A Comparative Assessment of Surface Water Quality in Lake Yuriria, Guanajuato, Using the Water Quality Index

Abstract

The pollution of water bodies has deteriorated the quality of freshwater and the health of the natural ecosystem. In the present study, the water quality index (WQI) was used to evaluate the spatial and temporal contamination levels in Lake Yuriria, Guanajuato, Mexico. Water quality was monitored at 27 different locations (monitoring points) in the dry season (April) and after the rainy season (November), measuring 21 physicochemical water parameters, 2 biological parameters, and 19 metal concentrations. The data analysis revealed that Yuriria Lake is a eutrophic water body. Six monitoring points exhibited a poor WQI (25–50) in April, and seven monitoring sites were classified as having poor water quality in November. The remaining monitoring points showed a WQI categorized as fair (51–70) in both periods. The present study analyzes an extensive distribution of monitoring points over the lake’s surface in two periods, showing a significant spatial and temporal representation of water quality. In addition, the major pollution sources identified include agricultural runoff and effluents from a nearby waterway and freshwater river. Finally, the key physicochemical parameters that determined the water quality were identified. BOD₅, NH₄⁺, P, orthophosphates, DO, conductivity, TSS, and color were linked to anthropogenic pollution sources, and Li, Ni, Zn, Cd, Ba, and Pb concentrations were linked to natural contamination sources. This study demonstrates the utility and versatility of these methodologies in water quality research, and it is the first spatial and temporal WQI analysis of Yuriria Lake.

1. Introduction

Accelerated population growth is altering the quality of freshwater bodies and the balance of natural ecosystems. The preservation of surface freshwater reservoirs is essential, as they provide numerous benefits, including in domestic use, irrigation, tourism, recreation, and functioning as ecosystems for aquatic life [1,2]. Consequently, freshwater pollution in surface bodies represents a global public health issue [3,4,5].

Anthropogenic activities, primarily industrial and agricultural, are the main contributors to the degradation of aquatic systems [5,6,7,8]. Furthermore, water quality is affected by natural processes such as soil erosion and weathering, although human activities play a significant role [5,9,10,11]. Given the increasing presence of contaminants, the study of these systems has gained considerable attention [12]. Among the wide range of contaminants, metals are considered persistent pollutants in natural aquatic systems due to their stability and long residence time [5,13]. In research carried out by Kumar et al. [13], information was compiled from a database that covered 233 articles published between 2010 and 2022 to analyze the presence of heavy metals in different bodies of water in Asia, Africa, Europe, North America, and Central America. The results showed that several metals (Fe, Pb, Co, Ni, Cd, Cr, and Hg) exceeded the limits that the World Health Organization established for drinking water in all the regions evaluated.

Lake Yuriria, Guanajuato, is an example of a freshwater body currently under severe environmental stress. It is located within the Lerma–Chapala–Santiago basin, an area characterized by high levels of pollution that directly affect water quality. The lake is in a Natural Protected Area, categorized for ecological restoration since 2001. In 2004, it was included in the Ramsar Convention’s list of wetlands of international importance due to its role as part of a network of wetlands in central Mexico. It supports vulnerable species, both resident and migratory birds, and functions as a microclimate regulator for the region. The water body provides key ecosystem services, supporting fishing, recreational activities, flood mitigation, and irrigation for District 011, which fosters local and regional socioeconomic development [14].

The lake of Yuriria is located within a Protected Natural Area in Guanajuato and constitutes an ecosystem of significant ecological importance, hosting remarkable biodiversity that includes 189 plant species. This biological richness coexists with diverse land uses characteristic of the lake’s zone of influence. The vegetation found in the body of water includes with species such as cattails (Typha domingensis) and water hyacinth (Eichhornia crassipes), along with xerophytic scrublands dominated by huisache (Acacia farnesiana) and mesquite (Prosopis laevigata) [15].

However, the landscape is significantly shaped by intensive agricultural activities (primarily maize and sorghum crops) covering approximately 60% of the area [16]. This biodiversity–human activity interface has generated environmental challenges including wetland area reduction due to agrochemical pollution and water hyacinth proliferation, which compromises water quality and local biodiversity [17].

The presence of numerous endemic and protected species, coupled with the ecosystem services it provides, makes Laguna de Yuriria an exceptionally valuable yet vulnerable natural space [18]. This necessitates sustainable management strategies to preserve its fundamental ecological characteristics and maintain its crucial role in regional biodiversity conservation.

The water body is currently infested with aquatic weeds, primarily water hyacinth (Eichhornia crassipes). The Sustainability Office of the Yuriria Municipality and the state government have implemented integrated management strategies to mitigate ecological impacts on the aquatic ecosystem. Considering the lake’s regional importance and benefits, it is imperative to implement monitoring programs to assess spatial and temporal variations in water quality parameters. Such programs enable a reliable and representative estimation of the lake’s current water quality status, supporting the effective management and conservation of this critical water resource.

Several tools, such as the water quality index (WQI), allow a spatial and temporal assessment of contamination in freshwater systems. These methodologies aim to facilitate water quality management by gathering and analyzing large datasets, exhibiting significant challenges in interpretation and evaluation [11]. Numerous studies have employed WQI models to assess historical variations in water quality across a variety of water bodies—including rivers, lakes, streams, canals, wells, dams, and reservoirs—in different regions around the world [2,11]. For example, Alobaidy et al. [19] applied the WQI to Lake Dokan, located in the Kurdistan Region of Iraq, using ten water quality parameters (pH, dissolved oxygen, turbidity, conductivity, hardness, alkalinity, sodium, biochemical oxygen demand, nitrate, and nitrite). In another study, Sutadian et al. [20] employed a 13-parameter WQI to assess water quality in rivers in West Java, Indonesia. In the case of Mexico, several relevant studies can be highlighted. For example, Sedeño-Díaz and López-López [21] assessed the spatial and temporal variations in water quality in the Lerma River basin over a 25-year period, using a weighted and multiplicative WQI along with principal component analysis. Another study, conducted by Rubio-Arias et al. [22], developed a WQI to evaluate the water quality of the Luis L. León Dam, located in the state of Chihuahua, Mexico. Their study included eleven parameters: pH, electrical conductivity, dissolved oxygen, color, turbidity, ammonia nitrogen, fluorides, chlorides, sulfates, total solids, and phosphorus. All these studies demonstrated the feasibility of using the WQI to assess water quality, highlighting its importance in guiding preventive or corrective actions to restore polluted aquatic sites.

Particularly, the 23 WQI and 10 Pollution Index (PI) models are commonly used in studies assessing water quality and pollution levels. These models follow a four-step methodology: selecting an appropriate set of quality parameters, determining sub-indices for the parameters, assigning weights to all of them, and applying a function to calculate the quality index. When selecting a model, several criteria should be considered, including the natural properties of the parameters, the intended use of the water, the environmental importance of the parameters, and the extent to which quality is guaranteed [2].

Continuous water quality evaluation is crucial for monitoring and predicting pollution in water bodies, as well as for providing information to support sustainable planning for water resource use [23]. Accordingly, the objective of this study was to assess the current water quality status of Lake Yuriria, Guanajuato, using the WQI model during two monitoring seasons. This evaluation seeks to support the effective management and conservation of this essential water resource.

2. Materials and Methods

2.1. Study Site

Lake Yuriria, Guanajuato, Mexico, is located at 20°13′00″–20°17′20″ N and 101°12′30″–101°03′35″ W, in the Lerma–Chapala–Santiago hydrological region, at an altitude of 1740 m above sea level. Lake Yuriria is an artificial reservoir constructed in 1548 by Fray Diego Chávez y Alvarado, representing the first hydraulic engineering work in colonial Latin America. The water body measures 14 km long by 6 km wide, with a surface area of 60 km² and an average depth of 3.2 m. It has an average storage capacity of 130 million m³ (Figure 1).

The lake’s main water inputs are the Lerma River and La Cinta Canal, which originates from Lake Cuitzeo. The predominant climate in the area is warm and subhumid with summer rainfall, featuring an average temperature ranging from 18 to 20 °C and an annual precipitation of 700 to 800 mm. The dominant soil type is vertisol, and the surrounding vegetation is primarily dedicated to agricultural activities.

2.2. Sampling and Parameter Analysis

Sampling was conducted using a gasoline-powered motorboat. During sample collection, an approximate 3 min waiting period was observed to allow surface water stabilization and avoid disturbance-induced alterations. The monitoring points were established using a systematic square grid of 1.5 km, optimized for the lake’s scale and adjusted for Eichhornia crassipes presence. This design was based on spatial representativeness criteria [24]. Transects were georeferenced and field-validated, ensuring coverage of both littoral zones (higher anthropogenic impact) and pelagic areas. Two sampling campaigns were conducted: the first in April 2018, during which 25 samples were collected, and the second in November 2018, with 27 samples collected (Figure 1). During both campaigns and at all sampling sites, duplicate surface water samples were collected in 500 mL plastic containers for physicochemical parameter analysis. Additionally, samples were collected in Whirl-Pak plastic bags to detect coliform organisms. The samples were stored under refrigeration at 4 °C until subsequent analysis.

2.3. Field Parameter Analysis

The parameters measured in situ at each monitoring point included depth (m), temperature (°C), pH, electrical conductivity (µS/cm), and total dissolved solids (TDS) in mg/L. Except for depth, these parameters were analyzed using a portable HANNA pH meter. Dissolved oxygen (DO) in mg/L was measured with a HANNA HI 9146 dissolved oxygen meter (HANNA MEX). All measurements were performed in duplicate.

2.4. Parameter Analysis

In the laboratory, duplicate determinations were performed for the concentrations of nitrites, nitrates, ammonium, sulfates, phosphates, total phosphorus, and total chlorine using a HANNA HI 83099 multiparameter photometer (HANNA MEX), following the procedures established by APHA 2005 methods. Additional parameters measured included color, total suspended solids, hardness, alkalinity, total nitrogen, orthophosphates, biochemical oxygen demand, and chemical oxygen demand. Furthermore, the most probable number of total coliforms and fecal coliforms was determined following APHA 2005 methods [25].

Finally, the concentrations of lithium, beryllium, magnesium, potassium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, gallium, total arsenic, selenium, silver, cadmium, barium, lead, and uranium in µg/L were analyzed using inductively coupled plasma mass spectrometry (ICP-MS).

2.5. Water Quality Index Analysis

Water quality analysis for optimal conditions is based on a maximum value of 100, which decreases as pollution levels increase. To quantify the WQI (water quality index) value at a monitoring point, the analysis of nine parameters is required: fecal coliforms, pH, BOD₅, nitrates, phosphates, temperature, turbidity, total dissolved solids, and dissolved oxygen.

The calculation of the WQI involves a multiplicative and weighted technique, incorporating specific weight assignments as proposed and adapted by Brown in 1965. Mathematically, the Brown index is calculated using a linear summation of the sub-indices, expressed as follows [2,11]:

$$ICA={\displaystyle \sum }_{i=1}^9\left(Su{b}_i\ast w_i\right)$$

The value of Sub_i is determined by using predefined graphs for each of the nine parameters, based on their respective concentrations. The final value is multiplied by the predetermined weighted factor w_i, which is provided in the

Table 1. Weighting of sub-indicators for calculating WQI.

i	Subi	wi
1	Fecal coliforms	0.15
2	pH	0.12
3	BOD5	0.10
4	Nitrates	0.10
5	Phosphates	0.10
6	Temperature change	0.10
7	Turbidity	0.08
8	Total dissolved solids	0.08
9	Dissolved oxygen	0.17

. Subsequently, a summation is performed. The resulting value, ranging from 0 to 100, indicates the water quality [11].

Water quality is determined based on the WQI value obtained, which can be classified as shown in the

Table 2. Water quality index (WQI) classification.

WQI Range	Classification	Color
(0 < WQI ≤ 25)	Very poor	Black
(26 < WQI ≤ 50)	Poor	Red
(51 < WQI ≤ 70)	Regular	Yellow
(71 < WQI ≤ 90)	Good	Green
(91 < WQI ≤ 100)	Excellent	Blue

[11].

2.6. Statistical Analysis of Water Quality Parameters

The nine parameters used for the water quality index (WQI) analysis in Lake Yuriria were subjected to ANOVA to identify statistically significant differences demonstrating water quality variations between monitoring periods.

3. Results and Discussion

3.1. Physicochemical Parameters of Water in the Lake

Table 3. Descriptive statistics of the physicochemical water parameters analyzed in Lake Yuriria during two sampling seasons.

Parameters	April (n = 25)		November (n = 27)		WHO 2022 1
	Mean ± SD	Min–Max	Mean ± SD	Min–Max
Depth (m)	1.8 ± 0.4	1.0–2.1	3.5 ± 0.4	2.5–4.0	-
Temperature (°C)	25.8 ± 1.6	22.7–28.2	22.4 ± 2.1	20.0–29.9	25
pH	7.7 ± 0.2	7.3–8.0	7.6 ± 0.3	7.1–8.1	6.5-8.5
Conductivity (µS/cm)	678.0 ± 15.0	650.0–700.0	387.4 ± 37.2	310.0–440.0	750
TDS (mg/L)	334.8 ± 7.7	320.0–350.0	192.6 ± 17.9	160.0–220.0	500
DO (mg/L)	2.7 ± 1.5	0.2–5.1	4.8 ± 1.6	2.3–10.1	5-8
Color (Units)	515.6 ± 148.1	311.1–1014.8	503.8 ± 298.2	274.1–1922.2	-
Turbidity (UTN)	34.6 ± 10.1	17.1–61.0	26.5 ± 7.0	16.8–53.5	5
Hardness (mg/L)	196.8 ± 3.5	192.0–203.0	72.1 ± 11.6	49.0–93.0	-
Alkalinity (mg/L)	304.4 ± 7.3	294.0–324.0	36.7 ± 4.4	25.0–44.0	-
TSS (mg/L)	56.1 ± 2.4	52.5–62.5	55.6 ± 28.9	50.0–200.0	-
Cl− (mg/L)	0.05 ± 0.04	0.01–0.2	0.01 ± 0.02	ND–0.1	250
NH4+ (mg/L)	0.3 ± 0.1	0.2–0.5	0.2 ± 0.1	ND–0.6	-
NO2− (mg/L)	0.01 ± 0.01	ND–0.02	0.01 ± 0.01	ND–0.1	-
NO3− (mg/L)	ND ± ND	ND–ND	ND ± ND	ND–ND	45
N (mg/L)	2.0 ± 0.1	2.0–2.4	2.0 ± 0.0	2.0–2.0	-
P (mg/L)	0.7 ± 0.2	0.3–1.3	2.9 ± 4.7	ND–15.0	-
PO43− (mg/L)	2.1 ± 0.7	0.7–3.8	8.8 ± 14.5	0.1–46.0	-
Orthophosphates (mg/L)	1.8 ± 0.2	1.6–2.5	0.4 ± 0.1	0.3–0.6	-
SO42− (mg/L)	29.9 ± 2.5	25.0–35.0	25.7 ± 6.0	10.0–40.0	250
BOD5 (mg/L)	29.7 ± 9.7	16.4–46.9	23.4 ± 21.2	8.0–116.2	5
COD (mg/L)	55.8 ± 11.38	37.4–84.2	47.0 ± 38.9	9.4–187.2	3
TC (NMP/100 mL)	2400.0 ± 0.0	2400.0–2400.0	1607.7 ± 1084.9	15.0–2400.0	-
FC (NMP/100 mL)	320.8 ± 784.6	9.0–2400.0	1550.8 ± 1089.6	7.0–2400.0	2500

Notes: ND: not detected. ¹ [26].

summarizes the average value, standard deviation, and minimum and maximum values for each parameter analyzed across the monitoring points during both sampling seasons.

The pH value serves as an indicator of water quality by reflecting acidity or alkalinity levels. The recorded average pH was 7.7 during the dry season and 7.6 in the post-rainy season, with minimum and maximum values of 7.3 and 8.1, respectively, indicating compliance with the 6–9 range set by NOM-001-SEMARNAT-2021. Similar pH values have been reported in studies of water bodies [27].

The parameters showing the highest average values were conductivity, alkalinity, hardness, turbidity, sulfates, BOD₅, COD, and total coliforms in April. These concentrations are comparable to those reported by Espinal et al. [14]. Increased organic matter presence currently correlates with population growth and a rise in untreated wastewater discharges. This is evidenced by elevated TDS and BOD₅ concentrations, similar to the findings in [28].

Regarding phosphorus (P) levels, 94% of concentrations exceeded 0.3 mg/L, a threshold for classifying a water body as eutrophic [14]. NOM-001-SEMARNAT-2021 establishes a permissible daily average limit of 5 mg/L, with 90.38% of samples below this limit.

For dissolved oxygen (DO), 26% of measurements were below the optimal 5 mg/L, similar to the values reported by Sheela et al. and Iqbal et al. [29,30]. Espinal et al. reported that Yuriria Lake previously lacked water hyacinth, leading to higher oxygen levels [14]. Currently, the increased presence of water hyacinth is associated with reduced DO concentrations, particularly during the dry season. However, during the post-rainy season (November), DO levels improved, likely influenced by rainfall. Suspended solids (SSs) also contributed to low oxygen levels, with all samples exceeding the 20 mg/L daily average limit set by NOM-001-SEMARNAT-2021 (Table 3).

Significant concentrations of SO₄²⁻, NH₄⁺, and NO₂⁻ were detected, as reported by Razmkhah et al. [31]. These compounds primarily enter the lake through runoff from surrounding agricultural fields. Agriculture is the dominant activity in Yuriria, and its prevalence near the lake makes it a major source of these compounds, alongside untreated domestic wastewater discharges and canal effluents. Sulfate concentrations ranged from 10 to 40 mg/L, ammonium from 0 to 0.56 mg/L, and nitrites from 0 to 0.06 mg/L (Table 3).

In Rawal Lake, metals such as Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Pb, Sr, and Zn were analyzed, with K, Li, Mg, and Mn being more concentrated than in Yuriria Lake. In this study, Mg and K showed the highest concentrations at all monitoring points during both sampling periods. This is attributed to runoff from surrounding agricultural fields, as this activity is predominant in the area. Figure 1 illustrates the land use surrounding the lake and the Puquichapio, Cienega, La Cinta, and Laurel canals, as well as the Lerma River diversion, contributing to mineral input into Yuriria Lake [30].

3.2. Metals in the Lake Water

Table 4. Descriptive statistics of the metals analyzed in Lake Yuriria during two sampling seasons.

Parameters (µg/L)	April (n = 25)		November (n = 27)
	Mean ± SD	Min–Max	Mean ± SD	Min–Max
Li	14.3 ± 12.2	ND–40.5	7.6 ± 1.6	6.0–11.5
Be	0.05 ± 0.02	0.02–0.08	0.05 ± 0.02	0.02–0.14
Mg	18,856.7 ± 1192.4	16,188.6–20,698.5	9542.2 ± 1239.9	7356.8–11654.9
K	26,133.2 ± 715.0	24,801.0–27,439.5	15,839.0 ± 1482.1	12,325.6–18,159.4
V	3.5 ± 0.9	2.0–5.6	4.1 ± 1.1	2.7–8.2
Cr	0.4 ± 0.3	0.2–1.9	0.4 ± 0.6	0.1–3.5
Mn	46.0 ± 21.3	14.9–128.3	29.2 ± 14.8	15.0–78.0
Co	0.3 ± 0.1	0.2–0.5	0.2 ± 0.1	0.2–0.6
Ni	2.1 ± 1.1	1.0–5.5	2.0 ± 1.0	1.0–4.6
Cu	2.5 ± 3.0	0.7–15.4	1.6 ± 1.5	0.6–7.1
Zn	9.8 ± 9.1	ND–27.7	8.6 ± 9.8	ND–39.4
Ga	0.04 ± 0.03	0.01–0.10	0.02 ± 0.02	0.01–0.09
As	6.3 ± 0.3	5.5–6.6	3.3 ± 0.5	2.1–3.9
Se	4.2 ± 0.8	2.7–5.4	1.5 ± 0.3	1.0–2.2
Ag	0.02 ± 0.01	0.01–0.03	0.01 ± 0.01	ND–0.03
Cd	0.1 ± 0.1	ND–0.4	0.1 ± 0.2	ND–0.7
Ba	222.5 ± 11.6	204.4–263.9	130.3 ± 12.2	102.9–160.6
Pb	2.0 ± 2.5	ND–7.9	2.4 ± 3.2	ND–13.0
U	0.3 ± 0.04	0.2–0.4	0.2 ± 0.03	0.2–0.3

Note: ND: not detected.

presents the results for metals analyzed during both monitoring periods. A total of 19 metals were measured, with Mg and K showing the highest concentrations. A study in Mangla Lake, Pakistan, reported higher concentrations of Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn [5] compared to this study.

3.3. Water Quality Index (WQI)

The WQI calculation classified water quality across monitoring points during both seasons. Figure 2a shows the WQI values for April, with P3 = 48.16, P6 = 45.35, P10 = 46.10, P20 = 44.31, P22 = 49.24, and P25 = 48.73, categorized as poor (red). For November, poor quality values were observed at P9 = 48.42, P11 = 47.62, P12 = 42.52, P13 = 46.57, P14 = 48.86, P19 = 47.25, and P21 = 49.38 (Figure 2b). The remaining points for both periods were classified as fair (yellow).

In April, the average WQI was 53.80 with a standard deviation of 5.46, while in November, the average was 56.21 with a standard deviation of 7.12. These values indicate fair water quality in Yuriria Lake, with a slight improvement in the second monitoring period, likely due to increased water levels from rainfall.

Braga et al. 2022 performed an analysis of the water quality index in two rivers, with their WQI results resembling those found in the present study [32]. It is noteworthy that the mentioned study included thermotolerant coliforms, whereas this parameter was not analyzed in the present WQI calculation.

On the other hand, Zanor and collaborators in 2023 [33] reported a study where they analyzed various physicochemical parameters of the water in Lake Yuriria, as well as analyzing the water quality index. In the present study, the high prevalence of TDS was found at an average of 393.83 mg/L, with average DO levels of 3.86 mg/L. In addition, they classified water quality indices as good for agricultural irrigation; on the other hand, there were low or poor quality indices for the preservation of aquatic life. The concentration levels of the parameters they report are highly similar to those shown and presented in this study, denoting that the pollution problem prevails. With low quality index values that show no variation.

With the results and the statistical analysis carried out, it was concluded that the quality of the water in the lake is severely altered, mainly by human activities such as runoff from nearby agricultural activity and wastewater that flows to the lake, both domestic and industrial. The study showed openness to establishing proposals and strategies for the monitoring and adequate management of the body of water.

Figure 3 highlights areas with poor water quality in Yuriria Lake, marked in red. In April, the poorest quality was observed in the eastern and northern zones due to contributions from the Lerma River diversion. In November, poor quality persisted in the eastern zones but showed slightly lower contamination levels. Additionally, the western area displayed low WQI values, attributed to contamination from the Cienega and La Cinta canals. For the present study, the monitoring points marked in red correspond to areas exhibiting a low water quality index due to elevated concentrations of the nine physicochemical and biological parameters used in the assessment. This degradation is attributed to water movement within the lake, as well as the influx of anthropogenic point-source pollution, primarily domestic wastewater discharges.

The lake’s water quality is compromised by multiple pollution sources associated with human activities, primarily intensive agricultural operations along its eastern and western margins, where irrigated agriculture predominates. This land use facilitates the generation of surface runoff laden with fertilizers and agrochemicals, constituting a diffuse non-point pollution source. These findings align with reports by Kumar et al. [34] and Subramaniam et al. [1], who identified agricultural runoff as a major pathway for nutrient and contaminant influx into lacustrine ecosystems, contributing to eutrophication.

Furthermore, as shown in Figure 1, runoff systems are also utilized by rural communities (Cahuageo, La Angostura, Los Tepetates, Cuadrilla de Andaracua, El Granjenal) and Yuriria city to discharge wastewater directly into the lake. Field surveys confirmed multiple raw-sewage discharge points along the shoreline, exacerbating organic and microbiological loading and further degrading water quality. This combination of agricultural runoff and untreated domestic discharges reflects a pattern of anthropogenic pressure on the lacustrine ecosystem, mirroring findings from other studies linking water quality deterioration to land use changes and inadequate liquid waste management in agricultural watersheds.

To address this issue, key mitigation strategies include implementing wastewater treatment plants in adjacent communities to prevent further graywater discharge into the lake. Currently, only 13% of the existing treatment plants are operational.

These red pollution hotspots spatially identify areas with critically low water quality indices, highlighting the urgent need to implement strategies for lake water quality preservation.

In a study conducted in Lake Yuriria, where the water quality index (WQI) was analyzed, highly consistent results were found, with low quality indices. The study concluded that the water is suitable for agricultural use but unfit for the preservation of aquatic life. Additionally, point and diffuse pollution sources were detected, confirming that nutrient enrichment in the water body stems largely from anthropogenic activities, particularly urban and agricultural runoff. Previous findings on pollutant inflows were validated by our current study.

3.4. Statistical Analysis

A one-way ANOVA of the nine water quality parameters (

Table 5. Statistical analysis (ANOVA).

Parameters	p-Value
Fecal coliforms	2.55 × 10−5 *
pH	0.04 *
BOD5	0.23
Nitrates	0.36
Phosphates	0.02 *
Temperature change	3.47 × 10−8 *
Turbidity	0.001 *
Total dissolved solids	7.95 × 10−38 **
Dissolved oxygen	2.06 × 10−5 *

Notes: *, ** parameters with statistically significant difference.

) showed the following: (1) highly significant variation in TDS concentrations (p < 0.01) and (2) significant differences in six other parameters (p < 0.05). These findings suggest substantial nutrient inputs from point sources (particularly domestic wastewater), contrasting with the lower nutrient levels reported [33].

The study results from Lake Yuriria reveal an environmental degradation pattern characteristic of inland wetlands under anthropogenic pressure, particularly from agricultural activities. The identification of advanced eutrophic conditions (with WQI values < 50% at multiple sampling points) and heavy metal presence reflects global trends documented in aquatic systems across various regions [13]. These findings suggest that Yuriria’s degradation processes operate through mechanisms like those reported in other wetlands impacted by agricultural runoff, where nutrient overload and metal accumulation disrupt natural biogeochemical cycles and affect aquatic trophic networks.

While the implemented methodology—based on the WQI and multiple physicochemical parameter analyses—provides a robust framework for environmental monitoring [35], there emerges a need to complement these indicators with biological assessments that fully capture the system’s ecological response. Yuriria’s situation presents the characteristic challenge of many wetlands: balancing productive activities with the conservation of critical ecosystem services, for which Nature-Based Solutions (NBSs) emerge as a promising alternative [36]. This case underscores the importance of approaching wetland management through integrated perspectives that consider both local specificities and global conservation frameworks.

4. Conclusions

To evaluate the pollution of surface water in Yuriria Lake, the water quality index (WQI) was utilized, providing insights into the spatial and temporal behavior of the lake’s physicochemical parameters. During both monitoring periods, the WQI indicated that most of the lake exhibited fair quality. Additionally, the primary effluents entering the lake were identified, and the WQI confirmed that these sites exhibited poor water quality, with percentages below 50%.

The sources responsible for altering the aquatic system’s quality were also identified. The primary sources affecting the lake were anthropogenic pollution, such as untreated domestic wastewater discharges and agricultural runoff.

Agriculture is a predominant activity covering extensive areas around the lake and significantly contributes to these impacts.

Consequently, the organic load and nutrient concentrations increased, as evidenced by the elevated levels of BOD₅, NH₄⁺, phosphorus, and orthophosphates. These high contamination levels have led to decreased DO levels and, subsequently, to the eutrophication of the lake.

The WQI also identified sites with similar water quality characteristics. During both monitoring periods, the area of the lake with the highest anthropogenic pollution was associated with discharges from the La Cinta Canal.

The WQI can be a valuable tool for optimizing the number of sampling sites in a lake without losing critical information. However, it highlights the challenges of interpreting and analyzing large datasets for assessing spatial and temporal variations in contamination within freshwater systems. It also aids in identifying locations where periodic water quality monitoring strategies can be implemented, enabling effective resource management.

The one-way ANOVA (α = 0.05) identified significant temporal variations in water quality, with seven out of nine WQI parameters showing statistically significant differences (p < 0.05) between monitoring campaigns. Peak contamination levels were recorded during the April dry season sampling, suggesting seasonal influences on pollutant loading.

Key Considerations from This Study:

Methodological innovation: the implementation of systematic 1.5 km grid sampling combined with biannual monitoring establishes a replicable protocol for RAMSAR lake assessment, addressing current standardization gaps in tropical wetland studies.

Temporal analysis: the identification of seasonal pollution trends enables the data-driven scheduling of governmental inspections (recommended quarterly during critical hydrological periods: dry season [March–May] and post-rainy season [October–November]).

Biodiversity impact: quantitative evidence of contaminant effects on 172 faunal species (including 7 endangered endemics) provides a scientific basis for policy reform, particularly for updating NOM-059-SEMARNAT-2010 conservation thresholds using eco-system-specific vulnerability indices.

Furthermore, the water quality results directly support Sustainable Development Goal 6.3 (improving water quality through pollution reduction and wastewater treatment) and SDG 15.1 (the conservation and sustainable use of wetlands, particularly as a designated RAMSAR site).

Economic implications:

A cost–benefit analysis framework can be developed to quantify short-term (3–5 year) investments in water treatment infrastructure vs. long-term (10+ year) economic gains from enhanced agricultural productivity (reduced irrigation contamination), sustainable fishery yield increases, and ecotourism revenue from biodiversity preservation.

Based on the results, wastewater treatment plants meeting discharge criteria are suggested as the most effective solution to reduce pollution levels in Yuriria Lake. This recommendation stems from the observation that the existing treatment plants are not operating well.