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Distribution Characteristics of Typical Heavy Metals in Sludge from Wastewater Plants in Jiangsu Province (China) and Their Potential Risks

School of Civil Engineering, Southeast University, Nanjing 211189, China
School of Environmental and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
Author to whom correspondence should be addressed.
Water 2023, 15(2), 313;
Submission received: 2 December 2022 / Revised: 8 January 2023 / Accepted: 9 January 2023 / Published: 11 January 2023
(This article belongs to the Section Urban Water Management)


Recently, increasing attention has been paid to heavy metals in sludge. However, limited literature could be found on the distribution characteristics of heavy metals in sludge and their potential risks. In this study, sludges from wastewater plants in Jiangsu Province (China) were selected for the investigation of heavy metal loadings, showing that typical heavy metal levels were in the order of Zn > Cu > Cr > Ni > Pb > As > Hg > Cd, ranging from 154 to 2970 mg/kg, 28 to 1150 mg/kg, 10 to 136 mg/kg, 9 to 262 mg/kg, 0 to 79 mg/kg, 12.1 to 41.6 mg/kg, 0.67 to 19.50 mg/kg and 0.21 to 2.77 mg/kg, respectively. Analysis of the typical heavy metal distribution in sludge indicated that Hg, Zn and Cu were obviously influenced by the degree of industrial intensity and exploitation of human activities, while Ni, Cd, Pb, As and Cr were more evenly distributed. Effects of sewage sources and wastewater-treatment processes on heavy metal levels implied that different industrial wastewaters resulted in different metal contents, but the distribution of Ni, Cd, Pb, As and Cr in different treatment processes was similar. Furthermore, Hg and Cd had the strongest ecological risk, with their levels reaching severe, suggesting that sludge was not recommended for agricultural reuse in this study.

1. Introduction

Sludge is considered to be a biologically active mixture that usually contains water, organic matter (from human life, waste from production activities, food waste, etc.), live microorganisms (including pathogens) and inorganic or organic toxic contaminants, as well as carbohydrates, proteins, fats and nutrients [1]. The high caloric and nutrient content of sludge means that it is considered an available energy resource [2], and the resource utilization of sludge is of great interest internationally [3]. China was an emerging sludge market [4], and sludge production has increased rapidly in the last decade, yet the majority of sludge is not properly treated [5]. High sludge-management costs [6], the pollutants brought by the large amount of sludge [7] and the recovery of substances and energy from sludge [8] are increasingly being a hot topic at present [9].
Sludge contains a large number of contaminants, such as inorganic (nitrogen and phosphorus nutrients [10], flocculants, heavy metals, etc.) contaminants, organic contaminants (e.g., PAHs, PCBs, AOX, pesticides, hormones, drugs, nanoparticles) [11] and some pathogenic biological species. The content for both Zn and Cu in municipal sludge was generally high, while the content of Cd, Hg and As was generally low. [12] The content of heavy metals in sludge generally showed Zn > Cu > Cr > Pb > Ni > As > Cd > Hg. This was consistent with the content of heavy metals in water pollution Cr > Pb > Cd > Hg. Sludge heavy metals have strong geographical distribution characteristics. Cheng et al. [13] found that accumulation was higher in the eastern and southern regions of China, and the risk of heavy metal accumulation was higher in the eastern and western regions of China. Higher levels of heavy metals in municipal sludge will be found in regions with higher industrial development [14]. The content of heavy metals in sludge varies from country to country due to different national industrial characteristics [13].
The heavy metal content is mainly influenced by a combination of factors, such as the source of wastewater, the sludge treatment process and its related effects [15] and also related to the geographical location of the city where it is located, the level of economic development and the industrial layout [16]. For example, leaching contains large amounts of Cr, and metallurgical, printing and electroplating industry wastewater contains large amounts of Cu [17]. Vehicle traffic and auto-part tire factories produce pollution from Cu, Pb and Zn [18]. Hg pollution was often associated with chemical activities and coal combustion. As usually originates from the production of pesticides, emissions from paint factories, textile factories and leather factories. Feng et al. [15] found that the anoxic–anaerobic–carousel oxidation ditch (AAC) process heavy metal concentrations are usually higher than those of the aerobic–anoxic–aerobic (AAO) process. Yang et al. [19] showed that the oxidation ditch (OD) process removed As, Cu and Ni from water higher than the sequencing batch reactor (SBR), conventional activated sludge (CAS) and AAO processes, and that the heavy metals removed are retained in the sludge. Robert et al. [20] found that the sludge collected from the MBR plant had significantly higher Cu and Cd content than the sludge collected from the SBR plant and concluded that the membranes in the plant line absorbed more Cd and Cu from the treated wastewater. Therefore, we hypothesize that different effluent sources and different operating processes have some influence on the load and distribution of heavy metal contamination of sludge.
Due to their complex chemical composition and natural non-biodegradability, heavy metals could accumulate in soil and water for a long time and enter the food chain, thus, producing bioconcentration in the environment; they are harmful to animals and microorganisms [21]. It is important to accurately identify the level of sludge contamination and manage it. Ecological risk assessment was originally mainly derived from human health risk-assessment methods [22], including the evaluation of the degree of contamination and ecological risk. Men et al. [23] used the Nemerow integrated index to analyze heavy metal content. Liu et al. [24] employed Igeo and the potential ecological risk index (RI) for analyzing the risk of contamination in sediments. Ntzala et al. [25] used the pollution load index (PLI) to evaluate soil contamination. Islam et al. [26] applied PLI and RI to sludge from different industries for ecological risk evaluation. Igeo could not yet effectively identify changes in environmental processes. PLI did not consider the influence of background differences in different pollutants. The Nemero index method strengthened the weight of the most-polluting factor in the pollutant in the pollution degree, taking into account the combined effect of pollution of multiple heavy metals. RI introduced toxicity response factors for specific substances, providing quantitative value for ecological risk assessment [27], and the method has been widely applied by taking into account the heavy metal content, inter-element synergy, toxicity level, pollution concentration and pollution sensitivity [28]. Considering the above, this study selected the Nemero index method to determine the degree of sludge contamination and RI to determine the ecological risk of sludge.
In summary, this study takes 20 wastewater plants in Jiangsu Province, with dewatered sludge as the research object, and focuses on pollutant concentration load, distribution characteristics, effects of different industrial wastewater sources and treatment process on the composition of typical heavy metals (Zn, Cu, Cr, Ni, Pb, As, Hg, Cd) in sludge, and potential risk evaluation to provide data for different regions and categories of sewage plant sludge pollution assessment and treatment.

2. Materials and Methods

2.1. Study Area

Sludge samples were collected from 20 typical wastewater treatment plants in Jiangsu Province, covering 13 prefecture-level cities in the province. In consideration of the geographical factors and the economic development factors of each region, the classification was made according to northern Jiangsu, central Jiangsu and southern Jiangsu. The sludge samples were selected using the principle of stratified sampling. The regions were divided into northern Jiangsu: Xuzhou, Yancheng, Huai’an, Suqian and Lianyungang; central Jiangsu: Zhenjiang, Yangzhou, Taizhou and Nantong; and southern Jiangsu: Nanjing, Suzhou, Wuxi and Changzhou. The 20 wastewater plants in the Jiangsu Province location distribution map are shown in Figure 1.

2.2. Sample Collection

The sludge collected was dewatered sludge from the wastewater plant before it was shipped out and was mixed using multi-point sampling, or in the case of multiple filter presses working at the same time, it is sampled and mixed at each filter press. Sampling on each filter press was carried out by taking approximately 200 g of sludge every 1 min on the conveyor belt and mixing it 10 times in a row. Samples were repeated six times at each treatment plant, with a sample mass greater than 1 kg to ensure that the total amount of samples was sufficient to support the subsequent experimental unfolding. The collected sludge was packed in clean and dense bags or polyethylene bottles and stored temporarily in an insulated box, and subsequently stored under refrigeration and sealed at 4 °C. Samples were collected in December 2021.

2.3. Analysis

The content of heavy metals Cr, Ni, Cu, Zn, Pb and Cd in sludge was determined by AA-6880F atomic absorption spectrophotometer, the content of heavy metals As and Hg in sludge was determined by AFS-8220 atomic fluorescence photometer, the content of organic matter in sludge was determined by potassium dichromate volumetric method, the pH value of sludge was determined by PHS-3E benchtop pH meter, the content of total nitrogen in sludge was determined by Kjeldahl method and the content of total phosphorus in sludge was determined by D-8 UV-visible spectrophotometer. The content of total nitrogen in the sludge was determined by the Kjeldahl method, and the content of total phosphorus in the sludge was determined using a D-8 UV-Vis spectrophotometer. Pearson correlation test was used to analyze the possible relationship between different heavy metals, and principal component analysis (PCA) was used to analyze the percentage of heavy metal contents and heavy metal forms in sewage sludge, revealing the changing patterns of heavy metal contents in sludge from different industries. Considering the comprehensive reflection of the impact of various heavy metals on sludge, highlighting the impact of high-concentration pollutants on the environment, and avoiding the phenomenon that the average value weakens the weight of heavy metals, the Nemero index method was chosen to analyze the degree of sludge pollution. By calculating the single-factor pollutant ecological risk index Eri and the total potential ecological risk RI index for risk classification, the ecological hazards of heavy metals were reflected comprehensively.
IBM SPSS Statistics 26 software was used for the above analysis, ArcGIS 10.6 software was used to draw the point distribution map of the sewage plant and Origin 2022 was used for other related graphical drawings.

2.3.1. Nemero Index Method

The Nemero individual pollution index P was the ratio of the actual measured value of heavy metals to the standard limit of that heavy metal content, Pi was the pollution index of heavy metal i, and the integrated pollution index PI was the arithmetic squared value of the maximum value of the Nemero individual index Pi-max and the average value Pi-ave, calculated as follows.
P i = C i C 0
P I = P i max 2 + P i ave 2 2
The evaluation results are PI ≤ 0.7 as “clean”, 0.7 < PI ≤ 1.0 as “still clean”, 1.0 < PI ≤ 2.0 as “light pollution”, 2.0 < PI ≤ 3.0 is “obvious pollution” and PI > 3.0 is “serious pollution”.

2.3.2. Potential Ecological Risk

The potential ecological hazard index was introduced to assess the graded degree of ecological hazard of different heavy metals. The specific formula is as follows.
E r i = C i C r i × T r i
R I = i = 1 n E r i
The reference values of Cu, Ni, Cd, Zn, Pb, As, Cr and Hg were 100, 100, 0.3, 250, 120, 30, 200 and 2.4, and the toxicity response coefficients were 5, 5, 30, 1, 5, 10, 2 and 40, respectively [27]. The results of the potential ecological risk evaluation are: the single-factor potential ecological risk index E r i < 40 is low ecological risk, 40 ≤ E r i < 80 is medium ecological risk, the comprehensive potential ecological risk index RI < 150 is low ecological risk, 150 ≤ RI < 300 is medium ecological risk, 300 ≤ RI < 600 is heavy ecological risk and RI ≥ 600 means the ecological risk is serious.

3. Results and Discussion

3.1. Typical Heavy Metal Loadings in Sludge

Table 1 shows basic information of the measured sample sludge. The mean values of heavy metal contents in order of magnitude were Zn > Cu > Cr > Ni > Pb > As > Hg > Cd, which was approximately the same order of heavy metal contents in Chinese sludge, as described by Geng et al. [29] Among them, Zn, Cu, Cr, Ni, Pb, As, Hg and Cd varied from 154 to 2970 mg/kg, 28 to 1150 mg/kg, 10 to 136 mg/kg, 9 to 262 mg/kg, 0 to 79 mg/kg, 12.1 to 41.6 mg/kg, 0.67 to 19.50 mg/kg and 0.21 to 2.77 mg/kg. The heavy metal content was highly variable and related to the different sludge sources, sludge content and sludge processes in different regions.
It is generally believed that pH is negatively correlated with sludge heavy metal activity. Low pH is favorable for the migration and release of heavy metals [30], while high pH is favorable for the stability of heavy metals [31]. The water content and temperature of sludge also affect heavy metal levels, due to the water solubility. The heavy metal level in sludge decreased during composting [32], but the leach decreased with an increase in temperature and further accumulates substantially in the solid phase [33]. In this sample, it can be found that Cu and Cd contents were higher at the lowest pH, and the other metal elements were the lowest, meaning that this was related to the morphology of Cu and Cd in the sludge. Heavy metals, such as F, E and B, are in the middle position, while the water content was higher, suggesting that some heavy metals were dissolved in water and also their interference caused by external factors accounts for a larger proportion.
Table 2 shows the heavy metal content in Jiangsu Province, as well as other regions from a previous report. According to Table 2, it could be seen that there is a wide range of variation in the content of different heavy metals in different regions, which was presumed to be related to the geographical environment and industries in different regions, and the comparison with other regions in the chart showed that all heavy metal indicators except Cd in Jiangsu Province (China) are within the range of the previous study. Cd levels fluctuate widely throughout China [13], and the lower Cd levels in Jiangsu Province (China) may be attributed to seasonal factors affecting the physical and chemical conditions of the water column, such as pH, temperature, redox potential and organic matter content [34].

3.2. Distribution Characteristics of Heavy Metals in Sludge

The geographical distribution of the exceedances of total nitrogen, total phosphorus, organic matter and heavy metals in sewage plant sludge samples is shown in Figure 2. It can be seen that the indicators are more dispersed geographically, and the exceedances of total nitrogen, total phosphorus, organic matter content and heavy metals are high in Yangzhou, Changzhou and Nanjing, while with the previous paper, it could be noted that the sludge pH of the three cities with exceedances are all located below 7, which was consistent with the conclusions obtained by the previous authors. The highest value of total phosphorus content was reached in Lianyungang, and the highest value of organic matter content was reached in Suzhou.
According to the overall economic and geographical distribution, Jiangsu was divided into northern, central and southern Jiangsu regions, and the pollutant contents in different regions are listed in Table 3. It can be seen that there were large differences in the distribution of the north and south of the Yangtze River, Ni, Cd, Pb, As and Cr were more evenly distributed in northern, central and southern Jiangsu; however, the Hg content was also the highest in northern Jiangsu, more than twice the content in central and southern Jiangsu, which may be due to more coal used for heating in northern areas during the cold season [46]. Further, Zn content in northern Jiangsu was the highest, more than the central and southern, nearly double the content, Cu content in southern Jiangsu was the highest, more than double the content of the central, more than three times the content of the north, the north and south of the heavy metal differences, which may be related to the degree of industrial density and human activity development in these areas [47], In addition, the north–south domain of Jiangsu province spanned a large area, and there were certain climatic rainfall differences in the north and south of the Huaihe River. The regional differences may also be attributed to seasonal factors in the physical and chemical conditions of the water bodies, with the rainy season increasing surface runoff and, thus, bringing in more heavy metals [34].

3.3. Effects of Sewage Sources and Wastewater Treatment Processes on Heavy Metal Levels

Table 4 shows the variation in sludge composition containing industrial wastewater from different industries. Different industries had different heavy metal content in industrial sludge [47]; the sludge samples containing industrial wastewater were divided into metallurgical, chemical, food and metal industry wastewater according to different influent industries. There was little difference in the organic matter and total nitrogen content (Table 4) and large variability in total phosphorus, which was mainly derived from domestic wastewater and phosphorus-containing substances carried by different industries, thus, causing corresponding differences. Cu and Zn content in sludge with wastewater from the metallurgical industry increased significantly, Zn and Cr content in sludge with wastewater from the chemical industry increased significantly, Cu, Ni, Zn and Cr content in sludge with wastewater from the food industry increased significantly, Ni and Zn content in sludge with wastewater from the printing and dyeing industry increased significantly, which was similar to the changes in heavy metals brought by different industries, as mentioned before [17,18]. This was roughly similar to the changes in heavy metals brought about by different industries, as described before, and there would be some deviations due to the fact that the measured sludge was affected by multiple factors, such as different levels of inflowing industrial wastewater and different processes in water plants. Zhang et al. [48] showed that Zn can be used as an indicator for printing and dyeing sludge to significantly distinguish it from sludge from other industries. The Zn content in the measured sludge containing printing and dyeing sludge was significantly higher than that of sludge without industrial wastewater and lower than that of wastewater containing metallurgical and chemical industries, meaning that this may be related to the content of industrial wastewater discharged into it.
Table 5 shows the sludge quality variation in different water treatment processes (AAO and its deformation process, SBR and its deformation process, OD and its deformation process and biofilter). AAO and its deformation process include UCT, inverted A2O, Paracord, multi-modal AAO, etc.; OD deformation process includes Aubert OD, five-trench OD, three-tank OD, etc.; SBR and its improvement processes include CAST, CASS, UNITANK, etc. A comparative analysis of the sludge composition of different categories showed that Ni, Cd, Pb, As and Cr had similar distributions in the four different processes, while Cu was highest in the OD and its deformation process, exceeding its content in the biofilter by a factor of 5; Zn was highest in the biofilter process, exceeding its content in the SBR and its deformation process by a factor of 5; Hg was highest in the AAO process, exceeding its content in the biofilter process by a factor of 3. The heavy metal content in the sludge of the OD process was higher than that in the sludge of the AAO process, which is consistent with a study by Feng et al. [15] It was observed that the As content in the sludge was OD < SBR < AAO < biofilter, which has some differences with the best treatment effect of OD process for As in wastewater treatment, as described by Yang et al. [19]. The reason for this was analyzed as the different sources of heavy metals and the percentage of industrial wastewater in the sludge of different processes, so the effect was different.

3.4. Potential Risks of Heavy Metals in Sludge

3.4.1. Analysis of the Sources of Heavy Metal Contamination in Sludge

Table 6 shows analysis of the sludge sources using principal component analysis. The Kaiser-Meyer-Olkin (KMO) was 0.689, and the Bartlett’s Test of Sphericity was significant. This indicates that the data set can be used for principal component analysis. The variance in the extracted common factors was all greater than 0.6, so the extracted principal components had a high degree of explanation. The results of the principal component analysis are shown in Table 7. The results showed that the contribution of the three principal components is 77.898% in total, which could reflect the majority of information. The contribution of the first principal component was 45.326%, which showed that Ni, Cd, Zn, Cr and Hg contents had high positive loadings, the contribution of the second principal component was 19.263%, which showed that Cu and Pb had high positive loadings, and the contribution of the third principal component was 13.309%, which showed that As had high positive loadings. It indicated that several metals may be homologous pollutants, and the first principal component homology relationship was similar to the results of Zhang et al. [49] for arable soils in Chongqing, further indicating the correlation of several heavy metals and also indicating that the source of heavy metal accumulation is similar to the natural source soil parent material. In addition, Cd and its compounds generally originate from paints, batteries and electrical appliances [50]. Ni and Zn also originate from the use of fertilizers and pesticides [51]. Therefore, principal component 1 can be retrospectively interpreted as pollution from electroplating, metallurgy, paints, electrical appliances, chemical fertilizer and pesticide industries. The second principal components Cu and Pb were often found together in alloy products, which can be interpreted as pollution from mining, printing and dyeing, and chemical industries. The third principal component As was analyzed separately, and As, as an active ingredient in pesticides and herbicides [52], flows into the pipeline as surface-source pollution, increasing the overall sludge As content, and also feeds would contain elemental As, so its source may also be related to human activities such as agriculture. Therefore, principal component 3 can be explained as pollution from special industries, such as wood preservatives, pigment preservatives, detergents, thermal power generation, pesticides and pharmaceuticals.
Figure 3 shows the Pearson correlation analysis of each heavy metal. It can be seen from the figure that there is a significant positive correlation between Ni, Cd, Cr, Zn and Hg, which corresponds to the results of principal component analysis. In summary, the five metals Ni, Cd, Cr, Zn and Hg had strong correlations. These metals and the electroplating and smelting industries should be focused on in sludge management.

3.4.2. Evaluation of the Degree of Heavy Metal Contamination and Ecological Risk of Sludge

Table 7 shows the results of the contamination level and potential ecological risk analysis of the sludge heavy metals assessed by the Nemero index method and the potential ecological hazard index method. As can be seen from the table, the contamination of heavy metals in the sludge samples was: Hg > As > Zn > Ni > Cu > Cd > Cr > Pb, the contamination level of As and Hg was “still clean” and the contamination level of other heavy metals was “clean”. This indicated that the sludge was not polluted by heavy metals and had some space for dispatching, while As and Hg tended to be close to the initial pollution value and needed to be warned. The integrated index of Nemero was 1.04, which was slightly greater than the critical value of 1, showing that the maximum value of Hg (0.97) contributes more, and the overall sludge in Jiangsu Province showed a light-pollution state. The RI values of ecological risk ranged from 12.95 to 1388, with mild to very strong ecological risk, and Hg and Cd showed very strong ecological risk, and the ranking of each potential ecological hazard index was Cd > Hg > As > Cu > Zn > Ni > Pb > Cr. The combination of the two showed that the As content caused environmental pollution but not ecological risk, while the lower content of Cd caused greater ecological risk. Cd content was a major ecological risk.
To conclude, the ecological risk of Hg and Cd in sludge in Jiangsu Province was high, and it was not recommended to use for agricultural reuse but could be adopted to adjust the industrial structure to utilize sludge, such as for planting mulberry trees to develop farming; [46] focus on the discharge of wastewater next to the sewage plant and access to the coal mining industry, etc.; and strengthen the monitoring of Hg and Cd emissions.

4. Conclusions

Typical heavy metal levels were in the order of Zn > Cu > Cr > Ni > Pb > As > Hg > Cd, ranging from 154 to 2970 mg/kg, 28 to 1150 mg/kg, 10 to 136 mg/kg, 9 to 262 mg/kg, 0 to 79 mg/kg, 12.1 to 41.6 mg/kg, 0.67 to 19.50 mg/kg and 0.21 to 2.77 mg/kg, respectively. Hg, Zn and Cu were obviously influenced by the degree of industrial intensity and exploitation of human activities, while Ni, Cd, Pb, As and Cr were more evenly distributed. Different industrial wastewaters resulted in different metal contents, but the distribution of Ni, Cd, Pb, As and Cr in different treatment processes was similar. Furthermore, Hg and Cd had the strongest ecological risk, with their levels reaching severe, suggesting that sludge was not recommended for agricultural reuse in this study.
Further studies are suggested to investigate inorganic reuse of sludge and reuse as building materials. In addition, heavy metal toxicity is related to morphology, and the effects of different geographical and process sources on chemical forms as well as toxicity can be further analyzed.

Author Contributions

Conceptualization, H.L.; Software, G.W.; Validation, C.W.; Formal analysis, H.L. and G.W.; Investigation, D.X., Y.W. and G.W.; Resources, H.L.; Data curation, D.X. and G.W.; Writing—original draft, D.X. and Y.W.; Writing—review & editing, D.X.; Supervision, C.W. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Kacprzak, M.; Neczaj, E.; Fijalkowski, K.; Grobelak, A.; Grosser, A.; Worwag, M.; Rorat, A.; Brattebo, H.; Almas, A.; Singh, B.R. Sewage sludge disposal strategies for sustainable development. Environ. Res. 2017, 156, 39–46. [Google Scholar] [CrossRef] [PubMed]
  2. Tyagi, V.K.; Lo, S.L. Sludge: A waste or renewable source for energy and resources recovery? Renew. Sustain. Energy Rev. 2013, 25, 708–728. [Google Scholar] [CrossRef]
  3. Zeng, Q.; Huang, H.; Tan, Y.K.; Chen, G.H.; Hao, T.W. Emerging electrochemistry-based process for sludge treatment and resources recovery: A review. Water Res. 2022, 209, 117939. [Google Scholar] [CrossRef] [PubMed]
  4. Jain, S.; Jain, S.; Wolf, I.T.; Lee, J.; Tong, Y.W. A comprehensive review on operating parameters and different pretreatment methodologies for anaerobic digestion of municipal solid waste. Renew. Sustain. Energy Rev. 2015, 52, 142–154. [Google Scholar] [CrossRef]
  5. Zhang, Q.H.; Yang, W.N.; Ngo, H.H.; Guo, W.S.; Jin, P.K.; Dzakpasu, M.; Yang, S.J.; Wang, Q.; Wang, X.C.; Ao, D. Current status of urban wastewater treatment plants in China. Environ. Int. 2016, 92–93, 11–22. [Google Scholar] [CrossRef]
  6. Romero-Guiza, M.S.; Vila, J.J.; Mata-Alvarez, J.; Chimenos, J.M.; Astals, S. The role of additives on anaerobic digestion: A review. Renew. Sustain. Energy Rev. 2016, 58, 1486–1499. [Google Scholar] [CrossRef]
  7. Arvaniti, O.S.; Stasinakis, A.S. Review on the occurrence, fate and removal of perfluorinated compounds during wastewater treatment. Sci. Total Environ. 2015, 524, 81–92. [Google Scholar] [CrossRef]
  8. Zhou, G.J.; Lin, L.; Li, X.Y.; Leung, K.M.Y. Removal of emerging contaminants from wastewater during chemically enhanced primary sedimentation and acidogenic sludge fermentation. Water Res. 2020, 175, 115646. [Google Scholar] [CrossRef]
  9. Mehta, C.M.; Khunjar, W.O.; Nguyen, V.; Tait, S.; Batstone, D.J. Technologies to Recover Nutrients from Waste Streams: A Critical Review. Crit. Rev. Environ. Sci. Technol. 2015, 45, 385–427. [Google Scholar] [CrossRef] [Green Version]
  10. Zhang, Q.G.; Hu, J.J.; Lee, D.J.; Chang, Y.J.; Lee, Y.J. Sludge treatment: Current research trends. Bioresour. Technol. 2017, 243, 1159–1172. [Google Scholar] [CrossRef]
  11. Siebielska, I. Comparison of changes in selected polycyclic aromatic hydrocarbons concentrations during the composting and anaerobic digestion processes of municipal waste and sewage sludge mixtures. Water Sci. Technol. 2014, 70, 1617–1624. [Google Scholar] [CrossRef] [PubMed]
  12. Tytla, M.; Widziewicz, K.; Zielewicz, E. Heavy metals and its chemical speciation in sewage sludge at different stages of processing. Environ. Technol. 2016, 37, 899–908. [Google Scholar] [CrossRef] [PubMed]
  13. Cheng, X.Q.; Wei, C.; Ke, X.; Pan, J.M.; Wei, G.R.; Chen, Y.; Wei, C.H.; Li, F.S.; Preis, S. Nationwide review of heavy metals in municipal sludge wastewater treatment plants in China: Sources, composition, accumulation and risk assessment. J. Hazard. Mater. 2022, 437, 129267. [Google Scholar] [CrossRef] [PubMed]
  14. Praspaliauskas, M.; Pedisius, N. A review of sludge characteristics in Lithuania’s wastewater treatment plants and perspectives of its usage in thermal processes. Renew. Sustain. Energy Rev. 2017, 67, 899–907. [Google Scholar] [CrossRef]
  15. Feng, J.J.; Jia, L.; Liu, Q.Z.; Chen, X.L.; Cheng, J.P. Source identification of heavy metals in sewage sludge and the effect of influent characteristics: A case study from China. Urban Water J. 2018, 15, 381–387. [Google Scholar] [CrossRef]
  16. Guo, G.; Chen, T.; Yang, J.; Zheng, G.; Gao, D. Regional distribution characteristics and variation of heavy metals in sewage sludge of China. Acta Sci. Circumstantiae 2014, 34, 2455–2461. [Google Scholar]
  17. Chen, S.Y.; IOP. Occurrence Characteristics and Ecological Risk Assessment of Heavy Metals in Sewage Sludge. In Proceedings of the 5th International Conference on Energy Materials and Environment Engineering (ICEMEE), Kuala Lumpur, Malaysia, 12–14 April 2019. [Google Scholar]
  18. Hou, H.; Zhao, L.; Zhang, J.; Xu, Y.F.; Yan, Z.G.; Bai, L.P.; Li, F.S. Organochlorine pesticides and polychlorinated biphenyls in soils surrounding the Tanggu Chemical Industrial District of Tianjin, China. Environ. Sci. Pollut. Res. 2013, 20, 3366–3380. [Google Scholar] [CrossRef]
  19. Yang, J.; Gao, D.; Chen, T.B.; Lei, M.; Zheng, G.D.; Zhou, X.Y. Comparison of heavy metal removal efficiencies in four activated sludge processes. J. Cent. South Univ. 2015, 22, 3788–3794. [Google Scholar] [CrossRef]
  20. Kowalik, R.; Latosinska, J.; Metryka-Telka, M.; Porowski, R.; Gawdzik, J. Comparison of the Possibilities of Environmental Usage of Sewage Sludge from Treatment Plants Operating with MBR and SBR Technology. Membranes 2021, 11, 722. [Google Scholar] [CrossRef]
  21. Riaz, U.; Murtaza, G.; Saifullah; Farooq, M. Influence of different sewage sludges and composts on growth, yield, and trace elements accumulation in rice and wheat. Land Degrad. Dev. 2018, 29, 1343–1352. [Google Scholar] [CrossRef]
  22. Panseriya, H.Z.; Gosai, H.B.; Sankhwal, A.O.; Sachaniya, B.K.; Gavali, D.J.; Dave, B.P. Distribution, speciation and risk assessment of heavy metals: Geochemical exploration of Gulf of Kachchh, Gujarat, India. Environ. Earth Sci. 2020, 79, 1–10. [Google Scholar] [CrossRef]
  23. Men, C.; Liu, R.M.; Xu, L.B.; Wang, Q.R.; Guo, L.J.; Miao, Y.X.; Shen, Z.Y. Source-specific ecological risk analysis and critical source identification of heavy metals in road dust in Beijing, China. J. Hazard. Mater. 2020, 388, 121763. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, Y.F.; Sun, J.X.; Zhao, M.L.; Ni, Y.Y.; Wang, X.R.; Fan, Z.Q. Seasonal Variation and Contamination Risk Assessment of Heavy Metals in Surface Sediment of an Estuary Alluvial Island in Eastern China. Bull. Environ. Contam. Toxicol. 2022, 108, 337–343. [Google Scholar] [CrossRef] [PubMed]
  25. Ntzala, G.; Kalavrouziotis, I.K.; Koukoulakis, P.H.; Papadopoulos, A.H. Impact of sludge and wastewater on lactuca sativa l growth and on soil pollution. Glob. Nest J. 2015, 17, 148–161. [Google Scholar]
  26. Islam, M.S.; Ahmed, M.K.; Raknuzzaman, M.; Habibullah-Al-Mamun, M.; Kundu, G.K. Heavy metals in the industrial sludge and their ecological risk: A case study for a developing country. J. Geochem. Explor. 2017, 172, 41–49. [Google Scholar] [CrossRef]
  27. Hakanson, L. An ecological risk index for aquatic pollution-control—A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  28. Islam, M.S.; Ahmed, M.K.; Habibullah-Al-Mamun, M.; Masunaga, S. Potential ecological risk of hazardous elements in different land-use urban soils of Bangladesh. Sci. Total Environ. 2015, 512, 94–102. [Google Scholar] [CrossRef]
  29. Geng, Y.M.; Zhang, C.B.; Zhang, Y.; Huang, D.D.; Yan, S.X.; Sun, T.F.; Wang, J.; Mao, Y.X. Heavy metal(loid)s in sewage sludge in China: Concentrations and spatial-temporal variations. Environ. Sci. Pollut. Res. 2021, 28, 29146–29156. [Google Scholar] [CrossRef]
  30. Ito, A.; Kusanagi, J.; Matsukura, T.; Aizawa, J.; Umita, T. Relationship between partition of heavy metals in sewage sludge and elution of heavy metals. Water Sci. Technol. 2002, 46, 25–32. [Google Scholar] [CrossRef]
  31. Merrigton, G.; Oliver, I.; Smernik, R.J.; McLaughlin, M.J. The influence of sewage sludge properties on sludge-borne metal availability. Adv. Environ. Res. 2003, 8, 21–36. [Google Scholar] [CrossRef]
  32. Kouki, S.; Saidi, N.; M’Hiri, F.; Hafiane, A.; Hassen, A. Co-Composting of Macrophyte Biomass and Sludge as an Alternative for Sustainable Management of Constructed Wetland By-Products. Clean-Soil Air Water 2016, 44, 694–702. [Google Scholar] [CrossRef]
  33. Zhang, Z.P.; Baroutian, S.; Munir, M.T.; Young, B.R. Variation in metals during wet oxidation of sewage sludge. Bioresour. Technol. 2017, 245, 234–241. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, Z.; Wang, Y.; Chen, L.; Yan, C.; Yan, Y.; Chi, Q. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. 2015, 99, 43–53. [Google Scholar] [CrossRef] [PubMed]
  35. El Hammoudani, Y.; Dimane, F. Assessing behavior and fate of micropollutants during wastewater treatment: Statistical analysis. Environ. Eng. Res. 2021, 26, 200359. [Google Scholar] [CrossRef]
  36. Busetti, F.; Badoer, S.; Cuomo, M.; Rubino, B.; Traverso, P. Occurrence and removal of potentially toxic metals and heavy metals in the wastewater treatment plant of fusina (Venice, Italy). Ind. Eng. Chem. Res. 2005, 44, 9264–9272. [Google Scholar] [CrossRef]
  37. Filipovic, J.; Grcic, I.; Bermanec, V.; Kniewald, G. Monitoring of total metal concentration in sludge samples: Case study for the mechanical-biological wastewater treatment plant in Velika Gorica, Croatia. Sci. Total Environ. 2013, 447, 17–24. [Google Scholar] [CrossRef] [PubMed]
  38. Olofsson, U.; Brorstrom-Lunden, E.; Kylin, H.; Haglund, P. Comprehensive mass flow analysis of Swedish sludge contaminants. Chemosphere 2013, 90, 28–35. [Google Scholar] [CrossRef] [Green Version]
  39. Yakamercan, E.; Ari, A.; Aygun, A. Land application of municipal sewage sludge: Human health risk assessment of heavy metals. J. Clean. Prod. 2021, 319, 128568. [Google Scholar] [CrossRef]
  40. Chen, M.H.; Oshita, K.; Mahzoun, Y.; Takaoka, M.; Fukutani, S.; Shiota, K. Survey of elemental composition in dewatered sludge in Japan. Sci. Total Environ. 2021, 752, 141857. [Google Scholar] [CrossRef]
  41. Westerhoff, P.; Lee, S.; Yang, Y.; Gordon, G.W.; Hristovski, K.; Halden, R.U.; Herckes, P. Characterization, Recovery Opportunities, and Valuation of Metals in Municipal Sludges from US Wastewater Treatment Plants Nationwide. Environ. Sci. Technol. 2015, 49, 9479–9488. [Google Scholar] [CrossRef]
  42. Tang, J.; Tang, H.J.; Sima, W.P.; Wang, H.Y.; Zou, D.B.; Qiu, B.A.; Qu, J.; Liang, R.; Dong, J.W.; Liao, Y.; et al. Heavy metal pollution level and potential ecological risk assessment of sludge landfill. Environ. Prog. Sustain. Energy 2022, 41, e13795. [Google Scholar] [CrossRef]
  43. Yang, T.; Huang, H.-j.; Lai, F.-y. Pollution hazards of heavy metals in sewage sludge from four wastewater treatment plants in Nanchang, China. Trans. Nonferrous Met. Soc. China 2017, 27, 2249–2259. [Google Scholar] [CrossRef]
  44. Lin, X.; Li, S.; Wei, Z.; Chen, Y.; Hei, L.; Wu, Q.-T. Indirect application of sludge for recycling in agriculture to minimize heavy metal contamination of soil. Resour. Conserv. Recycl. 2021, 166, 105358. [Google Scholar] [CrossRef]
  45. Duan, B.; Feng, Q. Risk Assessment and Potential Analysis of the Agricultural Use of Sewage Sludge in Central Shanxi Province. Int. J. Environ. Res. Public Health 2022, 19, 4236. [Google Scholar] [CrossRef] [PubMed]
  46. Wang, R.; Yu, J.; Li, Y.; Zhou, J.; Jia, Z.-M.; Yu, F.; Zhang, Y.-Y.; Jiang, Y.-L. Zoning and Safe Utilization Method of Heavy Metal Contaminated Cultivated Land at Block Scale. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 4190–4198. [Google Scholar] [CrossRef] [PubMed]
  47. Zhang, X.F.; Chen, S.Y.; Ai, F.T.; Jin, L.M.; Zhu, N.Z.; Meng, X.Z. Identification of industrial sewage sludge based on heavy metal profiles: A case study of printing and dyeing industry. Environ. Sci. Pollut. Res. 2022, 29, 12377–12386. [Google Scholar] [CrossRef]
  48. Beckers, F.; Rinklebe, J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit. Rev. Environ. Sci. Technol. 2017, 47, 693–794. [Google Scholar] [CrossRef]
  49. Fei, Y.; Jiabin, W.; Rui, W.; Yu, W.; Huan, N.M.; Yunyi, Z.; Liming, S.; Jinxiu, D. Analysis and risk evaluation of heavy metal sources in arable soils in a high geological background area based on township scale. Environ. Sci. 2022, 1–17. [Google Scholar] [CrossRef]
  50. Zhang, L.J.; Xu, H.; Yang, H.; Du, S.H. Source Identification and Health Risk Assessment of Heavy Metals in the Soil from a Typical Agricultural Area in the Sanjiang Plain, China. Chiang Mai J. Sci. 2020, 47, 973–983. [Google Scholar]
  51. Espinoza-Guillen, J.A.; Alderete-Malpartida, M.B.; Gallegos-Huaman, R.L.; Paz-Rosales, Y.M.; Dominguez-Vivar, R.M.; Bujaico-Leon, C. Ecological risk assessment and identification of sources of heavy metals contamination in sewage sludge from municipal wastewater treatment plants in the Metropolitan Area of Lima-Callao, Peru. Environ. Dev. Sustain. 2022, 1–32. [Google Scholar] [CrossRef]
  52. Han, L.; Xu, X.-B. Quantitative Evaluation of Human Health Risk of Heavy Metals in Soils Based on Positive Matrix Factorization Model and Geo-statistics. Environ. Sci. 2020, 41, 5114–5124. [Google Scholar] [CrossRef]
Figure 1. The location distribution of 20 sewage plants in Jiangsu Province.
Figure 1. The location distribution of 20 sewage plants in Jiangsu Province.
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Figure 2. Geographical distribution of total nitrogen, total phosphorus, organic matter and heavy metal exceedances in sewage sludge in Jiangsu Province.
Figure 2. Geographical distribution of total nitrogen, total phosphorus, organic matter and heavy metal exceedances in sewage sludge in Jiangsu Province.
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Figure 3. Sludge heavy metal Pearson correlation analysis.
Figure 3. Sludge heavy metal Pearson correlation analysis.
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Table 1. Typical sludge concentration index of wastewater plants.
Table 1. Typical sludge concentration index of wastewater plants.
/(Million m3·d−1)
Proportion of Industrial Wastewater
pHHeavy Metals/(mg/kg)
Average 7.4161.6558.530.71011.836.2725.4663.422.26
Table 2. Table heavy metal content in different regions.
Table 2. Table heavy metal content in different regions.
RegionHeavy Metals/(mg/kg)
Thessaloniki, Greece [35] 797703.347039 40
Venice, Italy [36]10846135631.18.417.8
Velika Gorica, Croatia [37]6301093.06 137 74.82.54
Sweden [38]280120.78560213.4210.57
United Kingdom [39]562593.5778221.5 160
Japan [40]255402.397953 69
Turkey summer [39]147751.7113364.29.41110.7
Turkey winter [39]161722.0129992.86.51450.9
USA [41]436283.662024.010.036
China [13]25956.2210.78906.7381.7428.01188.374.41
Shenyang, China [42]82154.73.5466650.911.968.44.32
Nanchang, China [43]38369311.7609113 113
Guangzhou, China [44]112.7 0.841389.944.72
Shanxi, China [45]175.99 2.71146.0345.0915.1126.682.85
Jiangsu, China111.6158.530.7427.1736.2725.4663.422.26
Table 3. Pollutant content in different regions in Jiangsu Province.
Table 3. Pollutant content in different regions in Jiangsu Province.
RegionHeavy Metals/(mg/kg)
Northern Jiangsu63.8866.380.811183.0028.4328.7962.753.64
Central Jiangsu117.5069.500.62654.0027.3326.3767.331.11
Southern Jiangsu230.4654.210.63600.6031.0323.3164.191.24
Table 4. Variation in sludge composition containing industrial wastewater from different industries.
Table 4. Variation in sludge composition containing industrial wastewater from different industries.
IndustriesHeavy Metals/(mg/kg)
Printing and dyeing941210.331380031.1430.72
No industrial wastewater98.3351.50.5735.1727.8326.4755.833.67
Table 5. Variation in sludge composition for different treatment processes.
Table 5. Variation in sludge composition for different treatment processes.
ProcessesHeavy Metals/(mg/kg)
AAO and deformation process85.565.910.8846841.7826.4473.553.05
SBR and deformation process137.6751.170.4228225.2522.4750.671.16
OD and its deformation process282600.837223216.5681.68
Table 6. Principal component analysis of heavy metals in sludge.
Table 6. Principal component analysis of heavy metals in sludge.
First principal component0.0830.9060.8010.7800.1880.4490.8190.8013.62645.32645.326
Second principal component0.835−0.1560.0780.0450.824−0.0660.141−0.3311.54119.26364.589
Third principal component−0.329−0.108−0.3750.2110.3360.785−0.022−0.1711.06513.30977.898
Table 7. Average pollution level and ecological risk grade of each heavy metal in sludge.
Table 7. Average pollution level and ecological risk grade of each heavy metal in sludge.
ElementNemerow Pollution Coefficient P Pollution LevelPotential Ecological Risk Coefficient ErPotential Ecological RiskComprehensive Potential Ecological Risk Index RIDegree of Comprehensive Potential Ecological Risk
As0.87still clean8.74slight174.85moderate
Hg0.97still clean48.45medium968.92severe
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Xiao, D.; Li, H.; Wang, Y.; Wen, G.; Wang, C. Distribution Characteristics of Typical Heavy Metals in Sludge from Wastewater Plants in Jiangsu Province (China) and Their Potential Risks. Water 2023, 15, 313.

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Xiao D, Li H, Wang Y, Wen G, Wang C. Distribution Characteristics of Typical Heavy Metals in Sludge from Wastewater Plants in Jiangsu Province (China) and Their Potential Risks. Water. 2023; 15(2):313.

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Xiao, Dandan, He Li, Yizhuo Wang, Guixin Wen, and Chencheng Wang. 2023. "Distribution Characteristics of Typical Heavy Metals in Sludge from Wastewater Plants in Jiangsu Province (China) and Their Potential Risks" Water 15, no. 2: 313.

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