This study is significant for several reasons. First, it provides the first integrated assessment of physicochemical water quality, heavy metal contamination, microbial pollution, and human health risk for the Ichu River, offering a comprehensive evaluation rather than isolated parameter analysis. Second, by combining water quality indices (NSF-WQI and CCME-WQI), multivariate statistical analysis (PCA and correlation), and quantitative health risk models (CDI, HQ, and CR), the study aligns with current international best practices and enables robust comparison with global and regional river systems.

Third, the results identify clear upstream–downstream contamination gradients and pollution hotspots, directly linking land-use pressures (urbanization, mining, and wastewater discharge) with ecological degradation and public health risks. Fourth, the findings provide decision-relevant evidence for local authorities by highlighting priority areas where intervention, wastewater treatment, and pollution control are urgently required. Finally, the study establishes a baseline dataset for future monitoring and modeling in a high-Andean river system, supporting long-term water resource management and protection of river-dependent communities.

Community members in Peru depend on the Ichu River for their freshwater needs because they supply drinking water, as well as for both farming and industrial applications. Little human activity occurred at Sampling Site 1 (S1), which serves as the baseline reference site situated within the remote upper region of the river. Semi-urbanized locations that contained industrial and agricultural activities were selected as Sampling Sites 2 to 4 (S2–S4), thus contributing to possible water pollution. The district receives effluents from mining activities and untreated sewage and industrial wastewater discharges between Sampling Sites 5 and 8 (S5–S8) located in this area. All sampling site locations have been provided below as S1, S2, S3, … , corresponding to the different river stretches covered in this study. All water samples were collected under consistent basic conditions, including sampling at a depth of 30 cm below the surface to avoid atmospheric interference, using pre-rinsed containers, during daylight hours under stable weather conditions (no rainfall within the preceding 24 hours), and following APHA guidelines to ensure representative and uncontaminated samples. Photographic illustrations of these sites highlight the surrounding land use and visible pollution sources, offering clearer context for the environmental conditions observed ( Figure 1). •

S1 (Upper Ichu River): Remote headwater region with minimal human activity (baseline reference site) ( Figure 1).

The polyethylene bottles were cleaned prior to use as containers for physicochemical tests and heavy metal evaluations, whereas glass bottles served as sterile containers for microbial analyses. The procedure for avoiding contamination included rinsing all bottles with river water immediately at the sampling site before specimen collection. A depth of 30 cm beneath the water surface served as the sampling point because scientists wanted to bypass atmospheric influences. Physicochemical and heavy metal samples were stored in ice boxes set to 4°C before laboratory analysis was performed. The laboratory examined microbiological samples no later than six hours after their collection time to stop bacterial breakdown. To stop heavy metal adsorption or precipitation during the analysis, the samples were treated with nitric acid (HNO ₃, pH < 2) (Sigma-Aldrich, 21641-1L). The laboratory followed the American Public Health Association standard methods for all sampling and preservation stages to establish consistent and reliable testing procedures.

Water samples underwent physicochemical testing through field instruments and laboratory methods to measure pH with a Hanna Instruments digital pH meter (USA) and turbidity with a Hach 2100P turbidimeter alongside TDS and EC analysis using a YSI 556 MPS multi-parameter probe (USA). The multi-parameter probe YSI 556 MPS (USA) measured the TDS and EC to evaluate the dissolved ion concentrations within the water samples. The Extech DO600 oxygen meter enabled DO measurements in the water column, and the 5-day incubation method at 20°C allowed the determination of BOD ₅ according to APHA Standard Method 5210 B. Researchers used UV-Vis spectrophotometry to measure nitrate (NO ₃ ^-) and phosphate (PO ₄ ^3-) concentrations because these chemicals indicate agricultural runoff pollution. All measuring tools underwent pre-use calibration to generate reliable and accurate data outputs. The National Sanitation Foundation Water Quality Index (NSF-WQI) and Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were calculated to standardize the interpretation of physicochemical parameters. These indices are widely used for comparing water quality across regions ( Su et al., 2021; Singh et al., 2021).

The use of composite water quality indices such as NSF-WQI and CCME-WQI is widely recommended in recent literature because they integrate multiple parameters into a single, interpretable metric that facilitates spatial and temporal comparisons across river systems. These indices have been successfully applied in diverse environmental settings to support regulatory assessment, public communication, and ecosystem management, thereby strengthening the scientific robustness and policy relevance of river water quality evaluations ( Gaur et al., 2022; Singh et al., 2024b).

In addition to NSF-WQI and CCME-WQI, the study framework is consistent with recent advances in integrated river water quality assessment that recommend the use of complementary indices and modeling concepts to enhance interpretability and decision relevance. Heavy Metal Pollution Index (HPI) and Metal Index (MI) are widely used to summarize multi-metal contamination into a single quantitative indicator, facilitating comparison across sites and regions. The Nemerow Pollution Index (NPI) is also commonly applied to emphasize worst-case parameter dominance in polluted river systems. Furthermore, recent studies recommend coupling water quality indices with multivariate and predictive approaches, including regression-based models and scenario analysis, to improve source apportionment and pollution forecasting. Although the present study primarily applied descriptive statistics and principal component analysis, the adopted framework provides a strong basis for future integration of uncertainty-based risk modeling (e.g., Monte Carlo simulation) and machine-learning-assisted prediction of water quality deterioration under changing anthropogenic pressures ( Gaur et al., 2022; Singh et al., 2024b; Choudhury et al., 2022).

The analysis focused on measuring arsenic (As), lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr) using Inductively Coupled Plasma Mass Spectrometry (ICP-MS; Agilent 7500 Series, Agilent Technologies, Santa Clara, California, USA). Physicochemical parameters were analyzed using a Hanna digital pH meter (Hanna Instruments, Woonsocket, Rhode Island, USA), a Hach 2100P turbidimeter (Hach Company, Loveland, Colorado, USA), a YSI 556 MPS multiparameter probe (YSI Inc., Yellow Springs, Ohio, USA), and an Extech DO600 dissolved oxygen meter (Extech Instruments, Nashua, New Hampshire, USA). The acid digestion required treating 50 mL of water sample solution with HNO ₃ under 95°C heat for two hours before organic matter breakdown. ICP-MS analysis of filtered and diluted digestions took place through 0.45 μm membrane filtration. The analysis procedure adopted Certified Reference Materials (CRMs, NIST 1643f ) for instrument calibration and required multiple runs for each sample to verify the consistency of the results. The interim procedure included the execution of blanks and standard solutions to prevent sample contamination between tests. Heavy metal concentrations in tap water samples were measured to evaluate their compliance with the WHO drinking water quality standards as well as the USEPA limits.

The Most Probable Number (MPN) method (APHA Standard Method 9221 B) allowed for the assessment of microbial contamination through E. coli and total coliform enumeration. Each water sample received 10 mL, 1 mL and 0.1 mL aliquots which were inserted into Lauryl Tryptose Broth (LTB) tubes before being placed at 35°C for 24–48 hours of incubation. Tubes producing gas signified a presumptive positive result, and post-confirmation transfers occurred in Brilliant Green Lactose Bile (BGLB) broth. The researchers counted positive tubes to determine microbial concentrations within the water samples using the Most Probable Number tables. Sterile glassware and autoclaved media maintained the reliability of microbial results throughout the entire process. Regular testing was performed twice for every sample, and control bacterial cultures of E. coli ATCC 25922 were included as part of the testing procedure. The data from the tests were cross-referenced with the WHO drinking water standards, where E. coli and total coliforms must not appear in drinking water (0 MPN/100mL).

To evaluate the potential health risks associated with long-term exposure to heavy metals, three key risk assessment models were applied: Chronic Daily Intake (CDI), Hazard Quotient (HQ), and Carcinogenic Risk (CR).

The CDI (mg/kg/day) was calculated using the following equation: CDI = C × IR × EF × ED \ BW × AT where C represents the metal concentration (mg/L), IR is the ingestion rate (2 L/day for adults and 1 L/day for children), EF is the exposure frequency (365 days/year), ED is the exposure duration (70 years for adults and 6 years for children), BW is the body weight (70 kg for adults and 15 kg for children), and AT is the averaging time (ED × 365 days).

Non-carcinogenic risk was assessed using the Hazard Quotient (HQ) formula: HQ = CDI \ RfD where RfD (mg/kg/day) is the reference dose per the USEPA guidelines. An HQ value greater than one indicates a potential health risk. The Carcinogenic Risk (CR) was estimated using: CR = CDI × SF where SF is the cancer slope factor (mg/(kg · d)) provided by the USEPA. A CR value exceeding 1E-04 is considered a significant risk factor for cancer.

The research data were statistically analyzed using IBM SPSS v26 and Microsoft Excel for processing. All parameters received descriptive statistical treatment using the mean and standard deviation assessment. A Pearson correlation test was used to examine the relationship between water quality and heavy metal content, as well as microbial contamination levels. Principal Component Analysis (PCA) used to detect the main pollution sources affecting the Ichu River.

A range of physicochemical parameters from the Ichu River showed considerable variations among sampling stations S1 through S8, as human activities such as industrial waste release, agricultural water flows, and uncontrolled domestic waste contributed to these changes. The standard deviation (±σ) represents the mean values used to measure both the natural variability and measurement uncertainty in the study results. The pH measurements at the upstream site (S1) amounted to 7.9 ± 0.41 while the downstream site (S8) recorded a value of 6.7 ± 0.41 ( Table 2). This downward trend in pH likely results from industrial and domestic wastewater emissions. Observational data show that the most basic condition exists at S1, where pH indicates neutral conditions, whereas S8 exhibits the lowest reading, indicating that the pollution effect leads to acidic conditions. The downstream water bodies showed higher levels of sediment and organic matter as turbidity values increased from 2.8 ± 1.95 NTU (S1) to 7.9 ± 1.95 NTU (S8). The possible contributors include erosion, agricultural runoff, and sewage discharge. The examination of the total dissolved solids showed rising numbers from 41.2 ± 5.24 mg/L (S1) to 54.8 ± 5.24 mg/L (S8) because of increased dissolved pollutants and minerals during the journey towards the river’s lower section. The pollution from organic substances resulted in a reduced DO concentration ranging from 6.3 ± 0.62 mg/L (S1) to 4.5 ± 0.62 mg/L (S8) ( Table 2). The organic matter decomposition increased in the lower reaches as shown by the BOD levels which rose from 14.8 ± 4.61 mg/L (S1) to 28.5 ± 4.61 mg/L (S8). The water temperature decreased steadily from 22.5 ± 0.58°C (S1) to 20.8 ± 0.58°C (S8) when considering seasonal changes alongside shading effects. The water samples collected at S1 had 3.2 ± 0.84 mg/L NO ₃ ^- while S8 samples showed 5.5 ± 0.84 mg/L NO ₃ ^- and 0.21 ± 0.10 mg/L PO ₄ ^3- increased to 0.50 ± 0.10 mg/L PO ₄ ^3- indicating nutrient accumulation resulting from agricultural runoff and wastewater discharges that poses eutrophication risks. The ion concentration and buffering capacity of the water bodies showed an upward trend since water conductivity increased from 85 ± 12.3 μS/cm (S1) to 120 ± 12.3 μS/cm (S8) concurrently with alkalinity values rising from 110 ± 10.8 mg/L CaCO ₃ (S1) to 140 ± 10.8 mg/L CaCO ₃ (S8) ( Table 2). Water quality worsens as pollutants from human-related activities continue to escalate downstream.

Heavy metals displayed an upward trend in concentration from S1 upstream to S8 in the downstream section due to anthropogenic activities, including mining, industrial effluents, and wastewater discharges. The concentrations in the water samples between S1 and S8 spanned from 0.040 ± 0.002 mg/L to 0.072 ± 0.005 mg/L which exceeded WHO’s set limit of 0.01 mg/L thus posing substantial health risks. The mining sites and industrial waste facilities appeared to have left their marks with elevated heavy metal content in the S6–S8 regions ( Table 3). The measurement results showed that Lead content in water ranged from 0.014 ± 0.001 mg/L (S1) to 0.032 ± 0.003 mg/L (S8) and exceeded the WHO permitted limit of 0.01 mg/L in all measured areas. Lead contamination results from industrial discharges, battery leakage, and vehicle runoff based on rising analytical data ( Table 3). Cadmium (Cd) concentrations increased from 0.002 ± 0.0003 mg/L (S1) to 0.012 ± 0.0007 mg/L (S8) above the WHO limit (0.003 mg/L) and were most prominent in downstream areas because of electroplating activities, plastic industries, and fertilizer usage. The chromium (Cr) content in the river rose steadily from 0.026 ± 0.002 mg/L at S1 to 0.042 ± 0.004 mg/L at S8, which was near the WHO limit (0.05 mg/L). The elevated level measurements in industrial locations S5 to S8 signify pollution resulting from tanneries and metal industries, alongside waste disposal activities ( Table 3). Heavy metal pollution in the Ichu River has become increasingly serious as we move further downstream because contamination meets or exceeds WHO safety limits, which creates important health and ecological threats.

The bacterial contamination levels of the Ichu River worsened from upstream (S1) to downstream (S8), showing a continuous increase in pollution stemming from human activities. The river demonstrates high pollution levels through fecal and environmental bacterial contamination, because E. coli and Total Coliforms exceed the WHO standard limit of 0 MPN/100mL. This indicates that unhygienic water is unsuitable for direct human consumption ( Figure 2). The bacterial content at S1 showed low levels of contamination because E. coli reached 1500 MPN/100mL while Total coliforms were measured at 3200 MPN/100mL. Bacterial levels experienced substantial growth in the river until they reached the location at S8 where E. coli reached a peak of 3600 MPN/100mL along with Total Coliforms reaching 5300 MPN/100mL. The data showed that microbial contamination originates from sewage outflows, together with runoff from agricultural and industrial facilities that release wastewater. The Total Coliform counts remained higher than the E. coli measurements, indicating that the river water contained bacterial elements from both intestinal sources and general environmental pollution. The rising E. coli numbers at S6–S8 indicate that raw feces entered the water column through unprocessed sewage and livestock waste disposal into the river ( Figure 2). At all measurement points, the Ichu River exceeded the WHO drinking water standards to such an extent that it created a major health threat because individuals exposed to these bacteria risk contracting diarrhea dysentery cholera and typhoid. This study shows an urgent need to implement corrective measures to combat microbial pollution throughout the Ichu River. Three essential measures must be implemented to decrease water pollutant levels and safeguard public wellbeing: a system of improved wastewater treatment, stricter agricultural runoff controls, and effective sanitation management.

The results indicated a progressive increase in Chronic Daily Intake (CDI), Hazard Quotient (HQ), and Carcinogenic Risk (CR) for both As and Pb from upstream (S1) to downstream (S8), reflecting worsening contamination levels. The CDI for arsenic increased from 0.0012 ± 0.0001 mg/kg/day (S1) to 0.0026 ± 0.0003 mg/kg/day (S8), whereas that for lead increased from 0.0008 ± 0.0001 mg/kg/day (S1) to 0.0015 ± 0.0002 mg/kg/day (S8) ( Table 4). This steady rise suggests increasing anthropogenic pollution, likely from industrial discharge, mining, and wastewater contamination. The HQ values for arsenic range from 4.00 ± 0.30 at S1 to 8.67 ± 0.65 at S8, indicating that exposure at all sites exceeds the non-carcinogenic safety threshold (HQ > 1), posing potential chronic health risks such as neurological and cardiovascular disorders. Similarly, lead HQ values increase from 2.50 ± 0.20 at S1 to 4.60 ± 0.42 at S8, suggesting continuous exposure risks from drinking or using contaminated water. Both metals exhibited HQ values far beyond the acceptable limits, emphasizing the need for immediate intervention. The carcinogenic risk (CR) for arsenic was notably high, exceeding the acceptable risk limit (1E-04) at all sites. The CR values ranged from 6.0E-04 ± 0.5E-05 (S1) to 1.3E-03 ± 1.2E-05 (S8), indicating that long-term exposure to arsenic substantially increased cancer risk. Likewise, CR values, although lower than arsenic, increased from 1.2E-04 ± 0.1E-05 (S1) to 2.6E-04 ± 0.5E-05 (S8), suggesting a growing risk of carcinogenic effects. The higher values in the downstream locations (S6–S8) highlight worsening contamination, requiring urgent mitigation measures. The experimental data in Table 4 show increasing CDI, HQ, and CR values at downstream sampling sites, which can be compared with the recommended values in Table 5 as provided by WHO and USEPA. This comparison highlights deviations from the safety thresholds, emphasizing potential health risks in these areas.

CDI, HQ, and CR for Cadmium, Mercury, and Chromium across sampling sites (S1–S8) revealed a progressive increase in contamination downstream ( Figure 3). Cr exhibited the highest CDI values, exceeding the WHO limits in S6–S8, while Cd and Hg also showed increasing trends, indicating growing contamination at lower sites. The WHO-recommended CDI thresholds consistently surpassed those of downstream locations. The HQ values demonstrated that Cr and Cd exceeded the non-carcinogenic safety limit (HQ = 1) at all sites, confirming their potential chronic health risks ( Figure 3). Mercury HQ values remained below the critical threshold, but increased steadily downstream, reflecting a rising exposure risk over time. The WHO safety guidelines for HQ were violated for Cr and Cd, highlighting their higher toxicity impact. The CR values indicated that Cr posed the greatest cancer risk, surpassing the acceptable carcinogenic threshold (1E-04) in multiple locations. Cadmium and Mercury also displayed increasing CR values downstream, confirming the elevated health risks for exposed populations. The WHO-recommended carcinogenic limits were exceeded at several sites, particularly in S6–S8, where contamination levels were the most severe. Overall, the results confirmed that heavy metal pollution intensified downstream, with Cr and Cd presenting the most significant health risks. Urgent intervention strategies, including water treatment, pollution control, and regulatory enforcement, are necessary to prevent long-term exposure hazards and safeguard public health.

The scree plot revealed that PC1 explained most of the variance in the dataset, indicating that most water quality parameters were highly correlated and contributed significantly to overall water quality degradation. PC2 accounted for much less variance, suggesting that only a few dominant parameters drove the primary variations in water quality. The sharp drop in variance after PC1 confirmed that the water pollution trends could be effectively summarized using a limited number of components ( Figure 4). The PCA scatter plot demonstrated a clear separation between the upstream and downstream sampling sites (S1–S8), reflecting increasing pollution levels as the river flowed downstream. The upstream sites (S1, S2) appeared distinctly separated from the downstream sites (S7 and S8), reinforcing the notion that water quality deteriorates due to industrial discharge, wastewater inflow, and agricultural runoff. The extreme position of S8 indicated that it had the highest contamination levels, particularly for heavy metals, microbial loads, and nutrient accumulation. Sites S3 to S6 showed a progressive transition, suggesting a gradual decline in water quality, where pollution accumulated and became more severe. The alignment of sites along PC1 suggested that pollution indicators such as heavy metals, coliforms, and organic matter were the primary factors differentiating water quality conditions.

All components of the Turbidity-BOD-Heavy Metal (arsenic-lead-cadmium-chromium)-Microbial Contamination (E. Coli-Total Coliforms) analyses were strongly correlated with each other. Water quality deterioration occurs mainly through industrial effluents, untreated sewage, and agricultural runoff, which carry organic and inorganic contaminants to water sources. The mining sector, along with industries, has substantially elevated both dissolved solids and metal concentrations in rivers by creating high correlations between TDS, Conductivity and Heavy Metals. Scientific analysis showed a clear negative relationship between Dissolved Oxygen readings and all pollution indicators, including BOD, heavy metals, and microbial results ( Figure 5). This study proved that higher pollution concentrations caused the water environment to become dangerous for aquatic organisms. The degradation of water quality worsened because the rising BOD measurements indicated greater organic matter decomposition, which simultaneously reduced the DO levels. The gradual decline of the DO measured downstream matched the rising amounts of waste disposal from industrial and residential sources, which proved the destructive impact of waste emissions. The analysis showed that specific parameters that were strongly associated with each other created redundancy, which made it possible to use important selected variables for monitoring general water quality. Both types of contaminants demonstrated strong associations, which confirmed that they originated from identical sources, such as industrial wastewater streams, urban water runoff, and industrial facilities. The upstream sites S1 and S2 showed decreased indicator correlations, yet the downstream sites S6 through S8 demonstrated increased correlations, indicating that contaminants accumulated during river transport towards the downstream areas.

Key findings and highlights •

Progressive upstream downstream deterioration of physicochemical water quality.

The overall water quality and ecological condition of the Ichu River are consistent with degradation patterns reported in river systems worldwide that are influenced by urbanization, mining, and untreated wastewater discharge. Globally, rivers in South Asia, East Asia, and parts of Africa and Latin America commonly exhibit declining dissolved oxygen, elevated biochemical oxygen demand, increased electrical conductivity, and excessive microbial contamination as a consequence of cumulative anthropogenic pressures ( Singh et al., 2024a). For instance, long-term studies of the Hindon River in India have reported similar upstream downstream deterioration, with strong associations between organic pollution, heavy metal enrichment, and fecal contamination, leading to substantial ecological stress and public health risks ( Singh et al., 2024a, 2024b). Comparable conditions have also been documented in large Asian river basins such as the Ganges and Yellow River systems, where intensive urban growth and industrial activity have resulted in heavy metal accumulation, nutrient enrichment, and loss of ecological integrity ( Li et al., 2022; Su et al., 2021). These studies demonstrate that the physicochemical and biological degradation observed in the Ichu River follows a globally recognized trajectory of river ecosystem decline under mixed pollution sources. At the regional scale, rivers in the Andean and Latin American context show striking similarities to the Ichu River. Mining-impacted rivers in the Central Andes of Peru frequently exhibit elevated arsenic and lead concentrations exceeding international guideline values, along with reduced water quality index scores and increased health risks for local populations ( Custodio et al., 2020; Sánchez-Araujo et al., 2024). Similar patterns have been observed in urban and mining-influenced rivers across Latin America, where integrated assessments combining water quality indices and health risk models have revealed widespread ecological degradation and compromised water usability ( Choudhury et al., 2022). In comparison with these global and regional case studies, the Ichu River displays a moderate-to-severe level of degradation, particularly in downstream sections where contamination accumulates. The convergence of physicochemical deterioration, heavy metal pollution, and microbial contamination indicates reduced ecosystem resilience, impaired aquatic habitat quality, and increased vulnerability of river-dependent communities. These comparisons confirm that the Ichu River represents a typical yet critical example of river ecosystem degradation in developing and mining-influenced regions, underscoring the urgency of integrated management and restoration efforts.

Heavy metals analyzed in the water increased along the downstream direction because of mining operations, improper waste disposal, and industrial discharge. This conclusion is supported by the distinct spatial gradient observed in the dataset, where metal concentrations increased consistently from upstream sites (S1–S2), located in minimally disturbed headwaters, toward downstream sites (S6–S8), which are situated near active mining zones, industrial facilities, and urban wastewater discharge points. The highest concentrations of As, Pb, Cd, and Cr were recorded precisely at sites adjacent to known mining operations and wastewater outlets, matching documented contamination pathways reported in regional environmental reports and prior studies conducted in the Central Andes ( Custodio et al., 2020; Sánchez-Araujo et al., 2024). The strong positive correlations between heavy metals, conductivity, TDS, and turbidity further support an anthropogenic source, as these indicators typically rise in response to industrial effluents and mine-drainage inflows. The arsenic levels increased downstream from 0.040 mg/L at S1 to 0.072 mg/L at S8 which surpassed the WHO drinking water standard of 0.01 mg/L creating critical health risks including cancer formation. The analysis revealed that lead mineral substance concentrations doubled from 0.014 mg/L to 0.032 mg/L above the WHO standards, presenting potential neurotoxic hazards. The heavy metal pollutant levels exceeded both national and international standards as Cd increased from 0.010 mg/L to 0.012 mg/L and Cr increased from 0.026 mg/L to 0.042 mg/L. The obtained results match historical findings about rivers adjacent to mining and industrial areas, whose heavy metal contamination stems from tailing ponds alongside electroplating industries and wastewater emissions ( Parida et al., 2021). Our findings are consistent with studies of the Hindon River in India, where high nutrient and heavy metal loads were also reported ( Singh et al., 2024a). Similar contamination has been observed in the Ganges basin ( Li et al., 2022), though regional variations exist due to industrial patterns. This comparative approach strengthens the ecological interpretation of our results. The results of this study support worldwide observations because industrial wastewater and rock dissolution processes act as principal heavy metal pollution pathways in riverine environments ( Zeb et al., 2024; Islam et al., 2018). The microbial tests verified high fecal contamination because total coliform counts increased from 3200 MPN/100mL at S1 to 5300 MPN/100mL at S8, and E. coli numbers rose from 1500 MPN/100mL to 3600 MPN/100mL. The total counts of microorganisms (0 MPN/100mL) set by the WHO exceed what the Ichu River sustains, making it unsafe for human consumption or direct interaction, except through the proper treatment of water. The high detection levels of microbial contaminants in the examined samples were correlated with the effects of urban runoff, untreated sewage, and agricultural waste pollution, in line with previous research on microbial river contamination ( Zahoor and Mushtaq, 2023). A correlation heatmap demonstrated that microbial indicators, together with turbidity and BOD, showed strong positive relationships, which establishes that sewage discharges and organic pollution increase bacterial numbers ( Bowes et al., 2020). The overall water quality trends in this study parallel findings from the Yamuna River in India ( Singh et al., 2021), Bangladesh’s Buriganga River ( Jyethi et al., 2022), and Peru’s Mantaro River ( Choudhury et al., 2022). However, variations in land use, industrial activity, and governance explain differences across these regions, underscoring the need for context-specific solutions.

Assessments of HQ and CR revealed serious safety threats associated with heavy metal exposure. Arsenic HQ levels at S1 measured 4.00 and rose up to 8.67 at S8 thus exceeding the safety threshold (HQ < 1) which suggests a strong probability of developing chronic health problems such as cancer and cardiovascular diseases. An increase in lead levels rose from 2.50 to 4.60 HQ produced substantial neurological disorder risks, mainly affecting children. The CR values exceeded the WHO standards (1E-04) for arsenic and lead, which indicates an unacceptable hazard for developing cancers during long-term exposure. Studies of heavy metal toxicity in polluted rivers have shown similar results with these findings ( Xia et al., 2018). Principal Component Analysis (PCA) showed pollution-related elements to be the fundamental causes of water quality degradation. Heavy metals, microbial contamination, and organic pollution (BOD and turbidity) were the dominant factors in PC1, which explained the most variability. Monitoring data through the PCA scatter plot displayed upstream locations S1 and S2, situated independently from downstream sites S6 through S8, indicating a trend of increasing pollution throughout the watercourse. Downstream water sites cluster due to centralized pollution influences, which also occur in industrialized and urban river systems ( Singh et al., 2005; Yamin et al., 2015. The correlation heatmap confirmed these insights by showing extreme connections between heavy metals and turbidity, and between microbial indicators and BOD, which implies that pollution sources work together as a system. Research worldwide has demonstrated that industrial production, together with urbanization, has adverse effects on freshwater systems ( Ouma et al., 2022).

This study shows that the Ichu River does not meet the water quality criteria defined by the WHO or those set by the USEPA for safe drinking water consumption and protection of aquatic ecosystems. Environmental safety and public health remain at significant risk due to the high levels of heavy metals, microbial contaminants, and organic pollutants that require immediate regulatory action. Authorities should establish a system of strict wastewater treatment regulations while installing advanced heavy metal removal systems through adsorption and coagulation, reverse osmosis, and strengthening the water quality monitoring system. Communities must actively participate in conservation activities, whereas public education programs must teach the public how to conduct proper waste disposal and maintain sanitary conditions. The current study presents significant pollution dynamics knowledge for the Ichu River, but research needs continuous expansion to evaluate water quality changes across seasons and wet and dry periods. Bioaccumulation studies on heavy metals in aquatic organisms must be conducted because they indicate how heavy metals affect fish populations and human food security. Hydrodynamic system simulators and predictive models should be developed to forecast pollution patterns and assist with effective river management practices. Nature-based remediation strategies that use cost-effective techniques for constructed wetlands and phytoremediation offer potential sustainable solutions to combat pollution problems. A key highlight of this study is the combination of physicochemical and biological parameters to provide a comprehensive view of river health. The prospects include developing predictive water quality models and expanding monitoring to evaluate climate change impacts on river ecosystems ( Choudhury et al., 2022; Singh et al., 2024b). From a management perspective, the integration of water quality indices, health risk metrics, and multivariate statistical analysis enhances the scientific and practical value of the study. Similar frameworks have been successfully used to support regulatory decision-making, pollution control prioritization, and public health protection in degraded river basins worldwide. The present study provides a foundation for future application of predictive models, including regression-based forecasting and machine-learning approaches, to simulate pollution scenarios and evaluate the effectiveness of mitigation strategies under increasing urban and climatic pressures ( Gaur et al., 2022; Singh et al., 2024b).

Limitations of the study

This study is limited by its short-term sampling period, which restricts the ability to capture long-term seasonal fluctuations. Additionally, while multiple water quality parameters were examined, advanced modeling approaches such as machine learning were beyond the current scope. Future studies should address these gaps to provide a more holistic assessment.

Despite the comprehensive assessment, this study has several limitations that should be acknowledged. First, the sampling campaign represents a short-term monitoring period, which limits the ability to capture seasonal variability associated with wet and dry seasons, storm-driven runoff, and fluctuations in industrial and agricultural discharge. Second, the analysis was based on eight sampling sites, which, while sufficient to characterize upstream–downstream trends, may not fully resolve localized pollution hotspots near specific discharge points.

Third, the human health risk assessment relied on standard exposure assumptions (ingestion rate, body weight, and exposure duration) recommended by international guidelines; however, actual exposure patterns of local communities may differ, potentially leading to under- or overestimation of risk. Fourth, the study focused primarily on water-column concentrations, without including sediment or biota analyses, which are important for understanding metal accumulation, long-term persistence, and food-chain transfer. Finally, microbial contamination was assessed using indicator organisms (E. coli and total coliforms) rather than pathogen-specific or quantitative microbial risk assessment (QMRA) approaches, which could provide a more refined estimation of infection risk.

This study offers several new highlights and future prospects. First, it demonstrates a co-occurrence pattern of physicochemical degradation, heavy metal enrichment, and microbial contamination along an upstream–downstream gradient, confirming that river pollution in the Ichu basin is driven by multiple interacting stressors rather than isolated sources. Second, the combined use of water quality indices (NSF-WQI and CCME-WQI), multivariate statistical analysis (PCA and correlation), and human health risk models (CDI, HQ, and CR) provide a robust, integrated framework that enhances both scientific interpretation and policy relevance.

Third, the study identifies downstream hotspots (S6–S8) where contamination and health risks are consistently highest, offering a clear spatial basis for prioritizing management interventions. Fourth, the dataset establishes a baseline reference for a high-Andean river system, which is currently underrepresented in the international literature, enabling future temporal comparisons and model-based forecasting.

The findings of this study have direct and significant implications for local communities living along the Ichu River, many of whom rely on the river for domestic water use, irrigation, livestock watering, and informal economic activities. The elevated levels of E. coli and total coliforms indicate a high risk of waterborne diseases such as diarrhea, dysentery, and typhoid, particularly affecting children, the elderly, and immunocompromised individuals. Simultaneous exposure to toxic heavy metals, especially arsenic and lead, raises concerns about long-term health effects, including neurological disorders, cardiovascular diseases, and increased cancer risk.

Beyond health, declining water quality threatens agricultural productivity and food safety, as irrigation with contaminated water can degrade soil quality and facilitate metal transfer into crops. These impacts can exacerbate economic vulnerability and increase healthcare burdens for households already facing limited access to treated water. By clearly identifying pollution hotspots and associated risks, this study provides critical information to support community risk awareness, public health interventions, and local decision-making, ultimately contributing to improved quality of life and environmental justice in the Huancavelica region.

Prospects and recommendations

Future research on the Ichu River should prioritize long-term and seasonal monitoring to capture temporal variability associated with wet and dry seasons, storm events, and fluctuations in mining, industrial, and agricultural activities. Seasonal datasets would improve understanding of contaminant transport dynamics, dilution effects, and peak exposure periods for local communities.

From a scientific perspective, future studies should expand the analytical scope to include sediment and aquatic biota, allowing assessment of metal accumulation, persistence, and food-chain transfer, which are critical for evaluating long-term ecological and human health risks. The application of advanced modeling approaches, such as multivariate regression, Monte Carlo–based uncertainty analysis, and machine-learning techniques, would further enhance pollution source apportionment and enable predictive forecasting of water quality trends under increasing anthropogenic and climatic pressures.

From a management and policy standpoint, immediate actions are recommended, including strengthening municipal wastewater treatment infrastructure, enforcing stricter control of mining and industrial effluent discharges, and implementing best agricultural practices to reduce nutrient and runoff inputs. Establishing a routine water quality monitoring program based on integrated indices (e.g., CCME-WQI, metal pollution indices) would support early warning and regulatory decision-making.

At the community level, public awareness and risk communication programs should be promoted to inform residents about water-related health risks and safe water-use practices. Encouraging stakeholder participation in river conservation initiatives and integrating scientific findings into local water governance frameworks will be essential for achieving sustainable watershed management and protecting the long-term health of the Ichu River ecosystem.