The River Ravi is a river that flows across borders, starting in the northeast of the Kangra district in Himachal Pradesh and the southeast of the Chamba district in the Jammu Kashmir Region of India (Chand and Sharma, 2015). It flows across Pakistan and serves as a notable tributary of the Indus System in the Punjab state (Akhtar et al., 2014). After covering a distance of 100 km from its upper catchments in India, the Ravi River enters Pakistan. The Ravi River is the most diminutive of the five other rivers in the Indus basin system that flow through Pakistan (Aslam et al., 2020).

The total length of the Ravi River is approximately 720 kilometres. The study region is situated within the latitude range of 31° 1’ 45.30” N to 31° 55’ 19.43” N and the longitude range of 73° 20’ 6.19” E to 74° 39’ 21.00” E. The study region is the Lahore division, which has four districts: Sheikhupura, Lahore, Kasur, and Nankana Sahib ( Figure 1 ). The length of the Ravi River within the research region is 219.87 kilometres.

This investigation involved the collection of sediment samples from various sites along the Ravi River. The collection of these samples were done during the May-June 2023. Twenty-two stations were sampled along the mainstream of the River. The sampling locations were chosen randomly. The sediment samples were obtained using a securely sealed grab sampler from a boat. Subsequently, the specimens were enclosed in plastic bags and preserved in an ice box maintained at 4°C. In the laboratory, samples were allowed to dry naturally at the ambient temperature. Before screening, the sediment samples were subjected to disaggregation and sorted into different particle sizes using a stainless-steel mesh (Feng et al., 2017). Samples were grounded to a fine powder using an electric agate mortar for less than 20 minutes, with around ten grams of each sample. The obtained powder was subsequently employed for the analysis of heavy metal presence. To conduct heavy metal analysis, 0.5 g of the powdered samples were treated with concentrated nitric acid (HNO₃) and concentrated chloric acid (HClO₃) in a ratio of 3:1 until practically all the liquid evaporated (Chester et al., 1994). Subsequently, deionized distilled water was added. After dilution, the insoluble residue was eliminated, and deionized water was added until the total volume reached 25 ml. The samples were analyzed for the existence of certain heavy metals using a flame atomic absorption spectrometer (AAS, GBC-932, Australia). To ensure the highest levels of accuracy, three replicates of each measurement were performed, with variations between them not exceeding 3% (Das et al., 2022).

The measurement of selected metals in sediment samples was done using the methodologies from the literature with certain modifications (Bu et al., 2020). After every fifth sample run, an examination was conducted to assess contamination and drift in a certified standard and a blank solution. All the chemicals and reagents utilized were of duly confirmed analytical purity. The metals were analyzed with a Buck 230ATS Atomic Absorption Spectrophotometer (AAS). The device was utilized at its utmost sensitivity and adjusted according to the manufacturer’s guidelines. The replication findings were computed following data acquisition for each metal parameter, expressed as milligrams per kilogram (mg/kg).

Sediment quality assessment involves the use of several indices to determine the extent of heavy-metal contamination. The indices employed include geo-accumulation index (Igeo), contamination factor (CF), and enrichment factor (EF). Collectively, these indices offer a robust framework for evaluating heavy-metal pollution in sediments. The degree of ecological risk associated with heavy-metal contamination can be systematically categorized as listed below.

The non-cancerous and cancerous risk conditions due to exposure to heavy meats have been assessed by considering the overall metal content of specific heavy metals. The equations used in this investigation to calculate the human health risk assessment (HRA) associated with metal exposure are derived from existing literature and methodologies (Zheng et al., 2010; Li et al., 2015) ( Table 2 ).

Hence, regardless of gender, children and adults were categorized. The metal exposure can occur through three main pathways: direct inhalation, dermal absorption,” and ingestion of deposited atmospheric particles as indicated in Equation 4–Equation 6

The metal content (mg/kg) is represented by C. The estimates are based on parameters provided by the literature (Li et al., 2015).

The HQ was measured per body weight and represented as milligrams per kilogram per day (mg/kg/day). To achieve this objective, the estimated ADDi values were used as the numerator, and the matching RfD values were used as the divisor to calculate HQi using Equation 7.

The study examines the maximum allowable risk of exposure to RfD, considering both children and adults, throughout a lifetime. If HQ< 1 (Zheng et al., 2010). Noncarcinogenic substances have a threshold limit below which no adverse response occurs. The RfD values employed in this study are cited by other studies (Li et al., 2015). The hazard index (HI), which is calculated by summing the hazard quotients (HQs) for the three main pathways of exposure, is represented by Equation 8 (De Miguel, 2007; Zheng et al., 2010).

A hazard index (HI) value of less than one is considered safe, while an HI value of more than 1 indicates the potential for non-cancerous events (Zheng et al., 2010; Hussain et al., 2015).

The heavy metals concentration was analyzed using Microsoft Office Excel to determine the minimum, maximum, and average concentrations and Pearson correlation analysis. ArcGIS software (version 10.8) was utilized for the spatial interpolations of heavy metal concentration in the River, employing spatial distribution techniques (Shetaia et al., 2023). The R programming language (Shetaia et al., 2023) was used to identify the correlation (corrplot) between heavy metals.

Figure 2 presents the statistical data and regional patterns of heavy metal concentrations in the sediment of River Ravi. The presence of heavy metals (HMs) in the soil is influenced by factors such as pH soil chemistry (including moisture, soil organic matter, clay, silt, and sand). The mean concentrations of metals were as follows: manganese (Mn) had the highest average value at 779.996 mg/kg, followed by nickel (Ni) at 131.163 mg/kg, chromium (Cr) at 129.594 mg/kg, zinc (Zn) at 128.112 mg/kg, copper (Cu) at 62.514 mg/kg, lead (Pb) at 50.504 mg/kg, arsenic (As) at 33.742 mg/kg, and cadmium (Cd) at 1.491 mg/kg. Regarding spatial distribution, most metal concentrations increase near densely inhabited and industrialized regions, particularly in the river section adjacent to Lahore. Various anthropogenic effluents are discharged into this area, such as home sewage, industrial wastes from the city, and agricultural wastes from neighbouring cultivated fields. Additionally, the presence of domestic and industrial waste may also contribute to the increased metal content. Zinc is readily absorbed by soil organic matter (SOM), although it is subsequently liberated in an acidic environment (Metsalu and Vilo, 2015; Nassar and Fahmy, 2016).

Water pollution significantly threatens aquatic life, including fish, shrimp, and crabs, thereby escalating health risks at various trophic levels (Ariyaee et al., 2015). This is particularly concerning for commercially valuable fish species because elevated levels of heavy metals could have deleterious health effects on humans, especially in vulnerable populations such as children, women, and individuals with pre-existing health conditions. Consequently, the vigilant monitoring of heavy-metal concentrations in organisms is imperative for the effective regulation and management of aquatic ecosystems, ensuring the safety of seafood for human consumption (Caeiro et al., 2005).

Table 3 summarizes the concentrations of heavy metals in the River Ravi with other rivers in Pakistan and globally. The River Ravi has moderate heavy metal concentration levels, with all measured elements falling within the range observed in other rivers, except for metals As, Cr, Ni, and Cu, which show higher levels at some sampling sites. The heightened levels of the elements in the River indicate that, in addition to the River’s natural flow, pollutants from other sources also infiltrate this unique ecosystem and contribute to the increase in pollution. Examples of causes contributing to water pollution can be categorized as anthropogenic, such as urban and industrial wastewater, and natural, connected to sediment texture and geological features (Hashemi, 2018). Upon comparing the average concentration of Arsenic (As) in various elements, it was discovered that the River Ravi exhibited a lower concentration (33.74 mg/kg) in comparison to the Zhejiang River in China (6.90–74.34 mg/kg) and the Yellow River in China (14-48 mg/kg) (Liu et al., 2009; Zhang et al., 2018). The Sediment of the River Ravi exhibited a significantly higher concentration of heavy metals compared to the Tanjan River in Iran (12.8 mg/kg) (Liu et al., 2016), the Yangtze River in China (29.9 mg/kg) (Yang et al., 2009), the Jialu River in China (2.39–14.57 mg/kg) (Fu et al., 2011), the Kabul River in Pakistan (1.26 mg/kg), the Chenab River in Pakistan (.65 mg/kg), and the Indus River in Pakistan (7.65 mg/kg) (Nawab et al., 2018). This phenomenon can be explained by the substantial influx of this element from rivers. Arsenic can be found naturally in rocks along the River, but it can also come from fertilizers and fungicides in rice crops along the River (Szolnoki et al., 2013; Wang et al., 2019).

The heavy-metal concentrations in sediment samples can be evaluated using various pollution assessment indicators (Hahladakis et al., 2013; Liu et al., 2018; Zhou et al., 2022). These include the geo-accumulation index (Igeo), which measures the degree of contamination compared with pre-industrial levels; the contamination factor (CF), which quantifies the relative enrichment of metals; the enrichment factor (EF), which assesses the potential harm to living organisms. The levels of these indicators are listed below

The highest level of contamination, as indicated by the Igeo values, was attributed to Cd (-0.519 – 2.348), in agreement with the pollution degree defined by Igeo (Muller, 1969). The sediments in the research area are categorized as uncontaminated by copper (Cu), nickel (Ni), lead (Pb), manganese (Mn), iron (Fe), and zinc (Zn). The Igeo analysis indicates that the sediments of River Ravi displayed a range of pollution levels, ranging from moderate to extremely high, as depicted in Figure 3 . The mean Igeo values ranged from -0.792 (Mn) to 1.583 (Cd), with the minimum and maximum Igeo values being -1.783 (Pb) and 2.348 (Cd), respectively. According to the criteria, it is essential to highlight that As, Cd, Cr, Ni, Cu, Zn, and Pb displayed distinct contamination patterns. Cadmium showed moderate pollution, whereas lead contamination varied from unpolluted to moderate. The soil samples primarily exhibited low pollution levels for chromium (Cr), nickel (Ni), copper (Cu), and zinc (Zn), ranging from unpolluted to moderately polluted. The soil analysis revealed varied degrees of contamination, ranging from non-polluted to moderately polluted, as indicated by the presence of As and Pb. The soil samples were sorted in descending order based on the average sequence of Igeo values as follows: Cd (1.583), As (0.654), Pb (0.467), Ni (0.328), Cr (-0.130), Zn (-0.213), Cu (-0.234), Mn (-0.792).

The contamination factor values exhibited a range of 0.436 (Pb) to 7.637 (Cd), with average values spanning from 0.9176 (Mn) to 4.9714 (Cd), suggesting significant regional variation. Analysis of the average contamination factor values from all test locations revealed no evidence of soil contamination by Mn. Nevertheless, As, Ni, Cr, Cu, and Zn in soils suggest a moderate pollution level. In contrast, Cd indicates a high level of contamination based on the criteria. The soils in different sampling sites displayed varying contamination patterns. Specifically, 9.0% of the sites had low contamination, 50% had moderate contamination, and 43.0% had considerable contamination. Regarding Cd (cadmium) levels in the soils, 9% had low contamination, 22% had moderate contamination, 36% had considerable contamination, and 33% exhibited very high contamination. Remarkably, the soil sample recovered from S-14 displayed the greatest Igeo and CF values for Cd, although elements like Cr, Cu, and Ni did not demonstrate a comparable trend. These data indicate that soil pollution in the study area is likely caused by processes other than only metal contamination.

The research region faces significant heavy metal pollution, as indicated by the Contamination Factor (CF) evaluation results. The Enrichment Factor (EF) was also utilized to assess the quantity of pollution caused by a particular metal ( Figure 4 ). The EF analysis reveals a consistent pattern of pollution in heavy metals, which is also observed in the Igeo analysis. This indicates a certain amount of consistency in the assessed repercussions of human activities on pollution. Based on the EF categorization (Hakanson, 1980), approximately 55% of the sampling locations exhibited a moderate to high pollution level regarding Cd contamination. The average concentration of arsenic (As), lead (Pb), and nickel (Ni) indicate a low level of pollution, ranging from deficiency to minor contamination. The soil samples exhibited the following sequence of EF values: Cd (5.4581) > As (2.8254) > Pb (2.6236) > Ni (2.2159) > Cr (1.6189) > Zn (1.5159) > Cu (1.4985). The mean EF values for magnesium were below 1, suggesting a natural source. The Ni, Cr, Cu, and Zn levels ranged from 1 to 2, indicating that the sources are most likely of anthropogenic origin (Zhang et al., 2018). The concentrations of Cd, As, and Pb exhibited a moderate level of enrichment, suggesting the presence of human-induced impacts and a moderate impact from human activities ( Figure 4 ).

The study aimed to assess the potential human health risks from exposure to some aspects of the Ravi River. Specifically, the non-cancerous effects of arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), lead (Pb), and manganese (Mn), as well as the carcinogenic risks of As, Cd, Cr, Ni, Cu, and Pb, were investigated. The study focused on three main pathways of exposure: inhalation, ingestion, and dermal interaction. Figure 5 demonstrated that the eating route had the highest danger, followed by cutaneous interaction and inhalation. Other scholars have also documented this phenomenon (Xu et al., 2017; Ur Rehman et al., 2018). Among the metals tested, the sequence of causing health impairment in children through diverse exposure routes was as follows: Cd > Mn > Cr > Ni > Pb > As > Cu > Zn. A similar pattern was seen in adulthood except for Copper (Cu), which came before Nickel (Ni). The sequencing of the remainder of the metals in the HI sequence remained consistent. According to the health risk assessment model employed by USEPA and IRIS, it was determined that children had a higher level of exposure to metals compared to adults. This conclusion was based on many criteria and approaches. Children are more exposed to dust and soil particles than adults because of their active behaviour, extended playtime, and lack of caution while eating and drinking. Precautionary steps should be implemented to reduce children’s exposure to potentially harmful health conditions. Unlike several other studies (Xu et al., 2017; Ur Rehman et al., 2018). In our investigation, Cd exhibited the most considerable noncarcinogenic risk in adults, followed by As, Mn, Cr, Ni, Cu, Pb, and Zn. Hence, Cd, As, Mn, and Cr are the elements that should be given the highest consideration regarding their propensity to cause health concerns. Multiple paths contribute to the intake of harmful substances during the day and night. In addition to these pathways, various food products (both wet and dry) from the same area are significant sources of health hazards in toxicology and hygiene. The hazard quotient (HQ) values for children were more important than those for adults, although they were still within the safe limit (HQ < 1). However, due to exposure to Cd, the HQ (Hazard Quotient) for children exceeded the established limit of 1.08E+00. Furthermore, HQderm was discovered to exceed the acceptable threshold (HQ > 1) in both children and adults due to exposure (Aslam et al., 2020). The study demonstrates that Cd and As were the sole metals shown to have detrimental health impacts in our research. These effects are expected to intensify with an increase in the HQ value. Similarly, the computed Hazard Index (HI) values for children were higher than those for adults in all of the metals investigated. Nevertheless, the analysis revealed that all these elements’ exposure remained below the established safety threshold (HI < 1).

Figure 5B unambiguously demonstrated that the cancer risks associated with several metals studied much exceeded the permissible threshold of RI (1 × 10−4). The values for children were ranked in the following order: Cu (6.65E-03) > As (3.65E-03) > Ni (3.88E-04) > Cd (3.65E-04) > Cr (3.62E-04) and Pb (7.15E-07). Only As (5.22E-04) and Cu (4.22E-04) exhibited RI values above the acceptable limit for adults, indicating a high risk of human carcinogenicity. Nevertheless, Cd, Ni, Cr, and Pb concentrations were determined to be under the acceptable threshold of 1 × 10⁻⁴.

Correlation analyses can reflect whether different heavy metals share a common source or undergo similar contamination processes (Hu et al., 2013). To further elucidate the origins of the heavy metals in the sediments and quantify their respective source contributions, Positive Matrix Factorization (PMF) was employed for source apportionment and contribution elucidation.

The metal concentration in aquatic habitats is affected by anthropogenic activities and natural phenomena (Li et al., 2020). Human-induced pollution includes agricultural effluents caused by the overuse of pesticides, herbicides and fertilizers, together with traffic pollution, industrial discharges, and home wastewater (Magnusson et al., 2018; Shetaia et al., 2022). Furthermore, natural characteristics refer to an ecosystem’s chemical and physical properties, its environmental context, and the biological factors that affect it (Zhang et al., 2019; Ytreberg et al., 2022). The interrelationships among the analyzed metals can aid in determining the origin(s) of pollution (Zhao et al., 2023). Moreover, the HCA approach offers a fitting explanation for variables in the sample region that exhibit similar geochemical behaviour or have been impacted by human activities. The reference is attributed to Thiombane et al. (2018). Figure 6 displayed the correlation of the heavy metals samples.

The examination of heavy metals indicated a substantial correlation between the existence of arsenic (As) and manganese (Mn), as well as between As and chromium (Cr). Furthermore, a moderate positive association was detected between arsenic (As) and copper (Cu) elements with a correlation coefficient of 0.48. Corelation analysis of heavy metals showed that surface sediments of the River Ravi originate from natural sources and three anthropogenic sources. Anthropogenic source 1 originated primarily from industrial effluent, which is characterized by a high load of metals. Anthropogenic source 2 originated primarily from metal processing and vehicular activities. Anthropogenic source 3 originated primarily from coastal industrial activities and fossil fuel combustion.

Heavy metal pollution in rivers can have detrimental effects on aquatic ecosystems. Metals can accumulate in sediments, water, and biota, leading to toxicological effects on aquatic organisms. Rivers serve as conduits for transporting heavy metal pollution from inland areas to coastal regions and ultimately the ocean. As rivers discharge into the sea, they carry their pollutant loads, including dissolved metals and sediment-bound contaminants. Coastal ecosystems may act as sinks for heavy metals, where deposition and accumulation occur, affecting marine organisms and habitats (Zhang et al., 2019; Ytreberg et al., 2022; Zhao et al., 2023).