Surface sediment properties and heavy metal contamination assessment in typical urban areas from middle and upper reaches of Yellow River

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Heavy metal pollution has become a serious problem in global river systems and adversely affects humans through the food chain.The contents of eight types of heavy metals (Fe, Mn, Cu, Ni, Zn, Cr, Pb, and Cd) in the sediments of six typical urban areas in the middle and upper reaches of the Yellow River were analyzed to explore the spatial distribution characteristics between cities and evaluate the degree of pollution.The main research objectives of this study were as follows: (1) to analyze the distribution characteristics of heavy metals in sediments along rivers in six typical urban areas to evaluate the degree of heavy metal pollution in sediments; (2) to reveal the enrichment characteristics and pollution level of eight types of heavy metals in six typical urban areas in the middle and upper reaches of the Yellow River; (3) to propose the ecological risk of heavy metals in sediments of six typical urban areas in the middle and upper reaches of the Yellow River using the potential ecological risk index method. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences typical urban areas heavy metal the Yellow River spatial distribution pollution assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rivers have multiple functions in aquatic environments, including ecological services, geochemical cycles, and habitat provision for plants and animals 1 , 2 . However, with rapid economic development and urban expansion in many developing countries, a large number of uncontrolled pollutants flow into rivers, posing a significant challenge to aquatic environments 3 – 5 .Heavy metals in river sediments are predicted to be an important source of pollutants and are specific indicators of pollutants because of their hydrophobicity and accumulation characteristics 6 – 9 . Heavy metals in river sediments originate from natural and anthropogenic sources. It has attracted worldwide attention owing to its persistence in the environment and radioactive toxins 9 – 11 . Heavy metals quickly migrate from water to sediment, adsorb onto the surface of particles, and then migrate further and are released into the water body through changes in the external environment, threatening the aquatic ecosystem 12 – 14 . Therefore, river sediments can be regarded as reservoirs for heavy metals and are the main research object for monitoring heavy metal pollutants in aquatic ecosystems. Its spatial distribution characteristics, contents, and pollution levels will help determine the source of heavy metals and play a key role in evaluating the pollution and potential ecological risk status of rivers 15 – 17 . Heavy metal pollution assessments provide theoretical support for environmental risk management of water 18 , 19 . The Yellow River is the second largest river in China and is located between 96 °—19 ° E and 32 °—42 ° N. It originates in the Qinghai Province, flows through Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan and Shandong provinces, and finally flows into the Bohai Sea. As the most important river in northern China, the Yellow River provides water to 15% of China's arable land and nearly 160 million people 20 . The Yellow River has been polluted in recent decades owing to agricultural, urban, and industrial activities, particularly heavy metal pollution 21 , 22 . In recent years, the amount of domestic sewage and industrial wastewater discharged into the middle and upper reaches of the Yellow River has increased annually, and water quality has deteriorated significantly 23 . Additionally, the construction of a series of dams in the Yellow River has changed the quantity and quality of river sediment to ensure hydropower generation, flood control, and water supply 11 . Previous studies have analyzed heavy metal pollution in the sediment of the Yellow River, but they were limited to a specific or typically small area, such as a typical city, estuary, wetland, nature reserve, or heavy industrial area 24 . Sediment samples were also collected from different locations (riverbed, floodplain, alluvial area, main stream, or tributary) 23 , 25 , 26 . Therefore, a comprehensive study of the content, distribution characteristics and pollution assessment of heavy metals in the sediments of several typical urban areas in the middle and upper reaches of the Yellow River is of great significance to further reveal the overall pollution status of the Yellow River 27 , 28 . Material and methods Study area The middle and upper reaches of the Yellow River start from the source of the Yellow River in the bay har, Qinghai Province, pass through seven provinces of Qinghai, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, and Henan, and end in Mengjin District, Luoyang City, Henan Province, with a total length of approximately 4,678 km and a drainage area of 772,000 square kilometers 29 , 30 . In this study, the content, spatial distribution, and pollution status of heavy metals in river sediments in six typical urban areas of Xining, Lanzhou, Yinchuan, Baotou, Weinan, and Luoyang in the middle and upper reaches of the Yellow River were analyzed (Figure. 1) 31 , 32 . Collection of samples Sixty sampling points were evenly distributed in the river basins of six typical urban areas along the middle and upper reaches of the Yellow River (According to the accessibility principle and administrative units, 10 sampling points were set in each city on average). Sixty sediment samples were collected at set sampling points (0–5 cm) along the riverbed using a grabbed gravity mud collector. Samples were collected from mixed samples, that is, sediment samples were collected at different positions on the same section three times and fully mixed. The sampling point number, sampling time, location, latitude and longitude, altitude, landform unit where the sampling point was located, river width, water depth, and distance between the sampling point and shore were recorded in detail, and photographs of the river section where the sampling point was located and the surrounding landform (including vegetation status and erosion status) were taken. The collected sediment samples were packed in plastic bottles and the soil samples were packed in polyethylene plastic bags. The sample number, sampling location, date, and other information on the plastic bottle or plastic bag were returned to the laboratory for air-drying, grinding, and screening before the next experiment. Sample digestion 0.5 g (to the nearest 0.0001 g) of sample was accurately weighed and digested in a polytetrafluoroethylene (PTFE) crucible using an electronic balance. The digestion steps were as follows: first, wet the soil sample with three drops of distilled water, add 10mL of concentrated hydrochloric acid, control the temperature to 90 ℃ on an electric heating plate, and heat it to a viscous state at a constant temperature; second, adding 10mL of concentrated nitric acid of superior grade pure was added, and the mixture was continuously heated to become viscous; Add 10mL of hydrofluoric acid was added again, and the mixture was heated until it became viscous. Finally, add 10mL of perchloric acid was added and the mixture was heated until the white smoke was exhausted. The digested samples were white or yellow, and sticky when the crucible was tilted. Digested samples were rinsed with water and poured into a funnel for filtration. The inner wall of the crucible was then rinsed twice and poured into a funnel. When the amount of solution in the funnel was less than one- third, the filter paper was rinsed twice with water. Finally, the filter paper was removed, the funnel was washed with distilled water, and distilled water was added to a constant volume of 100mL, shake well, placed, and tested. The digestion process was repeated four times. Sample determination The contents of heavy metals such as Fe, Mn, Cu, Zn, Ni, Pb, Cd and Cr in all treated sediment samples were determined by inductively coupled plasma mass spectrometry (ICP-MS, Thermo Fisher Scientific, USA). All samples were processed three times, the final data are the mean values, and the relative standard deviations were all within 10% (Supplementary Table 1). Pollution assessment methods There are many methods for evaluating heavy metal pollution in sediments, and the evaluation system is relatively accurate 33 . In this study, the most widely used enrichment factor, geo-accumulation index, and potential ecological risk index methods were selected to evaluate heavy metal pollution in the sediments. Enrichment factors evaluation method The enrichment factor (EF) is an important method for evaluating the degree of water sediment pollution and is widely used to determine the degree of sediment pollution in aquatic ecosystems 34 . The most commonly used reference elements were Al, Fe, Mn, Mg, and Ca. The normalized metal was used to identify abnormal metal content, and Mn was used as the reference material in this study 35 . The calculation method is as follows: $$\:EF=\frac{{\left(\frac{M}{Mn}\right)}_{sample}}{({\frac{M}{Mn})}_{backgroud}}$$ 1 Where, \(\:{\left(\frac{M}{Mn}\right)}_{sample}\) is the ratio of measured values of a metal element M and Mn in the same sample, the average crustal contents (Fe: 47200 mg/kg、Mn༚850 mg/kg、Cu༚45 mg/kg、Ni༚68 mg/kg、Zn༚95 mg/kg、Cr༚90 mg/kg、Pb༚20 mg/kg、Cd༚0.30 mg/kg) 36 were taken as the background value in this study, and \(\:{\left(\frac{M}{Mn}\right)}_{backgroud}\) is the ratio of background values of a metal element M and Mn. The evaluation criteria for enrichment factors can be expressed in seven grades (Table 1 ) 37 . Geo-accumulation Index method Professor Müller of the University of Heidelberg, Germany, first proposed the geological accumulation index (I geo ) method to evaluate the degree of heavy metal pollution in sediment and water in 1969 38 . The formula used was as follows: $$\:{I}_{geo}={log}_{2}\left(\frac{{C}_{n}}{{1.5B}_{n}}\right)$$ 2 where C n is the measured content of heavy metal n and B n is the geochemical background value of heavy metal n. In this study, the average contents of elements in the upper crust (Fe: 35000 mg/kg、Mn༚600 mg/kg、Cu༚25 mg/kg、Ni༚20 mg/kg、Zn༚71 mg/kg、Cr༚35 mg/kg、Pb༚20 mg/kg、Cd༚0.10 mg/kg)were used as the background value 37 , 39 . 1.5 is a correction index that considers the influence of different rocks on the background values 40 . The geological accumulation index can be divided into seven grades to indicate changes in the degree of pollution (Table 1 ) 41 . Potential ecological risk index method The potential ecological risk index (ERI) and comprehensive potential ecological risk index (RI) were proposed by Swedish scientist Hakanson in 1980. The degree of heavy metal pollution in the sediment was evaluated according to the content, type, toxicity level, environmental response, and water sensitivity to heavy metal pollution in sediment 42 . The following formula was used: $$\:ERI={T}_{r}^{i}\times\:\frac{{C}^{i}}{{C}_{n}^{i}}$$ 3 $$\:RI={\varSigma\:}_{1}^{i}ERI={\varSigma\:}_{1}^{i}{T}_{r}^{i}\times\:\frac{{C}^{i}}{{C}_{n}^{i}}$$ 4 where ERI is the potential ecological risk index of a single heavy metal; \(\:{T}_{r}^{i}\:\) is the toxicity coefficient of the ith heavy metal in the sediment, and the toxicity coefficients of Cu, Ni, Zn, Pb, Cr, and Cd are 5, 5, 1, 5, 2 and 30 respectively 43 , 44 . \(\:{C}^{i}\) is the measured value of the first heavy metal; \(\:{C}_{n}^{i}\) is the background value of the second heavy metal; RI is the comprehensive potential ecological risk index. The evaluation criteria for ERI and RI were expressed using different grading levels (Table 1 ) 45 . Table 1 Classification standard of EF, I geo , ERI and RI EF classes Enrichment level I geo value Pollution level ERI classes RI classes Ecological risk level EF<1 No enrichment I geo ≤0 No pollution —— —— —— 1 ≤ EF<3 Micro-enrichment 0<I geo ≤1 Light pollution ERI<40 RI<95 Low risk 3 ≤ EF<5 Medium enrichment 1<I geo ≤2 Moderate pollution 40 ≤ ERI<80 95 ≤ RI<190 Medium risk 5 ≤ EF<10 Heavier enrichment 2<I geo ≤3 Heavy pollution 80 ≤ ERI<160 190 ≤ RI<380 Strong risk 10 ≤ EF<25 Strong enrichment 3<I geo ≤4 Severe pollution 160 ≤ ERI<320 RI ≥ 380 Extra strong risk 25 ≤ EF<50 Extremely rich 4<I geo ≤5 Serious pollution ERI ≥ 320 —— Extremely risky Results and discussion Contents and spatial distribution characteristics of heavy metals The average crustal contents of the eight heavy metals were used as background values. The average heavy metal contents in Luoyang City exceeded the background values, while the average contents of Zn, Cr, and Cd in the other five cities were relatively high, and their accumulation was obvious. Compared with the average content of the upper crust in China as the background value, all the heavy metals in Luoyang City also exceeded the background value, and the average content of five types heavy metals (Mn, Ni, Zn, Cr and Cd) in the other five cities were relatively high, indicating that some urban basins in the middle and upper reaches of the Yellow River were polluted by heavy metals, and the Yellow River was polluted by heavy metals. Heavy metals with high variability (moderate variability CV values ranging from 15–36%) were exceeded in all six cities based on the coefficient of variation 46 , especially in Weinan. This shows that there were significant differences in the spatial distribution of heavy metals, which was also confirmed by the spatial distribution map of heavy metals (Supplementary Table 1 and Figure. 2). Xining, Lanzhou, Yinchuan and Baotou are distributed in the upper reaches of the Yellow River, while Weinan and Luoyang are located in the middle reaches of the Yellow River 27 . The geographical environment of the middle and upper reaches of the Yellow River is complex, and human activities on both sides of the river are diverse, which explains the similarities and differences in the spatial distribution of heavy metals in the sediments of the middle and upper reaches of the Yellow River 47 . ArcGIS10.7 software was used for the heavy metal content in sediments of the middle and upper reaches of the Yellow River and was interpolated by kriging. The buffer zones of the middle and upper reaches of the Yellow River were constructed, and the spatial distribution map of each heavy metal was obtained through mask extraction. Cr had a relatively high-value distribution in four typical urban areas in the upper reaches of the Yellow River. The main reasons for the distribution of high-value areas in Xining may be the poor water quality of the Huangshui River flowing through it and the discharge of wastewater from various industrial productions 48 . The main reasons for the distribution of high-value areas in Lanzhou City may be the discharge of wastewater from large-scale chemical production enterprises in Xigu District near the middle reaches and industrial production on both sides of the lower reaches 21 . The Cr content in Yinchuan City was higher than that of the other heavy metals, which is also suspected to be related to the disorderly discharge of industries on both sides of the river. The main reasons for the formation of medium-high value areas in Baotou City may be the use of chemical fertilizers and pesticides in agricultural production and the inflow of industrial wastewater from industrial areas in the southern part of the city into rivers 49 . Weinan is a heavy industrial city located in the middle reaches of the Yellow River, the Weihe River is the largest tributary of the Yellow River, runs through the city, and is seriously polluted by industrial production emissions on both sides of the river; the polluted Weihe River flows into the Yellow River, which is the main reason for the formation of a high value area in the southeast of Weinan 46 ; Luoyang is also located in the middle reaches of the Yellow River, the overall heavy metal content is high, mainly in the middle and high value areas, because the upper reaches of Luoyang are close to Sanmenxia City, the Xiaolangdi Water Conservancy Project is located in the city, and excessive water conservancy and power development leads to the reduction of natural flow and ecological flow of rivers in the sensitive period, and heavy metal deposition affects the sediment content. There are intensive industries on both sides of the middle and lower reaches of Luoyang, and the discharge of wastewater from industrial production affects the heavy metal content in the sediment exceeding the standard 50 (Supplementary Table 1 and Supplementary Figure. 1). Assessment of heavy metal pollution Enrichment factors The comprehensive average values of the enrichment factors of the eight types of heavy metals in order from large to small were as follows: Weinan (1.73) > Xining (1.60) > Yinchuan (1.59) > Lanzhou (1.56) > Baotou (1.51) > Luoyang (1.44). There was little difference in the values and they were all between 1 and 3, which belonged to the micro-enrichment level. The average enrichment factors of Fe, Cu and Ni were all less than 1, indicated that they had no enrichment level in sediments and no affected by human beings; The average enrichment factors of Mn, Zn and Cr were all between 1 to 3, indicated that they were micro-enriched in sediments and less affected by human beings; The average enrichment factor of Pb was less than 1 in Xining, Lanzhou, Yinchuan and Baotou, and ranged from 1 to 3 in Luoyang and Weinan, indicated that Pb was not enriched or slightly enriched in sediments; The enrichment degree of Cd is the largest, Xining (5.55), Lanzhou (5.24), Yinchuan (5.49) and Baotou (5.23) belong to heavy enrichment levels, Weinan (4.97) and Luoyang (3.81) belong to the medium enrichment level, but the maximum enrichment factors of Cd in these 2 cities reached 26.36 and 16.60, the results showed that the strong enrichment sites of Cd in these 2 cities were relatively concentrated, and there were local pollution sources with strong enrichment levels(Supplementary Table 2 and Figure. 3). Seven types of heavy metals, excluding Cd, were non-enriched or micro-enriched levels in Xining, Lanzhou, Yinchuan and Baotou in the upper reaches of the Yellow River and Luoyang City in the middle reaches of the Yellow River. In Weinan City, in the middle reaches of the Yellow River, two Zn sites reached a moderate enrichment level, and two Pb sites reached a moderate or severe enrichment level. There were sites with moderate and above Cd enrichment levels in the six typical urban watersheds, especially in Weinan City and Luoyang City in the middle reaches, where two sites reached strong enrichment levels or above, indicating that there might be industrial production or other human activities near these sites. The discharge of Cd-containing pollutants into the water body led to a strong enrichment level, which had a serious impact, and relevant departments should pay attention to it 51 (Supplementary Table 2). Geo-cumulative index The comprehensive average values of geo-accumulation indices of eight types of heavy metals in the order from large to small were: Luoyang (0.29) > Xining (0.04) > Weinan (0.01) > Yinchuan (0) = Lanzhou (0) > Baotou (-0.02) (Table 3.2 and Figure. 3), with values ranging from 0 to 1 in Luoyang, Xining, and Weinan, and less than or equal to 0 in the other three cities, indicating that the Yellow River Basin in these six cities is non-polluted or slightly polluted. The average geo-accumulation indices of Fe, Mn, Cu, Zn, and Pb were less than 1 in all five cities, except Luoyang City, indicating that these heavy metals were not polluted in the sediment and were less affected by human beings. The average geo-accumulation indices of the seven types of heavy metals, except Fe, in Luoyang City were between 0 and 1, indicating that the entire city was polluted by heavy metals, but the degree of pollution was relatively low (Supplementary Table 3 and Figure. 4). Most of the sampling points belonged to non-pollution or light pollution levels in the sample sites of the six typical urban areas studied; the sample sites with moderate pollution existed only for Cd, and the site with moderate or above heavy metal pollution did not exist in Lanzhou City, indicating that the heavy metal Cd is an important pollution factor in the study area and should be studied as a key point. Potential ecological risk assessment The average, maximum, and minimum ERI values of Cu, Ni, Zn, Cr, and Pb were less than 40, indicating a low risk level. The average ERI value of Cd was between 80 and 160, indicating a strong risk level. The maximum values of ERI of Cd were between 80 and 160 in Lanzhou (137.6) and Yinchuan (156.1), which showed that there was a strong risk of ecological pollution at the corresponding sites; The values were between 160 and 320 in Xining (185. 23) and Baotou (165. 7). The corresponding sites had a strong potential ecological for pollution risk, with maximum values exceeding 320 in Weinan (400. 42), and Luoyang (471. 34), and the corresponding sites had a strong potential ecological pollution risk level (Supplementary Table 4 and Figure. 5). The RI values of the six cities were between 95 and 190, indicating that there was a moderate risk of ecological pollution in the 6 cities, which should be paid attention to by the relevant departments of environmental governance. The maximum RI for Xining was 210. 97, indicating a strong potential ecological pollution risk in the local watersheds. The maximum RI value for Weinan (431. 13), and Luoyang (504. 87) were greater than 380, which indicates that the potential ecological pollution risk level was very strong, and the relevant departments must reduce and control pollution in these local watersheds. Conclusions Eight types of heavy metals (Fe, Mn, Cu, Ni, Zn, Cr, Pb and Cd) were studied in the sediments of six typical urban areas in the middle and upper reaches of the Yellow River (Xining, Lanzhou, Yinchuan, Baotou, Weinan and Luoyang) were studied in this paper. The contents and spatial distribution characteristics of heavy metals were analyzed by collecting sediments in the field and measuring their contents in the laboratory, taking the average contents of heavy metals in shale worldwide as the background value. The pollution level and potential ecological risk of heavy metals in the sediments of the middle and upper reaches of the Yellow River were studied using three evaluation methods: the enrichment factor, ground accumulation index, and potential ecological risk. The main results are as follows: (1) The Cr and Cd contents were relatively high in the six cities, mainly because of the influence of human agricultural and industrial production activities, which were the main pollution factors in the study area. Compared to the other five cities, the accumulation of heavy metals in Luoyang was the highest, and Cd pollution exceeded the standard, whereas the Cr content in Weinan was the highest. (2) The spatial distributions of the eight heavy metals in the six cities were significantly different. The heavy metal concentrations in the four typical urban areas in the upper reaches of the Yellow River were relatively low, whereas those in Weinan and Luoyang in the middle reaches of the Yellow River were relatively high. The spatial distribution of heavy metals was mainly affected by human factors, and the heavy metal content was high near industrial areas and downstream of the water conservancy project. Therefore, relevant departments should control the discharge of industrial wastewater. (3) The eight types of heavy metals were micro-enriched in the rivers as a whole, of which Fe, Cu, and Ni were not enriched in the sediment, Cr and Cd were relatively enriched, and there were strong enrichment sites in Weinan and Luoyang. Based on the evaluation of the geo-accumulation index, Cd produced light pollution, Pb produced light pollution only in Luoyang, and the other heavy metals did not cause pollution in the six cities. Based on the potential ecological risk assessment, there were potential pollution risks in the six typical urban areas; however, the risk level was not high, and the potential ecological risk index of Cd was the highest. Cd also poses a strong ecological risk to Weinan and Luoyang. (4) Weinan and Luoyang are the two cities with serious heavy metal pollution among the six typical cities, and the excessive heavy metal Cd is the direct cause of river pollution, which is caused by the emission of Cd in the process of human industrial and agricultural production. Therefore, relevant environmental protection and pollution control units should focus on and strengthen control. Declarations Data availability Data is provided within the manuscript or supplementary information files. Ethics approval and consent to participate Not applicable. Consent for publication All authors approved the manuscript for publication in ecological processes. Competing interests No competing interest exists in this manuscript. Funding This work was financially supported by the Industrial Support Program of The Education Department of Gansu Province (2021CYZC-31), the project of Gansu Province Science and Technology Plan (22CX3GA076), the Special Project of Gansu Science and Technology Commissioner (23CXGA0082). Authors' contributions Junzhang Wang: Conceptualization, Software, Writing—original draft. Ling Tao: Methodology, Formal analysis. Hanru Ren: Resources, Data acquisition. Xiangyu Xue: Data curation, Software. Zhijie Yang: Software. Yucheng Jiang: Software. Jun Ren: Writing—review and editing, Funding acquisition. Acknowledgment This study received significant assistance from the Key Laboratory of Water Environment of Lanzhou Jiaotong University and Gansu Hanxing Environmental Protection Co. Ltd. The convenient conditions provided by the teachers provided a strong guarantee for sample collection and experimental procedures. We are grateful to Professor Ren Jun for the guidance. Throughout this thesis, he carefully reviewed the draft and offered invaluable critique. His academic excellence and insightful comments are remarkable. I would also like to express my appreciation to other scholars and experts who offered suggestions and assistance during the thesis writing process. Without your contribution, this thesis would not have been smoothly completed. References Wang, Z., Lin, K. & Liu, X. Distribution and pollution risk assessment of heavy metals in the surface sediment of the intertidal zones of the Yellow River Estuary, China. Mar Pollut Bull 174 , 113286 https://doi.org/10.1016/j.marpolbul.2021.113286 (2022). Ren, J., Hao, J. & Tao, L. Concentrations, spatial distribution, and pollution assessment of heavy metals in surficial sediments from upstream of Yellow River, China. Environ Sci Pollut Res Int 28 , 2904-2913 https://doi.org/10.1007/s11356-020-10349-3 (2021). Hao, J.X. et al. Pollution Evaluation and Sources Identification of Heavy Metals in Surface Sediments from Upstream of Yellow River. Polish Journal of Environmental Studies 30 , 1161-1169 https://doi.org/10.15244/pjoes/125485 (2021). Jaskula, J. & Sojka, M. Assessment of spatial distribution of sediment contamination with heavy metals in the two biggest rivers in Poland. Catena 211 https://doi.org/ARTN 10595910.1016/j.catena.2021.105959 (2022). Muhammad, S. & Ullah, I. Spatial and temporal distribution of heavy metals pollution and risk indices in surface sediments of Gomal Zam Dam Basin, Pakistan. Environmental Monitoring and Assessment 195 https://doi.org/ARTN 115510.1007/s10661-023-11763-z (2023). Hossain, H.M.Z. Spatial distribution and pollution assessment of heavy metals in sediments from the Brahmaputra River watershed in Bangladesh. Environmental Science and Pollution Research 29 , 81557-81570 https://doi.org/10.1007/s11356-022-21522-1 (2022). Haddout, S., Priya, K.L., Casila, J.C.C. & Samad, B. Assessment of heavy metal contamination in the surficial sediments from the Sebou River Estuary, Morocco. Environmental Forensics 23 , 23-31 https://doi.org/10.1080/15275922.2021.1887970 (2022). Kodikara, K.A.S. et al. Spatial distribution of heavy metals in surface sediments of the Kalametiya Lagoon in southern Sri Lanka: Insights into the pollution status and socio-economic interactions. Journal of the National Science Foundation of Sri Lanka 51 , 401-411 https://doi.org/10.4038/jnsfsr.v51i3.11193 (2023). Zhuang, W. & Zhou, F. Distribution, source and pollution assessment of heavy metals in the surface sediments of the Yangtze River Estuary and its adjacent East China Sea. Mar Pollut Bull 164 , 112002 https://doi.org/10.1016/j.marpolbul.2021.112002 (2021). Li, M. et al. Heavy metals in surface sediments in the trans-Himalayan Koshi River catchment: Distribution, source identification and pollution assessment. Chemosphere 244 , 125410 https://doi.org/10.1016/j.chemosphere.2019.125410 (2020). Xie, Y.F., Huo, X.D., Hu, C.H. & Tao, Y.Q. Contamination, Ecological Risk and Source Apportionment of Heavy Metals in the Surface Sediments in the Hailar River, the Upper Source of the Erguna River between China and Russia. Sustainability 15 https://doi.org/ARTN 3655 10.3390/su15043655 (2023). Mohanta, V.L., Naz, A. & Mishra, B.K. Distribution of heavy metals in the water, sediments, and fishes from Damodar river basin at steel city, India: a probabilistic risk assessment. Human and Ecological Risk Assessment 26 , 406-429 https://doi.org/10.1080/10807039.2018.1511968 (2020). Anbazhagan, V., Rajendran, R., Arumugam, G. & Ganeshan, A. Identification of heavy metal pollution source due to idol immersion activity across the Cauvery river basin, Tamil Nadu, South India. Current Science 120 , 200-208 https://doi.org/10.18520/cs/v120/i1/200-208 (2021). Islam, M.S. et al. Assessment of ecological risk for heavy metals in surface sediment of an urban river in a developing country. International Journal of Sediment Research 38 , 834-846 https://doi.org/10.1016/j.ijsrc.2023.07.002 (2023). Budianta, W. Heavy metal pollution and mobility of sediment in Tajum River caused by artisanal gold mining in Banyumas, Central Java, Indonesia. Environ Sci Pollut Res Int 28 , 8585-8593 https://doi.org/10.1007/s11356-020-11157-5 (2021). Goswami, A.P., Das, S. & Kalamdhad, A.S. Assessment of possible pollution risk using spatial distribution and temporal variation of heavy metals in river sediments. Environmental Earth Sciences 80 https://doi.org/ARTN 67710.1007/s12665-021-09983-y (2021). Islam, M.S., Nakagawa, K., Abdullah-Al-Mamun, M., Siddique, M.A.B. & Berndtsson, R. Toxicity and source identification of pollutants in an urban river in Bangladesh. Environmental Earth Sciences 82 https://doi.org/ARTN 14010.1007/s12665-023-10812-7 (2023). Klein, O., Zimmermann, T., Hildebrandt, L. & Pröfrock, D. Technology-critical elements in Rhine sediments - A case study on occurrence and spatial distribution. Science of the Total Environment 852 https://doi.org/ARTN 15846410.1016/j.scitotenv.2022.158464 (2022). Rezapour, S., Asadzadeh, F., Nouri, A., Khodaverdiloo, H. & Heidari, M. Distribution, source apportionment, and risk analysis of heavy metals in river sediments of the Urmia Lake basin. Scientific Reports 12 https://doi.org/ARTN 1745510.1038/s41598-022-21752-w (2022). Liu, S.T. et al. Spatial distribution of heavy metal contaminants: The effects of water-sediment regulation in the Henan section of the Yellow River. Science of the Total Environment 892 https://doi.org/ARTN 16456810.1016/j.scitotenv.2023.164568 (2023). Ren, J., Shang, Z., Tao, L. & Hao, J.X. Speciation and Contamination Assessment of Metals in the Sediments from the Lanzhou Section of the Yellow River, China. Environment Protection Engineering 43 , 113-124 https://doi.org/10.5277/epe170307 (2017). Hao, J.X., Ren, J., Fang, H.B. & Tao, L. Identification Sources and High-Risk Areas of Sediment Heavy Metals in the Yellow River by Geographical Detector Method. Water 13 https://doi.org/ARTN 110310.3390/w13081103 (2021). Ge, M., Liu, G.J., Liu, H.Q. & Liu, Y. Distribution, bioavailability and contamination assessment of mercury and arsenic in the surface sediments from the Yellow River Estuary, China. Human and Ecological Risk Assessment 27 , 274-288 https://doi.org/10.1080/10807039.2019.1710460 (2021). Ren, H.R., Tao, L., Guo, Y.C. & Ren, J. Spatial Distribution and Ecotoxicological Assessment of Heavy Metals in Bottom Sediments of Yellow River from Inner Mongolia, China. Geochemistry International 59 , 1354-1362 https://doi.org/10.1134/S0016702921100074 (2021). Liu, X. et al. Heavy Metal Distribution and Bioaccumulation Combined With Ecological and Human Health Risk Evaluation in a Typical Urban Plateau Lake, Southwest China. Frontiers in Environmental Science 10 https://doi.org/ARTN 81467810.3389/fenvs.2022.814678 (2022). Li, Z. et al. Spatial distribution, ecological risk, and human health assessment of heavy metals in lake surface sections - a case study of Qinghai Lake, China. Environmental Science and Pollution Research 30 , 5137-5149 https://doi.org/10.1007/s11356-022-22293-5 (2023). Li, W.Q. et al. Distribution characteristics, source identification and risk assessment of heavy metals in surface sediments of the Yellow River, China. Catena 216 https://doi.org/ARTN 10637610.1016/j.catena.2022.106376 (2022). Cai, S.W., Zhou, S.Q., Cheng, J.W., Wang, Q.H. & Dai, Y. Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China. Polish Journal of Environmental Studies 30 , 5479-5491 https://doi.org/10.15244/pjoes/136185 (2021). Wang, G.Y., Zhang, Y.Z., Wang, J.H., Zhu, L.S. & Wang, J. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Sediments of a Heavily Polluted Maozhou River, Southern China. Bulletin of Environmental Contamination and Toxicology 106 , 844-851 https://doi.org/10.1007/s00128-021-03202-x (2021). Guo, Z.Q., Tang, S.N., Wang, D.J. & Zhang, J.Q. Spatial Distribution and Factors Influencing Ecological Efficiency of the Yellow River Basin in China. Frontiers in Environmental Science 10 https://doi.org/ARTN 81589810.3389/fenvs.2022.815898 (2022). Liu, P. et al. Comparison of heavy metals in riverine and estuarine sediments in the lower Yangtze River: Distribution, sources, and ecological risks. Environmental Technology & Innovation 30 https://doi.org/ARTN 10307610.1016/j.eti.2023.103076 (2023). Liu, C., Yang, D., Sun, J. & Cheng, Y. The Impact of Environmental Regulations on Pollution and Carbon Reduction in the Yellow River Basin, China. Int J Environ Res Public Health 20 https://doi.org/10.3390/ijerph20031709 (2023). Xie, F.Y. et al. Spatial distribution, pollution assessment, and source identification of heavy metals in the Yellow River. Journal of Hazardous Materials 436 https://doi.org/ARTN 12930910.1016/j.jhazmat.2022.129309 (2022). Fadlillah, L.N., Utami, S., Rachmawati, A.A., Jayanto, G.D. & Widyastuti, M. Ecological risk and source identifications of heavy metals contamination in the water and surface sediments from anthropogenic impacts of urban river, Indonesia. Heliyon 9 , e15485 https://doi.org/10.1016/j.heliyon.2023.e15485 (2023). Salati, S. & Moore, F. Assessment of heavy metal concentration in the Khoshk River water and sediment, Shiraz, Southwest Iran. Environ Monit Assess 164 , 677-89 https://doi.org/10.1007/s10661-009-0920-y (2010). Turekian, K.K. & Wedepohl, K.H. Distribution of the elements in some major units of the earth's crust. Geol Soc America Bull Vol.72 , 175-192 https://doi.org/10.1130/0016-7606(1961)72[175:Doteis]2.0.Co (1961). Yan, M.C., Chi, Q.H., Gu, T.X. & Wang, C.S. Average contents of chemical elements in various sediments of China(in Chinese). Geophysical and geochemical exploration , 468-472 (1995). Müller, G. Index of geoaccumulation in sediments of the Rhine River. GeoJournal Vol.2 , 108-118 (1969). Hawkesworth, C.J., Cawood, P.A. & Dhuime, B. The Evolution of the Continental Crust and the Onset of Plate Tectonics. Frontiers in Earth Science 8 https://doi.org/ARTN 32610.3389/feart.2020.00326 (2020). Rajmohan, N., Prathapar, S.A., Jayaprakash, M. & Nagarajan, R. Vertical distribution of heavy metals in soil profile in a seasonally waterlogging agriculture field in Eastern Ganges Basin. Environmental Monitoring and Assessment 186 , 5411-5427 https://doi.org/10.1007/s10661-014-3790-x (2014). Sun, W. et al. Distribution characteristics and ecological risk assessment of heavy metals in sediments of Shahe reservoir. Scientific Reports 12 https://doi.org/ARTN 1623910.1038/s41598-022-20540-w (2022). Hakanson, L. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Research Vol.14 , 975-1001 https://doi.org/10.1016/0043-1354(80)90143-8 (1980). Jahan, S. & Strezov, V. Comparison of pollution indices for the assessment of heavy metals in the sediments of seaports of NSW, Australia. Marine Pollution Bulletin 128 , 295-306 https://doi.org/10.1016/j.marpolbul.2018.01.036 (2018). Zhang, W., Liu, M. & Li, C. Soil heavy metal contamination assessment in the Hun-Taizi River watershed, China. Sci Rep 10 , 8730 https://doi.org/10.1038/s41598-020-65809-0 (2020). Wu, D., Liu, H., Wu, J. & Gao, X. Spatial Distribution, Ecological Risk Assessment and Source Analysis of Heavy Metals Pollution in Urban Lake Sediments of Huaihe River Basin. International Journal of Environmental Research and Public Health 19 https://doi.org/ARTN 1465310.3390/ijerph192214653 (2022). Chen, R. et al. Assessment of Soil-Heavy Metal Pollution and the Health Risks in a Mining Area from Southern Shaanxi Province, China. Toxics 10 https://doi.org/10.3390/toxics10070385 (2022). Li, M., Zhu, S., Ouyang, T., Tang, J. & Tang, Z. Magnetic properties of the surface sediments in the Yellow River Estuary and Laizhou Bay, Bohai Sea, China: Implications for monitoring heavy metals. J Hazard Mater 410 , 124579 https://doi.org/10.1016/j.jhazmat.2020.124579 (2021). Hua, C.Y. et al. Ecological risk, dynamics in fingerprinting, and source apportionment of heavy metals in soils from plateau in Upper Yellow River, Qinghai Province, China. Journal of Soils and Sediments 24 , 189-203 https://doi.org/10.1007/s11368-023-03600-0 (2024). Xie, S.C. et al. Spatial distribution and ecological risk of heavy metals and their source apportionment in soils from a typical mining area, Inner Mongolia, China. Journal of Arid Land 15 , 1196-1215 https://doi.org/10.1007/s40333-023-0109-1 (2023). Zhang, M. et al. Impact of Xiaolangdi Reservoir on the Evolution of Water Infiltration Influence Zones of the Secondary Perched Reach of the Lower Yellow River. Water 15 https://doi.org/ARTN 430810.3390/w15244308 (2023). Bazarzhapov, T.Z. et al. Distribution of Heavy Metals in Water and Bottom Sediments in the Basin of Lake Gusinoe (Russia): Ecological Risk Assessment. Water 15 https://doi.org/ARTN 338510.3390/w15193385 (2023). Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Tao","suffix":""},{"id":362769125,"identity":"cb63e6fd-20a8-43a1-9130-151a7d10f8c9","order_by":2,"name":"Hanru Ren","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hanru","middleName":"","lastName":"Ren","suffix":""},{"id":362769126,"identity":"d13a1bef-befc-4057-836b-44081e897b51","order_by":3,"name":"Xiangyu Xue","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Xue","suffix":""},{"id":362769127,"identity":"34650d1d-754b-4531-a1e1-fc3e13676184","order_by":4,"name":"Zhijie Yang","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Yang","suffix":""},{"id":362769128,"identity":"03b0ae83-c550-4db0-80cd-c0e74972c466","order_by":5,"name":"Yucheng Jiang","email":"","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yucheng","middleName":"","lastName":"Jiang","suffix":""},{"id":362769129,"identity":"ae295a22-475f-46b0-8a12-c5a1213f03bd","order_by":6,"name":"Jun Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBAC+/bmAwcSDCTk+InWYsBzLPHAhwobY8kGorVI5BgfnHEmLdHgALFazCUSDA7zth1OMD6evIHhR8U2wlosex4kgLTkmZ15VsDYc+Y2EdYcTzgA0lJsdiPHgJmxjRgtBxIbQFoSN88gVovBiWQGsPc3SBCrRbLnGAM4kCWAfjlIlF/42fs/fwBHZXvyxgc/KojxCwIkEB81CC2k6hgFo2AUjIIRAgArZUbEorjZIgAAAABJRU5ErkJggg==","orcid":"","institution":"Lanzhou Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-09-03 12:33:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5024997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5024997/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87503-9","type":"published","date":"2025-03-19T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69080394,"identity":"84be992c-25dd-450e-af4e-5828ce73840c","added_by":"auto","created_at":"2024-11-15 11:51:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143015,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/1e2253d58c11b72de30cc35a.png"},{"id":69081223,"identity":"732af604-b3bc-41dd-a494-45a506fe8372","added_by":"auto","created_at":"2024-11-15 11:59:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46840,"visible":true,"origin":"","legend":"\u003cp\u003eContents of heavy metals in sediments from six different typical urban areas.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/d7a811fa87472398cd4a5b06.png"},{"id":69080392,"identity":"cfc2b435-a654-4cb0-8a94-5f2d17b4bf5c","added_by":"auto","created_at":"2024-11-15 11:51:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31332,"visible":true,"origin":"","legend":"\u003cp\u003eAccumulation of enrichment factors of heavy metals in six typical urban areas.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/a2deb9ad1b51b39e51ef8af1.png"},{"id":69080397,"identity":"4f8bf78c-731b-4fa2-a8e3-4f01019cfdc6","added_by":"auto","created_at":"2024-11-15 11:51:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40270,"visible":true,"origin":"","legend":"\u003cp\u003eGeo-accumulation Indices of heavy metals in six different typical urban areas.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/e3721b5c467f56a0a50fba37.png"},{"id":69080395,"identity":"aa38ac53-fe08-49a3-9e4f-072faf2f5f54","added_by":"auto","created_at":"2024-11-15 11:51:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29031,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential ecological risk index of heavy metals in six different typical urban areas.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/f75b2c4e537d2da707103a73.png"},{"id":79120486,"identity":"ffe9de30-c2d6-45dd-afea-8f6b67aab905","added_by":"auto","created_at":"2025-03-24 16:08:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/494122ee-4df7-45a7-8a76-ae7c8d37c88e.pdf"},{"id":69080393,"identity":"af966410-4fcb-4dd4-84d6-d9880a35e823","added_by":"auto","created_at":"2024-11-15 11:51:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54270,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1ScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/9218f03a50563adf2f7f10b8.docx"},{"id":69080396,"identity":"ccf701e8-b819-4c2a-af8c-d98dfb61ee20","added_by":"auto","created_at":"2024-11-15 11:51:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1090719,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2ScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-5024997/v1/e5b1c4efc6f15a49422277ed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surface sediment properties and heavy metal contamination assessment in typical urban areas from middle and upper reaches of Yellow River","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRivers have multiple functions in aquatic environments, including ecological services, geochemical cycles, and habitat provision for plants and animals \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, with rapid economic development and urban expansion in many developing countries, a large number of uncontrolled pollutants flow into rivers, posing a significant challenge to aquatic environments\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.Heavy metals in river sediments are predicted to be an important source of pollutants and are specific indicators of pollutants because of their hydrophobicity and accumulation characteristics\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Heavy metals in river sediments originate from natural and anthropogenic sources. It has attracted worldwide attention owing to its persistence in the environment and radioactive toxins\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHeavy metals quickly migrate from water to sediment, adsorb onto the surface of particles, and then migrate further and are released into the water body through changes in the external environment, threatening the aquatic ecosystem\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, river sediments can be regarded as reservoirs for heavy metals and are the main research object for monitoring heavy metal pollutants in aquatic ecosystems. Its spatial distribution characteristics, contents, and pollution levels will help determine the source of heavy metals and play a key role in evaluating the pollution and potential ecological risk status of rivers \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Heavy metal pollution assessments provide theoretical support for environmental risk management of water\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Yellow River is the second largest river in China and is located between 96 \u0026deg;\u0026mdash;19 \u0026deg; E and 32 \u0026deg;\u0026mdash;42 \u0026deg; N. It originates in the Qinghai Province, flows through Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan and Shandong provinces, and finally flows into the Bohai Sea. As the most important river in northern China, the Yellow River provides water to 15% of China's arable land and nearly 160\u0026nbsp;million people\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The Yellow River has been polluted in recent decades owing to agricultural, urban, and industrial activities, particularly heavy metal pollution\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, the amount of domestic sewage and industrial wastewater discharged into the middle and upper reaches of the Yellow River has increased annually, and water quality has deteriorated significantly\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, the construction of a series of dams in the Yellow River has changed the quantity and quality of river sediment to ensure hydropower generation, flood control, and water supply\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous studies have analyzed heavy metal pollution in the sediment of the Yellow River, but they were limited to a specific or typically small area, such as a typical city, estuary, wetland, nature reserve, or heavy industrial area\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Sediment samples were also collected from different locations (riverbed, floodplain, alluvial area, main stream, or tributary)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, a comprehensive study of the content, distribution characteristics and pollution assessment of heavy metals in the sediments of several typical urban areas in the middle and upper reaches of the Yellow River is of great significance to further reveal the overall pollution status of the Yellow River\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe middle and upper reaches of the Yellow River start from the source of the Yellow River in the bay har, Qinghai Province, pass through seven provinces of Qinghai, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, and Henan, and end in Mengjin District, Luoyang City, Henan Province, with a total length of approximately 4,678 km and a drainage area of 772,000 square kilometers\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, the content, spatial distribution, and pollution status of heavy metals in river sediments in six typical urban areas of Xining, Lanzhou, Yinchuan, Baotou, Weinan, and Luoyang in the middle and upper reaches of the Yellow River were analyzed (Figure. 1) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of samples\u003c/h2\u003e \u003cp\u003eSixty sampling points were evenly distributed in the river basins of six typical urban areas along the middle and upper reaches of the Yellow River (According to the accessibility principle and administrative units, 10 sampling points were set in each city on average). Sixty sediment samples were collected at set sampling points (0\u0026ndash;5 cm) along the riverbed using a grabbed gravity mud collector. Samples were collected from mixed samples, that is, sediment samples were collected at different positions on the same section three times and fully mixed. The sampling point number, sampling time, location, latitude and longitude, altitude, landform unit where the sampling point was located, river width, water depth, and distance between the sampling point and shore were recorded in detail, and photographs of the river section where the sampling point was located and the surrounding landform (including vegetation status and erosion status) were taken. The collected sediment samples were packed in plastic bottles and the soil samples were packed in polyethylene plastic bags. The sample number, sampling location, date, and other information on the plastic bottle or plastic bag were returned to the laboratory for air-drying, grinding, and screening before the next experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample digestion\u003c/h2\u003e \u003cp\u003e0.5 g (to the nearest 0.0001 g) of sample was accurately weighed and digested in a polytetrafluoroethylene (PTFE) crucible using an electronic balance. The digestion steps were as follows: first, wet the soil sample with three drops of distilled water, add 10mL of concentrated hydrochloric acid, control the temperature to 90 ℃ on an electric heating plate, and heat it to a viscous state at a constant temperature; second, adding 10mL of concentrated nitric acid of superior grade pure was added, and the mixture was continuously heated to become viscous; Add 10mL of hydrofluoric acid was added again, and the mixture was heated until it became viscous. Finally, add 10mL of perchloric acid was added and the mixture was heated until the white smoke was exhausted.\u003c/p\u003e \u003cp\u003eThe digested samples were white or yellow, and sticky when the crucible was tilted. Digested samples were rinsed with water and poured into a funnel for filtration. The inner wall of the crucible was then rinsed twice and poured into a funnel. When the amount of solution in the funnel was less than one- third, the filter paper was rinsed twice with water. Finally, the filter paper was removed, the funnel was washed with distilled water, and distilled water was added to a constant volume of 100mL, shake well, placed, and tested. The digestion process was repeated four times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample determination\u003c/h2\u003e \u003cp\u003eThe contents of heavy metals such as Fe, Mn, Cu, Zn, Ni, Pb, Cd and Cr in all treated sediment samples were determined by inductively coupled plasma mass spectrometry (ICP-MS, Thermo Fisher Scientific, USA). All samples were processed three times, the final data are the mean values, and the relative standard deviations were all within 10% (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePollution assessment methods\u003c/h2\u003e \u003cp\u003eThere are many methods for evaluating heavy metal pollution in sediments, and the evaluation system is relatively accurate\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In this study, the most widely used enrichment factor, geo-accumulation index, and potential ecological risk index methods were selected to evaluate heavy metal pollution in the sediments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment factors evaluation method\u003c/h2\u003e \u003cp\u003eThe enrichment factor (EF) is an important method for evaluating the degree of water sediment pollution and is widely used to determine the degree of sediment pollution in aquatic ecosystems\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The most commonly used reference elements were Al, Fe, Mn, Mg, and Ca. The normalized metal was used to identify abnormal metal content, and Mn was used as the reference material in this study\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The calculation method is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:EF=\\frac{{\\left(\\frac{M}{Mn}\\right)}_{sample}}{({\\frac{M}{Mn})}_{backgroud}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{M}{Mn}\\right)}_{sample}\\)\u003c/span\u003e\u003c/span\u003e is the ratio of measured values of a metal element M and Mn in the same sample, the average crustal contents (Fe: 47200 mg/kg、Mn༚850 mg/kg、Cu༚45 mg/kg、Ni༚68 mg/kg、Zn༚95 mg/kg、Cr༚90 mg/kg、Pb༚20 mg/kg、Cd༚0.30 mg/kg) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003ewere taken as the background value in this study, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{M}{Mn}\\right)}_{backgroud}\\)\u003c/span\u003e\u003c/span\u003e is the ratio of background values of a metal element M and Mn. The evaluation criteria for enrichment factors can be expressed in seven grades (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGeo-accumulation Index method\u003c/h2\u003e \u003cp\u003eProfessor M\u0026uuml;ller of the University of Heidelberg, Germany, first proposed the geological accumulation index (I\u003csub\u003egeo\u003c/sub\u003e) method to evaluate the degree of heavy metal pollution in sediment and water in 1969 \u003csup\u003e38\u003c/sup\u003e. The formula used was as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{I}_{geo}={log}_{2}\\left(\\frac{{C}_{n}}{{1.5B}_{n}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e is the measured content of heavy metal n and \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e is the geochemical background value of heavy metal n. In this study, the average contents of elements in the upper crust (Fe: 35000 mg/kg、Mn༚600 mg/kg、Cu༚25 mg/kg、Ni༚20 mg/kg、Zn༚71 mg/kg、Cr༚35 mg/kg、Pb༚20 mg/kg、Cd༚0.10 mg/kg)were used as the background value \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003e1.5\u003c/em\u003e is a correction index that considers the influence of different rocks on the background values\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The geological accumulation index can be divided into seven grades to indicate changes in the degree of pollution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePotential ecological risk index method\u003c/h2\u003e \u003cp\u003eThe potential ecological risk index (ERI) and comprehensive potential ecological risk index (RI) were proposed by Swedish scientist Hakanson in 1980. The degree of heavy metal pollution in the sediment was evaluated according to the content, type, toxicity level, environmental response, and water sensitivity to heavy metal pollution in sediment \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The following formula was used:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:ERI={T}_{r}^{i}\\times\\:\\frac{{C}^{i}}{{C}_{n}^{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:RI={\\varSigma\\:}_{1}^{i}ERI={\\varSigma\\:}_{1}^{i}{T}_{r}^{i}\\times\\:\\frac{{C}^{i}}{{C}_{n}^{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eERI\u003c/em\u003e is the potential ecological risk index of a single heavy metal; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{r}^{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the toxicity coefficient of the ith heavy metal in the sediment, and the toxicity coefficients of Cu, Ni, Zn, Pb, Cr, and Cd are 5, 5, 1, 5, 2 and 30 respectively\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}^{i}\\)\u003c/span\u003e\u003c/span\u003e is the measured value of the first heavy metal; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{n}^{i}\\)\u003c/span\u003e\u003c/span\u003e is the background value of the second heavy metal; \u003cem\u003eRI\u003c/em\u003e is the comprehensive potential ecological risk index. The evaluation criteria for ERI and RI were expressed using different grading levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\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\u003eClassification standard of EF, I\u003csub\u003egeo\u003c/sub\u003e, ERI and RI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnrichment level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003csub\u003egeo\u003c/sub\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePollution level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eERI classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRI classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEcological risk level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u0026lt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003csub\u003egeo\u003c/sub\u003e\u0026le;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMicro-enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026lt;I\u003csub\u003egeo\u003c/sub\u003e\u0026le;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLight pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eERI\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRI\u0026lt;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026lt;I\u003csub\u003egeo\u003c/sub\u003e\u0026le;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u0026thinsp;\u0026le;\u0026thinsp;ERI\u0026lt;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026thinsp;\u0026le;\u0026thinsp;RI\u0026lt;190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavier enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026lt;I\u003csub\u003egeo\u003c/sub\u003e\u0026le;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeavy pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u0026thinsp;\u0026le;\u0026thinsp;ERI\u0026lt;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190\u0026thinsp;\u0026le;\u0026thinsp;RI\u0026lt;380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrong risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong enrichment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026lt;I\u003csub\u003egeo\u003c/sub\u003e\u0026le;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160\u0026thinsp;\u0026le;\u0026thinsp;ERI\u0026lt;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRI\u0026thinsp;\u0026ge;\u0026thinsp;380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExtra strong risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026thinsp;\u0026le;\u0026thinsp;EF\u0026lt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely rich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026lt;I\u003csub\u003egeo\u003c/sub\u003e\u0026le;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerious pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eERI\u0026thinsp;\u0026ge;\u0026thinsp;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExtremely risky\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 \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eContents and spatial distribution characteristics of heavy metals\u003c/h2\u003e \u003cp\u003eThe average crustal contents of the eight heavy metals were used as background values. The average heavy metal contents in Luoyang City exceeded the background values, while the average contents of Zn, Cr, and Cd in the other five cities were relatively high, and their accumulation was obvious. Compared with the average content of the upper crust in China as the background value, all the heavy metals in Luoyang City also exceeded the background value, and the average content of five types heavy metals (Mn, Ni, Zn, Cr and Cd) in the other five cities were relatively high, indicating that some urban basins in the middle and upper reaches of the Yellow River were polluted by heavy metals, and the Yellow River was polluted by heavy metals. Heavy metals with high variability (moderate variability CV values ranging from 15\u0026ndash;36%) were exceeded in all six cities based on the coefficient of variation\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, especially in Weinan. This shows that there were significant differences in the spatial distribution of heavy metals, which was also confirmed by the spatial distribution map of heavy metals (Supplementary Table\u0026nbsp;1 and Figure. 2).\u003c/p\u003e \u003cp\u003eXining, Lanzhou, Yinchuan and Baotou are distributed in the upper reaches of the Yellow River, while Weinan and Luoyang are located in the middle reaches of the Yellow River\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The geographical environment of the middle and upper reaches of the Yellow River is complex, and human activities on both sides of the river are diverse, which explains the similarities and differences in the spatial distribution of heavy metals in the sediments of the middle and upper reaches of the Yellow River\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eArcGIS10.7 software was used for the heavy metal content in sediments of the middle and upper reaches of the Yellow River and was interpolated by kriging. The buffer zones of the middle and upper reaches of the Yellow River were constructed, and the spatial distribution map of each heavy metal was obtained through mask extraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCr had a relatively high-value distribution in four typical urban areas in the upper reaches of the Yellow River. The main reasons for the distribution of high-value areas in Xining may be the poor water quality of the Huangshui River flowing through it and the discharge of wastewater from various industrial productions\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The main reasons for the distribution of high-value areas in Lanzhou City may be the discharge of wastewater from large-scale chemical production enterprises in Xigu District near the middle reaches and industrial production on both sides of the lower reaches\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The Cr content in Yinchuan City was higher than that of the other heavy metals, which is also suspected to be related to the disorderly discharge of industries on both sides of the river. The main reasons for the formation of medium-high value areas in Baotou City may be the use of chemical fertilizers and pesticides in agricultural production and the inflow of industrial wastewater from industrial areas in the southern part of the city into rivers\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Weinan is a heavy industrial city located in the middle reaches of the Yellow River, the Weihe River is the largest tributary of the Yellow River, runs through the city, and is seriously polluted by industrial production emissions on both sides of the river; the polluted Weihe River flows into the Yellow River, which is the main reason for the formation of a high value area in the southeast of Weinan\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e; Luoyang is also located in the middle reaches of the Yellow River, the overall heavy metal content is high, mainly in the middle and high value areas, because the upper reaches of Luoyang are close to Sanmenxia City, the Xiaolangdi Water Conservancy Project is located in the city, and excessive water conservancy and power development leads to the reduction of natural flow and ecological flow of rivers in the sensitive period, and heavy metal deposition affects the sediment content. There are intensive industries on both sides of the middle and lower reaches of Luoyang, and the discharge of wastewater from industrial production affects the heavy metal content in the sediment exceeding the standard\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;1 and Supplementary Figure. 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of heavy metal pollution\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eEnrichment factors\u003c/h2\u003e \u003cp\u003eThe comprehensive average values of the enrichment factors of the eight types of heavy metals in order from large to small were as follows: Weinan (1.73)\u0026thinsp;\u0026gt;\u0026thinsp;Xining (1.60)\u0026thinsp;\u0026gt;\u0026thinsp;Yinchuan (1.59)\u0026thinsp;\u0026gt;\u0026thinsp;Lanzhou (1.56)\u0026thinsp;\u0026gt;\u0026thinsp;Baotou (1.51)\u0026thinsp;\u0026gt;\u0026thinsp;Luoyang (1.44). There was little difference in the values and they were all between 1 and 3, which belonged to the micro-enrichment level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe average enrichment factors of Fe, Cu and Ni were all less than 1, indicated that they had no enrichment level in sediments and no affected by human beings; The average enrichment factors of Mn, Zn and Cr were all between 1 to 3, indicated that they were micro-enriched in sediments and less affected by human beings; The average enrichment factor of Pb was less than 1 in Xining, Lanzhou, Yinchuan and Baotou, and ranged from 1 to 3 in Luoyang and Weinan, indicated that Pb was not enriched or slightly enriched in sediments; The enrichment degree of Cd is the largest, Xining (5.55), Lanzhou (5.24), Yinchuan (5.49) and Baotou (5.23) belong to heavy enrichment levels, Weinan (4.97) and Luoyang (3.81) belong to the medium enrichment level, but the maximum enrichment factors of Cd in these 2 cities reached 26.36 and 16.60, the results showed that the strong enrichment sites of Cd in these 2 cities were relatively concentrated, and there were local pollution sources with strong enrichment levels(Supplementary Table\u0026nbsp;2 and Figure. 3).\u003c/p\u003e \u003cp\u003eSeven types of heavy metals, excluding Cd, were non-enriched or micro-enriched levels in Xining, Lanzhou, Yinchuan and Baotou in the upper reaches of the Yellow River and Luoyang City in the middle reaches of the Yellow River. In Weinan City, in the middle reaches of the Yellow River, two Zn sites reached a moderate enrichment level, and two Pb sites reached a moderate or severe enrichment level. There were sites with moderate and above Cd enrichment levels in the six typical urban watersheds, especially in Weinan City and Luoyang City in the middle reaches, where two sites reached strong enrichment levels or above, indicating that there might be industrial production or other human activities near these sites. The discharge of Cd-containing pollutants into the water body led to a strong enrichment level, which had a serious impact, and relevant departments should pay attention to it\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGeo-cumulative index\u003c/h2\u003e \u003cp\u003eThe comprehensive average values of geo-accumulation indices of eight types of heavy metals in the order from large to small were: Luoyang (0.29)\u0026thinsp;\u0026gt;\u0026thinsp;Xining (0.04)\u0026thinsp;\u0026gt;\u0026thinsp;Weinan (0.01)\u0026thinsp;\u0026gt;\u0026thinsp;Yinchuan (0)\u0026thinsp;=\u0026thinsp;Lanzhou (0)\u0026thinsp;\u0026gt;\u0026thinsp;Baotou (-0.02) (Table\u0026nbsp;3.2 and Figure. 3), with values ranging from 0 to 1 in Luoyang, Xining, and Weinan, and less than or equal to 0 in the other three cities, indicating that the Yellow River Basin in these six cities is non-polluted or slightly polluted.\u003c/p\u003e \u003cp\u003eThe average geo-accumulation indices of Fe, Mn, Cu, Zn, and Pb were less than 1 in all five cities, except Luoyang City, indicating that these heavy metals were not polluted in the sediment and were less affected by human beings. The average geo-accumulation indices of the seven types of heavy metals, except Fe, in Luoyang City were between 0 and 1, indicating that the entire city was polluted by heavy metals, but the degree of pollution was relatively low (Supplementary Table\u0026nbsp;3 and Figure. 4). Most of the sampling points belonged to non-pollution or light pollution levels in the sample sites of the six typical urban areas studied; the sample sites with moderate pollution existed only for Cd, and the site with moderate or above heavy metal pollution did not exist in Lanzhou City, indicating that the heavy metal Cd is an important pollution factor in the study area and should be studied as a key point.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePotential ecological risk assessment\u003c/h2\u003e \u003cp\u003eThe average, maximum, and minimum ERI values of Cu, Ni, Zn, Cr, and Pb were less than 40, indicating a low risk level. The average ERI value of Cd was between 80 and 160, indicating a strong risk level. The maximum values of ERI of Cd were between 80 and 160 in Lanzhou (137.6) and Yinchuan (156.1), which showed that there was a strong risk of ecological pollution at the corresponding sites; The values were between 160 and 320 in Xining (185. 23) and Baotou (165. 7). The corresponding sites had a strong potential ecological for pollution risk, with maximum values exceeding 320 in Weinan (400. 42), and Luoyang (471. 34), and the corresponding sites had a strong potential ecological pollution risk level (Supplementary Table\u0026nbsp;4 and Figure. 5).\u003c/p\u003e \u003cp\u003eThe RI values of the six cities were between 95 and 190, indicating that there was a moderate risk of ecological pollution in the 6 cities, which should be paid attention to by the relevant departments of environmental governance. The maximum RI for Xining was 210. 97, indicating a strong potential ecological pollution risk in the local watersheds. The maximum RI value for Weinan (431. 13), and Luoyang (504. 87) were greater than 380, which indicates that the potential ecological pollution risk level was very strong, and the relevant departments must reduce and control pollution in these local watersheds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eEight types of heavy metals (Fe, Mn, Cu, Ni, Zn, Cr, Pb and Cd) were studied in the sediments of six typical urban areas in the middle and upper reaches of the Yellow River (Xining, Lanzhou, Yinchuan, Baotou, Weinan and Luoyang) were studied in this paper. The contents and spatial distribution characteristics of heavy metals were analyzed by collecting sediments in the field and measuring their contents in the laboratory, taking the average contents of heavy metals in shale worldwide as the background value. The pollution level and potential ecological risk of heavy metals in the sediments of the middle and upper reaches of the Yellow River were studied using three evaluation methods: the enrichment factor, ground accumulation index, and potential ecological risk. The main results are as follows:\u003c/p\u003e\n\u003cp\u003e(1) The Cr and Cd contents were relatively high in the six cities, mainly because of the influence of human agricultural and industrial production activities, which were the main pollution factors in the study area. Compared to the other five cities, the accumulation of heavy metals in Luoyang was the highest, and Cd pollution exceeded the standard, whereas the Cr content in Weinan was the highest.\u003c/p\u003e\n\u003cp\u003e(2) The spatial distributions of the eight heavy metals in the six cities were significantly different. The heavy metal concentrations in the four typical urban areas in the upper reaches of the Yellow River were relatively low, whereas those in Weinan and Luoyang in the middle reaches of the Yellow River were relatively high. The spatial distribution of heavy metals was mainly affected by human factors, and the heavy metal content was high near industrial areas and downstream of the water conservancy project. Therefore, relevant departments should control the discharge of industrial wastewater.\u003c/p\u003e\n\u003cp\u003e(3) The eight types of heavy metals were micro-enriched in the rivers as a whole, of which Fe, Cu, and Ni were not enriched in the sediment, Cr and Cd were relatively enriched, and there were strong enrichment sites in Weinan and Luoyang. Based on the evaluation of the geo-accumulation index, Cd produced light pollution, Pb produced light pollution only in Luoyang, and the other heavy metals did not cause pollution in the six cities. Based on the potential ecological risk assessment, there were potential pollution risks in the six typical urban areas; however, the risk level was not high, and the potential ecological risk index of Cd was the highest. Cd also poses a strong ecological risk to Weinan and Luoyang.\u003c/p\u003e\n\u003cp\u003e(4) Weinan and Luoyang are the two cities with serious heavy metal pollution among the six typical cities, and the excessive heavy metal Cd is the direct cause of river pollution, which is caused by the emission of Cd in the process of human industrial and agricultural production. Therefore, relevant environmental protection and pollution control units should focus on and strengthen control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the manuscript for publication in ecological processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interest exists in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the Industrial Support Program of The Education Department of Gansu Province (2021CYZC-31), the project of Gansu Province Science and Technology Plan (22CX3GA076), the Special Project of Gansu Science and Technology Commissioner (23CXGA0082).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJunzhang Wang:\u003c/strong\u003e Conceptualization, Software, Writing—original draft.\u0026nbsp;\u003cstrong\u003eLing Tao:\u0026nbsp;\u003c/strong\u003eMethodology, Formal analysis.\u0026nbsp;\u003cstrong\u003eHanru Ren:\u003c/strong\u003e Resources, Data acquisition.\u0026nbsp;\u003cstrong\u003eXiangyu Xue:\u0026nbsp;\u003c/strong\u003eData curation, Software.\u0026nbsp;\u003cstrong\u003eZhijie Yang:\u0026nbsp;\u003c/strong\u003eSoftware.\u003cstrong\u003e\u0026nbsp;Yucheng Jiang:\u0026nbsp;\u003c/strong\u003eSoftware.\u0026nbsp;\u003cstrong\u003eJun Ren:\u003c/strong\u003e Writing—review and editing, Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received significant assistance from the Key Laboratory of Water Environment of Lanzhou Jiaotong University and Gansu Hanxing Environmental Protection Co. Ltd. The convenient conditions provided by the teachers provided a strong guarantee for sample collection and experimental procedures.\u003c/p\u003e\n\u003cp\u003eWe are grateful to Professor Ren Jun for the guidance. Throughout this thesis, he carefully reviewed the draft and offered invaluable critique. His academic excellence and insightful comments are remarkable.\u003c/p\u003e\n\u003cp\u003eI would also like to express my appreciation to other scholars and experts who offered suggestions and assistance during the thesis writing process. Without your contribution, this thesis would not have been smoothly completed.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang, Z., Lin, K. \u0026amp; Liu, X. Distribution and pollution risk assessment of heavy metals in the surface sediment of the intertidal zones of the Yellow River Estuary, China. \u003cem\u003eMar Pollut Bull\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 113286 https://doi.org/10.1016/j.marpolbul.2021.113286 (2022).\u003c/li\u003e\n \u003cli\u003eRen, J., Hao, J. \u0026amp; Tao, L. Concentrations, spatial distribution, and pollution assessment of heavy metals in surficial sediments from upstream of Yellow River, China. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 2904-2913 https://doi.org/10.1007/s11356-020-10349-3 (2021).\u003c/li\u003e\n \u003cli\u003eHao, J.X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Pollution Evaluation and Sources Identification of Heavy Metals in Surface Sediments from Upstream of Yellow River. \u003cem\u003ePolish Journal of Environmental Studies\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1161-1169 https://doi.org/10.15244/pjoes/125485 (2021).\u003c/li\u003e\n \u003cli\u003eJaskula, J. \u0026amp; Sojka, M. Assessment of spatial distribution of sediment contamination with heavy metals in the two biggest rivers in Poland. \u003cem\u003eCatena\u003c/em\u003e \u003cstrong\u003e211\u003c/strong\u003e https://doi.org/ARTN 10595910.1016/j.catena.2021.105959 (2022).\u003c/li\u003e\n \u003cli\u003eMuhammad, S. \u0026amp; Ullah, I. Spatial and temporal distribution of heavy metals pollution and risk indices in surface sediments of Gomal Zam Dam Basin, Pakistan. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e \u003cstrong\u003e195\u003c/strong\u003e https://doi.org/ARTN 115510.1007/s10661-023-11763-z (2023).\u003c/li\u003e\n \u003cli\u003eHossain, H.M.Z. Spatial distribution and pollution assessment of heavy metals in sediments from the Brahmaputra River watershed in Bangladesh. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 81557-81570 https://doi.org/10.1007/s11356-022-21522-1 (2022).\u003c/li\u003e\n \u003cli\u003eHaddout, S., Priya, K.L., Casila, J.C.C. \u0026amp; Samad, B. Assessment of heavy metal contamination in the surficial sediments from the Sebou River Estuary, Morocco. \u003cem\u003eEnvironmental Forensics\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 23-31 https://doi.org/10.1080/15275922.2021.1887970 (2022).\u003c/li\u003e\n \u003cli\u003eKodikara, K.A.S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Spatial distribution of heavy metals in surface sediments of the Kalametiya Lagoon in southern Sri Lanka: Insights into the pollution status and socio-economic interactions. \u003cem\u003eJournal of the National Science Foundation of Sri Lanka\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 401-411 https://doi.org/10.4038/jnsfsr.v51i3.11193 (2023).\u003c/li\u003e\n \u003cli\u003eZhuang, W. \u0026amp; Zhou, F. Distribution, source and pollution assessment of heavy metals in the surface sediments of the Yangtze River Estuary and its adjacent East China Sea. \u003cem\u003eMar Pollut Bull\u003c/em\u003e \u003cstrong\u003e164\u003c/strong\u003e, 112002 https://doi.org/10.1016/j.marpolbul.2021.112002 (2021).\u003c/li\u003e\n \u003cli\u003eLi, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Heavy metals in surface sediments in the trans-Himalayan Koshi River catchment: Distribution, source identification and pollution assessment. \u003cem\u003eChemosphere\u003c/em\u003e \u003cstrong\u003e244\u003c/strong\u003e, 125410 https://doi.org/10.1016/j.chemosphere.2019.125410 (2020).\u003c/li\u003e\n \u003cli\u003eXie, Y.F., Huo, X.D., Hu, C.H. \u0026amp; Tao, Y.Q. Contamination, Ecological Risk and Source Apportionment of Heavy Metals in the Surface Sediments in the Hailar River, the Upper Source of the Erguna River between China and Russia. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e https://doi.org/ARTN 3655 10.3390/su15043655 (2023).\u003c/li\u003e\n \u003cli\u003eMohanta, V.L., Naz, A. \u0026amp; Mishra, B.K. Distribution of heavy metals in the water, sediments, and fishes from Damodar river basin at steel city, India: a probabilistic risk assessment. \u003cem\u003eHuman and Ecological Risk Assessment\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 406-429 https://doi.org/10.1080/10807039.2018.1511968 (2020).\u003c/li\u003e\n \u003cli\u003eAnbazhagan, V., Rajendran, R., Arumugam, G. \u0026amp; Ganeshan, A. Identification of heavy metal pollution source due to idol immersion activity across the Cauvery river basin, Tamil Nadu, South India. \u003cem\u003eCurrent Science\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 200-208 https://doi.org/10.18520/cs/v120/i1/200-208 (2021).\u003c/li\u003e\n \u003cli\u003eIslam, M.S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Assessment of ecological risk for heavy metals in surface sediment of an urban river in a developing country. \u003cem\u003eInternational Journal of Sediment Research\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 834-846 https://doi.org/10.1016/j.ijsrc.2023.07.002 (2023).\u003c/li\u003e\n \u003cli\u003eBudianta, W. Heavy metal pollution and mobility of sediment in Tajum River caused by artisanal gold mining in Banyumas, Central Java, Indonesia. \u003cem\u003eEnviron Sci Pollut Res Int\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 8585-8593 https://doi.org/10.1007/s11356-020-11157-5 (2021).\u003c/li\u003e\n \u003cli\u003eGoswami, A.P., Das, S. \u0026amp; Kalamdhad, A.S. Assessment of possible pollution risk using spatial distribution and temporal variation of heavy metals in river sediments. \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e https://doi.org/ARTN 67710.1007/s12665-021-09983-y (2021).\u003c/li\u003e\n \u003cli\u003eIslam, M.S., Nakagawa, K., Abdullah-Al-Mamun, M., Siddique, M.A.B. \u0026amp; Berndtsson, R. Toxicity and source identification of pollutants in an urban river in Bangladesh. \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e https://doi.org/ARTN 14010.1007/s12665-023-10812-7 (2023).\u003c/li\u003e\n \u003cli\u003eKlein, O., Zimmermann, T., Hildebrandt, L. \u0026amp; Pr\u0026ouml;frock, D. Technology-critical elements in Rhine sediments - A case study on occurrence and spatial distribution. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e852\u003c/strong\u003e https://doi.org/ARTN 15846410.1016/j.scitotenv.2022.158464 (2022).\u003c/li\u003e\n \u003cli\u003eRezapour, S., Asadzadeh, F., Nouri, A., Khodaverdiloo, H. \u0026amp; Heidari, M. Distribution, source apportionment, and risk analysis of heavy metals in river sediments of the Urmia Lake basin. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e https://doi.org/ARTN 1745510.1038/s41598-022-21752-w (2022).\u003c/li\u003e\n \u003cli\u003eLiu, S.T.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Spatial distribution of heavy metal contaminants: The effects of water-sediment regulation in the Henan section of the Yellow River. \u003cem\u003eScience of the Total Environment\u003c/em\u003e \u003cstrong\u003e892\u003c/strong\u003e https://doi.org/ARTN 16456810.1016/j.scitotenv.2023.164568 (2023).\u003c/li\u003e\n \u003cli\u003eRen, J., Shang, Z., Tao, L. \u0026amp; Hao, J.X. Speciation and Contamination Assessment of Metals in the Sediments from the Lanzhou Section of the Yellow River, China. \u003cem\u003eEnvironment Protection Engineering\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 113-124 https://doi.org/10.5277/epe170307 (2017).\u003c/li\u003e\n \u003cli\u003eHao, J.X., Ren, J., Fang, H.B. \u0026amp; Tao, L. Identification Sources and High-Risk Areas of Sediment Heavy Metals in the Yellow River by Geographical Detector Method. \u003cem\u003eWater\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e https://doi.org/ARTN 110310.3390/w13081103 (2021).\u003c/li\u003e\n \u003cli\u003eGe, M., Liu, G.J., Liu, H.Q. \u0026amp; Liu, Y. Distribution, bioavailability and contamination assessment of mercury and arsenic in the surface sediments from the Yellow River Estuary, China. \u003cem\u003eHuman and Ecological Risk Assessment\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 274-288 https://doi.org/10.1080/10807039.2019.1710460 (2021).\u003c/li\u003e\n \u003cli\u003eRen, H.R., Tao, L., Guo, Y.C. \u0026amp; Ren, J. Spatial Distribution and Ecotoxicological Assessment of Heavy Metals in Bottom Sediments of Yellow River from Inner Mongolia, China. \u003cem\u003eGeochemistry International\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 1354-1362 https://doi.org/10.1134/S0016702921100074 (2021).\u003c/li\u003e\n \u003cli\u003eLiu, X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Heavy Metal Distribution and Bioaccumulation Combined With Ecological and Human Health Risk Evaluation in a Typical Urban Plateau Lake, Southwest China. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e https://doi.org/ARTN 81467810.3389/fenvs.2022.814678 (2022).\u003c/li\u003e\n \u003cli\u003eLi, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Spatial distribution, ecological risk, and human health assessment of heavy metals in lake surface sections - a case study of Qinghai Lake, China. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 5137-5149 https://doi.org/10.1007/s11356-022-22293-5 (2023).\u003c/li\u003e\n \u003cli\u003eLi, W.Q.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Distribution characteristics, source identification and risk assessment of heavy metals in surface sediments of the Yellow River, China. \u003cem\u003eCatena\u003c/em\u003e \u003cstrong\u003e216\u003c/strong\u003e https://doi.org/ARTN 10637610.1016/j.catena.2022.106376 (2022).\u003c/li\u003e\n \u003cli\u003eCai, S.W., Zhou, S.Q., Cheng, J.W., Wang, Q.H. \u0026amp; Dai, Y. Distribution, Bioavailability and Ecological Risk of Heavy Metals in Surface Sediments from the Wujiang River Basin, Southwest of China. \u003cem\u003ePolish Journal of Environmental Studies\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 5479-5491 https://doi.org/10.15244/pjoes/136185 (2021).\u003c/li\u003e\n \u003cli\u003eWang, G.Y., Zhang, Y.Z., Wang, J.H., Zhu, L.S. \u0026amp; Wang, J. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Sediments of a Heavily Polluted Maozhou River, Southern China. \u003cem\u003eBulletin of Environmental Contamination and Toxicology\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 844-851 https://doi.org/10.1007/s00128-021-03202-x (2021).\u003c/li\u003e\n \u003cli\u003eGuo, Z.Q., Tang, S.N., Wang, D.J. \u0026amp; Zhang, J.Q. Spatial Distribution and Factors Influencing Ecological Efficiency of the Yellow River Basin in China. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e https://doi.org/ARTN 81589810.3389/fenvs.2022.815898 (2022).\u003c/li\u003e\n \u003cli\u003eLiu, P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Comparison of heavy metals in riverine and estuarine sediments in the lower Yangtze River: Distribution, sources, and ecological risks. \u003cem\u003eEnvironmental Technology \u0026amp; Innovation\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e https://doi.org/ARTN 10307610.1016/j.eti.2023.103076 (2023).\u003c/li\u003e\n \u003cli\u003eLiu, C., Yang, D., Sun, J. \u0026amp; Cheng, Y. The Impact of Environmental Regulations on Pollution and Carbon Reduction in the Yellow River Basin, China. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e https://doi.org/10.3390/ijerph20031709 (2023).\u003c/li\u003e\n \u003cli\u003eXie, F.Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Spatial distribution, pollution assessment, and source identification of heavy metals in the Yellow River. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e \u003cstrong\u003e436\u003c/strong\u003e https://doi.org/ARTN 12930910.1016/j.jhazmat.2022.129309 (2022).\u003c/li\u003e\n \u003cli\u003eFadlillah, L.N., Utami, S., Rachmawati, A.A., Jayanto, G.D. \u0026amp; Widyastuti, M. Ecological risk and source identifications of heavy metals contamination in the water and surface sediments from anthropogenic impacts of urban river, Indonesia. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e15485 https://doi.org/10.1016/j.heliyon.2023.e15485 (2023).\u003c/li\u003e\n \u003cli\u003eSalati, S. \u0026amp; Moore, F. Assessment of heavy metal concentration in the Khoshk River water and sediment, Shiraz, Southwest Iran. \u003cem\u003eEnviron Monit Assess\u003c/em\u003e \u003cstrong\u003e164\u003c/strong\u003e, 677-89 https://doi.org/10.1007/s10661-009-0920-y (2010).\u003c/li\u003e\n \u003cli\u003eTurekian, K.K. \u0026amp; Wedepohl, K.H. Distribution of the elements in some major units of the earth\u0026apos;s crust. \u003cem\u003eGeol Soc America Bull\u003c/em\u003e \u003cstrong\u003eVol.72\u003c/strong\u003e, 175-192 https://doi.org/10.1130/0016-7606(1961)72[175:Doteis]2.0.Co (1961).\u003c/li\u003e\n \u003cli\u003eYan, M.C., Chi, Q.H., Gu, T.X. \u0026amp; Wang, C.S. Average contents of chemical elements in various sediments of China(in Chinese). \u003cem\u003eGeophysical and geochemical exploration\u003c/em\u003e, 468-472 (1995).\u003c/li\u003e\n \u003cli\u003eM\u0026uuml;ller, G. Index of geoaccumulation in sediments of the Rhine River. \u003cem\u003eGeoJournal\u003c/em\u003e \u003cstrong\u003eVol.2\u003c/strong\u003e, 108-118 (1969).\u003c/li\u003e\n \u003cli\u003eHawkesworth, C.J., Cawood, P.A. \u0026amp; Dhuime, B. The Evolution of the Continental Crust and the Onset of Plate Tectonics. \u003cem\u003eFrontiers in Earth Science\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e https://doi.org/ARTN 32610.3389/feart.2020.00326 (2020).\u003c/li\u003e\n \u003cli\u003eRajmohan, N., Prathapar, S.A., Jayaprakash, M. \u0026amp; Nagarajan, R. Vertical distribution of heavy metals in soil profile in a seasonally waterlogging agriculture field in Eastern Ganges Basin. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e \u003cstrong\u003e186\u003c/strong\u003e, 5411-5427 https://doi.org/10.1007/s10661-014-3790-x (2014).\u003c/li\u003e\n \u003cli\u003eSun, W.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Distribution characteristics and ecological risk assessment of heavy metals in sediments of Shahe reservoir. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e https://doi.org/ARTN 1623910.1038/s41598-022-20540-w (2022).\u003c/li\u003e\n \u003cli\u003eHakanson, L. An ecological risk index for aquatic pollution control.a sedimentological approach. \u003cem\u003eWater Research\u003c/em\u003e \u003cstrong\u003eVol.14\u003c/strong\u003e, 975-1001 https://doi.org/10.1016/0043-1354(80)90143-8 (1980).\u003c/li\u003e\n \u003cli\u003eJahan, S. \u0026amp; Strezov, V. Comparison of pollution indices for the assessment of heavy metals in the sediments of seaports of NSW, Australia. \u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 295-306 https://doi.org/10.1016/j.marpolbul.2018.01.036 (2018).\u003c/li\u003e\n \u003cli\u003eZhang, W., Liu, M. \u0026amp; Li, C. Soil heavy metal contamination assessment in the Hun-Taizi River watershed, China. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 8730 https://doi.org/10.1038/s41598-020-65809-0 (2020).\u003c/li\u003e\n \u003cli\u003eWu, D., Liu, H., Wu, J. \u0026amp; Gao, X. Spatial Distribution, Ecological Risk Assessment and Source Analysis of Heavy Metals Pollution in Urban Lake Sediments of Huaihe River Basin. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e https://doi.org/ARTN 1465310.3390/ijerph192214653 (2022).\u003c/li\u003e\n \u003cli\u003eChen, R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Assessment of Soil-Heavy Metal Pollution and the Health Risks in a Mining Area from Southern Shaanxi Province, China. \u003cem\u003eToxics\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e https://doi.org/10.3390/toxics10070385 (2022).\u003c/li\u003e\n \u003cli\u003eLi, M., Zhu, S., Ouyang, T., Tang, J. \u0026amp; Tang, Z. Magnetic properties of the surface sediments in the Yellow River Estuary and Laizhou Bay, Bohai Sea, China: Implications for monitoring heavy metals. \u003cem\u003eJ Hazard Mater\u003c/em\u003e \u003cstrong\u003e410\u003c/strong\u003e, 124579 https://doi.org/10.1016/j.jhazmat.2020.124579 (2021).\u003c/li\u003e\n \u003cli\u003eHua, C.Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Ecological risk, dynamics in fingerprinting, and source apportionment of heavy metals in soils from plateau in Upper Yellow River, Qinghai Province, China. \u003cem\u003eJournal of Soils and Sediments\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 189-203 https://doi.org/10.1007/s11368-023-03600-0 (2024).\u003c/li\u003e\n \u003cli\u003eXie, S.C.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Spatial distribution and ecological risk of heavy metals and their source apportionment in soils from a typical mining area, Inner Mongolia, China. \u003cem\u003eJournal of Arid Land\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1196-1215 https://doi.org/10.1007/s40333-023-0109-1 (2023).\u003c/li\u003e\n \u003cli\u003eZhang, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Impact of Xiaolangdi Reservoir on the Evolution of Water Infiltration Influence Zones of the Secondary Perched Reach of the Lower Yellow River. \u003cem\u003eWater\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e https://doi.org/ARTN 430810.3390/w15244308 (2023).\u003c/li\u003e\n \u003cli\u003eBazarzhapov, T.Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Distribution of Heavy Metals in Water and Bottom Sediments in the Basin of Lake Gusinoe (Russia): Ecological Risk Assessment. \u003cem\u003eWater\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e https://doi.org/ARTN 338510.3390/w15193385 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"typical urban areas, heavy metal, the Yellow River, spatial distribution, pollution assessment ","lastPublishedDoi":"10.21203/rs.3.rs-5024997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5024997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiver sediments are considered a source and sink of potential heavy metal pollutants and can provide important information for the health of aquatic ecosystems. Heavy metal pollution has become a serious problem in global river systems and adversely affects humans through the food chain.The contents of eight types of heavy metals (Fe, Mn, Cu, Ni, Zn, Cr, Pb, and Cd) in the sediments of six typical urban areas in the middle and upper reaches of the Yellow River were analyzed to explore the spatial distribution characteristics between cities and evaluate the degree of pollution.The main research objectives of this study were as follows: (1) to analyze the distribution characteristics of heavy metals in sediments along rivers in six typical urban areas to evaluate the degree of heavy metal pollution in sediments; (2) to reveal the enrichment characteristics and pollution level of eight types of heavy metals in six typical urban areas in the middle and upper reaches of the Yellow River; (3) to propose the ecological risk of heavy metals in sediments of six typical urban areas in the middle and upper reaches of the Yellow River using the potential ecological risk index method.\u003c/p\u003e","manuscriptTitle":"Surface sediment properties and heavy metal contamination assessment in typical urban areas from middle and upper reaches of Yellow River","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 11:51:05","doi":"10.21203/rs.3.rs-5024997/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-04T05:36:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-03T09:19:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-18T16:12:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84913031136803248666221871006847271244","date":"2024-09-11T13:38:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140256562669597391848381586914374976718","date":"2024-09-10T10:40:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-10T10:22:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-10T10:20:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-10T10:16:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-10T08:34:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-03T12:32:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"415c1595-e196-400d-b395-b845a61aa9f9","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38589132,"name":"Earth and environmental sciences/Ecology"},{"id":38589133,"name":"Earth and environmental sciences/Environmental sciences"},{"id":38589134,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-03-24T16:02:41+00:00","versionOfRecord":{"articleIdentity":"rs-5024997","link":"https://doi.org/10.1038/s41598-025-87503-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-19 15:57:58","publishedOnDateReadable":"March 19th, 2025"},"versionCreatedAt":"2024-11-15 11:51:05","video":"","vorDoi":"10.1038/s41598-025-87503-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87503-9","workflowStages":[]},"version":"v1","identity":"rs-5024997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5024997","identity":"rs-5024997","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}