1 Introduction

Urban soils are regarded as a crucial component of the city ecological system which closely related to the general life quality of residents (Pecina et al. 2021). However, with the accelerated urbanization and industrial expansion in and around cities, frequent anthropogenic disturbances have led to the accumulation of various pollutants in urban soils. Among numerous types of pollutants, heavy metal contaminants are of great concern due to their biotoxicity and irreversibility, and they are considered to be important indicators for monitoring the impact of anthropogenic processes on soil environmental quality (Tong et al. 2020).

The sources of heavy metals in the soil can be either natural or anthropogenic or both (Siddiqui et al. 2020). Urban soils differ significantly from other natural ones in that they are more susceptible to human activity. Therefore, anthropogenic sources are seen as the primary cause of the increased pollution elements in urban soils, for instance, vehicle emissions, municipal and industrial wastes disposal, mining and smelting activities, construction waste and domestic waste, fossil fuel combustion, huge usage of agrochemicals(Ma et al. 2016; Men et al. 2018; Hou et al. 2019; Faisal et al. 2021). It is reported that over five million sites were contaminated by heavy metals worldwide(Sun et al. 2019). In view of the non-degradable properties, heavy metals tend to accumulate in organisms, and they may damage the nervous, lungs, liver, kidneys skeletal, enzymatic, endocrine, and immune systems promoting several disease conditions and even lead to cancer ultimately (Jaishankar et al. 2014; Li et al. 2014; Vareda et al. 2019). Heavy metals can do harm to humans in three ways: ingestion, inhalation, and dermal contact (Safiur Rahman et al. 2019). Previous studies have showed that ingestion is the as the main exposure route for risk of humans, furthermore, children were more susceptible health threats from toxic elements than adults (Tong et al. 2019, 2020; Zhang et al. 2019; Huang et al. 2021; Siddiqui et al. 2021).

Since the origins of elements in urban soils were complex and varied, source identification and distinction of heavy metals was regarded as precondition of preventing and controlling pollution (Wu et al. 2018). The positive matrix factor (PMF) model cannot only allow for non-negative constraints on both the loadings and contribution, but can also handle some missing data (Li et al. 2020). In consequence, the PMF model has been used previously to apportion the source allocation of atmospheric, water, sediment and soil pollutants (Zhang et al. 2018, 2020; Liu et al. 2019b; Srivastava et al. 2021). Nonetheless, the approach relies predominantly on the researcher's analysis for the local background parameter values, and its accuracy and application were constrained (Huang et al. 2021). Consequently, a comprehensive research method based on PMF in conjunction with correlation coefficients was employed to ensure the reliability of source assignment results in the study.

Meanwhile, the health risk levels varied by element type and population group, and the contribution of each pollution source to health risk accounts for different shares. To accurately control the health risks related to heavy metals in soil, there is a need for conducting the health risk assessment and identify highlight risky elements. In term of health risk assessments, previous studies tended to depend upon traditional models with fixed parameters, resulting in overestimation or underestimation results (Chen et al. 2019; Zeng et al. 2019; Qiao et al. 2020). Fortunately, Monte Carlo simulation technique, which take both the variability of specific exposure parameters and uncertainty of heavy metal concentrations into account, can minimize the uncertainties in the evaluation of health risks. Given this advantage of the Monte Carlo simulation approaches, it has been shown to be one of the most powerful method for probabilistic risk assessment (Ma et al. 2017; Wang et al. 2020b). Therefore, it was employed to evaluate health risks resulting from soil elements and to identify preferent heavy metals for risk control in the study.

Urban soils have been regarded as a good tool for diagnosing environmental pollutions (Awasthi et al. 2016; Liu et al. 2009). The spatial distributions, pollution level analysis, source identification and concentration-oriented health risk caused by toxic elements in urban soils have been studied a lot (Wang et al. 2017, 2019; Men et al. 2018; Sun et al. 2019). However, very few studies explored source apportionment and source-oriented health risk of heavy metals in urban soils, especially that in large megacity. As the core city of central plains city group, the soil pollution characteristics and the related health risks to human beings in the urban areas of Zhengzhou remains to be lack of study, in spite of the fact that many people may be involved. On account of this, the study focused on the contamination characteristics and probabilistic health risk of pollution elements in urban soils from Zhengzhou. The core objectives include (1) to investigate pollution characteristics and the potential ecological risk of eight heavy metals in urban soils, (2) to identify and quantify the source apportionment of targeted pollutants, (3) to evaluate concentration-oriented and source-oriented health risk for both adult and children exposed to heavy metals.

2 Experimental

2.1 Site description

Zhengzhou city (112°42′–114°14′ E, 34°16′–34°58′ N) which locates in the geographical center of China is the capital of Henan Province, and is an important transportation junction, as well as a national logistics and population catchment area. The city covers an area of 7,446 km2, and it contains 12.6 million residents according to the seventh national census. The climate of Zhengzhou is characterized as northern temperate continental monsoon climate with an average rainfall of 633.1 mm per year. In the past decades, the predominant geological location, policies, cultural conditions played key roles in attracting more people to visit or settle in the city. While the huge population drives economic development, and it also contributes directly or indirectly to the formation of massive amounts of pollutants (Wang et al. 2020a). In Zhengzhou, there are ring roads surrounding the Erqi Tower up to various distances. The areas within 4th ring road have the higher population density and vehicle stream, and it’s the regions of concern for the study.

2.2 Sample collection and preparation

In this study, the area within 4th ring road of Zhengzhou was divided into a sampling grid of 2.5 km × 2.5 km. A total of 78 urban soils were gathered in March 2019 (Fig. 1), and renovation soil sites were avoided. At each sampling location, no less than 5 subsamples were sampled using a stainless steel spatula within the radius of 50 m. Samples at each sub-sites were blended to obtain one composite sample and sealed in a self-sealing polyethylene pouch subsequently. After being naturally dried at room temperature, stone oak, construction debris, dry branches and fallen leaves were removed from the samples. The remaining were abraded and passed through a 0.15-mm nylon sieve, and then the homogeneous sieved samples were kept in polyethylene bags before analysis.

Fig. 1
figure 1

Sampling sites distribution of urban soils in Zhengzhou City

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2.3 Heavy metal determination

The contents of As, Cd, Co, Cu, Mn, Ni, Pb and Zn were measured according to previously described methods with minor modification (Ma et al. 2016). A microwave digestion (Mars One, CEM Corporation, Matthews, USA) was employed to extract target analytes from soil samples. Approximately 0.50 g sample was weighed and digested under pressure into a polytetrafluoroethylene (PTFE) vessel with 9 mL of concentrated HNO3 (70%) and 3 mL of HCl (37%). On completion of the digestion procedure, the digestion solutions were heated to less than 5 mL in the acid detector. After adequate cooling, solutions were filtered and then replenished to 50 mL with deionized water (18.2 MΩ cm, ULUP-III-10 T-ZL, Sichuan Youpu Ultrapure Technology Co., Sichuan China). The element concentrations were determined by ICP-MS (Agilent 7700x, Tokyo, Japan). A certified reference material (CRM) soil GBW07444 was applied to verify the digestion efficiency. Results showed that the recovery rates for all the heavy metals were around 85–105%.

2.4 Statistical analysis

The descriptive statistics of eight metals were performed on the strength of SPSS software (version 21.0). Pearson correlation matrix analysis was performed by Origin 2020b to quantify the relationship between each pair of targeted pollutants. Positive matrix factorization (PMF) model was carried out through the EPA PMF software (version 5.0) to allocate the hypothetical sources of pollutants. Given the great impact of outliers on PMF results, the dataset was detected before performing PMF model, and the outliers were identified and eliminated on the basis of interquartile ranges and histograms (Guan et al. 2018; Huang et al. 2021). In addition, to prevent the underestimation or overestimation of risks because of utilizing constant parameters, Monte Carlo simulation was selected to keep the uncertainties of the health risk evaluation to a minimum (Jiang et al. 2021). Crystal Ball software (version 11.1.24) was applied to perform the Monte Carlo simulation by considering 10,000 iterations with 95% confidence level. The spatial distribution of the contaminants in study area was drawn by Geographic Information System (GIS) software. The geostatistical interpolation method of ordinary kriging was selected to predicted the heavy metal concentrations at unsampled area (Zhang et al. 2021).

2.5 Ordinary kriging interpolation method

Geostatistical analyses based on ArcGIS software were used to describe all spatial variations. In order to develop better predictions, ordinary kriging interpolation method was introduced to estimate the attributes values at non-sampled sites by combining observed values with weights linearly. The predicted value could be calculated as follows:

$${\mathrm{Z}}^{*}\left({x}_{0}\right)={\sum }_{i=1}^{n}{\lambda }_{i}Z({x}_{i})$$
(1)

where Z*(x0) is the predicted value at an unknown location x0, Z (xi) is the observed values at location xi, λi is the weight placed on Z (xi).

2.6 Pollution levels and potential ecological risk of heavy metals

Geo-accumulation index (Igeo) was applied to assess the pollution level of individual heavy metal in soils, and it can be achieved by the following formula (Huang et al. 2021):

$${I}_{geo}={log}_{2}(\frac{{C}_{i}}{1.5\times {B}_{i}})$$
(2)

where Ci is the determined content of metal, Bi represents the geochemical background value of individual element and 1.5 is the constant value allowing the analysis of fluctuations heavy metals based on natural processes. Igeo is unitless, and it can be divided into seven classes, as shown in Table S1.

Different from the Igeo, all the individual evaluation factors and highlights the contribution of the highest enrichment elements were taken into account in the Nemerow composite index (Ps) method (Cui et al. 2021), which expressed as follows:

$$C_{f}^{i} = \frac{{C_{i} }}{{B_{i} }}$$
(3)
$${P}_{s}=\sqrt{\frac{{({C}_{fmax}^{i})}^{2}+{({C}_{fave}^{i})}^{2}}{2}}$$
(4)

where \({C}_{f}^{i}\) is the individual pollution index of element i, \({C}_{fmax}^{i}\) represents the maximum value of \({C}_{f}^{i}\), and \({C}_{fave}^{i}\) is the average of single pollution index of all the elements. Ps can be classified into five grades and listed in Table S1.

Ecological risk index (RI) was selected to estimate the adverse effect caused by heavy metals on the urban ecosystem, which considers the toxicity and environmental response of different heavy metals based on the toxicity response (Liu et al. 2019a). The RI was defined as ((Mihankhah et al. 2020):

$$RI=\sum_{i=1}^{n}{E}_{r}^{i}=\sum_{i=1}^{n}{T}_{r}^{i}{C}_{f}^{i}=\sum_{i=1}^{n}\left[{T}_{r}^{i}\left({C}_{i}/{B}_{i}\right)\right]$$
(5)

where \({T}_{r}^{i}\) is the biological toxic response factor of each element. According to previous studies, the values of \({T}_{r}^{i}\) for As, Cd, Co, Cu, Mn, Ni, Pb and Zn are 10, 30, 1, 5, 1, 2, 5 and 1, respectively (Hakanson 1980; Dehghan Madiseh et al. 2009). \({E}_{r}^{i}\) represents the potential ecological risk factor for a given substance. The division of RI values was suggested as Table S1.

2.7 Source apportionment model and uncertainty analysis

2.7.1 Source apportionment model

In the research, the PMF model was introduced for source allocation of heavy metals following the guidelines of the USEPA (USEPA 2014). The model divided the original matrix into two factor matrices gik and fjk and a residual matrix eij. The basic equation can be formulated as follows:

$${X}_{ij}=\sum_{k=1}^{p}{g}_{ik}{f}_{kj}+{e}_{ij}$$
(6)

where Xij is the concentration of heavy metal j measured in sample i, gik is the contribution of source k to sample i (mg/kg) and fkj is the amount of element j from source k. The residual eij is calculated by adjusting gik and fkj to minimum value of the objective function Q, which is defined as:

$$Q=\sum_{i=1}^{n}\sum_{j=1}^{m}{\left(\frac{{e}_{ij}}{{u}_{ij}}\right)}^{2}$$
(7)
$${u}_{ij}=\left\{\begin{array}{c}\frac{5}{6}\times MDL C\le MDL\\ \\ \sqrt{{( Error Fraction \times C)}^{2}+{(0.5MDL)}^{2}} C>MDL\end{array}\right.$$
(8)

where uij represents the uncertainty of heavy metal j in sample i, Error fraction is the measurement percentage, and MDL represents the method detection limit.

2.7.2 Methods of uncertainty analysis

The goodness of fit, bias and variability were considered to be the common method for testing the uncertainties of PMF model results (Men et al. 2019; Chai et al. 2021). In the research, fitting coefficients (r2) between soil heavy metals observed concentrations and predicted concentrations by PMF model were applied to evaluate goodness of fit, and it could be calculated based on formula below:

$${r}^{2}=\frac{{\sum }_{i=1}^{n}{({\widehat{y}}_{i}-\overline{y })}^{2}}{{\sum }_{i=1}^{n}{({y}_{i}-\overline{y })}^{2}}$$
(9)

where \({\widehat{y}}_{i}\) is the predicted concentration at site i, yi is the observed concentration at site i, \(\overline{y }\) is the mean observed concentration and n is the number of sampling sites.

Bootstrap (BS) could be estimate effects from random errors and part effects of rotational ambiguity, and it was used to test bias and variability of PMF model results consequently (Paatero et al. 2014). The essence of the method is to generate replicate datasets from the original observations by re-sampling with replacement. The bootstrap samples (X* = [x1*, x2*, ⋯, xn*]) were developed by sampling from empirical distribution function (Fn) of original data set (X = [x1, x2, ⋯, xn]). The statistical object (R* (X*, Fn)) could be calculated from the following formula:

$$R^{*} \left( {X^{*} ,F_{n} } \right) = \theta \left( {F_{n}^{*} } \right) - \theta \left( {F_{n} } \right)$$
(10)

where Fn* represents the empirical distribution function, θ(Fn*) and θ(Fn) are the estimation of parameter θ of population distribution of X. The distribution of R and the parameter θ could be obtained after repeating the sampling and calculation of R* (X*, Fn) for enough times.

The BS data sets were specified number of 100 in this study and then processed with PMF. For each BS run, the BS factors were compared with the base run factors. If no base factors correlate above the threshold for a given BS factor, that factor is considered “unmapped”. This process would be repeated for as many BS runs as the specifies. There could be instances when multiple BS factors from the same run may be mapped to the same base factor.

Based on the BS simulations, the coefficient of variation (CV) values were applied to reflect the deviation level of heavy metals source apportionments, greater CV values suggest greater level of deviation (Liu et al. 2018). It could be obtained from standard deviation (SD) values divided by average values of the 200 iterations BS results.

2.8 Probabilistic health risk assessment

The hazard quotient (HQ) and carcinogenic risk (CR) were widely employed to quantify the potential health risk posed by chemical elements for both children and adults (Gu and Gao 2018; Islam et al. 2020; Lee et al. 2021). HQ and CR were calculated using the corresponding average daily dose (ADD), reference dose (RfD) and cancer slope factor (SF), respectively. Given that the risk generated by heavy metals in soil comes from ingestion (ADDing), inhalation (ADDinh) and dermal contact (ADDderm), the ADD could be calculated by Monte Carlo simulation according the following Eqs. (11)–(13). The optimal probability distributions for concentration database of exposure heavy metals and exposure parameters used in the method were presented in Table S2 and Table S3.

$${\text{ADD}}_{{{\text{ing}}}} {\text{ = }}\frac{{{\text{C}} \times {\text{R}}_{{{\text{ing}}}} \times {\text{EF}} \times {\text{ED}}}}{{{\text{BW}} \times {\text{AT}}}} \times {\text{10}}^{{{\text{ - 6}}}}$$
(11)
$${ADD}_{inh}=\frac{C\times {R}_{inh}\times EF\times ED}{PEF\times BW\times AT}$$
(12)
$${ADD}_{derm}=\frac{C\times SL\times SA\times ABF\times EF\times ED}{BW\times AT}\times {10}^{-6}$$
(13)

The evaluation indicators of hazard index (HI) and total carcinogenic risk (TCR) for elements could be estimated by the following formulas (Han et al. 2020; Li et al. 2020):

$$HI=\sum_{i=1}^{n}{HQ}_{i}=\sum_{i=1}^{n}{(\frac{{ADD}_{ing}}{RfD}+\frac{{ADD}_{inh}}{RfD}+\frac{{ADD}_{derm}}{RfD})}_{i}$$
(14)
$${\text{TCR}} = \sum\limits_{{i = 1}}^{n} {{\text{CR}}_{{\text{i}}} } {\text{ = }}\sum\limits_{{{\text{i = 1}}}}^{{\text{n}}} {({\text{ADD}}_{{{\text{ing}}}} \times {\text{SF + ADD}}_{{{\text{inh}}}} \times {\text{SF + ADD}}_{{{\text{derm}}}} \times {\text{SF}})_{{\text{i}}} }$$
(15)

When HQ < 1 or HI < 1, adverse health effect of heavy metals to the population is unlikely. If HQ > 1 or HI > 1, it indicates that harmful effects on health caused by soil heavy metals may occur. When CR is less than 1.00E−06, the carcinogenic risk of elements is deemed negligible. When CR between 1.00E−06 and 1.00E−04, indicates that the risk to human beings is within the acceptable range. When CR is ore than 1.00E−04, shows that the carcinogenic risk produced by pollutants is unacceptable. The corresponding RfD and SF values used in equations were shown in Table S4.

3 Results and discussion

3.1 Pollution levels and spatial distribution of the heavy metals

Statistical descriptive analysis results of elements including minimum, maximum, mean, standard deviation, and coefficient of variation, in soils from study area are listed in Table 1. The results showed that the arithmetic mean concentrations of the elements were less than the natural background reference values of soils from Henan Province, with the exception of Cd and Pb. However, the peak values for As, Cd, Co, Cu, Pb and Zn were13.845, 0.744, 11.420, 48.830, 131.860 and 168.760 mg/kg, respectively, and they were 1.2, 10.1, 1.1, 2.5, 6.7 and 2.8 times of the corresponding reference values. Especially, the average values of Cd and Pb were 2.51 and 1.90 times higher than the corresponding background reference values, respectively (Fig.S1), which revealed that heavy metal contamination occurred in the study area. Additionally, Cd levels were above background values in 97.44% of samples, and Pb levels above background values in all samples, demonstrating that Cd and Pb were the dominant concentrated elements in urban soils. The CV shows that the concentration distribution of Cd, Cu, Pb and Zn with considerable high spatial differentiation (CV > 30%), especially Cd with the variation coefficient of 65.05%. The higher variation coefficients may be caused by elevated levels of elements in some sampling sites, suggesting that point sources pollution may exist for Cd, Cu, Pb and Zn in the study area (Huang et al. 2021). The remarkably high kurtosis values of the Pb distribution suggested the occurrence of abnormally high values in some sample, and it was presumably related to anthropogenic discharges (Huang et al. 2020).

Table 1 Statistical results of heavy metal concentrations in urban soils from Zhengzhou
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In order to clarify the transformation of the heavy metals in the research area, ordinary kriging interpolation was introduced to obtain the spatial distribution patterns of the target elements. The spatial distribution of As, Cd, Co, Cu, Mn, Ni, Pb, and Zn in downtown area of Zhengzhou were described in Fig. S3. As illustrated in Fig. S3, the spatial distribution maps for As, Co, Mn and Ni were similar, all showing high levels in the northwest and low levels in the southeast. The distribution trends of the four elements were gently and without hot-pot areas (exclude Co). Furthermore, the maximum values of all elements were not significantly different from the background values, and all the mean values were lower than the corresponding background values. The above certificate proves that As, Mn and Ni in urban were uncontaminated, and derive mainly from the soil parent material. A hot-pot area can be observed in contour maps of Co, which locates the intersection of Jinshui Road and Zhongzhou Avenue, Zhengzhou’s main thoroughfares, and it is the location of two large commercial plazas (Manhattan Plaza and Chinatown Cultural Plaza) in the area. The slightly elevated Co in this region may be associated with deliberate applications of Co salts, such as Co-containing plating materials in infrastructures, Co treated phosphate fertilizers for greening (Salah et al. 2015; Han et al. 2020). The overall Co distribution map showed that the slight increase of Co concentration in the region has only a minor effect on the mean values, indicating that the Co was also primarily derived from soil parent material.

Spatial distribution patterns of Cd, Cu, Pb and Zn were similar with significant spatial variability as presented in Fig. S3. All hot-pot areas of Cu, Pb and Zn appeared in downtown areas with high traffic volumes or near outer rings of dense traffic lanes, indicating that high concentrations of them might be attributed to engine wear, leaded gasoline, braking and other traffic sources (Dao et al. 2014; Du et al. 2019; Huang et al. 2021). Two hot-pot areas of Cd could be observed in Fig. S3, one near the Zijinshan interchange where traffic volumes were high, and the other in the commercial and industrial cluster near Chemical Road, suggesting that Cd pollution was not only related to traffic emissions, but also influenced by commercial activities and industrial discharges (Ma et al. 2016).

3.2 Potential ecological risk of heavy metals

The Igeo and Ps were calculated to evaluation pollution degree of soils in study area, and the results are represented in Fig. 2a and b. As shown in Fig. 2a, the contamination levels of As, Co, Cu, Mn, Ni and Zn were found negligible on the basis of Igeo categories. The average Igeo for Cd and Pb ranged from 0 to 1, and the 75% data points are below 2. Therefore, the levels for Cd and Pb were in the range of uncontaminated and moderately contaminated. As demonstrating in Fig. 2b, the Ps values ranged from 0.96 to 7.27. According to the classification of Ps values mentioned before, sample sites in the class of slightly polluted, moderately polluted and seriously polluted domain were 51 (65% of total sites), 15(19% of total sites), 11(14% of total sites), respectively. The results of Nemerow index method indicated that heavy metal pollution in some locations were depressing.

Fig. 2
figure 2

Pollution levels of heavy metals in urban soil. a Box-plots of the geo-accumulation index, b Nemerow index and c potential ecological risk indices

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The potential ecological risk indices at different sampling sites assessed according to Eq. (4) was depicted in Fig. 2c. Referring to the RI category aforementioned, the ecological risks were between low risk (RI < 150) and considerable risk (300 ≤ RI < 600). The RI of heavy metals at 63 sample points were within 150, demonstrating a low ecological risk for most sample sites. However, 15 sample sites (approximately 20% of total sites) with 150 ≤ RI < 300 were perceived as moderate risk, and 2 sites with RI ≥ 300 were categorized as considerable risk. The Cd contributed the maximum to RI of all elements, with the exception of sample site 65. The findings of the ecological risk evaluation suggested that contamination control in the study area was imminent, especially for Cd.

3.3 Source allocation and uncertainty assessment

3.3.1 Source allocation of heavy metals

To distinguish the origin and migration of eight elements effectively, Pearson's correlation analysis was performed to ascertain the relationships among the target element concentrations in soils (Fig. 3c) (Ke et al. 2017). Then, the possible sources of contaminants were apportioned and quantified by means of PMF model (Fig. 3a, b). A stable and minimum Q was introduced in the model to guarantee the residual matrix and the optimum factor number was confirmed as four. The Cd with signal-to-noise (S/N) ratios 0.53 was defined as "weak", and other heavy metals with S/N ratios above 1 were defined as "strong". The values of scaled residuals for all heavy metals were between − 3 and 3, indicating that there were no abnormal data in the model. The extracted factor loadings and contributions to individual heavy metals by PMF model were presented in Figs. S4 and S5. Further, spatial distributions of elements (Fig. S3) were applied to verify the source identification results.

Fig. 3
figure 3

Source apportionment of heavy metals (HMs) in urban soils of the study area. a The percentage of contribution for each factor by PMF model. b Factor profiles of heavy metals in soils derived from PMF model. c Identify the correlations between heavy metals by combining Pearson correlation analysis and PMF model

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The results of Pearson's correlation analysis indicated As, Co, Cu, Mn, Ni and Pb were found significantly positively correlated with each other, indicating that they may be from a common origin, while Cd, Cu, Pb and Zn were significantly positively correlated with each other, which may suggest the same source of them (Han et al. 2006). More importantly, the significant correlations of Cu and Pb to other metals indicate multiple sources (Fang et al. 2019). Contribution percentages of the extracted four Factors to the aggregate pollution and individual heavy metals were presented in Fig. 3a and b, respectively.

Factor 1 accounted for 18.73% of total contributions, was characterized by Ni, Co, Cu, Zn, Pb, Mn, As and Cd in descending order (Fig. 3a and b,). The Factor profiles were less than 30% for all elements. Furthermore, the observed distribution trend of primarily element Ni was gently without any obvious point source (Fig. S3) and not enriched in any sample site (Fig. S2). Meanwhile, it has been proved to be available in the soil parent materials and pedogenic process in previous studies (Ma et al. 2016; Zhang et al. 2018; Jin et al. 2019; Wang et al. 2019). Therefore, Factor1 may represent the natural source.

Factor 2 was taken 33.22% of total variance, and Mn (36.88%) and Cd (30.01%) received higher weighting than the other elements. Both of the two elements were reported in numerous literatures to be associated with industrial activities, such as smelting, coal consumption, metal processing, galvanization, batteries and plastic application (Li et al. 2017, 2020; Zhang et al. 2018; Men et al. 2020). The proportions of Cd and Mn exceeding background values were 97.44% and 1.28% (Fig. S2), suggesting that the presence of Factor 2 did not have a significant effect on Mn contamination compared to Cd. Two hot-pot areas of Cd could be observed in Fig.S3, one near the Zijinshan interchange and the other in the commercial and industrial cluster near Chemical Road, suggesting that Cd pollution was not only related to industrial production, but also influenced by other sources (Ma et al. 2016). Hence, Factor 2 could be interpreted as industrial production.

Factor 3 was weighted primarily on As (40.79%), Ni (31.19%) and Co (30.55%) (Fig. 3b), which made up 23.91% of total contribution (Fig. 3a). Although the mean values of As and Co did not surpass their corresponding local background values, the percentage of sample sites with As and Co exceeding background values were 30.77% and 3.85%, respectively (Fig. S2). Many previous studies confirmed that they were common elements in pesticides and fertilizers (Chen et al. 2016; Zhang et al. 2018; Wang et al. 2019). Pesticides and fertilizers are widely used and overused in urban afforestation and agricultural production (Onakpa et al. 2018). Coincidentally, the spatial distributions of As and Co with high values were in the north region (Fig. S3), which was a typical farming area and had a long history of traditional agricultural of Zhengzhou. As a result, Factor 3 could be assigned to agricultural activities.

Factor 4 consisted predominantly of Cd (68.19%), Zn (48.75%), Cu (32.39%) and Pb (29.49%) (Fig. 3b), was taken 24.14% of total variance (Fig. 3a). Spatial distribution patterns of Cd, Zn, Cu and Pb were similar with significant spatial variability as presented in Fig. S3. Moreover, the proportion of samples that exceed the local background values for Cd, Zn, Cu and Pb were 97.44%, 17.95%, 8.97% and 100%, respectively (Fig. S2). The results showed that Cd and Pb was significantly enriched in urban soil and all of them may be associated with anthropogenic activities. All hot-pot areas of Zn, Cu and Pb could be observed in downtown areas with high traffic volumes or near outer rings of dense traffic lanes, indicating that high concentrations of them might be attributed to leaded petrol, braking, engine wear and other traffic pollution (Du et al. 2019; Huang et al. 2021). Similarly, one hot-pot area of Cd appeared near the Zijinshan interchange where traffic volumes were high, and Cd was described as a component of tyres and lubricants (Huang et al. 2021). As a consequence, Factor 4 was classified as traffic emissions.

3.3.2 Uncertainty assessment for factor contributions

In this study, fitted coefficients (r2), variability and CV values were applied to test uncertainties of pollutant source analysis. The r2 between the observed and predicted concentrations were shown in Fig. S6. As illustrated in the figure, the r2 of all elements were above 0.692, and r2 values of As, Cd, Co, Cu, Mn, Ni and Zn were even higher than 0.882, which indicated that the PMF model predictions could better explain the values and variation of observed measured values(Men et al. 2019).

The variability of the contribution of Four sources based on BS runs in the PMF model were presented in Fig. S7. Median values in BS runs corresponded to the most accurate Factor contributions, while interquartile ranges represented the variability of Factors’ contributions. As shown in Fig. S7, the correlations could be found between median contributions of different slope values and interquartile ranges. The higher R2, the lower variability of contributions in bootstrap in different species. The determination coefficient (R2) ranged from 0.984 to 0.997 for all Factors, indicating lower variations of simulated contributions among different heavy metal species.

Fig. S8 showed the base run concentrations of eight heavy metals and CV values based on the BS run in every Factor. Generally, higher CV values indicate significant deviations. According to the CV values in the line chart, Cd exhibited higher CV values in Factor 1, Factor 3 and Factor 4, and the CV value of As was higher in Factor 2, indicating higher deviation degrees. The CV values of Co, Mn and Pb in Factor 1, Cd, Cu, Pb and Zn in Factor 2, Pb and Ni in Factor 3, Cu, Pb, Co and Ni in Factor 4 were all less than 30%, indicating lower deviation degrees. Moreover, higher CV values tended to correlate with lower concentration in the base run of the Four Factors, which might be related to the re-sampling process based on BS simulation, or to its own low determined values (Brown et al. 2015). Despite the fact that the BS results could not reflect the uncertainty of rotation normally, they can identify the factors with poorly reproducible (Xie et al. 2013). In addition, the lower concentrations of heavy metal were allocated by the PMF model, the less impact on the results of the PMF base run(Liu et al. 2018). Therefore, it could be concluded that all four source Factors allocated by the PMF model were reliable.

3.4 Probabilistic health risk assessment

3.4.1 Concentration-oriented health risk analysis

In order to further clarify the health effects of pollutants on the local residents, the non-carcinogenic and carcinogenic risk base on heavy metal concentrations were evaluated, and the results were demonstrated in the Figs. 4 and 5.

Fig. 4
figure 4

Probability distribution of non-carcinogenic hazard index for a HI and HQ of b As, c Cd, d Co, e Cu, f Mn, g Ni, h Pb and i Zn, the vertical dashed lines with blue or green colour represented the mean values for adults and children, respectively

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Fig. 5
figure 5

Probability distribution and the percentage surpassed 1E-06 for a TCR and CR of b As, c Cd, d Co, e Ni and f Pb

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As presented in Fig. 4, the HI and HQ of individual element for both children and adult were no more than the USEPA’s specified value of 1, suggesting that the potential non-carcinogenic risks could be ignored. The mean HQ values of heavy metals exhibited a trend of As > Pb > Mn > Ni > Co ≈ Cu > Cd > Zn. Among three exposure routes, the ingestion way was the primary contributor of potential risk (Table S5). Additionally, compared with adults, children were more susceptible to non-carcinogenic risks. Coincidentally, this observation was also previously reported in studies (Tong et al. 2019; Huang et al. 2021), which alerted the public to the benefits of personal hygiene, reducing the frequency of hand-mouth contact and taking appropriate skin protection measures outdoors, especially for developing children.

The probability distribution of TCR in Fig. 5 revealed that the carcinogenic risk of five elements in the studied area cannot be negligible. As showed in Fig. 5a, the mean values of TCR caused by heavy metal pollutants for adults (1.03E−04) and children (1.28E−04) all exceed the upper acceptable limit of carcinogenic risk (1E−04). Further, the high exceedance percentage of threshold were found in Fig. S9, indicating that both adults and children were more likely to cause cancer and pose an unacceptable risk. The CR values of heavy metals were different from each other, and they could be available in decreasing order of Ni, Co, As, Cd and Pb (Fig. 5b–f). The proportion of risk values for Ni, Co and As above the threshold of 1E −04 could be found in Fig. S9. Besides, the mean CR values of Ni, Co and As were all above the acceptable limit of 1E−06 to the extent that they were at the 5th percentile (Table S6), and they were considered to be principal contributor to cancer risk. Therefore, those elements were considered as priority pollutants for further risk control.

3.4.2 Source-oriented health risk analysis

To differentiate the contribution of different heavy metal pollution sources to health risk, a comprehensive method on the basis of probabilistic health risk assessment developed by Huang (Huang et al. 2021) was adopt to identify source-oriented health risk. As depicted in Fig. 6, the risk profiles of the four sources showed similar distributional characteristics between children and adults, and agricultural pollution was considered to be the dominant source of health risks for the two groups. According to Fig. 4, As was identified as the dominant element for non-carcinogenic health risks owing to its highest mean HQ value. The Fig. 5 showed that Ni, Co and As were the main elements causing carcinogenic risks. The three elements mentioned above held relatively high weightings in Factor 3 (Fig. S5), and they were distributed highly in the north of the city (Fig. S3). In consequence, reducing emissions of heavy metals from agricultural activities is an effective step in decreasing potential risk to the public.

Fig. 6
figure 6

Comparison of health risks caused by different pollution sources of heavy metals. a Non-carcinogenic risk and b Carcinogenic risk for children, adult, respectively

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4 Conclusions

The contaminated level, source recognition and risk evaluation of eight elements in urban soils from Zhengzhou city were carried out in the study. Results found that Cd and Pb were the dominant concentrated contaminants and presented higher ecological risks compared to the others. The probability distribution of concentration-oriented health risk revealed that adverse health effect of heavy metals for all populations was unlikely, while the carcinogenic risk produced by five elements could not be acceptable. Moreover, the children were more susceptible to health risks than adults, and the ingestion way was considered to be the major route of risk exposure. Four Factors were identified based on the conjunction of PMF model and correlation analysis. Nevertheless, sampling locations in this study were mostly close to traffic lanes, which may interfere with the source analysis results to some extent. Uncertainty analysis revealed that there were certain uncertainties in PMF source analysis, but pollution source Factors still retained reliable. Subsequently, the source-oriented health risk was carried out through PMF model in conjunction with Monte Carlo simulation technique, and the agricultural source was confirmed to be the main source of health risks, and As, Co and Ni were identified as the highlight risky elements, which should be given special consideration for further risk control.