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This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 μm), fine (0.43-2.1 μm) and coarse mode (>2.1 μm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO 3 - and SO 4 2- from the usual 0.43-0.65 μm to 1.1-2.1 μm. Our results demonstrated the dominance of primary combustion sources in the <0.43 μm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This study aims to critically evaluate the source apportionment of fine particles by multiple receptor modelling approaches, including carbon mass balance modelling of filter-based radiocarbon ( 14 C) data, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) analysis on filter-based chemical speciation data, and PMF analysis on Aerosol Mass Spectrometer (AMS-PMF) or Aerosol Chemical Speciation Monitor (ACSM-PMF) data. These data were collected as part of the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) field observation campaigns from 10 th November to 12 th December in winter 2016 and from 22 nd May to 24 th June in summer 2017. 14 C analysis revealed the predominant contribution of fossil fuel combustion to carbonaceous aerosols in winter compared with non-fossil fuel sources, which is supported by the results from other methods. An extended Gelencsér (EG) method incorporating 14 C data, as well as the CMB and AMS/ACSM-PMF methods, generated a consistent source apportionment for fossil fuel related primary organic carbon. Coal combustion, traffic and biomass burning POC were comparable for CMB and AMS/ACSM-PMF. There are uncertainties in the EG method when estimating biomass burning and cooking OC. The POC from cooking estimated by different methods was poorly correlated, suggesting a large uncertainty when differentiating this source type. The PM 2.5 source apportionment results varied between different methods. Through a comparison and correlation analysis of CMB, PMF and AMS/ACSM-PMF, the CMB method appears to give the most complete and representative source apportionment of Beijing aerosols. Based upon the CMB results, fine aerosols in Beijing were mainly secondary inorganic ion formation, secondary organic aerosol formation, primary coal combustion and from biomass burning emissions.

Iron (Fe) in aerosol particles is a major external source of micronutrients for marine ecosystems and poses a potential threat to human health. To understand the impacts of aerosol Fe, it is essential to quantify the sources of dissolved Fe and total Fe. In this study, we applied receptor modeling for the first time to apportion the sources of dissolved Fe and total Fe in fine particles collected under five different weather conditions in the Hangzhou megacity of Eastern China, which is upwind of the East Asian outflow. Results showed that Fe solubility (dissolved Fe to total Fe) was the largest on fog days (6.7 ± 3.0 %), followed by haze (4.8 ± 1.9 %), dust (2.1 ± 0.7 %), clear (1.9 ± 1.0 %), and rain (0.9 ± 0.5 %) days. Positive matrix factorization (PMF) analysis suggested that industrial emissions were the largest contributor to dissolved Fe (44.5 %–72.4 %) and total Fe (39.1 %–55.0 %, except for dust days) during haze, fog, dust, and clear days. Transmission electron microscopy analysis of individual particles showed that > 75 % of Fe-containing particles were internally mixed with acidic secondary aerosol species on haze, fog, dust, and clear days. Furthermore, Fe solubility showed significant positive correlations with aerosol acidity/total Fe and liquid water content. These results indicated that the wet surface of aerosol particles promotes heterogeneous reactions between acidic species and Fe aerosols, contributing to a high Fe solubility. [ABSTRACT FROM AUTHOR]

Carbonaceous aerosol is a dominant component of fine particles in Beijing. However, it is challenging to apportion its sources. Here, we applied a newly developed method which combined radiocarbon (14C) with organic tracers to apportion the sources of fine carbonaceous particles at an urban (IAP) and a rural (PG) site of Beijing. PM2.5 filter samples (24 h) were collected at both sites from 10 November to 11 December 2016 and from 22 May to 24 June 2017. 14C was determined in 25 aerosol samples (13 at IAP and 12 at PG) representing low pollution to haze conditions. Biomass burning tracers (levoglucosan, mannosan, and galactosan) in the samples were also determined using gas chromatography–mass spectrometry (GC-MS). Higher contributions of fossil-derived OC (OCf) were found at the urban site. The OCf / OC ratio decreased in the summer samples (IAP: 67.8 ± 4.0 % in winter and 54.2 ± 11.7 % in summer; PG: 59.3 ± 5.7 % in winter and 50.0 ± 9.0 % in summer) due to less consumption of coal in the warm season. A novel extended Gelencsér (EG) method incorporating the 14C and organic tracer data was developed to estimate the fossil and non-fossil sources of primary and secondary OC (POC and SOC). It showed that fossil-derived POC was the largest contributor to OC (35.8 ± 10.5 % and 34.1 ± 8.7 % in wintertime for IAP and PG, 28.9 ± 7.4 % and 29.1 ± 9.4 % in summer), regardless of season. SOC contributed 50.0 ± 12.3 % and 47.2 ± 15.5 % at IAP and 42.0 ± 11.7 % and 43.0 ± 13.4 % at PG in the winter and summer sampling periods, respectively, within which the fossil-derived SOC was predominant and contributed more in winter. The non-fossil fractions of SOC increased in summer due to a larger biogenic component. Concentrations of biomass burning OC (OCbb) are resolved by the extended Gelencsér method, with average contributions (to total OC) of 10.6 ± 1.7 % and 10.4 ± 1.5 % in winter at IAP and PG and 6.5 ± 5.2 % and 17.9 ± 3.5 % in summer, respectively. Correlations of water-insoluble OC (WINSOC) and water-soluble OC (WSOC) with POC and SOC showed that although WINSOC was the major contributor to POC, a non-negligible fraction of WINSOC was found in SOC for both fossil and non-fossil sources, especially during winter. In summer, a greater proportion of WSOC from non-fossil sources was found in SOC. Comparisons of the source apportionment results with those obtained from a chemical mass balance model were generally good, except for the cooking aerosol. [ABSTRACT FROM AUTHOR]

Fine particles were sampled from 9 November to 11 December 2016 and 22 May to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, organic carbon (OC), elemental carbon (EC), and organic compounds, including biomarkers, hopanes, polycyclic aromatic hydrocarbons (PAHs), n -alkanes, and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+ , Na+ , Cl-), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial and residential coal burning, biomass burning, gasoline and diesel vehicles, cooking, and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by the CMB model was mostly associated with SOC. PM2.5 apportioned by the CMB model showed that the SNA and secondary organic matter were the two highest contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from the positive matrix factorization (PMF) analysis of co-located aerosol mass spectrometer (AMS) data. The CMB model was found to resolve more primary organic aerosol (OA) sources than AMS-PMF, but the latter could apportion secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC, correlated well with the results from the CMB model. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors. [ABSTRACT FROM AUTHOR]

The "Campaign on Atmospheric Aerosol Research" network of China (CARE-China) is a long-term project for the study of the spatio-temporal distributions of physical aerosol characteristics as well as the chemical components and optical properties of aerosols over China. This study presents the first long-term data sets from this project, including 3 years of observations of online PM2.5 mass concentrations (2012-2014) and 1 year of observations of PM2.5 compositions (2012-2013) from the CARE-China network. The average PM2.5 concentration at 20 urban sites is 73.2 µg m-3 (16.8-126.9 µg m-3), which was 3 times higher than the average value from the 12 background sites (11.2-46.5 µg m-3). The PM2:5 concentrations are generally higher in east-central China than in the other parts of the country due to their relatively large particulate matter (PM) emissions and the unfavourable meteorological conditions for pollution dispersion. A distinct seasonal variability in PM2.5 is observed, with highs in the winter and lows during the summer at urban sites. Inconsistent seasonal trends were observed at the background sites. Bimodal and unimodal diurnal variation patterns were identified at both urban and background sites. The chemical compositions of PM2.5 were analysed at six paired urban and background sites located within the most polluted urban agglomerations -- North China Plain (NCP), Yangtze River delta (YRD), Pearl River delta (PRD), North-east China region (NECR), South-west China region (SWCR) -- and the cleanest region of China -- the Tibetan Autonomous Region (TAR). The major PM2.5 constituents across all the urban sites are organic matter (OM, 26.0 %), SO2-4 (17.7%, mineral dust (11.8 %), NO-3 (9.8 %), NH + 4 C4 (6.6 %), elemental carbon (EC) (6.0 %), Cl- (1.2 %) at 45% RH and unaccounted matter (20.7 %). Similar chemical compositions of PM2.5 were observed at background sites but were associated with higher fractions of OM (33.2 %) and lower fractions of NO-3 (8.6 %) and EC (4.1 %). Significant variations of the chemical species were observed among the sites. At the urban sites, the OM ranged from 12.6 µg m-3 (Lhasa) to 23.3 µg m-3 (Shenyang), the SO2-4 ranged from 0.8 µg m-3 (Lhasa) to 19.7 µg m-3 (Chongqing), the NO-3 ranged from 0.5 µg m-3(Lhasa) to 11.9 µg m-3 (Shanghai) and the EC ranged from 1.4 µg m-3 (Lhasa) to 7.1 µg m-3 (Guangzhou). The PM2.5 chemical species at the background sites exhibited larger spatial heterogeneities than those at urban sites, suggesting different contributions from regional anthropogenic or natural emissions and from long-range transport to background areas. Notable seasonal variations of PM2.5-polluted days were observed, especially for the megacities in east-central China, resulting in frequent heavy pollution episodes occurring during the winter. The evolution of the PM2.5 chemical compositions on polluted days was consistent for the urban and nearby background sites, where the sum of sulfate, nitrate and ammonia typically constituted much higher fractions (31-57 %) of PM2.5 mass, suggesting fine-particle pollution in the most polluted areas of China assumes a regional tendency, and the importance of addressing the emission reduction of secondary aerosol precursors including SO2 and NOx. Furthermore, distinct differences in the evolution of [NO-3]/[SO2-4] ratio and OC=EC ratio on polluted days imply that mobile sources and stationary (coal combustion) sources are likely more important in Guangzhou and Shenyang, respectively, whereas in Beijing it is mobile emission and residential sources. As for Chongqing, the higher oxidation capacity than the other three cities suggested it should pay more attention to the emission reduction of secondary aerosol precursors. This analysis reveals the spatial and seasonal variabilities of the urban and background aerosol concentrations on a national scale and provides insights into their sources, processes and lifetimes. [ABSTRACT FROM AUTHOR]

A comprehensive year-round field campaign was conducted in the coastal city of Ningbo, China to examine the driving factors of oxidative potential (OP) of PM 2.5 and hence its potential health impacts. Using dithiothreitol (DTT) cell-free assay, OP of water-soluble (OPws) and methanol-soluble components (OPmeth) of PM 2.5 -based samples were measured. Volume-normalised OP (OP v ws and OP v meth) were higher in heating seasons, and mass-normalised OP (OP m ws and OP m meth) peaked in summer. Five sources were identified to be DTT active in OP v ws, with road dust (RD) being dominant, while four sources contributed to OP v meth, with industrial emission (IE) as the largest contributor. Water extracted more toxic components from RD while toxic components in IE and vehicle emission (VE) were more soluble in methanol. We further compared OP values between PM 2.5 of similar concentrations and observed that OP v ws prevailed in PM 2.5 toxicity during clean days (PM 2.5 concentration < 38.5 µg m−3), while OP v meth became dominant when PM 2.5 concentration was larger than 69.1 µg m−3, implying the need to extract whole PM 2.5 using both solvents to develop the mitigation strategy for reducing the overall PM 2.5 toxicity. Efforts should be devoted to controlling the emissions from dominant sources of both OP indicators in specific episode. [Display omitted] • Road dust dominated OP v ws, while industry and vehicle emissions governed OP v meth. • OP v ws and OP v meth prevailed PM 2.5 toxicity in respective clean and polluted days. • O 3 , selected metals and ions drove OP v ws under high Temp, RH and sun radiation. • OP v meth was affected by high-molecular-weight PAHs, Ni, Co, and Cl-. [ABSTRACT FROM AUTHOR]

Recurrent smoke-haze episodes that occur in Southeast Asia (SEA) are of much concern because of their environmental and health impacts. These haze episodes are mainly caused by uncontrolled biomass and peat burning in Indonesia. Airborne particulate matter (PM) samples were collected in the southwest coast of Singapore from 16 August to 9 November in 2009 to assess the impact of smoke-haze episodes on the air quality due to the long-range transport of biomass and peat burning emissions. The physical and chemical characteristics of PM were investigated during pre-haze, smoke-haze, and post-haze periods. Days with PM mass concentrations of ≥35 μg m were considered as smoke-haze events. Using this criterion, out of the total 82 sampling days, nine smoke-haze events were identified. The origin of air masses during smoke-haze episodes was studied on the basis of HYSPLIT backward air trajectory analysis for 4 days. In terms of the physical properties of PM, higher particle surface area concentrations and particle gravimetric mass concentrations were observed during the smoke-haze period, but there was no consistent pattern for particle number concentrations during the haze period as compared to the non-haze period except that there was a significant increase at about 08:00, which could be attributed to the entrainment of PM from aloft after the breakdown of the nocturnal inversion layer. As for the chemical characteristics of PM, among the six key inorganic water-soluble ions (Cl, NO, nss-SO, Na, NH, and nss-K) measured in this study, NO, nss-SO, and NH showed a significant increase in their concentrations during the smoke-haze period together with nss-K. These observations suggest that the increased atmospheric loading of PM with higher surface area and increased concentrations of optically active secondary inorganic aerosols [(NH)SO or NHHSO and NHNO] resulted in the atmospheric visibility reduction in SEA due to the advection of biomass and peat burning emissions. [ABSTRACT FROM AUTHOR]

This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 μm), fine (0.43–2.1 μm) and coarse mode (>2.1 μm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO 3 − and SO 4 2− from the usual 0.43–0.65 μm to 1.1–2.1 μm. Our results demonstrated the dominance of primary combustion sources in the <0.43 μm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity. [Display omitted] • Combining chemical speciation and particle sizes gives improved source apportionment. • Combustion processes are the dominant source of pseudo-ultrafine particles by mass. • Secondary and combustion processes are dominant sources of fine particles by mass. • The three-way receptor model provides the best source apportionment results. [ABSTRACT FROM AUTHOR]