Fuzzy synthetic evaluation and health risk assessment quantification of heavy metals in Zhangye agricultural soil from the perspective of sources

Science of the Total Environment 697 (2019) 134126

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Fuzzy synthetic evaluation and health risk assessment quantification of heavy metals in Zhangye agricultural soil from the perspective of sources Rui Zhao, Qingyu Guan ⁎, Haiping Luo, Jinkuo Lin, Liqin Yang, Feifei Wang, Ninghui Pan, Yanyan Yang Key Laboratory of Western China's Environmental Systems (Ministry of Education) and Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Apportioning heavy metal source by GPCA/APCS • Providing pollution control plan from the perspective of source • Cr from leather industry and metal processing should be paid attention • Pb from steel industry and traffic need to be controlled

a r t i c l e

i n f o

Article history: Received 11 June 2019 Received in revised form 17 August 2019 Accepted 25 August 2019 Available online 30 August 2019 Editor: Paulo Pereira Keywords: Agricultural soil heavy metals GPCA/APCS Fuzzy synthetic evaluation Health risk assessment

a b s t r a c t Heavy metals in agricultural soil receive much attention because they are easily absorbed by crop into the ecosystem. Managing the discharge of heavy metals from the source is an effective way to prevent and control heavy metals pollution. Grouped principal component analysis (GPCA) and Positive Matrix Factorization (PMF) receptor models were utilized in this study to conduct source apportionment, and the former was optimal because of the accuracy of predicting. Based on the source contribution by GPCA/APCS, heavy metals were evaluated by fuzzy synthetic evaluation model and health risk assessment model. The results of source apportionment showed that heavy metals in Zhangye agricultural soil were mainly affected by steel industry, traffic, agrochemicals, manures, mining activities, leather industry and metal processing industry source. Fuzzy synthetic evaluation showed that the pollution levels of Chromium (Cr) derived by leather industry and metal processing industry and Nickel (Ni) derived by steel industry and traffic source were higher. Health risk assessment revealed that the non-carcinogenic and carcinogenic risks of Cr derived by leather industry and metal processing industry and Lead (Pb) derived by steel industry and traffic source were higher. © 2018 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author. E-mail address: [email protected] (Q. Guan).

https://doi.org/10.1016/j.scitotenv.2019.134126 0048-9697/© 2018 Elsevier B.V. All rights reserved.

Rapid socioeconomic development is a worldwide phenomenon that is often accompanied by a series of environmental problems (Li et al., 2016). Among them, the rapid development of industry and agriculture has made heavy metal pollution an important threat to the

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R. Zhao et al. / Science of the Total Environment 697 (2019) 134126

environment, food safety and health (Sarwar et al., 2017; Kumar et al., 2019). The heavy metals in agricultural soil have reached high pollution levels due to influences from fossil fuel combustion, mining, metal smelting, sewage irrigation, application of fertilizers and pesticides, and discharge of traffic sources (Nriagu, 1996; Shi et al., 2018). When heavy metals enter the soil, they not only reduce environmental quality but also endanger the health of people and other organisms through the food chain (Nabulo et al., 2010). Therefore, it is of great importance to study the pollution characteristics and sources of heavy metal in agricultural soil to protect the environment and human health (Huang et al., 2018b; Li et al., 2018a). Receptor model can conduct source identification and contribution quantification for pollutants by a multivariate statistical method (Callén et al., 2009). Therefore, it can provide a reference for the establishment of effective environmental management (Larsen and Baker, 2003; Yang et al., 2013; Hu et al., 2018). Currently, the commonly used receptor models include principal component analysis/absolute principal component scores (PCA/APCS) (Larsen and Baker, 2003; Deng et al., 2018; Ma et al., 2018), edge analysis (UNMIX) (Song et al., 2006; Peter et al., 2018), positive matrix factorization (PMF) (Paatero, 1997; Guan et al., 2018) and chemical mass balance (CMB) (Watson et al., 1984; Chow and Watson, 2002). The PCA/APCS model uses orthogonal decomposition to identify component corresponding individual groups, and then correspond to the variable by a load factor. Varimax rotation is applied to group the strong correlation factors in this model, and there is little or no correlation between individual components (Thurston and Spengler, 1985; Jain et al., 2018). This model is widely used because it can obtain a reliable source contribution by inputting only the concentration of pollutants (Gholizadeh et al., 2016; Qi et al., 2017; Deng et al., 2018). A grouped principal component analysis (GPCA) method is the improved principal component analysis (PCA). Based on PCA and factor analysis, it can classify factors more carefully and has been successfully applied in energy studies (Zhang and Su, 2016), though its application in pollutant identification research is relatively limited. The PMF model is a conventional method for source apportionment (Jiang et al., 2017; Liang et al., 2017; Guan et al., 2018). Therefore, it can be used to verify the results of GPCA/APCS model. Soil heavy metals pollution assessment is an important reference for identifying pollution levels and formulating environmental protection polices (Li et al., 2018a). Recently, the evaluation indices widely used are the Enrichment factors (EF), Single factor index (SF), Nemerow index (NI), the Geo-accumulation index (Igeo) (Han et al., 2018; Li et al., 2018a; Zhuang et al., 2018; He et al., 2019). However, the above indicators do not account for the natural changes and the randomness and fuzziness of human activities in the environmental system (Hu et al., 2016), particularly pollutant data near the boundary value, where a small fluctuation will change its class (Onkal-Engin et al., 2004). The fuzzy synthetic evaluation method divides the data into different categories according to the quality standards of pollutants and uses the function to reflect the absence of obvious boundaries between adjacent standards. It can evaluate the contribution of pollutants based on predetermined weights and decrease fuzziness by using membership functions (Onkal-Engin et al., 2004; Lu et al., 2010). This method has been widely used to evaluate environment quality due to its advantages of feasible indicator quantification and classification, objectivity and method certainty (Liu et al., 2016a; Li et al., 2018b). Therefore, it has been widely used in environmental quality assessment research (Huang et al., 2010; Li et al., 2018a; Yang et al., 2018b). Different indices demonstrate different contributions to the evaluation objective, so it is necessary to determine the weight of each index (Xu et al., 2018). The entropy weight method is an objective method to evaluate index weight, and it is also a significant means of environmental assessment (Safari et al., 2012; Liu et al., 2014; Yang et al., 2016; Li et al., 2018b). This method takes into account the relationship between multiple evaluation objects in the calculation process, and does not introduce subjective factors (Zhang et al., 2018). Therefore, the weights determined are

relatively objective and reliable, and it can be used to define the weight in fuzzy synthetic evaluation. Heavy metals enter the body and affect human health via ingestion, inhalation and dermal contact (US EPA, 1996; De Miguel et al., 1998; Fang and Zheng, 2014). The health risk assessment model is able to assess the health risk of individual heavy metals based on these three pathways (non-carcinogenic risk and carcinogenic risk) (Li et al., 2017). However, there are few studies on health risk assessment based on the sources of heavy metals (Huang et al., 2018a). To control and manage pollutants more scientifically, the pollution assessment model, health risk assessment model and receptor model were combined to evaluate the soil heavy metals generated by different sources. Zhangye is a commodity grain production base in northwest China. It is of great importance to guarantee the quality of its grain for making sure the safety of agricultural products in northwest China and even the whole country. In this study, the content of heavy metals in Zhangye agricultural soil (Titanium (Ti), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As) and Lead (Pb)) were determined. GPCA/APCS model as an improved model was used to conduct source apportionment for the first time. PMF model, a mature receptor model, was used to verify and assist the GPCA/APCS model. Fuzzy synthetic evaluation as a rarely evaluation model combined with health risk assessment on Cr, Ni, Cu, Pb according to the source contribution. The aim of this work is to put forward more detailed suggestions about controlling agricultural soil heavy metals from the perspective of sources based on the results of fuzzy synthetic evaluation and health risk assessment. The specific objectives of this study are (1) identifying the source and quantifying the source contribution of agricultural soil heavy metals by GPCA/APCS to understand the source of heavy metals; (2) performing pollution assessment by fuzzy synthetic evaluation based on source contribution to recognize the situation of agricultural soil pollution; (3) conducting the risk assessment by health risk assessment on the basis of source contribution to know the effects of pollution on human health. The results obtained in this study can provide reference for the prevention and control of heavy metal pollution from the perspective of source. 2. Materials and methods 2.1. Study area Zhangye is located in northwest Gansu Province and the middle reaches of the second largest inland river in China-Heihe river (Fig. 1). Mountain belt is zonal distribution, along the northwest to southeast. It has a typical temperate continental climate, with an annual average precipitation of 140 mm and annual average evaporation of 1600–2400 mm (Xu et al., 2016), the annual sunshine hours are 3000–3600 h. The regional water use is mainly ice and snow melt water in Qilian mountains. Plus, the agricultural irrigation systems are well developed (Liu et al., 2016b). These advantages make Zhangye become one of the top ten commodity grain production bases in China (Xu et al., 2016). The soil type of the sampling area is Irragric Anthrosols (aridic, endofluvic) with parent materials of fluvic modified by longcontinued cultivation or sediments from irrigation water. There are temperate tufted dwarf grass, one harvest a year's crop, and coldresistant cash crops in Zhangye. The main crops in this area are corn, wheat, barley, oilseed rape, and vegetables (Liu et al., 2016b). In 2017, the cultivated land area of Zhangye is 27.63 ha, the population is 1.23 million, with a natural population growth rate of 5.06‰ (The Statistical Bureau of Zhangye, 2018). 2.2. Sample collection and instrumental analysis Based on predominant farmland distribution, 80 sites were determined by GPS with the interval of 5 km and all samples were collected from 0 to 20 cm depth by shovel in October 2017, all the samples were

R. Zhao et al. / Science of the Total Environment 697 (2019) 134126

3

Fig. 1. Map of study area and sampling sites of heavy metals in agricultural soil of Zhangye (The Land use map (digitized 1:100,000)) was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn).

collected several hundred meters away from the road to eliminate the interference of traffic (Fig. 1). Samples were collected and packed in sealed plastic bags after removing pebbles and plant roots to keep them clean. In laboratory, samples were natural dried for 3 months with room temperature of 25 °C. Olympus Delta Professional Handheld X-ray Fluorescence Analyzer can accurately measure elements without any requirement of grinding or even can analyze elements in situ, so the contents of Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As and Pb were measured by an Olympus Delta Professional Handheld X-ray Fluorescence Analyzer without any grinding and sieving. The mode of Olympus Delta Professional Handheld X-ray Fluorescence Analyzer was set as Soil, and the collection times of the three beams running continuously were all set at 90 s. Triplicate samples were collected at each site to ensure strict quality assurance and control procedures. The standard method 6200 has been established for Handheld X-ray Fluorescence Analyzer's use (US EPA, 2007). Calibration of instrument were conducted by a 316 Stainless Steel Calibration Check Reference Coin provided by Olympus company every 30 samples at most. After calibration, instrument can be used to analyze samples directly. Elements concentrations discussed were higher than the limits of detection (LOD) of the analyzer (Table 1). Results from the Handheld X-ray Fluorescence Analyzer are always affected by environmental humidity (Rouillon and Taylor, 2016; Caporale et al., 2018), so the samples were air dried naturally in the laboratory before being measured. Results from Handheld X-ray Fluorescence Analyzer are more considered as total content of soil heavy metal, while the results of the ICP-MS incomplete dissolution of the metal-bearing silicates (Caporale et al., 2018). However, when the source of soil metal pollution is anthropogenic, the results from Handheld X-ray Fluorescence Analyzer were similar to those from ICP-MS (Caporale et al., 2018).

2.3. The fuzzy synthetic evaluation model based on sources To meet the requirement of receptor models, the heavy metals concentration data were checked by interquartile ranges and histograms to remove the outliers (Guan et al., 2018). Kolmogorov-Smirnov (KS) test was performed to test the normal distribution of the data, most of the elements can pass the test (Table 3). So, data were used without any transformation (Guan et al., 2016). 2.3.1. Receptor models In this study, GPCA/APCS model was conducted in SPSS 18.0. PCA is a mathematical method to transform the potentially correlated variables into uncorrelated principal components by using the orthogonal transformation of the correlation matrix, it can reduce the data dimension without losing data information (Zhang and Su, 2016). For subdividing the hybrid sources, grouped principal component analysis (GPCA) method was acquired to replace the PCA. In order to examine the validity of PCA, Kaiser-Meyer-Olkin (KMO) and Bartlett's sphericity tests were performed (Gu and Gao, 2018). The procedure of GPCA/APCS can be conducted as (Thurston and Spengler, 1985; Guo et al., 2004): The concentration data are standardized as:

Zij ¼

Cij −C j σj

ð1Þ

where, Cij is the concentration of jth species in ith sample; C j and σj are the arithmetic average concentration and standard deviation for jth species, respectively. Since the PCA is based on normal distribution of concentration, the true zero for each factor score is described as:

ðZ0 Þ j ¼

0−C j Cj ¼− σj σj

ð2Þ

Table 1 The limits of detection (LOD) of heavy metals. Elements

Ti

V

Cr

Mn

Fe

Ni

Cu

Zn

As

Pb

LOD Range (ppm)

5–10 1–10 3–10 5–8 10–20 5–10 5–8 3–5 2–4 3–5

Based on the PCA method, the principal components of the certain group are extracted and repeat the PCA (Zhang and Su, 2016). The APCS for each component is estimated by subtracting the factor score of true zero from the one of true sample.

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Multivariate linear regression is performed on standardized concentrations and APCS: Zj ¼

X

Zk APCSkj

ð3Þ

where, Zj is the concentration of jth species after standardization; APCSkj is the adjusted fraction of factor k; Zk is the coefficient of multiple regression (Larsen and Baker, 2003). The average relative contribution rate of source k can be calculated from Zk:  X  Zk  100% CSk ð%Þ ¼ Zk =

ð4Þ

Since this model is based on the assumption that total content of pollutant is equal to the sum of contributions from all individual sources (Huang et al., 2018b), predicted values can be calculated as follows to verify the accuracy of the model (Larsen and Baker, 2003): P    X X  CS ¼ C j  Zk = Zk þ Zk δ j C j

ð5Þ

K¼1

where, δj is the standard deviation of jth species. The contribution rate is calculated to analyze the distribution of the source contribution, and then compared the calculated contribution with the observed value to analyze the operation effect of this model. PMF model, recommended by U.S. Environmental Protection Agency (USEPA) (Norris et al., 2014), was utilized to verify the results by GPCA/ APCS and assist it to identify sources. The uncertainty file is introduced into the process of evaluating the quality and reliability of data, and combines with the concentration file to perform (Hu et al., 2018). The result is acquired by minimizing the objective function Q: Q¼

 n X m  X eij 2 i¼1 j¼1

uij

5  MDL 6

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðError fraction  concentrationÞ þ ð0:5  MDLÞ2

Level

Cr

Pb

Ni

Cu

1 2 3 4 5

Non-pollution Light pollution Moderate pollution Heavy pollution Extreme pollution

60 90 150 250 400

15 25 35 70 150

25 35 55 120 200

20 35 55 120 250

If j = 1, 8 1 xkil ≤vij <   > k k rij xil ¼ xil −vijþ1 > v bxk bv 0 xk ≥v : vij −vijþ1 ij il ijþ1 il ijþ1

ð7Þ

ð8Þ

where, Error fraction is the method uncertainty percentage (Tan et al., 2016). In order to verify GPCA/APCS by PMF, Pearson correlation coefficients analysis were used to quantify the relationship between these two models. Significant correlation is considered when two factors are significantly correlated at a p b 0.05. Factors that are positively significant correlated always are generally considered to the same source. 2.3.2. Fuzzy synthetic evaluation model For fuzzy evaluation, the concentrations of heavy metals based on sources were classified on 5 levels according to the expert consultation method and Soil Environmental Quality Standard (Table 2; Liu et al., 2016a; MEE, 2018). Membership functions of heavy metals were calculated according to the trapezoidal algorithm as follows (Lourenço et al., 2010):

ð9Þ

If j = 2, 3 and 4, 8 0 vijþ1 ≤xkil ≤vij−1 <   > k rij xil ¼ xkil −vij−1 xk −vijþ1 > v bxk bv il v ≤xk ≤v : vij −vij−1 ij−1 il ij vij −vijþ1 ij il ijþ1

ð10Þ

If j = 5, 8 0 xkil ≤vij−1 <   > k k rij xil ¼ xil −vij−1 > v bxk bv 1 xk ≥v : vij −vij−1 ij−1 il ij il ij

ð11Þ

where, xkil is the predicted value for the ith heavy metal from kth source at the lth monitoring site, vij is the standard value of the ith heavy metal at the jth level. Therefore, the fuzzy matrix (R) can be obtained as following: 0

r11 B r21 ¼B @ ⋮ rm1

  R ¼ rij mp

when the concentration exceeds its corresponding MDL value, the calculation is: uij ¼

Class

ð6Þ

where, eij is the model uncertainty, and uij is the method uncertainty (Norris et al., 2014). when the concentration does not exceed the method detection limit (MDL) value, the uncertainty can be calculated as: uij ¼

Table 2 Environmental quality classes for heavy metals (mg/kg) (Liu et al., 2016a).

r12 r22 ⋮ rm2

1 ⋯ r1p ⋯ r2p C C ⋱ ⋮ A ⋯ rmp

ð12Þ

Different indicators have their own contribution to the evaluation objective, so it is essential to determine the weight of each indicator (Xu et al., 2018). Entropy is a measure of the disorder degree of information in a specific system, which can make full use of the information of the original data (Shannon, 1948). Entropy weight method is an objective method to evaluate and determine the weight of index (Yang et al., 2016; Xu et al., 2018; Zhang et al., 2018). The steps of entropy weight method as follows (Jaynes, 1982; Zhang et al., 2018): Establishing the decision matrix X: 2

x11 6 x21 6 X¼4 ⋮ xm1

x12 x22 ⋮ xm2

3 ⋯ x1n ⋯ x2n 7 7 ⋱ ⋮ 5 ⋯ xmn

ð13Þ

Calculating contribution pxy: Xxy pxy ¼ Pm x¼1 Xxy

ð14Þ

Calculating total contribution Ey: Ey ¼ −K

m X x¼1

pxy lnpxy

ð15Þ

R. Zhao et al. / Science of the Total Environment 697 (2019) 134126

3. Results

Calculating the weight Wy: Wy ¼

1−Ey  n  ∑y¼1 1−Ey

ð16Þ

where, xxy is the predicted concentration of heavy metal species (mg/kg); pxy is the contribution of Xx when the heavy metal is Xy; Ey is the total contribution of all programs when heavy metal is Xy; Wy is the weight of each heavy metal. The fuzzy synthetic matrix (B) can be calculated via multiplying the entropy weight matrix (W) by the fuzzy matrix (R) as:

B ¼ W  R ¼ b1 ; b2; ⋯; bm

5

ð17Þ

where, bj (j = 1, 2, …, m) is the result of the entropy weight of ith heavy metal and the fuzzy matrix. The max(bj) can be used to determine the quality of soil (Yang et al., 2016).

3.1. The statistical analysis of soil heavy metals Most of the elements passed the test at the significance level of 0.05 except Cr, but the Z value of Cr was small. Therefore, all of the elements can be used for subsequent steps without any transformation (Guan et al., 2016). The standard deviation and coefficient of variation values of Cr and Ni in this study were both large, and the average concentration of the former was much larger than its median value (Table 3). Compared with the background value in Gansu province, the average and median Ti, V, Cr, Mn, Fe and Zn concentration in Zhangye agricultural soil were lower, while those of Ni, Cu, As and Pb were much higher. However, compared with the agricultural regions in China, the average of Cr in Zhangye was similar, but the As and Pb were lower (Table 3). Overall, the agricultural soil heavy metals in this area did not exceed the risk screening values.

2.4. Health risk assessment model

3.2. Results of receptor models

In this study, the health risks of adult and children have been calculated by non-carcinogenic risk and carcinogenic risk according to the US EPA. Hazard index (HI) is the sum of more than one hazard quotient (HQ) for multiple substances and/or multiple exposure pathways (US EPA, 1989). Farmland soil heavy metals can harm human health through the following main pathways: (a) direct ingestion (Ding); (b) inhalation through mouth and nose (Dinh); and (c) direct dermal contact (Dder). Therefore, the HI of heavy metals can be calculated as following (US EPA, 1996):

KMO and Bartlett's tests indicated that data were useful in dimensionality reduction for PCA (KMO: 0.871; Bartlett's: 925.924; df = 45, p b 0.01). PCA/APCS extracted three factors and explained 87.173% of the data variance. PMF model ran 20 times with the seed number of 51, and then four factors were extracted. Factor 2 of PCA/APCS was significantly correlated with Factor 2 and Factor 3 of PMF (Table 4). Therefore, the PCA can be performed again for the heavy metals with high load in Factor 2 of PCA/APCS to obtain two factors, which explained 89.092% of the total variance. Factor 1 of GPCA/APCS and PMF had the same major elements (Ti, Pb, V, Mn, Fe; Fig. 2a, b), and the correlation of these two was significant (Table 4). The main elements of Factor 2 and Factor 3 of PMF were the main elements of Factor 2 of GPCA/APCS (Cu, Ni, Zn and As; Fig. 2a, b). The main elements of Factor 2-1 and Factor 2-2 obtained by further principal component analysis of Factor 2 of GPCA/APCS were consistent with those of Factor 2 and Factor 3 of PMF, respectively. (Fig. 2a, c). Factor 4 of PMF was significantly correlated with Factor 3 of GPCA/APCS (Fig. 2a, b), and their main element was Cr. Therefore, the PMF and the GPCA/APCS can verify each other. Both PMF model and GPCA/APCS model can well predict heavy metals concentrations, the predicted and observed values had good fitting effect (r2 N 0.67; Table 5). In PMF model, the concentrations of V, Cr and Ni were slightly overestimated, while Ti, Mn, Fe, Cu, Zn, As and Pb were slightly underestimated. However, overall, this model worked well (Table 5). For the GPCA/APCS model, the predicted errors of all elements were 0.00% (Table 5). In addition, these two models were consistent (Table 4; Fig. 2). Thus, GPCA/APCS model was slightly optimal than PMF model in predicting heavy metals concentrations.

Ding ¼ CSk 

Ring  EF  ED  CF BW  AT

ð18Þ

Dinh ¼ CSk 

Rinh  EF  ED PEF  BW  AT

ð19Þ

Dder ¼ CSk 

AF  SA  ABS  EF  ED  CF BW  AT

ð20Þ

HI ¼

X Di RfD

ð21Þ

where, CSk is the concentration of heavy metal (mg/kg) from kth source. Di is the Ding, Dinh or Dder. CF is the unit conversion factor: 1E-06 kg/mg All other parameters (RIng, RInh, EF, ED, BW, AT, PEF, AF, SA, ABS and RfD) were displayed in Table S1 and Table S2. The carcinogenic risk (CR) represents the probability that if an individual develops a type of cancer from lifetime exposure to carcinogenic hazards. Generally, CR surpassing 1E-04 can be considered to unacceptable and can cause adverse effect to human body, CR of 1E-06 - 1E-04 is widely considered acceptable, and CR below 1E-06 is always considered that there is no risk of cancer or adverse effects (US EPA, 2011). And it can be calculated as: CR ¼

X ðDi  SFÞ

ð22Þ

where, SF is the slope factor of heavy metal and it showed in Table S2. 2.5. Pollution index To judge the applicability of a fuzzy synthetic evaluation method, a pollution index (PI) was introduced in this study (Lee et al., 2006). Fe was determined as the reference element for the PI method, because it was the highest content element in this study area (Tasdemir and Kural, 2005).

3.3. Pollution assessment of soil heavy metals from different factors After performing GPCA/APCS model, heavy metals concentrations were subdivided into concentrations of each factor to heavy metals. On the basis of entropy-weight and environmental quality classes for heavy metals (Table 2), fuzzy synthetic evaluation of source contribution was conducted. The contribution of Factor 3 to Pb had the highest score in Class 1, and the trend of Class 1 and entropy-weight were similar (Fig. 3a, b). The high scores in Class 2 were Cr-3, Pb-1, Ni-1, Ni-2-2 and Ni-3, while those who scored higher in Class 3 were Cr-3, Ni-1, Ni-3 and Ni-2-2 (Fig. 3c). Therefore, the Cr pollution level of the source corresponding to Factor 3 was the highest, and the contribution of the source corresponding to Factor 1, Factor 2-2 and Factor 3 to Ni and the contribution of the source corresponding to Factor 1 to Pb should also be given high attention. Results from PI was consistent with the Entropy weight fuzzy levels, while Ni-1 was the exception (Figs. 3, 4).

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R. Zhao et al. / Science of the Total Environment 697 (2019) 134126

Table 3 Descriptive statistics for heavy metals in farmland soil of Zhangye. Elements

Ti

V

Cr

Mn

Fe

Ni

Cu

Zn

As

Pb

Mina (mg/kg) Maxb (mg/kg) Mean (mg/kg) Median (mg/kg) Standard deviation (mg/kg) CVc% Skewness Kurtosis Kolmogorov–Smirnov Test (Z)

2324.00 3847.00 3039.14 3028.00 313.92 10.33 −0.122 −0.386 0.20 (0.08) 3400.00

55.73 88.00 72.47 72.78 7.22 9.97 0.028 −0.396 0.20 (0.06) 81.90

38.33 152.00 63.11 58.00 18.49 29.30 2.059 6.393 0.00 (0.16) 70.20

417.33 756.33 594.38 595.67 70.37 11.84 −0.313 0.035 0.20 (0.06) 653.00

18,446.67 34,518.00 27,536.11 27,745.83 3605.25 13.09 −0.102 −0.359 0.20 (0.07) 30,900.00

22.00 81.00 55.09 54.17 10.99 19.95 0.147 0.145 0.19 (0.09) 35.20

18.20 47.00 29.85 30.63 5.22 17.48 0.128 0.708 0.06 (0.09) 24.10

34.47 84.00 59.14 61.03 9.84 16.64 −0.323 −0.004 0.19 (0.09) 68.50

7.40 18.33 13.41 13.82 2.50 18.67 −0.252 −0.585 0.20 (0.08) 12.60

13.73 28.30 20.80 20.75 2.97 14.26 0.101 −0.345 0.20 (0.06) 18.80

– –

– –

62.65 250.00

– –

– –

– 190.00

– 100.00

– 300.00

17.61 25.00

48.43 170.00

Background value in Gansu provinced (mg/kg) Agricultural regions in Chinae (mg/kg) Risk screening valuesf (mg/kg) a b c d e f

The minimum values of heavy metals. The maximum values of heavy metals. Coefficient of variation. Geochemical background value of heavy metal in Gansu province (CEMS, 1990). Heavy metal concentration of agricultural regions in China (Yang et al., 2018a). Soil environmental quality standard (MEE, 2018).

4. Discussion

in China, therefore, the adjacent Zhangye is inevitably affected by industrial activities in Jinchang, which results in serious Cr and Ni pollution (Yang et al., 2018a). The concentrations of Ni, Cu, As and Pb in the study area were higher than the background values in Gansu Province (Table 3), indicating the intense anthropogenic enrichment of Ni, Cu, As and Pb (Gu et al., 2016). Compared with the agricultural regions in China, the concentrations of As and Pb in agricultural soil in Zhangye were lower, which was related to the differing soil properties as well as various environmental factors, such as soil type, pH, precipitation, parent material and crop (Luo et al., 2016; Hamid et al., 2019). Generally, soil pH can significantly affect the presence and adsorption of heavy metals in soil (Huang et al., 2013). The agricultural soil in the study area is high in Ca, was alkaline and was low in oxide (Zhao et al., 2005). In this scenario, it is difficult for As to attach to the soil (Smith et al., 1999), and the high content of Ca is related to competitive adsorption of Pb (Yan et al., 2017), thus making it difficult for Pb to be retained by soil particles. In addition, all elements in the study area did not exceed the risk screening value, which indicated that the risk of soil to the quality and safety of agricultural products, crop growth or the soil ecological environment was relatively low (MEE, 2018).

4.1. General characteristics of heavy metals

4.2. Source identification

The standard deviation and variation coefficient of heavy metal concentration can indicate spatial variability (Jia et al., 2018). Among the ten heavy metals in the study area, the spatial variability of Cr and Ni were the highest, and the average concentration of the former was much higher than the median concentration, indicating that Cr and Ni were more affected by human activities (Dong et al., 2019). Jinchang in the east of the study area is an important industrial and mining city

PMF is a mature model, it can be more accurate by considering the uncertainties of variables through utilizing point-by-point estimates of the data errors (Callén et al., 2009). PCA/APCS is very simple to operate and fast to calculate (Banerjee et al., 2015), while it unable to separate highly correlated groups (Guo et al., 2004). GPCA/APCS is improved PCA/APCS to subdivide group. Therefore, using PMF to verify GPCA/ APCS can make sense. GPCA/APCS model was optimal to explain the

3.4. Quantifying the health risk of soil heavy metals from different sources The results of health risk assessment suggested that the health risks of heavy metals in children were notably higher than that in adults except for the carcinogenic risk of Ni (Fig. 5). The HI of heavy metals to children was N6 times that of adults, and the non-carcinogenic risks caused by heavy metals Cr and Pb were higher. Factor 3, in particular, had the highest contribution to Cr (Fig. 5a). Compared with other factors, the non-carcinogenic risk of Factor 1 contributing to Pb, Factor 3 contributing to Ni and Factor 2-1 contributing to Cu were the highest among all the corresponding factors. On the contrary, the noncarcinogenic risk of Factor 3 contributing to Cu was the lowest among all factors (Fig. 5a). Among them, Cr had the highest carcinogenic risk, while Ni had the lowest (Fig. 5b, c). After comparing the contribution of each factor to the same element, it was found that the contribution of Factor 3 to Cr and Ni and Factor 1 to Pb had a higher carcinogenic risk than other factors (Fig. 5b, c).

Table 4 Pearson correlation coefficients among the factor contribution by PCA/APCS and PMF model. PMF

PMF

GPCA/APCS

a b

Factor 1 Factor 2 Factor 3 Factor 4 Factor 1 Factor 2 Factor 3

PCA/APCS

Factor 1

Factor 2

Factor 3

Factor 4

Factor 1

Factor 2

Factor 3

1.000 −0.048 −0.777 −0.115 0.890b −0.503 −0.389

1.000 0.255 −0.832 0.305 0.812b −0.885

1.000 −0.408 −0.453 0.680a 0.065

1.000 −0.540 −0.709 0.839b

1.000 −0.116 −0.677

1.000 −0.505

1.000

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

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Fig. 2. Results by PMF (a) and GPCA/APCS (b and c) model.

concentrations of heavy metals (Table 5), which made the source pollution evaluation and health risk assessment quantification more convincing. Factor 1 of both models had the same high-load elements, which were Ti, V, Pb, Mn and Fe (Fig. 2a, b). Previous studies have shown that Ti, V, Mn and Fe are primarily produced by steel mills and tailings dams (Guagliardi et al., 2018; Wang et al., 2018), while Pb is derived Table 5 Relationship of concentrations observed by instrument and predicted by the two models. Elements

Observed (mg/kg)

PMF Predicted (mg/kg)

GPCA/APCS r2

Error %

Predicted (mg/kg)

r2

Error %

3039.14 72.47 63.11 594.38 27,536.11 55.09 29.85 59.14 13.41 20.80

0.9030 0.9128 0.9182 0.8913 0.9501 0.8195 0.7162 0.7785 0.6798 0.7804

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

a

Ti V Cr Mn Fe Ni Cu Zn As Pb

3039.14 72.47 63.11 594.38 27,536.11 55.09 29.85 59.14 13.41 20.80

3037.87 72.49 63.16 593.57 27,489.13 55.13 29.77 59.13 13.35 20.74

0.9490 0.9368 0.9998 0.9074 0.9603 0.9938 0.8194 0.9736 0.7260 0.7071

−0.04 0.02 0.07 −0.14 −0.17 0.07 −0.27 −0.02 −0.45 −0.29

a Error = (predicted concentration-observed concentration) ∗ 100 / observed concentration.

mainly from steel mills and traffic emissions (Dietrich et al., 2018). Therefore, Factor 1 can be reasonably identified as a steel industry and traffic source. Factor 2 of PMF corresponded to Factor 2-1 of GPCA/ APCS, and its primary elements, Zn, As and Cu (Fig.2a, c) are often used as feed additives to prevent diseases and improve feed conversion rate (Mondal et al., 2007; Jing et al., 2018), and they enter the farmland through manure. In addition, these three elements are also commonly found in fertilizers, pesticides and insecticides (Chen et al., 2008; Gupta et al., 2014). Farmers also generally use manure, fertilizers, pesticides and insecticides to improve the crop yield in the study area. Therefore, this factor could be considered to be a source of agrochemicals and manures, and this source was generally regarded as the main source of heavy metals in agricultural soil (Hou et al., 2014). Factor 3 of PMF was related to Factor 2-2 of GPCA/APCS, and the primary element in both was Ni (Fig. 2a, c). Generally, Ni was derived from mining activities (Guan et al., 2018; Petrik et al., 2018). Zhangye is the concentration area of metal minerals in Gansu province. In addition, Jinchang, bordering Zhangye, is rich in Ni minerals (Yang et al., 2018a). Therefore, this factor can be identified as the source of mining activities. Factor 4 of PMF was the same as Factor 3 of GPCA/APCS, and the element with the highest load was Cr (Fig. 2a, b). Previous studies have verified that Cr was primarily produced by the leather industry (EconomouEliopoulos et al., 2012; Petrik et al., 2018) and metal processing industry (Wuana and Okieimen, 2011; Lima et al., 2014). In Zhangye, the leather and metal processing industries are relatively developed, so this factor

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Fig. 3. Entropy weight (a) and Entropy weight fuzzy levels (b and c) of heavy metals contributed by different factors (Element-n: concentration of certain element contributed by factor n; The criteria of Class were showed in Table 2).

can be identified as the source of leather industry and metal processing industry.

Guan et al. (2018) used the PMF model to conduct source apportionment of heavy metals in farmland in the Hexi Corridor including Zhangye, and found that heavy metals in agricultural soil in this area were mainly affected by industrial activities including mining, the steel industry and coal burning, agricultural activities and traffic emissions. Yang et al. (2017) analyzed the soil of crop and vegetable growing areas in the eastern Qingshan district of Wuhan, and found that the main sources of heavy metals in the soil of this area were industrial activities including the metal processing industry, agricultural activities and traffic emission. These studies indicated that the results of source apportionment in this study were credible. 4.3. Pollution assessment of heavy metals from different sources

Fig. 4. The pollution index of heavy metals contributed by each factor.

The results of the pollution index and the fuzzy synthetic evaluation were relatively consistent. Both showed that Cr generated by the leather industry and metal processing industry source and Ni generated by mining activities source and leather industry and metal processing industry source reached relatively high pollution levels (Figs. 3c, 4), which suggested that the fuzzy synthetic evaluation method was feasible for the evaluation of heavy metal pollution. The results of PI showed that the pollution level of Ni generated by the steel industry and traffic source was lower than that of Pb, which was contrary to the results of the fuzzy synthetic evaluation (Figs. 3c, 4). This is likely because the result of the fuzzy synthetic evaluation was determined by the most polluted sampling site (Yang et al., 2016). In this study, the variation

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Fig. 5. Health risk (non-cancer risk (HI; a) and cancer risk (CR; b and c)) quantification of heavy metals contributed by each factor.

coefficient and the standard deviation of Ni were both higher than Pb. In addition, the concentration of Ni at the sampling site with the most serious Ni pollution exceeded the Class 3 limit of moderate pollution, while the concentration of Pb at the site with the most serious Pb pollution did not exceed the Class 3 limit (Table 3). Therefore, it can be concluded that the fuzzy synthetic evaluation method can evaluate the heavy metal pollution more objectively. In using the fuzzy synthetic evaluation model, there is no need for soil background samples, which can also solve the problem of difficult to determine background points. The results of the entropy weight analysis showed that the contribution of the leather industry and metal processing industry to Pb had the highest information weight (Fig. 3a), which indicated that the index had the lowest entropy and provided the largest amount of useful information. In contrast, the contribution of the leather industry and metal processing industry to Ni had the lowest information weight, indicating that this index had the highest entropy and played the smallest role in the evaluation (Fig. 3a; Li et al., 2018b). Similarly, the coefficient of variation and standard deviation of Ni were very high (Table 3), which also demonstrated that Ni had a large information entropy relative to other heavy metals (Ursacescu and Cioc, 2016). Fuzzy synthetic evaluation of heavy metals was conducted based on entropy weight. The results of the fuzzy synthetic evaluation showed that in the Class 1 level of non-pollution, the leather industry and metal processing industry scored the highest contribution to Pb, which indicates that the pollution level of Pb generated by this process was the lowest. At the Class 3 level of moderate pollution, the leather industry and metal processing industry scored the highest contribution to Cr. It is followed by Ni generated by the steel industry, traffic sources, leather industry, metal processing industry and mining activities.

4.4. Health risk assessments of heavy metals from different sources The health risks from heavy metals are due to exposure through three pathways: ingestion, inhalation and skin contact. The pathway of ingestion is the more important route of exposure (Yang et al., 2018a). In most cases, the health risk of heavy metals for children is higher than that for adults, primarily because children have special physiological characteristics, such as hand-finger sucking (White et al., 1998) and lower toxicity tolerance (Akoto et al., 2014). A comparative analysis showed that the non-carcinogenic risk of Cr was higher in these four sources, among which the non-carcinogenic risk of Cr caused by leather industry and metal processing industry was the highest (Fig. 5a). Previous studies have shown that the agricultural soil of China was greatly affected by atmospheric deposition (Luo et al., 2009; Hu et al., 2018; Shi et al., 2018), and Pb was an important element imported into the soil during atmospheric deposition (Luo et al., 2009). In this study, the non-carcinogenic risk of Pb was second only to that of Cr, and the non-carcinogenic risk of Pb contributed by the steel industry and traffic source was the highest among all sources (Fig. 5a). By calculating the carcinogenic risks of Cr, Ni and Pb, it can be found that all these carcinogenic risks were within the acceptable range (US EPA, 2011). Previous works have shown that long-term exposure to low concentrations of Cr can produce carcinogenic and toxic effects in humans (Angelone and Udovic, 2014; Khan et al., 2015). In this study, the carcinogenic risk caused by Cr was the highest, being two orders of magnitude higher than Pb and four orders of magnitude higher than Ni (Fig. 5b, c). Among the carcinogenic risks posed by Cr, Cr derived from the leather industry and metal processing industry is the highest, followed by Cr derived from the iron and steel industry and traffic source (Fig. 5b). Unlike other heavy metals, the carcinogenic risk of Ni for adults was higher than that for children because Ni is primarily

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affected by the inhalation pathway (Table S2), and the Rinh in adults was higher than that for children (Table S1). Based on the results of health risk assessment, it can be determined that in order to fundamentally control the heavy metal pollution of agricultural soil in Zhangye, it is necessary to control Cr emissions from leather industry and metal processing sources, and Pb emissions from steel industry and traffic sources.

5. Conclusions The Cr and Ni of agricultural soil in Zhangye were greatly influenced by human. Both GPCA/APCS model and PMF model can effectively carry out source analysis of heavy metals in this region, especially the former one. The GPCA/APCS model indicated that the study area was mainly affected by steel industry and traffic source, agrochemicals and manures source, mining activities source, leather industry and metal processing industry source. The results of fuzzy synthetic evaluation showed that Cr derived by leather industry and metal processing industry, Ni derived by steel industry and traffic source produced relatively heavy pollution to the study area. The results of health risk assessment revealed that Cr derived by leather industry and metal processing industry and Pb derived by steel industry and traffic emission were the highest carcinogenic and non-carcinogenic risks among all the sources. The control of heavy metal pollution in agricultural soil in this region should be based on the results of fuzzy synthetic evaluation and health risk assessment, focusing on the control of Cr emissions from leather industry and metal processing industry, Pb and Ni emissions from steel industry and traffic activities. In addition, we will analyze more metals to make source apportionment more objective in future studies. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We would like to express our sincere gratitude to the editors and reviewers who have put considerable time and effort into their comments on this paper. We are grateful to the professional editing service (Elsevier Language Editing Services) for improving the language of our manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 41671188). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.134126.

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Zhuang, Q., Li, G., Liu, Z., 2018. Distribution, source and pollution level of heavy metals in river sediments from South China. Catena 170, 386–396.