Organophosphate esters (OPEs) are a kind of emerging pollutants. Tree rings are long-lasting reservoirs of OPEs in the environment and can be analyzed to indicate OPEs contamination in the sampling region. Currently, research on OPEs in tree rings is still limited. In this study, tree rings of 5 arbor species from Sichuan Province, China, were sampled to study the occurrence and distribution of 6 OPEs, which were quantitatively analyzed by gas chromatography- mass spectrometry (GC-MS). The total concentrations of OPEs in all samples ranged from 189.79 (Fir species) to 341.23 ng/g (Toona sinensis), with average concentration of 284.77 ± 46.66 ng/g. The concentrations of OPEs in the 5 arbor species showed no significant difference. Tree rings of arbor species could be used as good passive samplers to show the occurrence and distribution of OPEs in the regional environment. Tris(2-butoxyethyl) phosphate (TBEP) was the dominant OPEs followed by tri(2-chloroethyl) phosphate (TCEP). Tris (2-ethylhexyl) phosphate (TEHP) and tri-n-butyl phosphate (TnBP) showed relatively stable concentrations in each arbor species, while the other 4 OPEs including TBEP, triphenyl phosphate (TPhP), tri(chloropropyl) phosphate (TCPP) and TCEP had significantly different concentrations. Interestingly, the absorption and accumulation of OPEs by tree rings of arbor species was quite different from that of inorganic elements reported by other studies.
Research Article
Distribution and pollution characteristics of organophosphate esters: reflected by tree rings of arbor species
https://doi.org/10.21203/rs.3.rs-2106473/v2
This work is licensed under a CC BY 4.0 License
Journal Publication
published 29 Dec, 2022
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Organophosphate esters
Emerging pollutants
Arbor
Tree rings
Pollution characteristics
Inter-annual variation
As the brominated flame retardants (BFRs) being gradually banned in many countries, Organophosphate esters (OPEs), a kind of emerging pollutants, have become the main substitute of BFRs because of its high flame retardant efficiency and economic availability. OPEs are widely used in different industries and consumer products such as textile, construction, electronic equipment, etc. (Pantelaki and Voutsa 2020; Wang et al. 2015). However, some OPEs have been proved to have reproductive toxicity, neurotoxicity, carcinogenicity and other health risks (Xu et al. 2018; Reemtsma et al. 2008). At present, OPEs have been detected in various environmental matrixes such as water (Ren et al. 2019; Yin et al. 2021), sediment (Castro-Jiménez et al. 2016), atmosphere (Yin et al. 2020), dust (Saito et al. 2007), soil (Wang et al. 2019), human urine (Schindler et al. 2009) and breast milk (Sundkvist et al. 2010), even in remote Arctic and Antarctic regions (Fu et al. 2021). In this paper, tri-n-butyl phosphate (TnBP), tris-(2-ethylhexyl) phosphate (TEHP), tris-(2-butoxyethyl) phosphate (TBEP), triphenyl phosphate (TPhP), tris-(2-chloroethyl)-phosphate (TCEP) and trichloropropyl phosphate (TCPP) were selected because they were common in literature reports (Bacaloni et al. 2008; Shi et al. 2016), and covered three classes of OPEs: alkyl OPEs (TnBP, TBEP, TEHP), chlorinated OPEs (TCEP and TCPP) and phenyl OPEs (TPhP). OPEs with different substituents have different physical and chemical properties (Table 1), so there are great differences in their bioaccumulation capacity (Wang et al. 2016; Chen et al. 2019).
Dendrochemistry is based on the fact that elements in the environment can be transported through plant tissues under the physiological activity of trees and accumulate in the xylem. Their existence is relatively stable, and no more obvious migration occurs. Therefore, the use of chemical element content in the annual ring can trace the history of environmental pollution and the migration characteristics of elements in the environment. Many studies have shown that tree rings can serve as a useful passive air sampler to monitor the long-term trends of pollutants such as inorganic pollutants, heavy metals and PAHs, and reveal the historical changes in the bioavailability level of polluting elements in the environment (Wang et al. 2021; Kuang et al. 2011). Contaminants can enter tree rings in many ways (Wen et al. 2004), and tree rings in different ages can truly reflect the environmental information in that year (Yang et al. 2011). Therefore, the study of tree rings can reproduce the high-resolution history of environmental pollution, and determine the source and spatial path of pollutants entering the environment as well (Scanlon et al. 2020). However, large number of studies have focused on heavy metals in tree rings (Xu 2005; He et al. 2021; Yin et al. 2011), while the research on trace organic pollutants, such as OPEs is almost blank.
As a commonly used passive sampler, arbor has the characteristics of wide distribution, easy availability, high resolution, large time span and strong continuity. Compared with shrub growth rings, arbor growth rings are generally wider with clear boundaries and easy to distinguish. When compared with other plants commonly used in biological monitoring (such as lichens and mosses), arbor growth rings have a clear temporal trend. Therefore, arbor was selected as the research object. Considered that OPEs only have anthropogenic sources and no natural sources. In addition, tree rings over 5 years can fully provide the information of the use and pollution history of OPEs in the region (Yin et al. 2011). Therefore, monitoring the pollution level and distribution characteristics of OPEs in tree rings is conducive to infer the pollution degree and profile of OPEs as well as provide basic data for the study of organic pollution in this area. It can take a good large-area sample and evaluate the pollution level of certain POPs in a large area.
In this study, 5 arbor species in a township far away from the point source discharge were selected and the objectives are to: (a) observe if the concentration level in tree rings among different arbor species has significant differences; (b) understand the correlation of the distribution and pollution characteristics of OPEs in 5 kinds of trees with the physicochemical properties of OPEs; and (c) evaluate whether arbor are suitable species which can be used to compare the use and pollution level of OPEs and other organics with similar physicochemical properties, especially emerging pollutants, between different regions, even if only in a crude way.
| classification | abbreviation | CAS | molecular weight | Molecular formula | logKOW | logKOA | BCF | T1/2 (in air: h) |
|---|---|---|---|---|---|---|---|---|
| Alkyl phosphates | TnBP | 126-73-8 | 266 | C12H27O4P | 4 | 9.21 | 1.03×103 | 3.26 |
| TBEP | 78-51-3 | 398 | C18H39O7P | 3.75 | - | 1.08×103 | - | |
| TEHP | 78-42-2 | 434 | C24H51O4P | 9.49 | 14.9 | 1.00×106 | 2.62 | |
| Phenyl phosphates | TPhP | 115-86-6 | 326 | C18H15O4P | 4.59 | 8.45 | 113 | 23.7 |
| Chlorinated phosphates | TCEP | 115-96-8 | 285.5 | C6H12Cl3O4P | 1.44 | 7.42 | 1.37 | 11.7 |
| TCPP | 13674-84-5 | 327.5 | C9H18Cl3O4P | 2.59 | - | 42.4 | - |
Sample collection
Liang'an Town is located in Ziyang City, Sichuan Province, China which is in a temperate region, 46 km away from the county seat and 110 km away from Chengdu. The trees here grow faster in spring and summer to form loose xylem (earlywood), and slower in autumn and winter to form dense xylem (latewood). The alternate distribution of loose and dense ring makes it easy to distinguish tree-rings. At the same time, Liang'an town is a typical traditional agricultural town dominated by agriculture, animal husbandry and aquaculture activities. There is no industrial facilities, so the impact of OPEs point source pollution could be neglected. Therefore, the forest farm in Liang'an town (30°30′28.35″N 104°56′34.43″E) was a good proxy for typical pollution in the area and was selected as the sampling site, and 5 common perennial arbor tree species: Toona sinensis, Cypress, Fir, Pterocarya stenoptera and Dongmu were selected as the research objects. They were in good growth condition, with similar tree ages. All of them were 8–10 years old, and the number of tree rings could meet the analysis needs of the latest eight years.
After the collected wooden piles being air-dried, the bark was removed and the rough surface of the tree disk was polished smoothly to distinguish the growth rings. By taking a complete circle of earlywood and latewood near the outermost layer as the first ring (the samples were collected in 2020, so the latest ring was formed in 2019). Every two growth rings from sapwood to heartwood were segmented for one study period, and the samples were numbered from 1 to 4 by year. More details of sampling were illustrated in Table 2.
Instruments and reagents
The main instruments included gas chromatography-mass spectrometry (Shimadzu, Japan, GC-MS 2010 plus) and vacuum concentrator (Buchi, Switzerland, R-215/v-700). The main reagents, such as ethyl acetate, acetone, hexane and acetonitrile, were high-performance liquid chromatography (HPLC) grade (Kelon Chemical Corp., China). The standard solution (Sigma Aldrich Corp., U.S.A.) included tri-n-butyl phosphate (TnBP), tris-(2-butoxyethyl) phosphate (TBEP), tris-(2-ethylhexyl) phosphate (TEHP), triphenyl phosphate (TPhP), tris-(2-chloroisopropyl) phosphate (TCPP) and tris-(2-chloroethyl) phosphate (TCEP). Copper, aluminium oxide, silica gel, Na2SO4 and other chemicals were purchased from Kelon Chemical Corp., China. The surrogate standard was triphenyl phosphate-d15 (TPhP-d15). Ultrapure water was obtained from Milli-Q equipment.
Sample pretreatment and clean up
In the process of sample preparation, the circular wooden pile was divided into fan-shaped areas with an electric saw. Considering the difference in growth speed between the north and south sides of the tree, a fan-shaped area of the same size was taken from the sparse surface (sunny surface) and dense surface (shady surface), respectively, so as to ensure the preciseness. The divided piles were mixed and crushed with an electric grinder and screened through 100 mesh, then the samples were wrapped in tin foil paper and labeled.
1.0000 g sample was accurately weighed and added into the test tube, soaked in 20 mL ethyl acetate: acetone (3:2 V/V) for 12 h, and then ultrasonic extracted for 30 min twice. The extraction solution was combined and centrifuged at 3000 r/min. The supernatant was transferred to a concentration bottle and concentrated to about 1 mL with a vacuum concentrator. 5–10 mL n-hexane was added for solvent conversion, and the supernatant continued concentrating to about 1 mL again. Then, it was separated and purified by silica gel/alumina/anhydrous sodium sulfate chromatography column activated with n-hexane. After 10 mL of n-hexane was added for removing the co-extracts, the target compounds were eluted with 20 mL of ethyl acetate: acetone (3:2 V/V), and the eluent was concentrated to 200 µL.
Instrumental analysis
The GC was equipped with a capillary column Rti-5MS (30 m × 0.25 mm× 0.25 µm), with a 280 °C inlet temperature using splitless injection. The MS source was electron impact (EI) and operated in selected ion monitoring (SIM) mode. Helium was used as a carrier gas with a flow rate of 1.00 mL/min. The GC oven temperature was held at 50 °C for 1 min, increased to 200 °C at 15 °C/min and held for 1 min, increased to 250 °C at 4.00 °C/min, and then increased to 260 °C at 20 °C/min and held for 4 min. The interface temperature was 280 °C, and the ion source temperature was 200 °C.
Quality assurance and Quality control (QA / QC)
The external standard method was used for quantification. The R2 of the standard curve of each OPEs component was greater than 0.99. Surrogate standard (TPhP-d15: 100 ng) was added to each sample to determine the recoveries, and the recoveries ranged from 64–83%. The limits of detection (LOD) (Signal-to-noise ratio = 3) ranged from 0.02 ng to 0.4 ng. The instrument precision ranged from 1.9–8.3%. Only TBEP was detected in the blank experiment, and its maximum concentration was lower than 5% of the sample concentration.
The pictures in this paper were drawn by Origin 2017. Statistical analysis was performed with SPSS Statistics version 25 for Windows. Correlations between OPEs monomers concentrations in 5 tree species were analyzed by Pearson correlation coefficients. Non-parametric test was used to analyze the difference between Σ6OPEs concentrations of 5 tree species. In order to explore the distribution differences of each monomer in 5 tree species, Friedman test was used for multiple comparison.
Concentration of OPEs in tree rings
As shown in Fig. 1 (see Table 2 for more sample number details), the detection rate of 6 OPEs in all samples was 100%, indicating that OPEs pollution was widespread in trees in the sampling area. High potential of long-range atmospheric transport and persistence of OPEs would be responsible for their presence in areas where there are few pollution sources (Wang et al. 2018). In addition, even though their reported half-lives were short at atmosphere, OPEs could bind with air particles, which greatly enhance their persistence in the environment (Wei et al. 2015). This can explain the high detected frequencies of each individual OPEs in present study. Σ6OPEs concentration in the tree-rings ranged from 189.79 to 341.23 ng/g, with an average of 284.77 ± 46.66 ng/g. The order of Σ6OPEs concentration in various arbor tree species was: Toona sinensis (320.64 ± 13.63 ng/g) > Cypress (301.46 ± 33.28 ng/g) > Pterocarya stenoptera (275.26 ± 48.53 ng/g) > Dongmu (274.12 ± 40.44 ng/g) > Fir (264.70 ± 50.82 ng/g). The concentration of OPEs in different arbor tree species in the sampling area was all in the hundreds of ng/g level, so the arbor trees have a good absorption of atmospheric OPEs, which can be used to evaluate the OPEs pollution in a region. Compared with OPEs concentrations in leaves reported by other studies, the pollution degree of Σ6OPEs in tree-rings in this study could not be ignored. For example, Santos et al. (2020) reported OPEs concentration in leaves of Bitter orange in Seville (TPhP: 47.5–265 ng/g, TCPP: 321–1058 ng/g) and Chen et al. (2019) reported that the concentration of Σ7OPEs in leaves of Magnolia and Loquat were 191.3 ng/g and 83.1 ng/g, respectively. Moreover, OPEs in different arbor tree species were at the same level, indicating that different arbor tree species had little effect on OPEs concentration. SPSS software was used to conduct significant difference analysis between Σ6OPEs concentration of 5 arbor species. The results showed that P = 0.433 > 0.05, indicating that there was no significant difference. Therefore, different types of arbors can reflect the pollution degree of local OPEs. In terms of the average concentration of Σ6OPEs over the last eight years, OPEs in the Toona sinensis had the highest Σ6OPEs concentration which was 1.2 times that of the Fir with the lowest concentration. This may be because stoma and lenticel are the main pathways for organic pollutants to enter the xylem. The larger the leaf size and the more the number of stomata and lenticels, the easier it is for organic matter to enter in the growth rings (Kuang et al. 2011).
Compared with other literatures, Xu (2005) studied the accumulation of 12 inorganic elements in 5 trees in Xiangshan, Beijing, and found that there were significant differences in the absorption and accumulation of inorganic elements among different tree species. Wu (2017) found that a single tree species had different absorption capacities for different heavy metal elements by comparing the concentration of 8 heavy metal elements in 5 tree-rings, and the ability of 5 trees absorbing the same heavy metal element was also different. Xue et al. (2016) measured the accumulation of Zn, Cu, Pb, Cd, Hg and As in the trunk of seven plantations in Harbin and found that there were differences in the comprehensive accumulation ability of different plantations to heavy metals. Therefore, the absorption and accumulation of arbors to inorganic elements was inconsistent with that of organic pollutants in this paper. Compared with the data reported by Odabasi et al. (2015) that the concentration of other POPs in tree rings in industrial areas (Σ16PAHs: 2526 ng/g, Σ41PCBs: 15.6 ng/g, Σ32PCNs: 2.35 ng/g, Σ7PBDEs: 16.65 ng/g) was higher than that in background areas (Σ16PAHs: 1767 ng/g, Σ41PCBs: 6.7 ng/g, Σ32PCNs: 1.31 ng/g, Σ7PBDEs: 6.51 ng/g) in Aliaga, Turkey, the concentration of OPEs in this study was higher than many POPs (PCBs, PBDEs, PCNs, etc.), suggesting that OPEs pollution should be paid attention to and could be used as an excellent indicator of air pollution. Therefore, arbor are suitable species to be used to roughly compare the use and pollution level of OPEs between different regions or countries.
Distribution of OPEs in tree rings
The distribution of OPE monomers in samples from 5 arbor species was shown in Fig. 2. The concentration of TBEP (73.77-184.57 ng/g) in each sample was the highest, accounting for 31.60%-61.33% of Σ6OPEs, followed by TCEP, TCPP, TPhP and TnBP, respectively, and TEHP was the lowest covering 2.11%-6.12% of Σ6OPEs. In order to explore the distribution differences of each OPE monomer in 5 arbor tree species, Friedman test of SPSS Statistics version 25 was used for multiple comparison of 6 OPEs in samples. P = 0.1027 > 0.05 indicated that there was no significant difference in OPEs composition distribution among 5 tree species. Therefore, the trunk rings of different tree species collected in this study have no obvious selectivity to the accumulation of OPEs, suggesting that different types of arbor species can be used to indicate OPEs contamination in regional environment.
According to different substituents, the 6 OPEs can be divided into alkyl phosphate (TnBP, TBEP, TEHP), chlorinated phosphate (TCEP and TCPP) and phenyl phosphate (TPhP). The proportion of different kinds of OPEs in this 5 arbor species was alkyl phosphate (680.03 ± 89.00 ng/g) > chlorinated Phosphate (337.49 ± 42.13 ng/g) > phenyl phosphate (129.26 ± 34.98 ng/g) (Fig. 3). The accumulation ability of all kinds of arbor species to alkyl phosphate was the highest which presented that the alkyl phosphate was the dominant OPEs in this area. The profile of OPEs in Fir was alkyl phosphate: chlorinated phosphate: phenyl phosphate ≈ 5:4:1, while the general profile of OPEs in the other 4 tree rings was alkyl phosphate: chlorinated Phosphate: phenyl phosphate ≈ 6:3:1, and the concentration of chlorinated phosphate in Fir (396.20 ng/g) was also higher than that in the other 4 arbor species (Toona sinensis: 349.04 ng/g > Cypress: 340.55 ng/g > Pterocarya stenoptera: 321.10 ng/g > Dongmu: 280.56 ng/g). It showed that among the various arbor species, the Fir had higher enrichment ability for chlorinated phosphate. However, in general, the profile of OPEs in different arbor species were similar which further prove that arobrs are suitable species to be used to compare the pollution of OPEs between different regions or countries.
Inter-annual variation characteristics of OPEs in tree rings
In all kinds of trees, the variation trend of Σ6OPEs concentration with years was different (Fig. 4). Among them, the concentration of Σ6OPEs in Toona sinensis and Dongmu decreased year by year, and there was an obvious linear relationship between Σ6OPEs concentration and year (Toona sinensis: R2 = 0.9882, Dongmu: R2 = 0.9173). The difference was that the Σ6OPEs concentration decreased faster in Dongmu than in Toona sinensis. However, the Σ6OPEs concentration in Cypress basically showed a slowly increasing trend while Σ6OPEs concentration in Fir and Pterocarya stenoptera fluctuated greatly which showed no obvious unidirectional trend. For Fir, the concentration of Σ6OPEs increased year by year in the first three groups, and reached the highest value (341.23 ng/g) in the third group (2015–2016), then decreased by about 1/3 in the fourth group (2017–2018). For Pterocarya stenoptera, the concentration of Σ6OPEs in the first three groups fluctuated greatly, with a fluctuation range of 189.79-314.82 ng/g (inter-group variation: 9.9%-66.1%). For Cypress, the concentration of Σ6OPEs fluctuated in the range of 254.00-329.30 ng/g (inter-group variation: 4.4%-23.6%), and the maximum value (329.33 ng/g) appeared at the second group (2014–2015), while the fourth group had a significant decline, which was 23.6% lower than that in the third group. The results showed that the sensitivity of different tree species to OPEs in the same area was different, which may be due to the different leaf size and lipid content of different tree species. Therefore, to better understand the changes of OPEs pollution in different years, the changes of OPEs in different tree ring samples could be further analyzed, and more detailed results could be obtained by combining the physical and chemical properties of OPEs.
The variation characteristics of OPEs monomer in different tree species varied greatly with the growth of rings (Fig. 5). Among them, TBEP concentration showed the greatest influence on the variation of Σ6OPEs in different tree species in the sampling area. TBEP belongs to alkyl OPEs, which has a short half-life in the atmosphere and is easily biodegraded. However, the content of TBEP was highest in different tree species, indicating that there was continuous non-point source input in this area. The concentrations of TEHP and TnBP were relatively stable, while the concentrations of the other 3 monomers fluctuated significantly. Compared with other OPE components, TnBP and TEHP have higher logKOA (TnBP: 9.21, TEHP: 14.9) and BCF values (TnBP: 1.03×103, TEHP: 1.00×106) (Table 1), so they were easier to be enriched in trees. Moreover, the half-lives of these two compounds (TnBP: 3.26, TEHP: 2.62) were short and they could not exist in the environment for a long time (Li et al. 2018). Therefore, similar with TBEP, their inter-annual variation characteristics also indicated that there was continuous and stable pollution input in the local area. In addition, TnBP is often added as plasticizer and additive to varnishes, concrete and plastics, and is widely used in construction and agricultural production (Wang et al. 2022). The study area in this paper mainly relies on agricultural development, so sewage irrigation and plastic film application in agricultural activities may be an important source of local OPEs pollution. As mentioned above, the accumulation of OPEs did not differ significantly among the five arbor species. However, the fluctuation of each monomer in Fir was relatively obvious, indicating that Fir was more sensitive to the change of OPEs monomer. According to other studies, Conifer xylem has a high concentration of organic matter (such as Turpentine and Rosin), which showed the strong ability to absorb organic matter from the atmosphere and preserved them over time (Kuang et al. 2011). Therefore, the adaptability of tree species should be considered when selecting trees to reflect the bioavailability and historical changes of pollutants in the environment.
Hundreds ng/g of OPEs were detected in tree rings of the 5 arbor species and there was no significant difference of the total concentrations of OPEs indicating that arbor could be used as a good passive sampler to show the occurrence and distribution of emerging pollutants with similar physicochemical properties to OPEs in the regional environment. Among the 5 arbor species, Fir species was more sensitive to the change of OPE monomers. Therefore, the use history and pollution status of OPEs in a region or country could be reflected by any arbor tree species. If the same arbor species with sensitive response to OPEs was selected, we could accurately infer the usage history and pollution status of OPEs in a region or country.
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Authors' contributions：Hongling Yin and Shuhong Fang contributed to the study conception and design. Methodology, material preparation, data analysis and writing were performed by Jiaojiao Song and Xiaoyu Xiong. Experiments were carried out by Xiaoyu Xiong, Jiaojiao Song and Yuanming Xiong. Writing-review was performed by Hongling Yin and Shuhong Fang. All authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript.
Funding: This work was supported by National Natural Science Fundation of China (Grant numbers No.41773072).
Competing interests：The authors declare that they have no competing interests.
Acknowledgements: This work was financially supported by the National Natural Science Fundation of China. (No.41773072).
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No competing interests reported.