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ORIGINAL PAPER
Exposure assessment, chemical characterization and sourceidentification of PM2.5 for school children and industrialdownwind residents in Guangzhou, China
Jia Wang • Senchao Lai • Zhaoyue Ke •
Yingyi Zhang • Shasha Yin • Junyu Zheng
Received: 18 October 2012 / Accepted: 1 August 2013
� Springer Science+Business Media Dordrecht 2013
Abstract To assess the exposure doses of PM2.5 and
to investigate its chemical components for the subpop-
ulation (i.e., school children and industrial downwind
residents), simultaneous sampling of indoor and outdoor
PM2.5 was conducted at an elementary school close to
traffic arteries and a residence located in the downwind
area of a steel plant in metropolitan Guangzhou in 2010.
Chemical components, i.e., organic carbon, elemental
carbon and 6 water soluble ions were analyzed in PM2.5.
A survey was also conducted to investigate the time-
activity patterns of the school children and the industrial
downwind residents. Indoor and outdoor PM2.5 were
63.2 ± 20.1 and (76.7 ± 35.8) lg/m3 at the school, and
118.8 ± 44.7 and 125.7 ± 57.1 lg/m3 in the commu-
nity, respectively. Indoor PM2.5 was found to be highly
related to outdoor sources, and stationary sources
were the significant contributors to PM2.5 at both sites.
The daily average doses of PM2.5 for the school children
at the school (Dchildren) and the industrial downwind
residents in the community (Dresidents) were (7.6 ± 1.9)
and (36.1 ± 36.8) lg/kg-day, respectively. The daily
average doses of particulate organic mass and SO42-
were the two most abundant chemical components in
PM2.5. PM2.5 exposure for the school children was
contributed by indoor and outdoor environments by 48.8
and 51.2 %, respectively; for the industrial downwind
residents, the contributions were 66.0 and 34.0 %,
respectively. Age and body weight were significantly
and negatively correlated with Dchildren, while age, body
weight and education level were significantly and
negatively correlated with Dresidents; gender was not a
significant factor at both cases.
Keywords PM2.5 � Exposure assessment �Time-activity pattern � School children �Industrial downwind residents
Introduction
Fine particulate matter (PM), also known as PM2.5, is
referred to the particulate less than 2.5 micrometers in
aerodynamic diameter and is suggested to have strong
impacts on human health. Epidemiologic studies have
shown that long-term exposure to PM2.5 has enormous
impacts on human health, including damages to
respiratory and cardiovascular systems (Miller et al.
2007; Pope et al. 2002; Pope and Dockery 2006).
An exposure assessment is the process of estimating
or measuring the magnitude, frequency and duration of
J. Wang � S. Lai � Z. Ke � Y. Zhang � S. Yin �J. Zheng (&)
College of Environment and Energy, South China
University of Technology, Guangzhou 510006,
People’s Republic of China
e-mail: [email protected]
J. Wang � S. Lai � Z. Ke � Y. Zhang � S. Yin � J. Zheng
Pearl River Delta Atmospheric Environment Research
Joint Laboratory, Guangzhou 510006,
People’s Republic of China
123
Environ Geochem Health
DOI 10.1007/s10653-013-9557-4
exposure to an agent, along with the number and
characteristics of the population exposed (Zartarian
et al. 2007). Not only ambient and microenvironmen-
tal concentrations but also time-activity patterns and
inhalation rates within the specified activity category
are needed to be considered (Burke et al. 2001).
Compared to concentrations, the potential population
exposure assessment has direct implications for human
health effects.
Recently, the pollution of PM2.5 and its environ-
mental and human health impacts have attracted the
attention of the public in China. However, only a few
studies have been conducted to estimate the personal
exposure to PM in several Chinese cities, such as
Beijing (Du et al. 2010), Chongqing (Wang et al.
2008), Anqing (Pan et al. 2001) and Hong Kong (Chau
et al. 2002). Guangzhou, located in the Pearl River
Delta (PRD) region, is the largest city in southern
China with a population of about 12.7 million
(Statistics Bureau of Guangzhou 2011). Although
PM2.5 pollution in Guangzhou has been studied for
decades, only a few studies have been done concerning
the estimation of the potential population exposure to
PM2.5 and its chemical components (He et al. 2011;
Lai et al. 2007; Wang et al. 2011; Xie et al. 2010). A
recent study has revealed that each 10 lg/m3 elevation
in atmospheric PM2.5 concentration in PRD region is
associated with approximately a 0.40, 0.53 and 1.43 %
increased risk of overall, cardiovascular and respira-
tory mortality, respectively (Xie et al. 2010). There-
fore, there are still further needs to assess the PM2.5
exposure and its health risk in this city.
Industries and motor vehicles are typically the
major sources of various atmospheric pollutants
including PM2.5 (Chen et al. 2012; Gummeneni et al.
2011; Zheng et al. 2009). There are a large number of
industrial factories located in both urban and suburban
areas of Guangzhou city. Among them, steel plant is
one of the industrial sources that has severe effects on
local and regional air quality. The processes of coal
combustion, thermoelectricity, ironmaking and steel-
making in steel plants can directly and/or indirectly
cause the increase in atmospheric PM2.5 within the
factory area as well as in the downwind areas
(Oravisjarvi et al. 2003). On the other hand, the
present population of motor vehicles in Guangzhou
has exceeded 2 million and still continues to increase
rapidly, which also has significant contribution to
ambient PM2.5 (Zheng et al. 2009).
Residential area and school are two important
microenvironments for air pollution exposure assess-
ment (Ozkaynak et al. 2008). Survey results showed
that most people spent more than 80 % of a day
indoors and about 60 % of a day at home; while
susceptible population such as the elderly and children
may stay longer at home (Chau et al. 2002; Klepeis
et al. 2001). Besides home, schools are the second
largest environments for children activities. There-
fore, the indoor air quality of home and school may
have considerable impact on human health.
Here, we present a study on assessment of potential
population exposure to PM2.5 and its components in an
elementary school and in an industrial downwind
community of a steel plant in Guangzhou. The main
objectives of this study are (1) to provide indoor and
outdoor concentrations and chemical components of
PM2.5 in the two microenvironments and to identify
the sources of PM2.5; (2) to collect the time-activity
patterns of school children at the school and industrial
downwind residents in the community; (3) to assess
the potential exposure doses of PM2.5 and chemical
components for the targeted subpopulations; (4) to
reveal the influencing factors on the potential exposure
doses of PM2.5 in both microenvironments.
Materials and methods
To assess the exposure to PM2.5 and its chemical
components for school children and industrial down-
wind residents, the concentrations of PM2.5 and its
chemical components in the related microenvironments,
personal time-activity patterns and inhalation rates of
the targeted subpopulations were collected in this study.
PM2.5 sampling
Indoor and outdoor PM2.5 were collected at an
elementary school from March 25 to April 1, 2010,
and in a residence in an industrial downwind commu-
nity of a steel plant from October 28 to November 6,
2010, in Guangzhou. The community is located about
2 km in the downwind area of the steel plant, and the
distance to the traffic artery is more than 1 km. The
locations of the sampling sites are shown in Fig. 1.
PM2.5 samplers, i.e., continuous pDR-1500 personal
dust monitor (Thermo, Franklin, USA) and filter-
based MiniVol TAS PM2.5 sampler (Airmetrics,
Environ Geochem Health
123
Eugene, USA) were used during the two sampling
campaigns. pDR-1500 is a online sampler collecting
PM2.5 mass concentrations in an interval of 10 s and
can also collect PM2.5 sample in a preloaded filter with
U = 37 mm. MiniVol TAS PM2.5 sampler is designed
to collect PM2.5 sample in a preloaded filter with
U = 47 mm. Quartz filter (Whatman, Maidstone, UK)
was used in this study.
Indoor and outdoor PM2.5 were measured simulta-
neously. The sampler was placed with a height of
1–1.5 m above the ground to simulate the typical
breathing height of people during the sampling. The
sampling duration was 24 h. Laboratory and field
blanks were used for quality assurance. In addition,
instrument inter-comparison has been conducted and
the data of continuous PM2.5 have been accordingly
adjusted.
Meteorological conditions, i.e., temperature, rela-
tive humidity (RH), atmospheric pressure, precipita-
tion, wind speed and wind direction were recorded by
a wireless weather station (Vantage Pro2 Plus, Davis,
USA). The daily average concentrations of PM2.5 at
Wan Qingsha monitoring station in Guangzhou in
2010 were collected from the PRD regional air quality
monitoring network.
Sample analysis
All the filters were pre-baked at 500 �C for 10 h.
Filters were conditioned for 24 h [(25 ± 1) �C and
(50 ± 5) % RH] and weighed using a microbalance
(MX5, Mettler-Toledo, Switzerland) with a sensitivity
of 1 lg. Anion (Cl-, NO3-, SO4
2-) and cation (Na?,
K?, NH4?) were measured by a ICS-1000 ion
chromatography system (Dionex, Sunnyvale, USA).
Elemental carbon (EC) and organic carbon (OC) were
determined by an off-line carbon analyzer (Sunset,
Tigard, USA) using NIOSH protocol (Cincinnati,
USA). Blank filters were used to correct the sample
determinations.
Time-activity pattern questionnaires
Questionnaire survey was conducted during the sam-
pling to obtain the time-activity patterns of the school
children and the industrial downwind residents. A total
of 220 pupils (from 6 to \13 years) were randomly
selected with a 7-day follow-up questionnaire survey
during school time. Personal information (such as
gender, age, height and weight), various activities and
time spent at the school were required to record by the
chosen pupils. A total of valid samples were 216.
During the industrial downwind campaign, 189
industrial downwind residents (from 5 months to
\85 years) in the community were randomly selected
for the survey. They were classified as infants (0 to
\6 years), children (6 to\13 years), adolescents (13
to \18 years), adults (18 to \60 years) and elderly
([60 years). The questionnaire comprised of a recall
diary of major activities and time spent in each major
Fig. 1 The locations of the sampling sites in Guangzhou
Environ Geochem Health
123
microenvironment (including indoors at home, out-
doors, enclosed transit and other indoors away from
home) on weekdays and weekends, personal informa-
tion (i.e., gender, age, height, weight, occupation,
education, smoking and respiratory disease) and
housing conditions. The questionnaires with total time
spent for 1 day between 23–25 h were regarded as
valid samples, and the total of valid samples were 187.
Calculation of daily average potential exposure
doses
The daily average potential exposure doses of PM2.5
and its chemical components were estimated using the
following equation (Exposure Factors Handbook, US
EPA 2011):
D ¼X
i¼1
Ci �X
j¼1
Tij � IRj ð1Þ
where D = daily average potential exposure doses
(lg/kg-day). Ci = daily average concentrations of
pollutants at microenvironment i, including indoor and
outdoor (lg/m3). Tij = daily average time spent by an
individual at microenvironment i with activity inten-
sity j, j including sleep, sedentary, light, moderate and
high activity intensity (min). IRj = average inhalation
rate per unit of body weight within the specified
activity intensity j, based on US EPA Exposure
Factors Handbook (2011) and Child-Specific Expo-
sure Factors Handbook (2008) (m3/min-kg).
The potential lifetime average daily dose (LADD)
of PM2.5 in the community was estimated using the
following equations (Exposure Factors Handbook, US
EPA 2011):
LADD ¼�
D�infants � 6 yearsþ D�children � 7 years
þ D�adolescents � 5 yearsþ D�adults � 42 years
þ D�elderly � 11 years�.
71 years ð2Þ
D� ¼X
i¼1
C�i �X
j¼1
Tij � IRj ð3Þ
C�i ¼ Ci � AFAC ð4ÞAFAC ¼ Cam=Csm ¼ 0:516 ð5Þ
where LADD = the potential lifetime average daily
dose of PM2.5 for the industrial downwind residents in
the community (lg/kg-day), and the lifetime was
assumed 71 years for average Chinese lifetime (Wang
et al. 2009). D* = estimated annual average potential
exposure doses of PM2.5 in the community in 2010
(lg/kg-day). C�i = estimated annual average concen-
trations of PM2.5 in the community in 2010 (lg/m3).
AFAC = adjustment factor of annual average PM2.5
concentration in the community, the value was 0.516.
Cam = annual average concentration of PM2.5 at Wan
Qingsha monitoring station in Guangzhou in 2010
(lg/m3). Csm = daily average concentration of PM2.5
at Wan Qingsha monitoring station in Guangzhou
during the sampling (from October 28 to November 6,
2010; lg/m3).
To compare the potential lifetime average daily
dose (LADD) of PM2.5 for the industrial downwind
residents in the community, the acceptable potential
lifetime average daily dose of PM2.5 on the basis of the
annual average PM2.5 limit from Chinese Ambient Air
Quality Standard was estimated using the following
equation (Exposure Factors Handbook, US EPA
2011):
LADDa ¼ Cs � IR� EDð Þ= BW� ATð Þ ð6Þ
where LADDa = the acceptable potential lifetime
average daily dose of PM2.5 (lg/kg-day). Cs = the
annual average PM2.5 limit from Chinese Ambient Air
Quality Standard (GB3095–2012, MEP of PRC 2012;
35 lg/m3). IR = average inhalation rate, which is
referenced from US EPA Exposure Factors Handbook
(2011; 16 m3/day). ED = exposure duration, which is
assumed to the lifetime (days). BW = body weight
(kg), which is assumed to 58.6 kg for Chinese (Wang
et al. 2009). AT = lifetime (days).
Results and discussion
PM2.5 and its chemical components
Descriptive statistics are summarized in Table 1 for
both indoor and outdoor concentrations of PM2.5, ion
and carbonaceous components. The daily average
concentrations of indoor and outdoor PM2.5 at the
school were 63.2 ± 20.1 and 76.7 ± 35.8 lg/m3,
respectively. PM2.5 at the school was dominated by
OC (12.9 % indoor and 11.2 % outdoor) and SO42-
(6.9 % indoor and 19.0 % outdoor). According to
Russell (2003), the amount of particulate organic mass
Environ Geochem Health
123
(OM) could be estimated by multiplying the amount of
OC by a correction factor of 1.4. Thus, the total
carbonaceous aerosol (TCA) was calculated by the
sum of OM and EC. TCA accounted for an averaged
19.4 and 19.2 % of indoor and outdoor PM2.5 at the
school, respectively. Secondary ions of NO3-, SO4
2-
and NH4?, mainly formed by atmospheric reactions,
contributed to 14.4 and 32.0 % of indoor and outdoor
PM2.5, respectively.
The wind directions were northwest by north
(17.1 ± 8.4)� with the wind speed of (6.6 ± 3.8) km/h
during the industrial downwind sampling. The selected
site was located in the downwind area and was supposed
to be mainly influenced by the emissions of the steel
plant. The daily average concentrations of indoor and
outdoor PM2.5 were (118.8 ± 44.7) and (125.7 ± 57.1)
lg/m3, respectively. OC (19.8 % indoor and 19.4 %
outdoor) and SO42- (25.0 % indoor and 24.9 % outdoor)
were the major components in PM2.5. TCA and
secondary ions contributed to 32.4 and 38.5 % of
indoor PM2.5, as well as 31.7 and 39.6 % of outdoor
PM2.5, respectively.
Recently, China launched its Ambient Air Quality
Standard for PM2.5 (GB3095–2012, MEP of PRC
2012) and the annual and 24-h limits were set to 35 and
75 lg/m3, respectively. Daily outdoor average PM2.5
observed at the both sites exceeded the daily PM2.5
standard.
Source identification
The ratios of indoor to outdoor concentrations (I/O
ratios) of PM may suggest the origin of indoor PM and
the association with outdoor sources (Chao et al.
1998). When in the absence of indoor sources, the I/O
ratios would be expected to be less than or equal to 1.
The value of correlation coefficient (r) between the
indoor and outdoor PM is used as an indicator of the
Table 1 Indoor and outdoor concentrations of PM2.5 and chemical components in an elementary school and in an industrial
downwind community
Sites Species Indoor (lg/m3) Outdoor (lg/m3)
Median AMb SDc Median AMb SDc
School (Na = 8) PM2.5 57.5 63.2 20.1 71.9 76.7 35.8
OC 7.3 8.1 3.6 8.5 8.6 4.0
OM 10.2 11.4 5.1 11.9 12.0 5.6
EC 0.7 0.9 0.6 2.8 2.7 1.2
NH4? 1.7 2.1 0.9 3.1 3.3 1.1
Na? 1.0 1.7 2.2 0.5 0.6 0.3
K? 1.1 1.8 2.1 1.1 1.1 0.3
Cl- 0.8 2.3 3.2 0.8 1.0 0.7
NO3- 1.5 2.6 2.1 6.0 6.7 4.9
SO42- 3.7 4.4 2.0 13.7 14.6 5.2
Community (Na = 10) PM2.5 117.4 118.8 44.7 120.4 125.7 57.1
OC 24.2 23.5 9.0 24.9 24.4 10.0
OM 33.9 32.9 12.7 34.9 34.1 14.0
EC 4.7 5.6 3.0 4.8 5.7 3.0
NH4? 6.5 7.4 2.9 7.3 7.7 3.0
Na? 2.9 3.0 2.9 3.7 3.3 2.2
K? 2.0 1.8 0.8 1.9 1.8 0.7
Cl- 0.7 0.9 0.5 1.5 1.5 1.0
NO3- 7.0 8.6 5.7 8.0 10.8 7.6
SO42- 24.8 29.7 14.3 28.0 31.2 13.8
a Number of samplesb Arithmetic meanc Standard deviation
Environ Geochem Health
123
degree of outdoor infiltration (Geller et al. 2002). In
this study, Pearson correlation analysis has been
performed on the correlation between indoor and
outdoor PM2.5 at the both sites and strong correlations
have been obtained (at the school: r = 0.803,
p = 0.016; in the community: r = 0.981, p \ 0.001).
Meanwhile, the I/O ratios of PM2.5 were close to 1.0 (at
the school: I/O = 1.0 ± 0.5; in the community:
I/O = 1.0 ± 0.2), suggesting that outdoor sources
contributed largely to indoor PM2.5 at the both sites.
Generally, indoor PM2.5 was influenced by the
indoor source, outdoor infiltration and air exchange
rate (Meng et al. 2005; Thatcher and Layton 1995;
Wainman et al. 2000). In this study, good ventilation
was found at the both sites, outdoor fine particles can
enter indoor environments by convective flow through
open windows and doors (Meng et al. 2005). General
people activities such as household cleaning and
walking had few influences on concentrations of PM2.5
(Jones et al. 2000). There were nearly no smoking and
cooking at the both sites, which were previously
suggested to be the major indoor sources of PM2.5.
EC is mainly from primary anthropogenic sources;
OC can be formed from both primary sources and
secondary organic aerosols (SOA) transformed in the
atmosphere from the low vapor pressure products by
atmospheric chemical reactions. OC/EC ratio can be
used to study emission sources and the formation of
SOA. OC/EC ratio exceeding 2 became an indicator
for the presence of SOA (Gray et al. 1986). The
average OC/EC ratios in the atmosphere at the school
and in the community were 3.2 and 4.4, respectively,
indicating the possible presence of SOA, which was
consistent with the previous studies on Guangzhou
atmosphere (Cao et al. 2003; 2004; Duan et al. 2007;
Hagler et al. 2006). Strong OC–EC correlations in the
atmosphere at the school (r = 0.865, p = 0.012) and
in the community (r = 0.924, p \ 0.000) were also
observed. The relationship between OC and EC
concentrations can reflect the origin of carbonaceous
particles. If major fractions of OC and EC are emitted
by a dominant primary source, the correlation between
the OC and EC concentrations should be high because
the relative rates of OC and EC emission would be
proportional to each other (Na et al. 2004). Therefore,
no matter at the school or in the community, OC and
EC were likely attributed to common sources though
the major sources could be different at the two sites
(Cao et al. 2003; 2004; Duan et al. 2007).
The mass ratio of NO3-/SO4
2- has been used as an
indicator of the relative importance of mobile versus
stationary sources of nitrogen and sulfur in the atmo-
sphere (Arimoto et al. 1996; Wang et al. 2006). Previous
studies showed that in China, the estimated ratios of
NOx to SOx from the emissions of gasoline and diesel
fuel burning were 13:1 and 8:1, respectively; while the
estimated ratio of NOx to SOx from coal burning was
1:2. Consequently, it is reasonable to use SO42- as an
indicator of stationary emission and NO3- of mobile
emission in China (Wang et al. 2006). The average
NO3-/SO4
2- ratios in the atmosphere at the school and
in the community were 0.4 and 0.3, respectively,
revealing that stationary sources were the significant
contributors to PM2.5 at the both sampling sites, which
was consistent with the previous results (Lai et al. 2007).
Overall, compared to those at the school, in the
community, the average ratio and correlation coeffi-
cient of OC/EC were larger, while the average NO3-/
SO42- ratio was smaller. This may be well explained
by the influence of the steel plant. The processes of
coal combustion, thermoelectricity, ironmaking and
steelmaking can emit a large amount of OC, SO2 and
SO3, which may directly or indirectly increase OC and
SO42- concentrations in the downwind sampling area
(Oravisjarvi et al. 2003).
However, it should be noted that the sampling
seasons can also cause the differences of ratios and
correlations between the school and the community.
According to Cao et al. (2003) and (2004), in the PRD
region, the correlation between OC and EC in summer
was lower than that in winter, while OC/EC ratio in
winter was similar to that in summer. On the other
hand, higher NO3-/SO4
2- ratio was found in winter
than in summer in the PRD region (Lai et al. 2007).
Time-activity patterns
Time-activity patterns for the school children and the
industrial downwind residents are summarized in
Table 2. On average, the school children spent
(467 ± 67) min/day at the school, accounting for
(32.5 ± 4.6) % of a day with 81.5 % indoors and
18.5 % outdoors. Indoor sedentary and indoor sleep
activities were the major activities for the school
children at the school, accounting for 62.9 and 16.1 %
of school time, respectively. There was no significant
difference in the time-activity patterns between the
female and the male children.
Environ Geochem Health
123
Ta
ble
2D
aily
aver
age
tim
esp
ent
un
der
dif
fere
nt
acti
vit
yp
atte
rns
by
the
sch
oo
lch
ild
ren
atth
esc
ho
ol
and
the
ind
ust
rial
do
wn
win
dre
sid
ents
inth
eco
mm
un
ity
Su
b-
po
pu
lati
on
Gro
up
sN
To
tal
tim
e
(min
)
Ind
oo
rac
tiv
ity
tim
e(m
in)
Ou
tdo
or
acti
vit
yti
me
(min
)
Sle
epS
eden
tary
Lig
ht
Mo
der
ate
Hig
hS
eden
tary
Lig
ht
Mo
der
ate
Hig
h
Sch
oo
l
chil
dre
n
All
21
64
67
75
29
41
11
04
35
38
9
Gen
der
Fem
ale
99
46
47
62
92
11
10
43
73
59
Mal
e1
17
47
17
52
95
12
10
43
44
01
0
Do
wn
win
dre
sid
ents
All
18
71
,16
15
38
26
19
55
51
39
87
74
11
Gen
der
Fem
ale
90
1,2
12
56
32
62
11
66
21
33
71
95
9
Mal
e9
71
,11
45
15
26
17
54
81
45
10
15
61
3
Ag
e
Infa
nts
36
1,3
32
72
41
77
51
15
91
74
45
99
1
Ch
ild
ren
39
1,0
66
56
02
29
71
57
49
27
88
23
Ad
ole
scen
ts3
19
11
44
02
35
57
17
12
97
13
92
2
Ad
ult
s5
51
,19
65
00
32
01
50
23
02
11
20
60
4
Eld
erly
26
1,2
91
44
73
36
12
01
70
88
18
29
48
Ed
uca
tio
n
Jun
ior
edu
cati
on
or
bel
ow
15
71
,19
95
53
26
71
01
64
24
08
08
21
1
Hig
hsc
ho
ol
edu
cati
on
25
94
14
37
21
76
27
04
11
28
40
9
Un
iver
sity
edu
cati
on
or
abo
ve
51
,05
95
67
31
16
70
00
92
17
5
Environ Geochem Health
123
The industrial downwind residents spent (1161 ±
259) min/day in the community, accounting for (80.6 ±
18.0) % of a day with 81.8 % indoors and 18.2 %
outdoors. Indoor sleep and indoor sedentary activities
were the major activities for the industrial downwind
residents in the community, accounting for 46.3 and
22.5 % of community time, respectively. The industrial
downwind residents were grouped with respect to their
genders, ages and education levels. As presented in
Table 2, the total time in the community was shown as:
(1) female[ male; (2) infants [ elderly [ adults[children[ adolescents; (3) junior education or below [university education or above [ high school education.
Exposure assessment
Potential exposure doses
As shown in Table 3, the daily average potential
exposure dose of PM2.5 for the school children at the
school (Dchildren) was (7.6 ± 1.9) lg/kg-day, which
was smaller than that for the industrial downwind
residents of the same age (6 to \13 years) in the
community [(34.2 ± 11.4) lg/kg-day]. Besides dif-
ferent time-activity patterns and pollutant sources
between both subpopulations, seasonal difference
might also be a major reason that there are higher
exposure dose levels at the industrial downwind
residents—the monthly averaged PM2.5 concentra-
tions from Wanqingsha monitoring station were 45.4
and 40.0 lg/m3 in March and April, as well as 58.7
and 67.9 lg/m3 in October and November, respec-
tively. As a result of relatively unified time activities at
the school and similar inhalation rates, small personal
differences of PM2.5 exposure have been found among
the school children at the school. As shown in Fig. 2a,
there were about 76 % surveyed pupils whose Dchildren
were in the range of 5–10 lg/kg-day.
The daily average potential exposure dose of PM2.5
for the industrial downwind residents in the commu-
nity (Dresidents) was (36.1 ± 36.8) lg/kg-day. When
Table 3 Daily average potential exposure doses (D) of PM2.5 and components for the school children at the school and the industrial
downwind residents in the community
Subpopulation D
(lg/kg-day)
PM2.5 OM EC SO42- NO3
- NH4? Na? K? Cl-
School children Median 7.7 1.3 0.2 1.0 0.5 0.3 0.1 0.2 0.2
AM 7.6 1.3 0.2 1.0 0.5 0.3 0.1 0.2 0.2
SD 1.9 0.3 0.1 0.3 0.1 0.1 0.0 0.0 0.0
Range 3.3–11.8 0.6–1.9 0.1–0.3 0.4–1.8 0.2–0.8 0.1–0.5 0.1–0.2 0.1–0.2 0.1–0.3
Downwind
residents
Median 21.6 5.9 1.0 5.3 1.6 1.3 0.5 0.3 0.2
AM 36.1 9.9 1.7 9.0 2.8 2.2 0.9 0.5 0.3
SD 36.8 10.1 1.7 9.2 2.8 0.2 0.9 0.5 0.3
Range 3.9–176.0 32.9–34.1 0.2–8.1 1.0–43.7 0.3–14.3 0.2–10.8 0.1–4.6 0.1–2.6 0.0–1.9
0.00 0.10 0.20
0.30 0.40
0.50 0.60 0.70
0.80 0.90
1.00
0.00 0.02 0.04
0.06 0.08
0.10 0.12 0.14
0.16 0.18
0.20
3 4 5 6 7 8 9 10 11 12
0.000.100.200.300.400.500.600.700.800.901.00
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.450 10 20 30 40 50 60 70 80 90 100
110
120
130
140
150
160
170
180
Frequency Cumulative Frequency
Frequency Cumulative Frequency(a)
(b)
Fig. 2 Histograms and cumulative frequency curves of the
daily average potential exposure doses of PM2.5 for the school
children at the school (a) and the industrial downwind residents
in the community (b)
Environ Geochem Health
123
more time was spent in the community during
weekends, higher dose [(43.3 ± 38.8) lg/kg-day]
was found compared to that [(33.2 ± 36.9) lg/kg-
day] during weekdays. As shown in Fig. 2b, there
were approximately half of the industrial downwind
residents whose Dresidents were ml.lore than 20 lg/kg-
day.
The potential lifetime average daily dose (LADD)
of PM2.5 for the industrial downwind residents in the
community was 12.8 lg/kg-day, which exceeded
about 32 % of the acceptable potential lifetime
average daily dose of PM2.5 (9.6 lg/kg-day). The
relatively high estimated LADD would be due to the
industrial downwind residents living in this high
polluted community throughout their lives and the
higher inhalation rates referenced from the American
health data than Chinese’s (Wang et al. 2009).
However, our results showed that the industrial
downwind residents living in the sampling community
for long term would be highly susceptible population
and their health affected by the emissions of the plant
should arouse more close attention.
The daily average potential exposure doses of the
components in PM2.5 for the school children at the
school and the industrial downwind residents in
the community are also presented in Table 3. The
daily average potential exposure doses of OM and
SO42- for the school children and the industrial
downwind residents were much higher than those of
other chemical components in PM2.5. This is the first
study to report the potential exposure doses of PM2.5
components, and the results can be used as references
for further study in epidemiology.
Various activities contributing to the potential
exposure doses
Previous studies have shown that schools and resi-
dences were identified to be the major microenviron-
ments for PM10 exposure (Chau et al. 2002). In this
study, we further studied the percent contributions of
various activities to Dchildren and Dresidents.
Figure 3a showed that the indoor sedentary activity
contributed the most largely to Dchildren. Besides the
indoor sedentary activity, outdoor moderate, light and
high activities were observed to be the major contrib-
utors to Dchildren. Indoor and outdoor environments
accounted for 48.8 and 51.2 % of Dchildren, respec-
tively. Reducing both indoor and outdoor exposure to
PM2.5 at the school was almost of equal importance to
control the whole exposure to PM2.5 for the school
children at the school.
As shown in Fig. 3b, indoor sleep, indoor moderate
and outdoor moderate activities were the major
contributors to Dresidents; and indoor and outdoor
environments accounted for 66.0 and 34.0 % of
Dresidents, respectively. Consequently, reducing indoor
exposure to PM2.5 in the community was even more
critical for the industrial downwind residents in the
community. Since reducing exposure time and chang-
ing time-activity patterns are difficult and impractical,
improving indoor air quality is essential to reduce the
potential exposure dose of PM2.5 for the industrial
downwind residents in the community. In view of the
previously mentioned fact that outdoor source was
mainly contributing to indoor PM2.5, thus, improving
outdoor air quality is also important to reduce the
Fig. 3 Various activities
contributing to the daily
average potential exposure
doses of PM2.5 for the
school children at the school
(a) and the industrial
downwind residents in the
community (b)
Environ Geochem Health
123
whole exposure to PM2.5 for the industrial downwind
residents in the community.
Demographic factors related to exposure
The possible influencing factors were analyzed by
Spearman’s rank correlation analysis (applied in
software SPSS 18.0) between age, body weight and
Dchildren. Age (rs = -0.650, p \ 0.01) and body
weight (rs = -0.435, p \ 0.01) were significantly
and negatively correlated with Dchildren. Table 4
showed that Dchildren for junior pupils were higher
than that for senior pupils because junior pupils spend
more time at the school and had higher average
inhalation rates per unit of body weight. Dchildren for
the male pupils were a little higher than that for the
female pupils. More time at the school and higher
average inhalation rates per unit of body weight were
the reasons. However, gender was not a significant
factor on Dchildren, because the statistical significance
of the differences of the means between the male and
female pupils by t test was not found (p [ 0.05).
The same method was used to analyze the influenc-
ing factors of Dresidents. Age, body weight and educa-
tion level (ternary variable; 1 = junior education or
below, 2 = high school education, 3 = university
education or above) were considered in the analysis.
Age (rs = -0.708, p \ 0.01), body weight (rs =
-0.759, p \ 0.01) and education level (rs = -0.453,
p \ 0.01) were significantly and negatively correlated
with Dresidents.
Table 4 showed that Dresidents followed the order of:
infants [ children [ elderly [ adolescents [ adults;
junior education or below [ high school educa-
tion [ university education or above. Dresidents for
the infants were the highest, which was due to the
Table 4 Daily average
potential exposure doses
of PM2.5 for different
subpopulation groups
Subpopulation Groups D (lg/kg-day)
Median AM SD
School children All 7.7 7.6 1.9
Gender
Female 7.4 7.4 1.9
Male 8.0 7.9 2.0
Age
6 years (first grade) 9.6 9.2 1.3
7 years (second grade) 9.1 8.8 1.6
8 years (third grade) 8.2 8.3 1.7
9 years (fourth grade) 8.0 7.8 1.2
10 years (fifth grade) 6.5 6.6 1.6
11 years (sixth grade) 4.9 5.0 1.1
Downwind residents All 21.6 36.1 36.8
Gender
Female 21.7 38.3 39.2
Male 18.7 34.1 34.5
Age
Infants 85.8 100.0 37.7
Children 33.5 34.2 11.4
Adolescents 15.2 16.1 6.3
Adults 13.5 15.2 6.2
Elderly 16.6 18.4 9.1
Education
Junior education or below 25.2 40.7 38.5
High school education 12.3 12.4 4.4
University education or above 11.4 11.1 1.8
Environ Geochem Health
123
longer time spent in the community and higher
average inhalation rate per unit of body weight.
Dresidents for the elderly were also high due to the
longer time spent in the community. It indicates that
time spent in the community is a key factor for the
exposure dose assessment in this typical community.
Nevertheless, it should be noticed that the exposure to
PM2.5 in other environments may not necessary be
lower for those industrial downwind residents who
stay less time in this community. They have the
chance to be exposed to PM2.5 in other microenvi-
ronments, such as working place, roadside and other
places which has not been considered in this study.
Education level has been suggested as another factor.
The industrial downwind residents in higher educa-
tion level spent less time in the community and,
therefore, had lower Dresidents. Dresidents for the female
were higher than that for the male. Although the
average inhalation rates per unit of body weight for
the male were higher than those for the female, the
female spent more time in the community, which may
lead to the fact that gender was not a significant factor
on Dresidents since the statistical significance of the
differences of the means between the male and female
residents by t test was not investigated (p [ 0.05).
Conclusions
Indoor and outdoor PM2.5 concentrations measured in
an elementary school and in an industrial downwind
residence in Guangzhou were at relatively high levels.
Observed PM2.5 was dominated by TCA and second-
ary ions (i.e., NO3-, SO4
2-, and NH4?). Strong
correlations between indoor and outdoor PM2.5 and the
average I/O ratios (close to 1.0) of PM2.5 indicated that
outdoor sources were the major contributors to indoor
PM2.5. The average NO3-/SO4
2- ratios suggested that
stationary sources were the significant contributors to
atmospheric fine particles. The daily average potential
exposure doses of OM and SO42- for both subpopu-
lations were much higher than those of other chemical
components in PM2.5. Based on the survey on time-
activity patterns, it also showed that indoor sedentary
and outdoor moderate activities contributed the most
largely to Dchildren, while indoor sleep, indoor moder-
ate and outdoor moderate activities were the major
contributors to Dresidents. Age and body weight were
significantly and negatively correlated with Dchildren,
while age, body weight and education level were
significantly and negatively correlated with Dresidents;
gender was not a significant factor on both of them.
Since at present no handbook on exposure factors
has been established for the Chinese population, the
physical parameters such as inhalation rates cited from
the American health data may have caused some
inaccuracies in our results. Therefore, the localized
handbook on exposure factors is necessary in China.
On the other hand, given that our exposure assessment
was based on a microenvironmental model, it is an
efficient indirect way of exposure assessment and it
can be applied to population exposure assessment in
large scale due to its low cost, high applicability and
good accuracy. Personal sampling, a direct approach
of assessing exposure with high cost and accuracy,
should also be used which can compare and calibrate
the results of exposure assessment using the microen-
vironmental model. Besides, in the future, more efforts
are expected to be made including: (1) to increase
exposure studies on more typical microenvironments
and subpopulations; (2) to establish direct quantitative
models from pollutant sources to potential exposure
doses even to health risks, which would be more
effective for policymaking; (3) to study regional
population exposure by combining geographic infor-
mation system (GIS) and air quality models.
Acknowledgments This work was supported by the 2008
New Century Excellent Scholar Support Plan by the Ministry of
Education of China (Project No. NCET-08-208).
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