Organophosphate esters (OPEs) have garnered significant attention for their extensive environmental presence and potential risk to biota, including humans. This study investigated the levels, influencing factors, sources and health risks of OPEs partitioning in atmospheric fine particulate matters (PM2.5) and total suspended particles (TSP) from Nanjing, China. The total concentrations of eleven OPEs (detection frequency > 50%) in PM2.5 and TSP were range of 57.0 − 404 pg/m3 and 37.7 − 354 pg/m3. OPEs tended to partition into fine particles for their higher adsorption capacity and octanol-air partition coefficient (KOA) of OPEs. Meteorological factors such as temperature and air pressure had opposite effects on halogenated and aryl OPEs, with high temperatures and low pressures causing halogenated OPEs to volatilize and partition into particles. Air mass trajectory analysis indicated differing sources for particulate matter and OPEs, with particulate matter originating mainly from Anhui, Jiangsu, Zhejiang, Henan, Jiangxi and Hubei provinces, and OPEs primarily from Jiangxi, Zhejiang and Shandong provinces. Correlation analysis and principal component analysis identified building constructions, traffic emissions and foam products, and indoor emissions as OPEs sources. The non-cancer risk assessment indicated no potential risk of concern, as the evaluation by hazard quotient was far below the acceptable risk.
Research Article
Levels, influencing factors, sources and exposure assessment of organophosphate esters in fine particulate matter and total suspended particle from Nanjing, China
https://doi.org/10.21203/rs.3.rs-5238908/v1
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Organophosphate esters
particulate matter
meteorological factors
backward trajectories
health risk
Organophosphate esters (OPEs), a group of semi-volatile organic chemicals serving roles primarily as flame retardants and plasticizers, which are extensive used in building materials, chemical and transportation products (Van der Veen and de Boer, 2012; Xu et al., 2019). In 2015, OPEs constituted approximately 680 thousand metric tons of market volume, and increased to 860 thousand metric tons by 2023 (ATSDR, 2012; Bi and Su, 2023; Pantelaki and Voutsa, 2019). Since OPEs are additives not by chemically but physically incorporate into materials, which makes them easily released into the environment via abrasion, leaching, and volatilization (Doherty et al., 2019). Some OPEs have low degradation, persistence and bioaccumulation (Xiang et al., 2023; Zhang et al., 2019). As a result, OPEs are frequently detectable in multiple environmental matrices (such as air, dust, soil, sediment and water) (Esplugas et al., 2022; Li et al., 2023; Liu et al., 2020; Tian et al., 2023) and biota (including animal and human) (He et al., 2019; Zhao et al., 2023). Also, OPEs have drawn increasing attention for their proven carcinogenicity, mutagenicity, neurotoxicy and reproductive effects (Salamova et al., 2017; Xiang et al., 2023).
Atmospheric particulate matter is an important reservior of contaminants, OPEs prone to bind with particles makes them resistant to degradation (Zhang et al., 2019), so the remarkable persistence allowing for medium to long-range transport in the atmosphere before deposition (Castro-Jiménez et al., 2014; Fu et al., 2021), affecting the global distribution of contaminants (Reemtsma et al., 2008). This highlight the importance of paticle size (aerdynamic equivalent diameter of particles) in research, as it not only determines the fate and transport of contaminants, but also influence their deposition in the lungs. Fine particle (PM2.5, with aerodynamic diameter ≤ 2.5 µm) can transfer deeper into respiratory system, posed higher risks to human health than larger particles (Li et al., 2021).
The partitioning of OPEs in atmospheric particles can be ascribed to a complex interplay of factors, including particle size distribution, the physico-chemical properties of pollutants, and meteorological conditions (Yin et al., 2020). Lately, comprehensive studies have delved into OPEs within the atmosphere, encompassing both the gaseous phase and airborne particles. Gas-particle partitioning (Suehring et al., 2016; Wang et al., 2020) and size distribution (Cao et al., 2022) stand out as key factors affecting the environmental fate of semi-volatile organic compounds. For OPEs partitioning in particles, there are some contraditory conclusion in coarse and fine particles. Some studies have displayed the distribution OPEs in size-segregated particles, roughly higher or equally to OPEs concentration in coarse particles than fine particles (Cao et al., 2019; Li et al., 2021; Yang et al., 2014; Zeng et al., 2021b). Contrarily, other reports conclude the higher OPEs concentration occurred on fine particles than coarse particles (Kung et al., 2022; Wang et al., 2023; Zhang et al., 2020). It appears association with the species and concentration of detected OPEs, the physico-chemical properties, octanol-air partition coefficient (KOA) is consider as a crucial factor affecting partition behavior (Zhang et al., 2020).
Nanjing, a central city in the eastern region in China, a core city in the Yangtze River Delta. In our study, we investigated the levels of thirteen OPEs in PM2.5 and TSP in the urban area of Nanjing, China. We aim to i) investigate the distribution profiles of OPEs bounded in PM2.5 and TSP collected synchronously, ii) investigate the factors affecting the partition behavior of OPEs, combine with air mass backward trajectory and principal component analysis (PCA) to revealing the primary sources and transportation pathway of particle matters and OPEs, iii) assess the OPEs inhalation exposure risks for adults and children.
2.1 Chemical and reagent
In our study, we examined a total of thirteen OPEs categorized into three groups. Alkyl OPEs including trimethyl phosphate (TMP), triethyl phosphate (TEP), tripropyl phosphate (TPP), tributyl phosphate (TnBP), tris(2-butoxyethyl) phosphate (TBEP), tris(2-ethylhexyl) phosphate (TEHP). Halogenated OPEs including tris(2-chloroethyl) phosphate (TCEP), tris(2-chloroisopropyl) phosphate (TCPP), tris(1,3-dichloroisopropyl) phosphate (TDCPP), tris(2,3-dibromopropyl) phosphate (TDBPP). Aryl OPEs including tritolyl phosphate (TCP), triphenyl phosphate (TPhP), 2-ethylhexyl diphenyl phosphate (EHDPP). The OPEs were purchased from J&K Chemical, Ltd. Additionally, two labelled TnBP-d27 and TPhP-d15 as internal standards were purchased from Cambridge Isotope Laboratories. Deionized water prepared by the Milli-Q purification system (18.2 MΩ·cm at 25°C) was employed in all experimental procedures. Methanol and n-hexane were procured from Sigma-Aldrich of HPLC grade.
2.2 Sample collection
Simultaneous collection of PM2.5 (n = 23) and TSP (n = 23) particle samples was conducted at a sampling height is 10 − 15 m above the ground, on the rooftop of the Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment (118°49′25″ E, 32°4′54″ N), in Nanjing, China from June 20 to December 4, 2017. The particle collection was conducted using a higher volume air sampler equipped with glass fiber filters, operating at a flow rate of 600 L/min. The total sampling volume reached 2160 m3. Each sample underwent consecutive sampling for three days, with a duration of four hours and an hourly interval. Following collection, the samples were promptly sent to the laboratory and stored at -20 ℃ prior to analysis.
2.3 Analysis methods
OPEs were detected in two particle-size samples (PM2.5 and TSP). Detailed parameter information on OPEs is provided in Table S1. A mixture of 20.0 ng surrogate standards and filter samples were prepared for quantitative detection and completely incubated for 12 h. The extraction solution was at the ratio 1:3 of n-hexane and acetone, ultrasonically extracted for 30 min. The combined solution was purified with Oasis HLB cartridges (500 mg, 6.0 mL, Waters), according to our previous report (Chen et al., 2019). The solution was concentrated to dryness by a rotary evaporator and finally kept in a 1.0 mL acetonitrile. High-performance liquid chromatography (HPLC, Agilent Technologies 1290 Infinity) and tandem mass spectrometry (MS, AB SCIEX QTRAP 4500) equipped with a ZORBAX Eclipse C18 column (150 mm×2.1 mm, 3.5 mm) were used for OPEs analysis. The column temperature was 30 ℃, the flow velocity at 0.3 L/min, the injection volume was 5.0 µL, and the gradient elution was performed by 0.02% formic acid (A) and methanol (B), the mobile phase of 30% solvent A at 0 min and kept to 8.0 min, then decrease to 5.0% of solvent A and kept to 16.0 min, finally return to 30% of solvent A. The detection was carried out by multiple reaction monitoring modes, the parameters for quantitative determination of OPEs are shown in Table S2.
2.4 Quality assurance and Quality control
Plastic or rubber products were strictly avoided during collection, storage, and extraction. Pre-baked glass fiber filters were prepared at 450 ℃ for 4.0 h, all glass tubes were ultrasonic cleaned with acetone. Procedural blanks, sampling site blanks, and solvent blanks spiked with TnBP-d27 as internal standard were conducted, and matrix-spiked samples were quantified by internal standard. The presence of OPEs has not been detected in blank samples. The recoveries of eleven OPEs were 73.6–111%, with relative standard deviations of 3.21–11.1%. The method detection limits (MDLs) was identified as mean values of field blanks plus three times the starndard deviation of the field blanks shown in Table S2. Concentrations below the MDLs were set as MDL/√2 in data analysis. The correlation coefficients (r2 > 0.990) of OPEs concentration and response were in a good linear relationship.
2.5 Data sources and statistical analysis
The meteorological parameters, including humidity, pressure, temperature, and wind speed, are shown in Table S3. For data that were not normally distributed, spearman correlation was calculated to analyze the relationships between OPEs concentration and influencing factors, including meteorological variables and mass concentration of PM2.5 and TSP, the correlations were identified as p ≤ 0.05, p ≤ 0.01, p ≤ 0.001. Mantel test analyses were used to test the correlation performed by software R version 3.5. Mann-Whitney U test and PCA was employed by SPSS Statistics 23.
2.6 Backward trajectory
Air mass backward trajectory was performed by MeteoInfo version 3.2.7 software to trace the transmission routes and sources of air mass particles and OPEs at the sampling site. The meteorological information of the Global Data Assimilate System was collected from National Centers for Environmental Prediction (ftp://ftp.arl.noaa.gov/pub/achives/gdas1) datasets. During the sampling periods, 72 h air mass backward trajectory analyses were performed at 100 m above the ground for each hour. The concentration weight trajectory method was used to evaluate the sources and to distinguish the pollution intensity, a i × j equal grid cells (0.5°×0.5°) were created and calculated by the following equation:
Where M is total trajectories, Cij\(\:\:\)is the measured concentration of trajectory l, and \(\:{\text{τ}}_{\text{ijl}}\) is the time spent by trajectory l. The cell size we used was 0.5˚ × 0.5˚. Generally, higher concentration weight trajectory (CWT) values in cell indicate a higher contribution at the region site. The weighting function Wij is used to minimize the uncertainty in grid cells defined as follows:
2.7 Human risk assessment
Inhalation is regarded as the major atmospheric exposure pathway, we used the estimated daily intakes (EDI, ng/kgžbw/day) to evaluate the exposure dose via inhalation, EDI = C×Rinh×T/BW, C (ng/m3) is the concentration of individual OPE in PM2.5 and TSP, Rinh (m3/day) is the inhalation rate with the value of 8.50 m3/day and 16.0 m3/day for children and adults (Liu et al., 2021; Zhang et al., 2019). T is a fraction of one day, which assumed to 1/3 of a day people spend outdoors (Chen et al., 2020). BW (kg) is the average body weight of 18.2 kg and 60.5 kg for children and adults (Liu et al., 2021).
The hazard quotient (HQ) that used to assess the non-cancer risks were calculated by HQ = EDI/RfD, RfD is reference dose (mg/kgžday) of individual OPE were as follows: TEP (0.125), TnBP (0.020), TBEP (0.015), TEHP (0.035), TCEP (0.007), TCPP (0.010), TDCPP (0.020), TCP (0.020), TPhP (0.07) and EHDPP (0.015) (Lao et al., 2022).
3.1 Concentrations and distribution profiles of OPEs in PM2.5 and TSP
The mass concentration of PM2.5 and TSP were measured to 18.9 − 145 µg/m3 and 40.2 − 263 µg/m3, respectively, as detailed in Table S4. During simultaneous collection, the mass concentration of PM2.5 was lower than those of TSP, with the proportion of PM2.5 to TSP ranging from 42.2–82.1%. Notably, 87.0% of PM2.5 samples exceeded the primary concentration limits set by the China National Ambient Air Quality Standard (GB3095-2012), which stipulated a 24-hour average of 35.0 µg/m3. Additionally, more than half (52.2%) of PM2.5 samples surpassed the secondary concentration limits (24-hour average of 75.0 µg/m3), underscoring severe pollution of PM2.5 in Nanjing. Furthermore, concentration of OPEs in particulate matter were analyzed. Detection frequencies (DF) for the majority of the eleven identified OPEs exceeded 71.7%, except for TMP (6.50%) and TPP (10.9%), as shown in Table S2. Consequently, the discussion is limited to these eleven OPEs. The composition profiles of OPEs are presented in Fig. 1 and Table S5. The ΣOPEs concentrations in PM2.5 and TSP were in the range of 57.0 − 404 pg/m3 and 37.7 − 354 pg/m3, respectively. With median concentration of 93.0 pg/m3 for PM2.5 and 126 pg/m3 for TSP. The ΣOPE concentrations observed in this study were higher than those reported for the South China Sea (TSP, 47.0 − 161 pg/m3) (Lai et al., 2015), and comparable to the North Atlantic and Arctic Oceans (gas + TSP, 35.0 − 343 pg/m3) (Li et al., 2017) and Bohai and Yellow Seas (TSP, 44.0 − 520 pg/m3) (Li et al., 2018). However, they were significant lower than concentrations measured in other urban areas, such as Beijing (PM2.5, 257 − 8360 pg/m3) (Liu et al., 2022), the Beijing-Tianjin-Hebei region (PM2.5, 90.0 − 8291 pg/m3) (Zhang et al., 2020), Guangzhou (PM2.5, 314 − 9721 pg/m3) (Zeng et al., 2020), Xinxiang (PM2.5, mean: 9990 ± 5690 pg/m3) (Yang et al., 2019) and Houston in US (PM2.5, 160 − 2400 pg/m3; TSP, 320 − 3500 pg/m3) (Clark et al., 2017).
The OPE composition profiles in PM2.5 and TSP were similar, as shown in Fig. 1 (c) and (d). TCPP, TEHP, TPhP, and TCEP were the predominant congeners in both PM2.5 and TSP. The profiles revealed that halogenated-OPEs, specifically, TCPP (PM2.5: 35.2 ± 27.8%, TSP: 27.0 ± 26.0%) and TCEP (PM2.5: 15.5 ± 6.27%, TSP: 15.9 ± 8.05%), were the dominant compounds. The high abundance of TCPP and TCEP are consistent with their widespread use in products such as flame retardant and plastic, as well as their long-term persistence (Wang et al., 2020). Non-halogenated OPEs, including TEHP (PM2.5: 18.6 ± 9.23%, TSP: 25.0 ± 9.73%) and TPhP (PM2.5: 23.8 ± 18.9%, TSP: 22.1 ± 16.0%) were also preminent in both PM2.5 and TSP. TEHP is commonly used in lubricants and agricultural products (Wang et al., 2018). While TPhP, used in polymers and as a substitute of pentaBDE, was a major congener, corroborating findings from several studies (Zeng et al., 2020).
The ΣOPE levels in PM2.5 and TSP also varied over the sampling period, with several peak concentrations from June to July attributed to high TCPP levels, as illustrated in Fig. 1. Notably, TCPP exhibited the widest concentration range among all detected OPEs, ranging from ND to 353 pg/m3 in PM2.5 and ND to 272 pg/m3 in TSP. The unexpectedly high concentrations of TCPP in June and July, which were two orders of magnitude higher than in other months, were likely due to strong emission sources from nearby sites combined with meteorological factors. The TCPP/TCEP ratios, which primarily depend on usage patterns and atmospheric processes in a region, ranged from 0.33 to 1.67 from August to December, comparable to previous reported values of 0.56 − 2.44 in China. However, the ratio from June to July (2.88 − 20.6) was significantly higher. Given the properties of TCPP and TCEP, the higher vapor pressure of TCPP facilitates its volatilization, while the longer atmospheric lifetime of TCEP (11.7 h) compared to TCPP (5.7 h) supports its long-distance transport. The elevated TCPP/TCEP ratio from June to July (2.88 − 20.6) indicates that TCPP was primarily associated with nearby emissions during these months, differing from other sampling periods.
3.2 Influence of particle size on OPEs partition behavior
The mass-normalized concentrations of ΣOPEs in particulate matter are illustrated in Fig. S1 and detailed in Table S6. The mass-normalized concentrations of ΣOPEs in coarse particles (474 to 3889 ng/g) were significantly lower than (Z=-2.01, p < 0.05 in Table S6) those found in fine particles (424 to 7628 µg/g). This suggests that fine particles may have higher adsorption capacities due to their large specific surface area. The normalized results are consistent with previous report (Zeng et al., 2021b). Furthermore, the ratios of ΣOPEs in PM2.5 to TSP were ranged from 0.38 to 1.67 (median: 0.85). The fraction of individual OPEs bounded in PM2.5 to TSP (χ) was also calculated, with ratios ranging from 0.71 to 1.26 (median value). This indicates that certain OPEs are more likely to associate with fine particle rather than coarse particle, a trend that can be attributed to higher adsorption capacity of fine particles (Cao et al., 2022).
The observed ratio gap in this study, derived from separately collected air samples, is significant wider than that report previous with size-segregated particles (Cao et al., 2019; Luo et al., 2016), where the ratio was approximately equal to or lower than 1. This finding reinforces the conclusion that OPEs preferentially distribute in fine particles when sampled at the same volume, highlighting the high adsorption capacity of fine particulate matter. Additionally, this distribution pattern may be relate to physicochemical properties of semi-volatile compounds, as evidenced by the significant correlation (r2 = 0.728, p < 0.05) between the χ value and the octanol-air partition coefficient (log KOA), as shown in Fig. 2.
3.3 Effects of meteorological factor and mass concentration on OPE levels
Three sets of OPEs from different sampling periods in PM2.5 and TSP were shown in Fig. S2. The content of Σalkyl-OPEs fluctuates over time, with higher concentration in PM2.5 than TSP, showing no clear trend. The concentrations of Σhalogenated-OPEs and Σaryl-OPEs varied significantly, with higher Σhalogenated-OPEs levels observed from June to July, and higher Σaryl-OPEs levels during the colder sampling period. The impact of meteorological conditions on OPE levels is presented in Table 1, Σhalogenated-OPEs and Σaryl-OPEs responded oppositely to air pressure and temperature (p < 0.01). Significant correlations were found for Σhalogenated-OPEs (PM2.5, r = 0.734, p < 0.001), possibly because high temperature facilitate the release of halogenated-OPEs from products, particularly TCPP (PM2.5, r = 0.663, p < 0.001) and TCEP (PM2.5, r = 0.574, p < 0.01). There was a negative correlation between temperature and Σaryl-OPEs (PM2.5, r=-0.763, p < 0.001; TSP, r=-0.905, p < 0.001) and TPhP (PM2.5, r=-0.866, p < 0.001; TSP, r=-0.921, p < 0.001). These findings align with previous studies indicating that meteorological factors, such as temperature, are primary drivers influencing particle OPE distribution (Zeng et al., 2020; Zhang et al., 2019; Zhang et al., 2020).
Air pressure showed a negative correlation to Σhalogenated-OPEs, particularly TCPP and TCEP, and a positive correlation with Σaryl-OPEs (r = 0.763, p < 0.001) especially TPhP (r = 0.827, p < 0.001). TCPP and TCEP, with their high vapor pressures, are prone to volatilization into the air and then partitioning to particles as temperature increases and air pressure decreases. In contrast, TPhP, which has a low vapor pressure (8.25×10− 8 pa), remains difficult to volatilize into the air even when temperature rises, but its particles levels increase with rising air pressure (Zeng et al., 2021a). Wind speed had a negative correlation to ΣOPEs in PM2.5 (r=-0.467, p < 0.05) and TSP (r=-0.529, p < 0.01), as higher wind speeds enhance the dilution and dispersion of pollutants. Further studies are needed to elucidate the underlying mechanism of the effect of of meteorological conditions on OPE levels in PM2.5 and TSP.
Most OPE concentrations (including ΣOPEs and four dominant pollutants) showed no significantly correlated with the mass concentration of PM2.5 and TSP, as shown in Table 1. The result is similar to findings from Chengdu (Yin et al., 2020). However, TEP (PM2.5, r = 0.671, p < 0.001; TSP, r = 0.792, p < 0.001) and TDBPP (PM2.5, r = 0.699, p < 0.001; TSP, r = 0.694, p < 0.001), TPhP (TSP, r = 0.692, p < 0.001) and ΣAryl OPEs (TSP, r = 0.674, p < 0.001) shown a highly significant correlation with mass concentration, which indicate that high concentration of these pollutants are often accompanied by high mass concentration of particulate matter (Zhao et al., 2023). Therefore, poor air quality does not necessarily indicate higher levels of OPE pollution, suggesting the existence of diverse sources of particulate matter and pollution.
| OPEs | PM2.5 | TSP | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Humidity (%) | Press (hpa) | Temp (℃) | Wind (Km/h) | Mass concentration (µg/m3) | Humidity (%) | Press (hpa) | Temp (℃) | Wind (Km/h) | Mass concentration (µg/m3) | |
| TEP | -0.544** | 0.651** | -0.688*** | -0.076 | 0.671*** | -0.452* | 0.629** | -0.708*** | -0.150 | 0.792*** |
| TnBP | 0.325 | -0.076 | 0.095 | -0.335 | -0.243 | 0.306 | 0.038 | -0.061 | -0.402 | -0.293 |
| TBEP | 0.427* | -0.465* | 0.400 | 0.103 | -0.161 | 0.557** | -0.272 | 0.021 | 0.009 | -0.037 |
| TEHP | -0.056 | 0.114 | -0.155 | -0.018 | 0.250 | -0.171 | 0.270 | -0.347 | -0.237 | 0.456* |
| TCEP | 0.199 | -0.596** | 0.574** | -0.279 | 0.088 | 0.016 | -0.006 | -0.127 | -0.312 | 0.298 |
| TCPP | 0.463* | -0.717*** | 0.663*** | -0.241 | -0.275 | 0.213 | -0.523* | 0.423* | -0.471* | -0.263 |
| TDCPP | 0.156 | -0.194 | 0.150 | -0.112 | 0.248 | -0.058 | 0.088 | -0.186 | -0.179 | 0.426* |
| TDBPP | -0.261 | 0.028 | -0.268 | -0.231 | 0.699*** | -0.263 | 0.261 | -0.505* | -0.291 | 0.694*** |
| TCP | -0.074 | 0.045 | -0.075 | -0.160 | -0.041 | -0.065 | 0.127 | -0.231 | -0.330 | 0.122 |
| TPhP | -0.346 | 0.827*** | -0.866*** | 0.070 | 0.283 | -0.292 | 0.782*** | -0.921*** | -0.001 | 0.692*** |
| EHDPP | 0.367 | -0.144 | 0.039 | 0.357 | -0.017 | 0.219 | 0.036 | -0.191 | 0.063 | 0.110 |
| ΣAlkyl OPEs | 0.109 | -0.010 | -0.022 | -0.170 | 0.071 | -0.057 | 0.198 | -0.262 | -0.324 | 0.344 |
| ΣHalogenated OPEs | 0.412 | -0.786*** | 0.734*** | -0.328 | -0.189 | 0.112 | -0.553** | 0.406 | -0.652*** | -0.124 |
| ΣAryl OPEs | -0.386 | 0.763*** | -0.763*** | 0.003 | 0.235 | -0.280 | 0.778*** | -0.905*** | 0.023 | 0.674*** |
| ΣOPEs | -0.098 | -0.293 | 0.091 | -0.467* | 0.201 | -0.195 | -0.090 | -0.085 | -0.529** | 0.296 |
| *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 | ||||||||||
3.4 Backward trajectory analysis
To analyze air mass trajectories and pollution sources, a backward trajectory method was employed. The Meteoinfo model revealed that air masses in Nanjing primarily originated from three directions: the east within Shanghai and Jiangsu (37.6%), the northwest spanning Mongolia, Hebei, Shandong (36.5%), and the southwest covering the boundary of Guangdong and Hunan, Jiangxi, Anhui (25.9%) (Fig. 3). These air mass trajectories indicate the sources of particle matters, and the CWT model was used to further trace the source contributions of particle matters and pollutants.
Based on the CWT results, the concentration of particle matters (PM2.5 and TSP) and particle-bound ΣOPEs are illustrated in Fig. 3(a-d). The grid contributions of particulate matters (PM2.5 and TSP) were consistent with the MeteoInfo analysis in ranking in the order of east > northwest > southwest. Evidently, the sources of particulate matter and pollutants varied. Anhui, Jiangsu, Zhejiang, Henan, Jiangxi and Hubei provinces were predominantly contributed to the levels of PM2.5 and TSP, while Jiangxi, Zhejiang and Shandong provinces were mainly responsible for the levels of OPEs. Fig. S3 shows the air mass trajectories from different directions during the months of June to July and September to December. The unexpectedly high concentration of TCPP from nearby emission in June to July (at section 3.1) can explained by the significant contributions from Jiangxi and Zhejiang Province, as indicated by the source contribution and air mass trajectories shown in Fig. 3 and Fig. S3. The disparity between particulate matter and OPEs sources suggests that the origins of particulate matter can differ from those of pollutants, highlighting the need to consider the distinct characteristics and sources of particulate matter and pollutant pollution.
3.5 Correlations and source apportionment
The correlations of individual OPEs were analyzed to illustrate their associations and sources in airborne particles shown in Fig. 4. In PM2.5, the dominant TCPP and TCEP exhibited a positive correlation (r = 0.489, p < 0.05), suggesting similar sources such as paint, polyester resin, textiles, polyurethane foam and construction materials. TPhP showed a negative correlated with TCPP (r=-0698, p < 0.001) and TCEP (r=-0.501, p < 0.05) in PM2.5, which may be attributed to the differing emission sources and atmospheric progress. Among PM2.5-bound alkyl-OPEs, TEP positively correlated to TPhP, and TEHP positively correlated with TBEP and TCP. More significant correlations were found between OPE congeners bound in TSP. TEP and TEHP exhibited positive correlation with TDBPP, TCP, and TPhP (r = 0.453 − 0.783, p < 0.05). Additionally, TDBPP showed positive correlations with TDCPP, TCP and TPhP (r = 0.480 − 0.630, p < 0.05).
The PCA is commonly used to indentify pollutant sources. Four major components with eigenvalues greater than 1 were extracted to explain 75.2% and 75.9% of OPEs bound in PM2.5 and TSP, as shown in Table S7. In PM2.5, TEP, TPhP and TEHP have high loadings under PC1 (28.2% variance explained), TCEP and TCPP have high loadings under PC2 (19.7%), TBEP and TCP have high loadings under PC3 (15.9%), and TnBP and EHDPP have high loadings under PC4 (11.4%). In TSP, TDBPP, TEP and TEHP have high loadings under PC1 (38.6%), TCPP have high loadings under PC2 (15.6%), TDCPP have high loadings under PC3 (11.9%), EHDPP and TDCPP have high loadings under PC4 (9.82%).
TEP, TPhP, TEHP, and TCP are used in polyvinyl chloride, while TPhP and TCP are known to be used in hydraulic fluids (van der Veen and de Boer, 2012; Williams et al., 2017). TCEP is mainly used in building insulation as a flame retardant, TCPP is commonly used in polyurethane foam as a substitute of pentaBDE, TBEP is used in home cleaning and care products, as well as office writing equipment products (van der Veen and de Boer, 2012; Williams et al., 2017), TnBP, a high-carbon alcohol defoamer, finds extensive use in non-food and non-cosmetic industries, as well as in antistatic agents and extractants for rare earth elements. EHDPP primarily acts as a flame retardant in indoor items such as floor coverings, furniture, and various plastic and rubber products. TDCPP is used as additives in foam product in furniture (Krystek et al., 2019). PC1 is assigned to traffic emissions and foam products, PC2 is assigned to building construction, PC3 and PC4 is assigned to indoor emissions from different sources. It should be noted that due to the widespread application of OPEs, it is challenging the precisely identify their sources solely based on PCA results.
3.6 Health risk assessment
Table 2 present the EDI and median HQ values for both children and adults. The median EDI values of PM2.5-bound individual OPE ranged from 0.77 to 550×10− 4 ng/kgžbw/day for children and 0.43 to 311×10− 4 ng/kgžbw/day for adults. TEHP exhibited the highest EDI values, followed by TPhP, TCPP, and TCEP. For TSP-bound individual OPEs, the median EDI values ranged from 0.71 to 424×10− 4 ng/kgžbw/day for children and 0.40 to 240×10− 4 ng/kgžbw/day for adult, with TEHP again showing the highest values, followed by TPhP, TCEP, and TCPP. The higher EDI values in children compared to adults indicate a greater exposure risk for children. The cumulative EDI values of PM2.5-bound eleven OPEs (DF > 50%) ranged from 88.7 to 629×10− 4 ng/kgžbw/day for children and 50.3 to 365×10− 4 ng/kgžbw/day for adults, with the median values of 145×10− 4 ng/kgžbw/day and 82.0×10− 4 ng/kgžbw/day, respectively. These values are significantly lower than those reported for the Pearl River Delta, China (median ΣOPEs: 1.89 ng/kgžbw/day for children, 1.06 ng/kgžbw/day for adults) (Liu et al., 2021). For eleven TSP-bound OPEs (DF > 50%), the cumulative EDI values ranged from 58.7 to 551×10− 4 ng/kgžbw/day for children and from 33.3 to 312×10− 4 ng/kgžbw/day for adults, with the median value of 197×10− 4 ng/kgžbw/day and 111×10− 4 ng/kgžbw/day. These values are considerably lower than those reported for Shijiazhuang (median ΣOPEs: 940×10− 4 ng/kgžbw/day for adults), Tianjin (median ΣOPEs: 106×10− 3 ng/kgžbw/day for adults), Beijing (median ΣOPEs: 189 − 822×10− 4 ng/kgžbw/day for adults) and Shanghai (ΣOPEs: 7.00 − 169×10− 2 ng/kgžbw/day for children, 3.00 − 73.0×10− 2 ng/kgžbw/day for adults) (Pang et al., 2019; Wang et al., 2017).
The HQ values were calculated to assess non-cancer inhalation risks of PM2.5 and TSP. The median HQ values of PM2.5-bound ΣOPEs were 75.8×10− 8 for children and 42.9×10− 8 for adults, while for TSP-bound ΣOPEs, the values were 103×10− 8 for children and 58.3×10− 8 for adults. The HQ value were approximately 1.77 times higher for children than adults, reflecting the increased vulnerability of children’s developing respiratory and immune systems. The observed HQ values were 5 − 6 orders of magnitude lower than the acceptable risk threshold (HQ = 1), indicating negligible non-cancer risks.
| OPEs | PM2.5 | TSP | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Children | Adult | Children | Adult | |||||||||||||
| EDI (×10− 4) | HQ (×10− 8) | EDI (×10− 4) | HQ (×10− 8) | EDI (×10− 4) | HQ (×10− 8) | EDI (×10− 4) | HQ (×10− 8) | |||||||||
| Range | Mean | Median | Range | Mean | Median | Range | Mean | Median | Range | Mean | Median | |||||
| TEP | 1.33 − 4.88 | 3.02 | 2.91 | 0.23 | 0.75 − 2.77 | 1.71 | 1.65 | 0.13 | 1.95 − 7.93 | 4.51 | 4.32 | 0.35 | 1.11 − 4.49 | 2.55 | 2.44 | 0.20 |
| TnBP | 0.95 − 35.0 | 6.20 | 3.12 | 1.56 | 0.54 − 19.8 | 3.51 | 1.77 | 0.88 | 1.05 − 34.3 | 6.89 | 2.75 | 1.37 | 0.59 − 19.4 | 3.90 | 1.56 | 0.78 |
| TBEP | 0.77 − 1.33 | 1.00 | 1.01 | 0.67 | 0.43 − 0.75 | 0.57 | 0.57 | 0.38 | 0.71 − 1.63 | 1.13 | 1.09 | 0.73 | 0.40 − 0.92 | 0.64 | 0.62 | 0.41 |
| TEHP | 8.35 − 45.7 | 28.2 | 28.0 | 8.00 | 4.73 − 25.9 | 15.9 | 15.9 | 4.53 | 15.9 − 84.1 | 49.1 | 45.0 | 12.8 | 8.98 − 47.6 | 27.9 | 25.5 | 7.27 |
| TCEP | 15.3 − 41.9 | 24.5 | 22.5 | 32.1 | 8.64 − 23.7 | 13.9 | 12.7 | 18.2 | 18.4 − 41.0 | 29.2 | 28.8 | 41.1 | 10.4 − 23.2 | 16.6 | 16.3 | 23.3 |
| TCPP | 12.8 − 550 | 119 | 24.0 | 24.0 | 7.23 − 311 | 67.5 | 13.6 | 13.6 | 13.0 − 424 | 87.0 | 22.8 | 22.8 | 7.33 − 240 | 51.0 | 12.9 | 12.9 |
| TDCPP | 2.48 − 5.31 | 3.63 | 3.51 | 1.75 | 1.40 − 3.00 | 2.05 | 1.99 | 0.99 | 2.48 − 5.13 | 3.78 | 3.54 | 1.77 | 1.40 − 2.91 | 2.14 | 2.00 | 1.00 |
| TDBPP | 1.45 − 3.32 | 2.12 | 2.19 | - | 0.82 − 1.88 | 1.20 | 1.24 | - | 1.46 − 3.79 | 2.35 | 2.52 | - | 0.83 − 2.15 | 1.33 | 1.43 | - |
| TCP | 1.03 − 5.45 | 2.75 | 2.51 | 1.25 | 0.58 − 3.09 | 1.56 | 1.42 | 0.71 | 1.20 − 6.91 | 3.65 | 3.55 | 1.78 | 0.68 − 3.91 | 2.07 | 2.01 | 1.01 |
| TPhP | 9.61 − 128 | 35.8 | 25.2 | 3.61 | 5.44 − 72.6 | 20.3 | 14.3 | 2.04 | 7.23 − 142 | 45.0 | 31.5 | 4.50 | 4.10 − 80.7 | 25.5 | 17.9 | 2.55 |
| EHDPP | 2.31 − 8.53 | 3.96 | 3.79 | 2.52 | 1.31 − 4.83 | 2.24 | 2.14 | 1.43 | 2.31 − 9.94 | 4.65 | 4.61 | 3.08 | 1.31 − 5.63 | 2.63 | 2.61 | 1.74 |
| ΣOPEs | 88.7 − 629 | 207 | 145 | 75.8 | 50.3 − 365 | 117 | 82.0 | 42.9 | 58.7 − 551 | 230 | 197 | 103 | 33.3 − 312 | 130 | 111 | 58.3 |
Our study conducted an extensive investigation into the occurrence of OPEs in airborne PM2.5 and TSP over a six-month period in Nanjing, China. More than half of the PM2.5 samples surpassed secondary concentration limits, but the levels of PM2.5 and TSP-bound OPEs were generally lower than reports in other Chinese cities. The higher concentration of OPEs in PM2.5 compared to TSP is likely due to the enhanced adsorption capacity of fine particles and the physicochemical properties of KOA. Correlation analysis revealed that meteorological factors such as temperature and air pressure have significant but opposing effects on OPEs levels in particulate matter. Additionally, air mass trajectories play a crucial role in shaping pollution characteristics. The observed discrepancy between particulate matter and OPE levels suggests that sources of particulate matter may differ from those of specific pollutants. The sources of OPEs were identified as traffic emissions, foam products, building construction activities, and indoor emissions. Our assessment of non-cancer risk exposure to OPEs through inhalation indicates no potential risk.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Ethical Approval
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Consent to Participate
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Consent to Publish
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Funding
This work was supported by the National Natural Science Foundation of China (Grant number 21507036, 42307468 and 42107398); the Natural Science Research Project of Anhui Educational Department (Grant number 2022AH050690); and the Jiangsu Funding Program for Excellent Postdoctoral Talent (Grant number 2023ZB688).
Author Contributions
Huaizhou, Xu: Conceptualization, Investigation, Data curation, Formal analysis, Methodology, Writing-review and editing, Project administration, Funding acquisition. Xinyong, Fei: Data curation, Investigation. Kaili, Wang: Writing-review and editing. Beicun, Wu: Data curation. Han Gao: Writing-review and editing, Funding acquisition. Bingyu Wang: Conceptualization, Writing-review and editing, Funding acquisition. Yiqun Chen: Conceptualization, Data curation, Formal analysis, Writing-original draft, Writing-review and editing, Funding acquisition.
Availability of data and materials
The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request. Source data are provided with this paper.
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Appendix A. Supplementary Information Supplementary information to this article, including 7 tables, and 3 figures with a total page of 15, can be found online.