1

Supplementary Material for Online Measurement of Gas-1

Phase Nitrated Phenols Utilizing CI-LToF-MS: Primary 2

Sources and Secondary Formation 3

Kai Song1,2, Song Guo1,2*, Haichao Wang3, Ying Yu1, Hui Wang1, Rongzhi Tang1, 4

Shiyong Xia4, Yuanzheng Gong1, Zichao Wan1, Daqi Lv1, Rui Tan1, Wenfei Zhu1, 5

Ruizhe Shen1, Xin Li1, Xuena Yu1, Shiyi Chen1, Liming Zeng1, Xiaofeng Huang4 6

7

1 State Key Joint Laboratory of Environmental Simulation and Pollution Control, 8

International Joint Laboratory for Regional Pollution Control, Ministry of Education 9

(IJRC), College of Environmental Sciences and Engineering, Beijing, 100871, China 10

2 Collaborative Innovation Center of Atmospheric Environment and Equipment 11

Technology, Nanjing University of Information Science & Technology, Nanjing 12

210044, China P. R. 13

3 School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China. 14

4 Key Laboratory for Urban Habitat Environmental Science and Technology, School of 15

Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 16

518055, China. 17

* Correspondence to: S. Guo: songguo@pku.edu.cn 18

19

2

Figures 20

Figure S1. Chemical structures and high-resolution peak fits of reagent ions and 21

nitrated phenols (NPs) investigated in this study. 22

Figure S2. (a) Background ions and ions detected during the calibration period (local 23

time); (b) Calibration line of ions (y) and the standard gas-phase concentration of 24

nitrophenol (x). 25

Figure S3. The measured concentration of nitrated phenols and their secondary 26

formation simulation by the box model. 27

Figure S4. Air quality and meteorology conditions during the sampling period in 28

Beijing: time series of (a) wind speed, (b) RH, (c) PM2.5, (d) NOy and (e) CO from Dec 29

1 to Dec 31, 2018. 30

Figure S5. Consensus maps of brunet, KL, offset, lee, nsNMF and snmf/l algorithms 31

in NMF. The consensus approach was used to estimate the proper method and cluster 32

method of simulation. The color of the consensus map indicated the coefficient and an 33

ideal consensus map was a color-coded heat map in which red blocks along the diagonal 34

on a blue background (Monti et al., 2003; Simpson et al., 2010). KL approach was the 35

optimal one. 36

Figure S6. NMF rank survey of factors 3 to 7. The cophenetic coefficient and RSS 37

curves were used for the judgment of factor numbers. The first decreasing cophenetic 38

value (Brunet et al., 2004)and an inflection point of the RSS curve (Hutchins et al., 39

2008)was the optimal factor number, that was, four factors in this study. 40

Figure S7. Diurnal profiles of coal combustion (a), biomass burning (b), industry (c) 41

and vehicle exhaust (d) sources. Coal combustion and biomass burning displayed a 42

nighttime peak while the source of vehicle exhaust showed peaks at rush hour which 43

were evidence of the NMF source apportionment. 44

Figure S8. Source profile from the PMF model. (a) Source profile of PMF results. SO2, 45

chloromethane, aromatics and 1,3-butadiene as the markers of coal combustion, 46

biomass burning, industry and vehicle exhaust sources. (b) Contribution of primary 47

emission (in blue borderline) and second formation (in red borderline) of NPs. 48

49

50

51

3

52

Figure S1. Chemical structures and high-resolution peak fits of reagent ions and nitrated 53

phenols (NPs) investigated in this study. 54

55

4

56

Figure S2. (a) Background ions and ions detected during the calibration period (local 57

time); (b) Calibration line of ions (y) and the standard gas-phase concentration of 58

nitrophenol (x). 59

60

5

61

Figure S3. The measured concentration of nitrated phenols and their secondary 62

formation simulation by the box model. 63

64

6

65

Figure S4. Air quality and meteorology conditions during the sampling period in 66

Beijing: time series of (a) wind speed, (b) RH, (c) PM2.5, (d) NOy and (e) CO from Dec 67

1 to Dec 31, 2018. 68

69

7

70

Figure S5. Consensus maps of brunet, KL, offset, lee, nsNMF and snmf/l algorithms in 71

NMF. The consensus approach was used to estimate the proper method and cluster 72

method of simulation. The color of the consensus map indicated the coefficient and an 73

ideal consensus map was a color-coded heat map in which red blocks along the diagonal 74

on a blue background (Monti et al., 2003; Simpson et al., 2010). KL approach was the 75

optimal one. 76

77

8

78

Figure S6. NMF rank survey of factors 3 to 7. The cophenetic coefficient and RSS 79

curves were used for the judgment of factor numbers. The first decreasing cophenetic 80

value (Brunet et al., 2004) and an inflection point of the RSS curve (Hutchins et al., 81

2008) was the optimal factor number, that was, four factors in this study. 82

83

9

84

Figure S7. Diurnal profiles of coal combustion (a), biomass burning (b), industry (c) 85

and vehicle exhaust (d) sources. Coal combustion and biomass burning displayed a 86

nighttime peak while the source of vehicle exhaust showed peaks at rush hour which 87

were evidence of the NMF source apportionment. 88

89

10

90

Figure S8. Source profile from the PMF model. (a) Source profile of PMF results. SO2, 91

chloromethane, aromatics and 1,3-butadiene as the markers of coal combustion, 92

biomass burning, industry and vehicle exhaust sources. (b) Contribution of primary 93

emission (in blue borderline) and second formation (in red borderline) of NPs. 94

95

11

References 96

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pattern discovery using matrix factorization, Proc. Natl. Acad. Sci. U. S. A., 98

doi:10.1073/pnas.0308531101, 2004. 99

Hutchins, L. N., Murphy, S. M., Singh, P. and Graber, J. H.: Position-dependent motif 100

characterization using non-negative matrix factorization, Bioinformatics, 101

doi:10.1093/bioinformatics/btn526, 2008. 102

Monti, S., Tamayo, P., Mesirov, J. and Golub, T.: Consensus clustering: A resampling-103

based method for class discovery and visualization of gene expression microarray data, 104

Mach. Learn., doi:10.1023/A:1023949509487, 2003. 105

Simpson, T. I., Armstrong, J. D. and Jarman, A. P.: Merged consensus clustering to 106

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