Distribution, source apportionment and ecological risk assessment of polycyclic aromatic hydrocarbons in the surface sediments of coal mining subsidence waters

Abstract

To probe into the concentration, composition and other pollution characteristics of polycyclic aromatic hydrocarbons (PAHs) in the surface sediments of coal mining subsidence water, Yangzhuang coal mining subsidence waters in Panyi Mine of China are determined as the object of this research, where 11 surface sediment samples are collected to detect and analyze PAHs with a Gas Chromatography-Mass Spectrometer (GC-MS) and make source apportionment with factor analysis and ecological risk assessment. The results show that 16 kinds of PAHs have all been detected, with the content level of ∑PAHs reaching 55 to 755 ng g⁻¹ and the average content of that reaching 288 ng g⁻¹. PAHs with three or four rings dominate. The pollution level of PAHs is quite low and mostly comes from combustion and oil. Most of these 16 kinds of PAHs have been proved to be of no potential ecological risk.

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1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are a sort of benzene ring with two or more rings that exist extensively in the environment.¹ They are also a series of nonpolar organic compounds fused together in a linear way or in a corniform or cluster, which belong to typical persistent organic pollutants (POPs).² Most PAHs in the environment primarily come from the emission of human activities, including the leaks of oil and refined products and the incomplete combustion or thermal cracking of coal, wood and other hydrocarbon materials. PAHs have stable chemical properties and are degradation-resistant, hydrophilic and lipophilic, most of which have potential high toxicity, carcinogenicity, teratogenesis and mutagenesis so that they have gained wide concern among people.^3–6 More than half of the countries in the globe have listed PAHs as the priority pollutants. Among the priority pollutants listed by the U.S. Environmental Protection Agency (US EPA), sixteen of them are PAHs and seven are included in China's priority-controlled list.^7,8

In China, coal-driven energy holds over 70% of the total primary energy production. China's coal production has reached nearly 3.69 billion tons by the year of 2015. As one of the thirteen billion-ton coal bases in China, Huainan City, Anhui Province is one of the largest coal producing areas and makes enormous economic benefits from coal. Particularly, in the coal mines of east-central China, Huainan Coal Mines represent a typical case, generating 75.68 million tons of raw coal in 2014. But meantime, it's reported that potential harm has been done to local ecological environment, such as soil, air, and water pollutions and the generation of solid wastes, like coal gangue and fly ash. Particularly, land subsidence is of great concern among all these issues. Excessive mining leads to ground displacement and deformation and finally forms the massive subsidence area. Such extensive area transforms into different and scattering subsidence waters because of regional hydrological (shallow groundwater) and geological conditions, the afflux and supply of atmospheric precipitation. Coal mining subsidence waters have become a kind of surface water which is common but special as well. The government of Huainan municipality has initiated to regulate these areas as large plain reservoirs, lakes and fishponds, performing multiple functions as flooding buffer zones and aquatic ecological rehabilitation.

Compared to rivers and lakes, coal mining subsidence area has evolved into aquatic environment from previous terrestrial environment as water accumulates. As previous soil turns into the sediments of the subsidence water, pollutants in the soil migrate and cluster. The properties of the sediments is directly determined by the soil before subsidence most of which comes from farmland. At the meantime, the sediments and waters in the subsidence area are also influenced by surrounding agricultural environment and become a vital potential pollution source. Early researches concerning coal mining subsidence waters mainly focus on the assessment of water eutrophication and conventional index of water quality, such as heavy metals and so on. Researches on persistent organic pollutants like PAHs in coal mining subsidence area haven't been reported yet. Because of the low solubility and high hydrophobicity, PAHs have relatively low content in water. Most of them are absorbed by suspended particles when getting into the water and quickly enter into the sedimentary environment with gravity. Thus, the sediments in the rivers and lakes have the highest content of PAHs.⁹ Also, they are considered as one of the main carrier and the final destination of the migration and transformation of PAHs in the environment. PAHs can be taken up by organisms.¹⁰ The PAHs that accumulate in the sediments can be transmitted to high trophic level organisms through food chain, posing potential harm to the living and health conditions of organisms and humans.¹¹ While the PAHs that store in the sediments will again enter into the water by desorption so that a secondary pollution will be caused to the water. Therefore, it's significant to conduct pollution researches of PAHs in the sediments of the coal mining subsidence area in Huainan. This research determines Yangzhuang coal mining subsidence waters in Panyi Mine, Huainan as the object, detects the concentrations of the PAHs in the surface sediments, analyzes the pollution characteristics of them such as the composition and the distribution, discusses the possible sources and carries out the ecological risk assessment in a bid to provide scientific evidences for the sustainable development of the subsidence waters in the future.

2. Materials and methods

2.1 Study area

Located in the north of Huainan municipality, Panji Mine, which is actually the whole coal mining belt in Panji area, faces Huaihe River in the south and Cihuaixin River in the north, borders on Huaiyuan County in the east and neighbors on Fengtai County in the west. Panji Mine has flat terrain and interconnected waterways. There are five modern mines in Panji Mine: Panyi Mine, Paner Mine, Pansan Mine, Pansidong Mine and Zhuji Mine. There are multiple coal mining subsidence waters in Panji Mine, among which Yangzhuang coal mining subsidence waters (116°49′30′′–116°52′00′′E, 32°47′00′′–32°48′30′′N), the largest one (an area of nearly 4 km²) that has the longest subsidence period (nearly 20 years), is determined as the research object. Located in the east of the main mine of Panyi Mine, Yangzhuang subsidence waters evolve from terrestrial environment and still keep certain properties of farmland soil. A gangue dump site is in the surrounding area. The main river is the Mud River which flows from northwest to southeast, crossing Pansan Mine, Panyi Mine and many residential areas and finally connects to this subsidence water. The Mud River enters the water from the southwest and flows out from the southeast. Its influx brings massive industrial wastewater and sanitary sewage of Panyi Mine. Agricultural plant zones spread in the west and south areas of the subsidence waters, causing agricultural non-point source pollution to this area to a certain extent. From data sources, the major pollutants in Yangzhuang subsidence waters are suspended substances, COD and ammonia nitrogen. The water quality of subsidence area is slightly alkaline. Dissolved oxygen is in supersaturated state because of the algae growth. The pH value reaches between 8 and 9. The content of the dichromate oxidizability (CODcr) is between 17.96 and 39.00 mg L⁻¹. The concentration of the permanganate index (COD_Mn) is between 6.79 and 8.10 mg L⁻¹. The concentration of ammonia nitrogen is between 0.44 and 1.16 mg L⁻¹. The average value of total phosphorus concentration is 0.14 mg L⁻¹. The average value of total nitrogen concentration is 1.4 mg L⁻¹. The contents of COD, permanganate index, total nitrogen and ammonia nitrogen all exceed the standard of the III-type water, according to the environment quality standards for surface water published by the Ministry of Environmental Protection of the People's Republic of China (GB 3838-2002). Waters are facing severe pollution problems such as eutrophication.

According to the investigation, the average water depth of Yangzhuang subsidence waters is 3.6 meters with the largest depth reaching over 6.0 meters. Currently, the whole research waters are developed as separate fishing areas for farmers who lost their lands to make a living. Fish larvae will be put into the water every year without fish bait. The feeding model is scattered-feeding type. The nutrients for the ecological system mainly come from external supply and internal environment that has already subsided. According to the average deposition rate of common lakes (1–20 mm per year), Yangzhuang subsidence area will emerge as a lacustrine deposit after about 20 years, covering the original plough layer of farmland. Thus, researches on this area are typical and representative to some extent.

2.2 Sampling

Research area is rather broad so that the overall distribution should be ensured to be relatively even, and representative detection points should be collected. The authors went to Yangzhuang subsidence area in Panyi Mine to collect samples with GPS in the end of August, 2014, based on the Technical Regulation on the Design of Sampling Programmes (HJ 495-2009) and Guidance on Sampling Techniques (HJ 494-2009) published by the Ministry of Environmental Protection of the People's Republic of China. Surface sediments (0–5 cm) were collected by Beeker-type sediments core samplers (made by the Dutch Eijkelkamp Company) and kept in aluminum containers. Images of sediments sampling equipment and field sampling in research area can be seen in Fig. 1. Samples should be brought back to the laboratory immediately, stored at −20 °C and kept away from sunlight, waiting for analysis, based on the Technical Regulation of the Preservation and Handling of Samples (HJ 493-2009). Research area and sampling points can be seen in Fig. 2, from which 11 sediments sampling points are set, uniformly distributing in Yangzhuang subsidence waters. Feature points like the confluence with the Mud River, shoresides and the middle of the fish ponds were all collected as required (HJ 495-2009).

Fig. 1 Sediments sampling equipment and field sampling in research area.

Fig. 2 Location and sampling points distribution in the research area.

2.3 Materials and reagents

Materials mainly include chromatography silica gel, anhydrous sodium sulfate, neutral alumina, sodium hydroxide and copper sheet. Before use, silica gel should be soaked in normal hexane and oscillated by ultrasonator for 30 minutes. Then, it will be moved to evaporating dish and continually activated for 16 hours under 130 °C in vacuum drier. It will be stored in the dryer as it cools to room temperature. After anhydrous sodium sulfate has been soaked in normal hexane and oscillated by ultrasonator for two hours, it should be dried by vacuum drier and kept under seal as it cools. After neutral alumina has been soaked in normal hexane and oscillated by ultrasonator for 30 minutes, it should be moved to vacuum dish and burned for 12 hours under 450 °C in muffle furnace after being dried. Then, it should be stored when cooled naturally.

Reagents mainly include normal hexane, dichloromethane and methanol (all are chromatographic ally pure and made by the U.S. TEDIA Company). The 16 kinds of mixed standards (made by the U.S. O2si Company) include naphthalene (NaP), acenaphthylene (Acy), acenaphthene (Ace), fluorine (Fl), phenanthrene (Phe), anthracene (Ant), fluoranthene (Flu), pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3 cd]pyrene (InP), dibenzo[a,h]anthracene (DBA) and benzo[g,h,i]perylene (BgP). The standard reagents of recovery rate indicators include 2-fluorobiphenyl (made by Germany Dr Ehrenstorfer Company) and para-terphenyl (made by the U.S. O2si Company).

2.4 Sample extraction

As the collected sediments are dried in the freeze drier, they should be ground by the agate mortar and screened through 200 mesh. Then, they should be separately kept in brown glass bottles. Soxhlet extraction is applied to process the prepared samples. The concrete steps: weigh 10 gram (g) of samples and 10 g of anhydrous sodium sulfate and put them into the filter bags; add known amount of recovery rate indicators before samples are extracted; add 200 milliliter (mL) of dichloromethane and normal hexane (the volume ratio is 1 to 1) into the round-bottle flask where appropriate glass beads and 2 g of the activated copper sheet. The next is to take soxhlet extraction; the temperature of the water bath and the cooling water are 50 °C and 10 °C; keep boiling and extract them for 24 hours. As the extraction is accomplished, pass the extraction liquid through the dry column of anhydrous sodium sulfate (around 3 centimeter (cm) long). Wash the extraction flasks and the sodium sulfate column with certain amount of normal-hexane to finish the quantitative transfer. Add the collected extraction liquid into the rotary evaporator to concentrate. The temperature should be kept at about 45 °C. The volume should be concentrated to less than 2 mL. Then, add 10 mL of normal-hexane and continue to concentrate. This step should be repeated 2 or 3 times. The last is purification and elution. Concrete steps: fill it with tailored glass chromatography column and use normal-hexane to fill the columns with wet methods. Fill anhydrous sodium sulfate, alumina, silica gel and anhydrous sodium sulfate from top to bottom. The volume ratio is 1 to 1 to 2 to 1. Rinse the columns with certain amount of normal-hexane at a low speed. Transfer the concentrated extraction liquid to the chromatography columns to purify. Leach the predicted component with 50 mL of normal-hexane to dichloromethane (7 : 3). Then, the concentration with rotary evaporator should be conducted again. As the eluant is concentrated to around 2 mL, small amount of normal-hexane is added to continue the concentration. This step should be repeated for 2 or 3 times. Transfer the above concentrated solutions into the nitrogen-blowing tubes to be concentrated by the pressure blowing concentrator. After that, move them to the sampling bottles of GC-MS and add fixed-concentration internal standards into the solution. Keep the volume to 1 mL with normal-hexane and conduct determination and analysis on the instrument.

2.5 Instrumental analysis

The Clarus SQ 8 GC-MS made by the U.S. PerkinElmer Corporation which is applied includes two parts: gaschromatograph (Clarus 680) and mass spectrometer (SQ 8). Chromatographic column is Elite-5. The carrier gas of instruments is helium. The flow speed is 1 mL min⁻¹. The heating procedures of instrument determination: set the initial temperature at 80 °C and keep this temperature for 1 minute. Then, raise it to 160 °C at the speed of 30 °C min⁻¹ and keep this temperature for 1 minute. The last is to heat it to 265 °C at the speed of 3 °C min⁻¹ and keep this temperature for 1 minute. The sample size should be 1 μL and the sampling method should be splitless injection. Conditions for mass spectrum: the temperatures of the EI ion sources and the transmission lines should respectively reach 250 °C and 280 °C. The internal standard method should be adopted to conduct quantitative analysis. PCBs material composition should be reached through the retention time, peak heights and peak areas of each chromatographic peak.

2.6 Quality assurance/quality control (QA/QC)

To ensure the reliability and the veracity of the monitoring data of the experiment, quality assurance and quality control measures recommended by the U.S. EPA are adopted. All the data passes through strict quality control. At the meantime, parallel samples, standard sample recovery and methods in blank should be set. The recovery rates of the 16 kinds of mixed standard substances are all within the range of 70–140% that the method has ruled. The recovery rates of the indicators are all in the range of 70–130%. The relative standard deviation among parallel samples is less than 5%. The internal standard method is used to precisely determine the concentrations. The fitting coefficients of calibration curves are all bigger than 0.999, which indicates the analytical quality is reliable.

3. Results and discussion

3.1 Distributional and compositional characteristics

Eleven surface sediments samples have been collected in the Yangzhuang coal mining subsidence waters. Sixteen kinds of PAHs that are in priority control of USEPA have been detected. The detection rate is 100%, which reveals the extensive existence of the PAHs residues in the surface sediments of subsidence waters. The PAHs distribution in each sampling point can be seen in Fig. 3. The total PAHs content of sediments samples is in the range of 55 to 755 ng g⁻¹. The average of total concentration is 288 ng g⁻¹. From the characteristics of space distribution of PAHs in the subsidence waters, the point with highest total concentration is in DN009, the east side of the subsidence waters. The point with least total concentration appears to be in DN001, the north of the subsidence waters. DN008, DN009, DN010 and DN011, the four sampling points with quite high total concentration of PAHs, are all located in the bed of the Mud River. As the affluent of the Huaihe River, the Mud River accumulates huge amounts of agricultural and industrial wastewater during the development of the agriculture and industry of Panji Mine. Large amounts of PAHs also accumulate in the sediments of the riverbed gradually. Thus, from the view of spatial distribution of subsidence waters, the concentration of PAHs in the bed of the Mud River is apparently higher than that in other sampling points. The concentration ranges, means and coefficients of variation (CV) of the 16 kinds of PAHs in the subsidence area can be seen in Table 1. CV represents the strength of the variability of PAHs content in the sediments of research area. The value of CV is the ratio between standard deviation and mean value. If CV is 0.1 or less, the variability is in the low intensity; if CV is more than 0.1 but less than 1, the variability is in the middle intensity; if the CV is equal to 1 or more, the variability is in the strong intensity. Due to the elimination of the influences of measurement scale and dimension, CV can reflect the dispersion and variation degrees of data in an objective manner. As CV gets bigger, the difference of PAHs contents gets wider. At the same time, CV can further embody the conditions of human activities and energy fix that have a close bearing with the emission of PAHs, providing necessary reference value to the study on the source apportionment of PAHs.^12,13 The 16 congeners have all been detected. But the CV of each congener fluctuates. The stability of the PAHs is not strong, which is related to the instability of the environment of coal mining subsidence waters.

Fig. 3 Distribution of the 16 kinds of PAHs in the surface sediments of each sampling point in research area.

Table 1 Concentrations and CV of 16 kinds of PAHs in the surface sediments of research area (ng g⁻¹)

PAHs	Range	Mean	CV/%
NaP	11.1–70.7	32.1	57.4
Acy	2.9–11.9	6.2	52.1
Ace	1.5–27.8	6.3	122.0
Fl	2.1–72.8	26.1	112.8
Phe	1.6–183.3	71.5	83.7
Ant	2.6–17.4	6.7	75.0
Pyr	2.1–129.4	39.1	106.2
Flu	2–137.7	36.1	113.9
BaA	1.5–70.2	17.5	120.1
Chr	0.9–70.2	16.7	128.4
BbF	1.8–13.0	4.0	83.3
BkF	0.9–6.5	2.2	65.0
BaP	3.5–14.6	5.5	59.1
InP	1.2–5.8	2.6	73.1
DBA	1.1–3.2	2.6	20.0
BgP	4.7–51.1	12.7	108.5

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PAHs can be divided by the number of benzene rings. The composition of the PAHs in the surface sediments of research area can be seen in Fig. 4. The concentration range of 2-ring PAHs (Nap) is from 11.14 to 70.70 ng g⁻¹, averaging 32.06 ng g⁻¹. The concentration range of 3-ring PAHs (include Ace, Acy, Fl, Phe and Ant) is from 11.75 to 287.24 ng g⁻¹, averaging 116.79 ng g⁻¹. The concentration range of 4-ring PAHs (include Flu, Pyr, BaA and Chr) is from 15.34 to 350.46 ng g⁻¹, averaging 109.42 ng g⁻¹. The concentration range of 5-ring PAHs (include BbF, BkF, BaP and DBA) is from 9.77 to 32.13 ng g⁻¹, averaging 14.46 ng g⁻¹. The concentration range of 6-ring PAHs (include InP and BgP) is from 5.95 to 52.6 ng g⁻¹, averaging 15.3 ng g⁻¹. The PAHs in the research area are mainly three and four rings aromatic hydrocarbons, accounting for 60–80%. Two-ring PAHs come third. Referring to the PAH congener, Phe has the highest concentration, taking up 25% of the total PAHs. The percentages of Pyr, Flu and Nap are respectively 14%, 13% and 11%.

Fig. 4 Composition of PAHs in the surface sediments of research area.

Generally, two and three rings are taken as low molecular weight (LMW) PAHs which are mainly contributed by petroleum hydrocarbons. Four rings are taken as a transition. High molecular weight (HMW) PAHs include five and six ring. The main sources are the combustion of petroleum, coal, higher plants and organic compounds.^14–17 The triangular distribution of the compositional percentage of each kind of the PAHs in the research area can be seen in Fig. 5, from which, it can be seen that low rings (2 rings, 3 rings and 4 rings) of PAHs in the sediments of subsidence areas take up a relatively large percentage. PAHs in the subsidence waters turn out to be: four rings have the largest concentration, followed by the sum of two and three rings, and the sum of five and six rings come third. Some researches finds that four ring substances are the symbol of coal combustion.^18,19 Moreover, the incomplete combustion of coal is the main cause of four rings substances. The naphthalene and phenanthrene are mainly caused by the combustion of agricultural waste, such as crop straws. Li indicates in the researches that the content of Phe can reach to 30% in the granular PAHs that are released in the coal burning process.²⁰ According to the research materials and existing data, the compositional characteristics of the PAHs in this area form possibly because of three points: firstly, the massive development of the coal in surrounding areas; secondly, thermal power stations can cause large amounts of substances to subside; thirdly, the combustion of various agricultural waste such as crop straws.

Fig. 5 Triangular diagram of PAHs proportions in the surface sediments of research area.

3.2 Source apportionment by factor analysis

There are primarily two sources of the PAHs in the sediments: natural source and artificial source. As humans keep developing and applying the nature and get more deeply involved in the environment, the influence of artificial source of PAHs will far exceed the natural factors. The artificial source includes the combustion of fossil fuel like the coal and oil, the incomplete combustion of hydrocarbons like the wood and the leaks of petrochemical materials. This research focuses on the ratio method to conduct qualitative analysis of the sources of PAHs.

The pollution sources of the surface sediments in the Yangzhuang coal mining subsidence waters in Panyi Mine can be initially predicted from some certain symbolic pollutants in the PAHs and the concentration ratios of these pollutants. Common ratios are: Ant/(Ant + Phe), Flu/(Flu + Pyr) and ∑COMB/∑PAHs (∑COMB = the sum concentration of Flu, Pyr, BaA, Chr, BkF, BbF, BaP, InP, DBA and BgP which are the major compounds coming from combustion source; as the value of ∑COMB gets bigger, more considerable portion of PAHs originates from the combustion source).^21,22 Generally, the ratio method has these laws: if the ratio of Ant/(Ant + Phe) is less than 0.1, the main source of PAHs is the leaks of substances like oil; if the ratio is bigger than 0.1, combustion source will play its role; if the ratio of Flu/(Flu + Pyr) is smaller than 0.4, oil will be the main source; if it is bigger than 0.5, the grass, woods and coal will be the combustion source. If the ratio is between 0.4 and 0.5, oil burning will be the main source.^23–25 If the ratio of ∑COMB and ∑PAHs are both more than 0.5, PAHs are the main combustion source. To separately draw the scatter diagrams of ∑COMB/∑PAHs, Ant/(Ant + Phe) and Flu/(Flu + Pyr) can qualitatively analysis the possible pollution sources of PAHs in the research area(Fig. 6 and 7). From the figure, there are only 2 points of which the Ant/(Ant + Phe) ratios are obviously bigger than 0.1. Most of the Ant/(Ant + Phe) ratios of the 9 sampling points are near 0.1. Four points of Flu/(Flu + Pyr) ratio are in the position of petroleum source. One is on the 0.4 split line, and two are in the position of combustion source. Most of the ∑COMB and ∑PAHs ratios of sampling points surround the 0.5 split line. The sources of them are not so distinguishing. The source of the qualitative analysis of PAHs is mixed. Both the combustion source and the petroleum source haven't taken up an obvious advantage.

Fig. 6 Scatter diagrams of Ant/(Ant + Phe) and Flu/(Flu + Pyr) in the surface sediments of research area.

Fig. 7 Scatter diagrams of ∑COMB/∑PAHs and Flu/(Flu + Pyr) in the surface sediments of research area.

To further quantificationally distinguish the pollution sources of PAHs, factor analysis (FA) is conducted in the 16 kinds of PAHs by using Statistical Product and Service Solutions (SPSS) software for Windows. SPSS is an integrated common software used for computer processing and statistic analysis. It is an efficient way of conducting research on factor analysis. Its basic idea is to use dimensional reduction to turn multiple variables into several important ones by linear transformation and classify the variables with strong relevance into one type.

The analytical results are listed by way of matrix.^26,27 Model of FA:

Its matrix form can be represented as:

x = AF + ε (1)

X₁, X₂, X₃ to X_p represent p numbers of variables. F₁, F₂, F₃ to F_m represent m numbers of common factors. m shall be less than p. A represents the factor loading matrix. a_ij refers to factor loading, representing the coefficient of association between no. i original variable and no. j factor variable. As the absolute value of a_ij gets bigger, the relationship between common factors, F_j and x_i, becomes closer. ε is a specific factor.

The variance contribution (S_j) of common factor F_j is:

S_j reflects the interpretability of factors towards the total variance of all original variables. As the value gets bigger, the importance of factors becomes higher.

Factor analysis based on SPSS software includes the determination of factor loading and factor rotation, etc. Principal component analysis (PCA) is applied to determine factor loading. Specific steps of calculation are:

Step one: data standardization. The matrix of original data is (X_ij)_n×p (n numbers of samples and p numbers of variables). Based on , σ_j² = (x_ij − x_j)²/n and (i = 1,..., n, j = 1,2,..., p), the standardized matrix is .

Step two: calculation of the correlation coefficient matrix (R). According to the standardized matrix X, R is calculated based on .

Step three: calculation of the eigenvalue (λ_i) and corresponding eigenvector (μ_i) of correlation coefficient matrix (R) and determination of the number of factors. Eigenvalue is the variance of corresponding common factor. Eigenvector constitutes the coefficient of common factor. The contribution rate of variance is . When factor analysis is being conducted, m numbers of integrative factors are selected. The principle of determining m is that the accumulated variance contribution rate, d, should be sufficiently big (generally d shall be 85% ).

Step four: rotating factors and calculating factor loading matrix. Firstly, factor loading is obtained based on (e_i is unit eigenvector). Secondly, factor loading matrix is obtained. The coefficients in the linear combination of common factors constitute factor loading matrix. For the purpose of obtaining better factor explanation, factor loading matrix can be rotated. This research adopts maximum-variance algorithm.

FA deepens and expands the PCA, which better explains factors with rotation. The multiple varieties of PAHs enlarge the complexity of analysis. Due to the crossing relationship of variables, more representative variables will be selected by taking the pair wise correlation among new variables. The first factor of FA explains the most information. The explanatory ability decreases successively. PAHs can be generally divided into three main groups. The first includes Nap, Acy and Fl which belong to LMW and alkyl-substituted PAHs, existing extensively in petroleum source and mainly coming from the leaks of petroleum products. The second includes BaP, DBA, BgP, BbF, BkF, Chr, BaA, Pyr, Flu, InP, Phe and Ace components, mainly existing in combustion sources, such as the burning of coal, woods and fossil fuel. The third has only Ant which is considered as the biological source, bringing PAHs pollutions through biological transformation.

Firstly, dimensionality reduction is used to turn multiple variables into few new variables by linear transformation to find out the principle influence factors of the PAHs. The explanatory situation of each factor variance of the 16 kinds of main PAHs can be seen in Table 2, from which the initial eigenvalues the initial three extracted factors all exceed 1, and the total variance explained of the three factors are all beyond 10%, totaling 89.04%. These three factors can be the extracted factors of the principal variance to have variance extraction. The component matrix of the 16 kinds of PAHs in the sediments after variance extraction and rotation can be seen in Table 3, and matrix displays the factor loading. Three principal factors embody the data change of 89.04% of PAHs. The first factor contributes 60.78%. The factor loadings of the Ant, BaA, Chr, BbF, BaP, BgP, Acy, Flu and Phe in the first principal component which represents the combustion source are all quite high. The second main group interprets 18.7% of the change of data matrix. The factor loadings of Nap, Acy, Flu, Fl, Pyr and BkF are quite high. The Nap, Acy and Fl correspond to the petroleum source. So the second main group corresponds to the petroleum source. The third main group explains 9.5% of the change of data matrix. Among them, the factor loadings of Ace, InP and DBA are quite high. The third main group corresponds to the combustion source.

Table 2 Fact sheet of the total variance explanation of the main factor in the 16 kinds of PAHs in the surface sediments of research area

Components	Initial eigenvalues
	Sum	Variance%	Accumulation%
1	9.726	60.788	60.788
2	2.988	18.675	79.463
3	1.532	9.578	89.041
4	0.923	5.769	94.810
5	0.711	4.442	99.252
6	0.058	0.360	99.612
7	0.026	0.160	99.772
8	0.023	0.146	99.918
9	0.010	0.065	99.983
10	0.003	0.017	100.000

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Table 3 Matrix of rotated principal components of 16 kinds of PAHs in the surface sediments of research area

PAHs	Components
	1	2	3
Nap	0.399	0.879	0.202
Acy	0.703	0.680	0.166
Ace	0.036	−0.194	0.856
Fl	0.588	0.728	0.023
Phe	0.677	0.691	0.188
Ant	0.891	0.423	−0.083
Pyr	0.759	0.611	−0.073
Flu	−0.022	0.961	0.187
BaA	0.965	0.208	−0.130
Chr	0.967	0.221	−0.104
BbF	0.986	0.052	0.081
BkF	0.105	0.907	−0.027
BaP	0.989	0.093	−0.018
InP	−0.130	0.243	0.803
DBA	−0.059	0.177	0.513
BgP	0.963	0.092	−0.184

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From the pollution sources analysis of the PAHs in the surface sediments of Yangzhuang coal mining subsidence waters, the first major sources is combustion which contributes about 70.28%. The second is petroleum source which contributes about 18.7%. It is the environment of the research area and the primary energy mix of the surrounding places of current waters. There are three possible explanations of the combustion sources: firstly, before subsidence, most of the lands in coal mining subsidence area are farmlands and villages. The main crops are rice, corn and etc. According to the survey, farmers used to burn the straws. The living fuel that are in most frequent use is the coal that hasn't been essentially processed. The incomplete combustion causes certain influence on the surrounding environment. Secondly, the surrounding places of subsidence area are rich in coal resource. Massive coal mining and thermal power stations take great use of coal, bringing heavy-duty vehicles exhaust, coal ash and smoke produced by power stations and especially the incomplete combustion of some raw coal, which carries large amounts of organic pollutants. But meantime, the possibilities of natural volatilization and leaks cannot be excluded. Thirdly, there is a gangue dump in the near place of the coal mining subsidence area. Long-time stockpiling and rain wash can move the PAHs to aquatic environment, causing non-point source pollution. It's been proved that areas with massive mining of coal have higher content of PAHs than other areas. The possible explanations of petroleum sources mainly come from the leaks and atmospheric transport. Before subsidence, the abandoned transformers in the villages were dismantled. During the process of digging raw coal, the leaks of emulsion and lubricating oil of the equipments cause the loss of oils which are left over in the soil (the sediments) with the subsidence of rainwater and atmosphere. In the second place, LMW PAHs with quite strong volatilization float some distance and subside into the aquatic environment with atmosphere flow. Except the above two major contribution sources, the aquatic environment near the mining area is easy to be directly affected by the raise dust, the subsidence of raw coal dust and the rain wash during the process of coal mining, stockpiling and processing. Moreover, the influx of the Mud River brings lots of industrial wastewater, mining well water and sanitary sewage into the subsidence waters. Mining well water mainly comes from the water burst of working face and the water use of equipment operation. There is no sewage collection network inside the village. From all mentioned above, all these transport certain amount of PAHs into the surface sediments of Yangzhuang coal mining subsidence waters.

3.3 Ecological risk assessment

Table 4 lists the contents of PAHs in the sediments of waters through out the world.^28–36 By comparison, the pollution level of the PAHs in the research area is relatively low. Currently, the research area is developed as the separate fishing areas for farmer. Fries are put into the waters every year. According to the research, low rings PAHs have acute toxicity, and high rings PAHs have carcinogenicity. They can influence the enzyme system and immune system inside the biological bodies. The acute toxicity can affect the majority of the aquatic organism. Pollution are easy to accumulate through food chain and further influence human's physical health and the whole ecological system. Thus, the ecological risk assessment on the PAHs in the surface sediments of subsidence waters is significant. Ecological risk assessment means to assess the possibilities of harmful ecological results as the ecological system is influenced by one or more stress factors. It is an effective means to quantitatively study the ecological harm of pollutants. Now, many countries have conducted large-scale ecological risk assessment and also set a barrage of ecological risk assessment standards concerning the PAHs in the sediments. Long figures out the effects range low (ERL) of risk assessment of the pollutants in sediments.^37–39 It means the harmful effect probability of organisms is less than 10%. Effects range median (ERM) means the harmful effect probability of organisms is bigger than 50%. If the concentration of PAH congeners in the subsidence waters is lower than ERL, there will be low potential risks, which means the toxic and side effect to aquatic organisms is not apparent. If the concentration of PAHs congener is higher than ERM, there will be potential risks, causing toxic and side effect to aquatic organisms. Also, negative ecological effects will appear. If it is just in between the ERL and ERM, potential ecological risks may exist, and the negative ecological effects may sometimes appear.^40–42 The above benchmarks are applied in assessing the possible ecological effects of the PAHs in the surface sediments of subsidence waters. Generally, the PAH congeners in the surface sediments of Yangzhaung subsidence area are all lower than ERM, posing no severe ecological risks. Therefore, what needs to be considered is whether they are beyond ERL. The excess coefficient K value should be adopted to further assess the ecological risks of the PAHs in the surface sediments.⁴³ The ratio of the concentration of PAH congeners in the surface sediments (Environmental Exposure Concentration) and the ERL is the K value. The results can be seen in Table 5. Here is the empirical synthetical risk degree of K values: K < 0.1, no potential risks; 0.1 ≤ K < 3, low potential risks; 3 ≤ K < 7, middle-level potential risks; 7 ≤ K ≤ 10, high potential risks; K > 10, risks possibly exist. The excess coefficient result indicates that the 16 kinds of PAHs in Yangzhuang coal mining subsidence waters in Panyi Mine have low potential risks, and most have no potential risks.

Table 4 Comparison of PAHs concentrations in this research area and other water areas (ng g⁻¹)

Location	Range	Mean	References
Bohai Sea, China	34–202	92	28
Daliao River Estuary, China	272–1607	743	29
Coastal Marine Areas, Hong Kong	123–947	450	30
Mediterranean Sea, Egypt	30–358	152	31
Lake Maryut, Egypt	106–57 800	6950	32
Hugli Estuary, India	25–1081	271	33
Marine Protected Areas, Italia	0.7–1550	155	34
Gironde Estuary, France	19–4888	1400	35
Casco Bay, USA	16–20 748	2900	36
Yangzhuang Subsidence Area, China	55–755	288	This study

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Table 5 Ecological risk assessment of PAHs in the surface sediments of research area

PAHs	ERL	ERM	PAHs
			Concentration/ng g^−1	K value
NaP	160	2100	32.1	0.20
Acy	44	640	6.2	0.14
Ace	16	500	6.3	0.39
Fl	19	540	26.1	1.37
Phe	240	1500	71.5	0.30
Ant	85.3	1100	6.7	0.08
Pyr	665	2600	39.1	0.06
Flu	600	5100	36.1	0.06
BaA	261	1600	17.5	0.07
Chr	384	2800	16.7	0.04
BbF	320	1880	4.0	0.01
BkF	280	1620	2.2	0.01
BaP	430	1600	5.5	0.01
InP	—	—	2.6	—
DBA	63.4	260	2.6	0.04
BgP	430	1600	12.7	0.03

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4. Conclusions

The analytical results of the PAHs in the surface sediments of Yangzhuang coal mining subsidence waters indicate that the 16 kinds of PAHs that are in priority control of the USEPA have all been detected with a detection rate of 100%. The content level of total PAHs is in the range of 55 to 755 ng g⁻¹ with an average content of 288 ng g⁻¹. The concentration of PAHs in the bed of the Mud River is apparently higher than that in other sampling points, reflecting that external human activities can influence the distribution of the PAHs in the sediments. Referring to the PAH congeners, Phe has the highest content, holding 25% of the total. Pyr, Flu and Nap respectively account for 14%, 13% and 11%. The PAHs in the surface sediments are mainly low rings of PAHs, such as four and three ring PAHs, accounting for 60–80%. Two rings aromatic hydrocarbons take the second place. The PAHs pollution in the surface sediments mainly comes from combustion source and oil source. The former one contributes 70.28%. The latter one comes second, contributing 18.7%. The reasons of these sources are the combustion of local coal and crop straw and the leaks of substances like industrial oil. Compared with the sediments in other waters, the pollution level of PAHs is quite low. Based on the excess coefficient, the 16 kinds of PAHs in the coal mining subsidence waters have low potential risks, most of which have no potential risks. Thus, the subsidence waters there are suitable for aquaculture.

Acknowledgements

This research is funded by the Natural Science Foundation of Anhui Province (No. 1508085MD68) and the National Key Technology Support Program (No. 2012BAC10B02). The authors are grateful for the support.

References

1. M. Tobiszewski and J. Namieśnik, Environ. Pollut., 2012, 162, 110–119.
2. X. Gu, S. Tian, Q. Zhou, J. Adkins, Z. Gu, X. Li and J. Zheng, RSC Adv., 2013, 3, 25989–25996.
3. W. H. Li, Y. Z. Tian, G. L. Shi, C. S. Guo, X. Li and Y. C. Feng, Ecotoxicol. Environ. Saf., 2012, 75, 198–206.
4. G. L. Shi, Y. Z. Tian, C. S. Guo, Y. C. Feng, J. Xu and Y. Zhang, J. Soils Sediments, 2012, 12, 1154–1163.
5. Y. Zhang, C. S. Guo, J. Xu, Y. Z. Tian, G. L. Shi and Y. C. Feng, Water Res., 2012, 46, 3065–3073.
6. T. Meyer, Y. D. Lei and F. Wania, Water Res., 2011, 45, 1147–1156.
7. Y. Hu, G. Li, M. Yan, C. Ping and J. Ren, Sci. Total Environ., 2014, 473, 459–464.
8. L. Y. Liu, J. Z. Wang, G. L. Wei, Y. F. Guan, C. S. Wong and E. Y. Zeng, Environ. Sci. Technol., 2012, 46, 6497–6504.
9. I. D. Manariotis, H. K. Karapanagioti and C. V. Chrysikopoulos, Water Res., 2011, 45, 2587–2594.
10. Z. Wang, M. Liu and Y. Yang, Environ. Earth Sci., 2015, 73, 2453–2462.
11. H. S. Wang, C. Zhang, P. Liang, D. D. Shao, Y. Kang, S. C. Wu, C. K. Wong and M. H. Wong, Ecotoxicol. Environ. Saf., 2010, 73, 900–906.
12. V. Piazza, A. Ferioli, E. Giacco, N. Melchiorre, A. Valenti, F. Del Prete, F. Biandolino, L. Dentone, P. Frisenda and M. Faimali, Ecotoxicol. Environ. Saf., 2012, 79, 134–138.
13. S. Cui, Q. Fu, T. Li, Y. Li, Y. Liu, Z. Lv and P. Li, Environ. Sci. Pollut. Res., 2016, 29, 509–515.
14. W. J. Hong, H. Jia, Y. F. Li, Y. Sun, X. Liu and L. Wang, Ecotoxicol. Environ. Saf., 2016, 128, 11–20.
15. Z. Wang, Z. Liu, Y. Yang, T. Li and M. Liu, Chemosphere, 2012, 89, 221–227.
16. J. Ribeiro, T. Silva, J. G. Mendonca Filho and D. Flores, J. Hazard. Mater., 2012, 199, 105–110.
17. E. H. Denis, J. L. Toney, R. Tarozo, R. S. Anderson, L. D. Roach and Y. Huang, Org. Geochem., 2012, 45, 7–17.
18. C. Geng, J. Chen, X. Yang, L. Ren, B. Yin, X. Liu and Z. Bai, J. Environ. Sci., 2014, 26, 160–166.
19. A. Malik, P. Verma, A. K. Singh and K. P. Singh, Environ. Monit. Assess., 2011, 172, 529–545.
20. R. K. Larsen and J. E. Baker, Environ. Sci. Technol., 2003, 37, 1873–1881.
21. A. El Nemr, M. M. El-Sadaawy, A. Khaled and A. El-Sikaily, Desalin. Water Treat., 2014, 52, 7964–7982.
22. A. Kamal, R. N. Malik, T. Martellini and A. Cincinelli, Environ. Sci. Pollut. Res., 2015, 22, 12644–12654.
23. S. Reynaud and P. Deschaux, Aquat. Toxicol., 2006, 77, 229–238.
24. N. L. Devi, I. C. Yadav, Q. Shihua, Y. Dan, G. Zhang and P. Raha, Chemosphere, 2016, 144, 493–502.
25. N. Hu, P. Huang, J. Liu, D. Ma, X. Shi and J. Mao, Environ. Earth Sci., 2014, 71, 873–883.
26. M. Basto and J. M. Pereira, J. Stat. Software, 2012, 46, 1–29.
27. M. G. Mago, L. Valles and J. J. Olaya, Ingeniare. Revista chilena de ingeniería, 2012, 32, 40–45.
28. L. Hu, Z. Guo, X. Shi, Y. Qin, K. Lei and G. Zhang, Org. Geochem., 2011, 42, 1181–1193.
29. B. Men, M. He, L. Tan, C. Lin and X. Quan, Mar. Pollut. Bull., 2009, 58, 818–826.
30. H. S. Wang, P. Liang, Y. Kang, D. D. Shao, G. J. Zheng, S. C. Wu, C. K. C. Wong and M. H. Wong, Environ. Pollut., 2010, 158, 3298–3308.
31. K. Z. E. Deeb, T. O. Said, M. H. E. Naggar and M. A. Shreadah, Bull. Environ. Contam. Toxicol., 2007, 78, 373–379.
32. A. O. Barakat, A. Mostafa, T. L. Wade, S. T. Sweet and N. B. E. Sayed, Water, Air, Soil Pollut., 2011, 218, 63–80.
33. L. Guzzella, C. Roscioli, L. Viganò, M. Saha, S. K. Sarkar and A. Bhattacharya, Environ. Int., 2005, 31, 523–534.
34. G. Perra, K. Pozo, C. Guerranti, D. Lazzeri, V. Volpi, S. Corsolini and S. Focardi, Mar. Pollut. Bull., 2011, 62, 874–877.
35. H. Budzinski, I. Jones, J. Bellocq, C. Piérard and P. Garrigues, Mar. Chem., 1997, 58, 85–97.
36. M. C. Kennicutt, T. L. Wade, B. J. Presley, A. G. Requejo, J. M. Brooks and G. J. Denoux, Environ. Sci. Technol., 1994, 28, 1–15.
37. E. R. Long, D. D. Macdonald, S. L. Smith and F. D. Calder, Environ. Manage., 1995, 19, 81–97.
38. Z. Liu, L. He, Y. Lu, J. Su, H. Song, X. Zeng and Z. Yu, Environ. Monit. Assess., 2015, 187, 1–10.
39. G. B. Yu, Y. Liu, S. Yu, S. C. Wu, A. O. W. Leung, X. S. Luo, B. Xu, H. B. Li and M. H. Wong, Chemosphere, 2011, 85, 1080–1087.
40. A. Liu, Y. Lang, L. Xue and J. Liu, Environ. Monit. Assess., 2009, 159, 429–436.
41. A. O. Barakat, A. Mostafa, T. L. Wade, S. T. Sweet and N. B. E. Sayed, Mar. Pollut. Bull., 2011, 62, 1969–1978.
42. K. Pozo, G. Perra, V. Menchi, R. Urrutia, O. Parra, A. Rudolph and S. Focardi, Mar. Pollut. Bull., 2011, 62, 1572–1576.
43. Z. T. Liu, Y. Z. Wang, Z. Y. Pang, J. L. Yang, J. L. Zhou and Y. H. Zhang, Environ. Sci. Pollut. Res., 2009, 22, 768–772.

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