Assessment of Chemical Pollution Load in Surface Waters of the Turkestan Region and Its Indirect Impact on Landscapes: A Comprehensive Study

1. Introduction

Due to the increasing anthropogenic pressure and the effects of global climate change, there is a growing need to reassess the chemical pollution of surface waters, which significantly impacts the stability of landscape systems. The preservation of the natural environment and ecological balance has become a critical issue at present [1,2,3,4,5]. In recent decades, the pollution of natural water resources has intensified as a result of human economic and industrial activities [6]. Especially in arid and semi-arid regions, including the Turkestan region, the change in the chemical composition of surface water sources has indirect effects on landscape processes, making it one of the key areas for interdisciplinary research [7].

The deterioration of surface water quality disrupts biogeochemical cycles and geochemical equilibrium, negatively affecting ecological sustainability and landscape morphodynamics. The entry of pollutants (such as heavy metals, organic and inorganic contaminants) into aquatic ecosystems alters natural feedback mechanisms, damaging landscape morphology and stability [8].

The diversity of geological and hydroclimatic conditions in the Turkestan region creates complex factors that affect landscapes and ecological systems. Land use and land cover changes negatively impact the chemical quality of surface waters and influence soil properties and vegetation structure [9,10,11,12]. These processes are exacerbated by the effects of climate change, making regional landscapes more vulnerable to natural stressors [13]. Pollutants have a long-term negative impact on both aquatic and terrestrial landscapes, leading to changes in landscape structure and function.

As a result of agricultural and industrial activity, heavy metals (such as Al) enter surface waters, disrupting landscapes and lowering water quality in the Turkestan region. Fertilizers and pesticides used in agriculture are washed into rivers through runoff, increasing the levels of NO₃⁻ and PO₄³⁻. This intensifies eutrophication, leading to the destabilization of aquatic ecosystems [14,15,16]. Waste from construction, industry, and livestock farming enters rivers through precipitation, further deteriorating water quality.

A systematic analysis of the dynamics of water quality and its impact on landscape stability is crucial. The distribution of pollutants alters the chemical properties of soils, the availability of nutrients to plants, and their structure, leading to significant changes in landscape morphology [17,18,19,20,21,22,23]. To ensure water quality and landscape stability, comprehensive measures must be taken.

Previously, several physicochemical and biological parameters such as total hardness, pH, dry residue, electrical conductivity, and total salinity were studied to assess water quality [24,25,26]. Various organizations have adopted indices based on mathematical and statistical methods [27,28,29]. To effectively assess the variability in heavy metals in water samples for water quality evaluation, principal component analysis (PCA) was used, employing variational-statistical indicators based on scientific principles. The overall pollution index (OIP) is applied as it reflects the cumulative effect of multiple pollutants [30]. Additionally, the Nemerov pollution index (NPI) allows for the assessment of the cumulative impact of several parameters affecting water quality [31,32,33]. The heavy metal pollution index (HPI) is used to assess heavy metal pollution, comparing the concentration of heavy metals with standard thresholds to determine their ecological impact [34]. For the analysis of the landscape features in the Turkestan region, land use and land cover (LULC) classification was performed based on satellite data from Landsat between 2000 and 2020. This method facilitates the effective management and planning of land resources.

In the Turkestan region, the presence of heavy metals such as Al, Cd, and Pb has increased the HPI value, indicating that water resources are polluted to levels that pose a health risk. Heavy metals negatively impact ecosystem stability, leading to the slowdown of natural processes and degradation [35,36,37,38,39]. Such pollution is associated with natural mineral decay, soil erosion, industrial effluents, agricultural waste, rainwater, and construction activities [40,41]. These factors degrade water quality and cause long-term damage to landscapes.

Several projects are being implemented in the Turkestan region to improve the quality of surface waters. In 2023, major repairs were carried out on a 62-kilometer canal, and 488 kilometers of drainage systems underwent mechanical cleaning. These measures improved the meliorative condition of 19.4 thousand hectares of irrigated land. Additionally, monitoring was conducted at 143 water bodies, including 93 rivers and 31 lakes. As a result, the level of chemical pollution in the Syr Darya, Eginsu, and Katta-Bogen rivers confirmed the deterioration of water quality [43,44,45]. The importance of small-scale studies to identify factors affecting landscapes is increasing. Such studies help in developing measures to improve water quality and in creating pollution prevention strategies. However, despite initiatives to improve water quality in the region, programs aimed at completely eliminating chemical pollution have yet to yield full results. To address these issues, additional investments and the enhancement of ecological monitoring mechanisms are necessary. A systematic analysis of the factors affecting water quality and landscape stability will form the basis for maintaining landscape balance and the sustainable use of natural resources.

The main objective of the study is to systematically analyze the level of chemical pollution in surface waters of the Turkestan region and determine their indirect effects on regional landscapes.

To achieve this goal, the following tasks were set:

• Evaluate water quality based on chemical parameters obtained from various sampling points;
• Perform variational-statistical analysis, principal component analysis (PCA), and calculate the OIP, NPI, and HPI indices to determine the pollution level;
• Identify land use and land cover changes (LULC);
• Apply statistical methods to explore the relationships between water quality and land use;

Identify the indirect effects of polluted surface waters on landscapes and develop strategic recommendations for improving the ecological situation.

This study comprehensively analyzed the anthropogenic and indirect impacts of surface water quality on landscapes in the Turkestan region. To evaluate water quality, the mechanisms of how heavy metals (especially Al and Fe) concentrations affect landscapes were studied based on the OIP, NPI, and HPI indices. The results indicated that at the S8 sampling point, the concentration of Al was tens of times higher than the threshold level (22.38 mg/l), identifying extreme water pollution. The 2024 water sample analysis revealed spatial variations of heavy metals and chemical parameters in the Turkestan region. The highest pollution levels were recorded at point S8, with moderate levels observed at points S1, S5, and S6. Spatial autocorrelation results showed the influence of anthropogenic factors on regional imbalances. High pollution levels were closely associated with agriculture, industry, and construction processes. Agricultural waste, industrial effluents, and urbanization exacerbated landscape degradation. The expansion of such studies allows for the integrated consideration of landscape and ecological factors. The obtained results will serve as the basis for developing new strategies for water resource management and improving the ecological monitoring system in the region.

2. Materials and Methods

2.1. Study Area

This study focused on the Turkestan region, aiming to systematically analyze the level of chemical pollution in surface waters and assess their potential indirect effects on regional landscapes. The Turkestan region is located in the middle latitudes of Central Eurasia, in the southern part of Kazakhstan, and covers the eastern part of the Turan Depression and the western slopes of the Tian Shan Mountains. The area spans approximately 117,300 km² and borders the Ulytau, Kyzylorda, and Zhambyl regions, as well as the Republic of Uzbekistan [46]. The study area is characterized by its terrain, shaped by various geological and geomorphological processes.

Surface waters in the Aral-Syrdarya Basin include the Shardara Reservoir and the Syr Darya, Arys, Bogen, Keles, Badam, Aksu, and Kurkeles rivers. The main components of the rivers’ annual flow are: groundwater (50%), snowmelt (45%), and rainfall (5%). In the Karatau mountain range, annual runoff can reach up to 500 mm, and river valleys are mostly distinct at the foothills. The low permeability of Paleozoic marl and clayey limestones contributes to an increase in surface runoff. Surface waters are fresh, with a mineralization level up to 1 g/l, and their chemical composition is either bicarbonate-calcium or bicarbonate-calcium-magnesium. Through the irrigation system, waters originating from the foothills are distributed across the Syr Darya alluvial plain.

The annual average flow volume of surface waters in the Turkestan region is 37.203 km³, with the total useful volume of reservoirs amounting to 5,232 million m³. Ninety percent of the river flow is used for agricultural needs, and reservoirs have increased the flow regulation level up to 0.94 [47]. Surface waters play a crucial role in agriculture, fishing, forestry, tourism, and industry. However, these resources face ecological issues such as contamination with heavy metals, a decrease in groundwater levels, soil erosion, and the reduction of sand deposits.

This study included eight sampling locations (S1–S8) for water quality analysis. The sampling points were determined based on the potential impact of anthropogenic factors on natural landscapes, using land use maps and 2023 Maxar high-resolution satellite imagery (Figure 1, Table 1).

2.2. Water Sampling and Analytical Methods

Field research on surface waters was conducted in August 2024. Water samples were collected from eight locations (S1–S8) using a selective sampling method, considering anthropogenic factors. The samples were taken from a depth of 15–20 cm from the water surface and stored in polyethylene bottles.

During the study, 24 chemical parameters were analyzed three times, and their average values were calculated. The samples were analyzed at the certified “Structural and Biochemical Materials” engineering-testing laboratory in Shymkent. The chemical composition was determined using physical-chemical methods with an X-ray spectrometer “Spektroskan MAX GF – 2E.”

To reduce data deviation and ensure accuracy, all equipment and containers were washed with a 10% HCl solution and rinsed with double deionized water. Chemical solutions were prepared using Merck-GR grade reagents. For proper calibration of the instruments for each heavy metal, stock samples were prepared from the initial solutions. The samples were analyzed three times, and deviations of the instruments were checked after every three measurements.

2.3. Variational-Statistical Analysis

First, the chemical parameters of the surface water samples collected from the Turkestan region were processed using variational-statistical analysis with MS Excel 2016. The following variational-statistical indicators were used during data processing. Each variational-statistical indicator has its own specific formula [49,50]. Below are their formulas (1):

Mean ± Standard Error (X ± Sx):

$$\mathrm{X}={\displaystyle \frac{\mathit{\sigma }}{\sqrt{\mathit{n}}}}$$

(1)

where:

X — mean value;

σ — standard deviation;

n — number of observations.

Limit rate (lim): This indicator represents the range between the highest and lowest values. (2):

$$\mathrm{l}\mathrm{i}\mathrm{m}={\mathit{X}}_{\mathit{m}\mathit{a}\mathit{x}}-{\mathit{X}}_{\mathit{m}\mathit{i}\mathit{n}}$$

(2)

Difference of Limits (p): This is also used to determine the difference between the limits (3):

$${\mathrm{p}=\mathit{X}}_{\mathit{m}\mathit{a}\mathit{x}}-{\mathit{X}}_{\mathit{m}\mathit{i}\mathit{n}}$$

(3)

Standard Deviation (σ): The standard deviation describes how far the data points are from the mean (4):

$$\mathit{\sigma }=\sqrt{{\displaystyle \frac{\sum {(\mathit{X}}_{\mathit{i}}-{\mathit{X})}^2}{\mathit{n}-1}}}$$

(4)

where:

${\mathit{X}}_{\mathit{i}}$— each observation;

X — mean value;

n— number of observations.

Coefficient of Variation (CV%): The coefficient of variation indicates the level of dispersion in the data (5):

$$\mathrm{C}\mathrm{V}\%={\displaystyle \frac{\mathit{\sigma }}{\mathit{X}}}\times 100$$

(5)

where:

σ — standard deviation;

X — mean value.

Principal Component Analysis (PCA): The Principal Component Analysis (PCA) method was used to reduce the dimensionality of the data and identify the primary sources of variability. The method consists of the following steps:

Data Standardization: To enable the comparison of variables with different units and ranges, the data was standardized using the z-score (6):

$${\mathit{Z}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{X}}_{\mathit{i}\mathit{j}}-{\mathit{\mu }}_{\mathit{j}}}{{\mathit{\sigma }}_{\mathit{j}}}}$$

(6)

where:

${\mathit{X}}_{\mathit{i}\mathit{j}}-$ the i-th observation value of the j-th variable;

${\mathit{\mu }}_{\mathit{j}}$ — the mean value of the variable;

${\mathit{\sigma }}_{\mathit{j}}$ — the standard deviation.

Constructing the Correlation Matrix: To analyze the relationships between standardized variables, the correlation matrix (R) was calculated (7):

$$\mathrm{R}={\displaystyle \frac{1}{\mathit{n}-1}}{\mathit{Z}}^{\mathit{T}}\mathrm{Z}$$

(7)

where:

Z— standardized data matrix,

n — number of observations..

Eigen Decomposition: Eigenvalues ($\mathit{\lambda }$) and eigenvectors (v) were derived from the correlation matrix using the following Equation (8):

$${\mathit{R}}_{\mathit{v}}={\mathit{\lambda }}^{\mathit{v}}$$

(8)

The eigenvalues represent the variance explained by each principal component, while the eigenvectors define the directions of these components.

Projection of Data onto Principal Components: The standardized data were projected onto the eigenvector space to compute the principal components (9):

PC=ZV

(9)

where PC is the matrix of principal components, and V is the matrix of eigenvectors.

Explained Variance: The contribution of each principal component to the total variance was calculated as follows (10):

$$\mathrm{Explained} \mathrm{Variance} \mathrm{Ratio}={\displaystyle \frac{{\mathit{\lambda }}_{\mathit{i}}}{\sum \mathit{\lambda }}}$$

(10)

where ${\mathit{\lambda }}_{\mathit{i}}$ is the eigenvalue of the i - th principal component.

The values of the principal components (PC1 and PC2) represent the contribution of variables or objects (sampling sites) to the total variance:

PC1 (Principal Component 1) — the direction that explains the largest portion of the variance. It describes the most significant variation in the data.

PC2 (Principal Component 2) — explains the remaining variance but is uncorrelated with PC1 (i.e., orthogonal).

The values of the sampling sites (- or +) indicate the direction and magnitude of their effect in relation to the components:

Positive values — strongly related to the component and increase the overall variation.

Negative values — negatively affect the component and decrease the variation.These steps enabled the identification of the most significant components, which were further used to interpret the relationships among variables and reduce the dimensionality of the dataset.

2.4. Water Quality Analysis:

2.4.1. Overall Pollution Index (OIP)

The Overall Pollution Index (OIP) is an important indicator for the comprehensive assessment of water resource quality. This index determines the quality of water by comparing various parameters within the water and allows for the safe use of water resources.

The OIP is calculated by determining the ratio between the actual value and the standard value of each parameter. Water quality is assessed in accordance with the water resource evaluation system in Kazakhstan (Table 2) [51]. The calculation is carried out using Formula (11), with the pollution index for each parameter determined based on the average value.

The OIP characterizes the overall condition of water quality and plays a crucial role in ecological monitoring and the development of strategies for improving water quality [52].

Formula for calculating the OIP:

$$\mathrm{OIP}={\displaystyle \frac{1}{\mathit{n}}}{\sum }_{\mathit{i}=1}^{\mathit{n}}{\mathit{P}}_{\mathit{i}}$$

(11)

where:

OIP — Overall Pollution Index;

n— number of parameters considered;

${\mathit{P}}_{\mathit{i}}$ — pollution index for the i-th parameter.

The pollution index for each parameter is determined using the following Formula (12):

$${\mathit{P}}_{\mathit{i}}{\displaystyle \frac{{\mathit{V}}_{\mathit{n}}}{{\mathit{V}}_{\mathit{s}}}}$$

(12)

where:

${\mathit{V}}_{\mathit{n}}$ — measured value of the parameter;

${\mathit{V}}_{\mathit{s}}$ — standard allowable value of the parameter (MAC).

This ratio determines how much each pollutant affects the water quality.

OIP Interpretation:

OIP ≤ 1.9 — Very Clean Water (C1 Class). The quality of the water source is excellent, meeting ecological and sanitary standards.

1.9 < OIP ≤ 3.9 — Satisfactory Quality (C2 Class). The quality of the water resources is generally acceptable, but some pollutants may be present in small amounts.

3.9 < OIP ≤ 7.9 — Moderate Pollution (C3 Class). The water resources are moderately polluted, affecting ecological stability.

7.9 < OIP ≤ 15.9 — Significant Pollution (C4 Class). The water quality is considerably degraded, potentially leading to ecological hazards.

OIP > 15.9 — Highly Polluted (C5 Class). The water resources do not meet ecological and sanitary standards and may pose significant risks to human health and the environment.

2.4.2. Nemerov’s Pollution Index (NPI)

Nemerov’s Pollution Index (NPI) [53] is one of the important methods for assessing water quality. The NPI allows for determining the overall pollution level by calculating various water indicators and comparing them with standards. This method quantitatively describes the environmental quality, making it an effective tool in ecological monitoring and water quality assessment.

To calculate the NPI, the following formula is used for each indicator (13):

$$\mathrm{NPI}={\displaystyle \frac{{\mathit{C}}_{\mathit{i}}}{{\mathit{M}\mathit{A}\mathit{C}}_{\mathit{i}}}}$$

(13)

where:

${\mathit{C}}_{\mathit{i}}$— the actual value of each chemical parameter in your water sample (e.g., aluminum, calcium, etc.).

${\mathit{M}\mathit{A}\mathit{C}}_{\mathit{i}}$— the Maximum Allowable Concentration (MAC) of the parameter, i.e., the maximum permissible concentration.

In other words, for each parameter, the actual concentration (MAC) is divided by the maximum allowable concentration to determine how much the parameter exceeds the permissible concentration.

Interpretation of NPI values:

NPI < 1 — The parameter does not exceed the allowable limit. This indicates that the water quality is good and there is no pollution.

NPI = 1 — The parameter is at the allowable concentration level. This parameter is within the safe limit, but it is approaching the maximum permissible concentration.

NPI > 1 — The parameter exceeds the maximum permissible concentration. This indicates that the parameter negatively affects the water quality, showing a high pollution level. The higher the NPI value, the stronger the water pollution.

2.4.3. Heavy Metal Pollution Index (HPI)

The Heavy Metal Pollution Index (HPI) is a quantitative indicator used to assess the overall pollution level of heavy metals in water. The HPI determines the contribution of each chemical parameter to water quality and was first proposed in the literature by [54].

HPI allows for studying the cumulative effect of heavy metals and determining their impact on water quality. The method evaluates the toxicity and significance of heavy metals using a weighted arithmetic mean, considering their effects on human health and river ecosystems. When calculating HPI, all analyzed metals are considered, and their compliance with the Maximum Allowable Concentration (MAC) standards is assessed.

The formula for calculating HPI is as follows (14):

$$\mathrm{HPI}={\displaystyle \frac{{\sum }_{\mathit{i}=1}^{\mathit{n}}{\mathit{W}}_{\mathit{i}}{\mathit{Q}}_{\mathit{i}}}{\sum _{\mathit{i}=1}^{\mathit{n}}{\mathit{W}}_{\mathit{i}}}}$$

(14)

In this equation ${\mathit{W}}_{\mathit{i}}$ represents the weight assigned to each heavy metal, and ${\mathit{Q}}_{\mathit{i}}$ refers to the sub-index for that heavy metal. The value of ${\mathit{W}}_{\mathit{i}}$ is calculated using the following Equation (15):

$${\mathit{W}}_{\mathit{i}}={\displaystyle \frac{\mathit{k}}{{\mathit{S}}_{\mathit{i}}}}$$

(15)

In this context, k is the proportionality constant (typically assumed to be k=1k = 1k=1), and ${\mathit{S}}_{\mathit{i}}$ represents the allowable standard value for the heavy metal. The variable ${\mathit{W}}_{\mathit{i}}$ ranges between 0 and 1.The sub-index ${\mathit{Q}}_{\mathit{i}}$ is calculated using the following Equation (16):

$${\mathit{Q}}_{\mathit{i}}={\displaystyle \frac{({\mathit{V}}_{\mathit{a}}-{\mathit{V}}_{\mathit{i}})}{({\mathit{S}}_{\mathit{i}}-{\mathit{V}}_{\mathit{i}})}}\times 100$$

(16)

In this equation, ${\mathit{V}}_{\mathit{a}}$ represents the value obtained through laboratory analysis, ${\mathit{V}}_{\mathit{i}}$ represents the ideal value, and ${\mathit{S}}_{\mathit{i}}$ represents the standard value for that heavy metal.

Interpretation Scale for HPI Values:

HPI < 50 — Safe and Clean Water: The concentration of heavy metals complies with ecological and sanitary standards. Such water is considered safe for ecosystems and human health.

50 ≤ HPI < 100 — Moderately Polluted Water: At this level, the concentration of heavy metals is close to the allowable limits but still acceptable. However, prolonged use may have an impact on ecosystems and health.

HPI ≥ 100 — Highly Polluted Water: The water is significantly polluted, and the concentration of heavy metals is far above the allowable limits. Such water is harmful to ecosystems and poses a significant health risk, so its use should be restricted or completely prohibited.

2.5. Land Use and Land Cover (LULC) Classification

The land use/land cover (LULC) classification for the Turkestan region is an effective method for analyzing the landscape features of the region using thematic information derived from satellite data. The global Land Cover and Land Use Change dataset from 2000-2020, obtained from the Landsat archive, was used [56]. The data obtained through LULC contributes to the effective management of land resources and land use planning, as it allows for a broad-scale assessment of land use types [57].

3. Results and Discussion

3.1. Physicochemical Indicators of Surface Waters in the Turkestan Region

Table 3 presents a variational-statistical overview of the surface water quality analysis. Based on the variational-statistical indicators, Principal Component Analysis (PCA) was conducted to effectively assess the variability of heavy metal concentrations in the water samples. Figure 2 shows that the PCA analysis identified two principal components, which explain 64.8% of the variability in the overall data.

The climate of the Turkestan region is distinctly continental, with very hot summers and relatively cool winters. In July, temperatures exceed 40°C, and in winter, the average temperature drops between -5°C and -10°C. The water temperature in the summer months ranges from 22°C to 30°C [47], which affects biological activity.

In the temperate climatic conditions, especially in summer, water parameters such as acidity (pH), hardness, and dissolved oxygen remain mostly stable. Seasonal fluctuations are relatively small, but the high summer temperatures affect plants and animals.

The Turkestan region is a very arid area, with annual precipitation ranging from 150-300 mm, which is a critical factor for water resources and landscape stability.

The average value of total hardness in the water samples is 7.00 ± 0.89 mg-equiv/l, which corresponds to the average hardness level of water and falls within the allowable limits (3–7 mg-equiv/l). The primary sources of total hardness are Ca and Mg ions, which influence the dissolution and distribution of heavy metals [58].

In water samples with low hardness, the concentration of heavy metals is higher, as metals dissolve more in such an environment. According to the results of the PCA analysis, PC1 indicated high concentrations of heavy metals, particularly Al and Fe. For example, at the S8 sampling point, the concentration of aluminum was found to be 22.38 mg/l, while iron was 12.76 mg/l, which indicates pollution of the water sources [59].

The average pH level of the water is 8.13 ± 0.04, indicating an alkaline environment. The solubility of heavy metals depends on the pH level: at lower pH, the solubility of metals increases, while at higher pH, they tend to precipitate. The pH value (8.13) observed in the study increases the likelihood of heavy metals precipitating. PC1 results showed higher concentrations of Al and Fe in samples with an alkaline pH [60].

The average concentration of Total Dissolved Solids (TDS) is 949.75 ± 110.39 mg/l, which is within the allowable limit of 1000 mg/l. The high levels of TDS at the S1 and S8 sampling points are associated with an increase in the concentration of dissolved minerals and heavy metals. The high concentrations of heavy metals, particularly Al and Fe, affect the TDS levels. PC1 and PC2 components describe the overall variation in the concentration of these metals.

The average concentration of Al is 5.39 ± 2.15 mg/l, which is approximately 24 times higher than the standard limit in the unified water quality classification system of the Republic of Kazakhstan (0.5 mg/l). This indicates significant water pollution. The high concentration of Al is mainly caused by industrial emissions and the entry of aluminum-based coagulants into the water [61].

In the PC1 component, high concentrations of Al and Fe were identified, indicating the highest level of heavy metal contamination. For example, at the S8 sampling point, the concentration of Al was 22.38 mg/l, demonstrating significant pollution in this area [62].

The average concentration of Ca was 108.30 ± 11.85 mg/l, and Mg concentration was 55.73 ± 4.82 mg/l, both of which are key indicators affecting water hardness. The high concentrations of heavy metals are related to the hardness levels, as the abundance of calcium and magnesium ions reduces the solubility of heavy metals [55]. The average concentration of SO₄²⁻ is 337.18 ± 64.78 mg/l, which is within the permissible limits. SO₄²⁻ participates in chemical reactions with heavy metals, influencing their solubility or precipitation. The PC2 component is closely related to sulfate concentration, as lower levels of sulfate were associated with lower concentrations of Cd and Co heavy metals [63].

The average value of electrical conductivity is 1107.75 ± 133.94 µS/cm, indicating a high presence of dissolved ions and heavy metals in the water. The high electrical conductivity may be linked to the elevated concentrations of Al and Fe.

According to the PCA analysis results, two principal components explained 64.8% of the total variability, allowing for a reliable description of the variation in heavy metal concentrations. PC1 is associated with the concentrations of Al and Fe, while PC2 is linked to the levels of Cd and Co. High concentrations of aluminum and iron were detected in the S1 and S8 samples, indicating significant contamination with heavy metals in these areas [60].

Although the concentrations of Cd and Co are low, they can still pose a risk to the aquatic ecosystem. This study revealed significant contamination of surface waters in the Turkestan region with heavy metals and their impact on water quality. The results of the PCA analysis described the distribution dynamics of heavy metals and identified the main factors of pollution.

The study demonstrates that the water ecosystem is impacted by anthropogenic factors, underscoring the need for continuous monitoring and the implementation of water resource management measures. Eliminating the main sources of pollution and preventing the accumulation of heavy metals will contribute to preserving the stability of the water ecosystem and improving water quality.

3.2. Water Quality Analysis:

3.2.1. Water Quality Analysis Through the Overall Pollution Index (OIP)

Water quality evaluation was conducted through the Overall Pollution Index (OIP). This index includes the analysis of 24 physicochemical parameters: total hardness, pH, total dissolved solids, electrical conductivity, total salt content, Al, As, B, Ca, Cd, Co, Cr, Ti, Fe, Pb, Cu, Mg, K, Mn, Na, Ni, Zn, SO₄²⁻, and C₆H₅OH. These parameters are based on the analyses conducted at the sampling sites in the Turkestan region.

These parameters were selected as key factors affecting water quality and assist in determining the suitability of the chemical composition and physical properties for ecological safety and industrial use. The OIP is calculated by considering the maximum allowable concentrations (MAC) of substances in water and their potential impacts on landscapes (Figure 3, Table 5).

The results of the water quality analysis of the main rivers in the Turkestan region showed that the quality of surface waters is directly dependent on both natural processes and anthropogenic factors. The water quality at the S1-S8 sampling points is characterized by various indicators. The study identified differences in the chemical composition, physical, and microbiological indicators of the water, as well as the level of pollution at each sampling point.

The water quality at the sampling sites S2 (OPI = 0.56), S3 (OPI = 0.43), and S7 (OPI = 0.73) is at a satisfactory level. The good water quality at these points is primarily due to the absence of Al, with its concentration being 0.00 mg/l. The absence of Al prevents the negative impact of heavy metals on landscapes, contributing to the higher water quality. Additionally, high levels of total hardness were recorded in these rivers, indicating a high degree of mineralization of the water. However, the abundance of Ca²⁺ and Mg²⁺ ions can lead to equipment wear and increased maintenance costs [64].

The high level of electrical conductivity indicates the presence of a large number of dissolved ions and salts in the water, which can lead to soil salinization. For example, the sodium concentration at the S7 sampling point is 180.05 mg/l, which increases the risk of soil salinization [65]. Although the water quality is rated as satisfactory according to the OIP indicators, the high levels of Ca²⁺, Mg²⁺, Na⁺, and Cl⁻ ions could negatively impact the quality of water resources in the long term.

At the S4 sampling point (OIP = 1.27), the water quality is considered to be at a moderate pollution level. The concentration of Al is 2.42 mg/l, which is the main cause of water contamination. The high level of aluminum contributes to the increase in the OIP value. The presence of high levels of Al in the water ecosystem negatively affects the stability of landscapes [66]. The low electrical conductivity value (555 µS/cm) indicates that the water contains fewer dissolved ions and salts, which is an important factor for agriculture.

At the S1 (OIP = 3.1), S5 (OIP = 2.38), and S6 (OIP = 3.68) sampling points, the water quality indicates a high level of pollution, confirming their classification as Class 4. The concentration of Al is 6.68 mg/l at S1, 5.47 mg/l at S5, and 6.15 mg/l at S6. Total hardness is 10.64 mg-equiv/l at S1, 7.36 mg-equiv/l at S5, and 8.07 mg-equiv/l at S6, with electrical conductivity recorded as 1635 µS/cm, 617 µS/cm, and 825 µS/cm, respectively. These values indicate a high level of mineralization [71].

At S1, the Na⁺ concentration is recorded at 146.35 mg/l, leading to an increase in salinity. The SO₄²⁻ concentration is 639 mg/l at S1, 92.2 mg/l at S5, and 153 mg/l at S6, which are indicators of mineralization.

At the S8 (OIP = 12.14) sampling point, the water quality shows an extreme level of pollution. The Al concentration (22.38 mg/l) is several times higher than the maximum allowable concentration (MAC), which has a toxic effect on the water ecosystem and plants.

The high level of total hardness (10.92 mg-equiv/l) indicates a significantly high degree of mineralization in the water. The elevated Na⁺ concentration (157.42 mg/l) presents a salinity issue.

The Cd concentration (0.001 mg/l) and Co concentration (0.001 mg/l) pose a threat to the water ecosystem, while the Cr concentration (0.003 mg/l) could negatively affect water quality. The Fe concentration (12.76 mg/l) leads to excessive algae growth and a reduction in oxygen levels [71].

The water quality indicators at the sampling points in the Turkestan region reflect the impact of both geological and anthropogenic factors. High concentrations of Al and Fe are the result of natural erosion and industrial waste. The study highlights the need for proper fertilizer use in agriculture, control of industrial pollution, and enhanced anti-erosion measures [72].

3.2.2. Analysis Using Nemerov’s Pollution Index (NPI):

The pollution index (NPI) values for the sampling points show significant variation in the pollution levels of different chemical elements (Table 6). The results for the S8 sampling point indicate very poor water quality, while other points show moderate levels of pollution. The concentration of Al exceeded the threshold at all sampling points, with particularly high levels observed at S1 (NPI = 26) and S8 (NPI = 45). Such high concentrations of Al are harmful to landscapes, agriculture, and human health, presenting a significant risk for water resource use. The high levels of Al indicate a persistent pollution source, which could be due to natural erosion or anthropogenic influences [73].

The concentration of Fe was highest at the S8 sampling point (NPI = 43), indicating ecological hazards associated with the water. The Fe pollution at S5 (NPI = 8) was also notably high, exceeding the standard limits. Excessive Fe can disrupt ecological balance, alter the taste and color of water, and negatively impact living organisms [72].

Total Hardness (CaCO₃) remains within the allowable limits at all sampling points (NPI < 1), indicating that the water quality is good. The pH levels are within the permissible range, meaning the water’s acid-base balance is normal. High electrical conductivity values were recorded at the S1 and S7 sampling points (NPI = 1.6 and 1.7), indicating a high degree of mineralization in the water.

The elevated levels of Al and Fe in the water significantly lower water quality and make it ecologically hazardous. At the S8 sampling point (OIP = 12.14), the water quality was found to be at an extreme level of pollution. The Al concentration (22.38 mg/l) is several times higher than the MAC, which has a toxic effect on the water ecosystem and plants.

The deterioration in water quality is associated with the high concentration of Al, while the high levels of Fe (12.76 mg/l) and total hardness (10.92 mg-equiv/l) indicate a high degree of mineralization. The concentrations of Cd (0.001 mg/l) and Co (0.001 mg/l) can harm the ecosystem, while Cr (0.003 mg/l) has carcinogenic effects.

Both NPI and OIP provide valuable information for assessing water quality, but each reflects pollution levels from different perspectives. NPI highlights the impact of specific parameters (e.g., Al, Fe) on water quality, while OIP describes the overall quality. A comparison of the two indices shows an increase in pollution levels, which helps to understand the factors influencing water quality [74]. It is crucial to take necessary actions for water resource management and to reduce pollution levels.

3.2.3. Heavy Metal Pollution Index (HPI)

The Heavy Metal Pollution Index (HPI) was calculated individually for each sampling point, allowing for a comparison of pollution loads and an assessment of water quality (Table 7). HPI reflects the cumulative effect of various heavy metals. According to the MAC (2016) standards, if the HPI value exceeds 100, the water is considered significantly polluted.

In the study, the HPI values for the S4 and S8 sampling points significantly exceeded 100, reaching 120.38 and 4253.33, respectively. These values indicate high levels of pollution with heavy metals and confirm that the water sources are ecologically and health-wise hazardous to human health [75].

The analyzed data allows for the assessment of heavy metal pollution levels at various points. The HPI indicator clearly distinguishes between high and low levels of pollution.

The S8 sampling point falls under the category of extremely dangerous pollution, with an HPI value of 4253.33, the highest value in the region. This high value is primarily due to the very high concentration of Fe (12.76 mg/l), which is 42 times above the maximum allowable concentration (MAC) level of 0.3 mg/l. Such pollution renders the water source unsuitable for ecological and agricultural purposes. Therefore, urgent measures are needed at the S8 sampling point to address pollution sources and implement water treatment.

At the S4 sampling point, the HPI value is 120.38, indicating a dangerous level of pollution. The concentration of Fe at this point is 12.76 mg/l, significantly exceeding the MAC level. While the pollution level is lower than at S8, this area is still categorized as highly polluted.

At the S1, S2, S3, and S5 sampling points, water quality is good, with HPI values below 50. These water sources are safe for both ecological and agricultural use. At these points, the concentration of heavy metals is at its lowest, which results in good water quality. However, continued monitoring is important to ensure that pollution levels remain under control [77].

At the S8 and S4 sampling points, extremely high levels of pollution were recorded, while S1, S2, and S5 showed moderate pollution, and S3 had the least pollution. At the most polluted points, restrictions on water use should be implemented, and measures to eliminate pollution sources need to be taken. Even in moderately polluted areas, it is important to strengthen monitoring efforts.

Principal Component Analysis (PCA) was conducted based on the correlation matrix for the Turkestan region. The analysis resulted in the identification of four principal components, with eigenvector values above 1, explaining 92.34% of the total variance [78]. Factor loadings above 0.5 were considered significant, and the correlation coefficients between the metals ranged from 0.87 to 1, indicating that the metals were appropriately distributed across the identified factors.

3.3. Land Use Changes and Water Quality

Changes in land use affect the stability of landscapes and directly impact the quality of water resources. The deterioration of water ecosystems poses risks to biodiversity, agricultural productivity, and public health. In the Turkestan region, landscape changes and anthropogenic factors contribute to the decline in water quality. Based on global land cover and land use change data from 2000-2020 obtained from the Landsat archive, the land cover classification in the Turkestan region was divided into the following categories: short vegetation, permanent tree cover, trees after degradation, seasonal water bodies, permanent snow/ice, permanent water, permanent agricultural land, and permanent structures. This map is shown in Figure 4.

The study results showed that the expansion of agriculture and urbanization has caused significant changes in land cover. The excessive use of fertilizers and pesticides in agriculture, increased construction, and the expansion of grazing lands have had negative impacts on water bodies. Soil erosion, chemical pollution, and salinization processes have contributed to the deterioration of water quality.

The Overall Pollution Index (OIP) was used to assess water quality. Water samples were taken from eight points (S1–S8) for analysis. At the S4 sampling point, the OIP was 1.27, indicating that the water was moderately polluted (Class 3). The main cause of pollution was the Al concentration reaching 2.42 mg/l. Other indicators, such as hardness and Na levels, showed lower values and had less impact on the OIP [79,80,81].

At the S8 sampling point, the OIP reached 12.14, indicating an extremely high level of pollution (Class 5). The water contamination at this point is primarily due to the concentration of Al (22.38 mg/l), which is several times higher than the maximum allowable concentration (MAC). Additional indicators showed that total hardness was 10.92 mg-equiv/l, electrical conductivity was 1493 µS/cm, and Na concentration was 157.42 mg/l. These results confirm that the water at the S8 point is unsuitable for ecological and agricultural purposes [82].

To assess the concentration of heavy metals, the Heavy Metal Pollution Index (HPI) was used. The HPI was calculated based on the concentrations of Cd, Co, Cr, and Fe. At the S8 point, the Fe concentration reached 12.76 mg/l, which is significantly higher than the MAC (0.3 mg/l). Such a high level of Fe poses a significant threat to the water ecosystem and makes the water unsafe for use.

Other metals, such as Cd and Cr, were within permissible limits at several points, but it is important to consider their cumulative effects. The study results showed that inefficient land use negatively impacts water resources. The excessive use of fertilizers and chemicals in agriculture contributed to the increase in heavy metals and chemical indicators [83].

The expansion of construction and urbanization has increased the electrical conductivity and hardness of the water, leading to salinization issues. The increase in grazing areas has intensified soil erosion and contributed to the influx of solid waste into the water. The exceedance of these indicators from quality standards was mainly due to domestic, industrial, and agricultural runoff [84,85,86,87,88]. Table 8 shows pollutants that require attention, their sources, impacts, and management methods.

3.4. Indirect Effects of Chemical Pollution Load in Surface Waters on Landscapes

Key water quality indicators (total hardness, Al, electrical conductivity, Na) directly affect soil fertility and vegetation cover. The increase in Al levels (S8: 22.38 mg/l) enhances soil acidity, reducing the quality of land suitable for agriculture. This accelerates soil salinization and degradation processes, increasing the risk of desertification.

The high value of electrical conductivity (1493 µS/cm) leads to the accumulation of Na salts and the disruption of soil structure. This negatively impacts the growth of agricultural crops and weakens ecosystem function. The S8 sampling point indicates that the ecosystem has surpassed its resilience threshold (OIP = 12.14), demonstrating that the water is unsuitable for use [95].

The accumulation of heavy metals, particularly Fe and Cd, accelerates landscape degradation. The Fe concentration at the S8 point reached 12.76 mg/l, causing eutrophication and oxygen depletion in aquatic ecosystems. Although Cd is found in low concentrations (S1, S2, S5: 0.001 mg/l), it can bioaccumulate in living organisms over time.

3.5. Water Pollution’s Impact on Local Hydrological Cycles and Landscapes

Water pollution affects local hydrological cycles and alters the chemical composition of groundwater. The pollution levels at the S4 and S8 sampling points (OIP: 1.27 and 12.14) influence river flows and groundwater levels. These changes may lead to soil swampification or desertification, particularly observed near the S6 sampling point [96].

At S2, water quality (OIP = 0.56) is normal, but long-term monitoring is necessary to prevent future increases in pollution [97]. The Fe concentration at S4 and S8 points (2.42–12.76 mg/l) slows down plant growth and degrades fish habitats. As a result, vegetation along riverbanks becomes sparse, forcing birds and animals to migrate.

The interconnection between water quality and landscapes is a critical factor for the stability of ecosystems in the Turkestan region. High concentrations of heavy metals and physicochemical indicators exacerbate landscape degradation and disrupt ecological balance.

Reducing water pollution through long-term ecological monitoring and implementing restoration strategies is essential to preserve the stability of natural ecosystems and enable adaptation to climate change [99].

3.6. Purification of River Water Contaminated with Heavy Metals

Purifying rivers contaminated with heavy metals is crucial for maintaining ecological balance and protecting public health. Cd, Pb, and Fe are harmful to the environment and biological organisms, causing various diseases when accumulated in the human body. The effectiveness of purification technologies depends on the level of contamination and economic costs. The primary methods used include adsorption, ion exchange, membrane filtration, and advanced oxidation processes (AOPs).

Kosar Hama Aziz and colleagues (2023) [100] found that biochar and zeolite effectively adsorbed heavy metals (Cd, Pb, Cr). Inglezakis V.J. (2003) [101] studied the effectiveness of clinoptilolite in removing heavy metals through ion exchange, but the contamination of the resin made its long-term use difficult. Mashangwa T.D. (2016) [102] investigated the effectiveness of using egg shells as a low-cost adsorbent to remove heavy metals. Roongtanakiat and colleagues (2007) [103] determined the effectiveness of vetiver grass in accumulating Pb, Cd, and Cr. Kidd and Monterroso (2005) [104] studied the hyperaccumulation properties of this plant. Ingole and Bhole (2003) [105] demonstrated the Pb and Cd absorption capabilities of water hyacinth.

Analyzing these studies, we focus on biological methods for heavy metal purification and propose phytoremediation. This method offers an ecologically safe and economically efficient solution by using plants with high effectiveness in accumulating heavy metals.

Phytoremediation is a method of purifying water using plants capable of accumulating heavy metals. Plants such as Populus spp., Tamarix spp., Phragmites australis, Carex spp., Medicago sativa, Lupinus spp., Typha latifolia, and Salix spp. are recommended for use in the riverbanks of the Turkestan region [106]. These plants absorb metals like Fe, Cd, and Zn, and help protect the banks from erosion (Table 8). The use of the rhizofiltration method, where plant roots are directed towards wastewater, can enhance the process of metal absorption. Aquatic plants – whether emergent, floating, or submerged – have unique roots and shoots that increase their ability to absorb substances from their growth environment, helping to reduce the concentration of pollutants in targeted water bodies.

Phytoremediation involves physical, chemical, and biological processes. This method helps reduce heavy metal levels in water ecosystems and restores natural balance.

The phytoremediation process relies on several key mechanisms:

• Accumulation of pollutants (phytoextraction and rhizofiltration),
• Immobilization of pollutants (phytostabilization),
• Biodegradation (rhizodegradation and phytodegradation),
• Dissipation (phytovolatilization).

These mechanisms help limit the spread of pollutants in the environment, promote their degradation, or transform them into a more stable form.

The combination of mechanisms used with macrophytes for the removal and degradation of pollutants primarily depends on the plant species, the properties of the pollutant, and the location of the pollution within the water body (water column, lake, or sediment at the bottom of flowing water).

To assess the potential for phytoremediation, it is necessary to calculate the Bioconcentration Factor (BCF) by comparing the concentration of the pollutant in the plant and the water body. This coefficient is expressed in L/kg and describes the plant’s ability to absorb the pollutant. The phytoremediation mechanisms commonly found in aquatic plants are summarized in Table 9 [110,111,112,113,114].

By applying this method, the potential for comprehensive water purification and ensuring the stability of river ecosystems increases. This approach will not only improve the water quality of the rivers in the Turkestan region but also help maintain ecological balance and allow for the efficient use of water resources. Recycling plant biomass for bioenergy production or using it as fertilizer will enhance economic efficiency. Additionally, utilizing plant species adapted to the local climate will contribute to maintaining the ecosystem’s stability.

In conclusion, the following actions are recommended for the phytoremediation of water resources in the Turkestan region:

• Continuous Water Quality Monitoring: Use sensors to monitor the dynamics of heavy metals.
• Adaptation of Local Plant Species: Utilize plants adapted to the local climatic conditions.
• Recycling Plant Biomass: Use the biomass obtained after purification as a source of bioenergy.
• Conduct Additional Scientific Research: Continue research to find effective solutions adapted to different ecosystems.
• These recommendations will allow for the ecological, safe, and sustainable restoration of the water resources in the Turkestan region.

Limitations of the Study:

Currently, there are several limitations in the methods used to assess water quality, which complicate the practical application of research results. Firstly, assessment methods often focus on individual indicators and do not employ a comprehensive approach. Researchers typically focus on changes in pollutant levels and the physical-chemical properties of water, but fail to provide a holistic evaluation of the overall ecosystem. This approach reduces the effectiveness of project management and remediation measures and hinders the development of accurate solutions. The lack of real-world examples of these methods also limits the applicability of the research. Additionally, the failure to account for the accumulation characteristics of heavy metals in ecosystems remains a major issue. For instance, some metals accumulate in biological tissues and pose a threat to ecosystems and human health. However, more detailed analysis is needed regarding the interactions of metals with each other and other chemicals, as their effects may depend on their interactions. These findings highlight the need to acknowledge the shortcomings in heavy metal monitoring and remediation techniques and emphasize the importance of employing a multidimensional approach to address pollution problems [121,122,123].

Future Research Needs:

Future studies should focus on fully assessing temporal changes by collecting samples across different seasons. This will provide a deeper understanding of the seasonal dynamics of water quality and its impact on landscapes.

Including additional rivers and water sources in the study will help better understand the spatial distribution patterns of water quality. It will also contribute to identifying ecologically vulnerable areas in the region.

Future research should also incorporate the analysis of BOD, nitrates, and phosphates, as they are crucial for evaluating the health of water ecosystems. These data will enhance the understanding of the ecological functions of water bodies.

The use of remote sensing, GIS technologies, and climate models will help determine the connections between water quality, land use, and climate change. This approach will enable a more detailed analysis of the development patterns of landscapes.

Assessing the impact of changes in water quality on biodiversity and ecosystem services will be an important direction for future research. Long-term biological monitoring will help define the irreversible thresholds of changes in ecosystems.

Establishing a sustainable monitoring system will allow for continuous tracking of water quality and landscape changes over time. This will contribute to the effective management of natural resources in the region and the development of scientifically based policies.

Considering these limitations and following the proposed future research directions will deepen the understanding of the interconnection between water quality and landscapes in the Turkestan region, and will contribute to the sustainable development of the region.

4. Conclusions

The comprehensive analysis of surface water quality in the Turkestan region revealed the complexity of the ecological condition of water resources and the significant impact of anthropogenic influences. The analysis using the OIP, NPI, and HPI indices demonstrated that the concentrations of heavy metals, particularly aluminum (Al) and iron (Fe), exceeded the normative limits by several times. At the S8 sampling point, the OIP value reached 12.14, indicating extreme pollution and rendering the water unsuitable for economic use. The accumulation of heavy metals is harmful to ecosystems and leads to the deterioration of water quality. The increase in pollution reflects the combined effect of anthropogenic and natural factors.

The comprehensive analysis results for surface waters in the Turkestan region showed that heavy metals and chemical parameters significantly impacted water quality. The concentrations of aluminum (Al) and iron (Fe) at several sampling points were significantly higher than the permissible limits, posing a serious threat to ecosystem stability. The findings allowed for a comprehensive evaluation of the condition of water resources through the Overall Pollution Index (OIP), Nemerow Pollution Index (NPI), and Heavy Metal Pollution Index (HPI).

The OIP value at the S8 sampling point reached 12.14, indicating that the water was at an extreme level of pollution. At this point, the concentration of aluminum was 22.38 mg/l, and the level of iron was 12.76 mg/l, both of which were several times higher than the permitted limits. These values rendered the water resources unsuitable for both ecological and economic purposes and highlighted the potential for toxic effects on living organisms.

Significant pollution was also recorded at the S1, S5, and S6 sampling points. The concentrations of aluminum were 6.68 mg/l, 5.47 mg/l, and 6.15 mg/l, respectively, which further exacerbated landscape degradation and negatively impacted the functioning of ecosystems. At the S4 sampling point, the OIP value reached 1.27, indicating a moderate level of water pollution, with aluminum concentration at 2.42 mg/l.

The analysis revealed elevated levels of sodium (Na), magnesium (Mg), and sulfates (SO₄²⁻). These indicators increased the mineralization level of the water, heightened the risk of salinization, and adversely affected agricultural activities. The rise in electrical conductivity and total hardness also deteriorated water quality in certain areas, negatively impacting the productivity of irrigation systems and ecosystems.

The HPI index showed that the accumulation of heavy metals, particularly aluminum (Al) and iron (Fe), poses a long-term threat to the ecological stability of the region. The Nemerow Pollution Index (NPI) also confirmed the pollution levels of water quality at certain points, providing evidence that key anthropogenic factors are affecting the water ecosystems.

These comprehensive research findings identified the direct impact of heavy metal-induced pollution on the quality of water resources and the alteration of landscapes in the Turkestan region. Continuous monitoring and effective management measures are essential to ensure water quality and restore ecosystem services.

Human activities in the Turkestan region have had complex indirect effects on water resources and landscapes due to intensive land use. Agriculture, industry, and construction activities have disrupted ecosystem stability and increased the concentrations of heavy metals, particularly aluminum (Al) and iron (Fe). Agricultural runoff and industrial waste have raised levels of nitrates (NO₃⁻) and phosphates (PO₄³⁻), intensifying the process of eutrophication. Industrial waste has caused iron and aluminum levels to exceed permissible limits several times at the S8 sampling point. Construction and urbanization processes have exacerbated soil erosion and water salinization. Landscape changes have slowed down natural processes, increased the vulnerability of ecosystems to anthropogenic impacts, reduced agricultural productivity, and weakened the services provided by aquatic ecosystems.

The study results showed that heavy metal contamination in surface waters of the Turkestan region, including aluminum (Al), iron (Fe), cadmium (Cd), and lead (Pb), poses a significant threat to both ecosystems and human health. High concentrations of heavy metals negatively affect soil fertility, plant productivity, and water quality, accelerating the degradation of ecosystems. The application of phytoremediation has been identified as an effective solution for improving the ecological situation and restoring natural balance.

This study emphasized the relevance of a comprehensive assessment of surface water quality based on multiple indices, multidimensional statistical methods, and geospatial analysis. The results provide a foundation for environmental protection organizations and policymakers to identify pollution sources effectively and make informed decisions to reduce or eliminate them. Furthermore, it highlighted the importance of implementing management strategies promptly to ensure the long-term sustainability of water resources and the preservation of ecosystems.