Levels, sources, and risk of heavy metals in soils from northwest and eastern industrial areas of China

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It adversely affects human health and the ecosystem. However, the relevant research on heavy metals contamination in typical petrochemical (PIA) and coking industries areas (CIA) was few. In this study, a total of 24 and 21 surface topsoil (< 20 cm) samples were collected in petrochemical and coking industrial areas, respectively. The geo-accumulation index (Igeo), enrichment factor (EF), and potential ecological risk index (ERI) were calculated to assess the Cu, Pb, Ni, Co, Cr, Zn, V, and Mn pollution levels in soils. The hazard index (HI), carcinogenic risk (CR), and non-carcinogenic risk (NCR) were used to assess the human health risk of heavy metals. The mean levels (mg/kg) of heavy metals were ranked as Mn (601.25) > Zn (154.63) > Cr (76.78) > V (76.04) > Cu (39.11) > Pb (36.88) > Ni (31.73) > Co (12.97) in PIA, and Mn (915.14) > Zn (307.64) > Cr (115.98) > Pb (93.20) > V (92.56) > Cu (44.42) > Ni (34.45) > Co (16.65) in CIA, respectively. Pollution indices indicated that the extent of heavy metals contamination in CIA soils is higher than PIA. Source apportionment of heavy metals in soil was performed using Spearman's correlation coefficient, principal component analysis (PCA) and matrix cluster analysis, suggesting that industrial activities and the transshipment process were the major contributors to heavy metals. About NCR, the THI values were higher than 1 in both typical industrial areas, implying that there is potential health risk to humans. Except for the CR values of Pb for children and adults in both industrial areas and the CR values of Cr for adults in PIA, the CR are between 1.00 × 10 − 6 and 1.00 × 10 − 4 , other heavy metals of the CR values were higher than 1 \(\times\) 10 −4 . This result reflects the fact that there were seriously adverse impacts on human health. Overall, the NCR and CR of the heavy metals for different populations ranked as follows: children > adults and Cu, Ni, Co, and Cr were identified as the major contributors to CR and NCR. The result of the present study provides timely information for developing control and management strategies to reduce soil contamination by heavy metals in typical petrochemical and coking industries areas. Heavy metal Industries area Potential ecological risk index (ERI) Principal component analysis (PCA) Carcinogenic risk (CR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Rapid industrialization has caused a large amount of excessive toxic chemicals emitted into the environment (Long et al., 2021 ), which poses significant ecosystem and public health on a global scale due to their wide abundance, toxicity, non-biodegradable properties, and accumulative behaviors (Ahmad Bhat et al., 2019 ; Qing et al., 2015 ). Soils are a major sink for heavy metals released into the environment (Long et al., 2021 ; X. Xiao et al., 2020 ), where they can persist for extended periods of time (Aminiyan et al., 2021 ). Many studies have reported that human exposure to heavy metals can induce adverse effects on the lungs, kidneys or other organs and lead to skeletal, cerebrovascular, and cardiovascular diseases with carcinogenic risks (Noor et al., 2024 ; Wallace et al., 2020 ; Yao et al., 2020 ), even at a low concentration level (Bing et al., 2018 ; Luo et al., 2019 ). Therefore, it is urgent to understand the contamination characteristics, pollution indices and assess the potential ecosystem and health risks posed by heavy metals in the environment, especially in the important sources of heavy metal emissions areas. There are more than 2500 national and provincial industrial parks profoundly contributed to economic development in the past decades in China (Jing et al., 2023 ). Meanwhile, soil contamination of heavy metals stemming from the long-term industrial activities is becoming a serious environmental problem, particularly in the old industrial areas of China. In 2014, the Ministry of Environmental Protection of China and the Ministry of Land and Resources published a national communique on soil pollution survey, which revealed that soil pollution is serious in some areas, and the quality of industrial soil is particularly concerning (Yang et al., 2018 ). The total exceedance rate of soil compared to the second level of China's Soil Environmental Quality Standard (GB15618-1995 was 16.1% in China. Furthermore, the over-standard rate of polluted Industrial wasteland and Industrial parks were 34.9% and 29.4%, respectively. The main pollutants in the soil surrounding industrial areas are heavy metals, such as Cu, Pb, and Zn. The government of China has called on the government to ramp up efforts in promoting the development of soil pollution control technology enforcement to help address soil contamination ( https://english.mee.gov.cn/ ; accessed on 18 February. 2024). However, reviews on soil heavy metal pollution in industries are limited. In particularl whether the different industrial areas show a significant difference in the degree of contamination and carcinogenic risk, and potential ecological risk of heavy metals in the soil still needs further research. Therefore, in our research, we selected typical important sources of heavy metal emissions industry, coking factory and petrochemical factory with operational histories exceeding 50 years. Therefore, the purposes of this study were (1) to determine the concentration and distribution of heavy metals (Cu, Pb, Ni, Co, Cr, Zn, V, and Mn) in soil from typical coking and petrochemical industrial areas, (2) to assess the pollution level, potential ecological risk and human health risk (adults and children) of heavy metals in the two typical industrial areas soils, (3) to identify the source apportionment of heavy metals in contaminated soils by using a combined method of Spearman's correlation coefficient, principal component analysis (PCA) and matrix cluster analysis. The present study can provide some insight into the adverse environmental and health impacts of heavy metal pollution caused by coking and petrochemical industrial activities, which can help prevent and control soil pollution and environmental risks. 2. Materials and methods 2.1. Study area PIA is located in the core industrial area of Gansu Provinces and the largest petrochemical base in western China, with a total area of 385.3 square kilometers and a population of 0.41 million. CIA is an important steel and energy base, located in the center of Shandong Province, with a total area of 506.42 square kilometers and a population of 0.29 million. PIA and CIA operational histories exceeded 50 years, which emitted a large amount of heavy metals into the surrounding soil (Fig. 1 ). 2.2. Sampling and soil analysis In this study, a total of 24 and 21 surface topsoil (< 20 cm) samples were collected from 2021 to 2022 in petrochemical and coking industrial areas, respectively. Soil sampling points were determined with a grid centered on the park, and the sampling areas of the petrochemical industrial park and the coking industrial park were 15 × 15 km2 and 25 × 25 km2, respectively. Five sub-samples were mixed and sealed in a clean plastic bag away from light. The soil samples were freeze-dried for 48 h, ground with agate mortar, screened with 100-mesh sieves, and finally stored in a refrigerator at -20°C until analysis. The soil samples concentrations of Pb, Cu, Cr, Zn, Mn, Co, V and Ni were analyzed via x-ray fluorescence spectrometer (XRF, ZSXPrimusⅡ, Rigaku Corporation, JP). The limits of detection for Pb, Cu, Cr, Zn, Mn, Co, V and Ni were 2.0, 1.2, 3.0, 2.0, 10, 1.6, 4.0, and 1.5 mg/kg, respectively. 2.3 Soils pollution indices In this study, individual and integrated pollution indices, such as enrichment factor (EF) and pollution indicators including potential ecological risk index (ERI), and geo-accumulation index (Igeo) were calculated. Generally, the average crustal abundance or average shale values data as reference baselines. All ranges characterizing pollution indices can be seen in the Table S1 . 2.3.1 Enrichment Factor (EF) The enrichment Factor ( EF ) provides similar information as Igeo, which was a useful parameter to track heavy metals originating from anthropogenic activities or natural sources (K. Xiao et al., 2022 ). In general, the abundant elements in the earth’s crust were used as reference elements, such as Al (K. Xiao et al., 2022 ), Cn (X. Zhang et al., 2020 ), Mn (Zajusz-Zubek et al., 2017 ), Fe(Giordano et al., 2024 ), Ti, V, and Si (Redwan & Rammlmair, 2017 ). In this study, V was selected as the reference element due to its relative stability in the earth’s crust. The EF was calculated using the following equation (K. Xiao et al., 2021 ) (1): EF = (C soli /V soil ) / (C lithosphere /V lithosphere ) (1) where C and V were denoted the concentrations of heavy metals in soils and in the lithosphere (the outer layer of the earth’s crust) (mg/kg), respectively. There is no generally recognized grade for EF. The EF values 1.0 indicate that the heavy metal has been influenced by anthropogenic activities (Xiao et al., 2021 ) (Taati et al., 2020 ). 2.3.2 Geo-accumulation index The geo-accumulation index ( I geo) was computed with the aim of understanding the characteristic variability of the natural distribution of heavy metals in the soil and the historical accumulation of pollution (Y. Zhang et al., 2023 ). The index of Igeo was calculated by comparing the investigated heavy metal contents with the pre-industrial levels of the soil (Peng et al., 2022 ): I geo =Log 2 \(\frac{Csoil}{1.5{C}_{background}}\) (2) where C soil represent the measured concentration of heavy metals in soil (mg/kg), C background is the background concentration of heavy metals (mg/kg). while the value of 1.5 was a constant, which was considered to minimize the effect of possible variations in the background values due to lithogenic effects (Boumaza et al., 2024 ; Taati et al., 2020 ). The average heavy metals of China were used as background in the present study. 2.3.3 Potential ecological risk index (ERI) The potential ecological risk index (ERI) integrates the four influencing factors (Wu et al., 2021 ): the concentration of heavy metals, type of contamination, level of toxicity and sensitivity of media to heavy metal contamination, to evaluate the potential combined ecological risk of heavy metals in soil (Boumaza et al., 2024 ; Gui et al., 2023 ). The calculation formula is expressed as (3–4) (Hakanson, 1980 ; Shi et al., 2023 ): ERI= \(\sum _{i=1}^{n}{E}_{i}\) (3) E i =Ti \(\times\) C soil /C background (4) Where Ei represents the potential ecological risk index of individual heavy metal; Ti represents the metal toxic response coefficient (Xiang et al., 2021 ): 1 for Zn and Mn, 2 for Cr and V, 5 for Pb, Ni, Cu, and Co (Mitran et al., 2024 ; Tan et al., 2023 ). 2.4. Potential human health risk Human exposure to the impacts of heavy metals may occur from contaminated surface soils, especially, in industrial areas. Generally, heavy metals in soil can enter the human body via direct oral ingestion (Ing), dermal contact (Der) and inhalation (Inh) that can cause non-carcinogenic and carcinogenic health hazards to humans (Boumaza et al., 2024 ). The reference parameters of the average daily dose (ADD) for adults and children are listed in Table 2 . The ADD for three exposure routes was formulated by the following equations ( 5 – 7 ) (Gui et al., 2023 ): Table 1. A statistical summary of the element concentrations in the soil samples (units: mg/kg). Heavy metal types Pb Zn Co Cu Ni Mn V Cr PIA (n=24) Minimum 18.70 43.10 9.40 13.90 12.30 367.00 47.30 36.00 Maximum 61.50 538.00 16.50 69.70 55.90 680.00 86.40 107.00 Standard deviation 13.57 102.94 1.79 15.58 8.45 72.59 8.76 16.10 Coefficient of variation (%) 36.79 66.57 13.84 39.83 26.64 12.07 11.51 20.97 Mean 36.88 154.63 12.97 39.11 31.73 601.25 76.04 76.78 CIA (n=21) Minimum 30.80 67.10 8.10 18.60 16.20 349.00 44.10 39.30 Maximum 241.00 754.00 25.20 99.10 50.60 2603.00 136.00 220.00 Standard deviation 51.78 201.76 4.27 19.74 8.66 511.62 18.99 40.01 Coefficient of variation (%) 55.56 65.58 25.62 44.43 25.15 55.91 20.51 34.50 Mean 93.20 307.64 16.65 44.42 34.45 915.14 92.56 115.98 Average of China 27.00 74.00 13.00 23.00 27.00 582.00 82.00 61.00 Chinese soil criteria (Grade II) 250.00 200.00 ­ 50.00 40.00 ­ ­ 150.00 Table 2 Mean value of the noncarcinogenic and carcinogenic risk index. Human health risk assessment Adults Children PIA CIA PIA CIA NCR Pb 2.78E-02 7.01E-02 1.69E-01 4.28E-01 Zn 1.35E-03 2.69E-03 8.26E-03 1.64E-02 Co 2.06E + 00 2.64E + 00 8.62E + 00 1.11E + 01 Cu 2.55E-03 2.90E-03 1.56E-02 1.77E-02 Ni 4.11E-03 4.47E-03 2.52E-02 2.73E-02 V 3.55E-02 4.32E-02 2.09E-01 2.54E-01 Cr 3.55E-01 5.36E-01 1.50E + 00 2.26E + 00 Total HI 2.48E + 00 3.30E + 00 1.05E + 01 1.41E + 01 CR Pb 1.93E-06 4.89E-06 9.66E-06 2.44E-05 Cu 1.13E-04 1.29E-04 8.08E-04 9.17E-04 Ni 1.16E-04 1.26E-04 7.56E-04 8.21E-04 Cr 9.50E-05 1.44E-04 5.90E-04 8.92E-04 TCR 3.26E-04 4.03E-04 2.16E-03 2.65E-03 $$ADD ing=Csoil\times \frac{IngR\times EF\times ED}{BW\times AT}\times CF$$ 5 ADD der = C soil \(\times\) \(\frac{SL\times SA\times ABS\times EF\times ED}{BW\times AT}\times\) CF (6) $$ADDinh= Csoil \times \frac{InhR\times EF\times ED}{BW\times AT\times PEF}$$ 7 where ADD ing , ADD der , ADD inh are the average daily intake by ingestion, dermal contact, and inhalation, respectively. Other exposure parameters used to estimate the potential human health risk are given in Table S2. The human health hazard of heavy metals can be categorized into cumulative non-carcinogenic risk (NCR) and carcinogenic risk (CR). In general, NCR is non-cancer health effects of chronic exposure, including teratogenic and genetic effects. The hazard index (HI), which represents the cumulative NCR, was calculated as a sum of hazard quotients (HQ): HQ = \(\frac{ADD}{RfD}\) (8) HI= \(\sum HQ\) =HQ ing + HQ der + HQ inh (9) The CR and the lifetime cancer risk (LCR) were calculated by the following equations (10–11) (Gui et al., 2023 ; Wang et al., 2020 ): CR = ADD \(\times CSF\) (10) LCR = CR total =CR ing + CR der + CR inh (11) Reference dose (RfD) and carcinogenicity slope factor (CSF) of heavy metals via the three main pathways of human exposure are listed in Table S3. The potential human health risk classifications of HI, HQ and CR are listed in Table S4. 2.5 Statistical analysis The basic descriptive statistical analysis of minimum, maximum, mean, standard deviation, and coefficient of variation for the data were calculated by SPSS 27.0 software. Multivariate statistical methods including correlation analysis and principal component analysis (PCA) were used to analyse the sources of these heavy metals via reduced dimensionality and unravel relationships between the studied variables. 3. Results and discussion 3.1. The concentrations of metal elements in the soil samples The basic statistics for several metals in the topsoil are shown in Table 1 and Fig. 2 . The mean concentrations of the sampled heavy metals in the soils of two industrial areas showed a significant difference. Except for Co and V in PIA, Pb, Zn, Co, Ni, Mn, and Cr were higher than their corresponding average of China (Zhou & Wang, 2019 ). Among the samples, the concentration of 25% (Zn), 29.17% (Cu), and 8.33% (Ni) in PIZ and 71.43% (Zn), 42.86% (Cu) and 19.05% (Ni), and 23.81% (Cr) in CIA were beyond the corresponding Grade II criterion of the National Environmental Quality Standards(Zhou & Wang, 2019 ). The mean levels (mg/kg) of heavy metals in the soil were ranked as Mn (601.25) > Zn (154.63) > Cr (76.78) > V (76.04) > Cu (39.11) > Pb (36.88) > Ni (31.73) > Co (12.97) in PIA, meanwhile a similar distribution patterns with PIA and higher concentration than those in CIA followed by Mn (915.14) > Zn (307.64) > Cr (115.98) > Pb (93.20) > V (92.56) > Cu (44.42) > Ni (34.45) > Co (16.65), indicating that the soils from the two typical industrial areas were contaminated by heavy metals at different degrees. Figure 2 . Boxplots of the heavy metal concentrations (mg/kg) of soils from the two typical industrial areas by boxplot. In general, high heavy metal concentrations and wide concentration ranges with high CV values commonly suggest a strong anthropogenic influence(Manta et al., 2002 ). In PIA, the CV values of heavy metals were as follows: Zn (66.57%) > Cu (39.83%) > Pb (36.79%) > Ni (26.64%) > Cr (20.97%) > Co (13.84%) > Mn (12.07%) > V (11.51%). In PIA, the CV values of heavy metals were as follows: Zn (66.58%) > Mn (55.91%) > Pb (55.56%) > Cu (44.43%) > Cr (34.50%) > Co (25.62%) > Ni (25.15%) > V (20.51%). This indicated that Zn, Cu, Pb, Ni, Cr, Co, Mn, and V pollution in PIZ and CIZ may be caused by varying degrees of human activities. Overall, among the two typical industrial areas, there were more types of heavy metal contamination in CIA than in PIA due to industrial activities. As an area with a high concentration of industries, industrial activities may be the main influencing factor for heavy metal contamination of soil. To verify whether and to what extent industrial activities contribute to heavy metal pollution, further studies are presented below. 3.2 Soils pollution indices 3.2.1 Enrichment Factor (EF) in soil The enrichment degree of eight heavy metals for the PIA soils and CIA soils are presented in Table S5 and Fig. 3 . The obtained results showed Pb, Zn, Co, Cu, Ni, Mn, V, and Cr have an enrichment factor greater than 1.0 in all PIA soil and CIA soil samples, these trace elements can be influenced by anthropogenic activities, except for Co and Mn (EF < 1) in PIA soil which is considered as associated with natural processes. Among them, the EF values of Pb, Zn in PIA soli and Zn in CIA soil have moderate enrichment, especially, the EF values of Pb in CIA soil have significant enrichment. Based on the mean values of EFs, the descending order of heavy metals enrichment in the PIA soil is observed as follows: Zn > Pb > Cu > Ni > Cr > V (EF = 1) > Mn > Co. As for the CIA soil, the order of enrichment of heavy metals are: Pb > Zn > Cu > Cr > Mn > Ni > Co. Based on the results, different heavy metals varied in the enrichment degree. The mean EF values of the investigated heavy metals were found in the order of EF CIA soils > EF PIA soils . 3.2.2 Geo-accumulation index ( Igeo ) The Igeo was also used to assess the contamination of heavy metals in the soil (X. Xiao et al., 2020 ). The Igeo values for all eight heavy metals of the two typically industrial area soils are depicted in Fig. 4 and Table 7. The Igeo values are similar to the results of (Mitran et al., 2024 ; Peng et al., 2022 ) who found that Pb was the most contaminated in both industrial areas. 58.33 %, 54.17 %, 41.67 %, 12.50 % and 8.33 % of the PIA samples fo Zn, Cu,Pb, Cr ad Ni exhbit Igeo vlues greater than 0, indicating that these heavy metals were contaminated. In contrast, the Co and V were unpolluted. In CIA, 90.47 %, 80.95 %, 61.90 %, 57.14 %, 38.10 %, 28.57%, 19.05 % and 4.6 % of te sample for Pb,Zn, Cr, u, Mn, Co, Ni, ad V for CI samples exhibit Igeo values greater than 0. This result may be due to the fact that PIA and CIA are in mountainous areas and certain samples were collected nearby. The above discussion indicating that the extent of heavy metal contamination in CIA soils is higher than in PIA. This result is consistent with the EF discussion. Further, it demonstrates that not all heavy metals in industrial soils are contaminated (Khademi et al., 2019 ). 3.2.3 Potential ecological risk index ( ERI ) of heavy metals The potential ecological risk index (ERI) quantifies the vulnerability of diverse biological communities to toxic substances and illustrates the potential ecological hazards posed by these hazardous heavy metals(Q. Han et al., 2023 ). The average ERI values for eight heavy metals in soils are all below 150, meaning slight ecological risks for the two typical industrial soils, as shown in Table S6. The potential ecological risk index ( E ) for individual metals indicated that the basic trend of mean E values of heavy metals was Cu > Pb > Ni > Co > Cr > Zn > V > Mn in soils and Pb > Cu > Co > Ni > Zn > Cr > V > Mn in CIA soils, demonstrating that all the heavy metals were classified as slight ecological risk. Our results are consistent with the previous studies conducted in industries that assessed ecological risk (Y. Han & Gu, 2023 ; Mitran et al., 2024 ). In addition, comparing the values of E in surface soils under different industrial area types across all the sampling sites (Table S6) showed that the ecological risk of heavy metals in CIA soils was significantly higher than those in PIA soils. Therefore, it has been suggested that industrial production processes may affect the ecological risk of heavy metals in surface soil (Wu et al., 2021 ). 3.3 Health risk assessment Cu, Pb, Ni, Co, Cr, Zn, V, and Mn have been categorized as priority elements that are crucial for public health due to their high levels of toxicity. Thus, an evaluation of human exposure to heavy metals from typical industrial soils through ingestion, inhalation, and dermal contact was conducted in terms of their potential carcinogenic and non-carcinogenic health risks. In this research, according to the results of health risk assessment, Cu, Pb, Ni, Co, Cr, Zn, V, and Mn in both typical PIA and CIA soils potentially posed high non-carcinogenic and carcinogenic risks to the local residents. Overall, the NCR and CR of the heavy metals for different populations ranked as follows: children > adults (Table 2 ), which was consistent with other worldwide studies (Boumaza et al., 2024 ; Gui et al., 2023 ; Peng et al., 2022 ; X. Xiao et al., 2020 ). It was worth mentioning that the NCR and CR of the heavy metals in CIA soils were higher than in CIA soils. Compared with petrochemical industrial activities, the coal industry contributes more to Cu, Pb, Ni, Co, Cr, Zn, V, and Mn accumulation in soil (X. Xiao et al., 2020 ). Regarding non-carcinogenic risk, the THI values were higher than 1 in both typical industrial areas, implying that there is has potential health risk to humans. Regardless of the type of samples, the HI values of heavy metals for adults decreased in the order of Co > 1 > Cr > V > Pb > Ni > Cu > Zn, and for children decreased in the order of Co > Cr > 1 > Pb > V > Ni > Cu > Zn (Table S7). The study revealed that THI values were higher among children in comparison to adults, it can be concluded that children have much more chances of non-carcinogenic risk from heavy metals in typical industrial soils than adults. The reason for children facing greater non-carcinogenic risk in children is mostly due to the larger single-day ingestion rate (such as pica behavior and hand or finger sucking) and lower body weight (Men et al., 2018 ; Wei et al., 2015 ). Considering the poor hygiene practices and physiological vulnerability of children to toxic metals, it is recommended that more protective measures be taken to reduce children's exposure to soil and dust, especially in industrial areas. Except for the CR values of Pb for children and adults in both industrial areas and the CR values of Cr for adults in PIA, the carcinogenic risks are between 1.00 × 10 − 6 and 1.00 × 10 − 4 , which is a relatively acceptable or tolerable risk range. Irrespective of the type of samples, for other heavy metals the CR values were higher than 1 \(\times\) 10 −4 and CR values were higher among children in comparison to adults. This result reflects the fact that there were seriously adverse impacts on human health. Overall, the long-term health effects for children and adults are serious, furthermore more attention should be paid to the carcinogenic effects of Cu and Ni. 3.4. Source of heavy metal contamination To evaluate further the extent of metal contamination in the study area and identify its sources, Spearman's correlation coefficient, PCA and matrix cluster analysis were carried out. Table 3 demonstrates that Pb, Zn, Cu, and Cr were highly correlated with each other in PIA, suggesting that these four heavy metals clearly originated from the same anthropogenic source such as industrial activities (p > 0.01). Between Mn and V, Co and Ni showed high correlation, however, their concentration in soils was relatively low to their corresponding average of China, indicating that they were influenced by industrial activities in addition to natural sources. In CIA, Co, Cr, Ni, and V were highly correlated with each other in CIA, indicating that may come from the same source. Mn showed very weak insignificant correlation with Co, Cu, Pb and Zn (p > 0.5), indicating that Mn may the come from natural sources because of its low concentration (Table 5). The results of the Spearman's correlation coefficient analysis indicated that heavy metals in the two typical industrial area soils from both natural and anthropogenic sources. Table 3 Spearman's correlation coefficient between heavy metal elements in the two typical industrial area soils Correlation analysis Pb Zn Co Cu Ni Mn V Cr PIA Pb 1 Zn 0.92 ** 1 Co 0.72 ** 0.77 ** 1 Cu 0.79 ** 0.90 ** 0.74 ** 1 Ni 0.66 ** 0.70 ** 0.73 ** 0.67 ** 1 Mn 0.69 ** 0.72 ** 0.61 ** 0.64 ** 0.75 ** 1 V 0.51 * 0.59 ** 0.62 ** 0.53 ** 0.66 ** 0.69 ** 1 Cr 0.78 ** 0.87 ** 0.67 ** 0.84 ** 0.57 ** 0.68 ** 0.60 ** 1 CIA Pb 1 Zn 0.80 ** 1 Co 0.42 0.64 ** 1 Cu 0.77 ** 0.80 ** 0.64 ** 1 Ni 0.17 0.29 0.59 ** 0.40 1 Mn 0.19 0.06 0.36 0.40 0.45 * 1 V 0.37 0.36 0.73 ** 0.64 ** 0.49 * 0.82 ** 1 Cr 0.47 * 0.70 ** 0.73 ** 0.59 ** 0.24 0.35 0.64 ** 1 * p < 0.05; ** p < 0.01 According to the results of cross-validation, the first three components could explain more than 80% of the total variance in the three urban agglomerations and were selected as the principal components (Table 4 ). This method showed that in PIA, PC1 explained 69.24% of the total variance and was dominated by Pb, Zn, Cu, and Cr. Pb and Cr are released during the refining and burning of residual oil (Kabir et al., 2012 ). Pb, Zn and Cu may originate from vehicular exhaust, braking and tyre wear (Hjortenkrans et al., 2006 ). On the other hand, these sampling sites were located in the petrochemical industrial area, thus the heavy metals may come from the leakage of diesel, lead gasoline, and fuel oil during the production and transportation processes. Based on the above discussion, PC1 can be divided into traffic emissions and petrochemical industrial activities. PC2 explained an additional 9.87% of the total variance and was dominated by Mn and V. The concentration of Mn was similar to the corresponding average of China, suggesting that a great amount of Mn has its origin in the soils (Nadal et al., 2004 ). PC3 explained an additional 6.63% of the total variance and was dominated by Co and Ni. During the coal combustion, Co, and Ni were the main heavy metals redistributed into fly ash, and bottom ash (Altlkulaç et al., 2022 ). Therefore, the PC3 can be interpreted as coal combustion. In CIA, PC1 explained 50.23% of the total variance and was dominated by Co, Ni, V and Cr. The most important source of V is the combustion of residual fuels and coals (Nadal et al., 2004 ). The PC1 can be defined as the coal combustion and coking process of coal. PC2 explained an additional 17.80% of the total variance and was dominated by Pb, Cu and Zn. Therefore, the PC2 represented the contribution of traffic emissions such as vehicular exhaust, braking and tyre wear make a major contribution to PC2. PC3 explained an additional 15.75% of the total variance and was dominated by Mn. The PC3 can be expressed as a natural source. Table 4 The rotated component matrix of the factor loadings. Region PIA CIA PC1 PC2 PC3 PC1 PC2 PC3 Pb 0.82 0.24 0.32 0.02 0.90 0.40 Zn 0.87 0.26 0.12 0.38 0.85 -0.34 Co 0.54 0.35 0.58 0.90 0.30 -0.07 Cu 0.72 0.23 0.51 0.30 0.78 0.46 Ni 0.23 0.35 0.88 0.69 0.01 0.29 Mn 0.33 0.79 0.38 0.17 0.18 0.93 V 0.26 0.89 0.26 0.84 0.09 0.41 Cr 0.71 0.56 0.22 0.72 0.25 -0.05 A heatmap with a dendrogram was obtained by matrix cluster analysis using the 24 samples for PIA and 21 samples for CIA as the Y-axis and 8 heavy metals (Cu, Pb, Ni, Co, Cr, Zn, V, and Mn) as the X-axis (Fig. 5 ). Each grid represents the concentration of single element for one soil sample. For the red color, the concentration of heavy metal was higher with the grid color gradually changing lighter; oppositely, for the blue color, the concentration was lower with the grid color becoming dark. In general, the results of the classification of heavy metals in the two typical industrial oils by the matrix cluster were consistent with the with the PCA (Table 11). Overall, the heavy metals were major from anthropogenic activities such as industrial production activities, transportation of products and geogenic sources. 4. Conclusions Based on the pollution and ecological risk assessment results, the soils surrounding coking industrial areas are higher contaminated than petrochemical industrial, indicating that different functional industrial areas are contaminated by heavy metals in varying degrees. In both industrial areas, the THI values were higher than 1, implying that there is has non-carcinogenic risk to humans. Co and Cr were identified as the major contributors to NCR. Except for the CR values of Pb and the CR values of Cr for adults in PIA, other heavy metals of the CR values were higher than 1 \(\times\) 10 −4 . This result reflects the fact that there were seriously adverse impacts on human health. The NCR and CR of the heavy metals for different populations ranked as follows: children > adults in both typical industrial areas. Cu and Ni were identified as the major contributors to CR. sources analysis suggests that the heavy metals were major from the anthropogenic activities such as industrial production activities, transportation of products and geogenic sources. Overall, this study suggests that long-term industrial activities could cause significant heavy metal pollution to the topsoil layer of industrial areas and provide significant information for further management and reduction of soil heavy metal pollution. Combining the analyses, a) the protection of children should be prioritized; b) Co, Cr, Cu and Ni should be taken priority control. Declarations CRediT authorship contribution statement Kai Xiao: Methodology, Writing – original draft, Software, Formal analysis, Investigation, Funding acquisition. Yousong Zhou, Yongqiang Zhang, and Donglei Fu: Investigation, Conceptualization, Supervision. Rabin Mominul Haque and Senlin Lu : Methodology, Formal analysis, Investigation, Data curation. 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Study on the characteristics of size-segregated particulate water-soluble inorganic ions and potentially toxic metals during wintertime in a high population residential area in Beijing, China. Processes , 9 (3). https://doi.org/10.3390/pr9030552 Xiao, K., Wang, Q., Lu, S., Lin, Y., Enyoh, C. E., Chowdhury, T., Rabin, M. H., Islam, M. R., Guo, Y., & Wang, W. (2022). Pollution levels and health risk assessment of potentially toxic metals of size-segregated particulate matter in rural residential areas of high lung cancer incidence in Fuyuan, China. Environmental Geochemistry and Health . https://doi.org/10.1007/s10653-022-01374-x Xiao, X., Zhang, J., Wang, H., Han, X., Ma, J., Ma, Y., & Luan, H. (2020). Distribution and health risk assessment of potentially toxic elements in soils around coal industrial areas: A global meta-analysis. In Science of the Total Environment (Vol. 713). https://doi.org/10.1016/j.scitotenv.2019.135292 Yang, Q., Li, Z., Lu, X., Duan, Q., Huang, L., & Bi, J. (2018). A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment. In Science of the Total Environment (Vol. 642). https://doi.org/10.1016/j.scitotenv.2018.06.068 Yao, B., Lu, X., Xu, L., Wang, Y., Qu, H., & Zhou, H. (2020). Relationship between low-level lead, cadmium and mercury exposures and blood pressure in children and adolescents aged 8–17 years: An exposure-response analysis of NHANES 2007–2016. Science of the Total Environment , 726 . https://doi.org/10.1016/j.scitotenv.2020.138446 Zajusz-Zubek, E., Radko, T., & Mainka, A. (2017). Fractionation of trace elements and human health risk of submicron particulate matter (PM1) collected in the surroundings of coking plants. Environmental Monitoring and Assessment , 189 (8). https://doi.org/10.1007/s10661-017-6117-x Zhang, X., Xu, Y., & Su, J. (2020). Temporal and spatial characteristics of particulate matters in metro stations of Shanghai, China. Building and Environment , 179 . https://doi.org/10.1016/j.buildenv.2020.106956 Zhang, Y., Song, B., & Zhou, Z. (2023). Pollution assessment and source apportionment of heavy metals in soil from lead - Zinc mining areas of south China. Journal of Environmental Chemical Engineering , 11 (2). https://doi.org/10.1016/j.jece.2023.109320 Zhou, X. Y., & Wang, X. R. (2019). Impact of industrial activities on heavy metal contamination in soils in three major urban agglomerations of China. Journal of Cleaner Production , 230 . https://doi.org/10.1016/j.jclepro.2019.05.098 Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4133831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281959222,"identity":"d0b6b1b2-f38b-4bfd-9c31-8b0fc138abf6","order_by":0,"name":"Kai Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3RsWrDMBCA4ROC66K4HU8E7FewMTgdAn0VhYKnUAKFkKlJKGhyd+ctkiVdXTJ0MZm9taKQuRDoXItOpSB77KB/lPiQdALw+f5hlxfrtflc0BKjj4qt7FLVQWRxOKRUX6sActWPxE2eXw30QoUwjfsRqKYZSE13CPWX2WgIg0ax88wh2KrO3mdHukf29JzsNKSyUXxYOghnxSimOTHNB3tpNEy2jUIuHKTdzUhgS1CcLFl2EoGYk9A00UKgbC+m4i5CgtshU4qEo6Q8UrKpzePQRW7ejP3KhzAq+ckU83EUvN6+nF3k17vsqWCn2BP8EJ/P5/P97RuKPEsE3ZZJ0wAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xiao","suffix":""},{"id":281959223,"identity":"65dc5e4c-4bc5-4b04-a3d5-fd364549be0b","order_by":1,"name":"Yousong Zhou","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yousong","middleName":"","lastName":"Zhou","suffix":""},{"id":281959224,"identity":"39bd1882-c65d-4956-b123-7eaa3d2d7704","order_by":2,"name":"Yongqiang Zhang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yongqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":281959225,"identity":"9f7676d6-865b-439c-8706-a947dee54d2e","order_by":3,"name":"Fu Donglei","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Fu","middleName":"","lastName":"Donglei","suffix":""},{"id":281959226,"identity":"9fa034c7-f714-4f80-a662-7726fae7a064","order_by":4,"name":"Senlin Lu","email":"","orcid":"","institution":"Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Senlin","middleName":"","lastName":"Lu","suffix":""},{"id":281959227,"identity":"6690f85c-9457-4c85-a16f-59ea657fa985","order_by":5,"name":"Mominul Haque Rabin","email":"","orcid":"","institution":"Bangla Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mominul","middleName":"Haque","lastName":"Rabin","suffix":""},{"id":281959228,"identity":"defdd3b1-0526-4117-83b9-2d815c195e0a","order_by":6,"name":"Abrar Chowdhury","email":"","orcid":"","institution":"Islamic University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Abrar","middleName":"","lastName":"Chowdhury","suffix":""}],"badges":[],"createdAt":"2024-03-20 03:29:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4133831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4133831/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53265886,"identity":"701c0f95-bdb2-448c-b1f4-3d7d3f6a3ce7","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124519,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the two typical industrial area and sampling points (PIA: n=24; CIA: n=21)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/1aa155e1718489249a092d79.png"},{"id":53265884,"identity":"ed71a5a2-59d0-4579-8cfa-df63a99cffa9","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19018,"visible":true,"origin":"","legend":"\u003cp\u003efor the examined sampling sites.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/b1d8117d23e8a3148c98d364.png"},{"id":53265885,"identity":"800e3aa2-06ba-4572-aa48-50f934415b88","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226427,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of enrichment factor (EF) values of heavy metals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/75e5d531ae59b6ec98454090.png"},{"id":53265888,"identity":"e37ea2b1-bcb3-41a8-bec6-56e37e025906","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231392,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of geo-accumulation (I\u003csub\u003egeo\u003c/sub\u003e) values of heavy metals.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/40b4a9d11bf545c82230f947.png"},{"id":53265889,"identity":"061e26d6-a563-4dc5-8857-b771f0176dee","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81597,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with dendrogram on the heavy metals of the two typical industrials soils\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/9b7b482c54ccd5ad11772000.png"},{"id":54368487,"identity":"77fcd967-d628-4db6-b52e-7786e1df584e","added_by":"auto","created_at":"2024-04-09 12:55:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/f9e2bdd7-eae6-4820-8d7b-96665601e565.pdf"},{"id":53265887,"identity":"6cc572ef-8a5f-40cb-af49-a1d2eac0ea34","added_by":"auto","created_at":"2024-03-22 15:37:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":122380,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4133831/v1/89d14ba7e89ca7f1c297cd89.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Levels, sources, and risk of heavy metals in soils from northwest and eastern industrial areas of China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRapid industrialization has caused a large amount of excessive toxic chemicals emitted into the environment (Long et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which poses significant ecosystem and public health on a global scale due to their wide abundance, toxicity, non-biodegradable properties, and accumulative behaviors (Ahmad Bhat et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Qing et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Soils are a major sink for heavy metals released into the environment (Long et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; X. Xiao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where they can persist for extended periods of time (Aminiyan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many studies have reported that human exposure to heavy metals can induce adverse effects on the lungs, kidneys or other organs and lead to skeletal, cerebrovascular, and cardiovascular diseases with carcinogenic risks (Noor et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wallace et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), even at a low concentration level (Bing et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, it is urgent to understand the contamination characteristics, pollution indices and assess the potential ecosystem and health risks posed by heavy metals in the environment, especially in the important sources of heavy metal emissions areas.\u003c/p\u003e \u003cp\u003eThere are more than 2500 national and provincial industrial parks profoundly contributed to economic development in the past decades in China (Jing et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, soil contamination of heavy metals stemming from the long-term industrial activities is becoming a serious environmental problem, particularly in the old industrial areas of China. In 2014, the Ministry of Environmental Protection of China and the Ministry of Land and Resources published a national communique on soil pollution survey, which revealed that soil pollution is serious in some areas, and the quality of industrial soil is particularly concerning (Yang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The total exceedance rate of soil compared to the second level of China's Soil Environmental Quality Standard (GB15618-1995 was 16.1% in China. Furthermore, the over-standard rate of polluted Industrial wasteland and Industrial parks were 34.9% and 29.4%, respectively. The main pollutants in the soil surrounding industrial areas are heavy metals, such as Cu, Pb, and Zn. The government of China has called on the government to ramp up efforts in promoting the development of soil pollution control technology enforcement to help address soil contamination (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://english.mee.gov.cn/\u003c/span\u003e\u003cspan address=\"https://english.mee.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 18 February. 2024). However, reviews on soil heavy metal pollution in industries are limited. In particularl whether the different industrial areas show a significant difference in the degree of contamination and carcinogenic risk, and potential ecological risk of heavy metals in the soil still needs further research. Therefore, in our research, we selected typical important sources of heavy metal emissions industry, coking factory and petrochemical factory with operational histories exceeding 50 years.\u003c/p\u003e \u003cp\u003eTherefore, the purposes of this study were (1) to determine the concentration and distribution of heavy metals (Cu, Pb, Ni, Co, Cr, Zn, V, and Mn) in soil from typical coking and petrochemical industrial areas, (2) to assess the pollution level, potential ecological risk and human health risk (adults and children) of heavy metals in the two typical industrial areas soils, (3) to identify the source apportionment of heavy metals in contaminated soils by using a combined method of Spearman's correlation coefficient, principal component analysis (PCA) and matrix cluster analysis. The present study can provide some insight into the adverse environmental and health impacts of heavy metal pollution caused by coking and petrochemical industrial activities, which can help prevent and control soil pollution and environmental risks.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Study area\u003c/h2\u003e\n \u003cp\u003ePIA is located in the core industrial area of Gansu Provinces and the largest petrochemical base in western China, with a total area of 385.3 square kilometers and a population of 0.41\u0026nbsp;million. CIA is an important steel and energy base, located in the center of Shandong Province, with a total area of 506.42 square kilometers and a population of 0.29\u0026nbsp;million. PIA and CIA operational histories exceeded 50 years, which emitted a large amount of heavy metals into the surrounding soil (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Sampling and soil analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, a total of 24 and 21 surface topsoil (\u0026lt;\u0026thinsp;20 cm) samples were collected from 2021 to 2022 in petrochemical and coking industrial areas, respectively. Soil sampling points were determined with a grid centered on the park, and the sampling areas of the petrochemical industrial park and the coking industrial park were 15 \u0026times; 15 km2 and 25 \u0026times; 25 km2, respectively. Five sub-samples were mixed and sealed in a clean plastic bag away from light. The soil samples were freeze-dried for 48 h, ground with agate mortar, screened with 100-mesh sieves, and finally stored in a refrigerator at -20\u0026deg;C until analysis. The soil samples concentrations of Pb, Cu, Cr, Zn, Mn, Co, V and Ni were analyzed via x-ray fluorescence spectrometer (XRF, ZSXPrimusⅡ, Rigaku Corporation, JP). The limits of detection for Pb, Cu, Cr, Zn, Mn, Co, V and Ni were 2.0, 1.2, 3.0, 2.0, 10, 1.6, 4.0, and 1.5 mg/kg, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Soils pollution indices\u003c/h2\u003e\n \u003cp\u003eIn this study, individual and integrated pollution indices, such as enrichment factor (EF) and pollution indicators including potential ecological risk index (ERI), and geo-accumulation index (Igeo) were calculated. Generally, the average crustal abundance or average shale values data as reference baselines. All ranges characterizing pollution indices can be seen in the Table \u003cspan\u003eS1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.3.1 Enrichment Factor (EF)\u003c/h2\u003e\n \u003cp\u003eThe enrichment Factor (\u003cem\u003eEF\u003c/em\u003e) provides similar information as Igeo, which was a useful parameter to track heavy metals originating from anthropogenic activities or natural sources (K. Xiao et al., \u003cspan\u003e2022\u003c/span\u003e). In general, the abundant elements in the earth\u0026rsquo;s crust were used as reference elements, such as Al (K. Xiao et al., \u003cspan\u003e2022\u003c/span\u003e), Cn (X. Zhang et al., \u003cspan\u003e2020\u003c/span\u003e), Mn (Zajusz-Zubek et al., \u003cspan\u003e2017\u003c/span\u003e), Fe(Giordano et al., \u003cspan\u003e2024\u003c/span\u003e), Ti, V, and Si (Redwan \u0026amp; Rammlmair, \u003cspan\u003e2017\u003c/span\u003e). In this study, V was selected as the reference element due to its relative stability in the earth\u0026rsquo;s crust.\u003c/p\u003e\n \u003cp\u003eThe EF was calculated using the following equation (K. Xiao et al., \u003cspan\u003e2021\u003c/span\u003e) (1):\u003c/p\u003e\n \u003cp\u003eEF = (C\u003csub\u003esoli\u003c/sub\u003e/V\u003csub\u003esoil\u003c/sub\u003e) / (C\u003csub\u003elithosphere\u003c/sub\u003e /V\u003csub\u003elithosphere\u003c/sub\u003e) (1)\u003c/p\u003e\n \u003cp\u003ewhere C and V were denoted the concentrations of heavy metals in soils and in the lithosphere (the outer layer of the earth\u0026rsquo;s crust) (mg/kg), respectively. There is no generally recognized grade for EF. The EF values\u0026thinsp;\u0026lt;\u0026thinsp;1.0 indicate a possible mobilization or depletion of trace elements, while EF values\u0026thinsp;\u0026gt;\u0026thinsp;1.0 indicate that the heavy metal has been influenced by anthropogenic activities (Xiao et al., \u003cspan\u003e2021\u003c/span\u003e) (Taati et al., \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.3.2 Geo-accumulation index\u003c/h2\u003e\n \u003cp\u003eThe geo-accumulation index (\u003cem\u003eI\u003c/em\u003egeo) was computed with the aim of understanding the characteristic variability of the natural distribution of heavy metals in the soil and the historical accumulation of pollution (Y. Zhang et al., \u003cspan\u003e2023\u003c/span\u003e). The index of Igeo was calculated by comparing the investigated heavy metal contents with the pre-industrial levels of the soil (Peng et al., \u003cspan\u003e2022\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003eI\u003csub\u003egeo\u003c/sub\u003e=Log\u003csub\u003e2\u003c/sub\u003e\u003cspan\u003e\u003cspan\u003e\\(\\frac{Csoil}{1.5{C}_{background}}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e\n \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003esoil\u003c/sub\u003e represent the measured concentration of heavy metals in soil (mg/kg), \u003cem\u003eC\u003c/em\u003e\u003csub\u003ebackground\u003c/sub\u003e is the background concentration of heavy metals (mg/kg). while the value of 1.5 was a constant, which was considered to minimize the effect of possible variations in the background values due to lithogenic effects (Boumaza et al., \u003cspan\u003e2024\u003c/span\u003e; Taati et al., \u003cspan\u003e2020\u003c/span\u003e). The average heavy metals of China were used as background in the present study.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.3.3 Potential ecological risk index (ERI)\u003c/h2\u003e\n \u003cp\u003eThe potential ecological risk index (ERI) integrates the four influencing factors (Wu et al., \u003cspan\u003e2021\u003c/span\u003e): the concentration of heavy metals, type of contamination, level of toxicity and sensitivity of media to heavy metal contamination, to evaluate the potential combined ecological risk of heavy metals in soil (Boumaza et al., \u003cspan\u003e2024\u003c/span\u003e; Gui et al., \u003cspan\u003e2023\u003c/span\u003e). The calculation formula is expressed as (3\u0026ndash;4) (Hakanson, \u003cspan\u003e1980\u003c/span\u003e; Shi et al., \u003cspan\u003e2023\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eERI=\u003c/em\u003e \u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\sum _{i=1}^{n}{E}_{i}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (3)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eE\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e=Ti\u003c/em\u003e \u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\times\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003eC\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003esoil\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e/C\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ebackground\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e (4)\u003c/p\u003e\n \u003cp\u003eWhere \u003cem\u003eEi\u003c/em\u003e represents the potential ecological risk index of individual heavy metal; \u003cem\u003eTi\u003c/em\u003e represents the metal toxic response coefficient (Xiang et al., \u003cspan\u003e2021\u003c/span\u003e): 1 for Zn and Mn, 2 for Cr and V, 5 for Pb, Ni, Cu, and Co (Mitran et al., \u003cspan\u003e2024\u003c/span\u003e; Tan et al., \u003cspan\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.4. Potential human health risk\u003c/h2\u003e\n \u003cp\u003eHuman exposure to the impacts of heavy metals may occur from contaminated surface soils, especially, in industrial areas. Generally, heavy metals in soil can enter the human body via direct oral ingestion (Ing), dermal contact (Der) and inhalation (Inh) that can cause non-carcinogenic and carcinogenic health hazards to humans (Boumaza et al., \u003cspan\u003e2024\u003c/span\u003e). The reference parameters of the average daily dose (ADD) for adults and children are listed in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. The ADD for three exposure routes was formulated by the following equations (\u003cspan\u003e5\u003c/span\u003e\u0026ndash;\u003cspan\u003e7\u003c/span\u003e) (Gui et al., \u003cspan\u003e2023\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;1.\u0026nbsp;A statistical summary of the element concentrations in the soil samples (units: mg/kg).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eHeavy metal types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003ePIA (n=24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e18.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e43.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e9.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e13.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e12.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e367.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e47.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e36.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e61.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e538.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e16.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e69.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e55.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e680.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e86.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e107.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e13.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e102.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e1.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e15.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e8.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e72.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e8.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e16.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e36.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e66.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e13.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e39.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e26.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e12.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e11.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e20.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e36.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e154.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e12.97\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e39.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e31.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e601.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e76.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e76.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eCIA (n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e30.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e67.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e8.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e18.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e16.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e349.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e44.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e39.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e241.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e754.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e25.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e99.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e50.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e2603.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e136.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e220.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e51.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e201.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e4.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e19.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e8.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e511.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e18.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e40.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e55.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e65.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e25.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e44.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e25.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e55.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e20.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e34.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e93.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e307.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e16.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e44.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e34.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e915.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e92.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e115.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eAverage of China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e27.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e74.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e13.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e23.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e27.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e582.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e82.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e61.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89473684210526%\"\u003e\n \u003cp\u003eChinese soil criteria (Grade II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e250.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e200.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026shy;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e50.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e40.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026shy;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u0026shy;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e150.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMean value of the noncarcinogenic and carcinogenic risk index.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHuman health risk assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.01E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.62E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.47E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.55E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.55E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal HI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.66E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.08E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.17E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.56E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.50E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.92E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.03E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$ADD ing=Csoil\\times \\frac{IngR\\times EF\\times ED}{BW\\times AT}\\times CF$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eADD\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003eder\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cem\u003e= C\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003esoil\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e \u003cspan\u003e\u003cspan\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e \u003cspan\u003e\u003cspan\u003e\\(\\frac{SL\\times SA\\times ABS\\times EF\\times ED}{BW\\times AT}\\times\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eCF\u003c/em\u003e (6)\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$ADDinh= Csoil \\times \\frac{InhR\\times EF\\times ED}{BW\\times AT\\times PEF}$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003eADD\u003c/em\u003e\u003csub\u003e\u003cem\u003eing\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eADD\u003c/em\u003e\u003csub\u003e\u003cem\u003eder\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eADD\u003c/em\u003e\u003csub\u003e\u003cem\u003einh\u003c/em\u003e\u003c/sub\u003e are the average daily intake by ingestion, dermal contact, and inhalation, respectively. Other exposure parameters used to estimate the potential human health risk are given in Table S2.\u003c/p\u003e\n \u003cp\u003eThe human health hazard of heavy metals can be categorized into cumulative non-carcinogenic risk (NCR) and carcinogenic risk (CR). In general, NCR is non-cancer health effects of chronic exposure, including teratogenic and genetic effects. The hazard index (HI), which represents the cumulative NCR, was calculated as a sum of hazard quotients (HQ):\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHQ =\u003c/em\u003e \u003cspan\u003e\u003cspan\u003e\\(\\frac{ADD}{RfD}\\)\u003c/span\u003e\u003c/span\u003e (8)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHI=\u003c/em\u003e \u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\sum HQ\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cem\u003e=HQ\u003c/em\u003e \u003csub\u003e\u003cem\u003eing\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ HQ\u003c/em\u003e \u003csub\u003e\u003cem\u003eder\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e+ HQ\u003c/em\u003e \u003csub\u003e\u003cem\u003einh\u003c/em\u003e\u003c/sub\u003e (9)\u003c/p\u003e\n \u003cp\u003eThe CR and the lifetime cancer risk (LCR) were calculated by the following equations (10\u0026ndash;11) (Gui et al., \u003cspan\u003e2023\u003c/span\u003e; Wang et al., \u003cspan\u003e2020\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCR\u0026thinsp;=\u0026thinsp;ADD\u003c/em\u003e \u003cspan\u003e\u0026nbsp;\u003cspan\u003e\\(\\times CSF\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (10)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLCR\u0026thinsp;=\u0026thinsp;CR\u003c/em\u003e \u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e=CR\u003c/em\u003e \u003csub\u003e\u003cem\u003eing +\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCR\u003c/em\u003e \u003csub\u003e\u003cem\u003eder\u003c/em\u003e \u003cem\u003e+\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eCR\u003c/em\u003e \u003csub\u003e\u003cem\u003einh\u003c/em\u003e\u003c/sub\u003e (11)\u003c/p\u003e\n \u003cp\u003eReference dose (RfD) and carcinogenicity slope factor (CSF) of heavy metals via the three main pathways of human exposure are listed in Table S3. The potential human health risk classifications of HI, HQ and CR are listed in Table S4. 2.5 Statistical analysis\u003c/p\u003e\n \u003cp\u003eThe basic descriptive statistical analysis of minimum, maximum, mean, standard deviation, and coefficient of variation for the data were calculated by SPSS 27.0 software. Multivariate statistical methods including correlation analysis and principal component analysis (PCA) were used to analyse the sources of these heavy metals via reduced dimensionality and unravel relationships between the studied variables.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. The concentrations of metal elements in the soil samples\u003c/h2\u003e\n \u003cp\u003eThe basic statistics for several metals in the topsoil are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean concentrations of the sampled heavy metals in the soils of two industrial areas showed a significant difference. Except for Co and V in PIA, Pb, Zn, Co, Ni, Mn, and Cr were higher than their corresponding average of China (Zhou \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among the samples, the concentration of 25% (Zn), 29.17% (Cu), and 8.33% (Ni) in PIZ and 71.43% (Zn), 42.86% (Cu) and 19.05% (Ni), and 23.81% (Cr) in CIA were beyond the corresponding Grade II criterion of the National Environmental Quality Standards(Zhou \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The mean levels (mg/kg) of heavy metals in the soil were ranked as Mn (601.25)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (154.63)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (76.78)\u0026thinsp;\u0026gt;\u0026thinsp;V (76.04)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (39.11)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (36.88)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (31.73)\u0026thinsp;\u0026gt;\u0026thinsp;Co (12.97) in PIA, meanwhile a similar distribution patterns with PIA and higher concentration than those in CIA followed by Mn (915.14)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (307.64)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (115.98)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (93.20)\u0026thinsp;\u0026gt;\u0026thinsp;V (92.56)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (44.42)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (34.45)\u0026thinsp;\u0026gt;\u0026thinsp;Co (16.65), indicating that the soils from the two typical industrial areas were contaminated by heavy metals at different degrees.\u003c/p\u003e\n \u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Boxplots of the heavy metal concentrations (mg/kg) of soils from the two typical industrial areas by boxplot.\u003cbr\u003eIn general, high heavy metal concentrations and wide concentration ranges with high CV values commonly suggest a strong anthropogenic influence(Manta et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). In PIA, the CV values of heavy metals were as follows: Zn (66.57%)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (39.83%)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (36.79%)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (26.64%)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (20.97%)\u0026thinsp;\u0026gt;\u0026thinsp;Co (13.84%)\u0026thinsp;\u0026gt;\u0026thinsp;Mn (12.07%)\u0026thinsp;\u0026gt;\u0026thinsp;V (11.51%). In PIA, the CV values of heavy metals were as follows: Zn (66.58%)\u0026thinsp;\u0026gt;\u0026thinsp;Mn (55.91%)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (55.56%)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (44.43%)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (34.50%)\u0026thinsp;\u0026gt;\u0026thinsp;Co (25.62%)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (25.15%)\u0026thinsp;\u0026gt;\u0026thinsp;V (20.51%). This indicated that Zn, Cu, Pb, Ni, Cr, Co, Mn, and V pollution in PIZ and CIZ may be caused by varying degrees of human activities. Overall, among the two typical industrial areas, there were more types of heavy metal contamination in CIA than in PIA due to industrial activities. As an area with a high concentration of industries, industrial activities may be the main influencing factor for heavy metal contamination of soil. To verify whether and to what extent industrial activities contribute to heavy metal pollution, further studies are presented below.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Soils pollution indices\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Enrichment Factor (EF) in soil\u003c/h2\u003e\n \u003cp\u003eThe enrichment degree of eight heavy metals for the PIA soils and CIA soils are presented in Table S5 and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The obtained results showed Pb, Zn, Co, Cu, Ni, Mn, V, and Cr have an enrichment factor greater than 1.0 in all PIA soil and CIA soil samples, these trace elements can be influenced by anthropogenic activities, except for Co and Mn (EF\u0026thinsp;\u0026lt;\u0026thinsp;1) in PIA soil which is considered as associated with natural processes. Among them, the EF values of Pb, Zn in PIA soli and Zn in CIA soil have moderate enrichment, especially, the EF values of Pb in CIA soil have significant enrichment. Based on the mean values of EFs, the descending order of heavy metals enrichment in the PIA soil is observed as follows: Zn\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;V (EF\u0026thinsp;=\u0026thinsp;1)\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Co. As for the CIA soil, the order of enrichment of heavy metals are: Pb\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Co. Based on the results, different heavy metals varied in the enrichment degree. The mean EF values of the investigated heavy metals were found in the order of EF\u003csub\u003eCIA soils\u003c/sub\u003e \u0026gt; EF\u003csub\u003ePIA soils\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Geo-accumulation index (\u003cem\u003eIgeo\u003c/em\u003e)\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003eIgeo\u003c/em\u003e was also used to assess the contamination of heavy metals in the soil (X. Xiao et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u003cem\u003eIgeo\u003c/em\u003e values for all eight heavy metals of the two typically industrial area soils are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table 7. The \u003cem\u003eIgeo\u003c/em\u003e values are similar to the results of (Mitran et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Peng et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) who found that Pb was the most contaminated in both industrial areas. 58.33 %, 54.17 %, 41.67 %, 12.50 % and 8.33 % of the PIA samples fo Zn, Cu,Pb, Cr ad Ni exhbit \u003cem\u003eIgeo\u003c/em\u003e vlues greater than 0, indicating that these heavy metals were contaminated. In contrast, the Co and V were unpolluted. In CIA, 90.47 %, 80.95 %, 61.90 %, 57.14 %, 38.10 %, 28.57%, 19.05 % and 4.6 % of te sample for Pb,Zn, Cr, u, Mn, Co, Ni, ad V for CI samples exhibit \u003cem\u003eIgeo\u003c/em\u003e values greater than 0. This result may be due to the fact that PIA and CIA are in mountainous areas and certain samples were collected nearby. The above discussion indicating that the extent of heavy metal contamination in CIA soils is higher than in PIA. This result is consistent with the EF discussion. Further, it demonstrates that not all heavy metals in industrial soils are contaminated (Khademi et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3 Potential ecological risk index (\u003cem\u003eERI\u003c/em\u003e) of heavy metals\u003c/h2\u003e\n \u003cp\u003eThe potential ecological risk index (ERI) quantifies the vulnerability of diverse biological communities to toxic substances and illustrates the potential ecological hazards posed by these hazardous heavy metals(Q. Han et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The average ERI values for eight heavy metals in soils are all below 150, meaning slight ecological risks for the two typical industrial soils, as shown in Table S6. The potential ecological risk index (\u003cem\u003eE\u003c/em\u003e) for individual metals indicated that the basic trend of mean \u003cem\u003eE\u003c/em\u003e values of heavy metals was Cu\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Mn in soils and Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Mn in CIA soils, demonstrating that all the heavy metals were classified as slight ecological risk. Our results are consistent with the previous studies conducted in industries that assessed ecological risk (Y. Han \u0026amp; Gu, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mitran et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, comparing the values of \u003cem\u003eE\u003c/em\u003e in surface soils under different industrial area types across all the sampling sites (Table S6) showed that the ecological risk of heavy metals in CIA soils was significantly higher than those in PIA soils. Therefore, it has been suggested that industrial production processes may affect the ecological risk of heavy metals in surface soil (Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Health risk assessment\u003c/h2\u003e\n \u003cp\u003eCu, Pb, Ni, Co, Cr, Zn, V, and Mn have been categorized as priority elements that are crucial for public health due to their high levels of toxicity. Thus, an evaluation of human exposure to heavy metals from typical industrial soils through ingestion, inhalation, and dermal contact was conducted in terms of their potential carcinogenic and non-carcinogenic health risks. In this research, according to the results of health risk assessment, Cu, Pb, Ni, Co, Cr, Zn, V, and Mn in both typical PIA and CIA soils potentially posed high non-carcinogenic and carcinogenic risks to the local residents. Overall, the NCR and CR of the heavy metals for different populations ranked as follows: children\u0026thinsp;\u0026gt;\u0026thinsp;adults (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), which was consistent with other worldwide studies (Boumaza et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gui et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peng et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; X. Xiao et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). It was worth mentioning that the NCR and CR of the heavy metals in CIA soils were higher than in CIA soils. Compared with petrochemical industrial activities, the coal industry contributes more to Cu, Pb, Ni, Co, Cr, Zn, V, and Mn accumulation in soil (X. Xiao et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegarding non-carcinogenic risk, the THI values were higher than 1 in both typical industrial areas, implying that there is has potential health risk to humans. Regardless of the type of samples, the HI values of heavy metals for adults decreased in the order of Co\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Zn, and for children decreased in the order of Co\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Zn (Table S7). The study revealed that THI values were higher among children in comparison to adults, it can be concluded that children have much more chances of non-carcinogenic risk from heavy metals in typical industrial soils than adults. The reason for children facing greater non-carcinogenic risk in children is mostly due to the larger single-day ingestion rate (such as pica behavior and hand or finger sucking) and lower body weight (Men et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wei et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Considering the poor hygiene practices and physiological vulnerability of children to toxic metals, it is recommended that more protective measures be taken to reduce children\u0026apos;s exposure to soil and dust, especially in industrial areas.\u003c/p\u003e\n \u003cp\u003eExcept for the CR values of Pb for children and adults in both industrial areas and the CR values of Cr for adults in PIA, the carcinogenic risks are between 1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e and 1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, which is a relatively acceptable or tolerable risk range. Irrespective of the type of samples, for other heavy metals the CR values were higher than 1\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e and CR values were higher among children in comparison to adults. This result reflects the fact that there were seriously adverse impacts on human health. Overall, the long-term health effects for children and adults are serious, furthermore more attention should be paid to the carcinogenic effects of Cu and Ni.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Source of heavy metal contamination\u003c/h2\u003e\n \u003cp\u003eTo evaluate further the extent of metal contamination in the study area and identify its sources, Spearman\u0026apos;s correlation coefficient, PCA and matrix cluster analysis were carried out. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates that Pb, Zn, Cu, and Cr were highly correlated with each other in PIA, suggesting that these four heavy metals clearly originated from the same anthropogenic source such as industrial activities (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01). Between Mn and V, Co and Ni showed high correlation, however, their concentration in soils was relatively low to their corresponding average of China, indicating that they were influenced by industrial activities in addition to natural sources. In CIA, Co, Cr, Ni, and V were highly correlated with each other in CIA, indicating that may come from the same source. Mn showed very weak insignificant correlation with Co, Cu, Pb and Zn (p\u0026thinsp;\u0026gt;\u0026thinsp;0.5), indicating that Mn may the come from natural sources because of its low concentration (Table 5). The results of the Spearman\u0026apos;s correlation coefficient analysis indicated that heavy metals in the two typical industrial area soils from both natural and anthropogenic sources.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpearman\u0026apos;s correlation coefficient between heavy metal elements in the two typical industrial area soils\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAccording to the results of cross-validation, the first three components could explain more than 80% of the total variance in the three urban agglomerations and were selected as the principal components (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This method showed that in PIA, PC1 explained 69.24% of the total variance and was dominated by Pb, Zn, Cu, and Cr. Pb and Cr are released during the refining and burning of residual oil (Kabir et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Pb, Zn and Cu may originate from vehicular exhaust, braking and tyre wear (Hjortenkrans et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). On the other hand, these sampling sites were located in the petrochemical industrial area, thus the heavy metals may come from the leakage of diesel, lead gasoline, and fuel oil during the production and transportation processes. Based on the above discussion, PC1 can be divided into traffic emissions and petrochemical industrial activities. PC2 explained an additional 9.87% of the total variance and was dominated by Mn and V. The concentration of Mn was similar to the corresponding average of China, suggesting that a great amount of Mn has its origin in the soils (Nadal et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). PC3 explained an additional 6.63% of the total variance and was dominated by Co and Ni. During the coal combustion, Co, and Ni were the main heavy metals redistributed into fly ash, and bottom ash (Altlkula\u0026ccedil; et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the PC3 can be interpreted as coal combustion. In CIA, PC1 explained 50.23% of the total variance and was dominated by Co, Ni, V and Cr. The most important source of V is the combustion of residual fuels and coals (Nadal et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The PC1 can be defined as the coal combustion and coking process of coal. PC2 explained an additional 17.80% of the total variance and was dominated by Pb, Cu and Zn. Therefore, the PC2 represented the contribution of traffic emissions such as vehicular exhaust, braking and tyre wear make a major contribution to PC2. PC3 explained an additional 15.75% of the total variance and was dominated by Mn. The PC3 can be expressed as a natural source.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe rotated component matrix of the factor loadings.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCIA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eA heatmap with a dendrogram was obtained by matrix cluster analysis using the 24 samples for PIA and 21 samples for CIA as the Y-axis and 8 heavy metals (Cu, Pb, Ni, Co, Cr, Zn, V, and Mn) as the X-axis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Each grid represents the concentration of single element for one soil sample. For the red color, the concentration of heavy metal was higher with the grid color gradually changing lighter; oppositely, for the blue color, the concentration was lower with the grid color becoming dark. In general, the results of the classification of heavy metals in the two typical industrial oils by the matrix cluster were consistent with the with the PCA (Table\u0026nbsp;11).\u003c/p\u003e\n \u003cp\u003eOverall, the heavy metals were major from anthropogenic activities such as industrial production activities, transportation of products and geogenic sources.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eBased on the pollution and ecological risk assessment results, the soils surrounding coking industrial areas are higher contaminated than petrochemical industrial, indicating that different functional industrial areas are contaminated by heavy metals in varying degrees. In both industrial areas, the THI values were higher than 1, implying that there is has non-carcinogenic risk to humans. Co and Cr were identified as the major contributors to NCR. Except for the CR values of Pb and the CR values of Cr for adults in PIA, other heavy metals of the CR values were higher than 1\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e. This result reflects the fact that there were seriously adverse impacts on human health. The NCR and CR of the heavy metals for different populations ranked as follows: children\u0026thinsp;\u0026gt;\u0026thinsp;adults in both typical industrial areas. Cu and Ni were identified as the major contributors to CR. sources analysis suggests that the heavy metals were major from the anthropogenic activities such as industrial production activities, transportation of products and geogenic sources.\u003c/p\u003e\n\u003cp\u003eOverall, this study suggests that long-term industrial activities could cause significant heavy metal pollution to the topsoil layer of industrial areas and provide significant information for further management and reduction of soil heavy metal pollution. Combining the analyses, a) the protection of children should be prioritized; b) Co, Cr, Cu and Ni should be taken priority control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKai Xiao:\u003c/strong\u003e Methodology, Writing \u0026ndash; original draft, Software, Formal analysis, Investigation, Funding acquisition. \u003cstrong\u003eYousong Zhou, Yongqiang Zhang, and Donglei Fu:\u003c/strong\u003e Investigation, Conceptualization, Supervision. \u003cstrong\u003eRabin Mominul Haque and Senlin Lu\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Methodology, Formal analysis, Investigation, Data curation. \u003cstrong\u003eAbrar Chowdhury\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Methodology, Software, Investigation, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Youth Science Foundation of China \u0026nbsp;(42307465).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad Bhat, S., Hassan, T., \u0026amp; Majid, S. 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Y., \u0026amp; Wang, X. R. (2019). Impact of industrial activities on heavy metal contamination in soils in three major urban agglomerations of China. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e230\u003c/em\u003e. https://doi.org/10.1016/j.jclepro.2019.05.098\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heavy metal, Industries area, Potential ecological risk index (ERI), Principal component analysis (PCA), Carcinogenic risk (CR)","lastPublishedDoi":"10.21203/rs.3.rs-4133831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4133831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe heavy metal pollution caused by widespread industrial activities is an important and difficult issue for environmental pollution control in China. It adversely affects human health and the ecosystem. However, the relevant research on heavy metals contamination in typical petrochemical (PIA) and coking industries areas (CIA) was few. In this study, a total of 24 and 21 surface topsoil (\u0026lt;\u0026thinsp;20 cm) samples were collected in petrochemical and coking industrial areas, respectively. The geo-accumulation index (Igeo), enrichment factor (EF), and potential ecological risk index (ERI) were calculated to assess the Cu, Pb, Ni, Co, Cr, Zn, V, and Mn pollution levels in soils. The hazard index (HI), carcinogenic risk (CR), and non-carcinogenic risk (NCR) were used to assess the human health risk of heavy metals. The mean levels (mg/kg) of heavy metals were ranked as Mn (601.25)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (154.63)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (76.78)\u0026thinsp;\u0026gt;\u0026thinsp;V (76.04)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (39.11)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (36.88)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (31.73)\u0026thinsp;\u0026gt;\u0026thinsp;Co (12.97) in PIA, and Mn (915.14)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (307.64)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (115.98)\u0026thinsp;\u0026gt;\u0026thinsp;Pb (93.20)\u0026thinsp;\u0026gt;\u0026thinsp;V (92.56)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (44.42)\u0026thinsp;\u0026gt;\u0026thinsp;Ni (34.45)\u0026thinsp;\u0026gt;\u0026thinsp;Co (16.65) in CIA, respectively. Pollution indices indicated that the extent of heavy metals contamination in CIA soils is higher than PIA. Source apportionment of heavy metals in soil was performed using Spearman's correlation coefficient, principal component analysis (PCA) and matrix cluster analysis, suggesting that industrial activities and the transshipment process were the major contributors to heavy metals. About NCR, the THI values were higher than 1 in both typical industrial areas, implying that there is potential health risk to humans. Except for the CR values of Pb for children and adults in both industrial areas and the CR values of Cr for adults in PIA, the CR are between 1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e and 1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, other heavy metals of the CR values were higher than 1\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e10\u003csup\u003e\u0026minus;4\u003c/sup\u003e. This result reflects the fact that there were seriously adverse impacts on human health. Overall, the NCR and CR of the heavy metals for different populations ranked as follows: children\u0026thinsp;\u0026gt;\u0026thinsp;adults and Cu, Ni, Co, and Cr were identified as the major contributors to CR and NCR. The result of the present study provides timely information for developing control and management strategies to reduce soil contamination by heavy metals in typical petrochemical and coking industries areas.\u003c/p\u003e","manuscriptTitle":"Levels, sources, and risk of heavy metals in soils from northwest and eastern industrial areas of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 15:37:29","doi":"10.21203/rs.3.rs-4133831/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c64a882-4329-41bd-870d-9df291b6305b","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-09T12:47:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 15:37:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4133831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4133831","identity":"rs-4133831","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}