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Article

Detection and Assessments of Sources and Health Hazards Caused by Heavy Metals in the Dust of Urban Streets in Harbin, Northeast China

1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(18), 11657; https://doi.org/10.3390/su141811657
Submission received: 10 August 2022 / Revised: 4 September 2022 / Accepted: 6 September 2022 / Published: 16 September 2022

Abstract

:
To investigate heavy metals (HMs) in the dust of the urban streets and evaluate health hazards through dust pollution exposure, this research implements an analysis method called principal component analysis and a model called positive matrix factorization to investigate the associations between HMs and their plausible allocation of sources. A total number of 118 dust samples were collected from Harbin, China, which is one of the most eloquent industrial hubs and tourist destinations. The results suggest that the mean concentrations of Cd, Cr, Cu, Zn, Ni, Pb, and Mn are 1.79 ± 1.618, 67.23 ± 32.84, 57.76 ± 51.50, 328.52 ± 117.62, 27.11 ± 4.66, 83.03 ± 25.39, and 745.34 ± 153.22 mg kg−1, respectively. The erratic enrichment of Cu, Zn, Pb, and Cd is succeeded by a geo-accumulation index and the factors that are used for enrichment. Both the spatial distribution and correlation analysis imply that Cu, Zn, Pb, and Cd can be controlled by anthropogenic activities. On the contrary, Cr, Mn, and Ni can be ascribed to natural sources. The hazard quotients are less than 1, and the hazard indexes for seniors and kids are 0.129 and 0.852, respectively. So, kids had more non-carcinogenic hazards than the older individuals did. Both groups have carcinogenic risks of less than 1 × 10−6. The results indicated that street dust could not be potentially accepted as a health hazard for dwellers. Cu, Zn, Pb, Cr, Ni, and Cd existed in the street dust of the research region and have been influenced by the combination of industrial and traffic sources and domestic coal combustion, and the parent material that forms soil affects the levels of Mn. A model, called the PMF, is implemented in the study of street dust pollution sources, enhancing the reliability and accuracy of pollution source determination, and presenting some potential applications.

1. Introduction

Dust that is accumulated in an urban road is a composite mix consisting of sedimented particles in the formation of airborne, dislocated urban soils, and biogenic entities (like fractured rocks and plants), which could be swiftly transferred by various conditions such as blowing winds, the movement of vehicles and pedestrians, and steer-sweeping maneuvers [1,2]. Human activities including industrial ones (e.g., petroleum refinery), activities related to traffic, coal combustion (in thermal plants), and atmospheric deposition could emanate inorganic pollutants into urban environments [3,4,5]. Metallic pollutants can be easily trapped in street dust that is scattered over a large area [6], so various types of heavy metals (HMs) can be found easily [7,8]. Unfortunately, metals that are piled in street dust could damage both the ingestion and respiration systems of humans which would have various negative impacts on, especially, the organs, the central nervous system, the blood and urine system, and the degradation of pulmonary and renal functions [9,10,11]. Therefore, exposure to HMs in suspended dust could cause certain health risks to pedestrians, even if a short amount of time is spent daily on such a road [12]. Therefore, HMs could easily accumulate in street dust, which can be used as an indicator to estimate the contaminant exposure of inhabitants in cities [4,6,13].
Detecting the various sources of HMs is a critical process since it is tough to identify whether the metals arise from anthropogenic or natural sources. Also, measuring, quantitatively, the fluxes of metal loadings concerning the whole pollution source is impossible. [14,15]. Natural sources include the weathering of rocks and jungle fires. Metal contamination sources that are related to anthropogenic ones generally include activities of mining and smelting, electronics, farming, waste sludge, and the combustion of fossil fuels. Different kinds of analysis methods have been recently constructed to find the primal sources of HMs, including principal component analyses, cluster, factor, and multiple regression analysis [16,17,18]. The ratios that are presented by the group of each metal are not quantified by the approaches that are mentioned in the previous lines. So, their ratios are obtained by using several contamination references. For example, USEPA, the U.S. Environmental Protection Agency, have provided a model that is used for a professional source-receptor called the Positive Matrix Factorization (PMF), which quantitatively attain the ratios of the environmental contamination sources and their distributions [19]. The quantification is dependent upon decomposing the primary data into a matrix of contributions by employing the profiles of the factors. Also, the contributing source factors of the contaminants could help to determine both the behavior and source of them. The methods classify the HMs, based on the changes that occur in their spatial concentrations [20]. In addition, the metals in each group contain the same sources [16,21,22]. The PMF appears to be a promising approach since the estimations of single error employ both outliers and discrete observations like missing ones and the below detection limit [23].
Harbin, the capital of Heilongjiang Province, is one of the leading industries and well-known tourist spots in China. The major industries are ferrous smelters, coal-run power plants, chemical, machinery, and automobile industries. Harbin is becoming an epicenter for various areas such as the economy, manufacturing, culture, and education in Northeast China since rapid industrialization, urbanization, and high-tech advancements have recently contributed to its expansion. Harbin is located in the northernmost part of the country and is characterized by cold and long winters, with the lowest air temperature reaching −40 °C. Central heating in Harbin will last for at least six months, beginning in late October and ending in late April of the next year. Coal combustion-based heating is one of the main reasons that causes the serious pollution of heavy metals. Moreover, a large number of automobiles, 1.45 million, resulting in the dense traffic jams in 2016, is a reality in Harbin. Therefore, haze pollution is becoming more and more serious. Nevertheless, to prevent street icing and endangering traffic safety, the environmental sanitation departments of Harbin in winter cannot sprinkle water or wash the streets to suppress the amount of street dust which eventually leads to more accumulation of it. Unfortunately, there exists an ostensive lack of perception about the accumulation and spatial distribution of HMs in Harbin. Furthermore, city inhabitants pay little attention to the health effects of HMs. Therefore, it is imperative and representative to study the pollution behavior of HMs that are accumulated in the street dust of the colder regions in northern China during the months when heating systems are widely used to provide scientific decision-making for improving the urban environmental quality and creating livable cities.
Therefore, the goal of the article includes the following issues: (1) to verify the pollution scene of metals such as Cd, Cr, Cu, Zn, Ni, Mn, and Pb in street dust and assess the degree of HM contamination concerning various geostatistical methods such as Igeo and EF; (2) to specify the spatial distribution of HMs in the dust; (3) to obtain the CDI, HQ, HI, and CR for senior citizens and kids to evaluate the risks that are related to cancer-specific or other diseases that are caused by ingesting, respiring, and coming into contact with dust; (4) to discuss the sources of HMs. The results could provide insights into heavy metal accumulation and a basis to eradicate the metal inputs from urban street dust.

2. Materials and Methods

2.1. The Research Area

Harbin is situated at 44°04′–46°40′ N and 125°42′–130°10′ E, with a territory that covers 7086 km2 and is populated by 5.51 million people. The research area is situated in the north temperate zone, having a climate that is characterized by the continental monsoon with long and harsh winters and generally short and warm summers. The average yearly temperature is 3.6 °C, with 141 frost-free days. The average yearly rainfall is 569 mm, with around 60% of the rainfall occurring between June and September. The concentrated snowfall period continues for at least three months, from November to January of the next year. The average temperature in winter is about −19 °C in January. While the northeast has dominant winds in winter and autumn, the southwest has this feature in summer and spring.

2.2. Sampling and Pretreatment

A sample of 118 collections were selected from the Harbin Fourth Ring Road (Figure 1) in April 2018 when no snow had occurred one week before the collection. Twelve to twenty samples were collected on the edges of both the road and the pavement and were mixed thoroughly to provide bulk samples in a representative manner. Those sites were typically scattered by covering the whole urban area. The amount of dust that was collected at the main intersection of all the locations was conducted by using a plastic brush and dustpan is between 800 and 1500 g. By sweeping slowly and in one direction, the samples were put into nylon bags, in which resuspending minute specks could be avoided when the sampling was conducted [24]. Unrelated objects like cigarette remains, miniature rocks, sludge plastics, metal scruple, and others were disregarded by a person before the sampling process was started [25]. Each sample was air-dried and screened out for particles whose sizes were less than 53 µm since ingesting, breathing, and adsorbing through the skin is highly probable for the small particles [25,26]. Many researchers have claimed that particles that are less than 63 µm in size present the highest loading for HMs in all chemical phases [27,28]. Therefore, this research picked particles that were less than 53 µm [25]. The coarse sand fraction (200–2000 µm) was not investigated since it was left out of the research scope [25], instead focusing just on inhalable or ingested particles. After sieving, the samples were stored at lower than 4 °C until a further study was conducted.

2.3. Chemical Analysis

The conventional approaches that were run by Chinese researchers (GB/T 17,138–1997, GB/T 17,141–1997) suggest that digesting metals in the dust is possible through HClO4-HNO3-HF by employing ultrapure grade acids. For the solutions that were derived through digestion, a graphite furnace atomizer (EX7i, Shimadzu, Japan) was utilized to detect the concentrations of heavy metals concentrations with the use of a flame (air acetylene) atomic absorption spectrophotometer, FAAS (AA-6300C, Shimadzu, Japan) with insufficient sensitivity for the measurements. The limits for Pb, Cr, Cu, Zn, Cd, and Mn were found to be 1, 4, 2, 2, 0.001, and 2 mg kg−1, respectively. The standard reference material (GBW 07405 (GSS-5)) was obtained from the National Standard Reference Material of China. The rates of recovery were between 95–105%. The investigations were conducted in a triplicate manner, and the sample collection and analytical process were implemented to achieve reagent blanks, analytically. All of the glasses were immersed overnight in 20% of HNO3 and bathed completely with deionized water to avoid contamination.

2.4. Geo-Chemical Measurements

2.4.1. Geo-Accumulation Index

Igeo, which is called the geo-accumulation index, has been widely implemented to assess the contamination of the HMs in street dust [25,29].
Igeo= log2 (Cn /1.5 Bn)
where Cn denotes the concentration of the metal in street dust, and Bn represents the geochemical basis concentration of the HM [25]. A fixed number of 1.5 was utilized to mitigate the effect of potential alterations in the basis scores that were caused by lithogenic alterations in the samples. As underlined by [29], seven Igeo classes were expressed. (Igeo < 0), (0 < Igeo < 1), (1 < Igeo < 2), (2 < Igeo < 3), (3 < Igeo < 4), (4 < Igeo < 5), and (Igeo > 5) correspond to, none, between none and moderate, moderate, between moderate and heavy, heavy, between heavy and extreme, and extreme, respectively.

2.4.2. The Enrichment Factor (EF)

The EF, called the enrichment factor, was employed to identify the type of the particle’s origin, which was either anthropogenic or natural, to assess the degree of the human exertion effect [30]. Mn, a traditional element, was used in the current study for reference [5,31]. While a score of EF = 1 denotes a natural source, a score that is greater than 10 represents an anthropogenic source. The basic values of the soil elements in Harbin were obtained from the National Environmental Monitoring Center of China [32] and were employed as reference concentrations.
EF = [Cn. (sample)/Cref. (sample)]/[Bn (baseline)/Bref. (baseline)]
where Cn represents the metal concentrations that were experimented on; Cref denotes the concentration of Mn in the dust; Bn(baseline) denotes the basis value of the metal that was experimented on; Bref (baseline) represents the background value of Mn. The scores of EF < 2, 2 ≤ EF < 5, 5 ≤ EF < 20, 20 ≤ EF < 40, and EF ≥ 40 correspond to deficiently to a minimum, medium, remarkable, very high, and extremely high enrichments, respectively [5].

2.4.3. The Assessment of Health Hazards of HMs

The CDI, which is called the chronic daily intake, was attained for (1) directly ingested particles (CDIing), (2) inhaled, resuspended particles (CDIinh), and (3) those particles that were absorbed through the skin (CDIdermal), respectively. The derivations of different CDIs are expressed by [25,33].
CDIing = (C × Ring × Fexp × Texp)/(ABW × Tavrg) × 10−6
CDIinh = ((C × Rinh × Fexp × Texp)/(PEF × ABW × Tavrg)
CDIdermal = (C × SAF × Askin × DAF × Fexp × Texp)/(ABW × Tavrg) × 10−6
where CDI represents the chronic daily intake (mg kg−1 day−1); C presents the concentration of the metal, Ring denotes the rate of ingestion (200 mg dust day−1 for kids (1–6 years), 100 mg dust day−1 for seniors), Rinh denotes the rate of the inhalation (20 m3 day−1 for seniors, 7.6 m3 day−1 for kids), Fexp denotes the frequency of the exposure (365 days year−1), Texp denotes the period of the exposure (6 years for kids and 24 years for seniors), Askin represents the area of the skin (2800 cm2 for kids and 5700 cm2 for seniors), SAF denotes the factor called skin adherence (0.2 mg cm−2 h−1 for kids and 0.07 mg cm−2 h−1 for seniors), DAF represents the factor called dermal absorption (unitless) [(0.001 for both kids and senior citizens), PEF denotes the factor used for the emission of particles (1.36 × 109 m3 kg−1 for both groups), ABW denotes the mean body weight (18 kg for kids and 60 kg for senior citizens), Tavrg defines the mean time for non-carcinogens, Tavrg = Texp × 365 [25,34]. The likely non-carcinogenic hazards for single metals are expressed in [25,35,36].
Hazard quotient (HQ) = CDI/RfD
Hazard index (HI) = ∑ HQ (ingestion/inhalation/dermal)
RfD denotes the dose that is used for the reference (Table 1) [25,36], and the CDIing/inh/dermal represents the chronic rate of sickness that us caused by the daily ingesting, inhaling, and dermatological acquisition of HMs, respectively. If the HQ is less than 1, then the people are not exposed to any important toxic effects. If a value of more than 1 is observed, the apparent toxic effects could be observed with an increasing probability [36]. The HI was calculated by adding the scores of the HQ. The non-carcinogenic effects were observed when HI > 1 while no significant risk is indicated when HI < 1 [36].
The CR, called the carcinogenic risk, is attained when there is an aggregation of single cancer hazards of each exposure pathway concerning the posterior equation [25,35]:
CR = ∑CDIi × CSFi
where i denotes the pathways of various exposures such as through ingesting, inhaling, and dermal contact, and CSF [35] denotes the slope factor of a carcinogenic effect in Table 2. Those scores for the inhalation exposure pathway are presented for Cd, Cr, and Ni. Therefore, the manuscript just extracts the carcinogenic risk when the exposure of the inhalation route is considered. Carcinogenic risks that are larger than 1 × 10−4 are assumed to be impermissible. On the other hand, scores that are less than 1 × 10−6 are assumed to have no significant health effects. When the scores vary between 10−6 and 10−4, a carcinogenic health effect occurs.

2.4.4. The PMF Model

The PMF is a conventional model that is used to assess the source allocation of the HMs, quantitatively [37,38,39], which is dependent upon factorizing the dataset into matrices. It is defined by:
X ij = k = 1 p g i k f j k + e i j
where Xij denotes the concentration matrix of the heavy metal, where i and j represent the position of the sampling and heavy metal, respectively, p represents the number that is pertinent to the source, gik denotes the contribution of the factor to the sampling, fkj denotes the contribution of the factor to the profile of the pollution source, and eij denotes the residual matrix. The gik and fkj can be extracted by lowering the degree of the objective function defined by:
Q = i = 1 n j = 1 m ( e i j u i j ) 2
where uij denotes that uncertainty exists in the sample, and where i and j represent the sample and the heavy metal, respectively. The two inputs, the sample and uncertainty measurements, are required to conduct the PMF. The uncertainty measurement can be extracted by:
Uij = 5 / 6   ×   MDL   ( X ij M D L )
U i j = ( σ × c ) + ( 0.5 × M D L ) 2   ( X ij M D L )
Sample concentration and uncertainty are two necessary datasets for any PMF model. In this study, the concentration of each sample was above the detection limit, and the uncertainty value was calculated according to the Equation (11).

2.5. Statistical Analyses

2.5.1. Factor Analysis

To simplify the dataset, a factor analysis was implemented to reduce the number of dimensions. The common variability in the concentrations of the sedimentary elements was represented by eigenvectors. The principal components analysis (PCA) was employed to extract the factors from the geochemical dataset. A correlation matrix was computed. All computations were conducted by using SPSS 24. The number of metals that were used in this research is seven. In addition, the PMF (ver. 5.0) software can be downloaded from the website (https://www.epa.gov/air-research/positive-matrix-factorization-model-environmental-data-analyses (accessed on 9 August 2022)).

2.5.2. Geostatistical Analysis

Two different transformations, the Box-Cox and logarithmic, were utilized for the data normalization. Then, the normalized dataset was investigated by using ArcGIS. The Cokriging interpolation was implemented to construct the spatial distribution of heavy metals.

3. Results and Discussion

3.1. The Accumulation of HMs in Dust

Table 3 depicts the concentrations of the HMs (mg kg−1, dw) in the road dust that was collected in Harbin when a dry weight basis was implemented. The mean concentrations of Cd, Cr, Cu, Zn, Ni, Pb, and Mn are 1.79 ± 1.618, 67.23 ± 32.84, 57.76 ± 51.50, 328.52 ± 117.62, 27.11 ± 4.66, 83.03 ± 25.39, and 745.34 ± 153.22 mg kg−1, respectively. When they are compared with the background values of the soil in Harbin, there are increased scores for Cu, Zn, Pb, and Cd. The most common HMs are Cu, Zn, Pb, and Cd which correspond to 315.27, 934.36, 229.54, and 6.63 mg kg−1, respectively. In other words, they are 15.76, 13.22, 9.49, and 77.13 times larger than the background values are. Hence, the studied metals may be derived from anthropogenic origins. Besides, the mean concentrations of Cd and Zn are 20.84 and 4.65 times larger than the background values. Moreover, Cr, Ni, and Mn did not present differences between the background values and the measured ones.
Based on the SD and CV, there was a broad alteration in the concentrations of heavy metals in the street dust samples. The assessments of the CV values [8]: CV ≤ 20%, 20% < CV ≤ 50%, 50% < CV ≤ 100%, CV > 100% correspond to low, moderate, high, and very high variabilities, respectively. The research has found that the CV (%) is lowered to Cd (90.26) > Cu (89.16) > Cr (48.84) > Zn (35.80) > Pb (30.58) > Mn (20.56) > Ni (17.20). This indicates that Cd and Cu show a high variability and a heterogeneous nature in the environment, implying that their origins are anthropogenic.
Table 4 presents the comparison outcomes of the concentrations of HMs using several samples that were collected from many locations in both China and abroad. The concentrations of seven kinds of HMs in the street dust in Harbin can be moderate or comparable, relative to Xi’an (China), Baotou (China), Changchun (China), and Turin (Italy). On the contrary, the concentrations of Cu, Zn, and Pb in the research are significantly lower than those in Beijing (China), Shanghai (China), Hong Kong (China), Guangzhou (China), Wuhan (China), Thessaloniki (Greece), Tehran (Iran), and Rawang (Malaysia), which are alarmingly contaminated regions. However, the Cd concentrations in the dust of Harbin are higher than they are in Shanghai (China), Lanzhou (China), Changchun (China), Turin (Italy), Thessaloniki (Greece), Tehran (Iran), and Eslamshahr (Iran). On the contrary, the Cd content in the research location is lower than it is in other metropolitans like Beijing (China), Guangzhou (China), Changsha (China), Petra (Jordan), and Rawang (Malaysia). The Cd is the key affecting attribute of urban street dust in China due to the anthropogenic sources of fertilizers, and waste materials that are related to construction and manufacturing.
Table 5 presents that the EF of each metal is extracted by picking the reference element, Mn. The average EF scores of the HMs are sorted out as follows: Cd (31.37) > Zn (6.89) > Pb (5.11) > Cu (4.28) >Ni (1.80) > Cr (1.66). The average EF scores of Cd (31.37), Zn (6.89), and Pb (5.11) are bigger than 5, which reflects an erratic enrichment of the dust in Harbin. The average EF value of Ni (1.80) and Cr (1.66) is approximately 1, which reflects a natural origin for Ni and Cr. The average EF score of Cd (31.37) is much bigger than 10, which reflects that its presence is the contribution of anthropogenic exertions.
An Igeo was calculated for each one to assess the range of the contamination concerning the activities of both geogenic and anthropogenic nature. As shown in Table 6, Cd is found to be moderate with Igeo 2.93, which shows the significantly impact of anthropogenic activities. The Igeo of Cu (0.45) showed that it uncontaminated to moderately contaminated the samples. All sampling locations in Harbin were moderately polluted with Zn (1.55) and Pb (1.14), which show the significantly impacts of anthropogenic activities. All the sampling locations in Harbin are unpolluted with Mn (−1.14), Cr (−0.66), and Ni (−0.36). The Igeo value for Mn was found to be negative, varying between −2.75 to −0.46. Thus, its origins are more probably related to geological activities. Further, the dust is classified as uncontaminated by Mn. The Igeo of Ni was altered from −1.06 to 0.20, with 94.07% of dust samples including a minus Igeo, indicating that the street dust is uncontaminated by Ni. This is also the case for Cr (−6.68 < Igeo < 0.95).

3.2. Spatial Distribution of Heavy Metals in Street Dust

The estimated distribution maps of HMs are depicted in Figure 2. The same spatial distributions of Cu, Zn, and Pb are found to exist over a wide territory, except for the lower concentrations of them outside of Second Ring Road and the higher concentrations of them in the southwest of Second Ring Road. There exists a significant relationship between Cu and Zn (r2 = 0.4, p < 0.01) (Table 5), which display similar origins. The correlation between Pb and Zn (r2 = 0.359, p < 0.01) (see Table 7) is completely agreeable with the spatial distributions. The distribution patterns of Cu, Zn, and Pb are similar since they are controlled by human activities [44]. In this study, in Harbin, inside the Second Ring Road there are serious traffic congestions due to the dense population and traffic in the city. High Pb values are on the southern and northern parts of the Third Ring Road, indicating that Pb is deposited in the soil, which is attributed to huge vehicular traffic emissions before 2002 and this may be the reason for the street dust having to be resuspended [7,49,50], although Pb-containing gasoline has been prohibited in China since 2002. Based on the spatial distribution of the studied elements, the locations with higher contamination results can be seen. Especially, the southern and western parts of the studied regions display higher scores. The reason for this could be the local dominant wind (north-easterly wind).
The distribution patterns of Cr, Mn, and Ni display low spatial heterogeneity. This fact displays that they have natural origins as a result of the natural abundance of resources in Harbin. In clean agricultural soil, Cr, Mn, and Ni display temporal and spatial variabilities weakly, worldwide [51,52]. These outcomes suggest that industrial activities have not utilized Cr, Mn, and Ni-contaminating materials or the contamination of the region has not occurred yet. Similar observations can be made in the areas in Hefei, China [26], Xi’an, China [46], and Zhuzhou, China [53]. Among these metals, the distribution of Cd is dispersed widely. So, the hotspots that are polluted with Cd exist in the center of the Second Ring Road. Hence, Cd is still the key contaminant in the street dust of Harbin, which indicates a significant metal when pollution control is a concern. Even though local governmental bodies have implemented programs mitigating contaminations, removing pollutants that consist of heavy metals takes a longer time, and it takes a long time to recover the unpolluted state of the studied region.

3.3. Potential Health Risk Caused by HMs in Street Dust

Table 8 depicts the results of non-carcinogenic health hazards that are caused by HMs in dust for seniors and kids, concerning several routes. 0.129and 0.852 are the corresponding scores for them, respectively. The HI score for the kids is less than 1, which reflects that there is a non-carcinogenic effect for the HMs in the dust. The HQ scores of several metals for the kids are displayed in the order of Cr (0.279) > Pb (0.269) > Mn (0.235) > Cd (0.0257) > Cu (0.0162) > Ni (0.0152) > Zn (0.0123). The ratio of the HQ to HI score by ingesting for kids is the highest (88.70%), which is succeeded by dermal contact (6.04%), and inhaling (5.26%). The seniors have the same outcomes too. The ratio of the HQ to HI score by ingesting for seniors is the highest (87.56%), succeeded by dermal contact (8.50%), and inhaling (3.95%). All these data suggest that ingesting is the most significant way to harm the health of humans when HMs are under consideration. On the other hand, higher non-carcinogenic hazards concerning the three routes of each metal pathway exist for kids, which demonstrates their susceptibility to environmental contaminations in the street. This may be led by the characteristics and behaviors of kids such as relatively higher rates of aspirations depending on body weight and not paying attention to personal hygiene [54,55].
It must be noted that some people such as firemen [56], sanitation labors [57], bus drivers [12], and traffic officers [58] may be more exposed than the average person to the dust, which leads to its intake in higher amounts. The oral bio-accessibility of HMs in street dust was ignored in the research. So, while the contents of HMs decrease, overestimating dust exposure to HMs occurred [6].
Table 8 depicts the results of the carcinogenic health hazards that are caused by HMs in dust for seniors and kids, concerning several routes. The missing scores occurred due to us not having the corresponding CSF scores. The carcinogenic group did not include As Cu, Pb, Mn, and Zn. However, the presence of Cd, Cr, and Ni are predicted. Since the corresponding CFS scores are missing the inclusion of some metals, they were assessed in only one pathway [35]. 2.66 × 10−7 and 2.33 × 10−6 are the values of CR for seniors and kids, respectively. Both of the values are lower than the negligible level of 1 × 10−6. Therefore, negligible carcinogenic hazards for seniors and kids are found. The CR values for several metals are conformed in the order of: Cr > Cd > Ni. The values of the CR for Ni, Cd, and Cr concerning seniors and kids are less than the negligible hazard value of 1 × 10, which demonstrates that there are not any significant health hazards. Neither carcinogenic nor non-carcinogenic health hazards for human are observed. Nevertheless, their bioavailable fragments should be a key feature to precisely assess the health hazards for humans [3,42,59]. Furthermore, other metals such as As and Hg [60,61] that penetrate the human body through ingestion pathways should be validated in future research. Although activities that are aimed at alleviating the concentration of HMs should be conducted such as the better sweeping of streets and the replacement of contaminated soil with a clean soil, the priority should be restricted to reducing the emissions that occur at their sources such as factories since it is an economically better method.

3.4. Source Allocation of Heavy Metals from Street Dust

A correlation analysis and a PCA have been widely implemented to investigate the sources of the HMs. This research combined a correlation analysis, a PCA, and the PMF model to investigate the sources of the HMs. Therefore, the seven heavy metal sources could be classified into three categories. Table 9 shows the outcomes of the PCA, and the maximum variance method was utilized to highlight the influencing factors. The seven factors were eventually split into three principal components, providing 63.96% of the explained variance, and characterizing most of the information. The first principal component represents 31.14% of the explained variance and had higher factor loadings for the Mn, Cr, and Pb elements. The second component had high loadings for the Ni and Cu elements, representing 17.72% of the explained variance. On the contrary, the third component did have higher factor loadings for the Cd and Zn elements, representing 15.1% of the variance.
The outcomes of the PMF model (Figure 3) showed that factor 1 explained 36.57% of the ratio for the different HMs origins to the HM concentrations in the street dust, and it had high factor loadings for Cr, Cu, Zn, and Pb (each element contributed 82.56%, 78.49%, 29.87%, and 24.2% of the factor 1 loading, respectively). Furthermore, the outcomes of F 1 and PCA 2 were found to be consistent, indicating that the studied metals had similar values when their geochemical properties or sources in the urban street dust in Harbin were under consideration. Cu and Cr are found to be indicator elements that are associated with vehicle traffic pollution. Thus, they contribute to vehicle exhaust emissions, the mechanical wear of tires and metal parts, and the leakage of lubricants during transportation61]. Besides, they are also influenced by the electronics and metallurgical industries. Therefore, F1 represents a source of mixed industrial transportation.
Factor 2 explained 34.04% of the explained variance in the different sources and had high factor loadings for Mn, Ni, and Zn, which is agreeable with the PCA 1 results. Mn is negatively correlated with Ni, indicating that they have inconsistent sources. According to the Igeo and EF scores, Mn shows no contamination, and the overall mean value is lower than that of the background value. This is mainly derived from characteristic elements of natural origin and is also a key element in soil, probably originating from the suspension and remigration of soil particles. Ni, Pb, and Zn are traffic indicator elements for traffic pollution. As the study area is distributed with more traffic arteries, the transportation process may release large amounts of Zn, Ni, and Pb into the environment, resulting in a higher enrichment status in the dust. F2 is classified as a traffic pollution source.
Factor 3 explained 29.39% of the contributions of different sources. It is mainly dominated by Cd, which is agreeable with the results of the PCA. Since Cd and its compounds are mainly utilized in the electroplating industry, in the pigment industry, for the production of batteries and electronic tools, the combustion of coal, oil, and waste-containing compartments also produce Cd in the environment. Copper mining, metallurgical industries, and copper that is produced by coal combustion are key copper sources in the environment. Nickel from incinerator combustion in urban areas is accumulated in the environment. Therefore, the preliminary judgment may be mainly on industrial production and its fuel and waste combustion. F3 originates from traffic-domestic pollution sources.

4. Conclusions

The attained outcomes have displayed the pertinent information for the accumulated amount of heavy metals in the street dust of Harbin, Northeast China. The eccentric Cu, Zn, Pb, and Cd enrichment scores are displayed by utilizing Igeo and EF scores. Conducting both spatial distribution and correlation has shown that human exertions are the source of certain heavy metal accumulations such as Cu, Zn, Pb, and Cr. The HI scores that are below 1 demonstrate the exposure of the mean concentration of the heavy metal that may not cause significant non-carcinogenic hazards to seniors and kids. On the other hand, the HQ scores concerning the pathway of the exposure to these metals are ordered based on their non-carcinogenic effects: ingesting > contacting dermally > inhaling. The CR scores that are below 1 × 10−6 reflect the absence of carcinogenic hazards for seniors and kids in Harbin when the HMs of street dust is a concern. Combined with the factor analysis, the seven HM factors in the street dust of Harbin have different sources, with the Mn element mainly coming from the soil-forming parent material. On the contrary, the rest of the elements come from anthropogenic activities (traffic-industrial and domestic pollutant sources). This research would be will be helpful for dwellers to conduct safe practices and for the government to reduce HM contamination in the road environment.

Author Contributions

All authors contributed to the design and development of this manuscript. R.Y. carried out the data analysis and prepared the first draft of the manuscript; N.L. and Z.C. provided important advice on the concepts and structuring of the manuscript, Y.Y. edited of the manuscript prior to submission and during revisions. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China (41701372) and Natural Science Foundation of Jilin Province (20210101109JC).

Data Availability Statement

The data presented in this study are available in the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of Harbin and distribution of sampling sites.
Figure 1. Location of Harbin and distribution of sampling sites.
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Figure 2. Spatial distribution of HMs in street dusts of Harbin, Northeast China.
Figure 2. Spatial distribution of HMs in street dusts of Harbin, Northeast China.
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Figure 3. Factor profiles showing the percentage of metal concentration derived from the PMF model through different sources of street dusts from Harbin city, Northeast China.
Figure 3. Factor profiles showing the percentage of metal concentration derived from the PMF model through different sources of street dusts from Harbin city, Northeast China.
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Table 1. The Reference Doseof HMs through different pathways.
Table 1. The Reference Doseof HMs through different pathways.
RfDCuZnPbCrNiCdMn
Ingestion4.00 × 10−23.00 × 10−13.50 × 10−33.00 × 10−32.00 × 10−21.00 × 10−34.60 × 10−2
Inhalation4.02 × 10−23.00 × 10−13.52 × 10−32.80 × 10−52.06 × 10−25.70 × 10−61.43 × 10−5
Dermal1.20 × 10−26.00 × 10−25.25 × 10−47.50 × 10−55.40 × 10−31.00 × 10−51.84 × 10−3
The data was referenced from [25,36].
Table 2. Carcinogenic risks caused by HMs through street dusts from Harbin, Northeast China.
Table 2. Carcinogenic risks caused by HMs through street dusts from Harbin, Northeast China.
CRCSFChildrenAdults
Cr422.31 × 10−62.63 × 10−7
Ni0.841.86 × 10−82.12 × 10−9
Cd6.39.22 × 10−91.05 × 10−9
Total-2.33 × 10−62.66 × 10−7
CSF: carcinogenic slope factors [8].
Table 3. Descriptive statistics of heavy metal concentrations (mg kg−1, dry weight) in street dusts from Harbin, Northeast China (n = 118).
Table 3. Descriptive statistics of heavy metal concentrations (mg kg−1, dry weight) in street dusts from Harbin, Northeast China (n = 118).
CuZnPbCrNiCdMn
Min0.86108.4951.940.8616.440.008238.29
Max315.27934.36229.54170.3339.246.631164.59
Mean57.76328.5283.0367.2327.111.79745.34
Median49.15307.6679.7065.3626.921.32755.16
SD51.50117.6225.3932.844.661.61153.22
CV (%)89.1635.8030.5848.8417.2090.2620.56
Skewness3.411.713.070.380.131.62−0.35
Kurtosis14.125.8514.550.140.042.180.96
Background20.070.724.258.622.80.0861065.0
SD: standard deviation; CV (%): coefficient of variance.
Table 4. The concentrations of HMs (mg kg−1) in street dusts from Harbin and other cities.
Table 4. The concentrations of HMs (mg kg−1) in street dusts from Harbin and other cities.
CitynCuZnPbCrNiCdMnReferences
Harbin, China11857.76328.5283.0367.2327.111.79745.34This study
Beijing, China62138.4722.7167.986.045.22.29607.1[40]
Changsha, China5143.917166.671.6-7.48-[41]
Guangzhou, China30192.41777.2387.5176.241.42.14539.8[42]
Wuhan, China21138835281130331.6-[3]
Shanghai, China10141699148242-0.9904[43]
Hong Kong, China85344024240324-1.8639[43]
Hefei, China9141.6130.10.9139.328.6-240.5[44]
Lanzhou, China3277.456.634.796.9-0.81-[45]
Xi’an, China9054.7268.6124.5145.030.8-510.5[46]
Baotou, China8329.489.564.9189.621.6-572.1[47]
Changchun, China23268.4465.493.696-0.62692[48]
Turin, Italy29181200745192940.8527[6]
Thessaloniki, Greece10526.2671191187.395.710.59529.1[13]
Tehran, Iran3027566621376.557.70.8864[10]
Eslamshahr, Iran3023957371.335.142.40.34-[45]
Rawang, Malaysia51425.1526.8593.3501.3113.371.71-[47]
Table 5. The enrichment factors of HMs in street dusts from Harbin, Northeast China.
Table 5. The enrichment factors of HMs in street dusts from Harbin, Northeast China.
EFCuZnPbCrNiCd
Min0.072.442.570.030.670.09
Max29.5918.4516.844.415.80124.39
Mean4.286.895.111.661.8031.37
SD4.262.721.920.820.6628.36
CV (%)99.5839.5337.5849.4636.5190.42
SD: standard deviation; CV (%): coefficient of variance.
Table 6. The geo-accumulation index of street dusts in Harbin, Northeast China.
Table 6. The geo-accumulation index of street dusts in Harbin, Northeast China.
IgeoMinMaxMean<0 (%)0–1 (%)1–2 (%)2–3 (%)3–4 (%)4–5 (%)>5 (%)
Cu−5.133.390.4521.1938.9835.590.853.3900
Zn0.033.141.55011.0272.8815.250.8500
Pb0.522.661.14032.2065.252.54000
Cr−6.680.95−0.6675.4224.5800000
Ni−1.060.20−0.3694.075.9300000
Cd−4.005.682.937.633.395.9319.4933.0520.3410.17
Mn−2.75−0.46−1.14100000000
Table 7. Pearson’s correlation of heavy metal in street dusts from Harbin, Northeast China (n = 118).
Table 7. Pearson’s correlation of heavy metal in street dusts from Harbin, Northeast China (n = 118).
CuZnPbCrNiCdMn
Cu1
Zn0.400 **1
Pb0.292 **0.359 **1
Cr0.1450.314 **0.281 **1
Ni0.234 *0.1500.0810.252 **1
Cd0.0590.247 **−0.002−0.0740.0071
Mn0.0690.190 *0.250 **0.382 **−0.041−0.0571
* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).
Table 8. The non-carcinogenic risks of HMs through street dusts from Harbin, Northeast China.
Table 8. The non-carcinogenic risks of HMs through street dusts from Harbin, Northeast China.
HQChildrenAdults
IngestionInhalationDermalHI = ∑HQIngestionInhalationDermalHI = ∑HQ
Cu1.60 × 10−21.17 × 10−61.50 × 10−41.62 × 10−22.41 × 10−31.34 × 10−73.20 × 10−52.44 × 10−3
Zn1.22 × 10−28.95 × 10−71.70 × 10−41.23 × 10−21.83 × 10−31.02 × 10−73.64 × 10−51.86 × 10−3
Pb2.64 × 10−11.93 × 10−54.92 × 10−32.69 × 10−13.95 × 10−22.20 × 10−61.05 × 10−34.06 × 10−2
Cr2.49 × 10−11.96 × 10−32.79 × 10−22.79 × 10−13.73 × 10−22.24 × 10−45.96 × 10−34.35 × 10−2
Ni1.51 × 10−21.08 × 10−61.56 × 10−41.52 × 10−22.26 × 10−31.23 × 10−73.34 × 10−52.29 × 10−3
Cd1.99 × 10−22.57 × 10−45.58 × 10−32.57 × 10−22.99 × 10−32.93 × 10−51.19 × 10−34.21 × 10−3
Mn1.80 × 10−14.26 × 10−21.26 × 10−22.35 × 10−12.70 × 10−24.85 × 10−32.69 × 10−33.46 × 10−2
Total7.56 × 10−14.48 × 10−25.15 × 10−28.52 × 10−11.13 × 10−15.11 × 10−31.10 × 10−21.29 × 10−1
Table 9. Rotated component matrix for concentration data of street dusts from Harbin, Northeast China.
Table 9. Rotated component matrix for concentration data of street dusts from Harbin, Northeast China.
ElementsComponents
/123
Cu0.2170.5680.404
Zn0.450.2890.63
Pb0.6120.1640.265
Cr0.710.307−0.12
Ni−0.0110.871−0.099
Cd−0.175−0.110.815
Mn0.801−0.228−0.064
Cumulative variance%31.1417.7215.1
Rotation Method: Varimax with Kaiser Normalization.
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Yu, R.; Cui, Z.; Luo, N.; Yu, Y. Detection and Assessments of Sources and Health Hazards Caused by Heavy Metals in the Dust of Urban Streets in Harbin, Northeast China. Sustainability 2022, 14, 11657. https://doi.org/10.3390/su141811657

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Yu R, Cui Z, Luo N, Yu Y. Detection and Assessments of Sources and Health Hazards Caused by Heavy Metals in the Dust of Urban Streets in Harbin, Northeast China. Sustainability. 2022; 14(18):11657. https://doi.org/10.3390/su141811657

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Yu, Rui, Zhengwu Cui, Nana Luo, and Yong Yu. 2022. "Detection and Assessments of Sources and Health Hazards Caused by Heavy Metals in the Dust of Urban Streets in Harbin, Northeast China" Sustainability 14, no. 18: 11657. https://doi.org/10.3390/su141811657

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