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

Abstract

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, P_i 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 P_i-max and the average value P_i-ave, calculated as follows.

$$P_i=\frac{C_i}{C_0}$$

(1)

$$PI=\sqrt{\frac{P_{i-\max }{}^2+P_{i-\mathrm{ave}}{}^2}{2}}$$

(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.

$$E_r^i=\frac{C_i}{C_{ri}}\times T_{ri}$$

(3)

$$RI=\sum _{i=1}^n{E}_r^i$$

(4)

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. Typical sludge concentration index of wastewater plants.

No.	City	Region	Size /(Million m3·d−1)	Proportion of Industrial Wastewater /(%)	pH	Heavy Metals/(mg/kg)
						Cu	Ni	Cd	Zn	Pb	As	Cr	Hg
A	Nanjing	South	67	0	7	74	115	0.5	1450	16	40.8	90	15.2
B	Zhenjiang	Central	2	8	6.69	196	50	0.66	2760	79	33.4	113	1.13
C	Zhenjiang	Central	4	2	7.51	94	15	0.65	326	10	12.2	21	1.98
D	Yangzhou	Central	26	10	6.91	193	129	0.69	1210	26	34.1	99	1.18
E	Taizhou	Central	8	7	6.29	59	82	0.83	387	24	20.3	80	0.67
F	Yancheng	North	10	0	6.06	28	9	0.26	154	\	13.7	10	0.9
G	Huai’an	North	10.5	20	7.01	64	32	0.5	477	60	27.9	31	1.23
H	Huai’an	North	10	0	9.99	46	20	0.35	1380	35	41.6	24	0.76
I	Suqian	North	1.5	33	7.27	91	262	2.77	2920	22	30.6	136	19.5
J	Suqian	North	6	20	6.84	69	58	0.33	2970	12	19.6	126	1.25
K	Xuzhou	North	20	5	7.36	61	34	0.58	668	\	28.4	60	1.96
L	Changzhou	South	20	5	5.94	1150	56	1.16	1040	74	22.8	79	1.31
M	Wuxi	South	6	56	9.16	368	19	0.49	435	14	17.5	36	1.09
N	Wuxi	South	3	30	7.9	75	20	0.54	346	47	25.7	43	1.41
O	Nantong	Middle	15	15-20	9.62	69	20	0.55	523	25	27.1	48	0.99
P	Nantong	Middle	12.8	60	8.31	94	121	0.33	1380	\	31.1	43	0.72
Q	Lianyungang	North	12	0	6.77	92	96	0.84	512	70	33.5	70	1.84
R	Lianyungang	North	4	20	7.16	60	20	0.81	383	\	35	45	1.69
S	Suzhou	South	3	2	\	68	9	0.21	193	14	12.7	73	1.65
T	Suzhou	South	6	0	6.74	282	60	0.83	722	32	16.5	68	1.68
Average					7.4	161.65	58.53	0.7	1011.8	36.27	25.46	63.42	2.26

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. Table heavy metal content in different regions.

Region	Heavy Metals/(mg/kg)
	Cu	Ni	Cd	Zn	Pb	As	Cr	Hg
Thessaloniki, Greece [35]	79	770	3.3	470	39		40
Venice, Italy [36]	108	46	1	356	31.1	8.4	17.8
Velika Gorica, Croatia [37]	630	109	3.06		137		74.8	2.54
Sweden [38]	280	12	0.78	560	21	3.4	21	0.57
United Kingdom [39]	562	59	3.5	778	221.5		160
Japan [40]	255	40	2.3	979	53		69
Turkey summer [39]	147	75	1.7	1133	64.2	9.4	111	0.7
Turkey winter [39]	161	72	2.0	1299	92.8	6.5	145	0.9
USA [41]	436	28	3.6	620	24.0	10.0	36
China [13]	259	56.22	10.78	906.73	81.74	28.01	188.37	4.41
Shenyang, China [42]	821	54.7	3.54	666	50.9	11.9	68.4	4.32
Nanchang, China [43]	383	693	11.7	609	113		113
Guangzhou, China [44]	112.7		0.841	389.9	44.72
Shanxi, China [45]	175.99		2.71	146.03	45.09	15.1	126.68	2.85
Jiangsu, China	111.61	58.53	0.7	427.17	36.27	25.46	63.42	2.26

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. Pollutant content in different regions in Jiangsu Province.

Region	Heavy Metals/(mg/kg)
	Cu	Ni	Cd	Zn	Pb	As	Cr	Hg
Northern Jiangsu	63.88	66.38	0.81	1183.00	28.43	28.79	62.75	3.64
Central Jiangsu	117.50	69.50	0.62	654.00	27.33	26.37	67.33	1.11
Southern Jiangsu	230.46	54.21	0.63	600.60	31.03	23.31	64.19	1.24

. 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. Variation in sludge composition containing industrial wastewater from different industries.

Industries	Heavy Metals/(mg/kg)
	Cu	Ni	Cd	Zn	Pb	As	Cr	Hg
Metallurgy	130	41	0.58	1618.5	69.5	30.65	72	1.18
Chemical	69	58	0.33	2970	12	19.6	126	1.25
Food	193	129	0.69	1210	26	34.1	99	1.18
Printing and dyeing	94	121	0.33	1380	0	31.1	43	0.72
No industrial wastewater	98.33	51.5	0.5	735.17	27.83	26.47	55.83	3.67

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. Variation in sludge composition for different treatment processes.

Processes	Heavy Metals/(mg/kg)
	Cu	Ni	Cd	Zn	Pb	As	Cr	Hg
AAO and deformation process	85.5	65.91	0.88	468	41.78	26.44	73.55	3.05
SBR and deformation process	137.67	51.17	0.42	282	25.25	22.47	50.67	1.16
Biofilter	46	20	0.35	1380	35	41.6	24	0.76
OD and its deformation process	282	60	0.83	722	32	16.5	68	1.68

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. Principal component analysis of heavy metals in sludge.

	Cu	Ni	Cd	Zn	Pb	As	Cr	Hg	Eigenvalue	Variance%	Cumulative%
First principal component	0.083	0.906	0.801	0.780	0.188	0.449	0.819	0.801	3.626	45.326	45.326
Second principal component	0.835	−0.156	0.078	0.045	0.824	−0.066	0.141	−0.331	1.541	19.263	64.589
Third principal component	−0.329	−0.108	−0.375	0.211	0.336	0.785	−0.022	−0.171	1.065	13.309	77.898

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. Average pollution level and ecological risk grade of each heavy metal in sludge.

Element	Nemerow Pollution Coefficient P	Pollution Level	Potential Ecological Risk Coefficient Er	Potential Ecological Risk	Comprehensive Potential Ecological Risk Index RI	Degree of Comprehensive Potential Ecological Risk
Cu	0.32	clean	8.08	slight	161.65	moderate
Ni	0.61	clean	3.07	slight	61.35	low
Cd	0.23	clean	69.4	medium	1388	severe
Zn	0.67	clean	4.05	slight	80.94	low
Pb	0.09	clean	1.17	slight	23.33	low
As	0.87	still clean	8.74	slight	174.85	moderate
Cr	0.13	clean	0.65	slight	12.95	low
Hg	0.97	still clean	48.45	medium	968.92	severe

. 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.