Sources, Distribution Characteristics, and Risk Evaluation of Typical Heavy Metals in Sludge from Industrial Parks

Sources, Distribution Characteristics, and Risk Evaluation of Typical Heavy Metals in Sludge from Industrial Parks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sources, Distribution Characteristics, and Risk Evaluation of Typical Heavy Metals in Sludge from Industrial Parks Lianchuan Zhou, Hongmei Zhu, Sheng Yuan, Danyi Yao, Xianhe Gong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7286011/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Although pollution caused by heavy metals in sludge has recently attracted extensive attention, little is known about the distribution characteristics, potential ecological risks, and sources of heavy metals in sludge collected from industrial parks. In this study, we collected sludge from 89 centralized wastewater treatment plants in industrial parks, a certain city, and analyzed the content and related characteristics of eight heavy metals. The spatial distribution analysis of typical heavy metals showed relatively high contents of Cr, Ni, Cu, and Zn in the sludge in the main urban metropolitan area, which can be correlated with the degree of industrial development and human activities. The determined potential ecological risks (E i ) were higher for Hg and Ni than for other heavy metals; the combined ecological risk value (RI) of the main urban metropolitan area was increased compared to other districts and counties. The analysis of heavy metal sources indicated that Cr, Cu, and Zn in the sludge originated from the same source, associated with the mechanical equipment manufacturing industry. Hg and Pb came from the same source and could be related to electroplating, chemical, and electronic manufacturing industries. The presented results provide a basis for studying the heavy metal pollution characteristics of sewage sludge. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences heavy metals sludge from industrial parks spatial distribution ecological risk source analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Industrial parks are critical for promoting the development of high-tech industries. Sewage treatment plants in industrial parks take over the majority of domestic sewage and industrial wastewater, the treatment and disposal of which are accompanied by sludge generation [ 1 , 2 ] . The total annual global production of sewage sludge is up to 160 million tons. [ 3 ] . In 2023, the total amount of urban sewage treatment in China reached 64.27 billion cubic meters, while the total amount of sludge exceeded 75.27 million tons (in terms of 80% water content), where industrial sludge accounted for about 50% [ 4 ] . Sludge contains a large number of recyclable substances, and a considerable part of sludge is used for land use, while the fraction that does not meet the land use criterion is used for building materials or incinerated for power generation [ 5 , 6 ] . However, heavy metals in sludge have become an important factor that limits sludge up-cycling [ 7 , 8 ] . Compared with municipal sludge, certain heavy metals present in industrial sludge are highly stable, persistent, and difficult to naturally degrade or eliminate, posing potential environmental risks [ 9 ] . Once heavy metals from sludge enter the soil and water bodies, they are hard to degrade, and, thus, easily accumulate, causing irreversible and persistent pollution, and seriously threatening ecological safety and human health [ 10 ] . Therefore, the pollution characteristics of heavy metals in sewage sludge need to be assessed before further sludge use. The distribution of heavy metals in sludge has attracted increased research attention as it can be correlated with the distribution of local human and industrial activities to some extent. Cheng et al. [ 11 ] mapped the geographic distribution of nine heavy metals in urban sludge in China, finding that the differences in the geographic distribution of heavy metals were related to the level of economic development of the studied cities and seasonal variations. Xiao et al. [ 12 ] reported that the differences in heavy metal contents could be related to the regional industrial density and the development of human activities. In addition, to evaluate the combined risk of multiple heavy metals and their potential environmental impact, a potential ecological risk index was proposed [ 13 ] . Islam et al. [ 14 ] analyzed the potential ecological risk of seven heavy metals in sludge from Dhaka city, Bangladesh, finding that elemental Cd exhibited a higher ecological risk associated with the local industrial sector. Acidic media and microbial characteristics of sludge affect the enrichment and stability of heavy metals in sludge, so resolving their sources and contamination pathways is challenging [ 15 ] . Correlation analysis and principal component analysis can be used to explore the correlation between the contents of different heavy metals [ 16 , 17 ] . Principal component analysis was used to identify the main influential factors and pollution sources, and the contribution of each source was determined by multiple linear regression (APCS-MLR) using absolute factor scores and pollutant contents, providing important guidance for policymakers toward selecting and formulating sustainable policies to control and reduce pollution emissions [ 18 , 19 ] . The distribution characteristics of heavy metals in sludge from urban sewage treatment plants and their ecological risk evaluation were extensively studied. However, the compositional characteristics and factors that influence the heavy metal content in sludge from sewage treatment plants in industrial parks were significantly less explored. In this study, dewatered sludge from 89 centralized wastewater treatment plants in industrial parks in a certain city was studied. Its physical-chemical properties, the heavy metal distribution characteristics, the effects of different industrial wastewater sources on the composition of typical heavy metals (Zn, Cu, Cr, Ni, Pb, As, Hg, Cd) in sludge, and the associated potential risks were examined in detail. The study aimed to create a scientific basis for the evaluation and treatment of heavy metal pollution originating from sewage treatment plants’ sludge from different regions and categories of industrial parks. 2. Materials and methods 2.1 Study area and sampling 89 centralized wastewater treatment plants in a certain city industrial parks were investigated, covering a city's central and western regional town clusters (including the main urban area and the surrounding city clusters, hereafter: the main urban metropolitan area), northeastern regional town clusters (hereafter: the northeastern region), and southeastern regional town clusters (hereafter: the southeastern region).. The wastewater harbored by industrial parks comprises different categories of industrial wastewater and domestic wastewater, such as machining, automobile, electroplating, electronics, food, chemical, pharmaceutical, and paper-making. Figure 1 shows that the proportion of wastewater treatment plants accepting mainly industrial wastewater is 61%. Sludge was collected from all 89 centralized wastewater treatment plants, and the sampling point was at the exit of the conveyor belt after the filter press. Sampling and preservation were carried out according to the Technical specifications on sampling and sample preparation from industry solid waste [ 20 ] , and the detection methods and limits are listed in Table 1 in detail. Table 1 Detection methods and limits of heavy metals in collected sludge Heavy metals Detection methods Limit of detectio(mg/kg) Instruments Hg Atomic fluorescence by microwave digestion(HJ 702–2014) [ 21 ] 0.002 Atomic fluorescence photometer (AFS-9799) As 0.01 Cd Inductively Coupled Plasma Atomic Emission Spectrometry(GB5085.3-2007) [ 22 ] 1.5 Inductively Coupled Plasma Spectrometer (Optima8300DV) Pb 25 Cr 2.5 Ni 5 Cu 5 Zn 3 2.2 Analysis methods Risk grading was performed by calculating the potential ecological risk index, Ei, for single-factor pollutants, and the composite ecological risk index, RI, for multiple-factor pollutants, reflecting the ecological risk of each heavy metal and the composite ecological risk of heavy metals in the sludge of the investigated wastewater treatment plants. Pearson correlation analysis was used to analyze the possible interrelationships between different heavy metals, and Principal Component Analysis (PCA) and Absolute Principal Component Scores-Multivariate Linear Regression (APCS-MLR) were applied to determine the contents (%) of heavy metals and reveal the common sources of interrelated heavy metals in the sludge of the sewage treatment plants. 2.2.1 Potential ecological risk analysis The potential ecological risk index method, proposed by Hakanson [ 23 ] , uses the content of heavy metals in the soil and their ecotoxicity to evaluate the related ecological risks, using the following equations: $${E_i}=\frac{{{T_i} * {C_i}}}{{{C_o}}}$$ 1 $$RI=\sum {{E_i}}$$ 2 where RI is the comprehensive potential ecological risk index, and T i represents the toxicity coefficient of heavy metal i .; the toxicity coefficients of eight heavy metals studied here are: Hg (40) > Cd (30) > As (10) > Cu (5) = Pb (5) = Ni (5) > Cr (2) > Zn (1). C i is the measured concentration of heavy metal i . E i is the one-factor potential ecological risk index, and it is divided into 5 levels: <40, 40–80, 80–160, 160–320, and ≥ 320, representing low, medium, stronger, strong, and very strong risk, respectively. RI is divided into 4 levels: RI ≤ 150, low risk; 150 < RI ≤ 300, medium risk; 300 < RI ≤ 600, stronger risk; 600 1200, very strong risk [ 24 ] . 2.2.2 APCS-MLR The APCS-MLR model was proposed by Thurston and Spengler [ 25 ] . The independent variables were the principal component factors obtained from the factor analysis, while the dependent variables were the contents of each heavy metal. Multiple linear regression analyses were performed to derive the contribution of each factor corresponding to the source of each heavy metal: $${C_i}=\sum\limits_{{m=1}}^{n} {({a_{im}} \times APC{S_{im}})+{b_i}}$$ 3 where C i is the content of heavy metal i (mg∙kg − 1 ); a im is the regression coefficient of source m on heavy met al i ; APCS im is the absolute principal factor score of source m on heavy metal i ; n is the number of factors; and, b i is the constant term of multiple regression. The contribution of source m to heavy metal element i is given by Eq. ( 4 ): $$P{C_{im}}=\frac{{\left| {{a_{im}} \times \overline {{APC{S_{im}}}} } \right|}}{{\left| {{b_i}} \right|+\sum\limits_{{m=1}}^{n} {\left| {{a_{im}} \times \overline {{APC{S_{im}}}} } \right|} }}$$ 4 The equation for other source contributions is as follows: $$P{C_{im}}=\frac{{\left| {{b_i}} \right|}}{{\left| {{b_i}} \right|+\sum\limits_{{m=1}}^{n} {\left| {{a_{im}} \times \overline {{APC{S_{im}}}} } \right|} }}$$ 5 where PC im is the contribution of the source; \(\overline {{APC{S_{im}}}}\) is the average absolute principal factor score for heavy metal i from source m . 3. Results and discussion 3.1 Physical and chemical properties of sludge The pH value is very important for sludge treatment. The reported pH value of sludge from sewage treatment plants in China is between 6–9 [ 26 ] . However, the pH values of sludge collected from the studied 89 sewage treatment plants ranged from 6.4 to 12.6 (Table 2 ), plausibly because some effluent sources were wastewater from the alkali industry; the pH value of sludge from12 sewage treatment plans exceeded the limit (5.5–8.5) defined by the Control standards of pollutants in sludge for agricultural use (GB 4284 − 2018) [ 27 ] . In addition, sludge samples from 67 studied plants had a pH value between 6.5–7.5, and they might not affect the soil pH during sludge land application. The sewage sludge mostly exhibited a high water content, and the water content of only 11 dewatered sludge samples was 60% or less. Sludge samples with high water content should not be applied directly in different disposal methods and must be dewatered before proceeding to the next step [ 28 ] . The content (%) of organic matter in sludge ranged from 16.9 to 87.1%, while the total nitrogen and total phosphorus contents ranged from 0.27 to 12.2% and 0.03 to 5.03%, respectively. Table 2 Physical-chemical properties of sludge derived from sewage treatment plants. pH Water content Organic matter N P K unit dimensionless % % % % % Average value 7.6 75.6 49.1 5.99 1.38 0.68 Maximum value 12.6 91.7 87.1 12.20 5.03 3.06 Minimum value 6.4 33.5 16.9 0.27 0.03 0.12 Median value 7.3 78.8 49.1 6.34 1.31 0.56 Standard Deviation 7.6 75.6 49.1 2.72 0.99 0.68 GB4284-2018 [ 27 ] 5.5–8.5 60 3.2 Spatial distribution characteristics of heavy metals in sludge The statistical analysis of the heavy metal content in sludge from industrial parks is listed in Table 3 . The median contents of Cr, Ni, Cu, and Zn are much lower than the mean value, and the standard deviation is large, indicating that the sludge in a few parks has a high content of Cr, Ni, Cu, and Zn; the contents of different categories of heavy metals in the sludge are also significantly different: the contents of Hg, As, Cd, Pb, Cr, Ni, Cu, and Zn range from 0.02–18.55, 0.04–59.80, 1.5-21.85, 25–364, 2.5–57,950, 3.65-94,509, 1.39-18.501, and 10.95–83.750 mg∙kg − 1 , respectively. The mean content values are ranked in descending order as follows: Zn > Ni > Cr > Cu > Pb > As > Cd > Hg. Referring to the following protocols, Disposal of sludge from municipal wastewater treatment plant-Quality of sludge for co-landfilling (GB/T 23485 − 2009) [ 29 ] , Disposal of sludge from municipal wastewater treatment plant-Quality of sludge used in the production of cement clinker (CJ/T 314–2009) [ 30 ] , Disposal of sludge from municipal wastewater treatment plant-Quality of sludge used in making brick (GB/T 25031 − 2010) [ 31 ] , Control standards of pollutants in sludge for agricultural use (GB 4284 − 2018) [ 27 ] , Class B sludge standards, and local Municipal Soil Background Value [ 32 ] , the average content of each heavy metal in the sludge in industrial park is higher than the local soil background value. The average contents of Hg, As, Cd, Pb, and Cu are lower, while the average contents of Cr, Ni, and Zn are higher than the limit value for each type of reference standard, which may pose environmental risks and must be considered and controlled in the process of subsequent disposal and utilization [ 33 ] . Table 3 Statistical analysis of the heavy metal content in sludge from a certain city industrial parks. mg/kg Hg As Cd Pb Cr Ni Cu Zn Average value 1.22 9.48 2.11 41.52 1576.2 1835 639.88 4489.89 Maximum value 18.55 59.80 21.85 364 57950 94509 18501 83750 Minimum value 0.02 0.04 1.5 25 2.5 3.65 1.39 10.95 Median value 0.78 7.68 1.5 25 57.1 24.55 79.20 377.00 Standard deviation 2.17 9.45 2.76 44.83 7570.2 10616 2690.44 14482 Soil background value [ 32 ] 0.069 6.62 0.28 28.1 74.4 31.6 24.6 81.9 GB/T 23485 − 2009 [ 29 ] 25 75 20 1000 1000 200 1500 4000 CJ/T 314–2009 [ 30 ] 25 75 20 1000 1000 200 1500 4000 GB/T 25031 − 2010 [ 31 ] 5 75 20 300 1000 200 1500 4000 GB4284-2018 [ 27 ] 15 75 15 1000 1000 200 1500 3000 The distribution of Hg, As, Cd, Pb, Cu, Cr, Ni, and Zn in sewage plant sludge samples in a main urban area, the Northeast, and the Southeast. is shown in Fig. 2 . Sludge from districts and counties that produced less sewage that year was not collected, so the levels of the 8 heavy metals were zero. The concentrations of the heavy metals in sludge from other total 32 districts and counties greatly varied. Based on their contents from low to high, they were categorized into five grades by region. Ranks 1–5 contained 7, 7, 6, 6, and 6 districts and counties, respectively. Among them, the Hg content in the southeastern region is concentrated in the lower content classes (Ranks 1–3), and the Hg content in the main urban metropolitan area and tthe northeastern region is relatively high (Ranks 4 and 5). The content of As and Cd in the northeastern region is relatively low (rank 1–3), and the relatively high content is concentrated in the main urban metropolitan area and the southeastern region (rank 4 and rank 5).The content distribution of Pb and Ni content is relatively balanced in all districts and counties, and the content of each content rank two or more subdistricts.Cr, Cu and Zn in the content of the highest rank 5 are only in the main urban metropolitan area, and the remaining four ranks of the content distribution is Relatively uniform. In summary, most districts and counties with high contents of Cr, Cu, and Zn are concentrated in the main urban metropolitan area. The content of Pb is high in all three regions, while the contents of Ni are relatively uniformly distributed. Nie et al. [ 34 ] reported that human activities considerably influence the content of heavy metals in soil. According to Praspaliauskas et al. [ 35 ] , the higher the level of industrial development in the region, the higher the heavy metal content in the sludge. Thus, the determined relatively high heavy metal contents in sludge collected from the main urban metropolitan area may be related to the concentration of industrial parks, high population density, a relatively high degree of industrial development, and some electroplating parks in the main urban metropolitan area [ 36 ] . The spatial distribution results reflect the possible sources of heavy metal accumulation in soil and provide a reference for subsequent source modeling analysis. 3.3 Potential ecological risk assessment of heavy metals in sludge The potential ecological risk indices and the evaluation of heavy metals in sludge collected from 89 industrial parks in a certain city are shown in Table 4 . Table 4 Potential ecological risk coefficients ( E i ) and composite ecological risk indices ( RI ) for heavy metals in sludge collected from 89 industrial parks. Statistical term E i RI Hg As Cd Pb Cr Ni Cu Zn Maximum value 2968 39.87 655.50 26.00 1287.78 11813.64 1850.05 478.57 12483.52 Minimum value 2.80 0.03 45.00 1.79 0.06 0.46 0.14 0.06 62.83 Average value 194.69 6.32 63.30 2.97 35.03 229.42 63.99 25.66 621.37 Risk ratio% Low 25.84 100 0.00 100 93.26 92.13 88.76 88.76 31.46 Medium 10.11 0 93.26 0 2.25 1.12 3.37 4.49 35.96 Stronger 29.21 0 2.25 0 0.00 1.12 2.25 2.25 20.22 Strong 24.72 0 2.25 0 1.12 0 1.12 1.12 5.62 Very strong 10.11 0 2.25 0 3.37 5.62 4.49 3.37 6.74 The average value of RI of heavy metals in wastewater treatment plants in a certain city industrial parks is 623.37, representing a strong risk. However, the risk ratio shows that the ecological risk of heavy metals is in the low-risk and medium-risk range, accounting for 67.42% of the total, and indicating that the potential risk of most of the industrial wastewater treatment plants is in the acceptable range. A small portion of the wastewater treatment plants exhibit a very strong ecological risk, requiring further attention. The potential ecological risk coefficients, E i , of 8 heavy metals, are arranged in the following order: Ni > Hg > Cu > Cd > Cr > Zn > As > Pb. The results of the potential ecological risk evaluation show that the potential the ecological risk index ( E i ) of Hg is the highest, while the ratio of strong and very strong ecological risk reaches 34.83%, followed by Ni, Cu, Zn, Cd, Cr, where the ratio of strong and very strong ecological risk is not more than 6%. The results show no high ecological risks for As and Pb. Therefore, in the process of disposal and utilization of industrial sludge, the potential ecological risk of Hg and Ni must be carefully treated, and the identification and detection of heavy metals Zn, Cu, Cd, and Cr should be strengthened. 3.4 Heavy metal source analysis 3.4.1 Pearson correlation analysis We correlated the contents of eight heavy metals and various physicochemical indexes of sludge, and the results are listed in Table 5 . For the listed elements, the following correlations are found: for Zn, a significant positive correlation with pH, a highly significant negative correlation with total nitrogen, a significant negative correlation with moisture, a highly significant negative correlation with water content, and a significant negative correlation with total organic carbon; for Cr, a highly significant negative correlation with moisture and total phosphorus, a highly significant positive correlation with pH; for Hg, a strong positive correlation with total phosphorus (P < 0.01); for Cu, a highly significant positive correlation with pH and a significant negative correlation with total nitrogen (P < 0.05); for Pb, a highly significant negative correlation with total phosphorus; pH can affect the presence and solubility of Cr, Cu, and Zn in sludge; an increase in the moisture content decreases the concentration of some heavy metals, therefore, presenting a negative correlation. The total organic carbon is related to the microbial activity in sludge, i.e., the total N and the total P are related to nitrogen and phosphorus removal from the sludge, affecting the content of some heavy metals [ 13 ] . Table 5 Correlation between heavy metal content and physical-chemical properties of sludge. Hg As Cd Pb Cr Ni Cu Zn pH -0.091 -0.037 0.069 -0.025 0.217* 0.088 0.231* 0.317** moisture 0.093 0.12 -0.348 0.153 -0.291** -0.087 -0.125 -0.212* TOC -0.076 -0.052 -0.244 -0.238 -0.178 -0.091 -0.260* -0.238* TN -0.124 -0.013 -0.329 -0.233 -0.317** -0.085 -0.264* -0.343** TP 0.297** -0.038 -0.224 0.358* -0.056 -0.005 -0.067 -0.121 Note: **At a 0.01 level (two-tailed), the correlation is significant. *Significant correlation at a 0.05 level (two-tailed). The correlation between heavy metals determines whether there is homology between their sources [ 37 ] . The results of Pearson correlation analysis for each heavy metal are shown in Fig. 3 . Cr, Ni, Cu, and Zn heavy metals exhibit two highly significant positive correlations (P < 0.01), mainly due to their common source, similar chemical properties, and their interrelation during the sludge treatment process. Cd exhibits a highly significant positive correlation with Pb, Cu, and Zn (P < 0.01), while there is a significant positive correlation of Pb and Hg (P < 0.01) but also with Cr, Cu, and Zn (P < 0.05). The correlation between As and the other seven heavy metals is not significant, inferring that the source pathways of As and other heavy metals are different. The results of heavy metal correlation analysis indicate that the heavy metals with significant correlation most likely originate from the same industrial activity, entering the sewage system and eventually accumulating in the sludge. 3.4.2 Principal component analysis (PCA) The KMO test coefficient of the data related to heavy metals in sludge is 0.622 > 0.6, and Bartlett's sphere test is P<0.001, indicating that the variables provide a reasonable basis for factor analysis. The results of the principal component analysis are statistically significant, Table 6 . The main contributing elements for the first principal factor are Cr, Ni, Cu, and Zn, then Hg and Pb for the second principal factor, and As for the third principal factor. The statistical results of factor analysis are shown in Table 7 . The principal component factor 1 has high loadings for Cr, Cu, and Zn, followed by Ni, Pb, and Cd, and compared to them, Hg and As exhibit low loadings. The principal component factor 2 explains 21.344% of the variance, with both Hg and Pb having high loadings, followed by Cd and As, while Cr, Ni, Cu, and Zn are relatively low. The principal component factor 3 explains 14.742% of the variance with high loadings for As, followed by Ni and Hg, and low loadings for the remaining heavy metals. The main sources of Ni are electroplating, chemical, and other industries. Hg mainly originates from electronic manufacturing, chemical, and other industries; industrial activities or the use of galvanized copper-plated pipes for urban sewage pipes may be the source of Cu and Zn; Pb mainly stems from automobile maintenance and cleaning wastewater, Cd and Cr mainly from electronic and other industrial sectors, and As mainly from metal manufacturing and other industries [ 38 – 40 ] . Combined with the source of wastewater in a certain city industrial parks, most of the studied objects belong to industrial wastewater; further analysis refers to the results of linear regression analysis. Table 6 Principal component analysis of heavy metals in sludge. Component Initial eigenvalue Post-extraction eigenvalue Eigenvalue Explained variance/% Total Percentage of variance Cumulative /% 1 2.885 36.067 2.634 32.924 36.067 2 1.621 20.259 1.707 21.344 56.326 3 1.015 12.684 1.179 14.742 69.01 4 0.979 12.238 81.248 / / 5 0.696 8.698 89.946 / / 6 0.332 4.144 94.091 / / 7 0.285 3.558 97.649 / / 8 0.188 2.351 100 / / Table 7 The component matrix for the principal component analysis of heavy metals in sludge. Elemental Factor loads F1 F2 F3 Hg 0.139 0.874 0.258 As -0.162 0.134 0.709 Cd 0.441 0.208 -0.542 Pb 0.444 0.801 -0.094 Cr 0.83 -0.198 0.17 Ni 0.517 -0.274 0.336 Cu 0.855 -0.147 0.023 Zn 0.872 -0.133 0.029 3.4.3 Absolute principal component scores-multivariate linear regression (APCS-MLR) The results of APCS-MLR of eight heavy metals from sewage treatment plant sludge are shown in Fig. 4 . The predicted/measured values of the average content of each heavy metal are close to 1, and the adjusted R 2 ranges from 0.436 to 0.845, representing a good fit and highly credible analysis results. Source 1 possesses high contributions of Cr, Ni, Cu, and Zn, with 61.98, 51.75, 57.76, and 55.72% respectively. According to the results of Pearson correlation analysis, the four heavy metals have a strong correlation (P < 0.01) and may exist in the same source, coinciding with the results of the Source 1 contribution analysis. The survey results show that the content of Cr, Ni, Cu, and Zn in the sludge of a certain city industrial park is relatively large, which can be understood by keeping in mind that the sewage treatment plant receives sewage from machinery and equipment manufacturing industries which raw materials may contain Cr, Ni, Cu and other metal elements, while Zn may be used as additive or coating material in smelting, casting, heat and surface treatment processes [ 41 ] . In summary, Source 1 is mainly related to industrial wastewater from machinery and equipment industries and related sectors. Hg and Pb are the main contributing elements of Source 2, with contribution rates of 67.32 and 79.77%, respectively. Pb and Hg show a highly significant positive correlation (P < 0.01). The contribution rate of Source 2 and the correlation analysis results coincide with the results in Table 5 , showing a two-by-two correlation between Pb, Hg, and TP, and no significant correlation with other heavy metals. Pb and Hg source industries contain phosphorus and are not homologous with Cd, Ni, Cu, Zn, and As. The production and use of phosphorus fertilizers, the production of phosphorus-containing chemicals, and e-waste disposal can lead to the coexistence of Hg, Pb, and P [ 42 , 43 ] . Keeping in mind the results in Tables 3 and 4 , the contents of Hg and Pb in the sludge from a certain city industrial parks are relatively low, and the potential ecological risk of Pb is low. Electroplating, chemical, and electronic manufacturing industries deal with relatively high contents of Pb and Hg. As the current sample involves sludge partially from electroplating parks and other wastewater treatment plants mainly receiving industrial wastewater, it is deduced that Source 2 represents an industrial source, related to the mentioned industry sectors. The As contribution rate of Source 3 is 32.65%, much higher than that of other heavy metal elements. According to the correlation results, no significant correlation between As and other heavy metals appears, implying their different sources. The average content of As is about 1.5 times the background value of the soil in a certain city. The As content is relatively low, as well as the potential ecological risk, indicating that the source pathway of As does not yield a large amount of As. It was previously shown that, when As in sludge has a single source, it may originate from arsenic-containing detergents or industrial wastewater from pharmaceutical and fertilizer production, etc. [ 40 ] . Furthermore, most detergents contain N and P, but the results in Table 5 show no correlation between As and N or P. Combined with the characteristics of the a certain city area, it is inferred that Source 3 is related to industrial wastewater from the pharmaceutical and chemical fertilizer sectors. Since APCS-MLR is based on principal component analysis to extract components with eigenvalues greater than 1 for parsing, 31.99% of other sources are not included. Based on the interpretation of the total variance of principal components, the other sources originate from five components with variance contributions of 12.24, 8.70, 4.14, 3.56, and 2.35%. The most important contributing elements of other sources are As and Cd, the sources of which are influenced by a combination of factors, with As contributing 0.30, 13.59, 32.65, and 53.46% to source 1, source 2, source 3, and other sources, respectively, and Cd contributing 5.87, 19.72, 26.49, and 47.93% to source 1, source 2, source 3, and other sources, respectively. Meanwhile, the correlation analysis implies that Cd is highly correlated with many heavy metals, having certain loading coefficients in the first and the second principal components of the principal component analysis. In summary, other source data may originate from the mixed sources of multiple pathways. 4 Conclusions Sludge from 89 centralized wastewater treatment plants in industrial parks of a certain city was studied. Its physicochemical properties, heavy metal distribution characteristics, ecological risks, and heavy metal sources were analyzed, and the results are given as follows: (1) The content of elemental Hg, As, Cd, Pb, Cr, Ni, Cu, and Zn heavy metals varied in the ranges of 0.02 ~ 18.55, 0.04 ~ 59.80, 1.5 ~ 21.85, 25 ~ 364, 2.5 ~ 57.950, 3.65 ~ 94.509, 1.39 ~ 18.501, and 10.95 ~ 83.750 mg∙kg − 1 . Cr, Cu, and Zn were affected by the degree of industrial development and human activities, and most of the districts and counties with high content of these four heavy metals were concentrated in the main urban metropolitan area, while the distribution of Ni was relatively more uniform. (2) The results of ecological risk evaluation showed that the degree of risk (E i ) of each heavy metal in a certain city is arranged as follows: Ni > Hg > Cu > Cd > Cr > Zn > As > Pb, having a wide dispersion in the RI value of the comprehensive potential ecological risk. The comprehensive ecological risk of the heavy metals in sludge from some sewage treatment plants was high. The obtained results suggest that special care needs to be taken about the potential ecological risk of Hg and Ni in the process of industrial sludge disposal and utilization and that the identification and detection of Zn, Cu, Cd, and Cr heavy metals should be strengthened. (3) The heavy metal source analysis (including Pearson correlation analysis, PCA principal component analysis, and APCS-MLR model analysis) showed that Cr, Ni, Cu and Zn originate from the same source, mainly reflecting the influence of the mechanical equipment manufacturing industry; the modeling contribution rates of Cr, Ni, Cu and Zn are 61.98, 51.75, 57.76, and 55.72%, respectively; Hg and Pb mainly originated from industrial sources related to electroplating, chemical, and electronic manufacturing industries, having model contribution rates of 67.32 and 79.77%, respectively. The main source As could be related to industrial wastewater from pharmaceuticals and fertilizer production, with a model contribution rate of 32.65%. Finally, Cd mainly originates from mixed sources and a variety of pathways. Declarations Funding Declaration This research was funding by the Guidance Project of Chongqing Scientific Research Institutions: Ecological and Health Risk Study of Heavy Metals in Park Sludge Basedon Different Categories (No.Cqhky2021jxj100003). Author Contribution L.Z. and H. Z. were responsible for writing the original draft; S. Y. was responsible for reviewing, editing, and providing guidance; D.Y. and X.G. were responsible for verification; D.B. was responsible for situation analysis; L.F. was responsible for obtaining funding. Data Availability All data generated or analysed during this study are included in this published article. References Janaszek, A. et al. 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An ecological risk index for aquatic pollution control.a sedimentological approach. Water Res. 14 , 975–1001 (1980). Zhang, J. & Xu, X. Spatial Distribution Characteristics and Potential Risk Assessment of Heavy Metals in Sludge of Shanghai Sewage Treatment Plant: A Case Study. Sustainability 15 , 3465. 10.3390/su15043465 (2023). Thurston, G. D. & Spengler, J. D. A Quantitative Assessment of Source Contributions to Inhalable Particulate Matter Pollution in Metropolitan Boston. Atmos. Environ. 19 , 257–259 (1985). Xuan, C., Zhang, J. & Sun, C. Characteristics and risk assessment of sewage sludge from urban wastewater treatment plants in Shaanxi Province, China. Environ. Monit. Assess. 195 (2023). Control standards of pollutants in. sludge for agricultural use. Publishe. Chinese Standard. 2018 (GB 4284 – 2018, 2018, Beijing: State Administration for Market Regulation). Liang, Y., Wang, R., Sun, W. & Sun, Y. Advances in Chemical Conditioning of Residual Activated Sludge in China. Water 15 10.3390/w15020345 (2023). Disposal of sludge. from municipal wastewater treatment plant—Quality of sludge for co-landfilling. Publishe. Chinese Standard. 2009 (GB/T 23485 – 2009, 2009, Beijing: General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China). Disposal of sludge from municipal wastewater. treatment plant—Quality of sludge used in the production of cement clinker. Publishe. Chinese Standard. 2009 (CJ/T 314–2009, 2009, Beijing: Ministry of Housing and Urban-Rural Development). Disposal of sludge from municipal wastewater treatment plant-一Quality of sludge used in making brick. Publishe. Chinese Standard. 2010 (GB/T 25031 – 2010, 2010, Beijing:General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China). F, Y. et al. Source Analysis and Ecological Risk Assessment of Heavy Metals in the Arable Soil at the Geological High Background, Based on the Township Scale. Huanjing kexue . 44 , 2838–2848. 10.13227/j.hjkx.202206202 (2023). Rocha, F., Ratola, N. & Homem, V. Heavy metal(loid)s and nutrients in sewage sludge in Portugal – Suitability for use in agricultural soils and assessment of potential risks. Sci. Total Environ. 964 10.1016/j.scitotenv.2025.178595 (2025). Nie, S., Chen, H., Sun, X. & An, Y. Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model. Sustainability 16 10.3390/su16114358 (2024). Praspaliauskas, M. & Pedišius, N. A review of sludge characteristics in Lithuania's wastewater treatment plants and perspectives of its usage in thermal processes. Renew. Sustainable Energy Reviews . 67 , 899–907 (2017). Wang, H., Liu, X. & Zhang, Z. Approaches for electroplating sludge treatment and disposal technology: Reduction, pretreatment and reuse. J. Environ. Manage. 349 , 119535 (2023). Tang, J. et al. Physicochemical properties and heavy metal pollution characteristics and correlation analysis of sludge landfill. Int. J. Environ. Anal. Chem. 103 , 5815–5824. 10.1080/03067319.2021.1943375 (2021). Cui, Y., Bai, L., Li, C., He, Z. & Liu, X. Assessment of Heavy Metal Contamination Levels and Health Risks in Environmental Media in the Northeast Region (Sustainable Cities and Society, 2022). Chen, R., Zhang, Q., Chen, H., Yue, W. & Teng, Y. Source apportionment of heavy metals in sediments and soils in an interconnected river-soil system based on a composite fingerprint screening approach. J. Hazard. Mater. 411 , 125125 (2021). Luo, C., Routh, J., Luo, D., Wei, L. & Liu, Y. Arsenic in the Pearl River Delta and its related waterbody, South China: occurrence and sources, a review. Geoscience Lett. 8 10.1186/s40562-021-00185-9 (2021). Lin, C. H., Lai, C. H., Hsieh, T. H. & Tsai, C. Y. Source apportionment and health effects of particle-bound metals in PM2.5 near a precision metal machining factory. Air Qual. Atmos. Health . 15 , 605–617. 10.1007/s11869-021-01147-y (2022). Sheng, M. et al. Processing toxic metal source proxies appropriately for better spatial heterogeneity source apportionment. Sci. Total Environ. 898 10.1016/j.scitotenv.2023.165516 (2023). Ma, W. et al. Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China. Environ. Geochem. Health . 46 10.1007/s10653-024-02037-9 (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 13 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7286011","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502492673,"identity":"b132f2c0-6fa2-4b86-8f2f-dfee7782b303","order_by":0,"name":"Lianchuan Zhou","email":"","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lianchuan","middleName":"","lastName":"Zhou","suffix":""},{"id":502492674,"identity":"11c1a048-e5a4-4ba6-8242-5d76dbe933d3","order_by":1,"name":"Hongmei Zhu","email":"","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Zhu","suffix":""},{"id":502492675,"identity":"de907fd4-640b-45b9-9dc2-879b7a17b04c","order_by":2,"name":"Sheng Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3RMQrCMBTG8ZRAp9Sur4t4hCeF4NCbuLQI1aGu0qFoQUjHrh5D6AUKAaeAV8jk5BBwdVA3t2QUzG/+/sNLCPG8X2UQpjGlUjsXwalepEkXluicUKbq4nxlM3Cax12VmkhAMEhGkDTZ0pqAunNIBFAuo1GTS7ltbQlCxclcQMjlJMeglU5JagoBLD0yBNcEYVQASF0TULdd0taAIN+PnLvcEner4fHE/aHvpdSmyewJIZuvD8zt84+1dtt5nuf9rxf2aDwo1n6bHgAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Yuan","suffix":""},{"id":502492676,"identity":"41409f5b-eb57-40a3-bfdc-9b67f6109e7e","order_by":3,"name":"Danyi Yao","email":"","orcid":"","institution":"Chongqing Tongliang District Ecological Environment Monitoring Station","correspondingAuthor":false,"prefix":"","firstName":"Danyi","middleName":"","lastName":"Yao","suffix":""},{"id":502492677,"identity":"4bb08b95-a956-4624-98d2-f3e0b940fb26","order_by":4,"name":"Xianhe Gong","email":"","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xianhe","middleName":"","lastName":"Gong","suffix":""},{"id":502492678,"identity":"ef2b55f2-3d14-4e17-b4d1-a9163a5d3b13","order_by":5,"name":"Denghui Bin","email":"","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Denghui","middleName":"","lastName":"Bin","suffix":""},{"id":502492679,"identity":"623aec84-f249-4b2f-8ceb-ba86430132b6","order_by":6,"name":"Li Fan","email":"","orcid":"","institution":"Chongqing Academy of Ecology and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-08-04 01:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7286011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7286011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89603415,"identity":"f2a54481-0875-45b8-94c4-536ed9e2b48b","added_by":"auto","created_at":"2025-08-21 19:05:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40406,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of sewage treatment plants in different percentage zones of industrial wastewater.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7286011/v1/ecce1f98b565e0743fc30fd4.jpg"},{"id":89603416,"identity":"427a7e4b-2f85-454d-a23b-d4efc62683dc","added_by":"auto","created_at":"2025-08-21 19:05:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147156,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of eight heavy metals in sludge collected from industrial parks.in a certain city\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7286011/v1/5b85f20f4584087abcf7f35e.jpeg"},{"id":89604515,"identity":"22e79f21-0088-4eb5-82c2-699b7eee8f75","added_by":"auto","created_at":"2025-08-21 19:29:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121737,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the Pearson correlation analysis for heavy metals.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7286011/v1/284e6916f82d8cbeea2fbb07.png"},{"id":89603419,"identity":"c20ed7d5-2284-4488-ba30-93d743130eb4","added_by":"auto","created_at":"2025-08-21 19:05:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240513,"visible":true,"origin":"","legend":"\u003cp\u003eThe contribution of different sources to the heavy metal content in sludge.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7286011/v1/b09cd54288fa84ec843dff44.png"},{"id":89604635,"identity":"1672d474-8fe7-41d5-9994-3c6badb8efa9","added_by":"auto","created_at":"2025-08-21 19:37:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1690929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7286011/v1/6804da0c-42c0-4378-8975-4ade17a25694.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sources, Distribution Characteristics, and Risk Evaluation of Typical Heavy Metals in Sludge from Industrial Parks","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndustrial parks are critical for promoting the development of high-tech industries. Sewage treatment plants in industrial parks take over the majority of domestic sewage and industrial wastewater, the treatment and disposal of which are accompanied by sludge generation \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The total annual global production of sewage sludge is up to 160\u0026nbsp;million tons.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In 2023, the total amount of urban sewage treatment in China reached 64.27\u0026nbsp;billion cubic meters, while the total amount of sludge exceeded 75.27\u0026nbsp;million tons (in terms of 80% water content), where industrial sludge accounted for about 50%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSludge contains a large number of recyclable substances, and a considerable part of sludge is used for land use, while the fraction that does not meet the land use criterion is used for building materials or incinerated for power generation \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, heavy metals in sludge have become an important factor that limits sludge up-cycling \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Compared with municipal sludge, certain heavy metals present in industrial sludge are highly stable, persistent, and difficult to naturally degrade or eliminate, posing potential environmental risks \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Once heavy metals from sludge enter the soil and water bodies, they are hard to degrade, and, thus, easily accumulate, causing irreversible and persistent pollution, and seriously threatening ecological safety and human health\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Therefore, the pollution characteristics of heavy metals in sewage sludge need to be assessed before further sludge use.\u003c/p\u003e\u003cp\u003eThe distribution of heavy metals in sludge has attracted increased research attention as it can be correlated with the distribution of local human and industrial activities to some extent. Cheng et al. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e mapped the geographic distribution of nine heavy metals in urban sludge in China, finding that the differences in the geographic distribution of heavy metals were related to the level of economic development of the studied cities and seasonal variations. Xiao et al. \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e reported that the differences in heavy metal contents could be related to the regional industrial density and the development of human activities. In addition, to evaluate the combined risk of multiple heavy metals and their potential environmental impact, a potential ecological risk index was proposed \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Islam et al.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e analyzed the potential ecological risk of seven heavy metals in sludge from Dhaka city, Bangladesh, finding that elemental Cd exhibited a higher ecological risk associated with the local industrial sector.\u003c/p\u003e\u003cp\u003eAcidic media and microbial characteristics of sludge affect the enrichment and stability of heavy metals in sludge, so resolving their sources and contamination pathways is challenging\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Correlation analysis and principal component analysis can be used to explore the correlation between the contents of different heavy metals\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Principal component analysis was used to identify the main influential factors and pollution sources, and the contribution of each source was determined by multiple linear regression (APCS-MLR) using absolute factor scores and pollutant contents, providing important guidance for policymakers toward selecting and formulating sustainable policies to control and reduce pollution emissions \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe distribution characteristics of heavy metals in sludge from urban sewage treatment plants and their ecological risk evaluation were extensively studied. However, the compositional characteristics and factors that influence the heavy metal content in sludge from sewage treatment plants in industrial parks were significantly less explored. In this study, dewatered sludge from 89 centralized wastewater treatment plants in industrial parks in a certain city was studied. Its physical-chemical properties, the heavy metal distribution characteristics, the effects of different industrial wastewater sources on the composition of typical heavy metals (Zn, Cu, Cr, Ni, Pb, As, Hg, Cd) in sludge, and the associated potential risks were examined in detail. The study aimed to create a scientific basis for the evaluation and treatment of heavy metal pollution originating from sewage treatment plants\u0026rsquo; sludge from different regions and categories of industrial parks.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area and sampling\u003c/h2\u003e\u003cp\u003e89 centralized wastewater treatment plants in a certain city industrial parks were investigated, covering a city's central and western regional town clusters (including the main urban area and the surrounding city clusters, hereafter: the main urban metropolitan area), northeastern regional town clusters (hereafter: the northeastern region), and southeastern regional town clusters (hereafter: the southeastern region).. The wastewater harbored by industrial parks comprises different categories of industrial wastewater and domestic wastewater, such as machining, automobile, electroplating, electronics, food, chemical, pharmaceutical, and paper-making. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the proportion of wastewater treatment plants accepting mainly industrial wastewater is 61%. Sludge was collected from all 89 centralized wastewater treatment plants, and the sampling point was at the exit of the conveyor belt after the filter press. Sampling and preservation were carried out according to the \u003cem\u003eTechnical specifications on sampling and sample preparation from industry solid waste\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and the detection methods and limits are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in detail.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetection methods and limits of heavy metals in collected sludge\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeavy metals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetection methods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLimit of detectio(mg/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstruments\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAtomic fluorescence by microwave digestion(HJ 702\u0026ndash;2014)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAtomic fluorescence photometer (AFS-9799)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eInductively Coupled Plasma Atomic Emission Spectrometry(GB5085.3-2007)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eInductively Coupled Plasma Spectrometer (Optima8300DV)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Analysis methods\u003c/h2\u003e\u003cp\u003eRisk grading was performed by calculating the potential ecological risk index, Ei, for single-factor pollutants, and the composite ecological risk index, RI, for multiple-factor pollutants, reflecting the ecological risk of each heavy metal and the composite ecological risk of heavy metals in the sludge of the investigated wastewater treatment plants. Pearson correlation analysis was used to analyze the possible interrelationships between different heavy metals, and Principal Component Analysis (PCA) and Absolute Principal Component Scores-Multivariate Linear Regression (APCS-MLR) were applied to determine the contents (%) of heavy metals and reveal the common sources of interrelated heavy metals in the sludge of the sewage treatment plants.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Potential ecological risk analysis\u003c/h2\u003e\u003cp\u003eThe potential ecological risk index method, proposed by Hakanson \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, uses the content of heavy metals in the soil and their ecotoxicity to evaluate the related ecological risks, using the following equations:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${E_i}=\\frac{{{T_i} * {C_i}}}{{{C_o}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$RI=\\sum {{E_i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eRI\u003c/em\u003e is the comprehensive potential ecological risk index, and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the toxicity coefficient of heavy metal \u003cem\u003ei\u003c/em\u003e.; the toxicity coefficients of eight heavy metals studied here are: Hg (40)\u0026thinsp;\u0026gt;\u0026thinsp;Cd (30)\u0026thinsp;\u0026gt;\u0026thinsp;As (10)\u0026thinsp;\u0026gt;\u0026thinsp;Cu (5)\u0026thinsp;=\u0026thinsp;Pb (5)\u0026thinsp;=\u0026thinsp;Ni (5)\u0026thinsp;\u0026gt;\u0026thinsp;Cr (2)\u0026thinsp;\u0026gt;\u0026thinsp;Zn (1). \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the measured concentration of heavy metal \u003cem\u003ei\u003c/em\u003e. \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the one-factor potential ecological risk index, and it is divided into 5 levels: \u0026lt;40, 40\u0026ndash;80, 80\u0026ndash;160, 160\u0026ndash;320, and \u0026ge;\u0026thinsp;320, representing low, medium, stronger, strong, and very strong risk, respectively. \u003cem\u003eRI\u003c/em\u003e is divided into 4 levels: \u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;150, low risk; 150\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;300, medium risk; 300\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;600, stronger risk; 600\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1200, strong risk; \u003cem\u003eRI\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1200, very strong risk \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 APCS-MLR\u003c/h2\u003e\u003cp\u003eThe APCS-MLR model was proposed by Thurston and Spengler \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The independent variables were the principal component factors obtained from the factor analysis, while the dependent variables were the contents of each heavy metal. Multiple linear regression analyses were performed to derive the contribution of each factor corresponding to the source of each heavy metal:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${C_i}=\\sum\\limits_{{m=1}}^{n} {({a_{im}} \\times APC{S_{im}})+{b_i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the content of heavy metal \u003cem\u003ei\u003c/em\u003e (mg∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eim\u003c/em\u003e\u003c/sub\u003e is the regression coefficient of source \u003cem\u003em\u003c/em\u003e on heavy met al \u003cem\u003ei\u003c/em\u003e; \u003cem\u003eAPCS\u003c/em\u003e\u003csub\u003e\u003cem\u003eim\u003c/em\u003e\u003c/sub\u003e is the absolute principal factor score of source \u003cem\u003em\u003c/em\u003e on heavy metal \u003cem\u003ei\u003c/em\u003e; \u003cem\u003en\u003c/em\u003e is the number of factors; and, \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the constant term of multiple regression.\u003c/p\u003e\u003cp\u003eThe contribution of source \u003cem\u003em\u003c/em\u003e to heavy metal element \u003cem\u003ei\u003c/em\u003e is given by Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$P{C_{im}}=\\frac{{\\left| {{a_{im}} \\times \\overline {{APC{S_{im}}}} } \\right|}}{{\\left| {{b_i}} \\right|+\\sum\\limits_{{m=1}}^{n} {\\left| {{a_{im}} \\times \\overline {{APC{S_{im}}}} } \\right|} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe equation for other source contributions is as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$P{C_{im}}=\\frac{{\\left| {{b_i}} \\right|}}{{\\left| {{b_i}} \\right|+\\sum\\limits_{{m=1}}^{n} {\\left| {{a_{im}} \\times \\overline {{APC{S_{im}}}} } \\right|} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ePC\u003c/em\u003e\u003csub\u003e\u003cem\u003eim\u003c/em\u003e\u003c/sub\u003e is the contribution of the source; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline {{APC{S_{im}}}}\\)\u003c/span\u003e\u003c/span\u003eis the average absolute principal factor score for heavy metal \u003cem\u003ei\u003c/em\u003e from source \u003cem\u003em\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Physical and chemical properties of sludge\u003c/h2\u003e\u003cp\u003eThe pH value is very important for sludge treatment. The reported pH value of sludge from sewage treatment plants in China is between 6\u0026ndash;9 \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, the pH values of sludge collected from the studied 89 sewage treatment plants ranged from 6.4 to 12.6 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), plausibly because some effluent sources were wastewater from the alkali industry; the pH value of sludge from12 sewage treatment plans exceeded the limit (5.5\u0026ndash;8.5) defined by the \u003cem\u003eControl standards of pollutants in sludge for agricultural use\u003c/em\u003e (GB 4284\u0026thinsp;\u0026minus;\u0026thinsp;2018) \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In addition, sludge samples from 67 studied plants had a pH value between 6.5\u0026ndash;7.5, and they might not affect the soil pH during sludge land application. The sewage sludge mostly exhibited a high water content, and the water content of only 11 dewatered sludge samples was 60% or less. Sludge samples with high water content should not be applied directly in different disposal methods and must be dewatered before proceeding to the next step\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The content (%) of organic matter in sludge ranged from 16.9 to 87.1%, while the total nitrogen and total phosphorus contents ranged from 0.27 to 12.2% and 0.03 to 5.03%, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhysical-chemical properties of sludge derived from sewage treatment plants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWater content\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrganic matter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edimensionless\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGB4284-2018\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.5\u0026ndash;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Spatial distribution characteristics of heavy metals in sludge\u003c/h2\u003e\u003cp\u003eThe statistical analysis of the heavy metal content in sludge from industrial parks is listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The median contents of Cr, Ni, Cu, and Zn are much lower than the mean value, and the standard deviation is large, indicating that the sludge in a few parks has a high content of Cr, Ni, Cu, and Zn; the contents of different categories of heavy metals in the sludge are also significantly different: the contents of Hg, As, Cd, Pb, Cr, Ni, Cu, and Zn range from 0.02\u0026ndash;18.55, 0.04\u0026ndash;59.80, 1.5-21.85, 25\u0026ndash;364, 2.5\u0026ndash;57,950, 3.65-94,509, 1.39-18.501, and 10.95\u0026ndash;83.750 mg∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The mean content values are ranked in descending order as follows: Zn\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Hg.\u003c/p\u003e\u003cp\u003eReferring to the following protocols, \u003cem\u003eDisposal of sludge from municipal wastewater treatment plant-Quality of sludge for co-landfilling\u003c/em\u003e (GB/T 23485\u0026thinsp;\u0026minus;\u0026thinsp;2009) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eDisposal of sludge from municipal wastewater treatment plant-Quality of sludge used in the production of cement clinker\u003c/em\u003e (CJ/T 314\u0026ndash;2009) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eDisposal of sludge from municipal wastewater treatment plant-Quality of sludge used in making brick\u003c/em\u003e (GB/T 25031\u0026thinsp;\u0026minus;\u0026thinsp;2010) \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eControl standards of pollutants in sludge for agricultural use\u003c/em\u003e (GB 4284\u0026thinsp;\u0026minus;\u0026thinsp;2018) \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, Class B sludge standards, and local Municipal Soil Background Value \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, the average content of each heavy metal in the sludge in industrial park is higher than the local soil background value. The average contents of Hg, As, Cd, Pb, and Cu are lower, while the average contents of Cr, Ni, and Zn are higher than the limit value for each type of reference standard, which may pose environmental risks and must be considered and controlled in the process of subsequent disposal and utilization\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical analysis of the heavy metal content in sludge from a certain city industrial parks.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1576.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e639.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4489.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e83750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e377.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7570.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2690.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil background value\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGB/T 23485\u0026thinsp;\u0026minus;\u0026thinsp;2009\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCJ/T 314\u0026ndash;2009\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGB/T 25031\u0026thinsp;\u0026minus;\u0026thinsp;2010\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGB4284-2018\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe distribution of Hg, As, Cd, Pb, Cu, Cr, Ni, and Zn in sewage plant sludge samples in a main urban area, the Northeast, and the Southeast. is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Sludge from districts and counties that produced less sewage that year was not collected, so the levels of the 8 heavy metals were zero. The concentrations of the heavy metals in sludge from other total 32 districts and counties greatly varied. Based on their contents from low to high, they were categorized into five grades by region. Ranks 1\u0026ndash;5 contained 7, 7, 6, 6, and 6 districts and counties, respectively.\u003c/p\u003e\u003cp\u003eAmong them, the Hg content in the southeastern region is concentrated in the lower content classes (Ranks 1\u0026ndash;3), and the Hg content in the main urban metropolitan area and tthe northeastern region is relatively high (Ranks 4 and 5). The content of As and Cd in the northeastern region is relatively low (rank 1\u0026ndash;3), and the relatively high content is concentrated in the main urban metropolitan area and the southeastern region (rank 4 and rank 5).The content distribution of Pb and Ni content is relatively balanced in all districts and counties, and the content of each content rank two or more subdistricts.Cr, Cu and Zn in the content of the highest rank 5 are only in the main urban metropolitan area, and the remaining four ranks of the content distribution is Relatively uniform.\u003c/p\u003e\u003cp\u003eIn summary, most districts and counties with high contents of Cr, Cu, and Zn are concentrated in the main urban metropolitan area. The content of Pb is high in all three regions, while the contents of Ni are relatively uniformly distributed. Nie et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e reported that human activities considerably influence the content of heavy metals in soil. According to Praspaliauskas et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, the higher the level of industrial development in the region, the higher the heavy metal content in the sludge. Thus, the determined relatively high heavy metal contents in sludge collected from the main urban metropolitan area may be related to the concentration of industrial parks, high population density, a relatively high degree of industrial development, and some electroplating parks in the main urban metropolitan area \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The spatial distribution results reflect the possible sources of heavy metal accumulation in soil and provide a reference for subsequent source modeling analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Potential ecological risk assessment of heavy metals in sludge\u003c/h2\u003e\u003cp\u003eThe potential ecological risk indices and the evaluation of heavy metals in sludge collected from 89 industrial parks in a certain city are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePotential ecological risk coefficients (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) and composite ecological risk indices (\u003cem\u003eRI\u003c/em\u003e) for heavy metals in sludge collected from 89 industrial parks.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eStatistical term\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e\u003cp\u003eE\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMaximum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e655.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1287.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11813.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1850.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e478.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e12483.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMinimum value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e62.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAverage value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e229.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e25.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e621.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eRisk ratio%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e93.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e88.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e88.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e31.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e35.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStronger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e20.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery strong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe average value of \u003cem\u003eRI\u003c/em\u003e of heavy metals in wastewater treatment plants in a certain city industrial parks is 623.37, representing a strong risk. However, the risk ratio shows that the ecological risk of heavy metals is in the low-risk and medium-risk range, accounting for 67.42% of the total, and indicating that the potential risk of most of the industrial wastewater treatment plants is in the acceptable range. A small portion of the wastewater treatment plants exhibit a very strong ecological risk, requiring further attention. The potential ecological risk coefficients, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, of 8 heavy metals, are arranged in the following order: Ni\u0026thinsp;\u0026gt;\u0026thinsp;Hg\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Pb. The results of the potential ecological risk evaluation show that the potential the ecological risk index (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) of Hg is the highest, while the ratio of strong and very strong ecological risk reaches 34.83%, followed by Ni, Cu, Zn, Cd, Cr, where the ratio of strong and very strong ecological risk is not more than 6%. The results show no high ecological risks for As and Pb. Therefore, in the process of disposal and utilization of industrial sludge, the potential ecological risk of Hg and Ni must be carefully treated, and the identification and detection of heavy metals Zn, Cu, Cd, and Cr should be strengthened.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Heavy metal source analysis\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Pearson correlation analysis\u003c/h2\u003e\u003cp\u003eWe correlated the contents of eight heavy metals and various physicochemical indexes of sludge, and the results are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For the listed elements, the following correlations are found: for Zn, a significant positive correlation with pH, a highly significant negative correlation with total nitrogen, a significant negative correlation with moisture, a highly significant negative correlation with water content, and a significant negative correlation with total organic carbon; for Cr, a highly significant negative correlation with moisture and total phosphorus, a highly significant positive correlation with pH; for Hg, a strong positive correlation with total phosphorus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01); for Cu, a highly significant positive correlation with pH and a significant negative correlation with total nitrogen (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); for Pb, a highly significant negative correlation with total phosphorus; pH can affect the presence and solubility of Cr, Cu, and Zn in sludge; an increase in the moisture content decreases the concentration of some heavy metals, therefore, presenting a negative correlation. The total organic carbon is related to the microbial activity in sludge, i.e., the total N and the total P are related to nitrogen and phosphorus removal from the sludge, affecting the content of some heavy metals\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation between heavy metal content and physical-chemical properties of sludge.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.217*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.231*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.317**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emoisture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.291**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.212*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.260*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.238*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.317**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.264*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.343**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.297**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.358*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: **At a 0.01 level (two-tailed), the correlation is significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Significant correlation at a 0.05 level (two-tailed).\u003c/p\u003e\u003cp\u003eThe correlation between heavy metals determines whether there is homology between their sources \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The results of Pearson correlation analysis for each heavy metal are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Cr, Ni, Cu, and Zn heavy metals exhibit two highly significant positive correlations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), mainly due to their common source, similar chemical properties, and their interrelation during the sludge treatment process. Cd exhibits a highly significant positive correlation with Pb, Cu, and Zn (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while there is a significant positive correlation of Pb and Hg (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but also with Cr, Cu, and Zn (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation between As and the other seven heavy metals is not significant, inferring that the source pathways of As and other heavy metals are different. The results of heavy metal correlation analysis indicate that the heavy metals with significant correlation most likely originate from the same industrial activity, entering the sewage system and eventually accumulating in the sludge.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2 Principal component analysis (PCA)\u003c/h2\u003e\u003cp\u003eThe KMO test coefficient of the data related to heavy metals in sludge is 0.622\u0026thinsp;\u0026gt;\u0026thinsp;0.6, and Bartlett's sphere test is P\u0026lt;0.001, indicating that the variables provide a reasonable basis for factor analysis. The results of the principal component analysis are statistically significant, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The main contributing elements for the first principal factor are Cr, Ni, Cu, and Zn, then Hg and Pb for the second principal factor, and As for the third principal factor.\u003c/p\u003e\u003cp\u003eThe statistical results of factor analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The principal component factor 1 has high loadings for Cr, Cu, and Zn, followed by Ni, Pb, and Cd, and compared to them, Hg and As exhibit low loadings. The principal component factor 2 explains 21.344% of the variance, with both Hg and Pb having high loadings, followed by Cd and As, while Cr, Ni, Cu, and Zn are relatively low. The principal component factor 3 explains 14.742% of the variance with high loadings for As, followed by Ni and Hg, and low loadings for the remaining heavy metals. The main sources of Ni are electroplating, chemical, and other industries. Hg mainly originates from electronic manufacturing, chemical, and other industries; industrial activities or the use of galvanized copper-plated pipes for urban sewage pipes may be the source of Cu and Zn; Pb mainly stems from automobile maintenance and cleaning wastewater, Cd and Cr mainly from electronic and other industrial sectors, and As mainly from metal manufacturing and other industries\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Combined with the source of wastewater in a certain city industrial parks, most of the studied objects belong to industrial wastewater; further analysis refers to the results of linear regression analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrincipal component analysis of heavy metals in sludge.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInitial eigenvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePost-extraction eigenvalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExplained variance/%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage of variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative /%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe component matrix for the principal component analysis of heavy metals in sludge.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eElemental\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eFactor loads\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.4.3 Absolute principal component scores-multivariate linear regression (APCS-MLR)\u003c/h2\u003e\u003cp\u003eThe results of APCS-MLR of eight heavy metals from sewage treatment plant sludge are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The predicted/measured values of the average content of each heavy metal are close to 1, and the adjusted R\u003csup\u003e2\u003c/sup\u003e ranges from 0.436 to 0.845, representing a good fit and highly credible analysis results.\u003c/p\u003e\u003cp\u003eSource 1 possesses high contributions of Cr, Ni, Cu, and Zn, with 61.98, 51.75, 57.76, and 55.72% respectively. According to the results of Pearson correlation analysis, the four heavy metals have a strong correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and may exist in the same source, coinciding with the results of the Source 1 contribution analysis. The survey results show that the content of Cr, Ni, Cu, and Zn in the sludge of a certain city industrial park is relatively large, which can be understood by keeping in mind that the sewage treatment plant receives sewage from machinery and equipment manufacturing industries which raw materials may contain Cr, Ni, Cu and other metal elements, while Zn may be used as additive or coating material in smelting, casting, heat and surface treatment processes \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In summary, Source 1 is mainly related to industrial wastewater from machinery and equipment industries and related sectors.\u003c/p\u003e\u003cp\u003eHg and Pb are the main contributing elements of Source 2, with contribution rates of 67.32 and 79.77%, respectively. Pb and Hg show a highly significant positive correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The contribution rate of Source 2 and the correlation analysis results coincide with the results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, showing a two-by-two correlation between Pb, Hg, and TP, and no significant correlation with other heavy metals. Pb and Hg source industries contain phosphorus and are not homologous with Cd, Ni, Cu, Zn, and As. The production and use of phosphorus fertilizers, the production of phosphorus-containing chemicals, and e-waste disposal can lead to the coexistence of Hg, Pb, and P \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Keeping in mind the results in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the contents of Hg and Pb in the sludge from a certain city industrial parks are relatively low, and the potential ecological risk of Pb is low. Electroplating, chemical, and electronic manufacturing industries deal with relatively high contents of Pb and Hg. As the current sample involves sludge partially from electroplating parks and other wastewater treatment plants mainly receiving industrial wastewater, it is deduced that Source 2 represents an industrial source, related to the mentioned industry sectors.\u003c/p\u003e\u003cp\u003eThe As contribution rate of Source 3 is 32.65%, much higher than that of other heavy metal elements. According to the correlation results, no significant correlation between As and other heavy metals appears, implying their different sources. The average content of As is about 1.5 times the background value of the soil in a certain city. The As content is relatively low, as well as the potential ecological risk, indicating that the source pathway of As does not yield a large amount of As. It was previously shown that, when As in sludge has a single source, it may originate from arsenic-containing detergents or industrial wastewater from pharmaceutical and fertilizer production, etc.\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Furthermore, most detergents contain N and P, but the results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show no correlation between As and N or P. Combined with the characteristics of the a certain city area, it is inferred that Source 3 is related to industrial wastewater from the pharmaceutical and chemical fertilizer sectors.\u003c/p\u003e\u003cp\u003eSince APCS-MLR is based on principal component analysis to extract components with eigenvalues greater than 1 for parsing, 31.99% of other sources are not included. Based on the interpretation of the total variance of principal components, the other sources originate from five components with variance contributions of 12.24, 8.70, 4.14, 3.56, and 2.35%. The most important contributing elements of other sources are As and Cd, the sources of which are influenced by a combination of factors, with As contributing 0.30, 13.59, 32.65, and 53.46% to source 1, source 2, source 3, and other sources, respectively, and Cd contributing 5.87, 19.72, 26.49, and 47.93% to source 1, source 2, source 3, and other sources, respectively. Meanwhile, the correlation analysis implies that Cd is highly correlated with many heavy metals, having certain loading coefficients in the first and the second principal components of the principal component analysis. In summary, other source data may originate from the mixed sources of multiple pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eSludge from 89 centralized wastewater treatment plants in industrial parks of a certain city was studied. Its physicochemical properties, heavy metal distribution characteristics, ecological risks, and heavy metal sources were analyzed, and the results are given as follows:\u003c/p\u003e\u003cp\u003e(1) The content of elemental Hg, As, Cd, Pb, Cr, Ni, Cu, and Zn heavy metals varied in the ranges of 0.02\u0026thinsp;~\u0026thinsp;18.55, 0.04\u0026thinsp;~\u0026thinsp;59.80, 1.5\u0026thinsp;~\u0026thinsp;21.85, 25\u0026thinsp;~\u0026thinsp;364, 2.5\u0026thinsp;~\u0026thinsp;57.950, 3.65\u0026thinsp;~\u0026thinsp;94.509, 1.39\u0026thinsp;~\u0026thinsp;18.501, and 10.95\u0026thinsp;~\u0026thinsp;83.750 mg∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Cr, Cu, and Zn were affected by the degree of industrial development and human activities, and most of the districts and counties with high content of these four heavy metals were concentrated in the main urban metropolitan area, while the distribution of Ni was relatively more uniform.\u003c/p\u003e\u003cp\u003e(2) The results of ecological risk evaluation showed that the degree of risk (E\u003csub\u003ei\u003c/sub\u003e) of each heavy metal in a certain city is arranged as follows: Ni\u0026thinsp;\u0026gt;\u0026thinsp;Hg\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Pb, having a wide dispersion in the RI value of the comprehensive potential ecological risk. The comprehensive ecological risk of the heavy metals in sludge from some sewage treatment plants was high. The obtained results suggest that special care needs to be taken about the potential ecological risk of Hg and Ni in the process of industrial sludge disposal and utilization and that the identification and detection of Zn, Cu, Cd, and Cr heavy metals should be strengthened.\u003c/p\u003e\u003cp\u003e(3) The heavy metal source analysis (including Pearson correlation analysis, PCA principal component analysis, and APCS-MLR model analysis) showed that Cr, Ni, Cu and Zn originate from the same source, mainly reflecting the influence of the mechanical equipment manufacturing industry; the modeling contribution rates of Cr, Ni, Cu and Zn are 61.98, 51.75, 57.76, and 55.72%, respectively; Hg and Pb mainly originated from industrial sources related to electroplating, chemical, and electronic manufacturing industries, having model contribution rates of 67.32 and 79.77%, respectively. The main source As could be related to industrial wastewater from pharmaceuticals and fertilizer production, with a model contribution rate of 32.65%. Finally, Cd mainly originates from mixed sources and a variety of pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\u003cp\u003e This research was funding by the Guidance Project of Chongqing Scientific Research Institutions: Ecological and Health Risk Study of Heavy Metals in Park Sludge Basedon Different Categories (No.Cqhky2021jxj100003).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Z. and H. Z. were responsible for writing the original draft; S. Y. was responsible for reviewing, editing, and providing guidance; D.Y. and X.G. were responsible for verification; D.B. was responsible for situation analysis; L.F. was responsible for obtaining funding.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJanaszek, A. et al. The Assessment of Sewage Sludge Utilization in Closed-Loop Economy from an Environmental Perspective. \u003cem\u003eWater\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/w16030383\u003c/span\u003e\u003cspan address=\"10.3390/w16030383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, L., Deng, X. \u0026amp; Li, Z. 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Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China. \u003cem\u003eEnviron. Geochem. Health\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10653-024-02037-9\u003c/span\u003e\u003cspan address=\"10.1007/s10653-024-02037-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"heavy metals, sludge from industrial parks, spatial distribution, ecological risk, source analysis","lastPublishedDoi":"10.21203/rs.3.rs-7286011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7286011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough pollution caused by heavy metals in sludge has recently attracted extensive attention, little is known about the distribution characteristics, potential ecological risks, and sources of heavy metals in sludge collected from industrial parks. In this study, we collected sludge from 89 centralized wastewater treatment plants in industrial parks, a certain city, and analyzed the content and related characteristics of eight heavy metals. The spatial distribution analysis of typical heavy metals showed relatively high contents of Cr, Ni, Cu, and Zn in the sludge in the main urban metropolitan area, which can be correlated with the degree of industrial development and human activities. The determined potential ecological risks (E\u003csub\u003ei\u003c/sub\u003e) were higher for Hg and Ni than for other heavy metals; the combined ecological risk value (RI) of the main urban metropolitan area was increased compared to other districts and counties. The analysis of heavy metal sources indicated that Cr, Cu, and Zn in the sludge originated from the same source, associated with the mechanical equipment manufacturing industry. Hg and Pb came from the same source and could be related to electroplating, chemical, and electronic manufacturing industries. The presented results provide a basis for studying the heavy metal pollution characteristics of sewage sludge.\u003c/p\u003e","manuscriptTitle":"Sources, Distribution Characteristics, and Risk Evaluation of Typical Heavy Metals in Sludge from Industrial Parks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 19:05:17","doi":"10.21203/rs.3.rs-7286011/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T06:17:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T05:58:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T11:54:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T10:16:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143575875445399618742660985871485764263","date":"2025-08-17T06:51:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308633393472802584981729164284894741759","date":"2025-08-13T09:41:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218426527922925108178954140825673434565","date":"2025-08-13T09:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T09:12:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T09:08:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T08:57:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T06:27:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-12T06:24:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eafc77a4-436f-4fa6-b890-426a49ce2731","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53377551,"name":"Biological sciences/Ecology"},{"id":53377552,"name":"Earth and environmental sciences/Ecology"},{"id":53377553,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-02-17T14:40:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 19:05:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7286011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7286011","identity":"rs-7286011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}