Supplementary Material Molecular Marker Characterization and Source Appointment of Particulate Matter and Its Organic Aerosols Jong-Kyu Choia,d, Soo-Jin Banb, Yong-Pyo Kimc, Yong-Hee Kimd, Seung-Muk Yia, Kyung-Duk Zoha * a Department of Environmental Health, School of Public Health, Seoul National University, Seoul 151-742, Korea bNational Institute of Environmental Research, Ministry of Environment, Incheon, 404-708, Korea cDepartment of Environmental Science and Engineering, Ewha Womans University, Seoul, 120-750, Korea d Research Institute of Public Health & Environment, Incheon Metropolitan city, Incheon, 400-036, Korea Submitted to Chemosphere 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 2
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Abstract · Web viewThe RSDs of ionic species, metallic elements, and individual organic species averaged approximately 0.8, 1.4, and 1.4%, respectively. The method detection limit
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Ionization energy EM volt (1800)Temp Transfer line : 300 , Ion source chamber : 230℃ ℃Solvent Delay (min) 3MS Data Collection Mode ScanMS Scan Range (amu) 35-600
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- Quality assurance and control (QA/QC)
Quality assurance and control (QA/QC) procedures were carried out for data
certification. The detailed QA/QC data was described in supplemental materials (Table
S2). For QA in the analysis of the samples, blank filters were simultaneously examined
using the same methods as described above. Background contamination was
periodically monitored (every 20 samples) using field blanks that were simultaneously
processed with the field samples. The background contamination was less than 5% of
the associated samples for all analytes. The relative percent difference (RPD) between
sampled concentrations was also used to evaluate the accuracy of measurement for each
pollutant and was typically within ±10 % of the standard value. The relative standard
deviation (RSD, %) expresses the standard deviation as a percentage of the mean. The
RSDs of ionic species, metallic elements, and individual organic species averaged
approximately 0.8, 1.4, and 1.4%, respectively. The method detection limit (MDL) was
calculated as three times the value of the standard deviation, obtained from seven
consecutive analyses of low level samples. The MDL values of ionic species, metallic
elements, and individual organic species were estimated to be 0.01~0.05 g/m3,
0.0005~0.004 g/m3, and 0.003~0.079 ng/m3, respectively. Recoveries of ionic species
and metallic elements were determined by spiking a standard solution into a blank filter
once every 20 samples and the recovery (%) of organic species was calculated from the
extraction recovery of the surrogate organic standards spiked. The recoveries were
estimated to be 91, 98, 80, 81, and 83% for ionic species, metallic elements, alkanes,
alkanoic acids, and polycyclic aromatic hydrocarbons (PAH), respectively.
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Table S2. Method detection limits, RSD (%), and RPD (%) of target analytes.
1. The method detection limit (MDL) was calculated as three times the value of the standard deviation, obtained from seven consecutive analyses of low level samples.
2. The relative standard deviation (RSD, %) expresses the standard deviation as a percentage of the mean.
3. The RPD (relative percent difference, %) was estimated from two time measurement of sample.
Fig. S1. Location of the study sites in Incheon, Korea
Fig. S2. The diagonistic factor of PMF model using 41 molecular markers only. (a) IM,
IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for
the different factor solutions and the change of “FPEAK” parameter.
Fig. S3. The diagonistic factor of PMF model using traditional 21items couple with 41
molecular markers. (a) IM, IS, and rotational freedom as a function of the factors
chosen in PMF, (b) Q-value for the different factor solutions and the change of
“FPEAK” parameter.
Fig. S4. Source profiles obtained from organic data (prediction ± standard deviation)
using 41organic marker species in Incheon, Korea.
Fig. S5. Timeseries plot for each source contribution to OC mass concentrationscalculated from PMF model using 41 organic marker species.Fig. S6. Source profiles obtained from TSP samples (prediction ± standard deviation)
using 63species in Incheon, Korea.
Fig. S7. Timeseries plot for each source contribution of TSP using 63species in
Incheon, Korea
Fig. S8. The source contributions (%) of identified sources to TSP mass concentrations
calculated from PMF model using 63species.
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Fig.S1. Location of the study sites in Incheon, Korea
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- PMF analysis for Organic Carbon using 41 molecular markers
PMF diagnostics (e.g., model error, Q and rotational ambiguity, rotmat) were based
on those described by Lee et al. (1999). We investigated the Q-value for different
numbers of factors and values of the rotational parameter (FPEAK), as well as
variations in the maximum individual column mean (IM), the maximum individual
column standard deviation (IS), and rotational freedom for the different factors used in
PMF models (see Fig. S2(a) and S2(b)). As the number of factors approached a critical
value, IM and IS clearly decreased. We also investigated the maximum rotmat, which
exhibited a significant increase from seven to ten factors (Figs. S2).
(a)
(b)
Fig.S2. The diagonistic factor of PMF model using 41 molecular markers only. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.
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- PMF analysis for TSP using 62 compounds (traditional 21 items + 41
molecular markers)
(a)
(b)
Fig.S3.The diagonistic factor of PMF model using traditional 21items couple with 41 molecular markers. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.
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Fig. S4. Source profiles obtained from organic data (prediction ± standard deviation)
using 41organic marker species in Incheon, Korea.
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Fig. S5. Timeseries plot for each source contribution to OC mass concentrations
calculated from PMF model using 41 organic marker species.
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Fig.S6. Source profiles obtained from TSP samples (prediction ± standard deviation) using 63 species in Incheon, Korea.
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Fig.S7. Timeseries plot for each source contribution of TSP using 63 species in
Incheon, Korea.
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Fig.S8. The source contributions (%) of identified sources to TSP mass
concentrations calculated from PMF model using 63 species.