Evaluation of trace elements pollution in deposited dust on residential areas and agricultural lands around Pb/Zn mineral areas using modified pollution indices

Abstract

Dust deposited on residential and agricultural lands can have serious consequences for the ecosystem when toxic trace elements are present. This study aimed to assess the ecological risk of the trace elements found in the deposited dust around the Mehdi Abad Pb/Zn mine, Yazd, Iran, using several modified pollution indices. The dust samples were collected by the grid method and a Marble Dust Collector (MDCO) sampler to evaluate the concentration of thirty trace elements: nickel (Ni), lead (Pb), barium (Ba), beryllium (Be), chromium (Cr), copper (Cu), dysprosium (Dy), lanthanum (La), lithium (Li), niobium (Nb), tin (Sn), neodymium (Nd), praseodymium (Pr), rubidium (Rb), Ferrum (Fe), sulfur (S), selenium (Se), strontium (Sr), tantalum (Ta), terbium (Tb), zirconium (Zr), tellurium (Te), thorium (Th), titanium (Ti), uranium (U), vanadium (V), yttrium (Y), ytterbium (Yb), thulium (Tm), and cobalt (Co). This study employed multivariate statistical techniques, including Hierarchical cluster analysis (HCA) and the IDW interpolation technique, as well as modified pollution indices such as Enrichment Factor (EF), Modified Pollution Index (MPI), Modified Potential Ecological Risk Index (MRI), and Modified Hazard Quotient (mHQ). All the statistical data analyses were performed via SPSS Statistics, version 22.00. The HCA results showed that all of these trace elements, except Fe, form a group and had similar behavior. The average levels of all elements in the dust samples except for Cr, S, Sr, Ta, Tb, and Te exceeded the background value. The results confirmed that both anthropogenic activities and natural factors were responsible for the trace elements found in the dust. The average EF value for Pb (43.26) indicated its extremely high enrichment in the study area. The MPI, mHQ, and MRI results showed that 33%, 100%, and 33.33% of the dust samples were in the heavily polluted, extreme severity, and high risk categories, respectively. The IDW analysis results revealed that the highest value of the MRI and mHQ indices was in agricultural lands and residential areas; the predominant wind direction also played a role in transferring the elements from the mine to these areas. In general, the results indicated that mining activities increased the ecological risk in Mehdi Abad due to the presence of trace elements, especially Pb.

Introduction

Dust is a solid particulate matter that can adsorb trace elements and a diverse range of contaminants from various anthropogenic and natural sources [1]. Therefore, atmospheric depositions have always been considered the dominant pollutant in regard to urban air quality [2]. Trace elements, especially heavy metals, in deposited dust may come from agriculture activities, urbanization, industrialization, and mining activities [1]. Mine activity is considered one of the most dangerous anthropogenic activities in the world. Mining and milling operations that include grinding, concentrating ores, mine and mill wastewater, and tailing disposal are sources of contamination in the surface environment [3]. Mining activities generate vast quantities of wastes that are laden with harmful and hazardous heavy metals (HMs), as well as some noble metals. In spite of the occupational improvements within the mining industry, the release of metalliferous dust into the environment remains a health concern, which is true for regions with poorly developed regulatory systems and where historic mining has left a significant legacy of exposed metalliferous mine wastes [4]. The contamination of the environment caused by trace elements due to mining activities has become a global issue because of the potential health risks it poses to the local communities. The contamination of soil around mining localities can lead to the contamination of plants, food crops, and grass grown on them. When consumed by humans and other animals, these contaminated food crops can cause health hazards [5]. Nowadays, studies investigating trace elements in dust focus mainly on the total content, distribution pattern, and associated environmental and ecological risk assessment. However, as for the deposited dust from the mining area, the source of trace elements is more complicated. It can result from release during mineral resource exploitation and dispersion during the transporting process [6]. Numerous recent studies have been conducted to determine trace element content and contamination in the dust around mining areas in the world. Obiora et al. (2016) analyzed the content of thirty-three trace elements (Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, Au, Th, Sr, Cd, Sb, Bi, V, Ca, P, La, Cr, Mg, Ba, Ti, Al, Na, K, Hg, Sc, Tl, S, Ga and Se) in soil samples close to or away from three major Pb/Zn mines: Ndinwanu Ishiagu Enyigba/Ikwo (Ameka), Ishiagu Enyigba (Enyigba), and Alibaruhu mines. Their results showed that Pb, Zn, Cd, Cu, and Cr concentrations are elevated compared with concentrations documented in the international agricultural soil standards [7]. Krishna et al. (2013) studied the content of toxic trace elements (As, Ba, Co, Cr, Cu, Mo, Ni, Pb, Sr, V, Zn and Zr) in fifty-seven soil samples collected from active (Tagdur) and abandoned (Jambur) chromite mining sites, as well as the residential zone around Chikkondanahalli of the Nuggihalli Schist Belt, Karnataka, India. Their results indicated that the soil in the study area had elevated Cr, Ni, and Co concentrations and exceeded the Soil Quality Guideline limits (SQGL) [8]. Baghaie and Aghili (2019) determined the concentrations of Cd, Zn, Ni, Cu, Mn, and Pb around the Nakhlak Pb/Zn mine (located in the Anarak district, Nain county of Isfahan province) were equal to 2.72, 347, 26, 36, and 355 mg/kg, respectively. These concentrations were higher than the background values of world soils [8]. Other studies have determined the level of toxic element pollution in mining areas: Bu et al. (2020) analyzed heavy metals in surface soils of the coal-mining city in Wuhai, China [9]; Li et al. (2019) evaluated heavy metals (Cu, Zn, As, Cr, Cd, Pb, and Ni) in topsoil samples in the Zhangji coal mine, Huainan City, Anhui Province [10]; Jahromi et al. (2020) analyzed heavy metals (Cu, Zn, Cd and Pb) present in the surface soil of the Irankouh Pb/Zn mine close to Isfahan, Iran [11]; and Kolawole et al. (2018) analyzed heavy metal (Mn, Cr, Pb, Cu, Cd, Co and Ni) contamination in the soils and sediments of an industrial area in Southwestern Nigeria [12].

An ecological risk assessment is a process to evaluate an environment’s likelihood of being impacted due to exposure to one or more environmental stressors. It is a flexible process to organize and analyze data, information, assumptions, and uncertainties, as well as to evaluate the probability of adverse ecological effects [13]. This process has received the utmost attention during recent years and has been performed using many indices such as Modified Hazard Quotient (mHQ), Enrichment Factor (EF), Modified Potential Ecological Risk Index (MRI), and Modified Pollution Index (MPI). These indices help interpret dust and soil quality by evaluating the degree of pollution the trace elements [5]. Ali et al. (2015) evaluated the pollution of tannery-affected soil in Pakistan’s Sialkot district using EF. Their results showed that the soils were enriched with Cd followed by Cr, Pb, Ni, Cu, Co, Zn, and Mn [14]. Mokhtarzadeh et al. (2020) used EF and MPI indices to assess the anthropogenic sources of potentially toxic elements (PTEs (in the urban and industrial soils of the Arvand Free Zone area in Iran. Their results showed that the soils had significant enrichment for elements such as Cd, Cu, Hg, Mo, Pb, Sb, and Zn. In addition, MPI values showed that the Abadan industrial district (MPI = 1091) and Abadan petrochemical complex (MPI = 1068) were the most polluted sites in the studied area [13]. Keshavarzi and Kumar (2020) studied heavy metal pollution in agricultural soils of the Mashhad plain, northeastern Iran. They concluded that the EF and MRI indices of 99% and 52.4%, respectively, of the samples showed very high enrichment and ecological risks from heavy metals [15]. Rayhan Khan et al. (2019) determined that due to anthropogenic activities, the EF index of the heavy metals Mn, Fe, Cu, Zn, Cd, and Pb was strongly to extremely enrichment in the soil of the Mongla industrial area, Bangladesh [16]. Yuan et al. (2019) studied the ecological risks of metalloids in riverbed sediments of the Jinsha River, China. The mHQ values revealed a very high degree of Cr contamination, while Hg, Zn, Cd, and Pb had a low degree of contamination [17]. Huang et al. (2017) measured the ecological risk potential of Cd, Pb, Zn, Cu, and Ni to be at a moderate level around the Pb/Zn mine in Hunan, China [18].

The assessment and monitoring of the levels of trace elements in the environment due to anthropogenic activities, including mining, are vital since they can alert for possible pollution that harms the ecology. Therefore, this study aimed to investigate trace element distribution in deposited dust on residential areas and agricultural lands around the Mehdi Abad Pb/Zn mine in Yazd, Iran, and predict the ecological risks they may pose to communities in the vicinity of the mine.

Materials and methods

Study area

The Mehdi Abad Pb/Zn mine is located in the Mehdi Abad region in north-eastern Mehriz in the center of Kalmand Bahadoran that is a geomorphological unit in Yazd, Iran. It is one of the largest mines in the world that contains huge sources of sulfide and oxide minerals [19]. It is situated in latitude 31° 28′ 25″ N and longitude 55° 00′ 23″ E with a height of 1473 m above mean sea level; it is surrounded by high to moderately high (1000–4000 m) mountains in the north, northwest, east, and southeast (Fig. 1). The inhabitants of surrounding villages include mainly farmers with their farms only a few kilometers from the Pb/Zn mine (Google Earth). The geology study area is made up of Sangestan, Taft, and Abkooh formations [19]. Vegetation in the area consists of the Artemisia sieberi (based on field visits). The climate is arid. The region is characterized by two seasons: a rainy season from December to May and a dry season from June to November. The average annual rainfall, temperature, and relative humidity are 72 mm, 26 °C, and 41%, respectively (https://data.irimo.ir).

Fig. 1.

Map of the Mehdi Abad Pb/Zn mine, Mehriz, Yazd Province, Iran and MDCO Sampling Stations

Sampling and analysis

Samples of deposited dust were collected in May–June-July of 2020 from nine stations installed at the Mehdi Abad Pb/Zn mine and the surrounding area. The method of systematic random sampling was used to collect the samples within a 2 km × 2 km grid. Most of the samples were collected from locations within the surrounding agricultural lands and villages at varying distances from the Mehdi Abad Pb/Zn mine pit: S1 (~14.08 km), S2 (~19.28 km), S3 (~17.63 km), S4 (~0.19 km), S5 (~14.45 km), S6 (~7.75 km), S7 (~12.90 km), S8 (~2.76 km), and S9 (~18.55 km). The sampling positions were recorded using Global Positioning System (GPS) localization. The collector designed for this study included a circular plastic container with a diameter of 315 mm. Furthermore, inside the container were three rows of marbles with a diameter of 1.6 cm to prevent the dust from escaping (Marble Dust Collector sampler). In order to compare the concentration of trace elements in the deposited dust with the background value, soil samples were taken at a depth of 2–3 m where there was no agricultural or mining activity [20].

The dust samples were analyzed in the Zar Azma laboratory in Tehran, Iran. For this purpose, the elements in the dust samples were measured using Perkin Elmer ELAN 9000 Inductively Coupled Plasma Mass Spectrometry (ICP-MS). For the trace element analysis, four acids, namely HF, HCL, HCLO₄, and HNO_3, were used to prepare the solutions required in order to digest the samples [21]. In the analytical procedure, the limit of detection (LOD) was considered as the lowest amount of analyte that can be quantified with a known degree of reliability. In contrast, the limit of quantitation (LOQ) refers to the concentration at which quantitative results can be produced with a sufficient amount of confidence [22]. These were determined using a statistical approach, which is based on measuring replicate blank samples or through the measurement of progressively dilute concentrations of the analyte. The LOD was calculated as 3 × SD and the LOQ as 3 × LOD, where SD is the standard deviation of the blank. The LOD and LOQ values ​​for the studied trace elements are presented in Table 1 [23].

Table 1.

LOD, LOQ and recovery values for selected elements

Elements	LOD	LOQ	Recovery values
	PPM		(%)
Ni	1	3	96–102
Pb	1	3	99–105
Ba	1	3	96–106
Be	0.2	0.6	95–97
Cr	1	3	96–103
Co	1	3	96–98
Cu	1	3	97–106
Dy	0.02	0.06	100–104
La	1	3	101–105
Li	1	3	98–106
Nb	1	3	99–104
Sn	0.1	0.3	96
Nd	0.5	1.5	99–104
Pr	0.05	0.15	95–105
Rb	1	3	95–102
Fe	100	300	101–104
S	50	150	98–106
Se	0.5	1.5	96
Sr	1	3	96–106
Ta	0.1	0.3	103–106
Tb	0.1	0.3	97–99
Zr	5	15	96–102
Te	0.1	0.3	102
Th	0.1	0.3	103–104
Ti	10	30	100–106
U	0.1	0.3	98–103
V	1	3	97–105
Y	0.5	1.5	103–105
Yb	0.05	0.15	99–102
Tm	0.1	0.3	99–106

Hierarchical cluster analysis (HCA)

Cluster analysis is a multivariate statistical technique used to efficiently classify trace elements into clusters of similarity in the n-dimensional space in which the elements are compared [24]. The similarities between elements are determined by cophenetic correlation or the distance between the elements in the space (elements closest to each other are most similar). In this study, HCA was used to identify the same geochemical behavior of the trace elements to determine their origin. The HCA was formulated according to the Ward algorithmic method, and the squared Euclidean distance was employed for measuring the distance between clusters of similar trace element contents. Clustering by means of Ward’s method reduces the total within-cluster variance [25]. The cluster analysis results are shown as a dendrogram. In the dendrogram, equal weight elements connect to create larger clusters [26]. Elements in a large cluster is an indication of their common origin.

Evaluation of new ecological indices

Modified pollution index (MPI)

The MPI is a multiple element index proposed by Brady et al. (2015) to assess the degree of contamination of soil using EF values; it is thought to be more reliable than other indices due to normalization versus the reference element, as shown in Eq. 1 [5, 24].

$$\mathrm{MPI}=\sqrt{\frac{{\left({\mathrm{Ef}}_{\text{average}}\right)}^2+{\left({\mathrm{Ef}}_{\max }\right)}^2}{2}}$$ 1

where Ef_average and Ef_max indicate the average and maximum enrichment factor, respectively. The MPI values are grouped into six classes: MPI <1 (Unpolluted), 1 < MPI <2 (Slightly Polluted), 2 < MPI <3 (Moderately Polluted), 3 < MPI <5 (Moderately-Heavily Polluted), 5 < MPI <10 (Severely Polluted), and MPI > 10 (Heavily Polluted) [27]. The enrichment factor is calculated from Eq. 2.

$${\mathrm{EF}}_{\mathrm{n}}=\frac{{\left(\frac{{\mathrm{C}}_{\mathrm{X}}}{{\mathrm{C}}_{\mathrm{Fe}}}\right)}_{\text{deposited dust}}}{{\left(\frac{{\mathrm{C}}_{\mathrm{X}}}{{\mathrm{C}}_{\mathrm{Fe}}}\right)}_{\text{back ground}}}$$ 2

where (C_x) _{deposited dust} and (C_Fe) _{deposited dust} are the concentration of the trace elements and Fe in the deposited dust sample, respectively. (C_x) _background and (C_Fe) _background are the concentration of the trace elements and Fe in the background sample, respectively [28]. Background values were used to calculate EF in soil samples collected from a depth of 2 to 3 m. The basis of the EF was defined using five contamination categories: 1. If the element EF is <2, it can be considered that the element showed no enrichment compared to the soil source and is mainly composed of soil particles; 2. Values in the range of 2–5 show moderate enrichment, indicating that the elements of the deposited dust are influenced by human activities in addition to soil sources; 3. The range of 5–20 is significant enrichment; 4. The range of 20–40 is very high enrichment; and 5. If the element EF is >40, this is primarily an indication of extremely high enrichment [29].

Modified potential ecological risk index (MRI)

The potential ecological risk index (RI) is a criterion for assessing the biological community’s sensitivity to the overall contamination of toxic elements. It is calculated based on the sum of potential ecological risk factors (Er) of individual toxic elements. The equation below was proposed by Hakanson (1980) for calculating the RI.

$$\mathrm{RI}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Er}}^{\mathrm{i}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Tr}}^{\mathrm{i}}\times {\mathrm{CF}}^{\mathrm{i}}$$ 3

where Erⁱ is the potential ecological risk index of an individual element, Trⁱ is the biological toxic response factor of an individual element, and CF_i is the contamination factor for each individual element [30].

In many studies, the RI is used as an index for quantitative ecological risk assessment. The CF is used in the calculations of this index, which does not consider lithogenic and sedimentary inputs of the element. Therefore, to account for the effect of geogenic sedimentary input, the enrichment factor (EF) was used instead of the contamination factor in the calculation of RI [27, 31]. In order to determine the ecological risk potential of heavy metals in deposited dust under anthropogenic, lithogenic, or geogenic conditions, we can multiply the EF of each heavy metal in its biological toxic response to determine the risk degree of each heavy metal in the deposited dust. The MRI is calculated from Eq. 4.

$$\mathrm{MRI}={\mathrm{EF}}_{\mathrm{n}}\times {\mathrm{T}}_{\mathrm{r}}$$ 4

where EF_n and T_r represent the enrichment factor and the biological toxic response of each heavy metal, respectively. The biological toxic response factors for the Pb, Cr, Cu, Ni, and V heavy metals are 5, 2, 5, 5 and 2, respectively [32, 33]. The MRI values are grouped into five classes: MRI <40 (Low risk), 40–80 (Moderate risk), 80–160 (Considerable risk), 160–320 (High risk), and MRI > 320 (Very high risk) [15, 32].

Modified Hazard quotient (mHQ)

The modified hazard quotient is a tool for determining each metal’s degree of risk for living organisms. Benson et al. (2018) confirmed the reliability and accuracy of the mHQ index [34]. Also, other studies such as Ustaoglu and Islam (2020) [35], Eker (2020) [36], and Yuan et al. (2019) [17] have confirmed its validity and accuracy. This new approach enables the assessment of contamination by comparing metal concentration in sediment with adverse ecological effects distributions for slightly differing threshold levels (Threshold Effect Level (TEL), Probable Effect Level (PEL), and Sever Effect Level (SEL)), as earlier reported. The TEL values ​​for Pb, Ni, Cu, Cr, and Sn are estimated to be 35, 18, 35.7, 52, and 0.85, respectively. The PEL values ​​for Pb, Ni, Cu, Cr, and Sn are estimated to be 112, 42, 108, 160 and 3.3 mg/kg, respectively. The SEL values for Pb, Ni, Cu, Cr, and Sn are 250, 75, 110, 110, and 21, respectively. The mHQ index is calculated from Eq. 5.

$$\mathrm{mHQ}={\left[{\mathrm{C}}_{\mathrm{i}}\left(\frac{1}{{\mathrm{TEL}}_{\mathrm{i}}}+\frac{1}{{\mathrm{PEL}}_{\mathrm{i}}}+\frac{1}{{\mathrm{SEL}}_{\mathrm{i}}}\right)\right]}^{\frac{1}{2}}$$ 5

where Ci indicates the measured metal concentration of the deposited sample. The mHQ values are grouped into five classes: 0.5 > mHQ (Nil to Very Low Severity of Polluted), 1 > mHQ ≥ 0.5 (Very Low Severity of Pollution), 1.5 > mHQ ≥ 1 (Low Severity of Pollution), 2 > mHQ ≥ 1.5 (Moderate Severity of Pollution), 2.5 > mHQ ≥ 2 (Considerable Severity of Pollution), 3 > mHQ ≥ 2.5 (Very High Severity of Pollution), 3.5 > mHQ ≥ 3 (High Severity of Pollution), and mHQ ≤ 3.5 (Extreme Severity of Pollution) [34, 37].

Statistical analysis

The results obtained from statistical analysis were evaluated using SPSS 22.00 software. The data were subjected to descriptive statistics (minimum, maximum, mean, standard deviation, standard error, skewness, kurtosis, and coefficient of the variable), the Kolmogorov-Smirnov (K-S) test, and HCA. In this study, the inverse distance weighting (IDW) method was used to map the spatial distribution of some pollution indices. The IDW method is based on the premise that the predictions are a linear combination of the available data. The interpolating function is calculated from Eq. 6:

$$\begin{array}{c}\mathrm{Z}\left(\mathrm{X}\right)=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}{\mathrm{Z}}_{\mathrm{i}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}}\\ {\mathrm{W}}_{\mathrm{i}}={\mathrm{d}}_{\mathrm{i}}^{-\mathrm{u}}\end{array}$$ 6

where Z(x) represents the predicted value at an interpolated point, Z_i represents a known point, n represents the total number of known points used in interpolation, d_i indicates the distance between point i and the prediction point, and Wi indicates the weight assigned to point i. Greater weighting values are assigned to values closer to the interpolated point. The weight decreases as the distance increases, and u is the weighting power that decides how the weight decreases as the distance increases. However, the result is greatly affected when the predicted point is located near the sample data [38].

Figure 2 shows the flowchart of the research method.

Fig. 2.

Steps of research to ecological risk assessment of elements in deposited dust

Results and discussion

Descriptive statistics of trace elements concentrations

The descriptive statistics related to the concentration of trace elements in the deposited dust around the Mehdi Abad Pb/Zn mine are presented in Table 2. Based on these results, Fe had the highest (31,846 ppm) average concentration in the deposited dust, and Te had the lowest (0.10 ppm). The data showed that the content of some trace elements in the dust samples displayed abnormal distributions (Pb, Ba, Ni, Nb, S, Te, and Yb). The analytical data results indicated that the coefficient of skewness for Pb, Ba, Be, Fe, S, Se, Sr, and Tm were extensively higher than zero, showing a right-skewed distribution (Fig. 3). It showed that the samples with a high value of Pb, Ba, Be, Fe, S, Se, Sr, and Tm occurred in the collected samples and indicated the non-similar distribution of concentration values. In contrast, As demonstrated normal distributions with close to zero skewness coefficients. The high variability in concentrations of Pb, Ba, Cr, Fe, S, Sr, and Ti, as shown in the values of standard deviation, indicated that these metals come from several origins. The average levels of all elements in the dust samples except for Cr, S, Sr, Ta, Tb, and Te exceeded the background value, indicating the entry of elements from anthropogenic sources (mining) in the area [39]. However, the elements whose concentration is lower than the background is probably a result of natural resources such as the area’s geological structure [6, 25]. Baghaie and Aghili (2019) reported that the concentrations of Pb, Cd, Mn, Ni, Cu, and Zn in surface soils around the Nakhlak Pb/Zn mine were significantly higher than those of the background value. They mentioned that mining activity greatly influenced the concentration of these heavy metals in surface soils around the mine [8]. Huang et al. (2017) measured the Pb concentrations in soils around the Pb/Zn mineral area to be above the reference values [18]. The CV of each trace element is an indicator that determines the degree of variability within the concentrations of metal in dust samples [40]. Based on Table 1, the CV for S, Cr, and Se show a moderate degree of variability, while that of Ba and Pb indicate a high degree of variability, which suggests the non-homogeneous distribution of Ba and Pb concentrations that can be due to either mining activities and/or geological properties.

Table 2.

Statistical analysis of trace elements in the deposited dust around the Mehdi Abad Pb/Zn mine

Elements	Min.	Max.	Mean	Background	Std. Dev	Std. Error	Skew.	Kurt.	Cv	K-S.
unit	(PPM)								%
Ni	42.00	69.00	59.33	44.00	8.68	2.89	−1.19	0.76	14.63	0.03
Pb	33.00	1987	428.00	6.00	660.88	220.29	1.98	3.93	154	0.00
Ba	402.00	5637	2028.55	253	2182.75	727.58	1.04	−0.84	107	0.00
Be	0.80	1.00	0.88	0.60	0.07	0.02	0.21	−1.04	7.95	0.20
Cr	68.00	15,700	119.55	145	25.34	8.44	−0.77	1.51	21.19	0.20
Co	9.00	12.90	11.46	8.30	1.29	0.43	−0.86	0.00	11.25	0.20
Cu	35.00	51.00	43.00	20.00	5.78	1.92	−0.07	−1.50	13.44	0.20
Dy	1.60	2.12	1.87	1.13	0.17	0.05	−0.55	0.71	9.09	0.20
La	15.00	21.00	18.44	10.00	1.87	0.62	−0.54	−0.18	10.14	0.20
Li	15.00	28.00	22.66	14.00	1.41	22.66	−0.52	−0.45	6.22	0.20
Nb	3.10	4.70	4.11	3.10	0.63	0.21	−0.74	−1.19	15.32	0.03
Sn	0.80	1.30	1.11	0.80	0.20	0.06	−0.77	−0.95	18.01	0.20
Nd	7.00	10.70	9.17	3.10	1.21	0.40	−0.58	−0.32	13.19	0.20
Pr	1.19	2.37	1.83	0.22	0.33	0.11	−0.45	1.02	18.03	0.20
Rb	24.00	31.00	28.33	19.00	2.54	0.84	−0.36	−1.09	8.96	0.20
Fe	27,089	38,089	31,846	20,717	3457.45	1152.48	0.24	0.08	10.85	0.20
S	742	3854	1414.11	24,046	1075.28	358.42	1.76	2.80	76.03	0.00
Se	<0.50	2.44	1.34	<0.50	0.75	0.25	0.10	−1.66	55.97	0.20
Sr	316	445	359.88	1309	36.10	12.03	1.75	4.35	10.03	0.08
Ta	0.30	0.58	0.44	0.35	0.08	0.02	−0.01	0.64	18.18	0.20
Tb	0.29	0.38	0.34	0.22	0.02	0.00	−0.54	0.80	5.88	0.20
Zr	38	55.00	48.55	31.00	5.31	1.77	−0.96	0.55	10.93	0.20
Te	<0.10	0.11	0.10	1.94	0.00	0.00	0.27	−2.57	0.00	0.00
Th	3.19	4.69	4.09	2.44	0.45	0.15	−0.78	0.81	11.00	0.20
Ti	2164	3025	2738	1612	303.80	101.26	−0.91	−0.27	11.09	0.15
U	1.20	1.60	1.38	1.20	0.15	0.05	−0.08	0.71	10.86	0.20
V	65.00	86.00	76.44	60	7.12	2.37	−0.51	−1.05	9.31	0.20
Y	10.50	13.60	12.31	7.40	1.12	0.37	−0.64	−1.22	9.09	0.17
Yb	1.20	1.50	1.42	1.10	0.10	0.03	−1.28	0.77	7.04	0.00
Tm	0.13	0.20	0.16	<0.10	0.02	0.00	0.44	0.45	12.5	0.20

Fig. 3.

Box and whisker plots of elements in the deposited dust around the Mehdi Abad Pb/Zn mine

Hierarchical cluster analysis

The dendrogram generated from HCA is illustrated in Fig. 4. These trace elements can be classified into two distinct groups. Each cluster represents the common origin of the trace elements studied. The distance between the clusters indicates the degree of similarity between the elements. Cluster 1 consists of Ni, Pb, Ba, Be, Cr, Co, Cu, Dy, La, Li, Nb, Pr, Rb, S, Se, Sr, Ta, Tb, Zr, Te, Th, Ti, U, V, Y, Yb, Sn, Tm, and Nd. Cluster 2 only includes Fe, which indicates its different geochemical behavior and origin. The results of mineral exploration studies in the study area show that the Mehdi Abad Pb/Zn mine consists of three lower cretaceous sedimentary formations, namely Sangestan, Taft, and Abkooh formations. In terms of lithology, the Sangestan formation includes siltstone, Chilean limestone, sandy limestone, bioclastic limestone, and sandstone interlayers. The Taft formation is mostly composed of carbonate series, which include dolomite (in terms of lithology) accompanied by extensive karstification in the upper horizon [41]. The Abkooh formation includes clayey and chert rocks that cover the Taft formation. The lower part of the Abkooh formation has Pb and Zn mineralization [42]. In general, the Mehdi Abad deposits contains sulfide and oxide minerals. The sulfide minerals include Sphalerite (ZnS), Glena (PbS), and Baryte (BaSO₄), along with small amounts of Pyrite (FeS₂), Chalcopyrite (CuFeS₂), and Chalcocite (Cu2S). Oxide minerals in this deposits include smithsonite (ZnCO₃), Hydrozincite (Zn₅ [(OH)₃-CO₃] ₂), Hemimorphite (Zn₄ [(OH)₂ Si₂O₇]. H₂O), and Cerusite (PbCO₃); the tailings minerals include Ankerite (Cafe [CO₃] ₂), Calcite (Ca CO₃), Dolomite (Ca Mg (CO₃) ₂), Limonite (FeOOH.nH₂O), Hematite (Fe₂O₃), and clay minerals [43]. According to this description, mining activities are probably the most common source of trace elements in deposited dust.

Fig. 4.

Dendrogram of HCA for trace elements concentrations in the deposited dust of the around Mehdi Abad Pb/Zn mine

Deposited dust pollution assessment using EF, MPI, MRI, and mHQ

Figure 5 shows the EF results for the trace elements in the deposited dust. The box plot of EF indicated that the range for all thirty elements changed. The narrowest plot belonged to S and the largest one to Pb. The EF of Ni, Cr, Be, Co, Cu, Dy, La, Li, Nb, Rb, S, Sr, Ta, Tb, Zr, Te, Th, Ti, U, V, Y, Yb, Tm, and Sn values were EF < 2 in all the samples, indicating that the concentrations of these trace elements were just slightly enriched. The average EF value for Pb was generally EF > 40, indicating that this element was the subject of extremely high enrichment in the study area. The average EFs value of Ba (5.24) and Pr (5.44) were in the 5–10 range, indicating that Ba was a little less than Pr and that both suffered strongly moderate enrichment in the study area. The highest enrichment values of Ba (16.87) and Te (0.039) in the lead and zinc mine were obtained around the mine pit (S4). In agricultural lands, the maximum enrichment value was observed for Cu (1.66), Dy (1.20), Li (1.31), Se (3.31), and Sn (1.10) in S1, and the maximum enrichment value of Pb (180/12) and S (0.087) were observed in S5. In residential areas, Ni (1.07), Co (1.00), and Nb (1.11) had the highest enrichment in S9. Kianpor et al. (2019) measured the enrichment values of Pb, Ni, V, Co, and Cr in the dust collected from the Karun Industrial Zone, Iran, at Extremely High, Very High, Very High, High and High levels, respectively [44]. Rout et al. (2013) measured the enrichment values of Cr, Cu, and Co in low-level dust samples collected around the Indian Jharia coal mine [45]. Baghaie and Aghili (2019) measured the highest and lowest enrichment values of Pb and Ni in the soil around the Nakhlak Pb/Zn mine [8].

Fig. 5.

Boxplot of enrichment factors for trace elements in the deposited dust around Mehdi Abad Pb/Zn mine

The MPI was used to evaluate the degree of trace element pollution in the studied area (Fig. 6), and the results indicated that the samples were heavily polluted with trace elements. The maximum and minimum MPI values were 127.47 and 4.03, respectively. The average MPI value for the elements was 31.21. Nevertheless, 44% of the samples were in the moderately-heavily polluted level, 22% in the severely polluted level, and 33% the heavily polluted class, according to the MPI values. The severe pollution experienced at the sites was contributed mainly to Pr and Pb concentration. Hu et al. (2018) reported that 23.80% of all agricultural soil samples collected from the areas around the Dexing Pb/Zn mining region, Jiangxi province, China, were in the heavily polluted class of MPI. They suggested that MPI was a useful index for soil contamination assessment due to its sensitivity in classifying the soil quality with a succession of soil contamination [46]. Furthermore, Keshavarzi and Kumar (2019) classified the agricultural soils in the Mashhad Plain, Khorasan-e-Razavi region, Iran, in the highly polluted class using MPI [47]. Hadzi et al. (2019) reported the distribution of the MPI values in four mining areas in eight regions of Ghana: Oda River Forest Reserve (ODA) and AngloGold Ashanti mining area around Obuasi Municipality of Ashanti region (AOB), Bosomkese Forest Conservation (BB) and Newmont mining area around Kenyase in the Asutifi District of Brong-Ahafo region (BAM), Goldfields mining area of Tarkwa Municipal area in the Western region (WTB), and Atewa Range (EA) and Kwabeng mines (EAM) of Atewa district in the Eastern region. The values ranged from slightly polluted to severely polluted (MPI = 1.6–24.1), with all the sites recording pollution indices of more than 1.5. According to the MPI values, the BAM site was the least polluted (MPI = 1.6), and the AOB site was the most polluted (MPI = 24) [48].

Fig. 6.

The MPI of trace elements in the deposited dust around Mehdi Abad Pb/Zn mine

Furthermore, the MRI based on EF was computed (Table 3). The MRI results indicated that 44.44% and 22.22% of the samples were low and moderate risk with Pb, Cu, Ni, V, and Cr, whereas 33.33% of the dust samples were very high risk for these heavy metals. The maximum MRI value was obtained for Pb (900.62) and the minimum value for Cr (0.71). The mean value of MRI is as follows: Pb (216.34) > Cu (7.00) > Ni (4.40) > V (1.66) > Cr (1.07). The MRI values of Cr, Cu, Ni, and V for all the dust samples showed low risk, whereas Pb showed low, moderate, and very high risk in 44%, 22%, and 35% of the samples, respectively. Hu et al. (2019) classified the ecological risk of the elements in agricultural soils around the Pb/Zn, Dexing China mining zone as considerable and very high risk using MRI [46]. Keshavarzi and Kumar (2019) revealed the MRI values of Cu, Mn, and Zn for both surface and subsurface samples. The values showed very high risk for Cu in Mashhad’s agricultural soil samples, whereas Mn and Zn were a considerable risk in the surface soil samples of the studied area. The Mn and Zn values indicated moderate contamination in the subsurface soil samples [47]. Zhu et al. (2012) reported that the risks of heavy metals in the Xiawan Port sediments were in the decreasing order of Cd (MRI = 11,172) > Pb (MRI = 70.42) > Cu (MRI = 55.84) > Zn (MRI = 30.64) [49].

Table 3.

Grading of ecological risk of trace elements in the deposited dust around Mehdi Abad Pb/Zn mine using MRI

Grading
MRI	Low risk	Moderate risk	Considerable risk	High risk	Very high risk
Sample (N or %)	4 (%44.44)	2 (%22.22)	0	0	3 (%33.34)

The mHQ index indicated an extreme severity risk of five heavy metals (Cu, Cr, Ni, Pb, and Sn) in all the dust samples (Table 4). The calculated average values of mHQ indicated a high severity ecological risk only in the case of Pb, and a low severity ecological risk for Cu and Sn. A considerable severity ecological risk was stated for Cr and Ni. The ecological risk of the samples based on the mHQ index are as follows: a low severity risk of 44% with Pb, 78% with Cu, and 100% with Sn; a moderate severity risk of 11% with Pb, 12% with Ni, 22% with Cu, and 66% with Cr; and a considerable severity risk of 11% with Pb, 88% with Ni, and 34% with Cr. An extreme severity ecological risk was stated only for Pb in 34% of the samples. The mHQ values were not defined for Ba, Be, Co, Dy, La, Li, Nb, Pr, Rb, S, Se, Sr, Ta, Tb, Zr, Te, Th, Ti, U, V, Y, Yb, and Tm because of the lack of adverse ecological effect values. Traczyk and Gruszecka-Kosowska (2019) determined the contamination degree and ecological risk of heavy metals bound with deposited PM by calculating mHQ in Kraków, Poland. Their results showed that the calculated mHQ values of contamination were extreme for Zn, considerable for Cr, and moderate for As, Cu, and Pb [50]. Benson et al. (2018), using the mHQ index, found the pollution levels of Cr, Cu, Ni, and Pb in the aqueous sediments in the Gulf of Guinea to be very low, low, very low, and high class, respectively [51]. However, according to the average value of the mHQ index in the present study (2.06), the risk of dust deposited around the Mehdi Abad Pb/Zn mine is treated as considerable severity.

Table 4.

Grading of ecological risk of some trace elements in the deposited dust around the Mehdi Abad Pb/Zn mine using mHQ index

Grading
mHQ	Nil to Very Low Severity	Very Low Severity	Low Severity	Moderate Severity	Considerable Severity	Very High Severity	High Severity	Extreme Severity
Sample (N or %)	0	0	0	0	0	0	0	9 (%100)

Spatial distribution of MRI and mHQ indices

As one of the main deterministic methods, IDW was used to prepare the spatial distribution map of MRI and mHQ indices around the Mehdi Abad Pb/Zn mine. This method demonstrated that the MRI and mHQ indices of Cu, Cr, Ni, Pb, V, and Sn had the highest values in the southwestern areas (agricultural and residential areas) and gradually became less when the distance from the mining area increased (Fig. 7). The predominant wind direction in the Mehdi Abad area is from northeast to southwest. As a result, it probably caused the transfer of elements from the mine to agricultural and residential areas. Therefore, the highest ecological hazards were predicted for these areas (Fig. 8).

Fig. 7.

Spatial distribution of mHQ index of Cr, Cu, Ni, Pb and Sn elements in the deposited dust on residential areas and agricultural lands around Mehdi Abad Pb/Zn mine using mHQ index

Fig. 8.

Spatial distribution of MRI index of Cr, Cu, Ni, Pb and Sn elements in the deposited dust on residential areas and agricultural lands around Mehdi Abad Pb/Zn mine using mHQ index

Conclusions

The characteristics of thirty trace elements in dust samples collected from around the Mehdi Abad Pb/Zn mine were analyzed. The average concentrations of all the dust sample elements except for Cr, S, Sr, Ta, Tb, and Te were higher than the background values. The results of HCA indicated that all the trace elements except Fe formed a group and had similar behavior. The extent and degree of trace element contamination around mines vary depending upon geochemical characteristics. Pb severely enriched the deposited dust around the Pb/Zn mine; the Mehdi Abad zone showed the highest pollution, which could directly result from mining activities and local geology. The MPI results indicated that the trace elements of deposited dust posed heavy contamination and ecological risks in the agricultural and residential areas. The MRI values for Pb showed that 35% of the dust samples reported MRI values >320 and posed very high ecological risks, whereas the MRI values of Cr, Cu, Ni, and V for all the samples showed a low ecological risk. The mHQ index indicated an extreme severity risk for Cu, Cr, Ni, Pb, and Sn in all the dust samples. The calculated average values of mHQ indicated a high severity ecological risk only in the case of Pb. The results of the IDW analysis revealed the highest value of the MRI and mHQ indices for the agricultural and residential areas, and the predominant wind direction had an effective role in transferring elements from the mine to these areas. In general, the results of the present study indicate that mining activities have increased the ecological risk of trace elements in the area, especially Pb. According to this study, the quality of dust deposited on the agricultural and residential areas around the Mehdi Abad Pb/Zn mine decreased with the presence of trace elements. The results indicate that increased mining activities, an increase in the pit’s radius, and more tailings production in the long-term will further decrease the quality of the deposited dust, which will seriously endanger the ecological cycle and the residents of the area. However, it is suggested and encouraged to plant vegetation that can uptake the toxic heavy metals and prevent their dispersion in agricultural and residential lands in the vicinity of mining areas.

Authors’ contributions

SSB work concept, conducted the data collection, ecological risk calculations, preparation of inverse distance weighting maps of mHQ and MRI, manuscript writing, proofreading, results and discussion, tables and figures. SZMA, MRE, VTV and MN Analysis (ICP-MS), ecological risk calculations, results and discussion, implementation of HCA, tables, figures, proofreading and references. The author(s) read and approved the final manuscript.

Funding

All sources of this study were supported by the authors.

Declarations

Conflicts of interest/competing interests

There are no competing interests.

Ethics approval

Not applicable.

Consent to participate

Not applicable.