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Article

Insights on the Abiotic/Biotic Interactive Impacts on the Occurrence of PFASs in Municipal Solid Waste Landfill Leachate

by
Jia Li
1,†,
Rongchuan Ye
2,†,
Ganghui Zhu
1,
Shuhe Chen
3,
Wenbing Tan
3,* and
Weijiang Liu
1,*
1
Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
2
School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
3
State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
*
Authors to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Water 2024, 16(23), 3436; https://doi.org/10.3390/w16233436
Submission received: 1 November 2024 / Revised: 23 November 2024 / Accepted: 26 November 2024 / Published: 29 November 2024
(This article belongs to the Section Wastewater Treatment and Reuse)
Browse Figures
Review Reports Versions Notes

Abstract

:
Per- and polyfluoroalkyl substances (PFASs) presented in landfill leachate can be transported to groundwater and soil, posing risks to human health in food and water. However, the occurrence characteristics of PFASs in landfill leachate and the influence paths of abiotic and biotic factors have not been fully discussed. Our study found that the detection rate of PFASs in the leachate samples was 100%; ΣPFASs ranged from 1328 ng/L to 37,447 ng/L; and the average ΣPFASs was 9593 ng/L. Most of the physical and chemical indexes in the landfill leachate showed a significant positive correlation with PFASs concentration, with the main physical and chemical factors including TOC, sodium ion, chromium ion, and chloride ion. Moreover, dissolved organic matter had a very important influence on the environmental behavior of PFASs. The degree of dissolved organic matter (DOM) humification promoted the transportation of PFBA and PFBS from the landfill waste to leachate, while microbial DOM inhibited the transportation of ∑13 PFCAs. The microorganisms in the leachate were mainly Firmicutes, Campylobacterota and Proteobacteria, among which there was a negative correlation between PFBS and Proteobacteria, indicating that PFBS was highly toxic to Proteobacteria and would inhibit the growth of Proteobacteria in leachate. Firmicutes and Campylobacterota had little influence on PFASs. However, Synergistota, and Halanaerobiaeota, which had a low abundance, both positively correlated with the various PFASs. This result may imply that these rare microphyla are the main microphyla driving the transformation of PFASs in leachate. Microorganisms in leachate indirectly affected the occurrence of PFASs, mainly by influencing the environmental factors in leachate. Therefore, abiotic factors are important factors affecting PFASs in the landfill leachate. In summary, PFASs pollution management in landfills should be enhanced by regulating abiotic factors to control PFASs in leachate.

Graphical Abstract

1. Introduction

PFASs (per- and polyfluoroalkyl substances) refer to a class of organic fluorine compounds in which the hydrogen atoms on the aliphatic hydrocarbon carbon chain are completely or partially replaced by fluorine atoms [1]. Due to fluorine’s high electronegativity and small atomic radius, the C-F bond is thermodynamically and chemically stable, which results in the C-F chain portion of the PFASs molecule being resistant to degradation, including hydrolysis, photolysis, and biodegradation. Furthermore, PFASs are widely used in many fields, such as in industrial production and daily life [2,3]. The wide application and persistence of PFASs have made these ubiquitous in the natural environment and they have been widely detected in different environmental media such as surface water, groundwater, seawater, soil, sediments, and in the atmosphere [4,5,6]. Whilst thousands of PFASs exist commercially, PFOA (perfluorooctanoic acid) and PFOS (perfluorooctane sulfonate) are the most widely studied [7,8].
China’s total production of PFOS has increased rapidly as related industries have shifted from developed countries to Asian countries. It has been reported that China has become one of the largest countries producing PFOS and related chemicals [9]. Landfill sites known to receive industrial waste containing PFOS and PFOA have recorded PFOS concentrations up to 82,000 ng/L and PFOA concentrations up to 31,054 ng/L in municipal solid waste (MSW) leachate [10]. The release of PFASs from landfill waste to leachate may be affected by abiotic and biotic factors in leachate. As shown in existing studies [11], the DOM in the leachate will adsorb PFASs, promoting their release from landfill and their further transport to the environment with the leachate. DOM is a very important contaminant, which is the main part of the total organic matter in leachate [12]. Therefore, it is of great significance to study the path of influence of abiotic factors (especially DOM) on the occurrence of PFASs in leachate, to elucidate the influencing mechanism behind the occurrence characteristics of PFASs in leachate.
Because of its high organic matter content and high moisture content, landfill interior provides an ideal environment for the growth and development of microorganisms. Although PFASs are highly stable and not easily biodegradable, the microorganisms in leachate can significantly affect the physical and chemical properties of leachate, such as in the interaction between the microbial community and the pH of the leachate, as well as the groundwater polluted by the leachate, dissolved organic carbon, sulfate, and ammonia [13]. Our previous research results showed that the concentration of PFASs in leachate-contaminated groundwater was closely related to physical and chemical factors such as NH3-N, Na, Ca, and macromolecular humic acid in groundwater. Therefore, microorganisms in leachate may indirectly regulate the occurrence of PFASs in leachate, something which elucidates the path of influence of abiotic and biotic factors on the occurrence characteristics of PFASs in leachate.
In this study, we collected leachate samples from 16 MSW landfills in the Beijing–Tianjin–Hebei region of China and analyzed their physicochemical properties, DOM components and structure, PFASs concentrations and microbial communities’ abundance [14]. This process aimed to achieve the following goals: (1) to determine the occurrence characteristics of PFASs in the leachate of landfill sites with different landfill ages and specifications; (2) to reveal the influence path of non-biological factors on the occurrence of PFASs in leachate; (3) to reveal the influence of biological factors in leachate on the occurrence of PFASs. This study improved our understanding of the environmental behavior of PFASs in landfill leachate, providing a new scientific basis and ideas for strengthening the prevention and control of PFASs in landfills.

2. Materials and Methods

2.1. Sample Collection

Leachate samples were collected from 16 landfills in the Beijing–Tianjin–Hebei region of China (June 2023), namely Lixian County (LX), Xiongxian County (XX), Beijing Huanfeng (BJH), Dacheng (DChe), Beijing Dongnanzhao (BJD), Dachang (DCha), Bazhou (BZ), Laishui (LS), Xianghe (XH), Zhuozhou (ZZ), Sanhe (SH), Baoding Harmless (BDW), Tianjin Quantai (TJQ), Baoding Yue Fengkewei (BDY), Wenan County Runda (WAR) and Wenan County Jielv (WAJ). The basic information of the 16 landfills is presented in Table S1. Leachate samples were collected from the end of the landfill drain and stored at 4 °C in the laboratory for analytical testing purposes.

2.2. Determination of PFASs in Leachate

Sample pretreatment: a 500 mL sample was filtered with a 0.45 μm filter membrane, and an internal standard liquid was added. A solid-phase extraction column with styryl-divinylbenzene copolymer as the filling material, or an extraction column with the same column efficiency, was installed on the solid-phase extraction device, and 4 mL 0.1% ammonia/methanol was used. Ultra-pure water and methanol were added (4 mL, respectively). Samples injected to the inner target are passed through the SPE column at a flow rate of approximately 1 drop/sec. The column was washed by ammonium acetate buffer (4 mL, 25 mmol/L). Subsequently, the column was dried via freeze drying, and washed by methanol and ammonia/methanol successively. The eluent was collected by a centrifuge tube. Eluent was concentrated to near-dryness under high-purity nitrogen using a nitrogen evaporator, reconstituted with 1 mL of initial mobile phase, filtered through a 0.22 μm membrane, and stored in a 1.5 mL amber vial at 4 °C until LC-MS/MS analysis [15].
Sample analysis: The target PFASs was isolated and quantitatively analyzed by Waters Xevo TQ micro-LC-MS/MS [16]. An ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 µm, WATERS, Milford, MA, USA) was used for chromatographic separation at 30 °C. The mobile phase consisted of 5 mM ammonium acetate (A) and methanol (B), delivered at a flow rate of 0.4 mL/min. Initially, 30% methanol was maintained for 240 s, then the methanol phase soared to 90% for 180 s, and dropped to 30% after 6 s. The whole separation was processed for 16 min. Following the detection of PFASs, concentrations were achieved by multiple-reaction monitoring (MRM) mode using a negative-ion electrospray ionization source (-ESI) mode [17]. Spray pressure (50 psi (N2)), auxiliary gas (10 L/min (N2)), atomization temperature (300 °C), capillary voltage (4000 V), and ion source voltage (180 V) were set. Firstly, an appropriate amount was taken of PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA, PFTrDA, PFTeDA, PFHxDA, PFODA, PFBS, PFPeS, PFHxS, PFHpS, PFOS, PFNS, PFDS, Cl-PFNS, ADONA and 22 other PFASs standard solutions. Secondly, a standard solution with concentration gradients was prepared by diluting it with methanol. Thirdly, the standard curve was established by adding the internal standard solution. Lastly, the quantitative analysis of 22 PFASs was carried out [18].
Quality control: Every PFASs sample was extracted and stored in accordance with the quality control demands of the General PFASs Sampling Guidance in the United States [19]. To avoid cross-contamination, it is prohibited to use low-density polyethylene bottles, teflon tubes, fluorinated ethylene propylene laboratory utensils, and to wear waterproof, oil, stain, insect, or UV-resistant clothes for the collection and analysis of PFASs samples. Field and equipment gaps are used to support the presence of cross-infection.
Determination of PFASs concentration: the appropriate amount of 22 standard solution for perfluorinated compounds diluted with methanol was removed. The internal standard solution was added to establish the standard curve, and 22 kinds of PFASs were quantitatively analyzed.

2.3. DOM Spectroscopic Analysis

The DOM fluorescence spectrum of leachate was measured by Hitachi F-7000 fluorophotometer (HITACHI, Tokyo, Japan). The excitation light source used was a 150w Xenon arc lamp (HAMAMATSU, Shizuoka, Japan), the photomultiplier voltage was 700 V, the scanning speed was 12,000 nm·min−1, and the slit width of both the excitation monochromator and emission monochromator was 10 nm. The Ex range is 200–450 nm, with a gap of 5 nm. The Em range is 280–550 nm, with a gap of 5 nm. While Ex = 310 nm, intensities at Em = 380 nm and Em = 430 nm were measured. The ratio was recorded as BIX, indicating the ratio of the terrestrial source and microbial source [20]. While Ex = 370 nm, intensities were measured at Em = 470 nm and Em = 520 nm. The ratio was denoted as FI. If FI was less than, or equal to, 1.4, DOM was a terrestrial DOM; if FI was greater than, or equal to, 1.8, the DOM was microbial DOM. While Ex = 310 nm, the intensity was measured at Em = 380 nm, and the strongest intensity appears in Em = 420–435 nm. The ratio of two intensities was β/α, and the higher the value, the higher the freshness of DOM. While Ex = 254 nm, peak areas between Em = 435–480 nm and Em = 300–345 nm were calculated. The ratio of two areas was named HIX, and the higher the value, the higher the degree of DOM humification [21,22].
The structure and composition of DOM was determined by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS, SolariX 15T, Bruker, Karlsruhe, Germany) [23,24]. According to the TOC content of the sample, an appropriate volume of the sample was taken and passed through a 0.22 μm filter membrane to remove impurities such as particles, whilst adding formic acid drop by drop until the pH of the water sample was 2. The DOM in the leachate sample was then extracted in solid phase (extraction column type was Agilent Bond Elut PPL 1.0 g, 6 mL, Agilent Technologies Co., Ltd., Santa Clara, CA, USA). The activated column was washed with 12 mL methanol (chromatographic pure) and 12 mL 0.01 mol /L hydrochloric acid. A certain number of samples were slowly added to the PPL column, and the target compounds were enriched through the solid-phase extraction column at a flow rate of 5 mL/min. An 18 mL 0.01 mol /L hydrochloric acid was then added to wash the column, thereby removing the salt. After leaching, the column was dried by nitrogen blowing, eluted with 12 mL methanol, and the eluent collected. Finally, the nitrogen eluent was concentrated into a 1 mL methanol solution and tested on the machine [25].
In order to visualize ESIFT-ICR-MS data, a Van Krevelen diagram was drawn up and the Modified Aromaticity Index of the organic molecular formula (AImod), oxygen–carbon ratio (O/C), and hydrogen–carbon ratio (H/C) were used to divide the Van Krevelen criteria. The Van Krevelen diagram can be divided into five regions, namely Aliphatic (Ali., 2.0 ≥ H/C ≥ 1.5), Low O—highly unsaturated and phenolic compounds (Low O unsaturated., 0.5 ≥ AImod, H/C < 1.5, O/C < 0.5), High O—highly unsaturated and phenolic compounds (High O unsaturated, 0.5 ≥ AImod, H/C < 1.5, O/C ≥ 0.5), polyphenols (PolyPh., 0.66 ≥ AImod > 0.5), and polycyclic aromatics (PolyAro., AImod > 0.66) [26].

2.4. Physicochemical Factor Analysis

The pH, EC, and ORP of the collected leachate samples were measured using portable water quality instruments from “Yellow Spring Instruments” (YSI, Yellow Springs, OH, USA). TOC concentration in the leachate samples was determined by a total organic carbon (TOC) analyzer. NH4+-N was determined by ammonia nitrogen analyzer. Total nitrogen (TN) and total phosphorus (TP) were determined by alkaline potassium persulfate digestion ultraviolet spectrophotometry [27] and potassium persulfate digestion ammonium molybdate spectrophotometry [28]. Chemical oxygen demand (COD) was determined by dichromate titration [29]. BOD5 was determined by dilution inoculation method [30]. Ion chromatography (Dionex model ICS-2000, Thermo Scientific, Waltham, MA, USA) was undertaken for the determination of anions (Cl−, SO42−). The contents of sodium, potassium, calcium, magnesium, lead, manganese, chromium, and zinc were determined by ICP-MS (iCAP™ MTX, Thermo Scientific, MA, USA).

2.5. Determination of Microbial Diversity in Leachate

Here, 16S rRNA gene sequencing was performed by Shanghai Majorbio Biomedicine Technology Co., Ltd. (Shanghai, China) on the Illumina MiSeq platform (Novogene, Beijing, China). The original data series was spliced with FLASH and analyzed with QIIME (https://qiime2.org). Sequences with a similarity ≥97% were assigned to the same OTU. The representative sequences of each OTU were filtered for further annotation. Based on RDP Classifier version 2.13, the classification information was annotated using the Greengene database version 13_5 (threshold 0.7). More details are provided in Text S1.

2.6. Statistical Analysis

Three-dimensional fluorescence deRaman scattering and PARAFAC analysis were processed in Matlab (2018a) by “emoveScatter” and “DOMfluor”. SPSS 23.0 was used for correlation analysis. At the p < 0.05 level, the difference was considered statistically significant. The alpha diversity index (Sobs, Shannon, Simpson, Chao1 and Ace) was analyzed using the Majorbio cloud platform (www.majorbio.com). The structural equation model was analyzed by AMOS 23.0 [31].

3. Results and Discussion

3.1. Leachate Composition Difference in Physical and Chemical Properties

The results of physical and chemical parameters of the leachate are shown in Table S2. The results showed that the properties of the leachate differ greatly; pH ranges from 6.9 to 9.2. The leachate of the landfill site with the lowest pH level is the BDY landfill, which has a landfill age of 2 years, while the landfill site with a pH of 9.2 is the ZZ landfill, which has a landfill age of 14 years. The concentrations of NH3-N and TN were 74~31.8 × 102 mg/L and 118~34.5 × 102 mg/L, respectively, and the concentrations of Cl− and SO42− were 745~10.9 × 103 mg/L and 78.57~62.5 × 103 mg/L, respectively. The concentrations of TOC, COD, and BOD5 were 38~10.1 × 103 mg/L, 19.8~28.8 × 103 mg/L, and 6.9~99.5 × 102 mg/L, respectively, indicating that the leachate produced by landfill sites with different landfill ages showed significant differences in inorganic salts, organic matter, and the biodegradability of leachate. The concentration of metal ions in the leachate is shown in Table S3. The results showed that the metals in the leachate presented mainly K and Na, with the highest concentration of K reaching 18.8 × 102 mg/L and Na reaching 92.6 × 102 mg/L, while the concentrations of Pb, Zn, and Cr were relatively low. The highest concentrations of metal ions in all leachate were 0.035 mg/L (Pb), 0.99 mg/L (Zn), and 3.8 mg/L (Cr), respectively. The solubility of metal ions in leachate was affected by various environmental factors such as landfill heterogeneity, self-dissolution characteristics, the pH of leachate, and organic matter adsorption in landfill [32,33]. The concentration distribution of metal ions in leachate was very heterogeneous.

3.2. Occurrence Characteristics of PFASs in Leachate

The detected types and concentrations of PFASs in leachate are shown in Figure 1a,b. The results showed that at least one PFASs was detected in all leachate samples. A total of 13 PFASs were detected, including PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFBS, PFPeS, PFHxS, PFHpS, PFOS, and Cl-PFNS. It can be noted that the detection rate of short-chain PFASs was higher, and these were easier to enrich in the leachate, because short-chain PFASs had a higher water solubility and transport levels, and short-chain PFASs had a low molecular weight, weak hydrophobicity, and weak adsorption capacity with soil and other solid media [34], meaning they were more likely to transport from solid waste to the leachate. In addition, with the strengthening of the control of long-chain PFASs, the production and use of long-chain PFASs were significantly reduced, whilst the production and use of short-chain PFASs were increased. Moreover, long-chain PFASs might be degraded and converted into short-chain PFASs in the environment, which will further promote the enrichment of short-chain PFASs in leachate [35,36].
Leachate ΣPFASs ranged from 1328 to 37,447 ng/L, average ΣPFCAs was 3299 ng/L, average ΣPFSAs was 6292 ng/L, average ΣPFAAs was 9590 ng/L, and average ΣPFASs was 9593 ng/L. The concentration of leachate ΣPFASs in landfill was 37,447 ng/L (WAR), 19,713 ng/L (BJD), 14,293 ng/L (XX), 12,925 ng/L (TJQ), 11,827 ng/L (LX), 7979 ng/L (SH), 7875 ng/L (BDW), 7706 ng/L (WAJ), 6958 ng/L (Dcha), 5643 ng/L (BDY), 5483 ng/L (BZ), 4637 ng/L (BJH), 12,925 ng/L (XH), 4486 ng/L (DChe), 2216 ng/L (LS), and 1328 ng/L (ZZ), respectively. The values of ∑PFSAs, ∑PFAAs, and ∑PFASs in leachate of WAR landfill (landfill age of 10 years) were the highest. The values of ∑PFSAs, ∑PFAAs, and ∑PFASs in the leachate of ZZ landfill (the landfill age is 14 years) were the lowest. There was no significant relationship between the concentrations of ∑PFSAs, ∑PFAAs, and ∑PFASs in leachate and the landfill age. The main reasons may include the following: PFASs are extremely chemically stable and therefore they can persist in landfills for a length of time [34]; PFASs in landfills come from a variety of sources, such as industrial waste, consumer goods, and sludge from sewage treatment plants [34]; environmental conditions such as pH, temperature, and moisture content in landfills also vary greatly, which can affect the transportation of PFASs from solid waste to leachate [36]. Therefore, there is no obvious relationship between the concentrations of ∑PFSAs, ∑PFAAs, and ∑PFASs in the leachate and the landfill age under the combined action of multiple factors such as the diverse nature, complex source, and many influencing factors of transport and diffusion of PFASs [37].
The concentrations of different kinds of PFASs were analyzed, and the results are shown in Figure 1c. We determined that, although 13 kinds of PFCAs and 7 kinds of PFSAs were tested, the ∑PFCAs were less than the ∑PFSAs in some landfill sites, mainly because the concentration of PFBS in leachate samples was too high. According to the carbon chain length, the PFASs concentrations of C4~C8 were much larger than those of the C8 chain length (Figure 1d). This indicates that the PFASs detected were mainly short chains, and the concentration of PFBS were the highest. Due to the limited use of long-chain PFAAs such as PFOS and PFOA, PFBA and PFPeA are currently used to replace long-chain PFAAs such as PFOA and PFOS in fluorine industry production. In addition, this is also related to the properties of short-chain PFASs themselves. This shows that in addition to PFOA, PFOS and other long-chain PFAAs utilized by international conventions as a substitute for long-chain PFAAs, the environmental risks of short-chain PFAAs cannot be ignored. In previous research, in some landfills, the oxidation of PFASs happens to appear due to some precursors. On the other hand, under anaerobic conditions, microbes can defluorinate PFASs via the Feammox reaction, leading to shorter chains and the mineralization of PFASs [38,39].

3.3. Composition and Molecular Characteristics of DOM in Leachate

The three-dimensional fluorescence spectra of DOM in landfill leachate are shown in Figure S1. The main fluorescence peaks observed were as follows, corresponding to 480/400 nm (BDY), 360/440 nm (BDW), 370/460 nm (BZ), and 360/440 nm (DChe) at Ex/Em wavelengths, respectively: 380/460 nm (DCha), 390/480 nm (DCha), 410/470 nm (BJD), 380/460 nm (BJH), 350/430 nm (LS), 450/525 nm (LX), 390/470 nm (WAR), 390/470 nm (SH), 385/470 nm (TJQ), 370/450 nm (WAJ), 360/440 nm (XH), 390/470 nm (XX), and 250 (320)/440 (400) (ZZ). The EEM spectra of the leachate of BDY, BJD, XX, TJQ, WAR, SH, DCha, and BZ showed an obvious fluorescence peak at Ex/Em = 375–400 nm/475 nm and the fluorescence intensity gradually increased from BDY to BZ. The EEM spectra of leachate of DChe, WAJ, XH, LS, and BDW showed obvious fluorescence peaks at Ex/Em = 350–375 nm/450 nm and the fluorescence intensity of XH, LS, and BDW was at the same level and higher than that of DChe and WAJ. LX, HF, and ZZ showed obvious fluorescence peaks at Ex/Em = 450 nm/520 nm, Ex/Em = 400–410 nm/450–500 nm, and Ex/Em= 240–260 (295–310, 355–365) nm/450–500 nm, respectively. According to the “EEM” spectral study of 45 chemicals by “Ma et al.” [40], the DOM associated with the fluorescence peak in Figure S1a may be benzopyranone, and the DOM associated with the fluorescence peak in Figure S1b may be 4-methylumbellae ketone. The DOM with triple excitation peak characteristic in Figure S1c may be dye-derived material [41], derived from waste clothing in landfill, and its molecular structure may be naphthalene with an electron donor group (-NH2 or -OH) at position 1 and 2.
The fluorescence index (FI) represented the source of DOM in nature (microbial source when >1.9, terrestrial source when <1.4), and was calculated by the fluorescence intensity ratio between 470 nm and 520 nm when the excitation wavelength was 370 nm [42]. The humification index (HIX) was positively correlated with DOM humification, and was calculated as two spectral regions in the emission spectrum under excitation of a 254 nm (H: 300–345 nm, L: 435–480 nm) area ratio [43]—the higher the HIX, the higher the degree of humification and stability of DOM. When HIX is greater than 6, DOM has strong humus characteristics. The Autogenous Index (BIX) was the contribution index of the recently generated local DOM, calculated by the ratio of fluorescence intensity at 380 nm and 430 nm when the excitation wavelength was 310 nm [44]. When the BIX was >1, the DOM autogenous characteristics were obvious, whilst when the BIX was <1, DOM autogenous characteristics were not obvious. In the β:α ratio of the freshness index (β:α), β represents the recently obtained DOM, which is defined as the emission intensity of 420 nm under a 310 nm excitation. α stands for the highly decomposed DOM, defined as the maximum emission intensity between 435 nm at 310 nm excitation. The beta:α value is a locally input index indicating the relative contribution of DOM recently produced by microorganisms [45]. Fluorescence parameters (FI, HIX, BIX, and β:α) were calculated to evaluate the source, humification, and difficulty/ease of degradation of DOM in landfill leachate (Figure S2). The results of the fluorescence index (FI) showed that only LX had an FI less than 1.4, indicating that LX leachate was mainly land-based DOM, whilst 12 of the 16 landfill sites in Beijing–Tianjin–Hebei presented an FI greater than 1.9, indicating that the leachate DOM of the landfill sites in the Beijing–Tianjin–Hebei region was mainly microbial DOM. The results of the humification index (HIX) showed that the HIX value of the ZZ leachate was the lowest (6.7), and that of WAR leachate was the highest (170.3). There were five landfills with an HIX over 100, namely BDY, BJD, WAR, SH, and XX. This means that the DOM of leachate from 16 landfill sites in Beijing, Tianjin, and Hebei showed strong humification characteristics, and the DOM of leachate of BDY, BJD, WAR, SH, and XX had the highest humification degree and the best stability values. The results of freshness index (β:α) were consistent with the trend of the fluorescence index, FI, which both proved that the DOM in leachate of landfill in the Beijing–Tianjin–Hebei region was dominated by newly generated microbial-source DOM [46,47,48].
Three-dimensional fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) can be used to explore the main fluorescence components of DOM [49]. A total of four major fluorescent components were identified in all leachate samples, as shown in Figure 2. The fluorescence component C1 contains one excitation peak and one emission peak, and the Ex/Em is 330/410 nm. The fluorescence component C2 contained two excitation peaks and one emission peak, and the Ex/Em was 260/445 nm and 370/445 nm. The fluorescence component C3 contained two excitation peaks and one emission peak, and the Ex/Em was 300/480 nm and 410/480 nm. The fluorescence component C4 contained one excitation peak and one emission peak, and the Ex/Em was 250/405 nm. According to previous studies, among the four components, C2 and C3 are collectively referred to as terrigenous humic acids, among which C1 is β-marine humic acid and C4 is fulvic acid [22]. To clarify the main components of DOM in each landfill leachate, the relative contents of four fluorescence components were further analyzed (Figure S3). Among them, the proportion of C3 terrestrial humic acids in BDY, BJD, BJH, LX, WAR, SH, TJQ, and XX were the highest (55–95%), being significantly higher than that in other landfills; the proportion of C2 terrestrial humic acids in BDW, BZ, DChe, DCha, WAJ, and XH were the highest (25–56%). In addition, C1 β-marine humic acid accounted for the highest proportion of 46% in LS landfill, while C4 accounted for the highest proportion of 80% in ZZ samples [50].
To further clarify the molecular information of DOM in the leachate, the DOM in all the leachate samples was further characterized by Fourier transform ion cyclotron resonance mass spectrometry. The leachate-source DOM mainly contained five types of compounds (Figure 3a) and four types of heteroatoms (Figure 3b), and the proportion of each compound in the leachate was similar [51]. CHO and CHON accounted for a high proportion (Figure 3b).
The molecular composition of the DOM was visually analyzed using the Van Krevelen diagram [52], where the corresponding points were associated with specific compounds (Figure 4a). The Van Krevelen diagram can be divided into five regions corresponding to the five classes of compounds found in the DOM, including Aliphatic (Ali.) (1.5 ≤ H/C ≤ 2), Low O—highly unsaturated and phenolic compounds (Low O.) (AImod ≤ 0.5, O/C < 0.5, H/C < 1.5), (High O–highly unsaturated and phenolic compounds (High O.), AImod ≤ 0.5, O/C ≥ 0.5, H/C < 1.5), polyphenols (PolyPh., 0.5 ≤ AImod < 0.66), polycyclic aromatics (PolyAro.), AImod ≥ 0.66 [51]. As can be seen from Figure 4b, the main components of DOM were fatty substances and low-unsaturated phenols, accounting for about 80% of the total. In BDY, BJD, BZ, DCha, and WAJ, fatty substances accounted for more than 50%, while in XH and ZZ, low-unsaturated phenols accounted for more than 50%. In summary, in the leachate of 16 landfill sites in the Beijing–Tianjin–Hebei region, DOM mainly consisted of CHO, CHON, and other simple molecules, which are primarily concentrated in the region with an H/C ≥ 1.0 and O/C ≤ 0.5 [53].

3.4. Species Composition and Function of Microbial Community in Leachate

The mesokaryotic microbial communities of 16 landfill leachates from different landfill ages in the Beijing–Tianjin–Hebei region were analyzed by 16S rRNA technology to fully establish the diversity of microbial communities in landfill leachate [54]. The dilution curve combined with the diversity index can be used to assess whether the sequencing results can adequately reflect the real microbial communities in the samples. Figure S4 shows the dilution curves of the Beijing–Tianjing–Hebei leachate samples with the Sobs index (Figure S4a) and Shannon index (Figure S4b), and the results showed that the Sobs index tends to be flat at the end of the curve. It was proved that the sequencing results could reflect the number of species in leachate samples to a certain extent, among which the number of species in BDY was the highest and the number of species in LS was the lowest. The dilution curves of the Shannon index were all very smooth, which proved that the quantity of sequencing data was reasonable and the results could well reflect the microbial diversity in leachate samples. Even if more samples were added, only a very small number of new species (OTUs) would be generated, among which BDY had the highest Shannon index. DChe has the lowest Shannon index.
The alpha diversity index can reflect the richness and diversity of microbial communities in leachate samples [55]. The results of the α diversity of microbial communities in leachate samples are shown in Table S4. The Chao1 index and Ace index can indicate the microbial richness of samples. The results of the Chao1 index showed that BDY had the highest Chao1 index (2086.28) and LS had the lowest Chao1 index (1010.02). Although BDY was the second highest in the Ace index, there was a small gap between BDY and BDW, which was the highest, which means that the Ace index results also indicated that the BDY sample had a high richness, while Ace results also indicate that the LS sample had a low microbial richness, consistent with the results of the previous dilution curve. The results showed that the microbial richness of BDY samples was the highest, while that of LS samples was the lowest. The Shannon index and Simpson index can indicate the microbial diversity of the sample. The higher the Shannon index, the higher the diversity of the sample, which means that the complexity and diversity of the microbial community in the sample were more complex and diverse; the higher the Simpson index, the lower the diversity of the sample. In the Shannon index in Table S4, the Shannon index of BDY was the highest (5.29), the DChe index was the lowest (3.95), and the Simpson index of BDY and DChe were also at a lower and higher level, respectively, indicating that the microbial community complexity and diversity of the BDY sample were the highest. The complexity and diversity of the DChe samples were the lowest, which proved consistent with the results of the dilution curve.
The microbial community composition of all leachate samples at the phylum level is shown in Figure 5. The results show that Firmicutes, Campylobacterota, and Proteobacteria are the three main bacteria in all leachate samples. Among them, the relative abundance of Firmicutes in all samples ranged from 5.11% to 69.57%, and the average abundance was 32.74%. Firmicutes were the main bacteria that decomposed cellulose and polysaccharide in leachate [56,57]. The relative abundance of Campylobacterota in all samples ranged from 0.01% to 51.93%, and the average abundance was 14.03%. Campylobacterota can participate in the cycle of sulfur in the leachate [58]. The relative abundance of Proteobacteria in all samples ranged from 0.61% to 43.52%, with an average abundance of 13.77%. Proteobacteria can use heavy metal elements in the leachate and decompose soluble sugars into short-chain fatty acids. Previous studies on leachate microorganisms in landfill have found that these play a key role in the transformation of substances in leachate, indicating that they have an important part in the stabilization process of leachate [59,60]. The relative abundance of Firmicutes in BDY, BZ, and WAJ was high, while the ages of three landfills are less than 10 years. The relative abundance of Firmicutes in BDW, DCha, and XH was low, amongst which, the landfill ages of BDW and XH are more than 10 years. The abundance of Firmicutes decreased significantly, and Firmicutes was the bacteria with the highest average relative abundance in all leachate. Therefore, landfill age significantly affected the microbial community structure in leachate mainly by impacting the abundance of Firmicutes.
To further study the microbial function in leachate, we continued to predict the metabolic function of microorganisms in leachate through the KEGG database, with the prediction results shown in Figure S5. The results showed that the global and overview maps contained the largest distribution of functional genes. Carbohydrate metabolism was the second most distributed functional gene, followed by amino acid metabolism. Carbohydrate metabolism and amino acid metabolism have such a high functional abundance, perhaps due to a predominance of organic substances in the leachate. This can provide sufficient nutrients to microorganisms and promote the carbon cycle.

3.5. Influence of Biotic/Abiotic Factors on the Occurrence of PFASs in Leachate

3.5.1. Influence of Abiotic Factors on the Occurrence of PFASs in Leachate

Most of the physical and chemical indexes in landfill leachate showed a significant positive correlation with PFASs concentration (Figure 6a), and the main physical and chemical factors included TOC, sodium ions, chromium ions, and chloride ions. It is worth noting that sodium, chromium, and chloride ion concentrations significantly affected the levels of various PFASs. Previous studies have shown the influence of ionic strength on PFASs adsorption varies across different media. For example, on the alumina/boehmite surface, the adsorption capacity of PFASs decreased with the increase in ionic strength (such as with NaCl, KCl, MgCl2, and CaCl2). On certain minerals (such as montmorillonite, goethite, kaolinite, hematite), sediments, and quartz sands, the adsorption of PFASs increased significantly with the addition of ionic strength (NaCl and CaCl2). In the media sampled in the landfill in this study, an increase in ionic strength may have led to the resolution and dissolution of PFASs, thereby increasing PFASs concentration levels.
The inter-group correlation between PFASs concentration and DOM is shown in Figure 6b. Among them, PFOA-Ali, PFNA-CHOS, CHONS, PFFDA-Chons, PFBA, and PFBS-HIX showed a significant positive correlation, indicating that fatty substances, DOM containing sulfur and nitrogen, and DOM with a high humification degree promoted the release of PFASs. PFBS-high-o, PFBS-Polyph, PFBA, PFHxA, PFBS-C1, PFBA, PFBS-C4, PFBS-SUVA254, PFHxA, PFBS, PFHpS, and PFOS-FI showed a significant negative correlation. These results indicated that the release of PFASs was inhibited by high levels of oxygen unsaturated phenols, polyphenols, β-marine humic acid (C1), fulvic acid (C4), the aromatization index, and the fluorescence index. It has been mentioned that some PFASs groups in landfill leachate are associated with waste constituents, age, the operational situation, and precipitation. Abiotic factors also play a key role in further leachate treatment discharge. In that case, the correlation analysis of physical–chemical parameters and PFASs is instructive [61,62].

3.5.2. The Effects of Biotic Factors on the Occurrence of PFASs in Leachate

Leachate contains a variety of PFASs, with these different types of PFASs also having significantly affected the structure, growth, and reproduction of the microbial community in the leachate, and even participating in the degradation and transformation process of different types of PFASs. In order to study the effects of different types of PFASs on the microbial community of leachate under the influence of landfill age, Spearman correlation coefficients for 13 PFASs and microbial communities were calculated and visualized as heat maps, and the results are shown in Figure 6c. The results showed that among the 13 PFASs, only PFDA (negative correlation) had a correlation with Firmicutes, Campylobacterota, and Proteobacteria among the top three bacteria with the highest relative abundance. And the concentration of PFDA in the leachate was very low. PFHpA and PFDA are related to Campylobacterota (both are positively correlated), but the concentration of these two fluorides in the leachate was also very low and therefore these were not the main fluorides. Proteobacteria was correlated with PFBS, PFHpS, and PFOS (all of which are negatively correlated), and the concentrations of PFHpS and PFOS in the leachate were also low.
PFBS was the main component of all PFASs in the samples, and the highest concentration of PFBS in the leachate of the landfill WAR was 32,874.62 ng/L, whilst the relative abundance of Proteobacteria in the leachate of the WAR landfill was 4.02%; the concentration of PFBS in the leachate of the landfill ZZ was the lowest (748.19 ng/L), and the relative abundance of Proteobacteria in the leachate of the ZZ landfill was 43.52%. These results indicate that PFBS displayed great toxicity to Proteobacteria and would inhibit the growth of Proteobacteria in the leachate. Interestingly, Synergistota and Halanaerobiaeota, which were relatively less abundant and were positively correlated with a variety of PFASs, were consistent with PFASs that were positively correlated [63].

3.5.3. The Path of Influence of Biotic/Abiotic Factors on the Occurrence of PFASs in Leachate

Correlation analysis was used to obtain the relevant indicators of the influence of abiotic and biotic factors on PFASs. To further explore their path of influence on PFASs and the causal relationship between variables, structural equation models (SEMs) were constructed for these indicators. According to the evaluation parameters of SEMs, the fit was good (χ2 = 3.3 (mean, same as below), P = 0.94, NFI = 0.95, GFI = 0.92, AIC = 32.00, RMSEA = 0.00; Figure 7). It can be seen from the path analysis results that HIX, FI, and C4 had direct effects on the occurrence of PFASs; however, the positive and negative effects and path coefficients on different PFASs differed. The path coefficients of HIX for PFBA and PFBS were relatively high, but the path coefficients for ∑13 PFCAs were low. On the contrary, FI has less influence on PFBA and PFBS, but more influence on ∑13 PFCAs. This indicates that fulvic acid content, DOM humification degree, and microbial-source DOM had a direct influence on the occurrence of PFASs. The humification degree of DOM promoted the transportation of PFBA and PFBS from the landfill waste to leachate, whilst microbial-source DOM inhibited the transport of ∑13 PFCAs. In addition, we found that NH3-N, TOC, EC, and Mn could change the structure and composition of DOM, and then affect the occurrence of PFASs in leachate. The increase in NH3-N in the leachate microenvironment had a positive effect on the change in EC, while EC had a negative effect on the structure and composition of DOM, thereby changing the occurrence of PFASs. At the same time, it was found that the structure and properties of organic carbon changed with electron shuttle, thus affecting the performance and distribution of PFASs [64]. To some extent, this supports our conclusion.
Some studies have found that the electron transport rate is positively correlated with the relative abundance of Synergistota [65]. In our analysis, we found that Synergistota can positively affect EC content, while EC changes had negative effects on FI and ∑13 PFCAs. This may be due to the changes in ions and esterases in the leachate caused by microbial-life activities [66], which may have inhibited the degradation of PFCA precursors. Synergistota can negatively affect FI, but FI had no significant effect on PFBS path. In addition, the changes in microbial community structure and physicochemical properties in the leachate had different effects on the structure and composition of DOM. However, it could be observed from the structural equation model that the influence of DOM changes on PFASs was generally consistent; that is, HIX had a positive effect on PFASs, while FI and C4 have a negative effect. This indicates that the microorganisms in leachate indirectly affected the occurrence of PFASs in leachate, mainly by influencing the DOM in leachate and other key environmental factors.

4. Conclusions

In this research, the physical and chemical properties, microbial composition, and occurrence characteristics of PFASs in leachate of different landfill ages in the Beijing–Tianjin–Hebei region were studied, and the path of influence of abiotic and biotic factors on the occurrence of PFASs in leachate was also studied. PFASs were found in all leachate samples, with concentrations ranging from 1328 to 37,447 ng/L. PFBS was the highest-concentration PFASs in leachate, and short-chain PFASs were more likely to transport from solid-phase waste to leachate and exist for a longer time than long-chain PFASs. The three bacteria with the highest relative abundance in the leachate were Firmicutes, Campylobacterota, and Proteobacteria, among which there was a negative correlation between PFBS and Proteobacteria, indicating that PFBS was highly toxic to Proteobacteria and would inhibit the growth of Proteobacteria in leachate. Firmicutes and Campylobacterota presented little influence on PFASs. Synergistota and Halanaerobiaeota, which were relatively less abundant, were positively correlated with a variety of PFASs, suggesting that rare phyla in the leachate might have been the main control phyla driving PFASs transformation in the leachate. PFASs in leachate were directly affected by a variety of abiotic factors. For example, the degree of DOM humification promoted the transport of PFBA and PFBS from landfill waste to leachate, while microbial DOM inhibited the concentration PFCAs. The microorganisms in the leachate indirectly affected the occurrence of PFASs in the leachate, mainly by affecting the environmental factors in the leachate. Therefore, abiotic factors were important factors affecting the leachate in landfill. In addition, the total oxidizable precursor (TOP) assay offers a means of bridging this gap by oxidizing unknown PFASs precursors and intermediates and converting these into stable PFASs with established analytical standards—essential for understanding PFASs in the environment and also an aspect needing further improvement in the future. In a word, this study worked on the occurrence characteristics and influencing mechanism of PFASs in landfill environments, exploring the transport mechanism of emerging contaminants in this complex environment. The research results provided certain scientific reference for the prevention and control of emerging contaminants.
Future research should focus on the migration behavior of PFASs in landfill leachate. Additionally, prevent and control technologies for PFASs contamination are required. The removal rate of PFASs in the existing leachate treatment processes (such as biological treatment, membrane treatment, etc.) can be as high as 99.8%, but there are still problems in the treatment process, such as precursor pollution transformation and the unknown environmental fate of by-products. Therefore, optimizing the existing treatment process to improve the removal efficiency of PFASs while reducing the risk of secondary pollution is an important direction of future research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16233436/s1, Table S1. Information of 16 landfill sites in studied area. Table S2. The physico-chemical characteristics of 16 landfill samples. Table S3. The concentrations of heavy metals and metalloids in 16 landfill samples. Table S4. List of α diversity indices of microbial communities in 16 landfill samples. Figure S1. Three-dimensional fluorescence spectra of DOM in 16 landfill samples are divided into group a, b, c. Figure S2. Fluorescence characteristics of DOM in 16 landfill samples: (a) the humification index (HIX), (b) the biological index (BIX), (c) the freshness index (β/α), and (d) the fluorescence index (FI). Figure S3. The fluorescence intensity values (Fmax) of the four PARAFAC components in 16 landfill samples. Figure S4. The OTU dilution curve for 16 landfill samples. Figure S5. The heatmap of functional genes in 16 landfill samples at the functional level of KEGG pathways for the biosynthesis of secondary metabolites. Text S1. Determination of microbial diversity in leachate (References [67,68,69,70,71] are cited in the Supplementary Materials).

Author Contributions

J.L.: writing of the original draft, writing—review and editing, methodology, investigation, formal analysis, and software. R.Y.: writing of the original draft, methodology, and investigation. G.Z.: formal analysis and visualization. S.C.: formal analysis, methodology. W.L.: methodology and visualization, writing—review and editing. W.T.: formal analysis, methodology, visualization, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 42030704).

Data Availability Statement

Data will be made available on request.

Conflicts of 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 manuscript.

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Water 16 03436 g001
Figure 1. Concentration distribution of PFASs in leachate: (a) is calculated according to PFASs functional groups; (b) indicates PFASs statistics by item; (c) features concentrations of different types of PFASs analyzed, (d) features concentration of PFASs according to length of carbon chain.
Figure 1. Concentration distribution of PFASs in leachate: (a) is calculated according to PFASs functional groups; (b) indicates PFASs statistics by item; (c) features concentrations of different types of PFASs analyzed, (d) features concentration of PFASs according to length of carbon chain.
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Figure 2. Fluorescence components identified by PARAFAC analysis of three-dimensional fluorescence spectra.
Figure 2. Fluorescence components identified by PARAFAC analysis of three-dimensional fluorescence spectra.
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Figure 3. The proportion of different components (a) and compound composition (b) in 16 landfill samples.
Figure 3. The proportion of different components (a) and compound composition (b) in 16 landfill samples.
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Figure 4. Molecular VK diagram of DOM in leachate: different compounds (a) and components (b).
Figure 4. Molecular VK diagram of DOM in leachate: different compounds (a) and components (b).
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Figure 5. Microbial species composition in leachate.
Figure 5. Microbial species composition in leachate.
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Figure 6. Spearman analysis matrices between PFASs and chemical factors (a), and DOM molecular (b) and microbial species (c), respectively. “*” indicates significant correlation (* p < 0.05, ** p < 0.01, *** p < 0.001).
Figure 6. Spearman analysis matrices between PFASs and chemical factors (a), and DOM molecular (b) and microbial species (c), respectively. “*” indicates significant correlation (* p < 0.05, ** p < 0.01, *** p < 0.001).
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Figure 7. Structural equation model fitting results: (a–c) are the effects of physical and chemical factors and the DOM of leachate samples on Σ13PFCAs, PFBA, and PFBS, respectively; (d–f) are the effects of leachate microorganisms on Σ13PFCAs, PFBA, and PFBS, respectively. The red and blue arrows represent positive and negative interactions, respectively. Continuous arrows and dotted arrows represent significant and non-significant relationships, respectively. The number adjacent to the arrow is the path coefficient, and the width of the arrow is proportional to the strength of the path coefficient; the R2 value is expressed as the proportion of variance explained for each variable; the final model was fitted by an χ2 test, quasi-fitting index (NFI > 0.90), goodness of fit index (GFI > 0.90), Akaechi information criterion (AIC), and approximate root mean square error (RMSEA < 0.10) tests. The significance levels are as follows: *** p < 0.001. According to the evaluation parameters of the structural equation model, the fit was good (χ2 = 3.3, P = 0.94, NFI = 0.95, GFI = 0.92, AIC = 32.00, RMSEA = 0.00).
Figure 7. Structural equation model fitting results: (a–c) are the effects of physical and chemical factors and the DOM of leachate samples on Σ13PFCAs, PFBA, and PFBS, respectively; (d–f) are the effects of leachate microorganisms on Σ13PFCAs, PFBA, and PFBS, respectively. The red and blue arrows represent positive and negative interactions, respectively. Continuous arrows and dotted arrows represent significant and non-significant relationships, respectively. The number adjacent to the arrow is the path coefficient, and the width of the arrow is proportional to the strength of the path coefficient; the R2 value is expressed as the proportion of variance explained for each variable; the final model was fitted by an χ2 test, quasi-fitting index (NFI > 0.90), goodness of fit index (GFI > 0.90), Akaechi information criterion (AIC), and approximate root mean square error (RMSEA < 0.10) tests. The significance levels are as follows: *** p < 0.001. According to the evaluation parameters of the structural equation model, the fit was good (χ2 = 3.3, P = 0.94, NFI = 0.95, GFI = 0.92, AIC = 32.00, RMSEA = 0.00).
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MDPI and ACS Style

Li, J.; Ye, R.; Zhu, G.; Chen, S.; Tan, W.; Liu, W. Insights on the Abiotic/Biotic Interactive Impacts on the Occurrence of PFASs in Municipal Solid Waste Landfill Leachate. Water 2024, 16, 3436. https://doi.org/10.3390/w16233436

AMA Style

Li J, Ye R, Zhu G, Chen S, Tan W, Liu W. Insights on the Abiotic/Biotic Interactive Impacts on the Occurrence of PFASs in Municipal Solid Waste Landfill Leachate. Water. 2024; 16(23):3436. https://doi.org/10.3390/w16233436

Chicago/Turabian Style

Li, Jia, Rongchuan Ye, Ganghui Zhu, Shuhe Chen, Wenbing Tan, and Weijiang Liu. 2024. "Insights on the Abiotic/Biotic Interactive Impacts on the Occurrence of PFASs in Municipal Solid Waste Landfill Leachate" Water 16, no. 23: 3436. https://doi.org/10.3390/w16233436

APA Style

Li, J., Ye, R., Zhu, G., Chen, S., Tan, W., & Liu, W. (2024). Insights on the Abiotic/Biotic Interactive Impacts on the Occurrence of PFASs in Municipal Solid Waste Landfill Leachate. Water, 16(23), 3436. https://doi.org/10.3390/w16233436

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