Human health and ecological risk assessment of heavy metal(loid)s in agricultural soils of rural areas: A case study in Kurdistan Province, Iran

Abstract

Background

Agricultural soils pollution with heavy metal (loid) s (HMs) can create significant ecological and health problems. The aims of present study were to characterize HMs pollution profile of dry farmland soils in rural areas of Kurdistan province in Iran and evaluate potential associated ecological and health risks.

Methods

Different indices of Geo-accumulation index (I_Geo), Individual contamination factor (ICF), Nemerow composite pollution index (NCPI) and Potential Ecological Risk Index (PERI) were employed to assess the bio-accumulation of the HMs and evaluate associated ecological risks. Human health risks estimated with total hazard index (THI) and total carcinogenic risk (TCR) indices based on ingestion, inhalation and dermal exposure pathways for children and adults.

Results

As, Cd, Cr, Ni and Pb exceeded the soil standards. The spatial maps of the I_Geo showed that As pollution was at severe level in eastern part of the study region. According to the ICF results, the studied soils were extremely contaminated with As, Cd, Cr, Ni and Zn. Furthermore, based on the pollution indices, some of sampling sites were critically polluted by abovementioned HMs. For children and adults groups, the THI values in 13 and 97% of sampling sites were more than 1 and the TCR in 7 and 14% of sampling sites were more than 10⁻⁴, respectively. The farmland soil pollution of the study area by As and Cr were found to be quite serious and dangerous.

Conclusion

The findings of this study suggest that further attention should be paid by decision-makers to control the HMs pollution in the agricultural soils of the study area.

Electronic supplementary material

The online version of this article (10.1007/s40201-020-00475-y) contains supplementary material, which is available to authorized users.

Introduction

There are growing concerns about the soil contamination with heavy metal (loid) s (HMs), especially agricultural soils [1]. The term HMs refers to elements that have a density higher than 5 g/cm⁻³ [2]. Due to the toxic and carcinogenic characteristics of some HMs, any contamination of the soil and water resources with these trace elements should be considered carefully. As a fact, long-term human exposure to heavy metals and metalloids could result in adverse health effects. Although some HMs such as copper (Cu), zinc (Zn) and selenium (Se) are essential for humans and other living organisms in trace amounts, but high levels uptake of these metals can cause severe effects and impair the physiological function of various body organs [3, 4]. So far, it has been demonstrated that, HMs such as Cd, As, Cr, Ni and etc. are highly toxic, non-degradable, and can be biologically accumulated in different tissues of humans and animals body, thus result in irreversible health effects [5]. In this regard, high exposure to arsenic (As) has been shown to cause skin cancer, bladder and lung cancer, black foot disease, hypertension, cardiovascular diseases and diabetes [4, 6]. Exposure to cadmium (Cd) has been linked to adenocarcinoma, lung cancer, bone problems, and kidney and heart failure [7]. Moreover, different health problems including leukemia, kidney cancer and neurotoxicity have been attributed to exposure to lead [8]. Therefore, some of HMs are considered as carcinogenic agents by Agency for Toxic Substances and Disease Registry (ATSDR) [9]. The presence of HMs in soils could be a result of both natural and anthropogenic activities. Weathering, erosion, and other geological processes have been assumed as the natural process which can lead to pollution of surface soils with heavy metals [10]. However, mining, industrial activities, traffic emissions, pesticides and chemical fertilizers are the main anthropogenic sources of HMs pollution in environment [11, 12]. Human beings and animals can be exposed to HMs in agricultural soils by direct contact with the contaminated soil (inhalation, digestion and dermal contact), or indirectly via accumulation of HMs in the food chain as well as through the contamination of drinking water resources [13, 14]. In the last decades, the population growth and increasing food demands, has led to annual expansion of farmlands, which subsequently can lead to higher exposure of humans to soil HMs in different countries [1, 14]. Therefore, ecological assessment of agricultural soils has of considerable importance, and can help to prevent the cultivation of crops in contaminated soils, and thus lessening the human exposure to HMs especially in farmers and rural residents [15].

As a developing country, agriculture is one of the mainstays in Iranian economy. Because Iran has a dry and semi-arid climate, about two-thirds of its landmass is uncultivated and dry farming method accounts for 70% of its cultivations. Furthermore, the economy of residents of agricultural areas is mainly depends on cultivation, and in many cases whole families are involved in farming. Hence, daily and lifetime contact with agricultural soils in such areas is unavoidable [16, 17].

To the best of our knowledge, spatial distributions of HMs in farmland soils, and their associated risks to humans and ecosystems have not been investigated in rural areas of Kurdistan province, Iran. Therefore, deep knowledge is needed to address this issue. In the present study, our main objectives were to: 1) characterize the profile and concentration of HMs in dry farmland soils, 2) identify the main source of HMs using a geoaccumulation index (I_Geo) and principle component analysis (PCA), 3) describe spatial distributions and trend of soil HMs pollution in study area, 4) evaluate the potential ecological risk and human health risk of HMs in the farmland soils of subjected area.

Materials and methods

Study area and sample collection

Kurdistan province is located in the west part of Iran, with an area of 29,000 km² and 1,603,011 inhabitants, which makes it as one of the most populated provinces. Due to the good weather conditions and relatively high annual precipitation (average precipitation of 517 mm/year), the study area is one of the important agricultural regions of Iran [18]. In this regard, in 2018 Kurdistan province was ranked first in the wheat cultivation and second in the wheat production of the country [17].

In this study, the sample collection was done based on EPA method [19]. Hence, 29 surface soil samples were obtained from dry farmlands of 29 different locations (1 site/31 km²) in Kurdistan province as shown in Fig. 1. It has to be mentioned that, the sample collection was done in May 2018, due to lack of rainfall. To minimize sampling bias, each sampling site, was sampled with 20 replicates at about 20 cm depth level. Then, these replicates were mixed together to obtain a single sample for each site. Also to avoid any disturbances, all samples were collected using Polyvinyl chloride (PVC) samplers and containers then sent to the laboratory to be kept at 4 °C for further analyses.

Fig. 1.

Locations of the study area and sampling sites

Sample preparation and measurement of HMs

The soil samples were air dried in a drying cabinet with air circulation and then, sieved (2.0 mm mesh sieve) to remove any stones or large particles before analysis. In order to perform chemical analysis, ASTM acid digestion was done using acidic solution that contained HNO₃, HClO₄, H₂SO₄ (6:2:1) [20]. Afterwards, the extracted samples were filtered and the HMs content of subsequent extracts was measured. All the chemicals used in this study were purchased from Merck Company (Darmstadt, Germany). Analysis of metal ions was performed using the SPECTRO ARCOS ICP-OES (SPECTRO, Germany) coupled to a V-groove nebulizer and Scott spray chamber made from quartz glass equipped with a charge coupled device (CCD) detector.

Quality assurance (QA) and quality control (QC)

The calibration of ICP-OES was performed employing certified standard reference materials and regents based on the US National Institute of Standards and Technology (NIST). The limit of detection (LOD) values for As, Cd, Cr, Ni, Pb, Co, Cu, Mn, V, and Zn were obtained to be 0.179, 0.049, 1.33, 0.3, 2.166, 0.43, 0.306, 2.73, 0.63, and 0.270 ppb, while the limit of quantification (LOQ) for these metals were as 0.597, 0.163, 4.42, 0.981, 7.219, 1.433, 1.020, 8.84, 2.079 and 0.9 respectively. Moreover, a stable measurement process and accurate data for all the investigated heavy metals and the recovery percent of 96.73 ± 8.12%, 95.89 ± 7.56%, 94.35 ± 6.18%, 107.75 ± 5.45%, 96.25 ± 4.22%, 88.15 ± 3.7% and 92.25 ± 2.9% was obtained for As, Cd, Pb, Co, Cu, Fe, and Zn, respectively.

Ecological risk assessment

Geo-accumulation index

To quantify the degree of contamination and compare concentration of different HMs appeared in soil samples, the Geo-accumulation index (I_Geo) was used. I_Geo makes it possible to compare the measured concentration of HMs in the soil samples (current status) with the geochemical background values (pre-industrial concentrations). As described in detail elsewhere by Muller in 1979, the I_Geo is calculated as follow by eq. 1: [21].

$$I_{\mathit{Geo}}={\mathit{log}}_2\left(\frac{C_i}{K\ast C_0}\right)$$ 1

Where C_i is the concentration of each HMs at the sampling site (mg kg⁻¹); C₀ is the average concentration (mg kg⁻¹) of HMs in the soil samples of study area (the average value considered as reference point or background value) [22], K is a constant value and equals to 1.5, which refers to natural fluctuations of a given heavy metal in the environment due to the lithological variations [23]. The I_Geo consists of seven grades and the corresponding contamination levels were as follows: [21].

I_Geo ≤ 0, uncontaminated (Class 0);

0 < I_Geo ≤ 1, uncontaminated to moderately contaminated (Class 1);

1 < I_Geo ≤ 2, moderately contaminated (Class 2);

2 < I_Geo ≤ 3, moderately to strongly contaminated (Class 3);

3 < I_Geo ≤ 4, strongly contaminated (Class 4);

4 < I_Geo ≤ 5, strongly to extremely contaminated (Class 5); and.

I_Geo > 5, extremely contaminated (Class 6).

Individual contamination factor

In order to determine the degree of risk of HMs to the environment in relation with their retention time, the Individual Contamination Factor (ICF) was used. Herein, the high ICF valve of a defined metal is indicative of its high risk to the environment. The individual contamination factor of each heavy metal can be calculated using eq. 2 as follows:

$$\mathrm{ICF}=\frac{C_i}{S_i}$$ 2

Where ICF is the individual contamination factor for each heavy metal or metalloid, C_i denoted to the mean concentration of HMs at sampling sites, and S_i is background value of the individual heavy metal [22]. The ICF classifications are provided by and presented here as following: [24].

0 < ICF_i ≤ 0.5, unpolluted (Class 0);

0.5 < ICF_i ≤ 1, unpolluted to moderately polluted (Class 1);

1 < ICF_i ≤ 2, moderately polluted (Class 2);

2 < ICF_i ≤ 3, moderately to strongly polluted (Class 3);

3 < ICF_i ≤ 4, strongly polluted (Class 4);

4 < ICF_i ≤ 5, strongly to extremely polluted (Class 5); and.

ICF_i > 5, extremely polluted (Class 6).

Nemerow composite pollution index (NCPI)

The Nemerow composite pollution index (NCPI) reflects the contribution of each heavy metal to the composite pollution. This method was also used to assess the ecological risk of HMs and classify the contamination grade [25]. The following equation was used to obtain the NCPI:

$$\text{NCPI}=\sqrt{\frac{{\left({\overline{\mathrm{ICF}}}_{\mathrm{i}}\right)}^2+{\left({\mathrm{ICF}}_{\text{iMax}}\right)}^2}{2}}$$ 3

Where NCPI is the Nemerow comprehensive pollution index, ${\overline{\mathrm{ICF}}}_i$ is the mean value of the individual pollution factor of the HMs, and ICF_iMax is the maximum value among individual pollution index of each HMs. Since the NCPI is a comprehensive index, it is used to grade the soil HMs pollution into 5 classes as follows: [26].

NCPI ≤0.7, safety domain;

0.7 ≤ NCPI <1.0, precaution domain;

1.0 ≤ NCPI <2.0, slightly polluted domain;

2.0 ≤ NCPI <3.0, moderately polluted domain; and.

NCPI >3.0, seriously polluted domain.

Potential ecological risk index (PERI)

The Potential Ecological Risk Index (PERI) which was originally proposed by Hakanson (1980) was employed to assess the degree of ecological risk based on the characteristics, toxicity, concentrations and environmental behavior of HMs. the comprehensive potential ecological risk and the potential ecological risk of an individual heavy metal can be computed using following equations, respectively: [27].

$$\mathit{\text{PERI}}={\sum }_{i=1}^n{E}_r^i$$ 4

$$E_r^i=T_n^i\times {\mathit{ICF}}_i$$ 5

Where $E_r^i$ is the potential ecological risk of an individual HM. $T_n^i$ is the biological toxicity factor of a single HM and ICF_i refers to the individual contamination factor of each HMs. The toxic factors were developed by Hakanson (1980) and assigned for each heavy metal (loid). In this regard, $\left(T_n^i\right)$ for Cr, Ni, Cu, As, Cd, Zn, Co, V, Mn and Pb is 2, 6, 5, 10, 30, 1, 5, 2, 1 and 5, respectively [28, 29]. The PERI represents the potential ecological risk caused by HMs and defined in five categories as: low risk (PERI < 40), moderate risk (40 < PERI < 80), considerable risk (80 < PERI < 160), high risk (160 < PERI < 320), or very high risk (PERI > 320).

Human health risk assessment

Non-carcinogenic risk

In this step, the probable adverse health effects of soil HMs on humans who may be exposed with these elements in the context of contaminated farmland soil was examined. Generally, three exposure pathways of ingestion, inhalation and dermal contact were considered in present study. The risk assessment was performed according to the guidelines and Exposure Factors Handbook of the U.S. Environmental Protection Agency [30]. The average daily intake (ADIs) (mg.kg⁻¹.d⁻¹) equations were used to calculate the human exposure to HMs by different routes of ingestion (ADI_ing), dermal contact (ADI_derm), and inhalation (ADI_inh) for both adults and children as follows:

$${\mathit{ADI}}_{\mathit{ing}}=\frac{C_{\mathit{\text{soil}}}\times \mathit{\text{IngR}}\times \mathit{EF}\times \mathit{ED}}{\mathit{BW}\times \mathit{AT}}\times 10-6$$ 6

$${\mathit{ADI}}_{\mathit{inh}}=\frac{C_{\mathit{\text{soil}}}\times \mathit{\text{InhR}}\times \mathit{EF}\times \mathit{ED}}{\mathit{FEF}\times \mathit{BW}\times \mathit{AT}}\times 10-6$$ 7

$${\mathit{ADI}}_{\mathit{\text{dermal}}}=\frac{C_{\mathit{\text{soil}}}\times \mathit{SA}\times \mathit{SL}\times \mathit{ABS}\times \mathit{EF}\times \mathit{ED}}{\mathit{BW}\times \mathit{AT}}\times 10-6$$ 8

All individual data for calculate ADIs were used from health standards [31, 32]. Then, non-carcinogenic risk related to an individual HM was characterized by the target hazard quotient (THQ). The THQ is defined as the target quotient of the chronic special pathway daily intake of a single HMs or specific chemical and determined based eq. 9. Hazard Index (HI) is the integration of THQ’s from all exposure pathways for each HMs or specific chemicals (Eq. 10): [30].

$$\mathit{THQ}=\frac{\mathit{ADI}}{\mathit{RfD}}$$ 9

$$\mathrm{HI}={\mathrm{THQ}}_{\text{Pathway}}1+{\mathrm{THQ}}_{\text{Pathway}}2+\dots +{\mathrm{THQ}}_{\text{Pathway}}\mathrm{n}$$ 10

$$\mathit{THI}=\sum \mathit{HI}$$ 11

Where RfD is the chronic reference dose for each of the HMs (mg.kg⁻¹.d⁻¹) and ADI is the average daily intake. To assess the overall potential for non-carcinogenic effects posed by all tested HMs in any sampling sites, the total hazard index (THI) approach was applied [30]. The THI is the sum of the HIs, and calculated by eq. 11. The THI less than 1, indicate that the exposed population may experience no adverse health effects. If THI value is greater than 1, which indicates that a risk to human health potentially exists and by increasing of this values, the probability of the adverse health effects is also increases [30].

Carcinogenic risk

The carcinogenic risks associated with the exposure to HMs were estimated in this step. Actually, carcinogenic risk reflects the probability of cancer development over a lifetime as a result of exposure to specific carcinogen. As, Ni, Cr and Cd are considered as class I carcinogens by International Agency for Research on Cancer (IARC), which means that these metals are carcinogenic to humans. Therefore, the risk of carcinogenicity was examined using excess lifetime cancer risk (ELCR) and total carcinogenic risk (TCR) only for aforementioned metals by following equation: [33].

$$\mathit{\text{ELCR}}={\sum }_i{\left(\mathit{ADI}\times \mathit{CSF}\right)}_i$$ 12

$$\mathrm{TCR}=\sum \mathit{\text{ELCR}}$$ 13

Where, ADI is the average daily intake (mg.kg⁻¹.d⁻¹) and CSF is the carcinogenicity slope factor (mg.kg⁻¹.d⁻¹). Based on USEPA guideline, if ELCR and TCR < 10⁻⁶, the carcinogenic risks can be considered as negligible, and risks from 1 × 10⁻⁶ to 1 × 10⁻⁴ are often deemed as an acceptable range to human beings. However, ELCR and TCR > 10⁻⁴ implies that the development of cancer in human beings is very probable [30].

Data analysis

The spatial distribution patterns of HMs were drawn out using geographic information system (GIS) software (ArcGIS, version 10.3). The descriptive statics of soil HMs including mean, range, and standard deviation were calculated for all sampling sites. The data normality was determined by Shapiro-Wilk and Kolmogorov-Smirnov tests and Skewness-Kurtosis method was used to determine data distribution. Pearson’s correlation analysis and Principal Component Analysis (PCA) were performed to show significant relationships between HMs and to find out their sources. All the statistical analyses were performed using SPSS 19.0 (IBM SPSS Inc., Chicago, USA).

Results and discussion

HMs concentrations in farmland soil samples

The statistics of the HMs concentrations in the soil samples are summarized in Table 1. The results showed that the data of the investigated HMs obtained from different sampling areas with exceptions of As, Cd and Ni are normality distributed. As indicated in Table 1, Mn has the highest mean concentration in the soils of study area (580.36 mg.kg⁻¹), which followed by Zn (103.49 mg.kg⁻¹) and As (94.3 mg.kg⁻¹), however, Cd has the lowest mean concentration of (0.79 mg.kg⁻¹). Therefore, the trend of mean concentration of tested HMs in dry farmland soil of the study area was as follows; Mn > Zn > As > Cr > Ni > V > Pb > Cu > Co > Cd. Our results showed that in 41.38%, 13.8%, 65.5%, 3.5%, and 3.5% of sample sites the levels of As, Cd, Cr, Ni and Pb exceeded the national soil pollution standards of Iran [34]. The minimum and maximum concentrations of arsenic in dry farmland soil samples were found to be 3.71 and 683.24 mg.kg⁻¹, respectively, indicating a high concentration of arsenic in the agricultural soils of this region. A possible reason for this may be the presence of a volcanic zone in this area with a higher amount of arsenic in the upper layers of its bedrock [35]. Regardless of As, the concentration of other HMs in the agricultural soils were found to be somehow consistent with the results obtained by Beygi and Jalali (2018), who studied the farmland soils of Hamadan which is the neighboring province with Kurdistan [36]. Furth more, the minimum, maximum and mean concentration of Cr in the farmland soils of Kermanshah (another neighboring province of the study area) have shown to be 32, 235 and 79.21 mg.kg⁻¹, respectively, which are almost similar to our results. Although, in the aforementioned studies the average concentrations of Zn and Ni were higher than our results, but the mean concentration of Cu was less than that obtained in present study [37]. Considering these results, it can be assumed that the parent material and erosion of bedrocks in long time might affect on HMs content of agricultural soil in this area. Moreover, the CV values of 34.4%, 35.5%, 32%, 38.2% and 26% for Cu, Mn, Pb, V and Zn, respectively were found in the present study. Additionally, a CV of 39.3%, 51.5%, and 62.6%, were observed for Co, Cr, and Ni, respectively. These results demonstrate the lower variability of Cu, Mn, Pb, V and Zn and relatively higher variability of Co, Cr, and Ni. Taken together, the findings suggest that there is a great variability among subjected HMs, hence these HMs are not homogeneously distributed in study area. In addition, the highest coefficient variation of 194.8% and 263.3% was obtained for As and Cd, respectively which shows their higher variability compared with other heavy metals [35]. According to the findings of present study, about 90%, 60%, 20% and 13% of the farmland soil samples are contaminated with As, Ni, Cr and Cd, respectively. These values are exceeded the standards of the Ministry of the Environment, Finland (MEF), as a preferred soil standards in the world [38]. The comparison of the results with background levels that were obtained by Beygi and Jalali (2018), showed that concentrations of all the tested elements were higher than background levels, however the difference were significant for only As, Cd, Cr, and Ni [36]. Furthermore, higher amounts of Cd, Pb and Zn were seen in the present study compared with the background levels in areas nearby the studied regions [39].

Table 1.

Descriptive Statistics of HMs contents in soil samples (mg.kg⁻¹)

HMs	Min.	Max.	Med.	Mean	S.D.	V	CV%	Skewness	Kurtosis
As	3.71	683.24	11.83	94.3	183.67	33,730	194.8	2.03	3.01
Cd	0.001	9.32	0.001	0.79	2.08	4.35	263.3	1.08	3.61
Co	9.79	34.45	14.76	17.37	6.82	46.6	39.3	1.30	0.86
Cr	44.42	231.24	62.8	79.71	41.02	1683	51.5	1.17	2.78
Cu	14.49	48.51	25.4	28.13	9.68	93.65	34.4	0.77	−0.54
Mn	263.03	1135.49	536	580.36	205.87	42,380	35.5	1.00	0.72
Ni	36.1	264.46	55.2	70.8	44.3	1962	62.6	1.09	3.28
Pb	17.25	56.2	26.9	29.69	9.49	89.99	32.0	1.06	0.95
V	22.83	81.03	34.2	38.75	14.8	219.54	38.2	1.40	1.66
Zn	61.64	175.19	95.3	103.49	26.9	724.85	26.0	0.92	0.82

Various amounts of HMs have been reported in the farmland soils of the different regions of world. In this regard, Cai et al. (2019) has conducted a study to evaluate heavy metals contamination of the farmland soils of Guangdong Province in China. Compared with our results, higher concentration of Pb has obtained by Cai et al. (2019), however the mean concentration of As, Cd, Cr, Cu, Zn and Ni was higher in our study area [40]. Therefore, profile of HMs in farmland soils can be different in various areas, and this may be due to the different naturogenic or anthropogenic sources.

Analysis and geostatistical interpolation of the HMs contamination in the farmland soils

The Geo-accumulation Index was used to assess the degree of pollution in this area by comparing between current and background concentrations. The I_Geo level of 6.5 was found for As, indicating that the study area is extremely contaminated with this pollutant. Also, the I_Geo levels for other elements showed that the study area is uncontaminated by Mn (−0.4), uncontaminated to moderately contaminated by Cu (0.6) and V (0.5), moderately contaminated by Zn (1.7), Co (1.2) and Pb (1.1), moderately to heavily contaminated by Cr (2.3) and Ni (3), and heavily to extremely contaminated by Cd (4.4). The GIS inverse distance weighting (IDW) interpolation was used to visualize the spatial interpolation of soil properties based on the Geo-accumulation index. As illustrated in Fig. 1S, the IDW-interpolated maps of the I_Geo imply that the eastern part of the study area is highly contaminated by As and has the most severe pollution concerns. Also, the central and eastern parts of the study area were found to be strongly contaminated by Cd. In addition, the western part of the study region was contaminated moderate to strongly by other HMs with an almost regular pattern.

According to the results of Individual Contamination Factor (ICF), the contamination level of the study area in terms of the five tested HMs was in the category of extremely polluted. The Maximum ICF levels of 136.65, 31.07, 7.46, 12.02, and 5.01 were obtained for As, Cd, Cr, Ni and Zn, respectively. Moreover, ICF results of Co and Pb showed that the sampling areas are in the category of strongly contamination. The contamination levels with Cu (2.2) and V (2.13) are also high and classified in moderate to strongly contamination category. The level of Mn (1.34) contamination in sampling sites is in moderately contamination category. The IDW-interpolated maps obtained based on Individual Contamination Factor as shown in Fig. 2, demonstrate that the eastern part of the study area is highly contaminated by As, while the central part of the study area is strongly contaminated by Cd. The patterns of contamination degree by HMs in the other parts of the study area are also illustrated in Fig. 2.

Fig. 2.

Spatial distribution maps of Individual Contamination Factor (ICF) of HMs, A (As), B (Cd), C (Co), D (Cr), E (Cu), F (Mn), G (Ni), H (Pb), I (V) and J (Zn)

The result of nemerow composite pollution index (NCPI) revealed that the contamination level of the HMs in the farmland soils of all sampling sites are in category of moderate to seriously pollution domains. IDW-interpolated map of NCPI showed that (Fig. 2S) the NCPI of farmland soils in 8 sampling sites are in moderately polluted category, while 6 sampling sites had NCPI values more than 22 which indicates that farmland soils of these areas are very polluted by HMs. Considering the NCPI results and the results obtained by other indexes, our findings indicate that the agricultural soil of the study area is highly polluted with As and Cd which should be considered as a health threatening issue in the future.

In order to comprehensively evaluate the ecological risk of HMs, the Potential Ecological Risk Index (PERI) was used. The PERI results indicated that the soil of all sampling sites had a moderate to very high potential ecological risk. Minimum, maximum and mean value of PERI in the study area were obtained to be 49.96, 1483.84 and 322.33, respectively. The potential ecological risk levels of HMs in farmland soils of the study area illustrated in Fig. 3. Accordingly, 27.6% of all sampling sites have very high potential ecological risk ($E_r^i$ > 320). We obtained that As is the key influence factor to cause the potential ecological risk. Hence, all of the sampling sites have very high potential ecological risk of As. Considering the Fig. 3, severe comprehensive potential ecological risk were observed in the eastern and central part of the study region. We assumed that, this may be related to the strong pollution of these sites, especially with As and Cd. Altogether, spatial distribution of NCPI and PERI values showed very high pollution and potential ecological risk in term of subjected HMs in the sampling points. In accordance with our result, the presence of HMs, especially As, in water resources and agricultural products of these areas have been reported [35, 41, 42].

Fig. 3.

Spatial distribution maps of Potential Ecological Risk Index (PERI) of HMs for the study area

Correlation and principal component analysis (PCA) of HMs

The correlation tests were used to understand the relationship between different tested HMs and to find their possible sources. In this regard, the positive correlation between HMs may be indicative of their common source [43]. According to the Pearson’s correlation analysis of agricultural soil HMs in our study (Table 2), no significant correlation was obtained between As and other tested HMs. It was found that Cd has positive correlation with Pb and negative correlation with Cu (P_v < 0.05). The statistically significant positive correlations (P_v < 0.05) were also observed among Cr, Co, Cu, Mn, Ni, Pb, V, and Zn. The strong correlation between these HMs can be attributed to their common origin, common physicochemical characteristics as well as common anthropogenic sources. Also, the lack of any correlation between As and other HMs in this study area may be indicative of its anthropogenic origin.

Table 2.

Results of the Pearson’s correlation analysis of HMs concentrations

	As	Cd	Co	Cr	Cu	Mn	Ni	Pb	V	Zn
As	1	0.086	−0.226	−0.237	−0.278	−0.066	−0.227	−0.261	−0.306	−0.346
Cd		1	−0.157	−0.232	−0.437*	0.054	−0.215	0.413*	−0.193	−0.191
Co			1	0.885**	0.843**	0.816**	0.824**	0.694**	0.833**	0.895**
Cr				1	0.711**	0.551**	0.960**	0.513**	0.651**	0.808**
Cu					1	0.693**	0.640**	0.439*	0.832**	0.792**
Mn						1	0.531**	0.688**	0.627**	0.758**
Ni							1	0.448*	0.506**	0.792**
Pb								1	0.638**	0.723**
V									1	0.675**
Zn										1

Note: ^*.shows significant correlation at the 0.05 level (2-tailed)

^**.shows significant correlation at the 0.01 level (2-tailed)

Principal component analysis (PCA) is a useful tools in order to analyze complex linkages between different concentrations of HMs in agricultural soil, which help us to determine the possible sources of HMs. the PCA analysis results revealed close correlation of Co, Cr, Cu, Mn, Ni, V and Zn, which are included to the PC-1 describing 59.27% of the total variance and among which both siderophiles and chalcophiles (elements normally associated with the metallic and sulfide phases, respectively) can be found (Table 3). These results are consistent with the results of Pearson’s correlation analysis. These elements had some degree of homology and may be affected mainly by geochemical structure of bedrock in this area. PC-2, adds another 12.78% to the explanation of the variance in the system, by its main constituents, Pb and Cd, which are chalcophile elements. These elements may be affected by regional factors such as industrial development, urbanization, and transportation. However, PC-3 includes only As, one elements which likely indicate unexpected pollution. Three principal components (PC-1, PC-2 and PC-3) were obtained with eigenvalue greater than 1, explaining total variance of more than 75% (Table 3). Also, the eigenvalue of PC-1 was greater than that of PC-2 and PC-3, indicating its dominant contribution to the total variance. The association coefficient between variables and factors reflects the degree of proximity between them (Fig. 4). Putting the results of Pearson’s correlation analysis and principal component analysis together, it could be concluded that most of the investigated elements are originated from parent materials. The existence of HMs in different PCAs is related with their variable appearance in the agricultural soils. Similarly, comparing the correlation and PCA results obtained for HMs in agricultural soils of Hamadan province showed that Co, Cr, Cu, Mn, Ni, V and Zn most likely are originated from natural sources and changed with natural factors [36].

Table 3.

Results of the principal component analysis (PCA) on HMs

Variables (HMs)	Component
	PC-1	PC-2	PC-3
As	−0.316	0.159	0.897
Cd	−0.179	0.840	−0.038
Co	0.982	−0.008	0.097
Cr	0.869	−0.180	0.039
Cu	0.853	−0.299	0.043
Mn	0.805	0.205	0.279
Ni	0.798	−0.235	0.054
Pb	0.637	0.813	−0.140
V	0.851	0.028	−0.061
Zn	0.936	−0.022	−0.075
Explained variance (%)	59.277	12.78	7.236
Explained of cumulative Variance (%)	59.277	72.057	79.293

Note: Gray shadings indicate main constituents of the PCs

Fig. 4.

a: Factor loadings of HMs on three principal components. Yellow, green and red diamond symbols were clustered in first, second and third components, respectively. b: Scree plot of PCA

Human health risk assessment

As mentioned previously, ingestion, inhalation, and dermal contact are the main pathways by which a special pollutant can enter the human body upon occupational or environmental exposure. Herein, the THI was calculated for soil HMs in each sampling sites considering the above-mentioned three exposure pathways (Table 4). The result showed that THI for adults and children groups were greater than 1 in 28 and 4 sampling sites, respectively, which indicate potential adverse health impact to these population groups. The minimum and maximum values for THI in children and adult groups were 0.404, 2.01 and 0.996, 4.98, respectively. Based on this result the THI values in all the sampling sites for adults were more than children group. The main reason for this mode can be due to the physical activity of the adults on agriculture soils hence have more exposure to the HMs. This is in agreement with the observations showed that the farming and ranching are the main occupation of adults in the study area. It has been demonstrated that As, Cd, Cr and Ni have both carcinogenic and non-carcinogenic health effects on humans. Exposure to these metals can lead to a variety of health problems including bronchitis, cardiac and vascular disease, skin rashes, sickness and headaches and behavioral changes [44, 45]. In the present study the spatial distribution of THI values for HMs indicate that the west part of the study area is at higher risk and Cr content in this region is responsible factor for exceeded levels of THI values (Fig. 5). Based on Fig. 6, Cr and then As play the highest portion in the overall THI in both groups. Likewise, in the other studies the highest THQ values were found to be related with As, Cd, Cr, and Ni [46–49].

Table 4.

THI and TCR values of HMs in exposed populations

Sampling Site	Total Hazard Index (THI)		Total Carcinogenic Risk (TCR)
	Children	Adult	Children	Adult
1	4.41E-01	1.09E+00	1.27E-05	2.99E-05
2	8.23E-01	2.03E+00	1.25E-05	2.97E-05
3	8.04E-01	1.99E+00	7.89E-06	1.90E-05
4	2.00E+00	4.98E+00	1.00E-05	2.40E-05
5	5.44E-01	1.35E+00	6.88E-06	1.66E-05
6	9.95E-01	2.39E+00	5.68E-05	1.32E-04
7	5.61E-01	1.38E+00	9.89E-06	2.34E-05
8	4.65E-01	1.15E+00	6.62E-06	1.58E-05
9	6.80E-01	1.68E+00	6.90E-06	1.66E-05
10	4.76E-01	1.17E+00	1.06E-05	2.53E-05
11	5.83E-01	1.43E+00	1.55E-05	3.65E-05
12	7.87E-01	1.95E+00	7.15E-06	1.71E-05
13	5.60E-01	1.38E+00	1.33E-05	3.14E-05
14	6.06E-01	1.50E+00	6.92E-06	1.66E-05
15	8.78E-01	2.07E+00	7.38E-05	3.24E-05
16	9.20E-01	2.28E+00	9.11E-06	2.19E-05
17	7.34E-01	1.69E+00	8.92E-05	2.07E-04
18	5.39E-01	1.33E+00	7.61E-06	1.81E-05
19	4.37E-01	1.08E+00	5.96E-06	1.42E-05
20	4.18E-01	1.03E+00	6.85E-06	1.65E-05
21	4.04E-01	9.96E-01	7.36E-06	1.76E-05
22	1.05E+00	2.60E+00	1.25E-05	2.98E-05
23	5.70E-01	1.41E+00	7.45E-06	1.79E-05
24	1.32E+00	3.27E+00	1.05E-05	2.51E-05
25	4.47E-01	2.60E+00	5.82E-06	1.46E-05
26	9.62E-01	2.20E+00	1.29E-04	2.98E-04
27	1.24E+00	3.08E+00	1.35E-05	3.24E-05
28	9.32E-01	2.31E+00	1.04E-05	2.48E-05
29	8.44E-01	1.95E+00	1.02E-04	2.36E-04
Min	4.04E-01	9.96E-01	5.82E-06	1.42E-05
Max	2.00E+00	4.98E+00	1.29E-04	2.98E-04

Fig. 5.

Spatial distribution of total hazard index (THI) and total carcinogenic risk (TCR) in study area; A: THI-Children, B: THI-Adults, C: TCR-Children, D: TCR-Adults

Fig. 6.

The portion of each HMs in THIs and TCRs, A: TCR_Children, B: TCR_ Adults, C: THI_Chidren, D: THI_Adults

According to the IARC report As, Cr, Ni and Cd have carcinogenic risks for human; thus, we examined the potential total carcinogenic risk related to these metals (Table 4). According to our results, the TCR values higher than 10⁻⁴ were obtained in 5 sampling sites of farmland soils. The minimum and maximum TCR for children and adult assess were found 5.82E-06, 1.29E-04 and 1.42E-05, 2.98E-04 respectively. The spatial distribution of TCR values indicate that east part of the study area is at high risk which is correlated with As content in soil (Fig. 5). It was found that As has the highest role in overall TCR for adult and child groups (Fig. 6). This result indicates that high pollution of As in study area must be scrutinized attentively and its impact on food sources, especially accumulation in agricultural products, should be addressed.

Conclusion

The farmland soils contamination with heavy metal (loid) s can pose different threats to ecosystem as well as human beings. Thus, reliable information on the HMs concentrations and risk assessment of agricultural soils is needed essentially. In present study, different methods were used to investigate the distribution and ecological risk assessment of HMs (As, Cd, Cr, Ni, Pb, CO, Cu, V, Mn and Zn) in dry farm soils of Kurdistan province, Iran. The amount of HMs contamination in the agricultural dry farm soils of this area presents high carcinogenic and non-carcinogenic risks to the humans, especially to farmers who living and work in the most severely polluted areas. It was found that Cr and As are most likely responsible for the higher THI and TCR values in both children and adult groups. Based on the findings of this study we purpose that the toxicity arising from the bioaccumulation of HMs from soil to plants, especially in agricultural yields should be assessed in the future. Also, ecological risk assessments of other contaminants such as pesticides, pharmaceuticals, and persistent organic pollutants (POPs) may be needed for comprehensive studies.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest about considering or publishing of this work.