Methods for estimating uncertainty in factor analytic solutions

Paatero, Pentti; Eberly, Shelly; Brown, Samantha M.; Norris, Gary A.

doi:10.5194/amt-7-781-2014

Cited by 429 publications

(259 citation statements)

References 24 publications

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“…Base model displacement (DISP), bootstrap (BS) and bootstrap displacement (BS-DISP) methods are the main tools of assessing quality. It has been demonstrated that these three methods complement each other (for more details, see Paatero et al, 2014). EFA PMF 5.0 provides aerosol data obtained from Baltimore and guides the applicant step by step to robustly use the source apportionment program of EPA PM 5.0.…”

Section: Appendix Amentioning

confidence: 99%

“…The first file includes concentrations, whilst the second file contains uncertainty for each species. Uncertainty for PMF application can be calculated using different approaches such as an ad hoc formula (Anttila et al, 1995), a fixed fraction of the concentration (Paatero et al, 2014) or a more complicated way, as proposed by Polissar et al (1998). No matter how it is calculated, if uncertainty is too high for one parameter, species will be categorized as bad by the PMF.…”

Section: Appendix Amentioning

confidence: 99%

“…After the base run one has to estimate the quality of the obtained results from PMF (for more details, see the EPA PMF 5.0 User Guide and Paatero et al, 2014). Base model displacement (DISP), bootstrap (BS) and bootstrap displacement (BS-DISP) methods are the main tools of assessing quality.…”

Section: Appendix Amentioning

confidence: 99%

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Atmospheric water-soluble organic nitrogen (WSON) in the eastern Mediterranean: origin and ramifications regarding marine productivity

Nehir

Koçak

2018

Atmos. Chem. Phys.

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Abstract. Aerosol and rain sampling in two size fractions was carried out at a rural site located on the coast of the eastern Mediterranean, Erdemli, Turkey (36 • 33 54 N, 34 • 15 18 E). A total of 674 aerosol samples in two size fractions (337 coarse, 337 fine) and 23 rain samples were collected between March 2014 and April 2015. Samples were analyzed for NO − 3 , NH + 4 and ancillary water-soluble ions using ion chromatography and water-soluble total nitrogen (WSTN) by applying a high-temperature combustion method. The mean aerosol water-soluble organic nitrogen (WSON) was 23.8 ± 16.3 nmol N m −3 , reaching a maximum of 79 nmol N m −3 , with about 66 % being associated with coarse particles. The volume weighted mean (VWM) concentration of WSON in rain was 21.5 µmol N L −1 . The WSON contributed 37 and 29 % to the WSTN in aerosol and rainwater, respectively. Aerosol WSON concentrations exhibited large temporal variation, mainly due to meteorology and the origin of air mass flow. The highest mean aerosol WSON concentration was observed in the summer and was attributed to the absence of rain and resuspension of cultivated soil in the region. The mean concentration of WSON during dust events (38.2 ± 17.5 nmol N m −3 ) was 1.3 times higher than that of non-dust events (29.4 ± 13.9 nmol N m −3 ). Source apportionment analysis demonstrated that WSON was originated from agricultural activities (43 %), secondary aerosol (20 %), nitrate (22 %), crustal material (10 %) and sea salt (5 %). The dry and wet depositions of WSON were equivalent and amounted to 36 % of the total atmospheric WSTN flux.

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Section: Appendix Amentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

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Atmospheric water-soluble organic nitrogen (WSON) in the eastern Mediterranean: origin and ramifications regarding marine productivity

Nehir

Koçak

2018

Atmos. Chem. Phys.

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“…Paatero et al (2014) compared the effectiveness in estimating uncertainties of factor elements using two different approaches: the displacement (DISP) and bootstrap analysis (BS). BS involves applying the model to input matrices consisting of a subset of the entire dataset.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Long-term chemical analysis and organic aerosol source apportionment at nine sites in central Europe: source identification and uncertainty assessment

et al. 2017

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Abstract. Long-term monitoring of organic aerosol is important for epidemiological studies, validation of atmospheric models, and air quality management. In this study, we apply a recently developed filter-based offline methodology using an aerosol mass spectrometer (AMS) to investigate the regional and seasonal differences of contributing organic aerosol sources. We present offline AMS measurements for particulate matter smaller than 10 µm at nine stations in central Europe with different exposure characteristics for the entire year of 2013 (819 samples). The focus of this study is a detailed source apportionment analysis (using positive matrix factorization, PMF) including in-depth assessment of the related uncertainties. Primary organic aerosol (POA) is separated in three components: hydrocarbon-like OA related to traffic emissions (HOA), cooking OA (COA), and biomass burning OA (BBOA). We observe enhanced production of secondary organic aerosol (SOA) in summer, following the increase in biogenic emissions with temperature (summer oxygenated OA, SOOA). In addition, a SOA component was extracted that correlated with an anthropogenic secondary inorganic species that is dominant in winter (winter oxygenated OA, WOOA). A factor (sulfur-containing organic, SC-OA) explaining sulfur-containing fragments (CH 3 SO + 2 ), which has an event-driven temporal behaviour, was also identified. The relative yearly average factor contributions range from 4 to 14 % for HOA, from 3 to 11 % for COA, from 11 to 59 % for BBOA, from 5 to 23 % for SC-OA, from 14 to 27 % for WOOA, and from 15 to 38 % for SOOA. The uncertainty of the relative average factor contribution lies between 2 and 12 % of OA. At the sites north of the alpine crest, the sum of HOA, COA, and BBOA (POA) contributes less to OA (POA / OA = 0.3) than at the southern alpine valley sites (0.6). BBOA is the main contributor to POA with 87 % in alpine valleys and 42 % north of the alpine crest. Furthermore, the influence of primary biological particles (PBOAs), not resolved by PMF, is estimated and could contribute significantly to OA in PM 10 .

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“…Prior to the PMF analyses, data and 5 uncertainty matrices were prepared in the same manner as previous studies (Polissar et al, 1998;Song et al, 2001). Data screening and the source apportionment were performed in accordance to the protocols and recommendations set out by Paatero et al, and Brown et al (Paatero et al, 2014;Brown et al, 2015). The effect on the receptor modeling from Variables elements with low signal-to-noise ratios were excluded from the examined by alternate inclusion and exclusion and only those that could be 10 explained in association with source emissions have been included in the results receptor modeling due to the effect that random analytical noise can have on the receptor modeling process (Paatero and Hopke, 2003).…”

Section: Receptor Modeling Using Pmfmentioning

confidence: 99%

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

Zhou¹,

Davy²,

Huang³

et al. 2017

Preprint

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Abstract. Hazardous air pollutants, such as trace elements in particulate matter (PM), are known or highly suspected to cause detrimental effects on human health. To understand the sources and associated risks of PM to human health, hourly time-integrated major trace elements in size-segregated coarse (PM 2.5-10 ) and fine (PM 2.5 ) particulate matter were collected at the industrial city of Foshan in the Pearl River Delta region, China. Receptor modeling of the dataset by positive matrix factorization (PMF) 25 was used to identify six sources contributing to PM 2.5 and PM 10 concentrations at the site. Dominant sources included industrial coal combustion, secondary inorganic aerosol, motor vehicles and construction dust along with two intermittent sources (biomass combustion and marine aerosol). The biomass combustion source was found to be a significant contributor to peak PM 2.5 episodes along with 2 motor vehicles and industrial coal combustion. Conditional probability function (CPF) was applied to estimate the source locations using the PMF-resolved source contribution coupled with the surface wind direction data. Health exposure risk of hazardous trace elements (Pb, As, Si, Cr, Mn and Ni) and sourcespecific values were estimated. The total hazard quotient (HQ) of PM 2.5 was 2.09, higher than the acceptable limit (HQ = 1). The total carcinogenic risk (CR) was 3.3710 -3 for PM 2.5 , which was three 5 times higher than the most tolerable limit (1.010 -4 ). Among the selected trace elements, As and Pb posed the highest non-carcinogenic and carcinogenic risks to human health, respectively. In addition, our results showed that industrial coal combustion source was the dominant non-carcinogenic and carcinogenic risks contributor, highlighting the need for stringent control of this source. This study provides new insight for policy makers to prioritize sources in air quality management and health risk 10 reduction.Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-807 Manuscript under review for journal Atmos. Chem. Phys.

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Methods for estimating uncertainty in factor analytic solutions

Cited by 429 publications

References 24 publications

Atmospheric water-soluble organic nitrogen (WSON) in the eastern Mediterranean: origin and ramifications regarding marine productivity

Atmospheric water-soluble organic nitrogen (WSON) in the eastern Mediterranean: origin and ramifications regarding marine productivity

Long-term chemical analysis and organic aerosol source apportionment at nine sites in central Europe: source identification and uncertainty assessment

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

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