Technology, Clear Water Bay, Kowloon, Hong Kong, China. Atmospheric Research Center, HKUST Fok Ying Tung Graduate School, Guangzhou, China.

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1 Supporting information for Impact of Secondary Organic Aerosol Tracers on Tracer-Based Source Apportionment of Organic Carbon and PM.5: A Case Study in the Pearl River Delta, China Qiongqiong Wang, Xiao He, X. H. Hilda Huang, Stephen M. Griffith, Yongming ng, Ting Zhang, Qingyan Zhang, Dui Wu, and Jian Zhen Yu,,,* Department of Chemistry, and Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. Atmospheric Research Center, HKUST Fok Ying Tung Graduate School, Guangzhou, China. Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China *CORRESPONDING AUTHOR: Jian Zhen Yu E mail: jian.yu@ust.hk Number of pages: 7 Number of Appendices: Number of tables: Number of figures: 7 S

2 Appendix Temporal variation of isoprene SOA tracers The temporal variation of isoprene SOA tracers showed episodic higher concentrations during Aug Oct, with the average concentrations during these months times higher than other months (.8 vs.75 ng/m for MGA, 9 vs 7. ng/m for MTs, and vs 5.5 ng/m for C 5 alkene triols) (Figure Sa). The episodic high concentrations of isoprene SOA tracers are consistent with the atmospheric chemistry of isoprene, as elaborated below. It is well known that isoprene emission has a strong positive dependence on temperature. Photo oxidation of isoprene is dominated by its reaction with OH (k=. cm molecule s ), followed by NO (k=7. cm molecule s ), and O (k=. 7 cm molecule s ). It is found that in chamber simulation experiments, isoprene SOA production (e.g., MTs and C 5 alkene triols) is enhanced with increasing aerosol acidity., Isoprene ambient concentration measurements were not available for our study sites. We estimated the aerosol H + concentration using the E AIM model 5 for the sampling dates in GZ. The calculated aerosol acidity is shown in Figure Sb, together with daily average temperature, and the Ox (O +NO ) level, which is an indicator reflecting the available OH concentrations. During Aug Oct, multiple factors were conducive for isoprene SOA formation, i.e., high temperature, high oxidants level, and high aerosol acidity (Figure Sb). Therefore, we believe that the episodic higher concentrations of isoprene SOA tracers observed during these months are reasonable, should be considered as good data, and would be erroneous if PMF treated them as outliers. S

3 (a) C 5 -alkene triols (ng/m ) 6 5 -MTs (ng/m ) MGA (ng/m ) 8 6 -MGA -MTs C 5 -alkene triols -Jan- 6-Jan- -Jan- 8-Jan- -Jan- -Jan- 5-b- -b- 7-b- -b- 9-b- 6-Mar- -Mar- 8-Mar- -Mar- -Mar- 5-Apr- -Apr- 7-Apr- -Apr- 9-Apr- 5-May- -May- 7-May- -May- 9-May- -Jun- -Jun- 6-Jun- -Jun- -Jun- -Jul- -Jul- 6-Jul- -Jul- -Jul- -Jul- 6-Jul- 8-Jul- -Jul- -Aug- -Aug- 7-Aug- -Aug- 7-Aug- -Sep- 8-Sep- -Sep- -Sep- 6-Sep- -Oct- 8-Oct- -Oct- 6-Oct- -Nov- 7-Nov- -Nov- 9-Nov- 5-Nov- -Dec- 7-Dec- -Dec- 9-Dec- 5-Dec- -Dec- Date (b) T Ox H + H + (nmol) 6 Ox (mg/m )..8 T (K) MGA -MTs C 5 -alkene triols C 5 -alkene triols (ng/m ) MTs (ng/m ) 8 6 -MGA (ng/m ) Month c Figure S. (a) Temporal variation of three isoprene SOA tracers for the samples collected at the Guangzhou site; (b) Monthly variation of temperature, Ox (O +NO ) concentration, and calculated aerosol H + concentration using the E AIM model. S

4 Appendix Supplementary details about BS, DISP and BS DISP methods in PMF analysis In this work, the EPA PMF version 5. was used to perform the analysis. Three uncertainty estimation methods are available in EPA PMF 5. software: bootstrapping (BS), displacement (DISP), and bootstrapping enhanced with DISP (BS DISP). The variation among these bootstrapped/displaced model parameters estimates the uncertainty of the initial PMF solution. BS involves resampling from the original input data set, and generates a new PMF solution with each resample resulting in a distribution of PMF model parameters. In this study, BS runs in total were performed. The BS factor contributions from the resampled results were mapped with the base factor contribution at a preset R value of.8. The mapping of each BS factor contribution with the corresponding base factor contribution is shown in Table S. The 5 th and 95 th percentiles of the BS factor profiles provide the error estimation range derived from the BS method. In the DISP method, after getting the base run result, each species in the factor profile (conc. of species) is adjusted up and down one by one. After each adjustment, PMF runs all over again to calculate a new converged solution, with the change of dq (Q displaced run Q base run) less than the predetermined maximum allowable change dq max (dq max =, 8, 5, 5). The essence of DISP is to find the largest and smallest feasible values d max and d min such that: dq(f kj=d max ) dq max dq(f kj=d min ) dq max The obtained values d max and d min represent the upper and lower interval estimates for factor element f kj. During the displacement of the target species f kj, the changes of other species are not counted as their interval estimates. The upper and lower interval of the other species in the factor profiles were determined following the above mentioned process similar to f kj. The intervals (minimum and maximum source profile values) at the lowest dq max provide the error estimation range for each species in each factor profile derived from the DISP method. After adjusting each species in the base run profile up and down, it is possible to transform the factor gradually, so that factor identities change, i.e. factor swap. Cross correlations based and regression based method are used in PMF to detect a factor swap. Specifically, for cross correlation based method, a matrix of uncentered correlation coefficients is computed, so that each matrix element is the correlation coefficient between one column of G (or F ) and one column of G (or F ) ((G, F ) is the original solution and (G, F ) is the transformed solution). A factor swap is seen in this correlation matrix if two or more diagonal entries are small, while corresponding off diagonal entries are close to. For regression based method, a transformation matrix T is calculated for G from G. If there are no factor swaps, T is nearly diagonal, with off diagonal elements small positive and negative values. If factor swaps occur, at least two diagonal elements of T change position with smaller off diagonal elements. 8 The decrease of Q and factor swaps in DISP are shown in Table S. In this work, coal combustion swaps times, while vehicle exhaust only swaps times. Thus, vehicle exhaust probably did not transform to coal combustion in the same time. BS DISP is a combination of BS and DISP, where displacement occurs in source profiles derived from each resampled data matrix. In the BS DISP method, the highest loading species in each factor profile (i.e. sulfate, nitrate, α pint, C 5 alkene triols, levoglucosan,,, hopanes, PAHs5, PAHs76,,, and ) are actively displaced. The accepted runs (If all the runs were accepted, the value should be BS+) and factor swaps for each dq max (dq max =.,,, ) are shown in Table S. The 5 th and 95 th percentiles of the factor profiles at the lowest dq max provide the error estimation range derived from the BS DISP method. For more details about the three error estimation methods, readers can refer to Brown et al., 6 Norris et al., 7 and Pattero et al. 8 S

5 Table S. Abundance of measured PM.5 major components (μg/m ) used in the PMF analysis in this study. Compound Dongguan Guangzhou Nanhai avg. ± stdev avg. ± stdev avg. ± stdev S/N * PM a.5. ±. 6.7 ±. 57. ± 8. Na +.8 ±.9.5 ±.9.5 ± ± ± ± K +.7 ±..7 ±.. ± Cl.8 ±.98. ±..95 ± trate.6 ±.5 6. ± ± ± 6.5. ± ± ±.7. ±.5.9 ± ± ± 5.6. ± ±.9. ±.95.7 ± ±..7 ±..5 ± ±.6.6 ±.5.8 ± ±.8.9 ±.7. ± ±..9 ±.9.5 ± ±..5 ±..6 ±... ±..7 ±.6.58 ± ±.6.9 ±.5. ± * gnal to noise ratio: (concentration uncertainty)/uncertainty. a PM.5 mass concentration not included as PMF input. S 5

6 Table S. Chemical species constrained in each factor profile in PMF. Species Secondary sulfate Secondary nitrate SOA_I SOA_II Biomass burning ehicle exhaust Coal combustion Industrial emission Dust Na K Cl trate PAHs PAHs a - a a o-phthalic acid a MGA a MTs a C5-alkene triols a a a a Species not included in PMF without SOA tracers (PMF wo). S 6

7 Table S. Tracers used to identify sources in PMF. Tracers Sources References NH + & SO / NO Secondary sulfate/nitrate formation process 9,, Dust, Ship emission, Industrial emission Biomass burning 5 Fossil fuel combustion (e.g. vehicle exhaust, coal combustion, ship emission) 6 9 PAHs76 Combustion sources (mainly coal combustion) 8, o Phthalic acid Naphthalene derived SOA MGA, MTs, C 5 alkene triols Isoprene derived SOA α pint α Pinene derived SOA β caryt β Caryophyllene derived SOA S 7

8 Table S. Summary of error estimation diagnostics from BS, DISP and BS DISP for PMF w. BS Mapping (R.8) Secondary sulfate Secondary nitrate SOA I SOA II Biomass burning ehicle exhaust Coal combustion Industrial emission Ship emission Secondary sulfate 97 Secondary nitrate SOA I 97 SOA II 98 Biomass burning ehicle exhaust 6 89 Coal combustion 96 Industrial emission Ship emission Dust DISP Diagnostics Error Code: Largest Decrease in Q:.9 (%) Factor Swaps dq max = dq max =8 dq max =5 dq max =5 BS DISP Diagnostics # of runs accepted: 65 out of Largest Decrease in Q:. (.%) Factor Swaps # of Decreases in Q: 6 # of Swaps in Best Fit: # of Swaps in DISP: 8 dq max =.5 5 dq max = 5 dq max = 5 dq max = Dust Unmapped S 8

9 Figure S. Location of the three sampling sites in the PRD..5 Q/Q exp.5.5 6F 7F 8F 9F F F F Figure S. Change of Q/Q exp value from 6 factor to factor run for PMF w. S 9

10 % of species Secondary sulfate formation process Secondary nitrate formation process EF method EF method SOA_I SOA_II Biomass burning ehicle exhaust Coal combustion Industrial emission Ship emission Dust PAHs5 PAHs76 o Phthalic acid MGA MTs C5 alkene triols α pint β caryt trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs trate PAHs5 PAHs76 -MGA -MTs Cl trate trate PAHs5 PAHs76 -MGA -MTs Figure S. Comparison of factor profiles (percentage of each species in factor) of factor solutions from PMF w with constant EF and PMF w with nonfixed EF. S

11 S factor contribution, μgc/m S factor contribution, μgc/m Dongguan Guangzhou Nanhai Secondary sulfate formation process -Jan- -Jan- 9-b- -Mar- 9-Apr- 9-May- -Jun- 8-Jul- 7-Aug- 6-Sep- 6-Oct- 5-Dec- -Jan- -b- -Mar- -Apr- -May- Secondary nitrate formation process SOA_I -Jun- -Jul- 6-Jul- 7-Aug- -Sep- -Oct- 9-Nov- 9-Dec- 6-Jan- -b- -Mar- 5-May- -Jul- -Sep- 9-Oct- -Nov- 9-Dec- -Jan- -Jan- 9-b- -Mar- 9-Apr- 9-May- -Jun- 8-Jul- 7-Aug- 6-Sep- 6-Oct- 5-Dec- -Jan- -b- -Mar- -Apr- -May- -Jun- -Jul- 6-Jul- 7-Aug- -Sep- -Oct- 9-Nov- 9-Dec- 6-Jan- -b- -Mar- 5-May- -Jul- -Sep- 9-Oct- -Nov- 9-Dec- SOA_II -Jan- -Jan- 9-b- -Mar- 9-Apr- 9-May- -Jun- 8-Jul- 7-Aug- 6-Sep- 6-Oct- 5-Dec- -Jan- -b- -Mar- -Apr- -May- -Jun- -Jul- 6-Jul- 7-Aug- -Sep- -Oct- 9-Nov- 9-Dec- 6-Jan- -b- -Mar- 5-May- -Jul- -Sep- 9-Oct- -Nov- 9-Dec- -Jan- -Jan- 9-b- -Mar- 9-Apr- 9-May- -Jun- 8-Jul- 7-Aug- 6-Sep- 6-Oct- 5-Dec- -Jan- -b- -Mar- -Apr- -May- -Jun- -Jul- 6-Jul- 7-Aug- -Sep- -Oct- 9-Nov- 9-Dec- 6-Jan- -b- -Mar- 5-May- -Jul- -Sep- 9-Oct- -Nov- 9-Dec- Date Figure S5. Time series of the contributions (μgc/m ) from individual secondary factor. 5 Reconstructed PM.5 (μg/m ) 9 6 y =.x -.56 R p = Measured PM.5 (μg/m ) Figure S6. Comparison of reconstructed PM.5 mass from PMF modeled individual species to the measured PM.5 mass. S

12 6 6 5 S from SOA_I, µgc/m T (K) S from SOA_II, µgc/m 6 8 S from secondary sulfate, µgc/m S from secondary nitrate, µgc/m Figure S7. Correlation of S contributions from SOA I and SOA II vs secondary sulfate and secondary nitrate. S

13 % of species Secondary sulfate formation process Secondary nitrate formation process Biomass burning ehicle exhaust Coal combustion PAHs5 PAHs76 PAHs5 PAHs76 PAHs5 PAHs76 Industrial emission Ship emission Dust PAHs5 PAHs76 PAHs5 PAHs76 PAHs5 PAHs76 Cl trate Cl trate Cl trate trate PAHs5 PAHs76 Cl trate Cl trate Cl trate trate PAHs5 PAHs76 Figure S8. Factor profiles (percentage of each species in factor) from 8 factor constrained run by PMF wo. S

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