Significant N-containing brown carbon emission from heavy-duty diesel vehicles revealed by the molecular and chromophore analysis using ultra-high resolution mass spectrometry

Significant N-containing brown carbon emission from heavy-duty diesel vehicles revealed by the molecular and chromophore analysis using ultra-high resolution mass spectrometry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Significant N-containing brown carbon emission from heavy-duty diesel vehicles revealed by the molecular and chromophore analysis using ultra-high resolution mass spectrometry Xiao He, Xuan Zheng, Bin Jiang, Xubing Cao, Ting Chen, Shuwen Guo, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5715056/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 8 You are reading this latest preprint version Abstract Brown carbon (BrC) holds scientific significance by influencing radiative balance, cloud condensation dynamics, and regional air quality. This study demonstrated that heavy-duty diesel vehicles (HDDVs) emit substantially higher levels of light-absorbing carbonaceous aerosols under aggressive conditions, such as frequent acceleration and high-speed phases by investigating the emission profiles, formula distribution, and chromophore characteristics under various driving conditions. A non-targeted analysis of BrC was performed using Fourier-transform ion cyclotron resonance mass spectrometry coupled with a soft electrospray ionization source. The light absorption properties were assessed and revealed significantly higher light absorption during frequent acceleration and high-speed phases compared to low-speed phases. Formula distribution analysis highlighted a substantial presence of nitrogen-containing species, constituting 53-65% of the identified peaks, with high O/N ratios (≥ 3), double bond equivalent values (DBE > 10), and aromaticity equivalent (X c ≥ 2.5), which indicated the substantial contribution of aromatic structures, condensed aromatics, nitrooxy functionalities to BrC chromophores. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental sciences/Environmental chemistry HDDVs FT-ICR-MS brown carbon light absorption profiles chromophore analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Synopsis This study examined the emission profiles of brown carbon (BrC) from heavy-duty diesel vehicles under various driving conditions, revealing enhanced light absorption properties during aggressive driving phases, a prominent presence of nitrogen-containing species, and the intricate composition of BrC chromophores. Introduction The light-absorbing carbonaceous fraction of particulate matter (PM) has attracted wide attention due to its profound effects on air quality, atmospheric aerosol evolution, and radiative forcing. 1 – 3 Black carbon (BC) is noted for its strong light absorption across a broad spectral range. 4 In contrast, Brown carbon (BrC) consists of a complex ensemble of colored organic compounds with diverse chemical structures, absorbing light predominantly in the near-ultraviolet range, e.g., 300–400 nm. 5 , 6 While the anthropogenic and natural sources of BC, along with its optical and chemical properties, have been extensively studied, 7 – 9 substantial uncertainties exist in understanding the sources and molecular features of BrC and its associated radiative effects. Significant emission sources include biomass burning 10 , fossil fuel combustion, and residential heating. 11 – 13 Vehicle emission has traditionally been regarded as a minor source of BrC. 14 , 15 However, recent studies conducted in urban environments confirmed that vehicles emissions contributed significantly to BrC levels, especially during rush hours. 4 , 5 , 16 On-road measurements and regression analysis have revealed that diesel vehicles, despite representing less than 5% of the tested vehicle fleet, dominate the light-absorbing properties compared to gasoline vehicles. 16 The vehicular nitro-organic compounds, such as nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) and nitro-phenols, are hypothesized to form during the nitration processes of fuel combustion. 17 Quantifying the contribution of BrC to light absorption necessitates a detailed understanding of its molecular structure and evolution, especially the presence of chromophores. For example, nitrated aromatic compounds have been widely recognized as significant contributors to the light absorption capacity of BrC, exhibiting light absorption efficiencies that are disproportionately high (e.g., 2–10 times) relative to their mass fractions. 18 – 20 However, the complexity of BrC poses significant analytical challenges, complicating the molecular characterization of its constituents and their linkage to optical properties. Mass spectrometry (MS) has long been a key tool for elucidating the molecular composition of bulk organics and its resolution witnessed significant improvements over time. Among the wide ranges of MS, ultra-high-resolution mass spectrometry (UHRMS), such as Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) attracts special interest due to the extraordinary resolving power and mass accuracy. UHRMS facilitates the unambiguous assignment of molecular formulas to individual peaks, with or without prior chromatographic separation. 21 , 22 Previous studies have found that the overall light-absorbing properties of secondary organic aerosols (SOA) may be dominated by the trace amounts of potent BrC chromophores, e.g., conjugated products with high degrees of unsaturation and the incorporation of nitrogen atoms. 23 Recent investigations have explored the roles of nitrogen (N), sulfur (S), and oxygen (O) atoms in BrC absorption, suggesting that sulfur-containing functional groups do not contribute significantly to chromogenic activity. 24 Nevertheless, only a small subset of light-absorbing moieties has been identified, leaving a substantial portion of BrC constituents poorly understood. Despite the significant advances of UHRMS techniques, their application to the speciation of BrC is insufficient. To systematically characterize the molecular and chromophore distributions of BrC and their light absorption properties from vehicle emissions, heavy-duty diesel vehicles (HDDVs) were tested on the chassis dynamometer under various driving conditions (i.e., low-speed, middle-speed, and high-speed) and the tailpipe-emitted PM was collected separately. Organic aerosol 23 fractions were isolated using a methanol (MeOH) extraction protocol, followed by measurement of their respective light absorption spectra. 25 , 26 A non-targeted screening of the chemical compositions was conducted using the ultra-high-resolution FT-ICR-MS in both positive and negative ionization modes to comprehensively examine the distribution of BrC species. Light absorption analysis reveals markedly higher MAE 365 (the mass absorption efficiency at 365 nm) values (1.38 ± 1.30 and 0.68 ± 0.87 m 2 /gC) during frequent acceleration and high-speed phases compared with low-speed phases (0.26 ± 0.15 m 2 /gC), suggesting that HDDVs emit substantial amounts of BrC under aggressive conditions. The light absorption efficiencies of HDDV emitted BrC under aggressive conditions were comparable to those of a diverse of BB materials (0.1 to 3.4 m 2 /gC). 27–30 The copious presence of nitrogen-containing species with high O/N ratios ( \(\:\ge\:\) 3) and aromaticity equivalent (X c \(\:\ge\:\) 2.5) values underscores the chemical complexity and emphasizes that HDDVs may potentially be significant contributors to BrC than previously recognized. Results and Discussion The light absorption properties of HDDV-emitted OA Measurement of light absorption, the MAE, organic carbon (OC), elemental carbon (EC), and the total carbon (TC) is described in the supporting information (Text S5-S7). As an important parameter to describe the light absorption capability of carbonaceous aerosols, the MAE 365 values of OA form P1, P2, P3, and Full samples are color-coded by OC and EC concentrations (Fig. 1 ). The average MAE 365 values of P1, P2, P3, and Full samples were 0.26 ± 0.15, 1.38 ± 1.30, 0.68 ± 0.87, and 0.67 ± 0.57 m 2 /gC, respectively, encompassing all the vehicles and driving conditions. Reference lines in Fig. 1 indicate the MAE 365 values reported in previous studies for easy comparison. Significant differences in MAE 365 values were observed among the diverse raw materials. For example, for the light absorption properties of BrC from BB sources, wood logs and rice (corn and wheat) straw were reported to have relatively higher MAE 365 values of 3.4 ± 1.3 and 2.43 ± 1.36 m 2 /gC. 27, 31 MAE 365 of sorghum straw averaged at 0.95 ± 0.54 m 2 /gC, compared to a much lower MAE 365 of 0.1 m 2 /gC for jute sticks. 27 , 29 The MAE 365 of ambient PM 2.5 in Georgia exhibited a narrow and low range between 0.3 and 0.5 m 2 /gC, and it extended to 0.6–2.0 m 2 /gC in China, with an average of 1.3 m 2 /gC. 28, 30 The MAE 365 of P1 phase was statistically lower than those of P2 and P3 phases (P < 0.05), while no significant differences were observed between P2 and P3 phases. Figure 2 displays the blank-corrected spectra of methanol extracts from representative P1, P2, P3, and Full samples, color-scaled by their respective OC concentrations. As mentioned earlier, the full range of light absorption spectra covered 200 to 800 nm. However, signals below 250 nm and beyond 500 nm mainly consisted of noise and thus were removed for more concise illustration. Three distinct inferences can be made from the spectra. First, the overall trends of four absorption spectra are in good agreement, exhibiting shapes characteristic of BrC absorption where absorbance increases as the wavelength decreases to negligible levels in the near UV-Vis range. 28 Second, it is clearly displayed that the light absorption of P2 and P3 (light absorbance reaching 2.12 and 2.02, both peaking at 280 nm) is much higher than that of P1 and the Full (light absorbance reaching 0.9 and 1.6, peaking at 270 nm) samples, despite that the OC concentrations in the P1 sample was much higher than in other samples. Huang et al. (2022) also reported that the EFs of carbonaceous matter did not correspond directly to their light absorption properties. 5 Third, the light absorption profiles of the middle and high-speed phases (P2 and P3) are notably similar, though the OC concentration of P3 (31.8 µg/m 3 ) is higher than that of P2 (4.4 µg/m 3 ). It is proposed that the light-absorbing BrC is intensively emitted during periods of frequent acceleration and high-speed driving, when the engine control unit adjusts the air-fuel ratio to optimize performance and efficiency while minimizing fuel consumption. 32 In contrast, strong BC emissions occur dominantly in the low-speed phase (P1) under incomplete combustion scenarios. 33 Further discussions from the perspectives of oxidation state, element distribution, and the presence of the chromophores are provided below. Oxidation state analysis of BrC under different driving conditions The fractions of oxygenated organic aerosols (OOAs) across different O:C ratio intervals (0.1–0.8, with a step size of 0.1) are shown in Fig. 3 . It is clearly demonstrated that the tailpipe emissions of OA possess a significantly higher oxidation state in the P2 and P3 phases than P1 (P 0.2 in P2 and P3 are 0.43 and 0.46, compared to 0.27 in P1, reflecting increases of 58.2% and 69.0%, respectively. This trend was consistently observed across a wide range of O:C intervals. Previous studies have reported representative chemical structures with conjugated oxygenated groups in relation to the optical properties of BrC and a few example structures are provided in Figure S3, such as nitrophenol and the derivatives. 6 , 34 The enhancement of oxidation state of OOA corresponds with the stronger light absorption properties observed in P2 and P3 phases, compared to P1. Nevertheless, the links between oxidation state and BrC optical properties were complicated. Jiang et al (2022) reported a transition O/C of about 0.6, below and above which the photo enhancement and photobleaching of OA absorptivity were observed with increasing oxidation state. In particular, the co-increase of nitrogen and oxygen in the molecules suggested the important role of nitrogen-containing functional groups in enhancing the absorptivity of the less polar compounds, while further oxidation (O/C > 0.6) on high-polar compounds likely led to fragmentation and the bleaching of chromophores. 35 With the implementation of more stringent emission standards, manufacturers are increasingly inclined to raise engine combustion temperature and efficiency to reduce PM emissions to comply with the regulatory requirements. For instance, the PM emission limit has been reduced from 160 mg/kWh under China III emission standard to just 10 mg/kWh in China VI standard for heavy-duty diesel engines. It was reported that higher combustion temperature facilitates the oxidation processes and leads to greater production of highly oxidized organic compounds. 36 The oxidation catalysts in the aftertreatment devices (e.g., diesel oxidation catalyst) may also favor the partial oxidation of fuel components. 37 The increased total OH reactivity was detected in Sha et al. (2022), as the combined effects of high combustion temperature in the engine and the catalysts, which inadvertently promote the formation of OOAs in the exhaust. 38 Formula distribution analysis Table 1 summarizes the formula distribution in individual samples. More peaks were detected in positive mode, while peak abundance varied between positive or negative modes (Figure S4 and Figure S5). The formula assignments for duplicate samples demonstrated a high degree of similarity, justifying the selection of two representative samples, 20211222-1 and 20221228-1, for further analysis. The FT-ICR mass spectra obtained from these representative samples, after blank correction, are displayed in Fig. 4 a and 4 b. Embedded within these figures are pie charts representing the fraction of each formula category, with the framed sectors indicating the cumulative nitrogen-containing formulas. Table 1 Number of formulas in each category (negative mode only, both modes, and positive mode only) and the average values of DBE, O/C, and DBE/C, and the fraction of nitrogen-containing species for the individual samples. Elemental composition 20211222-1 20211222-2 20211223-1 20211223-2 20211224-1 20211224-2 20211227-1 20211227-2 20211228-1 20211228-2 20211228-3 20211229-1 20211229-2 20211229-3 20211229-4 20211229-5 CHO- only 507 327 497 528 515 472 363 402 318 199 189 362 321 621 421 368 CHON- only 919 825 1084 930 918 707 722 874 890 739 755 856 855 902 679 785 CHOS- 505 585 592 563 650 550 390 451 422 380 391 382 379 622 385 312 CHONS- 193 239 229 204 221 195 187 199 200 185 195 198 179 221 160 181 All in ESI- 2124 1976 2402 2225 2304 1924 1662 1926 1830 1503 1530 1798 1734 2366 1645 1646 CHO both * 779 626 875 848 890 685 500 537 438 282 188 444 495 943 540 510 CHON both * 477 362 471 464 497 315 175 414 366 152 126 384 401 420 269 321 All both * 1256 988 1346 1312 1387 1000 675 951 804 434 314 828 896 1363 809 831 CHO + only 842 700 845 722 821 691 903 623 638 720 782 661 671 812 803 622 CHN+ 187 151 148 145 147 129 119 135 213 190 130 135 118 333 126 112 CHON + only 1693 1358 1474 1513 1520 1487 1202 1529 1700 1702 1642 1435 1360 1842 1387 1279 All in ESI+ 2722 2209 2467 2380 2488 2307 2224 2287 2551 2612 2554 2231 2149 2987 2316 2013 All sum 6102 5173 6215 5917 6179 5231 4561 5164 5185 4549 4398 4857 4779 6716 4770 4490 DBE^ 4.14 4.04 5.34 3.22 2.36 2.46 4.80 3.12 4.03 4.11 5.06 2.72 2.92 2.91 3.84 2.82 O/C^ 0.22 0.28 0.23 0.24 0.22 0.22 0.27 0.20 0.21 0.21 0.18 0.16 0.19 0.16 0.26 0.19 DBE/C^ 0.19 0.22 0.23 0.19 0.15 0.11 0.17 0.15 0.18 0.21 0.23 0.14 0.16 0.13 0.21 0.15 Fraction of N- containing species 0.57 0.57 0.55 0.55 0.53 0.54 0.53 0.61 0.65 0.65 0.65 0.62 0.61 0.55 0.55 0.60 Fraction of N- containing species from other studies 22 , 29 , 30 0.58 ± 0.20 (0.28 to 0.85, min to max) * Formulas that were identified in both positive and negative modes simultaneously. ^ These metrics were weighted average values. In all samples, CHON species (formula containing only carbon, hydrogen, oxygen, and nitrogen) contributed the highest fraction among the identified formulas, ranging from 43–75% (including both ESI- and ESI + modes, as described hereinafter), followed by the sulfur-containing species (CHOS and CHONS, 30–42%). CHO species accounted for 12–40% of the identified peaks. Among them, CHO and CHON species were detected in both modes, with a notable preference for ESI + mode. Specifically, 50–65% of CHO- formulas and 19–84% of CHO + formulas were detected in both modes concurrently, and the percentages were 14–35% for CHON- species and 7–24% for CHON + species. In ESI+, the number fractions of CHN ranged from 5–11%, while CH species were excluded from further discussion due to their low ionization efficiencies. Compared with previous studies, three distinct features of the formula distribution of HDDV samples could be highlighted. First, CHO species were typically the dominant category in both ambient PM 2.5 samples and the BB samples. 22 For example, CHO species ranged from 50–63% in the ambient PM 2.5 samples in negative mode in He et al. (2024). However, this trend was not observed in HDDV samples. The fraction of CHO species is significantly lower than that of CHON species. Second, the presence of nitrogen-containing species in HDDV tailpipe emissions is confirmed to be notably abundant. Even in the HUmic-LIke Substances (HULIS) extracts and the methanol extracts of the BB samples, of which the nitrogen-containing species were presumed to be plentiful, the number of nitrogen-containing species was comparable to or less than those observed in HDDV samples, as shown in Table 1 . This suggests that the PM emissions from HDDVs are composed of way too many nitrogen-containing species than expected, which may significantly contribute to the light absorption properties of BrC emitted from HDDVs, particularly during frequent acceleration and high-speed phases. The 3D distribution of C, N, and DBE of the identified formulas for representative samples is plotted in Fig. 4 c and 4 d. The fractions of formulas with 1, 2, 3, and 4 nitrogen atoms were 36% (32%), 12% (20%), 5% (8%), and 4% (4%) for samples 20211222-1 (20211228-1), respectively. Third, the sulfur-containing species identified in HDDV samples are far more abundant than in ambient PM and BB samples. Over 600 peaks were assigned with unambiguous formulas in HDDV samples, compared to 100–200 formulas found in ambient PM and BB samples. It is hypothesized that these sulfur-containing species originate from the sulfur content in fuels and are extensively formed during the combustion processes. Discussions on the chromophores The nitrogen-containing species (including CHON n (n = 1, 2, 3, 4), CHON n S (n = 1, 2), and CHN n (n = 1, 2, 3)) extracted from the representative sample of 20211222-1 are displayed in Fig. 5 . 58.9% of the total nitrogen-containing species exhibited an O/N ratio \(\:\ge\:\) 3, indicating that oxygenated nitrooxy compounds, e.g., -ONO 2 , constituted a significant portion of the detected species. This suggests the important role of highly oxidized nitrogen compounds in the molecular composition of BrC from HDDV emissions. To further characterize the aromatic nature of these species, the threshold values of X c of 2.50 and 2.71 were applied, as these values were proposed to be distinguish the presence of aromatic and condensed aromatic compounds. 39 The fraction of nitrogen-containing species with both O/N \(\:\ge\:\) 3 and X c \(\:\ge\:\) 2.5 was 20.5%, indicating the potential contribution of aromatic and nitroaromatic compounds to the chromophoric properties of the BrC. The distribution of nitrogen-containing species across different O/N bins, as shown in Figure S6, revealed a significant presence of compounds with high DBE values, particularly those exceeding DBE > 10. These high DBE values are indicative of complex molecular structures of condensed aromatic rings or highly conjugated −C = C double bonds, both of which are known to enhance the light-absorbing capacity of BrC. The reduced nitrogen species, characterized by lower O/N ratios and positioned at the upper left region in Fig. 5 a, were consistently observed in the HDDV tailpipe emissions and accounted for 35.7% of total nitrogen-containing species. These compounds likely consist of amines, imines and imides. Among them, C = N and C-N functional groups were identified to be the two most important chemical bonding structures in BrC from Chinese megacities. 24 Example chemical structures of such molecules are provided in Figure S7. The nitrogen-containing species were constrained in a H/C versus N/C plot in Fig. 5 b, separated by CH2 unit and oxygen number. Subgroups of them, i.e., H n N (n = 1, 2, 3, 4…), formulate oblique line clusters (indicated by the dotted gray lines), differed by one hydrogen atom. The regular and frequent occurrence of the same KMD (CH2) was constructed to provide information on the overall chemical and chromophore compositions of the nitrogen-containing species (Fig. 5 c). The copious presences of aromatic structures, condensed aromatics, phenol and oxygenated nitrooxy groups are distinctly illustrated in Fig. 5 d, underscoring the complex nature of nitrogen-containing BrC chromophores in HDDV emissions. Despite these findings, the overall O/C ratios of the HDDV samples, ranging from 0.16 to 0.26 (Table 1 ), are typically lower than those reported for ambient PM and BB samples, 22 which indicates that the HDDV emissions are comparably less oxidized. Consequently, the chromophore compositions in HDDV emissions reflect a relatively lower degree of oxidation but contain strong light-absorbing components due to its nitrogenous and aromatic content. Summary and future perspectives This research provides critical insights into the emission profiles of BrC emitted from HDDVs, challenging traditional perspectives that primarily attribute BrC emissions to biomass burning and other combustion sources. The vehicle driving conditions, emission features, optical properties, and the molecular and chromophore characterizations were linked, and three key insights could be deduced. First, the light-absorbing properties of HDDV emissions have been underestimated previously and the MAE 365 values are comparable to the well-known sources such as coal combustion and straw burning under specific driving conditions. Second, light-absorbing organic substances are intensively emitted during frequent acceleration and high-speed phases. Third, aromatic structures, condensed aromatics, phenol and oxygenated nitrooxy groups are copiously present in HDDV tailpipe emissions, which are identified as the major light-absorbing chromophores. This study leverages ultra-high-resolution FT-ICR mass spectrometry, enables the detailed elemental and chromophores characterization HDDV-emitted BrC, provides new insights into their complex chemical nature, and assesses the optical properties of vehicle BrC in dynamic urban environments. These findings have broader implications for climate modeling. However, certain limitations should be considered. First, the study focuses on specific HDDVs which may not fully capture the variability of emissions across diverse vehicle types and mileages. For example, the China VI standard mandates the use of diesel particulate filters (DPFs) on all new HDDVs to reduce PM emissions to comply with the regulatory requirements. It could be expected that the presence of light-absorbing substances would be distinctly different. Also, the controlled experiment setup, i.e., the CVS system, may not perfectly represent atmospheric dispersion and the aging processes that modulate BrC’s optical properties over time. Future research may consider including a broader array of vehicle types and in-field measurements. The underestimation of BrC from HDDVs introduces notable uncertainties into current models, particularly in urban areas where vehicle emissions dominate. Quantifying the emission factors of BrC from HDDVs under aggressive conditions and incorporating the results into BrC model shed light on the non-traditional sources beyond biomass burning and enhanced the accuracy of regional and global climate simulations. Methodology Vehicles, driving cycles, and sampling The tailpipe emissions from two in-use HDDVs were collected at the China Automotive Technology & Research Center in Guangzhou, China. The tested HDDVs were equipped with selective catalytic reduction (SCR) systems, and the detailed information about the tested vehicles is provided in the supporting information (Test S1 and Table S1 ). HDDVs were tested on a chassis dynamometer (AIP-ECDM 72H/2AXLE) following the China heavy-duty commercial vehicle test cycle for heavy trucks (CHTC-HT), which represents the typical driving patterns of commercial diesel vehicles in urban China. The entire driving cycle (Full) spans 1800 s and is divided into three segments: low-speed (phase one (P1), 342 s), medium-speed (phase two (P2), 988 s), and high-speed (phase three (P3), 470 s). The average speeds are 5.2, 31.3, and 63.2 km/h, respectively. The speed trace and phase division are displayed in Figure S1 . It has been reported that the ambient temperature would influence the efficiency of internal combustion engine (e.g., the fuel − air ratio), and this factor should be treated independently. 40 Thus, vehicle #2 was further tested at a low ambient temperature of 0°C. Prior to the experiment, each vehicle was pre-conditioned overnight to cool the engine completely. For each HDDV, cold-start and hot-start tests were conducted consecutively, and each test cycle was duplicated three times. The experiment list is shown in Table S2. The vehicle exhausts were diluted using a constant volume sampler (CVS) system with the ambient air filtered through a high-efficiency particulate air (HEPA) filter. The CVS system was equipped with a real-time gas analyzer module (MEXA-7400HLE, HORIBA, Japan) as well as sensors for temperature, airflow, relative humidity, and pressure to monitor the variations of CO, CO 2 , and other experimental conditions. The detailed procedures for collecting HDDV-emitted OA have been described elsewhere 37 and a brief summary is provided below. In each test, the diluted diesel exhausts were collected by one 47 mm quartz fiber filter (Grade QM-A, Whatman, UK), which was pre-baked at 550°C to remove carbonaceous contamination. Four parallel sampling lines were adopted to collect the P1, P2, P3, and Full samples simultaneously. Field blank samples were collected upstream of the emission pipeline to correct for blank matrix interference (Test S2). 37 , 41 Sample preparation and FT-ICR analysis Filter samples (including blank samples) were cut into stripes and extracted with 5 mL of MeOH in an ultrasonic bath for 15 min. The extract was filtered through a 0.2 µm PTFE syringe filter (Millipore, Billerica, USA) and transferred to a 5 mL conical vial. The filters were further extracted twice, and the extracts were combined and blown to dryness under a gentle stream of N 2 . The dried extracts were re-dissolved in 1 mL MeOH for FT-ICR MS analysis. A solariX XR FT-ICR MS (Bruker Daltonics GmbH & Co. KG, Bremen, Germany) equipped with a 9.4 T refrigerated, actively shielded superconducting magnet (Bruker France S.A.S., Wissembourg, France), a ParaCell analyzer cell, and an electrospray ionization (ESI) source (Bruker Daltonics GmbH &Co. KG, Bremen, Germany) was adopted. The FT-ICR MS analysis followed the protocol described in details in Sun et al. (2021). 42 Briefly, samples were injected at a flow rate of 200 µL/h and analyzed using a nebulizer gas pressure of 1 bar, a dry gas velocity of 4 L/min, and a capillary voltage of + 4.5kV. The temperature and the end plate offset were set to 200 ºC and − 500 V, respectively. The instrument was calibrated externally, and the procedures are provided in the supporting information (Text S3). The mass resolving power (m/ \(\:\varDelta\:\) m 50%, where \(\:\varDelta\:\) m 50% represents the peak full width at half-maximum peak height) of the instrument achieved over 450,000 at m/z = 319. A non-targeted full scan was performed with an m/z range of 100 − 800 in both ESI + and ESI- modes. Prior to positive analysis, 15 µL of ammonium formate (HCOONH 4 ) was added to the solution to improve the ionization efficiency. 43 The preferences of ESI for detecting various functional groups were discussed in He et al. (2024). 22 Basically, aldehyde groups were ionized in both modes, with ESI + spectra exhibiting more peaks than ESI- spectra, as the oxygen in -C = O bond was more electronegative and more likely to form protonated molecules (M + H) + in ESI + mode. Alcohol groups were detected in both modes and higher signal intensities were typically observed in ESI- mode. Methoxy and ketone groups were exclusively detectable in ESI + mode while acid groups were more effectively identified in ESI- mode. Nitrogen-containing compounds, whether in reduced forms (e.g., amines, imines, and azoles) or in oxidized forms (e.g., organonitrates), were readily detected in both modes. 44 Unfunctionalized compounds are not efficiently ionized in either mode. Data pretreatment and elemental composition assignment All peaks with a signal-to-noise ratio higher than 10 were assigned with mathematically calculated formulas with the constraints of a mass tolerance of ± 1 ppm (part per million). In the calculation of negative ions, the element numbers were restricted to 1 − 60 12 C, 1 − 100 1 H, 0 − 20 16 O, 0 − 5 14 N, 0 − 2 32 S, 0 − 1 13 C, 0 − 1 18 O, and 0 − 1 34 S. In the calculation of positive ions, S was excluded due to the fact that reduced sulfur compounds in the aerosol phase were rarely reported and they predominantly partitioned into the gas phase. 45 , 46 Instead, Na was included due to its high tendency to form adducts with target analysts in the positive mode. The following empirical rules were further applied to constrain the measurement results: \(\:0.3\le\:\) H/C \(\:\le\:4.0\) , \(\:0\le\:\) O/C \(\:\le\:4.0\) \(\:0\le\:\) N/C \(\:\le\:0.5\) , \(\:0\le\:\) S/C \(\:\le\:0.2\) , and 2C + 2 \(\:>\) H. All the calculated formulas with negative double bond equivalent (DBE) values and those that violated the nitrogen rule (molecules with all paired electrons that contain an odd number of nitrogen atoms will have an odd nominal mass) were discarded. The calculation of DBE value of each assigned formula is detailed in the supporting information (Text S4). Blank corrections were performed on individual samples in both positive and negative analysis. Only sample peaks with positive intensities were exported for further treatment. Declarations Competing Interests All authors declare no financial or non-financial competing interests. Author Contribution X.H. conceived the idea, conducted the data analysis, and wrote the manuscript. X.B.C., B.J., S.W.G, and T.C. conducted lab experiments. Z.Y.L., Y.Y., Y.D., Y.C., J.K.J., S.X.W., and Y.W. provided important insights for interpreting the results. X.Z. and S.J.Z supervised this study. All authors contributed to the final manuscript. Acknowledgements The authors acknowledge the financial support of the National Natural Science Foundation of China (42105100, 51978404, and 42261160645), Shenzhen Outstanding Science and Technology Innovation Program (RCBS20231211090534051), the Open Research Fund of Key Laboratory of Vehicle Emission Control and Simulation of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences (VECS2024K04), the Fundamental Research Funds for the Central Public-interest Scientific Institution (2024YSKY-03), and the Scientific Research Fund at Shenzhen University (868- 000001032089 and 827–000907). Data Availability The measurement data used in this study are available on request. References Andreae, M. O., Gelencsér, A. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmos. Chem. Phys. 2006 , 6 (10), 3131-3148. https://dx.doi.org/10.5194/acp-6-3131-2006. Chen, D., Zhao, Y., Lyu, R., Wu, R., Dai, L., Zhao, Y., Chen, F., Zhang, J., Yu, H., Guan, M. Seasonal and spatial variations of optical properties of light absorbing carbon and its influencing factors in a typical polluted city in Yangtze River Delta, China. Atmos. Environ. 2019 , 199 , 45-54. https://dx.doi.org/10.1016/j.atmosenv.2018.11.022. Tang, J., Li, J., Su, T., Han, Y., Mo, Y., Jiang, H., Cui, M., Jiang, B., Chen, Y., Tang, J., Song, J., Peng, P. A., Zhang, G. Molecular compositions and optical properties of dissolved brown carbon in biomass burning, coal combustion, and vehicle emission aerosols illuminated by excitation–emission matrix spectroscopy and Fourier transform ion cyclotron resonance mass spectromet. Atmos. Chem. Phys. 2020 , 20 (4), 2513-2532. https://dx.doi.org/10.5194/acp-20-2513-2020. Zhang, Q., Shen, Z., Zhang, T., Kong, S., Lei, Y., Wang, Q., Tao, J., Zhang, R., Wei, P., Wei, C., Cui, S., Cheng, T., Ho, S. S. H., Li, Z., Xu, H., Cao, J. Spatial distribution and sources of winter black carbon and brown carbon in six Chinese megacities. Sci. Total Environ. 2021 , 762 , 143075. https://www.sciencedirect.com/science/article/pii/S0048969720366055. Huang, R.-J., Yuan, W., Yang, L., Yang, H., Cao, W., Guo, J., Zhang, N., Zhu, C., Wu, Y., Zhang, R. Concentration, optical characteristics, and emission factors of brown carbon emitted by on-road vehicles. Sci. Total Environ. 2022 , 810 , 151307. https://www.sciencedirect.com/science/article/pii/S0048969721063853. Laskin, A., Laskin, J., Nizkorodov, S. A. Chemistry of Atmospheric Brown Carbon. Chem. Rev. 2015 , 115 (10), 4335-4382. https://doi.org/10.1021/cr5006167. Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., Deangelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., Zender, C. S. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. 2013 , 118 (11), 5380-5552. https://dx.doi.org/10.1002/jgrd.50171. Fan, X. X., Ye, S. H., Zheng, H., Han, B., Zhang, G. T., Zheng, Z. S., Zhao, X. Y., Kong, S. F., Wang, X. L., Yang, W. Source apportionment of black carbon using an advanced Aethalometer model in a typical industrial city of China. J. Environ. Sci. 2025 , 151 , 42-53. https://doi.org/10.1016/j.jes.2024.03.036. Geng, X. F., Li, J., Zhong, G. C., Zhao, S. Z., Tian, C. G., Zhang, Y. L., Zhang, G. Ship emissions as the largest contributor to coastal atmospheric black carbon at a receptor island in southern China. Environ. Sci. Technol. Lett. 2024 , 11 (7), 723–729. https://doi-org.ezproxy.lib.szu.edu.cn/10.1021/acs.estlett.4c00362. Bian, R., Stubbings, W. A., Li, F., Wu, F., Wang, S. Distribution, Partitioning, Source Apportionment, and Ecological Risk Assessment of Legacy and Emerging PFAS in Water and Sediment of the Pearl River Delta. ACS ES&T Water 2025 , 5 (1), 253-263. https://doi.org/10.1021/acsestwater.4c00828. Lack, D. A., Langridge, J. M. On the attribution of black and brown carbon light absorption using the Ångström exponent. Atmos. Chem. Phys. 2013 , 13 (20), 10535-10543. https://dx.doi.org/10.5194/acp-13-10535-2013. Olson, M. R., Victoria Garcia, M., Robinson, M. A., Van Rooy, P., Dietenberger, M. A., Bergin, M., Schauer, J. J. Investigation of black and brown carbon multiple-wavelength-dependent light absorption from biomass and fossil fuel combustion source emissions. Journal of Geophysical Research: Atmospheres 2015 , 120 (13), 6682-6697. https://dx.doi.org/10.1002/2014jd022970. Bond, T. C. Spectral dependence of visible light absorption by carbonaceous particles emitted from coal combustion. Geophys. Res. Lett. 2001 , 28 (21), 4075-4078. https://dx.doi.org/10.1029/2001gl013652. Shen, Z., Lei, Y., Zhang, L., Zhang, Q., Zeng, Y., Tao, J., Zhu, C., Cao, J., Xu, H., Liu, S. Methanol Extracted Brown Carbon in PM2.5 Over Xi’an, China: Seasonal Variation of Optical Properties and Sources Identification. Aerosol Science and Engineering 2017 , 1 (2), 57-65. https://dx.doi.org/10.1007/s41810-017-0007-z. Andreae, M. O., Gelencsér, A. Black carbon or brown carbon?: The nature of light-absorbing carbonaceous aerosols. Atmos. Chem. Phys. 2006 , 6 , 3131-3148. Zhang, R., Li, S., Fu, X., Pei, C., Huang, Z., Wang, Y., Chen, Y., Yan, J., Wang, J., Yu, Q., Luo, S., Zhu, M., Wu, Z., Fang, H., Xiao, S., Huang, X., Zeng, J., Zhang, H., Song, W., Zhang, Y., Bi, X., Wang, X. Emissions and light absorption of carbonaceous aerosols from on-road vehicles in an urban tunnel in south China. Sci. Total Environ. 2021 , 790 , 148220. https://www.sciencedirect.com/science/article/pii/S0048969721032915. Zielinska, B., Sagebiel, J., McDonald, J. D., Whitney, K., Lawson, D. R. Emission Rates and Comparative Chemical Composition from Selected In-Use Diesel and Gasoline-Fueled Vehicles. J. Air Waste Manage. Assoc. 2004 , 54 (9), 1138-1150. https://dx.doi.org/10.1080/10473289.2004.10470973. Xie, M., Hays, M. D., Holder, A. L. Light-absorbing organic carbon from prescribed and laboratory biomass burning and gasoline vehicle emissions. Scientific Reports 2017 , 7 (1). https://dx.doi.org/10.1038/s41598-017-06981-8. Lin, P., Bluvshtein, N., Rudich, Y., Nizkorodov, S. A., Laskin, J., Laskin, A. Molecular Chemistry of Atmospheric Brown Carbon Inferred from a Nationwide Biomass Burning Event. Environmental Science & Technology 2017 , 51 (20), 11561-11570. https://doi.org/10.1021/acs.est.7b02276. Bluvshtein, N., Lin, P., Flores, J. M., Segev, L., Mazar, Y., Tas, E., Snider, G., Weagle, C., Brown, S. S., Laskin, A., Rudich, Y. Broadband optical properties of biomass‐burning aerosol and identification of brown carbon chromophores. Journal of Geophysical Research: Atmospheres 2017 , 122 (10), 5441-5456. https://dx.doi.org/10.1002/2016jd026230. Bao, M., Zhang, Y.-L., Cao, F., Hong, Y., Lin, Y.-C., Yu, M., Jiang, H., Cheng, Z., Xu, R., Yang, X. Impact of fossil and non-fossil fuel sources on the molecular compositions of water-soluble humic-like substances in PM 2.5 at a suburban site of Yangtze River Delta, China. Atmos. Chem. Phys. 2023 , 23 (14), 8305-8324. https://dx.doi.org/10.5194/acp-23-8305-2023. He, X., Huang, X. H., Ma, Y., Huang, C., Yu, J. Z. Unambiguous analysis and systematic mapping of oxygenated aromatic compounds in atmospheric aerosols using ultrahigh-resolution mass spectrometry. Anal. Chem. 2024 , 96 (5), 1880-1889. https://doi.org/10.1021/acs.analchem.3c03760. Laskin, J., Laskin, A., Nizkorodov, S. A., Roach, P., Eckert, P., Gilles, M. K., Wang, B., Lee, H. J., Hu, Q. Molecular selectivity of brown carbon chromophores. Environ. Sci. Technol. 2014 , 48 (20), 12047-12055. https://doi.org/10.1021/es503432r. Zeng, Y., Ning, Y., Shen, Z., Zhang, L., Zhang, T., Lei, Y., Zhang, Q., Li, G., Xu, H., Ho, S. S. H., Cao, J. The Roles of N, S, and O in molecular absorption features of brown carbon in PM 2.5 in a typical semi‐arid megacity in northwestern China. J. Geophys. Res. 2021 , 126 (16), e2021JD034791. https://dx.doi.org/10.1029/2021jd034791. Cheng, Y., Cao, X.-b., Liu, J.-m., Yu, Q.-q., Wang, P., Yan, C.-q., Du, Z.-y., Liang, L.-l., Zhang, Q., He, K.-b. Primary nature of brown carbon absorption in a frigid atmosphere with strong haze chemistry. Environmental Research 2022 , 204 , 112324. https://www.sciencedirect.com/science/article/pii/S001393512101625X. Sun, W., Fu, Y., Zhang, G., Yang, Y., Jiang, F., Lian, X., Jiang, B., Liao, Y., Bi, X., Chen, D., Chen, J., Wang, X., Ou, J., Peng, P., Sheng, G. Measurement report: Molecular characteristics of cloud water in southern China and insights into aqueous-phase processes from Fourier transform ion cyclotron resonance mass spectrometry. Atmos. Chem. Phys. 2021 , 21 (22), 16631-16644. https://acp.copernicus.org/articles/21/16631/2021/. Fang, Z., Deng, W., Wang, X., He, Q., Zhang, Y., Hu, W., Song, W., Zhu, M., Lowther, S., Wang, Z., Fu, X., Hu, Q., Bi, X., George, C., Rudich, Y. Evolution of light absorption properties during photochemical aging of straw open burning aerosols. Sci. Total Environ. 2022 , 838 , 156431. https://www.sciencedirect.com/science/article/pii/S0048969722035288. Liu, J., Bergin, M., Guo, H., King, L., Kotra, N., Edgerton, E., Weber, R. J. Size-resolved measurements of brown carbon in water and methanol extracts and estimates of their contribution to ambient fine-particle light absorption. Atmos. Chem. Phys. 2013 , 13 (24), 12389-12404. https://acp.copernicus.org/articles/13/12389/2013/. Runa, F., Islam, M. S., Jeba, F., Salam, A. Light absorption properties of brown carbon from biomass burning emissions. Environmental Science and Pollution Research 2022 , 29 (14), 21012-21022. https://doi.org/10.1007/s11356-021-17220-z. Zhang, L., Li, J., Li, Y., Liu, X., Luo, Z., Shen, G., Tao, S. Comparison of water-soluble and water-insoluble organic compositions attributing to different light absorption efficiency between residential coal and biomass burning emissions. Atmos. Chem. Phys. 2024 , 24 (10), 6323-6337. https://acp.copernicus.org/articles/24/6323/2024/. Zhang, L., Luo, Z., Du, W., Li, G., Shen, G., Cheng, H., Tao, S. Light absorption properties and absorption emission factors for indoor biomass burning. Environmental Pollution 2020 , 267 , 115652. https://www.sciencedirect.com/science/article/pii/S0269749120363405. Peng, Y.-H., Huang, Y.-P., Tang, J.-Y., Huang, Q.-F., Huang, Y.-R. Experimental study on the effects of air supply control on combustion and emissions performance at medium and low load for a dual-fuel diesel engine. In Energies , 2018; Vol. 11, p 2944. Hays, M. D., Preston, W., George, B. J., George, I. J., Snow, R., Faircloth, J., Long, T., Baldauf, R. W., McDonald, J. Temperature and Driving Cycle Significantly Affect Carbonaceous Gas and Particle Matter Emissions from Diesel Trucks. Energy & Fuels 2017 , 31 (10), 11034-11042. https://doi.org/10.1021/acs.energyfuels.7b01446. Mohr, C., Lopez-Hilfiker, F. D., Zotter, P., Prévôt, A. S. H., Xu, L., Ng, N. L., Herndon, S. C., Williams, L. R., Franklin, J. P., Zahniser, M. S., Worsnop, D. R., Knighton, W. B., Aiken, A. C., Gorkowski, K. J., Dubey, M. K., Allan, J. D., Thornton, J. A. Contribution of Nitrated Phenols to Wood Burning Brown Carbon Light Absorption in Detling, United Kingdom during Winter Time. Environ. Sci. Technol. 2013 , 47 (12), 6316-6324. https://dx.doi.org/10.1021/es400683v. Jiang, X., Liu, D., Li, Q., Tian, P., Wu, Y., Li, S., Hu, K., Ding, S., Bi, K., Li, R., Huang, M., Ding, D., Chen, Q., Kong, S., Li, W., Pang, Y., He, D. Connecting the Light Absorption of Atmospheric Organic Aerosols with Oxidation State and Polarity. Environmental Science & Technology 2022 , 56 (18), 12873-12885. https://doi.org/10.1021/acs.est.2c02202. Higgins, K. J., Jung, H., Kittelson, D. B., Roberts, J. T., Zachariah, M. R. Kinetics of diesel nanoparticle oxidation. Environ. Sci. Technol. 2003 , 37 (9), 1949-1954. https://doi.org/10.1021/es0261269. He, X., Zheng, X., Zhang, S., Wang, X., Chen, T., Zhang, X., Huang, G., Cao, Y., He, L., Cao, X., Cheng, Y., Wang, S., Wu, Y. Comprehensive characterization of particulate intermediate-volatility and semi-volatile organic compounds (I/SVOCs) from heavy-duty diesel vehicles using two-dimensional gas chromatography time-of-flight mass spectrometry. Atmospheric Chemistry and Physics 2022 , 22 (21), 13935-13947. https://dx.doi.org/10.5194/acp-22-13935-2022. Sha, Q. e., Liu, X., Yuan, Z., Zheng, J., Lou, S., Wang, H., Li, X., Yu, F. Upgrading Emission Standards Inadvertently Increased OH Reactivity from Light-Duty Diesel Truck Exhaust in China: Evidence from Direct LP-LIF Measurement. Environmental Science & Technology 2022 , 56 (14), 9968-9977. https://doi.org/10.1021/acs.est.2c02944. Yassine, M. M., Harir, M., Dabek‐Zlotorzynska, E., Schmitt‐Kopplin, P. Structural characterization of organic aerosol using Fourier transform ion cyclotron resonance mass spectrometry: Aromaticity equivalent approach. Rapid Communications in Mass Spectrometry 2014 , 28 (22), 2445-2454. https://dx.doi.org/10.1002/rcm.7038. Grange, S. K., Farren, N. J., Vaughan, A. R., Rose, R. A., Carslaw, D. C. Strong Temperature Dependence for Light-Duty Diesel Vehicle NOx Emissions. Environmental Science & Technology 2019 , 53 (11), 6587-6596. https://doi.org/10.1021/acs.est.9b01024. He, X., Zheng, X., You, Y., Zhang, S., Zhao, B., Wang, X., Huang, G., Chen, T., Cao, Y., He, L., Chang, X., Wang, S., Wu, Y. Comprehensive chemical characterization of gaseous I/SVOC emissions from heavy-duty diesel vehicles using two-dimensional gas chromatography time-of-flight mass spectrometry. Environ Pollut 2022 , 305 , 119284. https://www.ncbi.nlm.nih.gov/pubmed/35436508. Sun, W., Fu, Y., Zhang, G., Yang, Y., Jiang, F., Lian, X., Jiang, B., Liao, Y., Bi, X., Chen, D., Chen, J., Wang, X., Ou, J., Peng, P. A., Sheng, G. Measurement report: Molecular characteristics of cloud water in southern China and insights into aqueous-phase processes from Fourier transform ion cyclotron resonance mass spectrometry. Atmos. Chem. Phys. 2021 , 21 (22), 16631-16644. https://dx.doi.org/10.5194/acp-21-16631-2021. Lu, J., Zhang, Y., Shi, Q. Ionizing aromatic compounds in petroleum by electrospray with HCOONH4 as ionization promoter. Anal. Chem. 2016 , 88 (7), 3471-3475. https://doi.org/10.1021/acs.analchem.6b00022. Ditto, J. C., Machesky, J., Gentner, D. R. Analysis of reduced and oxidized nitrogen-containing organic compounds at a coastal site in summer and winter. Atmos. Chem. Phys. 2022 , 22 (5), 3045-3065. https://acp.copernicus.org/articles/22/3045/2022/. Novak, G. A., Fite, C. H., Holmes, C. D., Veres, P. R., Neuman, J. A., Faloona, I., Thornton, J. A., Wolfe, G. M., Vermeuel, M. P., Jernigan, C. M., Peischl, J., Ryerson, T. B., Thompson, C. R., Bourgeois, I., Warneke, C., Gkatzelis, G. I., Coggon, M. M., Sekimoto, K., Bui, T. P., Dean-Day, J., Diskin, G. S., DiGangi, J. P., Nowak, J. B., Moore, R. H., Wiggins, E. B., Winstead, E. L., Robinson, C., Thornhill, K. L., Sanchez, K. J., Hall, S. R., Ullmann, K., Dollner, M., Weinzierl, B., Blake, D. R., Bertram, T. H. Rapid cloud removal of dimethyl sulfide oxidation products limits SO 2 and cloud condensation nuclei production in the marine atmosphere. PNAS 2021 , 118 (42). https://doi.org/10.1073/pnas.2110472118. Van Rooy, P., Purvis-Roberts, K. L., Silva, P. J., Nee, M. J., Cocker, D. Characterization of secondary products formed through oxidation of reduced sulfur compounds. Atmos. Environ. 2021 , 256 , 118148. https://doi.org/10.1016/j.atmosenv.2020.118148. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf TOC.png Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Accepted 04 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 23 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5715056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434915538,"identity":"19953bc8-aac8-43e4-bdf0-c9cf734e0de6","order_by":0,"name":"Xiao He","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"He","suffix":""},{"id":434915540,"identity":"a5aa3609-d181-4711-b526-23d242dee247","order_by":1,"name":"Xuan Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACNhDBw8beAOYxNhCrRYaP5wCJWmzkJBKI1GJwgP3Zg487annYJJ8//szDYCO74QDzswf4tEg2MKQbzjxznIdNOsfAmIchzXjDATZzA3xa+OUfHJPmbTsG0sKQzMNwOHHDAR42CfweYWyT/gvSInn8wWEehv+EtfAzMLNJM7bVgJQZNvMwHCCsRbKBjU2ytw2ojCfHmHGOQbLxzMNsZni1gEJM4mdbnb18+/HHH95U2Mn2HW9+hlcLFByGmQDEzESoB4I64pSNglEwCkbByAQA/dA+i7+zSSwAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Zheng","suffix":""},{"id":434915541,"identity":"82c11018-3ef9-46f8-83a4-311bd3295580","order_by":2,"name":"Bin Jiang","email":"","orcid":"","institution":"Guangzhou Institute of Geochemistry, CAS","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Jiang","suffix":""},{"id":434915542,"identity":"47a33039-2a22-4353-8d1e-5323ac2c4bec","order_by":3,"name":"Xubing Cao","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xubing","middleName":"","lastName":"Cao","suffix":""},{"id":434915543,"identity":"e8c449d3-04eb-4f70-a5d7-aae3e5b02f28","order_by":4,"name":"Ting Chen","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Chen","suffix":""},{"id":434915544,"identity":"8795f230-5827-4460-93d7-6439545bb889","order_by":5,"name":"Shuwen Guo","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Guo","suffix":""},{"id":434915545,"identity":"ab79071b-f95b-49eb-bd27-9f8e1fdaafde","order_by":6,"name":"Zhiyuan Li","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Li","suffix":""},{"id":434915546,"identity":"64af595f-a854-4958-be3c-93e4b7ce1b97","order_by":7,"name":"Yan Ding","email":"","orcid":"","institution":"Chinese Research Academy of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Ding","suffix":""},{"id":434915547,"identity":"5ca5b896-4bf7-4e90-a902-2c22469da529","order_by":8,"name":"Shaojun Zhang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Shaojun","middleName":"","lastName":"Zhang","suffix":""},{"id":434915548,"identity":"8e07b823-85a3-4ad3-8338-ea875750481c","order_by":9,"name":"Yuan Cheng","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Cheng","suffix":""},{"id":434915549,"identity":"690ad8af-04aa-43bd-b9d3-2456ad434fa1","order_by":10,"name":"Jingkun Jiang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Jingkun","middleName":"","lastName":"Jiang","suffix":""},{"id":434915550,"identity":"58180757-7113-4975-a077-d4add5b20460","order_by":11,"name":"Shuxiao Wang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Shuxiao","middleName":"","lastName":"Wang","suffix":""},{"id":434915551,"identity":"04eca460-6b30-439e-a711-b02412d09e9c","order_by":12,"name":"Ye Wu","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-12-26 09:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5715056/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5715056/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-025-01034-8","type":"published","date":"2025-05-27T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79475834,"identity":"3b19f323-3e7e-48af-ad07-a18e12724145","added_by":"auto","created_at":"2025-03-29 02:37:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":465910,"visible":true,"origin":"","legend":"\u003cp\u003eMeasured MAE\u003csub\u003e365\u003c/sub\u003e values of OA from P1, P2, P3, and Full samples, color-coded by their respective (a) OC concentrations and (b) EC concentrations. Reference lines indicate the MAE values of rice, corn, and wheat straw burning, PM\u003csub\u003e2.5\u003c/sub\u003e extracts in China, coal combustion, sorghum straw burning, PM\u003csub\u003e2.5\u003c/sub\u003e extracts in Georgia, and jute stick burning reported in previous studies for ease of comparison.\u003ca href=\"#_ENREF_27\" title=\"Fang, 2022 #383\"\u003e\u003csup\u003e27-30\u003c/sup\u003e\u003c/a\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/1bd593f02daa56052e7c8636.png"},{"id":79476186,"identity":"f4117eea-dd0a-4e40-96a3-9319630df12c","added_by":"auto","created_at":"2025-03-29 02:45:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183655,"visible":true,"origin":"","legend":"\u003cp\u003eSpectra (blank-corrected) of methanol extracts of OA from representative P1, P2, P3, and Full samples, color-scaled by their respective OC concentrations.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/95a441efff3f1dba9f65bd14.png"},{"id":79476185,"identity":"cf47e9f6-61a4-4967-bc31-098d17efb468","added_by":"auto","created_at":"2025-03-29 02:45:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55796,"visible":true,"origin":"","legend":"\u003cp\u003eThe fractions of OOAs in separate O:C ratio intervals (0.1-0.8, with a step size of 0.1).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/e6fa38333264f51436f8af80.png"},{"id":79475829,"identity":"879a4a49-25b1-480d-873b-08c393ab849b","added_by":"auto","created_at":"2025-03-29 02:37:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1481173,"visible":true,"origin":"","legend":"\u003cp\u003eFT-ICR mass spectra for the representative samples of (a) 20211222-1 and (b) 20211228-1. Inserted pie charts are the relative fractions of CHO, CHON, CHOS, and CHONS in the negative mode and CHO, CHN, and CHON in the positive mode. The framed sectors within the pie charts refer to the sum of nitrogen-containing formulas. The 3D distribution of C, N, and DBE for the identified formulas in (c) 20211222-1 and (d) 20211228-1 samples in ESI+ and ESI- modes. The different colors indicate formulas assigned with varying number of nitrogen atoms. The circles, stars, and bars indicate peaks detected exclusively in negative mode, exclusively in positive mode, and in both modes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/b5e1cfdaf5e7637a95596498.png"},{"id":79475832,"identity":"2669ebae-93c0-4832-b7af-b072bdec1f06","added_by":"auto","created_at":"2025-03-29 02:37:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1148303,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Van Krevelen (VK) and aromaticity equivalent (X\u003csub\u003ec\u003c/sub\u003e) combined plot and (b) VK plot in form of H/C versus N/C for all nitrogen-containing species (including CHON\u003csub\u003en\u003c/sub\u003e (n = 1, 2, 3, 4), CHON\u003csub\u003en\u003c/sub\u003eS (n = 1, 2), and CHN\u003csub\u003en\u003c/sub\u003e (n = 1, 2, 3)) extracted from the representative sample of 20211222-1. The colors and shapes of the markers correspond to the DBE values and X\u003csub\u003ec\u003c/sub\u003e ranges. (c) Kendrick mass defect (KMD) plot differed by the CH2 unit vs m/z for CHON\u003csub\u003en\u003c/sub\u003e formulas identified in ESI-, ESI+, and both modes. (d) A zoomed-in view of plot (c) on the y-axis from 0.18 to 0.20. The regular and frequent occurrence of the same KMD (CH2) illustrates the presence of homologous species.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/b8e99375a9e3223107e08895.png"},{"id":83782917,"identity":"fbea3fa6-bf06-4274-a204-d093cec5ce10","added_by":"auto","created_at":"2025-06-02 16:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4029383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/e6d43b87-f266-4094-9a4c-a1f9e416ed34.pdf"},{"id":79475836,"identity":"57c6d2c7-cfc1-4ac9-973d-1187edee3a45","added_by":"auto","created_at":"2025-03-29 02:37:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":892663,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/cf8e42ae05c85ac805c14440.pdf"},{"id":79475828,"identity":"2bd83540-1c24-4a93-9460-7fe89aa71160","added_by":"auto","created_at":"2025-03-29 02:37:50","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":381111,"visible":true,"origin":"","legend":"","description":"","filename":"TOC.png","url":"https://assets-eu.researchsquare.com/files/rs-5715056/v1/0cce71b3ca98b265219a95f7.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Significant N-containing brown carbon emission from heavy-duty diesel vehicles revealed by the molecular and chromophore analysis using ultra-high resolution mass spectrometry","fulltext":[{"header":"Synopsis","content":"\u003cp\u003eThis study examined the emission profiles of brown carbon (BrC) from heavy-duty diesel vehicles under various driving conditions, revealing enhanced light absorption properties during aggressive driving phases, a prominent presence of nitrogen-containing species, and the intricate composition of BrC chromophores.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe light-absorbing carbonaceous fraction of particulate matter (PM) has attracted wide attention due to its profound effects on air quality, atmospheric aerosol evolution, and radiative forcing.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Black carbon (BC) is noted for its strong light absorption across a broad spectral range.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In contrast, Brown carbon (BrC) consists of a complex ensemble of colored organic compounds with diverse chemical structures, absorbing light predominantly in the near-ultraviolet range, e.g., 300\u0026ndash;400 nm.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e While the anthropogenic and natural sources of BC, along with its optical and chemical properties, have been extensively studied,\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e substantial uncertainties exist in understanding the sources and molecular features of BrC and its associated radiative effects.\u003c/p\u003e \u003cp\u003eSignificant emission sources include biomass burning \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, fossil fuel combustion, and residential heating.\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Vehicle emission has traditionally been regarded as a minor source of BrC.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e However, recent studies conducted in urban environments confirmed that vehicles emissions contributed significantly to BrC levels, especially during rush hours.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e On-road measurements and regression analysis have revealed that diesel vehicles, despite representing less than 5% of the tested vehicle fleet, dominate the light-absorbing properties compared to gasoline vehicles.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The vehicular nitro-organic compounds, such as nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) and nitro-phenols, are hypothesized to form during the nitration processes of fuel combustion.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eQuantifying the contribution of BrC to light absorption necessitates a detailed understanding of its molecular structure and evolution, especially the presence of chromophores. For example, nitrated aromatic compounds have been widely recognized as significant contributors to the light absorption capacity of BrC, exhibiting light absorption efficiencies that are disproportionately high (e.g., 2\u0026ndash;10 times) relative to their mass fractions.\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e However, the complexity of BrC poses significant analytical challenges, complicating the molecular characterization of its constituents and their linkage to optical properties. Mass spectrometry (MS) has long been a key tool for elucidating the molecular composition of bulk organics and its resolution witnessed significant improvements over time. Among the wide ranges of MS, ultra-high-resolution mass spectrometry (UHRMS), such as Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) attracts special interest due to the extraordinary resolving power and mass accuracy. UHRMS facilitates the unambiguous assignment of molecular formulas to individual peaks, with or without prior chromatographic separation.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies have found that the overall light-absorbing properties of secondary organic aerosols (SOA) may be dominated by the trace amounts of potent BrC chromophores, e.g., conjugated products with high degrees of unsaturation and the incorporation of nitrogen atoms.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Recent investigations have explored the roles of nitrogen (N), sulfur (S), and oxygen (O) atoms in BrC absorption, suggesting that sulfur-containing functional groups do not contribute significantly to chromogenic activity.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Nevertheless, only a small subset of light-absorbing moieties has been identified, leaving a substantial portion of BrC constituents poorly understood. Despite the significant advances of UHRMS techniques, their application to the speciation of BrC is insufficient.\u003c/p\u003e \u003cp\u003eTo systematically characterize the molecular and chromophore distributions of BrC and their light absorption properties from vehicle emissions, heavy-duty diesel vehicles (HDDVs) were tested on the chassis dynamometer under various driving conditions (i.e., low-speed, middle-speed, and high-speed) and the tailpipe-emitted PM was collected separately. Organic aerosol \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e fractions were isolated using a methanol (MeOH) extraction protocol, followed by measurement of their respective light absorption spectra.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e A non-targeted screening of the chemical compositions was conducted using the ultra-high-resolution FT-ICR-MS in both positive and negative ionization modes to comprehensively examine the distribution of BrC species. Light absorption analysis reveals markedly higher MAE\u003csub\u003e365\u003c/sub\u003e (the mass absorption efficiency at 365 nm) values (1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 and 0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 m\u003csup\u003e2\u003c/sup\u003e/gC) during frequent acceleration and high-speed phases compared with low-speed phases (0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 m\u003csup\u003e2\u003c/sup\u003e/gC), suggesting that HDDVs emit substantial amounts of BrC under aggressive conditions. The light absorption efficiencies of HDDV emitted BrC under aggressive conditions were comparable to those of a diverse of BB materials (0.1 to 3.4 m\u003csup\u003e2\u003c/sup\u003e/gC).\u003csup\u003e27\u0026ndash;30\u003c/sup\u003e The copious presence of nitrogen-containing species with high O/N ratios (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 3) and aromaticity equivalent (X\u003csub\u003ec\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 2.5) values underscores the chemical complexity and emphasizes that HDDVs may potentially be significant contributors to BrC than previously recognized.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe light absorption properties of HDDV-emitted OA\u003c/h2\u003e \u003cp\u003eMeasurement of light absorption, the MAE, organic carbon (OC), elemental carbon (EC), and the total carbon (TC) is described in the supporting information (Text S5-S7). As an important parameter to describe the light absorption capability of carbonaceous aerosols, the MAE\u003csub\u003e365\u003c/sub\u003e values of OA form P1, P2, P3, and Full samples are color-coded by OC and EC concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average MAE\u003csub\u003e365\u003c/sub\u003e values of P1, P2, P3, and Full samples were 0.26 ± 0.15, 1.38 ± 1.30, 0.68 ± 0.87, and 0.67 ± 0.57 m\u003csup\u003e2\u003c/sup\u003e/gC, respectively, encompassing all the vehicles and driving conditions. Reference lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicate the MAE\u003csub\u003e365\u003c/sub\u003e values reported in previous studies for easy comparison.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignificant differences in MAE\u003csub\u003e365\u003c/sub\u003e values were observed among the diverse raw materials. For example, for the light absorption properties of BrC from BB sources, wood logs and rice (corn and wheat) straw were reported to have relatively higher MAE\u003csub\u003e365\u003c/sub\u003e values of 3.4 ± 1.3 and 2.43 ± 1.36 m\u003csup\u003e2\u003c/sup\u003e/gC.\u003csup\u003e27, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e MAE\u003csub\u003e365\u003c/sub\u003e of sorghum straw averaged at 0.95 ± 0.54 m\u003csup\u003e2\u003c/sup\u003e/gC, compared to a much lower MAE\u003csub\u003e365\u003c/sub\u003e of 0.1 m\u003csup\u003e2\u003c/sup\u003e/gC for jute sticks.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The MAE\u003csub\u003e365\u003c/sub\u003e of ambient PM\u003csub\u003e2.5\u003c/sub\u003e in Georgia exhibited a narrow and low range between 0.3 and 0.5 m\u003csup\u003e2\u003c/sup\u003e/gC, and it extended to 0.6–2.0 m\u003csup\u003e2\u003c/sup\u003e/gC in China, with an average of 1.3 m\u003csup\u003e2\u003c/sup\u003e/gC.\u003csup\u003e28, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The MAE\u003csub\u003e365\u003c/sub\u003e of P1 phase was statistically lower than those of P2 and P3 phases (P \u0026lt; 0.05), while no significant differences were observed between P2 and P3 phases.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the blank-corrected spectra of methanol extracts from representative P1, P2, P3, and Full samples, color-scaled by their respective OC concentrations. As mentioned earlier, the full range of light absorption spectra covered 200 to 800 nm. However, signals below 250 nm and beyond 500 nm mainly consisted of noise and thus were removed for more concise illustration. Three distinct inferences can be made from the spectra. First, the overall trends of four absorption spectra are in good agreement, exhibiting shapes characteristic of BrC absorption where absorbance increases as the wavelength decreases to negligible levels in the near UV-Vis range.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Second, it is clearly displayed that the light absorption of P2 and P3 (light absorbance reaching 2.12 and 2.02, both peaking at 280 nm) is much higher than that of P1 and the Full (light absorbance reaching 0.9 and 1.6, peaking at 270 nm) samples, despite that the OC concentrations in the P1 sample was much higher than in other samples. Huang et al. (2022) also reported that the EFs of carbonaceous matter did not correspond directly to their light absorption properties.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Third, the light absorption profiles of the middle and high-speed phases (P2 and P3) are notably similar, though the OC concentration of P3 (31.8 µg/m\u003csup\u003e3\u003c/sup\u003e) is higher than that of P2 (4.4 µg/m\u003csup\u003e3\u003c/sup\u003e). It is proposed that the light-absorbing BrC is intensively emitted during periods of frequent acceleration and high-speed driving, when the engine control unit adjusts the air-fuel ratio to optimize performance and efficiency while minimizing fuel consumption.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In contrast, strong BC emissions occur dominantly in the low-speed phase (P1) under incomplete combustion scenarios.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Further discussions from the perspectives of oxidation state, element distribution, and the presence of the chromophores are provided below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOxidation state analysis of BrC under different driving conditions\u003c/h3\u003e\n\u003cp\u003eThe fractions of oxygenated organic aerosols (OOAs) across different O:C ratio intervals (0.1–0.8, with a step size of 0.1) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It is clearly demonstrated that the tailpipe emissions of OA possess a significantly higher oxidation state in the P2 and P3 phases than P1 (P \u0026lt; 0.05). For example, the fractions of OOA with O:C \u0026gt; 0.2 in P2 and P3 are 0.43 and 0.46, compared to 0.27 in P1, reflecting increases of 58.2% and 69.0%, respectively. This trend was consistently observed across a wide range of O:C intervals. Previous studies have reported representative chemical structures with conjugated oxygenated groups in relation to the optical properties of BrC and a few example structures are provided in Figure S3, such as nitrophenol and the derivatives.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e The enhancement of oxidation state of OOA corresponds with the stronger light absorption properties observed in P2 and P3 phases, compared to P1. Nevertheless, the links between oxidation state and BrC optical properties were complicated. Jiang et al (2022) reported a transition O/C of about 0.6, below and above which the photo enhancement and photobleaching of OA absorptivity were observed with increasing oxidation state. In particular, the co-increase of nitrogen and oxygen in the molecules suggested the important role of nitrogen-containing functional groups in enhancing the absorptivity of the less polar compounds, while further oxidation (O/C \u0026gt; 0.6) on high-polar compounds likely led to fragmentation and the bleaching of chromophores.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the implementation of more stringent emission standards, manufacturers are increasingly inclined to raise engine combustion temperature and efficiency to reduce PM emissions to comply with the regulatory requirements. For instance, the PM emission limit has been reduced from 160 mg/kWh under China III emission standard to just 10 mg/kWh in China VI standard for heavy-duty diesel engines. It was reported that higher combustion temperature facilitates the oxidation processes and leads to greater production of highly oxidized organic compounds.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The oxidation catalysts in the aftertreatment devices (e.g., diesel oxidation catalyst) may also favor the partial oxidation of fuel components.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e The increased total OH reactivity was detected in Sha et al. (2022), as the combined effects of high combustion temperature in the engine and the catalysts, which inadvertently promote the formation of OOAs in the exhaust.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eFormula distribution analysis\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the formula distribution in individual samples. More peaks were detected in positive mode, while peak abundance varied between positive or negative modes (Figure S4 and Figure S5). The formula assignments for duplicate samples demonstrated a high degree of similarity, justifying the selection of two representative samples, 20211222-1 and 20221228-1, for further analysis. The FT-ICR mass spectra obtained from these representative samples, after blank correction, are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. Embedded within these figures are pie charts representing the fraction of each formula category, with the framed sectors indicating the cumulative nitrogen-containing formulas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of formulas in each category (negative mode only, both modes, and positive mode only) and the average values of DBE, O/C, and DBE/C, and the fraction of nitrogen-containing species for the individual samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElemental composition\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20211222-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20211222-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20211223-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20211223-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20211224-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20211224-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20211227-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20211227-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20211228-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20211228-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20211228-3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20211229-1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20211229-2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e20211229-3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e20211229-4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e20211229-5\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHO- only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHON- only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e919\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1084\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e707\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e722\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e874\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e890\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e755\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e855\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e679\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e785\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOS-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e422\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHONS-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll in ESI-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1976\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2402\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2225\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2304\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1924\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1926\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1830\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1503\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1530\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1798\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1734\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2366\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1646\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHO both\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e848\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e890\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e943\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHON both\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll both\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e988\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1346\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1312\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e804\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e434\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e896\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1363\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e831\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHO + only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e845\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e722\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e691\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e661\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e671\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e812\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e803\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e622\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHN+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHON + only\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1358\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1474\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1513\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1520\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1487\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1202\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1529\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1702\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1642\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1435\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1360\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1279\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll in ESI+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2722\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2467\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2380\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2488\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2307\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2224\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2287\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2551\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2612\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2554\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2231\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2987\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2316\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll sum\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5173\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6215\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5917\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5231\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4561\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4549\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4398\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4779\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6716\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4770\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e4490\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBE^\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO/C^\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBE/C^\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFraction of N- containing species\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFraction of N- containing species from other studies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"16\" nameend=\"c17\" namest=\"c2\"\u003e \u003cp\u003e0.58 ± 0.20 (0.28 to 0.85, min to max)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003e*\u003c/sup\u003eFormulas that were identified in both positive and negative modes simultaneously.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003e^\u003c/sup\u003eThese metrics were weighted average values.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn all samples, CHON species (formula containing only carbon, hydrogen, oxygen, and nitrogen) contributed the highest fraction among the identified formulas, ranging from 43–75% (including both ESI- and ESI + modes, as described hereinafter), followed by the sulfur-containing species (CHOS and CHONS, 30–42%). CHO species accounted for 12–40% of the identified peaks. Among them, CHO and CHON species were detected in both modes, with a notable preference for ESI + mode. Specifically, 50–65% of CHO- formulas and 19–84% of CHO + formulas were detected in both modes concurrently, and the percentages were 14–35% for CHON- species and 7–24% for CHON + species. In ESI+, the number fractions of CHN ranged from 5–11%, while CH species were excluded from further discussion due to their low ionization efficiencies.\u003c/p\u003e \u003cp\u003eCompared with previous studies, three distinct features of the formula distribution of HDDV samples could be highlighted. First, CHO species were typically the dominant category in both ambient PM\u003csub\u003e2.5\u003c/sub\u003e samples and the BB samples.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e For example, CHO species ranged from 50–63% in the ambient PM\u003csub\u003e2.5\u003c/sub\u003e samples in negative mode in He et al. (2024). However, this trend was not observed in HDDV samples. The fraction of CHO species is significantly lower than that of CHON species. Second, the presence of nitrogen-containing species in HDDV tailpipe emissions is confirmed to be notably abundant. Even in the HUmic-LIke Substances (HULIS) extracts and the methanol extracts of the BB samples, of which the nitrogen-containing species were presumed to be plentiful, the number of nitrogen-containing species was comparable to or less than those observed in HDDV samples, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This suggests that the PM emissions from HDDVs are composed of way too many nitrogen-containing species than expected, which may significantly contribute to the light absorption properties of BrC emitted from HDDVs, particularly during frequent acceleration and high-speed phases. The 3D distribution of C, N, and DBE of the identified formulas for representative samples is plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed. The fractions of formulas with 1, 2, 3, and 4 nitrogen atoms were 36% (32%), 12% (20%), 5% (8%), and 4% (4%) for samples 20211222-1 (20211228-1), respectively. Third, the sulfur-containing species identified in HDDV samples are far more abundant than in ambient PM and BB samples. Over 600 peaks were assigned with unambiguous formulas in HDDV samples, compared to 100–200 formulas found in ambient PM and BB samples. It is hypothesized that these sulfur-containing species originate from the sulfur content in fuels and are extensively formed during the combustion processes.\u003c/p\u003e\n\u003ch3\u003eDiscussions on the chromophores\u003c/h3\u003e\n\u003cp\u003eThe nitrogen-containing species (including CHON\u003csub\u003en\u003c/sub\u003e (n = 1, 2, 3, 4), CHON\u003csub\u003en\u003c/sub\u003eS (n = 1, 2), and CHN\u003csub\u003en\u003c/sub\u003e (n = 1, 2, 3)) extracted from the representative sample of 20211222-1 are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. 58.9% of the total nitrogen-containing species exhibited an O/N ratio \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 3, indicating that oxygenated nitrooxy compounds, e.g., -ONO\u003csub\u003e2\u003c/sub\u003e, constituted a significant portion of the detected species. This suggests the important role of highly oxidized nitrogen compounds in the molecular composition of BrC from HDDV emissions. To further characterize the aromatic nature of these species, the threshold values of X\u003csub\u003ec\u003c/sub\u003e of 2.50 and 2.71 were applied, as these values were proposed to be distinguish the presence of aromatic and condensed aromatic compounds.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e The fraction of nitrogen-containing species with both O/N \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 3 and X\u003csub\u003ec\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 2.5 was 20.5%, indicating the potential contribution of aromatic and nitroaromatic compounds to the chromophoric properties of the BrC. The distribution of nitrogen-containing species across different O/N bins, as shown in Figure S6, revealed a significant presence of compounds with high DBE values, particularly those exceeding DBE \u0026gt; 10. These high DBE values are indicative of complex molecular structures of condensed aromatic rings or highly conjugated −C = C double bonds, both of which are known to enhance the light-absorbing capacity of BrC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reduced nitrogen species, characterized by lower O/N ratios and positioned at the upper left region in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, were consistently observed in the HDDV tailpipe emissions and accounted for 35.7% of total nitrogen-containing species. These compounds likely consist of amines, imines and imides. Among them, C = N and C-N functional groups were identified to be the two most important chemical bonding structures in BrC from Chinese megacities.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Example chemical structures of such molecules are provided in Figure S7.\u003c/p\u003e \u003cp\u003eThe nitrogen-containing species were constrained in a H/C versus N/C plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, separated by CH2 unit and oxygen number. Subgroups of them, i.e., H\u003csub\u003en\u003c/sub\u003eN (n = 1, 2, 3, 4…), formulate oblique line clusters (indicated by the dotted gray lines), differed by one hydrogen atom. The regular and frequent occurrence of the same KMD (CH2) was constructed to provide information on the overall chemical and chromophore compositions of the nitrogen-containing species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The copious presences of aromatic structures, condensed aromatics, phenol and oxygenated nitrooxy groups are distinctly illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, underscoring the complex nature of nitrogen-containing BrC chromophores in HDDV emissions. Despite these findings, the overall O/C ratios of the HDDV samples, ranging from 0.16 to 0.26 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), are typically lower than those reported for ambient PM and BB samples,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e which indicates that the HDDV emissions are comparably less oxidized. Consequently, the chromophore compositions in HDDV emissions reflect a relatively lower degree of oxidation but contain strong light-absorbing components due to its nitrogenous and aromatic content.\u003c/p\u003e\n\u003ch3\u003eSummary and future perspectives\u003c/h3\u003e\n\u003cp\u003eThis research provides critical insights into the emission profiles of BrC emitted from HDDVs, challenging traditional perspectives that primarily attribute BrC emissions to biomass burning and other combustion sources. The vehicle driving conditions, emission features, optical properties, and the molecular and chromophore characterizations were linked, and three key insights could be deduced. First, the light-absorbing properties of HDDV emissions have been underestimated previously and the MAE\u003csub\u003e365\u003c/sub\u003e values are comparable to the well-known sources such as coal combustion and straw burning under specific driving conditions. Second, light-absorbing organic substances are intensively emitted during frequent acceleration and high-speed phases. Third, aromatic structures, condensed aromatics, phenol and oxygenated nitrooxy groups are copiously present in HDDV tailpipe emissions, which are identified as the major light-absorbing chromophores.\u003c/p\u003e \u003cp\u003eThis study leverages ultra-high-resolution FT-ICR mass spectrometry, enables the detailed elemental and chromophores characterization HDDV-emitted BrC, provides new insights into their complex chemical nature, and assesses the optical properties of vehicle BrC in dynamic urban environments. These findings have broader implications for climate modeling. However, certain limitations should be considered. First, the study focuses on specific HDDVs which may not fully capture the variability of emissions across diverse vehicle types and mileages. For example, the China VI standard mandates the use of diesel particulate filters (DPFs) on all new HDDVs to reduce PM emissions to comply with the regulatory requirements. It could be expected that the presence of light-absorbing substances would be distinctly different. Also, the controlled experiment setup, i.e., the CVS system, may not perfectly represent atmospheric dispersion and the aging processes that modulate BrC’s optical properties over time. Future research may consider including a broader array of vehicle types and in-field measurements. The underestimation of BrC from HDDVs introduces notable uncertainties into current models, particularly in urban areas where vehicle emissions dominate. Quantifying the emission factors of BrC from HDDVs under aggressive conditions and incorporating the results into BrC model shed light on the non-traditional sources beyond biomass burning and enhanced the accuracy of regional and global climate simulations.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methodology","content":"\u003ch2\u003eVehicles, driving cycles, and sampling\u003c/h2\u003e\u003cp\u003eThe tailpipe emissions from two in-use HDDVs were collected at the China Automotive Technology \u0026amp; Research Center in Guangzhou, China. The tested HDDVs were equipped with selective catalytic reduction (SCR) systems, and the detailed information about the tested vehicles is provided in the supporting information (Test S1 and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHDDVs were tested on a chassis dynamometer (AIP-ECDM 72H/2AXLE) following the China heavy-duty commercial vehicle test cycle for heavy trucks (CHTC-HT), which represents the typical driving patterns of commercial diesel vehicles in urban China. The entire driving cycle (Full) spans 1800 s and is divided into three segments: low-speed (phase one (P1), 342 s), medium-speed (phase two (P2), 988 s), and high-speed (phase three (P3), 470 s). The average speeds are 5.2, 31.3, and 63.2 km/h, respectively. The speed trace and phase division are displayed in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. It has been reported that the ambient temperature would influence the efficiency of internal combustion engine (e.g., the fuel − air ratio), and this factor should be treated independently.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Thus, vehicle #2 was further tested at a low ambient temperature of 0°C. Prior to the experiment, each vehicle was pre-conditioned overnight to cool the engine completely. For each HDDV, cold-start and hot-start tests were conducted consecutively, and each test cycle was duplicated three times. The experiment list is shown in Table S2.\u003c/p\u003e\u003cp\u003eThe vehicle exhausts were diluted using a constant volume sampler (CVS) system with the ambient air filtered through a high-efficiency particulate air (HEPA) filter. The CVS system was equipped with a real-time gas analyzer module (MEXA-7400HLE, HORIBA, Japan) as well as sensors for temperature, airflow, relative humidity, and pressure to monitor the variations of CO, CO\u003csub\u003e2\u003c/sub\u003e, and other experimental conditions. The detailed procedures for collecting HDDV-emitted OA have been described elsewhere\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and a brief summary is provided below. In each test, the diluted diesel exhausts were collected by one 47 mm quartz fiber filter (Grade QM-A, Whatman, UK), which was pre-baked at 550°C to remove carbonaceous contamination. Four parallel sampling lines were adopted to collect the P1, P2, P3, and Full samples simultaneously. Field blank samples were collected upstream of the emission pipeline to correct for blank matrix interference (Test S2).\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003ch3\u003eSample preparation and FT-ICR analysis\u003c/h3\u003e\u003cp\u003eFilter samples (including blank samples) were cut into stripes and extracted with 5 mL of MeOH in an ultrasonic bath for 15 min. The extract was filtered through a 0.2 µm PTFE syringe filter (Millipore, Billerica, USA) and transferred to a 5 mL conical vial. The filters were further extracted twice, and the extracts were combined and blown to dryness under a gentle stream of N\u003csub\u003e2\u003c/sub\u003e. The dried extracts were re-dissolved in 1 mL MeOH for FT-ICR MS analysis.\u003c/p\u003e\u003cp\u003eA solariX XR FT-ICR MS (Bruker Daltonics GmbH \u0026amp; Co. KG, Bremen, Germany) equipped with a 9.4 T refrigerated, actively shielded superconducting magnet (Bruker France S.A.S., Wissembourg, France), a ParaCell analyzer cell, and an electrospray ionization (ESI) source (Bruker Daltonics GmbH \u0026amp;Co. KG, Bremen, Germany) was adopted. The FT-ICR MS analysis followed the protocol described in details in Sun et al. (2021).\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Briefly, samples were injected at a flow rate of 200 µL/h and analyzed using a nebulizer gas pressure of 1 bar, a dry gas velocity of 4 L/min, and a capillary voltage of + 4.5kV. The temperature and the end plate offset were set to 200 ºC and − 500 V, respectively. The instrument was calibrated externally, and the procedures are provided in the supporting information (Text S3). The mass resolving power (m/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003em 50%, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003em 50% represents the peak full width at half-maximum peak height) of the instrument achieved over 450,000 at m/z = 319.\u003c/p\u003e\u003cp\u003eA non-targeted full scan was performed with an m/z range of 100 − 800 in both ESI + and ESI- modes. Prior to positive analysis, 15 µL of ammonium formate (HCOONH\u003csub\u003e4\u003c/sub\u003e) was added to the solution to improve the ionization efficiency.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e The preferences of ESI for detecting various functional groups were discussed in He et al. (2024).\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Basically, aldehyde groups were ionized in both modes, with ESI + spectra exhibiting more peaks than ESI- spectra, as the oxygen in -C = O bond was more electronegative and more likely to form protonated molecules (M + H)\u003csup\u003e+\u003c/sup\u003e in ESI + mode. Alcohol groups were detected in both modes and higher signal intensities were typically observed in ESI- mode. Methoxy and ketone groups were exclusively detectable in ESI + mode while acid groups were more effectively identified in ESI- mode. Nitrogen-containing compounds, whether in reduced forms (e.g., amines, imines, and azoles) or in oxidized forms (e.g., organonitrates), were readily detected in both modes.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Unfunctionalized compounds are not efficiently ionized in either mode.\u003c/p\u003e\u003ch2\u003eData pretreatment and elemental composition assignment\u003c/h2\u003e\u003cp\u003eAll peaks with a signal-to-noise ratio higher than 10 were assigned with mathematically calculated formulas with the constraints of a mass tolerance of ± 1 ppm (part per million).\u003c/p\u003e\u003cp\u003eIn the calculation of negative ions, the element numbers were restricted to 1 − 60 \u003csup\u003e12\u003c/sup\u003eC, 1 − 100 \u003csup\u003e1\u003c/sup\u003eH, 0 − 20 \u003csup\u003e16\u003c/sup\u003eO, 0 − 5 \u003csup\u003e14\u003c/sup\u003eN, 0 − 2 \u003csup\u003e32\u003c/sup\u003eS, 0 − 1 \u003csup\u003e13\u003c/sup\u003eC, 0 − 1 \u003csup\u003e18\u003c/sup\u003eO, and 0 − 1 \u003csup\u003e34\u003c/sup\u003eS. In the calculation of positive ions, S was excluded due to the fact that reduced sulfur compounds in the aerosol phase were rarely reported and they predominantly partitioned into the gas phase.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Instead, Na was included due to its high tendency to form adducts with target analysts in the positive mode.\u003c/p\u003e\u003cp\u003eThe following empirical rules were further applied to constrain the measurement results: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0.3\\le\\:\\)\u003c/span\u003e\u003c/span\u003e H/C \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:4.0\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\\le\\:\\)\u003c/span\u003e\u003c/span\u003e O/C \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:4.0\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\\le\\:\\)\u003c/span\u003e\u003c/span\u003e N/C \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:0.5\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0\\le\\:\\)\u003c/span\u003e\u003c/span\u003e S/C \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:0.2\\)\u003c/span\u003e\u003c/span\u003e, and 2C + 2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e H. All the calculated formulas with negative double bond equivalent (DBE) values and those that violated the nitrogen rule (molecules with all paired electrons that contain an odd number of nitrogen atoms will have an odd nominal mass) were discarded. The calculation of DBE value of each assigned formula is detailed in the supporting information (Text S4). Blank corrections were performed on individual samples in both positive and negative analysis. Only sample peaks with positive intensities were exported for further treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.H. conceived the idea, conducted the data analysis, and wrote the manuscript. X.B.C., B.J., S.W.G, and T.C. conducted lab experiments. Z.Y.L., Y.Y., Y.D., Y.C., J.K.J., S.X.W., and Y.W. provided important insights for interpreting the results. X.Z. and S.J.Z supervised this study. All authors contributed to the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the financial support of the National Natural Science Foundation of China (42105100, 51978404, and 42261160645), Shenzhen Outstanding Science and Technology Innovation Program (RCBS20231211090534051), the Open Research Fund of Key Laboratory of Vehicle Emission Control and Simulation of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences (VECS2024K04), the Fundamental Research Funds for the Central Public-interest Scientific Institution (2024YSKY-03), and the Scientific Research Fund at Shenzhen University (868- 000001032089 and 827\u0026ndash;000907).\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eThe measurement data used in this study are available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndreae, M. O., Gelencs\u0026eacute;r, A. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e (10), 3131-3148. https://dx.doi.org/10.5194/acp-6-3131-2006.\u003c/li\u003e\n\u003cli\u003eChen, D., Zhao, Y., Lyu, R., Wu, R., Dai, L., Zhao, Y., Chen, F., Zhang, J., Yu, H., Guan, M. Seasonal and spatial variations of optical properties of light absorbing carbon and its influencing factors in a typical polluted city in Yangtze River Delta, China. \u003cem\u003eAtmos. Environ. \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e199\u003c/em\u003e, 45-54. https://dx.doi.org/10.1016/j.atmosenv.2018.11.022.\u003c/li\u003e\n\u003cli\u003eTang, J., Li, J., Su, T., Han, Y., Mo, Y., Jiang, H., Cui, M., Jiang, B., Chen, Y., Tang, J., Song, J., Peng, P. A., Zhang, G. Molecular compositions and optical properties of dissolved brown carbon in biomass burning, coal combustion, and vehicle emission aerosols illuminated by excitation\u0026ndash;emission matrix spectroscopy and Fourier transform ion cyclotron resonance mass spectromet. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e20\u003c/em\u003e (4), 2513-2532. https://dx.doi.org/10.5194/acp-20-2513-2020.\u003c/li\u003e\n\u003cli\u003eZhang, Q., Shen, Z., Zhang, T., Kong, S., Lei, Y., Wang, Q., Tao, J., Zhang, R., Wei, P., Wei, C., Cui, S., Cheng, T., Ho, S. S. H., Li, Z., Xu, H., Cao, J. Spatial distribution and sources of winter black carbon and brown carbon in six Chinese megacities. \u003cem\u003eSci. Total Environ. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e762\u003c/em\u003e, 143075. https://www.sciencedirect.com/science/article/pii/S0048969720366055.\u003c/li\u003e\n\u003cli\u003eHuang, R.-J., Yuan, W., Yang, L., Yang, H., Cao, W., Guo, J., Zhang, N., Zhu, C., Wu, Y., Zhang, R. Concentration, optical characteristics, and emission factors of brown carbon emitted by on-road vehicles. \u003cem\u003eSci. Total Environ. \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e810\u003c/em\u003e, 151307. https://www.sciencedirect.com/science/article/pii/S0048969721063853.\u003c/li\u003e\n\u003cli\u003eLaskin, A., Laskin, J., Nizkorodov, S. A. Chemistry of Atmospheric Brown Carbon. \u003cem\u003eChem. Rev. \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e115\u003c/em\u003e (10), 4335-4382. https://doi.org/10.1021/cr5006167.\u003c/li\u003e\n\u003cli\u003eBond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., Deangelo, B. J., Flanner, M. G., Ghan, S., K\u0026auml;rcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., Zender, C. S. Bounding the role of black carbon in the climate system: A scientific assessment. \u003cem\u003eJ. Geophys. Res. \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e118\u003c/em\u003e (11), 5380-5552. https://dx.doi.org/10.1002/jgrd.50171.\u003c/li\u003e\n\u003cli\u003eFan, X. X., Ye, S. H., Zheng, H., Han, B., Zhang, G. T., Zheng, Z. S., Zhao, X. Y., Kong, S. F., Wang, X. L., Yang, W. Source apportionment of black carbon using an advanced Aethalometer model in a typical industrial city of China. \u003cem\u003eJ. Environ. Sci. \u003c/em\u003e\u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e151\u003c/em\u003e, 42-53. https://doi.org/10.1016/j.jes.2024.03.036.\u003c/li\u003e\n\u003cli\u003eGeng, X. F., Li, J., Zhong, G. C., Zhao, S. Z., Tian, C. G., Zhang, Y. L., Zhang, G. Ship emissions as the largest contributor to coastal atmospheric black carbon at a receptor island in southern China. \u003cem\u003eEnviron. Sci. Technol. Lett. \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e11\u003c/em\u003e (7), 723\u0026ndash;729. https://doi-org.ezproxy.lib.szu.edu.cn/10.1021/acs.estlett.4c00362.\u003c/li\u003e\n\u003cli\u003eBian, R., Stubbings, W. A., Li, F., Wu, F., Wang, S. Distribution, Partitioning, Source Apportionment, and Ecological Risk Assessment of Legacy and Emerging PFAS in Water and Sediment of the Pearl River Delta. \u003cem\u003eACS ES\u0026amp;T Water \u003c/em\u003e\u003cstrong\u003e2025\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (1), 253-263. https://doi.org/10.1021/acsestwater.4c00828.\u003c/li\u003e\n\u003cli\u003eLack, D. A., Langridge, J. M. On the attribution of black and brown carbon light absorption using the \u0026Aring;ngstr\u0026ouml;m exponent. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e (20), 10535-10543. https://dx.doi.org/10.5194/acp-13-10535-2013.\u003c/li\u003e\n\u003cli\u003eOlson, M. R., Victoria Garcia, M., Robinson, M. A., Van Rooy, P., Dietenberger, M. A., Bergin, M., Schauer, J. J. Investigation of black and brown carbon multiple-wavelength-dependent light absorption from biomass and fossil fuel combustion source emissions. \u003cem\u003eJournal of Geophysical Research: Atmospheres \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e120\u003c/em\u003e (13), 6682-6697. https://dx.doi.org/10.1002/2014jd022970.\u003c/li\u003e\n\u003cli\u003eBond, T. C. Spectral dependence of visible light absorption by carbonaceous particles emitted from coal combustion. \u003cem\u003eGeophys. Res. Lett. \u003c/em\u003e\u003cstrong\u003e2001\u003c/strong\u003e, \u003cem\u003e28\u003c/em\u003e (21), 4075-4078. https://dx.doi.org/10.1029/2001gl013652.\u003c/li\u003e\n\u003cli\u003eShen, Z., Lei, Y., Zhang, L., Zhang, Q., Zeng, Y., Tao, J., Zhu, C., Cao, J., Xu, H., Liu, S. Methanol Extracted Brown Carbon in PM2.5 Over Xi\u0026rsquo;an, China: Seasonal Variation of Optical Properties and Sources Identification. \u003cem\u003eAerosol Science and Engineering \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e1\u003c/em\u003e (2), 57-65. https://dx.doi.org/10.1007/s41810-017-0007-z.\u003c/li\u003e\n\u003cli\u003eAndreae, M. O., Gelencs\u0026eacute;r, A. Black carbon or brown carbon?: The nature of light-absorbing carbonaceous aerosols. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e, 3131-3148.\u003c/li\u003e\n\u003cli\u003eZhang, R., Li, S., Fu, X., Pei, C., Huang, Z., Wang, Y., Chen, Y., Yan, J., Wang, J., Yu, Q., Luo, S., Zhu, M., Wu, Z., Fang, H., Xiao, S., Huang, X., Zeng, J., Zhang, H., Song, W., Zhang, Y., Bi, X., Wang, X. Emissions and light absorption of carbonaceous aerosols from on-road vehicles in an urban tunnel in south China. \u003cem\u003eSci. Total Environ. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e790\u003c/em\u003e, 148220. https://www.sciencedirect.com/science/article/pii/S0048969721032915.\u003c/li\u003e\n\u003cli\u003eZielinska, B., Sagebiel, J., McDonald, J. D., Whitney, K., Lawson, D. R. Emission Rates and Comparative Chemical Composition from Selected In-Use Diesel and Gasoline-Fueled Vehicles. \u003cem\u003eJ. Air Waste Manage. Assoc. \u003c/em\u003e\u003cstrong\u003e2004\u003c/strong\u003e, \u003cem\u003e54\u003c/em\u003e (9), 1138-1150. https://dx.doi.org/10.1080/10473289.2004.10470973.\u003c/li\u003e\n\u003cli\u003eXie, M., Hays, M. D., Holder, A. L. Light-absorbing organic carbon from prescribed and laboratory biomass burning and gasoline vehicle emissions. \u003cem\u003eScientific Reports \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e (1). https://dx.doi.org/10.1038/s41598-017-06981-8.\u003c/li\u003e\n\u003cli\u003eLin, P., Bluvshtein, N., Rudich, Y., Nizkorodov, S. A., Laskin, J., Laskin, A. Molecular Chemistry of Atmospheric Brown Carbon Inferred from a Nationwide Biomass Burning Event. \u003cem\u003eEnvironmental Science \u0026amp; Technology \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e51\u003c/em\u003e (20), 11561-11570. https://doi.org/10.1021/acs.est.7b02276.\u003c/li\u003e\n\u003cli\u003eBluvshtein, N., Lin, P., Flores, J. M., Segev, L., Mazar, Y., Tas, E., Snider, G., Weagle, C., Brown, S. S., Laskin, A., Rudich, Y. Broadband optical properties of biomass‐burning aerosol and identification of brown carbon chromophores. \u003cem\u003eJournal of Geophysical Research: Atmospheres \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e122\u003c/em\u003e (10), 5441-5456. https://dx.doi.org/10.1002/2016jd026230.\u003c/li\u003e\n\u003cli\u003eBao, M., Zhang, Y.-L., Cao, F., Hong, Y., Lin, Y.-C., Yu, M., Jiang, H., Cheng, Z., Xu, R., Yang, X. Impact of fossil and non-fossil fuel sources on the molecular compositions of water-soluble humic-like substances in PM\u003csub\u003e2.5\u003c/sub\u003e at a suburban site of Yangtze River Delta, China. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e23\u003c/em\u003e (14), 8305-8324. https://dx.doi.org/10.5194/acp-23-8305-2023.\u003c/li\u003e\n\u003cli\u003eHe, X., Huang, X. H., Ma, Y., Huang, C., Yu, J. Z. Unambiguous analysis and systematic mapping of oxygenated aromatic compounds in atmospheric aerosols using ultrahigh-resolution mass spectrometry. \u003cem\u003eAnal. Chem. \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e96\u003c/em\u003e (5), 1880-1889. https://doi.org/10.1021/acs.analchem.3c03760.\u003c/li\u003e\n\u003cli\u003eLaskin, J., Laskin, A., Nizkorodov, S. A., Roach, P., Eckert, P., Gilles, M. K., Wang, B., Lee, H. J., Hu, Q. Molecular selectivity of brown carbon chromophores. \u003cem\u003eEnviron. Sci. Technol. \u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e48\u003c/em\u003e (20), 12047-12055. https://doi.org/10.1021/es503432r.\u003c/li\u003e\n\u003cli\u003eZeng, Y., Ning, Y., Shen, Z., Zhang, L., Zhang, T., Lei, Y., Zhang, Q., Li, G., Xu, H., Ho, S. S. H., Cao, J. The Roles of N, S, and O in molecular absorption features of brown carbon in PM\u003csub\u003e2.5\u003c/sub\u003e in a typical semi‐arid megacity in northwestern China. \u003cem\u003eJ. Geophys. Res. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e126\u003c/em\u003e (16), e2021JD034791. https://dx.doi.org/10.1029/2021jd034791.\u003c/li\u003e\n\u003cli\u003eCheng, Y., Cao, X.-b., Liu, J.-m., Yu, Q.-q., Wang, P., Yan, C.-q., Du, Z.-y., Liang, L.-l., Zhang, Q., He, K.-b. Primary nature of brown carbon absorption in a frigid atmosphere with strong haze chemistry. \u003cem\u003eEnvironmental Research \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e204\u003c/em\u003e, 112324. https://www.sciencedirect.com/science/article/pii/S001393512101625X.\u003c/li\u003e\n\u003cli\u003eSun, W., Fu, Y., Zhang, G., Yang, Y., Jiang, F., Lian, X., Jiang, B., Liao, Y., Bi, X., Chen, D., Chen, J., Wang, X., Ou, J., Peng, P., Sheng, G. Measurement report: Molecular characteristics of cloud water in southern China and insights into aqueous-phase processes from Fourier transform ion cyclotron resonance mass spectrometry. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e21\u003c/em\u003e (22), 16631-16644. https://acp.copernicus.org/articles/21/16631/2021/.\u003c/li\u003e\n\u003cli\u003eFang, Z., Deng, W., Wang, X., He, Q., Zhang, Y., Hu, W., Song, W., Zhu, M., Lowther, S., Wang, Z., Fu, X., Hu, Q., Bi, X., George, C., Rudich, Y. Evolution of light absorption properties during photochemical aging of straw open burning aerosols. \u003cem\u003eSci. Total Environ. \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e838\u003c/em\u003e, 156431. https://www.sciencedirect.com/science/article/pii/S0048969722035288.\u003c/li\u003e\n\u003cli\u003eLiu, J., Bergin, M., Guo, H., King, L., Kotra, N., Edgerton, E., Weber, R. J. Size-resolved measurements of brown carbon in water and methanol extracts and estimates of their contribution to ambient fine-particle light absorption. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e (24), 12389-12404. https://acp.copernicus.org/articles/13/12389/2013/.\u003c/li\u003e\n\u003cli\u003eRuna, F., Islam, M. S., Jeba, F., Salam, A. Light absorption properties of brown carbon from biomass burning emissions. \u003cem\u003eEnvironmental Science and Pollution Research \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e29\u003c/em\u003e (14), 21012-21022. https://doi.org/10.1007/s11356-021-17220-z.\u003c/li\u003e\n\u003cli\u003eZhang, L., Li, J., Li, Y., Liu, X., Luo, Z., Shen, G., Tao, S. Comparison of water-soluble and water-insoluble organic compositions attributing to different light absorption efficiency between residential coal and biomass burning emissions. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e24\u003c/em\u003e (10), 6323-6337. https://acp.copernicus.org/articles/24/6323/2024/.\u003c/li\u003e\n\u003cli\u003eZhang, L., Luo, Z., Du, W., Li, G., Shen, G., Cheng, H., Tao, S. Light absorption properties and absorption emission factors for indoor biomass burning. \u003cem\u003eEnvironmental Pollution \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e267\u003c/em\u003e, 115652. https://www.sciencedirect.com/science/article/pii/S0269749120363405.\u003c/li\u003e\n\u003cli\u003ePeng, Y.-H., Huang, Y.-P., Tang, J.-Y., Huang, Q.-F., Huang, Y.-R. Experimental study on the effects of air supply control on combustion and emissions performance at medium and low load for a dual-fuel diesel engine. In \u003cem\u003eEnergies\u003c/em\u003e, 2018; Vol. 11, p 2944.\u003c/li\u003e\n\u003cli\u003eHays, M. D., Preston, W., George, B. J., George, I. J., Snow, R., Faircloth, J., Long, T., Baldauf, R. W., McDonald, J. Temperature and Driving Cycle Significantly Affect Carbonaceous Gas and Particle Matter Emissions from Diesel Trucks. \u003cem\u003eEnergy \u0026amp; Fuels \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e31\u003c/em\u003e (10), 11034-11042. https://doi.org/10.1021/acs.energyfuels.7b01446.\u003c/li\u003e\n\u003cli\u003eMohr, C., Lopez-Hilfiker, F. D., Zotter, P., Pr\u0026eacute;v\u0026ocirc;t, A. S. H., Xu, L., Ng, N. L., Herndon, S. C., Williams, L. R., Franklin, J. P., Zahniser, M. S., Worsnop, D. R., Knighton, W. B., Aiken, A. C., Gorkowski, K. J., Dubey, M. K., Allan, J. D., Thornton, J. A. Contribution of Nitrated Phenols to Wood Burning Brown Carbon Light Absorption in Detling, United Kingdom during Winter Time. \u003cem\u003eEnviron. Sci. Technol. \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e47\u003c/em\u003e (12), 6316-6324. https://dx.doi.org/10.1021/es400683v.\u003c/li\u003e\n\u003cli\u003eJiang, X., Liu, D., Li, Q., Tian, P., Wu, Y., Li, S., Hu, K., Ding, S., Bi, K., Li, R., Huang, M., Ding, D., Chen, Q., Kong, S., Li, W., Pang, Y., He, D. Connecting the Light Absorption of Atmospheric Organic Aerosols with Oxidation State and Polarity. \u003cem\u003eEnvironmental Science \u0026amp; Technology \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e56\u003c/em\u003e (18), 12873-12885. https://doi.org/10.1021/acs.est.2c02202.\u003c/li\u003e\n\u003cli\u003eHiggins, K. J., Jung, H., Kittelson, D. B., Roberts, J. T., Zachariah, M. R. Kinetics of diesel nanoparticle oxidation. \u003cem\u003eEnviron. Sci. Technol. \u003c/em\u003e\u003cstrong\u003e2003\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e (9), 1949-1954. https://doi.org/10.1021/es0261269.\u003c/li\u003e\n\u003cli\u003eHe, X., Zheng, X., Zhang, S., Wang, X., Chen, T., Zhang, X., Huang, G., Cao, Y., He, L., Cao, X., Cheng, Y., Wang, S., Wu, Y. Comprehensive characterization of particulate intermediate-volatility and semi-volatile organic compounds (I/SVOCs) from heavy-duty diesel vehicles using two-dimensional gas chromatography time-of-flight mass spectrometry. \u003cem\u003eAtmospheric Chemistry and Physics \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e22\u003c/em\u003e (21), 13935-13947. https://dx.doi.org/10.5194/acp-22-13935-2022.\u003c/li\u003e\n\u003cli\u003eSha, Q. e., Liu, X., Yuan, Z., Zheng, J., Lou, S., Wang, H., Li, X., Yu, F. Upgrading Emission Standards Inadvertently Increased OH Reactivity from Light-Duty Diesel Truck Exhaust in China: Evidence from Direct LP-LIF Measurement. \u003cem\u003eEnvironmental Science \u0026amp; Technology \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e56\u003c/em\u003e (14), 9968-9977. https://doi.org/10.1021/acs.est.2c02944.\u003c/li\u003e\n\u003cli\u003eYassine, M. M., Harir, M., Dabek‐Zlotorzynska, E., Schmitt‐Kopplin, P. Structural characterization of organic aerosol using Fourier transform ion cyclotron resonance mass spectrometry: Aromaticity equivalent approach. \u003cem\u003eRapid Communications in Mass Spectrometry \u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e28\u003c/em\u003e (22), 2445-2454. https://dx.doi.org/10.1002/rcm.7038.\u003c/li\u003e\n\u003cli\u003eGrange, S. K., Farren, N. J., Vaughan, A. R., Rose, R. A., Carslaw, D. C. Strong Temperature Dependence for Light-Duty Diesel Vehicle NOx Emissions. \u003cem\u003eEnvironmental Science \u0026amp; Technology \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e53\u003c/em\u003e (11), 6587-6596. https://doi.org/10.1021/acs.est.9b01024.\u003c/li\u003e\n\u003cli\u003eHe, X., Zheng, X., You, Y., Zhang, S., Zhao, B., Wang, X., Huang, G., Chen, T., Cao, Y., He, L., Chang, X., Wang, S., Wu, Y. Comprehensive chemical characterization of gaseous I/SVOC emissions from heavy-duty diesel vehicles using two-dimensional gas chromatography time-of-flight mass spectrometry. \u003cem\u003eEnviron Pollut \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e305\u003c/em\u003e, 119284. https://www.ncbi.nlm.nih.gov/pubmed/35436508.\u003c/li\u003e\n\u003cli\u003eSun, W., Fu, Y., Zhang, G., Yang, Y., Jiang, F., Lian, X., Jiang, B., Liao, Y., Bi, X., Chen, D., Chen, J., Wang, X., Ou, J., Peng, P. A., Sheng, G. Measurement report: Molecular characteristics of cloud water in southern China and insights into aqueous-phase processes from Fourier transform ion cyclotron resonance mass spectrometry. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e21\u003c/em\u003e (22), 16631-16644. https://dx.doi.org/10.5194/acp-21-16631-2021.\u003c/li\u003e\n\u003cli\u003eLu, J., Zhang, Y., Shi, Q. Ionizing aromatic compounds in petroleum by electrospray with HCOONH4 as ionization promoter. \u003cem\u003eAnal. Chem. \u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e88\u003c/em\u003e (7), 3471-3475. https://doi.org/10.1021/acs.analchem.6b00022.\u003c/li\u003e\n\u003cli\u003eDitto, J. C., Machesky, J., Gentner, D. R. Analysis of reduced and oxidized nitrogen-containing organic compounds at a coastal site in summer and winter. \u003cem\u003eAtmos. Chem. Phys. \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e22\u003c/em\u003e (5), 3045-3065. https://acp.copernicus.org/articles/22/3045/2022/.\u003c/li\u003e\n\u003cli\u003eNovak, G. A., Fite, C. H., Holmes, C. D., Veres, P. R., Neuman, J. A., Faloona, I., Thornton, J. A., Wolfe, G. M., Vermeuel, M. P., Jernigan, C. M., Peischl, J., Ryerson, T. B., Thompson, C. R., Bourgeois, I., Warneke, C., Gkatzelis, G. I., Coggon, M. M., Sekimoto, K., Bui, T. P., Dean-Day, J., Diskin, G. S., DiGangi, J. P., Nowak, J. B., Moore, R. H., Wiggins, E. B., Winstead, E. L., Robinson, C., Thornhill, K. L., Sanchez, K. J., Hall, S. R., Ullmann, K., Dollner, M., Weinzierl, B., Blake, D. R., Bertram, T. H. Rapid cloud removal of dimethyl sulfide oxidation products limits SO\u003csub\u003e2\u003c/sub\u003e and cloud condensation nuclei production in the marine atmosphere. \u003cem\u003ePNAS \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e118\u003c/em\u003e (42). https://doi.org/10.1073/pnas.2110472118.\u003c/li\u003e\n\u003cli\u003eVan Rooy, P., Purvis-Roberts, K. L., Silva, P. J., Nee, M. J., Cocker, D. Characterization of secondary products formed through oxidation of reduced sulfur compounds. \u003cem\u003eAtmos. Environ. \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e256\u003c/em\u003e, 118148. https://doi.org/10.1016/j.atmosenv.2020.118148.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HDDVs, FT-ICR-MS, brown carbon, light absorption profiles, chromophore analysis","lastPublishedDoi":"10.21203/rs.3.rs-5715056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5715056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrown carbon (BrC) holds scientific significance by influencing radiative balance, cloud condensation dynamics, and regional air quality. This study demonstrated that heavy-duty diesel vehicles (HDDVs) emit substantially higher levels of light-absorbing carbonaceous aerosols under aggressive conditions, such as frequent acceleration and high-speed phases by investigating the emission profiles, formula distribution, and chromophore characteristics under various driving conditions. A non-targeted analysis of BrC was performed using Fourier-transform ion cyclotron resonance mass spectrometry coupled with a soft electrospray ionization source. The light absorption properties were assessed and revealed significantly higher light absorption during frequent acceleration and high-speed phases compared to low-speed phases. Formula distribution analysis highlighted a substantial presence of nitrogen-containing species, constituting 53-65% of the identified peaks, with high O/N ratios (≥\u0026nbsp;3), double bond equivalent values (DBE \u0026gt; 10), and aromaticity equivalent (X\u003csub\u003ec\u003c/sub\u003e ≥\u0026nbsp;2.5), which indicated the substantial contribution of aromatic structures, condensed aromatics, nitrooxy functionalities to BrC chromophores.\u003c/p\u003e","manuscriptTitle":"Significant N-containing brown carbon emission from heavy-duty diesel vehicles revealed by the molecular and chromophore analysis using ultra-high resolution mass spectrometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-29 02:37:44","doi":"10.21203/rs.3.rs-5715056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-04T09:42:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T09:35:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T19:31:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24505103899658701839635218709673363073","date":"2025-03-28T14:15:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7772546905788919519959341775286523065","date":"2025-03-27T15:06:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T07:22:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T07:13:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate and Atmospheric Science","date":"2025-03-23T04:06:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7623898c-aa08-48f0-a64c-3d013b76ce95","owner":[],"postedDate":"March 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46310612,"name":"Earth and environmental sciences/Environmental sciences"},{"id":46310616,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry"}],"tags":[],"updatedAt":"2025-06-02T16:01:59+00:00","versionOfRecord":{"articleIdentity":"rs-5715056","link":"https://doi.org/10.1038/s41612-025-01034-8","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2025-05-27 15:57:36","publishedOnDateReadable":"May 27th, 2025"},"versionCreatedAt":"2025-03-29 02:37:44","video":"","vorDoi":"10.1038/s41612-025-01034-8","vorDoiUrl":"https://doi.org/10.1038/s41612-025-01034-8","workflowStages":[]},"version":"v1","identity":"rs-5715056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5715056","identity":"rs-5715056","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}