An Assessment of Water Quality and Pollution Sources in a Source Region of Northwest China

Abstract

China prioritizes ensuring drinking water safety, particularly in the water-scarce northwest region. This study, utilizing water quality data from 52 village and town water sources since August 2022, assesses water quality, with a specific focus on key indicators related to organic pollution sources. This study provides a scientific foundation for enhancing water quality in these sources. Employing category factor analysis for classification and grading, principal component analysis for qualitative analysis of key evaluation indicators, and the absolute principal component linear regression equation for quantitative calculation of pollution sources, this study reveals that all 52 water sources meet quality standards. Principal component analysis categorizes pollution sources as diverse types of organic compounds in surface water. Source analysis calculations highlight decay-type organic substances as major contributors to increased water color and permanganate index, with pollution contribution rates of 54.78% and 31.31%, respectively. Fecal-type organic substances dominate the increase in dissolved total solids and total coliforms, with pollution contribution rates of 56.65% and 40.16%, respectively. Additionally, high-molecular-weight organic substances exhibit lower concentrations in the water. This article presents a systematic water quality assessment methodology, which is used for the first time to qualitatively assess the types of water sources and to quantitatively trace specific sources of organic pollution in source water in northwest China. This systematic study’s results, involving initial assessment followed by traceability, recommend the adoption of a simple contact filtration and disinfection process to enhance water quality in the region.

1. Introduction

China has been focusing on rural drinking water safety, particularly in the northwestern region where water shortage is more severe. Therefore, it is crucial to select appropriate water quality evaluation methods to develop and utilize water resources further. This will help alleviate the contradiction between supply and demand and improve regional water resource allocation and utilization efficiency. Various methods are available for evaluating water quality, including the single-factor evaluation method, the category factor evaluation method, the artificial neural network method, the fuzzy comprehensive evaluation method, Horton’s water quality index method, the mean integrated pollution index, and the Nemero pollution index method [1,2,3]. The category factor evaluation method can provide objective and rational evaluation results, and identify potential risks associated with drinking water sources. This information can be used as a reference for subsequent water source management programs. Principal component analysis (PCA) is a widely used method for qualitative analysis and the determination of pollution sources in aquatic systems. However, it cannot estimate the proportional contribution of pollution sources. To further quantify the effects of different pollution sources, some studies have utilized absolute principal component score (APCS) analysis combined with multiple linear regression (MLR) [4,5,6]. The combination of PCA and APCS-MLR is currently mainly used to analyze air pollution and heavy-metal pollution in water [7]. This method focuses on analyzing point source and surface source pollutants and has not yet been applied to analyze the source partitioning of specific organic matter in surface water.

This paper presents a refined systematic analysis methodology for qualitatively and quantitatively assessing and tracing water quality conditions and pollutant sources in 52 township water sources in the northwest territories. Firstly, the single-factor evaluation method and the category factor evaluation method were used to assess the water quality categories; then, PCA combined with APCS-MLR was used to explore the relationship between specific organic matter pollution sources and water quality indicators; and finally, preliminary strategies to improve water quality were proposed. This approach comprehensively assesses the water quality status of water sources and helps to quickly identify water quality problems. The method is important for improving the assessment system, strengthening pollution source control, and formulating targeted preventive and control measures.

2. Materials and Methods

2.1. An Overview of the Study Area

The study area is situated in the northwestern part of Gansu Province and is a part of Longnan City. In comparison to other cities in Gansu Province, the area is relatively abundant in water resources. It is located in the Sanjiang River Basin and has significant terrain undulations, including deep mountains and ditches. In the past 2–3 years, the study area has experienced severe drought conditions, which has put a lot of pressure on agriculture and water resources [8]. With the guidance of national policies, the study area has intensified the protection of grasslands and forests [9]. Water resource management has also shifted toward a comprehensive model of “source + end”, placing more emphasis on flood prevention, drought resistance, and water source protection [10]. However, the unique geographic environment of the area results in widely distributed rural drinking water source points, which are often scattered and difficult to protect. This has led to challenges in irrigation and domestic water use between the growing water demand and sustainable use of water resources, as well as threats to endemic species that depend on water management.

The water collection process is susceptible to contamination by air pollutants, ground impurities, domestic garbage, and animal feces, posing a significant risk to the safety of the water sources. In addition, seasonal variations have a significant impact on river water quality [11]. Rainfall in the Gansu region is mainly concentrated in the summer, and the large amount of surface runoff may bring various pollutants in the surface soil into the water body, leading to changes in the water quality of the water source. To assess the water quality situation effectively, we selected 52 decentralized water sources in the region as monitoring points for water quality evaluation and pollution source analysis. Figure 1 shows the distribution of monitoring points. This study aims to gain an in-depth understanding of water source pollution and provide a scientific basis for effective protection measures.

2.2. Data Selection

To ensure the accuracy and reliability of test data, the water quality testing center follows the ‘Standard Testing Methods for Drinking Water Quality Analysis of Water Quality Control’ (GB/T5750.3-2006) [12] for sample collection, testing, and data processing, adhering to all quality control requirements. The local key evaluation factors for a number of testing indicators are pH, oxygen consumption, iron, total coliforms, chromaticity temperate, turbidity, and total solids. These factors are based on actual water quality data tested since August 2022 and practical experience. The standard for judging water quality strictly follows the ‘surface water environmental quality standards’ (GB3838-2002) [13]. The data are then compared with the ‘drinking water health standards’ (GB5749-2022) [14] to provide support for subsequent analysis of pollution sources and purification measures, as indicated in

Table 1. Statistical depictions of evaluation factors and grading limits of water quality standards.

Parameters	pH	Oxygen Consumption mg/L	Iron mg/L	Total Coliforms MPN/100 mL	Chromaticity Temperate	Turbidity NTU	Total Solids mg/L
Average value	7.84	1.26	0.06	518.90	4.26	0.78	293.58
(statistics) Standard deviation	0.26	1.29	0.03	705.36	5.36	0.57	155.51
Maximum value	8.52	7.20	0.12	1600.00	27.00	2.73	770.00
Minimum value	7.28	0.10	0.00	2.00	0.00	0.00	1.23
I a	6–9	2	0.3	200
II	6–9	4	0.3	2000
III	6–9	6	0.3	10,000
IV	6–9	10	0.3	20,000
V	6–9	15	0.3	40,000
b	6.5–8.5	5	0.3	not detectable	15	3	1000

Note: a. Environmental Quality Standards for Surface Water (GB3838-2002); b. Health Standards for Drinking Water (GB5749-2006).

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Overall, the drinking water sources in the area are in good hygienic condition. The pH, oxygen consumption, iron, total coliforms, chromaticity, turbidity, and total solids mean values are 7.84, 1.26 mg/L, 0.06 mg/L, 518.90 MPN/100 mL, 4.26 degrees, 0.78 NTU, and 293.58 mg/L, and the mean values of these indicators all meet the Class II water quality standards above, but there are individual areas of physical and chemical indicators that exceed the limit value, affecting the evaluation results. According to the “Health Standards for Drinking Water” (GB5749-2022), the pH, iron, and total dissolved solids indicators have a maximum value lower than the standard limit value, the turbidity maximum value is close to the standard limit value, and the chromaticity, total coliform bacteria, and oxygen consumption maximum values are beyond the standard limit value. The maximum values of color, total coliforms, and the permanganate index are above the standard limits. Therefore, it is necessary to choose a suitable water quality evaluation method and analyze the local pollution sources to provide a reference basis for further treatment of the source water.

2.3. Data Analysis Methods

2.3.1. Single-Factor Evaluation Method

The single-factor evaluation method selects the category of the most seriously polluted indicator in the study area as the final rating of the area [15]. For the indicators involved in this paper, the single-factor index is calculated as follows: single-factor index $I_i=C_i/S_i$, where $C_i$ is the measured concentration of indicator i, and $S_i$ is the limit value of the indicator’s concentration. When the single-factor index of an indicator is less than or equal to 1, this indicates that the indicator meets the requirements of water function zoning; when the indicator exceeds the requirements of water function zoning, the single-factor index is greater than 1.

2.3.2. Category Factor Evaluation Method

The category factor method is the method used by the United States, the European Union, Canada, and other developed countries or institutions as the water quality evaluation method for water sources; combined with the current situation of water quality evaluation for China’s water sources and the need for objective, accurate, and reasonable categories for China’s water quality, the evaluation system for the classification and the development of the [16]. specific evaluation criteria is shown in

Table 2. Classification of standardized properties in category factor approach.

Water Quality Category	Fulfill a Condition	Drinking Water Function
Reach a set standard	All indicators are better than Class III standard values or standard limits	Fulfillment
Basic compliance	“Physical and chemical indicators” or “microbiological indicators” exceeding the limits, but the monitoring results are still within the national standard V water quality	Basic necessity
Non-performance	The appearance of “toxic indicators” exceed the limits, or “physical and chemical indicators” or “microbiological indicators” monitoring results according to the national standard V water quality standards are outside the requirement	Unsatisfactory

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Qualitative evaluation of the water quality of the water source categorizes it into three categories in accordance with the standards set forth: (1) The water quality of the source meets all standards, indicating that all evaluation indicators align with the requirements of Class III surface water quality. (2) The water quality of the source essentially meets the standards, with the possibility of some “physical and chemical indicators” or “microbiological indicators” exceeding the specified limits. However, these exceedances remain within the boundaries of Class V water quality standards. The water treatment process can effectively mitigate these indicators, ensuring they do not pose significant short-term or long-term risks to human health and do not compromise public drinking water safety. (3) The water quality of the source fails to meet the standard, signifying that all monitoring indicators surpass the thresholds of Class V water bodies. The associated drinking water function of these water sources is classified as meeting, partially meeting, or not meeting the standards.

The category factors were organized based on the indicator items analyzed at the water quality testing center in the region, and the classification is presented in

Table 3. Division of water quality evaluation system by category factor evaluation method.

Division of the Indicator System	Examples of Indicators
Physical and chemical indicators	Chromaticity, smell and taste, turbidity, visible to the naked eye, total hardness, total solids, pH, oxygen consumption, total nitrogen, sulfate, chloride, nitrate, iron, manganese
Toxicity indicators	Arsenic, ammonia nitrogen, fluoride, free chlorine, chlorine dioxide
Microbiological indicator	(Total) fecal coliform, total bacteria

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2.3.3. Principal Component Analysis (PCA)

Principal component analysis is a dimensionality reduction technique that identifies the most informative indicators from a large set, ensuring they are uncorrelated [17]. This method preserves the essential information of the original data, simplifying the analysis of complex environmental issues. Principal component analysis is commonly employed in the selection of evaluation indicators and environmental quality assessments [18]. The analysis was conducted using the statistical software SPSS (IBM SPSS Statistics 26).

2.3.4. The APCS-MLR Model

Multivariate statistical methods, including PCA and factor analysis (FA), form the foundation of our approach, aimed at deciphering pollution sources through intricate substance interrelationships in the observed data [19]. In the realm of multivariate statistical analysis, our study utilized principal component analysis/factor analysis (PCA/FA), which constructs “principal components” (PCs) by linearly amalgamating pertinent variables. Subsequently, these variable loadings on the principal components are refined through maximum variance rotation, creating “variable factors” (VFs). This nuanced method enhances data interpretation. Variable factors falling within the ranges of 0.3–0.5, 0.5–0.75, and 0.75–1 are classified as low, medium, and strong, respectively [20].

After conducting PCA, APCS-MLR analysis was carried out using the APCS. In the absolute principal component linear regression equation model, the absolute factor scores serve as the independent variables, while pollutant concentrations act as the dependent variables [21]. This approach enables the quantification of individual pollution sources’ contributions to the overall pollution levels. Through absolute principal component-based pollution source identification, we initially perform principal component extraction of water quality indicators to discern and quantify pollution sources. For detailed calculation steps, please refer to the literature [22].

$${\left(A_Z\right)}_{jk}={\sum }_{j=1}^p{w}_j·z_k$$

(1)

$$z_k={\displaystyle \frac{c_{k-}\overline{c}}{\sigma }}$$

(2)

where ${\left(A_Z\right)}_{jk}$ is the standardized principal component score value, j is the PC serial number, p is the number of PCs, $w_j$ is the jth PC coefficient, $Z_k$ is the standardized pollutant concentration (mg-L⁻¹, hereinafter the same) at the kth monitoring point, $c_k$ is the corresponding pollutant concentration, $\overline{C}$ is the arithmetic mean of the pollutant concentration, and $\sigma$ is the standard deviation.

If you want to calculate the contribution of PCs to pollutants, ${\left(A_Z\right)}_{jk}$ has to transform the standardized values into unstandardized APCS. The calculation method is as follows:

$${APCS}_{jk}={\left(A_Z\right)}_{jk}-{\left(A_o\right)}_j$$

(3)

$${\left(A_o\right)}_j={\sum }_{i=1}^i{S}_{ij}·{{(Z}_o)}_i$$

(4)

$${\left(Z_o\right)}_i={\displaystyle \frac{O-\overline{c}}{\sigma }}$$

(5)

In the equation above, ${\left(A_Z\right)}_{jk}$ corresponds to Equation (1), where i represents the ordinal number of the water chemistry factor, ${\left(A_o\right)}_j$ is the principal component score at a value of zero, $S_{ij}$ is the factor score coefficient, ${{(Z}_o)}_i$ is the pollutant concentration normalized to a value of zero at the observation point, $\overline{c}$ is the arithmetic mean of the pollutant concentration, and $\sigma$ is its standard deviation.

Multiple linear regression analysis was performed to calculate the pollutant contribution, with the actual water quality concentration (C) as the dependent variable and APCS as the independent variable. The regression coefficients were derived to analyze the relationship between the measured concentration of pollutant j, $C_j$, and the pollutant source k (APCS), as illustrated below:

$$c_j={\sum }_k{a}_{kj}·{APCS}_{kj}+b_j$$

(6)

In the above equation, $a_{kj}$ denotes the regression coefficient of source k on pollutant j, $a_{kj}·{APCS}_{kj}$ denotes the contribution of pollutant source k to the pollution indicator concentration $C_j$, and $b_j$ is the constant term of multiple linear regression.

The mean of all samples represents the average contribution of the source, and the constant term b_j of the regression equation is usually considered the contribution of the unidentified source term. The percentage contribution of source k to pollutant j can be calculated by the following equation:

$${PC}_{kj}={\displaystyle \frac{a_{kj·\overline{{APCS}_{kj}}}}{b_j+\sum _k{a}_{kj}·\overline{{APCS}_{kj}}}}$$

(7)

The contribution from unidentified sources is calculated as follows:

$${PC}_{kj}={\displaystyle \frac{b_j}{b_j+\sum _k{a}_{kj}·\overline{{APCS}_{kj}}}}$$

(8)

where $\overline{{APCS}_{kj}}$ is the average absolute principal component factor score of all data for pollutant j.

3. Results

3.1. One-Way Evaluation Method

For the actual research data using the single-factor evaluation method, the evaluation results of the 52 monitoring points in the area are depicted in Figure 2. Taking common physicochemical indicators of surface water, such as the permanganate index, pH, iron, and total coliform, as examples, it is observed that a sampling point of the water body in the area slightly exceeds the permissible limit for the permanganate index. Consequently, the water source is classified as a below Class III water body (Figure 2a).

However, upon closer examination, it is evident that the sampling points for the remaining indicators (Figure 2b–d) all meet the environmental quality standards of surface water of above Class III. This discrepancy highlights the need for a comprehensive assessment of multiple indicators to accurately evaluate the water quality status.

3.2. Category Factor Approach Analysis

The water quality function of the water source is only one requirement for the basic standard, and the water quality category is important for the basic standard; the rest of the points have reached the required water quality category for the standard. But the monitoring sites’ physical and chemical indicators do not exceed the national V water quality standards, according to the “Rural Drinking Water Safety Project Hygiene evaluation of technical rules (for trial implementation)”; it can be determined that the region’s water sources can be appropriately increased by using the contact filtration plus disinfection water purification process and can meet the requirements of water quality, using the category factor method of the standard of basic compliance.

According to the Living Drinking Water Health Standard, GB5749-2006, the monitoring of the existence of drinking water indicators is shown in Figure 3. In all monitoring projects, pH and oxygen consumption exceeded the rate of 1.92%; the maximum was exceeded by 1 and 2.4 times. Chromaticity exceeded the rate of 5.8%, and the maximum was exceeded by 9 times; total coliform exceeded the rate of 100%, and the maximum was exceeded by 16 times. The rest of the indicators did not exceed the standard limits and thus are counted as 0.

3.3. Principal Component Analysis (PCA)

In the context of principal component analysis, the interpretation of the total variance involves ensuring that the eigenvalues of the evaluation factors exceed 1 and that the cumulative contribution of variance approaches 100%. Based on the rules for extracting principal components [23], the analysis results (

Table 4. Principal component loadings, eigenvalues, variance contributions, and cumulative contributions.

Evaluation Factor	Principal Component 1	Principal Component 2	Principal Component 3
pH	0.540	−0.365	−0.568
Oxygen consumption	0.678	0.483	0.167
Total solids	0.054	0.772	0.323
Total coliforms	0.484	0.615	−0.034
Iron	−0.185	−0.493	0.759
Chromaticity	0.769	−0.394	0.130
Turbidity	0.815	−0.316	0.285
Eigenvalue (math.)	2.278	1.840	1.130
Variance explained %	32.536	26.279	16.146
Cumulative %	32.536	58.815	74.962

) indicate the extraction of three principal components.

Based on the results of field research and practical experience analysis, the sampling site is situated in the northwestern mountainous region characterized by developed animal husbandry and dense forests. Organic matter emerges as a significant factor contributing to water quality issues. Therefore, a traceability analysis of principal components focusing on organic matter was conducted.

Organic matter in surface water originates from various sources, including terrestrial organism debris, exogenous organic matter from microbial decomposition, metabolic byproducts of water microorganisms (e.g., bacteria and algae), and endogenous organic matter released from water body sediment [24,25]. Organic matter exerts diverse influences on water body characteristics. Firstly, it notably impacts the optical properties of water bodies, such as color and transparency. Studies have highlighted humic acids in natural dissolved organic matter, derived from plant and animal decay processes, as key contributors to excessive color and turbidity in water bodies [26]. Secondly, organic matter affects the biochemical attributes of water bodies, including dissolved oxygen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total coliforms. Investigations on fecal-type organic matter have demonstrated varying antibiotic transport rates by animal fecal organic matter in purple soils [27], potentially linked to high levels of bacteria like total coliforms and Escherichia coli in animal feces [27]. Thirdly, organic matter influences the ecological aspects of water bodies, encompassing nitrogen, phosphorus, and algae. Algae-secreted mucus and polymer-type organic matter in organic wastewater contribute to algal density growth, leading to increased water body turbidity and total dissolved solids [28]. Notably, physicochemical indicators such as color, turbidity, and total coliforms in the evaluation factors stem from different organic matter types.

The principal component analysis revealed that principal component 1 elucidated 32.536% of the total variance, with the permanganate index, color, and turbidity exhibiting higher factor loadings. This suggests that decaying organic matter elevates dissolved colored and particulate suspended matter in water bodies, resulting in increased color and turbidity, hence denoting principal component 1 as decaying organic matter. Principal component 2, explaining 58.815% of the total variance, featured high factor loadings of total coliforms and total dissolved solids. This association likely arises from animal feces generated in expansive mountainous grazing areas, leading to elevated total coliform levels and increased total dissolved solids, hence termed fecal-type organic matter [29,30]. Principal component 3, explaining 74.962% of the total variance, highlighted iron, total dissolved solids, and turbidity with high factor loadings. Iron, being a metallic element, is not directly linked to organic matter. Field observations revealed trace aquatic plant moss on the local catchment well surface, potentially fostering algae growth and impacting turbidity and total dissolved solids content, thus designating principal component 3 as polymer-type organic matter.

3.4. An Estimation of the Contribution of Pollution Sources

The APCS-MLR model was employed to establish the functional relationship between each pollution source and every water quality indicator. The degree of fit (R²) was utilized to assess the model’s fitting accuracy to the monitoring values, with a fit considered better when R² > 0.5 [31]. Achieving an R² value greater than 0.5 indicated a good fit. Following the PCA, the pollution source characteristics were identified. Subsequently, the absolute principal component linear regression equation was utilized to calculate the contribution rates of the pollution sources. The calculation results are detailed in

Table 5. Pollution contribution of physical and chemical indicators in northwestern site.

Evaluation Factor	Principal Component 1	Principal Component 2	Principal Component 3	Unknown Origin	R2
	Animal and Plant Spoilage	Animal and Plant Feces	Hydrophobic Organic Substance
pH	2.69%	9.52%	0.27%	87.52%	0.731
Oxygen consumption	31.31%	28.33%	13.44%	26.93%	0.703
Total solids	5.07%	56.65%	1.43%	36.85%	0.845
Total coliforms	5.77%	40.16%	7.87%	46.19%	0.589
Iron	65.94%	7.72%	0.33%	26.01%	0.749
Chromaticity	54.78%	15.74%	1.97%	27.51%	0.836
Turbidity	17.03%	31.81%	11.04%	60.85%	0.685

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Based on the data presented in Table 5, the following conclusions can be derived:

Principal component 1 signifies that the primary source of pollution in surface water bodies stems from decaying organic matter, impacting water bodies predominantly in terms of color and the permanganate index, with pollution contributions quantified at 54.78% and 31.31%, respectively. This observation suggests the presence of humic substances in the water body, which absorb and scatter light while consuming oxygen, leading to an elevation in the permanganate index. Although iron levels are relatively elevated, they are not typically directly linked to organic matter. Nonetheless, dissolved iron can form complexes with organic matter, thereby influencing organic matter behavior.

Principal component 2 indicates that the predominant source of pollution in surface waters originates from fecal organic matter, primarily influenced by total dissolved solids, total coliforms, and turbidity, with pollution contributions quantified at 56.65%, 40.16%, and 31.81%, respectively. This finding suggests that nearly half of the total coliform content arises from plant and animal feces.

Principal component 3 reveals that pollution in surface water bodies primarily arises from polymer-type organic matter, influenced by total dissolved solids and turbidity, with pollution contributions quantified at 1.43% and 11.04%, respectively. This indicates a relatively low content of HOMs in water bodies.

4. Discussion of Correlation Analysis and Water Quality Enhancement Techniques

Spearman’s rank correlation coefficient is employed for linear correlation analysis by considering the rank order of variables, aiming to assess the strength of the relationship between two variables. By integrating the outcomes of the correlation analysis with the distinctive attributes of the representative substances associated with each principal component, valuable insights can be gleaned to propose initial strategies for enhancing water quality [32].

In Figure 4, the permanganate index exhibits a notable positive correlation with color, turbidity, and total coliforms. Through the discussion on the principal component analysis, it is elucidated that humic acid, the representative substance in principal component 1, plays a pivotal role in influencing the permanganate index, color, and turbidity. Conventional domestic drinking water treatment processes encompass coagulation, precipitation, filtration, and disinfection [33,34].

A significant positive correlation between the permanganate index and total coliforms, along with a significant negative correlation with iron, is observed. Table 5 highlights that principal component 2 contributes substantially to total coliforms, while total dissolved solids exhibit weak associations with other factors. The contribution of principal component 3 to all evaluation factors, including total dissolved solids, remains relatively low. This indicates that principal component 3, potentially originating from algae released in the water column, does not significantly contribute to water quality issues in the region.

The predominant sources of organic pollutants in the area are primarily attributed to spoilage-type and fecal-type organic matter, posing risks of exceeding the color, turbidity, permanganate index, and total coliform levels in the water body, as noted by Lucas A.T et al. [35,36,37]. Among the current drinking water treatment processes, coagulation, sedimentation, and filtration can reduce the turbidity, chromaticity, and organic content of water, while disinfection processes can effectively remove microorganisms such as coliforms from water [38,39,40], as shown in

Table 6. Water quality enhancement technologies for drinking water sources.

Type of Technologies	Removal Indicators	Advantages	Disadvantages	Reference
Coagulation and sedimentation	Turbidity	1. High efficiency 2. Simple operation 3. Low cost	1. Coagulant residues 2. Sludge generation 3. Limited microbiological treatment	[38]
	Chromaticity
	Total solids
Sand filtration	Turbidity	1. Simple operation 2. No chemical additives 3. Low cost	1. Limited removal of dissolved contaminants 2. Requires pre-processing 3. Requires regular cleaning	[39]
	Chromaticity
	Total solids
Ultraviolet disinfection	Total coliforms	1. Efficient inactivation 2. Flexible operation 3. No chemical additives	1. Electricity supply 2. Higher maintenance cost 3. Biological impact	[40]

. The utilization of slow sand filters in conjunction with ultraviolet disinfection for surface water sterilization and turbidity removal demonstrates substantial treatment efficacy, and is particularly suitable for practical implementation in economically underdeveloped regions. In addressing such challenges, UV disinfection can be employed to enhance water quality and disinfection while synergistically eliminating organic matter. The combination of filtration and disinfection emerges as a viable approach to ameliorate water quality in the area, aligning with the insights derived from the category factor analysis.

5. Conclusions

This study employed a rational water quality evaluation method to ascertain the nature of water sources by analyzing monitoring sample data from 52 village and town water sources in a northwestern region through a comparative analysis approach. Furthermore, the current study qualitatively and quantitatively delved into a comprehensive analysis of pollutant sources by leveraging principal component analysis and the APCS-MLR model, respectively [41,42]. Subsequently, a tailored water treatment plan was proposed, and the following conclusions were drawn based on the acquired data:

• The water quality evaluation results indicated that the single-factor evaluation method categorized the water source as below the Class III water body standard, whereas the category factor method deemed the water source to largely meet the standard. In accordance with the “Rural Drinking Water Safety Project Hygienic Evaluation Technical Rules (Trial)”, the water source could be enhanced by using the contact filtration plus disinfection water purification process to align with water quality requirements, consistent with the category factor method’s assessment. These rules are congruent with the category factor method.
• Through the principal component analysis for clustering, this study identified the primary sources of organic pollutants in the water body within the region as predominantly corrosive organic matter, fecal organic matter, and macromolecular organic matter.
• By integrating correlation analysis with the quantification outcomes from the APCS-MLR model, it was discerned that the region’s pollutant sources were primarily influenced by spoilage-type organic matter and fecal-type organic matter.

This study offers valuable insights for water quality managers and policymakers to comprehensively grasp the key pollution sources under spatial conditions, enabling the prioritization of water quality enhancement initiatives for pollution control and sustainable development in northwestern China. The recommendation of employing a filtration plus disinfection process serves as a viable solution to address water quality challenges in the region [43]. However, such source identification methods may not be accurate when pollution data from different times and locations exist, or when there is no obvious correlation between the data. Moreover, such methods mainly analyze static data sets and are less sensitive to dynamically changing system responses.

In the future, we can validate the assessed pollution sources and apply this systematic evaluation method to complete the water quality evaluation work and pollution source analysis work of the same type of water source. In addition, by combining the method with hydrological models of lakes and rivers [44] and machine learning technology, it is possible not only to simulate changes in water quality, but also to predict and assess the impact of various human activities on the quality of water sources. Rational utilization and protection of water resources can be achieved through hierarchical management of water sources by the government, intensive research on actual pollution problems in water sources by academia, the implementation of targeted solutions for pollution sources by the industry, and active participation of the public in the protection of water sources.