Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China

Wang, Xiaoping; Zong, Zheng; Tian, Chongguo; Chen, Yingjun; Luo, Chunling; Li, Jun; Zhang, Gan; Luo, Yongming

doi:10.1038/s41598-017-10762-8

Cited by 23 publications

(24 citation statements)

References 37 publications

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“…Figure 6. RCC profiles of PM 2.5 collected using the DTSM with (a) data collected from available published profiles (Ge et al, 2004;Kong, 2014;Liu et al, 2016Liu et al, , 2017Yan et al, 2017a;Dai et al, 2019), and the coal fly ash RSM with (b) data collected from Wang et al, 2016. eycomb briquette coals. Generally, OC and sulfur are the predominate species in PM 2.5 emitted from RCC.…”

Section: Residential Coal Combustion (Rcc)mentioning

confidence: 99%

“…Elements, ions, and carbon materials have gradually become the routine chemical species in the source apportionment of PM. With the development of advanced sampling and chemical analysis techniques, more valuable information has been explored to further expand the existing profiles or create new profiles, including data on the following: organic compounds (Schauer and Cass, 2000;Simoneit et al, 1999); isotopic measurement of radiocarbon (Wang et al, 2017), sulfur (Han et al, 2016), and nitrogen (Pan et al, 2016); high-resolution aerosol mass spectra (Zhang et al, 2011); and particle size distribution (Zhou et al, 2004). This information has been proven to provide source specificity capable of being incorporated into receptor models as new markers (Zheng et al, 2002), constraining source contributions (Amato et al, 2009), and developing new models (Ulbrich et al, 2012;Dai et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Dai

et al. 2019

Atmos. Chem. Phys.

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Abstract. Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.

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Section: Residential Coal Combustion (Rcc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Dai

et al. 2019

Atmos. Chem. Phys.

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“…They chose multiple types of composition data (aerosols, volatile organic compounds (VOCs) and major gaseous pollutants) and wind data in source apportionment of air pollutants and found it to result in better defined source factors and better fit diagnostics, compared to when non-combined data were used. Likewise, Wang et al (2017) report an improvement in source profiles when coupling the PMF model with 14 C data to constrain the PMF run as a priori information.…”

Section: Introductionmentioning

confidence: 89%

“…As alluded to, this work is a continuation of the study carried out by Beddows et al (2015), which analysed NSD and PM 10 chemical composition data collected at the NK receptor site. Number size distribution (NSD) data were collected continuously every 15 min using a Scanning Mobility Particle Sizer (SMPS), consisting of a CPC (TSI model 3775) combined with an electrostatic classifier (TSI model 3080) and air-dried according to the EUSAAR protocol (Wiedensohler et al, 2012). The particle sizes covered were 51 size bins ranging from 16 to 604 nm, the 15 min distributions were aggregated up to hourly averages (when there were at least three 15 min samples per hour) and all missing values were replaced using a value calculated using the method of Polissar et al (1998).…”

Section: Datamentioning

confidence: 99%

Receptor modelling of both particle composition and size distribution from a background site in London, UK – a two-step approach

Beddows

Harrison

2019

Atmos. Chem. Phys.

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Abstract. Some air pollution datasets contain multiple variables with a range of measurement units, and combined analysis using positive matrix factorization (PMF) can be problematic but can offer benefits through the greater information content. In this work, a novel method is devised and the source apportionment of a mixed unit dataset (PM10 mass and number size distribution, NSD) is achieved using a novel two-step approach to PMF. In the first step the PM10 data are PMF-analysed using a source apportionment approach in order to provide a solution which best describes the environment and conditions considered. The time series G values (and errors) of the PM10 solution are then taken forward into the second step, where they are combined with the NSD data and analysed in a second PMF analysis. This results in NSD data associated with the apportioned PM10 factors. We exemplify this approach using data reported in the study of Beddows et al. (2015), producing one solution which unifies the two separate solutions for PM10 and NSD data datasets together. We also show how regression of the NSD size bins and the G time series can be used to elaborate the solution by identifying NSD factors (such as nucleation) not influencing the PM10 mass.

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“…And the analysis of 16 USEPA priority PAHs was performed using a gas chromatograph coupled with a mass spectrometer (GC-MS) to determine source profile species (Cai et al, 2017a). Furthermore, for better discriminating sources, Pb stable isotopes, which are not obviously influenced by ordinary chemical, physical or biological fractionation processes (Gallon et al, 2005;Cheng and Hu, 2010), were determined with an ICP-MS. Additionally, some other isotope measurements, for example radiocarbon (Wang et al, 2017), sulfur (Han et al, 2016, and nitrogen (Pan Atmos. Chem.…”

Section: Development Of Sampling and Analysis Techniquesmentioning

confidence: 99%

Characteristics of the main primary source profiles of particulate matter across China: from 1987 to 2017

Bi¹,

Cheng²,

Dai³

et al. 2018

Preprint

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<p><strong>Abstract.</strong> Based on the published literatures and typical profiles from the source library of Nankai University, a total of 3244 chemical profiles of the main primary sources of ambient particulate matter across China from 1987 to 2017, including coal combustion, industrial emissions, vehicle emissions, fugitive dust, biomass burning, and cooking emissions, were investigated and reviewed to trace the evolution of their main components and identify the main influencing factors to the evolution. As a result, the most complicated profiles are likely attributed to coal combustion and industrial emissions, which are evidently influenced by the decontamination processes and sampling techniques as well as the coal nature and the boiler types. The profiles of vehicle emissions are dominated by OC and EC, and varied with the changing standard of sulfur and additives in the gasoline and diesel as well as the sampling methods. The profiles of fugitive dust, such as soil dust and road dust, are dominated by the crustal materials and influenced by the sampling methods to some extent. The profiles of biomass burning is impacted mainly by the biomass categories and sampling methods. As expected, the profiles of cooking emissions is impacted mainly by the cooking types and materials. The uncertainty analysis and cluster analysis of all these source profiles are conducted to reveal the variations of the different source profiles in the same source category and evaluate the differences between source categories. A relatively large variation has been founded in the source profiles of coal combustion, vehicle emissions, industry emissions and biomass burning, indicating that it is necessary to establish the local profiles for these sources due to their high uncertainties. While the profiles of road dust and soil dust present a less variation with the stable chemical characteristics among the different profiles in the same category, suggesting that the profiles of these sources could be referenced for the cities in China when such local profiles are not available. The presented results highlight the need for increased investigation of more specific markers beyond routine measured components (e.g., isotopes, organic compounds and gaseous precursors) to discriminate sources. Additionally, specific focus should be placed on the sub-type of source profiles in the future, especially for local industrial emissions and geographical areas in China, to support the air quality research communities in their efforts to develop high resolution source apportionment for making a more effective control strategies.</p>

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Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China

Cited by 23 publications

References 37 publications

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Receptor modelling of both particle composition and size distribution from a background site in London, UK – a two-step approach

Characteristics of the main primary source profiles of particulate matter across China: from 1987 to 2017

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