10.1111/ina.12145
Online supporting information for the following article published in
Indoor Air
DOI: TO BE ADDED BY THE PRODUCTION EDITOR
Characterizations, relationship and potential sources of outdoor and indoor
particulate matter bound polycyclic aromatic hydrocarbons (PAHs) in a
community of Tianjin, Northern China
Bin Han (1), Zhipeng Bai (1), Yating Liu (2),Yan You (3), Jia Xu (2), Jian Zhou (4),
Jiefeng Zhang (5), Can Niu (3), Nan Zhang (2), Fei He (6), Xiao Ding (7)
(1) Chinese Research Academy of Environmental Sciences, 8#, Anwai Beiyuan
Chaoyang District , Beijing 100012, China
(2) Nankai University - College of Environmental Science and Engineering, Tianjin,
China
(3) Chinese Academy of Science - Research Center for Eco-Environmental Science,
Beijing, China
(4) Nanyang Technological University - Energy Research Institute, Singapore
(5) Nanyang Technological University - Division of Environmental and Water
Resources, School of Civil and Environmental Engineering, Singapore
(6) Hubei Provincial Meteorological Service Center, Wuhan, Hubei, China
(7) National University of Singapore - Department of Building, School of Design and
Environment, Singapore
Corresponding author: Dr Han Bin (nk_hanbin@126.com)
1. Introduction of the occupants’ recruitment
1.1 General characteristics of the study population
General characteristics of 80 participants are summarized in Table 2. There were 47
male and 33 female with a mean age of 71 years. Based on questionnaires, 13
participants were found to be smokers (16.2% of the total number).
Table S1 General characteristics of participants (n=80).
Characteristics
Sex
Non-heating period
Heating period
Male
47
Female
33
Smoking in the
Yes
13
13
sampling period
No
67
67
Cooking in the
Yes
43
52
sampling period
No
37
28
Cleaning in the
Yes
48
45
sampling period
No
32
35
Windows opened
Yes
80
25
No
0
55
1.2 Smoking
As shown in Table S1, 13 occupants smoked in this study. Table S2 gave the t test
result between smoking and non-smoking occupants.
Table S2 ΣPAHs personal exposure associated with specific activities (t test)
Non-heating period
Activity
ETS-exposed
Mean
SD
78.79
34.05
Non-ETS
73.95
37.50
Cooking
85.65
39.34
Non-cooking
70.37
35.11
Cleaning
72.99
29.45
Non-cleaning
81.38
44.89
Heating period
p
0.68
0.09
0.35
Mean
SD
320.15
111.08
296.93
157.82
240.04
163.14
232.06
154.25
258.04
153.71
196.21
163.31
p
0.37
0.88
0.24
1.3 Cooking
Most occupants cook, however, the kitchens in Chinese apartments are usually
located at the balcony, and the door is closed when cooking. There also are some
kitchen ventilators to discharge the cooking fume. This is different from the western
style kitchen, which is adjacent to the sitting room and there is no wall or something
else to separate them from each other. What’s more, the occupants are old, and they
are prone to cook meals by boiling or steaming, instead of the fierce fry cook. This
kind of cooking style emits less gaseous or particulate pollutants.
1.4 heating mode
The heating mode adopted by occupants is central heating in winter, which is also a
common heating mode in Chinese urban family. The central heating companies,
which are managed by the government, use heating boiler to supply hot water or
steam to each family by heating pipeline. If the indoor temperature is not high enough,
some families will use air conditioner or electric radiator for heating.
1.5 ventilation mode
There is no central ventilation in the building where occupants live, so the
ventilation mode they used is opening or closing windows and doors. Moreover, the
sampling campaign in non-heating period was conducted in August and September. In
this period, the weather is not too hot. In addition, these occupants are old, and they
prefer to living without air conditioning, because it will make them uncomfortable.
2. PAHs chemical analysis
2.1 Standard Solution
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 S3.
Table. S3 PAHs concentration in standard solution grads (μg/ml)
PAHs 1#
PAHs 2#
PAHs 3#
PAHs 4#
PAHs 5#
1/100
1/200
1/385
1/500
1/1250
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
PAHs
Original solution
Flu
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 S4.
Table S4 Summary of the PAH analysis and Quality Control/Quality Assurance
PAH
Number
of rings
Ion
monitored
Surrogate
Standard
Internal
Standard
Method
Detection
Limit
(ng/ml)
FLU
PHE
PYR
BaA
BbF
CHR
BkF
BeP
BaP
IND
BghiP
COR
3
3
4
4
4
4
5
5
5
6
6
7
166
178
202
216
216
228
252
252
252
276
276
300
D10-Fluorene
D10-Fluoranthene
D10-Fluoranthene
D10-Fluoranthene
D10-Fluoranthene
D10-Fluoranthene
D12-Benzo(a)pyrene
D12-Benzo(a)pyrene
D12-Benzo(a)pyrene
D12-Benzo(a)pyrene
D12-Benzo(a)pyrene
D12-Benzo(a)pyrene
D10-Acenaphthene
D10-Phenanthrene
D12-Chrysene
D12-Chrysene
D10-Acenaphthene
D12-Chrysene
D12-Perylene
D12-Perylene
D12-Perylene
D10-Acenaphthene
D10-Acenaphthene
D10-Acenaphthene
6
8
6
10
10
10
10
12
12
10
10
12
Recovery
%
RSD ,
%
percentage
below the
LODs, %
86.61
89.60
84.54
81.22
84.91
84.91
87.08
88.83
92.68
81.15
82.48
86.90
7.71
3.78
5.61
6.78
9.25
7.14
9.63
4.26
6.79
12.74
14.52
6.80
1.6%
2.6%
5.2%
2.1%
6.8%
5.1%
5.7%
6.3%
4.0%
7.2%
7.5%
6.0%
3. City development data
According to Tianjin Statistical Yearbook 2012 (2013), the city development data
of Tianjin, including air pollutants emission amount, number of civil motor vehicles,
floor Space of building under construction, final consumption of energy, gross
domestic product and registered population, are listed in Table S5.
Table. S5 The urban development data of Tianjin
Urban development data
2010
2011
2012
SO2
NOx
dust
Number of Civil Motor Vehicles
Owned (million)
235150
239736
71915
230900
358900
75923
224521
334225
84061
1.59
2.10
2.21
Floor Space of Building under
Construction (10 000 m2)
7564.29
10058.78
12484.91
Final Consumption of Energy
(10 000 tons of Standard Coal Equivalent)
6574.87
7346.13
7927.48
Gross Domestic Product (100 million yuan)
9224.46
11307.28
12893.88
Registered Population (10 000 persons)
984.85
996.44
993.20
Air pollutants Emission
data (ton)
4. Data analysis
4.1 Random component superposition (RCS) statistical model based on linear
regression
Ott et al. (2000) presented a random component superposition (RCS) model based
not on micro-environmental concentrations and activity patterns but on statistical
interrelationships among variables and field study measurements. Meng et al. (2005)
applied this model and assumed perfect instantaneous mixing and that factors affecting
indoor concentrations are either constant or change slowly throughout the monitoring
period. The steady-state indoor PM mass concentration can be described with a single
compartment mass balance model. Indoor PM concentrations are described as the sum
of PM generated outdoors (ambient contribution) and PM generated indoors
(nonambient contribution), as follows
Ci = FINF Ca + Cpig
(1)
Where Ci is the indoor PM mass concentration, Ca is the ambient (outdoor) PM mass
concentration, FINF is the dimensionless infiltration factor, Cpig is the concentration of
indoor-generated PM found indoors, and FINF is related to the air exchange rate (h-1),
particle loss rate (h-1) and penetration coefficient. (Meng et al., 2005)
In this study, the RSC model computes a constant infiltration factor, FINF, from the
linear regression of all measured outdoor particulate PAH concentrations and indoor
PAH concentrations (see eq 1). The product of FINF with each outdoor concentration, Ca,
provides an estimated mean, median, and standard deviation of the outdoor
contribution (Cai) to indoor PAHs. The accuracy of FINF obtained this way increases as
the number of measurements increases. This is because the slope (FINF) is easily
influenced by outliers. The standard deviation of the outdoor contribution to indoor
PAHs obtained by the RCS model is not affected by this limitation.
4.2 Microscopic mixture model based on robust regression analysis
This model used all measured PAH values and combined them to calculate the
infiltration factor home by home, rather than species to species. In the same home, all
concurrently measured PAHs experience the same air exchange behavior. This method
assumed that indoor and outdoor PAH concentrations were independent. Meng et al.
(2005) reported that the PM infiltration factor for one home during the sampling period
could be estimated by regressing the indoor species concentrations on the outdoor
species concentrations measured concurrently in that home using a robust regression.
Robust regression is a regression analysis designed to avoid some limitations of
traditional parametric and non-parametric regression methods. Normal regression
analysis aims to identify the relationship between one or more independent
variables and a dependent variable. However, certain widely employed regression
methods, such as ordinary least squares, have favorable properties if their underlying
assumptions are true but can give misleading results if those assumptions are not true;
therefore, ordinary least squares is not robust when there are violations of its
assumptions. Robust regression methods are designed to not be overly affected by
violations of assumptions in the underlying data-generating process.
Reference
Meng, Q.Y., Turpin, B.J., Polidori, A., Lee, J.H., Weisel, C., Morandi, M., Colome, S.,
Stock, T., Winer, A. and Zhang, J.F. (2005) PM2.5 of ambient origin: Estimates and
exposure errors relevant to PM epidemiology. Environmental Science & Technology,
39, 5105-5112.
Ott, W., Wallace, L. and Mage, D. (2000) Predicting particulate (PM10) personal
exposure distributions using a random component superposition statistical model.
Journal of the Air & Waste Management Association, 50, 1390-1406.
Tianjin Municipal Bureau of Statistics. (2013) Tianjin Statistical Yearbook. China
Statistics Press, Beijing.