Single Particle Mass Spectra Research Articles

Technical note: Determining chemical composition of atmospheric single particles by a standard-free mass calibration algorithm

Abstract. The chemical composition of individual particles can be revealed by single-particle mass spectrometers (SPMSs). With higher accuracy in the ratio of mass to charge (m/z), more detailed chemical information could be obtained. In SPMSs, the conventional standard-based calibration methods (internal/external) are constrained by the inhomogeneity of ionization lasers and the finite focusing ability of the inlet system, etc.; therefore, the mass accuracy is restricted. In this study, we obtained the detailed and reliable chemical composition of single particles utilizing a standard-free mass calibration algorithm. In the algorithm, the characteristic distributions of hundreds of ions were concluded and collected in a database denoted as prototype. Each single-particle mass spectrum was initially calibrated by a function with specific coefficients. The range of coefficients was constrained by the magnitude of mass deviation to a finite vector space. To find the optimal coefficient vector, the conformity of each initially calibrated spectrum to the prototype dataset was assessed. The optimum calibrated spectrum was obtained with maximum conformity. For more than 98 % ambient particles, a 20-fold improvement in mass accuracy, from ∼ 10 000 ppm (integer) to ∼ 500 ppm (two decimal places), was achieved. The improved mass accuracy validated the determination of adjacent ions with a m/z difference ∼ 0.05 Th. Furthermore, atmospheric particulate trace elements that were poorly studied before are specified. The obtained detailed single-particle-level chemical information could help explain the source apportionment, reaction mechanism, and mixing state of atmospheric particles.

Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

Brake wear-derived particles: Single-particle mass spectral signatures and real-world emissions

Brake wear is an important but unregulated vehicle-related source of atmospheric particulate matter (PM). The single-particle spectral fingerprints of brake wear particles (BWPs) provide essential information for understanding their formation mechanism and atmospheric contributions. Herein, we obtained the single-particle mass spectra of BWPs by combining a brake dynamometer with an online single particle aerosol mass spectrometer and quantified real-world BWP emissions through a tunnel observation in Tianjin, China. The pure BWPs mainly include three distinct types of particles, namely, Ba-containing particles, mineral particles, and carbon-containing particles, accounting for 44.2%, 43.4%, and 10.3% of the total BWP number concentration, respectively. The diversified mass spectra indicate complex BWP formation pathways, such as mechanical, phase transition, and chemical processes. Notably, the mass spectra of Ba-containing particles are unique, which allows them to serve as an excellent indicator for estimating ambient BWP concentrations. By evaluating this indicator, we find that approximately 4.0% of the PM in the tunnel could be attributable to brake wear; the real-world fleet-average emission factor of 0.28 mg km−1 veh−1 is consistent with the estimation obtained using the receptor model. The results presented herein can be used to inform assessments of the environmental and health impacts of BWPs to formulate effective emissions control policies.

Au4 cluster inhibits human thioredoxin reductase activity via specifically binding of Au to Cys189

Studies have shown that Au triggers tumor cell apoptosis by directly inhibiting human thioredoxin reductase (hTrxR), thus making Au potential drugs for cancer treatment. However, the exact molecular mechanism of Au inhibiting hTrxR has never been demonstrated via experimental and theoretical studies. Here, we synthesized an Au4 cluster coated by an artificial RGD peptide. This gold cluster can bind with hTrxR (KD: ∼1.74 µM) in vitro, and significantly inhibit hTrxR activity in tumor cells. To analyze the underlying mechanism, we established hTrxR-overexpressing tumor cells and fished Au-hTrxR adducts from tumor cells previously treated by Au clusters. Using single-particle cryo-electron microscopy (Cryo-EM) and mass spectra (MS), we found unexpectedly that, Au (I) ion, metabolism of Au cluster, specifically binds to the Cys189 of hTrxR. Molecular dynamic simulation revealed such specific Au-Cys189 binding effectively broke the electron transfer from Sec498 of hTrxR to Cys32 of thioredoxin. This inhibition of hTrxR activity by Au4 cluster induced ROS-regulated tumor cell apoptosis and significantly suppressed tumor growth in an A549 lung cancer xenograft model. In summary, this study revealed the structural mechanism of Au inhibiting hTrxR activity and shed light to the design and application of novel hTrxR-inhibitory anti-tumor nanodrugs.

Unexpected rise of atmospheric secondary aerosols from biomass burning during the COVID-19 lockdown period in Hangzhou, China

After the global outbreak of COVID-19, the Chinese government took many measures to control the spread of the virus. The measures led to a reduction in anthropogenic emissions nationwide. Data from a single particle aerosol mass spectrometer in an eastern Chinese megacity (Hangzhou) before, during, and after the COVID-19 lockdown (5 January to February 29, 2020) was used to understand the effect lockdown had on atmospheric particles. The collected single particle mass spectra were clustered into eight categories. Before the lockdown, the proportions of particles ranked in order of: EC (57.9%) < K-SN (13.6%) < Fe-rich (10.2%) < ECOC (6.7%) < K–Na (6.6%) < OC (3.4%) < K–Pb (1.0%) < K–Al (0.7%). During the lockdown period, the EC and Fe-rich particles decreased by 42.8% and 93.2% compared to before lockdown due to reduced vehicle exhaust and industrial activity. By contrast, the K-SN and K–Na particles containing biomass burning tracers increased by 155.2% and 45.2% during the same time, respectively. During the lockdown, the proportions of particles ranked in order of: K-SN (39.7%) < EC (38.1%) < K–Na (11.0%) < ECOC (7.7%) < OC (1.2%) < K–Pb (0.9%) < Fe-rich (0.8%) < K–Al (0.6%). Back trajectory analysis indicated that both inland (Anhui and Shandong provinces) and marine transported air masses may have contributed to the increase in K–SN and K–Na particles during the lockdown, and that increased number of fugitive combustion points (i.e., household fuel, biomass combustion) was a contributing factor. Therefore, the results imply that regional synergistic control measures on fugitive combustion emissions are needed to ensure good air quality.

Application of Single-Particle Mass Spectrometer to Obtain Chemical Signatures of Various Combustion Aerosols.

A single-particle mass spectrometer (SPMS) with laser ionization was constructed to determine the chemical composition of single particles in real time. The technique was evaluated using various polystyrene latex particles with different sizes (125 nm, 300 nm, 700 nm, and 1000 nm); NaCl, KCl, MgCO3, CaCO3, and Al2O3 particles with different chemical compositions; an internal mixture of NaCl and KCl; and an internal mixture of NaCl, KCl, and MgCl2 with different mixing states. The results show that the SPMS can be useful for the determination of chemical characteristics and mixing states of single particles in real time. The SPMS was then applied to obtain the chemical signatures of various combustion aerosols (diesel engine exhaust, biomass burning (rice straw), coal burning, and cooking (pork)) based on their single-particle mass spectra. Elemental carbon (EC)-rich and EC-organic carbon (OC) particles were the predominant particle types identified in diesel engine exhaust, while K-rich and EC-OC-K particles were observed among rice straw burning emissions. Only one particle type (ash-rich particles) was detected among coal burning emissions. EC-rich and EC-OC particles were observed among pork burning particles. The single-particle mass spectra of the EC or OC types of particles differed among various combustion sources. The observed chemical signatures could be useful for rapidly identifying sources of atmospheric fine particles. In addition, the detected chemical signatures of the fine particles may be used to estimate their toxicity and to better understand their effects on human health.

Analysis of single‐cell microbial mass spectra profiles from single‐particle aerosol mass spectrometry

Single-particle aerosol mass spectrometry is a practical method for studying microbial aerosols. However, the related mass spectra characteristics of single-cell microorganisms have not yet been studied systematically; hence, further investigations are necessary. Different microbial cells were grown and directly aerosolized in the laboratory. These aerosols were then drawn into a single-particle mass spectrometer platform, and single-cell mass spectra profiles were obtained in real-time. The biological characteristics, ion variation trends, and microbial types were analyzed with either laser pulse energy or laser fluence. The single-particle mass spectra contained prominent peaks that could be attributed to the presence of biological matter, such as organic phosphate and nitrogen, amino acids, and spore-associated calcium complexes. Limited types of average mass spectrum patterns were present, and a significant correlation was found between the ion intensity trend (presence and absence of peaks) and laser ionization energy (expressed by the total positive ion intensity). Although a single spectral data point does not contain all peaks of the average spectrum, it covers most of the characteristics peaks and could be identified using a machine learning algorithm. After the analysis of single-particle mass spectra, we found that using multi-group features (e.g., peak intensity ratio of m/z +47 and +41, peak intensity ratio of 59 N(CH3 )3 + and 74 N(CH3 )4 + , and 12 peak variables) led to an identification accuracy of approximately 92.4% with the random forest algorithm. The results indicate that single-cell mass spectra profiles can be used to distinguish microbial aerosols and further illustrate their origin in a laboratory setting.

Measurement of Density and Shape for Single Black Carbon Aerosols in a Heavily Polluted Urban Area

Black carbon (BC) aerosol imposes adverse effects on atmospheric visibility, climate, and health. The particle density and morphology are often needed to investigate the mixing state and aging process of BC particles. A method, combining an aerodynamic aerosol classifier (AAC), a differential mobility analyzer (DMA), a single-particle soot photometer (SP2) and a single particle aerosol mass spectrometer (SPAMS), was developed to determine the density and dynamic shape factor (χ) of ambient BC particles with three different aerodynamic diameters (Da, 200 nm, 350 nm, and 500 nm) in Shanghai, China, a typical urban area. The BC particles were either classified as “BC-dominated particle” which is mainly made of black carbon or “BC-mixed particle” which is a mixture of both BC and non-BC substances. The results show that BC-dominated particles whose BC core mass (~2.2 fg) was almost equal to particle mass (~2.3 fg) were observed in particles with 200 nm Da. The morphology of these BC-dominated particles was near-spherical (χ ≈ 1.02), indicating that they had undergone rapid morphology modification from the initial highly irregular morphology to near-spherical shape. Most BC particles with 350 nm or 500 nm Da were BC-mixed particles. Combining the effective densities (1.62~1.77 g cm-3) and average single particle mass spectra of particle, the ammonium sulfate and ammonium nitrate were found to be the main secondary substances of these BC-mixed particles, indicating that condensation of inorganic species such as nitrates and sulfates could play a significant role in the aging process of fresh BC in Shanghai. Generally, the morphology and density information of single BC particle is crucial to identify the mixing state and aging process of BC aerosols.

Development of an automatic linear calibration method for high-resolution single-particle mass spectrometry: improved chemical species identification for atmospheric aerosols

Abstract. The mass resolution of laser desorption ionization (LDI) single-particle aerosol mass spectrometry (SPAMS) is usually low (∼500), which has been greatly improved by the recent development of the delayed ion extraction technique. However, due to large fluctuations among LDI processes during each laser shot, accurate calibration of the mass-to-charge ratio for high-resolution SPAMS (HR-SPAMS) spectra is challenging. Here we developed an automatic linear calibration method to improve the accuracy of mass-to-charge (m∕z) measurement for single atmospheric aerosol particles. Laboratory-generated sea spray aerosol and atmospheric ambient aerosol were tested. After the calibration, the fluctuation ranges of the reference ions' (e.g., Pb+ and SO4+) m∕z reaches ±0.018 for sea spray aerosol and ±0.024 for ambient aerosol in average mass spectra. With such m∕z accuracy, the HR-SPAMS spectra of sea spray aerosol can easily identify elemental compositions of organic peaks, such as Cx, CxHy and CxHyOz. While the chemical compositions of ambient aerosols are more complicated, CxHy, CxHyOz and CNO peaks can also be identified based on their accurate mass. With the improved resolution, the time series of peaks with small m∕z differences can be separated and measured. In addition, it is also found that applying high-resolution data with enhanced mass calibration can significantly affect particle classification (identification) using the ART-2a algorithm, which classify particles based on similarities among single-particle mass spectra.

Direct links between hygroscopicity and mixing state of ambient aerosols: estimating particle hygroscopicity from their single-particle mass spectra

Abstract. Hygroscopicity plays crucial roles in determining aerosol optical properties and aging processes in the atmosphere. We investigated submicron aerosol hygroscopicity and composition by connecting an aerosol time-of-flight mass spectrometer (ATOFMS) in series to a hygroscopic tandem differential mobility analyzer (HTDMA), to characterize hygroscopicity and composition of ambient aerosols in Shanghai, China. The HTDMA–ATOFMS data suggested that particle types, including biomass burning, elemental carbon (EC), dust/ash, organic particles, cooking particles and sea salt, were shown to have distinct hygroscopicity distributions. Peak intensities in particle spectra were found to be nonlinearly correlated with hygroscopicity, and the correlations were variant with particle types. Based on the measured hygroscopicity–composition relations, we developed a statistical method to estimate ambient particle hygroscopicity just from their mass spectra. The method was applied to another ambient ATOFMS dataset sampled from 12 to 28 September 2012 in Shanghai. The estimated hygroscopicity suggested that ambient particles were present in three apparent hygroscopicity modes, whose growth factors peaked at 1.05, 1.42 and 1.60 (85 % relative humidity, RH). The estimated growth factor (GF) were divided into four bins as &lt;1.1, 1.1–1.3, 1.3–1.5 and &gt;1.5 to represent the nearly hydrophobic (NH), less-hygroscopic (LH), more-hygroscopic (MH) and sea salt (SS) modes. Number contributions of particle types to hygroscopicity modes showed consistent results with the HTDMA–ATOFMS experiment. Based on the combined information on particle composition, hygroscopicity, air mass back trajectories and ambient pollutant concentrations, we inferred that the NH, LH, MH and SS modes were characterized by primary organic aerosol (POA) ∕ EC, secondary organic aerosol (SOA), secondary inorganic aerosol (SIA) and salt compositions, respectively. The proposed method would provide additional information to the study of particle mixing states, source identification and visibility variation.

Identification of specific sources of airborne particles emitted from within a complex industrial (steelworks) site

A case study is provided of the development and application of methods to identify and quantify specific sources of emissions from within a large complex industrial site. Methods include directional analysis of concentrations, chemical source tracers and correlations with gaseous emissions. Extensive measurements of PM10, PM2.5, trace gases, particulate elements and single particle mass spectra were made at sites around the Port Talbot steelworks in 2012. By using wind direction data in conjunction with real-time or hourly-average pollutant concentration measurements, it has been possible to locate areas within the steelworks associated with enhanced pollutant emissions. Directional analysis highlights the Slag Handling area of the works as the most substantial source of elevated PM10 concentrations during the measurement period. Chemical analyses of air sampled from relevant wind directions is consistent with the anticipated composition of slags, as are single particle mass spectra. Elevated concentrations of PM10 are related to inverse distance from the Slag Handling area, and concentrations increase with increased wind speed, consistent with a wind-driven resuspension source. There also appears to be a lesser source associated with Sinter Plant emissions affecting PM10 concentrations at the Fire Station monitoring site. The results are compared with a ME2 study using some of the same data, and shown to give a clearer view of the location and characteristics of emission sources, including fugitive dusts.

Sources and mixing state of summertime background aerosol in the north-western Mediterranean basin

Abstract. An aerosol time-of-flight mass spectrometer (ATOFMS) was employed to provide real-time single particle mixing state and thereby source information for aerosols impacting the western Mediterranean basin during the ChArMEx-ADRIMED and SAF-MED campaigns in summer 2013. The ATOFMS measurements were made at a ground-based remote site on the northern tip of Corsica. Twenty-seven distinct ATOFMS particle classes were identified and subsequently grouped into eight general categories: EC-rich (elemental carbon), K-rich, Na-rich, amines, OC-rich (organic carbon), V-rich, Fe-rich and Ca-rich particles. Mass concentrations were reconstructed for the ATOFMS particle classes and found to be in good agreement with other co-located quantitative measurements (PM1, black carbon (BC), organic carbon, sulfate mass and ammonium mass). Total ATOFMS reconstructed mass (PM2. 5) accounted for 70–90 % of measured PM10 mass and was comprised of regionally transported fossil fuel (EC-rich) and biomass burning (K-rich) particles. The accumulation of these transported particles was favoured by repeated and extended periods of air mass stagnation over the western Mediterranean during the sampling campaigns. The single particle mass spectra proved to be valuable source markers, allowing the identification of fossil fuel and biomass burning combustion sources, and was therefore highly complementary to quantitative measurements made by Particle into Liquid Sampler ion chromatography (PILS-IC) and an aerosol chemical speciation monitor (ACSM), which have demonstrated that PM1 and PM10 were comprised predominantly of sulfate, ammonium and OC. Good temporal agreement was observed between ATOFMS EC-rich and K-rich particle mass concentrations and combined mass concentrations of BC, sulfate, ammonium and low volatility oxygenated organic aerosol (LV-OOA). This combined information suggests that combustion of fossil fuels and biomass produced primary EC- and OC-containing particles, which then accumulated ammonium, sulfate and alkylamines during regional transport. Three other sources were also identified: local biomass burning, marine and shipping. Local combustion particles (emitted in Corsica) contributed little to PM2. 5 particle number and mass concentrations but were easily distinguished from regional combustion particles. Marine emissions comprised fresh and aged sea salt: the former was detected mostly during a 5-day event during which it accounted for 50–80 % of sea salt aerosol mass, while the latter was detected throughout the sampling period. Dust was not efficiently detected by the ATOFMS, and support measurements showed that it was mainly in the PM2. 5–10 fraction. Shipping particles, identified using markers for heavy fuel oil combustion, were associated with regional emissions and represented only a small fraction of PM2. 5 particle number and mass concentration at the site.

Mass spectra features of biomass burning boiler and coal burning boiler emitted particles by single particle aerosol mass spectrometer

In this study, single particle mass spectra signatures of both coal burning boiler and biomass burning boiler emitted particles were studied. Particle samples were suspended in clean Resuspension Chamber, and analyzed by ELPI and SPAMS simultaneously. The size distribution of BBB (biomass burning boiler sample) and CBB (coal burning boiler sample) are different, as BBB peaks at smaller size, and CBB peaks at larger size. Mass spectra signatures of two samples were studied by analyzing the average mass spectrum of each particle cluster extracted by ART-2a in different size ranges. In conclusion, BBB sample mostly consists of OC and EC containing particles, and a small fraction of K-rich particles in the size range of 0.2–0.5μm. In 0.5–1.0μm, BBB sample consists of EC, OC, K-rich and Al_Silicate containing particles; CBB sample consists of EC, ECOC containing particles, while Al_Silicate (including Al_Ca_Ti_Silicate, Al_Ti_Silicate, Al_Silicate) containing particles got higher fractions as size increase. The similarity of single particle mass spectrum signatures between two samples were studied by analyzing the dot product, results indicated that part of the single particle mass spectra of two samples in the same size range are similar, which bring challenge to the future source apportionment activity by using single particle aerosol mass spectrometer. Results of this study will provide physicochemical information of important sources which contribute to particle pollution, and will support source apportionment activities.

Contribution of bacteria-like particles to PM2.5 aerosol in urban and rural environments

We report highly time-resolved estimates of airborne bacteria-like particle concentrations in ambient aerosol using an Aerodyne aerosol mass spectrometer (AMS). AMS measurements with a newly developed PM2.5 and the standard (PM1) aerodynamic lens were performed at an urban background site (Zurich) and at a rural site (Payerne) in Switzerland. Positive matrix factorization using the multilinear engine (ME-2) implementation was used to estimate the contribution of bacteria-like particles to non-refractory organic aerosol. The success of the method was evaluated by a size-resolved analysis of the organic mass and the analysis of single particle mass spectra, which were detected with a light scattering system integrated into the AMS. Use of the PM2.5 aerodynamic lens increased measured bacteria-like concentrations, supporting the analysis method. However, at all sites, the low concentrations of this component suggest that airborne bacteria constitute a minor fraction of non-refractory PM2.5 organic aerosol mass. Estimated average mass concentrations were below 0.1 μg/m3 and relative contributions were lower than 2% at both sites. During rainfall periods, concentrations of the bacteria-like component increased considerably reaching a short-time maximum of approximately 2 μg/m3 at the Payerne site in summer.

Effect of Structural Heterogeneity in Chemical Composition on Online Single-Particle Mass Spectrometry Analysis of Sea Spray Aerosol Particles.

Knowledge of the surface composition of sea spray aerosols (SSA) is critical for understanding and predicting climate-relevant impacts. Offline microscopy and spectroscopy studies have shown that dry supermicron SSA tend to be spatially heterogeneous particles with sodium- and chloride-rich cores surrounded by organic enriched surface layers containing minor inorganic seawater components such as magnesium and calcium. At the same time, single-particle mass spectrometry reveals several different mass spectral ion patterns, suggesting that there may be a number of chemically distinct particle types. This study investigates factors controlling single particle mass spectra of nascent supermicron SSA. Depth profiling experiments conducted on SSA generated by a fritted bubbler and total ion intensity analysis of SSA generated by a marine aerosol reference tank were compared with observations of ambient SSA observed at two coastal locations. Analysis of SSA produced by utilizing controlled laboratory methods reveals that single-particle mass spectra with weak sodium ion signals can be produced by the desorption of the surface of typical dry SSA particles composed of salt cores and organic-rich coatings. Thus, this lab-based study for the first time unifies findings from offline and online measurements as well as lab and field studies of the SSA particle-mixing state.

Single particle mass spectral signatures from vehicle exhaust particles and the source apportionment of on-line PM2.5 by single particle aerosol mass spectrometry

In order to accurately apportion the many distinct types of individual particles observed, it is necessary to characterize fingerprints of individual particles emitted directly from known sources. In this study, single particle mass spectral signatures from vehicle exhaust particles in a tunnel were performed. These data were used to evaluate particle signatures in a real-world PM2.5 apportionment study. The dominant chemical type originating from average positive and negative mass spectra for vehicle exhaust particles are EC species. Four distinct particle types describe the majority of particles emitted by vehicle exhaust particles in this tunnel. Each particle class is labeled according to the most significant chemical features in both average positive and negative mass spectral signatures, including ECOC, NaK, Metal and PAHs species. A single particle aerosol mass spectrometry (SPAMS) was also employed during the winter of 2013 in Guangzhou to determine both the size and chemical composition of individual atmospheric particles, with vacuum aerodynamic diameter (dva) in the size range of 0.2–2μm. A total of 487,570 particles were chemically analyzed with positive and negative ion mass spectra and a large set of single particle mass spectra was collected and analyzed in order to identify the speciation. According to the typical tracer ions from different source types and classification by the ART-2a algorithm which uses source fingerprints for apportioning ambient particles, the major sources of single particles were simulated. Coal combustion, vehicle exhaust, and secondary ion were the most abundant particle sources, contributing 28.5%, 17.8%, and 18.2%, respectively. The fraction with vehicle exhaust species particles decreased slightly with particle size in the condensation mode particles.

More unsaturated, cooking-type hydrocarbon-like organic aerosol particle emissions from renewable diesel compared to ultra low sulfur diesel in at-sea operations of a research vessel

ABSTRACTThe aerosol particle emissions from R/V Robert Gordon Sproul were measured during two 5-day research cruises (29 September–3 October 2014; 4–7 and 26–28 September 2015) at four engine speeds (1600 rpm, 1300 rpm, 1000 rpm, and 700 rpm) to characterize the emissions under different engine conditions for ultra low sulfur diesel (ULSD) and hydrogenation derived renewable diesel (HDRD) fuels. Organic aerosol composition and mass distribution were measured on the aft deck of the vessel directly behind the exhaust stack to intercept the ship plume. The ship emissions for both fuels were composed of alkane-like compounds (H/C = 1.94 ± 0.003, O/C = 0.04 ± 0.001, CnH2n) with mass spectral fragmentation patterns consistent with hydrocarbon-like organic aerosol (HOA). Single-particle mass spectra from emissions for both fuels showed two distinct HOA compositions, with one HOA type containing more saturated alkane fragments (CnH2n+1) and the other HOA type containing more monounsaturated fragments (CnH2n−1). T...

A new, powerful technique to analyze single particle aerosol mass spectra using a combination of OPTICS and the fuzzy c-means algorithm

Single particle aerosol mass spectrometers record two mass spectra for each individual particle, producing large amounts of data. The analysis of these spectra is typically performed using data mining algorithms like fuzzy c-means clustering. Here we present a new approach by applying the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm in combination with fuzzy c-means clustering to single particle mass spectra. OPTICS treats spectra as points in n-dimensional space where each mass to charge ratio represents a dimension. The algorithm orders the spectra based on their density in this n-dimensional space.To demonstrate the strength of this combination of algorithms we applied it to an ambient dataset. The graphical representation of the results reflects chemical processing in the sampled aerosol. This allows for a detailed interpretation of the chemical evolution of the atmospheric aerosol.

Size distribution and mixing state of black carbon particles during a heavy air pollution episode in Shanghai

Abstract. A Single Particle Aerosol Mass Spectrometer (SPAMS), a Single Particle Soot Photometer (SP2) and various meteorological instruments were employed to investigate the chemical and physical properties of black carbon (BC) aerosols during a regional air pollution episode in urban Shanghai over a 5-day period in December 2013. The refractory black carbon (rBC) mass concentrations measured by SP2 averaged 3.2 µg m−3, with the peak value of 12.1 µg m−3 at 04:26 LT on 7 December. The number of BC-containing particles captured by SPAMS in the size range 200–1200 nm agreed very well with that detected by SP2 (R2 = 0.87). A cluster analysis of the single particle mass spectra allowed for the separation of BC-containing particles into five major classes: (1) Pure BC; (2) BC attributed to biomass burning (BBBC); (3) K-rich BC-containing (KBC); (4) BC internally mixed with OC and ammonium sulfate (BCOC-SOx); (5) BC internally mixed with OC and ammonium nitrate (BCOC-NOx). The size distribution of internally mixed BC particles was bimodal. Detected by SP2, the condensation mode peaked around ∼ 230 nm and droplet mode peaked around ∼ 380 nm, with a clear valley in the size distribution around ∼ 320 nm. The condensation mode mainly consisted of traffic emissions, with particles featuring a small rBC core (∼ 60–80 nm) and a relatively thin absolute coating thickness (ACT, ∼ 50–130 nm). The droplet mode included highly aged traffic emission particles and biomass burning particles. The biomass burning particles had a larger rBC core (∼ 80–130 nm) and a thick ACT (∼ 110–300 nm). The highly aged traffic emissions had a smaller core (∼ 60–80 nm) and a very thick ACT (∼ 130–300 nm), which is larger than reported in any previous literature. A fast growth rate (∼ 20 nm h−1) of rBC with small core sizes was observed during the experiment. High concentrations pollutants like NO2 likely accelerated the aging process and resulted in a continuous size growth of rBC-containing particles from traffic emission.

Characterization of Single Particles in Urban Atmospheric Aerosols Over Nagoya Based on Mass Spectral Analysis

Mass spectral analysis of single aerosol particles was performed using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) in the urban area of Nagoya, Japan, for five days in the autumn of 2010. An ion-series analysis was applied to the single-particle mass spectra; the sum of the masses at Δ of 1, 3, 4, and 5 divided by the total mass of organics (fΔ1,3-5) was used as an indicator of the degree of oxygenation of the organics. The organic mass fraction (Forg) and fΔ1,3-5 of single particles varied substantially, and the results for the entire period indicate that two groups of particles with high and low fΔ1,3-5 were present. The organics in particles with high Forg were less oxygenated, whereas those with low Forg were more oxygenated; these different particles were associated with hydrocarbon-like organic aerosols (HOA) and low-volatility oxygenated organic aerosols (LV-OOA), respectively, based on the PMF analysis of the MS mode data. The size distributions of the two types of part...