We conducted a field campaign at the Heshan supersite in the PRD region from 29 September–17 November 2019. Considering the integrity of the measurements, we focus primarily on the period from 16 October–16 November 2019 in this study. The measurement site was located in a rural area surrounded by farms and villages (at 22°42^′39.1^′′ N, 112°55^′35.9^′′ E, with an altitude of about 40 m), situated to the southwest of the PRD region. All online instruments were placed in air-conditioned rooms on the top floor of the supersite building.

A FIGAERO-CIMS, coupled with an X-ray source, was used to measure organic compounds in both the gas- and particle-phase, utilizing I- as the chemical ionization reagent. The instrument operated on 1 h cycle by switching between two modes (sampling mode and desorption mode) for measuring gas- and particle-phase oxygenated organic molecules. In the sampling mode, ambient gas was measured in the first 21 min, followed by a 3 min zero air background, while the PM2.5 sample was collected on a PTFE membrane filter for 24 min. Then, the instrument was switched to the desorption mode, in which the collected particles were desorbed using heated N2. The temperature of the N2 was increased from approximately 25–175 °C over a 12 min period, and then held at 175 °C for an additional 24 min. Calibration of a few chemicals was conducted in the laboratory. For the remaining organic species, a voltage scanning method was used to determine their sensitivities (referred to as semi-quantified species) (Ye et al., 2021; Iyer et al.,  2016; Lopez-Hilfiker et al., 2016). The detailed operation settings, data processing, and calibration can be found in Cai et al. (2023) and Ye et al. (2021).

A soot particle aerosol mass spectrometer (SP-AMS, Aerodyne Research, Inc., USA) was used to measure the chemical composition of PM1 particles, including nitrate, sulfate, ammonium, chloride, black carbon, and OA. More details on the quantification using ionization efficiency, composition dependent collection efficiency, data analysis, and source apportionment of OA from AMS data (defined as AMS-OA) could be found in Kuang et al. (2021) and Cai et al. (2024). In brief, AMS-OA consisted of two primary OA factors and four secondary OA factors. The primary OA factors include hydrocarbon-like OA (HOA, 11 %) and biomass burning OA (BBOA, 20 %), which were mainly contributed by traffic and cooking emissions and biomass burning combustion, respectively. For SOA factors, biomass burning SOA (BBSOA, 17 %) was likely formed through oxidation of biomass burning emission; less oxidized oxygenated OA (LO-OOA, 24 %), which results from strong daytime photochemical processes; more oxidized oxygenated OA (MO-OOA, 17 %), related to regional transport; and nighttime-formed OA (Night-OA, 11 %) which was associated with nighttime chemistry.

Trace gases such as O3 and NOx were measured by gas analyzers (model 49i and 42i, Thermo Scientific, US). Meteorological parameters, including wind speed and wind direction, were measured by a weather station (Vantage Pro 2, Davis Instruments Co., US).

Positive matrix factorization (PMF) is a widely used tool for source apportionment of long timeseries data (Paatero and Tapper, 1994). In the desorption mode, the particulate organic compounds are thermo-desorbed and simultaneously measured by the FIGAERO-CIMS. Organic molecules with different volatility were characterized by thermograms (desorption signals vs temperature of N2). Here, we performed PMF analysis to the thermogram data of organic compounds measured by the FIGAERO-CIMS (FIGAERO-OA) using the Igor-based PMF Evaluation Tool (PET, v3.01, Ulbrich et al., 2009), which can be expressed as follows: 1X=GF+E, where X is the thermogram organic compound data measured by the FIGAERO-CIMS. which can be decomposed into two matrices G and F. The matrix G, F, and E contain the factor time series, factor mass spectra, and the residuals between the measured data and the reconstructed data.

The raw normalized count per second (NCPS) thermogram data with a time resolution of 1 s was averaged to a 20 s-time grid, and then was background-corrected by subtracting linearly interpolated background thermogram signals. For each scan, only the data points when the desorption temperature increased were used as input data (corresponding to 25–170 °C, 1–70 data points in this study, Fig. S1 in the Supplement), since the main information lies during the species desorbing from the FIGAERO filter (Fig. S1, Buchholz et al., 2020). Then, we combined data from separate thermogram scans (without background scans) to a larger input data set.

To perform the PMF analysis, a data uncertainty matrix (Si,j) is needed, where the i and j represents the index of ions and data points, respectively. According to Buchholz et al. (2020), the uncertainty was assumed to be constant for each individual thermogram scan (constant error scheme). The Si,j of a specific thermogram scan can be determined by the following equation: 2Si,j=σ. For each thermogram scan, the last 20 data points are assumed to be in steady state. Thus, the σnoise was calculated as the median of the standard deviation (SD) of the residual (resi,j), which can be obtained from the difference between the data points (Datai,j) and the corresponding linear fitted value (FittedDatai,j) for the measured data points (Fig. S2): 3resi,j=Datai,j-FittedDatai,j,4σ=medianSD(resi,j). Due to the large volume of the data matrix (59500×1028) exceeding the processing capacity of the PET, we had to divide the data matrix into three parts and performed PMF analysis separately. An eight-factor solution was selected for each part based on Q/Qexp behavior and factor interpretability (Figs. S3–S6). To assess factor consistency, the mass spectra of resolved factors were compared across different parts, showing strong correlations (R > 0.9) for the each factor (Figs. S7 and S8). Weaker correlations during the early campaign period (2–5 October) likely reflect changes in dominant OA sources under different meteorological conditions (Figs. S8 and S9). After excluding this period, consistent factor profiles were obtained and combined for further analysis. Detailed evaluations are provided in the Sect. S1 in the Supplement.

Since input data sets of PMF analysis were the NCPS data, the signal of each thermograms factor was a combination of NCPS values of different ions. Thus, it is necessary to convert the signal of these factors into mass concentrations, which would increase the representativeness of the thermogram PMF results. The NCPS of a specific ion was linearly correlated with the corresponding mass concentration. Thus, for a signal running cycle (a thermogram scan), the mass concentration of a specific thermograms OA factor k (Mk) can be estimated as: 5Mk=∑i∑jSignalj,k×Profilei,k∑jNCPSj,i×mi, where i and j represent the index of species and data points; the Signalj,k is the signal of a thermograms OA factor k at a data index j; the Profilei,k represents the fraction of signal of factor k and ion i; the NCPSj,i is the NCPS of species i at a data index j; and mi is the mass concentration of species i in the particle-phase measured by the FIGAERO-CIMS.

For a specific organic compound, the temperature of the peak desorption signal (Tmax) has a nearly linear relationship with the logarithm of saturation vapor pressure (Psat) of the respective organic compound (Lopez-Hilfiker et al., 2014): 6ln⁡(Psat)=aTmax+b, where a and b are fitting coefficients. Psat can be converted to saturation vapor concentration (C∗, µgm-3) by following equation: 7C∗=PsatMwRT106, where Mw is the average molecular weight of the organic compound (determined by the FIGAERO-CIMS), R is the gas constant (8.314 Jmol-1K-1), and T is the thermodynamic temperature (298.15 K). The fitting parameters of a and b were calibrated by a series of polyethylene glycol (PEG 5–8) compounds before the campaign. PEG standards (dissolved in acetonitrile) were atomized using a homemade atomizer, and the resulting particles were size-classified by a differential mobility analyzer (DMA; model 3081L, TSI Inc.) to target diameters of 100 and 200 nm. The size-selected particles were then split into two flows: one directed go to a CPC (3775, TSI) for the measurements of number concentration, and the other to the FIGAERO-CIMS particle inlet. The collected mass by CIMS was calculated based on the particle diameter, number concentration, FIGAERO-CIMS inlet flow rate, and collection time. The details of the calibration experiments and selection of fitting coefficients (a and b) can be found in Table S1 in the Supplement and Cai et al. (2024). In this study, the fitting parameters (a=-0.206 and b=3.732) were chosen, as the mass loading (407 ng) and diameter (200 nm) are closest to the ambient samples, since the collected mass loading centered at about 620 ng and the particle volume size distribution (PVSD) centered at about 400 nm (Cai et al., 2024). It was worth noting that the volatility range of PEG 5–8 (-1.73≤log⁡10C∗≤3.34µgm-3) may not fully represent the volatility of ambient organic aerosol, particularly nitrogen-containing and highly oxygenated compounds that can exhibit much lower volatility (log⁡10C∗≤-2µgm-3, Ren et al., 2022; Chen et al., 2024). At present, saturation vapor pressure data for PEG standards are only available up to PEG-8 (Krieger et al., 2018). Ylisirniö et al. (2021) demonstrated that different extrapolation approaches for estimating the volatility of higher-order PEGs can lead to substantial discrepancies in calibration results, and they strongly recommended that higher-order PEGs should only be used to extend the volatility calibration range once their saturation vapor pressures are accurately determined. Very recently, Ylisirniö et al. (2025) derived saturation vapor pressures for higher-order PEGs up to PEG-15 and demonstrated that extending FIGAERO-CIMS calibration to much lower volatilities is feasible, but also showed that different estimation approaches for higher-order PEGs can lead to large discrepancies, highlighting substantial uncertainties when extrapolating volatility calibration beyond PEG-8. Therefore, uncertainties may remain in the calibration of low-volatility OA, and further calibration experiments using complementary techniques are highly recommended. Therefore, uncertainties may remain in the calibration of low-volatility OA, and further calibration experiments using complementary techniques are highly recommended.

In this study, the average mass concentration of FIGAERO-OA was about 5.3±2.4µgm-3. The thermogram data sets of FIGAERO-OA were analyzed with PMF and mass concentration of each factor was estimated based on Eq. (), which provide volatility and mass concentration information of OA originating from different formation pathways. An 8-factor solution was chosen to explain the thermogram of FIGAERO-OA. These factors included six associated with daytime photochemical reactions, one related to biomass-burning, and one factor contributed by nighttime chemistry. The diurnal variation, mass spectra, and thermograms of these factors can be found in Fig. S9. The estimated volatility (log⁡10C∗), Tmax, and elemental information of all factors are shown in Table 1. Given that the thermogram data can provide volatility information of organic compounds, the identified OA factors were categorized based on their potential formation pathway, volatility, and correlation with AMS PMF factors (Table 1 and Fig. S10). For example, if the PMF factor with a Tmax located in the ranges of low volatile organic compounds (LVOC, approximately corresponding to 78.8–112.3 °C then Fig. 1), this factor will be named after low volatility OA (LVOA). For the factors whose Tmax is above 112.3 °C, extremely low volatility (ELVOA) will be named.

The six daytime chemistry related factors include a low volatility OA factor likely formed under high NOx condition (Day-HNOx-LVOA, 12 %), a low volatility factor contributed by gas-particle partitioning (Day-LNOx-LVOA, 11 %), a low volatility and an extremely low volatility factor originating from the daytime aging process (Day-aged-LVOA and Day-aged-ELVOA, 16 % and 11 % respectively), and a low volatility and an extremely low volatility factors related to urban air masses (Day-urban-LVOA and Day-urban-ELVOA, 16 % and 7 %, respectively). These daytime factors accounted for about 76.4 % of the total mass of FIGAERO-OA and demonstrated distinct daytime peak. The total mass of daytime FIGAERO-OA factors showed a strong positive correlation with LO-OOA in AMS-OA (R=0.86), which was attributed to photochemical reactions (Fig. S10a).

Both Day-HNOx-LVOA and Day-LNOx-LVOA reached their peak values at about 14:00 LT (Fig. 1a1 and b1), implying strong photochemical production. Day-HNOx-LVOA had the highest N : C (0.06) and the lowest oxidation state (OS‾c=-0.01), which could be attributed to the “high NOx” formation pathway. It was also supported by significant positive correlation (R=0.93-0.94) with particulate phase nitrogen-containing organic compounds (e.g., C4H5NO6, C8H11NO8, and C8H11NO9). Previous studies found that high NOx concentration can suppress the production of molecules with a high oxidation degree (Rissanen, 2018; Praske et al., 2018), which could explain the low OS‾c value (-0.01) and relative high volatility (log⁡10C∗‾=-0.98) found for Day-HNOx-LVOA. Day-LNOx-LVOA had a higher OS‾c (0.18) and lower log⁡10C∗‾ (-2.71) than Day-HNOx-LVOA, consistent with that Day-LNOx-LVOA was composed of smaller and more oxidized non-nitrogen containing compounds (e.g., C2H2O3, C3H4O3, C4H6O4, and C6H8O4). Noting that C2–C3 group could originate from the decomposition of larger molecules during thermal desorption, since the thermogram of C2H2O3 and C3H4O3 demonstrated a bimodal distribution (Fig. 9a). Figure S12b and d further examine the contribution of all FIGAERO factors to the signals of C2H2O3 and C3H4O3. One mode, peaking in the LVOC range, was primarily associated with Day-LNOx-LVOA, and a second mode, peaking in the ELVOC range, was dominated by Day-aged-ELVOA. These results indicates that these two low molecular weight species are likely decomposition products of at least two distinct classes of higher molecular weight organic compounds.

Additionally, we identified two aged OA factors (Day-aged-LVOA and Day-aged-ELVOA) with an afternoon peak at about 18:00 LT (Fig. 1c1 and d1), which may be derived from the aging transformation of preexisting organic aerosols via daytime photochemical reactions. These aged factors exhibited the highest OS‾c (0.35 and 0.40) and relatively low volatility with a log⁡10C∗‾ of -2.02 and -4.80, respectively. Day-aged-LVOA was featured with a series of C4-C8 oxygenated compounds (e.g., C4H6O5, C5H8O5, C6H10O5, C7H10O5, and C8H12O5). In contrast, Day-aged-ELVOA had a higher fraction of smaller molecules (e.g., C2H4O3 and C3H6O3, Fig. 1d2). Chen et al. (2021b) found that low molecular weight carboxylic acids (LMWCA) could form through SOA aging processes and report a strong correlation (R2=0.90) between LMWCA and highly oxygenated OA. However, C2H4O3 and C3H6O3 had a weak correlation (R=0.49 and 0.13) with MO-OOA resolved from AMS (Fig. S11). In addition, the Tmax of C2H4O3 and C3H6O3 located in the ELVOC range and showed thermogram profiles similar to that of Day-aged-ELVOA (Fig. S12a). The thermogram signal of C2H4O3 and C3H6O3 was mainly contributed by Day-aged-ELVOA (Fig. S12c and e), supporting the interpretation that these species are more likely decomposition products of low volatility organic compounds rather than being directly formed through atmospheric aging processes.

Two urban air masses-related OA factors (Day-urban-LVOA and Day-urban-ELVOA) were identified, which would be discussed in the following section. Day-urban-LVOA demonstrated comparable OS‾c (0.08), O : C (0.80) and volatility (-0.90) to Day-HNOx-LVOA (-0.01, 0.75, and -0.98, respectively), but show a higher fraction of non-N-containing molecules (e.g., C4H6O4, C5H6O4, C5H8O5, and C7H10O5) and a reduced N : C ratio (Table 1). However, the oxidation state (OS‾c) of Day-HNOx-LVOA (-0.01) was significantly lower than that of Urban-LVOA (0.08), accompanied by a relatively higher N : C (0.06 vs. 0.04). Despite its lower oxidation state, the volatility of Day-HNOx-LVOA is comparable to that of Day-urban-LVOA, which may reflect differences in functional group composition. For example, a nitrate group (-ONO2) contributes to volatility reduction at a level comparable to that of a hydroxyl group (-OH) and generally more strongly than carbonyl functionalities such as aldehydes (-C(O)H) or ketones (-C(O)-) (Pankow and Asher, 2008). However, due to instrumental limitations, we are unable to directly resolve the functional group composition of individual OA factors, and further measurements employing new techniques are needed to better constrain the role of functional groups in controlling the volatility of ambient organic aerosol. Day-urban-ELVOA had the lowest volatility (log⁡10C∗=-7.18) but an OS‾c (0.27) lower than Day-aged-ELVOA (0.34) and composed of oxygenated compounds (e.g., C8H10O5, C7H8O5, C6H8O4, and C5H6O4). The thermogram of Day-aged-ELVOA demonstrates bimodal distribution (peaked at LVOC and ELVOC range) and had a highest Tmax (153.2 °C) among thermograms OA factors (Fig. 1f3). However, the majority of organic molecules (e.g., C5H6O4, C4H6O5, C6H8O4, and C8H10O5) do not exhibit thermograms similar to that of Day-urban-ELVOA (Fig. S13). Instead, their thermograms demonstrate multimodal distributions and are contributed by multiple FIGAERO factors. For example, a mode of C5H6O4 peaking in the LVOC range was mainly contributed by Day-urban-LVOA, while two modes peaking in the ELVOC range were primarily contributed by Day-aged-ELVOA and Day-urban-ELVOA, respectively. These results suggest that these molecules may originate from both direct desorption of organic aerosol and thermal decomposition of higher-molecular-weight compounds during heating.

The biomass-burning related factor, biomass-burning less volatile organic aerosol (BB-LVOA, 10 % of FIGAERO-OA), had a low OS‾c (-0.07), the lowest O : C (0.74), and positive correlation with BBOA resolved from AMS (R=0.64, Fig. S10b). It presented a prominent peak at 19:00 LT and was identified by the distinctive tracer levoglucosan (C6H10O5), nitrocatechol (C6H5NO4), and nitrophenol (C6H5NO3, Fig. 1) in the spectrum, which was frequently detected in biomass burning plumes (Gaston et al., 2016; Ye et al., 2021). In the upwind urban region of Heshan site, Ye et al. (2023) identified a biomass burning related factor in Guangzhou city using the FIGAERO-CIMS, with a distinct evening peak at 21:00 LT and more abundance oxygenated compounds (e.g., C7H10O5 and C8H12O6). The different oxidation level of BBOA between Guangzhou and Heshan, suggest the BB-LVOA in this study is more related to the direct BB emission, but the BB factor in Guangzhou is more resembled BB related SOA factor. This statement was supported by the fact that biomass burning activities were frequently observed near the measurement site during this study, while the biomass burning activities in urban areas was prohibited and can be transported from nearby suburban agricultural areas (Cai et al., 2023) .

The nighttime chemistry related less volatile OA (Night-OA, 15 % of FIGAERO-OA) has the highest N : C (0.07) and exhibited an enhanced at nighttime (22:00–24:00 LT, Fig. 1). Notably, this nighttime factor was composed of a series of organic nitrates (e.g., C8H11NO7, and C10H15NO7), which was related to the products from monoterpenes oxidized by the NO3 radical or oxidation of biomass burning products during nighttime (Faxon et al., 2018; Decker et al., 2019). Noting that the levoglucosan (C6H10O5) was also abundant in the Night-LVOA, suggesting that part of this factor could be attributed to the nighttime aging process of biomass burning products (Jorga et al., 2021). The detailed discussion about the potential formation pathway of these six daytime FIGAERO-OA factors will be discussed in Sect. 3.2.

The volatility of organic compounds was closely related to their chemical characteristics (Donahue et al., 2012). Figure 2 demonstrates the relationship between log⁡10C∗‾ of thermogram factors and OS‾c, O : C, and number of carbons (nC). In general, these factors exhibited a negative correlation (R=-0.60 and -0.73) with both the OS‾c and the O : C but showed a positive correlation (R=0.73) with nC (Fig. 2), except for Day-urban-ELVOA. As aforementioned, the major component of Day-urban-ELVOA could be decomposition products of larger oxygenated molecules. Thus, the chemical characteristic of Day-urban-ELVOA did not demonstrate a similar relationship of volatility versus molecule indicators (e.g., oxidation state, O : C and nC) as other factors. The increase of carbon number usually lead to a decrease in volatility (Donahue et al., 2011), while this trend overturned in this campaign (Fig. 2c). Figure 2d shows that OS‾c had a negative relationship (R=-0.84) with carbon number, suggesting that organic factors with a higher oxidation degree had a shorted carbon backbone. It could be partly owing to fragmentation of organic molecules during aging processes (Chacon-Madrid and Donahue, 2011; Jimenez et al., 2009). Consistently, two aged factors (Day-aged-LVOA and Day-aged-ELVOA) had a higher OS‾c and a lower carbon number than other factors. Additionally, it indicates that the increase in oxidation degree outweighed the effect of decreasing nC, leading to a reduction in the volatility of OA during this campaign.

The temporal variation of volatility distribution and mean C∗ of FIGAERO-OA, the sum of six daytime factors in FIGAERO-OA and LOOA in AMS OA, mass fraction of eight FIGAERO-OA factors, and wind direction and speed are demonstrated in Fig. 3. As shown in Fig. 3, the increase of mean C∗ during the daytime (6:00–18:00 LT, Fig. S14a) is usually accompanied by the enhancement of daytime factors in FIGAERO-OA and LO-OOA from AMS (Fig. S14b and c), indicating that the formation of these factors could notably increase OA volatility. Notably, FIGAERO-OA with a log⁡10C∗ of -1µgm-3 showed pronounced enhancements during the increasing of mean C∗, implying that the volatility of six daytime factors might cluster around 10-1µgm-3 (Fig. S14d). In Fig. 3b, distinct diurnal variation of Ox (Ox=O3+NO2) was observed during the campaign. The maximum of Ox can be as high as 230 µgm-3, highlighting strong photochemical reaction. The daytime factors, especially Day-HNOx-LVOA (Fig. 3c), exhibited markable enhancements under weak northwesterly to northeasterly wind (Figs. 3d and S15). A backward trajectory analysis revealed that the measurement site was mainly affected by the urban pollutants from the city cluster around Guangzhou (Fig. S16). Two periods, which were long-range transport and urban air massed periods, respectively, were selected to further analyze the impact of urban pollutants on the formation and aging process of OA. The variation of OA volatility based on wind direction andspeed, together with backward trajectory analysis, were also explored (Fig. S16 and Table S2). In general, during the urban air masses period, the site was influenced by regional urban plumes from the northeast city cluster, while the long-range transport period was primarily associated with air masses advected from the northeast inland regions. More detailed discussion will be shown in the following section.

Figure 4 demonstrates distinct differences in the diurnal variation of thermograms factors (including Day-HNOx-LVOA, Day-aged-LVOA, Day-urban-LVOA, and Day-urban-ELVOA) during long-range transport period and urban air masses period. During the urban air masses period, Day-HNOx-LVOA significantly increased from ∼ 0.4 to ∼ 4.8 µgm-3 in the daytime. The mass concentration of Day-urban-LVOA and Day-urban-ELVOA demonstrated daytime enhancements only during urban period, suggesting that the formation of these factors was closely related to the pollutants in the urban plumes. Consistently, During the urban air masses period, the maximum ozone concentration in the afternoon (12:00–18:00 LT, 208.3 µgm-3) was higher than that (185.5 µgm-3) during long-range transport period, indicating a stronger photochemical reaction in the urban plumes (Fig. 4). Thus, the daytime thermogram factors accounted for a higher fraction (79 % vs. 75 %) of FIGAERO-OA (Fig. S17). Additionally, the average mass concentration of all thermogram factors (8.9±5.1µgm-3) was noticeably increased compared to the long-range period (5.3±2.4µgm-3). Elevated NOx concentration was observed in the urban plumes in the afternoon (12:00–18:00 LT, 17.4 ppbv vs. 11.7 ppbv), which might also affect the formation pathway of SOA. Both NO and NO/NO2 remained at a relative low level (0.6–0.8 ppbv and <0.5) in the afternoon during these two periods (Fig. S18), suggesting an important role of low-NO-like pathway (Ye et al., 2023). Nihill et al. (2021) found that the production of OH and oxidized organic molecules would be suppressed under high NO/NO2 (> 1) condition. Notably, Day-HNOx-LVOA accounted for the largest portion (29 %) of FIGAERO-OA in the afternoon (12:00–18:00 LT, Fig. S19), followed by Day-aged-LVOA (21 %), while Day-LNOx-LVOA contributed only 6 %. In contrast, during the long-range transport period, the mass fraction of Day-LNOx-LVOA significantly increased (from 6 % to 15 %) along with a decrease in Day-HNOx-LVOA (from 29 %–21 %). These results indicate that elevated NOx concentration in urban plumes might alter the formation pathway of SOA (Cai et al., 2024). Note that the sum of six daytime FIGAERO factors showed a positive relationship (R=0.80 and 0.76, respectively) with LOOA during both periods (Fig. S20). However, the slope (0.81) of the linear regression during the urban air masses period was higher than that (0.58) during the long-range transport period, indicating that a higher fraction of LOOA could be detected by the FIGAERO-CIMS during urban air masses period. This difference could be related to the discrepancy in OA volatility. According to Cai et al. (2024), the volatility of OA was higher during the urban air masses period.

To explore the potential formation pathway of daytime factors, Fig. 5 demonstrates the variation of mass concentrations of six daytime factors as a function of Ox, total gas-phase organic molecules measured by the FIGAERO-CIMS (referred as organic vapors), and NO3-/SIA. Five factors, excluding Day-urban-LVOA, exhibited positive correlations with Ox, highlighting the critical role of photochemical reactions in their formation. Previous studies have demonstrated that gas–particle partitioning plays a key role in SOA formation (Nie et al., 2022; Wang et al., 2022). In this study, organic vapors had strong positive correlations with Day-HNOx-LVOA (R=0.73) and Day-LNOx-LVOA (R=0.74), suggesting that these factors were mainly formed via gas-particle partitioning. The median concentration of Day-HNOx-LVOA dramatically increased (from ∼ 0 to ∼ 5.6 µgm-3) with rising organic vapors, whereas a comparable enhancement was not observed for Day-LNOx-LVOA (Fig. 5b1 and b2).

Furthermore, NOx impact on Day-HNOx-LVOA and Day-HNOx-LVOA was investigated here. Figure S21 show Day-HNOx-LVOA concentrations were consistently higher under elevated NOx conditions, while Day-HNOx-LVOA decreased with increasing NOx level. Figure 6a displays the mass ratio of Day-HNOx-LVOA to Day-LNOx-LVOA obviously increased with organic vapors (up to 12–26) under high NOx condition (>20ppbv), while the ratio remained at approximately 2 at low NOx level (<10ppbv). These overall results suggest that Day-HNOx-LVOA formation was predominantly governed by gas-particle partitioning under high NOx condition, which were typically sustained during urban air masses period (Fig. 2d). Figure 6b compares the relative mass fraction of molecular composition in two gas-particle partitioning related factors, Day-HNOx-LVOA and Day-LNOx-LVOA. The mass fraction of species was derived from the signal profile of corresponding factors based on their sensitivity (Ye et al., 2021). Day-HNOx-LVOA presented greater proportions (10-5–10-3) of organic nitrates (ONs) than Day-LNOx-LVOA (10-11–10-9), including C4H7NO6, C8H9NO4, C8H11NO7, as well as nitrophenols (e.g., C7H7NO3), which are characterized by relatively low OS‾C. These compounds were probably attributed to the SOA formation under elevated NOx concentration (Fig. 2d). In contrast, Day-LNOx-LVOA was enriched in non-nitrogen-containing species (e.g., C4H6O3, C5H10O3, C11H17O6), which exhibited a higher OS‾C. These results indicate that NOx exerts contrasting effects on the formation of these two gas–particle partitioning-related factors.

Previous studies show that NOx has a nonlinear effect on the formation of highly oxygenated organic (HOM) compounds by influencing the atmospheric oxidation capacity and RO2 autoxidation (Xu et al., 2025; Pye et al., 2019; Shrivastava et al., 2019). NOx could suppress the production of low-volatility molecules by inhibiting autoxidation (Rissanen, 2018; Praske et al., 2018), while Nie et al. (2023) found that NO could enhance the formation of HOM at low NO condition (<82pptv). During this campaign, the average NOx and NO was about 24.0 and 2.3 ppbv, respectively, substantially higher than the “low-NO-regime” described by Nie et al. (2023). Our previous study reported a lower concentration of organic vapors with a high OS‾c within urban plumes during the same campaign (Cai et al., 2024). We investigate diurnal evolution of organic compositions under long-range transport and urban air masses periods (Fig. S22). Mass concentrations of CHON increase during the daytime in both periods, with a more pronounced enhancement observed in urban air masses (Fig. S22a). However, the mass fraction of CHON was lower during the urban air masses period than during the long-range transport period. We speculated that elevated NOx enhances overall oxidation and product formation rather than selectively enriching nitrogen-containing compounds. This interpretation is consistent with results from our previous observation-constrained box-model simulations, in which production rates of OH and organic peroxyl radicals (RO2) were evaluated under varying NOx and VOC conditions (Cai et al., 2024). The modeled P(OH) were close to the transition regime, indicating that elevated NOx can enhance atmospheric oxidation capacity. In contrast, the P(RO2) was in the VOC-limited regime and decreased with increasing NOx. Consistent with these results, Fig. S22c shows that the mass fraction of highly oxygenated organic molecules (O≥6) is lower during urban air masses period. Concurrently, species with low oxygen numbers (O≤3) become relatively more abundant in the urban plumes (Fig. S22c), indicating a shift in the oxidation product distribution toward less oxygenated and potentially more volatile compounds, the NOx-driven suppression of multigenerational autoxidation inferred from the box-model results. This suppression of oxidation is observed for both CHON and CHO species. The average O : C of CHON (Fig. S22b) and CHO (Fig. S22e) are both lower during the urban air masses period, suggesting that enhanced NOx broadly suppresses autoxidation across organic compound classes.

Furthermore, as illustrated in Fig. S23, the mass concentration of SVOC (-0.5<log⁡10C∗<2.5µgm-3) and LVOC (-3.5<log⁡10C∗<-0.5µgm-3, Donahue et al., 2012) in the gas phase exhibited an increase (2.5 µgm-3 at NOx<10ppbv vs. 3.3 µgm-3 at NOx≥30ppbv) with the increase in NOx, suggesting that these species likely contributed to the formation of Day-HNOx-LVOA. Xu et al. (2014) found that both SOA volatility and oxidation state exhibited a nonlinear response to NOx in a series of chamber environment. SOA volatility decreases with increasing NOx level when the ratio of initial NO to isoprene was lower than 3. At higher NOx level, higher volatile SOA was produced, probably owing to the more competitive RO2+NO pathway. Figure 5c1 and c2 investigate the relationship between these two factors and NO3-/SIA. Day-HNOx-LVOA had a weak correlation (R=0.30) with NO3-/SIA, while this trend overturned (R=-0.35) for Day-LNOx-LVOA. Yang et al. (2022) showed that OH+NO2 pathway mainly contribute to the formation of nitrate in this campaign. Together, these results indicate that elevated NOx suppressed the formation of highly oxygenated organic compounds, thereby limiting the contribution to Day-LNOx-LVOA. Thus, the Day-LNOx-LVOA was more likely formed via gas-particle partitioning under relatively low NOx condition.

It is worth noting that C4H7NO5, likely originating from isoprene photooxidation in the presence of NOx (Fisher et al., 2016; Paulot et al., 2009), also show a higher fraction in Day-LNOx-LVOA (9.36×10-5 vs. 4.93×10-11 in Day-HNOx-LVOA). A plausible explanation is that Heshan site, located at a suburban region, experienced ambient NOx levels (∼ 13 ppb in the afternoon) sufficient to facilitate the formation of C4H7NO5. It is further supported by the observation that both particle- and gas-phase C4H7NO5 showed no significant variation with increasing NOx (Fig. S24).

For the two urban-related factors, a positive correlation with Ox was observed only during the urban air masses period (R=0.46 and 0.64 vs. -0.05 and 0.28 in the long-range transport period, Fig. S20a and c). Notably, Day-urban-LVOA increased from ∼ 1.0 to ∼ 2.6 µgm-3 as Ox rose from 75–275 µgm-3 during this period, while it remained relatively stable (∼ 0.4 µgm-3) during the long-range transport period (Fig. S25). In addition, Day-urban-LVOA showed only a limited similarity in its variation trend to Day-HNOx-LVOA during the urban air mass period (Fig. S26). This finding supports the hypothesis that the daytime formation of urban-related OA factors was closely related to the urban pollutants. Additionally, Day-urban-ELVOA exhibited a positive correlation with organic vapors (R=0.65, Fig. S25b), while such a correlation was not observed for Day-urban-LVOA. It implies that Day-urban-ELVOA may primarily form through gas-particle partitioning during the urban air mass period.

Day-urban-LVOA was also positively correlated with NO3-/SIA (Fig. 5c), consistent with the concurrent enhancement of nitrate and SOA during haze episodes (Ye et al., 2023; Zheng et al., 2021). During the urban air masses period, nitrate demonstrates a bimodal diurnal variation with peaks in both the morning and afternoon (Fig. S27), the latter peak likely attributed to OH+NO2 pathway (Yang et al., 2022). Day-urban-LVOA had a significant correlation (R=0.97) with succinic acid (C4H6O4) in the particle phase (Fig. S28), which was previously reported to form via multiphase reaction during haze episode in megacity (Zhao et al., 2018; Zheng et al., 2021). As shown in Fig. S29, Day-urban-LVOA also increased with the ratio of the aerosol liquid water content (ALWC) to PM1, further indicating that aqueous processes in urban plumes played an important role in its enhancement.

For the aging factors, Day-aged-LVOA and Day-aged-ELVOA exhibited peak concentrations about 3 h later (at about 18:00 LT, Fig. 4) than other day factors (15:00 LT). It suggests that the two aged factors might originate from the photochemical aging processes of preexisting organic aerosols. To further explore the formation and aging process of these daytime factors, we estimated their daytime enhancement (Δ). For factors peaked at 15:00 LT, the Δ was estimated as the difference between the average mass concentration during 00:00–06:00 LT and 12:00–18:00 LT. For factors peaking at about 18:00 LT, Δ was regarded as the difference between the average mass concentration during 06:00–12:00 and 15:00–21:00 LT, since these factors remained at a relatively high-level during nighttime probably owing to lower boundary layer height. The Δ Day-aged-LVOA showed strong positive correlations with Δ Day-HNOx-LVOA (R=0.73), Δ Day-urban-LVOA (R=0.77), and Δ Day-LNOx-LVOA (R=0.64, Fig. 7a), suggesting that its formation might be closely associated with the aging processes of these three factors. Similarly, Δ Day-aged-ELVOA was positively correlated with both Δ Day-LNOx-LVOA (R=0.61), Δ Day-urban-LVOA (R=0.67), and Δ Day-urban-ELVOA (R=0.73, Fig. 7c). In contrast, we did not observe such correlations between Δ Day-aged-ELVOA and Δ Day-HNOx-LVOA (R=0.49, Fig. 7c). It implies that the formation of Day-aged-ELVOA was likely more influenced by the aging of urban-related factors and Day-LNOx-LVOA.

Adopting PMF analysis to thermogram datasets provides valuable insights into the formation and aging processes of SOA. However, the representativeness of FIGAERO-OA still requires evaluation. Figure 8 compares FIGAERO-OA with AMS-OA during two different periods. In general, FIGAERO-OA could not explain MO-OOA and HOA identified in AMS OA, given that all thermogram factors had a weak correlation (R=-0.18-0.36) between these two factors (Table S2). MO-OOA, which had the highest O : C (1.0) among all AMS factors (0.32–1.0) (Cai et al.,  2024), was likely low volatile, meaning that much of this fraction might not have been vaporized during the heating process. Xu et al. (2019) investigate the volatility of different OA factors using the TD + AMS method and found that MO-OOA evaporated ∼ 52 % at T = 175 °C. Another TD + AMS field study in the North China Plain suggested that the volatility of MO-OOA varied with RH levels, more MO-OOA evaporate at higher RH levels (RH>70, Xu et al., 2021), suggesting that MO-OOA compounds formed at high RH condition could be higher volatile. During this campaign, the RH varied from 25 %–92 % which likely caused variability in MO-OOA volatility and thus in the fraction desorbed at 175 °C. This variability might explain the low correlation between MO-OOA in AMS and all FIGAERO-OA factors. HOA mainly consists of hydrocarbon-like organic compounds, which could not be detected by the FIGAERO-CIMS. The iodide source of the FIGAREO-CIMS is selective towards multi-functional organic compounds (Lee et al., 2014), making it less sensitive to detection hydrocarbon-like species. Ye et al. (2023) preformed factorization analysis of data obtained from the FIGAERO-CIMS and AMS and suggested that FIGAERGAERO-CIMS and AMS and suggested that FIGAERO-derived OA factors could not account for all primary OA components resolved by AMS, including COA, NOA, and HOA. These findings highlight the need for further investigations into the chemical characteristics of primary OA to better understand their emission signatures and atmospheric evolution.

BBSOA in AMS-OA had a bimodal diurnal distribution with an afternoon peak (∼ 14:00 LT) and an evening peak (∼ 17:00 LT, Fig. S30). The enhancement was more pronounced in the afternoon (∼ 1.6 to ∼ 3.6 µgm-3) compared to the evening (∼ 2.9 to ∼ 4.0 µgm-3). Thus, we classify both BBSOA and LOOA as daytime SOA. Six thermogram daytime factors could explain the majority (82 % on average) of daytime SOA with the explained fraction increasing from 78 % during the long-range transport period to 85 % during the urban air masses period (Fig. 8a and b). In both periods, the summed thermogram daytime factors exhibited a diurnal variation like that of LOOA + BBSOA (Fig. 8c and d). Thermogram daytime OA was close to AMS daytime OA in the morning but fell below AMS OA afternoon. The discrepancy in the afternoon could be related to the decrease in OA volatility through strong photochemical reactions. Since the heating temperature of the FIGAERO-CIMS was set at 175 °C, compounds of very low volatility might not have been fully detected. This discrepancy narrowed during the urban air masses, likely owing to the strong SOA formation through gas-particle partitioning, which increased OA volatility (Cai et al., 2024). The gap persisted overnight, owing to suppressed vertical mixing under lower boundary layer conditions.

FIGAERO-OA explained about 13 % of BBOA in AMS OA during the campaign and this ratio remained relatively stable across different periods compared with daytime SOA (Fig. 8a and b). Because BBOA is closely tied to local biomass burning activities, air mass variations likely had only a minor influence on its chemical characteristics. BB-LVOA showed a diurnal pattern similar to both BBOA in AMS-OA and levoglucosan (Fig. 9a), with an evening peak around 18:30 LT, confirming their close association with biomass burning emissions. For nighttime chemistry related factor, both Night-OA (from AMS) and Night-LVOA (from thermograms) increase during the nighttime, while they did not share a similar diurnal pattern (Fig. 9b). Night-LVOA peaked at about 20:00 LT and decreased after 04:00 LT, while Night-OA peaked later, at about 06:00 LT, and declined in the morning. It suggested that Night-LVOA identified by FIGAERO-CIMS might not be able to fully capture the evolution of organic compounds involved in nighttime chemistry, which can explain 48 % of Night-OA in AMS-OA. Given that the majority of organic compounds formed through the nighttime chemistry were oxygenated and could be detected by FIGAERO CIMS (Wu et al., 2021), we speculated that the volatility of organic compounds decreased overnight, resulting that some low volatility organic aerosols would not be fully vaporized during the heating process. Xu et al. (2019) found that nighttime MO-OOA exhibited lower volatility compared with daytime MO-OOA, likely due to differences in precursors, formation mechanisms, and meteorological conditions.