Browsing by Subject "SOURCE APPORTIONMENT"

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  • Brown, Steven G.; Eberly, Shelly; Paatero, Pentti; Norris, Gary A. (2015)
    The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to randomerrors and rotational ambiguity. To demonstrate the utility of the EEmethods, results are presented for three data sets: (1) speciated PM2.5 data froma chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species inwater quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high- resolution aerosolmass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results fromsensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying toomany factors. DISP diagnosticswere consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
  • Zhao, Jian; Qiu, Yanmei; Zhou, Wei; Xu, Weiqi; Wang, Junfeng; Zhang, Yingjie; Li, Linjie; Xie, Conghui; Wang, Qingqing; Du, Wei; Worsnop, Douglas R.; Canagaratna, Manjula R.; Zhou, Libo; Ge, Xinlei; Fu, Pingqing; Li, Jie; Wang, Zifa; Donahue, Neil M.; Sun, Yele (2019)
    Organic aerosol (OA) constituted a large fraction of aerosol particles during severe haze episodes in winter in northern China, yet our understanding of its physical and chemical processing was limited. Here we investigate the sources and processes of OA during four haze episodes in winter in 2016 using high-resolution aerosol mass spectrometer. The PM2.5 reached 400 mu g/m(3) during the severest episode (Ep1) when Beijing issued a red alert and implemented strict emission controls. Our results showed that secondary OA (SOA) dominated OA during haze episodes on average accounting for 46-66% of OA and was comparable to secondary inorganic aerosol (SIA) with the SOA/SIA ratios being 0.51-0.72. Primary OA from fossil-fuel combustion, biomass burning, and cooking presented very strong diurnal variations during haze episodes and contributed up to 60% in OA at night. Comparatively, the changes in semivolatile and low-volatility SOA were relatively small except a substantial increase in aqueous phase-related oxidized OA (aq-OOA) during Ep1 with high relative humidity and aerosol water content. aq-OOA fell well into a small region in the middle of the triangle plot of f(44) versus f(43) (fraction of m/z 44 and 43 in OA, respectively), which can be used as a diagnostic for the presence of aqueous phase processing of SOA. In addition, the increases of SO2+/SO3+ as a function of relative humidity, the triangle plot of f(H2SO4+) versus f(HSO3+), and high nitrogen-to-carbon ratio in aq-OOA suggest the potential formation of sulfur- and nitrogen-containing organic compounds through aqueous phase processing.
  • Wang, Jiandong; Zhao, Bin; Wang, Shuxiao; Yang, Fumo; Xing, Jia; Morawska, Lidia; Ding, Aijun; Kulmala, Markku; Kerminen, Veli-Matti; Kujansuu, Joni; Wang, Zifa; Ding, Dian; Zhang, Xiaoye; Wang, Huanbo; Tian, Mi; Petäjä, Tuukka; Jiang, Jingkun; Hao, Jiming (2017)
    China is one of the regions with highest PM(2.5)concentration in the world. In this study, we review the spatio-temporal distribution of PM2.5 mass concentration and components in China and the effect of control measures on PM2.5 concentrations. Annual averaged PM2.5 concentrations in Central-Eastern China reached over 100 mu g m(-3), in some regions even over 150 mu g m(-3). In 2013, only 4.1% of the cities attained the annual average standard of 35 mu g m(-3). Aitken mode particles tend to dominate the total particle number concentration. Depending on the location and time of the year, new particle formation (NPF) has been observed to take place between about 10 and 60% of the days. In most locations, NPF was less frequent at high PM mass loadings. The secondary inorganic particles (i.e., sulfate, nitrate and ammonium) ranked the highest fraction among the PM2.5 species, followed by organic matters (OM), crustal species and element carbon (EC), which accounted for 6-50%, 15-51%, 5-41% and 2-12% of PM2.5, respectively. In response to serious particulate matter pollution, China has taken aggressive steps to improve air quality in the last decade. As a result, the national emissions of primary PM2.5, sulfur dioxide (SO2), and nitrogen oxides (NOx) have been decreasing since 2005, 2006, and 2011, respectively. The emission control policies implemented in the last decade could result in noticeable reduction in PM2,(5)concentrations, contributing to the decreasing PM2.5 trends observed in Beijing, Shanghai, and Guangzhou. However, the control policies issued before 2010 are insufficient to improve PM2.5 air quality notably in future. An optimal mix of energy-saving and end-of-pipe control measures should be implemented, more ambitious control policies for NMVOC and NH3 should be enforced, and special control measures in winter should be applied. 40-70% emissions should be cut off to attain PM2.5 standard. (C) 2017 Elsevier B.V.All rights reserved.
  • Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prevot, Andre S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael (2017)
    Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statisticsbased data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k -means C C, for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral simi-larity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.
  • Cai, Jing; Chu, Biwu; Yao, Lei; Yan, Chao; Heikkinen, Liine M.; Zheng, Feixue; Li, Chang; Fan, Xiaolong; Zhang, Shaojun; Yang, Daoyuan; Wang, Yonghong; Kokkonen, Tom V.; Chan, Tommy; Zhou, Ying; Dada, Lubna; Liu, Yongchun; He, Hong; Paasonen, Pauli; Kujansuu, Joni T.; Petäjä, Tuukka; Mohr, Claudia; Kangasluoma, Juha; Bianchi, Federico; Sun, Yele; Croteau, Philip L.; Worsnop, Douglas R.; Kerminen, Veli-Matti; Du, Wei; Kulmala, Markku; Dällenbach, Kaspar (2020)
    Although secondary particulate matter is reported to be the main contributor of PM2.5 during haze in Chinese megacities, primary particle emissions also affect particle concentrations. In order to improve estimates of the contribution of primary sources to the particle number and mass concentrations, we performed source apportionment analyses using both chemical fingerprints and particle size distributions measured at the same site in urban Beijing from April to July 2018. Both methods resolved factors related to primary emissions, including vehicular emissions and cooking emissions, which together make up 76% and 24% of total particle number and organic aerosol (OA) mass, respectively. Similar source types, including particles related to vehicular emissions (1.6 +/- 1.1 mu gm(-3); 2.4 +/- 1.8 x 10(3) cm(-3) and 5.5 +/- 2.8 x 10(3) cm(-3) for two traffic-related components), cooking emissions (2.6 +/- 1.9 mu gm(-3) and 5.5 +/- 3.3 x 10(3) cm(-3)) and secondary aerosols (51 +/- 41 mu gm(-3) and 4.2 +/- 3.0 x 10(3) cm(-3)), were resolved by both methods. Converted mass concentrations from particle size distributions components were comparable with those from chemical fingerprints. Size distribution source apportionment separated vehicular emissions into a component with a mode diameter of 20 nm ("traffic-ultrafine") and a component with a mode diameter of 100 nm ("traffic-fine"). Consistent with similar day- and nighttime diesel vehicle PM2.5 emissions estimated for the Beijing area, traffic-fine particles, hydrocarbon-like OA (HOA, traffic-related factor resulting from source apportionment using chemical fingerprints) and black carbon (BC) showed similar diurnal patterns, with higher concentrations during the night and morning than during the afternoon when the boundary layer is higher. Traffic-ultrafine particles showed the highest concentrations during the rush-hour period, suggesting a prominent role of local gasoline vehicle emissions. In the absence of new particle formation, our re-sults show that vehicular-related emissions (14% and 30% for ultrafine and fine particles, respectively) and cooking-activity-related emissions (32 %) dominate the particle number concentration, while secondary particulate matter (over 80 %) governs PM2.5 mass during the non-heating season in Beijing.
  • Yan, Chao; Nie, Wei; Äijälä, Mikko; Rissanen, Matti P.; Canagaratna, Manjula R.; Massoli, Paola; Junninen, Heikki; Jokinen, Tuija; Sarnela, Nina; Häme (o.s. Häkkinen), Silja A. K.; Schobesberger, Siegfried; Canonaco, Francesco; Yao, Lei; Prevot, Andre S. H.; Petäjä, Tuukka; Kulmala, Markku; Sipilä, Mikko; Worsnop, Douglas R.; Ehn, Mikael (2016)
    Highly oxidized multifunctional compounds (HOMs) have been demonstrated to be important for atmospheric secondary organic aerosols (SOA) and new-particle formation (NPF), yet it remains unclear which the main atmospheric HOM formation pathways are. In this study, a nitrate-ion-based chemical ionization atmospheric-pressure-interface time-of-flight mass spectrometer (CI-APi-TOF) was deployed to measure HOMs in the boreal forest in Hyytiala, southern Finland. Positive matrix factorization (PMF) was applied to separate the detected HOM species into several factors, relating these "factors" to plausible formation pathways. PMF was performed with a revised error estimation derived from laboratory data, which agrees well with an estimate based on ambient data. Three factors explained the majority (> 95 %) of the data variation, but the optimal solution found six factors, including two night-time factors, three daytime factors, and a transport factor. One nighttime factor is almost identical to laboratory spectra generated from monoterpene ozonolysis, while the second likely represents monoterpene oxidation initiated by NO3. The exact chemical processes forming the different daytime factors remain unclear, but they all have clearly distinct diurnal profiles, very likely related to monoterpene oxidation with a strong influence from NO, presumably through its effect on peroxy radical (RO2 / chemistry. Apart from these five "local" factors, the sixth factor is interpreted as a transport related factor. These findings improve our understanding of HOM production by confirming current knowledge and inspiring future research directions and provide new perspectives on using factorization methods to understand short-lived atmospheric species.
  • Hellén, Heidi; Leck, Caroline; Paatero, Jussi; Virkkula, Aki; Hakola, Hannele (2012)
  • Honkonen, Olga; Rantalainen, Anna-Lea (2016)
    The aim of this study was to evaluate the sources, transport and distribution of hydrophobic organic contaminants produced in an urban area. Passive sampling devices (PSDs) were employed in the storm-water drainage of the city of Lahti, in an adjacent boreal lake (Vesijarvi) and along its shore. Samples were analysed for 16 polycyclic aromatic hydrocarbons (PAHs) and 28 polychlorinated biphenyls (PCBs) with a gas chromatograph-mass spectrometer. Concentrations of contaminants were elevated in the stormwater drainage and in the vicinity of the stormwater outlets in Vesijarvi, but declined as a function of distance from the shore. Atmospheric PAH concentrations were significantly higher in the autumn than in the summer. Petrogenic PAHs contributed significantly to stormwater contamination, while pyrogenic pollutants mainly appeared to be carried to Vesijarvi by atmospheric transport.
  • de Jesus, Alma Lorelei; Rahman, Md Mahmudur; Mazaheri, Mandana; Thompson, Helen; Knibbs, Luke D.; Jeong, Cheol; Evans, Greg; Nei, Wei; Ding, Aijun; Qiao, Liping; Li, Li; Portin, Harri; Niemi, Jarkko V.; Timonen, Hilkka; Luoma, Krista; Petäjä, Tuukka; Kulmala, Markku; Kowalski, Michal; Peters, Annette; Cyrys, Josef; Ferrero, Luca; Manigrasso, Maurizio; Avino, Pasquale; Buonano, Giorgio; Reche, Cristina; Querol, Xavier; Beddows, David; Harrison, Roy M.; Sowlat, Mohammad H.; Sioutas, Constantinos; Morawska, Lidia (2019)
    Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM2.5 (mass concentration of particles with aerodynamic diameter <2.5 mu m) and factors that influence their concentrations. Hourly average PNC and PM2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 x 10 3 to 19.5 x 10(3) particles.cm(-3) and from 7.0 to 65.8 mu g.m(-3) for PNC and PM2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM2.5 demonstrated that a high PNC level does not lead to a high PM2.5, and vice versa. The particle number-to-mass ratio (in units of 10(9) particles.mu g(-1)) ranged from 0.14 to 2.2, > 1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM2.5. Therefore, PNC and PM2.5 measurements are not representative of each other; and regulating PM2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.
  • Dos Santos, V. N.; Herrmann, E.; Manninen, H. E.; Hussein, T.; Hakala, J.; Nieminen, T.; Aalto, P. P.; Merkel, M.; Wiedensohler, A.; Kulmala, M.; Petäjä, T.; Hämeri, K. (2015)
    Air ion concentrations influence new particle formation and consequently the global aerosol as potential cloud condensation nuclei. We aimed to evaluate air ion concentrations and characteristics of new particle formation events (NPF) in the megacity of Paris, France, within the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric Pollution and climate effects, and Integrated tools for assessment and mitigation) project. We measured air ion number size distributions (0.8-42 nm) with an air ion spectrometer and fine particle number concentrations (>6 nm) with a twin differential mobility particle sizer in an urban site of Paris between 26 June 2009 and 4 October 2010. Air ions were size classified as small (0.82 nm), intermediate (2-7 nm), and large (7-20 nm). The median concentrations of small and large ions were 670 and 680 cm 3, respectively, (sum of positive and negative polarities), whereas the median concentration of intermediate ions was only 20 cm 3, as these ions were mostly present during new particle formation bursts, i.e. when gas-to-particle conversion produced fresh aerosol particles from gas phase precursors. During peaks in traffic-related particle number, the concentrations of small and intermediate ions decreased, whereas the concentrations of large ions increased. Seasonal variations affected the ion population differently, with respect to their size and polarity. NPF was observed in 13% of the days, being most frequent in spring and late summer (April, May, July, and August). The results also suggest that NPF was favoured on the weekends in comparison to workdays, likely due to the lower levels of condensation sinks in the mornings of weekends (CS weekdays 09: 00: 18 x 10(-3) s(-1); CS weekend 09:00: 8 x 10(-3) s(-1)). The median growth rates (GR) of ions during the NPF events varied between 3 and 7 nm h(-1), increasing with the ion size and being higher on workdays than on weekends for intermediate and large ions. The median GR of small ions on the other hand were rather similar on workdays and weekends. In general, NPF bursts changed the diurnal cycle of particle number as well as intermediate and large ions by causing an extra peak between 09: 00 and 14:00. On average, during the NPF bursts the concentrations of intermediate ions were 8.5-10 times higher than on NPF non-event days, depending on the polarity, and the concentrations of large ions and particles were 1.5-1.8 and 1.2 times higher, respectively. Because the median concentrations of intermediate ions were considerably higher on NPF event days in comparison to NPF nonevent days, the results indicate that intermediate ion concentrations could be used as an indication for NPF in Paris. The results suggest that NPF was a source of ions and aerosol particles in Paris and therefore contributed to both air quality degradation and climatic effects, especially in the spring and summer.
  • Helin, A.; Virkkula, A.; Backman, J.; Pirjola, L.; Sippula, O.; Aakko-Saksa, P.; Väätäinen, S.; Mylläri, F.; Järvinen, A.; Bloss, M.; Aurela, M.; Jakobi, G.; Karjalainen, P.; Zimmermann, R.; Jokiniemi, J.; Saarikoski, S.; Tissari, J.; Rönkkö, T.; Niemi, J.; Timonen, H. (2021)
    The absorption Angstrom exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal-fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual-spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370-950 nm). AAE(470/950) varied greatly between the different emission sources, ranging from -0.2 +/- 0.7 to 3.0 +/- 0.8. The correlation between the AAE(470/950) and AAE(370-950) results was good (R-2 = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE(470/950) values (up to 2.0 +/- 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE(470/950) (0.9-1.1). In the diesel bus exhaust emissions, AAE(470/950) increased in the order of acceleration (0.8 +/- 0.1), deceleration (1.1 +/- 0.1), and steady driving (1.2 +/- 0.1). In the coal-fired power plant emissions, the variation of AAE(470/950) was substantial (from -0.1 +/- 2.1 to 0.9 +/- 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood-burning derived aerosols had AAE(470/950) from 1.1 +/- 0.1 (modern masonry heater) to 1.4 +/- 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.