3.1. Spatial Assessment of Heavy Metals in Surface Water and Sediment
The concentration of heavy metals in surface water among all samples indicated Cu (26.9 ± 2.3) and Ni (21.2 ± 3.9) as the most dominant heavy metals (
Figure 1a), whereas in sediments, it was Zn (174.3 ± 10.9) and Cr (97.05 ± 5.4) (
Figure 1b). The data suggest a noticeable increase in the concentrations of most heavy metals in sediments and surface water from Sample 1 to Sample 10, indicating a possible contamination gradient across the sampled area. Notably, the levels of Cu and Ni for surface water and As and Cd for sediments increased significantly from Sample 1 to Sample 10. Heavy metal concentrations, including Arsenic, Cadmium, Copper, Nickel, Mercury, and Zinc, exhibited notable differences across the surface water samples, as indicated by the analysis of variance (ANOVA) results (
p < 0.05) (
Table 2). Heavy metal concentrations, including Arsenic, Chromium, Copper, and Zinc, exhibited notable differences across the sediment samples, as indicated by the analysis of variance (ANOVA) results (
p < 0.05) (
Table 3). These variations suggest spatial heterogeneity in heavy metal distribution across the sampled area. This could indicate an external source of contamination, possibly due to anthropogenic activities, such as industrial discharges, agricultural runoff, or improper disposal of hazardous waste [
2,
36,
37]. The dominance of Cu and Ni could be due to stormwater and agricultural runoff, particularly from roads, and the use of copper-containing pesticides and fertilizers in agriculture can lead to Copper in river water. Nickel can also be found in certain fertilizers [
2,
25,
38]. Since this tributary is exposed to agricultural runoff, this could possibly explain why Cu and Ni were found to be dominant.
The hierarchy of heavy metal concentration in surface water is as follows: Cu > Ni > Zn > Cr > Pb > As > Cd > Hg. For sediments, it is the following: Zn > Cr > Cu > Ni > Pb > As > Cd > Hg. A medium coefficient of variation (CV) was observed for Ni (21.7) and Zn (19.5) in surface water (
Figure 2), while for sediments, it was observed for Cu (16.88), Ni (17.9), Pb (16.2), and As (15.58) (
Figure 3). The medium variations of heavy metal concentrations found in the surface water and sediments indicated potential variations in their origins, possibly influenced significantly by human or anthropogenic activities [
39,
40]. All the other heavy metals had a CV of <15, indicating no minor or significant changes in heavy metal variation.
All the mean concentrations of heavy metals in surface water were lower than the Chinese heavy metal guidelines [
41] and World Health Organization (WHO) guidelines, while for sediments, all the mean metal concentrations were higher than the heavy metal guidelines [
31]. The heavy metal concentrations of this tributary were compared with existing literature to identify the current pollution status in surface water and sediments.
Regarding surface water, we compared the heavy metals to other freshwater ecosystems, such as Poyang, Chishui River, Dongting, Taihu, Chaohu, Hongze, Hong, Daye, Dianchi, and specific areas of the Yangtze River [
10,
42,
43]. The mean concentration of As (5.44 ± 0.58 mg/L) in our surface water was similar to Chao and Taihu Lakes, and it was slightly higher than the standard value observed in the Yangtze River (3.4–4.7 mg/L). Likewise, most of our heavy metal values were similar or in range with the Chaohu, Taihu, and Poyang lakes, indicating that heavy metal pollution in surface waters is a massive problem most freshwater ecosystems face [
44]. Most of these lakes are prone to agricultural runoffs, discharges from wastewater treatment plants, and anthropogenic activities [
3,
9,
10]. However, we did observe that our heavy metal concentrations for surface water were higher than the concentrations found in the middle and lower parts of the Yangtze River [
7,
45]; we believe that this could be due to the exposure of this tributary to an excessive agricultural runoff that caused elevated levels of heavy metals, as this was also observed by [
38,
46].
The sediment heavy metal concentrations were compared with Dianchi, Taihu, Haohu, Dongting, Poyang, Yangtze, Haihe River, Yellow River, Pearl River, Honghu, Donghu, and Daye lakes [
6,
11,
47,
48]. Unlike surface water, the sediment metal concentrations were within range. Even though the values were within range, they were still higher than the guideline values, indicating that the sediments are exposed and vulnerable to diverse contamination sources, including industrial, agricultural, and anthropogenic activities [
14]. The observation of sediment metal concentrations being higher than standard guidelines has been commonly observed by other studies that have also explored lakes and river sediments for metals [
46,
47,
48]. The heavy metal concentration in this tributary indicates that government intervention with stricter guidelines and policies for lakes and rivers is highly required, and removing or reducing the heavy metal concentration could benefit the ecosystem.
3.2. Heavy Metal Assessment through Various Indices
An investigation of surface water quality through the analysis of the Nemerow pollution index was conducted on a tributary in Nanjing in September (
Figure 3). The indices showcased a fluctuating range from 0.5309 to 0.7988. Three sample points (Samples 8–10) illustrated indices surpassing 0.7, inferring a minor pollution level at these locations. The scenario implies a possibility of specific pollution sources or events near or upstream of these points. The diverse pollution levels within the tributary could emanate from various sources, given its exposure to agricultural, residential, and anthropogenic activities. Elevated indices, especially in Samples 8–10, might be attributed to agricultural runoff, a common concern in surface water pollution, especially following rainfall events, due to the washing away of fertilizers and pesticides into water bodies [
49]. September’s climate context might be pivotal, as late summer and early autumn can introduce climatic variables influencing runoff and pollution levels. Residential communities adjacent to the tributary can also significantly contribute to the pollution indices observed. Domestic waste, sewage mismanagement, and everyday activities can directly or indirectly introduce contaminants into the water body. Anthropogenic activities, such as industrial operations or recreational uses, might also adversely impact the water quality [
50]. Illegal discharges, inadequate waste management, or non-compliance with environmental regulations exacerbate pollution issues in urban water bodies. Given the multifaceted potential sources, managing and mitigating pollution in the tributary necessitates an integrated approach. Employing biological remediation, enforcing stringent regulations on pollutant discharges, and community awareness programs might reduce current pollution levels and safeguard the tributary against future pollution threats.
All sediment sampled locations exhibited PLI values considerably exceeding 1.0 and reaching values as high as 3.04, indicating the existence of concerning levels of pollution across all tested sites (
Figure 4). The range of PLI from 2.16 (Sample 2) to a strikingly elevated 3.05 (Sample 10) confirms a ubiquitous contamination presence and intriguingly suggests a gradient or potentially variable sources of contaminative input across the sampled sites. Dissecting the variability and progressive increment in PLI values from Sample 1 to Sample 10 could be emblematic of a spatial relation to contamination sources. Considering the observable trend, we believe that the potential contaminative activities—such as agricultural runoffs and domestic waste outputs—could be the reason for these elevated PLI values. PLI values in this tributary were as high as three, indicating a high level of environmental quality degradation and potential risk to aquatic organisms [
51,
52]. Elevated metal contents in sediments could result in bioaccumulation within aquatic organisms, thus infiltrating the food chain, consequently impacting higher trophic levels, and potentially posing threats to human health [
53]. The observed escalation in PLI warrants a profound exploration into the bioavailability of these contaminants and potential bioaccumulation within local aquatic biota.
The consistently elevated Igeo values for specific heavy metals, particularly Arsenic and Cadmium, across various sediment samples indicate a pervasive contamination issue (
Table 4). These findings are comparable to national and global contexts, where anthropogenic activities, notably agricultural runoff, anthropogenic activities, domestic discharge, and industrial emissions, have notoriously facilitated the percolation of heavy metals into aquatic ecosystems [
1,
54,
55]. Arsenic, particularly noted for its toxicity, bioaccumulation, and potential to propagate through trophic levels [
56], emerges as a conspicuous concern given its consistently high Igeo values, implying a pressing need for strategic intervention. A conspicuous disparity in the Igeo values among various metals underscores the complex contamination and sedimentation dynamics. Mercury, for instance, consistently manifested negative Igeo values across all samples, emblematic of comparatively unpolluted conditions. Contrasting sedimentation and dilution mechanisms might be influential, as proposed by [
57], who emphasized the role of sediment particle size, organic matter, and redox conditions in modulating Hg distribution and bioavailability in sediments. The elevated levels of certain metals, such as Cd and Pb, even at localized hotspots such as Sample 10, warrant a comprehensive evaluation considering their ecotoxicological implications. Cd, renowned for its nephrotoxic, carcinogenic, and mutagenic properties [
46], and Pb, noted for its neurological and developmental toxicity, elevate the ecological and health risk spectrums associated with the investigated tributary [
58]. Zonal differences in heavy metal accumulation, evidenced by the spatial variability in Igeo values, suggest divergent contamination sources or transport mechanisms. Investigating the disparate contaminant profiles and scrutinizing the potential sources and pathways using isotopic fingerprinting or spatial distribution modeling, akin to methodologies adopted by [
59], can discernibly elucidate contamination origins and trajectories. Implementing rigorous pollution source controls, enforcing stringent discharge norms, and adopting sustainable land-use practices will mitigate further contamination. Furthermore, community-centric approaches that amalgamate local knowledge, stakeholder engagement, and participative management will bolster the effectiveness and inclusivity of contamination management strategies.
The Ecological Risk Index (Ei) and Potential Ecological Response Index (Ri) illuminated a noticeable presence and an alarming level of certain heavy metals across all sampling sites (
Figure 5). Remarkably, Cd showcased elevated Ei values across all sites, reaching 245.7 in Sample 10. Similarly, As and Hg also demonstrated pronounced Ei, although not as critically elevated as Cd. A perceivable augmenting trend was discerned in Ri values, cascading from 280.94 (Sample 1) to 386.68 (Sample 10), potentially suggesting an augmenting ecological risk along the sampled path. The infiltration of heavy metals, notably Cd, As, and Hg, in the tributary sediments underscores a tangible ecological problem, revealing a scenario of pronounced metal contamination. The significantly high Ei values for Cd across all sampling sites burgeon above the typical range observed in uncontaminated sites, rendering it an element of critical concern. The previous literature documented Cd as a potent toxicant, even at minimal concentrations, imposing severe repercussions on aquatic life and potentially spiraling up the trophic chain [
38,
60]. As and Hg also disturbed the sediments with notably high Ei values, entailing potential detrimental consequences. Arsenic is renowned for its non-threshold toxicity, culminating in severe ecological ramifications [
56]. Concurrently, Hg’s potential for bioaccumulation and biomagnification poses tangible threats to aquatic ecosystems and human health via seafood consumption [
42]. The tendency of Ri to escalate from sample sites 1 to 10 warrants an exploration into potential contamination sources or a contamination gradient, even though it could be potentially stated that consistent agricultural and anthropogenic activity may have caused the values to increase from Samples 1 to 10. However, it would be ideal for establishing spatially resolved risk assessments and considering the hydrodynamic and sedimentary processes, potentially facilitating deciphering contamination dispersion mechanisms.
3.3. Relationship between Physicochemical Parameters and Heavy Metals in Surface Water and Sediments
Pearson correlation analysis was performed to identify potential pollution sources that could cause the presence of heavy metals in surface water and sediments. The analysis of surface water samples revealed intriguing relationships between various heavy metals, shedding light on potential pollution sources and the interplay of these metals in the environment (
Figure 6). A notable positive correlation was observed between Cd and Cu (r = 0.73 *). This finding suggests that these two heavy metals may share a common source or be transported via similar pathways. The presence of Cd and Cu in the water samples could be attributed to agricultural activity processes that release these metals into the surrounding environment [
61]. Elevated levels of Cadmium and Copper can potentially contribute to water quality degradation, leading to adverse effects on aquatic ecosystems and human health. We observed a strong positive correlation between Cr and Ni (r = 0.89 ****). Such a high correlation coefficient indicates a tightly interwoven connection between these heavy metals. Previous studies have shown that their presence in the water may pose significant risks to water quality, including potential toxicity to aquatic life. Cr and Ni are often associated with industrial discharges, raising concerns about the extent of contamination from these sources and the potential impacts on water quality [
62]. We observed Zn and Pb (r = 0.68 *) to be positively correlated, implying that these two metals may co-occur in the surface water samples. Anthropogenic sources, such as agricultural runoff, likely contribute to Zn and Pb concentrations in the tributary [
63]. Mercury negatively correlated with pH (r = −0.34), indicating an inverse relationship between pH and Hg concentration. This correlation underscores the influence of pH on the behavior and availability of mercury in aquatic ecosystems. pH levels can enhance the bioavailability and toxicity of Hg, which has implications for both water quality and ecosystem health [
53]. A negative correlation was observed between Cd and pH (r = −0.06). This suggests that the slight alkalinity of water may play a role in influencing the concentration and behavior of Cadmium. Moderate pH levels can increase the solubility and mobility of certain heavy metals, including Cadmium, potentially exacerbating their impact on water quality [
64]. Copper exhibited a significant positive correlation with temperature (r = 0.73 *), implying a connection between these two parameters. It is well-established that temperature can influence the solubility and reactivity of Copper in aquatic systems. As water temperature increases, Copper’s availability and potential to impact water quality may also rise [
39]. Zinc positively correlated with COD (r = 0.63 *), suggesting that organic pollution may contribute to the presence of Zinc in the water. Organic matter can be a carrier for certain heavy metals, such as Zinc, potentially impacting water quality and aquatic ecosystems [
65]. Chromium positively correlates with TN (r = 0.22); elevated TN concentrations are often associated with agricultural runoff, which may have fertilizers. Since agricultural runoff combines organic matter (rich in N) and fertilizers (rich in Cr), it may explain their positive correlation. Lead significantly correlates with TSS (r = 0.49), implying that high levels of suspended solids in water may facilitate the transport of lead particles. Mercury exhibits a robust positive correlation with Total Phosphorus (TP) (r = 0.83 **). This finding suggests that water’s Phosphorus levels can influence mercury’s behavior and bioavailability. Elevated TP concentrations may exacerbate mercury contamination since elevated levels indicate eutrophication, and since this tributary is exposed to anthropogenic pollution, it could explain the reason behind the strong correlation [
66].
However, for sediments, most heavy metals were found to have a significant positive correlation with each other and also with TN and TP, apart from Cd (positive but non-significant), Hg (positive but non-significant), and OM (positive but non-significant) (
Figure 7). The strong positive correlations among critical heavy metals, As, Cu, Pb, and Ni, indicate a potential convergence of sources or similar behavior patterns, likely occurring because of anthropogenic influences or geological attributes intrinsic to the river basin [
7]. This observed clustering of correlations emphasizes the interlinked nature of these heavy metals in the sediment matrix, highlighting the necessity for a complete approach to source identification. Conversely, the marked positive association between Cd and Zn suggests shared origins, possibly attributable to industrial activities or human activities [
67]. A separate cluster emerges with Cr exhibiting robust correlations with Cu and Pb, implying a degree of co-release, a phenomenon that may have sources in agricultural discharges within the river’s catchment area [
68]. The distinctive behavior of Hg, displaying only moderate correlations with its heavy metal counterparts, apart from Cd and Cr, is noteworthy. This abnormality suggests differentiated sources or factors governing its mobility in the sediment, warranting a focused examination of its intricate transport mechanisms and contamination sources [
40]. We observed positive associations between Total Nitrogen (TN) and Total Phosphorus (TP) with heavy metals, hinting at potential nutrient–metal interplays or shared inputs, unveiling yet another layer of complexity in the sediment’s elemental composition [
69]. Such associations underscore the interdependent nature of nutrient and heavy metal dynamics, contributing to the intricate ecological balance in river ecosystems. Organic matter (OM) positively correlated with multiple heavy metals, notably Chromium. This underscores the pivotal role of OM as a binding agent, influencing the retention and distribution of heavy metals within the sediment matrix [
70]. These findings accentuate the multifaceted nature of heavy metal interactions and underscore the potential significance of organic matter content in sediments as a regulator of metal mobility.
The exploration of heavy metal concentrations and environmental parameters in water samples yields critical insights into aquatic health and possible contamination sources. A robust Pearson correlation analysis among heavy metals (Arsenic, Cadmium, Chromium, Copper, Lead, Nickel, Zinc, and Mercury) and other environmental parameters (pH, Temperature, COD, TN, TSS, TP, and Chlα) exposes several noteworthy relationships. A profoundly strong correlation was observed between Nickel (Ni) and Chromium (Cr) (r = 0.89 ****), suggesting their simultaneous presence and potential common source, which may be attributed to industrial discharge, given the ubiquity of these metals in several industries [
71]. Simultaneously, the apparent strong association between Copper (Cu) and Lead (Pb) (r = 0.86 **), metals often conjointly found in electronic wastes and industrial emissions, denotes an imperative need for scrutinizing industrial activities occurring within the watershed [
48].
Interestingly, the relationships between certain metals and nutrients hint towards the intricate and multifaceted dynamics within the water body. For example, a significant positive correlation between Zinc (Zn) and Total Phosphorus (TP) (r = 0.60 *) might stem from agricultural runoff, where Zn often originates from fertilizers and TP from both fertilizers and organic matter [
72]. Additionally, high correlations between COD and TP (r = 0.83 **) and between COD and Chlα (r = 0.88) suggest a potential scenario where nutrient and organic matter enrichment is fostering eutrophication, which could detrimentally affect aquatic ecosystems [
73]. Moreover, the relationships between heavy metals indicate possible synergistic toxicological effects on aquatic life, warranting a deeper dive into understanding the bioavailability and speciation of these metals in the water body. High concentrations and the coincidence of heavy metals may induce adverse effects beyond what might be anticipated from their concentrations [
55,
62]. Emphasizing further potential sources, the noted correlations may point towards distinct anthropogenic inputs. For instance, the associations between metals might signify influences of urban and industrial activities, while relationships between nutrients and certain metals might allude to agricultural influences. Such integrated insights into pollutant sources can serve as a baseline to develop and implement strategic monitoring and management approaches to safeguard aquatic ecosystems. From a broader perspective, the illustrated correlations and their implications underline the importance of adopting an integrative approach to understanding aquatic environmental chemistry, considering the role and impact of anthropogenic activities in shaping these chemical dynamics. Future studies should encompass a thorough spatial-temporal analysis to elucidate pollution hotspots and trend shifts and a rigorous risk assessment to effectively comprehend ecological and health implications.
The Principal Component Analysis (PCA) of heavy metal concentrations in surface water and sediments revealed intriguing patterns, offering insights into the nuanced interactions between these environmental matrices. Principal components with an eigenvalue greater than 1 were selected. In surface water (
Table 5), the shared negative loadings of Mercury, Zinc, Chromium, Cadmium, Arsenic, Copper, Nickel, and Lead on PC 1 (explaining 60.11% of variance) suggest a unified influence, potentially originating from agricultural runoff. The fact that these heavy metals show similar behavior on PC 1 suggests a common source or process influencing their concentrations in surface water. We believe that this unified influence and the strong correlation shown in Pearson correlation may originate from agricultural runoff. Agricultural activities often involve the use of fertilizers, pesticides, and other chemicals, contributing to the presence of heavy metals in water bodies [
72]. The dominance of PC 1 in explaining the variance strengthens the argument that the identified unified influence of heavy metals is a significant factor shaping the heavy metal composition of surface water. The contribution of PC 2 (14.26%) indicates additional complexities, possibly linked to variations in agricultural practices, land use, or seasonal fluctuations impacting the dynamics of heavy metals in the water. The positive loadings of Cadmium, Chromium, Copper, and Lead suggest that variations in these elements positively correlate in PC 2, indicating potential commonalities, such as land use or geochemical characteristics in their sources or behaviors distinct from the negative correlations observed in PC 1. This contrasts with the negative correlations observed in PC 1, indicating a nuanced differentiation in the environmental dynamics of these heavy metals. This nuanced differentiation implies subtle distinctions in their relationships, possibly influenced by factors unique to PC 1, contributing to a more comprehensive understanding of the complex interactions between these elements.
In sediments (
Table 6), the positive loadings of Chromium, Nickel, Mercury, Arsenic, Cadmium, Copper, Lead, and Zinc on PC 1 (explaining 67.05% of variance) signify a unique sediment signature, likely reflecting the cumulative impact of agricultural activities. Furthermore, the contribution of PC 2 in sediments (18.08%) provides additional insights into variations not fully captured by PC 1. In PC 2, the substantial positive loading of Chromium implies a robust positive correlation with other heavy metals, suggesting potential common sources or shared environmental behaviors. This was in accordance with our findings from previous Pearson correlation analyses. The negative loadings of Nickel, Arsenic, Copper, and Lead indicate an inverse relationship, pointing to distinct patterns or sources compared with Chromium. Exploring PC 2 in sediments may unveil specific factors influencing heavy metal concentrations, such as sediment composition or dynamic interactions between the agricultural site and the tributary. Sediment composition, for example, can vary based on factors such as mineral content, organic matter, and particle size. The mineral composition of sediments can influence heavy metal concentrations. Certain minerals may adsorb or release heavy metals, impacting their availability in the sediment. The presence of organic matter in sediments can affect heavy metal mobility. Organic compounds may form complexes with heavy metals, influencing their solubility and retention in sediments [
45]. Dynamic interactions between the agricultural site and the tributary could involve factors such as seasonal changes, water flow patterns, or land use practices that influence sedimentary processes and subsequently impact heavy metal concentrations.
The contrasting patterns in PC loadings between surface water and sediments emphasize the multifaceted nature of heavy metal dynamics in the tributary. Agricultural runoff likely plays a pivotal role in influencing surface water quality, introducing metals that can be transported downstream. In sediments, the cumulative impact of these inputs and sedimentary processes contribute to a distinctive composition. The contrasting patterns between surface water and sediments suggest that diverse sources influence surface water. Negative loading values for all heavy metals surface water and positive loading for sediments in the tributary suggest unique origins not captured by Factor 1. This indicates potential contributions from seasonal changes, sediment deposition, and water flow movement [
74]. In sediment samples, Factor 1 explained 67.05% of the total variance and was strongly associated with concentrations of Cr, Ni, Zn, and Cd, indicative of common sources. These metals are widely found in fertilizers and sewage discharges [
3,
75].