Assessing risk to human health for heavy metal contamination from public point utility through ground dust: a case study in Nantong, China

Heavy metal contamination in ground dust presents potential environmental and human health threats. However, the heavy metal contamination status of ground dust in the vicinity of public point utilities remains poorly explored. Therefore, this study has been designed to analyze the heavy metal contaminations in the ground dust collected monthly near a public bronze sculpture in an urban campus of Nantong, China, using geo-accumulation indexes (Igeo), enrichment factors (EF), potential ecological risk indexes (RI), and health risks (noncarcinogenic risks (HI) and carcinogenic risks (CR)). This study revealed that the maximum Cr, Cu, Mn, Ni, Pb, and Zn concentrations in ground dust samples were 156.2, 708.8, 869.8, 140.8, 180.5, and 1089.7 mg kg−1, respectively, in which the mean Cu and Zn concentrations were 9 and 7 times higher than the background level in the soil. Temporally speaking, for the majority of heavy metals (with the exception of Ni), the high-concentration seasons tend to be mainly summer and autumn. It was observed that Cu and Zn exhibited significant enrichment (EF = 11.7 and 8.4, respectively), moderate-to-strong pollution (Igeo = 2.4 and 2.0, respectively), and moderate- and low-potential ecological risks (Eri\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {E}_r^i $$\end{document} = 45.6 and 6.6, respectively). The noncarcinogenic risks which adults exposed to the heavy metal concentrations suffered were found to be insignificant. However, the carcinogenic risks related to Ni (1.3E-04) had exceeded the acceptable level. Based on principal component analysis (PCA) and correlation analysis, the heavy metal concentrations in the ground dust of urban campuses could be related to public point utilities, traffic-related exhaust sources, and industrial activities. This study’s findings demonstrated that urban public utilities require more attention due to their significant enrichment, ecological risk factors, and the significant carcinogenic risks to the population.


Introduction
Heavy metals can cause serious contamination in many large cities (Jahandari 2020;Torghabeh et al. 2019;Rahman et al. 2019). Many heavy metals (for example, Cr, Cu, Mn, Ni, Pb, and Zn) are of particular concern for the environment due to their potential toxicity and known persistence in the environment (MHC 2006;US EPA 1996). Dust is a solid particulate matter that can absorb heavy metals and a diverse range of other contaminants from various sources (Žibret et al. 2013). Furthermore, the particles accumulate on the roadsides or outdoor ground surfaces, eventually becoming road dust or ground dust . Ground dust particles are the products of the interactions between airborne particulates, liquids, and gaseous materials originating from both natural and anthropogenic processes and are considered as one of the most important carriers of heavy metals (Bao et al. 2019;Hou et al. Responsible Editor: Lotfi Aleya 2019). These particles can be transported into the atmosphere through resuspension processes and may potentially become important sources of atmospheric particulate matter (Padoan et al. 2017). Specifically, fine dust particles may be subsequently absorbed by humans via ingestion, direct inhalation, and dermal contact, often resulting in various diseases (Cai et al. 2021;Torghabeh et al. 2019). Ground dust can also enter bodies of water through surface runoff during rainy seasons. This may result in sediment contamination, which may eventually enter the food chain (Kusin et al. 2017). Therefore, the heavy metals which are associated with ground dust pose significant threats to the environment in general and specifically to human health.
During the past few decades, heavy metals in ground dust have been extensively studied in large cities from the perspectives of concentrations, enrichment levels, source identifications, spatial distribution characteristics, and health risk assessments (Jahandari 2020;Rahman et al. 2019;Wang et al. 2019). The majority of the aforementioned studies have focused on metal concentrations in the capital region (Men et al. 2018;Tang and Han 2017) or provincial capital regions (Ali et al. 2017;Bao et al. 2019;Ghanavati et al. 2019;Leng et al. 2017;Sobhanardakani 2018), as well as highly populated cities (Aminiyan et al. 2018) and coal-mining cities . However, few data are currently available regarding the metal concentrations from public point utilities (such as sculptures and metal fences) in medium-sized cities with rapid industrialization. In addition, the data regarding the magnetic characteristics of ground dust have been suggested as a tool which could potentially be used for the rapid and efficient determinations of heavy metal pollution levels due to their observed strong correlations (Cao et al. 2015;Qian et al. 2015;Wang et al. 2019). However, it is still unknown whether or not one specific magnetic index can indicate the heavy metal concentrations for the different seasons due to the lack of data at the temporal scale. Furthermore, Torghabeh et al. (2019) showed that a specific metal (such as Cr) with a moderate metal enrichment level and a low potential ecological risk factor could also induce high health risks, including both noncarcinogenic and carcinogenic risks. Therefore, health risk assessments should also obviously be included during pollution evaluations, along with metal enrichment levels and potential ecological risks. Nantong City has experienced fast industrialization and urban expansion during the last few decades (Nantong Statistic Bureau, 2010. With the rapid increases in urban construction, including gardens, campus sculptures, and road guardrails, the possibilities of heavy metals being released into the natural environment from such strong point contamination sources remain unknown and worthy of deeper exploration. Previous studies have shown that the heavy metal concentrations (Cu, Pb, and Zn) in outdoor ground dust in Nantong City were highest compared to indoor ground dust of urban campus, while the surface soil indicated the lowest value (Qian et al. 2012(Qian et al. , 2015. Specifically, the outdoor ground dust in the vicinity of the bronze sculpture was heavily polluted by Cu, with a geo-accumulation index of 2.82 (moderate-to-strong pollution) (Qian et al. 2012). In addition, the magnetic properties (such as saturation isothermal remanent magnetization) of the ground dust could be proxies of the heavy metal concentrations (Qian et al. 2015). However, there was little information available regarding the seasonal characteristics, correlations between magnetic properties and heavy metal concentrations during different seasons, and the potential health risks of contaminated ground dust near public point settings in an urban campus.
In order to address the seasonal characteristics and ecological health risks of the heavy metal concentrations in ground dust near public utilities and to identify their potential contamination sources, ground dust samples were collected monthly from one special urban campus site in Nantong, China. The objectives of this research were as follows: (1) to determine the magnetic characteristics and the concentration levels of six toxic heavy metals in the ground dust samples collected in the vicinity of a public point source (bronze sculpture) and analyze their seasonal characteristics, (2) to evaluate the contamination levels and human health risks caused by heavy metals, and (3) to identify the probable metal sources.

Study area
Nantong (31°41′06″~32°42′44″N, 120°11′47″~121°54′33″ E) is a medium-sized city in China, which is situated at the eastern coastal border of the country (Fig. 1). It has a population of over 7.32 million and covers approximately 8554 km 2 . The city is situated in a subtropical monsoon climate zone, with an annual precipitation of 1040 mm and an average temperature of 15.1°C. Nantong is an economic center and modern port city situated in the north wing of China's Yangtze River Delta region. According to the Nantong Statistics Yearbook (Nantong Statistic Bureau, 2010, the urbanization rate and the amount of coal consumption in Nantong increased from 31.2 to 61.1%, and from 19,330,000 to 25,624,400 tons, respectively, for the period ranging from 2010 to 2014. Meanwhile, the total number of privateowned cars increased 2.4-fold during the same period (Fig.  S1). In 2018, the industrial added value in Nantong ranked 20th overall in China (https://www.sohu.com/a/349337020_ 760842). The construction of public point settings has experienced rapid development in recent years. In addition, the rapid development of shipbuilding and offshore engineering, high-end textiles, new materials, and intelligent equipment consumed large amounts of resources while simultaneously discharging massive amounts of pollutants into the air in the region.

Sampling methods
The sampling site in this study is 3 m 2 (1 m × 3 m) area and set up close to a bronze sculpture in Nantong University (Fig. 1). According to the seasonal difference in wind direction of Nantong, the ground dust samples were collected monthly using plastic brushes and dustpans under dry weather conditions after three continuous sunny days in all four seasons (southeast wind in summer and north wind in winter) . Based on the system layout method of "The Technical Specifications for Soil Environmental Monitoring (HJ/T166-2004)" (SEPA 2004), a total of 40 ground dust samples were collected from October 2011 to November 2013. The dust samples were air-dried for at least 7 days at room temperature and passed through a 1-mm nylon sieve in order to remove any large particles and all debris, such as hair, brick, stone, leaves, and other impurities and then stored in polyethylene bags ready for geochemical analysis (Cai et al. 2021).

Magnetic properties
The low-and high-frequency (470 Hz, χ lf , and 4700 Hz, χ hf , respectively) magnetic susceptibilities were measured at a room temperature of approximately 25°C using a BartingtonMS2 device. The percentage frequency-dependent magnetic susceptibility (χ fd %) was calculated using the following equation: where χ lf and χ hf represent the low-and high-frequency magnetic susceptibilities, respectively. Values of χ fd % < 2% indicated that the samples were "essentially" free of superparamagnetic (SP) particles. Values of χ fd % > 10% indicated that SP particles were abundant (over 75%) (Dearing et al. 1996). Anhysteretic remanent magnetization (ARM) was imparted using a DTECH 2000 alternating-field demagnetizer with a direct current (DC) biasing field of 0.04 mT and a peak alternating field of 100 mT and then measured using an Agico JR6 spinner magnetometer. ARM was expressed as an anhysteretic magnetization susceptibility, χ ARM .
The "hard" isothermal remanence (HIRM) was calculated using the following equation: where the SIRM (saturation isothermal remanent magnetization) and IRM (isothermal remnant magnetization) were imparted at 1000 mT using an MMPM10 pulse magnetizer and also measured using an Agico JR6 spinner magnetometer. Subsequently, the reversed DC field of −300 mT was applied, and the change in remanence was measured after each step. These magnetizations were referred to as SIRM and IRM −300 mT, respectively.

Heavy metals
The process of the ground dust samples was conducted by digesting 100 mg of the sample with a mixture of concentrated HNO 3 -HF-HClO 4 mixture on an electric hot plate (around 120°C) until the complete digestion (the digestion solution was clear and no more white smoke). After that, the extracts were diluted to 10 mL, and the concentrations of eight elements (Cr, Cu, Mn, Ni, Pb, Zn, Al, and Fe) were measured using an inductively coupled plasma-automatic emission The quality assurance and quality control measures included reagent blanks and analytical duplicates. The analytical precision was estimated to be <5% for the heavy metals, which was based on the duplicate analysis results of the samples and standards. Quality control assessments were performed using the National Standard of China reference material (GBW 07427).

Pollution assessments
Contamination assessment method Geo-accumulation index The geo-accumulation index (I geo ) was introduced by Müller in 1969 and has now become the most popular indices used to assess the degree of heavy metal contamination (Cai et al. 2021). In the present study, the I geo was calculated using the following equation: where C i and B i refer to the examined concentration of metal i and its background value. The factor 1.5 was applied in order to control the variations of the B i value caused by the environment (Müller 1969), and C i and B i are in the same concentration unit. Seven categories of I geo were identified ( (Cu), 585.0 mg kg −1 (Mn), 26.7 mg kg −1 (Ni), 26.2 mg kg −1 (Pb), 62.6 mg kg −1 (Zn), and 6.4% (Al), respectively.

Enrichment factor
The enrichment factor (EF) is widely used to evaluate the degrees of enrichment for certain elements within environmental samples (Aminiyan et al. 2018;Tan et al. 2018;Wang et al. 2019). In particular, the EF is recognized as an efficient means of distinguishing natural and anthropogenic sources and can be utilized to evaluate the potential impacts of heavy metal pollutants (Doabi et al. 2017). The EF value was calculated as follows: where M sample and M background represent the concentrations of targeted heavy metals and reference metals in the ground dust and backgrounds, respectively. In this study, Al was selected as the reference element. The background value of each examined metal is the same to B i value in Eq. 3. Six classes of EF are listed in Table 1. Generally speaking, an EF close to 1 indicates that the elements mainly originated from the Earth's crust or soil (Taylor and McLennan 1985). However, an EF > 1.5 indicates anthropogenic sources of the toxic metals, and an EF > 10 indicates that the elements in the samples have been enriched as a result of human activities.
Potential ecological risks The potential ecological risk index (RI) has been widely applied to contamination assessments  (Kusin et al. 2017;Men et al. 2018;Aminiyan et al. 2018). This index provides the calculation of adverse ecological effects caused by exposure to one or more pollutants in the dust (Saeedi et al. 2012). The calculation method includes the following formulas (Hakanson 1980): where RI is the total amount of potential ecological risk for all traced metals, E i r is the potential ecological risk factor for a given metal, and T i r refers to the toxic response coefficient developed by Hakanson (1980). In this study, the T i r follows the order of Zn = Mn = 1 < Cr = 2 < Cu = Ni = Pb = 5 (Aminiyan et al. 2018). C i f corresponds to the pollution factor for each metal, and C i and B i are the same to Eq. 3. The minimum, mean, and maximum E i r and RI were calculated for each metal in this investigation. The interpretation categories for the RI are detailed in Table 1.

Human health risk assessments
In order to determine the probability rates of the noncarcinogenic and carcinogenic risks to the public due to heavy metal contamination in ground dust, the risks to human health should be included in the assessments. Based on their behavioral and physiological differences, human subjects are usually divided into two groups: adults and children (Men et al. 2018). In this study, only adults were considered since the study area was an urban campus setting. The public was mainly exposed to the heavy metal contamination through the following pathways: direct ingestion of the dust particles, inhalation of the dust particles through mouth and nose, and dermal contact absorption. According to the Exposure Factors Handbook (US EPA 1996), the average daily dosage of a contaminant caused by the aforementioned three exposure pathways can be estimated as follows (Han et al. 2016a): where ADD ing , ADD inh , and ADD dermal (mg kg −1 day −1 ) represent the average daily dosages through direct ingestion, inhalation, and dermal contact absorption, respectively; C (mg kg −1 ) indicates heavy metal concentrations in ground dust; IngR (mg day −1 ) refers to the ingestion exposure rate of the soil; EF (days year −1 ) is the exposure frequency; ED (years) represents the exposure duration; BW (kg) donates the average body weight; AT (days) indicates the average time; InhR (m 3 day −1 ) is the inhalation rate; PEF (m 3 kg −1 ) refers to the particle emission factor; CF (kg mg −1 ) is the conversion factor; SA (cm 2 ) represents the surface area of the skin which comes in contact with dust; AF (mg cm −2 ) is the skin adherence factor; and ABF (dimensionless) represents the dermal absorption factor. In this study, in accordance with noncancerous risks of individual toxic metals and specific reference dosages (R f D i ), the hazard quotient (HQ i ) Man et al. 2010) was calculated as follows: where HQ i is hazard quotient of a single pathway; i indicates the route of exposure (ingestion, inhalation, and dermal contact absorption); ADD i is the average daily dosages through a single pathway; R f D i represents the specific reference dosage (mg kg −1 d −1 ), which is an estimation of the highest acceptable risk to a student through university life; HI is the hazard index due to multiple pathways, in which HI value ≤ 1 indicates that there are no obvious adverse health effects of toxic metals for students and residential population. Whereas HI > 1 indicates that hostile health effects can potentially occur (Man et al. 2010). The carcinogenic risks (CR) are considered as the possibility of cancer occurring during a lifespan due to contact with carcinogenic hazards (Nazarpour et al. 2018). CR can be evaluated using the following equations (Dehghani et al. 2017): where SF i (dimensionless) is the carcinogenic slope factor, and i is the same as in Eq. 11. CR values between 1.0 × 10 −6 and 1.0 × 10 −4 indicate acceptable or tolerable carcinogenic risks. CR values higher than 1.0 × 10 −4 are considered as unacceptable, and CR values lower than 1.0 × 10 −6 are regarded as insignificant health hazards (Chen et al. 2012).
In this study's evaluations, Cr, Cu, Mn, Zn, and Pb were not analyzed due to the lack of SF. The carcinogenic and noncarcinogenic risk parameters are listed in Table S1.

Statistical analysis
The Spearman correlation analysis method was adopted in this study to establish the correlations among the various metals and magnetic properties. In addition, PCA was performed using SPSS 16.0 for the purpose of exploring the heavy metal sources in the dust samples obtained from the urban campus in Nantong. PCA was used to simplify the data and to identify the factors which explained the majority of the variances in the data ).

Results and discussion
Basic magnetic properties and heavy metal concentrations The results of the basic magnetic measurements are summarized in Table 2. χ lf and SIRM typically reflect the concentrations of such ferromagnetic minerals as magnetite and maghemite, and χ ARM reflects the stable single domain (SSD) and fine-grained pseudo-single domain (PSD) ferromagnetic mineral content levels. The mean values of χ lf and SIRM for the four seasons decreased in the following order: summer > autumn > spring > winter. The mean values of χ lf during summer and autumn seasons (553.8 × 10 −8 and 502.9 × 10 −8 m 3 kg −1 , respectively) were significantly higher than the values observed during the winter seasons (369.4 × 10 −8 m 3 kg −1 ). Correspondingly, the highest mean SIRM value of 6486.3 × 10 −5 Am 2 kg −1 occurred during summer, and the lowest value of 4908.6 × 10 −5 Am 2 kg −1 occurred during winter. During the autumn season, the ground dust was found to have the highest mean χ ARM values (mean of 790.2 × 10 −8 m 3 kg −1 , ranging from 340.2 × 10 −8 to 1882.5 × 10 −8 m 3 kg −1 ), which were significantly higher than the χ ARM values during the winter months (621.1 × 10 −8 m 3 kg − 1 ). Additionally, the values of χ lf , SIRM, and χ ARM were found to be significantly correlated (P < 0.01; Table S3-whole year data). These results indicated that the ground dust had higher magnetic mineral concentrations during the summer and autumn months when compared with the winter months. These magnetic minerals mainly consisted of ferromagnetic and incomplete antiferromagnetic minerals. In this field of study, χ fd % is often used to reflect the relative amounts of SP grains in environmental samples. The mean values of χ fd % for the samples examined in this study were observed to be similar among the four seasons, and mainly close to 2%, indicating that they were generally dominated by relatively coarse magnetic grains. The descriptive statistical analyses (minimum, maximum, mean, and standard deviation) of the trace metal concentrations in the ground dust, along with their corresponding background values, are summarized in Table 3. The Cr, Cu, Mn, Ni, Pb, and Zn concentrations in the ground dust samples varied from 24.7 to 156.2 mg kg −1 , 65.6 to 708.8 mg kg −1 , 371.2 to 869.8 mg kg −1 , 19.4 to 140.8 mg kg −1 , 39.1 to 180.5 mg kg −1 , and 165.2 to 1089.7 mg kg −1 , respectively. Their mean values were 0.8, 9.1, 1.0, 1.3, 3.1, and 6.6 times that of the soil background values, respectively (China National Environmental Monitoring Center 1990). The highest concentrations of heavy metals found in ground dust were for Mn, followed by Zn, Cu, Pb, Cr, and Ni, respectively. These findings were consistent with the observations of previous related research studies (Qian et al. 2015), in which Zn, Pb, and Cu were predominantly present in the ground dust samples of the Nantong college campus.
In the current study, extreme measurement results were also found for the Cu and Zn concentrations, with mean values of 203.4 and 414.9 mg kg −1 , which greatly exceeded the reference values of 22.3 and 62.6 mg kg −1 ( Table 3). The extremely high concentrations of Cu and Zn were largely due to the erosion and corrosion of the point bronze sculpture. Such factors as high temperatures and exposure to wet weather may accelerate erosion processes, causing wear-to-bronze sculptures which consist of Cu and Zn. In addition, Cu is a heavy metal used in numerous applications due to its physical It was found that the heavy metal concentrations widely varied between the different seasons. For the majority of the trace metals (with the exception of Ni), the higher concentration times were mainly during the summer and autumn seasons (Fig. 2), which is consistent with the χ lf and SIRM seasonal variations. This result indicated that the seasonal magnetic characteristics could imply the seasonal characteristics for heavy metal concentrations. To be specific, among the six investigated heavy metals, Mn and Pb displayed significantly higher concentrations during summer and autumn seasons when compared to winter and spring (P < 0.05). These findings were similar to the seasonal observations of metal concentrations in atmospheric dust observed in Nantong (unpublished data) and Beijing (Tang and Han 2017), while different from those in outdoor dust samples obtained at a university dormitory in Lanzhou (Bao et al. 2019) and Qingshan District in Wuhan City (Cai et al. 2021). The seasonal variations in heavy metal concentrations of ground dust were determined to be jointly influenced overall by the monsoons, climate, and human activities in Nantong. For example, when compared with Lanzhou, little coal was consumed for heating in winter in Nantong (Nantong Statistic Bureau, 2010. In addition, strong erosion and corrosion mainly occurred during the warm and wet seasons (such as summer and autumn with high precipitation and high temperature) (Roy et al. 1998). Furthermore, during summer and autumn, the atmospheric particles containing metals fall to the ground with high precipitation (http://data.cma.cn), which may result in high metal concentrations in the ground dust. While during winter, the metals are mainly in the atmospheric particles due to less rainfall, weak convection, and low air humidity (http://data.cma.cn). Finally, heavy metal particles emitted from industrial activities, mostly in the southeastern part of the city, precipitate in the city center due to the southeastern wind in summer (http://data.cma.cn). The above-mentioned four aspects could be important reasons for the low-metal concentrations of ground dust during spring and winter and the high-metal concentrations during summer and autumn in the study area.
The heavy metal concentrations in the campus ground dust in Nantong were compared with the available global published data ( Table 4). The concentration levels of all the metals in the ground dust were within the global range. From this study's results, it was determined that the concentration levels of the majority of the metals in the ground dust samples obtained from the Nantong urban campus were lower than those in Shanghai, urban Beijing, Xi'an, and Baoji, which are large cities in China. They were also lower than those of Seoul (Korea), Thessaloniki (Greece), and Tehran (Iran). Meanwhile, they were found to be higher than those in Tianjin, Hefei, Maanshan, Huainan, Chibi, Lanzhou, and Baotou (China), and Villavicencio (Colombia). These differences may have been due to the different geochemical compositions and ongoing activities in the different cities. Specifically, the concentrations of Cu, Ni, and Fe were higher than those observed in the majority of China's cities, as well as the soil background values (China National Environmental Monitoring Center 1990) (Table 4). These findings suggested that they may have been associated with anthropogenic sources. It should also be noted that Ni is a carcinogenic element for humans, and long-term intake of this element can cause lung cancer (Shen and Zhang 1994). In addition, the current metal concentrations may have been increased due to recent increments in traffic density, industry, and population in Nantong.

Geo-accumulation index (I geo )
The average I geo values for seven traced metals were as follows: I geo -Cr, −1.1; I geo -Cu, 2.4; I geo -Mn, −0.7; I geo -Ni, −0.3; I geo -Pb, 0.9; and I geo -Zn, 2.0. The majority of the sampling times were not polluted by Cr, Mn, and Ni (Figs. 3a and 4a). The contamination levels of Cu, Zn, and Pb were higher than those of the other metals and followed a decreasing order of Cu > Zn > Pb > Ni > Mn > Cr. The average I geo values for Cu and Zn were in the range of 2 to 3, showing moderate-tostrong pollution levels. Therefore, the high-contamination levels of Cu (I geo =2.4) in urban campus ground dust should be given more attention in the future. Cu is usually used in making wires, utensils, and sculptures. It has been found that under hot and wet weather conditions, Cu can easily be released from artwork, such as statues (Roy et al. 1998). Therefore, this study speculated that the high Cu pollution levels in the examined ground dust samples were probably related to the weathering erosion and corrosion of a bronze sculpture near the sampling site.

Enrichment factor (EF)
In order to better identify the natural and anthropogenic sources of the dust in Nantong, the EF values were calculated (Figs. 3b and 4b). The mean enrichment factors for the investigated metals ranged from 1.1 (Cr) to 11.7 (Cu) and consistently decreased with the order of the I geo . The EF values for Cu and Zn exceeded 5, indicating significant enrichment. The mean EF values of Pb exceeded 3, demonstrating that this metal had contaminated the campus ground dust to a moderate degree. Meanwhile, Cr, Mn, and Ni with mean EF values of 1.1, 1.2, and 1.7, respectively, were considered to be at minimal enrichment levels. Previous studies have suggested that heavy metals with mean EF values higher than 1.5 demonstrate anthropogenic sources (Jadoon et al. 2018). It was  (1990) confirmed that the contaminations of Cr, Mn, and Ni are likely originated primarily from natural sources, such as wind-blown soil minerals.

Potential ecological risk and potential risk indexes (E i r and RI)
In order to further characterize dust metal pollution in the study areas, the potential ecological risks associated with heavy metals in the dust samples were further assessed by calculating the E i r of individual element and the RI for all elements (Figs. 3c and 4c). The trend pattern of the E i r in the dust samples was Cu > Pb > Ni > Zn > Cr > Mn. The average E i r values of Cr, Mn, Ni, Pb, and Zn were <40, indicating low potential ecological risks, while Cu had a considerably higher average E i r (45.6), suggesting moderate potential ecological risks. The calculated RI values ranged between 30.0 and 217.0, indicating low-to-considerable risks for selected metals in the study area. The average RI values for all the studied  Fig. 4 Monthly assessment indexes of the metals detected in the examined ground dust metals were 76.9, which was considered to be a moderate ecological risk level. Among the evaluated metals, Cu and Pb accounted for 59 and 20% of the potential ecological risks, respectively. Moreover, the RI values indicated that 65.0% of the street dust samples had ranged between 50 and 100, indicating a moderate risk level, and 17.5% were smaller than 50, indicating a low ecological risk level.
Comparing the assessment results of the I geo , EF, and E i r , it was revealed that Cu was in a moderate-to-strong pollution range in the I geo , significantly enriched in the EF, and in a moderate ecological risk in the E i r . Meanwhile, the Zn was in a moderate pollution range and displayed significant contamination and low ecological risks in the three assessments, respectively. The differences between the three indices were likely caused by the differences in evaluation criteria and toxicities of the evaluated metals (Kusin et al. 2017). Among the metals assessed, the I geo , EF, and E i r of the Cu, Zn, Pb, and Ni were higher than all of the other metals. Although these metals did not occur at levels posing high ecological risks to human health in the current study, their high accumulation in the urban campus environments merits further attention.  reported that although the ground dust contained moderate levels of Pb pollution, the potential ecological risks associated with those metals should be of concern due to their high ecological toxicities. In addition, the observed seasonal variations in each index revealed that the majority of the metals were enriched during the summer and autumn seasons (June to October), which may have been attributed to the southeastern wind, high precipitation, and air temperature factors during those periods (http://data.cma.cn).

Health risk assessments
Three exposure dosage means (ingestion, inhalation, and dermal adsorption) for heavy metals were considered for both noncarcinogenic and carcinogenic risks. This study's adult health risk assessment results of the metals detected in the examined ground dust are listed in Table 5. The highest AD ing value was for Mn (6.7 × 10 −4 ), while the lowest value was for Ni (4.3 × 10 −5 ). The hazard quotient (HQ) values of Cu, Ni, Pb, and Zn for ingestion of dust particles were approximately three orders of magnitude higher than those for inhalation of dust particles. The results revealed that ingestion of dust particles appeared to be the main exposure route for metals with the potential to negatively affect human health considering noncancerous risks, which was similar to the reported results from previous related studies (Bourliva et al. 2016;Ghanavati et al. 2019;Jahandari 2020;Men et al. 2018;Rahman et al. 2019;Tan et al. 2018). The exposure to dust particles by adults was determined to have likely been more dependent on hand-mouth contact than on dermal contact and inhalation. In addition, daily outdoor activities, such as rubbing sweat, consumption of food outdoors, or not washing hands or faces before eating or drinking, may also cause higher risks of ingestion.
In regard to the noncarcinogenic effects, the hazard index (HI) for each metal in the examined ground dust samples decreased in the order of Pb > Cr > Mn > Cu > Zn > Ni, respectively (Table 5). Furthermore, none of the HI values for the metals exceeded 1, indicating the minimal noncarcinogenic risks to the adults. The average risk contributions through ingestion and dermal to the HI were 64.5 and 25.2%, respectively. Among the six analyzed metals, the noncarcinogenic risks of Pb were the highest, followed by Cr, and approximately 31 times higher than the lowest noncarcinogenic risks of Ni. In contrast, the carcinogenic risks of Cr ranged from 1.0 × 10 −6 to 1.0×10 −4 , suggesting that the carcinogenic risks of Cr were within the acceptable range. Conversely, the carcinogenic risks of Ni (1.3 × 10 −4 ) were greater than 1.0 × 10 −4 , indicating that the carcinogenic risks of Ni were at a critical level and required more attention. Although the mean I geo values of both the Cr and Ni were less than 0, they still posed carcinogenic risks to exposed adults. It was suggested that the health risks of metals which were at low concentrations should also be assessed since the health risk posed by those metals may have also been unacceptable. Similar results were also found in previous research investigations (Men et al. 2018). Therefore, considering the findings obtained in this study and previous related studies, it was considered that if

Identification of the metal sources
In this study, the co-relationships between heavy metals and magnetic indicators were also investigated (Table S3). PCA methods were applied to assist in the identification of the probable sources of heavy metals in the ground dust of the study area ( Fig. 5 and Table 6). χ lf and SIRM were significantly correlated with Cr, Mn, and Pb (P < 0.01). In specific terms, the SIRM was less correlated with Cu and Zn (P < 0.05). The aforementioned relationships indicated that χ lf and SIRM can potentially be used as efficient proxy indicators for heavy metal pollution in ground dust, which was similar to the finding of previous studies . Meanwhile, the correlation coefficients between the magnetic parameters and heavy metals obviously varied during different seasons (Table S3), which suggested that the magnetic indexes indicated different metals in different seasons. Therefore, the applicability of magnetic susceptibility as a useful and rapid proxy method to investigate the seasonal variations of metal pollution may have some constraints. The trace metal concentrations were positively correlated with each other (Table S3). For example, for the entire year data results, one group of toxic metals in the ground dust (Cr, Cu, Pb, and Zn) showed a strong significant correlation with each other (P < 0.01). Due to the low EF values of Cr compared with the other three metals, the significantly positive correlations between Cr and other metals could potentially explain the possibility that they had originated from anthropogenic sources which contain Cr (Aminiyan et al. 2018). In addition, a significantly positive correlation (P < 0.05) was also observed among some metal pairs, such as Mn-Cu and Mn-Ni. Ni displayed weak correlations with Pb and Zn, respectively. These findings suggested that they were possibly derived from different sources.
In the examined campus ground dust, two eigenvalues were found to be higher than 1, and two principal components (PC) accounted for approximately 71.40% of the total variances (Table 6 and Fig. 5). The first principal component (PC1) accounted for 53.51% of the total variances, with strong positive loading of Cr (0.72), Cu (0.72), Mn (0.80), Pb (0.84), Zn (0.72), χ lf (0.75), SIRM (0.78), and χ ARM (0.74) detected, and moderate loading of Ni (0.45). The major fossil fuels combusted in Nantong included petrol, gasoline, and natural gas (Nantong Statistic Bureau 2010-2014). These fossil fuels and fuel additives contain Pb, Cu, Zn, Ni, and even Cr (Pant et al. 2017;Zhang et al. 2016). In addition, the erosion and corrosion of painted surfaces, such as street surface markings, are known to play a substantial role in the release of Pb (Cheng and Hu 2010;Vleeschouwer et al. 2007;Zannoni et al. 2016). Furthermore, the low EF values of Cr, Mn, and Ni indicated existing natural sources, such as wind-blown soil minerals. It was considered that traffic-related exhaust sources, erosion of painted surfaces, and wind-blown soil minerals may have been the primary factors in the obtained analysis results. The second principal component (PC2) explained 17.90% of the total variances and was dominated by the positive loading of Cu (0.56) and Zn (0.51), and the negative loading of χ lf (−0.56), SIRM (−0.51), and χ ARM (−0.46). Cu and Zn are widely used in metal building materials (Men et al. 2018). Based on the lower metal concentrations for Cu and Zn in the surface soil (43.5 and 213.0 mg kg −1 ) and indoor ground dust (74.6 and 111.7 mg kg −1 ) of this campus (Qian et al. 2012(Qian et al. , 2015, the weathering and erosion of urban metallic substances, such as bronze sculptures, could be strongly related to Cu and Zn pollution in the ground dust samples. In addition, the smelting activities of factories could also emit large amounts of atmospheric dust and particulate matter into the environment, including high amounts of Cu and Zn (Esmaeili et al. 2014;Gong et al. 2014).

Conclusions
In this study, the obtained results led to the following conclusions: (1) The Cu, Ni, Pb, and Zn concentrations of the sampling ground dust were higher to different degrees than that of soil background levels. The accumulation index (I geo ) and enrichment factors (EF) values of Cu and Zn indicated moderate-to-strong accumulation (I geo = 2.4 and 2.0, respectively) and significant enrichment (EF = 11.7 and 8.4, respectively). The potential ecological risks (E i r ) of the ground dust were moderate for Cu (45.6), and low for the other detected metals (1.0~15.4).
(2) The health risk analysis results revealed that ingestion of the contaminated dust particles was the major pathway of exposure to the metals. The hazard index (HI) values of all the detected metals indicated no significant noncarcinogenic health risks for adults (<1). While the carcinogenic risk level for Ni showed unacceptable carcinogenic risk (10 −4 to 10 −6 ).
(3) The examined ground dust displayed higher contamination levels during summer and autumn than spring and winter (with the exception of Ni), which was consistent with the χ lf and SIRM seasonal variations. Although the heavy metal concentrations were strongly correlated with magnetic concentrations in the entire year sampling data, the correlation coefficients varied during the different seasons. (4) The bronze sculpture near the sampling sites was the most important point source for the high Cu pollution in the ground dust. Traffic-related exhaust sources, industrial activities, and wind-blown soil minerals were also important potential sources. Due to increased public utility construction, such as sculptures and guardrails, in schools, parks, and groundsides, these are becoming important point sources for the metal pollution in the urban ground dust and require greater degrees of attention and monitoring.