Linking perceived and actual drinking water quality across rural landscapes of northern Kazakhstan

Access to safe drinking water in rural areas remains a global challenge, particularly where decentralized supply systems are common and water quality is highly variable. In this study, we analyze the relationship between the chemical composition of drinking water and community perceptions of its quality in rural settlements of the Akmola Region of Kazakhstan across different landscape types. Using a mixed-methods approach, hydrochemical analyses of household water samples were conducted, ANOVA and Spearman correlation tests were applied, and village-level survey data were collected to assess the complaints, satisfaction, and water purification practices of residents. Results indicate that most water sources are of Ca–Mg–Cl–HCO₃ type, with steppe villages showing higher levels of dissatisfaction. These findings highlight the importance of aligning technical water assessments with local perceptions to improve rural water governance and foster community trust in water safety initiatives.

1 Introduction

Access to safe and acceptable drinking water remains a persistent challenge in many rural regions worldwide, particularly where decentralized water supply systems are dominant and consistent monitoring and quality control are limited. Conventionally, the quality of drinking water is assessed using chemical and physical indicators such as salinity, hardness, ion composition, and contaminant concentrations. However, in everyday practice, rural communities frequently rely on subjective sensory experiences—taste, odor, color, and clarity—to judge water safety and acceptability. These perceptions are deeply shaped by cultural knowledge, local experience, and historical trust in water sources (Beisenova et al., 2024; Faniran, 1986).

Numerous studies demonstrate that perceived water quality does not always correspond to measured chemical safety. Water with high mineralization or hardness may generate dissatisfaction despite being chemically safe, whereas hazardous pollutants such as nitrates or microbial contaminants often remain undetected by consumers due to the absence of sensory cues (Odikamnoro et al., 2019; Mishra et al., 2024; Seeyan et al., 2021). This divergence between scientific assessment and local perception complicates water governance and public health interventions, underscoring the need to jointly consider technical and social indicators.

Empirical evidence from South Asia and Sub-Saharan Africa shows that there are frequently more complaints against private and community wells than against centralized piped systems, largely due to issues related to hardness, iron staining, turbidity, and taste, which is often linked to local hydrogeological conditions (Jehan et al., 2019; Nyika and Onyari, 2019). In rural Pakistan, well water was widely perceived as unsafe despite acceptable chemical profiles, primarily because of visible turbidity and storage-related bacterial contamination (Dogaru et al., 2009).

Centralized or semi-centralized systems, such as piped networks and community standpipes, generally receive fewer complaints when regular monitoring and maintenance are ensured (Ngenzebuhoro et al., 2023). However, intermittent supply and inadequate disinfection can significantly increase dissatisfaction, particularly regarding odor and microbial safety (Andrew et al., 2019). Similar patterns have been observed in Sub-Saharan Africa, where irregular chlorination led households to associate taste and smell with health risks (West et al., 2021). Surface water sources—including rivers, lakes, and ponds—are commonly associated with higher complaint rates due to visible turbidity, seasonal variability, and microbiological contamination (Gupta et al., 2022), as reported in Sri Lanka’s arid regions (Tleuova et al., 2023).

Despite a growing body of research linking drinking water sources, chemical characteristics, and community perceptions, such studies remain geographically uneven. Most empirical evidence originates from South Asia, Sub-Saharan Africa, and parts of the Middle East, whereas Central Asia—particularly rural Kazakhstan—remains largely unexplored in this regard.

Existing studies that apply statistical or correlation-based approaches have shown that the water source type often predicts the nature and frequency of complaints. For example, groundwater hardness in India was positively correlated with dissatisfaction related to taste and soap lathering (Nyika and Onyari, 2019), whereas in Ghana, community complaints were statistically associated with turbidity in shallow wells (Ayeta et al., 2024). In Egypt’s Nile Basin, satisfaction levels varied significantly between surface and groundwater users even when the chemical parameters overlapped (Seeyan et al., 2021; Ahmad et al., 2025). These findings suggest that perceptions are influenced not only by chemical quality but also by visibility, reliability, and trust in the source.

However, no comparable integrated studies have been conducted in Central Asia (Xiao et al., 2015; Xiao and Sang, 2025), where rural water supply systems, post-Soviet infrastructure legacies, landscape heterogeneity, and governance challenges in rural water supply create distinct socio-environmental conditions. The absence of such research limits the ability to design context-sensitive water management policies and hampers the progress toward Sustainable Development Goal 6 in the region.

In this study, we address the identified research gap of examining drinking water quality and community perceptions in rural settlements of the Akmola Region, northern Kazakhstan. The region surrounds the capital city, Astana, and represents a predominantly agrarian–industrial zone with diverse landscapes and decentralized water-supply systems (Maussymbayeva et al., 2025). Whereas official monitoring focuses primarily on chemical compliance, residents’ complaints and satisfaction levels remain poorly understood and underutilized in decision-making.

The research adopts a novel interdisciplinary approach, integrating hydrochemical analysis of drinking water with sociological assessments of community perceptions across different landscape contexts. Unlike previous studies that treat water quality monitoring and public perception as separate domains, in this work, we quantitatively link chemical indicators with subjective complaints, trust, and satisfaction.

This study is guided by the following research questions:

1. Is there a relationship between residents’ complaints about drinking water and its measured chemical quality?

2. Do areas with elevated concentrations of specific inorganic ions report higher levels of dissatisfaction or complaints?

3. Does the landscape type influence the quality characteristics of drinking water and the frequency of community complaints?

By answering these questions, the study contributes to the global evidence base on drinking-water perception while providing region-specific insights for Kazakhstan and Central Asia. The findings aim to support more culturally informed, socially responsive, and technically grounded water management interventions in rural settings.

2 Materials and methods

2.1 Study area

2.1.1 Geographical and landscape characteristics of the Akmola Region in Kazakhstan

Geographically, the Akmola Region lies on the western edge of the Kazakh Uplands (Kazakh Hummocks), between the Ulytau Mountains in the southwest and the Kokshetau Hills in the north. Its terrain generally slopes from east to west, following the direction of the Ishim River valley (Akmola region, 2004; Dictionary of modern geographical names/Rus, 2006). The Ishim River traverses the central part of the region before turning sharply northward near its western border (Kotlyakov, 2006). The Akmola Region borders the Kostanay Region in the west, the North Kazakhstan Region in the north, the Pavlodar Region in the east, and the Karaganda Region in the south (State Archives of Akmola Region, 2022).

The relief of the region can be divided into three main zones:

1. The northwestern part: it is a flat plateau adjacent to the Ishim Valley, dissected by dry ravines and gullies, and ends in a ledge toward the valley.

2. The southwestern part: it is an elevated plain with isolated hills and numerous shallow salt and freshwater lakes in the inter-hill depressions.

3. The eastern part: it is a segment of the ancient Kazakh Upland, formerly mountainous but now leveled by erosion, featuring low hills, ridges, and softly contoured slopes that are locally known as sopka (or melkosopochnik). These hills vary in height from 5 m–10 m to 50 m–60 m, occasionally reaching 80 m–100 m. Their shapes depend on the type of rock: rounded hills typically consist of granite, gently sloping hills consist of porphyry, and sharp-pointed hills consist of quartzite (Dobretsov et al., 2006).

Closed basins between hills, ranging from several dozen meters to several dozen kilometers in diameter, are often occupied by lakes. The extreme northeastern part of the region lies within the West Siberian Lowland. The highest point in the Akmola Region is Mount Kokshe (947 m above the sea level), whereas the lowest is Lake Sholaksor (67 m above the sea level) (Kazakhstan, 2005; Land Resources Management Committee, 2022).

2.1.2 Study area and villages

The rural area of the Akmola Region was selected for this research due to its diverse landscape, which ranges from steppe to forest ecosystems. Most of the region is characterized by steppe areas, whereas certain parts lie within forested and wetland areas. This geographical diversity formed the basis for selecting three districts within the study area: Korgalzhyn, Zerendy, and Tselinograd.

Three villages were selected from each district: Ortaagash, Karabulak, and Malik Gabdullin from the Zerendy District, which are located in the north-central part of the Akmola Region; Uiyaly, Zhanteke, and Karaegin from the Korgalzhyn District, located in the south-central part of the region; and Ilyinka, Karazhar, and Taitobe from the Tselinograd District, located in the southeastern part of the region (Figure 1).

Figure 1

Figure 1. Study area: Akmola Region of Kazakhstan. Yellow dots show the nine selected villages: Malik Gabdullin, Karabulak, and Ortaagash from the Zerendy District; Ilyinka, Karazhar, and Taitobe from the Tselinograd District; and Zhanteke, Karaegin, and Uiyaly from the Korgalzhyn District.

Figure 2 illustrates the nine villages selected from the three districts, along with the locations where household water samples were taken. Three villages were randomly chosen from each district. The villages are as follows: Ortaagash (V1), Karabulak (V2), Malik Gabdullin (V3), Uiyaly (V4), Zhanteke (V5), Karaegin (V6), Ilyinka (V7), Karazhar (V8), and Taitobe (V9). In each village, five households were selected for water sampling using an “envelope method.” These households were chosen to reflect potential variation in drinking water sources, as residents may rely on different supply types depending on their location within the village, including three households in Ortaagash and Uiyaly villages that have two drinking water sources. Despite the limited size, the selected households are considered representative of the local community level as they cover the typical socioeconomic and everyday characteristics of the rural population.

Figure 2

Figure 2. Rural study area in the Akmola Region. Ternary marks indicate households that were selected for water sampling. Note: blue on the map indicates water bodies (rivers and lakes), gray indicates villages, and green indicates parks, where present.

General information about the selected villages and surveyed households in the rural Akmola Region is presented in Table 1. The table shows data on the geographical location of each household, the center of each village, the landscape type (steppe, forest, and wetland), the primary sources of drinking water, population data by gender, and whether water purification is carried out. Purification practices are considered one of the key parameters influencing respondents’ perceptions of water quality.

Table 1

Table 1. General information on the rural Akmola Region and sampled households: location of research water-sampling households, population of villages, the number of people living in sampled households, drinking water sources of sampled households, and purification of drinking water.

Drinking water sources in households were identified as the following categories: BWS, bottled water from a store; PWIPS, public water intake pump on the street; PWWOWSH, private water well owned without supply inside the house; PWWOSH, private water well owned with supply inside the house; PB, public borehole; SW, spring water; WPS, water pumping station; CWSSIT, centralized water supply system with inside tap; PW, public well; PBWWSH, private borehole water supply to the house; PBWOWSH, private borehole without water supply to the house; PWS, purified water from the store; FTW, free tracked-in water, and other water sources that were encountered sporadically were combined into the category “other” (возможно надо убрать).

These categories encompass all types of drinking water sources used by surveyed households. The table shows the drinking water sources of households from where the water samples were taken. Among these sources, we decided to analyze the wells, boreholes, and centralized water-supply systems within the study areas.

Water samples were collected from different households with 1–11 residents, and their water consumption is 3.6 thousand liters per person per day (National Bureau of Statistics, 2024).

2.1.3 Sampling period and feature

Nine villages distributed across different parts of the region representing different landscapes were randomly selected to sample drinking water for quality assessment during September 2024. September 2024 was characterized by high average temperatures, average air humidity, average wind speed, and the lowest pressure of the year. These weather conditions showed that this month was convenient for the research period of our object as it was neither a dry nor a rainy season compared to other years. In addition, September in the Akmola Region, as in many regions at this latitude, is the middle of the vegetation period, which is also the subject of the study with regard to the types of landscape. Therefore, this period was chosen for the research.

2.1.4 Questionnaire data

The nine villages, where samples were collected and the population was interviewed, are inhabited by 5,127 residents. Five households located along the perimeter of the rural area and at its center were selected for drinking water sampling. For the population survey, a cluster sampling method was used near the households where drinking water samples were collected. In total, five clusters were selected at the sampling sites, and 453 households were interviewed, representing 2,081 residents.

A questionnaire survey was used as the main method of collecting primary data, allowing the acquisition of quantitative and qualitative data on the perception, behavior, and level of awareness of the respondents on the issue under study. The structure of the questionnaire included both closed and open questions, which ensured a balance between data standardization and the ability to better understand the opinions of the participants. The survey was conducted among the target group with predefined sociodemographic characteristics, and the results of the questionnaire formed the basis for the analysis of the relationships between individual factors and the stated attitudes of the respondents. The questionnaire items are given in Table 2.

Table 2

Table 2. Questionnaire used to conduct the study regarding the conditions of access to water, sanitation, and hygiene in households in rural areas for research purposes. The questionnaire was used for assessment of drinking water source type and the complaints of respondents for the quality of drinking water.

Data preparation for analysis was carried out according to the flowchart given in Figure 3. The research data flowchart illustrates the workflow of the study, from data collection (survey and drinking water sampling) to chemical analysis, perception analysis, evaluation of correlation, and the final data interpretation.

Figure 3

Figure 3. Research data flowchart. The visual representation of the workflow divided into four main stages: 1) data collection (survey data and drinking water samples); 2) chemical analysis of water samples (ions, pH, and mineralization) and survey data analysis (complaints and satisfaction); 3) correlation analysis (link between chemical parameters and perception); 4) final data analysis (conclusions and recommendations).

The overall research process is presented in Figure 3, highlighting the sequential steps: namely, (1) survey data collection, (2) water sampling, (3) chemical analysis of water samples, (4) analysis of perceived water quality, and (5) correlation and final evaluation.

2.2 Methods

2.2.1 Chemical analysis of water samples

Water samples were analyzed in the chemical laboratory of L.N. Gumilyov Eurasian National University with analytical methods of determination of the chemical parameters. The main chemical substances examined in the drinking water samples were cations (sodium and potassium, magnesium, and calcium) and anions (hydrocarbonates, chlorides, and sulfates).

The water parameters were chosen because cations [calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), and potassium (K⁺)] and anions [hydrocarbonate (HCO₃⁻), sulfate (SO₄^2-), and chloride (Cl⁻)] are the main components of the mineral composition of drinking water, causing its hardness, mineralization, and taste. Sanitary standards (WHO, SanR&R of RK, and EU Drinking Water Directive) are based on the concentrations of these ions, as they are directly related to the safety and organoleptic properties of water. For example, high content of sulfates or chlorides is associated with diarrhea and a salty taste, and a lack of calcium and magnesium is associated with the risks of cardiovascular diseases. Residents evaluate water primarily by taste, smell, and sensations during use. These parameters are directly caused by the anion–cation composition, such as the bitter taste from high sulfates, salinity due to excess sodium/chlorine, and hardness because of calcium and magnesium. Therefore, the choice of these indicators allows us to explain the subjective complaints of the population through objective chemical data. Unlike microelements or organic pollutants, macro-ions (anions and cations) are present stably and in significant concentrations, making them suitable for statistical clustering and perception analysis. These parameters reflect the geochemical specificity of the sources and allow comparisons between different villages/regions.

Ca²⁺ and Mg²⁺ cations were detected using the complexometric method, and K⁺ and Na⁺ were detected using the ion-selective electrode technique. HCO₃⁻ anions were determined using acid–base and potentiometric titration methods, and SO₄^2- anions were analyzed using turbidimetry with barium chloride. Cl⁻ was determined by the argentometric method. A spectrophotometer and an ionometer were used for this analysis.

Residents of the research area are not informed about the quality of drinking water. Monitoring and analysis of central water supply is carried out regularly, but these figures are not available to everyone; they are available in the sanitary and epidemiological stations and are controlled according to SanR&R. The decentralized systems are not controlled.

The length of the central water-supply pipe runs along the village perimeter, measuring approximately from 0.5 to 1.5 km. The pipe material is plastic, and the time for which water stays in the pipe from the source to the consumer depends on the intensity of water consumption from minutes to several hours. Water purification occurs only in the central water-supply system and mainly through chemical methods. The quality of drinking water in terms of disinfection is controlled by the central water-supply system.

In 1994, Piper (1994) proposed creating a graphical representation of the partitioning of relevant analytical data to assess the chemical components of water. This process is based on the assumption that most water samples contain cations and anions in chemical equilibrium. The most common cations are assumed to be two “alkaline earth” cations: Ca²⁺ and Mg²⁺, and one “alkaline” cation: Na⁺. The most common anions are one “weakly acidic” HCO₃⁻ and two “strongly acidic” anions, namely, SO₄^2- and Cl⁻. The Piper diagram is widely used in ecological and ecotoxicological studies (Jat et al., 2025; Jat Baloch et al., 2022a; Jat Baloch et al., 2022b; Jat Baloch et al., 2021).

2.2.2 Statistical and correlation analysis

Statistics for each water quality parameter are presented as ± SE (standard error) of the mean. One-way analysis of variance (ANOVA) was used to test the statistical differences among the villages through equal and unequal variance t-tests. Group means were compared for each water quality variable using ANOVA. Prior to ANOVA, we checked normality of the residuals (Shapiro–Wilk) and homogeneity of variances (Levene’s test). When homogeneity held, we performed a classic one-way ANOVA followed by Tukey’s HSD for pairwise comparisons. When variances were unequal, Welch’s ANOVA was used, followed by the Games–Howell post hoc test. For clearly non-normal data (especially with small n), we additionally report the Kruskal–Wallis test with Dunn’s pairwise comparisons (Holm adjustment). The results are visualized as bar charts of group means with standard error bars; distinct letters above bars denote significant pairwise differences (α = 0.05) (Beisenova et al., 2025).

The bar plots display the mean concentrations of the major ions, namely, K⁺, Na⁺, HCO₃⁻, Cl⁻, and SO₄^2- across multiple water sources, including BWIP, CWSSIT, PB, PWIPS, PWWOSH, PWWOWSH, SW, and WPS. Error annotations are indicated using lowercase letters (e.g., a, b, and c), which represent statistically significant groupings. Identical letters denote no significant difference between sources, whereas different letters indicate statistically significant differences (p < 0.05). For statistical analysis, R studio version was used.

We combined two datasets by village: one containing chemical water quality measurements and with the other containing the respective household survey responses. For the chemical dataset, we grouped the data by village and calculated the average concentration of each measured parameter: Ca²⁺, Mg²⁺, hydrocarbonate (HCO₃), Cl⁻, SO₄^2-, K⁺, and Na⁺. This produced a table of nine villages with water chemistry values.

For the questionnaire dataset, we similarly grouped responses by village. Key survey questions were converted to numeric form (yes = 1, no = 0), so we could compute village-level averages (proportions of respondents answering “yes”). Finally, two aggregated tables were merged, showing the combined dataset with both average water chemistry and average survey outcomes side-by-side.

With the datasets combined, we computed the pairwise Pearson and Spearman correlations between all numeric variables (the chemical concentrations and the survey response averages). Several matrices were constructed to determine the relationship between different chemical composition indicators and participants’ responses about water quality and purification practices. Additionally, the correlation between chemical parameters and landscape indicators of the areas was calculated using Spearman correlation matrix. Python version 3.12 was used for correlation analysis.

The following indicators were used in the study:

1. Chemical composition indicators (concentration of major cations and anions (mg/L)): Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, SO₄^2-, HCO₃⁻, total dissolved solids (TDS), pH level, electrical conductivity (EC), and water hardness (calculated from Ca²⁺ and Mg²⁺).

2. Perception indicators (from questionnaires): perceived taste of drinking water, perceived smell/odor, level of satisfaction with drinking water quality, complaints regarding water quality (yes/no and different types of complaints), and reported use of water purification (yes/no).

3. Landscape and environmental context indicators: type of landscape surrounding the village (steppe, forest, and wetland), proximity to natural water sources (spring and wells), and type of primary water source types (centralized water supply system, well, and open source).

3 Results

3.1 Chemical composition of drinking water

The results of the hydrochemical analysis of drinking water conducted across nine rural villages with eight water sources are shown in Figure 4. The analysis revealed that most water samples fall within the Ca–Mg–Cl–HCO₃ facies. The cation plot is dominated by calcium type⁺ in PB, PWIPS, SW, and WPS, and no dominant type was observed in the other sources.

Figure 4

Figure 4. Piper diagram of the main drinking water samples of the rural Akmola Region: samples from different drinking water sources in the study villages: 0-BWS, 1-CWSSIT, 2-PB, 3-PWIPS, 4-PWWOSH, 5-PWWOWSH, 6-SW, and 7-WPS.

The anion plot is dominated by the hydrocarbonate type in CWSSIT, SW, and WPS; chloride type in BWS; and no dominant type in the other sources. The diamond plot shows that the samples from BWS, PB, and PWWOSH have calcium chloride type of water. CWSSIT and SW have magnesium hydrocarbonate type of water, and other sources have mixed type.

Figure 5 presents the comparative analysis of key chemical parameters in water samples from various rural water sources in the Akmola Region. Potassium (5 c) concentrations range from approximately 0.5 mg/L to 1.6 mg/L. The CWSSIT source exhibits the highest mean value, whereas BWIP exhibits the lowest. The statistical markers indicate that CWSSIT is significantly different from several other sources, including BWIP (a vs. b). Sodium (5 days) levels vary widely, from near-zero in BWIP to over 250 mg/L in PWWOWSH. Both PWWOSH and PWWOWSH share the same grouping (b), confirming no significant difference between them but a significant increase compared with BWIP (a).

Figure 5

Figure 5. Chemical parameters of the water samples of the rural Akmola Region. The x-axis shows drinking water sources, and the y-axis shows inorganic ions (mg/L). LSM: least square means. (a–e) Letters in the graph show a significant difference between the indicators, taking into account the standard error at a confidence level of 0.5%. Note: water sources: BWIP, CWSSIT, PB, PWIPS, PWWOSH, PWWOWSH, SW, and WPS.

Hydrocarbonates (5 e) in all sources except BWIP exhibit high concentrations, ranging between 300 and 450 mg/L. CWSSIT and SW fall within the same statistical category (b), whereas BWIP stands out as significantly lower (a). Chloride (5 f) concentrations range from approximately 100 to more than 400 mg/L, with CWSSIT and PWWOSH having the highest levels. The grouping of CWSSIT and PWWOSH under b implies similarity, whereas BWIP (a) significantly differs from most others. Sulfate (5 g) values reached the highest point in PWWOSH at approximately 250 mg/L and was the lowest in BWIP. Statistically, BWIP and CWSSIT are grouped together (a), indicating elevated levels, whereas BWIP remains the lowest (b).

Table 3 contains data on the chemical composition of drinking water from households in the Akmola Region. The highest calcium, magnesium, sodium, potassium, and sulfate contents were found in PWWOWSH (village 7) and the lowest was found in PWIPS (village 1). The highest concentration of hydrocarbonates was found in CWSSIT (village 8) and the lowest was found in PWIPS (village 1). The highest chloride content was found in PWWOWSH (village 7) and the lowest was found in CWSSIT (village 3) (Table 3).

Table 3

Table 3. Chemical components of the drinking water sources from the households’ samples in the Akmola Region.

3.2 Respondents’ drinking water sources in the rural Akmola Region

The questionnaire identified the sources of water supply in rural areas for drinking and other needs, along with the storage conditions, purification, and respondents’ complaints about the quality of drinking water (Figure 6). The bar chart shows domestic and drinking water source types across the study areas.

Figure 6

Figure 6. Types of sources and quality of drinking water according to respondents’ answers. The x-axis shows the types of sources and complaints about water in households, and the y-axis shows the frequency of occurrence of the indicator.

According to the results of the study, more than a half of the respondents indicated PWWOSH and one-quarter of the respondents indicated CWSSIT as a source of water for domestic use. Residents also use these two sources for drinking, which were indicated by 25% of respondents. The remaining 50% of respondents use other sources for drinking, such as BWS, PWS, BWIP, FTW, PBWWSH, PWIPS, PBWOWSH, PW, or PB. Most of the respondents have no complaints about drinking water. However, some respondents complained about sediment (25%), salty water (18%), poor taste (16%), muddy water (8%), hard water (6%), and bad smell (4%). More than a half of the respondents indicated that they store water in plastic containers, and the rest use closed tanks, buckets, glass containers, ceramic, and metal containers. The respondents’ satisfaction with drinking water was distributed as follows: 35% are fully satisfied, 33% are rather satisfied, 18% are rather dissatisfied, 10% are fully dissatisfied, and 6% found it difficult to answer. More than 60% of respondents do not purify drinking water, 37% purify it, and 3% did not indicate an answer. The primary methods of water purification are filtering and boiling.

3.3 Respondents’ complaints about drinking water

Survey responses of residents who use different sources for drinking water were used to formulate correlation between the variety of indicators. Figure 7 illustrates how satisfaction with water differs with the type of the drinking water source. According to Figure 7, residents who use CWSSIT (61), PWWOSH (33), PWIPS (13), PW (11), PWWOWSH (9), other (5), PB (4), and BWS (3) are fully satisfied, whereas residents who use CWSSIT (51), PWWOSH (41), PWS (23), PWWOWSH (14), PWIPS (10), and PWS and CWSSIT (8) are rather satisfied with the quality of water (Figure 7).

Figure 7

Figure 7. Cross-tabulation of the sources of drinking water and satisfaction of respondents in rural areas of the Akmola Region. The x-axis shows attitudes of participants (fully unsatisfied, fully satisfied, rather unsatisfied, rather satisfied, and difficult to answer), and the y-axis shows the drinking water sources (BWIP, BWS, CWSSIT, FTW, PB, PBWOWSH, PBWWSH, PW, PWS, PWWOSH, PWWOWSH, SWSSIT, other, and their combinations). Respondents who use CWSSIT (5), BWS (9), PWIPS (9), BWIP (3), PWWOSH (3), and PWWOWSH (3) are fully unsatisfied.

The correlation analysis of data on drinking water sources and respondents’ complaints about water quality showed that the majority of respondents had no complaints about water quality. However, Figure 8 shows that complaints are still reported, especially regarding sediment: CWSSIT (21), PWWOSH (17), PWIPS (11), PWWOWSH (5), and other sources. Some respondents also complained about the salinity and poor taste of water.

Figure 8

Figure 8. Cross-tabulation of the sources of drinking water and complaints of respondents in rural areas of the Akmola Region. The x-axis shows respondents’ complaints about water in households (bad smell, poor taste, muddy water, salty water, hard water, sediment, and no complaints), and the y-axis shows the sources of drinking water (BWIP, BWS, CWSSIT, FTW, PB, PBWOWSH, PBWWSH, PW, PWS, PWWOSH, PWWOWSH, SWSSIT, other, and their combinations).

The results of the survey on the use of additional drinking water purification methods show that the majority of rural residents do not use any purification methods (Figure 9).

Figure 9

Figure 9. Cross-tabulation of respondents’ drinking water purification practice and the source of drinking water. The x-axis shows whether drinking water is purified (yes or no), and the y-axis shows the sources of drinking water (BWIP, BWS, CWSSIT, FTW, PB, PBWOWSH, PBWWSH, PW, PWS, PWWOSH, PWWOWSH, SWSSIT, other, and their combinations).

Among those who purify drinking water, the majority of people use CWSSIT (69), followed by PWWOSH (44), PWIPS (15), and BWS and PWWOWSH (12).

3.4 Correlation among water source, chemical parameters, and landscape types of the rural Akmola Region

The correlation of chemical parameters (Ca²⁺, Mg²⁺, HCO₃⁻, Cl⁻, SO₄^2-, K⁺, and Na⁺) based on the average values in each landscape group shows the correlation between the pairs of chemicals in the three landscape types (forest, wetland, and steppe). Strong positive correlations (red cells close to 1.00) were found among most ions, indicating that if one chemical is high in a particular landscape, others tend to be high as well. K⁺ was found to be less correlated with some other parameters (some blue/light cells) (Figure 10).

Figure 10

Figure 10. Spearman correlation matrix heatmap illustrating the correlation between chemical parameters of sampled water and landscape types. The axes show the chemical parameters of drinking water (anions and cations in mg/L) and the landscape types of the villages (forest, steppe, and wetland).

The correlation between the steppe zones and the content of sodium, chloride, and HCO₃⁻ was higher than that of other chemical elements. In addition, the chemical elements themselves correlate very closely with each other in different types of landscapes. Only hydrocarbonate does not correlate with other ions (Figure 10).

Figure 11 shows correlation heatmap of the landscape types and different sources of drinking water.

Figure 11

Figure 11. Spearman correlation matrix heatmap illustrating the correlation between different landscape types and drinking water sources. The x-axis shows the drinking water sources of selected households, namely, BWIP, CWSSIT, PB, PWIPS, PWWOSH, PWWOWSH, SW, and WPS; the y-axis shows the landscape types, namely, forest, steppe, and wetland.

Forest areas have strong correlations with most water sources, especially SW (0.98), PB (0.95), and PWWOSH (0.96). Steppe areas have a generally strong correlation, except for PB (0.73), SW (0.75), and WPS (0.67). Wetlands have very strong correlations with nearly all sources, particularly with PB (0.96), PWIPS (0.97), PWWOSH (0.97), and WPS (0.96).

3.5 Correlation between water source types and residents’ attitudes

Correlation analysis between the main sources of drinking water (wells, CWSS, and boreholes) and attitudes of residents (complaints and satisfaction) shows that most respondents have no complaints, but when they do, the complaints against water from wells are regarding sediment, salinity, muddiness, hardness, and poor taste. Bad smell correlated with fully unsatisfied attitude (0.29), poor taste with rather unsatisfied (0.36), and sediment with both rather unsatisfied (0.29) and fully unsatisfied (0.31) (Figure 12).

Figure 12

Figure 12. Spearman correlation matrix heatmap illustrating the correlation of the drinking water sources and community perceptions in the rural Akmola Region. Note: water sources: well, borehole, CWSSIT, CWSSIT and well, and others; types of complaints: bad smell, poor taste, muddy water, salty water, hard water, sediment, and no complaints; attitudes: fully satisfied, fully unsatisfied, rather satisfied, rather unsatisfied, and difficult to answer.

Respondents who use borehole water were rather unsatisfied with poor taste (0.65) and sediment (1) (Figure 12). Respondents who use water from the central water-supply system expressed both fully unsatisfied and rather unsatisfied attitude. The prior correlated with bad smell (0.27), hard water (0.48), muddy water (0.20), poor taste (0.20), salty water (0.40), and sediment (0.32), whereas the latter correlated with bad smell (0.32) and poor taste (0.41) (Figure 12). Several households use both well and CWSS as sources of drinking water, and respondents from these households were rather unsatisfied with hard water (1) and poor taste (1) (Figure 12). Participants who use other sources of water predominantly expressed fully unsatisfied attitude, which was linked to bad smell (0.42), muddy water (0.42), salty water (0.49), and sediment (0.31). However, included among these participants were respondents who were rather unsatisfied with poor taste (0.24) and respondents who were rather satisfied except for some complaints about sediment (0.29) (Figure 12).

4 Discussion

The Piper diagram (Figure 4) illustrates eight water samples from various drinking water sources in the rural Akmola Region. Most samples cluster in the zone with high concentrations of Ca²⁺, Mg²⁺ and Cl⁻, and HCO₃⁻, indicating dominance of hydrocarbonate–calcium and calcium–magnesium water types. Hydrocarbonate concentrations (Figure 4) are particularly high (300 mg/L–450 mg/L) in all sources.

Statistical analysis shows that CWSSIT and SW belong to the same cluster (group b), whereas BWIP is statistically distinct (group a), reflecting differences in the treatment or aquifer geology. The statistical markers indicate that CWSSIT (5a) is significantly different from several other sources, including BWIP, suggesting variation in water–rock interaction or treatment levels. Elevated Cl⁻ and SO₄^2- levels in some sources may indicate anthropogenic pollution, saline intrusion, or natural mineral dissolution. The presence of sulfate is possibly linked to fertilizer runoff or geological sources (Wei et al., 2024; Mohallel, 2024). Variability in Ca²⁺/HCO₃⁻ ratios may also result from the treatment effect or hydrogeological diversity (Wichterich et al., 2024).

PWS (purified water from the store) displays inconsistent chemical profiles depending on the source—river, reservoir, or groundwater—resulting in spatial variability. Similar patterns are observed in other regions, such as Sri Lanka and northern India, where water chemistry is influenced by seasonal shifts and source mixing (Akram et al., 2024). BWIP consistently shows the lowest ionic concentrations, indicating effective treatment, whereas CWSSIT, PWIPS, and PWWOWSH often contain elevated levels of multiple ions, suggesting limited or no treatment and higher exposure to contamination (Zhang et al., 2025; Udeshani et al., 2025).

Sociological surveys in Tselinograd, Zerendy, and Korgalzhyn districts show that residents use a variety of drinking water sources with differing levels of satisfaction. Dissatisfaction is most common in steppe areas, often because of high mineral content or poor management of the centralized water-supply system (Norvivor et al., 2024). In contrast, forested and wetland regions report more stable water quality and greater trust in water sources.

Correlation analysis confirmed a clear pattern: villages with less mineralized, softer water reported higher satisfaction, whereas those with high concentrations of Na⁺, Cl⁻, or SO₄^2- reported lower satisfaction and greater reliance on alternative sources (Biswas et al., 2024; Wei et al., 2024). Despite occasional complaints, centralized systems remain highly valued due to their perceived convenience and reliability (Makhlouf et al., 2024).

In this study, we confirm that access alone is insufficient; water quality, palatability, and perceived safety are critical for sustained use. By integrating chemical data with sociological insights, water management strategies can become more context-sensitive, addressing both technical and human dimensions. Prioritizing villages with poor-quality sources (e.g., high Cl⁻, Na⁺, and SO₄^2-) and low user satisfaction could lead to improved water trust and health outcomes (Ahmad et al., 2025; Norvivor et al., 2024).

Forest areas have strong correlations with most water sources, indicating that these sources in forest regions have highly consistent chemical compositions. Steppe areas show a generally strong correlation, except for PB, SW, and WPS, suggesting more variability in the chemical content for these sources in steppe landscapes. Wetlands have very strong correlations across nearly all sources, particularly with PB, PWIPS, PWWOSH, and WPS, indicating a stable chemical profile regardless of the source.

5 Conclusion

The chemical analysis confirms that drinking water in the Akmola Region is predominantly of Ca–Mg–Cl–HCO₃ type. Sociological surveys indicate that although most rural households rely on private wells or CWSSIT, satisfaction with water quality is mixed. Roughly one-third of respondents report dissatisfaction, citing issues such as sediment, salinity, and unpleasant taste. Despite this, more than 60% of residents do not use any water purification methods, which is likely due to affordability concerns or a lack of awareness.

The heatmaps show that correlations between different types of complaints and satisfaction levels are generally weak. This suggests that households may complain about one aspect of water but remain satisfied (or dissatisfied) for other reasons. The structure of correlations differs across water sources. In some sources, complaints such as taste or smell cluster together and are associated with lower satisfaction, whereas in others, complaints are more scattered. This indicates that the perceived water quality issues are source-specific. In certain water sources, there is a visible clustering of complaints (e.g., taste, color, and hardness) that align with lower satisfaction. This highlights that some problems tend to occur together, leading to compounded dissatisfaction. For some sources, the heatmaps show very limited variability, meaning either very few complaints were reported or the satisfaction levels are consistently high/low. These cases suggest stable but potentially problematic water supply, where perceptions are uniform.

To address these challenges, the following measures are recommended: regular water quality monitoring, especially in steppe areas showing elevated salinity or mineralization, public education campaigns on the benefits of water purification and proper sanitation practices, and targeted interventions for households using untreated or poorly maintained water sources (e.g., CWSS and wells). By integrating hydrochemical analysis with community-based insights, water management in rural Kazakhstan can become more sustainable, equitable, and responsive to local needs.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by the Ethics Committee of the Faculty of Natural Science, L.N. Gumilyov ENU. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AN: Conceptualization, Writing – original draft, Data curation, Formal Analysis. RB: Writing – original draft, Conceptualization, Funding acquisition, Supervision, Writing – review and editing. AK: Supervision, Writing – review and editing, Resources. AZ: Validation, Writing – original draft, Investigation, Visualization. IS: Writing – original draft, Project administration. DB: Software, Writing – original draft, Data curation.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Science Committee of the Ministry of High Education and Science of the Republic of Kazakhstan via grant number AP23486944 on the topic “Linking water quality and quantity of climate change condition in steppe zone of Kazakhstan.”

Conflict of interest

Author RB was employed by LLP Smart Eco-nnect.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Footnotes

^{Abbreviations:}V1–V9, village 1–village 9; BWS, bottled water from a store; PWIPS, public water intake pump on the street; PWWOWSH, private water well owned without supply inside house; PWWOSH, private water well owned with supply inside house; PB, public borehole; SW, spring water; WPS, water pumping station; CWSSIT, centralized water supply system with inside tap; PW, public well; PBWWSH, private borehole with water supply to the house; PBWOWSH, private borehole without water supply to the house; PWS, purified water from the store; FTW, free tracked-in water; WHO, World Health Organization; SanR&R, sanitary rules and regulations; RK, Republic of Kazakhstan; EU, European Union; BWIP, bottled water is imported and ordered by the company and paid for.

References

Ahmad, S., Shakeel, A., Ahmed, F., and Badar, M. S. (2025). Assessment of drinking and irrigation water quality indices using geospatial techniques in the coal mining region of Ramgarh, Jharkhand. Water, Air, Soil Pollut. 236, 421. doi:10.1007/s11270-025-08054-3

CrossRef Full Text | Google Scholar

Akmola region (2004). Kazakhstan. National encyclopedia. Almaty Kazakh Encycl. 1. Available online at: https://commons.wikimedia.org/w/index.php?title=File:Kazakhstan_National_encyclopedia_(ru)_-_Vol_1_of_5_(2004).pdf&page=143.

Google Scholar

Akram, M. W., Rahman, F., Khan, D., and Ahmad, S. (2024). Assessment of groundwater quality for drinking purposes of Malda District, India. World Water Policy 10 (3), 796–820. doi:10.1002/wwp2.12187

CrossRef Full Text | Google Scholar

Andrew, R. G., Burns, R. C., and Allen, M. E. (2019). The influence of location on water quality perceptions across a geographic and socioeconomic gradient in appalachia. Water 11 (11), 2225. doi:10.3390/w11112225

CrossRef Full Text | Google Scholar

Ayeta, E. G., Yafetto, L., Lutterodt, G., Ogbonna, J. F., and Miyi, M. K. (2024). Groundwater in the coastal areas of Ghana: quality and associated health risks. Heliyon 10 (11), e31652. doi:10.1016/j.heliyon.2024.e31652

PubMed Abstract | CrossRef Full Text | Google Scholar

Beisenova, R., Tussupova, K., Tazitdinova, R., Tulegenova, S., Rakhymzhan, Z., Orkeyeva, A., et al. (2024). Perceived and physical quality of drinking water in Pavlodar and Akmola rural regions of Kazakhstan. Sustainability 16 (17), 7625. doi:10.3390/su16177625

CrossRef Full Text | Google Scholar

Beisenova, R., Chen, J., Kussainova, M., Tussupova, K., Tazitdinova, R., Mujahid, N., et al. (2025). Influence of geographical locations on drinking water quality in rural Pavlodar Region, Kazakhstan. Water 17, 945. doi:10.3390/w17070945

CrossRef Full Text | Google Scholar

Biswas, S., Chattopadhyay, A., Shaw, S., and Hoffmann, R. (2024). Assessing groundwater quality and its association with child undernutrition in India. Sci. Total Environ. 15 (943), 173732. doi:10.1016/j.scitotenv.2024.173732

PubMed Abstract | CrossRef Full Text | Google Scholar

Dictionary of modern geographical names/Rus (2006). Geogr. o-vo. Mosk. center; under the general editorship of academician V. M. Kotlyakov. Ekaterinburg: U-Faktoriya: Institute of Geography of the Russian Academy of Sciences. Available online at: https://rus-geo-enc.slovaronline.com/175%22%20title=%22https://rus-geo-enc.slovaronline.com/175%22%3Ehttps://rus-geo-enc.slovaronline.com/175 (accessed on May 09, 2025).

Google Scholar

Dobretsov, N. L., Buslov, M. M., Zhimulev, F. I., Travin, A. V., and Zayachkovsky, A. A. (2006). Vendian-early Ordovician geodynamic evolution and model for exhumation of ultrahigh- and high-pressure rocks from the Kokchetav subduction-collision zone. Russ. Geol. Geophys. 47 (4), 428–444. doi:10.1016/j.rgg.2015.01.004

CrossRef Full Text | Google Scholar

Dogaru, D., Zobrist, J., Balteanu, D., Popescu, C., Sima, M., Amini, M., et al. (2009). Community perception of water quality in a mining-affected area: a case study for the Certej catchment in the Apuseni Mountains in Romania. Environ. Health Perspect. 117 (6), 895–900. doi:10.1007/s00267-008-9245-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Faniran, A. (1986). The perception of water quality among rural communities in South-Western Nigeria: Lesson for planners. Water Int. 11 (3), 169–174. doi:10.1080/02508068608686425

CrossRef Full Text | Google Scholar

Gupta, S., Tsai, C.-H., and Carroll, J. M. (2022). Not another day zero: design hackathons for community-based water quality monitoring. arXiv preprint arXiv 2210, 16381. doi:10.48550/arXiv.2210.16381

CrossRef Full Text | Google Scholar

Jat Baloch, M. Y., Zhang, W., Chai, J., Li, S., Alqurashi, M., Rehman, G., et al. (2021). Shallow groundwater quality assessment and its suitability analysis for drinking and irrigation purposes. Water 13, 3361. doi:10.3390/w13233361

CrossRef Full Text | Google Scholar

Jat Baloch, M. Y., Zhang, W., Shoumik, B. A. A., Nigar, A., Elhassan, A. A. M., Elshekh, A. E. A., et al. (2022a). Hydrogeochemical mechanism associated with land use land cover indices using geospatial, remote sensing techniques, and health risks model. Sustainability 14, 16768. doi:10.3390/su142416768

CrossRef Full Text | Google Scholar

Jat Baloch, M. Y., Zhang, W., Zhang, D., Al Shoumik, B. A., Iqbal, J., Li, S., et al. (2022b). Evolution mechanism of arsenic enrichment in groundwater and associated health risks in Southern Punjab, Pakistan. Int. J. Environ. Res. Public Health 19, 13325. doi:10.3390/ijerph192013325

PubMed Abstract | CrossRef Full Text | Google Scholar

Jat, B. M. Y., Zhang, W., Juanfen Chai, A., Baig, A.A., Aqil, T., Javed, I., et al. (2025). Groundwater contamination, fate, and transport of fluoride and nitrate in Western Jilin, China: implications for water quality and health risks. Environ. Chem. Ecotoxicol. 7, 1189–1202. doi:10.1016/j.enceco.2025.04.010

CrossRef Full Text | Google Scholar

Jehan, S., Muhammad, S., Khan, S., Khattak, S., and Rashid, A. (2019). Hydrochemical properties of drinking water and their sources apportionment of pollution in Bajaur Agency, Pakistan. Measurement 139, 127–135. doi:10.1016/j.measurement.2019.02.090

CrossRef Full Text | Google Scholar

Kazakhstan (2005). National Encyclopedia. Almaty: Kazakh encyclopedias. Available online at: https://commons.wikimedia.org/w/index.php?title=File:Kazakhstan_National_encyclopedia_(ru)_-_Vol_3_of_5_(2005).pdf&page=265 (accessed on May 09, 2025).

Google Scholar

Kotlyakov, V. M., Razumovskii, V. M., and Brinken, A. O. (2006). The Russian geographic society turns 160. Her. Russ. Acad. Sci. 76, 166–171. doi:10.1134/S1019331606020109

CrossRef Full Text | Google Scholar

Land Resources Management Committee (2022). Consolidated analytical report on the status and use of lands of the Republic of Kazakhstan for 2021 (Russian). Land Resour. Manag. Comm. Ministry Agric. Repub. Kazakhstan. Available online at: https://www.gov.kz/memleket/entities/land/documents/details/291911?lang=ru (accessed on 7 December 2024).

Google Scholar

Makhlouf, A., Sharaan, M., El-Rawy, M., Kanae, S., and Ibrahim, M. G. (2024). Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: a case study Minia Governorate, Egypt. J. Environ. Manag. 362, 121269. doi:10.1016/j.jenvman.2024.121269

PubMed Abstract | CrossRef Full Text | Google Scholar

Maussymbayeva, A., Umirova, G., Polina, S., Abdullina, A., Tileuberdi, N., and Nunoo, S. (2025). The search for small intrusions of the Stepnyak gold-bearing type within the Akmola region of Kazakhstan: a multi-geophysical technique and unmanned aerial vehicle approach. Phys. Chem. Earth, Parts A/B/C 140, 103988. doi:10.1016/j.pce.2025.103988

CrossRef Full Text | Google Scholar

Mishra, A. P., Singh, S., Sarkar, M.Sh., Singh, R., Chandra, N., Đurin, B., et al. (2024). Integrating community perceptions, scientific data and geospatial tools for sustainable water quality management. Environ. Challenges 8, 100818. doi:10.1016/j.envc.2024.100818

CrossRef Full Text | Google Scholar

Mohallel, S. A. (2024). Hydrogeochemical tracers to assess the groundwater in El Saieda Basin. Appl. Water Sci. 14, 157. doi:10.1007/s13201-024-02147-w

CrossRef Full Text | Google Scholar

National Bureau of Statistics (2024). “Population of the Republic of Kazakhstan by gender by regions, cities, districts and regional centers and villages at the beginning of 2024,” in The Bureau of national statistics agency for strategic planning and reforms of the Republic of Kazakhstan. Available online at: https://stat.gov.kz/industries/social-statistics/demography/publications/157719/(accessed on July 4, 2024).

Google Scholar

Ngenzebuhoro, P. C., Dassargues, A., Bahaj, T., and Hammani, O. (2023). Water geochemistry and inorganic quality: case of the lower rusizi Plain aquifer, North-Western Burundi. Water 16 (12), 1712. doi:10.3390/w16121712

CrossRef Full Text | Google Scholar

Norvivor, F. A., Azizi, S., Kamika, I., Olayiwola, A., Sibali, L., and Maaza, M. (2024). Evaluating socio-cultural influences on water quality in Ghana. Environ. Prot. Eng. 50 (3), 240309. doi:10.37190/epe240309

CrossRef Full Text | Google Scholar

Nyika, J., and Onyari, E. (2019). Hydrogeochemical analysis and spatial distribution of groundwater quality in roundhill landfill vicinity of South Africa. Environ. Health Insights 13, 1178622119872771. doi:10.1177/1178622119872771

CrossRef Full Text | Google Scholar

Odikamnoro, O. O., Ezugwu, C. N., Ezeah, C., Omunakwe, O. G., and Mihai, F.-C. (2019). A comparative assessment of domestic water quality in rural communities of southeast Nigeria. Environ. Eng. Manag. J. 18 (9), 1941–1950. doi:10.30638/eemj.2019.182

CrossRef Full Text | Google Scholar

Piper, A. M. (1994). A graphic procedure in the geochemical interpretation of water-analyses. Eos, Trans. Am. Geophys. Union 25 (6), 914–928. Available online at: https://hatarilabs.com/ih-en/how-to-create-a-piper-plot-online-with-aquifer-app-tutorial.

Google Scholar

Seeyan, S., Adham, A., Mahdi, K., and Ritsema, C. (2021). Water quality, availability, and uses in rural communities in the Kurdistan Region, Iraq. Water 13 (20), 2927. doi:10.3390/w13202927

CrossRef Full Text | Google Scholar

State Archives of Akmola Region (2022). Guide. Available online at: https://web.archive.org/web/20180423170511/http://www.netref.ru/putevoditele.html?page=15#2._%D0%B3._%D0%9A%D0%BE%D0%BA%D1%88%D0%B5%D1%82%D0%B0%D1%83 (accessed on April 23, 2022).

Google Scholar

Tleuova, Zh., Snow, D. D., Mukhamedzhanov, M., and Ermenbay, A. (2023). Relation of hydrogeology and contaminant sources to drinking water quality in Southern Kazakhstan. Water 15 (24), 4240. doi:10.3390/w15244240

CrossRef Full Text | Google Scholar

Udeshani, C., Yue, F.-J., Chandrajith, R., Koralegedara, N. H., and Li, S.-L. (2025). Spatial and seasonal variation of groundwater geochemistry in high-grade metamorphic aquifers. J. Hydrology 657, 133154. doi:10.1016/j.jhydrol.2025.133154

CrossRef Full Text | Google Scholar

Wei, S., Zhang, Y., Cai, Z., Bi, D., Wei, H., Zheng, X., et al. (2024). Evaluation of groundwater quality and health risk assessment in dawen river basin, North China. Environ. Res. 264 (1), 120292. doi:10.1016/j.envres.2024.120292

PubMed Abstract | CrossRef Full Text | Google Scholar

West, A. O., Nolan, J. M., and Scott, J. T. (2021). Optical water quality and human perceptions of rivers: an ethnohydrology study. Environ. Health Sci. 2 (1), e1230. doi:10.1002/ehs2.1230

CrossRef Full Text | Google Scholar

Wichterich, C. M., Welch, S. A., Gardner, C. B., Smith, D. F., and Lyons, W. B. (2024). Effects of agriculture and urbanization on the geochemistry of the Scioto River. Appl. Geochem. 168, 106016. doi:10.1016/j.apgeochem.2024.106016

CrossRef Full Text | Google Scholar

Xiao, B. F. W., Sun, S., Li, J. L., Huang, B. C., Han, C. M., Yuan, C., et al. (2015). A tale of amalgamation of three Permo-Triassic collage systems in Central Asia: oroclines, sutures, and terminal accretion. Annu. Rev. Earth Planet Sci. 43, 477–507. doi:10.1146/annurev-earth-060614-105254

CrossRef Full Text | Google Scholar

Xiao, W. X., Sang, M., Zhai, M., Huang, P., Li, L., et al. (2025). Formation and accretionary evolution of the Kokchetav complex, northwestern Kazakhstan: insights from structural styles, and detrital zircon geochronology and Lu-Hf isotopes. Lithos 506-507, 108056. doi:10.1016/j.lithos.2025.108056

CrossRef Full Text | Google Scholar

Zhang, B., Hu, X., Yang, Y., Deng, X., Li, B., Gong, X., et al. (2025). Comprehensive evaluation of groundwater quality in population-dense and extensive agricultural regions. Environ. Geochem. Health 47 (3), 62. doi:10.1007/s10653-025-02364-5

PubMed Abstract | CrossRef Full Text | Google Scholar