Heavy Metal Pollution in Agricultural Soil: Source Apportionment

Science of the Total Environment 747 (2020) 141293
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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Contamination assessment and source apportionment of heavy metals in
agricultural soil through the synthesis of PMF and GeogDetector models
Xufeng Fei a,b,⁎, Zhaohan Lou a, Rui Xiao c, Zhouqiao Ren a,b, Xiaonan Lv a,b
a
b
c
Zhejiang Academy of Agricultural Sciences, Hangzhou, China
Key Laboratory of Information Traceability of Agriculture Products, Ministry of Agriculture and Rural Affairs, China
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 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
• A novel framework based on spatial
analysis for source apportionment is
proposed.
• Combined with auxiliary data, the new
model provides foundations for source
analysis.
• Cr (80.72%) was derived mainly from
natural sources while As and Pb had
mix sources.
• Cd (73.68%) was closely associated with
agricultural activities.
• Hg (92.38%) was mainly attributed to
industrial activities.
a r t i c l e
i n f o
Article history:
Received 14 May 2020
Received in revised form 24 July 2020
Accepted 25 July 2020
Available online 28 July 2020
Editor: Filip M.G. Tack
Keywords:
Heavy metal
Positive matrix factorization
GeogDetector models
Spatial analysis
Source apportionment
a b s t r a c t
Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a
case study location, this research proposed the synthesis application of positive matrix factorization (PMF) and
GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area. Although the mean concentrations of As, Cd, Cr, Hg, and Pb in the soils were lower than the National
Environmental Quality Standards for Soils in China, the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively, indicating that heavy
metals have been enriched in topsoil. Agricultural activities, industrial activities, and soil parent materials were
the main sources of heavy metal pollution in the soils, accounting for 63.4%, 19.8%, and 16.8% of the total heavy
metal accumulation, respectively. Cr was derived mainly from soil parent materials (80.72%). Cd was closely associated with agricultural activities (73.68%), such as sewage irrigation and application of fertilizer. Mercury was
mainly attributed to industrial activities (92.38%), such as coal mining and smelting. As was related to agricultural
(57.83%) and natural (35.56%) sources, and Pb was associated with industrial (42.42%) and natural (41.83%)
sources. The new synthesis models are useful for estimating the source apportionment of heavy metals in soils.
© 2020 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author at: No. 198 Shiqiao Road, Hangzhou, Zhejiang 310021, China.
E-mail address: [email protected] (X. Fei).
https://doi.org/10.1016/j.scitotenv.2020.141293
0048-9697/© 2020 Elsevier B.V. All rights reserved.
Soil is one of the most important ecosystems for human survival and
development, and soil safety is the fundamental guarantee for national
food security and human health (Fei et al., 2019; He et al., 2019; Wang
et al., 2019). With the rapid urbanization and industrialization of
2
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
society, soil pollution, especially soil heavy metal contamination and accumulation, has become a serious problem, attracting great public attention worldwide (Hu et al., 2017a; Huang et al., 2018; Yang et al.,
2018). As China is a developing nation that has undergone rapid urbanization in recent decades, soil heavy metal pollution has become a main
environmental problem (Niu et al., 2013; Hu et al., 2017b; Liu et al.,
2019). According to the Chinese National Soil Contamination Survey Report released by the Ministry of Land and Resources and the Ministry of
Environmental Protection of China, approximately 16.1% of soil samples
analyzed were contaminated by heavy metals to various degrees (MEP,
2014). Because of the heavy metal characteristics of bioavailability, persistence, and toxicity, heavy metal accumulation in soil can lead to reductions in soil fertility and crop production. Heavy metals can also be
transferred easily and accumulate through biomagnification in the
food chain, which poses significant risks to food safety and human
health, as heavy metals are absorbed by the human body through inhalation, ingestion, and dermal absorption (Xu et al., 2017; Zang et al.,
2017; Zhao et al., 2014a). Therefore, it is necessary to quantitatively
evaluate the characteristics, contamination, and sources of heavy metals
in soil.
Generally, heavy metals in soil come from two major sources: natural sources, driven by weathering and pedogenesis processes, and
anthropogenic activities, such as industrial manufacturing, sewage irrigation, vehicle exhaust, agricultural fertilizer, mining, and smelting (Sun
et al., 2014, 2019; Yang et al., 2013). Therefore, to effectively reduce the
cost and workload of soil remediation, it is important to quantitatively
clarify the sources of soil heavy metal pollution (Fei et al., 2018;
Huang et al., 2018). Many previous studies have used multivariate qualitative/quantitative statistical methods, such as spatial deviation, correlation analysis, cluster analysis, geographic information systems, and
principal component analysis (PCA), often in combination with multiple
linear regression, to determine the variability in and possible sources of
heavy metals in soils (Davis et al., 2009; Dong et al., 2019; Gu et al.,
2012; Lv, 2019; Nanos and Martín, 2012). Among these approaches,
the positive matrix factorization (PMF) model recommended by the
US EPA, an ideal method for ensuring nonnegative source contribution
values and factor profiles through the use of correlation and covariance
matrices to simplify high-dimensional variables, has been applied
widely and successfully to identify and quantify pollution sources of
heavy metals in soils (Dong et al., 2019; Huang et al., 2018; Wang
et al., 2019).
However, most of these methods cannot analyze the effect of categorical variables such as soil type and soil parent materials, which
have an important influence on the accumulation of heavy metals.
Few studies have taken the spatial information of the sampling points
into consideration, and the definition of each principal component obtained from a single PCA/PMF model is based mainly on previous researches and experience, thus precluding the ability to quantitatively
define the detailed effects and spatial characteristics of every source
on different heavy metals. Moreover, previous methods are unable to
identify the importance of specific sources to the principal components
based on the spatial analytical data, which is vital for controlling local
HM pollution and remediation in soils. To address these gaps, this
study proposes the synthesis of PMF and GeogDetector models for soil
heavy metal source apportionment and evaluates the approach in a
case study using the city of Hangzhou, China. Compared to previous receptor models, by including spatial information and auxiliary data (categorical and numerical variables), such as soil parent material, road
networks, industrial production, etc., this approach could provide an
in-depth understanding of multiple source contributions and a better
definition of principal factors. To our knowledge, this is the first study
using GeogDetector to interpret results derived from a PMF receptor
model.
The main objectives of this study were to (1) analyze the contents
and basic characteristics of heavy metals in soil in the city of Hangzhou,
China, (2) identify the potential sources of heavy metals through the
PMF model, and (3) develop a new combined spatial method for quantitative source apportionment and definition through various forms of
auxiliary data. The results of this study provide useful information for
the prevention and control of heavy metal pollution in soil.
2. Materials and methods
2.1. Study area
Hangzhou is located in the eastern coastal area of China (29°11′–
30°33′ N, 118°21′–120°30′ E). It is the capital city of Zhejiang Province
and one of the most important cities in the Yangtze River Delta urban
agglomeration (Fig. 1). The city has a moderate subtropical climate
with a hot and humid summer and cold and dry winter. The annual
average values of temperature, precipitation, and sunshine hours are
17.8 °C, 1454 mm, and 1765 h, respectively. In total, 13 districts/
counties are included in the study area, which has a total area of approximately 16,854 km2. According to the population census in 2017, the
population of the area is approximately 9.47 million. The northeastern
area consists of plains, but the southwestern area is mountainous. The
main soil types are red soil and paddy soil derived from eluvium and alluvial soil parent materials, respectively. In the past few decades, this
area has experienced rapid industrialization and urbanization, and the
consequent increasing accumulation of heavy metals has caused great
concern (Chen et al., 2009; Fei et al., 2018; Zhang and Ke, 2004).
2.2. Soil sampling and chemical analysis
According to the agricultural soil vector map, there are 211,931 ha of
agricultural soil in the study area. The 1 km*1 km grid layer was overlapped with the distribution of agricultural soil to extract the expected
sampling point locations in the study region (Ren et al., 2019). In total,
2150 surface soil samples were collected across the study area (Fig. 1).
Five subsamples were collected and mixed thoroughly to obtain a representative sample, and the sampling locations were recorded by a global
positioning system (GPS). All soil samples were air-dried at room temperature and then ground to 100-mesh size for chemical analysis. For
the determination of Cd, Cr, and Pb contents, the soil samples were
digested with HNO3–HClO4–HF, and the metals were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). For the determination of As and Hg contents, soil samples were digested with
HNO3–HCl, and atomic fluorescence spectrometry (AFS) was used for
the analyses. Blind duplicates and standard reference materials (GSS3, China National Center for Standard Materials) were used for quality
assurance and control. Standard sample recovery ranged between 90
and 110%, and the relative standard deviations of duplicate samples
were between 3 and 8%.
2.3. Analysis methods
In this work, the analysis consisted of both a statistical and a spatial
analysis part, as follows.
Statistical analysis: (i) Statistical indicators (mean, median, coefficient
of variation, etc.) and Spearman correlation analysis were employed to
describe the basic characteristics of heavy metals in agricultural soil and
their internal correlation, and (ii) a PMF model was used for pollution
source apportionment of heavy metals, as well as for calculation of the
pollution component scores of each site.
Spatial analysis: (i) Getis–Ord Gi* analysis (Getis and Ord, 1992) was
implemented to explore the spatial distribution pattern of the component scores derived from PMF analysis. (ii) Ordinary kriging was used
to draw the spatial distribution map of each component score (grid format). (iii) The results of ordinary kriging and the detailed pollution
source dataset (soil parent materials, distance to the main road, etc.)
were combined, and the GeogDetector model was employed to determine the spatial correlation between each component score and specific
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
3
Fig. 1. Distribution of sampling points and soil parent materials.
pollution sources, which could provide additional valuable information
for quantitative source apportionment.
The overall flow chart of this study is shown in Fig. 2, and the specific
description of each method is as follows:
metal concentration is greater than the MDL, the uncertainty can be calculated as follows:
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
uij ¼ ðerrorfraction concentrationsÞ þ ðMDLÞ2 :
ð3Þ
2.4. PMF model
Otherwise, the uncertainty is estimated through the following equation:
The PMF model, an efficient e receptor model for pollution source
apportionment, was used in this study to analyze the sources of heavy
metals in the soil (Huang et al., 2018; Wang et al., 2019; Zheng et al.,
2018). In brief, this model decomposes the matrix of the original dataset
Xij into two factor matrices (the source contribution matrix gik and the
source profile matrix fjk) and a residual matrix eij. The basic equation is
as follows:
X ij ¼
p
X
g ik f kj þ eij ;
ð1Þ
k¼1
where Xij is the concentration of the jth heavy metal at the ith sampling location, gik is the contribution of the kth source to the ith sample, fkj is the concentration of the element j from the kth source, and
eij is the residual error matrix, which can be calculated through the
minimum value of the objective function Q. The value of Q is calculated as follows:
Q¼
n X
m 2
X
eij
i¼1 j¼1
uij
;
ð2Þ
where uij is the uncertainty of the jth heavy metal in the ith sample,
which is calculated from the species-specific method detection limit
(MDL), the concentration, and the provided error fraction. If the heavy
uij ¼
5
MDL:
6
ð4Þ
All calculations follow the US EPA PMF 5.0 User Guide (U.S.
Environmental Protection Agency, 2014).
2.5. Spatial analysis model
After the factor values of each component in the samples were estimated through PMF, Getis–Ord Gi* analysis (Getis and Ord, 1992) was
employed to explore the spatial distribution pattern of the component
scores. The spatial clustering of the samples' component scores was determined through the Z values, which were calculated as follows:
Pn
Pn
j¼1 wij x j −x
j¼1 wij
Z j ¼ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
;
2
u
!2 3
u X
n
n
u1 n 2 2 u
1 4 X
u
2
t
n
x −x t
wij − ∑ wij 5
n j¼1 j
n−1
j¼1
j¼1
ð5Þ
where Zj is the Getis–Ord Gi* Z value of sample j, xj is the component
score of sample j, n is the total number of samples, wij is the weight of
the neighbor sample i to j, and x is the mean value of the component
score in all samples. Z values higher than 1.96 indicate significantly high
4
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
Fig. 2. The overall flow chart of this study.
clustering, and values lower than −1.96 mean significantly low clustering at the 0.05 level (Getis and Ord, 1992; Fei et al., 2016).
In addition, ordinary kriging interpolation (Fei et al., 2019; Gribov and
Krivoruchko, 2011) was executed to construct spatial distribution maps of
each component score to validate the spatial distribution pattern of the
sampling points and provide a foundation for the GeogDetector model.
As one of the most commonly used geostatistical methods, ordinary
kriging has been successfully applied in various disciplines, especially in
the assessment of soil heavy metal pollution in recent years (Li et al.,
2020; Wang et al., 2020; Wu et al., 2020). This tool realizes the optimal
linear unbiased estimation of spatial data based on sampled data and is
useful for exploring the spatial distribution pattern and variation characteristics of variables (Christakos, 1998). The semivariogram in the ordinary kriging process can be calculated as follows:
_
γ X ðhÞ ¼
⌢
1
nðhÞ
2
∑ ½X ðsi þ hÞ−X ðsi Þ
2nðhÞ i¼1
ð6Þ
where γ X ðhÞ is the semivariogram value at the distance of h, X(si + h)
and X(si) are the values of studied variables at the locations of si + h
and si, respectively, and n(h) is the number of pairs of sample points
at a distance of h (Olea, 2006). Spatial distribution maps of component scores derived from ordinary kriging can be used for overlay
analyses with pollution source variables through GeogDetector
models. Moreover, semivariogram information could provide fundamental reference for source analysis (Wang et al., 2020; Wu et al.,
2020).
2.6. GeogDetector model
The GeogDetector model (Wang and Hu, 2012) was then used to determine the spatial correlation between each component score and the
detailed pollution source dataset (soil parent material data obtained
from Wu et al. (2013) as a proxy for natural sources; the distance
from the sampling points to the main roads calculated in ArcGIS 10.2
as a proxy for traffic, or vehicle emission sources; and industrial and agricultural production values from the Hangzhou Statistics Yearbook
(http://tjj.hangzhou.gov.cn) as proxies for industrial and agricultural
sources), which provided additional information for source identification of each component obtained from the PMF model.
The detailed theory of this model is available in previous works (Fei
et al., 2016; Fei et al., 2018; Li et al., 2013; Wang et al., 2010; Wang and
Hu, 2012). Briefly, there are four detectors in this model, i.e., a risk detector, factor detector, ecological detector and interaction detector
(Wang et al., 2010; Jiang et al., 2020; Zhang et al., 2020). In this study,
the factor detector and interaction detector were employed to quantitatively detect the degree of influence of the pollution sources on the
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
component scores derived from the PMF model. The basic assumption
of the factor detector model here is that if one component extracted
from the PMF can be defined as a specific source, then the spatial distribution of this component should be similar to that of its source. For example, if component 1 (F1) has a spatial distribution pattern similar to
that of the soil parent material, then F1 has a great probability of
representing a natural source. The strength of this spatial similarity
can be calculated quantitatively through the power determination
(PD) value, which can be estimated as follows:
PD ¼ 1−
n
1 X
AV ;
AV i¼1 i i
ð7Þ
where A is the total area of Hangzhou, V is the component score variance
of all samples, Ai is the area of subregion i (for the soil parent material,
the subregions were defined as every category of parent material; for
numerical variables such as distance and production, the subregions
were classified through maximizing/minimizing the dispersion variance between/within subregions, respectively), and Vi is the component
score variance of samples in subregion i. In this case, the PD value, ranging from 0 to 1, represents the strength of the spatial correlation, from
weakest to strongest.
Then the interaction detector was implemented to assess the combined influences of pairs of pollution sources on the component scores
(Ren et al., 2019; Jiang et al., 2020). The PD values of each pair of pollution sources were labeled PD(S1) and PD(S2), and their interaction influence was calculated by the interaction detector as PD(S1∩S2). Finally,
the interaction of the two sources was assessed by comparing the
value of PD(S1∩S2) to the sum of PD(S1) and PD(S2), which resulted in
five interaction phenomena (Table S1).
Descriptive statistics and Spearman correlation analysis were conducted using SPSS 19.0 software (IBM Inc., Armonk, NY, USA). The
PMF model was applied using US EPA PMF 5.0 (U.S. Environmental
Protection Agency, 2014), and Getis–Ord Gi* analysis and ordinary
kriging interpolation were conducted using ArcGIS 10.2 (Esri Inc.,
USA). GeogDetector software (Wang and Hu, 2012) was employed for
the GeogDetector model. P values at the 0.05 level were considered significant, and all P values and 95% CIs were two-sided.
5
The mean concentrations of these heavy metals, in decreasing order,
were Cr (52.90 mg/kg) N Pb (31.66 mg/kg) N As (8.99 mg/kg) N Cd
(0.27 mg/kg) N Hg (0.13 mg/kg). Compared with the risk screening
values defined by the National Environmental Quality Standards for
Soils in China (GB15618-2018), the mean concentrations of these
heavy metals were all lower than the strictest risk screening values, indicating that the soil is relatively safe for agricultural production and
human health. However, in comparison with the average values in
China (Teng et al., 2014), the contents of Cd, Hg, and Pb were higher
than their national values by approximately 2.78, 2.00, and 1.17 times,
respectively, whereas the contents of As and Cr were lower than their
corresponding national averages. Moreover, the concentration of As
and Cd were higher than their corresponding local background values
(Xu et al., 2012) by approximately 1.31 and 1.59 times, respectively, indicating that As and Cd were enriched in the topsoil through human activities. The mean contents of Cr, Hg, and Pb were lower than their
corresponding local background values. The proportions of each element above its background value were in decreasing order of Cd
(46.09%) N As (38.47%) N Hg (20.42%) N Pb (17.16%) N Cr (6.33%).
The highest value of the coefficient of variation (CV) occurred for Cd
(0.81), reflecting a wider extent of variability in relation to the mean.
Arsenic (0.69) and Hg (0.62) also had high CV values. The high CV values
and strongly positive skewness of Cd, As, and Hg indicated that these
metals were strongly affected by human activities (Liu et al., 2018; Lv,
2019). Cr (0.38) and Pb (0.38) showed moderate variation. According
to the skewness values, kurtosis values, and results of the K-S normality
test, these heavy metal contents were not all normally distributed. Thus,
Spearman's rho correlation, a nonparametric method, was adopted to
explore the relationships between selected heavy metals (Dong et al.,
2019; Fei et al., 2019). As there was a large number of samples, the correlation coefficients were small, but all were positive and significant
(Table 2), indicating that these heavy metals were significantly correlated with one another. Additionally, the pairs of Cd-Pb (0.466) and
Pb-Hg (0.475) had relatively high correlation coefficients, indicating a
high possibility that they had similar anthropogenic sources, such as vehicle emissions, coal-fired power plants, agricultural fertilizer, and sewage irrigation (Hu and Cheng, 2013; Yoon et al., 2006). The detailed
source analysis is provided in the following parts of this paper.
3. Results and discussion
3.2. Source apportionment of heavy metals in PMF
3.1. Descriptive statistics of heavy metals in soil
The PMF model was used further to identify the sources and quantify
the contributions of heavy metals in the soils. The number of factors for
the model was initially set at 2, 3, and 4, the start seed number was randomly obtained, and the number of runs was 20. The most suitable
number of factors was determined by assessing the smallest and most
stable Q value. Finally, it was determined that three factors resulted in
good model fitting, with prediction residuals normally distributed
within −3.0 to 3.0 and a prediction R2 greater than 0.68. Two error estimation models, classical bootstrap (BS) and displacement of factor elements (DISP), were implemented to assess the bias and uncertainty of
the PMF results (Hu et al., 2020). The results showed that approximately
91% of the base factors were reproduced in the BS model, and no factor
swaps were observed in the DISP model, indicating that the three-factor
PMF solution was stable.
The summary statistics of the heavy metal concentrations in the soil
samples are shown in Table 1. The contents of As, Cd, Cr, Hg, and Pb
ranged from 2.63 to 43.20 mg/kg, 0.05 to 1.42 mg/kg, 10.50 to
104.00 mg/kg, 0.03 to 0.50 mg/kg, and 17.00 to 62.60 mg/kg, with median values of 7.07, 0.20, 54.40, 0.11, and 30.90 mg/kg, respectively.
Table 1
Summary statistics of heavy metal concentrations (mg/kg) in Hangzhou soil samples.
Min
Max
Median
Mean
CV
Skewness
Kurtosis
Background of Zhejianga
Average of Chinab
Risk screening valuesc
a
As
Cd
Cr
Hg
Pb
2.63
43.2
7.07
8.99
0.69
2.309
6.222
6.88
11
20
0.05
1.42
0.2
0.27
0.81
2.431
6.846
0.17
0.097
0.3
10.5
104
54.4
52.9
0.38
−0.024
−0.572
55.99
61
150
0.03
0.5
0.11
0.13
0.62
1.602
3.073
0.17
0.065
0.5
17
62.6
30.9
31.66
0.38
0.72
0.454
35.7
27
70
Data from Xu et al. (2012).
b
Data from Teng et al. (2014).
c
Based on the lowest soil risk screening values in the National Environmental Quality
Standards for soil in China (GB15618-2018).
Table 2
Spearman correlation coefficients of heavy metal concentrations in the soils.
Cd
Cr
Hg
Pb
As
Cd
Cr
Hg
0.361⁎⁎
0.249⁎⁎
0.124⁎⁎
0.309⁎⁎
0.156⁎⁎
0.305⁎⁎
0.466⁎⁎
0.131⁎⁎
0.266⁎⁎
0.475⁎⁎
⁎⁎ Significant at the 0.01 level.
6
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
an indicator of industrial emission (Hu et al., 2018; Rachwa et al.,
2015). On the other hand, vehicle emissions have been regarded as
the main source of Pb in agricultural soil over the past few decades. It
has been estimated that automobile exhaust accounts for roughly
two-thirds of global Pb emissions (Guo et al., 2014; Cui et al., 2018). Additionally, coal mining and combustion emissions could be important
contributors of Pb in atmospheric deposition (Liang et al., 2017). Therefore, F2 may be considered to represent coal-related industrial activities
and transportation.
Factor 3 (F3) accounted for 63.4% of the total contribution and was
dominated by Cd (73.68%) and As (57.83%). Agricultural activities,
such as the use of phosphate fertilizer, pesticides, organic manures,
and sewage irrigation, generally provide considerable amounts of Cd
(Shao et al., 2016; Zhao et al., 2014b). In China, crop production is usually accompanied by the large-scale use of fertilizers and pesticides,
which result in heavy metal accumulation in soils and create environmental problems (Sun et al., 2019). Considering that Cd had the highest
CV in this farmland area, it can be inferred that agricultural activities are
the main source of Cd. Previous studies also concluded that longstanding farming practices, such as the application of fertilizers and pesticides, can lead to the accumulation of heavy metals such as As in soils
(Jiang et al., 2017; Liu et al., 2017; Qiao et al., 2011). Thus, F3 may be associated with agricultural activities.
3.3. Spatial analysis of components
Fig. 3. Factor profiles and source contributions of heavy metals from the PMF model.
The results of the PMF analysis are shown in Fig. 3. Factor 1 (F1) contributed to 16.8% of the heavy metals in the soils and included a heavy
loading for Cr (80.72%) and moderately loadings for Pb (41.83%) and
As (35.56%). The mean concentration of Cr was lower than the local
background values, and only 6.33% of the samples showed a concentration of Cr exceeding the background value, indicating that there were
few external pollution sources of Cr. In addition, Cr showed lower CV
values than the other heavy metals. The above statistics imply that Cr
mainly comes from natural sources. Previous studies have also reported
that Cr in soils originates from parent materials (Fei et al., 2019; Sun
et al., 2019; Zhou et al., 2016). Hence, F1 may represent a natural source,
such as soil parent materials and pedogenic processes.
Factor 2 (F2), accounting for 19.8% of the total contribution, was
weighted primarily on Hg (92.38%) and Pb (42.42%). Mercury had a
high CV value, indicating that anthropogenic sources were the main origin of Hg pollution (Cai et al., 2015; Mamut et al., 2017; Baltas et al.,
2020). In China, smelting, coal mining, and combustion are regarded
as the main sources of Hg pollution in soil, and Hg is typically used as
To understand the spatial distributions of the component factors further and provide information for source identification, the spatial pattern of sampling point component scores was detected by Getis–Ord
Gi* analysis and the distribution of component factors across the study
area was mapped by ordinary kriging (Fig. 4). Samples with low F1
values were clustered in the northern and central-eastern areas,
where eluvium and washland alluvium are the main soil parent materials; whereas high F1 values were distributed in a wide region including the northern, central, and southwestern areas, where various
eluvium and alluvium soil parent materials are distributed. The kriging
interpolation map of F1 was consistent with the Getis–Ord Gi* analysis
of the samples. It is worth noting that the variogram model of F1 had relatively strong spatial autocorrelation, as indicated by the low ratio
(0.31) of nugget/(nugget+sill) and the long model range (34,527 m).
Furthermore, there were no obvious point sources of F1 (Lv, 2019;
Sun et al., 2019). As discussed above, F1 had a high loading on Cr,
which had a concentration lower than the mean of local background
value and relatively small CV. Consequently, F1, with strong spatial autocorrelation and a long dependence range in the soils, was probably
controlled by parent materials.
There was only one sample with a low F2 value, which was located
in the southwestern area, whereas there were abundant samples with
high F2 values that were clustered in the northeastern suburban area,
where industrial development and urban expansion are intense
(Zhang et al., 2013). The kriging interpolation map of F2, with significant point sources in the high-cluster area, was in accordance with the
Fig. 4. Distribution maps of the significantly high/low points calculated from Getis–Ord Gi* analysis along with the factor scores from the PMF model.
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
point results. Moreover, the variogram model for F2 had moderate spatial autocorrelation, indicated by the high ratio (0.64) of nugget/(nugget
+sill) and the short dependence range (4136 m). As discussed above, F2
was heavily loaded on Hg, which had a relatively high CV. Therefore, F2,
with low spatial autocorrelation and significant point sources in suburban areas, was probably controlled by intense industrial and urbanization activities.
There was no sample with a low F3 value across the study area, but
many samples with high F3 values were clustered in the southern and
southwestern rural areas. The kriging interpolation map of F3 was also
in accordance with the point results. The variogram model for F3 had
strong spatial autocorrelation, indicated by the low ratio (0.21) of
nugget/(nugget+sill) and the short dependence range (4437 m). As
discussed above, F3 was heavily loaded on Cd, which had a relatively
high CV and is typically used as an indicator for agricultural activities
(Shao et al., 2016; Zhao et al., 2014b). Hence, F3, with strong spatial
autocorrelation in rural soils, was probably controlled by agricultural
nonpoint source pollution, such as sewage irrigation and fertilizer
application.
3.4. Source identification in GeogDetector
In the final step, the GeogDetector model was used to determine the
spatial correlation between each component factor and the detailed pollution sources. The PD values of each possible pollution source proxy for
the components extracted from the PMF are shown in Table 3. Soil parent material type had a significant spatial correlation with F1 (PD =
0.336, P b 0.01), which indicated that F1 represents natural sources of
soil pollution. However, industrial production and agricultural production had significant spatial correlations with F2 (PD = 0.178, P b 0.01)
and F3 (PD = 0.143, P b 0.01), indicating that F2 and F3 represent anthropogenic sources of soil pollution (industrial and agricultural activities, respectively).
The interaction influences of pairs of sources on the spatial distribution of the component factors derived from the PMF models are listed in
Table 4. The joint effect of soil parent materials and agricultural production had the strongest influence on F1 (0.437), followed by the interaction of soil parent materials and industrial production (0.423). For F2,
the joint effect of industrial production and agricultural production
had the highest PD value (0.326), which further confirmed that F2
mainly comes from anthropogenic activities. In addition, the interaction
effect of soil parent materials and agricultural production had the strongest influence on F3 (0.240). The PD values of the interaction of the two
sources were all greater than the sums of the single PD values, which
corresponded to previous studies that found that the interaction effects
of multiple pollution sources showed nonlinear enhancements, indicating that the sources of heavy metals in soils are complex and diverse
(Ren et al., 2019; Jiang et al., 2020).
3.5. Summary of source apportionment
Based on spatial analytical data with auxiliary datasets (both categorical and numerical variables), the proposed comprehensive model
(integrated PMF and GeogDetector models, as well as Getis–Ord Gi*
analysis and ordinary kriging) in this study could provide an in-depth
understanding of multiple source apportionment.
Table 3
The power determination (PD) value of the detailed pollution source proxies to the component factors.
Factor 1
Factor 2
Factor 3
Soil parent materials
Industry
Agriculture
Distance to road
0.336⁎⁎
0.075
0.086
0.028
0.178⁎⁎
0.024
0.004
0.014
0.143⁎⁎
0.013
0.033
0.01
⁎⁎ Significant at the 0.01 level.
7
Table 4
The interaction power determination (PD) value of two pollution source proxies to the
component factors.
SPM&IP
SPM&AP
SPM&DR
IP&AP
IP&DR
AP&DR
F1
F2
F3
0.423NLE
0.437NLE
0.376NLE
0.198NLE
0.054NLE
0.029NLE
0.282NLE
0.121NLE
0.123NLE
0.326NLE
0.212NLE
0.064NLE
0.121NLE
0.240NLE
0.099NLE
0.182NLE
0.040NLE
0.176NLE
SPM: Soil parent materials.
IP: Industrial production.
AP: Agricultural production.
DR: Distance to the main road.
NLE: Non-linear enhancement.
Bold data was used to highlight the biggest values in each column.
F1 derived from the PMF model showed low clusters in the northern and central-eastern areas and high clusters in a wide region in
the northern, central, and southwestern areas (Fig. 4). Combined
with the distribution map of F1 produced by ordinary kriging, the
spatial pattern of F1 showed high spatial consistency with the distribution of soil parent materials. Moreover, the variogram model of F1
also revealed strong spatial autocorrelation and a long model range,
indicating that F1 was more natural and continuous in space, with
less nonstructural heterogeneity caused by human activities. Finally,
the soil parent material type had the highest and most significant
spatial correlation with F1 in the GeogDetector model, and there
was a certain nonlinear enhancement of the interaction between
the soil parent material and other pollution proxies. In conclusion,
F1 could be defined as natural sources.
F2 obtained from the PMF model showed high clusters in the
northeastern suburban area, where industrial development and
urban expansion were intense (Fig. 2). It can also be seen from the
spatial distribution map of the kriging data that high values of F2
were mainly distributed in the suburbs. In addition, the variogram
model for F2 had moderate spatial heterogeneity and a short dependence range, indicating that F2 had low spatial autocorrelation and
significant point sources in suburban areas. Finally, industrial production had the highest and most significant spatial correlation
with F2 in the GeogDetector model. In terms of interaction effects,
the combined influence of industry and agriculture had the highest
PD value, which further confirmed that F2 mainly comes from anthropogenic activities. Thus, F2 is mainly controlled by intense industrial and urbanization activities.
F3 derived from PMF exhibited high clusters in the southern and
southwestern rural areas. The kriging interpolation map of F3 was
also in accordance with the Getis–Ord Gi* analysis results (Fig. 4).
In addition, the variogram model for F3 had strong spatial autocorrelation and a short dependence range, indicating nonpoint source pollution. Finally, agricultural production had the highest and most
significant spatial correlation with F3 in the GeogDetector model.
In terms of interactions effects, the combined influence of soil parent
material and agriculture had the highest PD value. Hence, F3 is associated with agricultural activities such as sewage irrigation and fertilizer application.
Regarding the contribution of various pollution sources to heavy
metals, 83.2% of the heavy metals in the soil were ascribed to anthropogenic sources, and 16.8% were attributed to natural sources. Among the
anthropogenic sources, agriculture accounted for 63.4% of the total pollution, and industry was responsible for 19.8%. The source determination of each heavy metal indicated that Cr was mainly from soil parent
materials, Cd was closely associated with agricultural activities such as
sewage irrigation and fertilizer application, Hg was mainly attributable
to industrial activities such as coal mining and smelting, As was related
to natural and agricultural sources, and Pb was related to natural and industrial sources.
8
X. Fei et al. / Science of the Total Environment 747 (2020) 141293
4. Conclusions
This study developed a new combined spatial method for quantitative source apportionment and definition of heavy metal pollution in
soils through various auxiliary data and performed a case study in the
city of Hangzhou, China. Although the mean concentrations of As, Cd,
Cr, Hg, and Pb in the soils were lower than the National Environmental
Quality Standards for Soils in China (GB15618-2018), the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively,
indicating that heavy metals were enriched in the topsoil through
human activities. F1 derived from the PMF model showed strong spatial
autocorrelation, a long model range and the highest spatial correlation
with soil parent material; thus F1 was defined as natural sources. F2
(high values were clustered in suburban areas where industrial development and urban expansion were intense) obtained from the PMF
model showed moderate spatial heterogeneity, a short dependence
range and the highest spatial correlation with industrial production;
hence, F2 was mainly controlled by intense industrial and urbanization
activities. F3 (high values were clustered in the southwestern rural
areas) extracted by the PMF model revealed strong spatial autocorrelation, a short dependence range and the highest spatial correlation with
agricultural production; thus, F3 could be attributed to agricultural activities such as sewage irrigation and fertilizer application. Agricultural
activities, industrial activities, and natural sources accounted for 63.4%,
19.8%, and 16.8% of the total heavy metal accumulation in the soils, respectively. Agricultural activities were the main source of Cd (73.68%)
and provided a portion of As (57.83%); industrial activities dominated
the contribution of Hg (92.38%) and provided a portion of Pb (42.42%).
In addition, Cr (80.72%) and a portion of As (35.56%) and Pb (41.83%)
were closely related to the soil parent materials. These results suggest
that anthropogenic activities have strong influences (83.2%) on heavy
metal accumulation and distribution in the soils of the study area,
which calls for the prevention and control of heavy metal pollution in
the region.
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.scitotenv.2020.141293.
CRediT authorship contribution statement
Xufeng Fei: Conceptualization, Methodology, Writing - original
draft. Zhaohan Lou: Formal analysis, Resources. Rui Xiao: Investigation,
Visualization. Zhouqiao Ren: Validation. Xiaonan Lv: Writing - review
& editing.
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.
Acknowledgements
This work was partially supported by the National Key Technology
Research & Development Program of China (2018YFD0200500) and
the National Natural Science Foundation of China (41801302 and
41671399). The foundation had no involvement in the design of the
study, collection of the data, analysis or interpretation of the results,
writing of the report, or the decision-making process for submission of
the article for publication.
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