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Article

Integrated Composition–Toxicity Assessment Reveals Seasonal Drivers of PM2.5 Health Risks in Hefei, China

by
Zhaoyin Ding
1,
Lei Cheng
2,* and
Tong Wang
3,*
1
Faculty of Engineering, Anhui Sanlian University, Hefei 230601, China
2
School of Environment and Life Health, Anhui University of Applied Technology, Hefei 230011, China
3
School of Public Health, Anhui University of Science and Technology, Hefei 231131, China
*
Authors to whom correspondence should be addressed.
Toxics 2026, 14(2), 172; https://doi.org/10.3390/toxics14020172
Submission received: 15 January 2026 / Revised: 11 February 2026 / Accepted: 13 February 2026 / Published: 15 February 2026
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Abstract

Amidst rapid urbanization, fine particulate matter (PM2.5) has emerged as a critical environmental challenge in China, posing substantial health risks due to its complex composition and diverse sources. This study provides a seasonally resolved analysis of PM2.5 composition and multi-faceted toxicity in Hefei, a major Chinese manufacturing center. PM2.5 samples collected across four seasons were chemically characterized for water-soluble ions, carbonaceous components, metals, and polycyclic aromatic hydrocarbons (PAHs) and derivatives. Their toxicological effects were evaluated through oxidative potential (OP), cytotoxicity, and reactive oxygen species (ROS) generation in the human bronchial epithelial cell line BEAS-2B. The results reveal significant seasonal variations in PM2.5 concentration and composition. Winter exhibited the highest PM2.5 levels (68.31 ± 17.12 μg/m3), with enrichment of secondary inorganic aerosols (SIAs), toxic metals (Pb, Cd, As), and high-molecular-weight PAHs. Spring showed elevated crustal elements (Al, Fe, Mn), while summer had the lowest pollutant concentrations. Toxicity assays reflected the following patterns: winter PM2.5 demonstrated the highest OP (0.1423 ± 0.0368 nmol DTT/min/μg), strongest cytotoxicity (51.85% cell viability), and greatest ROS induction (2.28-fold increase). Statistical analyses identified distinct toxicity drivers: OP was associated with SIA (NO3−, NH4+) and redox-active metals (Cu, Zn); cytotoxicity correlated with toxic metals and PAHs; whereas ROS showed weaker compositional correlations. This integrated “composition–toxicity” assessment reveals that the elevated health risk in winter stems from a synergistic mix of secondary aerosols and combustion-derived toxicants, urging a shift toward component-specific, risk-based air quality management strategies.

Graphical Abstract

1. Introduction

Fine particulate matter (PM2.5) pollution represents a formidable global environmental and public health challenge associated with rapid urbanization. Its complex physicochemical composition, encompassing water-soluble ions, carbonaceous components, various trace metals, polycyclic aromatic hydrocarbons (PAHs), and their derivative components, is intrinsically linked to a spectrum of adverse health outcomes, including respiratory and cardiovascular diseases [1]. The global burden of disease estimates for 2019 attribute approximately 5.5 million deaths to ambient PM2.5 exposure [2]. This substantial health risk is further underscored by the World Health Organization’s 2021 revision of its air quality guideline, wherein the annual PM2.5 standard was strengthened from 10 μg/m3 to 5 μg/m3 based on comprehensive evidence synthesis [3,4].
The chemical composition of PM2.5 primarily determined by emission sources, exhibits marked spatiotemporal heterogeneity due to varying meteorological and geographical conditions [5,6]. This compositional variability further translates into divergent health impacts. Moreover, local industrial structure and terrain play critical roles in modulating the generation mechanisms, regional transport, source apportionment, and ultimate health effects of PM2.5 across different regions [7,8]. Numerous studies have elucidated the mass concentration profiles and overall chemical composition of PM2.5 across cities worldwide, alongside the apportionment of its major sources. For instance, source apportionment analysis utilizing positive matrix factorization (PMF) has revealed that secondary nitrate constitutes the primary contributor to PM2.5 concentrations in Beijing, China, which accounting for approximately 25.5% of its particulate mass [9]. PM2.5 in Mabopane, South Africa, originates from both local and distant sources, with the most prevalent elements being Fe, S, K, Ca, and Si [10]. Biomass burning and secondary aerosols were identified as the principal PM2.5 sources during the dry season in Riau, Indonesia, through PMF modeling [11]. Organic carbon is projected to become the largest contributor to PM2.5 in the South Coast Air Basin based on long-term chemical trend analysis [12]. Due to significant regional disparities in industrial composition, energy infrastructure, topographic features, and urbanization patterns, PM2.5 control strategies developed for specific cities cannot be directly replicated for precise pollution prevention in other urban contexts [13]. Moreover, a quantitative connection between specific emission sources and their respective toxicological potencies remains to be established in existing studies. Conventional source apportionment studies, often employing receptor models, provide essential insights into source identification and quantification but typically fall short of systematically evaluating the differential health risks posed by distinct source categories [14]. Conversely, toxicological assessments frequently lack robust integration with high-resolution, source-resolved chemical profiles.
As a core manufacturing hub in the Yangtze River Delta with industries like home appliances and new energy vehicles, Hefei faces complex air pollution. However, systematic studies on PM2.5 source–composition–toxicity relationships relevant to its industrial features are scarce, hindering targeted pollution control, necessitating an integrated source–toxicity analytical framework.
To elucidate the intrinsic relationship between the chemical composition of PM2.5 and its biological effects, this study systematically investigates the seasonal variation characteristics of key chemical components—including water-soluble ions, carbonaceous species, metals, and polycyclic aromatic hydrocarbons—in PM2.5. The toxicological potential is assessed through multi-dimensional biological endpoints such as in vitro cytotoxicity, reactive oxygen species (ROS) generation, and oxidative potential (OP). Furthermore, statistical modeling is employed to identify the key chemical constituents driving the observed toxicological outcomes. By establishing an integrated “chemical composition–toxicity effect” assessment framework, this research aims to provide a scientific basis for developing health risk-informed air quality management strategies.

2. Materials and Methods

2.1. Collection of PM2.5

PM2.5 samples were collected at a site adjacent to provincial-level monitoring station in urban Hefei (31°30′38″ E, 117°4′21″ N) using a KB-120F sampler (Genetc, Qingdao, China). All sampling and pretreatment procedures followed the Chinese environmental protection standards (HJ 618-2011) [15]. Prior to sampling, quartz fiber filters were baked in a muffle furnace at 550 °C for 5 h to remove organic contaminants. The baked filters were then equilibrated in a constant temperature and humidity chamber (25 ± 3 °C, 40 ± 5% relative humidity) for 24 h and weighed twice on an electronic analytical balance. The average of the two measurements was recorded as the pre-sampling filter weight. Continuous sampling was conducted over 20 days period with daily filter replacement. Sampling campaigns were carried out in January, April, July, and October to represent winter, spring, summer, and autumn conditions, respectively. After sampling, the loaded filters underwent the same weighing procedure. The net mass of collected PM2.5 was determined by subtracting the pre-sampling weight from the post-sampling weight. All samples were stored at −18 °C for subsequent chemical and toxicological analyses.

2.2. Chemical Analysis of PM2.5

The collected quartz filter membranes were cut into 1 cm2 pieces for subsequent analyses. The carbonaceous components, including organic carbon (OC), elemental carbon (EC), and total carbon (TC), were determined using a thermal–optical carbon analyzer (DRI Model 2001A, Atmos-lytic, Calabasas, CA, USA) [7]. Additionally, the concentrations of water-soluble inorganic ions (WSIIs, including F−, SO42−, NO3−, NO2−, NH4+, Cl−, K+, Mg2+, Na+, and Ca2+), water-soluble metals (WSMs, including Al, Co, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Ba, Pb, Cd), parent polycyclic aromatic hydrocarbons (P-PAHs, referring to the 16 U.S. EPA priority PAHs), oxygenated PAHs (O-PAHs), and nitrated PAHs (N-PAHs)—collectively abbreviated as ON-PAHs—in the PM2.5 samples were quantitatively analyzed using an ion chromatography system (ICS-3000, Dionex, Sunnyvale, CA, USA), an inductively coupled plasma mass spectrometer (ICP-MS, PerkinElmer, Waltham, MA, USA), and a gas chromatography–mass spectrometer (GC-MS, 1300+ISQ LT, ThermoFisher, Waltham, MA, USA), respectively [16,17]. Detailed experimental procedures, including instrumentation, operational conditions, and quality control measures for each analytical method, are provided in the Supplementary Text S1.
In order to identify and evaluate the negative effects of PAHs on human health, B[a]P was taken as the toxicity index of PAHs (BaPeq), and the equivalent toxic equivalent of PAHs mixture was calculated with the formula as follows:
B a P e q = ∑ C i × T E F i
where C i is the concentration of each PAH; T E F i is a toxic equivalent factor defined by Nisbet and Lagoy, as shown in Table S2.

2.3. Toxicology Assay of PM2.5

PM2.5 samples were prepared via ultrasonication-assisted dual-solvent extraction using ultrapure water and CH2Cl2. Prior to toxicological assays, all PM2.5 samples were centrifuged at 12,000 rpm for 15 min, without 0.22 μm filtration to preserve the original characteristics of total PM2.5 particles. The filtrates were then dried under nitrogen and redispersed in DMSO. Final PM2.5 treatment concentrations in all assays were maintained within a non-cytotoxic range.
The human bronchial epithelial cell line BEAS-2B (Conservation Genetics CAS Kunming Cell Bank, Kunming, Yunnan, China) was cultured in high-glucose DMEM supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37 °C with 5% CO2. This cell line retains key epithelial functions and serves as a relevant model for studying PM2.5–lung interactions.
Cell viability was evaluated using a CCK-8 assay. Cells were seeded in 96-well plates (5 × 103 cells/well), allowed to attach for 24 h, and then treated with 100 μg/mL PM2.5 for 24 h. After incubation with 10% CCK-8 reagent for 4 h, absorbance was measured at 450 nm.

2.4. ROS Measurement

Total intracellular ROS levels were detected using the fluorescent probe 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA). Following PM2.5 exposure, cells cultured in 96-well plates were incubated with 10 μM DCFH-DA in serum-free medium for 30 min at 37 °C in the dark. After washing with PBS to remove excess probe, the fluorescence intensity (excitation/emission: 485/535 nm) was measured using a SpectraMax i3x multi-mode microplate reader (Molecular Devices, San Jose, CA, USA).

2.5. Determination of Oxidative Potential

The oxidative potential (OP) of PM2.5 was quantified via the dithiothreitol (DTT) assay, following the experimental protocol as described [18,19]. Specifically, ultra-pure water was employed for the extraction of PM2.5 from filter membranes, and the resulting extract was filtered through a 0.22 μm polytetrafluoroethylene membrane. A 10 μL aliquot of the PM suspension was mixed with 100 μL of 0.5 M potassium phosphate buffer (pH 7.4) and 340 μL of ultrapure water, followed by incubation at 37 °C for 10 min. Subsequently, the reaction was triggered by the addition of 50 μL of 1 mM DTT (Sigma-Aldrich, Zwijndrecht, The Netherlands). At predetermined time intervals (0, 10, 20, and 30 min), the reaction was terminated by introducing 5,5′-dithiobis (2-nitrobenzoic acid) (DTNB, Solarbio Science & Technology Co., Ltd., Beijing, China). Absorbance measurements were performed at a wavelength of 412 nm using a spectrophotometer. The DTT consumption rate was calculated through linear regression analysis of the absorbance time data, which was averaged from three independent replicate measurements. The final OP value was expressed as nmol DTT per minute per microgram (nmol DTT/min/μg). A reaction mixture without PM2.5 (utilizing a blank filter) served as the negative control, while 1,4-naphthoquinone was used as the positive control. All experimental treatments were conducted in triplicate to ensure reproducibility.

2.6. Statistical Analysis

All toxicology experiments were performed with at least three independent biological replicates. Data are presented as mean ± standard deviation (SD). Differences between groups were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test, with statistical significance set at p < 0.05. The associations between source apportionment results and toxicity data were evaluated using Spearman’s rank correlation analysis and multiple linear regression models. All statistical analyses were conducted using SPSS 17.0 software.

3. Results and Discussion

3.1. Seasonal Variations in Chemical Constituents of PM2.5

In line with the study’s objective to characterize source-resolved PM2.5 profiles in Hefei, pronounced seasonal fluctuations in both mass concentration and chemical makeup were observed. As shown in Figure 1A, the monthly average PM2.5 mass concentrations exhibited a clear pattern, with the highest level recorded in winter (January: 68.31 ± 17.12 μg/m3), followed by spring (April: 41.23 ± 17.65 μg/m3) and autumn (October: 33.86 ± 15.43 μg/m3). The lowest concentration occurred in summer (July: 19.82 ± 6.54 μg/m3). The values obtained in this study were systematically higher than regional background monitoring data (reported by the Anhui Provincial Environmental Protection Bureau, https://sthjt.ah.gov.cn (accessed on 1 January 2026)), which is consistent with the sampling site’s proximity to traffic arteries and underscores the significance of local emission sources. It should be noted that the present study employed discrete 20-day sampling campaigns during one representative month per season. While this design captures key seasonal differences in PM2.5 composition and toxicity, it does not account for potential intra-seasonal variability or short-term pollution episodes that may influence PM2.5 characteristics. Future studies incorporating longer sampling durations or higher temporal resolution across seasons would help to further validate and generalize the seasonal trends reported here.

3.1.1. Carbon Analysis

Analysis of carbonaceous constituents revealed distinct temporal patterns. As shown in Figure 1B, the mass fractions of organic carbon (OC), elemental carbon (EC), and total carbon (TC), expressed in grams per kilogram of PM2.5, showed significant variation. The highest concentration of TC was observed in spring (April: 320.53 ± 16.55 g/kg), followed by winter (January: 254.67 ± 20.01 g/kg) and autumn (October: 210.02 ± 18.56 g/kg), with the lowest value recorded in summer (July:195.33 ± 12.94 g/kg). This trend was primarily driven by OC, which also peaked in spring (285.32 ± 16.22 g/kg).
The OC/EC ratio (Table S1), a key indicator for inferring secondary formation and source contributions, exhibited a different seasonal pattern. The highest ratio occurred in winter (6.38 ± 5.17), suggesting a greater relative influence of secondary organic aerosol formation or specific combustion sources during this period under stagnant meteorological conditions [20]. Lower and more stable ratios were found in autumn (5.60 ± 0.55), and particularly summer (5.65 ± 0.52). The consistently low summer ratio points towards a dominant contribution from primary combustion emissions, such as vehicle exhaust, which aligns with the urban traffic-proximate nature of the sampling site. The notable elevation of the OC fraction across all seasons underscores the predominant role of organic aerosols in PM2.5 pollution in Hefei.

3.1.2. Water-Soluble Inorganic Ions

Analysis of water-soluble inorganic ions (WSII) in PM2.5 revealed significant variations across the sampling months. The total WSII loading (expressed in g per kg of PM2.5) was highest in winter (626.91 ± 58.17), followed by spring (511.02 ± 43.24) and autumn (470.86 ± 35.43), with the lowest value observed in summer (394.99 ± 39.07). As shown in Figure 2, secondary inorganic aerosols (SIA), namely nitrate (NO3−), sulfate (SO42−), and ammonium (NH4+), dominated the WSII composition across all seasons.NO3− showed the most pronounced seasonal fluctuation, peaking in January (195.63 ± 35.32 g/kg) and reaching its minimum in July (55.85 ± 17.80 g/kg). NH4+ followed a similar trend, with its highest concentration also in January (146.81 ± 31.20 g/kg). In contrast, SO42− demonstrated a more stable annual profile, although its lowest level was recorded in October (117.29 ± 16.72 g/kg). Other ions displayed specific seasonal patterns. Notably, fluoride (F−), nitrite (NO2−), and potassium (K+) reached their maximum concentrations in July (12.59 ± 3.54, 38.98 ± 12.56, and 17.57 ± 3.66 g/kg, respectively). Calcium (Ca2+) decreased steadily from its January peak (54.54 ± 21.71 g/kg) to an October minimum (18.18 ± 6.89 g/kg). Chloride (Cl−), sodium (Na+), and magnesium (Mg2+) did not exhibit strong seasonal trends.
The winter enrichment of NO3− and NH4+ is consistent with conditions that favor ammonium nitrate formation, including high precursor emissions from heating, low temperatures, high humidity, and a shallow boundary layer. The summer minimum in NO3− reflects its thermal instability [21,22]. The summer peaks in F−, NO2−, and K+ hint at specific seasonal sources, such as potential industrial processes, unique photochemistry, and agricultural biomass burning, respectively [23]. The relatively constant SO42− burden points to a steady regional source like coal consumption. The high SIA contribution underscores the critical role of precursor gases (NOx, SO2, NH3) in PM2.5 formation in Hefei.

3.1.3. Water-Soluble Metals

The concentrations of water-soluble metals (WSM) in PM2.5, expressed in milligrams per kilogram (mg/kg), displayed discernible seasonal and elemental variations. As shown in Figure 3A,E,F, soil-derived elements such as aluminum (Al), iron (Fe), and manganese (Mn) generally exhibited higher concentrations in spring (April), corresponding to increased dust resuspension driven by windy conditions characteristic of Hefei’s spring climate, compounded by intensified construction and agricultural activities during this season. Specifically, Fe reached its peak in spring (1396.24 ± 205.96 mg/kg), while Al and Mn also showed elevated levels during this period (986.82 ± 328.62 mg/kg and 104.59 ± 26.57 mg/kg, respectively). Consequently, crustal elements (Fe, Al) dominated the WSM compositional profile in spring, collectively constituting approximately 65% of the total mass (Figure 4). This dominance aligns with the well-documented influence of enhanced wind-driven resuspension and potential contributions from long-range transported dust [24].
Traffic-related and anthropogenic elements displayed distinct patterns. As shown in Figure 4 and Table S3, Zinc (Zn) maintained relatively high concentrations and accounted for a consistent proportion (9–14%) of the total throughout the year, with a maximum concentration observed in autumn (October: 370.01 ± 44.03 mg/kg). which peaked in relative terms during summer, underscores its origin from persistent, non-seasonally specific urban sources such as tire and brake wear, industrial processes, and waste incineration [25]. Copper (Cu) and lead (Pb) exhibited their highest concentrations in winter (January: 79.79 ± 9.23 mg/kg and 92.64 ± 22.31 mg/kg, respectively), followed by a gradual decline to their lowest levels in summer (July: 43.89 ± 5.00 mg/kg and 52.79 ± 8.68 mg/kg, respectively). Notably, cadmium (Cd), a toxic heavy metal, showed a similar seasonal trend to Cu and Pb, with its peak in winter (3.18 ± 0.34 mg/kg). This surge is likely driven by heightened emissions from domestic heating and intensified vehicular traffic during the cold season, with pollution accumulation exacerbated by a shallow atmospheric boundary layer and suppressed dispersion conditions.
Further highlighting specific source influences, vanadium (V) and arsenic (As) exhibited contrasting seasonal behaviors. V peaked distinctly in autumn (7.24 ± 2.26 mg/kg), a pattern potentially indicative of sources like residual fuel oil combustion. As, however, reached its maximum concentration in winter (17.44 ± 5.51 mg/kg), strongly suggesting a linkage to increased coal burning for heating. The co-elevation of these toxic metals (Pb, Cd, As) in winter results in a seasonally enhanced fractional abundance, underscoring a significantly elevated PM2.5 toxicity profile during this season [26,27].
The generally lower absolute concentrations of most metals in summer are primarily attributable to enhanced atmospheric dispersion, increased wet scavenging by precipitation, and potentially reduced anthropogenic emissions. However, toxic metals were consistently detected across all seasons, indicating persistent background pollution. The elevated concentrations observed in winter underscore a heightened seasonal health risk associated with PM2.5 exposure. These findings highlight the necessity for continuous air quality monitoring and the implementation of targeted, seasonally informed control measures. Priority should be given to reducing emissions from coal combustion and traffic in winter, controlling dust resuspension in spring, and managing perennial sources such as industrial activities and vehicular emissions, which contribute to the year-round presence of metals.

3.1.4. Parent Polycyclic Aromatic Hydrocarbons (P-PAHs)

The concentrations of 16 parent polycyclic aromatic hydrocarbons (P-PAHs) in PM2.5 exhibited pronounced seasonal and compositional variations. Total P-PAH concentrations followed a distinct seasonal pattern: winter (January) > autumn (October) > spring (April) > summer (July). This trend aligns with enhanced combustion emissions during colder periods and is modulated by seasonal meteorological conditions [28]. As shown in Figure 5A, 4-ring PAHs were the most abundant homologues across all seasons, with fluoranthene (FluA) and pyrene (Pyr) showing the highest concentrations, peaking in winter at 22.39 ± 7.66 mg/kg and 18.73 ± 8.47 mg/kg, respectively. The composition of P-PAHs shifted significantly with seasons. Low-molecular-weight PAHs (2–3 rings) contributed 22.5%, 24.1%, 28.7%, and 23.8% to the total P-PAH mass in January, April, July, and October, respectively. Their relative proportion was highest in summer, likely due to increased volatility and photochemical degradation of higher-ring PAHs [29]. In contrast, high-molecular-weight PAHs (4–6 rings) dominated the profile, constituting 77.5%, 75.9%, 71.3%, and 76.2% of the total mass across the four seasons. Their absolute concentrations were highest in winter, with the combined load of 4–6 ring PAHs being approximately 5.2 times greater in January than in July. This indicates a strong influence of high-temperature combustion sources (e.g., coal, biomass, vehicle engines) during cold months [30,31,32]. The carcinogenic marker benzo[a]pyrene (BaP) demonstrated a clear winter maximum (9.11 ± 2.76 mg/kg), with its concentration in January being about 3.7 times higher than its summer minimum (2.47 ± 1.25 mg/kg).
The cumulative carcinogenic potency, expressed as BaP-equivalent concentration (BaPeq), mirrored the seasonal trend of total P-PAHs (Figure 5B). The calculated BaPeq was highest in winter (14.65 ± 1.68 mg/kg), followed by autumn (10.29 ± 1.30 mg/kg), spring (8.86 ± 1.08 mg/kg), and summer (3.98 ± 0.59 mg/kg). Notably, 5-ring and 6-ring PAHs (particularly BaP, DahA, IcdP, and BbF) contributed over 85% to the total BaPeq in all seasons (Figure 5C), underscoring the disproportionate toxicity burden posed by high-molecular-weight congeners [28].
The seasonal pattern—winter/autumn enrichment of total and high-ring PAHs—strongly implicates heating-related solid fuel combustion as a dominant source during cold seasons, exacerbated by a shallow boundary layer and stagnant meteorological conditions [33]. Diagnostic isomer ratios (e.g., FluA/(FluA+Pyr) and IcdP/(IcdP+BghiP)) further corroborate significant contributions from coal and biomass combustion [34,35]. The elevated BaPeq in winter, driven primarily by high-ring PAHs, indicates a significantly heightened inhalation cancer risk during this period [36]. Although absolute concentrations decrease in warmer months, the persistent presence of carcinogenic PAHs across all seasons necessitates continuous monitoring and targeted emission controls.

3.1.5. Oxygenated and Nitrated Polycyclic Aromatic Hydrocarbons (O-/N-PAHs)

The seasonal concentrations of selected oxygenated PAHs (O-PAHs) and nitrated PAHs (N-PAHs) in PM2.5 are presented in Table 1, respectively. Both compound classes exhibited significant seasonal variability, mirroring the pattern observed for their parent PAHs. This consistency suggests common or co-located primary emission sources and similar influences from seasonal atmospheric conditions on their fate. Among the seven quantified O-PAHs, 9,10-anthraquinone (9,10-AtQ) was the most abundant species across all seasons, reaching its highest concentration in winter (4.22 ± 1.97 mg/kg). 1,4-Anthraquinone (1,4-AtQ), benzo[a]fluorenone (BaFlu), and benzo[b]fluorenone (BbFlu) also showed substantial levels, with winter maxima of 3.51 ± 1.12, 2.85 ± 1.05, and 3.22 ± 1.10 mg/kg, respectively. The concentrations of all O-PAHs decreased markedly in summer, typically to 25–40% of their winter values. This seasonal amplitude is less pronounced than that observed for some parent PAHs, potentially indicating significant secondary formation pathways for O-PAHs during warmer months, partially offsetting the reduction in primary emissions [37].
For N-PAHs, 2-nitrofluoranthene (2N-FluA) and 1-nitropyrene (1N-Pyr) were the dominant compounds, with peak concentrations in winter (1.87 ± 0.83 and 1.64 ± 0.64 mg/kg, respectively). 1-nitronaphthalene (1N-Nap) also showed considerable levels. The seasonal variation for N-PAHs was particularly strong; for instance, the winter concentration of 2N-FluA was approximately 3.8 times higher than its summer concentration. This steep gradient is indicative of a predominant contribution from primary combustion emissions (e.g., diesel engines, coal burning) during cold seasons, based on established chemical tracers and seasonal emission patterns [38,39]. The lower summer concentrations likely result from a combination of reduced primary emissions, increased photochemical degradation of nitroarenes, and potentially enhanced volatilization.
The parallel seasonal trends between O-/N-PAHs and parent PAHs strongly suggest that primary combustion emissions are a major direct source for these derivatives. However, the different relative seasonal amplitudes offer clues about secondary formation. The relatively smaller summer–winter difference for certain O-PAHs hints at non-negligible secondary production via photochemical oxidation of parent PAHs during periods of high solar irradiation [40,41]. Conversely, the sharp decline of most N-PAHs in summer aligns with their known photolability and suggests that secondary nitration in the atmosphere is not a dominant source in this region compared to direct emission.

3.2. Oxidation Potential

The oxidative potential (OP) of PM2.5, a key metric reflecting its capacity to generate reactive oxygen species (ROS) and induce oxidative stress in biological systems, exhibited significant seasonal variation (Figure 6A). The mean OP values followed a clear seasonal trend: winter (0.1423 ± 0.0368) > autumn (0.0835 ± 0.0562) > spring (0.0396 ± 0.0116) > summer (0.0198 ± 0.0147). The OP in winter was approximately 7.2 times higher than in summer, indicating a substantially greater intrinsic pro-oxidative toxicity of PM2.5 during the cold season. The pronounced seasonality in OP aligns closely with the observed trends in the concentrations of redox-active chemical components within PM2.5. The elevated OP in winter correlates strongly with the concurrent peaks in the concentrations of transition metals (e.g., Fe, Cu, Mn) and toxic carbonaceous species, including polycyclic aromatic hydrocarbons (PAHs) and their derivatives (e.g., quinones). While OP indicates the intrinsic oxidative capacity of PM2.5 components, intracellular ROS levels are modulated by additional biological factors such as cellular uptake efficiency, metabolic activation, and antioxidant response.

3.3. Cytotoxicity and Oxidative Stress Induction of PM2.5

The chemical composition data were from 0.22 μm filtered PM2.5 extracts (a prerequisite for accurate instrumental quantification), while the toxicological data here were obtained from unfiltered total PM2.5 particles. Subsequent composition–toxicity correlation analysis was to identify the key water-soluble/extractable chemical drivers of the overall toxicity of total PM2.5. The in vitro toxicological assessment of seasonal PM2.5 samples revealed significant variations in both cytotoxic effects and oxidative stress induction capacity (Figure 6C). We selected 100 μg/mL as the experimental treatment dose, as it induced a clear concentration-dependent reduction in cell viability while maintaining sufficient viable cells for subsequent functional assays(Figure S1). Although samples were processed through centrifugation to minimize the inclusion of insoluble particles such as potential quartz fragments from the collection filter, the observed biological effects are primarily attributed to the complex mixture of soluble chemical constituents in the PM2.5 particles, as supported by control experiments with unfiltered blank quartz filters (Figure S2). Cell viability, measured by CCK-8 assay, demonstrated a distinct seasonal pattern with the most pronounced cytotoxicity observed in winter (January: 51.85 ± 12.16% viability), followed by autumn (October: 58.42 ± 7.31%) and spring (April: 69.73 ± 3.14%). The summer sample (July) showed the highest cell viability (76.63 ± 2.57%), comparable to vehicle control levels (99.17 ± 2.56%). This seasonal variation in cytotoxic potency highlights distinct differences in PM2.5’s ability to impair cell survival across seasons. The marked viability reduction in winter samples (over 48% loss relative to control) reflects severe cell damage—Such a degree of viability loss typically links to downstream cellular dysfunction, such as apoptotic signaling and metabolic impairment [42]. These processes can compromise the structural and functional integrity of target cells even at sub-lethal doses, which can compromise the structural and functional integrity of target cells even at sub-lethal doses. By comparison, the milder viability reduction in summer samples suggests their soluble components exert far weaker toxic effects on cell survival.
Notably, intracellular ROS generation exhibited an inverse seasonal relationship. Winter PM2.5 extracts induced the highest ROS production (2.28 ± 0.47-fold increase relative to control), followed by autumn samples (2.14 ± 0.52-fold). Spring and summer samples showed moderate ROS induction (1.64 ± 0.33-fold and 1.65 ± 0.23-fold, respectively). Vehicle controls demonstrated minimal ROS induction (1.07 ± 0.12-fold). This inverse relationship supports oxidative stress as a key mechanistic mediator of seasonal PM2.5–induced cytotoxicity: higher ROS accumulation in winter samples likely drives more extensive oxidative damage to cellular macromolecules, including lipids, proteins and DNA [43,44], thereby exacerbating cell viability loss. These seasonal differences align with typical variations in PM2.5 chemical composition: winter PM2.5 often accumulates higher concentrations of toxic soluble constituents, such as polycyclic aromatic hydrocarbons and heavy metals [45]. Due to increased combustion emissions [46] and stagnant atmospheric diffusion, contributing to its stronger cytotoxic effects. In contrast, summer PM2.5 may contain lower levels of such toxic components [47], resulting in weaker impacts on cell viability.

3.4. Identification of Key Chemical Drivers of Toxicity

To identify the primary chemical constituents responsible for the observed toxicological effects, spearman correlation analyses were conducted between the measured biological endpoints OP, cytotoxicity, and intracellular ROS and the concentrations of major chemical components in PM2.5. As shown in Figure 7A, OP showed significant positive correlations with secondary inorganic aerosols (SIAs), including nitrate (NO3−, r = 0.67, p < 0.01) and ammonium (NH4+, r = 0.44, p < 0.01), suggesting that secondary formation processes contribute substantially to the oxidative burden of PM2.5. cytotoxicity was strongly and negatively correlated with nitrate (NO3−, r = –0.76, p < 0.01). In contrast, intracellular ROS generation was not significantly correlated with any major WSII. Significant positive correlations were observed between OP and several metallic elements, including Co, V, Cr, Ni, Cu, Zn, As, Pb, and Cd, with correlation coefficients (r) ranging from 0.55 to 0.72 (p < 0.01). In contrast, cytotoxicity exhibited significant negative correlations with the same set of elements, with correlation coefficients (r) spanning from –0.74 to –0.84 (p < 0.01). ROS showed no strong correlations with most metal elements, except for Al and Fe (p < 0.05). Both OP and cell viability showed significant correlations with each of the 16 parent PAHs (p < 0.01), whereas no such correlations were observed for ROS.
The correlation analyses reveal distinct associations between specific chemical components and each of the three toxicological endpoints, reflecting the multi-component nature of PM2.5 toxicity. The strong positive association of OP with secondary inorganic ions ((NO3−, NH4+) underscores the role of atmospheric processing in enhancing oxidative burden, consistent with winter elevation under conditions favoring secondary aerosol formation [21,48]. Notably, metals display divergent relationships: broad positive correlations between OP and redox-active/toxic metals (e.g., Cu, Zn, As, Pb) contrast sharply with strong negative correlations of the same metals with cell viability, suggesting that metal-induced oxidative stress (reflected by OP) and cytotoxicity may operate through different biological pathways or cellular compartments. The consistent correlations of both OP and cell viability with all 16 parent PAHs point to a shared influence from combustion-derived constituents, yet the lack of PAH-ROS association indicates that PAHs are not key factors of acute intracellular ROS bursts in this model [49,50,51]. The limited correlation of intracellular ROS with most chemical components—except weak links to Al and Fe—implies that ROS generation may be modulated by factors beyond bulk composition, such as particle bioavailability or synergistic interactions. It should be noted that the associations reported between specific chemical components and toxicological outcomes are based on correlation analyses and do not imply direct causality. Given the known collinearity among PM2.5 constituents (e.g., the wintertime co-enrichment of secondary inorganic aerosols, metals, and polycyclic aromatic hydrocarbons), observed correlations may reflect concomitant variation rather than independent mechanistic roles. Future studies employing source apportionment modeling, controlled component addition experiments, or advanced multivariate regression techniques would be valuable to further disentangle the individual contributions of specific components.

4. Conclusions

This study systematically links PM2.5 chemical composition with toxicological outcomes in Hefei, revealing strong seasonal heterogeneity. Winter pollution, characterized by elevated secondary inorganic aerosols, toxic metals, and high-molecular-weight PAHs, exhibited the highest oxidative potential, cytotoxicity, and ROS induction, indicating substantially greater health risks per unit mass during cold seasons. Toxicity endpoints showed distinct drivers: OP correlated with secondary aerosols and redox-active metals, cytotoxicity with toxic metals and PAHs, while ROS generation suggested complex particle-specific interactions. These findings underscore the necessity of shifting from mass-based to component-specific, risk-informed air quality management, prioritizing control of combustion-derived and secondary aerosol precursors during high-pollution seasons to more effectively protect public health. This study links soluble/extractable chemical composition (filtered for accurate quantification) with the integrated toxic effects of unfiltered total PM2.5, a rational framework for identifying the key chemical drivers of PM2.5 health risks.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics14020172/s1. Figure S1: Dose-dependent effects of seasonal PM2.5 samples on cell viability; Figure S2: Biological assessment of blank quartz filter extracts; Table S1: Carbon Composition in PM2.5; Table S2: Chemical information and TEF for 16 US-EPA PAHs.; Table S3: Seasonal concentrations of water-soluble inorganic ions and water-soluble metals in PM2.5 [7,16,17]. Text S1: Chemical analysis of PM2.5

Author Contributions

Conceptualization, methodology, supervision, project administration, funding acquisition, Z.D.; methodology, software, formal analysis, data curation, visualization, T.W.; validation, investigation, resources, L.C.; writing—original draft preparation, writing—review and editing, Z.D. and T.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Natural Science Research Projects of Anhui Sanlian University (KJZD2025019), and the Natural Science Foundation of Anhui Province Higher School (2024AH050908, 2025AHGXZK31124, 2025AHGXZK31029), the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (2024yjrc23).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are contained within the article and its Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Seasonal variations in PM2.5 mass concentrations (A) and carbonaceous component fractions (B) in Hefei. In panel (A), the bars represent the mean PM2.5 mass concentrations, and the solid green line shows the temporal trend. In panel (B), the bars represent the mass fractions of OC, EC, and TC across different seasons.
Figure 1. Seasonal variations in PM2.5 mass concentrations (A) and carbonaceous component fractions (B) in Hefei. In panel (A), the bars represent the mean PM2.5 mass concentrations, and the solid green line shows the temporal trend. In panel (B), the bars represent the mass fractions of OC, EC, and TC across different seasons.
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Figure 2. Seasonal variations in the concentrations of water-soluble inorganic ions in PM2.5 in Hefei (A–J): F−, Cl−, NO2−, NO3−, SO42−, NH4+, K+, Ca2+, Mg2+ and Na+); (K) Total water-soluble inorganic ions concentration; (L) Water-soluble inorganic ions abundance. *: p < 0.05, **: p < 0.01, ***: p < 0.001, compared with each group.
Figure 2. Seasonal variations in the concentrations of water-soluble inorganic ions in PM2.5 in Hefei (A–J): F−, Cl−, NO2−, NO3−, SO42−, NH4+, K+, Ca2+, Mg2+ and Na+); (K) Total water-soluble inorganic ions concentration; (L) Water-soluble inorganic ions abundance. *: p < 0.05, **: p < 0.01, ***: p < 0.001, compared with each group.
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Figure 3. Seasonal variations in the concentrations of water-soluble metals in PM2.5 in Hefei ((A–N) Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, As, Zn, Cd, Ba, Pb); (O) Total water-soluble metals concentration. *: p < 0.05, **: p < 0.01, ***: p < 0.001,compared with each group.
Figure 3. Seasonal variations in the concentrations of water-soluble metals in PM2.5 in Hefei ((A–N) Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, As, Zn, Cd, Ba, Pb); (O) Total water-soluble metals concentration. *: p < 0.05, **: p < 0.01, ***: p < 0.001,compared with each group.
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Figure 4. Seasonal variations in water-soluble metals abundance in PM2.5.
Figure 4. Seasonal variations in water-soluble metals abundance in PM2.5.
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Figure 5. (A) Individual concentrations of 16 P-PAHs. (B) Total concentrations of 16 P-PAHs; (C) Toxic potency of P-PAHs as indicated by BaP; (D) Total concentrations and (E) BaPeq of 16 P-PAHs categorized into low-molecular-weight PAHs (LMW-PAHs), high-molecular-weight PAHs (HMW-PAHs), and 8 key carcinogenic PAHs (Σ8cPAHs). **: p < 0.01, ***: p < 0.001,compared with each group.
Figure 5. (A) Individual concentrations of 16 P-PAHs. (B) Total concentrations of 16 P-PAHs; (C) Toxic potency of P-PAHs as indicated by BaP; (D) Total concentrations and (E) BaPeq of 16 P-PAHs categorized into low-molecular-weight PAHs (LMW-PAHs), high-molecular-weight PAHs (HMW-PAHs), and 8 key carcinogenic PAHs (Σ8cPAHs). **: p < 0.01, ***: p < 0.001,compared with each group.
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Figure 6. (A) DTT consumption rates for PM2.5; (B) Intracellular ROS of BEAS-2B cells exposed to 100 μg/mL PM2.5 for 24 h; (C) Cell viability of BEAS-2B cells incubated with 100 μg/mL PM2.5 for 24 h. *: p < 0.05, **: p < 0.01, ***: p < 0.001, compared with each group.
Figure 6. (A) DTT consumption rates for PM2.5; (B) Intracellular ROS of BEAS-2B cells exposed to 100 μg/mL PM2.5 for 24 h; (C) Cell viability of BEAS-2B cells incubated with 100 μg/mL PM2.5 for 24 h. *: p < 0.05, **: p < 0.01, ***: p < 0.001, compared with each group.
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Figure 7. Spearman correlation coefficients (r) between the toxicity and (A) Water–soluble inorganic ions, (B) Water-soluble metals, (C) P-PAHs. *: p < 0.05, **: p < 0.01.
Figure 7. Spearman correlation coefficients (r) between the toxicity and (A) Water–soluble inorganic ions, (B) Water-soluble metals, (C) P-PAHs. *: p < 0.05, **: p < 0.01.
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Table 1. Seasonal concentrations of selected oxygenated and nitrated polycyclic aromatic hydrocarbons in PM2.5.
Table 1. Seasonal concentrations of selected oxygenated and nitrated polycyclic aromatic hydrocarbons in PM2.5.
JanuaryAprilJulyOctober
Oxy-PAHs (O-PAHs, mg/kg PM2.5)
1-NapA3.58 ± 1.421.75 ± 0.791.02 ± 0.772.21 ± 0.88
9-Flu1.83 ± 0.771.18 ± 0.690.70 ± 0.341.55 ± 0.73
9,10-AtQ4.22 ± 1.973.01 ± 1.021.83 ± 1.173.84 ± 1.30
1,4-AtQ3.51 ± 1.122.58 ± 1.401.46 ± 0.943.22 ± 1.52
BaFlu2.85 ± 1.051.99 ± 1.021.23 ± 0.922.54 ± 1.07
BbFlu3.22 ± 1.102.21 ± 0.931.33 ± 0.692.79 ± 1.03
BAN1.53 ± 0.821.07 ± 0.600.61 ± 0.521.34 ± 0.81
Nitro-PAHs (N-PAHs, mg/kg PM2.5)
1N-Nap1.28 ± 0.830.75 ± 0.600.34 ± 0.330.88 ± 0.56
2N-Flu0.81 ± 0.440.48 ± 0.430.27 ± 0.340.60 ± 0.39
9N-Ant0.54 ± 0.370.37 ± 0.360.17 ± 0.230.37 ± 0.36
2N-FluA1.87 ± 0.831.10 ± 0.550.49 ± 0.331.29 ± 0.54
1N-Pyr1.64 ± 0.640.96 ± 0.620.48 ± 0.321.13 ± 0.54
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Ding, Z.; Cheng, L.; Wang, T. Integrated Composition–Toxicity Assessment Reveals Seasonal Drivers of PM2.5 Health Risks in Hefei, China. Toxics 2026, 14, 172. https://doi.org/10.3390/toxics14020172

AMA Style

Ding Z, Cheng L, Wang T. Integrated Composition–Toxicity Assessment Reveals Seasonal Drivers of PM2.5 Health Risks in Hefei, China. Toxics. 2026; 14(2):172. https://doi.org/10.3390/toxics14020172

Chicago/Turabian Style

Ding, Zhaoyin, Lei Cheng, and Tong Wang. 2026. "Integrated Composition–Toxicity Assessment Reveals Seasonal Drivers of PM2.5 Health Risks in Hefei, China" Toxics 14, no. 2: 172. https://doi.org/10.3390/toxics14020172

APA Style

Ding, Z., Cheng, L., & Wang, T. (2026). Integrated Composition–Toxicity Assessment Reveals Seasonal Drivers of PM2.5 Health Risks in Hefei, China. Toxics, 14(2), 172. https://doi.org/10.3390/toxics14020172

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