Assessing groundwater quality in Nand Samand catchment, Rajasthan, India: a GIS-based multi indices approach for drinking and irrigation water suitability Open Access

The accessibility to freshwater poses a significant challenge in the twenty-first century (Subba Rao et al. 2019). Groundwater (GW) is acknowledged as a crucial natural resource, playing a vital role in supplying clean and potable water across diverse climatic regions (Priya et al. 2022). Consequently, world's quarter of the irrigated farmland leans on GW for irrigation, with the majority, 75%, situated in Asia (Basunia et al. 2015). Many regions globally, particularly in rural settings, are solely dependent on wells and springs for their water supply. In the context of India, GW holds immense significance, with roughly 30% of the urban and 90% of the rural population even today relying on untreated water for domestic and irrigation purposes (Adimalla & Venkatayogi 2018; Adimalla & Li 2019). However, over the previous two decades, a noticeable increase in dependency on GW is found due to extensive growth in the agricultural sector, population expansion, and industrial development (Kant et al. 2018; Adimalla et al. 2020).

According to the WHO, approximately 80% of human illnesses are linked to water-related factors. In India, waterborne diseases have led to significant rates of illness and death (Singh & Hussian 2016). Consequently, experts specializing in GW have concentrated their efforts on evaluating GW appropriateness for various usages like drinking, residential, agricultural, and industrial purposes (Thapa et al. 2017; Adimalla 2019; Subba Rao et al. 2019). Global investigations have been undertaken to illustrate the quality of subterranean water, its fitness for consumption and farming, as well as the primary origins of pollutants (Alaya et al. 2014; Li et al. 2016; Dişli 2017; Adimalla et al. 2018; Dimple et al. 2022, 2023).

In terms of population size, India's largest economic sector is agriculture, which also produces the second-most food worldwide (Rane & Jayaraj 2021). GW is the most essential freshwater resource for subsistence, accounting for over 60% in irrigated agriculture, and hence plays an imperative part in India's total socio-economic stability (CGWB 2017a, 2017b). India ranks as the top global consumer of GW, with yearly abstractions of ⁠, or about one-fourth of the total global GW extraction annually (Murmu et al. 2019). Due to spiking GW demand by various sectors, India has seen a significant GW reduction in recent years (Selvakumar et al. 2018). According to the CGWB (2021) study, the nation's overall annually GW recharge is 436.15 billion cubic meters (BCM), annual obtainable GW is 397.62 BCM, yearly GW depletion is 244.92 BCM, and the current GW development stage is 61.6%. According to a World Bank (2016) study, if required efforts are not made, India is going to be water-stressed by 2025 and water-scarce by 2050. At present, India is confronting a grave water crisis, impacting around 100 million individuals who are directly affected by the nationwide water scarcity, while numerous major cities are facing severe water shortages. According to United Nations and NITI Ayog assessments, water demand will exceed available supply by 2030, and 40% of India's population would lack access to safe drinking water (Saha 2019).

Rajasthan stands as the largest state in the nation, encompassing a land area greater than the combined territories of 128 countries. Nevertheless, the state faces a critical water situation. Despite its extensive expanse, accounting for over 10% of the nation's total topographical extent and accommodating more than 5.5% of the population and 18.7% of the livestock, Rajasthan possesses access to only a mere 1.16% of the nation's overall surface water and 1.70% of its total GW (Sinha et al. 2018). The state's cumulative surface water availability reaches 29.78 BCM, distributed with 0.38 BCM in minor reservoirs, 1.71 BCM in medium and minor check dams, 4.22 BCM in major state dams, and 23.47 BCM in major interstate contract dams. Rajasthan's net dynamic fresh GW availability stands at approximately 11.99 BCM (Singh et al. 2021).

The WQI is a mathematical indicator utilized to determine the general superiority of water for a specific application use. It is termed as a score that represents the collective effects of several water quality parameters utilized in the computation of the WQI. At present, there are a number of water quality evaluation methods/models, including analytic hierarchy process (Pang et al. 2008), grey clustering (Yang & Yang 2017), fuzzy comprehensive evaluation (Wu et al. 2017), pair-comparison analysis (Qiu et al. 2008), etc. However, most of these evaluation methods can only show where the water body is polluted and not the degree of pollution. As a result, taking appropriate prevention and control measures based on the evaluation results is difficult (Li & Zhang 2008; Wu et al. 2017). The drawbacks of evaluating water quality just using parameters, however, can be efficiently solved using the WQI. To reflect the level of water quality, it combines various water quality criteria into a singular value. Due to its superiority in evaluating water quality, the WQI is frequently utilized (Chen et al. 2020). Several studies have highlighted the significance of WQI, which act as an indication for monitoring water quality and were first proposed by Horton (1965). Its integration with advanced technologies, such as the geographic information system (GIS), has made it incredibly useful for figuring out the distribution and temporal and spatial variability of different water quality indices (Dash & Kalamdhad 2021). Some are the studies that were conducted in the hard-rock region of Rajasthan for the assessment of GWQ for irrigation and drinking purposes: Dahiphale et al. (2019) assessed the GWQ in Jaisamand catchment water quality index (WQI). Kumar et al. (2018a) studied the ground water quality of the Upper Berach Riverbasin, originating in the hills of Udaipur and Rajasmand districts also, Machiwal et al. (2011) selected the Udaipur district as the study area for the GWQ assessment by developing groundwater quality index (GWQI) model. But not much of extensive research has been carried out till date in Nand Samand catchment (NSC) to assess the GWQ for drinking and irrigation purposes, the primary objective of this work was to assess the suitability of the available GW for agricultural and domestic use in NSC of Rajsamand and Udaipur districts, Rajasthan.

Seasonal influences on WQI could be revealed by mapping WQI and irrigation indices with their temporal behaviour. This dataset offers insights into the levels of water quality indicators within the surveyed region, aiding decision-makers in comprehending the GWQ status for diverse applications. Anions and cations are among the most common attributes in water resources, making ongoing monitoring essential. Water quality is paramount for both drinking and irrigation purposes. Clean drinking water is vital for human health, preventing waterborne diseases and long-term health issues caused by contaminants. In agriculture, water quality directly affects soil health and crop yield; poor-quality water can lead to soil degradation and reduced productivity. Monitoring, treatment, and sustainable practices are crucial for maintaining high water quality, ensuring safe drinking water, and promoting efficient irrigation methods that preserve soil fertility and ecosystem health. The WQI in the NSC catchment has not been evaluated for both drinking and irrigation purposes. Therefore, the current research study was undertaken to appraise the GWQ using different indices and to classify the NSC into different classes based on the indices' values. This research results may benefit the local population by serving as a gauge for the proper utilization of groundwater for both drinking and irrigation purposes. Because the topic is visually represented, even individuals without specialized knowledge can grasp it. Moreover, the paper explores various indices beyond the WQI and investigates their interrelationships. The paper is organized as follows: Section 2 provides an explanation of the proposed model's structure. Additionally, Section 3 delves into the analysis of the results conducted by the authors. In conclusion, Section 4 summarizes the key findings, acknowledges limitations, and outlines future research directions. This research plays a crucial role in optimizing science-based GW resource management. The study's outcomes can aid planners and decision-makers in selecting the most suitable GW management strategies for the studied area.

Standard methods for water analysis were used for all sample procedures and data processing. Samples were gathered in 500-ml polyethylene bottles and subsequently transported to the laboratories of AICRP (All India Co-ordinated Research Project on Integrated Farming Systems) on Irrigation Water Management CTAE (College of Technology and Engineering) and Soil Science laboratory of RCA (Rajasthan College of Agriculture), Udaipur. All water samples underwent analysis following the standard methods for water examination (APHA 1995, 2005; Adimalla et al. 2018). Also, magnesium (by calculation), calcium (ethylenediaminetetraacetic acid (EDTA) titrimetric), and chloride (titrimetric) were measured using the titration method (APHA 1995, 2005). Sodium (Na) and potassium (K) concentrations were assessed using flame photometric analysis. pH and EC levels were estimated utilizing a pH meter and an EC meter, respectively. Subsequently, the obtained analytical data were employed within a GIS system to generate numerical representations of parameter distributions across space. The inverse distance weight (IDW) method was employed to create spatial maps depicting the distribution of water quality indicators and the WQI. All analyses were conducted using Excel 2010 and ArcGIS 10.1 software tools. The classes of water quality index are presented in Table 1.

The WQI serves as a substantial and distinctive indicator in the assessment of water quality and its appropriateness for human consumption. It amalgamates multiple water quality parameters to offer both the general public and policymaker's insights into water quality conditions. Among various methodologies, the effectiveness of the WQI approach has been notably evident, playing a key role in enabling the efficient management of water resources (Dash & Kalamdhad 2021). The drinking water guideline employed in this study conforms to the guidelines set forth by the WHO in 2011 and BIS (2012) specifications for drinking water presented in Table 2.

Table 2

Water parameters criteria for drinking use

Parameter .	Indian standard (Ramakrishnalah et al. 2009; BIS 2012; Adimalla et al. 2018) .		WHO (2011)  .
	mg/l .	meq/l .	mg/l .	meq/l .
pH	6.5–8.5	–	6.5–8.5	–
Ca2+	75–200	3.75–10	300	3.75–10
Mg2+	30–100	2.5–8.33	30	2.47
	–	–	200	8.70
K+	12	–	10	0.26
	200–400	4.17–8.33	200	4.16
Cl−	250–1,000	7.04–28.16	250	7.05
TDS	<1,500	–	500
	120

Parameter .	Indian standard (Ramakrishnalah et al. 2009; BIS 2012; Adimalla et al. 2018) .		WHO (2011)  .
	mg/l .	meq/l .	mg/l .	meq/l .
pH	6.5–8.5	–	6.5–8.5	–
Ca2+	75–200	3.75–10	300	3.75–10
Mg2+	30–100	2.5–8.33	30	2.47
	–	–	200	8.70
K+	12	–	10	0.26
	200–400	4.17–8.33	200	4.16
Cl−	250–1,000	7.04–28.16	250	7.05
TDS	<1,500	–	500
	120

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The main objective of computing the WQI was to offer a thorough evaluation of the suitability of GW for drinking. The computation of the WQI employed the weighted arithmetic index method, which was originally introduced by Brown et al. (1970) and subsequently adopted by other researchers (e.g. Dwivedi & Pathak 2007; Sakizadeh et al. 2016; Nag & Das 2017; Roy et al. 2018). The WQI was computed as follows:

The primary factors in determining water quality are quantitative assessments of several physico-chemical parameters. Statistical values for the various parameters for both seasons are given in Table 3. Following a comparison with suggested standard values, the acceptability of the collected water samples for irrigation and drinking was separately examined. Results showed that the standard deviation (SD) and coefficient of variation (CV) for the pH were 0.59 and 0.08, respectively during the pre-monsoon (PRM) and the same were 0.39 and 0.05 during the post-monsoon (POM) season.

Table 3

Statistical data of water quality parameters of the study area

Parameters .	PRM .					POM .
	Min. .	Max. .	Mean .	SD .	CV .	Min. .	Max. .	Mean .	SD .	CV .
pH	6.12	8.14	7.11	0.59	0.08	6.36	8.36	7.29	0.39	0.05
EC	1.25	6.61	2.51	1.03	0.41	0.58	7.42	1.77	0.83	0.47
TDS	200	4,400	1,580	706.46	0.45	180	3,417.74	1,166.61	451	0.39
Ca2+	2.43	11.49	4.88	1.58	0.32	1.27	17.48	3.69	2.12	0.57
Mg2+	1.23	22.97	4.87	3.27	0.67	0.00	8.62	2.33	1.33	0.57
	1.71	17.41	4.20	2.45	0.58	0.23	7.46	2.90	1.53	0.53
K+	0.22	3.42	1.31	0.74	0.56	0.00	1.29	0.23	0.33	1.44
	0.76	6.70	4.25	1.22	0.29	1.58	7.46	5.26	1.27	0.24
	0.00	1.52	0.07	0.24	3.58	0.00	1.30	0.06	0.22	3.96
Cl−	2.97	20.68	5.08	2.62	0.52	1.73	15.73	4.50	3.07	0.68
	1.62	12.72	4.42	1.74	0.39	0.00	16.55	2.95	2.33	0.79

Parameters .	PRM .					POM .
	Min. .	Max. .	Mean .	SD .	CV .	Min. .	Max. .	Mean .	SD .	CV .
pH	6.12	8.14	7.11	0.59	0.08	6.36	8.36	7.29	0.39	0.05
EC	1.25	6.61	2.51	1.03	0.41	0.58	7.42	1.77	0.83	0.47
TDS	200	4,400	1,580	706.46	0.45	180	3,417.74	1,166.61	451	0.39
Ca2+	2.43	11.49	4.88	1.58	0.32	1.27	17.48	3.69	2.12	0.57
Mg2+	1.23	22.97	4.87	3.27	0.67	0.00	8.62	2.33	1.33	0.57
	1.71	17.41	4.20	2.45	0.58	0.23	7.46	2.90	1.53	0.53
K+	0.22	3.42	1.31	0.74	0.56	0.00	1.29	0.23	0.33	1.44
	0.76	6.70	4.25	1.22	0.29	1.58	7.46	5.26	1.27	0.24
	0.00	1.52	0.07	0.24	3.58	0.00	1.30	0.06	0.22	3.96
Cl−	2.97	20.68	5.08	2.62	0.52	1.73	15.73	4.50	3.07	0.68
	1.62	12.72	4.42	1.74	0.39	0.00	16.55	2.95	2.33	0.79

Note: All parameter units are in meq/l except (pH, EC in dS/m, TDS in ppm). Min. (Minimum), Max. (Maximum).

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Hydrogen ion (pH) levels of GW within the NSC exhibit a range between 6.12 and 8.14, with an average of 7.11 during the PRM period. Similarly, during the POM period, the pH ranges from 6.36 to 8.36, with an average of 7.29 (refer to Table 4). In the PRM season, approximately 83.06% of the catchment area falls within the pH range of 6.5–8.5, while 11.82% registers a pH above 8.5 (as depicted in Figure 3(a). In the POM season, the respective proportions are 82.26 and 17.46% for the same pH ranges (illustrated in Figure 3(b). A breakdown of pH ranges and coverage for both seasons can be found in Table 4. This trend of pH variation is consistent with findings by Dahiphale (2015), Kumar et al. (2018b), and Yadav & Singh (2018) in regions characterized by hard-rock terrain. A similar observation was reported by Adimalla (2019) within the study area, where around 80% of GW samples exhibited alkaline pH levels ranging from 7.3 to 8.6, with an average value of 7.96. Furthermore, more than 90% of the collected samples remained within the recommended pH limits of 6.5–8.5, as specified by WHO (2017).

Within the category of positively charged ions (cations), the concentrations of calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), and potassium (K⁺) ions exhibited a range of 2.43 to 11.49, 1.23 to 22.97, 1.71 to 17.41, and 0.22 to 3.42 meq/l, respectively, with average values of 4.88, 4.87, 4.20, and 1.31 meq/l during the PRM period. In the POM period, the concentrations of these ions varied from 1.27 to 17.48, 0.23 to 7.46, 0.00 to 8.62, and 0.00 to 1.29 meq/l, respectively, yielding average values of 3.69, 2.90, 2.33, and 0.23 meq/l (as presented in Table 3). Notably, sodium and calcium emerged as the most prevalent cations, closely followed by magnesium and potassium during both PRM and POM periods for both years. It is worth noting that while sodium and potassium occur naturally in GW, their levels can also be influenced by household and industrial waste inputs (Garg et al. 2009).

Sulphate, a component found in various minerals, is naturally present in water. BIS (2012) standard designates an acceptable level of 200 mg/l and a maximum permissible level of 400 mg/l for sulphate. Water alkalinity is primarily attributed to bicarbonate and carbonate ions. As per the WHO (2003) guidelines, chloride levels in water stem from both natural and human-related sources. Elevated chloride concentrations can lead to increased metal concentrations in water due to corrosion. According to BIS (2012), the acceptable chloride level is 250 mg/l, while the maximum permissible limit is 1,000 mg/l.

For negatively charged ions (anions), such as chloride (Cl⁻), sulphate (⁠⁠), bicarbonate (⁠⁠), and carbonate (⁠⁠), the dissolved concentrations ranged from 2.97 to 20.68, 1.62 to 12.72, 0.76 to 6.70, and 0.00 to 1.52 meq/l, respectively, during the PRM period. In the POM period, these anions exhibited ranges of 1.58 to 7.46, 1.73 to 15.73, 0.00 to 16.55, and 0.00 to 1.30 meq/l, respectively, with mean values of 5.26, 4.50, 2.95, and 0.06 meq/l (as displayed in Table 3). Across both PRM and POM periods, chloride and bicarbonate stood out as the most prevalent anions among the major constituents (⁠⁠, Cl⁻, ⁠, and ⁠), followed by sulphate and carbonate, respectively. It is noteworthy that, with the exception of a few samples, the majority of GW samples from the study area fell below the acceptable concentration threshold for sulphate.

Through the application of statistical analysis, it becomes feasible to establish relationships and fluctuations among the physico-chemical properties and ion concentrations present in GW samples. This analytical approach aids in interpreting data and comprehending the interactions that underlie them (Meireles et al. 2010). The correlation matrix for the 11 physico-chemical parameters is presented in Tables 11 and 12 for both the seasons, respectively. The correlation matrix serves to highlight substantial correlation coefficients, which point to several meaningful hydrochemical relationships.

Table 11

Correlation matrix for different water quality parameters in PRM

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
pH	1
	0.24*	1
	−0.05	−0.141	1
	0.146	0.146	−0.26**	1
EC	0.161	0.26*	−0.36**	0.61**	1
TDS	0.142	0.25*	−0.23*	0.51**	0.88**	1
	0.078	0.083	−0.06	0.09	0.23*	0.34**	1
	−0.024	0.080	−0.05	−0.03	0.20*	0.31**	0.68**	1
	−0.23*	0.04	−0.04	−0.11	0.09	0.27**	0.45**	0.47**	1
	0.097	0.019	0.090	0.235*	0.106	0.123	−0.015	−0.046	−0.205*	1
	0.028	−0.041	−0.204*	−0.082	−0.099	−0.087	0.063	0.058	0.009	0.058	1

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
pH	1
	0.24*	1
	−0.05	−0.141	1
	0.146	0.146	−0.26**	1
EC	0.161	0.26*	−0.36**	0.61**	1
TDS	0.142	0.25*	−0.23*	0.51**	0.88**	1
	0.078	0.083	−0.06	0.09	0.23*	0.34**	1
	−0.024	0.080	−0.05	−0.03	0.20*	0.31**	0.68**	1
	−0.23*	0.04	−0.04	−0.11	0.09	0.27**	0.45**	0.47**	1
	0.097	0.019	0.090	0.235*	0.106	0.123	−0.015	−0.046	−0.205*	1
	0.028	−0.041	−0.204*	−0.082	−0.099	−0.087	0.063	0.058	0.009	0.058	1

**Correlation is significant at the p < 0.01 level (two-tailed), *Correlation is significant at the p < 0.05 level (two-tailed). The highlighted bold values shows the significant correlation between the variables.

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Table 12

Correlation matrix for different water quality parameters in POM

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
pH	1
	0.20*	1
	0.005	0.014	1
	−0.053	0.102	0.175	1
EC	−0.14	−0.21*	0.25*	0.42**	1
TDS	−0.07	−0.04	0.44**	0.48**	0.54**	1
	−0.03	−0.02	0.45**	0.53**	0.55**	0.91**	1
	−0.11	−0.098	0.21*	0.40**	0.49**	0.79**	0.83**	1
	−0.19	−0.23*	0.14	0.26*	0.45**	0.51**	0.47**	0.51**	1
	−0.149	−0.15	0.126	0.08	0.125	0.08	−0.003	0.067	0.33**	1
	0.043	−0.042	0.015	−0.108	0.026	−0.146	−0.056	−0.108	−0.151	−0.023	1

Variables .	pH .	.	.	.	EC .	TDS .	.	.	.	.	.
pH	1
	0.20*	1
	0.005	0.014	1
	−0.053	0.102	0.175	1
EC	−0.14	−0.21*	0.25*	0.42**	1
TDS	−0.07	−0.04	0.44**	0.48**	0.54**	1
	−0.03	−0.02	0.45**	0.53**	0.55**	0.91**	1
	−0.11	−0.098	0.21*	0.40**	0.49**	0.79**	0.83**	1
	−0.19	−0.23*	0.14	0.26*	0.45**	0.51**	0.47**	0.51**	1
	−0.149	−0.15	0.126	0.08	0.125	0.08	−0.003	0.067	0.33**	1
	0.043	−0.042	0.015	−0.108	0.026	−0.146	−0.056	−0.108	−0.151	−0.023	1

**Correlation is significant at the p < 0.01 level (two-tailed), *Correlation is significant at the p < 0.05 level (two-tailed). The highlighted bold values shows the significant correlation between the variables.

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During both seasons, EC and TDS demonstrate a notable positive correlation with Na⁺, ⁠, Cl⁻, and Mg²⁺. The existence of sodium in the study area significantly influences TDS and EC levels. Particularly during the pre- and POM periods, EC exhibits a highly significant and positive correlation with TDS. This correlation also extends to sulphate, Cl⁻, Mg²⁺, and Ca²⁺ during the PRM period. A distinct correlation between Cl⁻ and Mg²⁺ suggests that hydrogeochemical processes involving water–rock interaction and mineral dissolution may play a central role in shaping GW chemistry within the catchment (Feng et al. 2020). Furthermore, a pronounced correlation observed between Cl⁻ and Ca²⁺ appears less attributable to recognized salinization processes and is more likely associated with secondary mechanisms such as ionic exchange. This phenomenon becomes particularly evident in highly salinized water, where Cl⁻ concentrations are elevated (Alaya et al. 2014). Conversely, no significant relation is observed between Ca²⁺ and ⁠, indicating that calcite may not be the primary source of Ca²⁺.

During the POM season, EC and TDS exhibit a notably significant and positive correlation with Ca²⁺, Mg²⁺, Na⁺, Cl⁻, and parameters and the presence of these parameters in the research area significantly impacts the levels of TDS and EC. In this season, chloride and sulphate also exhibit significant and positive correlations with calcium, magnesium, sodium, and potassium. Additionally, chloride demonstrates significant and positive correlations with both sulphate and potassium. Conversely, during the PRM and POM periods, pH, ⁠, and display weak or negative correlations with the majority of variables under consideration.

The SAR and RSC are commonly utilized as significant indicators of the sodium levels in irrigation water or soil solution (Feng et al. 2020). The main goal of analysing agricultural water is to evaluate its impact on the soil and, subsequently, on the cultivated crops (Adamu 2013). The inadequate quality of irrigation water is a significant global environmental concern (Millennium Ecosystem Assessment 2005), whereas poor-quality irrigation water adversely affects soil quality and crop production (Kadyampakeni et al. 2018). For sensitive fruits, the permissible threshold for the SAR in irrigation water should not surpass 4. On the other hand, for general crops and forages, a range of 8–18 is commonly considered acceptable and beneficial (NTAC 1968).

Assessing the GWQ for irrigation is extremely critical in regions with semi-arid and arid climate worldwide, especially in developing nations such as India. The findings of hydrogeochemical investigations conducted in the predominantly rocky semi-arid area of NSC were assessed by comparing with the WHO and BIS standards. The objective of the present study was to evaluate the suitability of water quality for drinking and irrigation usage. The pH was within the recommended limits of 6.5–8.5 during the PRM (83.06%) and POM (82.26%) seasons, respectively. A strong correlation coefficient was found between EC and TDS during PRM (r = 0.88) and POM (r = 0.90), respectively. The evaluation of the comprehensive water quality was done by utilizing the WQI, revealing that a higher number of water samples exhibited good and poor quality during the POM compared to the PRM season. The computed irrigation indices suggested that the GW in the NSC is good for agricultural use. Furthermore, concerning irrigation, it's feasible to identify appropriate areas for various crops based on the tolerance limits. However, the study's limitation is the sampling of 95 samples during both PRM and POM seasons in the entire catchment. There is a need for further investigation to determine the key physical, chemical, and biological indicators for monitoring GWQ. Additionally, to optimize the efficient utilization of GW in the examined area, it is strongly advisable to carry out a comprehensive study focused on allocating its usage across different sectors.

Dimple is thankful to the INSPIRE (DST-INSPIRE-Fellowship), Government of India and Soil and Water Engineering Department, CTAE, MPUAT, Udaipur, Rajasthan, India.

D. conceptualized the whole article, developed the methodology selection, and carried out formal analysis. P. K. S. overviewed the manuscript and methodology improvement. M. K. contributed to drafting the manuscript. K. K. Y. reviewed and edited the article. S.R.B. contributed to reviewing the draft. J.R. rendered support in data analysis and edited the article. A. participated in the manuscript language editing. All the authors have contributed significantly to this research work. All authors read and approved the final manuscript.

No external fund was received to carry out this work.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.