Pollution characteristics and probabilistic human health risks of thallium and other heavy metals in soils from a typical copper mining city in the Yangtze river Delta, eastern China

ABSTRACT

Heavy metals contamination in soil from copper mine areas is a serious and widespread problem in China. In this study, the content of Tl, Hg, Cu, Cd, Ni, Pb and As and probabilistic human health risks were investigated in a typical copper mining city. Copper showed the highest average content of 184.29 mg/kg, followed by Pb, As, Ni, Tl, Cd and Hg. Approximately 54.2% of study sites were moderately contaminated by Cu, Cd, As and Tl. Among the heavy metals, Hg, Cu and Cd showed relatively high mobility and medium risk in the environment based on the chemical speciation. In particular, Tl was mainly present as Tl(I) in soil with more biovailability and solubility. Moreover, the probabilistic human health risk assessment using Monte-Carlo approach revealed that heavy metals in soil can cause non-carcinogenic risks (adults: 0.03%, children: 48.45%) and carcinogenic risks (adults: 41.79%; children 61.98%). The soil oral ingestion is the main exposure pathway for human exposed to heavy metals. Our findings indicate that the copper mining activities can cause high level of heavy metals in soils, and corresponding health risks. Meanwhile, these results could provide important information and theoretical support the development of precise soil pollution management strategies in copper mining areas.

KEYWORDS:

• Thallium
• heavy metals
• health risk assessment
• Monte-Carlo simulation

1. Introduction

Thallium (Tl) is a non-essential and widely dispersed trace element in environment [1]. Due to the high potential toxicity of Tl, it has been listed as a priority pollutant by the U.S. Environmental Protection Agency. The level of Tl in the natural environment is relatively low, the background level of Tl in the earth’s crust, soil, and water is 0.75 mg/kg [2], 0.01–3.00 mg/kg [3], and 0.001–0.25 μg/L [4], respectively. However, due to the high bioavailability of Tl in soil, it can be easily accumulated in plant, and may cause potential harm to humans [5]. In addition, the high content of Tl was also reported in animals (sheep, rabbits, horses, etc.) through the consumption of leaves from Tl contaminated areas [6]. Serious endemic thallotoxicosis, such as hair loss, body-aches, reduced vision and blindness, has occurred due to the high level of Tl in water, soil, and food crops from Guizhou Province, China [7]. Due to its adverse health effect, the Hygiene Ministry of China issued the maximum urinary Tl concentration for the public is 5 μg/L. Furthermore, a four-year study indicated that even the urinary Tl concentration was lower than 5 μg/L for children after treatment, their liver and renal functions did not completely recover [8,9].

Globally, the annual input of Tl was 5000 tons derived from industries and coal combustion [10]. Meanwhile, Tl and Tl-based alloys have been widely used in recent decades for technologically advanced industries [11]. The emerging Tl pollution has attracted more and more public and environmental attention, especially in China [12]. With the development of analytical techniques in recent years, the pollution caused by Tl in soil has been numerously documented. In general, the content of Tl was shown at low content in environment, whereas ferrous and non-ferrous smelting activities and industry processes can increase its content in environment [13]. Some previous studies found that the high level of Tl was recorded in soils from Tl-rich mineralized area [14], Tl-rich pyrite mineralized zone [15], Carlin-type gold deposit area [16], steel-making area [17], etc. More than 10 poisoning events involving Tl have occurred in China since 1997 [14].

As an important mineral resource, copper (Cu) is an essential non-ferrous metal and widely used in electronics, construction industry, transportation, and so on [18]. China is the largest copper consumer in the world, with an annual consumption being 13.89 million tons in 2021. It is estimated that the cumulative demand for copper in China will reach 360 million tons from 2022 to 2050 [19]. Therefore, copper is also listed in the catalog of strategic key minerals in China. Tongling city, the most important copper product area, has a long history of Cu mining activities for more than 3000 years [20]. Due to its long mining history and various kinds of non-ferrous metal mines, this city is known as the ‘ancient Chinese copper capital’ and ‘modern Chinese copper base’ in China [21,22]. It produced approximately 1.1 million tons of electrolytic copper annually. Therefore, this area is believed to be representative Cu mining districts in eastern China [23]. Unfortunately, some heavy metals can be accumulated in the soil near copper mines due to the metallurgical process [24,25]. Previous studies demonstrated that the presence of heavy metals in soil can cause potential human health risk for local residents [26–28]. Furthermore, soil pollution by heavy metals is considered to be the biggest challenge to sustainable development [29]. Out of 17 sustainable development goals (SDGs) set by United Nations in 2015, eight issues are directly or indirectly relevant to soil pollution [30].

Previous studies have mentioned that Tl is a mildly chalcophile element [31,32], and can be incorporated into the pyrite structure as a substitution, which leads to a relatively high level of Tl in pyrite [33]. Currently, a considerable count of publications documented Tl pollution in soils [34], vegetables [35], teas [36], plants [37], surface water and sediments [38]. However, the content of Tl and its human health risk remains poorly studied in this representative Cu mining district. Therefore, the main objectives of this study were (1) to investigate the levels of Tl and other heavy metals in the soils near copper smelting plants in Tongling city and (2) to evaluate the health risk of human exposure to multiple heavy metals in soil.

2. Materials and methods

2.1. Study area and sample collection

Tongling (117°42′00″-118°10′6″, 30°45′12″-31°07′56″) is a typical medium-sized industrial city and belongs to the Yangtze River basin, located in the central-south of Anhui province. This region belongs to a mild climate, and the mean annual temperature is 16°C, the average annual precipitation is 1930 mm, the average humidity is 83%, as well the frost-free period is approximately 230 days per year. The mineral deposits of investigated area are polymetallic (iron-copper-gold-sulfide) deposits, and the metal minerals mainly contain magnetite, hematite, chalcopyrite, pyrite, siderite [39]. In addition, the soils in study area are mainly brown red soil (Ferralosols).

In this study, the abandoned industrial area was selected, and the levels of heavy metals in soils were measured. Totally, 211 soil samples from 65 sites were collected (Figure 1), and each soil sample was commixed by five subsamples. All the samples were stored in clean aluminum and then returned to the laboratory for naturally air dried and ground to 100 mesh. The processed soil samples were sealed in polyethylene plastic container and store at 4°C for future chemical analysis [40].

Figure 1. Overview map of the study area and sample site.

2.2. Measurement of metal concentrations

The soil sample digestion was performed using a microwave-assisted acid-digestion procedure following the national standard method HJ 832–2017 [41]. In brief, approximately 100 mg sieved soil was weighed in a Teflon digestion tube followed by an addition of 11 mL acid mixture digestion systems (6 mL HNO₃ +3 mL HCl +2 mL HF) [42]. The concentrations of heavy metals were measured using an inductively coupled plasma emission spectrometer (ICAP6300, Thermo Fisher Scientific, U.S.A.). In addition, the blank, standard reference material (Geochemical Standard Reference Soil of China, GB W07443) was used to control and calibrate the analysis procedure. All samples were analyzed in triplicate and the relative error of the duplicate samples was <5% on average, and the heavy metals recovery rate of the reference material ranged from 96% to 102%.

2.3. Measurement of metal speciation

The processed soil samples were extracted using the European Community Bureau of Reference (BCR) sequential extraction procedure according to our previous studies [42,43]. After extraction, the heavy metals can be described as four fractions (i.e. acid-soluble fraction, reducible fraction, oxidizable fraction, and residual fraction). The detailed BCR procedure for heavy metals in soils was shown in Table S1. The concentrations of heavy metals in all steps were determined by ICP-OES. Additionally, the recovery percentages of all the heavy metals were ranged from 92% to 115%. The chemical oxidation states of heavy metals in soil samples were examined using X-ray Photoelectron Spectroscopy (XPS, Thermo Fischer, ESCALAB Xi+, U.S.A.), which was equipped with Al Kα X-ray source (1486.6 eV). The binding energies were referenced to the C_1s signal at 284.6 eV.

2.4. Risk assessment

2.4.1. Index of geo-accumulation

The geo-accumulation index (I_geo) has been widely used to assess heavy metals pollution in soils or sediments since the late 1960s [44], which is calculated using the following equation:

${\mathrm{I}}_{geo}=\mathrm{l}\mathrm{o}{\mathrm{g}}_2\left({\mathrm{C}}_n/1.5{\mathrm{B}}_n\right)$

Where, n represents the certain metal in soils, C_n and B_n are the heavy metal contents in collected soil samples and the geochemical background values of soils, respectively. 1.5 represents the constant factor due to lithospheric effects [45]. On the basis of this index, seven grades of I_geo can be divided to present different contamination levels, which was shown in Table S2.

2.4.2. Exposure assessment

To describe the quantitative health risk of exposure to heavy metals in soils, the assessment model proposed by the USEPA was selected. In this study, the carcinogenic and noncarcinogenic risk from heavy metals for adults and children in study area was assessed under three exposure pathways (ingestion, inhalation and dermal absorption) [46]. The average daily intake (ADD, mg/kg/day) of heavy metals was calculated according to the following formulae:

$AD{D}_{ingestion}=C_{soil}\times \frac{IngR\times EF\times ED}{BW\times AT}\times {10}^{-6}$

$AD{D}_{dermal}=C_{soils}\times \frac{SA\times AF\times ABS\times EF\times ED}{BW\times AT}\times {10}^{-6}$

$AD{D}_{inhalation}=C_{soils}\times \frac{InhR\times EF\times ED}{PEF\times BW\times AT}$

Where ADD_ingestion, ADD_inhalation and ADD_dermal are the average daily doses through ingestion, inhalation and dermal contact, respectively, in mg/(kg∙day). C_soil is content of heavy metals in the soil (mg/kg); IngR is the soil ingestion rate (mg/day); InhR is the air inhalation rate (m³/day); EF and ED are the exposure frequency (day/year), and exposure duration (year), respectively; BW shows the body weight (kg); The term AT represents the average exposure time (day). SA is dermal surface area (cm²/day), and AF is the soil adherence factor (mg/(cm²∙day)). ABS demonstrates dermal fraction absorbed from the soil (unitless). PEF represents the particulate emission factor of heavy metals (m³/kg). The assumed values for health risk assessment are given in Table S3.

2.4.3. Human health risk assessment

The carcinogenic risk index (TCR) and the noncarcinogenic risk index (HI) were calculated [47].

$TC{R}_{soil}=\sum _{i=1}^{n}C{R}_i=\sum _{i=1}^n\sum _{j=1}^{3}AD{D}_{ij}\times S{F}_i$

$H{I}_{soil}=\sum _{i=1}^{n}H{Q}_i=\sum _{i=1}^n\sum _{j=1}^3\frac{AD{D}_{ij}}{Rf{D}_i}$

where i presents one heavy metal in soil; j indicates the three exposure pathways for soil; HI_soil is the sum of the hazard quotient of various heavy metals through different exposure pathway (unitless); TCR_soil represents the total carcinogenic risk through the three exposure pathways (unitless); CR_i is the carcinogenic risk of heavy metal i (unitless); HQ_i is the hazard quotient of heavy metal i (unitless); RfD_i is the reference oral dose available for heavy metal i (mg/(kg∙day)); SF_i is the cancer slope factor for heavy metal i (mg/(kg∙day)) . The toxicity parameters for heavy metals were shown in Table S4.

The criteria for risk are as follows: no significant health risk (CR or TCR < 10⁻⁶); acceptable/tolerable (10⁻⁶ < CR or TCR < 10⁻⁴); unacceptable (CR or TCR > 10⁻⁴). Humans may tolerate the potentially toxic effects if the HQ or HI value exceeds 1.0.

2.4.4. Probabilistic risk modeling and sensitivity analysis

To reduce the uncertainty of the health risk of elements, a Monte Carlo simulation method was conducted to assess the probabilistic risk using the Oracle Crystal Ball program (version 11.1, Oracle, U.S.A.) loaded in Microsoft Excel [48]. Furthermore, the sensitivity analysis was conducted to estimate the key factors that influenced the results of risk. The parameter distribution types used in this study were listed in Table S3. In this study, the THQ and CR were repeatedly and randomly repeated 10,000 times to obtain more creditable outputs and sensitivity index.

2.5. Data and statistical analyses

The arithmetic mean, range, and standard deviation were used to analyze the content of heavy metals in soils. Spearman’s correlation was used to explore sources of heavy metals. The Origin Pro 2022 (Origin Lab Corporation, Northampton, U.S.A.) was used to create the figures in this study.

3. Results and discussion

3.1. Contents of heavy metals in soils

The descriptive statistics of contents for heavy metals are shown in Table 1, including the items of percentage, median and mean. Comparing the contents of heavy metals with the national background values of China, all the heavy metals in soils exceeded the corresponding background values in the study area. This suggests that heavy metals in the soil were heavily influenced by pollution from mining activities. The range of heavy metals in the soils was: 31.00–562.00 mg/kg for Cu, 10.4–408.00 mg/kg for As, 0.60–9.7 mg/kg for Tl, 0.18–0.61 mg/kg for Hg, 0.28–5.44 mg/kg for Cd, 33.00–298.00 mg/kg for Pb, 26.00–197.00 mg/kg for Ni. The maximum contents of all heavy metals except Hg were higher than their corresponding recommended risk screening values in China (GB15618–2018). The mean value of content for heavy metals in soils showed a trend of Cu>Pb>As>Ni>Tl>Cd>Hg.

Table 1. Summary statistics for the content of heavy metals in soils (unit: mg/kg).

	Cu	As	Pb	Ni	TI	Cd	Hg
10^th	35.00	38.60	34.00	38.00	1.10	0.31	0.20
25^th	65.00	54.90	72.00	46.00	1.50	0.37	0.23
Median	133.00	82.70	1.00	55.00	2.00	0.50	0.30
Mean	184.29	99.89	134.88	65.38	2.76	1.16	0.34
75^th	270.00	134.00	234.00	66.00	3.40	1.27	0.46
90^th	415.00	175.00	278.00	93.00	4.60	3.21	0.51
BVSC^a	22.60	11.20	26.00	26.90	0.62	0.10	0.065
RSVSC^b	100.00	30.00	12.00	100.00	-	0.30	2.40

a CNEMC (China National Environmental Monitoring Centre). 1990. The background values of elements in Chinese soils. China Environmental Science Press, Beijing.

b CEPA (Chinese Environment Protection Administration). 2018. Soil environmental quality-Risk control standard for soil contamination of agriculture land. GB15618–2018.

Particularly, as shown in Table S5, the level of Tl in soils was higher than the geochemical background value of soils from the Yangtze river – Huaihe river basin of Anhui province (0.60 mg/kg), indicating that Tl was likely introduced into soils through some anthropogenic effects. Previous studies indicated that human activities (i.e. mining and agricultural activities) are the main factors affecting the behaviors of Tl in the soil environment [14]. Besides, the mean Tl content in this study exceeds that of vegetable soils, forest soils and grasslands from previous research [49–51]. Thallium contents in this study were lower than the soil samples collected from Spain and China reported by Cortada et al., Jiang et al. and Xiao et al. [16,52]. Since no environmental standards for Tl in soil are established in China, Xiao et al. suggested the maximum content of Tl in arable soils should be regulated at 1 mg/kg [14]. In this study, only 11 of 211 samples, the content of Tl was less than 1 mg/kg.

To further understand the pollution level of heavy metals, the geo-accumulation index (I_geo) was calculated, and the result was shown in Figure 2. Cd had the highest I_geo value (2.30), followed by Cu (2.01), Hg (1.71), Pb (1.48), As (0.95), Tl (0.59), and Ni (0.54). According to the classification of I_geo, approximately 54.2% of soils were moderately contaminated, and nearly 4.1% of soils were extremely contaminated by Cd. This result is highly consistent with those reported data in Tongling city [28].

Figure 2. Class distribution of I_geo value for heavy metals in soil.

The correlation analysis was used to analyze the inter-relationship between the contents of elements in this study. And the results are plotted in Figure 3. High positive linear relationships were found between Pb and Cd, Tl and Pb, Tl and Cd. The high correlations suggested that these elements might have similar sources or common pollution pathways.

Figure 3. Correlation matrix for pH and heavy metals in soils.

3.2. Speciation of heavy metals in soils

The chemical speciation of heavy metals can control their bioavailability, mobility, reactivity and toxicity in soils. The chemical speciation can be divided into four categories, the acid soluble/exchangeable fraction (F1), reducible fraction (F2), oxidizable fraction (F3) and residual fraction (F4). And the F1 and F2 can be considered the mobile fractions in the environment. The results of the BCR and risk assessment code (RAC) of heavy metals in soils are presented in Figure 4. According to Figure 4, As, Ni and Cu were more concentrated in the residual fraction (F4), with the percentage was higher than 50%, these heavy metals were likely immobilized and will not pose severe hazards in the environment. Furthermore, the high value of Cu in oxidizable fraction (F3) soils was due to the organic matter in soils, as Cu showed a strong affinity with humic substances in soils [53]. In contrast, the mobilization of heavy metals, such as Cd, Tl, Pb and Hg was high, due to their high values of ∑(F1+F2+F3), these fractions were regarded as directly bioavailable and potentially bioavailable [42]. In addition, Hg was found more in the exchangeable fraction (F1), with a percentage of 28.3%, which may cause negative effects because this fraction is bound with carbonates that can readily release to the environment [54].

Figure 4. The speciation and RAC of heavy metals using BCR sequential extraction scheme.

The environment implication of heavy metals was also calculated by risk assessment codes (RAC). In this study, based on the result of Figure 4, the RAC of heavy metals showed a trend: Hg>Cu>Cd>Ni>Tl>Pb>As. The Hg, Cu and Cd presented medium risk because their RAC values were higher that 10%. Low risk (<10%) was observed in soils for Ni, Tl, Pb and As.

To investigate the chemical states of heavy metals, the soil sample was characterized by XPS. The results of XPS analysis are shown in Figure 5, some intense peaks in wide-scan spectra can be attributed to O1s, C2s, Si2p and Fe2p. According to the results of XPS (Figure 5), Tl main occurs as weakly hydrated monovalent cation Tl(I) in soils [55], with a percentage of approximately 71.3%. The Tl(I) is the common speciation of Tl in the environment, and can be more bioavailable because of its high solubility. In contrast, the toxicity of Tl(III) is much less than Tl(I) [56]. This result was in good agreement with the study of Tl-As rich soils from Yunnan, China [56].

Figure 5. The XPS spectra of Tl in soil.

3.3. Human health risk assessment based on Monte Carlo simulation

3.3.1. Potential non-carcinogenic risk

The Monte Carlo simulation results of non-carcinogenic risk through soil exposure to heavy metals are shown in Table 2 and Figure 6. The median value of HI for children and adults were all <1, with the trend of As>Pb>Tl>Cu>Hg>Ni>Cd. Arsenic showed the highest contribution to the total non-carcinogenic risk by comparing the largest HQ value, this result was consistent with the research on the global copper mines, which found that the As accounted for more than 70% of HI value for eight heavy metals [27]. Meanwhile, the mean value of HI for adults was 0.23 with 95% CI of 0.08 to 0.25, and the mean value for children (HI_children) was 1.09 with 95% CI of 0.70 to 1.34. Compared with the guideline value of HI, the HI value for adult was much lower than 1.0, indicating no potential noncariogenic risk. Meanwhile, approximately 48.45% of the HI value for children surpassed 1.0, indicating non-negligible potential non-carcinogenic risk (Figure 7). Moreover, the non-cariogenic risk was mainly from soil ingestion, which accounted for 99.23% and 82.93% of the total risk for children and adults, respectively. This meant that oral ingestion was the major exposure pathway in this study. These results may be due to the hand-to-mouth behavior of children, thus children were more vulnerable to non-carcinogenic risks than adults [27]. Furthermore, the HI values were >10 folds as high as agricultural soil reported by Wang et al. [57]. Hence, the potential non-carcinogenic risk for soils in this study should pay closer attention to heavy metals exposure for children, especially to As.

Figure 6. The distribution of non-carcinogenic risk from adults (a) and children (b).

Figure 7. The probabilities distribution of carcinogenic risk (a) and non-carcinogenic (b) risks for local residents (the black short dot line presents the risk boundary, TCR = 1.0E–4, HI = 1).

Table 2. Summary statistics of health risk based on Monte Carlo simulation.

Risk	Mean		Median		95% CI
	Adults	Children	Adults	Children	Adults	Children
HI	0.23	1.09	0.16	0.99	0.08–0.25	0.70–1.34
TCR	1.21E–4	1.24E–4	9.10E–5	1.15E–4	3.99E–5 ~ 2.16E–4	6.39E–5 ~ 1.94E–4

Additionally, the probability distribution of HQ value for each heavy metal was shown in Fig. S1, all the heavy metal, except for As, with negligible non-carcinogenic risk (HQ < 1). And As with 0.01% and 26.56% probability had a higher HQ value than 1.0 for adults and children, respectively, which indicated the potential non-carcinogenic risk for local residents.

3.3.2. Potential carcinogenic risk

The potential carcinogenic risk (CR) of heavy metals for local residents is shown in Table 2. It was worth noticing that the CR of adults was higher than that of children, which was consistent with the results of HI. With regard to the exposure pathway, oral ingestion accounted for 93.18% and 95.68% of the TCR value for adults and children, respectively. The mean value of TCR for adults and children were 1.21E–4 and 1.24E–4, respectively, which was higher than the acceptable range (1.00E–6 to 1.00E–4) recommended by USEPA. The probability of carcinogenic risk (CR) for all heavy metals for adults and children was 41.79% and 61.98%, respectively. Furthermore, the CR value for Cu showed the highest percentage (60.06%) of CR, followed by As (19.67%), Ni (19.55%) and Cd (0.73%), indicating that Cu and As was the major carcinogenic factor in this study (Figure 8). In addition, the CR values of Pb and Cd were within the acceptable threshold that indicating no cancer risk for both adults and children.

Figure 8. The distribution of carcinogenic risk for adults and children.

3.3.3. Sensitivity analysis

The sensitivity analysis of the impacts of parameters on the risk was performed with Crystal Ball software. The result was shown in Figure 9. In general, the value of the total noncarcinogenic risk (HI) and total carcinogenic risk (TCR) was found positively sensitive to the content of heavy metals, exposure frequency (EF), the soil ingestion rate (IngR) and the air inhalation rate (InhR), while negatively sensitive to body weight (BW). For the HI, the content of Pb in soil showed the most notable contribution to the risk, with proportions of 48.64% and 51.38% for adults and children, respectively. Regarding the TCR, the content of Cu was significantly responsible for cancer risk estimation, accounting for 55.60% and 65.83% for adults and children, respectively. The results implied that heavy metals in the soil were the most sensitive factor for both noncarcinogenic and carcinogenic risk, and effective controlling measures were required to decrease Pb, As and Cu content to effectively reduce the health risk to local residents.

Figure 9. The sensitivity analysis of non-carcinogenic risks and carcinogenic risks (C-Metal: the content of heavy metals in soils; BW: the body weight; EF: exposure frequency; IngR: the soil ingestion rate; InhR: the air inhalation rate).

4. Conclusions and recommendations

The content, chemical speciation, human health risk of Tl and other heavy metals in soil from copper mining area were studied in this study. The results demonstrated that the content of all heavy metals exceeded the background value of Tongling city. Cadmium showed the highest geo-accumulation (I_geo) index, followed by Cu, Hg, Pb, As, Tl and Ni. Furthermore, approximately 54.2% of soils were moderately contaminated, based on the classification of I_geo. The chemical speciation demonstrated that the Hg, Cu and Cd had more bioavailable fractions, which can readily be released to the environment. In addition, Tl predominantly occurred in the form of Tl(I) in soil. The health risk assessment revealed that the non-carcinogenic risk and carcinogenic risk for children were higher than that for adults, the probability of risk for children was approximately 48.45% and 61.98% for non-carcinogenic risk and carcinogenic risk, respectively. Furthermore, based on the Monte Carlo sensitivity analysis, the health risk was most sensitive to heavy metal content, especially Cu, As and Pb. Furthermore, this study recommended that some soil management strategies should be implemented to protect public and environmental health. Further, comprehensive studies are needed to determine the sources of heavy metals in soil from this area.

Authors contributions

Kefei Sun, Hui Li and Zixuan Jiang: investigation, methodology, writing-original draft. Yunhua Zhang: resources, supervision, writing – review & editing, fund acquisition. Yanhong Shi: project administration. Jiale Gao, Jiaqi Shi and Yan Fang: investigation. Ting Fang and Jie Kong: writing – review & editing. Zixuan Jiang, Kongyong Ouyang and Ningyuan Zhang: sample collection. Xinju Dong: writing – review & editing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/26395940.2023.2250912

Additional information

Funding

This work was financially supported by the Yangtze River Ecological Protection Project (2022-LHYJ-02-0504-02), Key Research and Development Program of Anhui Province (2022m07020004), The College Students’ Innovative Entrepreneurial Training Plan Program AHAU (X202310364540), Special Fund for Anhui Agriculture Research System.