Water quality of the Chibunga River in central Ecuador, based on EPT, ISQA indices and GIS

Qualidade da água do rio Chibunga, no centro do Equador, com base nos índices EPT, ISQA e SIG

Abstract

This study evaluated the water quality of the Chibunga River in central Ecuador using the Ecological Index of Macroinvertebrates (EPT) (Ephemeroptera, Plecoptera, and Trichoptera) and the Simplified Water Quality Index (ISQA) from August to November 2022. Four monthly monitoring campaigns were conducted, collecting macroinvertebrate and water samples at three sites along the river: an upstream site (P1), a midstream site (P2), and a downstream site (P3), following the protocols of the Ecuadorian Institute of Standardization (INEN) 2176:2013 and 2169:2013. Macroinvertebrate abundance decreased progressively from P1 to P3, which corresponded with changes in physicochemical parameters, including temperature (T), total organic carbon (TOC), suspended solids (SS), dissolved oxygen (DO), and electrical conductivity (EC). At P2 and P3, water quality was classified as “Poor” by both indices, while P1 maintained a “Fair” condition according to the EPT index. In the final sampling campaign, the ISQA value at P1 improved from “Fair” to “Good,” likely due to increased rainfall in the upper basin. These findings indicate a progressive decline in ecological and physicochemical water quality from the upper to the lower reaches of the Chibunga River, primarily driven by anthropogenic activities and untreated wastewater discharges in urban and peri-urban areas. The integration of biological indices, physicochemical parameters, and GIS spatial analysis facilitated the identification of critical pollution zones and provided updated data for environmental monitoring of the river system.

Keywords:
water quality; anthropic activities; contamination; macroinvertebrates; physicochemical parameters

Resumo

Este estudo avaliou a qualidade da água do rio Chibunga, no centro do Equador, utilizando o Índice Ecológico de Macroinvertebrados (EPT) (Ephemeroptera, Plecoptera e Trichoptera) e o Índice Simplificado de Qualidade da Água (ISQA), de agosto a novembro de 2022. Foram realizadas quatro campanhas mensais de monitoramento, com coleta de amostras de macroinvertebrados e água em três pontos ao longo do rio: um ponto a montante (P1), um ponto no meio do rio (P2) e um ponto a jusante (P3), seguindo os protocolos do Instituto Equatoriano de Normalização (INEN) 2176:2013 e 2169:2013. A abundância de macroinvertebrados diminuiu progressivamente de P1 para P3, o que correspondeu a alterações nos parâmetros físico-químicos, incluindo temperatura (T), carbono orgânico total (COT), sólidos em suspensão (SS), oxigênio dissolvido (OD) e condutividade elétrica (CE). Em P2 e P3, a qualidade da água foi classificada como “Ruim” por ambos os índices, enquanto P1 manteve uma condição “Regular” de acordo com o índice EPT. Na campanha de amostragem final, o valor do ISQA em P1 melhorou de “Regular” para “Bom”, provavelmente devido ao aumento da precipitação na bacia superior. Esses resultados indicam um declínio progressivo na qualidade ecológica e físico-química da água do curso superior para o curso inferior do rio Chibunga, impulsionado principalmente por atividades antrópicas e pelo lançamento de efluentes não tratados em áreas urbanas e periurbanas. A integração de índices biológicos, parâmetros físico-químicos e análise espacial em SIG facilitou a identificação de zonas críticas de poluição e forneceu dados atualizados para o monitoramento ambiental do sistema fluvial.

Palavras-chave:
qualidade da água; atividades antrópicas; contaminação; macroinvertebrados; parâmetros físico-químicos

1. Introduction

Rivers are recognized globally as vital sources of natural wealth (Wang and He, 2022; Day and Hall, 2016; Zini et al., 2025). Nevertheless, anthropogenic activities and inadequate waste management have resulted in irreversible pollution, impacting 36% of rivers as reported by the Food and Agriculture Organization (Mateo-Sagasta et al., 2018). Consequently, rivers now pose significant risks to both human health and the environment (Ayala et al., 2019; Fernández-Rodríguez and Guardado-Lacaba, 2021; Rebolledo Monsalve et al., 2022; Dueñas-Muñoz et al., 2022). The World Water Development Report indicates that in developed countries, approximately 70% of wastewater is treated, compared to 38% in middle-income countries and only 28% in low-income countries. In the poorest countries, merely 8% of wastewater receives any treatment. These data suggest that over 80% of wastewater is discharged untreated into the environment, resulting in critical water shortages and severe environmental pollution (UNESCO, 2017; Bijekar et al., 2022; Tariq and Mushtaq, 2023).

In Latin America, wastewater discharges have been increasing in urban areas due to the rise in populations, from 314 million in 1990 to 496 million approximately, and it is estimated to reach 674 million in 2050 (Walteros and Ramírez, 2020; Sánchez et al., 2024; Anton, 1993). The negative impacts of river pollution have underscored the need to apply methods to assess watercourses' contamination levels (Béjar and Mendoza, 2018). In Ecuador, wastewater discharges remain insufficiently controlled due to inadequate environmental management policies (Sánchez, 2019; Voloshenko-Rossin et al., 2015; Arcentales-Ríos et al., 2022; Benítez et al., 2019).

The Chibunga River is part of the Chambo River sub-basin and the Pastaza River basin. Its waters flow into the Amazon macro-basin through the Pastaza-Amazon system; its tributary network originates on the lower slopes of Chimborazo Volcano and traverses Riobamba and its neighboring 25 communities before reaching the Chambo River (Echeverría-Puertas et al., 2023). This water resource constitutes a key driver in the canton’s development. In 2010, the canton had 234,170 inhabitants, with a projected population of 264,048 at the end of 2020, according to the National Institute of Statistics and Censuses of Ecuador (INEC, 2010). However, in the last few decades, anthropogenic activities related to agriculture, livestock, solid waste generation, and untreated wastewater discharges have increased, leading to increased pollution in this inter-Andean zone (Veloz and Carbonel, 2018; Cadme et al., 2018), resulting in diminished resource sustainability, which calls for the application of adequate treatment systems (Godoy-Ponce et al., 2020).

The aim of this study has been to evaluate the water quality of the Chibunga River from August 2022 to November 2022. Hereby, we employed aquatic macroinvertebrates as bioindicators, as established in the EPT index (Ephemeroptera, Plecoptera, and Trichoptera), and ISQA index physicochemical parameters (Simplified Water Quality Index), Temperature (T), Total Organic Carbon (TOC), Suspended Solids (SS), Dissolved Oxygen (DO), and Electrical Conductivity (EC). EPT bioindicators correspond to groups of aquatic macroinvertebrates sensitive to pollution, whose presence and diversity allow the evaluation of the ecological status and biological quality of water bodies. In contrast, the ISQA is a tool that integrates physicochemical parameters to quantitatively assess water quality, facilitating the interpretation of environmental conditions in aquatic ecosystems. The results of this extensive analysis may provide fundamental insights into the human impact on a sensitive river environment in the Andean region.

2. Methods and Materials

2.1. Sampling design and monitoring sites

Benthic macroinvertebrates and water samples were collected during four monthly campaigns from August to November 2022. At each monitoring site (P1, P2, and P3), three replicate samples (1R, 2R, and 3R) were obtained. This monitoring effort extended the work of Lara-Basantes and Andrade (2022), who previously identified key sampling points along the river based on the distribution of anthropogenic activities and locations of wastewater discharges (D1, D2, and D3) during field inspections. Monitoring point P1 was located upstream near the Cemento Chimborazo facility in a rural area. Point P2 was situated adjacent to the urban zone of Riobamba, while point P3 was positioned downstream near the confluence with the Chambo River (Figure 1).

Figure 1
Chibunga River location map with sampling points and wastewater discharges.

2.2. Sampling and macroinvertebrates characterization

Following the methodology described by Flores (2014), macroinvertebrates were collected at each sampling point by sampling the riverbed in rifles, lentic areas, and pools, using a Surber net positioned on the substrate at the bottom and oriented against the river current. The riverbed material was disturbed by dislodging organisms into the net. The collected material was preserved in 500-mL plastic jars containing 96% alcohol and labeled with the corresponding sampling point and date. Macroinvertebrates were separated and analyzed under an M280 (LED) stereomicroscope to identify taxa at the order and family levels, following standard taxonomic keys (Carrera and Fierro, 2001). The EPT index (Ephemeroptera, Plecoptera, and Trichoptera) was then calculated to provide an indicator of water quality based on percentages.

2.3. EPT index

The EPT index was calculated using macroinvertebrates from the orders Ephemeroptera, Plecoptera, and Trichoptera, which are widely recognized for their high sensitivity to pollution (Tubić et al., 2024; Ab Hamid and Rawi, 2017; Savic et al., 2017). The absence of these taxa indicates a level of water contamination consistent with ecological expectations for freshwater systems. The index is determined by dividing the number of EPT individuals by the total macroinvertebrate abundance and multiplying the result by 100%, resulting in a percentage value (Equation 1). The EPT index is categorized into four ranges reflecting water quality status: 0-24% (poor), 25-49% (fair), 50-74% (good), and 75-100% (very good) (Carrera and Fierro, 2001) (Equation 1).

$EPT\%=\frac{\begin{array}{c}Numberofindividuals\\ fromtheEPTorders\end{array}}{Totalmacroinvertebrateabundance}x100\%$ (1)

2.4. Sampling and physiochemical parameters characterization

Water samples were analyzed according to the Ecuadorian Technical Standard methodology (NTE) INEN 2176:2013. The resulting samples were used to characterize the five physicochemical parameters (TOC, SS, T, EC, DO) that compose the ISQA index, developed by Queralt in 1982 (Torres et al., 2009). For sampling, bottles of 1000 ml were previously rinsed three times with the water to be analyzed, and rinsing was omitted when the containers contained preservatives or had been sterilized (INEN, 2013a).

The Ecuadorian Accreditation Service (SAE) accredited the laboratory that conducted the TOC (mg L⁻¹) and SS (mg L⁻¹) analyses. Each sample was labeled with its identification number, monitoring point, date, and required analysis, in accordance with the storage and preservation procedures established in NTE INEN 2169:2013 (INEN, 2013b). They quantified TOC using the SM 5310c method (persulfate-UV or heated persulfate oxidation) described by Chamorro et al. (2010) to determine SS, and performed gravimetric analysis using fiberglass filters, an analytical balance (Sartorius ED224S +/- 0.0001g), a POL-EKO drying oven, and a Beyondsupply desiccator. The expressed SS results as a percentage of suspended solids in the water samples, following Marine and Coastal Research Institute procedures (INVEMAR, 2003).

Meanwhile, the in-situ determination of T (°C), EC (μS cm⁻¹), and DO (mg L⁻¹) adheres to procedures outlined in the Manual of Analytical Methods for the Determination of Physicochemical Parameters (Severiche-Sierra et al., 2013). These parameters required immediate analysis in accordance with NTE INEN 2169:2013. Finally, T and EC were determined with a portable meter, while DO was measured by using a Hach HQ40D multimeter.

2.5. ISQA index

The ISQA index was determined based using values ranging from 0 to 100 (Torres et al., 2009), applying the classification ranges established by Hernández (2021) and the ISQA index formulas. Each range represents a distinct water quality category: 0-25 (very poor), 26-50 (poor), 51-75 (fair), and 76-100 (good). These values were obtained by substituting Equation 2, based on the results from Equations 3 to 14.

$ISQA=E\left(A+B+C+D\right)$ (2)

Equivalence:

E = T in °C. For T, one of the following equations is chosen:

$E=1ifT\le 20℃$ (3)

$E=1-\left(T-20\right)x\mathrm{0,0125}ifT>20℃$ (4)

A = TOC in mg L⁻¹. For TOC, one of the following equations is chosen:

$A=30-TOCifTOC\le 5mgL-¹$ (5)

$A=21-\left(0.35xTOC\right)if12mg{L}^{-1}\ge TOC>5mgL-¹$ (6)

$A=0ifTOC>12mgL-¹$ (7)

B = SS in mg L⁻¹. For SS, one of the following equations is chosen:

$B=25-\left(0.15xSS\right)ifSS\le 100mg{L}^{-1}$ (8)

$B=17-\left(0.07xSS\right)if250mg{L}^{-1}\ge SS>100mg{L}^{-1}$ (9)

$B=0ifSS>250mg{L}^{-1}$ (10)

C = DO in mg L⁻¹. For DO, one of the following equations is chosen:

$C=2.5xDOifDO<10mg{L}^{-1}$ (11)

$C=25ifDO\ge 10mg{L}^{-1}$ (12)

D = EC in μS cm⁻¹ at 18 °C. For EC, one of the following equations is chosen:

$D=\left(3.6-\log EC\right)x15.4ifEC\le 4000\mu Scm-¹$ (13)

$D=0ifEC>4000\mu Scm-¹$ (14)

2.6. Statistical analysis

Descriptive statistics were used to summarize the three replicates for each variable, including measures of central tendency and variability, expressed as mean values and standard errors (SE). Prior to inferential analysis, data normality was evaluated using the Shapiro–Wilk test at a significance level of α = 0.05, confirming that the data followed a normal distribution. Subsequently, one-way Analysis of Variance (ANOVA) was applied to determine significant differences among the monitoring points for the EPT and ISQA indices. Values of p < 0.05 were considered statistically significant, leading to rejection of the null hypothesis. When significant differences were detected, Tukey Post-Hoc Test (Equation 15) was performed to identify the groups with significant differences. Statistical analyses were conducted using the Real Statistics add-in for Microsoft Excel.

$w=q_{\alpha }\left(a,v\right)\sqrt{\frac{CME}{n_g}}$ (15)

Equivalence: qα (a, v) = critical value of ranges; α = significance level; a = number of treatments or levels; v = degrees of freedom associated with the error mean square (CME), with v = n – a; CME = Mean square error; ng = number of observations at each of the levels.

2.7. GIS analysis

Spatial interpolation maps of the EPT and ISQA indices were generated using the Inverse Distance Weighting (IDW) method in ArcGIS software. This method was selected for its simplicity, wide applicability in environmental studies, and ability to represent spatial trends from discrete sampling points, given that the influence of each point decreases with distance. The interpolation was performed using the values obtained from the three sampling points (P1, P2, and P3), allowing the spatial representation of water quality distribution along the Chibunga River channel during each monitoring campaign. The thematic maps generated enabled the identification of spatial patterns of water quality deterioration and the visualization of the areas most affected by anthropogenic activities and wastewater discharges in urban and peri-urban sectors.

3. Results and Discussion

3.1. Macroinvertebrates

The sampling effort yielded 3,870 benthic macroinvertebrates representing 23 families and 11 taxonomic orders across the three monitoring points, with Chironomidae (Diptera) dominating the assemblage with 1,027 individuals. The dominance of Chironomidae may indicate tolerance to organic pollution and environmental disturbance, since this family is commonly associated with aquatic ecosystems affected by anthropogenic contamination and low dissolved oxygen conditions. The reduction of sensitive macroinvertebrate taxa toward the middle and lower sections of the river suggests ecological alteration associated with untreated wastewater discharges and urban runoff (Savic et al., 2017; Walteros and Ramírez, 2020; Tubić et al., 2024). Hereby, Table 1 presents the average total macroinvertebrate abundance and the average total EPT individuals from the three repetitions at each monitoring point. P1 indicated the highest family richness during all campaigns, which significantly decreased in P2 and P3, especially in the first two campaigns.

Table 1
Average total macroinvertebrate abundance and average total EPT abundance.

Campaign Date	Monitoring Points	Total abundance	SE Total abundance	Total EPT	SE Total EPT
15/08	P1	140	16.95	47	4.67
	P2	78	9.82	8	2.08
	P3	60	3.84	6	0.67
16/09	P1	202	26.91	51	5.61
	P2	69	54.56	9	2.08
	P3	9	2.33	1	0.58
15/10	P1	53	16.90	19	7.02
	P2	32	8.08	5	1.86
	P3	9	5.61	1	0.58
12/11	P1	87	25.99	30	10.17
	P2	40	18.61	9	4.63
	P3	26	12.55	5	0.88

• Note: The average obtained corresponds to the 3 replicates performed at each monitoring point of each campaign. The total macroinvertebrate abundance includes all individuals found as Amphipoda, Gastropoda, Diptera, Ephemeroptera, Plecoptera, Trichoptera, Coleoptera, Collembola, Acari, Annelida, and Tricladia/Seriata, while the total EPT abundance includes only Ephemeroptera, Plecoptera, and Trichoptera. SE: Standard error.

3.2. EPT index

According to this index, P1 demonstrated fair water quality, whereas P2 and P3 exhibited poor quality in all monitoring campaigns. The lowest percentage at P1 (25%) occurred in the second campaign (September), and the highest (36%) during the third campaign (October). The lowest percentages at P2 and P3 (both at 10%) occurred in the first campaign (August), with the highest percentages (23% and 19%) in the fourth campaign (November) (Figure 2).

Figure 2
EPT Index in the four monitoring campaigns for all sampling sites.

The reduced abundance of macroinvertebrates at P2 and P3 relative to P1 can be attributed to their geographic positioning and, more significantly, to the proximity of contaminant sources at each monitoring site, which likely exert adverse effects on macroinvertebrate communities (Meza-Salazar et al., 2012; Leaño and Pérez, 2020). Cadme et al. (2018) documented untreated wastewater discharges into the Chibunga River, along with agricultural activities and human settlements along its banks. Similarly, Lara-Basantes and Andrade (2022) observed that P2 and P3 consistently exhibited poor water quality across all monitoring campaigns, with values ranging from 0 to 13.21%. In contrast, P1 demonstrated regular water quality only during the first campaign (31.15%), whereas the second and third campaigns showed poor water quality (17.47% and 22.73%, respectively). Their study identified Chironomidae (Diptera) and Scirtidae (Coleoptera) as the most prevalent families.

3.3. Water physicochemical parameters characterization

Based on the characterization of water physicochemical parameters, Table 2 presents the mean values and standard errors from the three repetitions performed at each monitoring point for the five physicochemical parameters during the study period. The temperature (T) ranged from 11.10 to 17.10 °C; P1 consistently recorded the lowest values, whereas P3 recorded the highest. The Total Organic Carbon (TOC) varied from <1.5 to 90 mg L⁻¹, with P1 yielding the lowest concentration across all, showing a marked difference compared with P2 and P3. The Suspended Solids (SS) ranged from 29.70 to 712.33 mg L⁻¹, with the lowest values at P1 in all campaigns. These values increased at P2 and P3, with P3 showing the highest values. The Dissolved Oxygen (DO) ranged from 5.00 to 9.00 mg L⁻¹, with P2 and P3 maintaining the lowest concentrations in all campaigns, never exceeding 6.00 mg L⁻¹. Electrical Conductivity (EC) varied from 468.57 to 661.70 μS cm⁻¹, with P1 showing the lowest values throughout the study period.

Table 2
Mean values and standard errors of physicochemical parameters of the ISQA.

Campaign Date	Points	T (°C)	TOC (mg L−1)	SS (mg L−1)	DO (mg L−1)	EC (μS cm−1)
15/08	P1	11.22 ± 0.02	6.00 ± 0.12	37.67 ± 0.33	7.10 ± 0.01	468.57 ± 3.47
	P2	15.14 ± 0.02	19.32 ± 0.01	119.33 ± 2.19	5.54 ± 0.02	531.77 ± 6.53
	P3	16.00 ± 0.05	19.69 ± 0.02	217.33 ± 1.20	5.03 ± 0.03	591.53 ± 2.02
16/09	P1	11.10 ± 0.06	7.00 ± 0.23	32.10 ± 0.01	7.50 ± 0.01	486.70 ± 7.33
	P2	13.30 ± 0.02	18.40 ± 0.03	156.70 ± 0.90	5.20 ± 0.01	623.10 ± 6.35
	P3	15.50 ± 0.02	18.70 ± 0.01	214.70 ± 2.00	5.00 ± 0.02	661.70 ± 1.68
15/10	P1	13.20 ± 0.09	7.00 ± 0.28	29.70 ± 0.67	7.40 ± 0.03	536.20 ± 2.16
	P2	15.30 ± 0.02	19.80 ± 0.01	111.30 ± 0.35	5.90 ± 0.02	608.80 ± 9.92
	P3	16.00 ± 0.29	19.90 ± 0.01	712.70 ± 1.35	6.00 ± 0.01	617.21 ± 6.60
12/11	P1	12.21 ± 0.01	<1.5 ± *	43.32 ± 0.87	9.00 ± 0.15	473.53 ± 11.63
	P2	16.10 ± 0.06	18.60 ± 0.02	100.50 ± 0.01	5.30 ± 0.06	504.50 ± 3.33
	P3	17.10 ± 0.03	18.80 ± 0.04	120.3 ± 1.22	5.61 ± 0.02	531.90 ± 23.97

• ^*
  Standard error was not calculated for values below the detection limit (<1.5 mg L⁻¹).

Temperature (T) differences among the monitoring points indicate altitudinal variation, as higher elevations are associated with lower air temperatures. P1, located at 3,070 masl, is consequently expected to have lower water temperatures. In contrast, P2 and P3 are situated at lower elevations (2,754 masl) and are adjacent to channels that discharge untreated wastewater directly into the river from the Riobamba urban area (Lara-Basantes and Andrade, 2022; Cadme et al., 2018). García (2012) observes that wastewater typically exhibits higher temperatures than uncontaminated river water due to previous industrial and domestic use, with reported temperatures ranging from 7 to 18 °C in cold regions.

Alvarado et al. (2020) report that variations in TOC, SS, and EC values are attributable to significant contaminant activities. In the present study, untreated wastewater discharges near P2 and P3 are likely to increase microbial loads, organic matter, and sludge deposition, promoting anaerobic conditions and contributing to reduced DO concentrations. The observed increases in TOC, SS, and EC in the middle and lower sectors of the river are likely associated with untreated wastewater discharges and urban runoff, both of which introduce organic matter and dissolved ions into the aquatic system. These conditions can reduce dissolved oxygen availability and alter aquatic communities, particularly affecting sensitive macroinvertebrate taxa (Mateo-Sagasta et al., 2018; Walteros and Ramírez, 2020; Tariq and Mushtaq, 2023). In contrast, the vicinity of P1 has fewer contamination sources and has not experienced significant population growth (Lara-Basantes and Andrade, 2022). The Andean Union of Cement (UNACEM, 2020) states that Cemento Chimborazo has implemented environmental management programs to protect water sources.

During the monitoring period, the area experienced cloudy conditions, light drizzle, and occasional fog. These climatic factors, together with variations in runoff, may have exerted additional natural influences on the recorded parameters.

3.4. ISQA Index

According to this index, P1 exhibited regular water quality from the first through the third monitoring campaign and improved to good quality in the fourth campaign. In contrast, P2 and P3 demonstrated poor water quality in all campaigns. P1 lower indicator occurred in the first campaign (70.31), while P2 and P3 had their lowest values in the second campaign (31.33 and 26.47, respectively). The highest percentages in all the points occurred in the fourth campaign (Figure 3).

Figure 3
ISQA Index in the four monitoring campaigns.

P2 and P3 remain within the range of 26.47 to 36.52 across all campaigns, indicating that the water is sustainable only for recreational purposes and not for human consumption. On the other hand, P1 in the first three campaigns ranged from 70.31 to 71.55. This value increased to 85.24 in the fourth campaign, which possibly makes its use and consumption feasible according to this index (Torres et al., 2009; Hernández, 2021).

The results from P1 indicate that the upper section of the river maintains higher water quality, a pattern commonly observed within watersheds, as downstream areas typically receive greater effluent inputs (Ramírez and Viña, 1998), which modify physicochemical conditions. These findings are consistent with Toapanta (2022), who observed that upstream sampling points in the Chibunga River exhibited superior water quality compared to the most downstream point, which is directly affected by domestic and industrial wastewater discharges.

3.5. Anova analysis, EPT and ISQA indices

In Table 3 the ANOVA results for both the EPT and ISQA indices are detailed. For both indices, the p-value is lower than the significance level (0.00 < 0.05), confirming at a 95% confidence level that the water quality indices between the monitoring points are different or at least one is different, meaning they vary from upstream to downstream.

Table 3
Inferential Statistics One-Way ANOVA – EPT and ISQA.

Index	Source of Variation	Degrees of Freedom	Mean Square	F-ratio	p-value	Critical F value
EPT	Between groups	2	445.58	18.35	0.00	4.26
	Within groups	9	24.28	-	-	-
	Total	11	100.88	-	-	-
ISQA	Between groups	2	2419.07	92.15	0.00	4.26
	Within groups	9	26.25	-	-	-
	Total	11	461.31	-	-	-

• Significance level α = 0.05.

Table 4 presents the Tukey Post-Hoc Test results for both indexes. For EPT index, the test confirms significant differences in P1, presenting regular water quality in relation to P2 and P3; it is stated that there are no differences between P2 and P3, as both demonstrate poor water quality, confirming the results. Similarly, for ISQA, significant differences are confirmed at P1, indicating regular water quality, while P2 and P3 have similar poor water quality.

Table 4
Tukey Post-Hoc Test – EPT and ISQA.

Index	G1	G2	Standard Error	P - value	Alfa value	Critical value	Differences
EPT	P1a	P2b	2.46	0.00	0.05	9.73	appear
	P1a	P3c	2.46	0.00	0.05	9.73	appear
	P2b	P3c	2.46	0.71	0.05	9.73	absent
ISQA	P1a	P2b	2.56	0.00	0.05	10.11	appear
	P1a	P3c	2.56	0.00	0.05	10.11	appear
	P2b	P3c	2.56	0.28	0.05	10.11	absent

3.6. GIS analysis

Figure 4 presents spatial interpolation maps of the EPT index, revealing a longitudinal pattern of ecological deterioration along the Chibunga River. There is a progressive decline in biological quality from the upper to the middle and lower sections of the river channel. The spatial distribution observed during the four monitoring campaigns demonstrates persistent unfavorable ecological conditions in areas influenced by urban and peri-urban activities, particularly near P2 and P3, which are located close to the city of Riobamba. While minor temporal variations occurred between campaigns, the spatial pattern of the EPT index remained relatively stable throughout the study period. This stability suggests ongoing anthropogenic pressure on the benthic macroinvertebrate community. Additionally, the thematic maps facilitated the visualization of ecological fragmentation and enabled the spatial identification of areas most affected by contamination.

Figure 4
Spatial distribution of the EPT index using IDW interpolation in the Chibunga River.

Figure 5 displays the spatial interpolation of the ISQA index, revealing a gradient of physicochemical deterioration from the upper to the lower sections of the Chibunga River, which aligns with areas experiencing the highest levels of anthropogenic intervention. The maps indicate a persistent pattern of poor water quality in the middle and lower sectors throughout most of the monitoring period. In contrast, the final monitoring campaign recorded an improvement in water quality in the upper basin sector, likely due to dilution associated with increased precipitation. The spatial concordance between patterns identified by the ISQA and EPT indices indicates a relationship between physicochemical deterioration and ecological alteration within the fluvial system. Thus, GIS-based spatial analysis enhanced the interpretation of numerical results by enabling visualization of contamination patterns and identification of critical sectors along the river.

Figure 5
Spatial distribution of the ISQA index using IDW interpolation in the Chibunga River.

A primary limitation of this study was the limited number of sampling points used for GIS-based spatial interpolation, which may limit the spatial representativeness of the resulting maps. Furthermore, microbiological parameters and heavy metals were not assessed, so the evaluation concentrated mainly on ecological and physicochemical water quality.

Although the IDW method is one of the simplest and most widely used approaches for generating continuous surfaces from point data in environmental studies, it presents important limitations when the sampling density is low. By design, IDW assumes that closer observations have more influence than distant ones and calculates predicted values based on a weight inversely proportional to distance (Li and Heap, 2008). However, this assumption does not explicitly incorporate spatial autocorrelation structures or associated physical processes, which can generate artifacts such as “bull’s-eye” patterns around sampling points and less reliable estimates in areas with complex spatial variation (Workneh et al., 2024). Moreover, the accuracy of IDW depends on factors such as sampling point density, data variability, and sampling design, where a sparse or irregular sampling network can significantly reduce the precision of interpolated results (Li and Heap, 2008; Ohlert et al., 2023). Comparative studies of interpolation methods have shown that the density and distribution of points influence estimation accuracy, and geostatistical methods such as kriging usually handle spatial correlations better in contexts with trends or complex variability (Ohlert et al., 2023; Yasin et al., 2024). Therefore, maps generated using IDW should be interpreted with caution, especially when only a few sampling points are available, and it is recommended to increase both the number and distribution of sampling points in future studies to improve spatial resolution and the reliability of GIS-based assessments in fluvial systems such as the Chibunga River (Adedapo and Zurqani, 2024).

4. Conclusions

Throughout the study period, macroinvertebrate abundance and diversity declined progressively from the upper to the lower sections of the Chibunga River, indicating ecological deterioration linked to increasing anthropogenic pressure in urban and peri-urban areas. Sampling point P1 exhibited the most favorable environmental conditions, with lower temperature, total organic carbon, suspended solids, and electrical conductivity, as well as higher dissolved oxygen concentrations. In contrast, points P2 and P3 exhibited unfavorable physicochemical and biological conditions, as evidenced by reduced sensitive macroinvertebrate families and persistent values indicative of poor water quality, as measured by the EPT and ISQA indices.

The combined use of biological and physicochemical indices enabled the identification of a longitudinal pattern of water-quality deterioration from the upper to the lower reaches of the river, primarily associated with untreated wastewater discharges, urban runoff, and other anthropogenic activities along the river channel. Additionally, spatial analysis using GIS-IDW interpolation enabled the visualization of critical contamination zones and enhanced the ecological and physicochemical interpretation of the findings.

Compared with previous studies of the Chibunga River, these results confirm the ongoing presence of contamination processes in the middle and lower sectors of the fluvial system, indicating that environmental issues continue to affect the ecological and physicochemical quality of the river. This study offers updated data on environmental monitoring of the Chibunga River and demonstrates the value of integrating aquatic bioindicators, physicochemical parameters, and GIS tools to support the assessment and management of Andean fluvial ecosystems under anthropogenic pressure.

Acknowledgements

To ESPOCH, for lending us the facilities for this study

Data Availability Statement

The datasets generated during this study are available from the corresponding author upon reasonable request for academic and scientific purposes.

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Edited by

• Editor:
  Takako Matsumura Tundisi

Publication Dates

• Publication in this collection
  27 July 2026
• Date of issue
  2026

History

• Received
  26 Feb 2026
• Accepted
  07 June 2026