RESEARCH ARTICLE

 

Pollution level and risk assessment of heavy metals in sewage sludge from eight wastewater treatment plants in Wuhu City, China

 

Hanwen Zhang (Zhang, H)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

Yuee Huang (Huang, Y)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

Shu Zhou (Zhou, S)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

Liangchen Wei (Wei, L)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

Zhiyuan Guo (Guo, Z)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

Jinchun Li (Li, J)

School of Public Health, Wannan Medical College, Wuhu, Anhui, China

 

 

Abstract

Aim of study: To investigate the content, contamination levels and potential sources of five heavy metals (Hg, Pb, Cd, Cr, As) in sewage sludge from eight wastewater treatment plants (W1 to W8).

Area of study:Wuhu, located in southeastern Anhui Province, southeastern China.

Material and methods: The sewage sludge pollution assessment employed the single-factor pollution index, Nemerow’s synthetic pollution index, monomial potential ecological risk coefficient and potential ecological risk index. The potential sources among the five heavy metals were determined using the Pearson’s correlation analysis and principal component analysis (PCA).

Main results: The mean concentrations of the heavy metals were 0.27 mg/kg (Hg), 70.78 mg/kg (Pb), 3.48 mg/kg (Cd), 143.65 mg/kg (Cr) and 22.17 mg/kg (As). W1, W5 and W6 sewage sludge samples showed the highest levels of heavy metal contamination, and cadmium had the highest contamination level in the study area. Pearson’s correlation analysis and PCA revealed that Pb and Cd mainly derived from traffic emissions and the manufacturing industry and that As and Cr originated from agricultural discharges.

Research highlights: The pollution of cadmium in Wuhu should be controlled preferentially. The heavy metal pollution of W1, W5 and W6 sewage treatment plants is relatively high, they should be key prevention targets.

Additional keywords: contamination evaluation; source identification

Abbreviations used: Igeo (geoaccumulation index); PI (single-factor pollution index); PN (Nemerow’s synthetic pollution index); Eir (monomial potential ecological risk coefficient); RI (potential ecological risk index); PCA (principal component analysis);

Authors’ contributions: Conceived and designed the experiments: YH. Analyzed the data: HZ and YH. Wrote the paper: HZ, YH, SZ, LW, ZG and JL. Revised the paper: HZ and YH. All authors read and approved the final manuscript.

Citation: Zhang, H; Huang, Y; Zhou, S; Wei, L; Guo, Z; Li, J (2020). Pollution level and risk assessment of heavy metals in sewage sludge from eight wastewater treatment plants in Wuhu City, China. Spanish Journal of Agricultural Research, Volume 18, Issue 2, e1103. .https://doi.org/10.5424/sjar/2020182-15796

Received: 26 Sep 2019. Accepted: 25 May 2020

Copyright © 2020 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License.

Funding agencies/institutions Project/Grant
Natural Science Foundation of Anhui Province 1608085MH220
Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province gxyqZD2016180
Key Projects of Wuhu Science and Technology Plan 2014cxy08
Doctoral Scientific Research Foundation of Wannan Medical College WYRCQD201703
Students’ Innovation and Entrepreneurship Training Program of Anhui Province S201910368103

 

Competing interests: The authors have declared that no competing interests exist.

Correspondence should be addressed to Yuee Huang huangyewindow@163.com


 

CONTENTS

Abstract

Introduction

Material and methods

Results and discussion

References

IntroductionTop

Sewage sludge is generated during the process of treating municipal wastewater, and it is rapidly increasing (Dong et al., 2013). In China, approximately 56% of sludge is associated with disposed building materials, incineration waste, fertilizer, sanitary landfills, and the other sources; therefore, nearly half of the sludge has not been treated safely. Approximately one-third of the sludge is disposed of by “temporary means”, and more than 10% of the sludge is disposed of by unknown means (He et al., 2016).

Sludge that is not treated in a timely manner continues to accumulate and occupy a large amount of land, and it can contain various heavy metals, organic pollutants and other toxic substances, which can cause secondary pollution (Lister & Line, 2001). Urban industrial sewage, domestic sewage, commercial water mixed emissions, and surface runoff inevitably lead to heavy metal accumulation in urban sludge, and these metals are not easily biodegraded once they reach into the soil environment and pose a threat to human health once they enter into the food chain (Dou et al., 2013; Grotto et al., 2015). Heavy metals in sewage sludge can eventually be taken up by humans, accumulating in fatty tissues and influencing the nervous system, immune system, endocrine system and hematopoietic function (Zhao et al., 2014; Xu et al., 2016). However, sludge can also be disposed in the form of soil conditioners or fertilizers, and improper disposal leads to a loss of organic matter and nutrient elements, thus representing a waste of resources. Sludge is rich in organic matter and nutrients, by improving soil physical and chemical properties and increasing soil organic matter, nitrogen and phosphorus, has positive and long-term effects on soil remediation or improvement (Singh & Agrawal, 2008; Kendir et al., 2014; Liu et al., 2015). To evaluate the environmental risk and sources of heavy metals in sewage sludge, the geo-accumulation index (Igeo), single-factor pollution index (PI), Nemerow pollution index (PN), monomial potential ecological risk coefficient (Eir) and potential ecological risk index (RI), together a multivariate statistical analysis have been widely applied (Abrahim & Parker, 2008; Shafie et al., 2013; Kowalska et al., 2016; Birch, 2017; Yang et al., 2017; Zhu et al., 2018).

To use sewage sludge in an environmentally safe manner in Wuhu City, a risk assessment should be implemented. The aims of this research were to assess the contamination status of five heavy metals (Hg, Pb, Cd, Cr, and As) from different angles via Igeo, PI, PN,Eir and RI and to identify the potential sources of the heavy metals via Pearson’s correlation coefficient analysis and a principal component analysis (PCA).

Material and methodsTop

Study area

The city of Wuhu is located in southeastern Anhui Province in southeastern China, and ranks 10th out of 26 cities in the Yangtze River Delta City Group. The eight sewage treatment plants are located in: W1) Zhujiaqiao, in the Jinghu District; W2) Tianmenshan, in the Jiujiang District; W3) Binjiang, in the Yijiang District; W4) Chengnan, in the Sanshan District; W5) Wuhu Mingyuan, in the Nanling County; W6) Nanling County, in the Wuhu County; W7) Fanchang County, in the Fanchang County; and W8) Wuwei Modern, in the Wuwei County. The main sources of sewage were industrial and domestic effluents. The properties of these eight wastewater treatment plants are shown in Table 1.

 

Determination of the total heavy metal concentration

Dry sludge was collected from the terminals of the sewage treatment plants in the second and fourth quarters of 2014. Each month, 3~5 500-g samples were collected from each of the sewage treatment plants. The collected samples were dried at room temperature, ground, and then separated into 0.149-mm particles through a sieve. The samples were weighed and digested with HNO3-HClH2O2 and then used to determine the content of Cd, Cr and Pb (USEPA, 1996). Cd was analyzed using an atomic absorption spectrophotometer (AA-6300 Atomic Absorption Spectrometer, Shimadzu International Trading Co., Ltd., Shanghai, China). Pb and Cr were calculated using inductively coupled plasma mass spectrometry (ICP-OES 700 Inductively Coupled Plasma Mass Spectrometer, Agilent Technologies Inc., Tokyo, Japan). The sludge samples were also digested with HNO3:HCl (10 mL, 1:1 v/v) at 95 °C for 2 h to determine the content of As and Hg (Lacerda et al., 2004) using the atomic fluorescence method (AFS-830 Dual-Channel Atomic Fluorescence Spectrometer, Beijing Titan Instruments Co., Ltd., Beijing, China).

 

Geoaccumulation index (Igeo)

The Igeo was introduced by Müller (1969) to assess the contamination of heavy metals in soils and sediments, and it is defined as follows:

 

where Cn is the content of heavy metal n in samples, mg/ kg; Bn is the background content of the metal n using the Nanjing background concentration of heavy metal in the soils (Hg = 0.12 mg/kg, Pb = 24.80 mg/kg, Cd = 0.19 mg/ kg, Cr = 59.00 mg/kg and As = 10.60 mg/kg); and 1.5 is a constant factor applied to address the lithospheric effects. The classification of the Igeo is shown in Table 2

 

Table 1. Some properties of eight wastewater treatment plants in this study

[1] A2/O: Anaerobic-Anoxic-Oxic

 

Assessment of heavy metal pollution

The PI was used to evaluate the comprehensive level of heavy metals for each study site (Tomlinson et al., 1980), and it is defined as follows:

where Ci is the concentration of the heavy metal i, mg/kg; and Si is the standard of the heavy metal i according to CJT 309-2009 (Ministry of Housing and Urban-Rural Development, 2009). Nemerow’s synthetic pollution in-dex (PN) was applied to assess heavy metal contamina-tion caused by all the heavy metals at each study site. PN is defined as follows:

where Pave is the average value of the single-factor pollution index of the heavy metal i; and Pmax is the maximum value of the single-factor pollution index of the heavy metal i. The classification of PI and PN is shown in Table 3.

 

Assessment of potential ecological risk

The RI was proposed by Hakanson (1980) and is widely utilized to assess potential ecological risk, including heavy metal pollution risk. The index is defined as follows:

where Cif is the pollution of the metal i; cis is the concentration of heavy metal in samples: Cin is the standard of the heavy metal i according to Chinese Soil Environmental Standard (pH 6.5-7.5) GB15618-1995 (Ministry of Ecology and Environment, 1995) and the corresponding standard values Cin for Hg, Pb, Cd, Cr, and As are 0.5, 300, 0.6, 300 and 25 mg/kg, respectively; Eir is the monomial potential ecological risk coefficient; and Tir is the metal toxic response factor (Hg = 40, Pb= 5, Cd= 30, Cr = 2 and As = 10). The classification of Eir and RI is displayed in Table 4.

 

Table 2. Classifications for geoaccumulation index (Igeo)

 

Table 3. Classification for single-factor pollution index (PI) and Nemerow’s synthetic pollution index (PN)

 

Table 4. Classification for monomial potential ecological risk coefficient (Eir and potential ecologia risk index (RI)

 

Table 5. Heavy metal concentrations in sewage sludge from different sewage treatment plants (mg/kg)

[1] CJT 309-2009 (Ministry of Housing and Urban-Rural Development, 2009)

 

Table 6. Geoaccumulation index (Igeo) for heavy metals in sewage sludge of eight sampling sites

 

Statistical analyses

The relationships among five heavy metals were de-termined using the Pearson’s correlation analysis. A prin-cipal component analysis (PCA) was used to reduce the dimensionality, and the highly correlated heavy metal ele-ments were extracted into independent factors (Li et al., 2013; Lu et al., 2010).

 

ResultsTop

The concentration of heavy metals in sewage sludge

The measured concentrations of heavy metals are presented in Table 5. According to the mean concentration values, the corresponding order of heavy metals in sewage sludge samples was Cr > Pb > As > Cd > Hg. The variation coefficients of heavy metals were ranked in decreasing order as follows: Cd > Pb > Cr > Hg > As. Heavy metal content in the study area varied greatly among sewage treatment plants, which occurs probably because the sewage sludge samples were collected from different sites (Yang et al., 2014). The maximum concentrations of the heavy metals of the eight sewage treatment plants did not exceed the permissible content limits in the dis-charge standards (Class B) of CJT 309-2009, except for Cd at W1. Cd exceeded the permissible content limits at this site probably because the W1 sewage treatment plant collects water from an industrial area. The above results are consistent with other Chinese studies (e.g., Zhao et al., 2019), which showed that the electronics industry is a pollution source for Cd.

 

Three assessment methods of heavy metals contamination

Geoaccumulation index values for heavy metals in sewage sludge

The Igeo values for five heavy metals are presented in Table 6. The mean Igeo values for five heavy metals were in the following decreasing order: Cd > Pb > Cr = As > Hg. The pollution order of stations was W1 > W5 > W6 > W8 > W3 > W4 > W2 > W7.

The Igeo values were less than zero for Hg at sites W1, W5, W6 and W7; Pb at sites W3, W6 and W7; Cd at sites W2 and W7; Cr at sites W3, W4, W6, W7 and W8; and As at sites W4 and W7; these findings indicate that these sites were not polluted by these metals. The Igeo values were between 0 and 1 for Hg at sites W2, W3, W4 and W8; Pb at sites W2, W4, W5 and W8; Cd at site W8; Cr at sites W1 and W2; and As at sites W1, W2, W3 and W8; these findings indicate that the pollution level of these metals at these stations ranged from unpolluted to moderately polluted. The Igeo values were between 1 and 2 for Hg at site W8, Cd at site W3 and As at sites W5 and W6; and these findings indicate that the pollution levels of these metals at these stations were moderate. The Igeo values were between 2 and 3 for Pb at site W1, Cd at site W4 and Cr at site W5; these findings indicate that these metals at these stations were polluted at moderate to heavy levels. The Igeo values were higher than 3 for Cd at sites W1, W5 and W6, what indicates that the pollution level of Cd at these stations was heavy.

Assessment of heavy metal pollution

The PI values of heavy metals are presented in Table 7. According to the mean PI values, heavy metals were sorted in the following decreasing order: Cd > As > Cr > Pb > Hg. According to these results, the sewage sludge in the study area exhibited low pollution levels for most heavy metals except for Cd at sites W1, W5 and W6 and As at sites 5 and 6. According to the mean PN values, the heavy metals were sorted in the following decreasing or-der: W1 > W5 > W6 > W2 > W3 > W8 > W4 > W7. The PN values for sites W2, W3, W4, W7 and W8 were lower than 0.7, and the maximum concentrations of the heavy metals of five sampling sites did not exceed the permissible content limits in the discharge standards (Class B) of CJT 309-2009. This finding suggests that the sewage sludge in these sites was safe in terms of heavy metal dis-charged into the environment and could be directly used in agriculture. The PN values for sites W5 and W6 were between 1 and 2, and the value at W1 was higher than 3, indicating that sewage sludge at these sites had risk levels of heavy metals; therefore, heavy metal pollution should be considered when using sewage sludge from these sites for land treatments.

The potential ecological risk

The RI and Eir values for each studied site are shown in Table 8. The mean Eirvalue of five heavy metals decrea-sed in the following order: Cd > Hg > As > Pb > Cr. The Eirvalues for Hg, Pb, Cr and As in all sampling sites were lower than 40 except for Hg at site W1, suggesting that these sites did not pose a potential ecological risk. The Eir values for Cd at sites W5 and W6 were between 160 and 320, and the value for Cd at site W8 was higher than 320, suggesting that sewage sludge at these sites had high RI for Cd. W5 and W6 exhibited high risk, and W1 very high risk. The mean RI values for sites W2, W3, W4, W5, W7, and W8 were < 150, indicating that these sites had low RI. The RI values for sites W5 and W6 ranged from 150 to 300, indicating that these sites had moderate RI. For site W1, the RI values were > 600, indicating that this site had very high risk.

According to the results of Igeo, PI, PN, RI and Eirresults show that the highest risk levels of heavy metal contamination in W1, W5 and W6 wastewater treatment plants, pos-sibly may because W1 and W5 wastewater treatment plant is located near industrial area, and W6 sewage treatment plant is located in suburban areas, which is near steel woll, cement, textile and pharmaceutical manufacturing industries (Lin et al., 2002). Such heavy metal contamination emitted from industries is also consistent with other regions in China, In Shanxi Province, Cd pollution might be caused by the rich coal resources, and the large number of coal industries (Duan et al., 2017). In Guangzhou City, Cu and Cr pollution may be related to the industrial wastewater such as electroplating, chemical and machinery manufacturing industries (Li et al., 2015).

 

Table.7 Single-factor pollution index (PI) and Nemerow’s synthetic pollution index (PN) for heavy metals in sewage sludge of eight sampling sites

 

Table.8 Monomial potential ecological risk coefficient (Eir and potential ecological risk index (RI) for heavy metals in sludge of eight sampling sites

 

 

Correlation coefficient

Table 9 displays the correlation coefficients as a linear correlation matrix. The results of the correlation analysis suggested a low correlation occurred between Hg and Pb (r = -0.279), Cd and Cr (r = 0.249) at 0.05 level and between Hg and Cr (r = -0.341), Hg and As (r = -0.440) and Cd and As (r = 0.394) at 0.01 level. Furthermore, high correlation was observed between Hg and Cd (r = -0.550), Pb and Cd (r = 0.862) and Cr and As (r = 0.555) at 0.01 level.

The positive correlations among metals may reflect the fact that these metals had similar pollution levels, the same behavior during transport, and common sources or at least one major source (Suresh et al., 2011). The negative correlation between Hg and Pb, Cd, Cr and As indi-cated that the adsorption capacity of Hg may be restrained because of the competitive adsorption of the other coexisting heavy metals in sediments (Zhang & Zheng, 2007).

 

Table 9. Pearson’s correlation matrix for the metal concentrations in sewage sludge

**,*: correlation is significant at the 0.01 level or 0.05 (2-tailed), respectively.

 

Table 10. Eigenvalues, variables and rotation of principal component analysis (PCA) for heavy metals in sewage sludge

PC1, PC2: first and second principal component factor, respectively

 

Factor analysist

PCA was a performed to identify the probable sources between the heavy metals when they were interrelated (Mirzaei Aminiyan et al., 2018). Table 10 depicts the fac-tor loadings as well as the eigenvalues, percentile of va-riance, and cumulative percentages of the total loadings. According to Table 10, two principal components with eigenvalues of 2.15 and 1.39 were obtained, and they ac-counted for 78.61% of the total variance. The first princi-pal component was dominated by Pb (0.96) and Cd (0.93) and accounted for 50.90% of the total variance. These observations show that Cd and Pb probably originated from a similar source. Previous studies (Kabata-Pendias & Mukherjee, 2007; Wei et al., 2009; Al-Khashman, 2013; Zhang et al., 2013) have reported that vehicle emissions, diesel fuel, and fossil fuel combustion are the primary sources of Cd and Pb pollution. Cd and its compounds are also known to originate from different manufactured pro-ducts, such as paints, batteries, and electrical appliances (Mico et al., 2006).Thus, the component loading of PC1 can be defined as traffic emissions and the manufacturing industry. The second principal component was dominated by As (0.86) and Cr (0.85), and it accounted for 27.70% of the total variance. Based on the correlation analysis, a highly positive correlation was observed between As and Cr, suggesting that they may share a common source. A previous study reported that the main fertilizer products in China contain Cr, As and other harmful metals (Feng et al., 2009). Anhui is a major agricultural province, and the input of chemical pesticides and chemical fertilizers per unit area of cultivated land in Wuhu is well above the average level of Anhui Province of China as a whole. In addition, several studies (Yongming et al., 2006; Sharma et al., 2008; Duan & Tan, 2013) have reported that indus-trial and agricultural activities are major sources of As and Cr. In the study area, many industrial activities are obser-ved, including cement and asphalt plants, a paperboard factory, a shipyard, sand mining operations, and electrical industries. Thus, the component loading of PC2 can be considered to be agriculture activities.

In summary, the maximum concentrations of the heavy metals in the eight sewage treatment plants did not exceed the permissible content limits in the discharge standards (Class B) of CJT 309-2009, except for Cd at W1. Based on the total concentration results and the Igeo, PI, PN, RI and Eir results described above, heavy metal pollution reached the highest contamination levels in the ecosystem for W1, W5 and W6 sewage sludge samples in the city of Wuhu. Pb at site W1, Cd at sites W5 and W6 and As at sites W5 and W6 were identified as the main contributors to metal pollution. Thus, measures should be taken to con-trol these metals at these sampling sites. Cd exhibited the highest contamination level in the eight wastewater treatment plants, and the strongest ecological risk posed by Cd was primarily attributed to the fact that the toxicity coefficients of Cd were far higher than those of the other metals, although its concentration in the study area was relatively lower than those of the other metals. The correlation and PCA suggest that Pb and Cd mainly derived from traffic emissions and the manufacturing industry and that As and Cr originated from agriculture discharge.

ReferenesTop

Abrahim GM, Parker RJ, 2008. Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki Estuary, Auckland, New Zealand. Environ Monit Assess 1-3 (136): 227-238. https://doi.org/10.1007/s10661-007-9678-2
Al-Khashman OA, 2013. Assessment of heavy metals contamination in deposited street dusts in different urbanized areas in the city of Ma'an, Jordan. Environ Earth Sci 70 (6): 2603-2612. https://doi.org/10.1007/s12665-013-2310-6
Birch GF, 2017. Determination of sediment metal background concentrations and enrichment in marine environments - A critical review. Sci Total Environ 580: 813-831. https://doi.org/10.1016/j.scitotenv.2016.12.028
Dong B, Liu X, Dai L, Dai X, 2013. Changes of heavy metal speciation during high-solid anaerobic digestion of sewage sludge. Bioresour Technol 131: 152-158. https://doi.org/10.1016/j.biortech.2012.12.112
Dou Y, Li J, Zhao J, Hu B, Yang S, 2013. Distribution, enrichment and source of heavy metals in surface sediments of the eastern Beibu Bay, South China Sea. Mar Pollut Bull 67 (1): 137-145. https://doi.org/10.1016/j.marpolbul.2012.11.022
Duan B, Zhang W, Zheng H, Wu C, Zhang Q, Bu Y, 2017. Disposal situation of sewage sludge from municipal wastewater treatment plants (WWTPs) and assessment of the ecological risk of heavy metals for its land use in Shanxi, China. Int J Environ Res Public Health 14 (7): E823. https://doi.org/10.3390/ijerph14070823
Duan J, Tan J, 2013. Atmospheric heavy metals and arsenic in China: Situation, sources and control policies. Atmos Environ 74: 93-101. https://doi.org/10.1016/j.atmosenv.2013.03.031
Feng C, Liu H, Wang X, 2009. Content and evaluation of harmful elements in main fertilizer products in China. Soil Fertil Sci in China 4: 44-47.
Grotto D, Batista BL, Souza JMO, Carneiro MFH, Dos Santos D, Melo WJ, Barbosa F, 2015. Essential and nonessential element translocation in corn cultivated under sewage sludge application and associated health risk. Water Air Soil Poll 226 (8): 261-270. https://doi.org/10.1007/s11270-015-2527-y
Hakanson L, 1980. An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res 14 (8): 975-1001. https://doi.org/10.1016/0043-1354(80)90143-8
He Q, Ji F, Li J, 2016. Sludge treatment and disposal and resource utilization methods and new technologies. Water Sew Eng 52 (2): 1-3.
Kabata-Pendias A, Mukherjee AB, 2007. Trace elements from soil to human. Springer, Berlin Heidelberg: https://doi.org/10.1007/978-3-540-32714-1
Kendir E, Kentel E, Sanin D, 2014. Evaluation of heavy metals and associated health risks in a metropolitan wastewater treatment plant's sludge for its land application. Hum Ecol Risk Assess 21 (6): 1631-1643. https://doi.org/10.1080/10807039.2014.966590
Kowalska J, Mazurek R, Gasiorek M, Setlak M, Zaleski T, Waroszewski J, 2016. Soil pollution indices conditioned by medieval metallurgical activity - A case study from Krakow (Poland). Environ Pollut 218: 1023-1036. https://doi.org/10.1016/j.envpol.2016.08.053
Lacerda LD, de Souza M, Ribeiro MG, 2004. The effects of land use change on mercury distribution in soils of Alta Floresta, Southern Amazon. Environ Pollut 129 (2): 247-255. https://doi.org/10.1016/j.envpol.2003.10.013
Li HY, Hu XD, Wu QH, Wu ZY, Huang XX, Zhang FG, Leung YS, Fu J, Huang ZY, Xiong FK, et al., 2015. Heavy metal concentration, emission flux and potential ecological risk assessment for agriculture in Guangzhou. Chin J Environ Eng 9 (3): 1409-1416.
Li X, Liu L, Wang Y, Luo G, Chen X, Yang X, Hall MHP, Guo R, Wang H, Cui J, et al., 2013. Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China. Geoderma 192: 50-58. https://doi.org/10.1016/j.geoderma.2012.08.011
Lin YP, Teng TP, Chang TK, 2002. Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua county in Taiwan. Landscape Urban Plan 62 (1): 19-35. https://doi.org/10.1016/S0169-2046(02)00094-4
Lister SK, Line MA, 2001. Potential utilisation of sewage sludge and paper mill waste for biosorption of metals from polluted waterways. Bioresour Technol 79 (1): 35-39. https://doi.org/10.1016/S0960-8524(01)00035-9
Liu J, Zhuo Z, Sun S, 2015. Concentrations of heavy metals in six municipal sludges from Guangzhou and their potential ecological risk assessment for agricultural land use. Pol J Environ Stud 24 (1): 165-174. https://doi.org/10.15244/pjoes/28348
Lu X, Wang L, Li LY, Lei K, Huang L, Kang D, 2010. Multivariate statistical analysis of heavy metals in street dust of Baoji, NW China. J Hazard Mater 173 (1-3): 744-749. https://doi.org/10.1016/j.jhazmat.2009.09.001
Mico C, Recatala L, Peris M, Sanchez J, 2006. Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis. Chemosphere 65 (5): 863-872. https://doi.org/10.1016/j.chemosphere.2006.03.016
Mirzaei Aminiyan M, Baalousha M, Mousavi R, Mirzaei Aminiyan F, Hosseini H, Heydariyan A, 2018. The ecological risk, source identification, and pollution assessment of heavy metals in road dust: a case study in Rafsanjan, SE Iran. Environ Sci Pollut Res Int 25 (14): 13382-13395. https://doi.org/10.1007/s11356-017-8539-y
Müller G, 1969. Index of geoaccumulation in sediments of the Rhine River. Geo J 2 (3): 109-118.
Ministry of Ecology and Environment, 1995. GB15618-1995: environmental quality standard for soil. Beijing, PRC.
Ministry of Housing and Urban-Rural Development, 2009. CJT 309-2009: Disposal of sludge from municipal wastewater treatment plant - Control standards for agricultural use (Class A). Beijing, PRC.
Shafie NA, Aris AZ, Zakaria MP, Haris H, Lim WY, Isa NM, 2013. Application of geoaccumulation index and enrichment factors on the assessment of heavy metal pollution in the sediments. J Environ Sci Health A Tox Hazard Subst Environ Eng 48 (2): 182-190. https://doi.org/10.1080/10934529.2012.717810
Sharma RK, Agrawal M, Marshall FM, 2008. Atmospheric deposition of heavy metals (Cu, Zn, Cd and Pb) in Varanasi City, India. Environ Monit Assess 1-3 (142): 269-278. https://doi.org/10.1007/s10661-007-9924-7
Singh RP, Agrawal M, 2008. Potential benefits and risks of land application of sewage sludge. Waste Manag 28 (2): 347-358. https://doi.org/10.1016/j.wasman.2006.12.010
Suresh G, Ramasamy V, Meenakshisundaram V, Venkatachalapathy R, Ponnusamy V, 2011. Influence of mineralogical and heavy metal composition on natural radionuclide concentrations in the river sediments. Appl Radiat Isot 69 (10): 1466-1474. https://doi.org/10.1016/j.apradiso.2011.05.020
Tomlinson DL, Wilson JG, Harris CR, Jeffrey DW, 1980. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgol Meeresunters 33 (1): 566-575. https://doi.org/10.1007/BF02414780
USEPA, 1996. Method 3050B: Acid digestion of sediments, sludges and soils, revision 2. Washington, DC.
Wei B, Jiang F, Li X, Mu S, 2009. Spatial distribution and contamination assessment of heavy metals in urban road dusts from Urumqi, NW China. Microchem J 93 (2): 147-152. https://doi.org/10.1016/j.microc.2009.06.001
Xu Z, Li J, Pan Y, Chai X, 2016. Human health risk assessment of heavy metals in a replaced urban industrial area of Qingdao, China. Environ Monit Assess 188 (4): 229-240. https://doi.org/10.1007/s10661-016-5224-4
Yang J, Lei M, Chen T, Gao D, Zheng G, Guo G, Lee D, 2014. Current status and developing trends of the contents of heavy metals in sewage sludges in China. Front Env Sci Eng 8 (5): 719-728. https://doi.org/10.1007/s11783-013-0600-6
Yang Y, Jin Q, Fang J, Liu F, Li A, Tandon P, Shan A, 2017. Spatial distribution, ecological risk assessment, and potential sources of heavy metal(loid)s in surface sediments from the Huai River within the Bengbu section, China. Environ Sci Pollut Res Int 24 (12): 11360-11370. https://doi.org/10.1007/s11356-017-8732-z
Yongming H, Peixuan D, Junji C, Posmentier ES, 2006. Multivariate analysis of heavy metal contamination in urban dusts of Xi'an, Central China. Sci Total Environ 1-3 (355): 176-186. https://doi.org/10.1016/j.scitotenv.2005.02.026
Zhang M, Zheng S, 2007. Competitive adsorption of Cd, Cu, Hg and Pb by agricultural soils of the Changjiang and Zhujiang deltas in China. J Zhejiang Univ Sci A 8 (11): 1808-1815. https://doi.org/10.1631/jzus.2007.A1808
Zhang J, Deng H, Wang D, Chen Z, Xu S, 2013. Toxic heavy metal contamination and risk assessment of street dust in small towns of Shanghai suburban area, China. Environ Sci Pollut Res Int 20 (1): 323-332. https://doi.org/10.1007/s11356-012-0908-y
Zhao L, Xu Y, Hou H, Shangguan Y, Li F, 2014. Source identification and health risk assessment of metals in urban soils around the Tanggu chemical industrial district, Tianjin, China. Sci Total Environ 468-469: 654-662. https://doi.org/10.1016/j.scitotenv.2013.08.094
Zhao L, Liang Y, Chen Q, Xu Q, Jing H, 2019. Spatial distribution, contamination assessment, and sources of heavy metals in urban green space soil of central six districts of Beijing. Environ Sci 5: E1-13.

Zhu D, Wu S, Han J, Wang L, Qi M, 2018. Evaluation of nutrients and heavy metals in the sediments of the Heer River, Shenzhen, China. Environ Monit Assess 190 (7): 380-389. https://doi.org/10.1007/s10661-018-6740-1