Source Apportionment Of Organic Aerosol Research Articles

Simultaneous organic aerosol source apportionment at two Antarctic sites reveals large-scale and ecoregion-specific components

Abstract. Antarctica and the Southern Ocean (SO) are the most pristine areas of the globe and represent ideal places to investigate aerosol–climate interactions in an unperturbed atmosphere. In this study, we present submicrometer aerosol (PM1) source apportionment for two sample sets collected in parallel at the British Antarctic Survey stations of Signy and Halley during the austral summer of 2018–2019. Water-soluble organic matter (WSOM) is a major aerosol component at both sites (37 % and 29 % of water-soluble PM1, on average, at Signy and Halley, respectively). Remarkable differences between pelagic (open-ocean) and sympagic (influenced by sea ice) air mass histories and related aerosol sources are found. The application of factor analysis techniques to series of spectra obtained by means of proton-nuclear magnetic resonance (H-NMR) spectroscopy on the samples allows the identification of five organic aerosol (OA) sources: two primary organic aerosol (POA) types, characterized by sugars, polyols, and degradation products of lipids and associated with open-ocean and sympagic/coastal waters, respectively; two secondary organic aerosol (SOA) types, one enriched in methanesulfonic acid (MSA) and dimethylamine (DMA) and associated with pelagic waters and the other characterized by trimethylamine (TMA) and linked to sympagic environments; and a fifth component of unclear origin, possibly associated with the atmospheric aging of primary emissions. Overall, our results strongly indicate that the emissions from sympagic and pelagic ecosystems affect the variability in the submicrometer aerosol composition in the study area, with atmospheric circulation establishing marked latitudinal gradients only for some of the aerosol components (e.g., the sympagic components) while distributing the others (e.g., pelagic and/or aged components) both in maritime and inner Antarctic regions.

Bulk and molecular-level composition of primary organic aerosol from wood, straw, cow dung, and plastic burning

Abstract. During the past decades, the source apportionment of organic aerosol (OA) in ambient air has been improving substantially. The database of source retrieval model-resolved mass spectral profiles for different sources has been built with the aerosol mass spectrometer (AMS). However, distinguishing similar sources (such as wildfires and residential wood burning) remains challenging, as the hard ionization of the AMS mostly fragments compounds and therefore cannot capture detailed molecular information. Recent mass spectrometer technologies of soft ionization and high mass resolution have allowed for aerosol characterization at the molecular formula level. In this study, we systematically estimated the emission factors and characterized the primary OA (POA) chemical composition with the AMS and the extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) for the first time from a variety of solid fuels, including beech logs, spruce and pine logs, spruce and pine branches and needles, straw, cow dung, and plastic bags. The emission factors of organic matter estimated by the AMS and hydrocarbon gases estimated by the total hydrocarbon analyzer are 16.2 ± 10.8 g kg−1 and 30.3 ± 8.5 g kg−1 for cow dung burning, which is generally higher than that of wood (beech, spruce, and pine), straw, and plastic bag burning (in the range from 1.1 to 6.2 g kg−1 and 14.1 to 19.3 g kg−1). The POA measured by the AMS shows that the f60 (mass fraction of m/z 60) varies from 0.003 to 0.04 based on fuel types and combustion efficiency for wood (beech, spruce, and pine) and cow dung burning. On a molecular level, the dominant compound of POA from wood, straw, and cow dung is C6H10O5 (mainly levoglucosan), contributing ∼ 7 % to ∼ 30 % of the total intensity, followed by C8H12O6 with fractions of ∼ 2 % to ∼ 9 %. However, as they are prevalent in all burning of biomass material, they cannot act as tracers for the specific sources. By using the Mann–Whitney U test among the studied fuels, we find specific potential new markers for these fuels from the measurement of the AMS and EESI-TOF. Markers from spruce and pine burning are likely related to resin acids (e.g., compounds with 20–21 carbon atoms). The product from the pyrolysis of hardwood lignins is found especially in beech log burning. Nitrogen-containing species are selected markers primarily for cow dung open burning. These markers in the future will provide support for the source apportionment.

Source apportionment of anthropogenic and biogenic organic aerosol over the Tokyo metropolitan area from forward and receptor models

Organic aerosol (OA) is a dominant component of PM2.5, and accurate knowledge of its sources is critical for identification of cost-effective measures to reduce PM2.5. For accurate source apportionment of OA, we conducted field measurements of organic tracers at three sites (one urban, one suburban, and one forest) in the Tokyo Metropolitan Area and numerical simulations of forward and receptor models. We estimated the source contributions of OA by calculating three receptor models (positive matrix factorization, chemical mass balance, and secondary organic aerosol (SOA)-tracer method) using the ambient concentrations, source profiles, and production yields of OA tracers. Sensitivity simulations of the forward model (chemical transport model) for precursor emissions and SOA formation pathways were conducted. Cross-validation between the receptor and forward models demonstrated that biogenic and anthropogenic SOA were better reproduced by the forward model with updated modules for emissions of biogenic volatile organic compounds (VOC) and for SOA formation from biogenic VOC and intermediate-volatility organic compounds than by the default setup. The source contributions estimated by the forward model generally agreed with those of the receptor models for the major OA sources: mobile sources, biomass combustion, biogenic SOA, and anthropogenic SOA. The contributions of anthropogenic SOA, which are the main focus of this study, were estimated by the forward and receptor models to have been between 9 % and 15 % in summer 2019. The observed percent modern carbon data indicate that the amounts of anthropogenic SOA produced during daytime have substantially declined from 2007 to 2019. This trend is consistent with the decreasing trend of anthropogenic VOC, suggesting that reduction of anthropogenic VOC has been effective in reducing anthropogenic SOA in the atmosphere.

Chemical Characteristics and Source Apportionment of Organic Aerosols in Urban Shanghai During Cold Season Based on High Time-resolution Measurements of Organic Molecular Markers

Organic aerosols (OA) are closely related to the formation of both PM2.5 and O3 in the atmosphere. In this study, a thermal desorption aerosol GC/MS (TAG) online system was adopted to measure hourly concentrations of 94 total organic molecular markers in PM2.5 at an urban site in Shanghai from November 6th to December 31st, 2021. Combined with air mass cluster analysis and other online measurement data, the chemical characteristics of OA under the influence of different air masses, oxidant levels, and relative humidity (RH) levels were investigated. The results showed that OA was characterized by higher mass percentages of primary organic molecular markers (e.g., saturated fatty acids, unsaturated fatty acids, and alkanes) under the influence of local air masses. Further, high loadings of biomass burning tracers were observed in OA under the influence of long-range transported air masses. In contrast, OA impacted by marine air masses was laden with significantly higher fractions of secondary organic molecular markers, such as dicarboxylic acids and hydroxyl dicarboxylic acids, which were formed from a wide range of volatile organic precursors through photochemical and aqueous-phase processing. With the application of the positive matrix factorization (PMF) model, seven total primary source factors and five secondary source factors were resolved for PM2.5 and OA during the observation. Among them, secondary nitrate was the highest contributor to PM2.5 mass with a mass percentage of 25.2%, whereas vehicle emissions were the top contributor (24.0%) to OA mass. Primary source factors, including coal combustion, vehicle emission, and cooking emission as well as their corresponding secondary source factors (e.g., secondary nitrate, secondary organic aerosols 2, etc.) showed elevated contributions in PM2.5 and OA with the increase in PM2.5 masses, indicating that more stringent controls of local emission sources (e.g., coal combustion, vehicle emission, and cooking emission) are needed to further lower PM2.5 pollution and improve air quality in Shanghai.

Development and evaluation of an improved offline aerosol mass spectrometry technique

Abstract. The offline aerosol mass spectrometry technique is a useful tool for the source apportionment of organic aerosol (OA) in areas and periods during which an aerosol mass spectrometer (AMS) is not available. However, the technique is based on the extraction of aerosol samples in water, while several atmospheric OA components are partially or fully insoluble in water. In this work an improved offline technique was developed and evaluated in an effort to capture most of the partially soluble and insoluble organic aerosol material, reducing significantly the uncertainty of the corresponding source apportionment. A major advantage of the proposed approach is that no corrections are needed for the offline analysis to account for the limited water solubility of some OA components. The improved offline AMS analysis was tested in three campaigns: two during winter and one during summer. Collocated online AMS measurements were performed for the evaluation of the offline method. Source apportionment analysis was performed separately for the online and the offline measurements using positive matrix factorization (PMF). The PMF results showed that the fractional contribution of each factor to the total OA differed between the online and the offline PMF results by less than 15 %. The differences in the AMS spectra of the factors of the two approaches could be significant, suggesting that the use of factor profiles from the literature in the offline analysis may lead to complications. Part of the good agreement between the online and the offline PMF results is due to the ability of the improved offline AMS technique to capture a bigger part of the OA, including insoluble organic material. This was evident by the significant fraction of submicrometer suspended insoluble particles present in the water extract and by the reduced insoluble material on the filters after the extraction process. More than half of the elemental carbon (EC) was on average missing from the filters after the water extraction. Significant EC concentrations were measured in the produced aerosol that was used as input to the AMS during the offline analysis.

Secondary Formation of Submicron and Supermicron Organic and Inorganic Aerosols in a Highly Polluted Urban Area

AbstractDifferent adverse health effects of submicron (PM1) and fine particles (PM2.5) may be attributed to their chemical differences, requiring a better understanding of size‐resolved composition. Herein, extensive online measurements were conducted across seasons in Beijing by two aerosol mass spectrometers, one of which alternately sampled PM1 and PM2.5. Source apportionment of organic aerosol (OA) indicated that traffic‐ and cooking‐related OA together accounted for ∼20%−30% of the OA mass in PM2.5, showing insignificant seasonal variations. Coal‐combustion and biomass‐burning‐related OA had minor contributions. The two secondary OA (SOA) factors together accounted for 59%−73% of the OA mass in PM2.5. The mass distributions of particulate components in PM1 and PM2.5 varied greatly across seasons. Secondary formation played a key role in particle size growth during cold seasons. During severe hazes with high aerosol liquid water content (ALWC), the supermicron mass fraction (MF1−2.5) of secondary components reached ∼40%−50% while those for primary OA remained at ∼20%. Heterogeneous uptake, aqueous processing, and dissolution likely all contributed to the enhanced concentration of secondary components, and the former two were perhaps more important. The increase of MF1−2.5 for secondary components with increasing ALWC in spring was less than that in winter, possibly due to the shorter duration of stagnant conditions limiting secondary formation. Early autumn showed higher MF1−2.5 values than cold seasons with insignificant changes as ALWC varied, plausibly explained by intensive new particle formation hindering persistent particle growth. Our results highlight the importance of heterogeneous uptake and aqueous processing in distributing SOA in supermicron mode in polluted areas.

Real-Time Source Apportionment of Organic Aerosols in Three European Cities.

97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 μg/m3). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a priori knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers.

Cruise observation of the marine atmosphere and ship emissions in South China Sea: Aerosol composition, sources, and the aging process

Marine atmospheric aerosols impact the global climate and biogeochemical cycles. However, how the composition, sources, and aging of these aerosols affect the above processes has not been thoroughly studied. Here, we conducted ship-based measurements in the northern South China Sea to investigate the chemical composition and aging of aerosols from various sources during the summer of 2019. Separate measurements were conducted at the bow (marine environment) and stern (cooking, smoking, and engine exhaust) of the ship. Source apportionment of organic aerosols (OAs) was conducted using positive matrix factorization (PMF) and trajectory models. The results showed that ship exhaust and coastal submicron particles were composed of comparable sulfate and organic fractions (both approximately 43%), distinct from the sulfate-dominated particles in the marine atmosphere (52–77%). PMF using the multilinear engine-2 solver identified five factors for the stern sampling period: hydrocarbon-like OA (HOA-I, 9%), slightly oxidized HOA (HOA-II, 25%), cooking OA (COA, 13%), cigarette smoke OA (CSOA, 4%), and low-volatility oxygenated OA (LV-OOA, 49%). The primary OAs (HOA-I/II + COA + CSOA), derived mostly from direct ship-related emissions, contributed to approximately half of the OAs, whereas the contribution from the highly aged marine atmosphere was only 20%. Notably, certain living-related emissions (i.e., COA and CSOA), which were often neglected in previous studies, might represent a considerable contribution to OA emissions from the ship. Four factors were identified for the bow sampling periods: HOA (13%), biomass burning OA (BBOA, 9%), semi-volatile OOA (7%), and LV-OOA (71%). The BBOAs from the Indo-China and Malay peninsulas were aged, converted to secondary organic aerosols (SOAs) during transport, and influenced by the combined photo-oxidation and liquid-phase reactions, indicating a substantial impact of BB on SOA formation. Our study highlights the influence of ship and inland emissions and their aging during transport on marine atmospheric aerosols.

European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets.

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average=71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240min) and long-term data coverage (9-36months), providing essential information to improve/validate air quality, health impact, and climate models.

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

We apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes α-pinene and β-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ∼9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ∼50−60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

Organic aerosol source apportionment by using rolling positive matrix factorization: Application to a Mediterranean coastal city

We investigated the contributions and the evolution of organic aerosol (OA) sources at the Marseille-Longchamp supersite (MRS-LCP, France) based on Time-of-flight Aerosol Chemical Speciation Monitor (ToF-ACSM) measurements of non-refractory PM 1 over a fourteen-month period (1 February – 3 April 2018). The OA source apportionment was performed by positive matrix factorization (PMF) using the novel “rolling window” approach implemented in the Source Finder Professional (SoFi Pro). Here, PMF is performed over a 14-days window moving over the entire OA dataset, in order to account for the temporal variability of the source profiles. Six factors were resolved, including hydrocarbon-like organic aerosol (HOA) which is related to traffic exhausts, cooking-like organic aerosol (COA), biomass burning aerosol (BBOA), less oxidized organic aerosol (LOOA), more oxidized organic aerosol (MOOA) and a new defined source related to the mix between shipping and industrial plumes (Sh-IndOA). While HOA and COA consistently contribute to the total OA with on average 11.2% (ranging between 9.2 and 12.1% over the seasons) and 11.5% (11–12.1%), respectively, BBOA (11.7% on average) shows a larger seasonal variability with 18% in winter and no contribution in summer. BBOA profiles during winter were attributed to fresh biomass burning emissions from domestic heating, and more oxygenated profiles were assigned to regional land and agricultural waste burning for spring and early autumn. Sh-IndOA fraction is estimated to 4.5% (3.7–6.1%) and contributes to the total OA mass concentrations to a minor extent. The secondary organic aerosol (SOA) fraction includes both LOOA with 21.5% (18.8–27.2%) and MOOA with 39.6% (36.8–42.6%). Based on the f44/f43 analysis these sources appeared to be more linked to biogenic influences in summer, whereas the concentrations were associated with oxidized anthropogenic sources (biomass burning and road traffic) for the rest of the year. The investigation of MOOA geographical origins suggests some influence of air masses transported from the Rhône Valley and the west basin of the Mediterranean Sea. • OA source apportionment over 14 months at an urban background site. • Positive matrix factorization using the novel rolling window approach. • A new defined source of OA related to shipping and industrial plumes. • Highest contribution of SOA during every season.

Chemical Characterization and Source Apportionment of Organic Aerosols in the Coastal City of Chennai, India: Impact of Marine Air Masses on Aerosol Chemical Composition and Potential for Secondary Organic Aerosol Formation

Chemical Characterization and Source Apportionment of Organic Aerosols in the Coastal City of Chennai, India: Impact of Marine Air Masses on Aerosol Chemical Composition and Potential for Secondary Organic Aerosol Formation

Mass spectral characterization of secondary organic aerosol from urban cooking and vehicular sources

Abstract. In the present work, we conducted experiments of secondary organic aerosol (SOA) formation from urban cooking and vehicular sources to characterize the mass spectral features of primary organic aerosol (POA) and SOA using an high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). Our results showed that the cooking styles have a greater impact on aged COA (cooking organic aerosol) mass spectra than oxidation conditions. However, the oxidation conditions affect the aged HOA (hydrocarbon-like OA) spectra more significantly than vehicle operating conditions. In our study, we use mass spectra similarity analysis and positive matrix factorization (PMF) analysis to establish the POA and SOA mass spectra of these two sources. These mass spectra are used as source constraints in a multilinear engine (ME-2) model to apportion the OA (organic aerosol) sources in the atmosphere. Compared with the traditional ambient PMF results, the improved ME-2 model can better quantify the contribution of POA and SOA from cooking and vehicular sources. Our work, for the first time, establishes the vehicle and cooking SOA source profiles, and can be further used in the OA source apportionment in the ambient atmosphere.

Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

Persistence of Primary and Secondary Pollutants in Delhi: Concentrations and Composition from 2017 through the COVID Pandemic

We assess impacts of the 2020 COVID-19 lockdown on ambient air quality in Delhi, building on over three years of real-time measurements of black carbon (BC) and nonrefractory submicrometer aerosol (NR-PM1) composition from the Delhi Aerosol Supersite and public data from the regulatory monitoring network. We performed source apportionment of organic aerosol (OA) and robust statistical analyses to differentiate lockdown-related impacts from baseline seasonal and interannual variability. The primary pollutants NOx, CO, and BC were most reduced, primarily due to lower transportation emissions. Local and regional emissions such as agricultural burning decreased during the lockdown. PM2.5 declined but remained well above WHO guidelines. Despite the lockdown, NR-PM1 changed only moderately compared to prior years. Differences in the trends of hydrocarbon-like OA and BC suggest that some sources of primary aerosol may have increased. Despite notable reductions in some primary pollutants, the lockdown restrictions led to rather small perturbations in the primary fraction of NR-PM1, with secondary aerosol continuing to dominate. Overall, our results demonstrate the impact of secondary and primary pollution on Delhi's air quality and show that large changes in emissions within Delhi alone are insufficient to bring about needed improvements in air quality. © 2021 American Chemical Society.

Chemical Characteristics and Source Apportionment of Organic Aerosols in Atmospheric PM2.5 in Winter in Beijing

To explore the concentrations, characteristics, and sources of organic aerosols in winter in Beijing, atmospheric fine particulate matter (PM2.5) samples were collected from November 10, 2016 to December 10, 2016. One hundred and twenty-nine particulate organic matters (POM) were quantified by gas chromatography-mass spectrometry, accounting for approximately 9.3%±1.2% of the total concentration of organic matter. The most abundant class was sugar, among which levoglucosan alone accounted for 18% of the quantified organic matter mass. The next most abundant classes were alkanoic acids, normal alkanes, dicarboxylic acids, and polycyclic aromatic hydrocarbons. The influence of winter heating and biomass burning emissions on organic aerosols in winter in Beijing was analyzed by the characteristics of the molecular markers in the POM. Compared with those during the non-heating period, the concentrations and proportions of hopane species, which are tracers for fossil fuels, increased in the organic matters during the heating period. Moreover, the influence of coal burning emissions on the distribution of hopane species was enhanced. The species with the maximum concentration and carbon predominance index in n-alkanes also reflected the influence of enhanced fossil fuel emissions. The results of the concentration-weighted trajectory model for levoglucosan, a tracer for biomass combustion, suggested that straw burning pollution in the surrounding areas of Beijing would affect the composition of organic aerosols in Beijing via airmass transport. A molecular marker-based chemical mass balance model was used to apportion the sources of organic carbon in the winter of 2016 in Beijing, and the results were compared with those of research in 2006 to quantify the changes in the source contributions over 10 years. The contribution of motor vehicles increased significantly in 2016 compared with that in 2006, whereas the contribution of coal burning and wood burning decreased to a large extent. The contribution of cooking emissions could not be ignored. Therefore, the control of motor vehicle and cooking emissions is of great importance to reduce the problem of PM2.5 pollution in winter in Beijing.

Effect of COVID-19 lockdown on the concentration and composition of NR-PM2.5 over Ahmedabad, a big city in western India

This study investigates the impact of reduced anthropogenic emissions during the lockdown period on the concentration, composition, and characteristics of non-refractory particular matter ≤ 2.5 μm aerodynamic diameter (NR-PM2.5) and black carbon (BC) at Ahmedabad, a big city in western India. Online measurements were performed from February 29 to March 23 (before lockdown, P1), April 10 to May 01 (during the lockdown, P2), and June 1 to June 16 (post lockdown, P3) using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an Aethalometer. On average, organic aerosols (OA), NO3−, SO42−, NH4+, Cl−, BC at 370 nm (BC370), and BC at 880 nm (BC880) were reduced by 52, 64, 43, 62, 86, 52, and 57%, respectively, during P2 compare to P1. However, the diurnal trends of species were similar during the lockdown and no-lockdown periods. Source apportionment of OA using positive matrix factorization analysis revealed three factors: hydrocarbon–like organic aerosol (HOA), low volatile oxygenated OA (LV-OOA), and semi-volatile oxygenated OA (SV-OOA), contributing 26%, 44% and, 30%, respectively, to the total OA during the study period. Such studies are very crucial in assessing the effects of reduced anthropogenic emissions on the air quality of big cities, and planning appropriate mitigation strategies.

Altitude Aerosol Measurements in Central France: Seasonality, Sources and Free‐Troposphere/Boundary Layer Segregation

AbstractThe chemical composition of nonrefractory submicron aerosol particles was measured at the Puy‐de‐Dôme (PUY) station (1,465 m a.s.l) during 2015 using a Time‐of‐Flight Aerosol Chemical Speciation Monitor (ToF‐ACSM). These aerosol chemistry measurements are combined with online black carbon (BC) measurements to provide an overview of the submicron aerosol composition. Averaged over the entire year, and normalized to standard temperature and pressure, organic aerosol (OA) dominates the PM1 concentration during all seasons and within all air mass types (2.12 ± 1.73 µgm−3), and is responsible for summertime increases in aerosol concentration. Highest mass concentrations were measured during the summer, when air masses were arriving over mainland Europe and lowest in the winter months (when most air masses were of Atlantic origin). OA source apportionment was performed separately during each season, using the Source Finder (SoFi) interface for the multilinear engine. The PUY site, situated at 1,465 m a.s.l, although mainly sampling in the atmospheric boundary layer, it is sometimes sampling in the lower free troposphere (FT), providing the opportunity to identify the characteristics of FT aerosol. In order to accurately identify these sampling periods, the methodology described in Farah et al. (2018), during the same time period (2015/2016), was applied to the data. During this period, FT air masses are sampled approximately 20% of the time. This work provides, on one hand, a description of long‐term aerosol chemical properties at a remote regional site in central Europe and, on the other hand a characteristic chemical signature of FT aerosols over this region. This data can be used to improve our understanding of the transport and aging properties of aerosols at regional observation sites.

Seasonal variations in characteristics, sources and diurnal patterns of carbonaceous and water-soluble constituents in urban aerosols from the east coast of tropical India

Environmental context The export of various man-made pollutants from northern India has a large impact on aerosol formation processes, their transformations and regional environmental chemistry over tropical peninsular India. The quantitative source apportionment of organic aerosols performed in this study provides a better understanding of their sources and implications for climate and air-quality management policies in South Asia. Abstract This study highlights seasonal characteristics, sources, daytime (sea-breeze) and night-time (land-breeze) variations of carbonaceous and water-soluble ionic components in PM10 (<10 µm particulate matter) aerosols from the east coast (Chennai city) of tropical India. Elemental and organic carbon (EC and OC) were found to be higher in winter when air masses were delivered from the northern part of India covered by the Indo-Gangetic-Plains whereas lower concentrations were observed during summer and monsoon associated with marine air masses. Sea salts (Na+ and Cl–), dust (Ca2+ and Mg2+) and nitrates (NO3–) were found to be highest in monsoon, suggesting these species may be co-transported over the sampling site with marine air masses. Using air mass back-trajectory analysis, linear relationships between chemical species and specific mass ratios, we demonstrate that east coast urban aerosols are strongly influenced by aged anthropogenic sources including biomass burning in winter and post monsoon while aged marine emissions mixed with local pollutants (dust and vehicular) are important in monsoon and summer. Further, the mesoscale phenomenon was reflected in measured chemical constituents during the study period. Positive-matrix-factorisation (PMF) analysis confirmed that OC aerosols are largely attributable to chemically aged anthropogenic (53 % in the day and 39 % in the night) and combustion-derived (17 % and 39 %) sources in winter and sea salts mixed with dust and vehicular emissions (61 % and 52 %) during monsoon. These important insights about the sources and formation processes of organic aerosols will help in understanding the formation of atmospheric brown clouds over south Asia.

The formation and evolution of secondary organic aerosol during summer in Xi'an: Aqueous phase processing in fog-rain days

Secondary organic aerosol (SOA) is an important contributor to organic aerosol (OA), however, the model simulations of SOA concentrations and oxidation states remain significant uncertainties because of inadequate cognition of its formation and aging chemistry. In this study, SOA formation and evolution processes during summer in Xi'an were investigated, based on high-resolution online measurements of non-refractory PM2.5 (NR-PM2.5) species and OA source apportionment using positive matrix factorization. The results showed that the total SOA, including less oxidized-oxygenated OA (LO-OOA), more oxidized-oxygenated OA (MO-OOA), and aqueous-phase-processed oxygenated OA (aq-OOA), on average constituted 69% of OA, and 43% of NR-PM2.5, suggesting the high atmospheric oxidation capacity and the dominance of SOA during summer in Xi'an. Photochemical oxidation processes dominated the summertime SOA formation both during non-fog-rain days and fog-rain days, which were responsible for the formation of both LO-OOA and MO-OOA. Consistently, LO-OOA and MO-OOA in total contributed 59% to OA during non-fog-rain days and 56% to OA during fog-rain days, respectively. On the contrary, aq-OOA was mainly observed during fog-rain days, which increased dramatically from 2% of OA during non-fog-rain days to 19% of OA during fog-rain days with the mass concentration increasing accordingly from 0.3 μg m−3 to 2.5 μg m−3. Episodic analyses further highlighted the persistently high RH period with high aerosol liquid water content (ALWC) was the driving factor of aq-OOA formation, and high Ox condition could further enhance its formation. Meanwhile, air masses from east and southeast were much favorable for the formation of long-time fog-rain days, which facilitated aq-OOA production during summer in Xi'an.