# Variation Feature, Pollution Risk Assessment, and Source Analysis of Heavy Metals in Lanzhou City, Northwestern China

Authors: Yongyao Zeng; Bo Zhou
Journal: Geofluids
 (2022)

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## Abstract

Lanzhou is located in the northwest of China and is famous for its petrochemical and heavy industries of China. The city has suffered long-term pollution of heavy metals in recent years. Significant efforts, such as tax subsidies for new energy vehicles and treating solid waste, have been launched by the Lanzhou municipal government to address the pollution of heavy metals since 2010. What is the current status of heavy metal pollution in Lanzhou? In this study, the concentrations of 12 heavy metals (V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, and Pb) were tested from 20 sediment samples. Mn, Zn, V, and Cr are the predominant heavy metals, with mean concentrations of 534.65, 65.85, 56.29, and 36.05 mg/kg; Ni, Cu, Pb, Co, and As are in moderate amounts, with mean concentrations of 25.2, 21.5, 16.7, 9.87, and 4.86 mg/kg; Mo and Cd have the lowest mean concentrations of 0.54 and 0.15 mg/kg. A low ecological risk is confirmed by two indices, the Hakanson potential ecological risk index (RI) and the geoaccumulation index (Igeo). The RI index has lower Eir values (<40) and RI values (<150), which range from 18.11 to 37.95, with an average value of 25.61. Co, V, Cd, Cu, Zn, As, Cr, Ni, Pb, and Mn metals are classified as an uncontaminated environment (level 0) and a moderately to heavily contaminated environment (level 2-level 3) by Igeo values. Seven heavy metals (Pb, Cd, Cr, As, Cu, Zn, and Ni) in Lanzhou are the relatively lowest amount in the 5 cities in China. The changing trends of heavy metal concentrations firstly increase and then decrease gradually from 2005 to 2022. The time turning point of concentration shrinking occurred in approximately 2014, which is in response to the increased efforts of addressing the pollution launched by the Lanzhou municipal government since 2010. Source analysis by Pearson’s correlation and principal component analysis revealed that the accumulation of Mo metal was caused by the enrichment of MnO2 in the oxidic condition; the accumulations of Cu, Co, Ni, Mn, Cr, Zn, Cd, and Pb metals are influenced by natural sources and human activities (vehicle exhaust and industrial pollution), respectively.


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## Body

## 1. Introduction

Heavy metals, such as cobalt (Co), copper (Cu), and lead (Pb), are toxic and can accumulate in the human body and impact human health [1]. Therefore, heavy metals have been listed as priority control contaminants by the Chinese government. Even so, with the rapid urbanization and industrialization, heavy metal pollution has increased in many cities in China in recent decades, as revealed by the data from the annual Environmental Bulletin released by the Ministry of Environmental Protection of China in 2019.Among these cities, Lanzhou, one of the largest petroleum industry cities in northwestern China, has experienced this pollution (Figure1). The city has suffered long-term pollution of heavy metals in the last 40 years [2–7]. This is because many petrochemical and heavy industries (electrical machinery factory, steelworks, and automobiles factory) have been established in Lanzhou since the 1950s. Later, the pollution of heavy metals was gradually increased with rapid urbanization and industrialization that has been occurring since the 2000s [3, 4]. Lanzhou city was once ranked as one of the top cities polluted by heavy metals in China ([8, 9]; Figure 1). Studies on the pollution characteristics of heavy metals in Lanzhou have been conducted a decade ago [5, 6, 10, 11]. Among these, many studies have focused on the spatial distribution, source analysis, and pollution characteristics of heavy metals in soil, farmland, and vegetable fields in Lanzhou [2, 3, 5, 7, 10–13]. In addition, the magnetic properties of heavy metals in urban street dust of Lanzhou have been studied [6, 14, 15]. However, reviewing the literature on heavy metals in Lanzhou, main deficiencies are revealed: (1) many samples were the sole source. This is because the previous studies were mainly concentrated in the soil and in farmland samples. (2) The previous studies were limited to only 3-4 elements. Obviously, the above inadequacies are likely to bias the comprehensive research regarding heavy metal pollution in Lanzhou. More importantly, significant efforts, such as tax subsidies for new energy vehicles, safe solid waste, and domestic garbage treatment, have been launched by the Lanzhou municipal government to solve the pollution of heavy metals since 2010. What is the current status of heavy metal pollution in Lanzhou a decade later?Figure 1
Schematic map of sampling sites ((a) modified from [16]). Inset map (b), modified from Chai et al. [3], showing the location of Lanzhou city in Gansu Province, China.
(a)(b)Therefore, the concentrations of 12 heavy metals (V, Cr, Mn, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, and Pb) were tested in 20 samples from wetland, marsh, farmland, mountain, and river sediments in Lanzhou to resolve the inadequacies of previous studies and answer the above question (Figure1(a)). In this study, the environmental risk is assessed by the indices of the Hakanson potential ecological risk (RI) index and the geoaccumulation (Igeo) index. The trend of heavy metal concentration changes during different years in Lanzhou is discussed. The distribution characteristics of heavy metals in different cities are contrasted. The sources of heavy metal pollution, natural sources (weathering materials of parent rock) or anthropogenic activities (exhaust gas emission and fossil fuel combustion), are analyzed by Pearson’s correlation analysis and principal component analysis.


## 2. Materials and Methods

### 2.1. Description of the Study Area

Lanzhou city (~35°34′20″ N -37°07′ 07″ N, 102°35′ 58″ E -104°34′ 29″ E), located in the Chinese Loess Plateau, is the provincial capital of Gansu Province, China ([16]; Figure 1(b)), which is usually considered to be jointly influenced by westerlies and monsoon climatic zone [17]. The city is filled with loess. The city, also situated upstream of the Yellow River in northwest China, is characterized by many chemical and petroleum industries, steel mills, heavy industries, and coal-fired power plants [16]. One remarkable feature is that the Yellow River flows through Lanzhou city from west to east, resulting in the city being distributed in an east-west orientation within narrow valley. The east-west span of the city is approximately 30 km, while its north-south span is only 5-10 km. Another remarkable feature of the city is encircled by many mountains in the south and north orientations. All these features result in the city having typical mountain-valley topography with low winds, precipitation, and atmospheric humidity. In addition, the city experiences a semiarid climate and frequently suffers from sandstorms kicked up from the adjacent desert in the winter and spring seasons [6]. It has a continental climate with an annual average temperature of 9.5°C and an annual average precipitation of 290-360 mm [3]. The average altitude is ~1670 m [3]. The main crops are wheat, corn, and potato. The cultivated land area is about 210,000 hectare [3].The administrative regions of Lanzhou city are composed of five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu) and three counties (Yuzhong, Gaolan, and Yongdeng) (Figure1(a)). The main differences in districts and counties in China are dominated by industry and agricultures. Chengguan District, the central downtown, is located in the central south of Lanzhou (Figure 1(a)). Anning District and Qilihe District, as subdowntowns, are located in the northwest and south of Chengguan District (Figure 1(a)). Xigu and Honggu District, the famous industrial areas of petrochemical industry, coal-fired power stations, locomotives, machinery, and heavy industry, are located further west (Figure 1(a)). Yuzhong, Gaolan, and Yongdeng counties are typical rural areas with many food, farm, and livestock activities and a small population (Figure 1(a)).


### 2.2. Sample Collection and Description

A global positioning system (GPS from UniStrong Company of G120BD) was used to measure the latitude and longitude in the process of sampling. Surface sediments of approximately 10 cm were first wiped off during sampling. Then, specimen under the surface sediments at approximately 10-20 cm was collected by a small spade at each site. Three parallel sets of samples were collected at the same sampling site for cross-calibration measurements. Approximately 500 g of sediment was stored in pocket-sized sealable plastic bags at each site and transferred to the laboratory in a refrigerator at 4°C. In this study, 20 samples were collected from five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu) in Lanzhou (Figure1(a)), which were collected in wetlands, marshes, mountain sediment, river sediment, and farmland in October 2021. They are described in Supplement Table A (available here). Some samples are illustrated in Figure 2.Figure 2
Representative photos of sampling sites in Lanzhou city. (a) Profile of mountain sediment from HSG, (b) profile of wetland from HYRY, (c) profile of river sediment from QBSQ, (d) profile of mountain sediment from WQSE, (e) profile of farmland from FJQ, and (f) profile of marsh from PHG.
(a)(b)(c)(d)(e)(f)Why did we only sample from five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu), instead of three counties (Yuzhong, Gaolan, and Yongdeng) in Lanzhou? The reasons are as follows: (1) limited budget; (2) the human activities, urbanization, traffic pollution, and industrialization are chiefly focused on the five districts. For example, the Lanzhou Statistical Yearbook of 2021 revealed that the population in five districts is approximately 322 million, which is approximately 74 percent of the total population in Lanzhou (referring to the website of Lanzhou Statistical Bureau,http://tjj.lanzhou.gov.cn/); (3) the relatively small population, transportation, and industry in three counties indicate very little heavy metal pollution; and (4) the previous studies on heavy metals in Lanzhou only focused on five districts. We only sampled in the five districts, which can help compare the distribution characteristics of heavy metals in different years in Lanzhou.


### 2.3. Sample Testing

The samples were dried in a drying oven at 55°C. Then, all samples were used to remove coarse debris, small stones, and plant roots by a 150μm sieve before the measurement process. A portion of sediment was taken from the above sample after removing impurities. The above portion sediment was then dispelled by microwave digestion technology in a mixture environment of nitric acid-hydrofluoric acid-perchloric acid (HNO3-HF-HCLO4). The next step is to volatilize the epibiotic acidic material of the samples using an electric hot plate at 150°C. The samples were naturally cooled at a room temperature of 15-30°C. The samples were dissolved in ultrapure water in a 100 ml vessel. Finally, the samples were filtered by a 0.45 μm filtration membrane before testing with an ICP-MS (Agilent 7800). All the experiments were tested in the Guangzhou Testing Services Company of Chinese Academy of Sciences in January 2022. The China National Standard (HJ 803-2016), 12 metal elements extracted from soil and sediment by inductively coupled plasma mass spectrometry, released by the Ministry of Environmental Protection in China, was used for accuracy control in the process of testing. Each batch of sample analysis was repeated 3 times. Beyond that, the national standard soil samples Certified Reference Materials for the Chemical Composition of Soils (ID = GSS-8) were used for quality control. The relative deviation of the measurement was controlled within ±10%.


### 2.4. Pollution Monitoring Indices

In this study, two methods, the Hakanson potential ecological risk index (RI) and the geoaccumulation index (Igeo), are employed to assess the pollution risk of heavy metals in Section 4.3. The background values [18, 19] are adopted to assess the pollution risk of heavy metals (Table 1). The [18] was cited from Natural background values of soil elements in China, which was released by the China National Environmental Monitoring Centre. The [19] was cited from the China National Standard (Soil Environmental Quality-Risk Control Standard for Soil Contamination of Agricultural Land, GB15618-2018), which was released by the Ministry of Ecology and Environment of China. The background values [19] have priority in this study because of the novel data. If there is a data gap in MEEC [19], the background values are also cited from [18].Table 1
Statistical results of heavy metal concentrations (mg/kg) in this study.
IDVCrMnCoNiCuZnAsMoCdPbMax.108.0054.00778.0014.1036.0038.70201.009.601.460.3423.00Min.34.8018.00327.007.1818.0013.4039.002.400.130.1010.00Standard deviation (σ)15.0311.0593.821.674.536.1333.101.870.290.053.91Arithmetic mean (μ)56.2936.05534.659.8725.2021.5065.854.860.540.1516.70CV (σ/μ)0.270.310.180.170.180.290.500.380.540.370.23Background value81.90250.0065.3012.60190.00100.00300.0025.00No data0.60170.00μ/background value0.690.148.190.780.130.210.220.190.250.10The italics numbers of the background values in Table1 are cited from CNEMC [18]. Background values that are not italicized are cited from MEEC [19]. CV is the abbreviation for the coefficient of variation.


#### 2.4.1. Ecological Risk Index (RI)

The potential ecological risk index (RI), introduced by Hakanson [20], is used to evaluate the combined pollution risk caused by heavy metals. RI is defined as follows: Eri=Tri×Ci/Cni and RI=∑i=1nEri.Ci is the heavy metal concentration of element i in the sediment. Cin is the background value of metal i. Tir is the toxicity index for a given substance, e.g., Zn=Mn=1, V=Cr=2, Cu=Pb=Ni=Co=5, As=10, and Cd=30 [20]. Eir is the potential ecological risk index of heavy metal i. RI is the sum of all the risk indices for heavy metals in the sediment. The classification criteria of Eir and RI are listed in Supplement Table B [21].


#### 2.4.2. Geoaccumulation Index (Igeo)

The geoaccumulation index (Igeo), introduced by Muller [22], is a useful tool to evaluate the pollution level of heavy metals after eliminating the influence of geological contributions. The Igeo index defines the relationship between the total concentration of heavy metals in the sample and the average concentration of heavy metals in the study area. The geoaccumulation index is calculated by the following equation: Igeo=log2Ci/kBi. In this equation, Ci is the measured heavy metal concentration in the sample, and Bi is the geochemical background value of the same metal. k is the background matrix correction factor due to lithospheric effects, which is usually defined as 1.5 [23, 24]. The classification of the geoaccumulation index is listed in Supplemental Table C [21].


### 2.5. Methods of Source Analysis

Heavy metals may accumulate in sediments by two sources: human activities (transportation, fossil fuel combustion, and factory metal processing) and parent rock weathering [25]. If the sources, migration paths, and enrichment processes of elements are similar, there is a significant positive correlation with these elements and vice versa [3]. Pearson’s correlation analysis and principal component analysis (PCA) were employed to analyze the correlation of heavy metals by the IBM SPSS Statistics tool (version 26) in this study.


## 2.1. Description of the Study Area

Lanzhou city (~35°34′20″ N -37°07′ 07″ N, 102°35′ 58″ E -104°34′ 29″ E), located in the Chinese Loess Plateau, is the provincial capital of Gansu Province, China ([16]; Figure 1(b)), which is usually considered to be jointly influenced by westerlies and monsoon climatic zone [17]. The city is filled with loess. The city, also situated upstream of the Yellow River in northwest China, is characterized by many chemical and petroleum industries, steel mills, heavy industries, and coal-fired power plants [16]. One remarkable feature is that the Yellow River flows through Lanzhou city from west to east, resulting in the city being distributed in an east-west orientation within narrow valley. The east-west span of the city is approximately 30 km, while its north-south span is only 5-10 km. Another remarkable feature of the city is encircled by many mountains in the south and north orientations. All these features result in the city having typical mountain-valley topography with low winds, precipitation, and atmospheric humidity. In addition, the city experiences a semiarid climate and frequently suffers from sandstorms kicked up from the adjacent desert in the winter and spring seasons [6]. It has a continental climate with an annual average temperature of 9.5°C and an annual average precipitation of 290-360 mm [3]. The average altitude is ~1670 m [3]. The main crops are wheat, corn, and potato. The cultivated land area is about 210,000 hectare [3].The administrative regions of Lanzhou city are composed of five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu) and three counties (Yuzhong, Gaolan, and Yongdeng) (Figure1(a)). The main differences in districts and counties in China are dominated by industry and agricultures. Chengguan District, the central downtown, is located in the central south of Lanzhou (Figure 1(a)). Anning District and Qilihe District, as subdowntowns, are located in the northwest and south of Chengguan District (Figure 1(a)). Xigu and Honggu District, the famous industrial areas of petrochemical industry, coal-fired power stations, locomotives, machinery, and heavy industry, are located further west (Figure 1(a)). Yuzhong, Gaolan, and Yongdeng counties are typical rural areas with many food, farm, and livestock activities and a small population (Figure 1(a)).


## 2.2. Sample Collection and Description

A global positioning system (GPS from UniStrong Company of G120BD) was used to measure the latitude and longitude in the process of sampling. Surface sediments of approximately 10 cm were first wiped off during sampling. Then, specimen under the surface sediments at approximately 10-20 cm was collected by a small spade at each site. Three parallel sets of samples were collected at the same sampling site for cross-calibration measurements. Approximately 500 g of sediment was stored in pocket-sized sealable plastic bags at each site and transferred to the laboratory in a refrigerator at 4°C. In this study, 20 samples were collected from five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu) in Lanzhou (Figure1(a)), which were collected in wetlands, marshes, mountain sediment, river sediment, and farmland in October 2021. They are described in Supplement Table A (available here). Some samples are illustrated in Figure 2.Figure 2
Representative photos of sampling sites in Lanzhou city. (a) Profile of mountain sediment from HSG, (b) profile of wetland from HYRY, (c) profile of river sediment from QBSQ, (d) profile of mountain sediment from WQSE, (e) profile of farmland from FJQ, and (f) profile of marsh from PHG.
(a)(b)(c)(d)(e)(f)Why did we only sample from five districts (Chengguan, Anning, Qilihe, Xigu, and Honggu), instead of three counties (Yuzhong, Gaolan, and Yongdeng) in Lanzhou? The reasons are as follows: (1) limited budget; (2) the human activities, urbanization, traffic pollution, and industrialization are chiefly focused on the five districts. For example, the Lanzhou Statistical Yearbook of 2021 revealed that the population in five districts is approximately 322 million, which is approximately 74 percent of the total population in Lanzhou (referring to the website of Lanzhou Statistical Bureau,http://tjj.lanzhou.gov.cn/); (3) the relatively small population, transportation, and industry in three counties indicate very little heavy metal pollution; and (4) the previous studies on heavy metals in Lanzhou only focused on five districts. We only sampled in the five districts, which can help compare the distribution characteristics of heavy metals in different years in Lanzhou.


## 2.3. Sample Testing

The samples were dried in a drying oven at 55°C. Then, all samples were used to remove coarse debris, small stones, and plant roots by a 150μm sieve before the measurement process. A portion of sediment was taken from the above sample after removing impurities. The above portion sediment was then dispelled by microwave digestion technology in a mixture environment of nitric acid-hydrofluoric acid-perchloric acid (HNO3-HF-HCLO4). The next step is to volatilize the epibiotic acidic material of the samples using an electric hot plate at 150°C. The samples were naturally cooled at a room temperature of 15-30°C. The samples were dissolved in ultrapure water in a 100 ml vessel. Finally, the samples were filtered by a 0.45 μm filtration membrane before testing with an ICP-MS (Agilent 7800). All the experiments were tested in the Guangzhou Testing Services Company of Chinese Academy of Sciences in January 2022. The China National Standard (HJ 803-2016), 12 metal elements extracted from soil and sediment by inductively coupled plasma mass spectrometry, released by the Ministry of Environmental Protection in China, was used for accuracy control in the process of testing. Each batch of sample analysis was repeated 3 times. Beyond that, the national standard soil samples Certified Reference Materials for the Chemical Composition of Soils (ID = GSS-8) were used for quality control. The relative deviation of the measurement was controlled within ±10%.


## 2.4. Pollution Monitoring Indices

In this study, two methods, the Hakanson potential ecological risk index (RI) and the geoaccumulation index (Igeo), are employed to assess the pollution risk of heavy metals in Section 4.3. The background values [18, 19] are adopted to assess the pollution risk of heavy metals (Table 1). The [18] was cited from Natural background values of soil elements in China, which was released by the China National Environmental Monitoring Centre. The [19] was cited from the China National Standard (Soil Environmental Quality-Risk Control Standard for Soil Contamination of Agricultural Land, GB15618-2018), which was released by the Ministry of Ecology and Environment of China. The background values [19] have priority in this study because of the novel data. If there is a data gap in MEEC [19], the background values are also cited from [18].Table 1
Statistical results of heavy metal concentrations (mg/kg) in this study.
IDVCrMnCoNiCuZnAsMoCdPbMax.108.0054.00778.0014.1036.0038.70201.009.601.460.3423.00Min.34.8018.00327.007.1818.0013.4039.002.400.130.1010.00Standard deviation (σ)15.0311.0593.821.674.536.1333.101.870.290.053.91Arithmetic mean (μ)56.2936.05534.659.8725.2021.5065.854.860.540.1516.70CV (σ/μ)0.270.310.180.170.180.290.500.380.540.370.23Background value81.90250.0065.3012.60190.00100.00300.0025.00No data0.60170.00μ/background value0.690.148.190.780.130.210.220.190.250.10The italics numbers of the background values in Table1 are cited from CNEMC [18]. Background values that are not italicized are cited from MEEC [19]. CV is the abbreviation for the coefficient of variation.


### 2.4.1. Ecological Risk Index (RI)

The potential ecological risk index (RI), introduced by Hakanson [20], is used to evaluate the combined pollution risk caused by heavy metals. RI is defined as follows: Eri=Tri×Ci/Cni and RI=∑i=1nEri.Ci is the heavy metal concentration of element i in the sediment. Cin is the background value of metal i. Tir is the toxicity index for a given substance, e.g., Zn=Mn=1, V=Cr=2, Cu=Pb=Ni=Co=5, As=10, and Cd=30 [20]. Eir is the potential ecological risk index of heavy metal i. RI is the sum of all the risk indices for heavy metals in the sediment. The classification criteria of Eir and RI are listed in Supplement Table B [21].


### 2.4.2. Geoaccumulation Index (Igeo)

The geoaccumulation index (Igeo), introduced by Muller [22], is a useful tool to evaluate the pollution level of heavy metals after eliminating the influence of geological contributions. The Igeo index defines the relationship between the total concentration of heavy metals in the sample and the average concentration of heavy metals in the study area. The geoaccumulation index is calculated by the following equation: Igeo=log2Ci/kBi. In this equation, Ci is the measured heavy metal concentration in the sample, and Bi is the geochemical background value of the same metal. k is the background matrix correction factor due to lithospheric effects, which is usually defined as 1.5 [23, 24]. The classification of the geoaccumulation index is listed in Supplemental Table C [21].


## 2.4.1. Ecological Risk Index (RI)

The potential ecological risk index (RI), introduced by Hakanson [20], is used to evaluate the combined pollution risk caused by heavy metals. RI is defined as follows: Eri=Tri×Ci/Cni and RI=∑i=1nEri.Ci is the heavy metal concentration of element i in the sediment. Cin is the background value of metal i. Tir is the toxicity index for a given substance, e.g., Zn=Mn=1, V=Cr=2, Cu=Pb=Ni=Co=5, As=10, and Cd=30 [20]. Eir is the potential ecological risk index of heavy metal i. RI is the sum of all the risk indices for heavy metals in the sediment. The classification criteria of Eir and RI are listed in Supplement Table B [21].


## 2.4.2. Geoaccumulation Index (Igeo)

The geoaccumulation index (Igeo), introduced by Muller [22], is a useful tool to evaluate the pollution level of heavy metals after eliminating the influence of geological contributions. The Igeo index defines the relationship between the total concentration of heavy metals in the sample and the average concentration of heavy metals in the study area. The geoaccumulation index is calculated by the following equation: Igeo=log2Ci/kBi. In this equation, Ci is the measured heavy metal concentration in the sample, and Bi is the geochemical background value of the same metal. k is the background matrix correction factor due to lithospheric effects, which is usually defined as 1.5 [23, 24]. The classification of the geoaccumulation index is listed in Supplemental Table C [21].


## 2.5. Methods of Source Analysis

Heavy metals may accumulate in sediments by two sources: human activities (transportation, fossil fuel combustion, and factory metal processing) and parent rock weathering [25]. If the sources, migration paths, and enrichment processes of elements are similar, there is a significant positive correlation with these elements and vice versa [3]. Pearson’s correlation analysis and principal component analysis (PCA) were employed to analyze the correlation of heavy metals by the IBM SPSS Statistics tool (version 26) in this study.


## 3. Sample Results and Statistical Analysis

### 3.1. Sample Test Results

In this study, the raw data and the concentrations of 12 heavy metals are displayed in Supplement Table D. The concentration of Sb was not detected in many samples (Table D). Therefore, we do not study the Sb element in the following sections.


### 3.2. Statistical Analysis of Raw Data

The concentrations of the maximum, minimum, arithmetic mean (μ), the standard deviation (σ), and the coefficient of variation (CV) are listed in Table 1. The parameter CV in Table 1 is a statistical measure of the dispersion of data points in a data series around the mean. In this study, Wilding’s [26] classification of the CV is adopted, which is displayed in Supplemental Table E.The arithmetic mean of heavy metal concentrations are in the following order: Mn > Zn > V > Cr > Ni > Cu > Pb > Co > As > Mo > Cd (Table1). Mn, Zn, V, and Cr were the predominant heavy metals in this study, with mean concentrations of 534.65, 65.85, 56.29, and 36.05 mg/kg, respectively (Table 1). Ni, Cu, Pb, Co, and As were in moderate concentration, with mean concentrations of 25.2, 21.5, 16.7, 9.87, and 4.86 mg/kg, respectively (Table 1). Mo and Cd had the lowest mean concentrations of 0.54 and 0.15 mg/kg, respectively (Table 1). In this study, the arithmetic average values of 9 heavy metals (V, Cr, Co, Ni, Cu, Zn, As, Cd, and Pb) were lower than their local soil background values of 0.69, 0.14, 0.78, 0.13, 0.21, 0.22, 0.19, 0.25, and 0.10 times, respectively (Table 1). The arithmetic average value of Mn metal is higher than the background value of 8.19 times (Table 1).Based on the grade of the CV ([26]; Table E), the CV values of 4 heavy metals, including Cd (0.37), As (0.38), Zn (0.50), and Mo (0.54), showed a high degree of variation (Table 1). The CV values of 7 heavy metals, including Co (0.17), Mn (0.18), Ni (0.18), Pb (0.23), V (0.27), Cu (0.29), and Cr (0.31), showed moderate degrees of variation (Table 1). The higher CV values indicated that the variability and spatial dispersion of heavy metals are larger and are more easily affected by human activities [3]. The variability and spatial dispersion of heavy metals, revealed by the CV values, are discussed in Section 4.4.In this study, the calculated results ofEir and RI are displayed in Table 2. The statistical and classification results of RI are listed in Table 3. The maximum and minimum values of RI were 37.95 (site located in GYXC) and 18.11 (site located in PHG), respectively. All 20 RI values are classified as low risk (Table 3). The calculated Igeo values and Igeo arithmetic mean values are shown in Table 4. The box plot of Igeo values is presented in Figure 3, which can intuitively present the classification level of Igeo of different heavy metals.Table 2
The values ofRI and Eir.
IDEirRICrNiCuZnAsCdPbVMnCoHHQS0.360.551.010.221.368.500.621.057.533.2624.47YTDQN0.140.500.720.141.165.000.320.977.173.0419.17WQSW0.220.741.080.211.606.500.411.229.084.3325.38HSG0.190.951.480.292.209.000.651.6911.915.6033.95PHG0.160.500.710.151.085.000.290.856.522.8518.11HYRY0.190.630.920.191.166.500.411.078.353.8123.23WQSE0.380.761.940.283.4411.500.561.128.704.5633.24JZDD0.290.761.120.211.686.000.471.427.864.2524.05ZJG0.180.470.670.133.845.500.351.245.012.9320.32WQS0.280.630.940.193.086.000.411.147.703.6924.06WYS0.300.681.020.201.888.500.441.288.423.9726.70HCZ0.270.680.990.200.966.000.411.198.303.9222.93YJY0.300.791.110.222.207.000.471.569.424.6027.66QBSQ0.340.550.990.161.606.000.591.568.843.5024.13WHC0.390.581.010.181.769.000.651.597.523.6426.31YTHHQ0.420.550.960.161.885.500.501.498.153.4223.02XFY0.310.660.940.202.085.500.682.615.713.6722.35GYXC0.430.741.750.671.6817.000.681.479.104.4437.95DJS0.400.791.090.211.848.000.471.669.344.5628.37FJQ0.210.741.090.212.367.000.441.299.134.3326.78Table 3
The statistical results ofRI.
The statistical results ofRIThe classification of allRI (%)Min. value and siteMax. value and siteAve. valueExtremely high riskHigh riskModerate riskLow risk18.11, PHG37.95, GYXC25.61000100Table 4
TheIgeo values.
IDIgeo valuesVCrMnCoNiCuZnAsCdPbHHQS-1.51-3.062.33-1.20-3.76-2.90-2.77-3.46-2.40-3.60YTDQN-1.63-4.382.26-1.30-3.91-3.38-3.39-3.69-3.17-4.53WQSW-1.30-3.742.60-0.79-3.35-2.80-2.86-3.23-2.79-4.19HSG-0.83-3.972.99-0.42-2.98-2.35-2.39-2.77-2.32-3.53PHG-1.82-4.232.12-1.40-3.91-3.41-3.35-3.80-3.17-4.67HYRY-1.48-3.972.48-0.98-3.57-3.03-2.98-3.69-2.79-4.19WQSE-1.42-3.002.54-0.72-3.30-1.95-2.42-2.12-1.97-3.75JZDD-1.08-3.382.39-0.82-3.30-2.74-2.86-3.16-2.91-3.99ZJG-1.28-4.091.74-1.36-3.98-3.48-3.53-1.97-3.03-4.41WQS-1.39-3.422.36-1.02-3.57-3.00-3.01-2.28-2.91-4.19WYS-1.23-3.302.49-0.92-3.45-2.88-2.91-3.00-2.40-4.09HCZ-1.33-3.462.47-0.94-3.45-2.92-2.93-3.97-2.91-4.19YJY-0.94-3.342.65-0.70-3.25-2.76-2.79-2.77-2.68-3.99QBSQ-0.94-3.122.56-1.10-3.76-2.92-3.23-3.23-2.91-3.67WHC-0.92-2.942.33-1.04-3.70-2.90-3.09-3.09-2.32-3.53YTHHQ-1.01-2.852.44-1.13-3.76-2.97-3.26-3.00-3.03-3.91XFY-0.20-3.271.93-1.03-3.51-3.00-2.93-2.85-3.03-3.47GYXC-1.03-2.802.60-0.75-3.35-2.10-1.16-3.16-1.40-3.47DJS-0.85-2.912.64-0.72-3.25-2.78-2.81-3.03-2.49-3.99FJQ-1.22-3.852.61-0.79-3.35-2.79-2.86-2.67-2.68-4.09Arithmetic mean-1.17-3.452.43-0.96-3.52-2.85-2.88-3.05-2.67-3.97Figure 3
The box plot ofIgeo value.The classification ofIgeo (level 0-level 3) is presented in Supplemental Table C.


### 3.3. Correlation Analysis and Principal Component Analysis

Correlation analysis was performed on 11 heavy metals except Sb, which was not detected in many samples. The results are presented in Table5. However, the strong positive correlation (R>0.7) is only discussed in the following. Based on the criterion, the results of the correlation analysis are as follows: (1) there is no significant correlation among V, Cr, As, Pb, and the other 7 heavy metals; (2) Cu, Mo, Ni, Co, and Mn metals are positively correlated; and (3) Zn is significantly correlated with Cd (R=0.914) (Table 5).Table 5
Results of the Pearson’s correlation analysis of heavy metals.
VCrMnCoNiCuZnAsMoCdPbV1Cr0.3681Mn0.0300.1521Co0.3040.177.850∗∗1Ni0.3240.134.791∗∗.984∗∗1Cu0.127.480∗.596∗∗.732∗∗.680∗∗1Zn0.1190.4200.378.468∗0.431.740∗∗1As0.1110.042-0.1300.1450.1130.255-0.0111Mo0.3660.108.747∗∗.787∗∗.741∗∗.544∗0.3370.0881Cd0.049.544∗0.401.480∗0.413.827∗∗.914∗∗0.1110.4091Pb.649∗∗.659∗∗0.2980.4040.350.580∗∗.513∗0.048.507∗.586∗∗1∗∗Significance at P<0.01 (two tailed). ∗Significance at P<0.05 (two tailed).We use the 7 heavy metals, except for the V, Cr, As, and Pb elements, in PCA based on the same criterion as the Pearson’s correlation analysis (R>0.7). To examine the validity of PCA, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were performed. The results of the KMO and Bartlett’s tests indicated that the data were positive in dimensionality reduction for PCA (KMO: 0.729; Bartlett’s: 165.155; df=21, P<0.01). The results of PCA are presented in Tables 6 and 7 and Figure 4. The loading values, expressed in bold, are more than 0.7, which represent strong positive loading in the process of PCA (Table 7).Table 6
Total variance explained by the PCA.
ComponentInitial eigenvaluesExtraction sums of squared loadingsTotal% of varianceCumulative (%)Total% of varianceCumulative (%)14.81668.79668.7964.81668.79668.79621.37919.70188.4971.37919.70188.49730.3294.70193.19840.2323.30896.50650.1892.70699.21260.0490.69899.9170.0060.09100Table 7
Results of principal component analyses for concentrations of heavy metals. Bold values represent strong positive loading, which markedloadings>0.7.
Heavy metalsPC1PC2Mn0.833-0.367Co0.928-0.312Ni0.885-0.344Cu0.880.299Zn0.7160.637Mo0.795-0.366Cd0.7470.632Figure 4
Biplot is showing the scores of different site samples and the loading of 7 heavy metals.Two principal components (PC1 and PC2) were extracted based on the Kaiser criterion (eigenvalues>1) (Table 6). The percentages of cumulative variance for the two PCs are 68.796% and 88.497% (Table 6). PC1, the most important principal component, has an eigenvalue of 4.816 and is 68.796% of the total variance, which can be explained by the strong positive correlations of Cu, Mo, Mn, Co, Ni, Zn, and Cd (Tables 6 and 7). PC2 with an eigenvalue of 1.379 is 19.701% of the total variance, which can be explained by a moderate positive correlation of Zn and Cd (Tables 6 and 7).


## 3.1. Sample Test Results

In this study, the raw data and the concentrations of 12 heavy metals are displayed in Supplement Table D. The concentration of Sb was not detected in many samples (Table D). Therefore, we do not study the Sb element in the following sections.


## 3.2. Statistical Analysis of Raw Data

The concentrations of the maximum, minimum, arithmetic mean (μ), the standard deviation (σ), and the coefficient of variation (CV) are listed in Table 1. The parameter CV in Table 1 is a statistical measure of the dispersion of data points in a data series around the mean. In this study, Wilding’s [26] classification of the CV is adopted, which is displayed in Supplemental Table E.The arithmetic mean of heavy metal concentrations are in the following order: Mn > Zn > V > Cr > Ni > Cu > Pb > Co > As > Mo > Cd (Table1). Mn, Zn, V, and Cr were the predominant heavy metals in this study, with mean concentrations of 534.65, 65.85, 56.29, and 36.05 mg/kg, respectively (Table 1). Ni, Cu, Pb, Co, and As were in moderate concentration, with mean concentrations of 25.2, 21.5, 16.7, 9.87, and 4.86 mg/kg, respectively (Table 1). Mo and Cd had the lowest mean concentrations of 0.54 and 0.15 mg/kg, respectively (Table 1). In this study, the arithmetic average values of 9 heavy metals (V, Cr, Co, Ni, Cu, Zn, As, Cd, and Pb) were lower than their local soil background values of 0.69, 0.14, 0.78, 0.13, 0.21, 0.22, 0.19, 0.25, and 0.10 times, respectively (Table 1). The arithmetic average value of Mn metal is higher than the background value of 8.19 times (Table 1).Based on the grade of the CV ([26]; Table E), the CV values of 4 heavy metals, including Cd (0.37), As (0.38), Zn (0.50), and Mo (0.54), showed a high degree of variation (Table 1). The CV values of 7 heavy metals, including Co (0.17), Mn (0.18), Ni (0.18), Pb (0.23), V (0.27), Cu (0.29), and Cr (0.31), showed moderate degrees of variation (Table 1). The higher CV values indicated that the variability and spatial dispersion of heavy metals are larger and are more easily affected by human activities [3]. The variability and spatial dispersion of heavy metals, revealed by the CV values, are discussed in Section 4.4.In this study, the calculated results ofEir and RI are displayed in Table 2. The statistical and classification results of RI are listed in Table 3. The maximum and minimum values of RI were 37.95 (site located in GYXC) and 18.11 (site located in PHG), respectively. All 20 RI values are classified as low risk (Table 3). The calculated Igeo values and Igeo arithmetic mean values are shown in Table 4. The box plot of Igeo values is presented in Figure 3, which can intuitively present the classification level of Igeo of different heavy metals.Table 2
The values ofRI and Eir.
IDEirRICrNiCuZnAsCdPbVMnCoHHQS0.360.551.010.221.368.500.621.057.533.2624.47YTDQN0.140.500.720.141.165.000.320.977.173.0419.17WQSW0.220.741.080.211.606.500.411.229.084.3325.38HSG0.190.951.480.292.209.000.651.6911.915.6033.95PHG0.160.500.710.151.085.000.290.856.522.8518.11HYRY0.190.630.920.191.166.500.411.078.353.8123.23WQSE0.380.761.940.283.4411.500.561.128.704.5633.24JZDD0.290.761.120.211.686.000.471.427.864.2524.05ZJG0.180.470.670.133.845.500.351.245.012.9320.32WQS0.280.630.940.193.086.000.411.147.703.6924.06WYS0.300.681.020.201.888.500.441.288.423.9726.70HCZ0.270.680.990.200.966.000.411.198.303.9222.93YJY0.300.791.110.222.207.000.471.569.424.6027.66QBSQ0.340.550.990.161.606.000.591.568.843.5024.13WHC0.390.581.010.181.769.000.651.597.523.6426.31YTHHQ0.420.550.960.161.885.500.501.498.153.4223.02XFY0.310.660.940.202.085.500.682.615.713.6722.35GYXC0.430.741.750.671.6817.000.681.479.104.4437.95DJS0.400.791.090.211.848.000.471.669.344.5628.37FJQ0.210.741.090.212.367.000.441.299.134.3326.78Table 3
The statistical results ofRI.
The statistical results ofRIThe classification of allRI (%)Min. value and siteMax. value and siteAve. valueExtremely high riskHigh riskModerate riskLow risk18.11, PHG37.95, GYXC25.61000100Table 4
TheIgeo values.
IDIgeo valuesVCrMnCoNiCuZnAsCdPbHHQS-1.51-3.062.33-1.20-3.76-2.90-2.77-3.46-2.40-3.60YTDQN-1.63-4.382.26-1.30-3.91-3.38-3.39-3.69-3.17-4.53WQSW-1.30-3.742.60-0.79-3.35-2.80-2.86-3.23-2.79-4.19HSG-0.83-3.972.99-0.42-2.98-2.35-2.39-2.77-2.32-3.53PHG-1.82-4.232.12-1.40-3.91-3.41-3.35-3.80-3.17-4.67HYRY-1.48-3.972.48-0.98-3.57-3.03-2.98-3.69-2.79-4.19WQSE-1.42-3.002.54-0.72-3.30-1.95-2.42-2.12-1.97-3.75JZDD-1.08-3.382.39-0.82-3.30-2.74-2.86-3.16-2.91-3.99ZJG-1.28-4.091.74-1.36-3.98-3.48-3.53-1.97-3.03-4.41WQS-1.39-3.422.36-1.02-3.57-3.00-3.01-2.28-2.91-4.19WYS-1.23-3.302.49-0.92-3.45-2.88-2.91-3.00-2.40-4.09HCZ-1.33-3.462.47-0.94-3.45-2.92-2.93-3.97-2.91-4.19YJY-0.94-3.342.65-0.70-3.25-2.76-2.79-2.77-2.68-3.99QBSQ-0.94-3.122.56-1.10-3.76-2.92-3.23-3.23-2.91-3.67WHC-0.92-2.942.33-1.04-3.70-2.90-3.09-3.09-2.32-3.53YTHHQ-1.01-2.852.44-1.13-3.76-2.97-3.26-3.00-3.03-3.91XFY-0.20-3.271.93-1.03-3.51-3.00-2.93-2.85-3.03-3.47GYXC-1.03-2.802.60-0.75-3.35-2.10-1.16-3.16-1.40-3.47DJS-0.85-2.912.64-0.72-3.25-2.78-2.81-3.03-2.49-3.99FJQ-1.22-3.852.61-0.79-3.35-2.79-2.86-2.67-2.68-4.09Arithmetic mean-1.17-3.452.43-0.96-3.52-2.85-2.88-3.05-2.67-3.97Figure 3
The box plot ofIgeo value.The classification ofIgeo (level 0-level 3) is presented in Supplemental Table C.


## 3.3. Correlation Analysis and Principal Component Analysis

Correlation analysis was performed on 11 heavy metals except Sb, which was not detected in many samples. The results are presented in Table5. However, the strong positive correlation (R>0.7) is only discussed in the following. Based on the criterion, the results of the correlation analysis are as follows: (1) there is no significant correlation among V, Cr, As, Pb, and the other 7 heavy metals; (2) Cu, Mo, Ni, Co, and Mn metals are positively correlated; and (3) Zn is significantly correlated with Cd (R=0.914) (Table 5).Table 5
Results of the Pearson’s correlation analysis of heavy metals.
VCrMnCoNiCuZnAsMoCdPbV1Cr0.3681Mn0.0300.1521Co0.3040.177.850∗∗1Ni0.3240.134.791∗∗.984∗∗1Cu0.127.480∗.596∗∗.732∗∗.680∗∗1Zn0.1190.4200.378.468∗0.431.740∗∗1As0.1110.042-0.1300.1450.1130.255-0.0111Mo0.3660.108.747∗∗.787∗∗.741∗∗.544∗0.3370.0881Cd0.049.544∗0.401.480∗0.413.827∗∗.914∗∗0.1110.4091Pb.649∗∗.659∗∗0.2980.4040.350.580∗∗.513∗0.048.507∗.586∗∗1∗∗Significance at P<0.01 (two tailed). ∗Significance at P<0.05 (two tailed).We use the 7 heavy metals, except for the V, Cr, As, and Pb elements, in PCA based on the same criterion as the Pearson’s correlation analysis (R>0.7). To examine the validity of PCA, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were performed. The results of the KMO and Bartlett’s tests indicated that the data were positive in dimensionality reduction for PCA (KMO: 0.729; Bartlett’s: 165.155; df=21, P<0.01). The results of PCA are presented in Tables 6 and 7 and Figure 4. The loading values, expressed in bold, are more than 0.7, which represent strong positive loading in the process of PCA (Table 7).Table 6
Total variance explained by the PCA.
ComponentInitial eigenvaluesExtraction sums of squared loadingsTotal% of varianceCumulative (%)Total% of varianceCumulative (%)14.81668.79668.7964.81668.79668.79621.37919.70188.4971.37919.70188.49730.3294.70193.19840.2323.30896.50650.1892.70699.21260.0490.69899.9170.0060.09100Table 7
Results of principal component analyses for concentrations of heavy metals. Bold values represent strong positive loading, which markedloadings>0.7.
Heavy metalsPC1PC2Mn0.833-0.367Co0.928-0.312Ni0.885-0.344Cu0.880.299Zn0.7160.637Mo0.795-0.366Cd0.7470.632Figure 4
Biplot is showing the scores of different site samples and the loading of 7 heavy metals.Two principal components (PC1 and PC2) were extracted based on the Kaiser criterion (eigenvalues>1) (Table 6). The percentages of cumulative variance for the two PCs are 68.796% and 88.497% (Table 6). PC1, the most important principal component, has an eigenvalue of 4.816 and is 68.796% of the total variance, which can be explained by the strong positive correlations of Cu, Mo, Mn, Co, Ni, Zn, and Cd (Tables 6 and 7). PC2 with an eigenvalue of 1.379 is 19.701% of the total variance, which can be explained by a moderate positive correlation of Zn and Cd (Tables 6 and 7).


## 4. Discussion

### 4.1. Comparison of Heavy Metals in Different Cities

Due to the limited research data, the concentrations of 7 heavy metals in different cities in China are shown in Table8 and Figure 5. Table 8 and Figure 5 reveal that (1) the concentrations of Pb (16.7 mg/kg) and As (4.86 mg/kg) in Lanzhou were the lowest compared to other cities (Table 8 and Figure 5); (2) the concentrations of Cd (0.15 mg/kg), Cr (36.05 mg/kg), Zn (65.85 mg/kg), and Ni (25.2 mg/kg) in Lanzhou were the relatively lowest compared to other cities, except for Huizhou, China (Table 8 and Figure 5); and (3) the concentration of Cu (21.5 mg/kg) in Lanzhou was relatively lower than other cities, except for Huizhou and the central region of Gansu Province, China (Table 8). The comparison of heavy metals in different cities reveals that the concentrations of 7 heavy metals in Lanzhou were the lowest among the 5 major cities in China.Table 8
Summary of heavy metal concentrations of different cities in China.
RegionsHeavy metal concentrations (mg/kg)ReferencesPbCdCrAsCuZnNiLanzhou16.70.1536.054.8621.565.8525.2This studyCentral Gansu Province23.3No data40.18.817.159.1455.09[27, 28]Huizhou44.660.127.6110.1916.7457.2114.89[29]Taiyuan27.870.2574.110.732.1190.7629.74[30]Nanjing30.30.1776.29.5931.278.932.7[31]Figure 5
The histogram of heavy metal concentrations in different cities of China.


### 4.2. The Contrast in Heavy Metals during Different Years in Lanzhou

The contrasting results of heavy metal concentrations in different years in Lanzhou are shown in Table9 and Figure 6. Due to the limited research data, 8 heavy metals are discussed.Table 9
Summary of the mean values of heavy metal concentrations in different years.
Year and referencesMean values of heavy metals contents (mg·kg-1)PbZnCuCrMnAsCdNi2005 [5]23.9285.723.0865ND11.350.16431.32006 [6, 14] [1]62.65296.9272.9762.14592.4120.63ND38.222010 [2, 7, 11, 15, 32] [2]18.7574.9340.42158.55693.12.420.3728.72014 [4, 13] [3]44.54161.3941.6678.39624.6410.650.2429.842015 [12] [4]36.99ND27.570.69ND12.350.34ND2019 [3] [5]20.96101.9248.570.05ND8.360.2434.342022 this study16.765.8521.49536.05534.654.8550.14925.2ND = no data; [1]: the reference of Wang et al. [6] was published in 2012, but the samples were collected in 2006, which had been clarified in the paper; the time discrepancy of [2]-[5] is the same as [1].Figure 6
The histogram of the mean values of 8 heavy metals in different years in Lanzhou city.Table9 and Figure 6 suggest that (1) the trend of Mn metal is relatively stable with an approximate concentration of 500-600 mg/kg in recent decades; (2) the trend of Cr metal initially increases and then gradually decreases. The peak level of 158.55 mg/kg occurred in 2010; (3) the trends of Cu and Ni metals firstly increased (before 2006) and then stabilized (2010-2015) and finally decreased (later 2019). The concentration peak values of Cu and Ni, 72.97 mg/kg and 38.22 mg/kg, respectively, occurred in 2006; (4) the trends of Pb, Zn, Cd, and As increased first and then in recent decades gradually decreased. A turning point of the reduction in concentration occurred in 2014 (Pb and Zn) or 2015 (Cd and As).In summary, the changing trends of 8 heavy metals in Lanzhou from 2005 to 2022 are generally an initial increase and then a gradual decrease (Table9 and Figure 6). The shift to a shrinking concentration occurred in approximately 2014 (Table 9 and Figure 6). These changing trends of heavy metals are in response to the regional efforts to address pollution, which have been launched by the Lanzhou municipal government since 2010. For example, (1) a project of tax subsidy was released to replace oil-fueled automotive with new energy vehicles. New energy vehicles amounted to 145.6 million in 2021, which rose 17.3% compared with last year (referring to the Lanzhou Statistical Yearbook of 2021). (2) The rate of safely treating municipal solid waste and domestic garbage was intended to improve from 40.4% to 100% during the last decades (referring to the Lanzhou Statistical Yearbook of 2021). All these efforts to address pollution were amply rewarded. For example, a low ecological risk is confirmed by the RI and Igeo index (Tables 2–4 and Figure 3). More importantly, Lanzhou city, the top polluted city, has earned a reputation for antipollution in China.


### 4.3. Pollution Risk Assessment

The geoaccumulation index (Igeo) is widely applied to assess the ecological environment caused by heavy metal accumulation due to the advantages of simple formulas and visualizations [21, 33]. However, the Igeo index ignores the factors of chemical forms and geospatial heterogeneity of different heavy metals [21]. In consideration of the above disadvantages of the Igeo index, the Hakanson potential ecological risk index (RI) was introduced to comprehensively assess the pollution risk of the study area. The RI index reflects the synergistic effect of the toxicity level, contamination concentration, and environmental sensitivity to heavy metals [33]. The essential differences between the RI index and Igeo index are that the former considers adverse biological effects and potential ecological risks, but the Igeo index only considers a single heavy metal [13]. Therefore, two parameters, RI and Igeo, were employed to evaluate the pollution risk of heavy metals. The results of RI and Igeo are shown in Tables 2 and 4 and Figure 3.The results show that the potential ecological hazard coefficient (Eir) of all heavy metals is less than 40 (Table 2), which indicates that the environment of the study area is a low ecological risk based on the categories of Eir (Supplement Table B). The RI values ranged from 18.11 (site PHG) to 37.95 (site GYXC) (Table 2). The average value of RI (25.61) is much less than 150 (Supplement Table B). The RI values of all 20 sample sites are much less than 150 (Supplement Table B). These data indicate that the environment of the study area is a low ecological risk.Among allRI values, the maximum value of RI (37.95) comes from the GYXC site (Table 2). Although the study area, including the GYXC site, has a low ecological risk, the reason why the RI value (GYXC site) is the highest among all 20 sites deserves discussion (Table 2). The maximum value of RI (37.95) is equal to the accumulation of 20 values of Eir (Table 2). The maximum Eir values of Cr (0.43), Zn (0.67), Cd (17.00), and Pb (0.68) also come from the GYXC site (Table 2), which accounts for 40% of all Eir values. For example, the maximum Eir value of Cd (17.00) at the GYXC site was 2-3 times that of the other Eir values of Cd (Table 2). In addition, the maximum Eir values of Cr (0.43), Cd (17.00), and Pb (0.68) echo the maximum concentrations of Cr (54 mg/kg), Cd (0.34 mg/kg), and Pb (23 mg/kg) of the GYXC site (Supplement Table D).Why is theRI value of the GYXC site the highest among all 20 sites (Table 2)? In other words, why did the maximum Eir values of Cr (0.43), Zn (0.67), Cd (17.00), and Pb (0.68) arise at the GYXC site (Table 2)? The highest RI value or the maximum Eir values of Cr, Zn, Cd, and Pb at the GYXC site are likely caused by human activities. The reasons for these high values are stated as follows: (1) there is the large fruit and vegetable market at the GYXC site. Many logistic parks, factories, and heavy traffic were flooded in the region; (2) studies show that Cr, Zn, Cd, and Pb are the major emissions of vehicles, which can accumulate in sediments by air dust adsorption [34, 35]; (3) Chai et al. [3] revealed that Pb, Cd, and Zn are mainly caused by transportation in the Chengguan district of Lanzhou, where the GYXC site is located. The contribution rates of Pb, Cd, and Zn were 52.4%, 50.7%, and 56.7%, respectively [3]; (4) among these metals (Cr, Zn, Cd, Pb, and Cd) are typical metal associated with human activities [36]. (5) The natural source of Pb in the crust varied from 15 to 20 mg/kg [37]. Supplement Table D reveals that the Pb concentration at the GYXC site was 23 mg/kg, which was higher than the maximum value of the natural source of Pb (20 mg/kg).The arithmetic mean values ofIgeo are ranked in the order of Mn (2.43) > Co (-0.96) > V (-1.17) > Cd (-2.67) > Cu (-2.85) > Zn (-2.88) > As (-3.05) > Cr (-3.45) > Ni (-3.52) > Pb (-3.97) (Table 4). The classified results of the Igeo values in Figure 3 indicate that 9 heavy metals (Co, V, Cd, Cu, Zn, As, Cr, Ni, and Pb) are classified as level 0, which suggests that these heavy metals are uncontaminated in sediments (Figure 3). Figure 3 also indicates that the Mn metal is classified into level 2-level 3, a moderately to heavily contaminated sediment. The Igeo values of Mn metal ranged from 1.74 (site ZJG) to 2.99 (site HSG) (Table 4). The maximum concentration of Mn metal (778 mg/kg) also came from the HSG site (Supplement Table D), which corresponded to the maximum value of the Igeo index. The arithmetic mean of Mn metal is 8.19 times higher the background value (Table 1), which explains that the moderate to heavy contaminated risk assessment (level 2-level 3) caused by excess Mn metal is assigned in Lanzhou (Figure 3).Mn is the eleventh most abundant element on Earth, and its concentration in the crust ranges between 40 and 900 mg/kg [38–40]. The concentrations (mg/kg) of Mn ranged from 327 (the ZJG site) to 778 (the HSG site) with a median value (543.5) and arithmetic mean value (534.65), which are within the above range (Table 1). Figure 7 reveals that the Mn concentration of the vast majority of samples (approximately 80%) ranged between 500 and 600 mg/kg. In addition, previous studies on Mn metal in Lanzhou revealed that the average concentrations were in the range of 592.41-624.64 mg/kg, with a median value of 627.3 mg/kg [4, 6]. The minimum and maximum values were in the range of 415.5-833.3 mg/kg [4, 6]. Table 1 reveals that the statistical results of the arithmetic mean, minimum value, maximum value, and median value of Mn metal are coupled with the previous results of Wang et al. [6] and Sun [4]. Obviously, previous studies can provide references to reveal the reason why Mn metal showed higher accumulation in Lanzhou city.Figure 7
Scatter plot showing the concentration distribution trend of Mn.Wang et al. [6] confirmed that the higher Mn contamination across Lanzhou city resulted from combustion processes, which was revealed by the highly significant positive correlations between magnetic and heavy metals. Sun [4] revealed that the higher Mn contamination in Lanzhou was induced from traffic pollution and industrial activity by the method of positive matrix factorization (PMF). Therefore, we argue that the accumulation and enrichment of Mn in this study is also influenced by both natural processes and anthropogenic activities, such as vehicle exhaust and industrial pollution.AllEir values of the 10 heavy metals in all samples are less than 40, which indicate that the environment of the study area is a low ecological risk (Tables 2 and 3). In particular, the RI values, with an average value of 25.61, range from 18.11 to 37.95, which are much less than 150 (Table 2). This also means that the environment of Lanzhou is a low ecological risk (Table 3), which is echoed by the comparison of heavy metals in different cities in Section 4.1. In addition, the final results are also in response to the progressive decrease in heavy metals in recent years in Lanzhou (Table 9 and Figure 6), which is the result of reducing environmental pollution by the Lanzhou municipal government. Among these results, the maximum value of RI (37.95) from the GYXC site could be more caused by traffic emissions (Table 3). In addition, 9 heavy metals (Co, V, Cd, Cu, Zn, As, Cr, Ni, and Pb), except for Mn, are classified into level 0 by the Igeo values, which suggests that these heavy metals are uncontaminated in sediments (Figure 3). The Mn metal, a moderately to heavily contaminated metal in sediments, is classified into level 2-level 3 of pollution risk assessment by Igeo values ranging from 1.74 to 2.99 (Table 4 and Figure 3).


### 4.4. Source Analysis of Heavy Metals

Pearson’s correlation and principal component analysis (PCA) reveal that there are strong positive correlations of Cu, Mo, Mn, Co, Ni, Zn, and Cd heavy metals. The sources of Mn, Zn and Cd elements are discussed in Section4.3 for the process of assessing pollution risk. Therefore, we mainly discuss the sources of Cu, Mo, Co, and Ni metals in this section.The maximum concentrations of Cu, Mo, Co, and Ni metals are 38.70 mg/kg, 1.46 mg/kg, 14.10 mg/kg, and 36.00 mg/kg, respectively (Table1). The arithmetic mean concentrations of Cu, Mo, Co, and Ni metals are 21.50 mg/kg, 0.54 mg/kg, 9.87 mg/kg, and 25.20 mg/kg, respectively (Table 1). The arithmetic average values of Cu, Co, and Ni metals are lower than their background values of 0.21, 0.78, and 0.13 times, respectively (Table 1). The average concentrations of Cu, Mo, Co, and Ni metals present in the Earth’s crust are approximately 50, 1.5, 20, and 80 mg/kg, respectively [41, 42]. Although the arithmetic mean concentrations and maximum concentrations of Cu, Mo, Co, and Ni metals are lower than their concentrations in the Earth’s crust (Table 1), the provenances of the above 4 metals may be from multiple sources, including natural sources and anthropogenic activities. This deduction is supported by the CV value, an index that partly helps to analyze sources of heavy metals [3]. Based on the coefficient of variation (CV), the CV values of Cu (0.29), Co (0.17), and Ni (0.18) showed moderate variation (Table 1 and Supplement Table E), which means that there was moderate variability among these metals that is mainly affected by natural sources instead of human activity (Table 1). The CV value of Mo (0.54) has a high degree of variation, which indicates that the variability of Mo metal is larger and is more easily affected by the physical environment (Table 1).Among these Cu, Co, and Ni metals, the lower concentrations of arithmetic mean values and maximum values with moderate CV values together indicate that these metals are mainly affected by natural sources (Table1). For example, (1) many studies have revealed that Cu, Co, and Ni metals are mainly derived from soil parent materials [43, 44]; (2) Cu is easily adsorbed on water-borne suspended particles, which reflects the degree of pollution of the water body [45]. In our study area, the maximum concentration of Cu (38.7 mg/kg) is sampled at the WQSE site, which is a park famed for its five springs (Supplement Table D). The site also grows vegetation with local running water. The maximum concentrations of Co (14.1 mg/kg) and Ni (36 mg/kg) metals are sampled at the HSG site, which is mountain sediment that is little affected by human activity (Supplement Table D). Therefore, we argue that the accumulations of Cu, Co, and Ni metals in this study are influenced by natural sources, such as river impacts.The adsorption of Mn oxides is an important pathway for authigenic Mo formation [46], which also means that Mn oxides have a close correlation with the formation of Mo. This perspective was further developed by Bai et al. [47] in the process of seeking the redistribution of Mo metal in sediments. Bai et al. [47] revealed that the enrichments in Mo were related to the adsorption of Fe- and Mn-bearing sediments. The enrichment of Mo was caused by the enrichment of MnO2 during the increased oxidic condition [47]. In this study, the maximum concentrations of Mo (1.46 mg/kg) and Mn (778 mg/kg) were sampled at the HSG site (Supplement Table D). The HSG site consists of a high amount of red sediment, resulting in the Chinese naming of “Red Mountain” (Figure 2(a)). The reddish sediment in the HSG site indicates the adsorption of a large amount of Fe-bearing sediment under oxidic conditions. Therefore, we argue that the accumulation of Mo metal was caused by the enrichment of MnO2 due to the increased oxidizing conditions.


## 4.1. Comparison of Heavy Metals in Different Cities

Due to the limited research data, the concentrations of 7 heavy metals in different cities in China are shown in Table8 and Figure 5. Table 8 and Figure 5 reveal that (1) the concentrations of Pb (16.7 mg/kg) and As (4.86 mg/kg) in Lanzhou were the lowest compared to other cities (Table 8 and Figure 5); (2) the concentrations of Cd (0.15 mg/kg), Cr (36.05 mg/kg), Zn (65.85 mg/kg), and Ni (25.2 mg/kg) in Lanzhou were the relatively lowest compared to other cities, except for Huizhou, China (Table 8 and Figure 5); and (3) the concentration of Cu (21.5 mg/kg) in Lanzhou was relatively lower than other cities, except for Huizhou and the central region of Gansu Province, China (Table 8). The comparison of heavy metals in different cities reveals that the concentrations of 7 heavy metals in Lanzhou were the lowest among the 5 major cities in China.Table 8
Summary of heavy metal concentrations of different cities in China.
RegionsHeavy metal concentrations (mg/kg)ReferencesPbCdCrAsCuZnNiLanzhou16.70.1536.054.8621.565.8525.2This studyCentral Gansu Province23.3No data40.18.817.159.1455.09[27, 28]Huizhou44.660.127.6110.1916.7457.2114.89[29]Taiyuan27.870.2574.110.732.1190.7629.74[30]Nanjing30.30.1776.29.5931.278.932.7[31]Figure 5
The histogram of heavy metal concentrations in different cities of China.


## 4.2. The Contrast in Heavy Metals during Different Years in Lanzhou

The contrasting results of heavy metal concentrations in different years in Lanzhou are shown in Table9 and Figure 6. Due to the limited research data, 8 heavy metals are discussed.Table 9
Summary of the mean values of heavy metal concentrations in different years.
Year and referencesMean values of heavy metals contents (mg·kg-1)PbZnCuCrMnAsCdNi2005 [5]23.9285.723.0865ND11.350.16431.32006 [6, 14] [1]62.65296.9272.9762.14592.4120.63ND38.222010 [2, 7, 11, 15, 32] [2]18.7574.9340.42158.55693.12.420.3728.72014 [4, 13] [3]44.54161.3941.6678.39624.6410.650.2429.842015 [12] [4]36.99ND27.570.69ND12.350.34ND2019 [3] [5]20.96101.9248.570.05ND8.360.2434.342022 this study16.765.8521.49536.05534.654.8550.14925.2ND = no data; [1]: the reference of Wang et al. [6] was published in 2012, but the samples were collected in 2006, which had been clarified in the paper; the time discrepancy of [2]-[5] is the same as [1].Figure 6
The histogram of the mean values of 8 heavy metals in different years in Lanzhou city.Table9 and Figure 6 suggest that (1) the trend of Mn metal is relatively stable with an approximate concentration of 500-600 mg/kg in recent decades; (2) the trend of Cr metal initially increases and then gradually decreases. The peak level of 158.55 mg/kg occurred in 2010; (3) the trends of Cu and Ni metals firstly increased (before 2006) and then stabilized (2010-2015) and finally decreased (later 2019). The concentration peak values of Cu and Ni, 72.97 mg/kg and 38.22 mg/kg, respectively, occurred in 2006; (4) the trends of Pb, Zn, Cd, and As increased first and then in recent decades gradually decreased. A turning point of the reduction in concentration occurred in 2014 (Pb and Zn) or 2015 (Cd and As).In summary, the changing trends of 8 heavy metals in Lanzhou from 2005 to 2022 are generally an initial increase and then a gradual decrease (Table9 and Figure 6). The shift to a shrinking concentration occurred in approximately 2014 (Table 9 and Figure 6). These changing trends of heavy metals are in response to the regional efforts to address pollution, which have been launched by the Lanzhou municipal government since 2010. For example, (1) a project of tax subsidy was released to replace oil-fueled automotive with new energy vehicles. New energy vehicles amounted to 145.6 million in 2021, which rose 17.3% compared with last year (referring to the Lanzhou Statistical Yearbook of 2021). (2) The rate of safely treating municipal solid waste and domestic garbage was intended to improve from 40.4% to 100% during the last decades (referring to the Lanzhou Statistical Yearbook of 2021). All these efforts to address pollution were amply rewarded. For example, a low ecological risk is confirmed by the RI and Igeo index (Tables 2–4 and Figure 3). More importantly, Lanzhou city, the top polluted city, has earned a reputation for antipollution in China.


## 4.3. Pollution Risk Assessment

The geoaccumulation index (Igeo) is widely applied to assess the ecological environment caused by heavy metal accumulation due to the advantages of simple formulas and visualizations [21, 33]. However, the Igeo index ignores the factors of chemical forms and geospatial heterogeneity of different heavy metals [21]. In consideration of the above disadvantages of the Igeo index, the Hakanson potential ecological risk index (RI) was introduced to comprehensively assess the pollution risk of the study area. The RI index reflects the synergistic effect of the toxicity level, contamination concentration, and environmental sensitivity to heavy metals [33]. The essential differences between the RI index and Igeo index are that the former considers adverse biological effects and potential ecological risks, but the Igeo index only considers a single heavy metal [13]. Therefore, two parameters, RI and Igeo, were employed to evaluate the pollution risk of heavy metals. The results of RI and Igeo are shown in Tables 2 and 4 and Figure 3.The results show that the potential ecological hazard coefficient (Eir) of all heavy metals is less than 40 (Table 2), which indicates that the environment of the study area is a low ecological risk based on the categories of Eir (Supplement Table B). The RI values ranged from 18.11 (site PHG) to 37.95 (site GYXC) (Table 2). The average value of RI (25.61) is much less than 150 (Supplement Table B). The RI values of all 20 sample sites are much less than 150 (Supplement Table B). These data indicate that the environment of the study area is a low ecological risk.Among allRI values, the maximum value of RI (37.95) comes from the GYXC site (Table 2). Although the study area, including the GYXC site, has a low ecological risk, the reason why the RI value (GYXC site) is the highest among all 20 sites deserves discussion (Table 2). The maximum value of RI (37.95) is equal to the accumulation of 20 values of Eir (Table 2). The maximum Eir values of Cr (0.43), Zn (0.67), Cd (17.00), and Pb (0.68) also come from the GYXC site (Table 2), which accounts for 40% of all Eir values. For example, the maximum Eir value of Cd (17.00) at the GYXC site was 2-3 times that of the other Eir values of Cd (Table 2). In addition, the maximum Eir values of Cr (0.43), Cd (17.00), and Pb (0.68) echo the maximum concentrations of Cr (54 mg/kg), Cd (0.34 mg/kg), and Pb (23 mg/kg) of the GYXC site (Supplement Table D).Why is theRI value of the GYXC site the highest among all 20 sites (Table 2)? In other words, why did the maximum Eir values of Cr (0.43), Zn (0.67), Cd (17.00), and Pb (0.68) arise at the GYXC site (Table 2)? The highest RI value or the maximum Eir values of Cr, Zn, Cd, and Pb at the GYXC site are likely caused by human activities. The reasons for these high values are stated as follows: (1) there is the large fruit and vegetable market at the GYXC site. Many logistic parks, factories, and heavy traffic were flooded in the region; (2) studies show that Cr, Zn, Cd, and Pb are the major emissions of vehicles, which can accumulate in sediments by air dust adsorption [34, 35]; (3) Chai et al. [3] revealed that Pb, Cd, and Zn are mainly caused by transportation in the Chengguan district of Lanzhou, where the GYXC site is located. The contribution rates of Pb, Cd, and Zn were 52.4%, 50.7%, and 56.7%, respectively [3]; (4) among these metals (Cr, Zn, Cd, Pb, and Cd) are typical metal associated with human activities [36]. (5) The natural source of Pb in the crust varied from 15 to 20 mg/kg [37]. Supplement Table D reveals that the Pb concentration at the GYXC site was 23 mg/kg, which was higher than the maximum value of the natural source of Pb (20 mg/kg).The arithmetic mean values ofIgeo are ranked in the order of Mn (2.43) > Co (-0.96) > V (-1.17) > Cd (-2.67) > Cu (-2.85) > Zn (-2.88) > As (-3.05) > Cr (-3.45) > Ni (-3.52) > Pb (-3.97) (Table 4). The classified results of the Igeo values in Figure 3 indicate that 9 heavy metals (Co, V, Cd, Cu, Zn, As, Cr, Ni, and Pb) are classified as level 0, which suggests that these heavy metals are uncontaminated in sediments (Figure 3). Figure 3 also indicates that the Mn metal is classified into level 2-level 3, a moderately to heavily contaminated sediment. The Igeo values of Mn metal ranged from 1.74 (site ZJG) to 2.99 (site HSG) (Table 4). The maximum concentration of Mn metal (778 mg/kg) also came from the HSG site (Supplement Table D), which corresponded to the maximum value of the Igeo index. The arithmetic mean of Mn metal is 8.19 times higher the background value (Table 1), which explains that the moderate to heavy contaminated risk assessment (level 2-level 3) caused by excess Mn metal is assigned in Lanzhou (Figure 3).Mn is the eleventh most abundant element on Earth, and its concentration in the crust ranges between 40 and 900 mg/kg [38–40]. The concentrations (mg/kg) of Mn ranged from 327 (the ZJG site) to 778 (the HSG site) with a median value (543.5) and arithmetic mean value (534.65), which are within the above range (Table 1). Figure 7 reveals that the Mn concentration of the vast majority of samples (approximately 80%) ranged between 500 and 600 mg/kg. In addition, previous studies on Mn metal in Lanzhou revealed that the average concentrations were in the range of 592.41-624.64 mg/kg, with a median value of 627.3 mg/kg [4, 6]. The minimum and maximum values were in the range of 415.5-833.3 mg/kg [4, 6]. Table 1 reveals that the statistical results of the arithmetic mean, minimum value, maximum value, and median value of Mn metal are coupled with the previous results of Wang et al. [6] and Sun [4]. Obviously, previous studies can provide references to reveal the reason why Mn metal showed higher accumulation in Lanzhou city.Figure 7
Scatter plot showing the concentration distribution trend of Mn.Wang et al. [6] confirmed that the higher Mn contamination across Lanzhou city resulted from combustion processes, which was revealed by the highly significant positive correlations between magnetic and heavy metals. Sun [4] revealed that the higher Mn contamination in Lanzhou was induced from traffic pollution and industrial activity by the method of positive matrix factorization (PMF). Therefore, we argue that the accumulation and enrichment of Mn in this study is also influenced by both natural processes and anthropogenic activities, such as vehicle exhaust and industrial pollution.AllEir values of the 10 heavy metals in all samples are less than 40, which indicate that the environment of the study area is a low ecological risk (Tables 2 and 3). In particular, the RI values, with an average value of 25.61, range from 18.11 to 37.95, which are much less than 150 (Table 2). This also means that the environment of Lanzhou is a low ecological risk (Table 3), which is echoed by the comparison of heavy metals in different cities in Section 4.1. In addition, the final results are also in response to the progressive decrease in heavy metals in recent years in Lanzhou (Table 9 and Figure 6), which is the result of reducing environmental pollution by the Lanzhou municipal government. Among these results, the maximum value of RI (37.95) from the GYXC site could be more caused by traffic emissions (Table 3). In addition, 9 heavy metals (Co, V, Cd, Cu, Zn, As, Cr, Ni, and Pb), except for Mn, are classified into level 0 by the Igeo values, which suggests that these heavy metals are uncontaminated in sediments (Figure 3). The Mn metal, a moderately to heavily contaminated metal in sediments, is classified into level 2-level 3 of pollution risk assessment by Igeo values ranging from 1.74 to 2.99 (Table 4 and Figure 3).


## 4.4. Source Analysis of Heavy Metals

Pearson’s correlation and principal component analysis (PCA) reveal that there are strong positive correlations of Cu, Mo, Mn, Co, Ni, Zn, and Cd heavy metals. The sources of Mn, Zn and Cd elements are discussed in Section4.3 for the process of assessing pollution risk. Therefore, we mainly discuss the sources of Cu, Mo, Co, and Ni metals in this section.The maximum concentrations of Cu, Mo, Co, and Ni metals are 38.70 mg/kg, 1.46 mg/kg, 14.10 mg/kg, and 36.00 mg/kg, respectively (Table1). The arithmetic mean concentrations of Cu, Mo, Co, and Ni metals are 21.50 mg/kg, 0.54 mg/kg, 9.87 mg/kg, and 25.20 mg/kg, respectively (Table 1). The arithmetic average values of Cu, Co, and Ni metals are lower than their background values of 0.21, 0.78, and 0.13 times, respectively (Table 1). The average concentrations of Cu, Mo, Co, and Ni metals present in the Earth’s crust are approximately 50, 1.5, 20, and 80 mg/kg, respectively [41, 42]. Although the arithmetic mean concentrations and maximum concentrations of Cu, Mo, Co, and Ni metals are lower than their concentrations in the Earth’s crust (Table 1), the provenances of the above 4 metals may be from multiple sources, including natural sources and anthropogenic activities. This deduction is supported by the CV value, an index that partly helps to analyze sources of heavy metals [3]. Based on the coefficient of variation (CV), the CV values of Cu (0.29), Co (0.17), and Ni (0.18) showed moderate variation (Table 1 and Supplement Table E), which means that there was moderate variability among these metals that is mainly affected by natural sources instead of human activity (Table 1). The CV value of Mo (0.54) has a high degree of variation, which indicates that the variability of Mo metal is larger and is more easily affected by the physical environment (Table 1).Among these Cu, Co, and Ni metals, the lower concentrations of arithmetic mean values and maximum values with moderate CV values together indicate that these metals are mainly affected by natural sources (Table1). For example, (1) many studies have revealed that Cu, Co, and Ni metals are mainly derived from soil parent materials [43, 44]; (2) Cu is easily adsorbed on water-borne suspended particles, which reflects the degree of pollution of the water body [45]. In our study area, the maximum concentration of Cu (38.7 mg/kg) is sampled at the WQSE site, which is a park famed for its five springs (Supplement Table D). The site also grows vegetation with local running water. The maximum concentrations of Co (14.1 mg/kg) and Ni (36 mg/kg) metals are sampled at the HSG site, which is mountain sediment that is little affected by human activity (Supplement Table D). Therefore, we argue that the accumulations of Cu, Co, and Ni metals in this study are influenced by natural sources, such as river impacts.The adsorption of Mn oxides is an important pathway for authigenic Mo formation [46], which also means that Mn oxides have a close correlation with the formation of Mo. This perspective was further developed by Bai et al. [47] in the process of seeking the redistribution of Mo metal in sediments. Bai et al. [47] revealed that the enrichments in Mo were related to the adsorption of Fe- and Mn-bearing sediments. The enrichment of Mo was caused by the enrichment of MnO2 during the increased oxidic condition [47]. In this study, the maximum concentrations of Mo (1.46 mg/kg) and Mn (778 mg/kg) were sampled at the HSG site (Supplement Table D). The HSG site consists of a high amount of red sediment, resulting in the Chinese naming of “Red Mountain” (Figure 2(a)). The reddish sediment in the HSG site indicates the adsorption of a large amount of Fe-bearing sediment under oxidic conditions. Therefore, we argue that the accumulation of Mo metal was caused by the enrichment of MnO2 due to the increased oxidizing conditions.


## 5. Conclusion

(1)
The arithmetic average values of 9 heavy metals V (56.29 mg/kg), Cr (36.05 mg/kg), Co (9.87 mg/kg), Ni (25.20 mg/kg), Cu (21.5 mg/kg), Zn (65.85 mg/kg), Cd (0.15 mg/kg), As (4.86 mg/kg), and Pb (16.70 mg/kg), except Mn (534.65 mg/kg) metal, are lower than their local soil background values. The concentrations of 7 heavy metals (Pb, Cd, Cr, As, Cu, Zn, and Ni) in Lanzhou are the lowest in the 5 cities in China(2)
The changing heavy metal concentration trends in Lanzhou are increasing first and then decreasing gradually from 2005 to 2022. The shift of shrinking concentrations of Pb, Zn, Cu, Cr, Mn, As, Cd, and Ni heavy metals occurred in approximately 2014, which is in response to the increased efforts of addressing pollution launched by the Lanzhou municipal government since 2010(3)
A low ecological risk in Lanzhou is confirmed by the lowerEir (<40) and lower RI values (<150), which range from 18.11 to 37.95 with an average value of 25.61. Co, V, Cd, Cu, Zn, As, Cr, Ni, Pb, and Mn heavy metal level concentrations result in the classification of an uncontaminated environment (level 0) and a moderately to heavily contaminated environment (level 2-level 3) by Igeo values(4)
The accumulation of Mo metal was caused by the enrichment of MnO2 under oxidic conditions. The accumulations of Cu, Co, and Ni metals are influenced by natural sources. The accumulations of Mn, Cr, Zn, Cd, and Pb could be the result of local human activities, such as vehicle exhaust and industrial pollution



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*Source: 5165194-2022-07-18.xml*