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Supplementary material of Ambient Volatile Organic Compounds in a Suburban Site between Beijing and Tianjin: Concentration Levels, Source Apportionment and Health Risk AssessmentYuan Yang1, 2, Dongsheng Ji1, Jie Sun1, Yinghong Wang1, Dan Yao1, Shuman Zhao1, Xuena Yu3, Limin Zeng3, Renjian Zhang1, Hao Zhang5, Yonghong Wang1,4, Yuesi Wang1,2,6*1 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China2 University of the Chinese Academy of Sciences, Beijing 100049, China3 State Joint Key Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China4 Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, P.O.Box 64, 00014 University of Helsinki, Helsinki, Finland5 Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China6 Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China* Co-Corresponding authors: wys@mail.iap.; yonghong.wang@helsinki.fiThis supplementary document consists of including 2 texts, 4 Figures and 14 Tables Table of ContentsText 1. Source apportionment method: Positive matrix factorization (PMF)Text 2. Screening human health risk assessmentFigure S1. Samples of highly collinear speciesFigure S2. Correlations between isoprene and temperature, isoprene and 1,3-butadiene, isoprene and trans-2-Butene and isoprene and propyleneFigure S3. Comparison of the top ten VOC species in this study and former studies.Figure S4. Scatter plots of modeled versus measured total VOCs in this studyFigure S5. Time series of the PM2.5 concentrations during the entire observational periodTable S1. Minimum detection limit (MDL), and precision (relative standard deviation, RSD) of measured VOC compounds and their mixing ratios (mean concentration ± standard deviation) (ppbv) for different air quality levelsTable S2. Target VOCs and associated toxicity values of health risk assessmentTable S3. Comparisons of the top ten VOCs in Xianghe with other cities in China (ppbv)Table S4. Percentage of BS factors assigned to each base case factor with with 100 bootstraps and using a minimum correlation R-value of 0.6Table S5. The bootstrap calculation with the variability in the concentration / percentage of TVOCs for the factorsTable S6. Comparison of VOC source apportionment results with former studiesTable S7. Non-carcinogenic and carcinogenic risk assessment results of VOCs in ambient air in present study and of other cities in ChinaTable S8. Source specific carcinogenic and noncarcinogenic risk values and relative contribution to carcinogenic and noncarcinogenic inhalation risk of the sourcesTable S9. The top 20 VOC species with the highest increasing ratios from clean days to heavy pollution daysTable S10. The bootstrap calculation with the variability in the concentration of TVOCs for the factors at different pollution levelsTable S11. The bootstrap calculation with the variability in the percentage of TVOCs for the factors at different pollution levelsTable S12. Noncarcinogenic and carcinogenic risk assessment results of VOCs at different pollution levelsTable S13. Source specific noncarcinogenic risk values at different pollution levelsTable S14. Source specific carcinogenic risk values at different pollution levelsDetail operation of positive matrix factorization (PMF)1.1 PMF receptor model descriptionUS PMF 5.0 (U.S. EPA, 2014) was comprehensively applied to identify the initial contribution of sources to samples based on the composition or fingerprints of the sources. The method is reviewed briefly here and described in greater detail elsewhere PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYWF0ZXJvPC9BdXRob3I+PFllYXI+MTk5NzwvWWVhcj48

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ADDIN EN.CITE.DATA (Paatero and Tapper, 1994; Paatero, 1997). The PMF is a multivariate factor analytical tool that decomposes the speciated measurement data matrix x of i by j dimensions into two matrices-factor profiles (f) and factor contributions (g): xij=k=1pgikfkj+eij (1)where p is the total number of independent sources, gik stands for the contribution of kth factor in the ith sample, fkj is the load of jth compound in the kth source and eij is the relevant residual for each sample/species. Factor contributions and profiles are derived by the PMF model minimizing the objective function Q:Q=i=1nj=1mk=1pgikfkj+eij-k=1pgikfkjuij (2)where m and n are the numbers of samples and chemical species, respectively, u represents the uncertainty of each data. The theoretical Q (Qtheoretical) can be calculated as Eq. (3), and the best PMF solution should have Q/Qtheoretical with the value of～1. Qtheoretical=i×j-p×(i+j) (3)1.2 Data preparationTwo files including species concentrations and uncertainty file are required to be introduced into the EPA PMF 5.0 model. The concentrations file is a matrix (X matrix) of number of samples (column) plus the number of species (row), i.e., 2040×49 in this study. PMF analysis requires a complete data set; in order to reduce the error, the samples with missing values for individual species were excluded rather than replaced by the mean concentrations of the remaining observations PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IdWFuZzwvQXV0aG9yPjxZZWFyPjIwMTc8L1llYXI+PFJl

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ADDIN EN.CITE.DATA (Huang et al., 2017). EPA PMF 5.0 accepts two types of uncertainty files: observation-based and equation-based. The sample-specific uncertainty file is also a matrix with the same dimension as the concentrations matrix. The equation-based uncertainty dataset is constructed according to the method detection limit (MDL) and error fraction (%). If the concentration is less than or equal to the MDL provided, the uncertainty is calculated using a fixed fraction of the MDL, as Uncertainty=5/6×MDL. If the concentration is greater than the provided MDL, the calculation is based on a user provided fraction of the concentration and MDL, defined as Uncertainty=Error Fraction ×concentration2+(0.5×MDL)2. Not all 101 measured VOCs are introduced into the PMF model, there are some rules to decide which species should be included or excluded from the PMF model: 1) the chosen species had relatively high concentrations (i.e., ethane, ethylene and propane, the top three species in this study) and/or were typical tracers for specific emissions (i.e., acetylene is marker of combustion sources); 2) highly collinear species, such as iso-butane & n-butane, iso-pentane& iso-pentane, benzene & toluene, m/p-Xylene & o-Xylene are included (Figure S1); 3) species that are highly reactive are excluded, except for the source markers, since they are rapidly reacted away in the ambient atmosphere PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5aaGVuZzwvQXV0aG9yPjxZZWFyPjIwMTc8L1llYXI+PFJl

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ADDIN EN.CITE.DATA (Shao et al., 2016; Zheng et al., 2017; Li et al., 2019); 4) species were incorporated in PMF analysis based on their potential toxicity (e.g., 1,3-butadiene, tetrachloroethylene, bromoform). Finally, a total of 48 species, including 9 alkanes, 5 alkenes, 7 OVOCs, 10 aromatics and 15 halocarbons, acetylene and acetonitrile were selected for the input data. In addition, the total measured VOC was included as an input variable in the PMF model to directly obtain source contribution instead of using post-hoc regression analysis in this study. Prior to the PMF model run, the retained species are firstly classified into strong, weak, and bad based on their signal-noise-ratios (S/N). Species with S/N ratios less than 0.5 are grouped into bad and into weak if S/N ratios are in the range of 0.5-1.0 (US EPA, 2014). Finally, 41 species were categorized as strong and seven species (bromomethane, 1,1,1-Trichloroethane, 1,1,2-Trichloroethane, 1,2-Dibromoethane, bromoform, 1,1,2,2-Tertrachloroethane and 1,4-Dichlorobenzene) were characterized as weak. 1.3 Optimal number of factorsThe Q values, source profiles and scaled residuals distributions were examined to obtain the most reasonable factor solutions. Choosing the optimal number of factors in modeling is a critical question. Too many factors will result in meaningless factor profiles, while too few factors will make it difficult to segregate the mixing sources PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CcmVzc2k8L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxS

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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CcmVzc2k8L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA (Bressi et al., 2014; Zheng et al., 2017). In theory, if the number of sources is estimated properly, the Qtrue value should be approximately to Qexcept. If the number of sources is not well determined, the Q value may deviate from the theoretical value PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXVkaWM8L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA (Bressi et al., 2014; Baudic et al., 2016). Factors ranged from 4 to 9 were tested in this study. Finally, at five factors, the value of Qrobust is almost identical to that of Qtrue, suggesting that no specific event was affecting the solution. With five factors, more than 96% of the scaled residuals of the chemical species for the selected results are distributed between -3 to +3. In addition, the regression relationship between modeled and measured total VOCs concentrations (Figure S4) showed a good fit with square of the correlation coefficient (R2) of 0.97 suggesting that statistically, observed VOC concentrations at Xianghe were well represented by the PMF model-resolved source factors. For the 48 individual VOCs species, the PMF mode also well reproduced the predicted concentrations, with most R2 ranged from 0.41 to 0.96. Therefore, we considered that the 5-factor solution was the optimum solution for this PMF analysis.1.4 Uncertainty estimationAfter choosing the 5-factor solution, the bootstrap (BS) method was used to detect and estimate disproportionate effects of a small set of observations on the solution and also, to lesser extent, effects of rotational ambiguity PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CZWxpczwvQXV0aG9yPjxZZWFyPjIwMTU8L1llYXI+PFJl

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b3RlPn==

ADDIN EN.CITE.DATA (Belis et al., 2015; Brown et al., 2015; Debevec et al., 2017). BS datasets were constructed by randomly sampling blocks of observations from the original data set (US EPA, 2014). The base run with the lowest Qrobust was provided to map with each BS run in minimum Pearson correlation coefficient being 0.6. The number of BS was set as 100 to ensure the robustness of the statistics PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5KaTwvQXV0aG9yPjxZZWFyPjIwMTg8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA (Ji et al., 2018; Li et al., 2019). With four factors, the factor 1 (liquefied petroleum gas & Natural gas) containing factor 2 (secondary and long-lived species). There were no swaps with DISP and no unmapped BS factors. At five factors, results were more stable with all factors mapped in BS in 100%, as showed in Table S2. There are no unmapped BS factors and no swaps with DISP. However, the solution became less stable in moving from five to six factors. The factor1(natural gas) was only mapped in BS in 87% and factor 3 (industrial sources) was mapped in BS in 99%, factor 6 (liquefied petroleum gas) was mapped in 99%, other factors were mapped in 100% of runs. No swaps were found in DISP. According to the EPA PMF 5.0 User Guide, mapping over 80% of the factors indicated that BS uncertainties can be interpreted and the number of factors may be appropriate. Therefore, based on the above analysis, five factors were found to be the optimal solution in this study.2. Screening human health risk assessmentA screening-level health risk assessment is the process to characterize the nature and magnitude of health risks to humans and ecological receptors from chemical contaminants and other stressors that may be present in the environment (). To assess public health risks associated with chronic inhalation exposure, a four-step health risk assessment method proposed by the U.S. National Academy of Sciences has become an internationally recognized health risk assessment method and has been used in several studies to screen ambient concentrations of hazardous air pollutants PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXJpPC9BdXRob3I+PFllYXI+MjAxNzwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA (Bari et al., 2016; Bari and Kindzierski, 2017, 2018). The USEPA’s risk assessment methods are generally used in studies to evaluate carcinogenic and non-carcinogenic risks of pollutant-specific PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5aaGFuZzwvQXV0aG9yPjxZZWFyPjIwMTc8L1llYXI+PFJl

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PC9FbmROb3RlPgB=

ADDIN EN.CITE.DATA (Li et al., 2014; Zhang et al., 2017). However, pollutant-specific risk estimates may provide limited information for air quality management due to contributions of multiple sources to each compound. Source-specific risk estimates may add valuable information for understanding potential control strategies for particular sources PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXJpPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj

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dGU+AG==

ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXJpPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj

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dGU+AG==

ADDIN EN.CITE.DATA (Bari and Kindzierski, 2018). Source-risk apportionment, which is a combination of risk assessment and receptor modeling using actual measurements, has been applied in several studies worldwide to determine relative source contributions to human health risks PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XdTwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+PFJlY051

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Tm90ZT4A

ADDIN EN.CITE.DATA (Wu et al., 2009; Liao et al., 2015; Bari and Kindzierski, 2017). In this study, out of all detected species, only 31 VOC species with known toxicity values were considered, including 27 noncarcinogenic species and 13 carcinogenic species. Target VOCs and associated toxicity values of health risk assessment can be found in Table S3.2.1 Pollutant-specific health risk assessment Carcinogenic and non-carcinogenic risks of all risk-posing VOC species were evaluated using the EPA inhalation unit risk (IUR, ?g m-3) and reference concentrations (RfCs, mg m-3) associated with the exposure concentration (EC) based on the investigation of the Chinese behavior pattern for each trace element. In this study, EC was calculated using the following equation according to USEPA’s Superfund program:EC=C ×ET ×EF ×EDAT (4) where EC and C are the exposure concentration and concentration, respectively (?g m-3); ET is exposure time, h d-1; EF is exposure frequency, d y-1; ED is exposure duration, y; and AT is averaging time, h. In the present study, the risk assessment parameter EF was selected from U.S.EPA Integrated Risk Information System (IRIS), whose value was 365 d y-1, and ET, ED, and AT were selected from the Exposure Factors handbook of the Chinese population (adult), whose values were 3.7 h d-1, 74.8 y and 74.8×365×24 h, respectively, based on the investigation of the Chinese behavior pattern.Non-cancer risk was expressed as hazard index (HI), which is is the sum of hazard quotient (HQ) of various pollutants:HI=i=1nHQi (5)HQ of non-carcinogenic risk is expressed by the the ratio of EC to the reference concentration (RfC):HQ=ECRfC (6)Carcinogenic risk (R) was expressed in terms of risk, as the product of inhalation unit risk (IUR) and exposure concentration (EC):R=IUR×EC (7)The cumulative cancer risks (CR) are the summation of available individual compound risksCR=i=1nRi (8)2.2 Source-specific health risk assessment Noncarcinogenic and carcinogenic risk from inhalation exposure to the ith source was estimated as the sum of cancer and noncancer risks of all available n risk-posing VOC species in PMF-derived profiles.ECij=Cij ×ET ×EF ×EDAT (9)where ECij and Cij are the exposure concentration and concentration, respectively, of the jth species from the ith source (?g m-3).Non-carcinogenic riski=j=1nECijRfCj (10)Carcinogenic riski=j=1nECijIURj (11)where IURj and RfCj are the inhalation unit risk and the reference concentration, respectively, for the jth species. A non-cancer risk is represented by hazard index (HI). USEPA (2009) states that if HI <1, there is no appreciable risk of adverse health effects, while HI >1 indicates a chance of non-cancer effects occurring. The excess cancer risk range recommended by the USEPA (2009) for public health protection is a one in a million (1 x 10-6) acceptable risk level to one in ten thousand (1 x 10-4) tolerable risk level. center25590500Figure S1. Samples of highly collinear species.center25590500Figure S2. Correlations between (a) isoprene and temperature, (b) isoprene and 1,3-butadiene, (c) isoprene and trans-2-Butene and (d) isoprene and propylene. The green solid line is the regression line.center25590500Figure S3. Comparison of the top ten VOC species in this study and former studies. Beijinga, 2014/10/2-2014/10/14 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XdTwvQXV0aG9yPjxZZWFyPjIwMTY8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA (Liu et al., 2016a); Nanjing, 2011/3-2012/2 ADDIN EN.CITE <EndNote><Cite><Author>An</Author><Year>2014</Year><RecNum>2013</RecNum><DisplayText>(An et al., 2014)</DisplayText><record><rec-number>2013</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1528889337">2013</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>An, Junlin</author><author>Zhu, Bin</author><author>Wang, Honglei</author><author>Li, Yongyu</author><author>Lin, Xu</author><author>Yang, Hui</author></authors></contributors><titles><title>Characteristics and source apportionment of VOCs measured in an industrial area of Nanjing, Yangtze River Delta, China</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>206-214</pages><volume>97</volume><dates><year>2014</year></dates><isbn>13522310</isbn><urls></urls><electronic-resource-num>10.1016/j.atmosenv.2014.08.021</electronic-resource-num></record></Cite></EndNote>(An et al., 2014). * pollution. center25590500Figure S4. Scatter plots of modeled versus measured total VOCs in this studycenter25590500Figure S5. Time series of the PM2.5 concentrations during the entire observational periodTable S1. Minimum detection limit (MDL), and precision (relative standard deviation, RSD) of measured VOC compounds and their mixing ratios (mean concentration ± standard deviation) (ppbv) for different air quality levelsSpeciesMDL(ppbv)RSD(%)AVGCMPHPEthane*0.0372.19.60±0.086.02±5.8013.68±7.8022.18±14.42Propane*0.0323.455.07±5.712.98±2.967.59±4.0512.19±9.29iso-Butane*0.0163.331.49±1.750.87±1.092.30±1.203.54±2.55n-Butane*0.0352.592.63±3.271.55±2.274.08±2.146.06±4.71Cyclopentane*0.0083.980.12±0.220.07±0.080.20±0.100.29±0.29iso-Pentane*0.0343.281.02±1.220.56±0.631.75±0.842.36±1.36n-Pentane*0.0114.330.77±1.070.40±0.721.24±0.601.99±1.502,2-Dimethylbutane0.02450.06±0.150.04±0.050.09±0.050.15±0.262,3-Dimethylbutane0.0154.840.33±0.470.17±0.240.50±0.250.88±0.802-Methylpentane0.01950.60±0.150.31±0.420.91±0.451.68±1.503-Methylpentane0.0164.410.33±0.460.15±0.200.51±0.240.95±0.79n-Hexane*0.0244.850.49±0.620.25±0.290.81±0.381.29±0.972,4-Dimethylpentane0.014.10.05±0.100.03±0.030.07±0.040.12±0.24Methylcyclopentane0.0114.360.20±0.230.10±0.110.33±0.150.49±0.342-Methylhexane*0.0124.870.10±0.120.05±0.060.15±0.070.25±0.182,3-Dimethylpentane0.0153.780.06±0.060.03±0.030.09±0.050.13±0.09Cyclohexane*0.0113.920.13±0.140.07±0.090.21±0.100.29±0.203-Methylhexane0.0134.710.09±0.090.05±0.050.13±0.070.21±0.142,2,4-Trimethylpentane0.0034.520.06±0.070.04±0.030.09±0.050.14±0.014n-Heptane0.0094.860.13±0.150.07±0.090.19±0.100.33±0.22Methylcyclohexane0.0134.120.13±0.140.07±0.100.21±0.110.26±0.152,3,4-Trimethylpentane0.014.910.03±0.030.02±0.100.04±0.030.07±0.052-Methylheptane0.0154.750.04±0.050.03±0.020.06±0.030.10±0.073-Methylheptane0.0094.380.04±0.140.02±0.010.05±0.030.08±0.29n-Octane0.0094.820.08±0.160.05±0.180.11±0.060.18±0.10Nonane0.0175.010.09±0.120.05±0.040.13±0.070.21±0.22n-Decane0.0095.10.05±0.040.03±0.020.07±0.040.09±0.06n-Undecane0.0085.120.02±0.020.02±0.010.04±0.020.05±0.04n-Dodecane0.0215.210.02±0.020.02±0.010.03±0.020.02±0.03Ethylene*0.0394.568.73±11.734.05±5.0512.78±6.0827.08±19.20Propylene*0.01852.02±2.990.92±1.182.83±1.366.58±5.40trans-2-Butene0.0092.920.11±0.160.06±0.070.14±0.070.33±0.301-Butene0.023.140.31±0.460.15±0.160.41±0.210.97±0.90cis-2-Butene0.0184.60.25±0.570.11±0.150.31±0.160.84±1.241,3-Butadiene*0.0244.290.24±0.420.10±0.150.34±0.150.81±0.651-Pentene*0.0294.350.39±0.770.22±0.520.58±0.301.00±1.30trans-2-Pentene0.0094.950.05±0.080.02±0.030.07±0.040.17±0.16cis-2-Pentene0.0064.380.03±0.040.01±0.020.04±0.020.09±0.08Isoprene*0.014.830.07±0.110.04±0.040.10±0.050.22±0.191-Hexene0.0134.790.14±0.310.07±0.110.18±0.100.48±0.69Acetylene*0.0393.874.24±5.202.13±2.416.64±3.1711.67±8.19Acetaldehyde*0.0210.231.79±1.831.22±0.842.40±1.503.84±3.49Acrolein*0.02312.460.25±0.340.13±0.140.36±0.180.74±0.64Propanal0.0311.30.33±0.280.23±0.180.46±0.280.65±0.45Acetone*0.04310.581.53±1.490.97±0.682.12±1.233.60±2.42MTBE*0.0136.620.14±0.190.07±0.100.26±0.110.36±0.26Methacrolein0.0084.260.04±0.080.02±0.020.05±0.030.13±0.18n-Butanal*0.0116.470.09±0.070.06±0.040.12±0.070.17±0.13MethylVinylKetone0.04814.940.07±0.080.04±0.030.10±0.050.19±0.13MethylEthylKetone0.0196.40.64±1.250.32±0.730.97±0.471.84±2.332-Pentanone0.02611.090.04±0.060.03±0.030.06±0.040.11±0.11Pentanal*0.02214.480.04±0.030.03±0.020.05±0.040.07±0.053-Pentanone0.03114.350.02±0.020.02±0.010.03±0.020.04±0.03Hexanal*0.01512.390.19±0.140.16±0.130.22±0.170.28±0.17Benzene*0.0144.551.98±2.720.92±1.002.84±1.376.22±4.89Toluene*0.0164.011.91±2.350.97±1.392.98±1.435.29±3.19Ethylbenzene*0.0214.730.74±1.090.36±0.531.13±0.542.17±1.75m/p-Xylene*0.0244.321.99±2.831.02±1.503.01±1.485.56±4.57o-Xylene*0.0233.970.67±.000.32±0.511.03±0.481.94±1.63Styrene*0.0084.290.40±0.790.17±0.400.57±0.261.37±1.46Isopropylbenzene*0.0074.980.04±0.060.02±0.030.06±0.030.13±0.11n-Propylbenzene0.0095.010.04±0.040.03±0.020.06±0.040.10±0.06m-Ethyltoluene0.0155.350.11±0.140.06±0.070.18±0.090.30±0.21p-Ethyltoluene0.0145.20.07±0.070.04±0.030.10±0.050.16±0.111,3,5-Trimethylbenzene*0.023.30.06±0.060.03±0.030.08±0.050.14±0.10o-Ethyltoluene0.014.30.05±0.050.03±0.020.07±0.040.12±0.081,2,4-Trimethylbenzene*0.0294.650.12±0.150.06±0.070.19±0.090.33±0.231,2,3-Trimethylbenzene*0.0124.940.04±0.040.03±0.020.06±0.030.10±0.06Table S1 (continued). Minimum detection limit (MDL), and precision (relative standard deviation, RSD) of measured VOC compounds and their mixing ratios (mean concentration ± standard deviation) (ppbv) for different air quality levelsSpeciesMDL(ppbv)RSD(%)AVGCMPHPm-Diethylbenzene0.065.590.02±0.010.01±0.010.02±0.010.03±0.02p-Diethylbenzene0.0540.03±0.030.02±0.020.04±0.020.06±0.04Freon-1140.0277.950.02±0.030.01±0.000.02±0.020.02±0.05Chloromethane0.027.330.69±0.560.48±0.170.93±0.591.44±1.05Vinylchloride*0.0236.350.09±0.110.06±0.080.14±0.080.14±0.11Bromomethane*0.0247.010.01±0.010.01±0.000.01±0.010.01±0.01Chloroethane0.0215.510.04±0.050.02±0.020.05±0.030.09±0.10Freon-110.027.540.29±0.110.26±0.060.34±0.280.39±0.201,1-Dichloroethene0.0176.630.01±0.010.01±0.010.01±0.010.01±0.01Freon-1130.0267.790.07±0.010.07±0.010.08±0.070.07±0.01MethyleneChloride*0.0248.012.71±5.611.15±2.033.66±1.729.50±11.901,1-Dichloroethane0.0249.620.02±0.080.01±0.020.04±0.020.06±0.22cis-1,2-Dichloroethene0.0093.850.01±0.060.01±0.030.02±0.010.02±0.09Chloroform*0.0257.50.68±1.270.30±0.421.10±0.482.06±2.681,1,1-Trichloroethane*0.0269.610.00±0.010.00±0.010.00±0.000.00±0.01CarbonTetrachloride*0.028.60.09±0.010.08±0.010.10±0.090.11±0.211,2-Dichloroethane*0.03111.311.55±2.710.83±1.652.38±1.194.09±4.51Trichloroethylene*0.0144.970.05±0.050.03±0.040.07±0.040.07±0.041,2-Dichloropropane*0.03110.490.61±1.420.23±0.451.13±0.441.83±1.87Bromodichloromethane0.02810.120.00±0.000.00±0.000.00±0.000.00±0.00trans-1,3-Dichloropropene0.0177.760.01±0.120.00±0.000.02±0.010.02±0.21cis-1,3-Dichloropropene0.0179.590.01±0.030.01±0.020.02±0.010.04±0.051,1,2-Trichloroethane*0.03913.510.02±0.040.01±0.010.02±0.01005±0.09Tetrachloroethylene*0.0349.880.09±0.110.06±0.040.14±0.080.15±0.091,2-Dibromoethane*0.03112.010.00±0.000.00±0.000.00±0.000.00±0.00Chlorobenzene0.09110.470.02±0.040.01±0.010.03±0.010.05±0.09Bromoform*0.03612.410.00±0.000.00±0.000.00±0.000.00±0.001,1,2,2-Tertrachloroethane*0.08713.840.00±0.000.00±0.000.00±0.000.00±0.001,3-Dichlorobenzene0.02113.070.00±0.000.00±0.000.00±0.000.00±0.001,4-Dichlorobenzene*0.01713.190.01±0.010.01±0.010.02±0.010.02±0.01BenzylChloride0.02114.420.00±0.000.00±0.000.00±0.000.00±0.001,2-Dichlorobenzene0.057.850.00±0.000.00±0.000.00±0.000.00±0.00Acetonitrile*0.0399.180.32±0.840.16±0.550.44±0.221.02±1.43Note：* Potential VOC tracers used in PMF in this study and their corresponding sources can be found in Pjx1cmxzPjxyZWxhdGVkLXVybHM+PHVybD48c3R5bGUgZmFjZT0idW5kZXJsaW5lIiBmb250PSJk

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ADDIN EN.CITE.DATA (Guenther et al., 1993; Parrish et al., 1998; Jobson et al., 1999; Fujita, 2001; Chang et al., 2003; Jobson et al., 2004; Velasco et al., 2007; Liu et al., 2008a; Liu et al., 2008b; Yuan et al., 2009; Cai et al., 2010b; Russo et al., 2010; Suthawaree et al., 2010; Yuan et al., 2010b; Hecobian et al., 2011; Borbon et al., 2013; Gilman et al., 2013; Zhang et al., 2013; Ait-Helal et al., 2014; Chen et al., 2014; Huang et al., 2015; Zhang et al., 2015; Sun et al., 2016; Wang et al., 2016a; Wu et al., 2016; Zhang et al., 2016; Athanasopoulou et al., 2017; Liu et al., 2017a; Liu et al., 2017b; Saunier et al., 2017; Zhang et al., 2017; Darbyshire et al., 2018).C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively.Table S2. Target VOCs and associated toxicity values of health risk assessmentSpeciesRfC (mg m-3)IUR(?g m-3)BasisPoDmg m-3Sourcen-Hexane0.7-Peripheral neuropathy (decreased MCV at 12 weeks)BMCL (HEC): 215IRISCyclohexane6.0-Educed pup weights in the F1 and F2 generationsBMCL1sd (HEC): 1822?IRIS1,3-Butadiene2.0 x10-33.0 x10-5Ovarian atrophyMCL10 (HEC): 1.98IRISAcetaldehyde9 x10-32.2 x10-6Degeneration of olfactory epitheliumNOAEL (HEC): 8.7IRISAcrolein2.0 x10-5-Nasal lesionsLED05 (ADJ): 0.02IRISMTBE3.0-Increased absolute and relative liver and kidney weightsNOAEL (HEC): 2.59 x102IRISBenzene3.0 x10-27.8 x10-6Decreased lymphocyte countBMCL: 8.2IRISToluene5.0-Neurological effects in occupationally-exposed workersNOAEL (ADJ): 46 IRISEthylbenzene1.0-Developmental toxicityNOAEL (HEC): 4.34 x102 IRISm/p-Xylene0.1-Impaired motor coordination (decreased rotarod performance)NOAEL (HEC): 39IRISo-Xylene0.1-Impaired motor coordination (decreased rotarod performance)NOAEL (HEC): 39IRISStyrene1.0- CNS effectsNOAEL (HEC): 3.4 x 101IRISIsopropylbenzene0.4-Increased kidney weights in female rats and adrenal weights in male and female ratsNOAEL (HEC): 4.35 x 102IRIS1,3,5-Trimethylbenzene0.06-Decreased pain sensitivity in male Wistar ratsBMCL1SD (HEC): 18.15IRIS1,2,4-Trimethylbenzene0.06-Decreased pain sensitivity in male Wistar ratsBMCL1SD (HEC): 18.15IRIS1,2,3-Trimethylbenzene0.06-Decreased pain sensitivity in male Wistar ratsBMCL1SD (HEC): 18.15IRISvinylchloride0.18.8 x10-6IRISBromomethane5.0 x10-3-Degenerative and proliferative lesions of the olfactory epithelium of the nasal cavityLOAEL (HEC): 4.8 x 10-1IRISMethyleneChloride0.61.0 x10-8Hepatic effects (hepatic vacuolation)BMDL10 (HEC): 17.2IRIS1,2-Dichloroethane-2.6 x10-5Probable human carcinogen - based on sufficient evidence of carcinogenicity in animals-IRIS1,1,1-Trichloroethane5.0-Liver histopathologic changesNOAEL (HEC): 1553 (Chronic)IRISCarbonTetrachloride0.16.0 x10-6Fatty changes in the liverBMCL10 (HEC): 14.3IRISTrichloroethylene2.0 x10-34.1 x10-6Decreased thymus weight in mice and fetal heart malformations in rats-IRISChloroform-2.3 x10-5Moderate/marked fatty cyst formation in the liver and elevated SGPT-IRISBromoform-1.1 x10-6Hepatic lesions-IRIS1,2-Dichloropropane4.0 x10-3Hyperplasia of the nasal mucosaLOAEL (HEC): 1.3IRISTetrachloroethylene4.0 x10-22.6 x10-7Neurotoxicity in occupationally-exposed adults-IRIS1,1,2-Trichloroethane-1.6 x10-5Clinical serum chemistry -IRIS1,2-Dibromoethane9.0 x10-33.0 x10-4Nasal inflammationBMCL10 (HEC): 2.8IRIS1,4-Dichlorobenzene0.8-Increased liver weights in P1 malesNOAEL (HEC): 7.5 x 101IRISAcetonitrile0.06-MortalityNOAEL (HEC): 6.0 x 101IRISNote: RfC, the reference concentrations. IUR, the Inhalation Unit Risk. IRIS, U.S. EPA'S Integrated Risk Information System.Table S3. Comparisons of the top ten VOCs in Xianghe with other cities in China (ppbv)Top ten VOCsThis studyBJaBJa*BJbBJb*GZSHTJNJFS*Ethane9.605.0410.349.6813.463.66-10.656.9718.52Ethylene8.733.246.647.9111.372.99-5.945.6820.58Propane5.073.216.936.579.434.347.872.563.2912.98Acetylene4.242.787.827.5010.60---3.1223.38Methylene chloride2.712.464.44---1.28---n-Butane2.631.773.582.102.863.073.041.691.693.67Propylene2.020.741.192.553.541.321.600.772.456.84m/p-Xylene1.991.012.03--1.412.220.180.94-Benzene1.980.901.951.812.590.623.210.543.144.05Toluene1.911.372.841.672.384.598.220.272.1010.98BJ, Beijing, 2014/10/2-2014/10/14 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XdTwvQXV0aG9yPjxZZWFyPjIwMTY8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA (Wu et al., 2016); BJ, Beijing, 2015/12/15-2016/2/14 ADDIN EN.CITE <EndNote><Cite><Author>Liu</Author><Year>2017</Year><RecNum>1649</RecNum><DisplayText>(Liu et al., 2017a)</DisplayText><record><rec-number>1649</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523787243">1649</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Liu, Chengtang</author><author>Ma, Zhuobiao</author><author>Mu, Yujing</author><author>Liu, Junfeng</author><author>Zhang, Chenglong</author><author>Zhang, Yuanyuan</author><author>Liu, Pengfei</author><author>Zhang, Hongxing</author></authors></contributors><titles><title>The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China</title><secondary-title>Atmospheric Chemistry and Physics</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics</full-title><abbr-1>Atmos Chem Phys</abbr-1></periodical><pages><style face="normal" font="default" size="100%">10633</style><style face="normal" font="default" charset="134" size="100%">-10649</style></pages><number>17</number><dates><year>2017</year></dates><isbn>1680-7324</isbn><urls></urls><electronic-resource-num>10.5194/acp-17-10633-2017</electronic-resource-num></record></Cite></EndNote>(Liu et al., 2017a); GZ, Guangzhou, 2011/6-2012/5 ADDIN EN.CITE <EndNote><Cite><Author>Zou</Author><Year>2015</Year><RecNum>953</RecNum><DisplayText>(Zou et al., 2015)</DisplayText><record><rec-number>953</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1514422500">953</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zou, Y.</author><author>Deng, X. J.</author><author>Zhu, D.</author><author>Gong, D. C.</author><author>Wang, H.</author><author>Li, F.</author><author>Tan, H. B.</author><author>Deng, T.</author><author>Mai, B. R.</author><author>Liu, X. T.</author><author>Wang, B. G.</author></authors></contributors><titles><title>Characteristics of 1 year of observational data of VOCs, NOx and O3 at a suburban site in Guangzhou, China</title><secondary-title>Atmospheric Chemistry and Physics</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics</full-title><abbr-1>Atmos Chem Phys</abbr-1></periodical><pages>6625-6636</pages><volume>15</volume><number>12</number><section>6625</section><dates><year>2015</year></dates><isbn>1680-7324</isbn><urls></urls><electronic-resource-num>10.5194/acp-15-6625-2015</electronic-resource-num></record></Cite></EndNote>(Zou et al., 2015); SH, Shanghai, 2006-2008 ADDIN EN.CITE <EndNote><Cite><Author>Geng</Author><Year>2010</Year><RecNum>2270</RecNum><DisplayText>(Geng et al., 2010)</DisplayText><record><rec-number>2270</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1551319674">2270</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Geng, Fuhai</author><author>Cai, Changjie</author><author>Tie, Xuexi</author><author>Yu, Qiong</author><author>An, Junlin</author><author>Peng, Li</author><author>Zhou, GuangQiang</author><author>Xu, Jianming</author></authors></contributors><titles><title>Analysis of VOC emissions using PCA/APCS receptor model at city of Shanghai, China</title><secondary-title>Journal of Atmospheric Chemistry</secondary-title></titles><periodical><full-title>Journal of Atmospheric Chemistry</full-title><abbr-1>J Atmos Chem</abbr-1></periodical><pages>229-247</pages><volume>62</volume><number>3</number><dates><year>2010</year></dates><isbn>0167-7764&#xD;1573-0662</isbn><urls></urls><electronic-resource-num>10.1007/s10874-010-9150-5</electronic-resource-num></record></Cite></EndNote>(Geng et al., 2010); TJ, Tianjin, 2014/11-2015/10 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaXU8L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA (Liu et al., 2016a); NJ, Nanjing, 2011/3-2012/2 ADDIN EN.CITE <EndNote><Cite><Author>An</Author><Year>2014</Year><RecNum>2013</RecNum><DisplayText>(An et al., 2014)</DisplayText><record><rec-number>2013</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1528889337">2013</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>An, Junlin</author><author>Zhu, Bin</author><author>Wang, Honglei</author><author>Li, Yongyu</author><author>Lin, Xu</author><author>Yang, Hui</author></authors></contributors><titles><title>Characteristics and source apportionment of VOCs measured in an industrial area of Nanjing, Yangtze River Delta, China</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>206-214</pages><volume>97</volume><dates><year>2014</year></dates><isbn>13522310</isbn><urls></urls><electronic-resource-num>10.1016/j.atmosenv.2014.08.021</electronic-resource-num></record></Cite></EndNote>(An et al., 2014); FS, Foshan, 2008/12/1-2008/12/31 ADDIN EN.CITE <EndNote><Cite><Author>Guo</Author><Year>2011</Year><RecNum>2139</RecNum><DisplayText>(Guo et al., 2011)</DisplayText><record><rec-number>2139</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1534943890">2139</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Guo, Songjun</author><author>Tan, Jihua</author><author>Ma, Yongliang</author><author>Yang, Fumo</author><author>Yu, Yongchan</author><author>Wang, Jiewen</author></authors></contributors><titles><title>Characteristics of atmospheric non-methane hydrocarbons during high PM10 episodes and normal days in Foshan, China</title><secondary-title>Atmospheric Research</secondary-title></titles><periodical><full-title>Atmospheric Research</full-title><abbr-1>Atmos Res</abbr-1></periodical><pages>701-710</pages><volume>101</volume><number>3</number><dates><year>2011</year></dates><isbn>01698095</isbn><urls></urls><electronic-resource-num>10.1016/j.atmosres.2011.04.022</electronic-resource-num></record></Cite></EndNote>(Guo et al., 2011).* pollution- data were not available in the relative reference.Table S4. Percentage of BS factors assigned to each base case factor with with 100 bootstraps and using a minimum correlation R-value of 0.6.Boot FactorSecondary and long-lived speciesSolvent utilizationIndustrial emissionsVehicle exhaustLiquefied petroleum gas & natural gasUnmappedBoot Factor 110000000Boot Factor 201000000Boot Factor 300100000Boot Factor 400010000Boot Factor 500001000Table S5. The bootstrap calculation with the variability in the concentration / percentage of TVOCs for the factors.FactorsBase ValueBS 5thBS 25thBS 50thBS 75thBS 95thSecondary and long-lived species6.29 /10.415.34/8.826.00/9.946.52/10.826.95/13.188.02/13.18Solvent utilization8.17/13.567.97/13.188.56/14.158.51/14.0910.10/20.4712.54/20.47Industrial emissions15.91/26.419.93/16.5211.88/19.7515.36/25.5416.34/29.4217.91/29.42Vehicle exhaust18.69/31.0215.11/25.0317.74/29.3319.25/31.9021.27/37.6722.73/37.67Liquefied petroleum gas & natural gas11.18/18.578.30/13.749.87/16.3810.89/18.0811.60/23.4514.24/23.45Table S6. Comparison of VOC source apportionment results with former studiesSampling siteSampling periodModelSourcesVehicle exhaustSolvent utilizationliquefied petroleum gas & natural gasIndustrial emissionsSecondary and long-lived speciesVegetation emissionGasoline evaporationFuel combustionCoal or biomassOtherThis study, suburban6th Nov 2017 to 29th Jan 2018PMF31.013.618.626.410.4Nanjing industrial area a15th May to 31th August 2013PMF34309225Beijing urban b18th–31th Oct 2014PMF3512822689Tianjin, urban cNov 2014 to Oct 2015PMF39.14.718.619.98.710.6Hong Kong, urban d Jan 2001 to Dec 2001PCA/APCS38.9-4832-36.411-19.45.2-90.1Chengdu, urban e27th Oct 2016 to 30th Sep 2017PMF4426219Lanzhou, downtown f1th Jan to 31th Dec 2013 PCA-MLR58.322.219.5Wuhan, urban gFeb 2013 to Oct 2014PMF27.8±0916.2±0.419.8±0.914.4±0.921.8±09Baoji, industrial zone h15th June to 21th June 2016PMF44.4819.7320.995.123.536.16Junggar Basin iSep 2014 to Aug 2015PMF62.6±3.043.8±0.5021.5±2.9910.9±1.571.3±0.69a ADDIN EN.CITE <EndNote><Cite><Author>Shao</Author><Year>2016</Year><RecNum>979</RecNum><DisplayText>(Shao et al., 2016)</DisplayText><record><rec-number>979</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1514430214">979</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Shao, Ping</author><author>An, Junlin</author><author>Xin, Jinyuan</author><author>Wu, Fangkun</author><author>Wang, Junxiu</author><author>Ji, Dongsheng</author><author>Wang, Yuesi</author></authors></contributors><titles><title>Source apportionment of VOCs and the contribution to photochemical ozone formation during summer in the typical industrial area in the Yangtze River Delta, China</title><secondary-title>Atmospheric Research</secondary-title></titles><periodical><full-title>Atmospheric Research</full-title><abbr-1>Atmos Res</abbr-1></periodical><pages>64-74</pages><volume>176-177</volume><section>64</section><dates><year>2016</year></dates><isbn>01698095</isbn><urls></urls><electronic-resource-num>10.1016/j.atmosres.2016.02.015</electronic-resource-num></record></Cite></EndNote>(Shao et al., 2016); b ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2015</Year><RecNum>1639</RecNum><DisplayText>(Li et al., 2015)</DisplayText><record><rec-number>1639</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523674065">1639</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Xie, S. D.</author><author>Zeng, L. M.</author><author>Li, L. Y.</author><author>Li, Y. Q.</author><author>Wu, R. 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K.</author></authors></contributors><titles><title>Source apportionment of ambient non-methane hydrocarbons in Hong Kong : Application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model</title><secondary-title>Environmental Pollution</secondary-title></titles><periodical><full-title>Environmental Pollution</full-title><abbr-1>Environ Pollut</abbr-1></periodical><pages>489-498</pages><volume>129</volume><number>3</number><dates><year>2004</year></dates><urls></urls></record></Cite></EndNote>(Guo et al., 2004); e ADDIN EN.CITE <EndNote><Cite><Author>Song</Author><Year>2018</Year><RecNum>2126</RecNum><DisplayText>(Song et al., 2018)</DisplayText><record><rec-number>2126</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1534512361">2126</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Song, Mengdi</author><author>Tan, Qinwen</author><author>Feng, Miao</author><author>Qu, Yu</author><author>Liu, Xingang</author><author>An, Junling</author><author>Zhang, Yuanhang</author></authors></contributors><titles><title>Source Apportionment and Secondary Transformation of Atmospheric Nonmethane Hydrocarbons in Chengdu, Southwest China</title><secondary-title>Journal of Geophysical Research: Atmospheres</secondary-title></titles><periodical><full-title>Journal of Geophysical Research: Atmospheres</full-title></periodical><pages>9741-9763</pages><volume>123</volume><number>17</number><dates><year>2018</year></dates><isbn>2169897X</isbn><urls></urls><electronic-resource-num>10.1029/2018jd028479</electronic-resource-num></record></Cite></EndNote>(Song et al., 2018); f ADDIN EN.CITE <EndNote><Cite><Author>Jia</Author><Year>2016</Year><RecNum>2294</RecNum><DisplayText>(Jia et al., 2016)</DisplayText><record><rec-number>2294</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1552454290">2294</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jia, Chenhui</author><author>Mao, Xiaoxuan</author><author>Tao, Huang</author><author>Liang, Xiaoxue</author><author>Wang, Yanan</author><author>Shen, Yanjie</author><author>Jiang, Wanyanhan</author><author>Wang, Huiqin</author><author>Bai, Zhilin</author><author>Ma, Minquan</author></authors></contributors><titles><title>Non-methane hydrocarbons (NMHCs) and their contribution to ozone formation potential in a petrochemical industrialized city, Northwest China</title><secondary-title>Atmospheric Research</secondary-title></titles><periodical><full-title>Atmospheric Research</full-title><abbr-1>Atmos Res</abbr-1></periodical><pages>225-236</pages><volume>169</volume><number>117</number><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(Jia et al., 2016); g ADDIN EN.CITE <EndNote><Cite><Author>Lyu</Author><Year>2016</Year><RecNum>2295</RecNum><DisplayText>(Lyu et al., 2016)</DisplayText><record><rec-number>2295</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1552455241">2295</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lyu, X. 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ADDIN EN.CITE.DATA (Xue et al., 2017); i ADDIN EN.CITE <EndNote><Cite><Author>Zheng</Author><Year>2017</Year><RecNum>1605</RecNum><DisplayText>(Zheng et al., 2017)</DisplayText><record><rec-number>1605</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1520686234">1605</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zheng, Huang</author><author>Kong, Shaofei</author><author>Xing, Xinli</author><author>Mao, Yao</author><author>Hu, Tianpeng</author><author>Ding, Yang</author><author>Li, Gang</author><author>Liu, Dantong</author><author>Li, Shuanglin</author><author>Qi, Shihua</author></authors></contributors><titles><title>One year monitoring of volatile organic compounds (VOCs) from an oil-gas station in northwest China</title><secondary-title>Atmospheric Chemistry and Physics Discussions</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics Discussions</full-title></periodical><pages>1-57</pages><volume>18</volume><number>7</number><dates><year>2017</year></dates><isbn>1680-7375</isbn><urls></urls><electronic-resource-num>10.5194/acp-2017-828</electronic-resource-num></record></Cite></EndNote>(Zheng et al., 2017)Table S7. Non-carcinogenic and carcinogenic risk assessment results of VOCs in ambient air in present study and of other cities in China.SpeciesRfC (mg m-3)IUR (?g m-3)This studyBeijingaGuangzhoubShanghaicNoncarcinogenic riskCarcinogenic riskNoncarcinogenic riskCarcinogenic riskNon-carcinogenic riskCarcinogenic riskn-Hexane0.7-4.2x10-4-3.4x10-4-Cyclohexane6-1.2x10-4-5.2x10-5-1,3-Butadiene2x10-33x10-54.4x10-22.6x10-68.1x10-34.9x10-7Acetaldehyde9x10-32.2x10-66.0x10-21.2x10-6--Acrolein2x10-5-4.9---MTBE3-2.9x10-5---Benzene3x10-27.8x10-63.5x10-28.3x10-63.6x10-28.4x10-60.230.032Toluene5-2.4x10-4-1.5x10-4-4x10-35.9x10-4Ethylbenzene1-5.4x10-4-4.2x10-4-4.3x10-30m/p-Xylene0.1-1.5x10-2-1.3x10-3-3.1x10-31.0x10-2o-Xylene0.1-4.9x10-3-1.0x10-2-2.4x10-33.6x10-3Styrene1-2.9x10-4-6.5x10-5-1x10-4Isopropylbenzene0.4-9.1x10-5-1.5x10-5-1,3,5-Trimethylbenzene0.06-7.9x10-4---1,2,4-Trimethylbenzene0.06-1.7x10-3---1,2,3-Trimethylbenzene0.06-5.7x10-4---vinylchloride0.18.8x10-33.7x10-43.3x10-7-4.7x10-6Bromomethane5x10-3-1.3x10-3-5.4x10-3MethyleneChloride0.61x10-82.6x10-31.6x10-85.4x10-33.2x10-101,2-Dichloroethane-2.6x10-5----1,1,1-Trichloroethane5-4.6x10-7-2.4x10-7-CarbonTetrachloride0.16x10-69.6x10-45.8x10-700Trichloroethylene2x10-34.1x10-62.0x10-21.7x10-70.211.7x10-6Chloroform-2.3x10-5-1.3x10-5--Bromoform-1.1x10-6-3.2x10-9--1,2-Dichloropropane4x10-3-0.12-0.08-Tetrachloroethylene0.042.6x10-72.5x10-22.6x10-80.021.9x10-71,1,2-Trichloroethane-1.6x10-5-2.3x10-7-1,2-Dibromoethane9x10-33.0x10-42.6x10-47.1x10-74.9x10-52.7x10-71,4-Dichlorobenzene0.8-1.4x10-5-5.9x10-5-Acetonitrile0.06-1.5x10-3---a ADDIN EN.CITE <EndNote><Cite><Author>Zhang</Author><Year>2017</Year><RecNum>2138</RecNum><DisplayText>(Zhang et al., 2017)</DisplayText><record><rec-number>2138</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1534942652">2138</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zhang, Hao</author><author>Li, Hong</author><author>Zhang, Qingzhu</author><author>Zhang, Yujie</author><author>Zhang, Weiqi</author><author>Wang, Xuezhong</author><author>Bi, Fang</author><author>Chai, Fahe</author><author>Gao, Jian</author><author>Meng, Lingshuo</author><author>Yang, Ting</author><author>Chen, Yizhen</author><author>Cheng, Qi</author><author>Xia, Fenmei</author></authors></contributors><titles><title>Atmospheric Volatile Organic Compounds in a Typical Urban Area of Beijing: Pollution Characterization, Health Risk Assessment and Source Apportionment</title><secondary-title>Atmosphere</secondary-title></titles><periodical><full-title>Atmosphere</full-title><abbr-1>Atmosphere-Basel</abbr-1></periodical><pages>61</pages><volume>8</volume><number>12</number><dates><year>2017</year></dates><isbn>2073-4433</isbn><urls></urls><electronic-resource-num>10.3390/atmos8030061</electronic-resource-num></record></Cite></EndNote>(Zhang et al., 2017); b ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2013</Year><RecNum>2275</RecNum><DisplayText>(Li et al., 2013)</DisplayText><record><rec-number>2275</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1551322702">2275</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, Lei</author><author>Li, Hong</author><author>Wang, Xue-Zhong</author><author>Zhang, Xin-Min</author><author>Wen, Chong</author></authors></contributors><titles><title>Pollution characteristics and health risk assessment of atmospheric VOCs in the downtown area of Guangzhou, China</title><secondary-title>Huan jing ke xue= Huanjing kexue</secondary-title><alt-title>Huan Jing Ke Xue</alt-title></titles><alt-periodical><full-title>Huan Jing Ke Xue</full-title></alt-periodical><pages>4558-4564</pages><volume>34</volume><number>12</number><keywords><keyword>Risk Assessment</keyword><keyword>Environmental Monitoring</keyword></keywords><dates><year>2013</year><pub-dates><date>2013/12//</date></pub-dates></dates><isbn>0250-3301</isbn><accession-num>24640890</accession-num><urls><related-urls><url><style face="underline" font="default" size="100%">;(Li et al., 2013); c ADDIN EN.CITE <EndNote><Cite><Author>Cai</Author><Year>2010</Year><RecNum>2284</RecNum><DisplayText>(Cai et al., 2010a)</DisplayText><record><rec-number>2284</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1551673651">2284</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cai, Changjie</author><author>Geng, Fuhai</author><author>Tie, Xuexi</author><author>Yu, Qiong</author><author>An, Junlin</author></authors></contributors><titles><title>Characteristics and source apportionment of VOCs measured in Shanghai, China</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>5005-5014</pages><volume>44</volume><number>38</number><dates><year>2010</year></dates><urls></urls></record></Cite></EndNote>(Cai et al., 2010a).Rfc: inhalation reference concentration, IURL: inhalation unit risk. The values of RfC and IUR are taken from IRIS, U.S. EPA (). –: no data.Table S8. Source specific carcinogenic and noncarcinogenic risk values and relative contribution to carcinogenic and noncarcinogenic inhalation risk of the sourcesSourcesHealth riskRelative contribution (%)Noncarcinogenic riskCarcinogenic riskNoncarcinogenic riskCarcinogenic riskSecondary and long-lived species 0.371.0x10-623.110.6Solvent utilization0.013.1x10-60.832.6Industrial emissions0.281.4x10-617.715.0Vehicle exhaust0.902.8x10-656.230.3Liquefied petroleum gas & natural gas0.031.1x10-62.211.5Table S9. The top 20 VOC species with the highest increasing ratios from clean days to heavy pollution daysSpecies (potential sources)Increasing ratios (%)SpeciesIncreasing ratios (%)Methylene chloride (solvent usage a)728.411-Hexene (NG& LPG k)582.711,3-Butadiene (vehicle exhaust b) 722.80Benzene (vehicle exhaust, industrial production, paint solvent usage l)572.39Styrene (petrochemical industry c)688.64Ethylene (gasoline vehicle exhaust, petrochemical industry m)568.421,2-Dichloropropane (industrial sources d)680.10Acetonitrile (biomass burning n)540.10cis-2-Butene (vehicle exhaust e)656.241-Butene (Industrial production, Industrial combustion, Vehicle exhaust o)537.87Propylene (diesel vehicle exhaust, petrochemical industry f)615.08Methacrolein (Secondary and long-lived species p)525.12trans-2-Pentene (coal combustion, vehicle exhaust g)609.09Isoprene (biogenic sources, road traffic q)520.26trans-1,3-Dichloropropene (industrial sources h)598.013-Methylpentane (Vehicle exhaust r)515.26Chloroform (industrial sources i)586.35cis-2-Pentene (vehicle exhaust s)514.49Chlorobenzene (industrial sources j)585.45Ethylbenzene (paint solvent usage, industrial production t)506.69a ADDIN EN.CITE <EndNote><Cite><Author>Ying</Author><Year>2008</Year><RecNum>2282</RecNum><DisplayText>(Ying et al., 2008; Li et al., 2015)</DisplayText><record><rec-number>2282</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1551617392">2282</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ying, Liu</author><author>Min, Shao</author><author>Fu, Linlin</author><author>Lu, Sihua</author><author>Zeng, Limin</author><author>Tang, Dagang</author></authors></contributors><titles><title>Source profiles of volatile organic compounds (VOCs) measured in China: Part I</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>6247-6260</pages><volume>42</volume><number>25</number><dates><year>2008</year></dates><urls></urls></record></Cite><Cite><Author>Li</Author><Year>2015</Year><RecNum>1639</RecNum><record><rec-number>1639</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523674065">1639</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Xie, S. D.</author><author>Zeng, L. M.</author><author>Li, L. Y.</author><author>Li, Y. Q.</author><author>Wu, R. R.</author></authors></contributors><titles><title>Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014</title><secondary-title>Atmospheric Chemistry and Physics Discussions</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics Discussions</full-title></periodical><pages>12453-12490</pages><volume>15</volume><number>8</number><dates><year>2015</year></dates><isbn>1680-7375</isbn><urls></urls><electronic-resource-num>10.5194/acpd-15-12453-2015</electronic-resource-num></record></Cite></EndNote>(Ying et al., 2008; Li et al., 2015); b PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaXU8L0F1dGhvcj48WWVhcj4yMDA4PC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA (Liu et al., 2008b); c ADDIN EN.CITE <EndNote><Cite><Author>Ying</Author><Year>2008</Year><RecNum>2282</RecNum><DisplayText>(Ying et al., 2008)</DisplayText><record><rec-number>2282</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1551617392">2282</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ying, Liu</author><author>Min, Shao</author><author>Fu, Linlin</author><author>Lu, Sihua</author><author>Zeng, Limin</author><author>Tang, Dagang</author></authors></contributors><titles><title>Source profiles of volatile organic compounds (VOCs) measured in China: Part I</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>6247-6260</pages><volume>42</volume><number>25</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>(Ying et al., 2008); d ADDIN EN.CITE <EndNote><Cite><Author>Sun</Author><Year>2016</Year><RecNum>935</RecNum><DisplayText>(Sun et al., 2016)</DisplayText><record><rec-number>935</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1514381501">935</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sun, Jie</author><author>Wu, Fangkun</author><author>Hu, Bo</author><author>Tang, Guiqian</author><author>Zhang, Junke</author><author>Wang, Yuesi</author></authors></contributors><titles><title>VOC characteristics, emissions and contributions to SOA formation during hazy episodes</title><secondary-title>Atmospheric Environment</secondary-title></titles><periodical><full-title>Atmospheric Environment</full-title><abbr-1>Atmos Environ</abbr-1></periodical><pages>560-570</pages><volume>141</volume><section>560</section><dates><year>2016</year></dates><isbn>13522310</isbn><urls></urls><electronic-resource-num>10.1016/j.atmosenv.2016.06.060</electronic-resource-num></record></Cite></EndNote>(Sun et al., 2016); e ADDIN EN.CITE <EndNote><Cite><Author>Liu</Author><Year>2017</Year><RecNum>1649</RecNum><DisplayText>(Liu et al., 2017a)</DisplayText><record><rec-number>1649</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523787243">1649</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Liu, Chengtang</author><author>Ma, Zhuobiao</author><author>Mu, Yujing</author><author>Liu, Junfeng</author><author>Zhang, Chenglong</author><author>Zhang, Yuanyuan</author><author>Liu, Pengfei</author><author>Zhang, Hongxing</author></authors></contributors><titles><title>The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China</title><secondary-title>Atmospheric Chemistry and Physics</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics</full-title><abbr-1>Atmos Chem Phys</abbr-1></periodical><pages><style face="normal" font="default" size="100%">10633</style><style face="normal" font="default" charset="134" size="100%">-10649</style></pages><number>17</number><dates><year>2017</year></dates><isbn>1680-7324</isbn><urls></urls><electronic-resource-num>10.5194/acp-17-10633-2017</electronic-resource-num></record></Cite></EndNote>(Liu et al., 2017a); f PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5BaXQtSGVsYWw8L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFy

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ADDIN EN.CITE.DATA (Li et al., 2015; Liu et al., 2017a); h ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2015</Year><RecNum>1639</RecNum><DisplayText>(Li et al., 2015)</DisplayText><record><rec-number>1639</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523674065">1639</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Xie, S. D.</author><author>Zeng, L. M.</author><author>Li, L. Y.</author><author>Li, Y. Q.</author><author>Wu, R. R.</author></authors></contributors><titles><title>Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014</title><secondary-title>Atmospheric Chemistry and Physics Discussions</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics Discussions</full-title></periodical><pages>12453-12490</pages><volume>15</volume><number>8</number><dates><year>2015</year></dates><isbn>1680-7375</isbn><urls></urls><electronic-resource-num>10.5194/acpd-15-12453-2015</electronic-resource-num></record></Cite></EndNote>(Li et al., 2015); i ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2015</Year><RecNum>1639</RecNum><DisplayText>(Li et al., 2015)</DisplayText><record><rec-number>1639</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523674065">1639</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Xie, S. D.</author><author>Zeng, L. M.</author><author>Li, L. Y.</author><author>Li, Y. Q.</author><author>Wu, R. R.</author></authors></contributors><titles><title>Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014</title><secondary-title>Atmospheric Chemistry and Physics Discussions</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics Discussions</full-title></periodical><pages>12453-12490</pages><volume>15</volume><number>8</number><dates><year>2015</year></dates><isbn>1680-7375</isbn><urls></urls><electronic-resource-num>10.5194/acpd-15-12453-2015</electronic-resource-num></record></Cite></EndNote>(Li et al., 2015); j ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2015</Year><RecNum>1639</RecNum><DisplayText>(Li et al., 2015)</DisplayText><record><rec-number>1639</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1523674065">1639</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Xie, S. D.</author><author>Zeng, L. M.</author><author>Li, L. Y.</author><author>Li, Y. Q.</author><author>Wu, R. R.</author></authors></contributors><titles><title>Characterization of ambient volatile organic compounds and their sources in Beijing, before, during, and after Asia-Pacific Economic Cooperation China 2014</title><secondary-title>Atmospheric Chemistry and Physics Discussions</secondary-title></titles><periodical><full-title>Atmospheric Chemistry and Physics Discussions</full-title></periodical><pages>12453-12490</pages><volume>15</volume><number>8</number><dates><year>2015</year></dates><isbn>1680-7375</isbn><urls></urls><electronic-resource-num>10.5194/acpd-15-12453-2015</electronic-resource-num></record></Cite></EndNote>(Li et al., 2015); k PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5ZYW5nPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA (Chen et al., 2014; Li et al., 2015); q PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HdWVudGhlcjwvQXV0aG9yPjxZZWFyPjE5OTM8L1llYXI+

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ADDIN EN.CITE.DATA (Guenther et al., 1993; Pacifico et al., 2009; Ding et al., 2014; Saunier et al., 2017); r ADDIN EN.CITE <EndNote><Cite><Author>Chan</Author><Year>2006</Year><RecNum>2369</RecNum><DisplayText>(Chan et al., 2006)</DisplayText><record><rec-number>2369</rec-number><foreign-keys><key app="EN" db-id="waaezvfsirrx2iepa0gpftrn29wtxrestv2r" timestamp="1554283867">2369</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chan, Lo‐Yin</author><author>Chu, Kam‐Wah</author><author>Zou, Shi‐Chun</author><author>Chan, Chuen‐Yu</author><author>Wang, Xin‐Ming</author><author>Barletta, Barbara</author><author>Blake, Donald R</author><author>Guo, Hui</author><author>Tsai, Wai‐Yan</author></authors></contributors><titles><title>Characteristics of nonmethane hydrocarbons (NMHCs) in industrial, industrial‐urban, and industrial‐suburban atmospheres of the Pearl River Delta (PRD) region of south China</title><secondary-title>Journal of Geophysical Research Atmospheres</secondary-title></titles><periodical><full-title>Journal of Geophysical Research Atmospheres</full-title></periodical><volume>111</volume><number>D11</number><dates><year>2006</year></dates><urls></urls></record></Cite></EndNote>(Chan et al., 2006); s PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaXU8L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA (Ait-Helal et al., 2014; Chen et al., 2014).Table S10. The bootstrap calculation with the variability in the concentration of TVOCs for the factors at different pollution levels.CMPHPFactor1Factor2Factor3Factor4Factor5Factor1Factor2Factor3Factor4Factor5Factor1Factor2Factor3Factor4Factor5base value4.844.8410.418.605.7717.7320.6716.3530.3129.2122.7144.5944.8059.0235.00BS 5th3.733.957.357.534.6611.3016.669.9921.3613.5518.4632.1838.2721.2124.07BS 25th4.374.468.858.575.4513.6318.6612.7927.4019.3320.8041.4847.2030.6131.33BS 50th4.794.829.959.035.9116.7221.2015.7731.7421.9023.3647.1253.8940.5335.93BS 75th5.495.1510.689.556.3417.8524.3123.0636.5626.1027.9551.0962.1557.2543.94BS 95th6.625.9212.0910.856.9423.7629.4142.0445.5736.9645.9265.5773.9569.8855.83Note: C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively. Factor1, Factor 2, Factor 3, Factor 4 and Factor 5 denote secondary and long-lived species, solvent utilization, industrial sources, vehicular exhaust and Liquefied petroleum gas & Natural gas, respectively.Table S11. The bootstrap calculation with the variability in the percentage of TVOCs for the factors at different pollution levels.CMPHPFactor1Factor2Factor3Factor4Factor5Factor1Factor2Factor3Factor4Factor5Factor1Factor2Factor3Factor4Factor5base value14.4014.0430.2324.9516.7415.5218.0814.3126.5325.5611.0221.6321.7428.6316.98BS 5th10.8011.3021.3021.5313.308.3413.077.8218.2311.068.8915.3717.969.7411.11BS 25th12.5512.9825.6524.6415.7411.4615.7810.8423.3416.1410.1219.6522.3814.1714.96BS 50th13.9713.9528.9826.0817.1513.8317.5713.7127.0318.5111.2622.6626.2518.9217.19BS 75th19.2617.1934.4731.0319.9020.9925.7433.9438.9030.0521.2030.5234.4133.2925.82BS 95th19.2617.1934.4731.0319.9020.9925.7433.9438.9030.0521.2030.5234.4133.2925.82Note: C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively. Factor1, Factor 2, Factor 3, Factor 4 and Factor 5 denote secondary and long-lived species, solvent utilization, industrial sources, vehicular exhaust and Liquefied petroleum gas & Natural gas, respectively.Table S12. Noncarcinogenic and carcinogenic risk assessment results of VOCs at different pollution levelsSpeciesHQ Carcinogenic riskCMPHPCMPHPn-Hexane2.1x10-46.9x10-41.1x10-3Cyclohexane6.7x10-52.1x10-42.9x10-41,3-Butadiene1.8x10-26.4x10-21.5x10-11.1x10-63.8x10-69.0x10-6Acetaldehyde4.1x10-28.1x10-21.3x10-18.2x10-71.6x10-62.6x10-6Acrolein2.56.91.4MTBE1.4x10-55.3x10-57.3x10-5Benzene1.7x10-25.1x10-21.1x10-13.9x10-61.2x10-52.6x10-5Toluene1.2x10-43.8x10-46.7x10-3Ethylbenzene2.6x10-48.3x10-41.6x10-3m/p-Xylene7.5x10-32.2x10-24.1x10-2o-Xylene2.4x10-37.5x10-31.4x10-2Styrene1.2x10-44.1x10-49.8x10-4Isopropylbenzene5.0x10-51.3x10-42.6x10-41,3,5-Trimethylbenzene4.7x10-41.1x10-32.0x10-31,2,4-Trimethylbenzene8.9x10-42.6x10-34.6x10-31,2,3-Trimethylbenzene3.5x10-48.2x10-41.4x10-3Acetonitrile7.5x10-42.1x10-34.8x10-3Bromomethane1.1x10-31.7x10-31.8x10-31,1,1-Trichloroethane4.3x10-75.2x10-75.5x10-71,2-Dichloropropane4.6x10-22.2x10-13.6x10-11,4-Dichlorobenzene1.1x10-52.2x10-51.9x10-5Vinylchloride2.6x10-46.2x10-35.8x10-42.3x10-75.5x10-75.1x10-7Methylene chloride1.1x10-33.6x10-39.3x10-36.7x10-92.1x10-85.6x10-8Carbon tetrachloride8.9x10-41.1x10-31.1x10-35.3x10-76.5x10-76.8x10-7Trichloroethylene1.5x10-23.2x10-23.2x10-21.2x10-72.6x10-72.7x10-7Tetrachloroethylene1.7x10-24.1x10-24.2x10-21.8x10-84.2x10-84.3x10-81,2-Dibromoethane2.5x10-42.7x10-43.1x10-46.7x10-77.3x10-78.5x10-71,1,2-Trichloroethane1.2x10-73.3x10-76.9x10-7Chloroform5.7x10-62.1x10-53.9x10-5Bromoform3.0x10-93.6x10-93.7x10-91,2-Dichloroethane1.5x10-54.2x10-57. x10-5HI/ cumulative cancer risks2.77.415.12.8x10-58.3x10-51.5x10-4Note: C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively.Table S13. Source specific noncarcinogenic risk values at different pollution levelsSource factorsCMPHPSecondary and long-lived species 0.290.631.79Solvent utilization0.070.130.15Industrial emissions0.200.061.78Vehicle exhaust0.391.670.46Liquefied petroleum gas & natural gas0.000.430.99Note: C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively.Table S14. Source specific carcinogenic risk values at different pollution levelsSource factorsCMPHPSecondary and long-lived species 6.3x10-74.3x10-65.0x10-6Solvent utilization1.5x10-67.9x10-61.4x10-6Industrial emissions9.5x10-72.3x10-65.3x10-6Vehicle exhaust1.3x10-64.1x10-67.4x10-6Liquefied petroleum gas & natural gas4.1x10-72.6x10-64.9x10-6Note: C, MP, and HP represent clean days (PM2.5 <75 ?g m-3), moderate pollution days (75≤PM2.5 <150 ?g m-3), and heavy pollution days (PM2.5 ≥150 ?g m-3), respectively.References ADDIN EN.REFLIST Ait-Helal, W., Borbon, A., Sauvage, S., Gouw, J.A.d., Colomb, A., Gros, V., Freutel, F., Crippa, M., Afif, C., Baltensperger, U., Beekmann, M., Doussin, J.-F., Durand-Jolibois, R., Fronval, I., Grand, N., Leonardis, T., Lopez, M., Michoud, V., Miet, K., Perrier, S., Prév?t, A.S.H., Schneider, J., Siour, G., Zapf, P., Locoge, a.N., 2014. Volatile and intermediate volatility organic compounds in suburban Paris: variability, origin and importance for SOA formation. 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