Next Article in Journal
Effectiveness of Rain Gardens for Managing Non-Point Source Pollution from Urban Surface Storm Water Runoff in Eastern Texas, USA
Previous Article in Journal
Do Non-Agricultural Sectors Affect Food Security in Saudi Arabia?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 10
10.3390/su17104622
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Land-Use Patterns on Heavy Metal Pollution and Health Risk in the Surface Water of the Nandu River, China

by
Changchao Chen
1,
Wen Zhang
2,3,
Ping Li
1,*,
Yuanhao Ma
1,
Longru Liang
1,
Wanman Wu
1,
Jianlei Li
1 and
Xiaoshan Zhu
1
1
School of Ecology, Hainan University, Haikou 570228, China
2
College of Life Science, Hainan Normal University, Haikou 571158, China
3
Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Haikou 571101, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4622; https://doi.org/10.3390/su17104622
Submission received: 11 April 2025 / Revised: 7 May 2025 / Accepted: 13 May 2025 / Published: 18 May 2025
(This article belongs to the Section Sustainable Water Management)
Browse Figures
Versions Notes

Abstract

:
Rapid land-use changes have significantly changed the occurrence of heavy metals (HMs) in tropical watershed systems. However, the influence of land-use patterns on the spatial and temporal distribution of HMs in tropical river systems remains poorly understood. This study aims to explore the relationship between land-use types and HM pollution in the China’s largest tropical watershed, the Nandu River. Eight heavy metals (Cd, Pb, Cr, Cu, Zn, As, Hg, and Sb) in the surface water were monitored across river, estuary, and nearshore zones during wet and dry seasons. Our findings show a higher total concentration of eight heavy metals (ΣHMs) in the wet season (30.52 μg/L) compared to the dry season (21.53 μg/L). In the wet season, ΣHM concentrations followed the order: estuary (70.96 μg/L) > basin (31.03 μg/L) > nearshore (8.07 μg/L). In the dry season, it was basin (31.56 μg/L) > estuary (23.26 μg/L) > nearshore (7.49 μg/L). Land-use patterns had higher interpretation rates for HM distribution in the dry season (65.8–73.0%) compared to the wet season (31.0–42.4%). The 2000 m buffer zone had a greater impact on HM distribution than the 500 m and 1000 m zones. Agricultural land and construction areas were the primary contributors to HM pollution in the dry and wet seasons, respectively. Noteworthy, in the river basin, chromium (Cr) presented carcinogenic risks to both children and adults through ingestion in both seasons and arsenic (As) posed a risk to children in the dry season. This study provides valuable insights for the sustainable management of land use and improving river water quality by highlighting the relationship between land use and HM contamination in tropical river ecosystems.

Graphical Abstract

1. Introduction

Rivers serve as crucial channels for the migration and transportation of substances between land, lakes, and oceans, while also providing vital water resources that support social and economic development [1,2]. However, the intensification of anthropogenic disturbances has elevated heavy metal pollution to a pervasive environmental threat in aquatic systems worldwide, particularly within riverine ecosystems [3,4,5]. Heavy metals in river systems have attracted widespread attention because of their non-biodegradable, bioaccumulative, and highly toxic characteristics [6]. These pollutants typically originate from both natural sources, such as the weathering of rocks and minerals, and anthropogenic sources, including municipal and industrial discharge, agricultural runoff, mining, and smelting activities [7]. In tropical regions, the rapid growth of agriculture and urbanization has been particularly pronounced [8,9], contributing to escalating heavy metal contamination. A systems-level analysis of terrestrial–aquatic interface interactions, particularly how land-use heterogeneity modulates metal transport regimes in tropical fluvial networks, is pivotal for sustainable basin management. Understanding how land-use patterns influence heavy metal pollution in tropical river systems is critical for addressing these environmental challenges.
Land use denotes the organizational structures, operational practices, and resource allocations applied within specific land cover types to create, alter, or preserve their functional states [10,11]. Land-use types are typically classified into six classes: forest land, grassland, cultivated land, construction land, water areas, and unused land, according to the China Land use/cover Change Database (CNLUCC). Land-use patterns can significantly influence the pollutants in river ecosystems, as they may discharge different pollutants and change the transmission and exchange of contaminants from land to water bodies [12,13]. Urban land, for example, fragments natural landscapes and transforms them into impervious surfaces, leading to increased peak flows and higher pollutant-laden runoff [14,15]. In agricultural areas, the excessive use of metal-containing pesticides and fertilizers, which are not absorbed by crops, can be washed off from farmlands by rainfall and surface runoff, subsequently entering river systems [16]. Furthermore, the interactions of land use with fluvial water parameters are periodically mediated by hydrological cycles, meteorological variability, and anthropogenically driven operations [17]. The heterogeneous configurations of land-use patterns, which exhibit spatiotemporal variability across multiple scales, contribute to methodological uncertainties in assessing land–water quality interactions, as documented in previous studies [18,19]. Consequently, investigations into land use–heavy metal contamination correlations must adopt multi-scale analytical frameworks that explicitly address both temporal dynamics and spatial heterogeneity.
Current research predominantly examines heavy metal contamination profiles within discrete land use categories, including industrial zones, agricultural lands, and urban cores [20,21,22]. Nevertheless, the influence of land use on aquatic metal loads in tropical river basins remains underexplored through comprehensive spatial–temporal analytical frameworks. Yang et al. directly analyzed the relationship between land-use patterns and heavy metal concentrations in river sediments and found that urban land showed significant positive correlations with heavy metal pollution [23]. Mohammadi et al. found that irrigated agricultural land had the highest positive correlations with sediment heavy metals in the 250 m buffer zone in the Talar watershed [24]. However, no unified conclusion has been reached on the optimal or strongest spatial–temporal scale for studying the relationship between land use and heavy metals.
As the most extensive tropical river system on Hainan Island of China, the Nandu River functions as the primary freshwater source, supplying potable water, irrigation resources, and industrial process water to approximately four million residents. In 2018, the Chinese government declared to support Hainan in building an international free trade port, which attracted a large number of international firms and institutions to settle in Hainan. Increased population and urbanization have rapidly changed the landscape patterns along the Nandu River. Compared to the past three decades, the total areas of forest land and grassland in the Nandu River basin have decreased by 25.27 km2 and 82 km2, respectively, while land under construction has increased by 91.37% [25]. The Nandu River is mainly influenced by industrial discharges, agricultural runoff, and domestic wastewater. It is estimated that roughly 33.53 million tons of metal-bearing wastewater from these sources are released into the river each year [26,27]. However, the impacts of land-use patterns on heavy metal dispersion across spatiotemporal scales in Nandu River waters have not been thoroughly examined. There remains a scarcity of systematic and holistic assessments of metal contamination in the river’s basin, estuary, and nearshore environments. Given the ongoing development of the free trade island, understanding how land-use modifications regulate metal transport processes in this tropical river system is imperative.
Thus, we take the largest tropical river system of China, the Nandu River, as an example: (1) to comprehensively investigate the distribution, source apportionment, and health risk of heavy metals in surface water from the basin, estuary, and nearshore for the first time; (2) to quantify the impact of land-use patterns on heavy metal pollution in river water in spatiotemporal gradients; (3) to assess the human health risk posed by heavy metals in river water. This study is helpful in better understanding the linkages among land-use patterns and heavy metal contamination in tropical river ecosystems, provides reference values for heavy metal control and land-use management, and promotes sustainable development of the regional environment and health.

2. Materials and Methods

2.1. Study Area

The Nandu River occupies the north-central region of Hainan Island with a total course extending 334 km, draining a catchment of 7076 km2 [25]. It originates from the Nanfeng Mountains in Baisha and discharges into the Qiongzhou Strait through Haikou City. The Nandu River basin contains more than 20 secondary waterways, such as the Longzhou, Datang, and Yao Zi Rivers. The elevation of the Nandu River spans 2–1379 m (see Figure S1 in the Supplementary Materials), averaging 116 m. The topography of the Nandu River exhibits elevated terrain in the southwestern sector and depressed topography in the northeastern zone, flowing from southwest to northeast. The upper reaches are characterized as hilly areas underlain by granite and metamorphic rocks, and the middle reaches feature gently rolling terrain. The lower reaches consist of river terraces and alluvial plains formed from weathered granite and basalt [28,29]. Land use is dominated by forest land (including economic forests), mainly in the central hills and southern mountainous areas, followed by cropland in the northern part of the river basin. Construction land is primarily concentrated in the estuarine region and the main urban area [25]. There are no large-scale mining operations within the watershed; only small-scale artisanal mining for sand or clay occurs locally. The region is characterized by a tropical monsoon-dominated climatic regime, exhibiting a mean annual temperature of 23.5 °C coupled with annual precipitation accumulation approximating 1935 mm. Seasonal rainfall distribution shows that the wet season (May–October) contributes 81% of total precipitation, while the dry period (November–April) accounts for the remaining 19% proportionally. The total annual runoff is 67.52 × 108 m3, with the rainy season accounting for more than 70% of the total yearly runoff [30].

2.2. Land-Use Analysis

The land-use/cover classification data utilized in this 2023 research were acquired from the Resources and Environmental Science Data Center (RESDC) of the Chinese Academy of Sciences (http://www.resdc.cn, accessed on 9 August 2024). These datasets were derived through interpretation of Landsat 8 OLI-TIRS satellite images with 30 m spatial resolution, obtained from the United States Geological Survey (USGS) (Figure 1). Following established methodologies from prior research [23], the original 21 land-use classifications at the sampling locations were systematically reclassified into ten distinct categories to differentiate natural from human-influenced landscapes: paddy fields, dry farmland, aquaculture ponds, urban/built-up areas, forested land, grassland, rivers, marine areas, other water bodies, and undeveloped land. Considering the topography of the Nandu River, which is characterized by high mainstream reaches and low estuary and nearshore areas, three circular buffer spaces with radii of 500, 1000, and 2000 m were established in this study. In the basin areas, the geographical centers of the buffer zones were at 500, 1000, and 2000 m upstream of the sampling points, while in the estuary and nearshore areas, the geographical centers of the buffer zones were located at the sampling points [24]. The land-use patterns in the buffer zones were illustrated by ArcGIS 10.7.

2.3. Sample Collection and Analysis

Surface water samples were gathered from 37 representative sites along the Nandu River in August 2023 (wet season) and December 2023 (dry season) (Figure 1). Seventeen of the sampling sites were located in the basin of the Nandu River (N1–N17), seven were located in the estuary (E1–E7), and thirteen were located in the nearshore (S1–S13). Detailed information on the sampling sites is shown in Table S1 (Supplementary Material). Water samples (0~50 cm) were collected and stored in pre-cleaned plastic bottles, transported to the laboratory within 24 h, and kept at 4 °C until further analysis. Water parameters such as pH, temperature, and conductivity were determined in situ using a YSI ProDSS (Xylem Analytics, Yellow Springs, OH, USA) (see Table S1).
The water samples were filtered through 0.45 µm mixed cellulose ester membrane filters (JinTeng Experimental Equipment Co., Ltd., Tianjin, China). The pretreatment process of As, Cd, Pb, Cr, Cu, and Zn followed the national environmental protection standard method of China (HJ 700-2014). Water samples were acidified with nitric acid to a pH threshold of <2, transferred to microwave digestion vessels, and digested at 170 °C for 10 min with concentrated nitric and hydrochloric acids. The pretreatment process of Hg and Sb followed the method (HJ 694-2014). For Hg, water samples were mixed with a hydrochloric–nitric acid solution and heated in a boiling water bath for one hour with intermittent shaking and venting. For Sb, samples were digested with nitric–perchloric acid on a hot plate until white fumes appeared and, after cooling, treated with hydrochloric acid and reheated until yellow-brown fumes were fully expelled. All digested solutions were cooled and diluted to volume with deionized water for subsequent analysis. Inductively coupled plasma mass spectrometry (ICP-MS, NexIon 1000G, PerkinElmer, Waltham, MA, USA) was used to determine the concentrations of the metal elements (As, Cd, Pb, Cr, Cu, and Zn) in the water samples. The concentrations of Hg and Sb were measured using atomic fluorescence spectrometry (AFS-8220, Jitian Instrument Co., Ltd., Beijing, China). Detailed information on quality assurance and quality control is present in Supplementary Material S1.

2.4. Pollution Level and Risk Assessment

The single-factor pollution index (Pi) and the Nemerow integrated pollution index (NIPI) were determined to evaluate the heavy metal pollution levels in the water [31,32]. The hazard quotient (HQ) and the hazard index (HI) were used to assess the non-carcinogenic health risks of individual elements and the total non-carcinogenic risk under different exposure pathways, respectively. The carcinogenic risk for heavy metals (Pb, Cr, and As) was calculated using the carcinogenic risk (CR) and total cancer risk (TCR) [33,34]. Ingestion exposure (direct water intake) is rarely considered as a dominant exposure route for dissolved heavy metals in seawater [35]. Therefore, the present study focused principally on quantifying the health implications of heavy metals via ingestion exposure restricted to the basin area, with the estuarine and nearshore zones excluded. The computational equations are presented as follows:
P i = C i H i
N I P I = ( M a x P i ) 2 + ( A v e P i ) 2 2 .
where Ci is the measured concentration of metal I in the water samples; Hi is the threshold value of heavy metals from the Class II water standards of the Environmental Quality Standards for Surface Water (GB3838-2002) [36]; MaxPi is the maximum Pi among all metals; and AvePi is the arithmetic mean of all heavy metal pollution indexes at each sampling site.
H Q i n g e s t i o n = C w × I R W × E F × E D B W × A T × R f D o × 10 3
H Q d e r m a l = C w × S A × K p × E T × E V × E F × E D B W × A T × R f D o × G I A B S × 10 6
H I = ∑ ( H Q i n g e s t i o n + H Q d e r m a l )
C R i n g e s t i o n = C w × I R W × E F × E D × C S F B W × A T × 10 3
C R d e r m a l = C w × S A × K p × E T × E V × E F × E D × C S F B W × A T × 10 6
T C R = ∑ ( C R i n g e s t i o n + C R d e r m a l )
Cw is the concentration of metal in water samples, IRW is the water intake ratio (ingestion), EF is the exposure frequency, ED is the exposure duration, BW is the body weight, AT is the averaging exposure time, RfDo represents the values of oral reference dose, SA is the skin surface area, Kp is the dermal permeability constant, ET is the water exposure time, EV is the event, GIABS represents the gastrointestinal absorption, and CSF represents the slop factor. The parameter values in Formulas (1)–(6) are shown in Tables S2 and S3 [37].

2.5. Statistical Analysis

The normality of the data was confirmed by the Diagnostic QQ plots and the Shapiro–Wilk test (p > 0.05) using the SPSS v26.0 software (IBM). The spatial variations in heavy metal concentrations were statistically assessed using a univariate ANOVA (analysis of variance) coupled with Duncan’s post hoc multiple comparison test. The relationships among the environmental factors, land uses, and heavy metals were determined by a Pearson correlation analysis using SPSS v26.0. A redundancy analysis (RDA) was conducted to analyze the relationship between heavy metals in the river water and the land-use patterns using the CANOCO v5.0 software. The inverse distance weighted (IDW) method was used to create the spatial distribution of heavy metals by ArcGIS v10.7.

3. Results and Discussion

3.1. Heavy Metal Distributions in the Nandu River Surface Waters

The total concentration of heavy metals (ΣHMs) in the wet and dry seasons was in the range of 3.07–133.55 (mean 30.52 μg/L) and 4.35–87.35 (mean 21.53 μg/L), respectively (Table 1). During the wet season, the average heavy metal content followed the order Zn > Cr > Cu > As > Pb > Sb > Hg > Cd. In the dry season, the order was Zn > Cr > Cu > Pb > As > Sb > Hg > Cd, which aligns with the trends of heavy metals within the sediments of the Nandu River reported before [27]. The average concentrations of As, Cu, Cr, Hg, and Sb in the wet season were significantly higher than those in the dry season (ANOVA, p < 0.05, Table S4). Cd was only detected at site N16 in the downstream area during the dry season, with a concentration of 0.07 μg/L. Overall, the ΣHM concentrations were relatively higher in the wet season, which could be due to increased water erosion and surface runoff transporting heavy metal-laden substances to the Nandu River from the surrounding agricultural, industrial, and municipal wastes. This finding aligns with prior research demonstrating that the rainy season generally exhibits elevated levels of metallic contaminants compared to the dry season [38].
The spatial distribution of heavy metals in the surface waters varied significantly among the basin, estuary, and nearshore zones (single-sample t-test, p < 0.05) (Table 1 and Figure S2). In the wet season, the average ΣHM content was ranked as follows: estuary (70.96 μg/L) > basin (31.03 μg/L) > nearshore (8.07 μg/L). The highest ΣHM concentration was observed at site E7 (133.55 μg/L), located in the estuary at the urban center of Haikou City, suggesting a strong anthropogenic influence from urban runoff, domestic sewage. The lowest concentration was detected at site S3 (3.07 μg/L) in the nearshore. In this season, the average Pb and Cr contents were significantly higher in the basin compared to the estuary and nearshore (p < 0.05), while the Cu, Zn, and As contents were significantly higher in the estuary (p < 0.05), and the Hg and Sb contents were higher in the nearshore (p < 0.05). In the dry season, the average ΣHM contents followed the order of basin (31.56 μg/L) > estuary (23.26 μg/L) > nearshore (7.49 μg/L). The highest ΣHM concentrations were found at downstream sites N15 (87.35 μg/L) and N16 (46.29 μg/L), which are situated in agriculturally active regions, affected by intensive agricultural activities such as pesticides, fertilizers, and sewage irrigation. The lowest concentration was at site S7 (4.35 μg/L) in the nearshore. The concentrations of Pb, Cr, Cu, Zn, and As in the basin water were significantly higher than those in other areas (p < 0.05), whereas Hg was more concentrated in the estuary and Sb in the nearshore.
The higher concentration of ΣHMs detected in the estuary could be attributed to its location as the intersection of river and ocean, where anthropogenic terrestrial contaminants are continuously transported via fluvial systems into estuarine zones [37]. The estuary of the Nandu River flows through Haikou City, which is the most economically developed area with intensive anthropogenic activities. The heavy metal contents in the estuary decrease towards the nearshore in both seasons, which may be due to the stronger hydrodynamics in the nearshore waters [38]. Compared to the estuary, the nearshore has relatively more intensive water movement, which can reduce the residence time and result in lower concentrations of heavy metals in the water [39].

3.2. Pollution Level of Heavy Metals in the Nandu River Surface Waters

The pollution index of Pi and NIPI was calculated (see Section 2.4) to evaluate the heavy metal pollution levels. As shown in Table 2, except for Hg in the nearshore area (S1, S3, S5, S6, and S7) that showed moderate to heavy pollution levels in the wet season (Pi > 2), the Pi values for all metals indicated that no serious heavy metal pollution occurred in the Nandu River (Pi ≤ 2). The results for the NIPI show that, in the wet season, 78.4% of the sites had no heavy metal pollution (NIPI ≤ 1), 18.9% of the sites (almost all located in the nearshore) were lightly contaminated (1 < NIPI < 5), and only one site (S7) in the nearshore area was moderately polluted by heavy metals (5 < NIPI < 10). In the dry season, 97.3% of the sites showed no heavy metal pollution, with only one site (S2) near the shore lightly being contaminated by heavy metals.
In comparison to other tropical rivers worldwide (Table S5), the average heavy metal contents in the Nandu River were lower than those reported for the Selangor River in Malaysia, the Karnaphuli and Barnoi Rivers in Bangladesh and the Omo River in Ethiopia, which are intensively affected by industrial discharges, agricultural runoff, and domestic sewage [39,40,41,42]. In contrast, the Nandu River showed relatively higher levels of heavy metals than those found in the Mahananda River, which flows through Bangladesh and India, and the Amazon River in Brazil, where the anthropogenic pressure is generally lower [43,44]. The levels of Cu, As, Zn, Pb, and Hg during the wet season exceeded the global average [45]. The average concentrations of all elements in this study met the Class I standards of China’s environmental quality standards for surface water (GB3838-2002), except for Hg, which, in the wet season, only met the Class III criteria (0.05–0.10 μg/L). The water quality classification showed 18 sites as Class II and 10 sites as Class III during the wet season. In the dry season, there was one Class II site and nine Class III sites.

3.3. Structural Characteristics of Land Use

The proportions of different land-use types varied significantly across the river basin, estuary, and nearshore zones (Figure S3). In the river basin, the dominant land-use types were forests, paddy fields, and uplands. Within the 500 m buffer zone, the average proportions of forest, construction land, paddy fields, and uplands were 24.28%, 13.07%, 12.96%, and 12.46%, respectively. In the 1000 m buffer zone, the proportions of forest and paddy fields increased to 33.81% and 20.43%, respectively. In the 2000 m buffer zone, the proportions of forests, paddies, and uplands rose to 40.93%, 19.37%, and 19.69%, respectively, whereas the construction land area decreased to 7.45%. In the estuary area, construction land and the river itself were the primary land-use types. Within the 500 m buffer zone, construction land and the river accounted for 50.46% and 35.22% of the area, respectively. In the 1000 m buffer zone, the proportions shifted to 62.22% for the construction land and 21.91% for the river. By the 2000 m buffer zone, the proportion of construction land (62.23%) was comparable to that within the 1000 m zone, while the river area decreased to 12.84%. In the nearshore area, the dominant land types were the ocean and construction land. Within the 500 m buffer zone, the ocean area accounted for 99.77%. Within the 1000 m buffer zone, the ocean area decreased to 88.43%, and the construction land increased to 10.86%. Within the 2000 m buffer zone, the ocean and construction land areas were similar to those in the 1000 m buffer zone, accounting for 82.26% and 10.83%, respectively.

3.4. Source Identification of Heavy Metals in Surface Water Based on Land Uses

Pearson correlation analysis was conducted to identify the potential sources of heavy metals in the Nandu River. As shown in Figure 2, during the wet season, the Pb and Cr contents were significantly positively correlated with upland areas (r > 0.50, p < 0.01) and paddy fields (p < 0.05) in all three buffer zones, indicating agricultural inputs. The strong association between Pb, Cr, and agricultural land in the wet season may be attributed to the high background concentrations of these metals in the cultivated soils of northern Hainan Island, which are mobilized by surface runoff during the rainy season [27]. Cu and As were positively correlated with construction land in the 2000 m buffer zone (r > 0.50, p < 0.01), pointing to industrial and domestic sources. These two metals may be from the atmospheric deposition and discharge of the locally developed industry and urbanization [46].
In the dry season, significant positive correlations were found between Cd and the pond area, Cu and Zn and paddy fields, and Hg and construction land and river area (r > 0.30, p < 0.05). Zn and Cu in the agricultural areas of the Nandu Basin are likely sourced from fertilizers and untreated irrigation water, which enter the river system through surface runoff [46]. Hg is generally associated with human activities, such as domestic sewage discharge in densely populated areas [27]. Noteworthy, Pb, Cr, Cu, Zn, and As were significantly positively correlated with forest areas, particularly in the dry season (r > 0.40, p < 0.01). Forest land can play a role in improving water quality by effectively reducing soil erosion [47]. Forests in the Nandu River Basin include both natural forests and plantations of economic crops such as rubber, betel, and tropical fruits, etc., which are important artificial forests in the Nandu River Basin. Previous studies have shown that the surface runoff in rubber plantations tends to be higher than in natural forests [30]. Thus, the heavy metals associated with the forest area may be due to the irrigation practices in artificial forests and the high background content of heavy metals in the soil taken away by surface runoff.
Strong correlations among heavy metals indicate a shared origin and comparable chemical characteristics [40]. As shown in Figure S4, significant positive correlations were observed between the Pb-Cr (r = 0.90, p < 0.01) and Cu-As (r = 0.82, p < 0.01) pairs in the wet season, and among the Pb-Cr (r = 0.87, p < 0.01), Pb-As (r = 0.81, p < 0.01), Cr-As (r = 0.91, p < 0.01), and Cu-Zn (r = 0.82, p < 0.01) pairs in the dry season. These results suggest that the correlated metals may come from common sources or have similar transport mechanisms, corroborating the findings mentioned above.
The correlations between heavy metals and water parameters further highlight the influence of physicochemical conditions. During the wet season, the temperature was significantly negatively correlated with Pb, Cr, Cu, Zn, and As (p < 0.01), whereas dissolved oxygen (DO) exhibited significant positive correlations with Pb, Cr, Cu, and Zn (p < 0.01). Conductivity and salinity were both negatively correlated with Pb, Cu, and Zn (p < 0.05). Sb showed significant positive associations with the temperature, water pressure, conductivity, salinity, and pH, but a negative association with DO (p < 0.01). In the dry season, Pb and Cr were significantly negatively correlated with water pressure, conductivity, pH, and salinity (p < 0.01), while Hg displayed significant positive correlations with conductivity, salinity, and pH (p < 0.05). These findings align with previous studies demonstrating that heavy metal distribution, sedimentation, and speciation are strongly influenced by water properties such as pH, temperature, salinity, and DO [48,49]. Higher water temperatures during the wet season in the Nandu River may enhance the removal of Pb, Cr, Cu, Zn, and As from the water column through biological uptake processes [50]. A lower pH in the river basin area may diminish the adsorption capacity and bioavailability of Pb and Cr, thereby enhancing their mobility [51]. High salinity and conductivity in the estuarine and nearshore regions are associated with increased ionic strength, which can promote the adsorption of Pb, Cu, Zn, and Cr onto particles, leading to a reduction in their dissolved concentrations [52]. Sb and Hg showed positive correlations with conductivity, salinity, and pH, may due to their increase mobility in saline and alkaline waters, likely forming stable complexes such as Hg-Cl species [48,53].

3.5. Effect of Land-Use Patterns on Heavy Metal Pollution

The explanatory power of the land-use types on heavy metal pollution across different seasons and spatial scales was calculated, and those that had the greatest impact on heavy metal pollution were selected (Figure 3). The RDA results reveal obvious seasonal differences in how land-use patterns affect heavy metal pollution. The explanation rates of land-use patterns for heavy metal pollution in the three buffer zones (ranging from 31.0% to 42.4%) in the wet season were much lower than those in the dry season (ranging from 65.8% to 73.0%). In the wet season, the explanation rate for construction land was the highest, at 11.5% in the 500 m buffer zone, 11.2% in the 1000 m zone, and 21.8% in the 2000 m zone. In the dry season, the highest explanation rates for heavy metal pollution in the three buffer zones were the ocean area (explanation rate of 47.4–55.0%), followed by the uplands (5.9%) in the 500 m buffer zone, paddy fields (9.4%) in the 1000 m buffer zone, and grassland (8.6%) and ponds (5.5%) in the 2000 m buffer zone, respectively.
In terms of spatial scale, the impact of land-use type on the heavy metal pollution in the buffer zones was 2000 m > 500 m > 1000 m for both seasons. The total interpretation rate for land use in the 2000 m buffer zone during the dry season was 73.04%, indicating it had the greatest impact on heavy metal pollution. As the buffer zone scale expands moderately, more landscape pattern variables are included, which can more comprehensively explain the phenomenon of water quality differentiation and the impact of landscape patterns on water quality.
The landscape pattern demonstrates a superior capability in accounting for variations in heavy metal concentrations in river water during the dry season, as opposed to the wet season, which aligns with previous findings [54]. This disparity can be ascribed to the decreased precipitation and reduced river flow that occur during the dry season, which makes the concentration of river pollutants more responsive to changes in landscape patterns. During the wet season, construction land exerted the most significant influence on heavy metal pollution, likely due to the abundant urban runoff and domestic sewage containing heavy metals [55]. In the dry season, the ocean and agricultural land greatly influenced heavy metal pollution in the Nandu River. Oceans play an important role in diluting heavy metals in river water, which is confirmed by the significant negative correlation with heavy metal concentrations in both seasons (p < 0.05). Agricultural land impacts heavy metal pollution through intensive activities, including irrigation and the application of pesticides and fertilizers that contain heavy metals [56].

3.6. Risk Assessment of Heavy Metals in the Nandu River Surface Water

The HQ values for both children and adults induced by ingestion exposure were higher than those for skin contact and were higher in children than in adults (Table 2 and Table S6). These findings align with those of previous studies [33,57,58], confirming that ingestion is the dominant exposure pathway to heavy metals in river water and that children are more sensitive to heavy metals. In both seasons, all values of HQ and HI for heavy metals in children and adults were <1, suggesting no non-carcinogenic risk. In the wet season, the HI values for Cr in children (0.26) and adults (0.14) were the highest compared to other elements; in the dry season, the HI values for As in children (0.13) and adults (0.07) were the highest. Thus, Cr and As were identified as the primary non-carcinogenic factors affecting human health in this study area.
The carcinogenic risk indices (CR and TCR) were also higher for children than for adults in both seasons, highlighting that children have a higher susceptibility compared to adults in the same environment [40,59]. The average CR values for Pb and As for children and adults through ingestion and dermal exposure were lower than 1 × 10−4, suggesting the carcinogenic risk was tolerable. However, in the wet season, the average values of CRingestion for Cr in children and adults were 3.20 × 10−4 and 1.69 × 10−4 (>1 × 10−4), respectively, showing a carcinogenic risk via ingestion exposure. Cr in the basin area (including sites N1, N3, N4, N6, N8–N10, N13–N17) showed a carcinogenic risk (CR > 1 × 10−4). In the dry season, sites N1-N4 and N12 for Cr and sites N2 and N3 for As showed a carcinogenic risk (Table 2 and Table S7). Our findings reveal that more attention should be paid to the treatment of Cr and As in the water of the river basin.

3.7. Management Suggestions

With the ongoing construction of Hainan’s international free trade port and agricultural development, the ecological integrity of the Nandu River is under increasing pressure. It is essential to implement effective land-use management strategies to protect and enhance the water quality of the Nandu River. In the wet season, several measures should be implemented to mitigate urban runoff, including building urban green buffer zones, maintaining clean streets, and enhancing water treatments. Additionally, controlling the use of fertilizers and pesticides in urban green areas is essential. In the dry season, agricultural activities contribute significantly to the input of heavy metals, necessitating a stricter management of irrigation water, fertilizer, and pesticide use. Moreover, vegetation belts should be established around farmland to filter and absorb pollutants from agricultural runoff, and proper drainage systems should be designed to reduce soil erosion and minimize nutrient loss.

4. Conclusions

The relationship between land use and the distribution of heavy metals (including Cd, Pb, Cr, Cu, Zn, As, Hg, and Sb) in the surface waters of the Nandu River system was studied at multiple spatial and seasonal scales. Heavy metal concentrations varied significantly in different seasons and spaces, with higher ΣHM concentrations in the wet season, particularly in the estuary and basin regions. The interpretation rate for land-use type on heavy metal pollution in the dry season was higher than that in the wet season. For both seasons, the land-use types in the 2000 m buffer zone showed the highest interpretation rate on heavy metals. Agricultural land was the primary contributor to heavy metal pollution in the dry season, while construction land had a greater impact in the wet season. In the river basin, elemental Cr had a carcinogenic health risk for both children and adults through ingestion contact and As for children in the dry season. This study enhances our understanding of how land-use patterns influence heavy metal pollution in tropical river ecosystems and offers essential reference values for heavy metal control and sustainable land-use management. However, our study only focused on surface water and did not include sediment or biological media, which may also play key roles in heavy metal transport and accumulation. Future research should integrate multi-media assessments and long-term monitoring to better understand the interactions between land use and pollutant behavior.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17104622/s1. Table S1: Information on sampling sites and water parameters of the Nandu River in different seasons; Table S2: Definitions, symbols, units, and values that are associated with health risk assessment equations; Table S3: Dermal permeability coefficient, reference dose, and gastrointestinal absorption coefficient for each element; Table S4: The variance analysis results of each heavy metal element in the three regions of the river basin, estuary, and nearshore; Table S5: The comparison of heavy metal concentration (μg/L) in water with other rivers around the world; Table S6: Non-carcinogenic health risk (HQ, HI) values of each element in the surface water of the Nandu River; Table S7: Carcinogenic health risk (CR, TCR) values of each element in the surface water of the Nandu River; Figure S1: Elevation map of Nandu River, Hainan Island, China; Figure S2: Spatial distribution of heavy metals in surface water of the Nandu River in the wet and dry seasons; Figure S3: Land-use type characteristics in the study area; Figure S4: Correlation between heavy metals and environmental factors in the Nandu River during the (a) wet and (b) dry seasons.

Author Contributions

Conceptualization, P.L.; Data curation, C.C.; Formal analysis, Y.M., L.L., W.W. and J.L.; Investigation, C.C., Y.M., L.L., W.W. and J.L.; Methodology, X.Z.; Project administration, P.L.; Resources, W.Z., P.L. and X.Z.; Supervision, P.L. and X.Z.; Validation, W.Z.; Visualization, W.Z.; Writing—original draft, C.C.; Writing—review and editing, P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Hainan Provincial Key Research and Development Project of China (ZDYF2025SHFZ034 and ZDYF2022SHFZ317), Hainan Provincial Natural Science Foundation of China (323MS010), and Hainan Key Laboratory of Tropical Forestry Resources Monitoring and Application (SZDSYS2024-004).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We wish to thank the editors and anonymous reviewers for their valuable comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Grill, G.; Lehner, B.; Thieme, M.; Geenen, B.; Tickner, D.; Antonelli, F.; Babu, S.; Borrelli, P.; Cheng, L.; Crochetiere, H.; et al. Mapping the world’s free-flowing rivers. Nature 2019, 569, 215–221. [Google Scholar] [CrossRef] [PubMed]
  2. Palmer, M.; Ruhi, A. Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration. Science 2019, 365, eaaw2087. [Google Scholar] [CrossRef] [PubMed]
  3. Jaskula, J.; Sojka, M. Assessment of spatial distribution of sediment contamination with heavy metals in the two biggest rivers in Poland. CATENA 2022, 211, 105959. [Google Scholar] [CrossRef]
  4. Jin, X.; Liu, J.; Wang, L.; Yu, X.; Wang, J.; Jin, Y.; Qiu, S.; Liu, J.; Zhao, Y.; Sun, S. Distribution characteristics and ecological risk assessment of heavy metal pollution in seawater near the Yellow River Estuary of Laizhou Bay. Mar. Environ. Res. 2025, 203, 106776. [Google Scholar] [CrossRef]
  5. Liang, H.; Wang, G.; Guo, H.; Niu, L.; Yang, Q. Evaluation of heavy metal accumulation and sources in surface sediments of the Pearl River Estuary (China). Mar. Environ. Res. 2025, 204, 106948. [Google Scholar] [CrossRef]
  6. Kiran; Bharti, R.; Sharma, R. Effect of heavy metals: An overview. Mater. Today Proc. 2022, 51, 880–885. [Google Scholar] [CrossRef]
  7. Saravanan, P.; Saravanan, V.; Rajeshkannan, R.; Arnica, G.; Rajasimman, M.; Baskar, G.; Pugazhendhi, A. Comprehensive review on toxic heavy metals in the aquatic system: Sources, identification, treatment strategies, and health risk assessment. Environ. Res. 2024, 258, 119440. [Google Scholar] [CrossRef]
  8. Hotez, P. Global urbanization and the neglected tropical diseases. PLOS Neglected Trop. Dis. 2017, 11, e0005308. [Google Scholar] [CrossRef]
  9. Laurance, W.F.; Sayer, J.; Cassman, K.G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 2014, 29, 107–116. [Google Scholar] [CrossRef]
  10. Jansen, L.J.M.; Di Gregorio, A. Land-use data collection using the “land cover classification system”: Results from a case study in Kenya. Land Use Policy 2003, 20, 131–148. [Google Scholar] [CrossRef]
  11. Wang, Y.; Sun, Y.; Cao, X.; Wang, Y.; Zhang, W.; Cheng, X. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing. ISPRS J. Photogramm. Remote Sens. 2023, 206, 311–334. [Google Scholar] [CrossRef]
  12. Kändler, M.; Blechinger, K.; Seidler, C.; Pavlů, V.; Šanda, M.; Dostál, T.; Krása, J.; Vitvar, T.; Štich, M. Impact of land use on water quality in the upper Nisa catchment in the Czech Republic and in Germany. Sci. Total Environ. 2017, 586, 1316–1325. [Google Scholar] [CrossRef] [PubMed]
  13. Arcega-Cabrera, F.; Sickman, J.O.; Fargher, L.; Herrera-Silveira, J.; Lucero, D.; Oceguera-Vargas, I.; Lamas-Cosío, E.; Robledo-Ardila, P.A. Groundwater Quality in the Yucatan Peninsula: Insights from Stable Isotope and Metals Analysis. Groundwater 2021, 59, 878–891. [Google Scholar] [CrossRef] [PubMed]
  14. Liu, J.; Shen, Z.; Chen, L. Assessing how spatial variations of land use pattern affect water quality across a typical urbanized watershed in Beijing, China. Landsc. Urban Plan. 2018, 176, 51–63. [Google Scholar] [CrossRef]
  15. Schulte-Uebbing, L.F.; Beusen, A.H.W.; Bouwman, A.F.; de Vries, W. From planetary to regional boundaries for agricultural nitrogen pollution. Nature 2022, 610, 507–512. [Google Scholar] [CrossRef]
  16. Shu, W.; Wang, P.; Zhao, J.; Yu, X.; Xu, Q. Characteristics, Sources and Risk Assessment of Heavy Metals in the Ganjiang River Basin, China. Pol. J. Environ. Stud. 2020, 29, 1849–1868. [Google Scholar] [CrossRef]
  17. Zhang, F.; Chen, Y.; Wang, W.; Jim, C.Y.; Zhang, Z.; Tan, M.L.; Liu, C.; Chan, N.W.; Wang, D.; Wang, Z.; et al. Impact of land-use/land-cover and landscape pattern on seasonal in-stream water quality in small watersheds. J. Clean. Prod. 2022, 357, 131907. [Google Scholar] [CrossRef]
  18. Yao, X.; Zeng, C.; Duan, X.; Wang, Y. Effects of land use patterns on seasonal water quality in Chinese basins at multiple temporal and spatial scales. Ecol. Indic. 2024, 166, 112423. [Google Scholar] [CrossRef]
  19. Crites, R.; Beggs, R.; Leverenz, H. Perspective on Land Treatment and Wastewater Reuse for Agriculture in the Western United States. Water 2021, 13, 1822. [Google Scholar] [CrossRef]
  20. Bojanowski, D. Determination of Heavy Metal Sources in an Agricultural Catchment (Poland) Using the Fingerprinting Method. Water 2024, 16, 1209. [Google Scholar] [CrossRef]
  21. Liu, S.; Pan, G.; Zhang, Y.; Xu, J.; Ma, R.; Shen, Z. Risk assessment of soil heavy metals associated with land use variations in the riparian zones of a typical urban river gradient. Ecotoxicol. Environ. Saf. 2019, 181, 435–444. [Google Scholar] [CrossRef] [PubMed]
  22. Zeng, Y.; Bi, C.; Jia, J.; Deng, L.; Chen, Z. Impact of intensive land use on heavy metal concentrations and ecological risks in an urbanized river network of Shanghai. Ecol. Indic. 2020, 116, 106501. [Google Scholar] [CrossRef]
  23. Yang, J.; Sun, Y.; Wang, Z.; Gong, J.; Gao, J.; Tang, S.; Ma, S.; Duan, Z. Heavy metal pollution in agricultural soils of a typical volcanic area: Risk assessment and source appointment. Chemosphere 2022, 304, 135340. [Google Scholar] [CrossRef]
  24. Mohammadi, M.; Khaledi Darvishan, A.; Dinelli, E.; Bahramifar, N.; Alavi, S.J. How does land use configuration influence on sediment heavy metal pollution? Comparison between riparian zone and sub-watersheds. Stoch. Environ. Res. Risk Assess. 2022, 36, 719–734. [Google Scholar] [CrossRef]
  25. Mao, M.; Wei, L.; Gong, W.; Wu, G.; Liu, T. Impacts of Spatio-Temporal Changes in Anthropogenic Disturbances on Landscape Patterns in the Nandu River Basin, China. Sustainability 2024, 16, 2724. [Google Scholar] [CrossRef]
  26. Bolan, N.; Kumar, M.; Singh, E.; Kumar, A.; Singh, L.; Kumar, S.; Keerthanan, S.; Hoang, S.A.; El-Naggar, A.; Vithanage, M.; et al. Antimony contamination and its risk management in complex environmental settings: A review. Environ. Int. 2022, 158, 106908. [Google Scholar] [CrossRef]
  27. Sun, Y.; Yang, J.; Gong, J.; Duan, Z. Contamination and source of metals in surface sediments from the Nandu River of Hainan Island, China. Mar. Pollut. Bull. 2022, 182, 114037. [Google Scholar] [CrossRef]
  28. Ouyang, J.; Chen, G.; Yang, L.; Lu, W.; Zhou, Y. Tectonic Evolution of the Hainan Island, South China: Geochronological and Geochemical Constraints from Late Permian to Early Triassic Basalts. Minerals 2025, 15, 293. [Google Scholar] [CrossRef]
  29. Zhang, G.-L.; Pan, J.-H.; Huang, C.-M.; Gong, Z.-T. Geochemical features of a soil chronosequence developed on basalt in Hainan Island, China. Rev. Mex. De Cienc. Geológicas 2007, 24, 261–269. [Google Scholar]
  30. Cao, C.; Sun, R.; Wu, Z.; Chen, B.; Yang, C.; Li, Q.; Fraedrich, K. Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin. Sustainability 2023, 15, 13941. [Google Scholar] [CrossRef]
  31. American Public Health Association. Standard Methods for the Examination of Water and Wastewater, 18th ed.; Zenodo (CERN European Organization for Nuclear Research); APHA: Washington, DC, USA, 1992. [Google Scholar] [CrossRef]
  32. Effendi, H. River Water Quality Preliminary Rapid Assessment Using Pollution Index. Procedia Environ. Sci. 2016, 33, 562–567. [Google Scholar] [CrossRef]
  33. Canpolat, Ö.; Varol, M.; Okan, Ö.Ö.; Eriş, K.K.; Çağlar, M. A comparison of trace element concentrations in surface and deep water of the Keban Dam Lake (Turkey) and associated health risk assessment. Environ. Res. 2020, 190, 110012. [Google Scholar] [CrossRef] [PubMed]
  34. USEPA. Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk Assessment); U.S. Environmental Protection Agency: Washington, DC, USA, 2004.
  35. Wang, J.; Zheng, Y.; Li, Y.; Wang, Y. Potential risks, source apportionment, and health risk assessment of dissolved heavy metals in Zhoushan fishing ground, China. Mar. Pollut. Bull. 2023, 189, 114751. [Google Scholar] [CrossRef] [PubMed]
  36. GB 3838–2002; Environmental Quality Standards for Surface Water in China. State Environmental Protection Administration of China: Beijing, China, 2002.
  37. Meng, F.; Cao, R.; Zhu, X.; Zhang, Y.; Liu, M.; Wang, J.; Chen, J.; Geng, N. A nationwide investigation on the characteristics and health risk of trace elements in surface water across China. Water Res. 2024, 250, 121076. [Google Scholar] [CrossRef]
  38. Nguyen, B.T.; Do, D.D.; Nguyen, T.X.; Nguyen, V.N.; Phuc Nguyen, D.T.; Nguyen, M.H.; Thi Truong, H.T.; Dong, H.P.; Le, A.H.; Bach, Q.-V. Seasonal, spatial variation, and pollution sources of heavy metals in the sediment of the Saigon River, Vietnam. Environ. Pollut. 2020, 256, 113412. [Google Scholar] [CrossRef]
  39. Ali, M.M.; Ali, M.L.; Islam, M.S.; Rahman, M.Z. Preliminary assessment of heavy metals in water and sediment of Karnaphuli River, Bangladesh. Environ. Nanotechnol. Monit. Manag. 2016, 5, 27–35. [Google Scholar] [CrossRef]
  40. Haque, M.A.; Khatun, B.; Jewel, M.A.S.; Ara, J.; Islam Kazal, M.S.; Hasan, J. Assessment of water quality and heavy metal indices in a tropical freshwater river for aquatic life and public health standard. Ecol. Indic. 2024, 169, 112862. [Google Scholar] [CrossRef]
  41. Kotacho Abiy, A.; Yimer Girma, T.; Solomon, S.; Yohannes Seifu, B. Heavy Metal Contamination in Omo River, Ethiopia: Environmental and Human Health Risks. Environ. Health Insights 2024, 18, 1–3. [Google Scholar] [CrossRef]
  42. Othman, F.; Chowdhury, M.S.U.; Wan Jaafar, W.Z.; Faresh, E.M.; Shirazi, S. Assessing Risk and Sources of Heavy Metals in a Tropical River Basin: A Case Study of the Selangor River, Malaysia. Pol. J. Environ. Stud. 2018, 27, 1659–1671. [Google Scholar] [CrossRef]
  43. Shil, S.; Singh, U.K. Health risk assessment and spatial variations of dissolved heavy metals and metalloids in a tropical river basin system. Ecol. Indic. 2019, 106, 105455. [Google Scholar] [CrossRef]
  44. Moulatlet, G.M.; Yacelga, N.; Rico, A.; Mora, A.; Hauser-Davis, R.A.; Cabrera, M.; Capparelli, M.V. A systematic review on metal contamination due to mining activities in the Amazon basin and associated environmental hazards. Chemosphere 2023, 339, 139700. [Google Scholar] [CrossRef] [PubMed]
  45. Zhang, K.; Chang, S.; Tu, X.; Wang, E.; Yu, Y.; Liu, J.; Wang, L.; Fu, Q. Heavy metals in centralized drinking water sources of the Yangtze River: A comprehensive study from a basin-wide perspective. J. Hazard. Mater. 2024, 469, 133936. [Google Scholar] [CrossRef] [PubMed]
  46. Jiang, W.; Hou, Q.; Yang, Z.; Yu, T.; Zhong, C.; Yang, Y.; Fu, Y. Annual input fluxes of heavy metals in agricultural soil of Hainan Island, China. Environ. Sci. Pollut. Res. 2014, 21, 7876–7885. [Google Scholar] [CrossRef] [PubMed]
  47. Wang, Y.-B.; Junaid, M.; Deng, J.-Y.; Tang, Q.-P.; Luo, L.; Xie, Z.-Y.; Pei, D.-S. Effects of land-use patterns on seasonal water quality at multiple spatial scales in the Jialing River, Chongqing, China. CATENA 2024, 234, 107646. [Google Scholar] [CrossRef]
  48. Li, M.; Ding, D.; Xu, Y.; Yang, Q.; Sun, J.; Qu, K.; Cui, Z.; Wei, Y. Implications of seawater characteristics on dissolved heavy metals in near-shore surface waters of the Yellow Sea. Mar. Pollut. Bull. 2025, 211, 117469. [Google Scholar] [CrossRef]
  49. Huang, Y.; Zhang, D.; Xu, Z.; Yuan, S.; Li, Y.; Wang, L. Effect of overlying water pH, dissolved oxygen and temperature on heavy metal release from river sediments under laboratory conditions. Arch. Environ. Prot. 2017, 43, 28–36. [Google Scholar] [CrossRef]
  50. Fritioff, Å.; Kautsky, L.; Greger, M. Influence of temperature and salinity on heavy metal uptake by submersed plants. Environ. Pollut. 2005, 133, 265–274. [Google Scholar] [CrossRef]
  51. Li, H.; Shi, A.; Li, M.; Zhang, X. Effect of pH, Temperature, Dissolved Oxygen, and Flow Rate of Overlying Water on Heavy Metals Release from Storm Sewer Sediments. J. Chem. 2013, 2013, 434012. [Google Scholar] [CrossRef]
  52. Elder, J.F. Metal Biogeochemistry in Surface-Water Systems; A Review of Principles and Concepts; U.S. G.P.O.: Washington, DC, USA, 1988.
  53. Wang, H.; Xu, J.; Gomez, M.A.; Shi, Z.; Jia, Y. A study on the effects of anion, cation, organic compounds, and pH on the release behaviors of As and Sb from sediments. Environ. Sci. Pollut. Res. 2021, 28, 45199–45211. [Google Scholar] [CrossRef]
  54. Wu, J.; Lu, J. Landscape patterns regulate non-point source nutrient pollution in an agricultural watershed. Sci. Total Environ. 2019, 669, 377–388. [Google Scholar] [CrossRef]
  55. Wu, J.; Zeng, S.; Yang, L.; Ren, Y.; Xia, J. Spatiotemporal Characteristics of the Water Quality and Its Multiscale Relationship with Land Use in the Yangtze River Basin. Remote Sens. 2021, 13, 3309. [Google Scholar] [CrossRef]
  56. Fei, X.; Lou, Z.; Sheng, M.; Lv, X.; Ren, Z.; Xiao, R. Different “nongrain” activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands. Environ. Res. 2024, 251, 118642. [Google Scholar] [CrossRef] [PubMed]
  57. Tokatlı, C.; Varol, M. Variations, health risks, pollution status and possible sources of dissolved toxic metal(loid)s in stagnant water bodies located in an intensive agricultural region of Turkey. Environ. Res. 2021, 201, 111571. [Google Scholar] [CrossRef] [PubMed]
  58. Varol, M. Arsenic and trace metals in a large reservoir: Seasonal and spatial variations, source identification and risk assessment for both residential and recreational users. Chemosphere 2019, 228, 1–8. [Google Scholar] [CrossRef]
  59. Xiao, J.; Wang, L.; Deng, L.; Jin, Z. Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci. Total Environ. 2019, 650, 2004–2012. [Google Scholar] [CrossRef]
Sustainability 17 04622 g001
Figure 1. Land-use map and location of the sampling sites in the Nandu River, Hainan Island, China.
Figure 1. Land-use map and location of the sampling sites in the Nandu River, Hainan Island, China.
Sustainability 17 04622 g001
Sustainability 17 04622 g002
Figure 2. Correlation between land uses and heavy metals in the Nandu River, Hainan Island, China. * indicates that the correlation is significant (p < 0.05); ** indicates that the correlation is significant (p < 0.01); the size of the circles indicates the strength of the correlation coefficient.
Figure 2. Correlation between land uses and heavy metals in the Nandu River, Hainan Island, China. * indicates that the correlation is significant (p < 0.05); ** indicates that the correlation is significant (p < 0.01); the size of the circles indicates the strength of the correlation coefficient.
Sustainability 17 04622 g002
Sustainability 17 04622 g003
Figure 3. Redundant discriminant analysis (RDA) of the relationship between heavy metals and land uses at different spatiotemporal scales.
Figure 3. Redundant discriminant analysis (RDA) of the relationship between heavy metals and land uses at different spatiotemporal scales.
Sustainability 17 04622 g003
Table 1. Descriptive analysis of heavy metals in the surface water of the Nandu River (μg/L).
Table 1. Descriptive analysis of heavy metals in the surface water of the Nandu River (μg/L).
SeasonRegion CdPbCrCuZnAsHgSbΣHMs
Wet seasonBasin
(n = 17)		Minb.d.l.b.d.l.b.d.l.0.95 1.11 0.42 b.d.l.b.d.l.3.84
Meanb.d.l.1.43 12.84 3.18 12.11 0.99 0.02 0.46 31.03
Maxb.d.l.3.46 28.53 6.17 28.53 1.92 0.07 0.89 51.45
Estuary
(n = 7)		Minb.d.l.b.d.l.2.92 15.07 b.d.l.7.85 b.d.l.b.d.l.25.84
Meanb.d.l.b.d.l.6.14 24.73 16.71 23.38 b.d.l.b.d.l.70.96
Maxb.d.l.b.d.l.12.27 36.93 76.39 49.94 b.d.l.b.d.l.133.55
Nearshore
(n = 13)		Minb.d.l.0.11 0.84 0.37 b.d.l.0.27 b.d.l.0.40 3.07
Meanb.d.l.0.28 1.64 0.61 4.36 0.45 0.10 0.63 8.07
Maxb.d.l.0.58 2.30 1.49 16.20 0.60 0.39 1.64 19.41
Total
(n = 37)		Meanb.d.l.0.76 7.64 6.36 10.26 5.04 0.04 0.43 30.52
SD-0.98 8.38 9.31 10.76 8.85 0.09 0.33 30.23
Dry seasonBasin
(n = 17)		Minb.d.l.0.55 1.30 0.74 8.52 0.27 b.d.l.0.05 11.80
Mean<0.012.70 3.46 2.28 22.16 0.78 0.03 0.14 31.56
Maxb.d.l.10.47 11.17 5.23 58.10 2.18 0.07 0.34 87.35
Estuary
(n = 7)		Minb.d.l.b.d.l.0.49 1.49 12.50 0.28 b.d.l.0.28 15.56
Meanb.d.l.0.28 0.99 2.14 19.00 0.47 0.05 0.32 23.26
Maxb.d.l.0.55 1.77 3.49 26.27 0.73 0.07 0.37 29.74
Nearshore
(n = 13)		Minb.d.l.0.27 0.84 b.d.l.0.29 0.41 b.d.l.0.24 4.35
Meanb.d.l.0.97 2.36 0.55 2.48 0.73 0.01 0.38 7.49
Maxb.d.l.1.98 4.55 1.49 12.90 1.23 0.09 0.47 16.25
Total
(n = 37)		Mean<0.011.64 2.61 1.65 14.65 0.71 0.03 0.26 21.53
SD-2.082.061.2812.02 0.380.030.1315.99
n: the number of sample sites; b.d.l.: below the detection limit; ∑HMs: the sum of eight heavy metal concentrations; -: not available
Table 2. Pollution level and health risk of heavy metals in the surface water of the Nandu River, China.
Table 2. Pollution level and health risk of heavy metals in the surface water of the Nandu River, China.
CriteriaRange IndexDegreeWet SeasonDry Season
Pi≤1No pollutionHg (26 sites), other metals (37 sites)Hg (25 sites), other metals (37 sites)
1~2Low pollutionHg (N1, N3, S2, S4, S9, S10)Hg (N6, N7, N8, N11, N12, N16, N17, E3, E5, E6, S1, S2)
2~3Moderate pollutionHg (S1, S5)0
≥3Heavy pollutionHg (S3, S6, S7)0
NIPI≤1No pollution29 sites36 sites
1~5Low pollutionN3, S1, S3, S4, S5, S6, S9S2
5~10Moderate pollutionS70
>10Heavy pollution00
HQ, HI<1No adverse effect 37 sites37 sites
>1Adverse effects 00
CR<1 × 10−6Nonexistent or low carcinogenic riskPb (27 sites), Cr (14 sites), As (12 sites)Pb (29 sites), Cr (16 sites), As (20 sites)
1 × 10−6 ~
1 × 10−4		Possible carcinogenic riskPb (10 sites), Cr (11 sites), As (24 sites)Pb (8 sites), Cr (14 sites), As (12 sites)
>1 × 10−4Carcinogenic riskCr (N1, N3, N4, N6, N8-N10, N13–N17), As (N1)Cr (N1–N4, N6, N12, N13), As (N1–N4, N12)
TCR<1 × 10−6Low carcinogenic riskPb (34 sites), Cr (17 sites), As (12 sites)Pb (31 sites), Cr (20 sites), As (20 sites)
1 × 10−6 ~
1 × 10−4		Possible carcinogenic riskPb (3 sites), Cr (8 sites), As (25 sites) Pb (6 sites), Cr (12 sites), As (15 sites)
>1 × 10−4Carcinogenic riskCr (N1, N3, N4, N6, N8–N10, N13–N17)Cr (N1–N4, N12), As (N2, N3)
Note: The information in parentheses indicates the number of sampling sites where the corresponding element exceeded the specified thresholds.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, C.; Zhang, W.; Li, P.; Ma, Y.; Liang, L.; Wu, W.; Li, J.; Zhu, X. Effects of Land-Use Patterns on Heavy Metal Pollution and Health Risk in the Surface Water of the Nandu River, China. Sustainability 2025, 17, 4622. https://doi.org/10.3390/su17104622

AMA Style

Chen C, Zhang W, Li P, Ma Y, Liang L, Wu W, Li J, Zhu X. Effects of Land-Use Patterns on Heavy Metal Pollution and Health Risk in the Surface Water of the Nandu River, China. Sustainability. 2025; 17(10):4622. https://doi.org/10.3390/su17104622

Chicago/Turabian Style

Chen, Changchao, Wen Zhang, Ping Li, Yuanhao Ma, Longru Liang, Wanman Wu, Jianlei Li, and Xiaoshan Zhu. 2025. "Effects of Land-Use Patterns on Heavy Metal Pollution and Health Risk in the Surface Water of the Nandu River, China" Sustainability 17, no. 10: 4622. https://doi.org/10.3390/su17104622

APA Style

Chen, C., Zhang, W., Li, P., Ma, Y., Liang, L., Wu, W., Li, J., & Zhu, X. (2025). Effects of Land-Use Patterns on Heavy Metal Pollution and Health Risk in the Surface Water of the Nandu River, China. Sustainability, 17(10), 4622. https://doi.org/10.3390/su17104622

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop