Occurrence and Risk Assessment of Metals and Metalloids in Surface Drinking Water Sources of the Pearl River Basin

Abstract

Based on monitoring data from 2019 to 2024 at 270 typical surface drinking water sources (SDWS) in the Pearl River Basin (PRB), the occurrence and health risks of metal and metalloid pollutants (MMPs) were analyzed from a large watershed scale and long-term evolution. The results indicated that the overall pollution status of 8 MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) were at a low level and the concentrations of Cd, Pb, Ni, Ba, and V exhibited downward trends from 2019 to 2024. The distribution of MMPs exhibited significant regional differences with the main influencing factors including geological conditions, industrial activities, and urban development. River-type drinking water sources might be more affected by pollution from human activities such as industrial wastewater discharge, and the concentration levels of MMPs were generally higher than those in lake-type drinking water sources. Monte Carlo simulation revealed that 33.08% and 12.90% of total carcinogenic risks (TCR) exceeded the threshold of 10⁻⁶ for adults and children, respectively. Ba and Ni were the main contributors to the TCR, while As posed a certain non-carcinogenic risk to children. Sensitivity analysis indicated that concentrations of As and Ba were the main factors contributing to health risks. Although highly stringent water pollution control and a water resource protection policy have been implemented, it is still suggested to strengthen the control of As, Ba, and Ni in industrial-intensive areas and river-type water sources in the PRB.

1. Introduction

Metal and metalloid contamination in surface water bodies, in particular, remains one of the critical environmental challenges [1,2,3]. Metal and metalloid pollutants (MMPs) not only severely impact water quality but also pose potential threats to ecosystems and human health [4,5]. These contaminants exhibit persistent environmental characteristics, including acute biotoxicity and bioaccumulative potential [6,7], and can readily enter organisms through water sources, causing long-term health hazards [4,8]. The toxicological effects of MMPs manifest as damage to human nervous, hepatic, renal, and immune systems [6,9], even increasing cancer risks. For instance, Pb contamination may induce neurological impairment [6,10], particularly affecting children’s intellectual development, while Cd is associated with renal dysfunction and osteoporosis [6,11]. Arsenic and nickel have been categorized by the International Agency for Research on Cancer (IARC) as Grade 1 carcinogens. Additionally, when MMPs accumulate biologically within aquatic organisms, they potentially magnify threats to human well-being through trophic transfer across food webs [12,13,14].

The Pearl River Basin (PRB) extends across multiple Chinese administrative regions including Yunnan, Guizhou, Guangxi, Guangdong, Hunan, Jiangxi, and Fujian provinces, while also encompassing both Hong Kong and Macao Special Administrative Regions. This basin’s hydrological system is ecologically integrated with the Pearl River Delta ecosystem and serves as a crucial drinking water source for the south of China. However, industrial and agricultural intensification and high population density in certain areas of the PRB have resulted in varying degrees of metal and metalloid contamination in surface water bodies and drinking water sources [15,16,17]. The Beijiang River Basin, downstream of the PRB where concentrated metal processing industries are located, experienced Pb and Cd contamination events [18,19]. Industrial clusters in the northern part of Guangdong Province caused severe metal and metalloid pollution in adjacent constructed wetlands [20]. Currently, studies on MMPs in surface drinking water sources (SDWS) from a large watershed scale are still lacking. Studies indicated that the main source of MMPs in the water of the Pearl River Delta estuary in southern Guangzhou is various industrial wastewater from upstream areas [21,22]. Metal and metalloid pollution may originate from various sources, including industrial emissions, agricultural fertilization, and landfill of urban waste.

Although there have been extensive studies on metal and metalloid pollution in rivers and lakes, most existing studies have focused on the occurrence characteristics of MMPs in surface water or sediments of water body in specific areas [23,24]. Long-term monitoring of MMPs in the water environment of the PRB has not been systematically studied. Moreover, the pollution level and human health risk in SDWS of the PRB are still unknown. Therefore, this study selected eight typical MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) and systematically analyzed the pollution level and spatio-temporal distribution of these contaminants in SDWS of the PRB. Moreover, human health risk evaluations were conducted to establish a scientific foundation for MMPs regulation and health hazard management strategies within drinking water supplies.

2. Materials and Methods

2.1. Study Area

The PRB incorporates the Pearl River main flow (predominantly the Xijiang River) alongside its tributary systems, encompassing the Beijiang, Dongjiang, Liujiang, and Ganjiang watercourses. For analytical purposes, we segmented the PRB into three distinct zones based on hydrological flow patterns: upper, middle, and lower reaches. The upper segment primarily traverses Yunnan and Guizhou Provinces, while the middle section flows through Guangxi, and the lower portion runs within Guangdong. Our investigation examined 270 surface drinking water sources (SDWS), comprising 142 river-type and 128 lake-type water source locations. Figure 1 depicts both the geographical research domains and the spatial distribution of SDWS.

2.2. Data Sources

This investigation leveraged datasets from the Ministry of Ecology and Environment of China, gathered in meticulous adherence to the Technical Specifications for Surface Water Environmental Quality Monitoring (HJ/T 91-2022) guidelines [25]. Both water collection procedures and subsequent laboratory analyses followed stringent standardized methodologies to maximize data precision and ensure measurement consistency. The dataset comprises monthly monitoring records from 2019 to 2024 for all 270 SDWS, including eight typical MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) and six conventional parameters (COD_Mn, NH₃-N, TP, SO₄²⁻, NO₃⁻, Cl⁻). Annual averages were calculated from 12 monthly measurements to minimize seasonal variability. The carcinogenic grade and water quality standards of eight MMPs can be found in Table S1 of the Supporting Materials. The analytical methods and limits of detection for eight MMPs and six water chemical parameters are listed in Tables S2 and S3. The information on data quality assurance and quality control can be seen in Text S1.

2.3. Assessment of MMP Pollution Levels

The pollution levels of MMPs in SDWS were evaluated using the Heavy Metal Pollution Index (HPI) (Equation (1)), the Nemerow Index (NI) (Equation (2)), and the Contamination Degree (CD) (Equation (3)). The classification criteria for these indices were established based on methodologies from previous studies [26,27,28], which can be found in Table S4 of the Supporting Materials.

$$\mathrm{H}\mathrm{P}\mathrm{I}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}(\frac{{\mathrm{M}}_{\mathrm{i}}}{{\mathrm{S}}_{\mathrm{i}}}\times 100)\times \frac{1}{{\mathrm{S}}_{\mathrm{i}}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\frac{1}{{\mathrm{S}}_{\mathrm{i}}}}}$$

(1)

$$\mathrm{N}\mathrm{I}=\sqrt{{\displaystyle \frac{(\frac{{\mathrm{M}}_{\mathrm{i}}}{{\mathrm{S}}_{\mathrm{i}}}{)}_{\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}}^2+(\frac{{\mathrm{M}}_{\mathrm{i}}}{{\mathrm{S}}_{\mathrm{i}}}{)}_{\mathrm{m}\mathrm{a}\mathrm{x}}^2}{2}}}$$

(2)

$$\mathrm{C}\mathrm{D}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{M}}_{\mathrm{i}}}{{\mathrm{S}}_{\mathrm{i}}}}$$

(3)

where M_i represents the measured concentration of each specific metal/metalloid within the analyzed sample, S_i denotes the regulatory threshold concentration for that particular metal/metalloid in surface drinking water sources as stipulated by the Environmental Quality Standards for Surface Water (GB 3838-2002) [29], and n signifies the total quantity of distinct metal and metalloid species evaluated in this investigation.

2.4. Method of Health Risk Assessment

This investigation utilized the US EPA’s health risk assessment methodology as a quantitative framework for examining connections between hazardous compounds and human well-being. We implemented Monte Carlo simulations to determine non-carcinogenic hazards (expressed as hazard quotient, HQ) and carcinogenic risks (CR) from MMPs through consumption or skin exposure across diverse demographic segments [30,31]. The HQ represents the dimensionless risk ratio for water absorption via ingestion or dermal routes, while THI denotes the total hazard index (also dimensionless), encompassing the collective non-carcinogenic risk from all MMPs. When HQ or THI exceeds 1, potential adverse health impacts may emerge; conversely, values below 1 indicate absence of detrimental health effects [32]. CR quantifies the carcinogenic risk probability assuming sustained exposure to chemical contaminants, whereas TCR encompasses the aggregate carcinogenic risk from all MMPs. Remedial action generally becomes necessary when cumulative carcinogenic risk surpasses 10⁻⁴. For scenarios where baseline cancer risk falls within 10⁻⁶ to 10⁻⁴, intervention decisions require site-specific evaluation. Typically, risk-based preliminary remediation targets become unnecessary for substances exhibiting cumulative cancer risk below 10⁻⁶.

We quantified HQ values for adults and children through water consumption (HQ_ingestion) and cutaneous absorption (HQ_dermal):

$${\mathrm{H}\mathrm{Q}}_{\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}={\displaystyle \frac{\mathrm{M}\mathrm{E}\mathrm{C}\times \mathrm{I}\mathrm{R}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\times \mathrm{R}\mathrm{f}\mathrm{D}\times {10}^3}}$$

(4)

$${\mathrm{H}\mathrm{Q}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}}={\displaystyle \frac{\mathrm{M}\mathrm{E}\mathrm{C}\times \mathrm{S}\mathrm{A}\times \mathrm{K}\mathrm{P}\times \mathrm{E}\mathrm{T}\times \mathrm{E}\mathrm{V}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\times \mathrm{R}\mathrm{f}\mathrm{D}\times \mathrm{A}\mathrm{B}\mathrm{S}\times {10}^6}}$$

(5)

$$\mathrm{T}\mathrm{H}\mathrm{I}=\sum ({\mathrm{H}\mathrm{Q}}_{\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}+{\mathrm{H}\mathrm{Q}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}})$$

(6)

The CR for MMPs via ingestion and dermal absorption of surface water by residents was calculated using Equations (7)–(9):

$${\mathrm{C}\mathrm{R}}_{\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}={\displaystyle \frac{\mathrm{M}\mathrm{E}\mathrm{C}\times \mathrm{I}\mathrm{R}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}\times \mathrm{S}\mathrm{F}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\times {10}^3}}$$

(7)

$${\mathrm{C}\mathrm{R}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}}={\displaystyle \frac{\mathrm{M}\mathrm{E}\mathrm{C}\times \mathrm{S}\mathrm{A}\times \mathrm{K}\mathrm{P}\times \mathrm{E}\mathrm{T}\times \mathrm{E}\mathrm{V}\times \mathrm{E}\mathrm{F}\times \mathrm{E}\mathrm{D}\times \mathrm{S}\mathrm{F}}{\mathrm{B}\mathrm{W}\times \mathrm{A}\mathrm{T}\times \mathrm{A}\mathrm{B}\mathrm{S}\times {10}^6}}$$

(8)

$$\mathrm{T}\mathrm{C}\mathrm{R}=\sum ({\mathrm{C}\mathrm{R}}_{\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}+{\mathrm{C}\mathrm{R}}_{\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}})$$

(9)

In these equations, Measured Environmental Concentration (MEC) signifies individual pollutant concentrations in water (µg/L); Ingestion Rate (IR) reflects daily water consumption volume (L/day); EF quantifies yearly exposure frequency (days/year); ED represents exposure duration (years); ABS indicates the dimensionless gastrointestinal absorption coefficient; BW denotes average body mass (kg); AT represents average exposure duration (days); SA measures potentially exposed skin surface area (cm²); KP indicates metal dermal permeability coefficient (cm/hour); ET quantifies exposure duration (h/event); CF represents the conversion factor (cm³). RfD denotes pollutant reference dose, while SF indicates slope factor.

Table 1. Parameters of influencing factors in the health risk assessment model and best-fit distribution model for target pollutant concentrations.

Symbol	Parameters	Units	Distribution	Values
				Adults	Children
MEC	Measured Environmental Concentration	mg/L	-	Data value	Data value
IR	Ingestion Rate	L/da	Log-normal	(1.23, 0.27)	(1.12, 0.27)
EF	Exposure Frequency	d/a	Triangular	345 (180–365)	345 (180–365)
ED	Exposure Duration	years	Uniform	(0, 70)	(0, 10)
AT	Averaging time—non-carcinogen	days	Constant	365 × ED
	Averaging time—carcinogen		Constant	365 × 70
BW	Body Weight	kg	Log-normal	(59.78, 1.07)	(16.68, 1.48)
SA	Skin Surface Area	cm2	Log-normal	LN (17,900, 339.93)	LN (11,493, 2296)
ET	Exposure Time	hours/days	Triangular	0.11 (0.03–0.33)
As	Arsenic	µg/L	Weibull	(0.00066, 0.00063)
Cd	Cadmium	µg/L	Gumbel	(0.00002, 0.00003)
Pb	Lead	µg/L	Log-normal	(0.00101, 0.02875)
Mn	Manganese	µg/L	Gamma	(0.02661, 0.00023)
Sb	Antimony	µg/L	Log-normal	(0.00347, 0.2317)
Ni	Nickel	µg/L	Gamma	(0.00501, 0.00012)
Ba	Barium	µg/L	Gamma	(0.05683, 0.00019)
V	Vanadium	µg/L	Gumbel	(0.00037, 0.00071)
KP	Dermal Permeability Coefficient	cm/h	-	As = 0.001; Cd = 0.001; Pb = 0.001; Sb = 0.001; Ni = 0.0002; Ba = 0.001; V = 0.001
RfD	Reference Dose	mg/(kg·day)	-	As = 0.0003; Cd = 0.0001; Pb = 0.0035; Mn = 0.024; Sb = 0.0004; Ni = 0.02; Ba = 0.2; V = 0.007
SF	Slope Factor	kg·day/mg	-	As = 1.5; Cd = 0.63; Pb = 0.0085; Sb = 0.207; Ni = 1.7; Ba = 0.851; V = 0.122

compiles all parameters employed in our health risk assessment model [33,34].

2.5. Data Analysis

The spatial distribution of water sources and contaminants was mapped using the geographic information system ArcGIS (ESRI ArcGIS v10.3). Monte Carlo simulations were performed with Oracle Crystal Ball^® (v11.1.2.4) to predict human carcinogenic and non-carcinogenic risk values (10,000 iterations). An integrated approach combining ArcGIS and statistical data analysis was employed to evaluate MMP concentration levels by comparing monthly monitoring data across different regions and time points. Mann–Whitney U test was performed by SPSS (IBM SPSS statistics 26.0).

3. Analysis and Discussion

3.1. Water Chemical Parameters

The statistical results of basic water quality indicators (COD_Mn, NH₃-N, TP, SO₄²⁻, NO₃⁻, Cl⁻) are shown in Figure 2. Compared with 2019, NH₃-N concentrations showed a significant downward trend from 2020 to 2024 (p < 0.001, Figure 2b), while Cl⁻ showed an upward trend (Figure 2f). The NH₃-N concentration (mean value ± standard deviation) decreased from 0.159 ± 0.154 mg/L (2019) to 0.103 ± 0.112 mg/L (2024), while Cl⁻ increased from 5.123 ± 12.134 mg/L to 7.099 ± 11.26 mg/L. Additionally, concentrations of COD_Mn (1.74–1.82 mg/L), TP (0.033–0.039 mg/L), SO₄²⁻ (11–13 mg/L), and NO₃⁻ (0.85–0.90 mg/L) showed minimal inter-annual variations (Figure 2a,c–e), remaining relatively stable. The decline in NH₃-N concentration indicates effective control of urban sewage emissions [35,36].

3.2. Overall Pollution Level of MMPs

Table 2. Concentration statistics of metal and metal-like pollutants (MMPs) in SDWS of the Pearl River Basin.

Parameters	As	Cd	Pb	Mn	Sb	Ni	Ba	V
Detection Rate (%)	81.98	66.83	70.51	80.83	67.67	61.96	68.39	68.20
Mean Concentration (µg/L)	1.14	0.06	0.48	7.43	0.65	0.98	15.55	1.32
CV (%)	144.6	329.5	249.6	167.7	113.6	167.4	130.4	165.6
Median Concentration (µg/L)	0.70	0.03	0.10	5.05	0.31	0.46	13.50	0.74

presents the concentration statistics of the MMPs detected in SDWS of the PRB. Overall, the detection rates of these target contaminants ranged from 61% to 82%. Two or more MMPs were detectable in water samples, and the three MMPs with the highest mean concentrations were Ba (15.55 μg/L), Mn (7.43 μg/L), and V (1.32 μg/L). Additionally, the three MMPs with the highest coefficient of variation were Cd (329.48%), Pb (249.64%), and Mn (167.70%). Although Cd had the lowest detected concentration, it had the maximum coefficient of variation, which indicates the most dispersed concentration distribution and significant spatio-temporal heterogeneity. It is suggested that Cd in drinking water sources may be strongly influenced by human activities. Comparing with China’s Surface Water Environmental Quality Standards [29], none of the MMPs exceeded the limit concentrations for drinking water sources. However, the maximum concentrations of As and V exceeded the Standards for Drinking Water Quality of China and the Guidelines for Drinking-Water Quality of the World Health Organization (Table S1).

A recent large-scale survey of heavy metals in Chinese surface waters [37] found that the concentrations of Ba (mean = 30.60 μg/L) and As (mean = 1.74 μg/L) were higher than those in this study. Compared with SDWS in the Yangtze River and Yellow River Basins of China [38,39], the concentrations of the eight MMPs were at the same levels or lower in the PRB. A long-term study on the Freiberger Mulde river in Germany showed that the concentrations of Mn (mean = 74.27 µg/L) and Cd (mean = 2.98 µg/L) were higher than those in this study, and the concentrations of Mn and Cd in this study are also lower than the world river average [37].

HPI, NI, and CD indices were employed for comprehensive assessment of MMP contamination levels in SDWS (

Table 3. Statistics of pollution level index of MMPs (unit-less).

Pollution Level Index	Max.	Min.	Mean	Median	90th Percentile
HPI	87.08	1.37 × 10−6	4.81	2.28	11.93
NI	1.28	3.75 × 10−6	0.12	0.08	0.29
CD	5.20	5.0 × 10−6	0.25	0.19	0.51

). The HPI values exhibited a range from 1.37 × 10⁻⁶ to 87.08, yielding an average measurement of 3.95. Based on established HPI classification parameters, 14,897 samples (constituting 94.87% of the total) demonstrated low heavy metal contamination, 746 samples (representing 4.75%) displayed moderate contamination levels, while only 18 samples (comprising 0.38%) manifested relatively high contamination profiles. The NI metric ranged from 3.75 × 10⁻⁶ to 1.28, with a calculated mean value of 0.12, wherein over 99% source samples registered low contamination classifications (NI < 1). Furthermore, the CD index spanned from 5.0 × 10⁻⁶ to 5.20, averaging 0.25 across all measurements. Every drinking water source sample analyzed fell within the low pollution category (CD < 6.00). From a comparative perspective, both CD and NI methodologies produced concordant assessment outcomes among the three pollution evaluation frameworks implemented in this study. The average HPI was significantly lower than the critical value of 100.00, indicating acceptable levels of contamination. Sb and Cd have relatively larger contribution to HPI among the eight MMPs.

3.3. Temporal Variations in MMPs

From 2019 to 2024, the majority of eight MMPs displayed a decreasing trend in concentration level (Figure 3 and Figure S1). Specifically, the average concentration of Cd exhibited a continuous decrease from 0.086 ± 0.34 μg/L in 2019 to 0.030 ± 0.038 μg/L in 2024. The concentration of V showed a steady decline from 1.772 ± 3.417 μg/L in 2019 to 1.038 ± 1.336 μg/L in 2023, followed by a slight increase to 1.131 ± 1.617 μg/L in 2024. The concentration of Pb initially rose and then decreased, starting at 0.583 ± 1.279 μg/L in 2019, peaking at 0.673 ± 1.509 μg/L in 2021, and subsequently dropping to 0.213 ± 0.434 μg/L in 2024. Meanwhile, the concentration levels of Ni and Ba also decreased during the six years. The median and average concentrations of As, Mn, and Sb fluctuated slightly from 2019 to 2024, but remained relatively stable overall. Recent studies also reported that the Yangtze River and Yellow River Basins have experienced evident decrease in heavy metals content in SDWS over the past few years [38,39]. This can be attributed to the implementation of highly stringent water pollution control and water resource protection in China since 2015.

3.4. Spatial Distribution of MMPs

Figure 4 illustrates the concentration distribution of the eight MMPs in SDWS across the PRB. The metalloid As was mainly distributed in the lower reach of the PRB, particularly in the Beijiang River and its vicinity, potentially originating from industrial waste residues or wastewater discharged by sectors like smelting, dyeing, and tanning [40,41,42]. Ba and Sb were predominantly found in the central regions of the PRB, encompassing Guangxi Province and the downstream Pearl River Delta. Cd was more prevalent in Gejiu (Yunnan Province), southwestern Guizhou Province, and northern Guangdong Province. Ni was primarily concentrated in the downstream Pearl River Delta and southwestern Guangdong Province, while Pb exhibited higher levels in the upper reach of the PRB, as well as in northern and southwestern Guangdong Province. The presence of Sb, Cd, Ni, and Pb may be linked to emissions from mining, electroplating, and smelting industries, whereas Cd and Pb levels could also be influenced by the application of fertilizers and pesticides [43,44]. The metalloid V had broad distribution throughout the PRB, while Mn was primarily found in downstream areas and eastern Guangdong Province. Metal and metalloid levels in drinking water sources were notably elevated in the lower reach of the PRB, showing considerable regional disparities.

Major sources of MMPs in water environment comprised natural geological attributes, local industrial operations, and urban expansion. Among them, natural geological attributes refer to the weathering and dissolution of rock minerals and sediments, industrial operations result in the discharge of wastewater and waste from industries such as mining, metallurgy, chemical engineering, and electronics, while urban expansion contributes to the discharge of urban sewage and agricultural runoff. Climate and geochemical factors can affect the form and concentration of MMPs in water, including water temperature, pH, dissolved oxygen, redox potential, coexisting ions and adsorption, natural organic matter, etc. The MMPs in drinking water sources may be influenced by one or more of these factors, resulting in differences in the distribution of high concentration areas. These factors are very complex, but in general, the overall concentration level of MMPs is relatively high due to the denser population and industrial distribution in the lower reaches of the PRB.

Figure 5 displays the concentration distributions of the eight MMPs in river-type and lake-type SDWS in the PRB. The results of the Mann–Whitney U test show a significant difference in distribution of data between the two types of SDWS (p < 0.05, Table S5). The findings indicate that the concentration levels of As, Sb, Ni, and Ba were typically elevated in river-type water sources compared to lake-type sources, as evidenced by their ranges, medians, and means. Pb, V, and Mn also showed higher median and average concentrations in river-type SDWS.

This disparity implied that river-type SDWS were more vulnerable to human-induced pollution, like industrial effluent discharge [45], while the presence of MMPs in lake-type SDWS was predominantly linked to natural background factors or internal sediment release [46,47]. River-type SDWS is a relatively open system influenced by more sewage outlets and agricultural non-point sources from the upstream. The MMPs in lake-type SDWS may mainly come from natural geochemical processes. For example, lakes in enriched areas of manganese deposits, black shale, and apatite have higher natural background concentrations of Mn and V. Their distribution ratios in water and sediment are more susceptible to factors such as temperature, dissolved oxygen, pH, etc. The climate and hydrology changes in recent years may intensify the impact of these factors in lake-type SDWS. These could explain the significant difference in the distribution of concentration data between the two types of SDWS.

3.5. Health Risk Assessment

This study assessed the health risks associated with the eight MMPs present in drinking water sources within the PRB. The evaluation included the calculation of hazard quotient (HQ), total hazard index (THI), and carcinogenic risk (CR). Utilizing the health risk assessment model endorsed by the US EPA, both carcinogenic and non-carcinogenic risks posed by the identified pollutants were determined for different populations (adults and children) through ingestion and dermal exposure pathways. Considering that the conventional water purification process of waterworks had very limited removal efficiency (less than 10%) for the eight MMPs, this study directly used the concentration of metals and metalloids in the drinking source water for health risk assessment.

3.5.1. Non-Carcinogenic Risk

Monte Carlo simulations generated HQ and CR values for the target pollutants (

Table 4. Statistical data of the HQ and CR to adults and children via ingestion of MMPs based on Monte Carlo simulation.

Risk	MMPs	Mean (Median)		Std. Deviation		Min−Max
		Adults	Children	Adults	Children	Adults	Children
HQ	As	4.85 × 10−2 (3.45 × 10−2)	1.59 × 10−1 (1.12 × 10−1)	5.94 × 10−2	1.95 × 10−1	−7.58 × 10−2–6.39 × 10−1	−1.87 × 10−1–2.35
	Ba	6.84 × 10−4 (6.47 × 10−4)	2.25 × 10−3 (2.10 × 10−3)	1.14 × 10−3	3.79 × 10−3	−5.83 × 10−3–7.97 × 10−3	−2.10 × 10−2–2.75 × 10−2
	Cd	5.37 × 10−3 (4.05 × 10−3)	1.76 × 10−2 (1.33 × 10−2)	6.37 × 10−3	2.12 × 10−2	−9.33 × 10−3–5.83 × 10−2	−3.63 × 10−2–2.36 × 10−1
	Mn	3.78 × 10−3 (1.71 × 10−3)	1.25 × 10−2 (5.53 × 10−3)	7.12 × 10−3	2.42 × 10−2	−9.75 × 10−4–1.66 × 10−1	−3.62 × 10−3–6.84 × 10−1
	Ni	5.32 × 10−4 (5.74 × 10−6)	1.73 × 10−3 (1.83 × 10−5)	1.62 × 10−3	5.22 × 10−3	8.92 × 10−11–2.92 × 10−2	3.65 × 10−10–8.50 × 10−2
	Pb	1.63 × 10−3 (7.91 × 10−5)	5.42 × 10−3 (2.63 × 10−4)	4.16 × 10−3	1.42 × 10−2	−4.16 × 10−10–5.60 × 10−2	−1.35 × 10−9–3.33 × 10−1
	Sb	1.81 × 10−2 (5.71 × 10−3)	5.93 × 10−2 (1.86 × 10−2)	2.93 × 10−2	9.58 × 10−2	−2.51 × 10−3–3.05 × 10−1	−1.37 × 10−2–8.41 × 10−1
	V	1.96 × 10−3 (1.29 × 10−3)	6.45 × 10−3 (4.20 × 10−3)	2.73 × 10−3	8.91 × 10−3	−2.79 × 10−3–4.33 × 10−2	−9.14 × 10−3–9.82 × 10−2
THI	All MMPs	8.06 × 10−2 (6.61 × 10−2)	2.64 × 10−1 (2.14 × 10−1)	6.72 × 10−2	2.21 × 10−1	−4.07 × 10−2–6.75 × 10−1	−1.63 × 10−1–2.64
CR	As	1.10 × 10−5 (5.45 × 10−6)	5.08 × 10−6 (2.56 × 10−6)	1.69 × 10−5	7.63 × 10−6	−1.78 × 10−5–2.59 × 10−4	−9.17 × 10−6–9.47 × 10−5
	Ba	5.78 × 10−5 (3.57 × 10−5)	2.74 × 10−5 (1.65 × 10−5)	1.18 × 10−4	5.54 × 10−5	−7.17 × 10−4–1.02 × 10−3	−3.87 × 10−4–4.63 × 10−4
	Cd	1.70 × 10−7 (9.01 × 10−8)	7.94 × 10−8 (4.20 × 10−8)	2.54 × 10−7	1.21 × 10−7	−4.61 × 10−7–2.71 × 10−6	−2.96 × 10−7–1.42 × 10−6
	Ni	9.15 × 10−6 (6.86 × 10−8)	4.21 × 10−6 (3.14 × 10−8)	3.25 × 10−5	1.49 × 10−5	1.30 × 10−14–5.65 × 10−4	1.12 × 10−15–3.01 × 10−4
	Pb	2.41 × 10−8 (8.32 × 10−10)	1.15 × 10−8 (3.76 × 10−10)	7.47 × 10−8	3.70 × 10−8	−9.69 × 10−15–1.48 × 10−6	−2.42 × 10−15–1.12 × 10−6
	Sb	7.43 × 10−7 (1.52 × 10−7)	3.55 × 10−7 (7.03 × 10−8)	1.44 × 10−6	7.01 × 10−7	−1.72 × 10−7–1.56 × 10−5	−1.02 × 10−7–9.19 × 10−6
	V	8.43 × 10−7 (3.85 × 10−7)	3.90 × 10−7 (1.76 × 10−7)	1.43 × 10−6	6.49 × 10−7	−2.10 × 10−6–2.40 × 10−5	−9.61 × 10−7–9.44 × 10−6
CR	All MMPs	7.79 × 10−5 (4.97 × 10−5)	3.66 × 10−5 (2.30 × 10−5)	1.27 × 10−4	5.91 × 10−5	−7.59 × 10−4–1.65 × 10−3	−3.54 × 10−4–6.12 × 10−4

and

Table 5. Statistical data of the HQ and CR to adults and children via dermal exposure to MMPs based on Monte Carlo simulation.

Risk	MMPs	Mean (Median)		Std. Deviation		Min−Max
		Adults	Children	Adults	Children	Adults	Children
HQ	As	1.11 × 10−4 (7.21 × 10−5)	2.58 × 10−4 (1.61 × 10−4)	1.44 × 10−4	3.53 × 10−4	−1.50 × 10−4–1.60 × 10−3	−5.62 × 10−4–4.59 × 10−3
	Ba	1.60 × 10−6 (1.32 × 10−6)	3.65 × 10−6 (2.92 × 10−6)	2.77 × 10−6	6.76 × 10−6	−1.89 × 10−5–1.97 × 10−5	−4.08 × 10−5–9.10 × 10−5
	Cd	1.22 × 10−5 (8.39 × 10−6)	2.87 × 10−5 (1.86 × 10−5)	1.57 × 10−5	3.87 × 10−5	−3.00 × 10−5–1.68 × 10−4	−9.44 × 10−5–4.52 × 10−4
	Ni	2.40 × 10−7 (2.34 × 10−9)	5.53 × 10−7 (5.34 × 10−9)	7.88 × 10−7	1.77 × 10−6	2.25 × 10−14–1.90 × 10−5	6.06 × 10−14–2.86 × 10−5
	Pb	3.67 × 10−6 (1.72 × 10−7)	8.80 × 10−6 (3.80 × 10−7)	9.86 × 10−6	2.46 × 10−5	−1.01 × 10−12–1.51 × 10−4	−2.07 × 10−12–4.87 × 10−4
	Sb	4.20 × 10−5 (1.18 × 10−5)	9.54 × 10−5 (2.70 × 10−5)	7.31 × 10−5	1.67 × 10−4	−7.43 × 10−6–7.63 × 10−4	−2.05 × 10−5–2.06 × 10−3
	V	4.50 × 10−6 (2.65 × 10−6)	1.04 × 10−5 (5.87 × 10−6)	6.65 × 10−6	1.59 × 10−5	−7.34 × 10−6–8.30 × 10−5	−1.55 × 10−5–2.74 × 10−4
THI	All MMPs	1.74 × 10−4 (1.29 × 10−4)	4.04 × 10−4 (2.90 × 10−4)	1.69 × 10−4	4.09 × 10−4	−1.12 × 10−4–1.68 × 10−3	−3.13 × 10−4–5.62 × 10−3
CR	As	2.50 × 10−8 (1.14 × 10−8)	8.28 × 10−9 (3.71 × 10−9)	4.00 × 10−8	1.37 × 10−8	−5.06 × 10−8–4.99 × 10−7	−2.68 × 10−8–1.74 × 10−7
	Ba	1.35 × 10−7 (7.35 × 10−8)	4.44 × 10−8 (2.33 × 10−8)	2.81 × 10−7	9.87 × 10−8	−2.07 × 10−6–2.64 × 10−6	−8.86 × 10−7–1.10 × 10−6
	Cd	3.83 × 10−10 (1.87 × 10−10)	1.30 × 10−10 (6.06 × 10−11)	6.12 × 10−10	2.19 × 10−10	−1.52 × 10−9–8.72 × 10−9	−5.72 × 10−10–2.64 × 10−9
	Ni	4.09 × 10−9 (2.87 × 10−11)	1.36 × 10−9 (8.88 × 10−12)	1.53 × 10−8	5.24 × 10−9	4.31 × 10−18–2.98 × 10−7	3.00 × 10−19–1.32 × 10−7
	Pb	5.36 × 10−11 (1.78 × 10−12)	1.90 × 10−11 (5.73 × 10−13)	1.70 × 10−10	6.46 × 10−11	−2.06 × 10−17–2.86 × 10−9	−4.11 × 10−18–1.77 × 10−9
	Sb	1.73 × 10−9 (3.14 × 10−10)	5.69 × 10−10 (1.04 × 10−10)	3.61 × 10−9	1.22 × 10−9	−5.06 × 10−10–4.87 × 10−8	−2.29 × 10−10–2.18 × 10−8
	V	1.93 × 10−9 (7.99 × 10−10)	6.30 × 10−10 (2.50 × 10−10)	3.52 × 10−9	1.16 × 10−9	−5.01 × 10−9–5.85 × 10−8	−1.62 × 10−9–2.02 × 10−8
CR	All MMPs	1.59 × 10−7 (9.20 × 10−8)	5.40 × 10−8 (2.99 × 10−8)	2.87 × 10−7	9.93 × 10−8	−1.63 × 10−6–2.86 × 10−6	−5.20 × 10−7–1.19 × 10−6

). Analysis revealed that HQ values for the eight MMPs followed this hierarchical sequence: As > Sb > Cd > Mn > V > Pb > Ba > Ni. When HQ measurements register below 1.0, this signifies absence of potential non-carcinogenic human health hazards [48,49]. The 95th percentile threshold typically serves as the benchmark for determining whether pollutant health risks exceed regulatory standards [48,50]. Our investigation determined that adult mean HQ values through oral consumption and cutaneous exposure were 8.06 × 10⁻² and 1.74 × 10⁻⁴ respectively, while corresponding mean HQ values for pediatric subjects via ingestion and dermal pathways measured 0.26 and 4.04 × 10⁻⁴ respectively. Consequently, dermal contact HQ values consistently registered lower than ingestion-related measurements, thus contributing proportionally less to the overall THI calculations across both demographic categories.

This suggests that oral ingestion is the primary pathway for adverse effects and non-carcinogenic risks to humans when surface water is used as a drinking water source [51,52]. It also indicates differences in non-carcinogenic risks among different populations, with children exhibiting slightly higher HQ values than adults for both oral ingestion and dermal contact, which is consistent with previous studies [53,54,55,56,57].

In Figure 6, the HQ values of the MMPs, except for As, in the PRB remain within safe limits. The red shaded region in the diagram signifies an unacceptable risk level. It can be observed that the 95th percentile ranges of the HI for adults and children were 2.66 × 10⁻³ to 0.21 and 8.63 × 10⁻³ to 0.71, respectively. About 1.14% of children had THI values surpassing the acceptable threshold of 1.0, mainly due to the HQ_ingestion-As component, with 0.54% of children exceeding the 1.0 threshold for HQ_ingestion-As. Conversely, the 95th percentiles of the remaining seven MMPs ranged from 10⁻⁴ to 10⁻¹, contributing minimally to the THI. In summary, the metalloid As emerges as the primary factor causing potential non-carcinogenic risk in SDWS within the PRB.

3.5.2. Carcinogenic Risk

Among the selected target pollutants, the metal Mn is not listed as a carcinogen by the IARC. Therefore, this study calculated the carcinogenic risks of the other seven MMPs. As shown in Table 4 and Table 5, the calculated CR_ingestion values for adults and children ranged from −7.59 × 10⁻⁴ to 1.65 × 10⁻³ and −3.54 × 10⁻⁴ to 6.12 × 10⁻⁴, respectively, while the CR_dermal values ranged from −1.63 × 10⁻⁶ to 2.86 × 10⁻⁶ and −5.20 × 10⁻⁷ to 1.19 × 10⁻⁶, respectively. According to the US EPA classification of CR, a threshold of 10⁻⁶ is generally considered negligible [48]. Since the CR values for both adults and children via dermal exposure did not exceed 10⁻⁶, the carcinogenic risk posed by dermal exposure to the local population was negligible.

Figure 7 presents the cumulative frequency curves of CR for seven orally ingested MMPs, with the blue shaded area indicating the warning risk zone. The CR values of these seven MMPs were ranked in descending order as Ba > Ni > As > V > Sb > Cd > Pb. The mean CR values for both Cd and Pb fell below the threshold, indicating that their carcinogenic risks to the local population were negligible. As shown in Figure 7, 33.08% of adults and 12.90% of children exhibited TCR values exceeding the risk threshold (10⁻⁶). Ba and Ni were the primary contributors to TCR exceeding the risk threshold, with Ba making the most significant contribution. The selected pollutants consistently demonstrated higher carcinogenic risks in adults than in children, likely due to adults’ longer exposure duration. In summary, the MMPs in drinking water sources of the PRB posed potential carcinogenic risks. Unlike non-carcinogenic risk outcomes, adults represented a more sensitive population group.

3.5.3. Sensitivity Analysis

A comprehensive sensitivity analysis was implemented to clarify the relative contributions of various exposure routes and parameter fluctuations toward uncertainty quantification in aggregate carcinogenic and non-carcinogenic risk assessments. Examination of the non-carcinogenic risk evaluation outcomes identified the metalloid arsenic as the predominant contributor to observed health hazards. Consequently, our sensitivity investigation specifically focused on arsenic concentration variability alongside other pertinent parameters, excluding factors with minimal influence on overall risk characterization.

It is depicted in Figure 8a,b that the parameter sensitivity concerning HQ risks through dermal and ingestion pathways showed consistent patterns. Notably, all parameters, with the exception of body weight (BW), present positive contribution rates. The concentration of As stood out with the highest contribution rate, exceeding 90% across different pathways, particularly affecting adults. In the dermal pathway, exposure time (ET) emerged as another critical factor in non-carcinogenic risk assessment, contributing 26% for both adults and children. In the oral ingestion pathway, ingestion rate (IR) and exposure frequency (EF) exhibited positive correlations with health risks. Conversely, BW demonstrated negative contribution rates across different demographic groups. Despite the relatively small and negligible contribution values, it is essential to note that BW had a more pronounced impact on children, consistent with prior research [58,59]. Figure 8c,d illustrate the parameter sensitivity regarding TCR through dermal and ingestion pathways, considering all target pollutant concentrations and parameters.

Among these two pathways, the concentration of Ba contributed approximately 78.5% to the positive effect on TCR. ED was another major sensitive factor in health risk assessment, showing a positive correlation with TCR (39.8–40.0%). The TCR of MMPs accumulated with prolonged exposure duration. Although BW exhibited a negative correlation in both adult and child populations, its influence was minimal, which aligned with the findings of non-carcinogenic risk analysis. In summary, pollutant concentration was the most sensitive factor in the health risk assessment of MMPs, with As and Ba being the primary contributors to the sensitivity analysis of HQ and CR.

4. Conclusions

This study systematically examined the pollution characteristics and associated health risks of metal and metalloid pollutants (MMPs) in surface drinking water sources (SDWS) in the Pearl River Basin (PRB) from 2019 to 2024. The findings revealed that the overall water quality within the research area remained stable and satisfactory. Concentrations of eight specified pollutants (As, Cd, Pb, Mn, Sb, Ni, Ba, V) were all found to meet water quality standards for SDWS. Furthermore, the concentrations of Cd, Pb, Ni, Ba, and V displayed a decreasing trend annually, indicating that the current levels of MMPs in SDWS of the PRB are generally managed effectively. Notably, MMPs exhibited notable regional disparities, with downstream and river-type water sources in the PRB experiencing more pronounced impacts from human activities such as industrial operations and urban expansion. Oral ingestion was identified as the primary pathway for adverse health effects and non-carcinogenic risks posed by MMPs, with As posing a certain non-carcinogenic risk to children. For adults and children, 33.08% and 12.90% of the total carcinogenic risks (TCR), respectively, exceeded the risk threshold of 10⁻⁶, which indicated potential carcinogenic risk of MMPs to human heath. Ba and Ni were the major contributors to carcinogenic risk, among which Ba exhibited the highest contribution rate. The carcinogenic risk of MMPs was higher for adults than for children. Sensitivity analysis further confirmed that the concentration of As derived non-carcinogenic risk; while the concentration of Ba influenced carcinogenic risk. Subsequent research is recommended to enhance dynamic monitoring in industrial-intensive areas and river-type drinking water sources. Pollution control strategies need optimizing for high-risk factors of As, Ba, and Ni, including stricter discharge rules of wastewater and ecological restoration of mine tailings. Meanwhile, early warning capabilities for health risks and water purification technology should be improved to ensure drinking water safety.