Supplementary materials for “Assessing inhalation cancer risk of particulate matter bound polycyclic aromatic hydrocarbons (PAHs) for the elderly men in a retirement community of a mega city in North China”
1. Supporting information for Elderly Population by Age Groups in Tianjin
Table S1 illustrated the population of elderly in different age group in studying city in the year of 2012, according to Tianjin Statistical Yearbook. (Tianjin Statistical Bureau, 2013)
Table S1 Elderly Population by Age Groups in Tianjin (2012)
Grouped by Aged Population / Total / Urban Districts / Male / Urban Districts / Female / Urban DistrictsAge 60 and over / 187.74 / 88.30 / 90.29 / 41.77 / 97.45 / 46.53
Proportion in Total (%) / 18.90 / 22.56 / 18.11 / 21.44 / 19.71 / 23.67
Age 65 and over / 123.31 / 60.33 / 58.55 / 27.99 / 64.76 / 32.34
Proportion in Total (%) / 12.42 / 15.41 / 11.74 / 14.37 / 13.09 / 16.45
Age 80 and over / 25.31 / 14.73 / 11.42 / 6.62 / 13.89 / 8.11
Proportion in Total (%) / 2.55 / 3.76 / 2.29 / 3.40 / 2.81 / 4.13
2. Supporting information for Laboratory analysis for PAHs
2.1 Analysis Procedure
Before sampling, all blank filters were conditioned in our controlled balance lab (21±2 oC and 40±5% relative humidity) for at least 48 h. Collected filters were kept in sampling petri dishes and stored in the refrigerator with a temperature below -20°C before analysis.
The filter samples were extracted with dichloromethane by ultrasonication three times (20 min each time) in 15 mL of dichloromethane. After extraction, rotary evaporation was used to concentrate the extracts in a final volume of approximately 5 mL, and then, the extracts were concentrated under a gentle stream of 99.999% pure nitrogen gas flow, until 1 mL was obtained.
After each sample was transferred onto a separation column, a mixed solution of n-hexane and dichloromethane (1:1) was added to eliminate impurities. Finally, all of the purified extracts were reduced to 1.0 mL under a gentle stream of purified 99.999% pure nitrogen gas flow.
A gas chromatograph connected to a mass spectrometer (model 6890N-5973i, Agilent, USA) and an Agilent ChemStation data system was employed to determine these twelve PAH species. Separation of the compounds was carried out on a fused silica capillary column (30m×0.3mm×0.25μm). The injection temperature was 285°C. The oven temperature was 70°C for 4 min and was increased to 300°C at a rate of 10°C/min. This was held for 2 min and then increased to 340°C at 5°C/min, which was then held for 12 min. In this experiment, helium was used as the carrier gas. The MSsourcetemperature was 230 °C. Target PAHs were identified based on the retention time and qualitative ions of the standards in a selected ion monitoring mode that were then quantified by the internal standards (Table S3). The compounds monitored were as follows: phenanthrene (PHE), fluoranthene(FLU), pyrene (PYR), benz(a)anthracene (BaA), chrysene (CHR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), benzo(e)pyrene (BeP), indeno(l,2,3-cd)pyrene (IND), benzo(g,h,i)perylene (BghiP) and coronene (COR).
2.2 Standard Solution in PAHs chemical analysis
Standard reference materials used in this study was the mixture of EPA 610 (Polynuclear Aromatic Hydrocarbons Mix, Supelco #48743, containing 16 PAH individuals), BeP standard (Supelco #36962) and Cor Standard (Supelco #36963). The range of standard solution was shown in Table S2.
Table. S2 PAHs concentration in standard solution grads (μg/ml)
PAHs / Original solution / PAHs 1#1/100 / PAHs 2#
1/200 / PAHs 3#
1/385 / PAHs 4#
1/500 / PAHs 5#
1/1250
Flu / 200.2 / 2 / 1 / 0.52 / 0.4 / 0.32
Phe / 99.9 / 1 / 0.5 / 0.26 / 0.2 / 0.16
Pyr / 100.1 / 1 / 0.5 / 0.26 / 0.2 / 0.16
BaA / 100.1 / 1 / 0.5 / 0.26 / 0.2 / 0.16
Chr / 100.2 / 1 / 0.5 / 0.26 / 0.2 / 0.16
BbF / 200.2 / 2 / 1 / 0.52 / 0.4 / 0.32
BkF / 100.2 / 1 / 0.5 / 0.26 / 0.2 / 0.16
BeP / 102 / 1.02 / 0.51 / 0.27 / 0.2 / 0.16
BaP / 100 / 1 / 0.5 / 0.26 / 0.2 / 0.16
IND / 100 / 1 / 0.5 / 0.26 / 0.2 / 0.16
BghiP / 200.1 / 2 / 1 / 0.52 / 0.4 / 0.32
Cor / 100 / 1 / 0.5 / 0.26 / 0.2 / 0.16
The internal standards used in this study were: D12-Perylene for BeP, BaP and BkF, D12-Chrysene for BaA, CHR and PYR, D10-Acenaphthene for FLU, BghiP, IND, BbF and COR, D10-Phenanthrene for PHE. The detailed information was added in Table S3.
Table S3 Summary of the PAH analysis and Quality Control/Quality Assurance
PAH / Numberof rings / Ion
monitored / Surrogate
Standard / Internal
Standard / Method Detection Limit (ng/ml) / Recovery
% / RSD ,
% / percentage below the LODs, %
FLU / 3 / 166 / D10-Fluorene / D10-Acenaphthene / 6 / 86.61 / 7.71 / 1.6%
PHE / 3 / 178 / D10-Fluoranthene / D10-Phenanthrene / 8 / 89.60 / 3.78 / 2.6%
PYR / 4 / 202 / D10-Fluoranthene / D12-Chrysene / 6 / 84.54 / 5.61 / 5.2%
BaA / 4 / 216 / D10-Fluoranthene / D12-Chrysene / 10 / 81.22 / 6.78 / 2.1%
BbF / 4 / 216 / D10-Fluoranthene / D10-Acenaphthene / 10 / 84.91 / 9.25 / 6.8%
CHR / 4 / 228 / D10-Fluoranthene / D12-Chrysene / 10 / 84.91 / 7.14 / 5.1%
BkF / 5 / 252 / D12-Benzo(a)pyrene / D12-Perylene / 10 / 87.08 / 9.63 / 5.7%
BeP / 5 / 252 / D12-Benzo(a)pyrene / D12-Perylene / 12 / 88.83 / 4.26 / 6.3%
BaP / 5 / 252 / D12-Benzo(a)pyrene / D12-Perylene / 12 / 92.68 / 6.79 / 4.0%
IND / 6 / 276 / D12-Benzo(a)pyrene / D10-Acenaphthene / 10 / 81.15 / 12.74 / 7.2%
BghiP / 6 / 276 / D12-Benzo(a)pyrene / D10-Acenaphthene / 10 / 82.48 / 14.52 / 7.5%
COR / 7 / 300 / D12-Benzo(a)pyrene / D10-Acenaphthene / 12 / 86.90 / 6.80 / 6.0%
3. Model and software introduction
3.1 Monte Carlo model
Monte Carlo Analysis is a computer-based method of analysis developed in the 1940's that uses statistical sampling techniques in obtaining a probabilistic approximation to the solution of a mathematical equation or model (Firestone et al., 1997). This model relies on repeatedrandomsampling to obtain numerical results, and is often used in risk assessment.
Specifying distributions for all or most variables in a Monte Carlo analysis is useful for exploring and characterizing the full range of variability and uncertainty. We defined the possible values with a probability distribution, such as normal, lognormal, triangular, etc. The choice of input distribution should always be based on all information (both qualitative and quantitative) available for a parameter (Firestone et al., 1997). A simulation calculates multiple scenarios of a model by repeatedly sampling values from the probability distributions for the uncertain variables and using those values for the cell. The advantages of this model lie on that 1) The probability distributions of each variables within the model can be easily and flexibly used, without the need to approximate them; 2) Correlations and other relations and dependencies can be modeled without difficulty. Also this model has some disadvantages: 1) The number of random numbers that can be produced from a random number generating algorithm; 2) The time a computer needs to generate the iterations (and the time the risk analyst has). (EpiX Analytics LLC, 2007)
Sensitivity generally refers to the variation in output of a mathematical model with respect to changes in the values of the model’s input. A sensitivity analysis attempts to provide a ranking of the model’s input assumptions with respect to their contribution to model output variability or uncertainty.
3.2 Crystal Ball software(Oracle Corporation, 2008)
Oracle Crystal Ball is the leading spreadsheet-based software suite for predictive modeling, forecasting, simulation, and optimization. This software is built on existing Monte Carlo and predictive modeling tools, and could provide advanced optimization and calculation capabilities.
In risk assessment, the software works with spreadsheet models, specifically Microsoft® Excel spreadsheet models, helps people define those uncertain variables in a whole new way: by defining the cell with a range or a set of values. When running a Monte Carlo simulation with it, the software remembers the values for each forecast for each trial, and gives the statistics of the results (such as the mean forecast value) and the certainty of any outcome.
Reference
EpiX Analytics LLC (2007) ModelAssist for @RISK. http://www.epixanalytics.com/modelassist/AtRisk/Model_Assist.htm#Montecarlo/How_Monte_Carlo_Simulation_Works.htm.
Firestone M, Fenner-Crisp P, Barry T, Bennett D, Chang S, Callahan M, Burke A, Michaud J, Olsen M, Cirone P Guiding principles for Monte Carlo analysis. In, 1997. Risk Assessment Forum, US Environmental Protection Agency Washington, DC,
Oracle Corporation (2008) Risk Analysis Overview http://www.oracle.com/us/products/middleware/bus-int/crystalball/risk-analysis-overview-404902.pdf.
Tianjin Statistical Bureau (2013) Tianjin Statistical Yearbook (2013). China Statistics Press, Beijing