Integrated water quality assessment and health risk analysis of heavy metal and microbial contamination in the Ichu River, Peru

Study area and sampling sites

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.

Sample collection and preservation

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 (HNO₃, 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.

Physicochemical analysis

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 BOD₅ according to APHA Standard Method 5210 B. Researchers used UV-Vis spectrophotometry to measure nitrate (NO₃^-) and phosphate (PO₄^3-) concentrations because these chemicals indicate agricultural runoff pollution. All measuring tools underwent pre-use calibration to generate reliable and accurate data outputs.

Heavy metal analysis

The analysis focused on measuring arsenic (As), lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr) in water samples using Inductively Coupled Plasma Mass Spectrometry (ICP-MS, Agilent 7500 Series, USA). The acid digestion required treating 50 mL of water sample solution with HNO₃ 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.

Microbial contamination analysis

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).

Health risk assessment

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:

Non-carcinogenic risk was assessed using the Hazard Quotient (HQ) formula:

Statistical analysis and data Interpretation

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.

Physiochemical properties of Ichu River across various sites

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 1). 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 1). 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 NO₃^- while S8 samples showed 5.5 ± 0.84 mg/L NO₃^- and 0.21 ± 0.10 mg/L PO₄^3- increased to 0.50 ± 0.10 mg/L PO₄^3- 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 CaCO₃ (S1) to 140 ± 10.8 mg/L CaCO₃ (S8) ( Table 1). Water quality worsens as pollutants from human-related activities continue to escalate downstream.

Heavy metals concentration in the Ichu River across various sampling sites

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 2). 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 2). 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 2). 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.

Microbial contamination

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 1). 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 1). 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.

Figure 1. Bacterial contamination levels in the Ichu River.

Human health risk assessment

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 3). 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 3 show increasing CDI, HQ, and CR values at downstream sampling sites, which can be compared with the recommended values in Table 4 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 2). 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 2). 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.

Figure 2. Human health risk assessment along various sites Ichu River water sample.

Principal component analysis

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 3). 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.

Figure 3. Principal component analysis of water quality parameters.

Correlation matrix

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 4). 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.

Figure 4. The water quality parameter correlation analysis.