries and the presence of all the free access policy (no ownership scenario) to water rich tropical related biodiversity [15]. Other socio-eco- bodies will have had to change for Lake Ziway by giving nomic activities conducted along the lake’s shore include concession rights to users with the appropriate environ- livestock production and small-scale farming [16]. mental regulatory protocols and it also helps to develop Agriculture is the most dominant land use system con- mitigation and restoration strategies for the lake and tributing to the livelihoods of the majority of the catch- aquatic ecosystems in Ethiopia. ment population. The agricultural sector is characterized T ibebe et al. BMC Chemistry (2022) 16:11 Page 3 of 18 Figure1 Location and bathymetric Map of Lake Ziway and its tributaries with the sampling sites by small-scale subsistence-based farming and rising of Table 1 Geographic coordinates of the sample points livestock. About 74.3% of the total land-use types within Sampling site Abr North East Elevation (m) the catchment are agricultural lands. Lake Ziway water description demands have massively increased, along with increased Floriculture effluent Fb 07º54.715’ 038º44.020’ 1642 population and intensification of agriculture since the Floriculture after mixing Fa 07º54.79’ 038º144.111’ 1639 end of the last decade [17]. Bulbula River mouth B 07º53.943’ 038º44.134’ 1641 Ketar River mouth Ka 07º55.398’ 038º52.086’ 1640 Chemicals, reagents and standards Ketar River at Abura Kb 08º02.019’ 038º49.340’ 1646 Analytical reagent grade sodium hydroxide, concen- Town trated hydrochloric acid, concentrated sulfuric acid, con- Meki River at Meki Mb 08º03.019’ 039º01.144’ 1673 centrated phosphoric acid, anhydrous sodium sulfate, Town ammonium persulfate, potassium persulfate, Phenol, Meki River mouth Ma 08º03.379’ 038º56.459’ 1633 sodium nitroprusside, sulfanilamide, N-(1-naphthyl)- Korekonch Kt 07º55.494’ 038º43.697’ 1637 ethylenediamine dihydrochloride, Potassium chloride, Central station C 07º55. 49’ 038º52.934 1635 sodium salicylate, potassium sodium tartarate, boric acid, Tibebe et al. BMC Chemistry (2022) 16:11 Page 4 of 18 potassium antimony tartrate, ammonium molybdate, through 0.45-μm poly tetrafluoroethylene (PTFE) disk ascorbic acid ethanol (99.99%), phenolphthalein, methyl syringe filter, filled in high-density polyethylene (HDPE) orange. All chemical and reagents are products of Sigma- bottles, sealed with Parafilm were collected in the field Aldrich, Germany. and kept in the same environment with other water sam- ples. The results of these field blank samples showed neg - Apparatus and equipment ligible contamination during the sampling, filtering, and UV–Visible Spectrophotometer (Jenway 6405, UK); Kjel- storage processes, as the values of most hydrochemical dahl apparatus (Gallenhamp, USA); Oven dry (Binder, variables were below the detection limit [19–21].. The Germany); Turbidimeter (T-100, Singapore); portable average analytical precision for nutrients was better than multi meter (HACH MM150, China) were used in the 2%. The alkalinity as HCO was estimated by charge bal- experiments. ance [19]. To interpret the data and develop a conclusive understanding of the geochemistry of Lake Ziway water In‑situ measurements quality, a series of statistical tests were performed using All field equipments were calibrated according to the an IBM SPSS 22.0 [20]. These tests include a normal - manufacturer’s specifications. Temperature, pH, elec - ity test, descriptive statistics (mean, max, min, SD etc.), trical conductivity, total dissolved solids, and dissolved Spearman correlation, and principal component analysis oxygen (DO) were measured with a portable ion meter and factor analysis (PCA/FA) [19, 20]. The normality test (HACH model150 made in Spain. Secchi depth (SD) and correlation analysis were performed by considering was measured with a standard Secchi disk of 20  cm all of the parameters to predict the degree of dependent diameter. of one variable on others with a correlation significance level of 0.01. PCA/FA was applied to group the changing Sampling and laboratory analysis patterns of physico-chemicals parameters and nutrients The collection of data for analyses of major nutrients in order to explain the fluctuation in dataset with mini - and physico-chemical water quality parameters in water mum loss of original information. PCA/FA is attained by samples in different depths at the selected sampling sites analyzing the correlation matrix and transforming the taken at selected seasons for two years. Nine representa- original variables to uncorrelated ones, commonly called tive sampling sites were selected purposefully based on varifactors (VFs) [19]. Additionally, the eigen values in access, safety, waste disposal activities, lake inflow and PCA/ FA define how much variance is present in associ - outflow and geographical proximity. These sites were ated VFs. The VF that holds the maximum eigenvalue is evenly distributed along the course of Lake Ziway. found to have the most co-variability [22, 23]. Suitability Water samples were collected with a Van Dorn water of the dataset for PCA/FA was tested by using the Kai- sampler from different depths of the entire water column ser–Meyer–Olkin (KMO) and Bartlett’s sphericity meth- at 1  m intervals and mixed in equal proportions to pro- ods which is run prior to PCA/FA. duce composite samples. The collected water samples were kept in precleaned polyethylene plastic bottles for Chemical analysis nutrient analysis following the standard guideline values Concentrations of inorganic nutrients (NO-N, NO -N, 2 3 [18]. All water samples were stored in insulated dark ice PO-P, NH -N, total phosphorus (TP), total nitrogen 4 3 boxes and taken on the same day to the laboratory. (TN), total inorganic nitrogen (TIN) and soluble reactive For quality control and quality assurance, the stand-silica (SiO -Si) were determined for all samples following ard operating procedures were strictly followed during the standard procedures outlined in [18]. Table 2 summa- sampling and laboratory analysis as directed by [18]. rizes the analytical methods for surface water samples. In order to avoid contamination, powder-free nitrile exam gloves and mask was used during the sample col- Multivariate statistical methods lection and testing. At each sampling site, three water Lake water quality data sets were subjected to three mul- samples, i.e., at left bank, middle, and right bank of the tivariate techniques: cluster analysis (CA), principal com- lake, were taken and mixed before a composite sample ponent analysis (PCA) and factor analysis (FA) [24]. All was prepared. The sample bottles were prerinsed three statistical analyses were performed using the SPSS statis- times with the same water before the final sample was tical software (Version 20) and PAST statistical software acquired. Before the in  situ measurements, the instru- (Version 1.93) [24]. ments were properly calibrated. Triplicate samples were run, and the average recovery of quality control analysis Cluster analysis was 99 ± 4%, indicating the good quality of the data. In CA classifies objects, so that each object is similar to the addition, four blank samples of deionized water filtered others in the cluster with respect to a predetermined T ibebe et al. BMC Chemistry (2022) 16:11 Page 5 of 18 Table 2 Summary of analytical methods used for surface water sample (APHA [18]) Parameter Method Description Total alkalinity APHA 2320 B Titrimetric pH Membrane Electrode Portable HACH model 150 EC Membrane Electrode Portable HACH model 150 TDS Membrane Electrode Portable HACH model 150 Temperature Membrane Electrode Portable HACH model 150 Ammonia APHA4500-NH C Spectrophotometric, Phenate Nitrate Yang et al., 1998 Spectrophotometric, sodium salicylate NitriteAPHA4500- NO A Spectrophotometric, Colorimetric TN APHA4500- N C Spectrophotometric, Kjeldahl method Phosphate APHA4500-P C Spectrophotometric, Ascorbic Acid TP APHA4500-P C Spectrophotometric, Persulfate digestion method, then Ascorbic acid method Secchi depth Lind Field equipment Dissolved oxygen Membrane Electrode probe method (YSI model 58) Silica APHA4500-SiO Spectrophotometric, Molybdosilicate Method selection criterion. Hierarchical agglomerative clus- P = 1/n (Ci/Si) tering is the most common approach, which provides n=1 intuitive similarity relationships between any one sam- ple and the entire data set and is typically illustrated by where P is comprehensive pollution index, C is the a dendrogram (tree diagram). The dendrogram provides −1 measured concentration of the pollutant (mg L ), S a visual summary of the clustering processes, presenting represents the limits allowed by the State Environmen- a picture of the groups and their proximity with a dra- tal Protection Administration (SEPA) in the particular matic reduction in dimensionality of the original data [7, country for water quality standard, and n is the number 25, 26]. In this study, hierarchical agglomerative CA was of selected pollutants [24, 28, 29]. Ultimately, the values carried out on the normalized data by means of Ward’s determined for P could be used to classify the water qual- method, using squared Euclidean distances as a measure ity level of the lake (Table 3). of similarity. Principal component analysis (PCA)/factor analysis (FA) Statistical analysis In this research, PCA was applied to summarize the Different procedures of statistical analyses were used statistical correlation among water quality parameters. to analyze the data. Analysis of variance (ANOVA) The concentrations of physico-chemical parameters and was conducted to test the differences between, and nutrients tend to differ greatly; as such, the statistical within, sampling sites at 95% confidence interval using results should be highly biased by any parameter having SPSS (version 20) software (Chicago, USA). The differ - a high concentration. Thus, each water quality parameter ences between sites were examined to determine the was standardized before PCA the analysis was performed spatial variation while the differences within seasons in order to minimize the influence of different variables addressed the temporal variation for water samples. and their respective units of measurements. The calcula - Table 3 Standard of surface water quality classification (WHO, tions were performed based on the correlation matrix of 1996) chemical components, and the PCA scores were obtained from the standardized analytical data [25, 27]. Comprehensive pollution index (P) Water quality level ≤< 0.20 I cleanness Comprehensive evaluation of water quality in the lake 0.20 to 0.40 II sub-cleanness A comprehensive pollution index method has been 0.41to1.00 III slight pollution applied to evaluate water quality qualitatively in many 1.01 to 2.00 IV moderate pollution existing studies. The comprehensive pollution index can ≥ 2.01 V sever pollution be calculated as follows [28]: Tibebe et al. BMC Chemistry (2022) 16:11 Page 6 of 18 Table 4 Mean, mean standard error and range of the physicochemical parameters in dry season (Temp for temperature in C; DO for −1 + −1 −1 dissolved oxygen in mg L ; pH for H concentration; EC for electrical conductivity in μS cm ; TDS for total dissolved solids in mg L ; −1 SD for secchi depth in cm; TA for total alkalinity, mg L ; Turbidity in NTU) Site Temp DO pH EC TDS SD TA B x̄ ± Std. Err 24.8 ± 1.3 7.3 ± 1.8 8.3 ± 0. 3 385 ± 38 248 ± 24 24.6 ± 0.5 314 ± 41 Range 21–28 4.8–12.4 8–9 289–521 189–335 23–26 216–425 C x̄ ± Std. Err 21.2 ± 1.3 5.2 ± 1.1 8.1 ± 0.2 408 ± 43 268 ± 33.7 27.7 ± 1.3 225 ± 22 Range 18–25 4–8.4 8–9 337–558 215–393 23–30 184–300 Fa x̄ ± Std. Err 23.8 ± 1.0 6.8 ± 1.7 8.13 ± 0.2 639.73 ± 114 423.8 ± 83 26 ± 0.7 320 ± 50 Range 21–26 4.2–11.2 8–9 376–1028 249–720 24–28 200–425 Fb x̄ ± Std. Err 23. 5 ± 1.4 6.2 ± 1.4 7.56 ± 0.1 1233.6 ± 107 789.6 ± 68.3 25.8 ± 0.6 463 ± 71.7 Range 19–27 2.6–9.3 7–8 1050–1650 672–1056 24–27 200–625 Ka x̄ ± Std. Err 21.3 ± 0.5 4.4 ± 1.5 8.1 ± 0.16 382.6 ± 39.4 237.8 ± 18.6 24.4 ± 1.8 24 ± 29 Range 20–23 2.5–9.2 7–8 307–543 196–307 19–30 156–325 Kb x̄ ± Std. Err 20.15 ± 0.2 5.5 ± 0.6 7.4 ± 0.1 170.7 ± 14.3 107.7 ± 9 21 ± .7 187 ± 44 Range 20–21 4.0–7.0 7–8 134–204 86–130 19–23 104–275 Ko x̄ ± Std. Err 24.2 ± 1.3 5.4 ± 1.3 7.4 ± 0.8 399 ± 14.4 254 ± 10.5 25.6 ± 1.1 330 ± 38 Range 21–28 2.4–9.0 7–9 362–435 219–278 22–28 200–425 Ma x̄ ± Std. Err 22.2 ± 0.7 4.4 ± 0.6 7.98 ± 0.2 404.1 ± 43 291.9 ± 37.7 27.8 ± 1.8 227.6 ± 26 Range 21–25 3.3–6.5 8–9 333–576 213–403 22–31 160–300 Mb x̄ ± Std. Err 22.9 ± 0.8 7.6 ± 1.6 7.95 ± 0.2 424 ± 625 267 ± 38 21 ± 0.71 263 ± 48 Range 20–24 3.8–10 7–9 203–584 130–365 19.00 100–375 Table 5 Mean, mean standard error and range of the physicochemical parameters in wet season season ( Temp for temperature in C; −1 + −1 DO for dissolved oxygen in mg L ; pH for H concentration; EC for electrical conductivity in μS cm ; TDS for total dissolved solids in −1 −1 mg L ; SD for secchi depth in cm; TA for total alkalinity, mg L ; Turbidity in NTU) Site Temp DO pH EC TDS SD TA B x̄ ± Std. Err 22 ± 0.5 4.72 ± 0.4 8.59 ± 0.1 274.5 ± 9 175.7 ± 58.2 15.3 ± 0.3 207 ± 6.3 Range 21.5–23 4.27–5.4 8.4—8.7 176—456 113–292 15–16 200–220 C x̄ ± Std. Err 21.2 ± 0.3 4.75 ± 28 8.57 ± 0.1 218 ± 44 147.8 ± 37 17.3 ± 0.7 168 ± 6.1 Range 20.7–22 4.4–5.3 8.4–8.7 173–306 110–222 16–18 160–180 Fa x̄ ± Std. Err 22 ± 0.6 4.4 ± 0.3 8.78 ± 0.5 353 ± 89 226 ± 56 18.6 ± 0.7 172 ± 14 Range 20.8–23 3.95–4.8 7.9–9.7 187–488 120–312 18–20 148- 196 Fb x̄ ± Std. Err 21 ± 1.6 6.2 ± 0.7 8.53 ± 0.1 370 ± 98 216 ± 84 19.6 ± 0.7 250 ± 111 Range 18–24 4.9–7.2 8.3–8.7 175–478 49–306 19–21 120–472 Ka x̄ ± Std. Err 22 ± 0.3 3.1 ± 0.3 8.4 ± 0.1 230 ± 41.4 180 ± 32 16–1.2 117–35 Range 20—27 2.4 -3.5 8.3–8.5 183–312 117–221 14–18 80–188 Kb x̄ ± Std. Err 20 ± 0.2 2.6 ± 0.6 7.78 ± 0.2 101 ± 17.9 65 ± 11.7 17.3 ± 0.9 72.3 ± .3 Range 20–21 1.4–3.6 7.5–8.3 65–120 42–78.3 16–19 72–73 Ko x̄ ± Std. Err 22.5 ± 0.9 4.8 ± 0.3 8.7 ± 0.1 381 ± 101 234 ± 61 18.3 ± 0.9 175 ± 14.6 Range 21–24 4.35–5.3 8.5–8.8 179–496 116–318 17.-20 146–190 Ma x̄ ± Std. Err 23 ± 0.1 3.9 ± 0.1 8.6 ± 0.1 273 ± 49 153 ± 21.3 17 ± 1.2 130 ± 24.9 Range 22–23 3.7–4.1 8.6–8.61 176–337 113–185 15–19 100–180 Mb x̄ ± Std. Err 20.4 ± 0.7 4.8 ± 0.8 7.4 ± 0.5 120 ± 3 77 ± 1.9 18 ± 1.7 100 ± 20 Range 19.5–22 3.4–5.8 6.5–8.3 115–126 73.7–80.4 15–21.0 60–120 T ibebe et al. BMC Chemistry (2022) 16:11 Page 7 of 18 −1 All correlations were considered statistically significant showed the lowest mean EC (134 μScm ) in dry and 65 −1 when the significance level was p < 0.05. μS cm in wet seasons while site Fb recorded the high- −1 est mean values of 1650 μS cm in dry and Fa mean val- −1 Results ues 488 μS cm in wet seasons, respectively (Tables  4 Spatial and temporal variations in Physico‑chemical water and 5). Total dissolved solid (TDS) ranged from 119.77 −l quality parameters to 746.80  mg L with the lowest value was recorded in The average spatio-temporal values of physico-chem - sampling site Kb and the highest value was recorded in ical water quality parameters in dry and wet seasons sampling site Fb while in the wet season, it ranged from −1 are given separately in Tables  4 and 5, respectively. 129.5 to 547.76  mg L at sites Mb and Fb, respectively The surface-water temperature measured in the study (Tables 4 and 5). The mean SD values ranged from 0.20 to site ranged from 19.0 to 27.0  °C and 18.0 to 27.0  °C 0.22 m, with mean values of 0.21 m. in dry and wet seasons, respectively. The highest val - Total alkalinity (TA) in the study sites were ranged −1 ues were measured at B and Ka and the lowest values from 100 to 625 and 60 to 472 mg CaCO L in dry and were measured at C and Fb during dry and wet seasons, wet seasons, respectively (Tables  4 and 5). Mb showed −1 respectively. the lowest mean TA 100 mg CaCO L in dry and 60 mg −1 The level of dissolved oxygen (DO) ranged from 2.42 CaCO L in wet seasons while the highest mean values −1 −1 −1 to 12.4 mg L and 1.4 to 7.2 mg L in dry and wet sea- of TA was 625 and 472  mg CaCO L in dry and wet sons, respectively (Tables  4 and 5). The lowest values seasons, respectively in sampling site Fb. −1 in both seasons were at K (2.4  mg L ) in dry and K o b −1 (1.4 mg L ) in wet seasons where as the highest values Nutrients analyses −1 −1 were at B (12.4  mg L ) in dry and F (7.2  mg L ) in The spatial and temporal variations of nutrients are sum - wet seasons, respectively. Whilst the pH values ranged marized in Tables  6 and 7. The mean NO -N concentra- −1 from 7.0 to 9.0 and 6.5 to 9.7 in dry and wet seasons tion ranged from 0.1 to 5.26 mg L and 0.01 to 0.86 mg −1 respectively (Tables 4 and 5). L in dry and wet seasons, respectively. The highest The mean electrical conductivity (EC) values in the mean NO -N was recorded at Fb in dry and Kb in wet −1 study sites ranged from 134.0 to 1650 μS c m in the dry season while the lowest values were in Ma in both dry −1 −1 season and 65.0 to 488 μS c m in the wet season. Kb and wet seasons. N O -N ranged from 0.06 to 2.89 mg L −1 Table 6 Mean, mean standard error and range of nutrient concentrations (mg L ) measured in sampling sites at Lake Ziway in dry −1 −1 −1 season ( TP for total phosphorus in mg L ; SRP for soluble reactive phosphorus in mg L ; NO -N for nitrite-nitrogen in mg L ; NO -N 2 3 −1 −1 −1 for nitrate-nitrogen in mg L ; NH4-N for ammonia–nitrogen in mg L ; TIN for total inorganic nitrogen in mg L ; TN for total nitrogen −1 −1 in mg L; SiO -Si for soluble silica in mg L ) Site TP PO ‑P NO ‑N NO ‑N NH ‑N TIN TN SiO ‑Si 4 2 3 3 2 B x̄ ± Std. Err 0.12 ± 0.02 0.06 ± 0.01 0.48 ± 0.20 0.17 ± 0.04 0.21 ± 0.05 0.85 ± 0.25 5.7 ± .25 46.2 ± 6.2 Range 0.06–0.15 0.04–0.08 0.188–1.3 0.06–0.25 0.1–0.35 0.34–1.8 4.9–6.4 32–68 C x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.29 ± 0.05 0.26 ± 0.17 0.17 ± 0.03 0.72 ± 0.22 9.1 ± 0.65 46.8 ± 2.4 Range 0.1–0.185 0.03–0.07 0.18–0.41 0.01–0.91 0.09–0.26 0.29–1.5 7.34–11.20 39.5–54 Fa x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.96 ± 0.22 0.38 ± 0.13 0.24 ± 0.04 1.6 ± 0.36 6.1 ± .6 46.8 ± 4.9 Range 0.105–0.23 0.03–0.1 0.6–1.8 0.1–0.75 0.15–0.35 0.9–2.9 4.5–8.3 35–60 Fb x̄ ± Std. Err 0.19 ± 0.05 0.08 ± 0.03 1.7 ± 0.44 0.58 ± 0.19 0.29 ± 0.05 2.6 ± 0.6 8.1 ± .56 91.39.8 Range 0.05–0.32 0.04–0.16 0.72–2.89 0.08–0.97 0.15–0.42 1.0–4.2 6.5–9.8 56.9–114 Ka x̄ ± Std. Err 0.13 ± 0.02 0.05 ± 0.01 0.35 ± 0.08 0.12 ± 0.02 0.22 ± 0.05 0.68 ± 0.10 7.1 ± 1.1 50.5 ± 6.7 Minimum 0.09–0.19 0.04–0.07 0.155–0.64 0.08–0.18 0.10–0.4 0.40–0.95 5.0–11 40.1–77 Kb x̄ ± Std. Err 0.17 ± 0.03 0.05 ± 0.01 0.34 ± 0.03 0.22 ± 0.05 0.34 ± 0.03 0.89 ± 0.08 9.2 ± 1.3 68 ± 7.8 Range 0.08–0.24 0.04–0.09 0.24–0.42 0.07–0.35 0.24–0.42 0.76–1.2 14-Jul 43–88.7 Ko x̄ ± Std. Err 0.12 ± 0.02 0.05 ± 0.01 0.32 ± 0.09 0.10 ± 0.03 0.2 ± 0.04 0.62 ± 0.14 9.7 ± .42 39 ± 3.4 Range 0.07–0.19 0.04–0.07 0.05 ± 0.188 0.01–0.18 0.09–0.3 0.3–1.2 8.5–11 30–47 Ma x̄ ± Std. Err 0.14 ± 0.02 0.05 ± 0.01 0.23 ± 0.08 0.10 ± 0.02 0.17 ± 0.03 0.55 ± 0.08 6.3 ± .49 49 ± 3.8 Range 0.09–0.2 0.04–0.06 0.1–0.53 0.04–0.14 0.114–0.3 0.35–0.8 4.8–7.6 38–62 Mb x̄ ± Std. Err 0.97 ± 0.75 0.06 ± 0.01 0.41 ± 0.17 0.28 ± 0.19 0.41 ± 0.17 1.3 ± 0.53 9.4 ± 1.2 61.3 ± 3.9 Range 0.2–3.95 0.04–0.08 0.06–1.1 0.03–1.1 0.1–1.1 0.21–3.2 5.6–13 50.8–73 Tibebe et al. BMC Chemistry (2022) 16:11 Page 8 of 18 −1 Table 7 Mean, mean standard error and range of nutrient concentrations (mg L ) measured in sampling sites at Lake Ziway in wet −1 −1 −1 season( TP for total phosphorus in mg L ; SRP for soluble reactive phosphorus in mg L ; NO -N for nitrite-nitrogen in mg L ; NO -N 2 3 −1 −1 −1 for nitrate-nitrogen in mg L ; NH4-N for ammonia–nitrogen in mg L ; TIN for total inorganic nitrogen in mg L ; TN for total nitrogen −1 −1 in mg L; SiO -Si for soluble silica in mg L ) Site TP(mg/L) PO ‑P NO ‑N NO ‑N NH ‑N TIN TN SiO ‑Si 4 2 3 3 2 B x̄ ± Std. Err 0.35 ± 0.1 0.046 ± 0.01 0.47 ± 0.06 0.15 ± 0.03 0.11 ± 0.02 0.73 ± 0.08 5.13 ± 1.20 35.5 ± 11.60 Range 0.21–0.42 0.04–0.06 0.35–0.53 0.08–0.20 0.07–0.13 0.57–0.86 2.8–7.00 12.37–47.30 C x̄ ± Std. Err 0.38 ± 0.023 0.05 ± 0.01 0.33 ± 0.12 0.17 ± 0.12 0.09 ± 0.01 0.59 ± 0.24 6.02 ± 0.26 43.6 ± .72 Range 0.34–0.41 0.03–0.08 0.21–0.57 0.05–0.41 0.08–0.10 0.352–1.1 5.60–6.5 42.9–45.1 Fa x̄ ± Std. Err 0.29 ± 0.12 0.05 ± 0.01 0.74 ± 0.22 0.26 ± 0.12 0.09 ± 0.03 1.1 ± 0.33 7.0 ± 0.81 36 ± 7.5 Range 0.18–0.52 0.04–0.07 0.43–1.2 0.03–0.39 0.03–0.14 0.48–1.62 5.6–8.4 22.2–47.7 Fb x̄ ± Std. Err 0.42 ± 0.175 0.11 ± 0.04 0.89 ± 0.44 0.44 ± 0.19 0.09 ± 0.04 1.42 ± 0.62 6.66 ± 0.53 39.2 ± 26 Range 0.17–0.75 0.04–0.16 0.34–1.8 0.17–0.80 0.02–0.15 0.75–2.7 5.6–7.4 5.8–90.5 Ka x̄ ± Std. Err 0.23 ± 0.02 0.05 ± 0.01 0.84 ± 0.16 0.47 ± 0.04 0.09 ± 0.03 1.39 ± .18 7.6 ± 0.43 37.9 ± 4.68 Range 0.20–0.27 0.04–0.07 0.5—1.1 0.4–0.54 0.03–0.12 1.1 ± 1.7 7–8.4 30.3–46.45 Kb x̄ ± Std. Err 0.73 ± 0.27 0.06 ± 0.01 1.2 ± 0.20 0.86 ± 0.22 0.15 ± 0.10 2.2 ± .50 8.1 ± 0.86 38.15 ± 17.10 Range 0.24–1.2 0.05–0.08 0.89–1.6 0.52–1.3 0.05–0.3 1.5–3.1 7–9.8 11.24–70 Ko x̄ ± Std. Err 0.27 ± 0.08 0.05 ± 0.01 0.42 ± 0.12 0.20 ± 0.04 0.08 ± 0.02 0.7 ± 0.12 12 ± 1.9 34.3 ± 11 Range 0.12–0.376 0.04–0.07 0.2–0.6 0.1–0.3 0.05–0.1 0.5–0.9 8.4–14 12.6–48.2 Ma x̄ ± Std. Err 0.24 ± 0.04 0.06 ± 0.02 0.76 ± 0.06 0.23 ± 0.11 0.08 ± 0.02 1.06 ± 0.14 6.1 ± .55 42.62 ± 1.9 Range 0.19–0.33 0.04–0.082 0.66–0.86 0.01–0.369 0.03–0.11 0.8–1.3 4.97–7 38.96–45.42 Mb x̄ ± Std. Err 1.0 ± 0.30 0.09 ± 0.02 1.02 ± .2 0.50 ± 0.24 0.16 ± 0.06 1.66 ± .48 13.5 ± 4.5 39.1 ± 14.72 Range 0.47–1.5 0.06–0.12 0.7–1.42 0.02–0.75 0.05–0.23 0.76–2.4 5.6–21 11.2–61.2 −1 and 7). Mean TP concentration was highest at M in both in dry season and 0.20 to 1.8 mg L in wet season during seasons whereas the lowest concentrations were at B and the study period (Tables 6 and 7). Low concentrations of K in dry and K in wet seasons. NO -N were at M in both dry and wet seasons whereas o a 2 a The concentration of SiO -Si ranged from 39.4 to high concentrations were at F and K in dry and wet sea- b b −1 91.3 and 35.5 to 42.6  mg L with mean values of 55.4 sons, respectively (Tables 6 and 7). −1 and 38.5  mg L in dry and wet seasons, respectively Ammonia- nitrogen (NH -N) concentrations ranged −l −1 (Tables 6 and 7) whereas the highest and lowest concen- from 0.17 to 0.29 mg L in dry and 0.08 to 0.15 mg L trations were noticed during the dry and wet seasons, in wet seasons with the lowest concentrations at C and respectively (Tables  6 and 7). Significant fluctuations in M in dry and K and M in wet seasons while the highest a o a the mean SiO -Si concentrations were observed in both values in F and K in dry and wet seasons respectively b b seasons showed that the fluctuations in it over the differ - (Tables  6 and 7). The mean total nitrogen (TN) concen - −1 ent seasons and across the different sampling sites were trations ranged from 5.69 to 12.21 mg L in dry and 4.98 −1 significant in the lake. to 12.0  mg L in wet seasons. The highest concentra - tions were at K in dry and at F in wet season whereas o b Multivariate analysis the lowest concentrations were at B in both seasons Principal component analysis (PCA) (Tables 6 and 7). Four components of PCA analysis showed 88.10% of the Soluble reactive phosphorus (SRP) ranged from 0.05 to −1 variance in the data set of the wet season, as the eigen- 0.08 mg L and showed similar concentrations for lower vectors classified the 15 physico-chemical parameters values for most of the sampling sites and high values at F into four groups. P C (38.93% of the total variance in in the dry season, while in the wet season it ranged from −1 the data set) has strong positive loadings on TP, NH -N, 0.05 to 0.12  mg L (Tables  6 and 7). Most sites have NO-N, NO -N, TIN, pH and SD (Table  8). The sec - also similar concentrations in the wet season and only 2 3 ond component (P C ) accounted for 24.02% of the total Site F had highest values. Similarly, the mean TP con- −1 variance measured, demonstrated strong positive load- centrations ranged from 0.12 to 0.97 mg L and 0.23 to −1 ings for TN, EC, TDS and TA and the third component 1.02 mg L in dry and wet seasons respectively (Tables 6 T ibebe et al. BMC Chemistry (2022) 16:11 Page 9 of 18 Table 8 The Factor loadings values and explained variance of water quality in two seasons (positive and negative strong correlations are marked bold) Dry season Wet season Parameters PC1 PC 2 PC3 PC 4 Parameters PC1 PC2 PC3 PC 4 TP 0.065 − 0.69 0.48 0.10 TP 0.92 − 0.18 0.21 0.23 PO4 0.93 − 0.16 0.13 − 0.03 PO4 0.15 − 0.18 0.85 0.37 NH3 0.907 − 0.04 0.27 − 0.11 NH3 0.85 − 0.38 0.32 0.05 NO2 0.966 0.10 − 0.07 − 0.09 NO2 0.88 0.09 − 0.33 − 0.15 NO3 0.963 − 0.04 − 0.21 0.10 NO3 0.90 − 0.005 0.03 − 0.34 TIN 0.98 − 0.01 − 0.17 0.06 TIN 0.94 0.03 − 0.14 − 0.235 TN − 0.097 − 0.32 0.18 0.88 TN 0.56 0.68 − 0.28 0.20 SiO2 0.791 − 0.52 − 0.29 − 0.05 SiO2 0.11 − 0.10 − 0.62 0.49 Temp 0.283 0.79 0.43 − 0.09 Temp − 0.11 0.19 0.63 − 0.62 DO 0.349 − 0.42 0.76 − 0.09 DO 0.08 0.55 0.64 0.40 PH − 0.373 0.76 0.49 0.08 pH − 0.85 0.13 0.01 − 0.17 EC 0.963 0.16 − 0.02 0.09 EC − 0.25 0.96 − 0.07 0.03 TDS 0.955 0.17 − 0.05 0.08 TDS − 0.26 0.95 − 0.087 0.03 SD 0.035 0.66 − 0.34 0.32 SD 0.61 0.58 − 0.21 − 0.16 TA 0.801 0.54 0.20 0.11 TA 0.44 0.63 0.43 0.03 Eigen value 7.97 3.05 1.66 0.96 Eigen value 5.84 3.60 2.52 1.26 % variance 53.133 20.34 11.09 6.41 % variance 38.93 24.02 16.76 8.39 % Cumulative variance 53.133 73.47 84.56 90.97 % cumulative variance 38.93 62.95 79.71 88.10 (PC ) demonstrated 16.76% of the total variance and have of the following parameters: nutrients (NH-N, NO -N, 3 3 2 strong positive loadings on SiO-Si, PO -P, DO and tem- NO -N, TIN, PO-P, SiO -Si), TDS, EC, TA. The second 2 4 3 4 2 perature, while, the fourth component (P C ) accounts PC accounted for 20.34% of the total variance and had only 8.39% of the total variance in the season (Table 8). strong positive loading with temperature, pH, TP and The dry season PCA analysis showed that four principal SD as the associated parameters. The third PC explained components (PCs) represented about 90.97% of the total 11.09% of the total variations between sites comprising variation in the entire dataset. The first PC accounted for only DO. Scree plot showed the eigenvalues sorted from 53.4% of the total variations between sites and comprised large to small as a function of the principal components 28 42 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 Component Component Fig. 2 a Wet season Scree plot of the eigenvalues. b Dry season Scree plot of the eigenvalues Eigenvalue% Eigenvalue % Tibebe et al. BMC Chemistry (2022) 16:11 Page 10 of 18 Fig. 3 Results of the bi-plot of the correlation between for various water quality parameters with respect to studied sites using PCA in the dry season number after the fourth PC. After the fourth PC (Fig. 2a, Cluster analysis (CA) b), starting in the downward curve, other components A dendrogram of sampling sites obtained by Ward’s can be omitted. method is shown in Fig.  5. Nine sampling sites were The bi-plot of PCs associated with nutrients (NH -N, divided into three groups. Cluster 1 corresponded to site NO-N, NO-N, SiO -Si and PO -P), EC and TDS char- Fb, which was located in the western part of the lake. 3 2 2 4 acterizing Fb sampling site from axis 1 (Fig. 3) and Fa dis- Cluster 2 included site Kb, which were located in the tinctiveness was attributed to temperature, SD and TA. eastern portion of the lake. Cluster 3 contained sites Fa, The parameter influencing the distinction in the B site K and B the western part of the lake, C which was in the from axis 2 was mainly pH while Mb site from axis 2 was lake central station; site Mb and Ma in northern part of influenced by DO, TN and TP in the dry season. the lake and Ka was in the eastern part of the lake. The bio-plot of PCs associated with nutrients (NH -N, The wet season classification performed by the use of NO-N, NO-N, SiO -Si, TIN, PO -P and TP), which cluster analysis grouped in all the nine sampling sites 3 2 2 4 were the key parameters characterizing the Mb and Kb of the basin into three statistically significant clusters sampling sites (Fig.  4) and Fa distinctiveness was attrib- (Fig. 6). uted to temperature, TDS, EC and DO. The parameter influencing the distinction in the K site was mainly pH Comprehensive evaluation of Lake Ziway water quality while Fb site was influenced by DO, TN, TA and SD in analysis the wet season. The values of the comprehensive pollution index were For the two temporal clusters, 90.97% and 88.10% of 1.8, 1.0, 1.01 and 1.08 for sites Fb, Fa, B and Mb, respec- the variances in dry and wet seasons were explained by tively (Table  9), which demonstrated moderate pollution the four main factors, respectively. in dry season while sampling sites of Ka, Ma, C, and Kb T ibebe et al. BMC Chemistry (2022) 16:11 Page 11 of 18 Fig. 4 Results of the bi-plot of the correlation between for various water quality parameters with respect to studied sites using PCA in the wet season Fig. 5 Dendrogram based for agglomerative hierarchical clustering Fig. 6 Dendrogram based for agglomerative hierarchical clustering (wards method) based on the PCA scores in dry season (wards method) based on the PCA scores in the wet season Tibebe et al. BMC Chemistry (2022) 16:11 Page 12 of 18 Table 9 Paired samples test for dry and wet seasons Paired samples test Paired differences Sig. (2‑tailed) Mean Std. deviation Std. error mean Pair 1 TP in dry—TP in Wet − 0.20 0.15 0.05 0.00 Pair 2 PO -P in dry—PO4-P in wet − 0.01 0.02 0.01 0.27 Pair 3 NH -N in Dry—NH3-N wet 0.11 0.05 0.02 0.00 Pair 4 NO -N in Dry—NO2-N in wet − 0.171 0.502 0.17 0.34 Pair 5 NO -N in Dry—NO3-N in wet 0.48 1.68 0.56 0.42 Pair 6 TIN in dry—TIN in wet 0.41 2.14 0.71 0.58 Pair 7 TN in dry—TN in wet 0.57 2.79 0.93 0.56 Pair 8 SiO -Si in dry—SiO2- Si in wet 16.88 15.68 5.23 0.01 Pair 9 Temp in dry—Temp in wet 1.68 1.22 0.416 0.00 Pair 10 DO in dry—DO in wet 0.58 0.60 0.20 0.02 Pair 11 PH in dry—DO in wet 2.97 0.97 0.32 0.00 Pair 12 EC in dry—EC in wet 109.04 112.82 37.61 0.02 Pair 13 TDS in dry—TDS in wet 73.10 71.67 23.89 0.02 Pair 14 SD in dry—SD in wet 7.73 2.11 0.70 0.00 Pair 15 TA in dry—TA in wet 93.65 85.24 28.41 0.01 −1 Table 10 Comparison of the physico-chemical parameters and nutrients of Lake Ziway with other tropical lakes (mg L for nutrients Lakes Temp( c) DO P pH EC SRP TP NO ‑N SiO ‑Si SD References 3 2 Hawasa 23.5 5–7 8.66 846 0.015 0.034 0.025 37.6 0.85 [30] Chamo 26.3 5–9 8.84 1910 0.118 0.182 0.033 1.0 0.18 [30] Hayq 18.2 1–8.4 9 910 0.022 0.058 0.042 3.7 2.7 [46] Tana 20–27 5.9–7 7.3–8.5 115–148 1.8 0.1–1 0.51–1.82 [58] Abaya – – 8.9 623 0.04 – – 40 – [59] Langano – – 9.4 1810 0.09 – – 48 – [59] Bishoftu – – 9.2 1830 0.005 to 0.1 – – 38 – [59] Abijata – – 10.2 15,800 0.05 – – 128 – [59] Shala – – 9.9 19,200 0.76 – – 112 – [59] Chitu – – 9.8 28,600 1.7 – – 320 – [59] Ziway 23 5 8.1 404 0.06 0.311 0.21 40.7 0.2 Present study have pollution index of 0.71, 0.69, 0.81, 0.79 and 0.84, Discussion respectively, which demonstrated slight pollution in the Spatial and temporal variations Physico‑chemical water same season. However, in the wet season, the values of quality of Lake Ziway the comprehensive pollution index ranged from 0.38 to The spatial and temporal variation of mean water tem - 0.68 which demonstrated slight pollution of the whole perature in Lake Ziway was not significant (p > 0.05) sampling sites. during the study period. The mean temperature of the lake water was 23.0 °C in both seasons, which is almost similar to the previously reports in [30] but lower than Temporal variation of water quality the value reported by [31]. Lake Ziway has narrow sea- Significant temporal variations were observed in physico- sonal fluctuations in water temperature due to the lake chemical parameters and nutrients of Lake Ziway water is shallow tropical lake. quality where most of the physicochemical parameters The lowest DO values in dry season at K was attrib- have significantly higher values in the dry season as com - uted to human impacts like fishing, car and human pared to wet season (P < 0.05) (Table 10). washing while the low DO level at Kb in the wet season T ibebe et al. BMC Chemistry (2022) 16:11 Page 13 of 18 was attributed to its muddy water with agricultural sites were well below the WHO guideline values pre- −1 runoff. The highest values of DO at sampling sites B in scribed for drinking water purpose (1500 μS c m ) [36]. dry season might be attributed to the presence of mac- Accordingly, the value of EC in different water samples rophytes and phytoplankton with higher biomass and could not be water quality problem of the study area. abundance than other sites [31]. The high values of DO TDS also followed the same trend as that of EC as EC is at sampling site Fb in the wet season could be probably sensitive to variations in dissolved solids, mostly mineral due to high dilution. The overall mean DO concentra - salts, and there were significantly lower value of EC and −1 tion in this study (5.00 mg L ) is much lower than the TDS during the main rainy season which may be because −1 value reported by [32], (8.72  mg L ). Reference [33] of dilution. −1 has also reported the DO concentration of 1.4  mg L Similar result of mean SD values with this study (0.21 m) around the floriculture effluent which is smaller than was reported by [39] which was 0.19 m. however, the range −1 the present study. Concentrations below 4.0  mg L values of this study (0.20 to 0.22 m) was smaller than the val- adversely affect aquatic life [34]. The value of DO in ues which were ranged from 0.20 to 0.35 m and 0.4 to 1.06 m this study is within the [35] and [36] permissible limits. reported by [40, 41], respectively (Table  11). Moreover, [40] According to [35] and [36], the standard for DO value also reported that the mean SD value was 0.29  m in Lake for fisheries and aquatic life is between 5.0 to 9.0  mg Ziway. The declining trend in SD reading is one of the indica - −1 L (Table 10). tions which suggest the increasing trend in turbidity of the The overall mean pH value of the lake water was 8.10 lake, which can be mainly attributed to catchment degrada- which is in a close agreement with previous data reported tion and siltation. by [32] (8.39), [30] (8.65) and [31] (8.44), respectively. Reference [40] reported that the mean value of TA in −1 However, significant temporal variation was noted during the Lake was 247.5  mg C aCO L which was similar −1 the study as significantly lower value was measured dur - value in this study in dry season (239.3  mg C aCO L ) ing the rainy season than dry season. The pH value could where as the value in the wet season (154.6  mg CaCO −1 mainly be controlled by freshwater swamp exudates that L ) was very low. In the lake, TA was solely due to bicar- regulate the acidity of the water body. A pH range of 6 to bonates and carbonate alkalinity that could be traced at 8.5 is normal according to the [18]. In general, the pH of any station during the entire period of study. According Ziway Lake water is within the acceptable range accord- to [7] nutrient status classifications using TA, Lake Ziway ing to [36] (Table 10). can be considered nutrient rich. During all the seasons, −1 The overall mean value of EC (404.30 μS cm ) was fluctuations in TA across the sites were significant. TA comparable with previous report of [30, 31, 37] with EC has generally decreased in the wet seasons probably due −1 values of 410, 478, 419.14 μS cm , respectively. Higher to the dilution effect of the rains and fresh incoming run - conductivity values were measured at the floriculture offs [42, 43]. farming sites than other sampling sites could be attrib- uted to the use of high amount of dissolved agrochemi- Nutrients analyses cals from effluents of floriculture industry [33, 38]. For All the nutrient species analyzed in the surface water the present study, the EC values of different sampling of the lake showed increased trend. The mean nitrate Table 11 Single pollution index and comprehensive pollution index of nine sampling sites in some selected water quality parameters in dry and wet seasons Site Dry season Wet season P P P P P P P P P P P P PO4 NH3 NO2 NO3 DO PO4 NH3 NO2 NO3 DO Fb 0.83 0.20 1.91 0.53 1.20 1.80 0.60 0.06 0.99 0.53 1.21 0.68 Fa 0.48 0.16 1.06 0.04 1.09 1.0 0.50 0.06 0.93 0.04 1.02 0.51 B 0.55 0.14 0.53 0.02 1.33 1.01 1.10 0.07 0.37 0.02 1.18 0.55 Ka 0.50 0.14 0.39 0.01 0.89 0.71 0.50 0.06 0.52 0.01 0.78 0.38 Ma 0.51 0.11 0.31 0.01 0.88 0.69 0.57 0.05 0.84 0.01 0.84 0.46 Ko 0.51 0.13 0.36 0.01 1.07 0.81 0.50 0.05 0.47 0.01 1.03 0.41 C 0.46 0.11 0.32 0.03 1.04 0.79 0.51 0.06 0.82 0.03 0.97 0.48 Kb 0.52 0.12 0.37 0.02 1.10 0.84 0.59 0.10 1.32 0.02 0.70 0.55 Mb 0.62 0.15 0.45 0.03 1.43 1.08 0.85 0.10 1.13 0.03 1.25 0.67 Tibebe et al. BMC Chemistry (2022) 16:11 Page 14 of 18 −1 highly agricultural activities around the lake watershed nitrogen values found in this study (0.21  mg L ) −1 [17]. was higher than those values 0.17, 0.003, 0.06  mg L Higher concentration of SiO -Si was found in dry sea- reported by [30, 31, 44], respectively. The increasing son compared to wet season, this might be because of trend in nitrate concentration in the lake is probably dilution in the wet season. Similar results were reported because of nutrient enrichment of the littoral zone of by [48]. Significant fluctuations in mean SiO -Si concen- the lake from anthropogenic sources from the catch- trations were observed over the different seasons and ment area. The mean nitrite nitrogen values found in −1 across the different sampling sites in the Lake. The range this study (0.5  mg L ) was also higher than the values and mean concentration of SiO -Si in this study (39.36 to reported by previous studies on the lake. For instance, −1 −1 91.29 and 35.53 to 42.62 mg L with mean values of 55.4 [45] and [31] has reported 0.06 and 0.01  mg L nitrite −1 and 38.5  mg L in dry and wet seasons, respectively) is nitrogen respectively. Relatively higher nitrite concentra- higher than that of the reports by previous studies. [37, tions were measured near effluent of floriculture indus - 59] reported that SiO -Si concentrations of Lake Ziway try which could be due to the application of high amount −1 ranged 13.4 to 31 and 14.7 to 37.5  mg L with mean of agrochemicals (Tadele, 2012). Comparatively, higher −1 values of 19.0 and 22.9  mg L in dry and wet seasons, concentration of nitrite nitrogen value also measured in respectively. Lake Ziway than some other Ethiopian lakes for instance, The overall mean concentration of SiO -Si (40.68  mg Lake Hayq [46]. The mean concentration of nitrite nitro - −1 L ) in this study was higher than the previous reported gen in this study is beyond the concentration limit of the values in the same lake and other Ethiopian rift val- EU guide lines for drinking water (0.1  mg nitrite nitro- −1 ley Lakes, Awasa and Chamo by [30, 58, 59] which was gen L ) [35]. Consequently, it might cause environmen- −1 23.8, 37.6 and 1.00  mg L in Lake Ziway, Awasa and tal concern due to its toxicity to aquatic biota as well as Chamo, respectively. In view of the high silica concentra- because of human health effects. −1 −1 tions (> 10 mg SiO L ) commonly encountered in Afri- The mean concentration of NH -N (0.121  mg L ) can lakes [49], the lake might encounter some ecological in this study is closely similar with relatively recent −1 changes especially towards higher Diatom productivity reports; by [39] (0.111  mg L ), and [31] (0.143) but (Table 10). higher than that of earlier reports; for example, by [44] −1 Generally, a pattern of low mean concentrations (0.036 mg L ) indicating increasing trend. The mean of NH-N, NO -N, TN, TIN, SiO -Si in dry season TN concentration in both dry and wet season in this 3 3 2 have higher mean concentrations in wet season. This study is higher than the standard limit value even for strongly indicated point source pollution for this param- eutrophic waters [18] (Table 10). eter, which might be associated with industrial effluents, In addition, the mean SRP concentration (0.06  mg −1 human interference, municipal discharge and animal L ) was higher than that of the pervious reported waste [14]. During dry season both decreased precipita- data of [31, 37, 39, 44] which was 0.016, 0.01, 0.059 −1 tion and increased agricultural withdraws for irrigation and 0.029  mg L , respectively. The measured concen - contributed to lower flows of those nutrients, however, tration is also beyond the range of its threshold (0.05 −1 TP, PO -P and NO -N were observed in a higher con- to 0.1mgL ) as a nutrient for natural waters [47, 58]. 4 2 centration during wet season. Similarly [50, 58], noted This is because in recent times Lake Ziway is exposed that nutrients that have a high concentration during dry to anthropogenic activities due to over usage of agro- season than wet season tend to come from point sources chemicals like fertilizers, pesticides in which organic whose supply is constant, whereas the inverse pattern and inorganic pollutants releases and discharge of can be attributed to non-point sources that are mobilized water from domestic sources, agricultural runoff, and by high run-off during wet periods. horticulture including floriculture activities around the lake. Besides, the mean TP value of the lake water −1 Multivariate analysis (0.311 mg L ) is higher than the previous reported Principal component analysis (PCA) data of [44], and [30], which was 0.069 and 0.219  mg −1 As indicated in the PCA analysis, P C has strong positive L , respectively. Higher TP concentration was also loadings on N H-N, NO-N, NO-N, PO -P, TP, SiO -Si, measured in this lake in this study as compared to that 3 2 3 4 2 TIN, EC, TDS, TA and SD associated sampling sites Mb of other Ethiopian rift valley lakes like Lake Awasa and and Kb during wet season. The presence of nutrients in Chamo [30]. The increasing trend in TP is also probably PC demonstrated the intense of agricultural activities in because of nutrient enrichment of the lake from the 1 T ibebe et al. BMC Chemistry (2022) 16:11 Page 15 of 18 the environment of the lake ecosystem and this resulted In the present study, a scree plot also showed the eigen in pollution with nutrients coming from fertilizers and values sorted from large to small as a function of the prin- pesticides [14]. cipal components number. After the fourth PC (Fig.  2a, One of the main sources of TP in runoff is soils with b), starting in the downward curve, other components high phosphorus levels. In other words, the nutrient can be omitted. The scree plot was used to identify the parameters, pH and SD account for similar patterns seen number of PCs to be retained in order to comprehend in lake water samples. This group of nutrient param - the underlying data structure [53]. Thus, a new set of data eters also reflected the degree of eutrophication of the is obtained. This may explain the variation of data set lake, suggesting that the anthropogenic pollution mainly with fewer variables. Scree plots in PCA/FA to visually from the discharge of domestic and agricultural wastes, assess which components or factors explain most of the industrial sewage and agricultural runoff [14]. Moreo - variability in the data. ver, it might be due to farmers use ammonium fertilizers The bi-plot of PCs associated with nutrients (NH -N, and phosphate pesticides, and the lake receive ammo- NO-N, NO-N, SiO -Si and P O -P), EC and TDS which 3 2 2 4 nium via surface runoff and irrigation waters [17]. Nitrate were the key parameters characterizing the Fb sampling nitrogen source is due to numerous sources, such as, site (Fig.  3), which were due to the floriculture effluents geologic deposits, natural organic matter decomposi- [31, 33] and Fa distinctiveness was attributed to tempera- tion and agricultural runoff [51]. The second component ture, SD and TA. The parameter influencing the distinc - (PC ) demonstrated strong positive loadings for TN, EC, tion in sampling site B was mainly pH while Mb site was TDS and TA. The third components (PC ) demonstrated influenced by DO, TN and TP in the dry season [31]. strong positive loadings for SiO-Si, PO -P, DO and tem- The bio-plot of PCs associated with nutrients (NH -N, 2 4 3 perature. This factor indicates that PO -P source is from NO-N, NO-N, SiO -Si, TIN, PO -P and TP), which 4 3 2 2 4 domestic and agricultural wastes, detergents from indus- were the key parameters characterizing the Mb and Kb tries whereas SiO -Si is from bed rock materials and sampling sites (Fig.  4), can suggest an influence of agri - compounds containing silica from floriculture industry cultural activities in the catchment of the two rivers [33], while, the fourth component (PC ) had no strong feeding the lake (Meki and Ketar Rivers) and Fa distinc- loadings in any measured parameters. tiveness was attributed to temperature, TDS, EC and DO. In the dry season the PCA performed on the cor- The parameter influencing the distinction in the K site relation matrix of means of the analyzed water quality was mainly pH while Fb site was influenced by DO, TN, parameters by sites showed that four principal compo- TA and SD in the wet season. nents (PCs) represented about 90.97% of the total vari- The results from temporal PCA/FA suggested that ation in the entire dataset. The first PC accounted agrochemicals pollution were potential pollution sources nutrients (NH-N, NO-N, NO -N, TIN, PO-P, SiO -Si), for both temporal clusters but that the influence of each 3 2 3 4 2 TDS, EC, TA associated with Fa and Fb sampling sites. was different. The results of the present study showed The high values in these sampling sites were attributed to the existence of the contamination of Lake Ziway in both the point pollution sources from floriculture industry in inorganic and organic agrochemicals mainly in the lake dry season. catchment in particular to Fb, Fa, Mb and Kb sampling The second PC had strong positive loading with tem - sites. The major pollutant sources to the lake might be perature, pH, TP and SD as the associated parameters. mainly from agricultural activities, human interference TP demonstrating that intense agricultural activity had for different purposes, domestic wastes, industrial efflu - occurred at the sampling site Fa and B, causing pollu- ents and urban origin [14]. tion due to fertilizers and pesticides [14, 42] interpreted as nutrient pollution from anthropogenic sources, such Cluster analysis (CA) as eutrophication from domestic wastewater, indus- The three groups obtained by cluster analysis vary trial effluents and agricultural activities. The third PC according to natural backgrounds features, land use explained the total variations between sites compris- and land cover, industrial structure and anthropogenic ing only DO in sampling site Mb. The inverse relation - sources of pollution [14]. The cluster analysis revealed ship between temperature and DO is a natural process different properties at each site with respect to physical because it can hold less dissolved oxygen [42]. The fourth and chemical variables. PC explained site variations with TN only. [52] classified Sites mainly located at middle reach of the lake (Sta- the factor loadings as “strong,” “moderate,” and “weak,” tion C, M, K, K and F ) were grouped under Cluster III, a a o a corresponding to absolute loading values of > 0.75, 0.75 to which were basically at the center of the lake and shore 0.50, and 0.50 to 0.30, respectively. water. In addition, Station Ma and Ka located upstream Tibebe et al. BMC Chemistry (2022) 16:11 Page 16 of 18 of the lake, showed the similar water environment qual- of pollution sources based on parameter association. ity characteristics with these stations. Urbanization and Their findings agree with the present study particu- industrialization level is relatively low at these sites. larly in the association of nutrients with catchment Direct discharged domestic wastewater contaminated runoff in the wet season and point sources during the the water; the cluster III correspond to relatively less pol- dry season. Reference [55] used PCA and CA in 33 luted (LP), because the inclusion of the sampling location sampling sites for 13 physico-chemical and biological suggests the anthropogenic sources of pollution is less in water quality parameters which helped them in iden- the study period. tifying the underlying processes responsible for the Mb and Kb sampling sites were grouped under cluster heterogeneity in different parts of Lake Neusiedler in II; the two stations are the tributaries of the lake; one is Hungary. Their study also showed that the river input Meki River that drains part of the western high land and region was significantly different. Moreover, [56], 16 the second is the Ketar River which can drains the Arsi water quality parameters; in their finding also agreed Mountains to the eastern part of the lake. These two riv - with the present study that most parameters increase ers transported many agrochemicals from western high their concentrations in the dry season due to evapora- land and Arsi Mountains ([14, 17]. Therefore, these sam - tive effects, whereas lower values are observed in the pling stations received pollutants mostly from agricul- wet season as the lake water is diluted by rain water. tural runoff, domestic waste and industrial effluent from Furthermore, [50] also applied PCA and CA to iden- the local people and Meki and Abura towns ([14, 17]. tify the factors influencing the water quality in dif- Cluster II corresponds to moderately pollution. ferent seasons in Hyderabad lakes in India. The study Sampling site Fb is grouped under Cluster I; this revealed that water pollution was more significant cluster site is the effluents of the floriculture indus- during the dry season as compared to the rainy sea- tries which is directly enter to the lake and polluted son because of precipitation and tidal influence which the lake water. Cluster I correspond to relatively highly cause dilution. [57], applied PCA and CA in order to polluted (HP) site, because the inclusion of floricul- provide an insight on water quality in Lake Naivasha, ture industry, due to the untreated sewage of flori- Kenya showed the usefulness of such multivariate culture effluent at this site [33]. Accordingly, spatial analysis in establishing the characteristics of differ- variations of water quality in Lake Ziway showed that ent regions in aquatic ecosystems based on numerous water quality was better in center and some portions water quality parameters. of the shore water than in western and eastern areas in the lake. At the same time these results showed that Comprehensive evaluation of Lake Ziway water quality for a rapid assessment of water quality, only one site analysis in each cluster presents a useful spatial assessment of According to the comprehensive pollution index val- the water quality for the entire network in different ues sites Fb, Fa, B and Mb showed moderate pollution seasons. This implies that, the results indicate the CA in dry season. The low water qualities parameters in technique is useful in offering reliable classification of these sites might be the influences of floriculture indus - surface water in the whole region and make it possi- try and domestic wastes from Ziway and Meki Towns. ble to design a future spatial sampling strategy in an However, the wet season, the values of the comprehen- optimal method, which can reduce the number of sam- sive pollution index ranged from (0.38 to 0.68) dem- pling sites and associated costs. Similar reports have onstrated slight pollution of the whole sampling sites. been dispatched by different authors [7, 25]. The water quality of the lake was determined to have This implies that, for a rapid assessment of water been influenced by different major source of pollution quality, only one site in each cluster presents a useful such as agricultural activities, domestic wastes, fish - spatial assessment of the water quality for the entire ing industries, swimming and car washing. Similarly network in different seasons. In this study we found [24], applied comprehensive pollution index model to the PCA and CA analysis techniques are useful in explain the pollution status of Lake Baiyangdian, china. apportionment of pollution sources based on param- Their result revealed that Lake Baiyangdian has a pollu - eter association. Similar findings has been reported in tion status ranging from less polluted to sever polluted. the study of [50, 54–56]. Other water quality studies that applied PCA and CA analysis found the techniques helpful in the inter- Conclusion pretation of large datasets. 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Journal

BMC ChemistrySpringer Journals

Published: Mar 14, 2022

Keywords: Cluster analysis; Comprehensive pollution index; Factor analysis; Principal component analysis

References