Assessment of Heavy Metal Contamination and Ecological Risk in Urban River Sediments: A Case Study from Leyte, Philippines

Abstract

This study evaluated heavy metal (HM) contamination in sediments from the Malbasag River in the Ormoc City port, Leyte, Philippines. A total of thirty sediment samples were collected randomly from ten locations along the river using an Ekman grab sampler. Atomic absorption spectrophotometry revealed HM concentrations in the order of Mn > Zn > Cu > Ni > Pb > Cd. All HMs exceeded their sediment quality guideline (SQG) thresholds except for Mn. Contamination was assessed using indices such as the contamination factor (CF), pollution load index (PLI), geo-accumulation index (I_geo), and enrichment factor (EF). The CF values indicated “moderate to considerable” contamination for Zn, Ni, and Cd, while Cu and Pb showed “very high” contamination levels. The PLI results indicated severe sediment degradation in 20% of samples. The I_geo analysis classified 60% of the samples as “heavily to extremely polluted” for Cd, Cu, and Pb. EF analysis suggested that anthropogenic sources contributed to elevated HM levels, including port activities and agricultural runoff. Ecological risk index (RI) analysis revealed moderate risk in 40% and considerable risk in 20% of sampling locations. Multivariate analyses suggested significant anthropogenic contributions to HM contamination, highlighting the need for further studies to assess the ecological impacts.

1. Introduction

Toxic metal exposure in aquatic environments has attracted attention from all over the world in recent years due to its persistence, potentially rising levels, and environmental toxicity [1,2]. Around the world, a lot of dangerous substances, especially heavy metals (HMs), are released into rivers as a result of industrial processes, atmospheric precipitation, fast population expansion, and other man-made and natural activities. These pollutants can be transported through water and accumulate in riverbed sediments, posing risks to aquatic ecosystems and human health [3,4,5]. Moreover, dumping untreated household and commercial waste into rivers significantly increases metal concentrations in sediments, further contributing to issues with water quality [6,7].

While there are many other causes of HM contamination in sediment, the disposal of industrial waste and municipal wastewater drainage systems into rivers is one of the main contributors. Many wealthy countries dump waste effluents from their industrial areas into rivers after purifying them. This practice, mostly without proper planning, causes major damages to the surrounding ecosystems [1]. While the immediate impact of industrial waste dumping is evident in ecosystem damage, the long-term consequences are further intensified by the complex chemical processes that heavy metals undergo within aquatic environments. The chemical composition of the suspended sediment, the substrate sediment, and the water chemistry all affect how metals behave in natural water [8]. Heavy metals may experience several speciation changes during transportation as a result of sorption, complexation, dissolution, and precipitation mechanisms [9,10,11], which affect their behavior and bioavailability [1,12]. The range of habitats and ecosystems found in the river basin make sediment an essential and dynamic component. Because contaminants tend to accumulate in bottom sediments in aquatic systems, they are an important indicator of pollution. Microorganisms, aquatic plants, and animals may bio-accumulate HM residues in polluted environments. These organisms may then enter the human food chain and have long-term effects on ecosystems and human health [13,14]. Furthermore, biological activity changes the chemical makeup of deposited HMs in sediments to create organic compounds, some of which may be more dangerous to animal and human life through the food chain [15].

The majority of cities in poor nations lack adequate planning for proper hazardous waste management, resulting in waste being carelessly thrown into aquatic environments. In these newly industrialized countries, increased urbanization and industrialization have resulted in the discovery of heavy metal contamination in rivers [16,17,18]. Furthermore, the issue of pollution is worsened in port areas, which are critical places for trade and economic development. HM contamination of sediment in port environments has received increased attention recently due to its origins, abundances, rates of buildup and degradation, and possible ecotoxicity [19,20,21].

In addition to being a reservoir of pollutants that are crucial to preserving the trophic status of any body of water, sediments are biologically relevant elements of aquatic habitat [22,23]. The ecological risks and potential contamination linked to certain HMs should be taken into account to avoid possible danger. To provide a thorough understanding of the possible impacts, this evaluation should be carried out using a variety of environmental risk assessment indices, including the contamination factor (CF), pollution load index (PLI), enrichment factor (EF), geo-accumulation index (I_geo), toxicological unit (TU), toxic risk unit (TRI), modified hazard quotient (mHQ), and potential ecological risk (RI) [19,24]. Furthermore, sediment research is crucial because sediments have a lengthy residence duration, which translates into long-term environmental impacts [1,25]. As a result, river sediments are crucial resources for determining the degree of anthropogenic and natural pollution sources in riverine environments. In an aquatic system, sediments can serve as secondary sources of pollution in addition to being carriers of pollutants. Because sediment offers a variety of habitats, stores bulk contaminants, and significantly provides environmental and geochemical speciation, sampling it is therefore a faster, more cost-effective, reliable, and more thorough way to monitor the health of aquatic ecosystems than other methods [26].

This study was conducted in Malbasag River, situated in the main city center called Ormoc City, Philippines. It is one of the most important rivers in the country for the ecosystem, supporting and regulating services. The whole population of Ormoc City gets its drinking water supply, fishing, and irrigation sources from this river. One of the most crucial and busiest ports is located in the study area; it is important for both international trade and the nation’s economic development. In addition to the development of commercial, recreational, and industrial activities, it is regarded as the midpoint of all forms of transportation networks.

However, by releasing wastewater, spilling oil, leaking hazardous material storage, dumping garbage, painting ships, dredging sediment, emitting air pollution, making noise, and other activities, the port area harms the nearby aquatic ecology [27,28]. More HMs are entering into Malbasag River water bodies easily as a result of port area activities. Following that, these metals are distributed throughout the water and sediments due to hydrodynamics, tidal changes, and environmental factors [29,30]. The port, urbanization, and industrial activities are causing the gradual deterioration of the river water and sediment quality. Therefore, it is essential to track the spread of HM concentrations in sediment so that they may be compared to background values that are not contaminated, as well as to evaluate the threats to ecological health and environmental quality in this region. The Malbasag River, which provides significant ecosystem services to the population of Ormoc City, is increasingly at risk of heavy metal pollution, raising concerns about its ecological health. Despite its importance, no scientific studies have yet addressed the issue of heavy metal contamination in its riverbed sediment. This study aims to establish baseline data on heavy metal pollution in the sediment of the Malbasag River, contributing to future research and the development of strategies for managing aquatic ecosystem health.

This study specifically aims to (i) evaluate the heavy metal (Cu, Ni, Zn, Pb, Mn, and Cd) concentrations and the degree of heavy metal pollution by using pollution indices from the surface sediment samples in the Malbasag river, (ii) determine the spatial distribution of HMs in the study area by using Arc GIS mapping and Inverse Distance Weighting (IDW) techniques, (iii) find potential sources of heavy metals in sediment samples using multivariate statistical methods, and (iv) evaluate the potential ecological risks posed by heavy metal contamination in the sediment samples and compare the results with sediment quality guidelines (SQGs).

2. Materials and Methods

2.1. Study Area

This study was conducted on an important urban river that is found at the northwestern part of Leyte, Philippines. The Malbasag River is located in Ormoc City, which is the second largest city in Leyte and has a total land area of 55,774 hectares. The river is one of the two major drainages in the 4567-hectare Ormoc Watershed and these rivers converge upstream of Ormoc City and the Isla Verde Area [31]. The river is based in the city proper, directly connected with the Ormoc City port and also 100 m away from the city bus terminal. The port’s key uses are for residential, commercial, industrial, and recreational activities. The upstream section of the river is surrounded by agricultural lands, primarily pineapple and sugarcane plantations. These commercially cultivated crops rely on pesticides and fertilizers which contribute to the release of heavy metals into the environment. In the downstream areas, various industrial and commercial establishments, including a city port, bus terminals, restaurants, hospitals, malls, hotels, hardware stores, and an ice plant, are present. Additionally, the area is surrounded by residential communities. These anthropogenic activities likely contribute to heavy metal pollution in the river’s sediments. Those metals accumulate and deposit in the sediments and contaminate the waterbody through anthropogenic and natural pathways (e.g., release mechanisms at a higher temperature and lower pH), as reported by Zhang et al. [32] and Zhang, Y. et al. [25]. Those release mechanisms could potentially add extra amounts of toxic elements such as Pb, Mn, and Cd. The Malbasag River is around 3 km long, with a junction located 855 m below the watershed’s crest and 5 m above the average sea level (ASL). The river is a part of the government project to guarantee a consistent water supply to 82 percent of the communities within the next 25 years [33]. The coordinates given are 11.04036° latitude and 124.63433° longitude. According to the 2020 census, the city mentioned in the text has a population of 230,998 inhabitants.

On 5 November 1991, Ormoc City experienced a deadly flood that claimed more than 6000 lives. The Malbasag River was one of the rivers involved in the tragedy [31]. The Philippine Bureau of Soils has designated the soils in the Ormoc Watershed as “upland soils”. These soils are distinguished by their high potential for erosion and their unclear soil horizon. A high rate of soil formation is caused by the quick degradation of andesitic rock components and climate variables. Originally, soils were created from decomposed andesitic rocks [31]. The Malbasag River drains the watershed’s middle portion, which has just one subbasin. According to the Köppen–Geiger climate classification, the city’s weather pattern falls within the tropical rainforest climate category (Af). The mean yearly temperature observed in Ormoc is recorded to be 26.1 °C. The rainfall is around 2216 mm per year. Precipitation is the lowest in April, with an average of 61 mm. The maximum quantity of rainfall is observed during the month of July, exhibiting an average value of 298 mm. Between the driest and wettest months, the difference in precipitation is 237 mm. The degree of fluctuation in the yearly temperature is approximately 2.1 °C [34].

2.2. Sampling Sites

The sampling sites’ locations are exhibited in Figure 1. Samples of surface sediment were taken at 10 sites, namely Brgy Donghol (S1), Brgy Donghol (S2), Brgy Patag (S3), Brgy Patag (S4), Brgy District 29 (Nadongholan) (S5), Brgy District 29 (Nadongholan) (S6), Brgy North (S7), Brgy East (S8), Brgy East (S9), and Brgy South (S10), along the Malbasag River. Table S1 contains a brief summary of the sampling sites considered for this investigation.

2.3. Sample Collection

The Global Positioning System (GPS) device (Garmin, Olathe, KS, USA) was used to identify the sampling locations geographically. A total of thirty (30) sediment samples were collected during July 2024. The sample collection day was very sunny and the average temperature in Ormoc City in July 2024 was approximately 31 °C, with an average rainfall of 298 mm, typical for its wet season [35]. Ten different locations (S1–S10) were sampled from upstream to downstream of the river (Table S1). An Ekman grab sampler (7.5 × 3.2 × 3.1 cm) was used to collect sediment samples (top 2 cm of surface) from the river. To prevent cross-contamination and interference, the grab sampler was carefully cleaned repeatedly with deionized water after each sample collection. Three samples were collected from each sampling site. For precision, triplicate samples were taken into consideration for making a dried representative composite sample of each sampling point. Approximately 750 g of sediment was collected for making this composite mixture. For each site, we considered three composite samples for further processing and sample analysis and reported the average value with the appropriate standard deviation value. Following collection, sediment samples were stored in an icebox at 4 °C, sealed in sterile zip-lock polyethylene bags, and sent to Visayas State University’s Central Analytical Services Laboratory (CASL) in Baybay City, Leyte 6521, Philippines, for additional analysis. Those analyses were conducted on 10 August 2024, approximately two to four weeks after collection, ensuring proper sample preservation.

2.4. Chemical Analysis

2.4.1. Reagents and Standards

All reagents used in this study were of Super Pure or Analytical Grade, sourced from Merck (Darmstadt, Germany). The ultrapure water used for solution preparation was obtained from a Milli-Q purification system (Millipore, Darmstadt, Germany), ensuring high purity and minimal contamination. Certified atomic absorption (AA) standard solutions (Merck, Darmstadt, Germany) with a concentration of 1000 mg/L for each target metal (Cu, Ni, Pb, Zn, Fe, Mn, and Cd) were used to prepare calibration standards through serial dilution with ultrapure water. Before being used, every item of glassware was thoroughly cleaned by rinsing it in deionized water after being soaked in diluted acid for at least 24 h. For sample digestion, each 2.0 g sediment sample was treated with 10 mL nitric acid (HNO₃, 69%), 5 mL sulfuric acid (H₂SO₄, 98%), 5 mL perchloric acid (HClO₄, 70%), and 3 mL hydrogen peroxide (H₂O₂, 30%), all obtained from Merck (Germany). To ensure the accuracy and reliability of the analytical method, Standard Reference Material (SRM-1515, Apple Leaves, National Institute of Standards and Technology, NIST, Gaithersburg, MD, USA) was analyzed alongside the sediment samples. The concentrations of target elements in SRM-1515 were measured and compared to their certified values to assess analytical precision and accuracy.

2.4.2. Analysis of Sediment Samples

The samples were oven dried at 45 °C for 72 h to achieve a constant weight, then stones and plant fragments were removed physically. According to Islam et al. [36], the dried samples were then pulverized with a mortar and pestle and sieved through a 2 mm aperture. The samples were kept in borosilicate sealed glass vials with labels prior to the evaluation of chemical properties. The modified USEPA Method 3050B [37] was used to carry out the entire sediment digestion process in order to determine the heavy metal content. In brief, 2.0 g of each sediment sample was digested using concentrated 10 mL HNO₃ (69%), 5 mL H₂SO₄ (98%), 5 mL HClO₄ (70%) and 3 mL H₂O₂ (30%) in good standing. After digestion, Whatman No. 41 filter paper (pre-washed with 0.1 M HNO₃) was used to filter the solution and the final volume was 100 mL of double-distilled water. Heavy metal concentrations (Cu, Ni, Pb, Zn, Fe, Mn, and Cd) were determined using Flame Atomic Absorption Spectroscopy (AAS) with an Agilent 200 Series AA spectrophotometer (Agilent Technologies, Australia), operated in air–acetylene flame mode, with element-specific wavelengths (Cu: 324.8 nm, Ni: 232.0 nm, Pb: 217.0 nm, Zn: 213.9 nm, Fe: 248.3 nm, Mn: 279.5 nm, and Cd: 228.8 nm), a slit width of 0.7 nm, and lamp currents per manufacturer specifications. The method detection limits (mg/kg) were 0.026 (Cu), 0.080 (Ni), 0.035 (Pb), 0.010 (Zn), 0.060 (Fe), 0.020 (Mn), and 0.004 (Cd). Additional analytical conditions and statistical evaluations, including detection limits, calibration curves, and validation methods, are summarized in Table S2.

2.5. Analytical Process for Physicochemical Parameter

The physicochemical parameters, including pH; Electrical Conductivity, EC; % organic carbon, OC; % organic matter, OM; and % nitrogen, N, of the sediment samples were determined. The pH was determined using an OAKTON^® pH 700 Benchtop pH meter. Coarse sediments and double-distilled water were combined in a 1:2.5 ratio, stirred violently, and allowed to stand for an hour. In order to determine the EC, coarse sediments and double-distilled water were combined in a 50 mL beaker at a 1:5 ratio, vigorously stirred, and allowed to stand for an hour. After this, EC was determined via an EC meter (OAKTON CTSTestr™, Oakton, VA, USA). Before use of the pH and EC meters in sediment sample analysis, each was calibrated by standard solution, using pH values of 4.0, 7.0, and 10.0, as well as 1000 µs/cm for EC. Walkley and Black [38] measured OC and (OM) percentages using a spectrophotometric method at 627 nm. The calibration curve for organic carbon percentage showed a correlation coefficient of 0.991. The percentage of OM was used conversion factor 2.0 and multiple with percentage of OC. The percentage of N was analyzed following the Kjeldhal method’s manual titration procedure [39].

2.6. Quality Control

The quality of the analytical data was ensured through rigorous quality assurance and control measures, including adherence to standard operating procedures, calibration with standards, reagent blank analysis, and spiked replicate testing using AAS. The instrument was used three times to check for variances in the standard addition curve’s slope using the standard reference materials (SRMs) (R² > 0.991). The average measurements of heavy metals in the SRMs and the analytical conditions for AAS are provided in Table S3. The precision of the analytical results was confirmed by ensuring that the relative standard deviations (%RSDs) across sample replicates remained below 10% (Table S2) [40]. The proportion of the experimental value to the certified value and the Z-score threshold [41,42] were established to evaluate the accuracy and reliability of the laboratory results (Table S3). It is evident that the Z-scores for the reference materials range from 0.66 to 1.74 for all of the metals. The laboratory performance was seen as excellent based on the Z-score criterion (Z-score ≤ 2: satisfactory performance) and all of the elements’ Z-score values fell below 2. Additionally, the relative error (RE%) for the metals under study was less than 10% (Table S3), demonstrating a high agreement between the measurement results and certified values. The ratio between the experimental and verified values ranged between 0.93 and 1.14. The heavy metal measurement, and statistical approach for AAS, were given in the procedure that was used in AAS (Table S2). Each metal was analyzed twice and the average of the outcomes was recorded.

2.7. Sediment Quality Guidelines

According to MacDonald et al. [43], sediment quality assessment guidelines (SQGs) are invaluable for screening sediment contamination by comparing the concentration of sediment contaminants with the appropriate quality guideline. These guidelines help interpret sediment quality by assessing the potential impacts of sediment chemistry on aquatic life. They are useful for various purposes, such as designing monitoring programs, analyzing historical data, identifying the need for detailed sediment assessments, evaluating dredged material quality, conducting ecological risk studies, and setting remediation goals [43]. SQGs such as threshold effect level (TEL), probable effect level (PEL), effect range low (ERL), severe effect level (SEL), effects range medium (ERM), and lowest effect level (LEL) were utilized, however, to evaluate the possible biotic influence of the metal(oid)s assessed in the sediment samples.

2.8. Pollution Assessment

Pollution indicators may play a part in the thorough evaluation of the level of soil pollution [44,45]. A crucial factor in the interpretation of geochemical data is the selection of background values. Many researchers have utilized the average crustal abundance data or the average shale values as baselines for comparison [46,47,48]. The background values in this paper were sourced from pre-industrial global standard average shale values of heavy metal concentrations, as there were no available data for the background concentrations of the Malbasag River sediments and soils of nearby places that were analyzed. This study employed four primary indices, the enrichment factor (EF), geo-accumulation index (I_geo), pollution load index (PLI), and contamination factor (CF), to assess the degree of pollution in the sediment samples from the research region based on the concentrations of heavy metals.

2.8.1. Contamination Factor (CF)

The concentration of each metal in the sediment divided by the baseline or background value for the same metal provides the contamination factor (CF) [49].

$$\mathrm{CF}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{heavymetal}}}{{\mathrm{C}}_{\mathrm{background}}}}$$

(1)

CF values were interpreted as suggested by Hakanson [49], where CF < 1 indicates low contamination; 1 < CF < 3 is moderate contamination; 3 < CF < 6 is considerable contamination; and CF > 6 is very high contamination. The study obtained, by using the standard pre-industrial background value of metals (in mg/kg), 45 for Cu, 68 for Ni, 20 for Pb, 95 for Zn, 850 for Mn, and 0.3 for Cd [49,50].

2.8.2. Pollution Load Index (PLI)

PLI is an integrated method for evaluating the quality of sediment. The geometric mean of a metal’s contamination factor is known as the PLI. The PLI of each site was calculated by taking the n root of the contamination factor derived from all metals [51]. The PLI was designed by Tomilson et al. [51] and is shown below:

$$\mathrm{PLI}=\sqrt[\mathrm{n}]{{\mathrm{CF}}_1{\times \mathrm{CF}}_2{ \times \mathrm{CF}}_3{ \times \text{…} \times \mathrm{CF}}_{\mathrm{n}}}$$

(2)

An easy way to compare levels of heavy metal pollution is to use this empirical indicator. PLI > 1 indicates the presence of pollution; PLI < 1 indicates the absence of metal contamination [51].

2.8.3. Geo-Accumulation Index (I_geo)

Metal contamination in soils and aquatic sediments is measured using the geo-accumulation index (I_geo). The following formula defines the geo-accumulation index (I_geo):

$${{\mathrm{I}}_{\mathrm{geo}}=\log }_2{\displaystyle \frac{\mathrm{Cn}}{1\cdot {5 \mathrm{B}}_{\mathrm{n}}}}$$

(3)

where B_n is the metal’s geochemical background concentration [52] and Cn is the concentration of metals analyzed in sediment sample concentrations (n). The background matrix adjustment factor because of the lithospheric effects is 1.5. There are seven classes in the geo-accumulation index [52]. I_geo ≤ 0 for Class 0 (practically unpolluted); 0 < I_geo < 1 for Class 1 (unpolluted to moderately polluted); 1 < I_geo < 2 for Class 2 (moderately polluted); 2 < I_geo < 3 for Class 3 (moderately to heavily polluted); 3 < I_geo < 4 for class 4 (heavily polluted); 4 < I_geo < 5 for Class 5 (heavily to extremely polluted); and 5 > I_geo for class 6 (extremely polluted) [53].

2.8.4. Enrichment Factor (EF)

The enrichment factor (EF) is a useful tool for determining the extent of anthropogenic HMs contamination [48,54]. The following relationship is used to calculate the EF:

$$\mathrm{Enrichment} \mathrm{Factor} \left(\mathrm{EF}\right)={\displaystyle \frac{(\mathrm{Metal}/\mathrm{Fe}) \mathrm{Sample}}{(\mathrm{Metal}/\mathrm{Fe}) \mathrm{Background}}}$$

(4)

Iron (Fe) is used as the reference element for geochemical normalization in the present study for an array of reasons, including the fact that it is associated with solid surfaces, that its geochemistry is similar to that of many trace metals, and that its natural concentration tends to be uniform [53]. For the EF calculation, the background metal concentrations were 20 for Pb, 50 for Ni, 68 for Zn, 600 for Mn, 0.1 for Cd, and 25 for Cu [54,55]. The EF values were interpreted according to the guidelines proposed by Sakan et al. [48], where an EF < 1 denotes no enrichment, 3–5 moderate enrichment, 5–10 moderately severe enrichment, 10–25 severe enrichment, 25–50 very severe enrichment, and >50 extremely severe enrichment.

2.9. Potential Ecological Risk Index (RI)

Since heavy metal concentrations in sediments are increasing and can be harmful to ecological health when released into water, evaluating the possible ecological risk of heavy metal contamination was suggested as a helpful technique for water pollution mitigation [56]. In order to determine which lakes, rivers, and chemicals require special attention, Hakanson [49] devised a system to evaluate the prospective ecological risk index for aquatic pollution management reasons. Using this approach, the following formulas may be used to calculate the potential ecological risk factor (${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$) of a single element and the potential ecological risk index (RI) of a multi-element array [42]:

$${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}{=\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}{ \times \mathrm{CF}}^{\mathrm{i}}$$

(5)

$$\mathrm{RI}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{i}}{\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$$

(6)

where ${\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}$ is the toxic response factor for the specified element of “i” which takes into consideration both the sensitivity and the toxic requirements; CFⁱ is the contamination factor for the element of “i”. The toxic response factors for Pb, Ni, Cu, Mn, Cd, and Mn were 5, 6, 5, 1, 30, and 1, respectively [49,57]. The possible ecological risk assessment was calculated using Equations (1), (4) and (5).

2.10. Toxic Units

To compare the relative impacts, the toxicities of different heavy metals were normalized using toxic units (TUs) [58]. According to Pedersen et al. [59], TUs are the ratios of the observed levels of each heavy metal to the probable effect level (PEL) values. Equation (7) was used to get the TU for each heavy metal.

$$\mathrm{TU}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{PEL}}_{\mathrm{i}}}}$$

(7)

where PEL_i is the investigated heavy metal of the ith parameter’s associated value and C_i is the analyzed heavy metal concentration of the ith parameter in sediment. According to MacDonald et al. [43], these values are shown as Ni = 36, Pb = 91, Cd = 3.5, Cr = 90, Cu = 197, and Zn = 315. The toxic unit total (∑TU) was computed using Equation (8).

$$\sum \mathrm{TU}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{PEL}}_{\mathrm{i}}}}$$

(8)

where ∑TU is the total of all heavy metals’ toxic units.

2.11. Toxic Risk Index (TRI)

The ecotoxicity may be underestimated by the toxic units (TUs) since the TEL effects are not taken into account. Zhang et al. [60] developed a toxic risk index which was used to give a more thorough assessment of biota’s hazards in the aquatic environment. The TRI was computed using Equation (9) using two threshold values for SQGs (TEL and PEL standard).

$${\mathrm{TRI}}_{\mathrm{i}}=\sqrt{{\displaystyle \frac{{\left(\frac{{\mathrm{c}}_{\mathrm{i}}}{{\mathrm{TEL}}_{\mathrm{i}}}+\frac{{\mathrm{c}}_{\mathrm{i}}}{{\mathrm{PEL}}_{\mathrm{i}}}\right)}^2}{2}}}$$

(9)

$$\mathrm{TRI}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{TRI}}_{\mathrm{i}}$$

(10)

PEL and TEL stand for the probable impact level and threshold effect level for the ith metals, respectively, whereas C_i indicates the concentration of the ith metal. As shown in

Table 1. Concentration of heavy metals in sediment samples from Malbasag River, Leyte, Philippines.

	Concentration (mg/kg) as Dry Weight Basis
	Cu	Ni	Pb	Zn	Mn	Cd
Experimental Data
Mean (n = 30)	52.516	33.058	21.955	54.643	141.096	0.801
Std	13.468	22.833	7.984	28.820	13.239	0.650
RSD (%)	25.646	69.069	36.366	52.742	9.383	81.170
Median	50.164	22.680	20.790	42.724	142.746	0.810
Min.	32.568	11.760	12.915	27.459	100.502	0.014
Max.	74.865	81.480	42.350	105.391	165.984	2.014
SQG Threshold values
LEL (Persuad et al., 1993) [61]	16.0	16	31.0	120	460	0.6
SEL [61]	110	75	250	820	1100	10.0
TEL [43]	35.7	18	35	123	-	0.59
PEL [43]	197	36	91	315	-	3.5
ERL [62]	34	20.9	46.7	120	-	1.2
ERM [62]	270	51.6	218	410	-	9.6
TRV [63]	16.0	16	31	110	-	0.6
Impact (%) on ecology
<LEL	-	30.0	90.0	100	100	40.0
LEL-SEL	100	70.0	10.0	-	-	60.0
>SEL	-	-	-	-	-	-
<TEL	10.0	43.3	90.0	100	-	40.0
TEL-PEL	90.0	26.7	-	-	-	60.0
>PEL	-	30.0	-	-	-	-
<ERL	6.76	53.3	100	100	-	80.0
ERL-ERM	93.3	20.0	-	-	-	20.0
>ERM	-	30.0	-	-	-	-

, TEL and PEL standard values from MacDonland et al. [43] were used in this study. The following is an interpretation of the TRI values: ‘TRI ≤ 5’ denotes no toxic risk, ‘5 < TRI ≤ 10’ denotes low toxic risk, ‘10 < TRI ≤ 15’ denotes moderate toxic risk, ‘15 < TRI ≤ 20’ denotes major hazardous danger, and ‘TRI > 20’ denotes extremely high toxic risk.

2.12. Modified Hazard Quotient (mHQ)

Based on the severity of heavy metal contamination, a new index is created and proposed in this study to evaluate sediment pollution. Through the comparison of the metal content in sediment with the synoptic metal concentration, this unique technique allows contamination evaluation. According to earlier descriptions [43,64], the distributions of adverse ecological effects for three somewhat varied threshold values (TEL, PEL, and SEL). The following method may be used to calculate the metals’ modified hazard quotient (mHQ), which is a crucial assessment tool that clarifies the level of danger that each heavy metal poses to the aquatic environment and biota.

$$\mathrm{mHQ}=\sqrt[2]{{\mathrm{C}}_{\mathrm{i}}\left({\displaystyle \frac{1}{{\mathrm{TEL}}_{\mathrm{i}}}}+{\displaystyle \frac{1}{{\mathrm{PEL}}_{\mathrm{i}}}}+{\displaystyle \frac{1}{{\mathrm{SEL}}_{\mathrm{i}}}}\right)}$$

(11)

The threshold effect level, probable impact level, and severe effect level are denoted by the abbreviations TEL_i, PEL_i, and SEL_i for each metal. The detected concentrations of heavy metals in the sediment samples are denoted by C_i. For mathematical and ranking purposes, the square root is employed as a drawdown function in the equation. The Modified Hazard Quotient is categorized as follows: mHQ > 3.5 indicates extreme contamination severity; 3.0 ≤ mHQ < 3.5 indicates very high contamination severity; 2.5 ≤ mHQ < 3.0 indicates high contamination severity; 2.0 ≤ mHQ < 2.5 indicates significant contamination; 1.5 ≤ mHQ < 2.0 indicates moderate contamination; 1.0 ≤ mHQ < 1.5 indicates low contamination severity; 0.5 ≤ mHQ < 1.0 indicates very low contamination severity; and mHQ < 0.5 indicates nil to very low contamination severity [43,64].

2.13. Statistical Analyses

The data were statistically analyzed using R (V. 4.4.2), Arc GIS (V. 10.5), and Microsoft Excel 2021 software. To investigate the significant temporal and spatial differences, a one-way nested analysis of variance (ANOVA) was conducted at a 95% confidence level, p values < (0.001 ‘***’, 0.01 ‘**’, 0.05 ‘*’, 0.1 ‘.’, 1 ‘ ’). The data were summarized using ranges, median values, percentages of relative standard deviation (%RSD), mean values, and standard deviations (Std). This study also determined the coefficient of variation (CV) value for understanding the stability of data. A lower CV indicates that the data points are close to the mean and thus more stable, while a higher CV suggests greater dispersion and less stability. The geographical distribution of heavy metals in the study areas was calculated using the inverse distance weighting (IDW) method in Arc GIS software (V. 10.5). The IDW method was chosen for its ability to provide spatially interpolated values with high speed and accuracy. Pearson’s correlation matrix (PCM), principal component analysis (PCA), and cluster analysis (CA) are multivariate statistical techniques used to determine the geochemical process and the probable origins of heavy metals in sediments. PCA with varimax rotation was performed to enhance the interpretability of the factors without altering the original sediment dataset. This approach grouped variables based on their specific properties. Varimax rotation effectively minimizes the influence of less significant variables in the PCA dataset. The study results are explained using PCA using the mean value of each variable, with an eigenvalue > 1 and a loading value > 0.7 for each of the main components from the analysis table. Moreover, cluster analysis was performed to determine the similar groups based on their similar characteristics within the class and dissimilar characteristics among the other classes. In this study, to determine the relationship dependable variable (RI) and predictor (metals), stepwise linear regression analysis was undertaken. Correlation analysis was carried out to discarded the number of explanatory variables (Pb and Zn) and decrease the collinearity in the linear regression model. The regression model may have been overfitted because of the small sample size, which can reduce the reliability of the results and limit their applicability to other similar situations or locations.

3. Results and Discussion

3.1. The Physicochemical Properties of Sediment Samples in the Study Area

A river basin’s physicochemical characteristics and heavy metals are influenced by variations in topography and hydrogeology. Furthermore, even over short distances, variations in local temperature, salinity, rainfall, land runoff, anthropogenic activities, and geomorphological configuration are important factors in the fluctuation of heavy metals between catchment areas [65,66]. In

Table 2. Physicochemical properties in surface sediment of the Malbasag River associated with the Ormoc City port, Philippines.

	pH	EC (µS/cm)	%OC	OM (%)	%N	C/N Ratio
Mean (n = 30)	5.93	278.58	0.73	1.47	0.11	10.97
Std	0.32	467.33	0.40	0.81	0.08	8.14
RSD (%)	5.44	167.75	55.47	55.47	72.38	74.25
Median	5.98	126.65	0.58	1.16	0.09	9.56
Min.	5.29	4.34	0.21	0.42	0.01	1.78
Max.	6.59	1700.0	1.50	3.00	0.26	31.05

Min. = minimum; Max. = maximum; Std = standard deviation; RSD = relative standard deviation.

, the physicochemical properties of sediments are displayed.

The study area’s pH ranged from 5.29 to 6.59, with an average value of 5.93 ± 0.32, which meant that the sediments were moderately acidic. The EC values in the sediments varied from 4.34 to 1700.0 µS/cm, with average value of 278.56 ± 467.33 µS/cm. The organic carbon (OC) in the study sites ranged from 0.21 to 1.50 percent and the average value was 0.73 ± 0.40 percent. The differences in the EC values and organic carbon were due to discharging waste from residential and commercial areas, agricultural runoff, vegetation, salinity, and industrial sewage from the port area. Moreover, the percentage of nitrogen in the study sites varied from 0.01 to 0.26 and the average value was 0.11 ± 0.08%. The physicochemical parameters of sediments at different sampling locations had percentages of relative standard deviation (%RSD) ranging from 5.44 to 167.75. The %RSD for each sampling point is shown in

Table 3. Physicochemical and heavy metals concentration of surface sediment samples in the Malbasag River, Leyte, Philippines.

Sampling Point	pH	EC (µS/cm)	%OC	OM (%)	%N	C/N	Cu (mg/kg)	Ni (mg/kg)	Pb (mg/kg)	Zn (mg/kg)	Mn (mg/kg)	Cd (mg/kg)
S-1.1	6.30	188.20	1.20	2.41	0.07	17.18	32.57	25.20	22.47	34.38	146.14	0.02
S-1.2	6.32	178.70	1.09	2.18	0.07	15.12	34.09	28.14	23.80	36.22	132.84	0.02
S-1.3	6.05	184.70	1.24	2.48	0.09	13.98	38.48	31.08	24.89	37.73	157.32	0.01
Mean	6.22	183.87	1.18	2.36	0.08	15.42	35.05	28.14	23.72	36.11	145.43	0.02
Std	0.15	4.80	0.08	0.16	0.01	1.62	3.07	2.94	1.21	1.68	12.25	0.00
RSD (%)	2.42	2.61	6.68	6.68	13.33	10.52	8.76	10.45	5.10	4.64	8.43	10.38
S-2.1	6.39	174.60	0.53	1.07	0.09	5.86	53.11	25.20	24.15	35.35	140.19	0.19
S-2.2	6.07	154.70	0.47	0.94	0.10	4.71	49.21	28.77	20.93	34.16	114.13	0.16
S-2.3	6.24	161.60	0.56	1.12	0.10	5.55	46.73	33.18	23.87	27.64	100.50	0.19
Mean	6.23	163.63	0.52	1.04	0.10	5.37	49.68	29.05	22.98	32.38	118.27	0.18
Std	0.16	10.10	0.05	0.09	0.01	0.59	3.22	4.00	1.78	4.15	20.17	0.02
RSD (%)	2.57	6.18	8.83	8.83	5.66	11.06	6.48	13.76	7.76	12.82	17.05	8.48
S-3.1	6.33	122.80	0.21	0.43	0.01	19.36	71.07	14.70	15.10	28.14	144.52	1.01
S-3.2	5.83	132.80	0.21	0.42	0.01	19.09	71.98	11.76	13.02	27.46	146.23	1.01
S-3.3	6.17	114.60	0.23	0.46	0.01	16.29	64.17	12.83	12.92	33.46	122.66	1.01
Mean	6.11	123.40	0.22	0.43	0.01	18.25	69.07	13.10	13.68	29.69	137.80	1.01
Std	0.26	9.11	0.01	0.02	0.00	1.70	4.27	1.49	1.23	3.29	13.14	0.00
RSD (%)	4.18	7.39	4.44	4.44	14.43	9.34	6.18	11.36	8.98	11.07	9.53	0.21
S-4.1	6.13	143.10	0.38	0.76	0.08	4.66	45.69	16.80	14.98	28.16	137.73	0.85
S-4.2	5.68	125.20	0.42	0.84	0.10	4.43	51.98	17.22	14.35	29.41	139.61	0.74
S-4.3	6.14	126.10	0.39	0.78	0.10	3.91	42.73	15.18	13.65	29.36	124.12	0.93
Mean	5.98	131.47	0.40	0.80	0.09	4.33	46.80	16.40	14.33	28.98	133.82	0.84
Std	0.26	10.08	0.02	0.04	0.01	0.38	4.72	1.08	0.67	0.71	8.45	0.10
RSD (%)	4.39	7.67	5.13	5.13	10.06	8.86	10.09	6.56	4.64	2.44	6.31	11.54
S-5.1	5.98	126.60	0.37	0.75	0.21	1.78	33.37	14.93	18.90	31.41	139.79	0.89
S-5.2	5.48	126.70	0.38	0.76	0.20	1.92	41.90	14.28	18.55	30.66	139.48	0.77
S-5.3	6.03	110.80	0.44	0.89	0.22	2.00	40.13	14.05	17.44	31.59	160.60	0.77
Mean	5.83	121.37	0.40	0.80	0.21	1.90	38.47	14.42	18.30	31.22	146.63	0.81
Std	0.30	9.15	0.04	0.08	0.01	0.11	4.50	0.46	0.76	0.50	12.11	0.07
RSD (%)	5.22	7.54	9.79	9.79	5.96	5.85	11.69	3.17	4.18	1.59	8.26	8.53
S-6.1	5.89	118.90	0.50	1.00	0.02	27.78	36.79	19.11	20.65	51.53	138.66	0.14
S-6.2	5.52	111.00	0.53	1.06	0.02	25.19	38.78	20.16	20.34	51.52	140.98	0.17
S-6.3	6.11	112.20	0.59	1.18	0.02	31.05	45.19	17.22	19.57	62.15	131.78	0.14
Mean	5.84	114.03	0.54	1.08	0.02	28.01	40.25	18.83	20.18	55.07	137.14	0.15
Std	0.30	4.26	0.05	0.09	0.00	2.94	4.39	1.49	0.56	6.14	4.78	0.01
RSD (%)	5.11	3.73	8.51	8.51	7.90	10.49	10.90	7.91	2.77	11.14	3.49	9.13
Mean (n = 30)	5.94	278.59	0.74	1.47	0.11	10.97	52.52	33.06	21.96	54.64	141.10	0.80
Std	0.32	467.33	0.41	0.82	0.08	8.15	13.47	22.83	7.98	28.82	13.24	0.65
RSD (%)	5.44	167.75	55.48	55.48	72.38	74.26	25.65	69.07	36.37	52.74	9.38	81.17
Median	5.99	126.65	0.58	1.16	0.09	9.57	50.16	22.68	20.79	42.72	142.75	0.81
Min.	5.29	4.34	0.21	0.42	0.01	1.78	32.57	11.76	12.92	27.46	100.50	0.01
Max.	6.59	1700.00	1.50	3.00	0.27	31.05	74.87	81.48	42.35	105.39	165.98	2.01

, and the results of the ANOVA test at a 95% confidence interval that indicated the significant differences in all physicochemical properties across the sites (p < 0.001 for most properties) can be found in the supplementary section (Table S4). These results imply that site-specific factors (vegetation, industry, port activities, and agricultural farms) contribute to the variability in sediment properties in the river.

3.2. Heavy Metal Concentration

Urban and port effluents have been recognized as one of the main environmental hazards. As a result, sediment samples from sites on the Malbasag River that are directly connected to the port (Ormoc City port) were examined in order to ascertain their heavy metal concentrations, which are shown in Table 1. According to the average data analysis, the Malbasag River’s sediment has the following total heavy metal accumulation order (mg/kg as dry weight basis): Mn > Zn > Cu > Ni > Pb > Cd. The concentrations of the heavy metals (Cu, Ni, Pb, Zn, Mn, and Cd) for each sampling point are listed in Table 3. According to the data, Cd was only slightly deposited in the sediments, whereas Mn was extremely concentrated (Figure 2). The percentage of relative standard deviation (%RSD) for the studied heavy metals’ distribution in sediments at various sampling points showed that the abundance of Cu, Ni, Pb, Zn, Mn, and Cd varied widely (%RSD: 9.38–81.17%), shown in Table 1, and the ANOVA test at a 95% confidence interval indicated that most of the metals were highly significant among the sites. The ANOVA result in this investigation showed that there was spatial heterogeneity in the concentrations of heavy metals (Cu, Ni, Pb, Zn, and Cd) and that these variations were very significant (p < 0.001) among the sites (Table S5). It is mentioned that specific sites are might be affected by anthropogenic sources (agricultural runoff, domestic and urban waste, port activities, onloading fishing boats, and hospital and medical wastewater discharge). On the other hand, the metal Mn’s concentrations showed insignificance (p = 0.076) within the sites, indicating that it is possibly affected by natural geochemical and climatic factors.

Table 3 shows that heavy metal concentrations at some sites (Sites 6–10) were much higher than those at other sites due to their location downstream of the river and the considerable discharge of urban and port material waste. The highest concentrations of heavy metals were observed at Site 10 (Brgy South). These elevated levels are likely attributable to port activities and untreated waste discharges directly from the Ormoc City port, the city bus terminals, and the food park in the Township area. Site 9 (Brgy East) receives untreated medical and domestic waste from the urban area and Site 8 (Brgy East) receives wastewater from drains that mix partially treated domestic and industrial wastewater from Brgy East, which is situated in the main township. Additionally, Sites 8–10 easily receive pollution from the port because of the high tide. Site 7 (Brgy North) receives agricultural runoff from rice fields and Pura Agriventures and Development Corporation (PADC)’s untreated farm waste, as well as wastewater from Brgy 29 in Ormoc City. Site 6 (Nadongholan) receives pollution from a big agricultural field and residential areas from the Township. The lowest polluted site was estimated as Site 4 (Brgy Patag), where there is an established water treatment plant, and this plant supplies water for the whole city’s communities. The plant maintained good treatment process before discharging waste from the plant. According to Table 3, the total metal concentrations in this investigation were as follows: Site 10 > Site 9 > Site 7 > Site 6 > Site 8 > Site 1 > Site 3 > Site 2 > Site 5 > Site 4. It has been reported that the origins of these metals in sediments are primarily anthropogenic [21], and the examined heavy metals were dispersed uniformly. The sediment quality guidelines’ (SQGs’) threshold values—probable effect level (PEL), threshold effect level (TEL), severe effect level (SEL), effect range low (ERL), lowest effect level (LEL), and effects range medium (ERM)—were also compared with the concentrations of the heavy metals under investigation (mg/kg) in the sediment samples.

3.2.1. Copper (Cu)

The investigated area’s sediment samples had an average Cu concentration of 52.5 ± 13.4 mg/kg, with a range of 32.56 to 74.86 mg/kg (Table 1). The average Cu concentration in the sediment samples in the study area was found to be higher than the background value of soils [69] (25 mg/kg), the average sediment value (50 mg/kg) [50], and the upper continental crust value (28 mg/kg) [67] (Figure 2a). However, the coefficient of variance for Mn concentrations across sampling points showed moderate variability (25.64% < CV ≤ 50) [70], suggesting that the research area’s Cu sources could be mostly natural and anthropogenic (Table S5). The range of Cu concentrations in the sediment samples was then found to be lower than that of several different river sediments in the Philippines, including Manila Bay [71] and the Mangonbangon River [72]; additionally, it was higher than the results of river sediments in several other countries around the world [73,74,75] (Table S6). However, high Cu copper contents were found at Site 9 and 10 (Brgy East and South). Notably, Cu is typically released into the environment through car exhausts, smelting from coal burning furnaces, and other sources [40,76]. Due to the study sites’ proximity to ports, major towns, road dust, fishing boats, and urban areas, there may be a source of copper contamination in the sediment samples. It was found that the average concentration of copper in sediment samples was above all of the SQG threshold values (LEL, SEL, TEF, PEL, ERL, ERM, and TRV [61,62,63]. It was observed that 30%, 10%, and 6.76% of samples were classified as less than LEL, TEL, and ERL, respectively, whereas 100%, 90%, and 93.3% of samples fell into the LEL-SEL, TEL-PEL, and ERL-ERM categories. This element is essential for plant growth because it is found in many enzymes and proteins [77]. Cu is frequently used in electrical wire, roofing, alloys, pigments, culinary utensils, and pipelines [78].

3.2.2. Nickel (Ni)

The sediment samples had an average Ni concentration of 33.0 ± 22.8 mg/kg, with a range of 11.7 to 82.4 mg/kg (Table 1). Site 10 > Site 9 > Site 7 > Site 2 > Site 1 > Site 6 > Site 4 > Site 5 > Site 8 > Site 3 was the descending order of variability for the Ni concentration in the study sites (Figure 2b). Ni usually exists in soil in the organically bound form, which enhances its mobility and bioavailability in neutral and acidic environments [77]. In sediment samples, Site 1 (Brgy Donghol), Site 2 (Brgy Donghol), Site 7 (Brgy Norh), Site 9 (Brgy East), and Site 10 (Brgy North) showed above-background values (24.5 mg/kg) [69]. Values for Site 7, Site 9, and Site 10 were found to be higher than the upper continental crust (Rudnick and Gao, 2014) value (47 mg/kg). At Site 10, the Ni concentration showed above the average shale [50] value (68 mg/kg). However, the elevated Ni concentrations in Sites 9 and 10 (59.0 and 78.1 mg/kg) suggest a higher input, which could originate from urban waste, oil and gas refinery machinery and equipment, wood chips and preservative chemicals at the port, untreated hospital wastewater, household sludge, and agricultural runoff from sugarcane and pineapple fields. However, the Pb concentration’s coefficient of variation (CV) varied greatly across sampling points (69.06% < CV ≤ 100) [70], suggesting that the research area’s Ni sources may be predominantly human disturbances. Additionally, this study showed a range of Ni concentrations higher than those of other Philippine river sediment samples [71]. The Ni concentration in sediments was also compared with other international river sediment samples studies and it was revealed that this study’s sediments had concentrations less than those of several international rivers: Bangladesh [36], Turkey [79] (Table S6). The average Ni concentration in sediments was compared with sediment quality guideline values (LEL, SEL, TEF, PEL, ERL, ERM, and TRV). It was observed that 30%, 43.3%, and 53.3% samples were less than LEL, TEL, and ERL and 30% sediment samples were higher than the ERM value. Additionally, Table 1 shows that 70%, 26.7%, and 20% of the samples were classified as LEL-SEL, TEL-PEL, and ERL-ERM, respectively. The findings show a negative impact on bottom-dwelling creatures, which were expected to occur regularly [78].

3.2.3. Lead (Pb)

The ecology may be seriously threatened by Pb due to its extremely dangerous toxicity level, even at low concentrations [80]. Following a sequence of Site 10 > Site 9 > Site 1 > Site 7 > Site 2 > Site 6 > Site 5 > Site 8 > Site 4 > Site 3 (Figure 2c), the average Pb concentration was found to be 21.95 ± 7.98, ranging from 12.9 to 42.3 mg/kg (Table 1). Interestingly, there was no downstream trend in the declining order of heavy metals in the studied sites. This is likely because of the influence of mineralogical composition, source variability, and the dominance of physicochemical processes like organic matter variation, adsorption, absorption, precipitation, redox reactions, and rocks. In this study area, the Pb concentrations crossed the upper continental crust value (17.0 mg/kg), except at Site 3 (Brgy Patag), Site 4 (Brgy Patag), and Site 8 (Brgy East) (Figure 2c). The coefficient of variance for the Pb concentration in various sampling points showed moderate (36.36% < CV ≤ 50) variability [70], suggesting that the sources of Pb in the study area may be influenced by both natural and man-made sources. Site 9 (Bgy East) and Site 10 (Brgy South) were established close to the city center and port area. Although the Pb concentration in the study area was higher than in some international river sediments, such as Pakistan [74] and Italy [75], the Pb level was found to be lower than the value observed in national river sediments in Pasig, Marikana (Suthar et al., 2009) and Mangobangon [72] (Table S6). However, the 10% Pb concentration value fell between the LEL and SEL categories, although the average Pb content for 90%, 90%, and 100% sediment samples was determined to be below the severe effect level (SEL), threshold effect level (TEL), and effect range low (ERL) values (Table 1). Thus, similar to other basic divalent metals (Mn²⁺ and Zn²⁺), Pb²⁺ may have altered the osmotic balance of bacterial cells, the compliance of proteins and nucleic acids, and the inhibition of bacterial chemical movement [81]. These changes may have an effect on the ecology [42].

3.2.4. Zinc (Zn)

The sediment sample’s average Zn content was 54.6 ± 28.8 mg/kg, with a range of 27.4 to 105.3 mg/kg (Table 1). Site 4 (Brgy Patag) had the lowest Zn content, whereas Site 10 (Brgy South) had the highest. In this study, it was shown that three sites were more polluted; the same was true for the Ni concentration. Site 7, Site 9, and Site 10 were revealed to have average concentrations of Zn higher than the background value [69] and upper continental crust value [67]. Zn concentrations at Sites 9 and 10 were greater than the global average [50] (Figure 2d). Vehicle emissions as well as commercial and industrial discharge might be responsible for the higher content of Zn in the sediments [36,82]. In addition, Sites 10, 9, and 7 in this study are the points of entry for metals into the port, and the excessive land use for urbanization, industrialization, and economics (such as manufacturing, plantations, and animal farming) that takes place in this area of the river has an effect on the metals carried to the downstream sites. Furthermore, the average Zn concentration in the sediment samples was found to be lower than that of several different river sediments in the Philippines, including Manila Bay [71] and the Mangonbangon River [72]; it was also higher than the results of river sediments in a number of other countries, including Italy [75], Pakistan [74], and Malaysia [73] (Table S6). According to sediment quality guidelines (SQGs), the average observed Zn concentration in all sediment samples in this study area was lower than the LEL, TEL, and ERL threshold levels. This indicated the toxicity was negligible and good for a functioning environment for aquatic ecology.

3.2.5. Manganese (Mn)

This investigation showed that the sediment samples had an average Mn content of 141.0 ± 13.2 mg/kg (Table 1). The sample point of Site 2 (Brgy Donghol) had the lowest Mn concentration, whereas the sampling point of Site 9 (Brgy East) had the highest Mn concentration. The following order was determined for the average Mn concentration in the sediment samples taken from the various sampling locations: Site 9 > Site 7 > Site 10 > Site 5 > Site 1 > Site 8 > Site 3 > Site 4 > Site 2, correspondingly (Figure 2e). According to this order, the areas with the highest levels of Mn pollution were lower downstream and close to the port, while the study area’s Mn concentration showed low variability (9.38% < CV ≤ 10) across sample locations [70]. This suggested that natural sources and site-specific elements including rainfall, soil erosion, and andesite rock formations may be the primary sources of Mn in the research region. On the other hand, the average Mn concentration was found to be much lower than the global average soil value (Turekian and Wedephol, 1961), the upper continental crust value [67], and the background value of soils [69]. According to Suthar et al. [70], Pacle et al. [72], and Varol [79], the average Mn content was subsequently lower than the sediment values reported by national and international scientists worldwide (Table S6). According to Table 1, the average Mn content in all of the sediment samples used in this investigation was found to be below the LEL and SEL threshold values [61]. This suggested that there was less Mn pollution in the research area.

3.2.6. Cadmium (Cd)

The average Cd concentration in this investigation was 0.80 ± 0.65 mg/kg, with a range of 0.01 to 2.014 mg/kg (Table 1). Site 10 > Site 9 > Site 8 > Site 3 > Site 4 > Site 5 > Site 7 > Site 2 > Site 6 > Site 1 was the decreasing order of the average Cd content in the sediment samples. This investigation found that the Cd content was higher than the top continental crust [67] (0.09 mg/kg), soil background (0.2 mg/kg) according to UNEP [69], and world average shale [50] values (0.3 mg/kg) (Figure 2). The highest Cd concentrations were found downstream of the river (Sites 8 to 10) at sites which are directly connected to port activities, hospitals, city bus terminals, shipping boats, the repair and painting of ships, domestic and industrial drainage, and the discharge of untreated or slightly treated wastewater. Furthermore, as Cd is typically found in phosphorus fertilizer and animal dung, stormwater runoff from agricultural regions may further contribute to metal contamination [83]. According to Zhang et al. [70], the statistical analysis of the average values in several sample points showed very significant (CV < 100) variability, suggesting that the study area’s Cd sources may be mostly anthropogenic. The varied Cd concentrations of sediment samples from rivers in the Philippines and other countries were compared in this study. It was noted that the reported values for the Cd content were lower than those of many different countries, including China [84], Turkey [79], and India [85], but it crossed the level of some of the international rivers, such as those in Poland [86] and Malaysia [73] (Table S6). Additionally, this study compared the Cd content in evaluated sediment samples with the threshold values for SQGs (LEL, TEL, SEL, PEL, ERL, and ERM). It was found that 60%, 60%, and 20% of the sediment samples fell into the LEL-SEL, TEL-PEL, and ERL-ERM categories, respectively, whereas 40%, 60%, and 80% were less than the LEL, TEL, and ERL (Table 1). It could be suggested that there were localized moderate to high ecological risks. This includes the potential for bioaccumulation, sublethal toxicity, and disruptions in sediment-associated biodiversity and ecosystem services [87].

3.3. Geochemical Indicators of Contaminated Sediments

Contamination Factor (CF), Pollution Load Index (PLI), Geo-Accumulation Index (I_geo), and Enrichment Factor (EF)

Two significant indices were introduced by Hakanson [49] for calculating the metal pollution in sediment: the pollution load index (PLI) and the contamination factor (CF). However, many scientists have utilized such indicators significantly to assess the level of heavy metal contamination in sediment samples [51,88]. The outcomes for the pollution load index (PLI) and contamination factors (CFs) are shown in

Table 4. Heavy metals contamination factors (CF) and pollution load index (PLI) of all sites in the study area.

Sites	Contamination Factors (CFs)							PLI
	Cu	Ni	Pb	Fe	Zn	Mn	Cd
Site 1	0.779	0.414	1.186	0.559	0.380	0.171	0.052	0.356
Site 2	1.104	0.427	1.149	0.784	0.341	0.139	0.606	0.533
Site 3	1.535	0.193	0.684	0.356	0.312	0.162	3.368	0.533
Site 4	1.040	0.241	0.716	0.585	0.305	0.157	2.806	0.544
Site 5	0.855	0.212	0.915	0.447	0.329	0.173	2.694	0.527
Site 6	0.894	0.277	1.009	0.667	0.580	0.161	0.502	0.500
Site 7	1.452	0.865	1.126	0.804	0.843	0.175	0.760	0.745
Site 8	1.027	0.214	0.758	0.860	0.506	0.169	3.622	0.641
Site 9	1.603	0.869	1.348	0.755	1.052	0.179	5.891	1.067
Site 10	1.367	1.150	2.086	0.860	1.104	0.173	6.410	1.193
Mean	1.166	0.486	1.098	0.668	0.575	0.166	2.671	0.664
Min	0.779	0.193	0.684	0.356	0.305	0.139	0.052	0.356
Max	1.603	1.150	2.086	0.860	1.104	0.179	6.410	1.193

. The greatest CF values for all metals investigated were discovered at Site 10 (Brgy South) and Site 9 (Brgy East), which receive a significant volume of waste from port activities, hospitals, and the urban wastewater drainage system. Total CF was distributed as follows: Site 10, Site 9, Site 7, Site 8, Site 4, Site 3, Site 2, Site 5, Site 6, and Site 1. Site 7 receives stormwater runoff from agricultural areas and animal farming waste from Brgy District 29, Ormoc City. While Cu, Pb, and Fe exhibited extremely high levels of contamination (CF > 6), the contamination factor (CF) value for other metals indicated a moderate level of contamination (CF > 1) (Figure 3). Cu > Pb > Fe > Zn > Cd > Mn was the overall declining order of the CF for all metals.

The degree of heavy metal contamination in the specific sites under study was determined using the pollutant load index (PLI) [51,89]. This index is an easy method to compare the pollution levels of various locations. The mean value of the pollutant load index (PLI) was 0.66, while the range was 0.35 to 1.19 (Table 4). The mean value confirmed that the river had low contamination (PLI < 1). However, a higher PLI value was found in Site 9 (Brgy East) and Site 10 (Brgy North) that indicated the sediments in this site were polluted (PLI > 1). Other all sites’ PLI values were found to be less than one (Figure 4), indicating that the sites’ sediments were pollution free. In this study, it was also revealed that Cu, Pb, and Cd were the major contributors for sediment pollution. Site 10 > Site 9 > Site 7 > Site 8 > Site 4 > Site 3 > Site 2 > Site 5 > Site 6 > Site 1 was the decreasing site order that the PLI followed (Figure 4). The residents can gain some insight into the environmental quality through the PLI.

In order to determine the degree of heavy metal contamination in sediment samples, this study makes significant use of the geo-accumulation index (I_geo), which has been widely used in European trace metal investigations since the late 1960s [52]. The ranges of I_geo values for different sediment samples were found to be −0.95 to 0.10, −2.96 to −0.38, −1.13 to 0.48, −2.10 to −0.82, −2.29 to −0.44, −3.43 to −3.06, −1.52 to 5.41 for Cu, Ni, Pb, Fe, Zn, Mn, and Cd respectively (Figure 5). Each metal and each sampling point’s comprehensive data are included in the supplemental section (Table S8). The I_geo values of the heavy metals under study are displayed in Figure 5. In Site 3 (Brgy Patag) and Site 9 (Brgy East), the I_geo for Cu was 0.03 and 0.10, respectively, according to this analysis, indicating that both samples are class 1. At Site 9, the I_geo for Pb was individually 0.48, which means it fell into class 1, or is unpolluted to moderately polluted. The other Igeos for Cu, Ni, Pb, Fe, Zn, and Mn were less than zero, which indicated the sediments were not polluted. The I_geo for Cd in the study area had a mean concentration of 3.28 (Table S8). The maximal positive values of Cd are shown in Figure 5. However, the I_geo values for Cd in 10%, 10%, 20%, 40%, and 20% of the sampling points fell into class 0, class 2, class 3, class 5, and class 6, indicating that the sediments were, respectively, uncontaminated, moderately contaminated, moderately to heavily contaminated, heavily to extremely contaminated, and extremely contaminated [52,90]. The I_geo values for the heavy metals under study were arranged in the following decreasing sequence, as seen in Figure 5: Cd > Pb > Zn > Cu > Fe > Ni > Mn.

According to Zhang and Liu [91], a metal is entirely produced from natural processes or crustal materials if the EF value is between 0.05 and 1.5. However, if the EF value is larger than 1.5, the sources are more likely to be human-made. In the sediments of the Malbasag River, the mean EF values for every metal examined in this study—aside from Mn—were >1.5, suggesting that human activity has an effect on the metal levels in the river (Figure 6). The EF values for Cu and Cd in the sediments of Site 3 were 7.87 and 28.78, respectively, showing “severe” and “very severe enrichment”, which indicated highly anthropogenic activities; mainly, this site is located near a livestock farm and residential and agricultural areas. Moreover, among most of the sites, the highest EF values were found at Site 9 (Brgy East) and Site 10 (Brgy South) due to industrialization, urbanization, partially treated drainage wastewater, and deposition of untreated port and medical waste from the Ormoc Township. Total EF values followed the order of Site 3 > Site 9 > Site 10 > Site 5 > Site 4 > Site 8 > Site 7 > Site 6 > Site 2 > Site 1 (Figure 6).

3.4. Ecological Risk

The results of the evaluation for the potential ecological risk index (RI) and the prospective ecological risk factor (${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$) are shown in

Table 5. ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ and RI values of heavy metals of surface sediments in Malbasag River, Philippines.

Sites	Potential Ecological Risk Factors (${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$)						RI	Risk Level
	Cu	Ni	Pb	Zn	Mn	Cd
Site 1	3.894	2.483	5.930	0.380	0.171	1.562	14.420	Low
Site 2	5.520	2.563	5.746	0.341	0.139	18.170	32.479	Low
Site 3	7.675	1.156	3.420	0.312	0.162	101.035	113.759	Moderate
Site 4	5.200	1.447	3.582	0.305	0.157	84.175	94.866	Moderate
Site 5	4.274	1.272	4.574	0.329	0.173	80.815	91.436	Moderate
Site 6	4.472	1.661	5.046	0.580	0.161	15.054	26.975	Low
Site 7	7.259	5.188	5.632	0.843	0.175	22.798	41.895	Low
Site 8	5.136	1.285	3.792	0.506	0.169	108.659	119.547	Moderate
Site 9	8.014	5.214	6.738	1.052	0.179	176.739	197.936	Considerable
Site 10	6.833	6.899	10.430	1.104	0.173	192.289	217.728	Considerable

. Cu, Ni, Pb, Zn, Mn, and Cd were found to have possible ecological risk factors (${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$) ranging from 3.89 to 8.01, 1.15 to 6.89, 3.42 to 10.43, 0.30 to 1.10, 0.13 to 0.17, and 1.5 to 192, respectively, with average values of 5.82, 2.91, 5.48, 0.57, 0.16, and 80.12. According to Table 5, the possible ecological risk factors for heavy metals in the Malbasag River sediments were as follows: Cd > Pb > Cu > Ni > Zn > Mn.

Cd presented a moderate to significant ecological danger, with a mean value of 95.10 (Table 5). Cd may have been present in the sediments as a result of agricultural runoff into the river and the release of waste and oily effluents from port activities. Other heavy metal studies’ ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ results (Cu, Ni, Pb, Zn, and Mn) indicate minimal ecological risk due to a lower toxic response factor (${\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}$). The potential ecological risk indexes for the heavy metal according to this study in the sediments of the Malbasag River were found to be in the following order: Site 10 > Site 9 > Site 8 > Site 3 > Site 4 > Site 5 > Site 7 > Site 2 > Site 6 > Site 1. The RI values at the sampling sites, however, varied from 14.42 to 217.72, suggesting that all of the sampling sites had moderate to significant ecological risk (Table 5).

3.5. Assessment of Heavy Metal Contamination by Using Toxic Unit (TU) and Toxic Risk Index (TRI)

Through the computation of the TU and ∑TU, as seen in Figure 7, this study measured the potential acute toxicity of heavy metals on aquatic organisms. In the sites, according to this study’s results, the heavy metal TUs, in decreasing order, were Ni (0.91 ± 0.65) > Cu (0.266 ± 0.06) > Pb (0.24 ± 0.09) > Cd (0.22 ± 0.19) > Zn (0.17 ± 0.09) (Figure 7a). The ∑TU values for all sediment samples ranged from 1.12 to 3.82. Figure 7 indicates the values of ∑TU for Site 10 (Brgy South), Site 9 (Brgy East), and Site 7 (Brgy North) were increased, which was directly connected to the potential source of waste from port, urban, and residential areas and might be affected by human activity. However, the results in this study did not exceed the ∑TU reference value (∑TU > 4), which means there was a low toxic effect of the studied heavy metals. Although Cd had a higher amount of pollution based on the EF values, it contributed less toxicity to the ∑TUs. Because of the significantly higher PEL value of Cd, the TU assessment technique would undoubtedly underestimate its toxicity. For a more thorough and accurate evaluation of the environmental danger posed by metals, a supplementary strategy that incorporates conventional soil criteria and other assessment techniques should be taken into consideration [92].

The toxic risk index (TRI) is a different computation that has been approved to offer a more precise assessment of the potential toxicity of a certain metal(oid) in the ecosystem. Using the previously described index (Equation (10)), this study found that the average TRI values for Cu, Ni, Pb, Zn, and Cd were 2.01, 1.38, 0.45, 0.62, and 2.29, respectively. Given the average values for this investigation, it has been proposed that these metal(oid)s do not pose any concern. According to Figure 7, the toxic risk indexes (TRIs) for the heavy metals under study in the Malbasag River sediments were ordered as follows: Site 10 > Site 9 > Site 7 > Site 3 > Site 8 > Site 2 > Site 4 > Site 5 > Site 6> Site 1. Sites 10 (Brgy South), 9 (Brgy East), and 3 (Brgy Patag) had TRI values above 5 (TRI ≤ 5: no danger) because of increased accumulation and contamination. This indicates that the sediment-dwelling fauna in these regions experience a low toxic effect of the heavy metals under study. TRI values and ∑TUs showed a good connection (R² = 0.94), suggesting that the TRI is a suitable method for accurately assessing ecological toxicity (Figure S1). Furthermore, the TRI technique revealed a greater contributing ratio of Cd than the ratio in ∑TUs, indicating a higher risk of Cd pollution.

3.6. Sediment Hazard Quotient Evaluation

Benson et al. [64] recently developed a pollution index called the modified hazard quotient (mHQ) that is connected to the degree of contamination. The mHQ represents the concentration of each metal(oid) in a sediment to determine pollution levels by using the threshold edge of hazardous environmental dispersions, such as the SEL, PEL, and TEL. The assessment of mHQ is very significant because it measures the harm of specific metal(oid)s to the biota and the aquatic environment [93].

Table 4 exhibits the results of the mHQ calculation for particular metal contributions, which was done using Equation (11). According to this investigation of the Cu level in sediments, 43.33% of the sediments’ mHQ values fell into the 2 > mHQ ≥ 1.5 (moderate severity of contamination) range, while 56.6% of the samples’ mHQ values fell into the 1.5 > mHQ ≥ 1 (low severity of contamination) range (Table S10). As a result, it was proposed that Cu posed a moderate to severe ecological risk to the research area and that the ecology of the floral and faunal communities was at serious risk. More information about Ni contamination was found to show that 50% of the sediments had a low severity of contamination (1.5 > mHQ ≥ 1), whereas 20%, 20%, and 10% of the sediments had a moderate severity of contamination (2 > mHQ ≥ 1.5), considerable severity of contamination (2.5 > mHQ ≥ 2), and very high severity of contamination (3 > mHQ ≥ 2.5) based on mHQ values. However, 77%, 80%, and 30% of the sediment samples indicated very low concern for ecology and the environment from Pb, Zn, and Cd (Table 4). On the other hand, this study revealed that 20% and 40% of the sediment samples fell into the low severity of pollution category (1.5 > mHQ ≥ 1) for Zn and Cd; additionally, 20% of the sediment samples fell into the moderate severity of contamination category for Cd (2 > mHQ ≥ 1.5), which indicated the studied river and its associated aquatic environment had potential ecological risk.

3.7. Sources of Heave Metal in the Study Area

The distribution and variability in the content of heavy metals in sediment are often influenced by the possible sources [52,94]. Multivariate statistical methods such as factor analysis, principal component analysis, cluster analysis, and Pearson’s correlation coefficient analysis are crucial for assessing heavy metal levels and identifying pollution sources in the riverine [52,95]. Furthermore, multivariate methods are helpful for clustering, data reduction, and the study of temporal and spatial changes [95].

3.7.1. Correlation Coefficient Analysis (CCA)

The matrix of correlations for heavy metals in sediments was performed to find relationships between metals and identify mutual sources of metals in the Malbasag River. In the Cluster Coefficient Analysis (CCA), strongly positively correlated components suggest the same source of origin, whereas weakly or negatively correlated parts show several sources. In the current study (Figure 8), a strong positive correlation (p < 0.001) was found among the Cu–Zn (0.58), Ni–Pb (0.87), Ni–Zn (0.90), Ni–Fe (0.61), Pb–Zn (0.77), Fe–Zn (0.64), Zn–Cd (0.58), OC–Ni (0.78), OC–Pb (0.74), OC–Zn (0.67), OM–OC (1.0), OM–Pb (0.74), and OM–Ni (0.78) pairs, indicating similar sources of geogenic origin and mobility. A moderate positive (r = 0.05) correlation existed between the Cu–Ni (0.57), Cu–Cd (0.55), Pb–Fe (0.53), OC–Fe (0.57), and OM–Fe (0.57) pairs. The research makes it noticeable that Zn had a significant impact on the total OC, Cu, Ni, Pb, Fe, and Cd, indicating that the elements were obtained via ports, agricultural runoff, vegetation, and the movement of both urban and residential garbage combined (Figure 8).

3.7.2. Principal Component Analysis (PCA)

Principal component analysis (PCA), introduced by Hotelling in 1933 [96], was conducted to unravel the compositional patterns of sediment samples and identify factors influencing metal distribution. Positive loadings in PCA reflect the degree of influence specific metals have on sediment quality, while negative values indicate minimal impact [53]. Using normalized data, PCA extracted five principal components (PCs) with eigenvalues > 1, explaining 99.1% of the total variance (Table S9). The scree plot indicates the contribution of each principal component (PC) to the total variance, while the PCA biplot reveals that the variability in heavy metal concentrations can be effectively represented by two principal components, which together explain 75.5% of the total variance in the sediment samples (Figure 9). PC1 accounted for 58.5% of the total variance, with strong loadings on Zn, Ni, Pb, and Fe, while Ni and Pb showed a strong relationship. On the other hand, PC2 contributed 16.6% of the variance and showed significant loadings on Mn, Cd, and Cu, with Cu and Cd exhibiting a strong positive relationship.

The rotation table reflects the varimax-rotated components, which redistribute variance for better interpretability, making the contributions of individual parameters to the PCs more distinct (Table S9). PC1, PC2, PC3, PC4, and PC5 explain 30.3%, 18.6%, 18.0%, 17.6%, and 14.7% of the variance, respectively. Table S9 shows the relevance of each component based on its eigenvalues > 1, percentage of total variance, and cumulative variance. The redistributed first principal component (PC1), accounting for 30.1%, showed a moderately positive loading on Zn and a highly positive loading (loading > 0.70) on Ni and Pb. Moreover, the CCA section showed a strong positive correlation between Ni–Pb (Figure 8). The PC2 only showed positive loadings (loading > 0.70) for Fe, which was shown to account for 18.4% of the total variance. This study discussed the interactions between Ni, Pb, Zn, and Fe in the previous section (CA), and they came from the same cluster (number 1) and sites (2 to 6 and 8) (Figure S1a,b). Consequently, the PCA validated the influence of the four distinct factors in PC1 and PC2. They may have come from a variety of geological sources, surface runoff from farms, and natural (soil erosion, climate changes) and man-made activities like fishing boats, household and urban waste, poultry farms, ship painting, and wood processing facilities. Similarly, Mn contributed to the majority of PC3, which described 18.0% of the total variance. Cd contributed significantly to PC4, accounting for 17.6% of the total variance. As a result, the source may have included various biosolids that came from the manufacturing port, emissions from coal and car combustion from port traffic, leachate from Cd batteries, and Cd-plated objects. However, Cu also showed a notable contribution to PC5, which accounted for 14.7% of the total variance. Additionally, the CA section showed that the elements Mn, Cd, and Cu formed cluster 2 and were from Sites 7, 9, and 10 (Figure S1a,b), indicating that they possibly originated from the same region and pollution sources. These PCs represent mostly anthropogenic sources. The variables come from urban, hospital, animal farm, industrial, and residential wastewaters and metallic wastewaters of the nearby port and central city bus terminals.

3.8. Spatial Distribution

The concentrations of Cu, Ni, Pb, Zn, Mn, and Cd in river sediment samples from the Malbasag River connected to the port in Ormoc City were used to determine the spatial distributions of these elements using ArcGIS software (version 10.5) (Figure 10). The IDW interpolation method was performed to make spatial distribution maps of each metal from sediment samples in the study area. Based on the findings of the geographical distribution pattern (Figure 10a), high concentrations of Cu were found in the port region and center of the urban area (~Sites 8 to 10) and agricultural land area (Site 1). However, according to the spatial distribution pattern for the metals under study (Ni, Pb, Zn, Mn, and Cd), the maximum concentrations of these metals were found in the southeast part of the Malbasag River (Figure 10b–f), which is connected to the port, urban untreated wastewater drainage, and medical wastewater from Ormoc City, Philippines (Table 3). According to the sediment quality recommendations [43,74,75,76], the average concentrations of Cu, Zn, and Cd in sediment samples also exceeded all threshold values (LEL, SEL, TEF, PEL, ERL, ERM, and TRV). This may have happened due to the downstream pattern, as ship transportation activities release different kind of metals through ship painting, repair, and the unloading and or loading of cargo. The medical waste, especially from laboratories and pharmaceuticals, contributes HMs such as Cd and Zn. The southern part of the Malbasag River is also connected with the main city center, and city wastes such as domestic waste, industrial discharge, vehicle emissions are the main source of some metal (Cu, Zn, and Cd) pollution. The ecology is seriously threatened by these wastes, which are inevitable because metals are so versatile.

3.9. Limitations of the Study

This study was conducted without specific funding or grants, which limited the scope of the research, including the sample size and the ability to collect water and biological samples for a more comprehensive analysis. Additionally, the cross-sectional design captured contamination at a single time point, thereby not accounting for seasonal variations or temporal dynamics in pollutant levels. Future research should address these limitations by incorporating longitudinal sampling and evaluating health risks through water and biological sample analyses to provide a more holistic understanding of ecological and human health impacts.

4. Conclusions

Malbasag River sediment samples for heavy metal concentration showed maximum concentrations at Site 10 (Brgy South), due to the Ormoc port and the city’s untreated sewage and wastewater, and at other locations such as Site 9 (Brgy East), Site 7 (Brgy North), and Site 8 (Brgy East), due to elevated levels of heavy metals from nearby releases and runoff. The heavy metals’ concentration order was Mn > Zn > Cu > Ni > Pb > Cd. Most of the sites were above the UCC-recommended values for HMs, and that implied enormous ecological risks, particularly from Cu, Zn, and Cd. Pollution assessments using contamination factor (CF), pollutant load index (PLI), and geo-accumulation index (I_geo) indicated high levels of Cu, Cd, Pb, and Zn in Sites 10, 9, 8, and 3 (Brgy Patag). Ecological risk assessment indicated moderate to high risks to bottom-dwelling organisms, while toxic effects overall within the study site were low.

Multivariate analyses (CCA, CA, and PCA) identified the sources of pollution, and these indicated a high correlation between Ni, Cu, Zn, Pb, Cd, and Fe originating from the same sources related to port activities and urban wastewater drainage. This study recommends the establishment of wastewater treatment plants in the port and urban areas to avert pollution and preserve the ecosystem.