Study area
The Mianyang thermal power plant, which is located in Mianyang City, Sichuan Province, was selected as the study area (Fig. 1a). It currently faces pressures in relation to soil pollution and urban renewal. The examined region has an area of 569,100 m2, of which 20.06% makes up industrial land, 13.61% is agricultural, 5.47% is traffic land. The rest of the region is considered residential/commercial land. Furthermore, the industrial area may be divided into office, production, power, storage, and other types of spaces (Fig. 1b). The Mianyang thermal power plant area is representative of brownfield renewal in China. Mianyang City is the second largest city in Sichuan Province, with a strong industrial foundation. It is currently included in the Chinese government’s transformation plan of old industrial bases6. The city’s thermal power plant was established in the 1970s, providing heat and electricity for 70% of urban residents and had an irreplaceable role in urban industrial activities. However, the power plant was decommissioned in 2016 and the resulting brownfield site is presently faced with a number of serious environmental problems. As this area was included in the urban renewal projects of local government in 202022, there is an urgent need for the evaluation of soil pollution, as it is a prerequisite for brownfield renewal.
Figure 1
The study area: (a) Location of the study area; (b) Functional layout of the industrial area. (Note: The base map was designed by the author through referring to Baidu Map, https://map.baidu.com and painting the City boundary and urban road network with Adobe Photoshop CS6.)
Sampling and analysis
In order to analyze heavy metal soil contamination across the area, soil samples were collected from different land use types. The sampling points were arranged as shown in Fig. 1. The soil samples collected were mainly collected those representing different production activities in the industrial area. It should be noted that a few samples from the residential/commercial, traffic and agricultural areas were collected as well. Therefore, the spatial distribution of samples was balanced out. Despite limited accessibility to some sites, a total of 87 soil samples were collected in January 2021. Due to increased heating in winter, contaminants in the thermal power plant and its surrounding areas may have increased with seasonal change. After the sampling points were recorded by the GPS measuring instrument, the stainless steel soil sampler was used to collect the soil. Moreover, a composite topsoil sample was mixed by 5 samples nearby (depth = 0–20 cm). The samples were stored in Poly Vinyl Chloride (PVC) packages with sample information labels. They were brought to the laboratory on the day of sampling.
The soil samples were initially air-dried for 24 h, before being passed through a 2-mm sieve that removed stones and plant debris. Afterward, the samples were mechanically mixed, packed into PVC packages, and labeled with sample information to ensure the uniformity of subsequent analyses. Lead (Pb), chromium (Cr), zinc (Zn), nickel (Ni), and cuprum (Cu) concentrations were determined using flame atomic absorption spectrophotometry (PinAAcle 900H, PerkinElmer). In addition, arsenic (As) concentrations were determined using atomic fluorescence spectrometry (BOEN-35851, Fairborn). Duplicate samples were simultaneously analyzed for approximately 20% of soil samples in the assays, using a standard deviation range of 5%.
The validity and accuracy of the data have been ensured by the self-check of the instrument system and the measurement of standard soil samples. The determination results of the standard material for soil composition analysis, purchased from the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences (IGGE) showed that the recovery of each element was 90–120%.
Risk evaluation
The six heavy metals Pb, As, Cr, Zn, Ni, and Cu selected in this experiment have the features of persistence, latency, migration, and accumulation in soli. Due to the fact that the heavy metal pollution of soil is harmful to animals, plants, and humans, it is necessary to understand the environmental status through the evaluation of soil contamination using single factor index method, geo-accumulation index method and HHRE. This will provide a basis for the reuse of land resources as well as soil pollution control. In addition, the combination of Factor Analysis and geo-statistics can compensate the shortcomings of statistical methods in the study on soil pollution using spatial differences.
Single factor index method
The single factor index ((P_{i})) is a commonly used method for calculating the health quality of soil. This index was calculated as:
$$p_{i} = frac{{C_{i} }}{{S_{i} }}$$
(1)
In the above formula, Ci represents the measured concentration of element i (mg kg−1), while Si is the standard value of element i in the soil (mg kg−1) according to the Chinese standard23. The evaluation standard of the single factor index method is shown in Supplementary Table S1. Furthermore, (P_{i}) ≤ 1 represents soil without pollution, while (P_{i}) > 5 represents severely polluted soil. The larger the value of (P_{i}), the more serious the soil pollution.
Geo-accumulation index method
The geo-accumulation index ((I_{geo})) is used to quantitatively examine the degree of heavy metal pollution in sediments and soil24. This index was calculated as:
$$I_{geo} = log_{2} left( {frac{{C_{n} }}{{1.5B_{n} }}} right)$$
(2)
In the above formula, (C_{n}) represents the measured concentration of element n (mg kg−1), while (B_{n}) is the geochemical background value of heavy metals in the soil (mg kg−1) according to the standard value of Sichuan Province, China25. Furthermore, (I_{geo}) < 0 indicates soil without pollution, while 5 < (I_{geo}) ≤ 6 represents severely polluted soil. Therefore, the larger the value of (I_{geo}), the greater level of soil pollution.
Human health risk evaluation (HHRE)
The HHRE was formulated to explore the potential risk levels of human exposure to soil pollutants. In this study, the focus has been placed on evaluating the Hazard Quotient (HQ) and Carcinogenic Risk (CR) of heavy metals in the soil. HQ is used to characterize the level of harm caused by non-carcinogenic pollutants through a single or multiple pathways26. CR is used to examine the probability of carcinogenic diseases or injury caused by carcinogenic pollutants26. Firstly, the corresponding land risk coefficient was identified according to land use types. Secondly, by considering the actual situation of the study area, HQ and CR of oral ingestion ((HQ_{ois})), dermal contact ((HQ_{dcs})), and particulate inhalation ((HQ_{pis})) were calculated. Finally, in accordance with the acceptability level of CR of a single heavy metal in soil being ≤ 10–6, and the acceptability level HQ being ≤ 1, the possible health risk of the site was evaluated27. According to the Technical Guidelines for Risk Assessment of Contaminated Sites25, the three exposure pathways were calculated as:
$$HQ_{ois} = frac{{OSIR times ED times EF times C_{sur} times ABS_{o} times 10^{ – 6} }}{{RfD_{o} times BW times AT_{nc} times SAF}}$$
(3)
In formula (3), (HQ_{ois}) represents the hazard quotient from oral ingestion of soil, while (OSIR) is the daily oral ingestion rate of soils (children (100 mg d−1), adults (200 mg d−1)); (ED) is the exposure duration (children (6a), adults (24a)), while (EF) is the exposure frequency, children (350 ({text{d}};{text{a}}^{ – 1})), adults (350 ({text{d}};{text{a}}^{ – 1}) ). Moreover, the symbol (C_{{{text{su}}r}}) stands for the measured concentration of contaminants in the surface soil (mg kg−1) derived from the site investigation and (ABS_{O}) is the absorption factor of oral ingestion (1). The symbol (BW) is the average body weight (children (15.9 kg), adults (56.8 kg)), while (RfD_{o}) represents the reference dose for oral ingestion (mg kg−1 d−1), (RfD_{o}) of As is 3.00E-04 mg kg−1 d−1, (RfD_{o}) of Cr is 3.00E-03 mg kg−1 d−1, (RfD_{o}) of Zn is 3.00E-01 mg kg−1 d−1, (RfD_{o}) of Pb is 3.50E-03 mg kg−1 d−1, (RfD_{o}) of Cu is 4.00E-02 mg kg−1 d−1, (RfD_{o}) of Ni is 2.00E-02 mg kg−1 d−1. Lastly, (AT{}_{nc}) is the average time for non-carcinogenic effect, (children (2190 d), adults (8760 d)) and SAF is the distribution coefficient of the reference dose (0.20).
$$HQ_{dcs} = frac{{239 times H^{0.417} times BW^{0.517} times SER times SSAR times EF times E_{v} times C_{sur} times ABS_{d} times 10^{ – 6} }}{{RfD_{o} times BW times AT_{nc} times ABS_{gi} times SAF}}$$
(4)
Within the above formula, (HQ_{dcs}) is the hazard quotient of dermal contact with a soil and (H) is the average height (children (99.4 cm), adults (156.3 cm)). Additionally, the symbol ({text{SER}}) represents the skin exposure ratio (children (0.36), adults (0.32)), while ({text{SSAR}}) stands for the adherence rate of soil on skin (children (0.2 mg cm−2), adults (0.07 mg cm−2)). (E_{v}) is the daily exposure frequency of dermal contact (1 time d−1 ) and (ABS_{d}) is the absorption factor of dermal contact (chemically specific), (ABS_{d}) of As is 3.00E-02, (ABS_{d}) of Pb is 3.52E-03, (ABS_{d}) of Zn is 1.00E-03, (ABS_{d}) of Cr is 1.00E-03, (ABS_{d}) of Ni is 2.00E-02. Finally, (ABS_{gi}) represents the absorption factor of the digestive tract (chemically specific), (ABS_{gi}) of As is 1, (ABS_{gi}) of Cr is 0.013, (ABS_{gi}) of Ni is 0.04.
$$HQ_{pis} = frac{{PM_{10} times DAIR_{c} times ED times PIAF times left( {fspo times EFO + fspi times EFI} right) times C_{sur} times BW_{a} times 10^{ – 6} }}{{RfC times DAIR_{a} times AT_{nc} times SAF times BW_{c} }}$$
(5)
Within Formula (5), (HQ_{pis}) is the hazard quotient of inhaled soil particulates and (PM_{10}) is the content of inhalable particulates in ambient air (0.15 mg m−3). Furthermore, (DAIR) is the daily air inhalation rate (children (7.5 m3 d−1), adults (14.5 m3 d−1)), while (PLAF) represents the retention fraction of inhaled particulates in body (0.75). The ({text{fspo}}) represents the fraction of soil-bome particulates in outdoor air (0.5), while (fspi) is the fraction of soil-bome particulates in indoor air (0.8). Lastly, (EFO) symbolizes the outdoor exposure frequency (children (87.5 d a−1), adults (87.5 d a−1)), while (EFI) is the indoor exposure frequency (children (262.5 d a−1), adults (262.5 d a−1)) and (RfC) is the reference concentration of respiratory inhalation (mg m−3), (RfC) of As is 1.50E-05 mg m−3, (RfC) of Cr is 1.00E-04 mg m−3, (RfC) of Ni is 9.00E-05 mg m−3.
$$CR = OISER times C_{sur} times SF$$
(6)
Finally, in Formula (6), (CR) stands for the carcinogenic risk from soil ingestion (oral ingestion, dermal contact, or inhalation), while OISER is the exposed quantity of soil exposure through oral ingestion, dermal contact, or inhalation (mg kg−1 d−1). OISER (oral ingestion) is 1.568E-06, OISER (dermal contact) is 4.459E-06, and OISER (inhalation) is 9.729E-09. Lastly, SF is the carcinogenic slope factor (kg d mg−1), SF of As is 1.50E +00 kg d mg−1, SF of Ni is 1.02E +00 kg d mg−1 and SF of Cr is 5.00E-01 kg d mg−1.
Geospatial distribution
The use of geo-statistics
Geo-statistics28 provides a theoretical basis for studying the geospatial distribution of heavy metals in soil. Although this method is currently employed in environmental research to determine the characteristics of contaminated sites29, combining it with landscape planning and ecological remediation in urban brownfield sites requires further consideration. In the present paper, a soil database was constructed using the GIS (ArcGIS, Version 10.3, ESRI), into which geographical coordinates of soil samples, heavy metals concentrations, and potential health risks were input (Supplementary Table S2, Supplementary Table S5). The possible distribution of heavy metal concentrations and of potential health risks in the study area were determined using the Inverse Distance Weighting method.
Factor analysis method
Factor Analysis30 is a method used for determining the correlation and principal components of heavy metals in soil. This analysis can calculate the weight of heavy metals in contaminated soil and the weight is used for the superposition of spatial information of human health risk evaluation of various heavy metals. Moreover, factor analysis is the practice of condensing many variables into only a few because it groups together highly related variables into a single category. In comparison to Principal Component Analysis (PCA), Factor Analysis has the benefit of taking the strength of the correlation into account. Thus, it solves the computation obstacle of PCA through the rotation of the factor axis. In this study, the weight of heavy metals was calculated using the following steps:
Step 1: Data standardization was obtained as:
$$y_{ij} = frac{{x_{ij} – overline{x}_{j} }}{{s_{j} }}$$
(7)
where (x_{ij}) represents the (i)th data of the (j)th factor, where (j) = 1,2…(n), (overline{x}_{j} = frac{1}{n}sumlimits_{{{text{j}} = 1}}^{n} {x_{ij} }), (s_{j} = sqrt {frac{1}{n – 1}sumlimits_{j = 1}^{n} {left( {x_{ij} – overline{x}_{ij} } right)^{2} } }).
Step 2: The variance contribution rate of the common factor Fm was calculated as:
$$F_{{text{m}}} = u{}_{1m}x_{1} + u_{2m} x_{2} + cdots + u_{jm} x_{j}$$
(8)
where Fm is (m) represents the common factors obtained according to the principle that the cumulative variance proportion is greater than 60%. Furthermore, (u_{jm}) symbolizes the coefficient vector in the decision matrix, (u_{jm} = frac{{f_{jm} }}{{sqrt {lambda_{m} } }}), in which (f_{jm}) stands for the factor loading and (lambda_{m}) is the eigenvalue corresponding to the mth common factor.
Step 3: The score coefficient (x_{j}) of factors was determined as:
$$x_{j} = frac{{sum {F_{m} cdot u_{jm} } }}{{sum {F_{m} } }}$$
(9)
Step 4: The factor weight was then normalized to obtain (w_{j}), as:
$$w_{{text{j}}} = frac{{x_{j} }}{{sum {x_{j} } }}$$
(10)
Ethics statement
Our study is based on open source data, so there are no ethical issues and other conflicts of interest.
Consent to participate
Written informed consent for participation was obtained from all participants.
Consent to publish
Written informed consent for publication was obtained from all participants.
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