Hydrochemical Characterization of Surface Water in Phewa Lake, Gandaki Province, Nepal

doi:10.21203/rs.3.rs-2142071/v1

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Hydrochemical Characterization of Surface Water in Phewa Lake, Gandaki Province, Nepal

https://doi.org/10.21203/rs.3.rs-2142071/v1

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Securing water to satisfy the needs of humans and the ecosystem is one of the prime issues worldwide. This study aims to characterize the hydrochemical properties and water quality of Phewa Lake, Nepal. The mean pH and DO were found to be 7.98 and 6.08 mg/L, respectively. The ionic dominancy of water follows the order of Ca²⁺>Na⁺>K⁺>Mg²⁺, and HCO₃⁻>SO₄²⁻>Cl⁻>NO₃⁻. The Piper plot characterizes the Ca-HCO₃ type of water facies, and the Gibbs diagram indicates the rock weathering as a major controlling mechanism. The high ratios of (Ca²⁺ + Mg²⁺)/T_Z⁺, (Ca²⁺ + Mg²⁺)/(Na⁺+ K⁺), Ca²⁺/Na⁺, and HCO₃⁻ /Na⁺, i.e., 0.84, 5.33, 6.48, and 5.36, respectively also confirmed the dominancy of carbonate weathering. The drinking and irrigational indices revealed that the lake water was relatively better in monsoon compared to the pre-monsoon season. The study provides new insights on hydrochemical characteristics which contribute to the sustainable management of Ramsar-listed lakes in the Himalayas.

Hydrochemistry

Spatiotemporal variations

Multivariate analysis

Water quality index

Ramsar-listed lakes

Ecological sustainability and socioeconomic prosperity are largely dependent on the availability of quality and quantity of water (Pathak et al., 2020). Freshwater ecosystems are indispensable for livelihoods and ecological services, however, the quality of water in most countries has become a critical issue in recent years (Singh et al., 2004). Securing freshwater to satisfy the needs of humans and the environment is one of the crucial issues under the context of climate change which challenging the 21st century. Additionally, the natural and anthropic interferences have greatly impacted the quality of the freshwater bodies in the Himalayas (Singh et al., 2016). The anthropic pollution has become more severe including municipal, domestic, and industrial effluents; and agricultural runoffs where climate change has further added the risks of contamination in freshwater environments (Mapoma et al., 2017; Zhang et al., 2019). Owing to the stagnant nature, the lakes are more susceptible to contaminants as compared to the flowing water bodies. In the lake ecosystems, the pollutants may easily enter with the surface runoff and remain there for a long period of time. These activities are responsible for elevated dissolved chemicals in the form of major ions and toxic chemicals in the stagnant environments causing eutrophication, biological contaminations, and sedimentation (Tripathee et al., 2016). Hence, continuous monitoring, as well as effective mitigation measures of these lakes are essential for maintaining their sustainability.

The hydrochemical study of freshwater bodies allows the understanding of the weathering process, and controlling mechanism of chemical variables including the effect of seasonality (Acharya et al., 2020; Pant et al., 2018). Several kinds of literature are available on the assessment of hydrochemistry and the processes affecting the freshwater lakes in the Himalayas (Adhikari et al., 2020; Gurung et al., 2018; Rupakheti et al., 2017; Sharma et al., 2015; Singh et al., 2016; Zheng and Liu, 2009). The findings have underlined that the Himalayan lakes are more sensitive under the context of climate change and thus, getting more attention among the scientific communities (Xu et al., 2010). For instance, the hydrochemistry of Rara Lake (Gurung et al., 2018), Gosainkunda Lake (Sharma et al., 2015), and Rajarani Lake (Adhikari et al., 2020) were documented and elucidated that the haphazard developmental activities, coupled with the climate change effects and long-range transport of the pollutants have increased the risk of contaminations. It is also highlighted that the encroachment and shrinkage of the lake with such poor quality, especially during the last four decades is a serious social, economic and ecological challenge for the sustainability of Himalayan freshwater ecosystems.

Among the lake clusters of Pokhara Valley, the Phewa Lake is the biggest Ramsar- listed lake having multiple ecological and economic importance. Also, the lake basin is one of the most important touristic destinations in Nepal. The lake water is widely used for drinking, fishing, irrigation, and commercial purposes. Nevertheless, during the last few decades, the lake has been facing the heavy burden of eutrophication, sedimentation, and contamination due to waste disposal, fish-feeding, and land acquisition, etc. Most of the previous studies mainly focused on the explorations at the spatial levels, for instance, (Khadka and Ramanathan, 2020); (Sharma et al., 2015) have indicated anthropic contaminations are high in the lake, whereas, some other studies (e.g., Cui and Li, 2014; Watson et al., 2019) have observed the land use/cover change is one of the serious matter of concerns for the sustainability of the lake. In view of the above-mentioned literature, there are limited investigations on the assessment of the hydrochemistry of the lake basin using multivariate statistical techniques. Thus, the objective of this study is to characterize the hydrochemistry and water quality of the Phewa Lake by applying multivariate statistical techniques such as cluster analysis, and principal component analysis. Ultimately, the study could explore new insights on hydrochemical characteristics and contribute to implementing the appropriate management strategies for the sustainability of Ramsar-listed lakes in the Himalayas.

2.1 Study area

The Phewa Lake is located in the mid-hills of Gandaki Province, Nepal (28°1’N, 82°5’E) at an altitude of 782 m asl (Fig. 1, Table A1). With ~ 156 km² watershed area, the surface area of Phewa Lake is 4.35 km² having a maximum depth of 22.5 m. It is a wetland of international importance being a Ramsar site (No. 2257) listed lake (Paudel et al., 2017). The lake has a sub-tropical to a temperate climate with ~ 3710 mm/year average precipitation. It is the largest lake in the Pokhara Valley, and categorized as the second largest lake in Nepal after Rara Lake (9.8 km²). The lake is formed by a natural process and regulated by the artificial dam, representing a high anthropic pressure from diverse sources of pollutants from the vicinity. It receives a massive water influx from tributaries with municipal solid wastage from the Pokhara Metropolitan City. The lake is fed by two tributaries namely the Harpan and Andheri, and the water of the lake continues to flow outside from its outlet to the Damside area, throughout the year. The lake has socio-cultural importance; as a temple (Tal Barahi) is situated in the middle of the lake (Fig. 1). The lake provides various ecosystem services such as hydroelectricity, irrigation, fishery, and ecotourism activities. By land use patterns, the lake basin features have dense settlements especially on the northeast sides whereas rural settlements on the southern side, and stream channel with forest coverage on the western side of the lakeshore (Pant and Adhikari., 2015). The lake basin is predominated by sedimentary and metamorphic rocks with lesser Himalayan geological features i.e., slate, dolomite, limestone, siltstone, quartzite, schist, muscovite, and carbonaceous shale (Fort et al., 2018; Schwanghart et al., 2016; Stolle et al., 2017; Upreti, 1999).

2.2 Sampling and analytical techniques

In this study, 50 surface water samples were collected from 25 different locations (2 × 25 samples) from the Phewa Lake in pre-monsoon and monsoon seasons in 2017. The samples were collected in a representative way by incorporating the inlet, center, and outlet of the lake (Fig. 1). The samples from each site were collected at 20–30 cm depth in the polyethylene plastic bottles which were rinsed three times with the same water before actual sampling. Water temperature (WT), pH, electrical conductivity (EC), total dissolved solids (TDS); and dissolved oxygen (DO) were determined in-situ using a mul-timeter probe (HI-98129, HANNA, Romania); and DO meter, respectively. The major ions such as Ca²⁺, Mg²⁺, K⁺, Na⁺ (detection limit: 0.05 mg/L), Si (detection limit: 0.01 mg/L); and Cl⁻, NO₃⁻, and SO₄²⁻ (detection limit: 0.1 mg/L) were analyzed using Inductively Coupled Plasma Optical Emission Spectrometer and Ion Chromatography of Dionex, respectively, whereas HCO₃⁻ was determined by charge balance method (Pant et al., 2018).

For quality control, special care was taken during sampling and laboratory analyses. To avoid contamination, non-powder vinyl cleanroom gloves and masks were used during sample collection and laboratory works. The collected samples were stored in the refrigerator at 4°C until the laboratory analyses were performed. During the laboratory analyses, distilled deionized water was used. In addition, freshly prepared standards of known concentrations and procedural blanks were analyzed during each analytical run and no detectable contaminations were found. Furthermore, each calibration curve was evaluated by analysis of a set of samples. The charge balance error (E) was calculated using E = (Σcations – Σanions / Σcations + Σanions) × 100% and it was found within the limit of ± 10%.

2.3 Statistical analysis

The cluster analysis (CA) is one of the multivariate techniques whose primary aim is to assemble objects based on the characteristics they possess (Varol et al., 2013a). The hierarchical agglomerative CA was performed on the normalized dataset utilizing Ward’s method (Zhao et al., 2012). The principal component analysis (PCA) was used to identify the major controlling factors of the lake water quality (Pasquini et al., 2012; Zeng et al., 2019). All descriptive statistics and multivariate analysis were performed using SPSS version 26.00.

2.4 Geochemical analysis

To understand the chemical characteristics of hydrochemical variables in the Phewa Lake, the Piper, Gibbs, and mixing plots were applied. For this purpose, the trinary linear diagram of hydrochemical facies was plotted. Gibb’s (1970) plot was used to determine major processes controlling the hydrochemistry of the lake water. The mixing diagrams were used to examine the Na⁺-normalized Ca²⁺ versus Mg²⁺ and Na⁺-normalized Ca²⁺ versus HCO₃⁻ ratios were plotted to evaluate the contribution of silicate and carbonate dissolution in the study lake (Qaisar et al., 2018).

3.1 Hydrochemistry of surface water

The result of hydrochemical properties of water quality of the Phewa Lake during pre-monsoon and monsoon seasons was presented in Table 1. The lake water was mildly alkaline, with a pH ranging from 6.95 to 8.93. EC values varied from 36 to 164 µS/cm, while TDS values ranged from 19 to 89 mg/L, indicating the relatively low range of dissolved chemicals in the lake. Among the major ions, the Ca²⁺ (8.74 ± 3.75 mg/L) and HCO₃⁻ (22.65 ± 10.80 mg/L) were the most abundant cation and anion, respectively. The grand mean ionic concentrations were in the order of Ca²⁺>Na⁺>K⁺>Mg²⁺ and HCO₃⁻>SO₄²⁻>Cl⁻ >NO₃⁻ for cations and anions, respectively. Unlike the previous studies on hydrochemistry in the Himalayan lakes, relatively low concentrations of Mg²⁺ were obtained in this study, which could be due to the limited distribution of ferromagnesium and magnesium carbonates in the underlying rocks (Mapoma et al., 2017; Trower, 2009).

In general, low ionic strength indicated that the lake is pristine and less impacted by anthropic activities. Besides, the high rate of rainfall in central Himalaya has caused a dilution effect on the concentrations of the ions in the lake. It was observed that the mean values of the major ions composition in the Phewa Lake were comparable with some of the lakes like Begnas and lesser than most of the freshwater lakes in the world, and most of the variables are within WHO guidelines (Khadka and Ramanathan, 2012; Gorchev and Ozolins, 2011; Singh et al., 2016). Although most of the variables are within the guideline values, the results demonstrate that the elevated concentration of some ions indicates the point source of pollution including domestic wastewater effluents and agricultural runoff. Mainly, more attention should be paid to the effects of K⁺ on the lake water quality.

3.2 Spatiotemporal variations

The spatial pattern of major ions indicated that the influence of anthropic activities especially in the sampling points located in the north-east side of the Phewa Lake (Fig. 2, Table A2)). The Park Anedha (PL21) had the lowest concentrations of hydrochemical variables, which may be explained by the dense coverage of vegetation in the vicinity. At the proximity of urban settlement i.e., Fishtail Gate (PL4) demonstrated the highest concentrations of hydrochemical variables, indicating anthropic signature (Table A2). The results are more or less consistent in both the pre-monsoon and monsoon seasons confirming that the major source of the pollutant was anthropic activities at the proximity of the core urban sites. The highest pH value was reported from the inlet (i.e., 8.93), signifying the higher land-water interaction with the carbonate-dominated lithology, whereas the lowest values were observed at the site PL3 (i.e., 6.95). Similarly, the highest DO was observed in the center of the lake at the fishery site (PL15) and the lowest was observed from PL4 (i.e., 2.03 mg/L) in the pre-monsoon, which were below the permissible limit of the water quality standard (i.e., 5 mg/L). The results showed that the center of the lake and inlet sides have relatively higher concentrations of DO, while the nearby temples, hotels, and outlet sides have relatively less (Table A1).

The excessively high concentrations of K⁺ were observed in PL1 during the monsoon season, indicating the point source of the pollution. Similarly, the highest concentrations of Cl⁻ (PL1: Mon), and NO₃⁻ (PL14: Mon) were found to be 6.21 mg/L, and 2.61 mg/L, respectively.

Table 1

Summary statistics of hydrochemical variables in the water of Phewa Lake, Gandaki Province, Nepal (All units in mg L^− 1, except, Temperature: ℃, EC: µS/cm and pH)
Parameters		Temp.	pH	EC	TDS	DO	Ca²⁺	Mg²⁺	K⁺	Na⁺	Si	Cl⁻	NO₃⁻	SO₄²⁻	HCO₃⁻	Reference
Pre-monsoon	Mean	24.89	8.44	104	57	5.42	11.98	1.60	1.31	2.10	1.82	1.95	0.87	2.29	32.54	This Study
	SD	1.44	0.37	10	6	1.13	1.05	0.13	0.14	0.32	0.14	0.28	0.33	0.46	2.76
	Min	22.50	7.38	90	47	2.03	10.87	1.48	1.15	1.79	1.43	1.67	0.00	1.91	28.21
	Max	29.40	8.93	142	77	7.70	15.99	1.98	1.67	3.34	2.04	2.75	1.25	4.17	43.60
Monsoon	Mean	25.95	7.52	50	27	6.75	5.51	0.89	1.36	0.98	2.48	1.01	0.94	1.28	12.75
	SD	2.03	0.39	21	11	1.10	2.41	0.20	1.25	0.14	0.25	1.10	0.45	0.27	5.13
	Min	21.30	6.95	36	20	3.51	4.18	0.80	0.85	0.82	1.78	0.64	0.00	0.71	9.91
	Max	30.00	8.15	144	78	9.08	16.72	1.82	7.26	1.53	2.92	6.21	2.61	1.97	36.68
Grand mean	Mean	25.42	7.98	77.00	42	6.08	8.74	1.25	1.33	1.54	2.15	1.48	0.91	1.79	22.65
	SD	1.82	0.60	35	19	1.29	3.75	0.40	0.88	0.62	0.39	0.92	0.39	0.63	10.80
	Min	21.30	6.95	36	19	2.03	4.18	0.80	0.85	0.82	1.43	0.64	0.00	0.71	9.91
	Max	30.00	8.93	164	89	9.08	16.72	1.98	7.26	3.34	2.92	6.21	2.61	4.17	43.60
WHO		-	6.5–8.5	1000	500	-	100	50	100	200	-	250	50	250	600	WHO, 2011

The high concentrations of Cl⁻ at PL1 indicated the point source of pollution. Unlike with many previous studies, the concentrations of K⁺ were markedly higher in the center boat site during monsoon season, indicating the point source of water pollution in the lake (Khadka and Ramanathan, 2020). Similarly, the highest concentration of dissolved silica was observed from the sampling site down from the Harpan stream (PL7: Mon), signifying the intense rock-water interactions in the moving water as compared to the standing water bodies. Site PL21 indicates the organic pollution in the lake waters from the core urban settlements and farmlands. Most of the polluted sites of the lake were located on the northern side and near the feeding streams passing from the core urban area of Pokhara Valley. Whereas, the inlet area and the southern side of the lake were relatively pristine due to the natural source of small streams and less anthropic interferences. The concentrations decreased in the middle segments of the lake, suggesting that Phewa Lake has a strong ability of decontamination and self-purification (Fig. 2). The results indicated that the anthropic signatures were observed in the vicinity of dense settlements, which could have a potential threat to human and ecological health.

The seasonal trend implies that the concentrations of most of the hydrochemical attributes were higher in the pre-monsoon as compared to the monsoon season (Fig. 3). The results further specified that the seasonality impact on the concentrations of EC, TDS, and major ions is directly associated with the high rainfall-runoff discharge. The results are consistent with the previous studies indicating that most of the aquatic environments including lakes are highly diluted in the monsoon season (Khadka and Ramanathan, 2020). Particularly, pH, Ca²⁺, Mg²⁺, Na⁺, Cl⁻, SO₄²⁻, and HCO₃⁻ displayed higher mean values in the pre-monsoon, while the mean values of temperature, DO, K⁺_, Si, and NO₃⁻ were observed higher in the monsoon season. In general, the increase in temperature leads to the decrease of DO and affects the growth and development of the biota during the monsoon season, but the present study does not follow a similar pattern. This could be due to the accumulation of organic wastes in the pre-monsoon season owing to low discharge (Pant et al., 2019). Since the climate of the lake basin displayed a bipolarity (i.e., low and high rainfall in the pre-monsoon and monsoon seasons, respectively), exhibiting the marked differences in controlling the hydrochemistry that could be primarily associated with the monsoon-driven climatic seasonality. This could be the reason that most of the measured variables showed statistically significant seasonal differences between pre-monsoon and monsoon season based on one-way ANOVA (p < 0.05) except DO, K⁺, and NO₃⁻ (Fig. 3).

3.3 Hydrochemical characterizations

The hydrochemical facies of the lake water on a seasonal basis was presented in Fig. 4. The Piper plot showed Ca²⁺-HCO₃⁻ a type of water in the Phewa Lake during both the seasons. Alkaline earth metals (Ca²⁺ and Mg²⁺) were noticed to exceed alkali metals (Na⁺ and K⁺) and weak acid (HCO₃⁻) surpass strong acid (SO₄²⁻). The hydrochemistry was mainly dominated by natural phenomena, with monsoon mainly influenced by high rainfall in the region, with Ca²⁺ being noted as the dominant cation, whereas HCO₃⁻ was the dominant anion. Nevertheless, the results exhibited that the pre-monsoon water samples were more enriched in terms of ionic concentration indicating the impact of monsoon-driven climatic seasonality. Although all the water samples of the pre-monsoon and monsoon seasons characterized the Ca-HCO₃ type, the anthropic signature in the lake is obvious as the PL1 sample moving towards the Na-Cl type of water facies during the monsoon season (Fig. 4). Interestingly, there was no significant seasonal discrimination between the pre-monsoon and monsoon waters was observed from the point of hydrochemical facies in the lake.

3.4 Gibbs plots

The major sources of chemicals and their controlling mechanism including precipitation, rock weathering, and evapo-crystallization (Gibbs, 1970) could be explained by TDS versus Na⁺/Na⁺ + Ca²⁺, and TDS versus Cl⁻/(Cl⁻ + HCO₃ ⁻) for cations and anions, respectively (Fig. 5). In this study, the entire samples fell rock weathering domain, indicating that the geochemical weathering was the main source of hydrochemical variables in the Phewa Lake. Additionally, it was clearly illustrated that the significant contribution of precipitation to the ionic chemistry of Phewa Lake during the monsoon season (Fig. 5). Thus, the hydrochemistry of Phewa Lake was controlled by mainly two natural processes i.e., geogenic weathering and monsoon-driven climatic seasonality.

3.5 Ionic ratios and mixing diagrams

In this study the mean equivalent ratios of Na⁺ /Cl⁻ was 1.76, which was significantly higher than the sea aerosols (Na⁺/Cl⁻ = 0.85). This indicates a relatively minor contribution of these ions from atmospheric fallout to the chemical composition of the lake. The source of Ca²⁺ and Mg²⁺ can be determined from the (Ca²⁺+Mg²⁺)/Tz⁺ ratio. In this study the ratio was 0.84, clearly indicating that the major source of these ions was the carbonate weathering, which was further supported by high ratios of [HCO₃^˗/(HCO₃^˗+SO₄^2˗) = 0.90]. Similarly, the ratio of (Ca²⁺+Mg²⁺)/(Na⁺+K⁺) and HCO₃^˗/(Na⁺+K⁺) were 5.33 and 3.57, for (Ca²⁺+Mg²⁺)/(Na⁺+K⁺) and HCO₃^˗/(Na⁺+K⁺), respectively indicating the minimum contribution of silicate weathering in the lake basin (Rehman et al., 2018). On the other hand, to evaluate the contribution of evaporite dissolution, the ratio of Ca²⁺/SO₄²⁻ was found to be 11.58, clearly indicating that evaporate dissolution has very less role in determining major ions which is also consistent with the Gibbs plots. However, some of the ionic ratios (Table 2) such as the K⁺/Cl⁻ ratio during monsoon season was 1.28, indicating the anthropic signature.

Table 2

Ionic ratios of pre-monsoon, monsoon, and a total of the water samples in the Phewa Lake, Central Himalaya Nepal
Parameter	Pre-monsoon (n = 25)	Monsoon (n = 25)	Average (n = 50)
Ca²⁺/Na⁺	6.63	6.32	6.48
HCO₃^˗/Na⁺	5.92	4.81	5.36
HCO₃^˗/Ca²⁺	0.89	0.76	0.83
HCO₃^˗/(Na⁺+K⁺)	4.31	2.84	3.57
(Ca²⁺+Mg²⁺)/(Na⁺+K⁺)	5.91	4.74	5.33
Ca²⁺/SO₄^2˗	12.84	10.32	11.58
Na⁺/Cl^˗	1.66	1.86	1.76
K⁺/Cl⁻	0.62	1.28	0.95
HCO₃^˗/(HCO₃^˗+SO₄^2˗)	0.92	0.88	0.90
Si/(Na⁺+K⁺)	2.11	4.91	3.51
(Ca²⁺ +Mg²⁺)/Tz⁺	0.85	0.82	0.84
(Na⁺+K⁺)/Tz⁺	0.15	0.18	0.16

From the ionic ratios and mixing diagrams, it was clear that carbonate and silicate weathering were primary and secondary contributors for the major ions, respectively with a minor contribution of anthropic inputs (Fig. 6). During both seasons, the chemistry of lake water was mainly controlled by carbonate sources, however, most of the ratios were even higher in the pre-monsoon season indicating the impacts of dilution on the water quality during the monsoon period.

3.6 Multivariate analysis

3.6.1 Cluster analysis (CA)

CA is widely applied to evaluate the long-term monitoring of hydrochemical variables, and designing an optimal spatial sampling strategy (Varol et al., 2013b). In this study, CA was applied to the TDS, Ca²⁺, Mg²⁺, K⁺, Na⁺, Si, Cl⁻, NO₃⁻, SO₄²⁻, HCO₃⁻ and DO (n = 50). As shown in Fig. 7, Cluster 1 was formed with 24 sample sites of monsoon season, which can be considered as low pollution sites with high dilution. Cluster 2 included 25 sampling sites from pre-monsoon and one sample (PL21) from monsoon season. The relatively high concentration of major ions in Cluster 2 could be attributed to the anthropic contribution and relatively low dilution of lake water during the pre-monsoon season. Interestingly, there was only one sampling point from the monsoon season was clustered in Cluster 2 indicating the anthropic signature due to the mass flow of pilgrims in the Barahi Temple, located at the center of the lake (Fig. 1).

3.6.2 Principal component analysis (PCA)

To identify the important seasonal water quality parameters, PCA was executed on 11 variables (n = 50). For the reliability of the dataset, the KMO and Bartlett's Tests were performed and the values were obtained 0.80 and 854.78, respectively. The principal components (PCs) with an eigenvalue > 1 are selected for further analyses (Table 4). The loading values are classified according to the absolute loading values of > 0.75 and 0.75 − 0.50 were classified as strong and moderate, respectively (Varol et al., 2013b).

The contribution of each of these hydrogeochemical variables to the loading of the first three factors was displayed in Fig. 8. PC1 accounts for 63.38% of the total variance and has strong positive loading on EC, TDS, Ca²⁺, Mg²⁺, Na⁺, SO₄²⁻, and moderately loading HCO₃⁻, and strong negatively loading on Si. PC1 was, therefore, mainly representing the parameters affected by geogenic sources, mainly reflecting the dominant carbonate weathering. On the other hand, the strong negative loading of Si in PC1 implied that their sources are quite different. PC2 accounts for 15.15% of the total variance and had strong positive loading values on K⁺ and Cl⁻, indicating that these chemicals were mainly contributed from the anthropic activities. Similarly, PC3 accounts for 9.18% of the total variance and had strong positive loading on NO₃⁻ and moderate positive loading on HCO₃⁻, signifying their distinguishing characteristics than other hydrochemical variables. The distribution of the water quality parameters on PC1, PC2, and PC3 can also be found scattered in Fig. 8.

Table 4

Loadings of chemical variables on principal components for the water samples in the Phewa Lake, Gandaki Province Nepal
	PC1	PC2	PC3
TDS	0.97	0.14	-0.02
Ca²⁺	0.96	0.06	-0.10
Mg²⁺	0.97	0.08	-0.10
K⁺	-0.09	0.99	-0.05
Na⁺	0.96	0.14	-0.05
Si	-0.79	-0.05	0.00
Cl⁻	0.46	0.88	-0.08
NO₃⁻	-0.04	-0.07	0.96
SO₄²⁻	0.85	0.12	0.00
HCO₃⁻	0.98	0.07	-0.08
DO	-0.65	-0.12	0.39
Eigenvalue	6.97	1.67	1.01
Variance (%)	63.38	15.15	9.18
Cumulative (%)	63.38	78.53	87.71

This study was conducted to evaluate the hydrochemical properties of water on a seasonal basis in Phewa Lake, Nepal. Overall assessment of hydrochemistry during the study period showed that the surface water in the lake was alkaline with a mean pH value of 7.98. Alkaline earth metals (Ca²⁺ and Mg²⁺) were noticed to exceed alkali metals (Na⁺ and K⁺) and weak acid (HCO₃⁻) surpass over strong acid (SO₄²⁻) in both the seasons specifying Ca²⁺–HCO₃⁻ a type of hydrochemical facies in the lake. The principal component analysis confirmed that Ca²⁺, Mg²⁺, and HCO₃⁻ were mainly derived from the carbonate rock weathering while Na⁺ and K⁺ originated primarily from silicate weathering. The cluster analysis categorized the hydrochemistry of the lake into less and moderate pollution with distinct monsoon-driven climatic seasonality and anthropic impacts. Interestingly, the spatial classifications were exactly consistent with the geographical distribution of the sampling sites by cluster analysis where the site proximity to urban settlements was relatively more polluted.

Conclusively, the overall state of the Phewa Lake is pristine, however, sewerage disposal from the surrounding hotels and other anthropic activities have impacted the natural water quality of the lake. Thus, the finding of the research could improve the understanding of the current hydrochemical status of the Phewa Lake and contribute to sustainable water resource planning, and livelihood supports around the lake. It is recommended that the depth-wise study on trace elements in association with major ions should be carried out for the integrity of Ramsar-listed freshwater lakes in the Himalayas.

Acknowledgments

The authors acknowledge the Central Departmental of Environmental Science, Institute of Science and Technology, Tribhuvan University for providing the research facilities in Nepal. We would like to thank Professor Dr. Zhang Fan, Dr. Zeng Chen and Dr. Subash Adhikari for supporting the research. Special thanks to the anonymous reviewers and Editors who provided critical comments and insightful suggestions which greatly led to improving this manuscript.

Author contributions: Conceptualization: RRP, FUR, WG; data collection: RRP and FUR; writing/original draft preparation: RRP, FUR, GW, and KBP; analysis and GIS work: FUR, KB, and LBT; review and editing: ICY, KP and SJ. All authors have approved the final version.

Funding: We would like to confirm that there has been no significant financial support for this work that could have influenced its outcome.

Data availability: Once the manuscript is accepted, the original data will be shared with the journal.

Conflict of interest: The authors declare that they have no conflicts of interest.

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Hydrochemical Characterization of Surface Water in Phewa Lake, Gandaki Province, Nepal

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Abstract

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1. Introduction

2. Materials And Methods

2.1 Study area

2.2 Sampling and analytical techniques

2.3 Statistical analysis

2.4 Geochemical analysis

3. Results And Discussion

3.1 Hydrochemistry of surface water

3.2 Spatiotemporal variations

3.3 Hydrochemical characterizations

3.4 Gibbs plots

3.5 Ionic ratios and mixing diagrams

3.6 Multivariate analysis

3.6.1 Cluster analysis (CA)

3.6.2 Principal component analysis (PCA)

4. Conclusion And Future Perspective

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References

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