Distribution and Risk Assessment of Heavy Metals in the Wetlands in the Upper, Middle, and Lower Reaches of the Yellow River Basin: A Study Focusing on the Yellow River Delta, Henan Section and Ningxia Section

Distribution and Risk Assessment of Heavy Metals in the Wetlands in the Upper, Middle, and Lower Reaches of the Yellow River Basin: A Study Focusing on the Yellow River Delta, Henan Section and Ningxia Section | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distribution and Risk Assessment of Heavy Metals in the Wetlands in the Upper, Middle, and Lower Reaches of the Yellow River Basin: A Study Focusing on the Yellow River Delta, Henan Section and Ningxia Section Yiqiao Zhou, Shuo Li, Fan Yang, Qingsong Guan, Ning Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4378030/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wetlands serve as significant sinks and sources of heavy metals. In this study, surface soil samples (0–25 cm) were collected from 15 sampling sites across the wetlands on the Ningxia, Henan,and the delta wetlands reaches to investigate the contents, distributions, and ecologic risks of heavy metals such as As and Cd in the wetland sediments in the Yellow River. The results revealed that the wetland soils in the upper and lower reaches were alkalineand more conducive to heavy metal enrichment. There was no significant spatial distribution pattern of the heavy metals across the wetlands in the Yellow River.The contents of the heavy metals decreased with increasing soil depth vertical profile each sampling sites. Geoaccumulation index (Igeo) analysis revealed that heavy metals had a negative Igeo value at each sampling site, expect for the following metals and sampling sites: in the Tianhe Bay wetland, the Igeo values for Cd, Mn, and Ni were 0.71, 0.17, and 0.04, respectively; in the middle reaches, the Igeo value for Cd was 0.28; and in the lower reaches, the Igeo value for Sb in the delta wetlands was 0.21.Pollution load index analysis and enrichment factor (EF) analysis revealed the occurrence of severe Cd contamination in the Ningxia, with an EF of greater than 3, indicating a high degree of anthropogenic impact. There was a strong correlation (correlation coefficient > 0.8) among the various heavy metals in the wetlands in both the Ningxia and delta wetlands, suggesting a common source for these elements. Heavy metals Ecological risk assessment Yellow River Basin Wetlands Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Wetlands are referred to as the kidneys of the Earth and are one of the most important natural habitats on Earth(Wagner, Gallagher et al. 2010, Shan, Singh et al. 2021 ). Due to their low-lying topography, wetlands are readily subjected to constant material inputs transported by hydrodynamic forces, and thus, they become significant sinks for anthropogenically discharged heavy metals(Li, Bu et al. 2021 ). Heavy metals, as typical cumulative pollutants, are highly toxic and difficult to degrade, can cause the death of animals and plants, damage the food chain, and cause deterioration of the ecosystem, resulting in significant biological toxicity and persistent threats to the ecological environment(Xiaohui, Dongfang et al., Liu, Men et al. 2016 , Chen, Chen et al. 2019 ). Consequently, extensive studies have been conducted on the accumulation(Ramos-Miras, Roca-Perez et al. 2011 ), speciation(Hoque, Goswami et al. 2011 ), spatial distribution(Harikumar, Nasir et al. 2009 ), and ecological risks(Cui, Zang et al. 2014 ) of heavy metals in surface soils in wetlands. It has been found that wetland soils currently exhibit varying degrees of heavy metal accumulation globally, with a trend towards increasing levels(Ramos-Miras, Roca-Perez et al. 2011 ). Sterckeman(Sterckeman, Douay et al. 2000 ) posited that the vertical migration characteristics of heavy metals in soils at different depths can serve as a straightforward indicator of the migration capacity of heavy metals and the status of soil contamination. Currently, research on the vertical migration characteristics of heavy metals has primarily focused on urban soils, farmlands, and industrial tailings, and less research has been conducted on the vertical distribution and migration characteristics of heavy metals in wetland soil profiles. Moreover, the complex ecological structure and unique hydrological characteristics of wetlands result in the formation of a variety of soil types, which affects the distribution of heavy metals in wetland soils. Therefore, studying the vertical distribution characteristics of heavy metals in wetland soils and their influencing factors is crucial for revealing the distribution and migration patterns of heavy metals in wetland soils. The Yellow River, which flows through nine provinces and is revered as China’s Mother River, is the country’s second longest river. The total area of the wetlands in the Yellow River Basin is approximately 2.8 million hectares, including riverine and floodplain wetlands. These wetlands can be categorized into eight distinct zones based on their regional distribution, including the wetlands in the source area of the Yellow River, the Ningxia Plain wetlands, the Sanmenxia Reservoir wetlands, the estuarine delta wetlands. Consequently, the ecological quality of the Yellow River’s wetlands is crucial to achieving high-quality development in the Yellow River Basin. The wetlands of the Yellow River are characterized by a low organic matter content(Guan 2022 )and a high degree of salinization, and excessive industrial development has led to severe heavy metal contamination in the Yellow River Basin. In this study, based on the spatial distribution of the wetlands in the Yellow River Basin, we selected typical wetlands in three regions, namely, the Ningxia Plain in the upper reaches, the Sanmenxia-Luoyang-Zhengzhou reservoir area in the middle reaches, and the estuarine delta in the lower reaches, to elucidate the vertical distribution characteristics and migration patterns of the heavy metals in the wetland soils in the Yellow River Basin. This study provides fundamental data and a scientific basis for ecological environmental protection of the Yellow River wetlands. 2 Materials and methods 2.1 Study area In this study, we focused on the wetlands in three typical regions: the Ningxia Plain in the upper reaches, the Sanmenxia-Luoyang-Zhengzhou reservoir area in the middle reaches, and the estuarine delta in the lower reaches of the Yellow River. The distribution of the sampling sites is shown in Fig. 1 . The Tianhe Bay wetlands (THW) is located on the Ningxia Plain in the upper Yellow River and is under the administration of Pingluo County, Ningxia Hui Autonomous Region. The THW have a mid-temperate continental dry climate with little rainfall, and an annual average precipitation of about 200 mm. The vegetation type are limited, mainly consisting of Salix matsudana , Phragmites communis , and Calamagrostis pseudophragmites . The Sanmenxia-Luoyang-Zhengzhou reservoir wetlands (YJW-MZC-HYK-HHT) in the middle reaches are influenced by the Sanmenxia and Xiaolangdi dams. Specifically, the reservoir’s water level changes periodically, forming a diverse set of reservoir-type wetlands, including rivers, beaches, and lakes, distributed in Sanmenxia, Luoyang, and Zhengzhou cities in Henan Province. This region has a temperate monsoon climate and a diverse plant community, including Phragmites communis , Typha orientalis , Populus simonii , and Tamarix chinensis . Additionally, the Sanmenxia wetlands are located on the central migration route of the whooper swan, making them an important habitat and water conservation area for this species. The estuarine delta lower-reach wetlands are located in Kenli County, Shandong Province, and have a temperate semi-humid continental monsoon climate. These wetlands are representative of unique estuarine wetlands formed by the interaction between terrestrial and aquatic ecosystems, as well as between riverine wetlands and marine wetlands. Subjected to seawater intrusion and excessive land development, the soils of these wetlands are characterized by saline-alkaline conditions(Genua-Olmedo, Ana et al. 2016). There is severe vegetation degradation(Fan, XM et al. 2011), and the predominant vegetation species are Phragmites communis , Suaeda glauca , and Tamaris chinensis . 2.2 Sample collection and analysis In this study, we selected six typical sampling plots in the upper, middle, and lower reaches of the Yellow River based on the spatial distribution of wetlands. In the upper reaches, the THW sampling plot (106°37′E, 38°47′N) was selected. The sampling sites (THW-YL, THW-LW, and THW-CD) were covered by vegetation, and Populus spp. , phragmites communis , and grasses were the dominant species in these three sites, respectively. Site THW-GD was subjected to anthropogenic tilling and was considered to be a bare soil site. In the middle reaches, three sampling plots were selected in Sanmenxia, Luoyang, and Zhengzhou. (1) The Yangjiawan sampling plot in Sanmenxia (110°43′E, 34°37′N) included three sampling sites (YJW-LW, YJW-XP, and YJW-LS). These plots had vegetation cover, and the dominant species in these three plots were phragmites communis , Typha orientalis , and willows, respectively. (2) The Mengjin sampling plot in Luoyang (112°36′E, 35°07′N) included two sampling sites (MZC-LW and MZC-XP) with vegetation cover. The dominant species in these two sites were phragmites communis and Typha orientalis . There was also one sampling site (MZC-GT) without vegetation cover. (3) The Huayuankou sampling plot (HYK, 113°66′E, 34°89′N) and the Huanghetan sampling plot (HHT, 114°07′E, 35°11′N) in Zhengzhou both contained phragmites communis as the predominant vegetation species. In the lower reaches, two sampling plots were selected in the estuarine delta wetlands in Dongying (119°8′E, 37°44′N) Plot SJZ-ND was located in a bird activity area, and plot SJZ-XH was located in an artificial lake area used for crab cultivation. In both sampling sites, phragmites communis was the predominant vegetation species. In each sampling plot, 2–5 sampling sites were selected based on the functionality and plant type of the sampling plot. At each sampling site, parallel soil samples were collected from three points, and five surface sediment samples (0–25 cm) were collected from each point. The soil samples were sealed in plastic bags and transported to the laboratory. Then, they were air-dried and animal tissues, plant detritus, and stones were removed. The sample was ground into powder using a GM-S vibratory grinder, passed through a 200-mesh sieve, and stored under cool dry conditions for later use. The soil moisture content was determined via gravimetry. The pH was measured using a pH meter (soil to water ratio of 1:2.5). The total organic carbon (TOC) content was analyzed using an elemental analyzer (Elementar, vario MACRO cube). The As, Cd, Cu, Cr, Mn, Ni, Pb, Sb, and Zn contents of the soil and plant samples were determined via inductively coupled plasma mass spectrometry (ICP-MS), and the Al content was determined via inductively coupled plasma optical emission spectrometry (ICP-OES). 2.3 Ecological risk evaluation of heavy metals 2 . 3 . 1 Geo accumulation index analysis The geoaccumulation Index ( I geo ) is a metric for quantitatively measuring the contamination levels of heavy metals based on their environmental background contents in sediments(Han, Cheng et al. 2017 ). The formula is as follows: $${I}_{geo}={\text{l}\text{o}\text{g}}_{2}\left(\frac{{C}_{I}}{1.5{C}_{Bi}}\right),$$ where C i and C Bi are the measured and environmental background contents of heavy metal i , respectively. In this study, the background contents of the metals in the soils in the upper, middle, and lower reaches of the Yellow River were taken as those in the Ningxia sectio(China National Environmental Monitoring Centre 1990 ), Henan section(Qi 2009 ), and Shandong section of the Yellow River(Xugui, Jierui et al. 2018), respectively. The classification based on the I geo value is described in Table 1 . Table 1 Methods and grading standards for the evaluation of heavy metal pollution used in this study I geo Level CF Level PLI Level EF Level of enrichment ≤ 0 Not polluted < 1 Low pollution ≤ 0.7 Excellent 0.5–1.5 No 0–1 Slightly polluted 0.7–1.0 Clean 1.5–3 Minor 1–2 Partial moderately polluted 1–3 Moderate pollution 1.0–2.0 Slightly polluted 3–5 Moderate 2–3 Moderately polluted 2.0–3.0 Moderately polluted 5–10 Moderate-severe 3–4 Partial severely polluted 3–6 Considerable pollution > 3.0 Heavily polluted 10–25 Severe 4–5 Severely polluted 25–50 Very severe > 5 Extremely polluted > 6 Very high pollution > 50 Extremely severe 2 . 3 . 2 Pollution load index analysis The pollution load index ( PLI ) is a metric used to assess the contamination load of heavy metals in a study area based on the contamination factor ( CF ) of each heavy metal(Bedaiwi, Wysong et al. 2022 ). The calculation formulas are as follows: CF i (Hakanson 1980 )= \(\frac{{C}_{i sample}}{{C}_{o baseline}}\) , PLI i (Wu, Yang et al. 2019 )= \(\sqrt[n]{{CF}_{1}\times {CF}_{2}\times \cdots {CF}_{n}}.\) The background contents of the soil elements in different reaches of the Yellow River are the same as those mentioned in Section 2.3.1 . The classification based on the PLI values is described in Table 1 . 2.3.3 Enrichment factor The enrichment factor ( EF ) is a metric that is widely used for assessing heavy metal contamination levels and distinguishing potential sources of heavy metals (anthropogenic vs natural sources)(Rule 1986 , Roussiez, Ludwig et al. 2005 , Xia, Meng et al. 2012 ). The EF of a specific metal in a sample is determined by calculating the ratio of the measured content of the metal to that of a reference element, which is Al in this study, and then by dividing this ratio by the baseline. The equation is as follows: $$EF=\frac{\left(\frac{{C}_{x}}{{C}_{\text{A}\text{l}}}\right)\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}}{\left(\frac{{C}_{x}}{{C}_{\text{A}\text{l}}}\right)\text{b}\text{a}\text{s}\text{e}\text{l}\text{i}\text{n}\text{e}},$$ where C x and C Al are the contents of heavy metal x and Al, respectively(Christoforidis and Stamatis 2009 ). In this study, the background contents of the elements in the soil in the different reaches are the same as those mentioned in Section 2.3.1 . The classification based on the EF value is described in Table 1 . 2.4 Statistical analysis The relationships between the physicochemical properties of the wetlands and the contents of various heavy metals were assessed using Pearson correlation analysis in order to identify the influencing factors of the heavy metal distribution and the sources of the heavy metal contamination. The data were processed using Excel and SPSS. 3 Results and discussion 3.1 Physical and chemical properties of the soil profile and total heavy metal content in the Yellow River wetlands The physicochemical properties and total heavy metal contents of the soils in the Yellow River wetlands are presented in Table 2 . The soil pH values of the Tianhe Bay wetlands in Ningxia in the upper reaches of the Yellow River ranged from 7.63 to 8.54, with an average value of 8.12, indicating overall alkaline conditions. The total organic carbon (TOC) content of the soils varied between 0.25% and 0.57%. The soil moisture content ranged from 20.43–33.73% and decreased with increasing soil depth. The lowest TOC content and the highest moisture content were observed at sampling site NX-GD. The highest moisture content was caused by the fact that this site had been freshly tilled by human activities and exhibited evident signs of watering. The lowest TOC content was attributed to the absence of surface vegetation at the site, which led to the plant roots being the main source of the soil TOC(Guo and Gifford 2002 ). Table 2 Physical and chemical properties of soil profiles in wetlands in the upper reaches of the Yellow River Upper reaches Middle reaches Lower reaches THW-LW THW-CD THW-GD THW-YL YJW-LS YJW-LW YJW-XP MZC-LW MZC-XP MZC-GT HYK HHT SJZ-ND SJZ-YL SJZ-XH ph 7.63 8.54 8.27 8.05 7.81 7.95 7.90 7.45 7.57 7.77 7.89 7.74 7.95 8.19 7.98 TOC (%) 0.41 0.35 0.25 0.57 0.23 0.12 0.22 0.63 0.40 0.15 0.17 0.20 0.57 0.90 0.13 MC (%) 26.42 20.43 33.73 27.61 26.82 23.25 23.30 32.32 31.24 26.14 20.21 20.02 27.61 32.12 19.89 The soil pH values of the wetlands in Henan in the middle reaches of the Yellow River ranged from 7.44 to 7.95, with an average of 7.74, indicating mildly alkaline conditions. The soil TOC content ranged from 0.12–0.23% in the Sanmenxia reservoir wetlands (three sampling sites YJW LS–XP), ranged from 0.15–0.63% in the Luoyang reservoir wetlands (three sampling sites MZC LW–GT), and was 0.17% and 0.20% at two sampling sites (HYK and HHT) in the Zhengzhou reservoir wetlands, respectively. Among these, the MZC-GT sample site, which did not have surface vegetation, had the lowest soil TOC content among the sampling sites in the Luoyang reservoir wetlands, further confirming the positive correlation between the quantity of surface vegetation and the soil TOC content. Among the wetland sampling sites in the Henan section of the Yellow River, MZC-LW and MZC-XP had notably higher soil moisture contents than the other sites and had the highest soil TOC contents. This may have been due to the greater soil pore water at these two sites caused by the high-moisture conditions, which enhanced the transport of the dissolved organic carbon (DOC) and particulate organic carbon (POC) in the soils(A, A et al. 2018). The soil pH values of the delta wetlands in the lower reaches of the Yellow River ranged from 7.95 to 8.19, with an average of 8.04, indicating overall alkaline conditions. The soil TOC content ranged from 0.13–0.90%, and the soil moisture content ranged from 19.89–32.12%. 3.2 Vertical distribution of heavy metals The statistics of the contents of the various heavy metals across the different sampling sites in the Yellow River Basin are presented in Table 3 . The following observations were made. (1) In the upper reaches of the Yellow River, except for Pb, the contents (in mg·kg − 1 ) of all of the heavy metals exceeded the environmental background values. The contents were as follows: As (16.69), Cd (0.27), Cr (81.95), Cu (26.31), Mn (838.52), Ni (33.38), Pb (19.90), Sb (1.51), and Zn (72.36). (2) In the middle reaches of the Yellow River, except for Cu, Ni, Pb, and Zn, the contents (in mg·kg − 1 ) of the heavy metals exceeded the environmental background values. The contents were as follows: As (11.47), Cd (0.20), Cr (69.59), Cu (19.49), Mn (609.69), Ni (27.07), Pb (18.44), Sb (1.28), and Zn (55.26). (3) In the lower reaches of the Yellow River, except for Cu, Ni, and Pb, the contents (in mg·kg − 1 ) of the heavy metals exceeded the environmental background values. The contents were as follows: As (12.89), Cd (0.16), Cr (71.60), Cu (20.55), Mn (702.46), Ni (28.05), Pb (19.18), Sb (1.32), and Zn (60.69). The results also revealed that the contents of the heavy metals in the alkaline wetland soils in the upper and lower reaches of the Yellow River were higher than those in the mildly alkaline wetland soils in the middle reaches, reflecting the positive correlation between the soil pH and the amount of heavy metal adsorption. This positive correlation is attributed to the fact that alkaline conditions are favorable for the precipitation and stabilization of heavy metals(Wu, Hu et al. 2015 ). There is no significant pattern in the spatial distributions of the heavy metals across the various locations in the Yellow River Basin. Table 3 Heavy metal contents at various sampling sites in the Yellow River Basin Site As (mg·kg − 1 ) Cd (mg·kg − 1 ) Cr (mg·kg − 1 ) Cu (mg·kg − 1 ) Mn (mg·kg − 1 ) Ni (mg·kg − 1 ) Pb (mg·kg − 1 ) Sb (mg·kg − 1 ) Zn (mg·kg − 1 ) Upper reaches THW-YL 16.39 0.26 82.49 25.68 834.83 33.57 19.62 1.50 71.31 THW-LW 18.66 0.32 84.80 29.14 899.89 36.96 22.33 1.61 79.87 THW-GD 13.79 0.22 77.28 21.64 714.53 28.40 17.18 1.32 60.96 THW-CD 17.91 0.28 83.23 28.78 904.81 34.60 20.45 1.59 77.28 Average value 16.69 0.27 81.95 26.31 838.52 33.38 19.90 1.51 72.36 Background value 12.2 0.07 62.7 22.10 497 21.70 20.06 1.18 58.8 Middle reaches YJW-LS 11.80 0.13 69.00 18.15 602.83 27.94 15.85 1.26 51.50 YJW-XP 10.85 0.12 67.78 16.47 552.28 25.78 14.60 1.22 47.18 YJW-LW 11.52 0.14 67.62 18.40 549.23 26.92 15.49 1.26 52.22 MZC-LW 11.83 0.34 66.39 22.38 767.82 26.90 24.26 1.43 67.46 MZC-XP 10.95 0.25 86.19 21.72 564.29 30.64 20.08 1.30 51.91 MZC-GT 8.46 0.18 56.12 15.03 489.72 21.76 19.11 1.00 52.14 HYK 11.73 0.20 69.76 19.32 610.14 25.62 17.68 1.26 53.13 HHT 14.62 0.22 73.83 24.45 741.20 31.00 20.41 1.45 66.54 Average value 11.47 0.20 69.59 19.49 609.69 27.07 18.44 1.28 55.26 Background value 10.86 0.11 67.03 21.37 583.48 28.64 20.19 0.93 60.22 Lower reaches SJZ-ND 15.56 0.17 76.60 26.00 870.86 33.71 21.29 1.45 70.28 SJZ-YL 12.85 0.19 70.64 22.14 674.21 29.80 21.47 1.36 71.05 SJZ-XH 10.27 0.12 67.56 13.52 562.32 20.63 14.77 1.16 40.75 Average value 12.89 0.16 71.60 20.55 702.46 28.05 19.18 1.32 60.69 Background value 8.70 0.14 64.2 24.2 590 28.3 25.2 0.76 66.6 The vertical distribution characteristics of the heavy metals in the wetlands in the Yellow River Basin are shown in Figs. 2 – 4 . The heavy metal contents in the soil profiles of each sampling site generally decreased with increasing soil depth. This trend is attributed to the presence of humus, which is a product of plant decomposition, accumulates in the surface layer of wetland soils, and subsequently absorbs heavy metals, forming aggregates and leading to higher contents of heavy metals in the surface soil compared to the deeper layers(Jin, Ruhai et al. 2016 ). In the upper reaches of the Yellow River, at sampling site THW, the contents of the heavy metals, except for Cr, generally exhibited the following order: NX-LW > NX-CD > NX-YL > NX-GD (Fig. 2 ). The vertical variations in the heavy metal contents in the soil profile at NX-GD were notable, indicating that the anthropogenic tilling significantly altered the soil layer structure at this site. At sampling site NX-YL, the heavy metal contents increased at depths of 15–20 cm, and then, they decreased with increasing depth. The elevated contents at depths of 15–20 cm were attributed to the presence of a large amount of plant roots in this depth interval, which adsorbed heavy metals, thus promoting the accumulation of metals. At sampling sites NX-LW and NX-CD, the heavy metal contents linearly decreased with increasing soil depth, indicating that the natural migration and distribution processes of the heavy metals in vertical soil profiles at these two sites were not significantly disturbed by human activities. The vertical distribution characteristics of the heavy metals in the wetland sampling sites in Henan in the middle reaches of the Yellow River are shown in Fig. 3 . It ca be seen that compared to the other wetlands, the three sampling sites in sampling plot YJW in the Sanmenxia reservoir wetlands were all covered by vegetation, and the heavy metal contents did not exhibit significant vertical variations, indicating that this area was less affected by anthropogenic activities. In the Luoyang reservoir wetlands, sampling site MZC-GT (covered by bare soil) had markedly lower heavy metal contents and soil TOC contents than those of sites MZC-LW (covered by Phragmites communis ) and MZC-XP (covered by Typha orientalis ), confirming the strong adsorption effect of the plant roots and soil TOC on the heavy metals in the soil. In the Zhengzhou reservoir wetlands, sampling site ZZ-HYK had slightly lower heavy metal contents at depths of0–15 cm depth compared to site ZZ-HHT, and the heavy metals exhibited aggregation at depths of 5–10 cm depth. This was likely due to adsorption by plant roots. The heavy metal contents at site ZZ-HHT generally decreased with increasing soil depth and exhibited marked variations at depths of 0–15 cm, indicating a strong anthropogenic influence at this site. For sampling plot SJZ in the lower reaches of the Yellow River, the sampling sites varied markedly in terms of the sources of the materials and their physicochemical properties, leading to both similarities and differences in the vertical distribution characteristics of the heavy metal elements. Except for Cr, the contents of the heavy metals generally exhibited the following order: SJZ-ND > SJZ-LL > SJZ-XH (Fig. 4 ). Site SJZ-ND was located in an area where many birds, including the red-crowned crane ( Grus japonensis ) and the scaly-sided merganser ( Mergus squamatus ), engage in feeding and resting activities, which release heavy metals that have previously accumulated at this site(A, B et al. 2017). However, a previous study has shown that the deposition of bird feces can increase the contents of heavy metals in soil(De, La et al. 2018). At site SJZ-LL, except for Cd, Mn, and Zn, the contents of the heavy metals did not change significantly with increasing soil depth, indicating minimal anthropogenic impact. At site SJZ-XH, which was in an lake area used for crab cultivation, the contents of As, Ni, Sb, and Zn peaked in the middle soil layer and were significantly different than those at the other depths. This was likely due to changes in the hydrological conditions of the wetland caused by frequent changes in the lake water level. In marked contrast to the other heavy metals, Cr exhibited peak contents at depths of 0–5 cm and 20–25 cm. The underlying cause of this pattern still needs to be identified. 3.3 I geo analysis The I geo values are presented in Table 4 . In the Tianhe Bay wetlands in the upper reaches of the Yellow River, the I geo values of Cd, Mn, and Ni were 0.71, 0.17, and 0.04, respectively, indicating mild contamination, while the other heavy metals were assessed to be clean in terms of their contamination risk. In the wetlands of the middle reaches of the Yellow River, the I geo value of Cd was 0.28, also indicating mild contamination, while the other heavy metals were assessed to be clean. In particular, the Sanmenxia reservoir wetlands were ecologically healthy, and all of the heavy metals were assessed to be healthy. In the Luoyang and Zhengzhou reservoir wetlands, the I geo values of Cd were 0–1, indicating mild contamination. Additionally, in the Luoyang reservoir wetlands, there was mild Pb contamination at all of the sampling sites. In the Zhengzhou reservoir wetlands, mild Sb contamination only occurred at sampling site HYK. In the delta wetlands of the Yellow River, the I geo value of Sb was 0.21, while the I geo values of the other heavy metals were less than zero. Table 4 Geoaccumulation index ( I geo ) values at various sampling sites in the Yellow River wetlands Site I geo As Cd Cr Cu Mn Ni Pb Sb Zn Upper reaches THW −0.13 0.71 −0.20 −0.34 0.17 0.04 −0.64 −0.23 −0.29 Middle reaches YJW −0.52 −0.36 −0.56 −0.86 −0.59 −0.68 −0.85 −0.16 −0.84 MZC −0.65 0.64 −0.53 −0.70 −0.53 −0.70 0.04 −0.17 −0.66 HHT −0.47 0.28 −0.53 −0.73 −0.52 −0.75 −0.78 −0.15 −0.77 HYK −0.16 0.39 −0.54 −0.39 −0.24 −0.47 −0.57 0.06 −0.44 Average −0.44 0.28 −0.52 −0.66 −0.46 −0.64 −0.49 −0.10 −0.67 Estuary SJZ −0.02 −0.37 −0.43 −0.82 −0.33 −0.60 −0.98 0.21 −0.67 3.4 PLI analysis The PLI values of the heavy metals at the various sampling sites are presented in Table 5 . It can be seen that except for the Sanmenxia wetlands in the middle reaches of the Yellow River, the heavy metals at the other sampling sites were in a state of mild contamination. In the upper reaches of the Yellow River, it was found that there was heavy Cd contamination, mild Pb contamination, and moderate contamination of the other heavy metals. Among the wetland sampling sites in the middle reaches of the Yellow River, Cd had the highest CF value. There was heavy Cd contamination at sampling site MZC-LW in the Luoyang wetlands and moderate Cd contamination at the other sampling sites. The CF values of all of the heavy metals were lower at site MZC-GT than at the other sampling sites, indicating that plants may have a certain enrichment effect on heavy metals. In the Zhengzhou wetlands, the CF value was lower at site HYK than at site HHT, but both values were greater than 1, indicating mild contamination. In the delta lower-reaches wetlands of the Yellow River, sampling site SJZ-XH did not have heavy metal contamination, while the other two sampling sites (SJZ-ND and SJZ-YL) were classified as mildly contaminated. Specifically, except for Pb ( CF value of <!) at site SJZ-ND and for Cu and Pb ( CF valeus of < 1) at site SJZ-YL, all of the heavy metals had CF values of greater than 1, indicating a high level of pollution. Table 5 Pollution load index ( PLI ) values at various sampling sites in the Yellow River wetlands Site CF PLI As Cd Cr Cu Mn Ni Pb Sb Zn Upper reaches THW-LW 1.62 4.63 1.35 1.32 1.81 1.70 1.08 1.36 1.36 1.63 THW-CD 1.56 3.94 1.33 1.27 1.82 1.59 0.99 1.35 1.31 1.55 THW-GD 1.20 3.10 1.23 0.98 1.44 1.31 0.83 1.12 1.04 1.26 THW-YL 1.43 3.76 1.32 1.16 1.68 1.55 0.95 1.27 1.21 1.47 Middle reaches YJW-LS 1.09 1.23 1.03 0.85 1.03 0.98 0.78 1.36 0.86 1.01 YJW-XP 1.00 1.08 1.01 0.77 0.95 0.90 0.72 1.32 0.78 0.93 YJW-LW 1.06 1.20 1.01 0.86 1.02 0.94 0.77 1.35 0.87 0.99 MZC-LW 1.09 3.06 0.99 1.05 1.32 0.94 1.20 1.54 1.12 1.27 MZC-XP 1.01 2.28 1.29 1.02 0.97 1.07 0.99 1.39 0.86 1.16 MZC-GT 0.78 1.65 0.84 0.70 0.84 0.76 0.95 1.08 0.87 0.91 HYK 1.08 1.82 1.04 0.90 1.05 0.89 0.88 1.35 0.88 1.07 HHT 1.35 1.96 1.10 1.14 1.27 1.08 1.01 1.56 1.10 1.26 Lower reaches SJZ-ND 1.75 1.24 1.19 1.07 1.48 1.19 0.84 1.90 1.09 1.27 SJZ-YL 1.44 1.34 1.10 0.91 1.14 1.05 0.85 1.79 1.10 1.16 SJZ-XH 1.15 0.89 1.05 0.56 0.95 0.73 0.59 1.53 0.63 0.85 3.5 EF analysis The EF indicates the degree of enrichment of a heavy metal in a location and also reflects the main sources of the heavy metal. An EF value of 1 suggests that the heavy metal originates from crustal activities such as rock weathering. An EF value of > 1 indicates that the heavy metal came from non-crustal activities, such as pollutant emissions and biological activities. As shown in Table 6 , in sampling plots THW and MZC, which included both bare soil sites and vegetated sites, the EF values of the heavy metals were lower at the bare soil sites than at the vegetated sites, indicating that the local plants had a positive effect on the accumulation of heavy metals. In the upper reaches of the Yellow River, except for Mn and Sb, the EF values of the heavy metals at the different sampling sites exhibited the following order: THW-LW > THW-CD > THW-YL > THW-GD. The EF values of Pb at all of the sampling sites were approximately 1, indicating that the Pb in the THW area primarily originated from crustal activities such as rock weathering, and anthropogenic activities exerted little influence. The EF values of the other heavy metals were all greater than 1, suggesting that the enrichment of these metals may have been influenced by human activities. Moreover, the EF values of Cd were 3–5, indicating moderate enrichment, while those of the other heavy metals were classified as mildly enriched. Table 6 Enrichment factor ( EF ) values of heavy metals at various sampling sites in the Yellow River wetlands Site EF As Cd Cr Cu Mn Ni Pb Sb Zn Upper reaches THW-LW 1.43 4.32 1.26 1.23 1.69 1.59 1.01 1.27 1.27 THW-CD 1.42 3.80 1.28 1.23 1.76 1.54 0.96 1.30 1.27 THW-GD 1.17 3.21 1.28 1.01 1.49 1.35 0.86 1.16 1.07 THW-YL 1.29 3.62 1.27 1.12 1.62 1.49 0.92 1.23 1.17 Middle reaches YJW-LS 1.23 1.39 1.17 0.95 1.17 1.11 0.89 1.54 0.97 YJW-LW 1.21 1.37 1.15 0.91 1.17 1.08 0.88 1.55 0.99 YJW-XP 1.18 1.27 1.19 0.97 1.12 1.06 0.85 1.55 0.92 MZC-LW 1.21 3.41 1.10 0.99 1.47 1.05 1.34 1.71 1.25 MZC-XP 1.15 2.60 1.46 1.17 1.10 1.22 1.13 1.59 0.98 MZC-GT 0.91 1.92 0.97 1.16 0.98 0.88 1.10 1.26 1.01 ZZ-HYK 1.29 2.18 1.25 0.82 1.25 1.07 1.05 1.62 1.06 ZZ-HHT 1.46 2.12 1.19 1.08 1.38 1.17 1.10 1.69 1.20 Lower reaches SJZ-ND 1.86 1.29 1.23 1.11 1.53 1.24 0.88 1.97 1.13 SJZ-LL 1.64 1.50 1.22 1.02 1.27 1.17 0.95 1.99 1.22 SJZ-XH 1.50 1.13 1.33 0.71 1.21 0.92 0.74 1.94 0.78 In sampling plots YJW and MZ in the middle reaches of the Yellow River, the EF values of As, Ni, Pb, and Zn were higher at the sites covered by Phragmites communis (i.e., the sites with LW in their names) than at the sites covered by Typha orientalis (i.e., the sites with XP in their names), indicating that Typha orientalis had a mildly stronger capacity to enrich these four heavy metals compared to Phragmites communis . This is consistent with the findings of Chen et al.(Chen,Ning et al.2020). The sampling sites in plot YJW did not exhibit Cu, Ni, Pb, or Zn enrichment, and the EF values of the remaining heavy metals exhibited the following order: Sb > Cd > As > Cr > Mn. Among them, As and Sb, such as at the sampling sites in plots MZC and ZZ, exhibited more-than-mild enrichment based on the fact that their EF values ranked in the top three, indicating that the Henan section of the Yellow River Basin may be subjected to varying degrees of As and Sb pollution. In the lower reaches of the Yellow River, sampling site in plot SJZ did not exhibit metal enrichment or it exhibited only mild enrichment, and the EF values of As, Cu, Mn, and Ni exhibited the following order: SJZ-ND > SJZ-LL > SJZ-XH. Cu, Ni, Pb, and Zn were not enriched at site SJZ-XH, while Cu, Ni, and Z exhibited mild enrichment at sites SJZ-ND and SJZ-LL. This suggests that the degree of metal enrichment in these wetland soils is related to the hydrodynamic forces in the wetlands, under which the heavy metals in the soils may migrate with the water flow toward the center of the lake. 3.6 Correlation analysis Elements with similar chemical properties generally tend to cluster and coexist under the same or similar geological conditions(Zhang,Luo et al.2022). Therefore, correlation analysis of the heavy metals within the same research area can help to determine whether they share a common source༈Li,Zhang et al.2013). The physicochemical properties of the soils and their correlation coefficients with the heavy metals across the sampling sites in the Yellow River Basin are shown in Fig. 5 , except for the sampling sites in the Zhengzhou reservoir (HYK-HHT), which were too few to conduct correlation analysis. Except for Cr, there were positive correlations among the heavy metals across the sampling sites in plot JYW, which was in the Sanmenxia reservoir wetlands in the middle reaches of the Yellow River. There were positive correlations between the pH and heavy metals at the sampling sites in plot THW (in the upper reaches of the Yellow River) and in sampling plot MZC (in the lower reaches) where the soils were largely alkaline. In contrast, there were negative correlations between the pH and heavy metals at the sampling sites in plot JYW (in the Sanmenxia reservoir wetlands) and plot MZC (in the Luoyang reservoir wetlands), both of which were located in the middle reaches of the Yellow River and largely contained neutral soils. This was because low-pH soils contain a large amount of H + ions, which cause heavy metals to desorbed and become more reactive, making it difficult for them to cluster(Hu,Shen et al.2020). Due to its high cation exchange capacity and the presence of numerous different functional groups, the TOC can adsorb metal elements through surface precipitation, complexation, and ion exchange, thereby promoting the enrichment of heavy metals. A higher soil moisture content also facilitates the migration of heavy metals to lower-lying areas in wetlands. The soil moisture content at the sampling sites in plot THW exhibited negative correlations with the heavy metals. This was mainly due to the application of artificial watering at sampling site THW-GD, which led to higher migration and the loss of heavy metals from the soil. Both sampling plots THW (in the upper reaches of the Yellow River) and SZJ (in the lower reaches) had correlation coefficients of > 0.8 among the heavy metals, suggesting that the heavy metals may have originated from the same source(Bai, Xiao et al. 2011 ). Sampling plot YJW, located in the Henan section of the middle reaches of the Yellow River, exhibited strong correlations between As vs Cr, Ni, and Pb, a strong correlation between Cr and Pb, a moderate correlation between Cr and Mn, and strong correlations between Cu and Mn, Ni, Sb, and Zn. At sampling plot MZC, Cr exhibited weak correlations with the other heavy metals and no enrichment to mild enrichment, indicating that the primary source of the Cr in this area may be crustal activity instead of human activities. Conversely, Cd was strongly correlated with Cu, Ni, and Sb and exhibited mild to moderate enrichment, suggesting that all four of these metals may originate from human activities. 4 Conclusions In this study, we analyzed the contents, vertical distribution characteristics, and ecological risks of nine heavy metals in the surface sediments of different types of wetlands in the upper, middle, and lower reaches of the Yellow River. It was found that the heavy metals did not exhibit significant spatial patterns across the wetlands in the Yellow River Basin. In the upper reaches of the Yellow River, except for Pb, the contents of the heavy metals exceeded the soil background values in Ningxia. In contrast, the Cu, Ni, Pb, and Zn contents of the wetland soils in the middle reaches and the Cu, Ni, and Pb contents of the wetland soils in the lower reaches were below the environmental background values. The Pb contents of the wetland soils at all of the sampling sites in the Yellow River Basin were lower than the environmental background values. In the delta wetlands in the Yellow River Basin, bird activities may have a positive effect on the enrichment of heavy metals. The contents of the various heavy metals at depths of 0–25 cm in the vertical soil profiles generally decreased with increasing soil depth. Enrichment factor analysis revealed that the plants had a positive effect on the enrichment of the heavy metals. Geoaccumulation index analysis and pollution load index analysis revealed that there were varying degrees of heavy metal contamination risks, except for the Sanmenxia reservoir wetlands. In the upper-reach wetlands and the Luoyang-Zhengzhou reservoir wetlands in the middle reaches, Cd was the primary pollutant, and it exhibited more than mild contamination. Enrichment factor analysis revealed that the heavy metals in the upper reaches were more influenced by human activities compared to the middle and lower reaches. Correlation analysis revealed the occurrence of strong correlations among the heavy metals (correlation coefficients > 0.8) in both the upper-reach wetlands and lower-reach wetlands, suggesting that the heavy metals may have originated from the same source. The results of this study provide survey information for the distribution pattern recognition and risk assessment of heavy metals in the Yellow River Basin. Declarations Ethical approval Not applicable. Funding This research was supported by the Sanmenxia City Science and Technology Tackling Project (2022002012)；Open Fund for Key Lab. of Land Degradation and Ecological Restoration in northwestern China of Ningxia University；（Grant number2023Q03）；Open Research Fund of State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Sciences (HKHA2022009) and Fundamental Research Funds for the Central Public-interest Scientific Institution (2022YSKY-03) Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The data used in this study will be available upon request. References Shan V , Singh S K , Haritash A K .(Shan, Singh et al. 2021)[J].Applied Water Science, 2021, 11(1).DOI:10.1007/s13201-020-01334-9. Wagner K I , Gallagher S K , Hayes M ,et al.Wetland Restoration in the New Millennium: Do Research Efforts Match Opportunities?[J].Restoration ecology, 2008, 16(3):p.367-372.DOI:10.1111/j.1526-100x.2008.00433.x. LI W, BU D, SUN J, et al. Distribution and ecological risk assessment of heavy metal elements in the surface sediments of Bagaxue wetlands in Lhasa [J]. Environmental Chemistry, 2021, 40(1): 195-203(in Chinese). Chen R , Chen H , Song L ,et al.Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils[J].Science of the Total Environment, 2019, 694.DOI:10.1016/j.scitotenv.2019.133819. Liu R , Men C , Liu Y ,et al.Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment[J].Marine Pollution Bulletin, 2016, 110(1):564-571.DOI:10.1016/j.marpolbul.2016.05.060. Ramos-Miras J J , Roca-Perez L ,M. Guzmán-Palomino,et al.Background levels and baseline values of available heavy metals in Mediterranean greenhouse soils (Spain)[J].Journal of Geochemical Exploration, 2011, 110(2):186-192.DOI:10.1016/j.gexplo.2011.05.009. Hoque R R , Goswami K G , Kusre B C ,et al.Distribution and solid-phase speciation of toxic heavy metals of bed sediments of Bharali tributary of Brahmaputra River[J].Environmental Monitoring & Assessment, 2011, 177(1-4):457-466.DOI:10.1007/s10661-010-1647-5. Harikumar P S , Nasir U P , Rahman M P M .Distribution of heavy metals in the core sediments of a tropical wetland system[J].International Journal of Environmental Science & Technology, 2009, 6(2):225-232.DOI:10.1007/BF03327626. Cui J , Zang S , Zhai D ,et al.Potential ecological risk of heavy metals and metalloid in the sediments of Wuyuer River basin, Heilongjiang Province, China[J].Ecotoxicology, 2014, 23(4):589-600.DOI:10.1007/s10646-014-1182-1. Sterckeman T , Douay F , Proix N ,et al.Vertical Distribution of Cd, Pb and Zn in Soils near Smelters in the North of France[J].Environmental Pollution, 2000, 107(3):377-389.DOI:10.1016/S0269-7491(99)00165-7. Guan Q .Vertical Distribution Characteristics and Source Tracing of Organic Carbon in Sediment of the Yellow River Wetland, Sanmenxia, China[J].Limnologica, 2022.DOI:10.1016/j.limno.2022.125960. Genua-Olmedo,Ana,Alcaraz,et al.Sea level rise impacts on rice production: The Ebro Delta as an example.[J].Science of the Total Environment, 2016.DOI:10.1016/j.scitotenv.2016.07.136. Fan,XM,Pedroli,et al.Potential plant species distribution in the Yellow River Delta under the influence of groundwater level and soil salinity[J].ECOHYDROLOGY, 2011, 2011,4(6)(-):744-756.DOI:10.1002/eco.164. Han D , Cheng J , Hu X ,et al.Spatial distribution, risk assessment and source identification of heavy metals in sediments of the Yangtze River Estuary, China[J].Marine Pollution Bulletin, 2017, 115(1-2):141-148.DOI:10.1016/j.marpolbul.2016.11.062. China National Environmental Monitoring Centre. Background values of soil elements in China[M]. BeiJing:China Environmental Science Press, 1990:330-381. Qi S .Study on Soil Background Value and Reference Value in Henan Section of Yellow River[J].Journal of Anhui Agricultural Sciences, 2009. Pang Xugui, Dai Jierui, Hu Xueping, Song Zhiyong, Yu Chao, Chen Lei, Zhang Huaping, Liu Huafeng, Wang Hongjin, Wang Zenghui, Zhao Xiqiang, Zeng Xiandong, Ren Wenkai. Background Values of Soil Geochemistry in Shandong Province [J]. ShangDong Land and Resources, 2018, 34(1):5.DOI:10.3969/j.issn.1672-6979.2018.01.005. (in Chinese) Bedaiwi A , Wysong A , Rogan E G ,et al.Arsenic Exposure and Melanoma Among US Adults Aged 20 or Older, 2003-2016:[J].Public Health Reports, 2022, 137(3):548-556.DOI:10.1177/00333549211008886. Hakanson L .An Ecological Risk Index for Aquatic Pollution Control – A Sedimentological Approach[J]. 1980.DOI:10.1016/0043-1354(80)90143-8. Wu H , Yang F , Li H ,et al.Heavy metal pollution and health risk assessment of agricultural soil near a smelter in an industrial city in China[J].International Journal of Environmental Health Research, 2019:1-13.DOI:10.1080/09603123.2019.1584666. Rule J H .Assessment of trace element geochemistry of Hampton Roads Harbor and lower Chesapeake Bay area sediments[J].Environmental Geology, 1986, 8(4):209-219.DOI:10.1007/BF02524948. Roussiez V , Ludwig W , Probst J L ,et al.Background levels of heavy metals in surficial sediments of the Gulf of Lions (NW Mediterranean): an approach based on 133Cs normalization and lead isotope measurements.[J].Environmental Pollution, 2005, 138(1):167-177.DOI:10.1016/j.envpol.2005.02.004. Xia P , Meng X , Feng A ,et al.[J].Environmental Earth Sciences, 2012, 66(5):1337-1344.DOI:10.1007/s12665-011-1343-y. Christoforidis A , Stamatis N .Heavy metal contamination in street dust and roadside soil along the major national road in Kavala's region, Greece[J].Geoderma, 2009, 151(3-4):257-263.DOI:10.1016/j.geoderma.2009.04.016. Guo L B , Gifford R M .Soil carbon stocks and land use change: a meta analysis[J].Global Change Biology, 2002, 8(4):345-360.DOI:10.1046/j.1354-1013.2002.00486.x. A Y X , A B L , B E P ,et al.Soil carbon storage in mangroves is primarily controlled by soil properties: A study at Dongzhai Bay, China[J].Science of The Total Environment, 2018, 619-620:1226-1235.DOI:10.1016/j.scitotenv.2017.11.187. Wu W , Hu Y , Guo Q ,et al.Sorption/desorption behavior of triclosan in sediment–water–rhamnolipid systems: Effects of pH, ionic strength, and DOM[J].Journal of Hazardous Materials, 2015, 297(oct.30):59-65.DOI:10.1016/j.jhazmat.2015.04.078. Jin Z , Ruhai L , Jiaxin J ,et al.Vertical distribution and speciation characteristics of heavy metals in wetlands soils of Ziyaxin River downstream[J].Environmental Chemistry, 2016. A J W , B R O , B D T W ,et al.Antimony and arsenic exhibit contrasting spatial distributions in the sediment and vegetation of a contaminated wetland - ScienceDirect[J].Chemosphere, 2017, 180:388-395.DOI:10.1016/j.chemosphere.2017.03.142. S,De,La,et al.Enrichment of trace elements in colonies of the yellow-legged gull (Larus michahellis) in the Atlantic Islands National Park (Galicia-NW Spain).[J].Science of the Total Environment, 2018.DOI:10.1016/j.scitotenv.2018.08.284. CHEN T, BAO N Y,DU C X,et al.Absorption Characteristics of Heavy Metals by Emergent Plants from Polluted River in Ecological Restoration Areas[J].Environmental Science Research,2020,33(09):2110-2117.DOI:10.13198/j.issn.1001-6929.2020.02.06.(in Chinese). ZHANG X R, LUO H, ZHU A N, et al. Study on the migration of heavy metals in natural soil profile of a dryland in Wuhan City [J].Resources Environment & Engineering,2022,36(03):274-279.DOI:10.16536/j.cnki.issn.1671-1211.2022.03.002.(in Chinese). LI J C,ZHANG H,SHI W. Concentrations of soil heavy metals and their spatial distribution in the surrounding area of Fenhe reservoir [J]. Environmental Science, 2013, 34(1): 116-120.DOI:10.13227/j.hjkx.2013.01.027.(in Chinese). HU Q Q, SHEN Q, CHEN F, et al.Reconstructed soil vertical profile heavy metal Cd occurrence and its influencing factors [J].Environmental Science, 2020, 41(6): 2878-2888..DOI:10.13227/j.hjkx.201911023.(in Chinese). Bai J , Xiao R , Cui B ,et al.Assessment of heavy metal pollution in wetland soils from the young and old reclaimed regions in the Pearl River Estuary, South China[J].Environmental Pollution, 2011, 159(3):817-824.DOI:10.1016/j.envpol.2010.11.004.. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4378030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300180010,"identity":"1fcee8a4-65d2-421b-954e-58ff4399358d","order_by":0,"name":"Yiqiao Zhou","email":"","orcid":"","institution":"Sanmenxia Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Yiqiao","middleName":"","lastName":"Zhou","suffix":""},{"id":300180011,"identity":"bbfc6579-419a-4bb7-a802-eefaae1d3018","order_by":1,"name":"Shuo Li","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Li","suffix":""},{"id":300180012,"identity":"e33d130e-c94d-4832-88a4-b8db2501352e","order_by":2,"name":"Fan Yang","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""},{"id":300180013,"identity":"fff0f28d-aa21-4c67-b585-4ba7cc91f4b1","order_by":3,"name":"Qingsong Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYNACAyidUFEjx8befoAELQ/OHDPm4zmTQLxljA9bmBPnSTgY4FUlH5H87MGbgjt289t7D79IbGBLb5NgSGD4UbENpxbDG2nmhnMMniVvOHMuzSJxh0xum3TjAcaeM7dxa5mRYCbNY3A42UAix8wg8QxbbpvMgQRmxjZ8WtK/gbXIzwBpaWNOZ5NIMMCrRR5oOEiLHcONHOMHQC0JBLUY8Lwpk5xjcDjB4MwZM4aEM8cM24CBfBCfX+Tb07dJvPlz2F6+vcf444+KGnn59vaDD35U4LHlAJDgYWBIbGBgYJOAiR7AqR5kSwNEiz2QYv6AT+UoGAWjYBSMXAAA9yVb9mE0VCEAAAAASUVORK5CYII=","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Qingsong","middleName":"","lastName":"Guan","suffix":""},{"id":300180014,"identity":"096ac735-9cbc-4334-96d8-7029cd945515","order_by":4,"name":"Ning Zhang","email":"","orcid":"","institution":"Sanmenxia Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-05-06 15:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4378030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4378030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56436359,"identity":"f55ff90d-b50f-4143-9ca9-f7fb218cef44","added_by":"auto","created_at":"2024-05-14 07:30:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":392955,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the spatial distribution of the sampling plots\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/778c789f14ea1ffa741a144e.png"},{"id":56435814,"identity":"fe2f3904-f83d-430b-9b79-0a7f6cc16d58","added_by":"auto","created_at":"2024-05-14 07:22:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":400242,"visible":true,"origin":"","legend":"\u003cp\u003eVertical variation characteristics of heavy metals at various points in the wetlands on the Ningxia Plain in the upper reaches of the Yellow River\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/678d82bcff8890e0a2788e92.png"},{"id":56435816,"identity":"4ae4c6fd-d452-426f-b7e3-3f289f8f0a6e","added_by":"auto","created_at":"2024-05-14 07:22:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":581019,"visible":true,"origin":"","legend":"\u003cp\u003eVertical distribution characteristics of heavy metals at various points in Henan in the middle reaches of the Yellow River\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/4cd634715aa5f3cd3b344e12.png"},{"id":56435818,"identity":"20a3afd3-ae3e-4466-950a-512326f19905","added_by":"auto","created_at":"2024-05-14 07:22:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":354099,"visible":true,"origin":"","legend":"\u003cp\u003eVertical distribution characteristics of heavy metals at various points in the wetlands in the lower Yellow River Delta\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/3338130bb8a64d67940321c0.png"},{"id":56436360,"identity":"6cc1edd0-efdf-4f9c-9578-fee8594a0dbb","added_by":"auto","created_at":"2024-05-14 07:30:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1572540,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients between different physicochemical properties and heavy metals in the Yellow River\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/df38803ec4564aeaca0704cf.png"},{"id":76921288,"identity":"50923a67-7f65-4772-a087-9e68518f3588","added_by":"auto","created_at":"2025-02-22 11:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5181765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4378030/v1/c4bf5225-f876-4794-a986-a76ab79c0bf4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distribution and Risk Assessment of Heavy Metals in the Wetlands in the Upper, Middle, and Lower Reaches of the Yellow River Basin: A Study Focusing on the Yellow River Delta, Henan Section and Ningxia Section","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWetlands are referred to as the kidneys of the Earth and are one of the most important natural habitats on Earth(Wagner, Gallagher et al. 2010, Shan, Singh et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to their low-lying topography, wetlands are readily subjected to constant material inputs transported by hydrodynamic forces, and thus, they become significant sinks for anthropogenically discharged heavy metals(Li, Bu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Heavy metals, as typical cumulative pollutants, are highly toxic and difficult to degrade, can cause the death of animals and plants, damage the food chain, and cause deterioration of the ecosystem, resulting in significant biological toxicity and persistent threats to the ecological environment(Xiaohui, Dongfang et al., Liu, Men et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Chen, Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, extensive studies have been conducted on the accumulation(Ramos-Miras, Roca-Perez et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), speciation(Hoque, Goswami et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), spatial distribution(Harikumar, Nasir et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and ecological risks(Cui, Zang et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) of heavy metals in surface soils in wetlands. It has been found that wetland soils currently exhibit varying degrees of heavy metal accumulation globally, with a trend towards increasing levels(Ramos-Miras, Roca-Perez et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSterckeman(Sterckeman, Douay et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) posited that the vertical migration characteristics of heavy metals in soils at different depths can serve as a straightforward indicator of the migration capacity of heavy metals and the status of soil contamination. Currently, research on the vertical migration characteristics of heavy metals has primarily focused on urban soils, farmlands, and industrial tailings, and less research has been conducted on the vertical distribution and migration characteristics of heavy metals in wetland soil profiles. Moreover, the complex ecological structure and unique hydrological characteristics of wetlands result in the formation of a variety of soil types, which affects the distribution of heavy metals in wetland soils. Therefore, studying the vertical distribution characteristics of heavy metals in wetland soils and their influencing factors is crucial for revealing the distribution and migration patterns of heavy metals in wetland soils.\u003c/p\u003e \u003cp\u003eThe Yellow River, which flows through nine provinces and is revered as China\u0026rsquo;s Mother River, is the country\u0026rsquo;s second longest river. The total area of the wetlands in the Yellow River Basin is approximately 2.8\u0026nbsp;million hectares, including riverine and floodplain wetlands. These wetlands can be categorized into eight distinct zones based on their regional distribution, including the wetlands in the source area of the Yellow River, the Ningxia Plain wetlands, the Sanmenxia Reservoir wetlands, the estuarine delta wetlands. Consequently, the ecological quality of the Yellow River\u0026rsquo;s wetlands is crucial to achieving high-quality development in the Yellow River Basin. The wetlands of the Yellow River are characterized by a low organic matter content(Guan \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)and a high degree of salinization, and excessive industrial development has led to severe heavy metal contamination in the Yellow River Basin. In this study, based on the spatial distribution of the wetlands in the Yellow River Basin, we selected typical wetlands in three regions, namely, the Ningxia Plain in the upper reaches, the Sanmenxia-Luoyang-Zhengzhou reservoir area in the middle reaches, and the estuarine delta in the lower reaches, to elucidate the vertical distribution characteristics and migration patterns of the heavy metals in the wetland soils in the Yellow River Basin. This study provides fundamental data and a scientific basis for ecological environmental protection of the Yellow River wetlands.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eIn this study, we focused on the wetlands in three typical regions: the Ningxia Plain in the upper reaches, the Sanmenxia-Luoyang-Zhengzhou reservoir area in the middle reaches, and the estuarine delta in the lower reaches of the Yellow River. The distribution of the sampling sites is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Tianhe Bay wetlands (THW) is located on the Ningxia Plain in the upper Yellow River and is under the administration of Pingluo County, Ningxia Hui Autonomous Region. The THW have a mid-temperate continental dry climate with little rainfall, and an annual average precipitation of about 200 mm. The vegetation type are limited, mainly consisting of \u003cem\u003eSalix matsudana\u003c/em\u003e, \u003cem\u003ePhragmites communis\u003c/em\u003e, and \u003cem\u003eCalamagrostis pseudophragmites\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Sanmenxia-Luoyang-Zhengzhou reservoir wetlands (YJW-MZC-HYK-HHT) in the middle reaches are influenced by the Sanmenxia and Xiaolangdi dams. Specifically, the reservoir\u0026rsquo;s water level changes periodically, forming a diverse set of reservoir-type wetlands, including rivers, beaches, and lakes, distributed in Sanmenxia, Luoyang, and Zhengzhou cities in Henan Province. This region has a temperate monsoon climate and a diverse plant community, including \u003cem\u003ePhragmites communis\u003c/em\u003e, \u003cem\u003eTypha orientalis\u003c/em\u003e, \u003cem\u003ePopulus simonii\u003c/em\u003e, and \u003cem\u003eTamarix chinensis\u003c/em\u003e. Additionally, the Sanmenxia wetlands are located on the central migration route of the whooper swan, making them an important habitat and water conservation area for this species.\u003c/p\u003e \u003cp\u003eThe estuarine delta lower-reach wetlands are located in Kenli County, Shandong Province, and have a temperate semi-humid continental monsoon climate. These wetlands are representative of unique estuarine wetlands formed by the interaction between terrestrial and aquatic ecosystems, as well as between riverine wetlands and marine wetlands. Subjected to seawater intrusion and excessive land development, the soils of these wetlands are characterized by saline-alkaline conditions(Genua-Olmedo, Ana et al. 2016). There is severe vegetation degradation(Fan, XM et al. 2011), and the predominant vegetation species are \u003cem\u003ePhragmites communis\u003c/em\u003e, \u003cem\u003eSuaeda glauca\u003c/em\u003e, and \u003cem\u003eTamaris chinensis\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection and analysis\u003c/h2\u003e \u003cp\u003eIn this study, we selected six typical sampling plots in the upper, middle, and lower reaches of the Yellow River based on the spatial distribution of wetlands. In the upper reaches, the THW sampling plot (106\u0026deg;37\u0026prime;E, 38\u0026deg;47\u0026prime;N) was selected. The sampling sites (THW-YL, THW-LW, and THW-CD) were covered by vegetation, and \u003cem\u003ePopulus spp.\u003c/em\u003e, \u003cem\u003ephragmites communis\u003c/em\u003e, and grasses were the dominant species in these three sites, respectively. Site THW-GD was subjected to anthropogenic tilling and was considered to be a bare soil site. In the middle reaches, three sampling plots were selected in Sanmenxia, Luoyang, and Zhengzhou. (1) The Yangjiawan sampling plot in Sanmenxia (110\u0026deg;43\u0026prime;E, 34\u0026deg;37\u0026prime;N) included three sampling sites (YJW-LW, YJW-XP, and YJW-LS). These plots had vegetation cover, and the dominant species in these three plots were \u003cem\u003ephragmites communis\u003c/em\u003e, \u003cem\u003eTypha orientalis\u003c/em\u003e, and willows, respectively. (2) The Mengjin sampling plot in Luoyang (112\u0026deg;36\u0026prime;E, 35\u0026deg;07\u0026prime;N) included two sampling sites (MZC-LW and MZC-XP) with vegetation cover. The dominant species in these two sites were \u003cem\u003ephragmites communis\u003c/em\u003e and \u003cem\u003eTypha orientalis\u003c/em\u003e. There was also one sampling site (MZC-GT) without vegetation cover. (3) The Huayuankou sampling plot (HYK, 113\u0026deg;66\u0026prime;E, 34\u0026deg;89\u0026prime;N) and the Huanghetan sampling plot (HHT, 114\u0026deg;07\u0026prime;E, 35\u0026deg;11\u0026prime;N) in Zhengzhou both contained \u003cem\u003ephragmites communis\u003c/em\u003e as the predominant vegetation species. In the lower reaches, two sampling plots were selected in the estuarine delta wetlands in Dongying (119\u0026deg;8\u0026prime;E, 37\u0026deg;44\u0026prime;N) Plot SJZ-ND was located in a bird activity area, and plot SJZ-XH was located in an artificial lake area used for crab cultivation. In both sampling sites, \u003cem\u003ephragmites communis\u003c/em\u003e was the predominant vegetation species.\u003c/p\u003e \u003cp\u003eIn each sampling plot, 2\u0026ndash;5 sampling sites were selected based on the functionality and plant type of the sampling plot. At each sampling site, parallel soil samples were collected from three points, and five surface sediment samples (0\u0026ndash;25 cm) were collected from each point. The soil samples were sealed in plastic bags and transported to the laboratory. Then, they were air-dried and animal tissues, plant detritus, and stones were removed. The sample was ground into powder using a GM-S vibratory grinder, passed through a 200-mesh sieve, and stored under cool dry conditions for later use.\u003c/p\u003e \u003cp\u003eThe soil moisture content was determined via gravimetry. The pH was measured using a pH meter (soil to water ratio of 1:2.5). The total organic carbon (TOC) content was analyzed using an elemental analyzer (Elementar, vario MACRO cube). The As, Cd, Cu, Cr, Mn, Ni, Pb, Sb, and Zn contents of the soil and plant samples were determined via inductively coupled plasma mass spectrometry (ICP-MS), and the Al content was determined via inductively coupled plasma optical emission spectrometry (ICP-OES).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ecological risk evaluation of heavy metals\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2\u003c/b\u003e.\u003cb\u003e3\u003c/b\u003e.\u003cb\u003e1\u003c/b\u003e Geo\u003cb\u003eaccumulation index analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe geoaccumulation Index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e) is a metric for quantitatively measuring the contamination levels of heavy metals based on their environmental background contents in sediments(Han, Cheng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${I}_{geo}={\\text{l}\\text{o}\\text{g}}_{2}\\left(\\frac{{C}_{I}}{1.5{C}_{Bi}}\\right),$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eBi\u003c/em\u003e\u003c/sub\u003e are the measured and environmental background contents of heavy metal \u003cem\u003ei\u003c/em\u003e, respectively. In this study, the background contents of the metals in the soils in the upper, middle, and lower reaches of the Yellow River were taken as those in the Ningxia sectio(China National Environmental Monitoring Centre \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), Henan section(Qi \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and Shandong section of the Yellow River(Xugui, Jierui et al. 2018), respectively. The classification based on the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e value is described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethods and grading standards for the evaluation of heavy metal pollution used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePLI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLevel of enrichment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLow pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlightly polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u0026ndash;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial moderately polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModerate pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u0026ndash;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlightly polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerately polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate-severe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial severely polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConsiderable pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHeavily polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverely polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVery severe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely polluted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery high pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExtremely severe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2\u003c/b\u003e.\u003cb\u003e3\u003c/b\u003e.\u003cb\u003e2\u003c/b\u003e Pollution load index analysis\u003c/h2\u003e \u003cp\u003eThe pollution load index (\u003cem\u003ePLI\u003c/em\u003e) is a metric used to assess the contamination load of heavy metals in a study area based on the contamination factor (\u003cem\u003eCF\u003c/em\u003e) of each heavy metal(Bedaiwi, Wysong et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The calculation formulas are as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eCF\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u003c/em\u003e \u003c/sub\u003e(Hakanson \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1980\u003c/span\u003e)=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{C}_{i sample}}{{C}_{o baseline}}\\)\u003c/span\u003e\u003c/span\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003ePLI\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u003c/em\u003e \u003c/sub\u003e(Wu, Yang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sqrt[n]{{CF}_{1}\\times {CF}_{2}\\times \\cdots {CF}_{n}}.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe background contents of the soil elements in different reaches of the Yellow River are the same as those mentioned in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.3.1\u003c/span\u003e. The classification based on the \u003cem\u003ePLI\u003c/em\u003e values is described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Enrichment factor\u003c/h2\u003e \u003cp\u003eThe enrichment factor (\u003cem\u003eEF\u003c/em\u003e) is a metric that is widely used for assessing heavy metal contamination levels and distinguishing potential sources of heavy metals (anthropogenic vs natural sources)(Rule \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, Roussiez, Ludwig et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Xia, Meng et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The \u003cem\u003eEF\u003c/em\u003e of a specific metal in a sample is determined by calculating the ratio of the measured content of the metal to that of a reference element, which is Al in this study, and then by dividing this ratio by the baseline. The equation is as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$EF=\\frac{\\left(\\frac{{C}_{x}}{{C}_{\\text{A}\\text{l}}}\\right)\\text{s}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}}{\\left(\\frac{{C}_{x}}{{C}_{\\text{A}\\text{l}}}\\right)\\text{b}\\text{a}\\text{s}\\text{e}\\text{l}\\text{i}\\text{n}\\text{e}},$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eAl\u003c/sub\u003e are the contents of heavy metal \u003cem\u003ex\u003c/em\u003e and Al, respectively(Christoforidis and Stamatis \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study, the background contents of the elements in the soil in the different reaches are the same as those mentioned in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.3.1\u003c/span\u003e. The classification based on the \u003cem\u003eEF\u003c/em\u003e value is described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe relationships between the physicochemical properties of the wetlands and the contents of various heavy metals were assessed using Pearson correlation analysis in order to identify the influencing factors of the heavy metal distribution and the sources of the heavy metal contamination. The data were processed using Excel and SPSS.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cp\u003e \u003cb\u003e3.1 Physical and chemical properties of the soil profile and total heavy metal content in the Yellow River wetlands\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe physicochemical properties and total heavy metal contents of the soils in the Yellow River wetlands are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The soil pH values of the Tianhe Bay wetlands in Ningxia in the upper reaches of the Yellow River ranged from 7.63 to 8.54, with an average value of 8.12, indicating overall alkaline conditions. The total organic carbon (TOC) content of the soils varied between 0.25% and 0.57%. The soil moisture content ranged from 20.43\u0026ndash;33.73% and decreased with increasing soil depth. The lowest TOC content and the highest moisture content were observed at sampling site NX-GD. The highest moisture content was caused by the fact that this site had been freshly tilled by human activities and exhibited evident signs of watering. The lowest TOC content was attributed to the absence of surface vegetation at the site, which led to the plant roots being the main source of the soil TOC(Guo and Gifford \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical properties of soil profiles in wetlands in the upper reaches of the Yellow River\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUpper reaches\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c13\" namest=\"c6\"\u003e \u003cp\u003eMiddle reaches\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eLower reaches\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-LW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTHW-CD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTHW-GD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTHW-YL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYJW-LS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYJW-LW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYJW-XP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMZC-LW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMZC-XP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMZC-GT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHYK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSJZ-ND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSJZ-YL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSJZ-XH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eph\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e20.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e27.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe soil pH values of the wetlands in Henan in the middle reaches of the Yellow River ranged from 7.44 to 7.95, with an average of 7.74, indicating mildly alkaline conditions. The soil TOC content ranged from 0.12\u0026ndash;0.23% in the Sanmenxia reservoir wetlands (three sampling sites YJW LS\u0026ndash;XP), ranged from 0.15\u0026ndash;0.63% in the Luoyang reservoir wetlands (three sampling sites MZC LW\u0026ndash;GT), and was 0.17% and 0.20% at two sampling sites (HYK and HHT) in the Zhengzhou reservoir wetlands, respectively. Among these, the MZC-GT sample site, which did not have surface vegetation, had the lowest soil TOC content among the sampling sites in the Luoyang reservoir wetlands, further confirming the positive correlation between the quantity of surface vegetation and the soil TOC content. Among the wetland sampling sites in the Henan section of the Yellow River, MZC-LW and MZC-XP had notably higher soil moisture contents than the other sites and had the highest soil TOC contents. This may have been due to the greater soil pore water at these two sites caused by the high-moisture conditions, which enhanced the transport of the dissolved organic carbon (DOC) and particulate organic carbon (POC) in the soils(A, A et al. 2018).\u003c/p\u003e \u003cp\u003eThe soil pH values of the delta wetlands in the lower reaches of the Yellow River ranged from 7.95 to 8.19, with an average of 8.04, indicating overall alkaline conditions. The soil TOC content ranged from 0.13\u0026ndash;0.90%, and the soil moisture content ranged from 19.89\u0026ndash;32.12%.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Vertical distribution of heavy metals\u003c/h2\u003e \u003cp\u003eThe statistics of the contents of the various heavy metals across the different sampling sites in the Yellow River Basin are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The following observations were made. (1) In the upper reaches of the Yellow River, except for Pb, the contents (in mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of all of the heavy metals exceeded the environmental background values. The contents were as follows: As (16.69), Cd (0.27), Cr (81.95), Cu (26.31), Mn (838.52), Ni (33.38), Pb (19.90), Sb (1.51), and Zn (72.36). (2) In the middle reaches of the Yellow River, except for Cu, Ni, Pb, and Zn, the contents (in mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the heavy metals exceeded the environmental background values. The contents were as follows: As (11.47), Cd (0.20), Cr (69.59), Cu (19.49), Mn (609.69), Ni (27.07), Pb (18.44), Sb (1.28), and Zn (55.26). (3) In the lower reaches of the Yellow River, except for Cu, Ni, and Pb, the contents (in mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the heavy metals exceeded the environmental background values. The contents were as follows: As (12.89), Cd (0.16), Cr (71.60), Cu (20.55), Mn (702.46), Ni (28.05), Pb (19.18), Sb (1.32), and Zn (60.69). The results also revealed that the contents of the heavy metals in the alkaline wetland soils in the upper and lower reaches of the Yellow River were higher than those in the mildly alkaline wetland soils in the middle reaches, reflecting the positive correlation between the soil pH and the amount of heavy metal adsorption. This positive correlation is attributed to the fact that alkaline conditions are favorable for the precipitation and stabilization of heavy metals(Wu, Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). There is no significant pattern in the spatial distributions of the heavy metals across the various locations in the Yellow River Basin.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy metal contents at various sampling sites in the Yellow River Basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCd (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCr (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCu (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMn (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNi (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSb (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUpper reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-YL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e834.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e899.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e79.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e714.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e904.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e77.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e16.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e81.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e26.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e838.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e33.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e19.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e72.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBackground value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e62.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e22.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e497\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e21.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e20.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e58.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eMiddle reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e602.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e552.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e549.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e767.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e564.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-GT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e489.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e610.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e741.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e66.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e69.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e19.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e609.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e27.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e55.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBackground value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e67.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e21.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e583.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e28.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e20.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e60.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLower reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-ND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e870.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-YL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e674.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-XH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e562.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e71.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e20.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e702.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e28.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e19.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e60.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBackground value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e64.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e24.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e590\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e28.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e25.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e66.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe vertical distribution characteristics of the heavy metals in the wetlands in the Yellow River Basin are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The heavy metal contents in the soil profiles of each sampling site generally decreased with increasing soil depth. This trend is attributed to the presence of humus, which is a product of plant decomposition, accumulates in the surface layer of wetland soils, and subsequently absorbs heavy metals, forming aggregates and leading to higher contents of heavy metals in the surface soil compared to the deeper layers(Jin, Ruhai et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the upper reaches of the Yellow River, at sampling site THW, the contents of the heavy metals, except for Cr, generally exhibited the following order: NX-LW\u0026thinsp;\u0026gt;\u0026thinsp;NX-CD\u0026thinsp;\u0026gt;\u0026thinsp;NX-YL\u0026thinsp;\u0026gt;\u0026thinsp;NX-GD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The vertical variations in the heavy metal contents in the soil profile at NX-GD were notable, indicating that the anthropogenic tilling significantly altered the soil layer structure at this site. At sampling site NX-YL, the heavy metal contents increased at depths of 15\u0026ndash;20 cm, and then, they decreased with increasing depth. The elevated contents at depths of 15\u0026ndash;20 cm were attributed to the presence of a large amount of plant roots in this depth interval, which adsorbed heavy metals, thus promoting the accumulation of metals. At sampling sites NX-LW and NX-CD, the heavy metal contents linearly decreased with increasing soil depth, indicating that the natural migration and distribution processes of the heavy metals in vertical soil profiles at these two sites were not significantly disturbed by human activities.\u003c/p\u003e \u003cp\u003eThe vertical distribution characteristics of the heavy metals in the wetland sampling sites in Henan in the middle reaches of the Yellow River are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It ca be seen that compared to the other wetlands, the three sampling sites in sampling plot YJW in the Sanmenxia reservoir wetlands were all covered by vegetation, and the heavy metal contents did not exhibit significant vertical variations, indicating that this area was less affected by anthropogenic activities. In the Luoyang reservoir wetlands, sampling site MZC-GT (covered by bare soil) had markedly lower heavy metal contents and soil TOC contents than those of sites MZC-LW (covered by \u003cem\u003ePhragmites communis\u003c/em\u003e) and MZC-XP (covered by \u003cem\u003eTypha orientalis\u003c/em\u003e), confirming the strong adsorption effect of the plant roots and soil TOC on the heavy metals in the soil. In the Zhengzhou reservoir wetlands, sampling site ZZ-HYK had slightly lower heavy metal contents at depths of0\u0026ndash;15 cm depth compared to site ZZ-HHT, and the heavy metals exhibited aggregation at depths of 5\u0026ndash;10 cm depth. This was likely due to adsorption by plant roots. The heavy metal contents at site ZZ-HHT generally decreased with increasing soil depth and exhibited marked variations at depths of 0\u0026ndash;15 cm, indicating a strong anthropogenic influence at this site.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor sampling plot SJZ in the lower reaches of the Yellow River, the sampling sites varied markedly in terms of the sources of the materials and their physicochemical properties, leading to both similarities and differences in the vertical distribution characteristics of the heavy metal elements. Except for Cr, the contents of the heavy metals generally exhibited the following order: SJZ-ND\u0026thinsp;\u0026gt;\u0026thinsp;SJZ-LL\u0026thinsp;\u0026gt;\u0026thinsp;SJZ-XH (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Site SJZ-ND was located in an area where many birds, including the red-crowned crane (\u003cem\u003eGrus japonensis\u003c/em\u003e) and the scaly-sided merganser (\u003cem\u003eMergus squamatus\u003c/em\u003e), engage in feeding and resting activities, which release heavy metals that have previously accumulated at this site(A, B et al. 2017). However, a previous study has shown that the deposition of bird feces can increase the contents of heavy metals in soil(De, La et al. 2018). At site SJZ-LL, except for Cd, Mn, and Zn, the contents of the heavy metals did not change significantly with increasing soil depth, indicating minimal anthropogenic impact. At site SJZ-XH, which was in an lake area used for crab cultivation, the contents of As, Ni, Sb, and Zn peaked in the middle soil layer and were significantly different than those at the other depths. This was likely due to changes in the hydrological conditions of the wetland caused by frequent changes in the lake water level. In marked contrast to the other heavy metals, Cr exhibited peak contents at depths of 0\u0026ndash;5 cm and 20\u0026ndash;25 cm. The underlying cause of this pattern still needs to be identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e analysis\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In the Tianhe Bay wetlands in the upper reaches of the Yellow River, the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values of Cd, Mn, and Ni were 0.71, 0.17, and 0.04, respectively, indicating mild contamination, while the other heavy metals were assessed to be clean in terms of their contamination risk. In the wetlands of the middle reaches of the Yellow River, the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e value of Cd was 0.28, also indicating mild contamination, while the other heavy metals were assessed to be clean. In particular, the Sanmenxia reservoir wetlands were ecologically healthy, and all of the heavy metals were assessed to be healthy. In the Luoyang and Zhengzhou reservoir wetlands, the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values of Cd were 0\u0026ndash;1, indicating mild contamination. Additionally, in the Luoyang reservoir wetlands, there was mild Pb contamination at all of the sampling sites. In the Zhengzhou reservoir wetlands, mild Sb contamination only occurred at sampling site HYK. In the delta wetlands of the Yellow River, the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e value of Sb was 0.21, while the \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e values of the other heavy metals were less than zero.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeoaccumulation index (\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e) values at various sampling sites in the Yellow River wetlands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003egeo\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMiddle reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cem\u003ePLI\u003c/em\u003e analysis\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ePLI\u003c/em\u003e values of the heavy metals at the various sampling sites are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It can be seen that except for the Sanmenxia wetlands in the middle reaches of the Yellow River, the heavy metals at the other sampling sites were in a state of mild contamination. In the upper reaches of the Yellow River, it was found that there was heavy Cd contamination, mild Pb contamination, and moderate contamination of the other heavy metals. Among the wetland sampling sites in the middle reaches of the Yellow River, Cd had the highest \u003cem\u003eCF\u003c/em\u003e value. There was heavy Cd contamination at sampling site MZC-LW in the Luoyang wetlands and moderate Cd contamination at the other sampling sites. The \u003cem\u003eCF\u003c/em\u003e values of all of the heavy metals were lower at site MZC-GT than at the other sampling sites, indicating that plants may have a certain enrichment effect on heavy metals. In the Zhengzhou wetlands, the \u003cem\u003eCF\u003c/em\u003e value was lower at site HYK than at site HHT, but both values were greater than 1, indicating mild contamination. In the delta lower-reaches wetlands of the Yellow River, sampling site SJZ-XH did not have heavy metal contamination, while the other two sampling sites (SJZ-ND and SJZ-YL) were classified as mildly contaminated. Specifically, except for Pb (\u003cem\u003eCF\u003c/em\u003e value of \u0026lt;!) at site SJZ-ND and for Cu and Pb (\u003cem\u003eCF\u003c/em\u003e valeus of \u0026lt;\u0026thinsp;1) at site SJZ-YL, all of the heavy metals had \u003cem\u003eCF\u003c/em\u003e values of greater than 1, indicating a high level of pollution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollution load index (\u003cem\u003ePLI\u003c/em\u003e) values at various sampling sites in the Yellow River wetlands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePLI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUpper reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-YL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eMiddle reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-GT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLower reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-ND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-YL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-XH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cem\u003eEF\u003c/em\u003e analysis\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eEF\u003c/em\u003e indicates the degree of enrichment of a heavy metal in a location and also reflects the main sources of the heavy metal. An \u003cem\u003eEF\u003c/em\u003e value of 1 suggests that the heavy metal originates from crustal activities such as rock weathering. An \u003cem\u003eEF\u003c/em\u003e value of \u0026gt;\u0026thinsp;1 indicates that the heavy metal came from non-crustal activities, such as pollutant emissions and biological activities. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, in sampling plots THW and MZC, which included both bare soil sites and vegetated sites, the \u003cem\u003eEF\u003c/em\u003e values of the heavy metals were lower at the bare soil sites than at the vegetated sites, indicating that the local plants had a positive effect on the accumulation of heavy metals. In the upper reaches of the Yellow River, except for Mn and Sb, the \u003cem\u003eEF\u003c/em\u003e values of the heavy metals at the different sampling sites exhibited the following order: THW-LW\u0026thinsp;\u0026gt;\u0026thinsp;THW-CD\u0026thinsp;\u0026gt;\u0026thinsp;THW-YL\u0026thinsp;\u0026gt;\u0026thinsp;THW-GD. The \u003cem\u003eEF\u003c/em\u003e values of Pb at all of the sampling sites were approximately 1, indicating that the Pb in the THW area primarily originated from crustal activities such as rock weathering, and anthropogenic activities exerted little influence. The \u003cem\u003eEF\u003c/em\u003e values of the other heavy metals were all greater than 1, suggesting that the enrichment of these metals may have been influenced by human activities. Moreover, the \u003cem\u003eEF\u003c/em\u003e values of Cd were 3\u0026ndash;5, indicating moderate enrichment, while those of the other heavy metals were classified as mildly enriched.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnrichment factor (\u003cem\u003eEF\u003c/em\u003e) values of heavy metals at various sampling sites in the Yellow River wetlands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUpper reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-GD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHW-YL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eMiddle reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYJW-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-LW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-XP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMZC-GT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ-HYK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ-HHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLower reaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-ND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-LL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSJZ-XH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn sampling plots YJW and MZ in the middle reaches of the Yellow River, the \u003cem\u003eEF\u003c/em\u003e values of As, Ni, Pb, and Zn were higher at the sites covered by \u003cem\u003ePhragmites communis\u003c/em\u003e (i.e., the sites with LW in their names) than at the sites covered by \u003cem\u003eTypha orientalis\u003c/em\u003e (i.e., the sites with XP in their names), indicating that \u003cem\u003eTypha orientalis\u003c/em\u003e had a mildly stronger capacity to enrich these four heavy metals compared to \u003cem\u003ePhragmites communis\u003c/em\u003e. This is consistent with the findings of Chen et al.(Chen,Ning et al.2020). The sampling sites in plot YJW did not exhibit Cu, Ni, Pb, or Zn enrichment, and the \u003cem\u003eEF\u003c/em\u003e values of the remaining heavy metals exhibited the following order: Sb\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Mn. Among them, As and Sb, such as at the sampling sites in plots MZC and ZZ, exhibited more-than-mild enrichment based on the fact that their \u003cem\u003eEF\u003c/em\u003e values ranked in the top three, indicating that the Henan section of the Yellow River Basin may be subjected to varying degrees of As and Sb pollution.\u003c/p\u003e \u003cp\u003eIn the lower reaches of the Yellow River, sampling site in plot SJZ did not exhibit metal enrichment or it exhibited only mild enrichment, and the \u003cem\u003eEF\u003c/em\u003e values of As, Cu, Mn, and Ni exhibited the following order: SJZ-ND\u0026thinsp;\u0026gt;\u0026thinsp;SJZ-LL\u0026thinsp;\u0026gt;\u0026thinsp;SJZ-XH. Cu, Ni, Pb, and Zn were not enriched at site SJZ-XH, while Cu, Ni, and Z exhibited mild enrichment at sites SJZ-ND and SJZ-LL. This suggests that the degree of metal enrichment in these wetland soils is related to the hydrodynamic forces in the wetlands, under which the heavy metals in the soils may migrate with the water flow toward the center of the lake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation analysis\u003c/h2\u003e \u003cp\u003eElements with similar chemical properties generally tend to cluster and coexist under the same or similar geological conditions(Zhang,Luo et al.2022). Therefore, correlation analysis of the heavy metals within the same research area can help to determine whether they share a common source༈Li,Zhang et al.2013). The physicochemical properties of the soils and their correlation coefficients with the heavy metals across the sampling sites in the Yellow River Basin are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, except for the sampling sites in the Zhengzhou reservoir (HYK-HHT), which were too few to conduct correlation analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExcept for Cr, there were positive correlations among the heavy metals across the sampling sites in plot JYW, which was in the Sanmenxia reservoir wetlands in the middle reaches of the Yellow River. There were positive correlations between the pH and heavy metals at the sampling sites in plot THW (in the upper reaches of the Yellow River) and in sampling plot MZC (in the lower reaches) where the soils were largely alkaline. In contrast, there were negative correlations between the pH and heavy metals at the sampling sites in plot JYW (in the Sanmenxia reservoir wetlands) and plot MZC (in the Luoyang reservoir wetlands), both of which were located in the middle reaches of the Yellow River and largely contained neutral soils. This was because low-pH soils contain a large amount of H\u003csup\u003e+\u003c/sup\u003e ions, which cause heavy metals to desorbed and become more reactive, making it difficult for them to cluster(Hu,Shen et al.2020). Due to its high cation exchange capacity and the presence of numerous different functional groups, the TOC can adsorb metal elements through surface precipitation, complexation, and ion exchange, thereby promoting the enrichment of heavy metals. A higher soil moisture content also facilitates the migration of heavy metals to lower-lying areas in wetlands. The soil moisture content at the sampling sites in plot THW exhibited negative correlations with the heavy metals. This was mainly due to the application of artificial watering at sampling site THW-GD, which led to higher migration and the loss of heavy metals from the soil.\u003c/p\u003e \u003cp\u003eBoth sampling plots THW (in the upper reaches of the Yellow River) and SZJ (in the lower reaches) had correlation coefficients of \u0026gt;\u0026thinsp;0.8 among the heavy metals, suggesting that the heavy metals may have originated from the same source(Bai, Xiao et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Sampling plot YJW, located in the Henan section of the middle reaches of the Yellow River, exhibited strong correlations between As vs Cr, Ni, and Pb, a strong correlation between Cr and Pb, a moderate correlation between Cr and Mn, and strong correlations between Cu and Mn, Ni, Sb, and Zn. At sampling plot MZC, Cr exhibited weak correlations with the other heavy metals and no enrichment to mild enrichment, indicating that the primary source of the Cr in this area may be crustal activity instead of human activities. Conversely, Cd was strongly correlated with Cu, Ni, and Sb and exhibited mild to moderate enrichment, suggesting that all four of these metals may originate from human activities.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eIn this study, we analyzed the contents, vertical distribution characteristics, and ecological risks of nine heavy metals in the surface sediments of different types of wetlands in the upper, middle, and lower reaches of the Yellow River. It was found that the heavy metals did not exhibit significant spatial patterns across the wetlands in the Yellow River Basin. In the upper reaches of the Yellow River, except for Pb, the contents of the heavy metals exceeded the soil background values in Ningxia. In contrast, the Cu, Ni, Pb, and Zn contents of the wetland soils in the middle reaches and the Cu, Ni, and Pb contents of the wetland soils in the lower reaches were below the environmental background values. The Pb contents of the wetland soils at all of the sampling sites in the Yellow River Basin were lower than the environmental background values. In the delta wetlands in the Yellow River Basin, bird activities may have a positive effect on the enrichment of heavy metals. The contents of the various heavy metals at depths of 0\u0026ndash;25 cm in the vertical soil profiles generally decreased with increasing soil depth. Enrichment factor analysis revealed that the plants had a positive effect on the enrichment of the heavy metals. Geoaccumulation index analysis and pollution load index analysis revealed that there were varying degrees of heavy metal contamination risks, except for the Sanmenxia reservoir wetlands. In the upper-reach wetlands and the Luoyang-Zhengzhou reservoir wetlands in the middle reaches, Cd was the primary pollutant, and it exhibited more than mild contamination. Enrichment factor analysis revealed that the heavy metals in the upper reaches were more influenced by human activities compared to the middle and lower reaches. Correlation analysis revealed the occurrence of strong correlations among the heavy metals (correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.8) in both the upper-reach wetlands and lower-reach wetlands, suggesting that the heavy metals may have originated from the same source. The results of this study provide survey information for the distribution pattern recognition and risk assessment of heavy metals in the Yellow River Basin.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Sanmenxia City Science and Technology Tackling Project (2022002012)；Open Fund for Key Lab. of Land Degradation and Ecological Restoration in northwestern China of Ningxia University；（Grant number2023Q03）；Open Research Fund of State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Sciences (HKHA2022009) and Fundamental Research Funds for the Central Public-interest Scientific Institution (2022YSKY-03)\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial\u0026nbsp;\u003c/p\u003e\n\u003cp\u003einterests or personal relationships that could have appeared to influence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethe work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data used in this study will be available upon request. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShan V , Singh S K , Haritash A K .(Shan, Singh et al. 2021)[J].Applied Water Science, 2021, 11(1).DOI:10.1007/s13201-020-01334-9.\u003c/li\u003e\n\u003cli\u003eWagner K I , Gallagher S K , Hayes M ,et al.Wetland Restoration in the New Millennium: Do Research Efforts Match Opportunities?[J].Restoration ecology, 2008, 16(3):p.367-372.DOI:10.1111/j.1526-100x.2008.00433.x.\u003c/li\u003e\n\u003cli\u003eLI W, BU D, SUN J, et al. Distribution and ecological risk assessment of heavy metal elements in the surface sediments of Bagaxue wetlands in Lhasa [J]. Environmental Chemistry, 2021, 40(1): 195-203(in Chinese).\u003c/li\u003e\n\u003cli\u003eChen R , Chen H , Song L ,et al.Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils[J].Science of the Total Environment, 2019, 694.DOI:10.1016/j.scitotenv.2019.133819.\u003c/li\u003e\n\u003cli\u003eLiu R , Men C , Liu Y ,et al.Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment[J].Marine Pollution Bulletin, 2016, 110(1):564-571.DOI:10.1016/j.marpolbul.2016.05.060.\u003c/li\u003e\n\u003cli\u003eRamos-Miras J J , Roca-Perez L ,M. Guzm\u0026aacute;n-Palomino,et al.Background levels and baseline values of available heavy metals in Mediterranean greenhouse soils (Spain)[J].Journal of Geochemical Exploration, 2011, 110(2):186-192.DOI:10.1016/j.gexplo.2011.05.009.\u003c/li\u003e\n\u003cli\u003eHoque R R , Goswami K G , Kusre B C ,et al.Distribution and solid-phase speciation of toxic heavy metals of bed sediments of Bharali tributary of Brahmaputra River[J].Environmental Monitoring \u0026amp; Assessment, 2011, 177(1-4):457-466.DOI:10.1007/s10661-010-1647-5.\u003c/li\u003e\n\u003cli\u003eHarikumar P S , Nasir U P , Rahman M P M .Distribution of heavy metals in the core sediments of a tropical wetland system[J].International Journal of Environmental Science \u0026amp; Technology, 2009, 6(2):225-232.DOI:10.1007/BF03327626.\u003c/li\u003e\n\u003cli\u003eCui J , Zang S , Zhai D ,et al.Potential ecological risk of heavy metals and metalloid in the sediments of Wuyuer River basin, Heilongjiang Province, China[J].Ecotoxicology, 2014, 23(4):589-600.DOI:10.1007/s10646-014-1182-1.\u003c/li\u003e\n\u003cli\u003eSterckeman T , Douay F , Proix N ,et al.Vertical Distribution of Cd, Pb and Zn in Soils near Smelters in the North of France[J].Environmental Pollution, 2000, 107(3):377-389.DOI:10.1016/S0269-7491(99)00165-7.\u003c/li\u003e\n\u003cli\u003eGuan Q .Vertical Distribution Characteristics and Source Tracing of Organic Carbon in Sediment of the Yellow River Wetland, Sanmenxia, China[J].Limnologica, 2022.DOI:10.1016/j.limno.2022.125960.\u003c/li\u003e\n\u003cli\u003eGenua-Olmedo,Ana,Alcaraz,et al.Sea level rise impacts on rice production: The Ebro Delta as an example.[J].Science of the Total Environment, 2016.DOI:10.1016/j.scitotenv.2016.07.136.\u003c/li\u003e\n\u003cli\u003eFan,XM,Pedroli,et al.Potential plant species distribution in the Yellow River Delta under the influence of groundwater level and soil salinity[J].ECOHYDROLOGY, 2011, 2011,4(6)(-):744-756.DOI:10.1002/eco.164.\u003c/li\u003e\n\u003cli\u003eHan D , Cheng J , Hu X ,et al.Spatial distribution, risk assessment and source identification of heavy metals in sediments of the Yangtze River Estuary, China[J].Marine Pollution Bulletin, 2017, 115(1-2):141-148.DOI:10.1016/j.marpolbul.2016.11.062.\u003c/li\u003e\n\u003cli\u003eChina National Environmental Monitoring Centre. Background values of soil elements in China[M]. BeiJing:China Environmental Science Press, 1990:330-381.\u003c/li\u003e\n\u003cli\u003eQi S .Study on Soil Background Value and Reference Value in Henan Section of Yellow River[J].Journal of Anhui Agricultural Sciences, 2009.\u003c/li\u003e\n\u003cli\u003ePang Xugui, Dai Jierui, Hu Xueping, Song Zhiyong, Yu Chao, Chen Lei, Zhang Huaping, Liu Huafeng, Wang Hongjin, Wang Zenghui, Zhao Xiqiang, Zeng Xiandong, Ren Wenkai. Background Values of Soil Geochemistry in Shandong Province [J]. ShangDong Land and Resources, 2018, 34(1):5.DOI:10.3969/j.issn.1672-6979.2018.01.005. (in Chinese)\u003c/li\u003e\n\u003cli\u003eBedaiwi A , Wysong A , Rogan E G ,et al.Arsenic Exposure and Melanoma Among US Adults Aged 20 or Older, 2003-2016:[J].Public Health Reports, 2022, 137(3):548-556.DOI:10.1177/00333549211008886.\u003c/li\u003e\n\u003cli\u003eHakanson L .An Ecological Risk Index for Aquatic Pollution Control \u0026ndash; A Sedimentological Approach[J]. 1980.DOI:10.1016/0043-1354(80)90143-8.\u003c/li\u003e\n\u003cli\u003eWu H , Yang F , Li H ,et al.Heavy metal pollution and health risk assessment of agricultural soil near a smelter in an industrial city in China[J].International Journal of Environmental Health Research, 2019:1-13.DOI:10.1080/09603123.2019.1584666.\u003c/li\u003e\n\u003cli\u003eRule J H .Assessment of trace element geochemistry of Hampton Roads Harbor and lower Chesapeake Bay area sediments[J].Environmental Geology, 1986, 8(4):209-219.DOI:10.1007/BF02524948.\u003c/li\u003e\n\u003cli\u003eRoussiez V , Ludwig W , Probst J L ,et al.Background levels of heavy metals in surficial sediments of the Gulf of Lions (NW Mediterranean): an approach based on 133Cs normalization and lead isotope measurements.[J].Environmental Pollution, 2005, 138(1):167-177.DOI:10.1016/j.envpol.2005.02.004.\u003c/li\u003e\n\u003cli\u003eXia P , Meng X , Feng A ,et al.[J].Environmental Earth Sciences, 2012, 66(5):1337-1344.DOI:10.1007/s12665-011-1343-y.\u003c/li\u003e\n\u003cli\u003eChristoforidis A , Stamatis N .Heavy metal contamination in street dust and roadside soil along the major national road in Kavala\u0026apos;s region, Greece[J].Geoderma, 2009, 151(3-4):257-263.DOI:10.1016/j.geoderma.2009.04.016.\u003c/li\u003e\n\u003cli\u003eGuo L B , Gifford R M .Soil carbon stocks and land use change: a meta analysis[J].Global Change Biology, 2002, 8(4):345-360.DOI:10.1046/j.1354-1013.2002.00486.x.\u003c/li\u003e\n\u003cli\u003eA Y X , A B L , B E P ,et al.Soil carbon storage in mangroves is primarily controlled by soil properties: A study at Dongzhai Bay, China[J].Science of The Total Environment, 2018, 619-620:1226-1235.DOI:10.1016/j.scitotenv.2017.11.187.\u003c/li\u003e\n\u003cli\u003eWu W , Hu Y , Guo Q ,et al.Sorption/desorption behavior of triclosan in sediment\u0026ndash;water\u0026ndash;rhamnolipid systems: Effects of pH, ionic strength, and DOM[J].Journal of Hazardous Materials, 2015, 297(oct.30):59-65.DOI:10.1016/j.jhazmat.2015.04.078.\u003c/li\u003e\n\u003cli\u003eJin Z , Ruhai L , Jiaxin J ,et al.Vertical distribution and speciation characteristics of heavy metals in wetlands soils of Ziyaxin River downstream[J].Environmental Chemistry, 2016.\u003c/li\u003e\n\u003cli\u003eA J W , B R O , B D T W ,et al.Antimony and arsenic exhibit contrasting spatial distributions in the sediment and vegetation of a contaminated wetland - ScienceDirect[J].Chemosphere, 2017, 180:388-395.DOI:10.1016/j.chemosphere.2017.03.142.\u003c/li\u003e\n\u003cli\u003eS,De,La,et al.Enrichment of trace elements in colonies of the yellow-legged gull (Larus michahellis) in the Atlantic Islands National Park (Galicia-NW Spain).[J].Science of the Total Environment, 2018.DOI:10.1016/j.scitotenv.2018.08.284.\u003c/li\u003e\n\u003cli\u003eCHEN T, BAO N Y,DU C X,et al.Absorption Characteristics of Heavy Metals by Emergent Plants from Polluted River in Ecological Restoration Areas[J].Environmental Science Research,2020,33(09):2110-2117.DOI:10.13198/j.issn.1001-6929.2020.02.06.(in Chinese).\u003c/li\u003e\n\u003cli\u003eZHANG X R, LUO H, ZHU A N, et al. Study on the migration of heavy metals in natural soil profile of a dryland in Wuhan City [J].Resources Environment \u0026amp; Engineering,2022,36(03):274-279.DOI:10.16536/j.cnki.issn.1671-1211.2022.03.002.(in Chinese).\u003c/li\u003e\n\u003cli\u003eLI J C,ZHANG H,SHI W. Concentrations of soil heavy metals and their spatial distribution in the surrounding area of Fenhe reservoir [J]. Environmental Science, 2013, 34(1): 116-120.DOI:10.13227/j.hjkx.2013.01.027.(in Chinese).\u003c/li\u003e\n\u003cli\u003eHU Q Q, SHEN Q, CHEN F, et al.Reconstructed soil vertical profile heavy metal Cd occurrence and its influencing factors [J].Environmental Science, 2020, 41(6): 2878-2888..DOI:10.13227/j.hjkx.201911023.(in Chinese).\u003c/li\u003e\n\u003cli\u003eBai J , Xiao R , Cui B ,et al.Assessment of heavy metal pollution in wetland soils from the young and old reclaimed regions in the Pearl River Estuary, South China[J].Environmental Pollution, 2011, 159(3):817-824.DOI:10.1016/j.envpol.2010.11.004..\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heavy metals, Ecological risk assessment, Yellow River Basin, Wetlands","lastPublishedDoi":"10.21203/rs.3.rs-4378030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4378030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWetlands serve as significant sinks and sources of heavy metals. In this study, surface soil samples (0\u0026ndash;25 cm) were collected from 15 sampling sites across the wetlands on the Ningxia, Henan,and the delta wetlands reaches to investigate the contents, distributions, and ecologic risks of heavy metals such as As and Cd in the wetland sediments in the Yellow River. The results revealed that the wetland soils in the upper and lower reaches were alkalineand more conducive to heavy metal enrichment. There was no significant spatial distribution pattern of the heavy metals across the wetlands in the Yellow River.The contents of the heavy metals decreased with increasing soil depth vertical profile each sampling sites. Geoaccumulation index (Igeo) analysis revealed that heavy metals had a negative Igeo value at each sampling site, expect for the following metals and sampling sites: in the Tianhe Bay wetland, the Igeo values for Cd, Mn, and Ni were 0.71, 0.17, and 0.04, respectively; in the middle reaches, the Igeo value for Cd was 0.28; and in the lower reaches, the Igeo value for Sb in the delta wetlands was 0.21.Pollution load index analysis and enrichment factor (EF) analysis revealed the occurrence of severe Cd contamination in the Ningxia, with an EF of greater than 3, indicating a high degree of anthropogenic impact. There was a strong correlation (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.8) among the various heavy metals in the wetlands in both the Ningxia and delta wetlands, suggesting a common source for these elements.\u003c/p\u003e","manuscriptTitle":"Distribution and Risk Assessment of Heavy Metals in the Wetlands in the Upper, Middle, and Lower Reaches of the Yellow River Basin: A Study Focusing on the Yellow River Delta, Henan Section and Ningxia Section","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 07:22:14","doi":"10.21203/rs.3.rs-4378030/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8c2a3d9-d4b6-4fd3-bb5d-8b0e043d9348","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-22T11:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 07:22:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4378030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4378030","identity":"rs-4378030","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}