Energy and Buildings 41 (2009) 139–145
Contents lists available at ScienceDirect
Energy and Buildings
journal homepage: www.elsevier.com/locate/enbuild
A comparative analysis of urban and rural residential thermal comfort under
natural ventilation environment
Jie Han a, Wei Yang a, Jin Zhou a, Guoqiang Zhang a,*, Quan Zhang a, Demetrios J. Moschandreas b
a
b
Key Lab of Building Safety and Energy Efficiency, Ministry of Education, China, College of Civil Engineering, Hunan University, Changsha 410082, China
Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL, USA
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 25 May 2008
Received in revised form 30 July 2008
Accepted 2 August 2008
The paper presents a field study of occupants’ thermal comfort and residential thermal environment
conducted in an urban and a rural area in Hunan province, which is located in central southern China. The
study was performed during the cold winter 2006. Twenty-eight naturally ventilated urban residences
and 30 also naturally ventilated rural residences were investigated. A comparative analysis was
performed on results from urban and rural residences. The mean thermal sensation vote of rural
residences is approximately 0.4 higher than that of urban residences at the same operative temperature.
Thermal sensation votes calculated by Fanger’s PMV model did not agree with these obtained directly
from the questionnaire data. The neutral operative temperature of urban and rural residences is 14.0 and
11.5 8C, respectively. Percentage of acceptable votes of rural occupants is higher than that of urban
occupants at the same operative temperature. It suggests that rural occupants may have higher cold
tolerance than urban occupants for their physiological acclimatization, or have relative lower thermal
expectation than urban occupants because of few air-conditioners used in the rural area. The research
will be instrumental to researchers to formulate thermal standards for naturally ventilated buildings in
rural areas.
ß 2008 Elsevier B.V. All rights reserved.
Keywords:
Comparative analysis
Urban thermal comfort
Rural thermal comfort
Thermal sensation
1. Introduction
In recent years, many researchers studied residential thermal
environment and occupant comfort in urban residences of
different climatic zones [1–7]. Specifically, in China, such field
studies have been conducted in large cities, such as Beijing,
Harbin, Shanghai, Changsha, Xi’an, Hong Kong, Guangzhou and
Shenzhen [8–15]. However, China is a developing country, the
majority of Chinese live in rural areas rather than in urban areas.
In order to improve people’s living conditions in rural areas, the
Chinese government is now promoting new rural constructions
all over the country. This study seeks responses to the following
questions: what is the difference of occupants’ thermal comfort
between urban and rural residences, and should we provide the
same residential thermal environment for rural and urban
residences? It is well known that the lifestyle and economic
status of individuals in rural areas are different from those in
urban areas in China. For example, air-conditioning in rural areas
is less popular than in urban areas, which leads to occupants
* Corresponding author. Tel.: +86 731 882 5398; fax: +86 731 882 1005.
E-mail address: gqzhang@188.com (G. Zhang).
0378-7788/$ – see front matter ß 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.enbuild.2008.08.005
living in rural areas to expect less thermal comfort than those
living in urban areas. Fanger and Toftum [16] have introduced an
expectancy factor to explain the difference between non-airconditioned buildings in regions where the weather is warm only
during the summer where there are none or few buildings with
air-conditioning and regions with same or similar environmental
conditions but widespread use of air-conditioning. The expectancy factor is 0.7–0.8 with few air-conditioned buildings and
0.8–0.9 where there are many air-conditioned buildings. Moreover, thermal adaptive theory indicates that behavioral adjustment, past thermal history and expectation influence occupants’
thermal comfort [17].
It is evident that there are many differences between urban and
rural occupants’ comfort because of their different economic
condition, lifestyle, context factors, physiological acclimatization
and expectation. However, to the best of our knowledge, there is
little information available concerning the occupant’s comfort and
residential thermal environment in the rural area. Thus, two
purposes of this study were proposed, firstly this paper provides
the information of occupant’s thermal comfort and residential
thermal environment in the rural area through field studies,
secondly, a comparative and statistical analysis of urban and rural
occupants’ thermal comfort was conducted, which will be assistant
140
J. Han et al. / Energy and Buildings 41 (2009) 139–145
to recommend the sustainable thermal standards for buildings in
the rural area in China in the future.
2. Research methods
2.1. Building types, envelop characteristics and possible use of heating
Changsha, which represents conditions of typical hot summer
and cold winter zone of central southern China, is the capital of
Hunan province. Changsha was selected as the urban area site for
our study. Yuping, a village about 150 km away from Changsha,
was selected as the rural area site. The outdoor meteorological
conditions of the two areas are very similar: cold winters and hot
summers. The surveys were performed in both Changsha and
Yuping in the cold winter of 2006.
There are two common types of buildings in the rural area:
earth and brick-masonry buildings. The envelopes of earth
buildings are rammed earth walls made of yellow earth and sand.
The envelopes of brick-masonry buildings are cavity walls made of
brick and cement mortar. Reinforced-concrete buildings are
usually in the urban area. The envelopes of reinforced-concrete
buildings are filled wall which are made of aerated concrete block
or hollow brick. During the winter months, occupants of naturally
ventilated buildings without heating and cooling systems warm
themselves using basins in which charcoal is burnt, while urban
occupants warm themselves using electric heaters. In summer, the
majority of rural and urban occupants make use of oscillating fans
for cooling occupied areas of naturally ventilated buildings. All
residences both in the rural and the urban areas were investigated
by this study without installing central HVAC systems or any
mechanical ventilated systems. The size of rural residences is
usually larger than urban residences.
2.2. Subjects
A sample size of 103 subjects in 58 different residences of the
urban and rural areas participated in the study, occupants of 28
residences in urban Changsha and 30 in rural Yuping responded to
the winter surveys. The subjects participating in the survey were
composed of 51 females (49.5%) and 52 males (50.5%). The average
age of all respondents is 37.5 years old, ranging from 10 to 70. The
questionnaire covered several areas including demographics (sex,
gender, height, weight, age, etc.), years of living in their current
places, economic condition, educational level and measures for
improving residential thermal environment and advancing
occupants’ thermal comfort level. The questionnaire also includes
the traditional scales of thermal sensation and thermal preference, current clothing garment and metabolic activity checklists. The thermal sensation scale was the ASHRAE seven-point
scale of warmth ranging from cold (3) to hot (+3) with neutral (0)
in the middle. The thermal preference scale is a three-point scale
with the following options: (1) ‘‘want warmer’’, (2) ‘‘no change’’
and (3) ‘‘want cooler’’. Intrinsic clothing insulations were
estimated using the garment values published in ISO 7730.
Metabolic rates were assessed by a checklist of residential
activities databases published in ASHRAE Standard 55-1992.
Summary of the background characteristic of the subjects are
represented in Table 1.
Table 1
Summary of the subjects of residential occupants and personal thermal variables
Place
Urban area
Rural area
Sample size (male/female)
53 (21/32)
50 (31/19)
Mean age (year)
Mean, standard deviation
Minimum, maximum
34.8, 15.6
10, 67
40.1, 13.9
12, 70
Mean height (m)
Mean, standard deviation
Minimum, maximum
1.63, 8.6
1.35, 1.78
1.60, 0.09
1.25, 1.76
Mean weight (kg)
Mean, standard deviation
Minimum, maximum
59.3, 12.9
32, 90
54.3, 11.2
25, 76
Mean years living in local address
Mean, standard deviation
Minimum, maximum
7, 8.6
0.5, 50
30.6, 19.6
3, 70
Mean metabolism (met)
(58.2 W/m2 = 1 met)
Mean, standard deviation
Minimum, maximum
1.25, 0.43
1, 2
1.53, 0.5
1, 2
Mean clothing insulation
Mean, standard deviation
Minimum, maximum
2.0, 0.48
0.92, 2.86
2.15, 0.46
1.16, 2.89
conditioning and ventilation. Moreover, Swema 3000 incorporates powerful built-in calculation and documentation features
that vastly simplify field study. Three probes are equipped with
Swema 3000, Swa03 probe measures air velocity and temperature
with sensory accuracy of 0.3 m/s, 0.3 8C, respectively. Hygroclip
S probe measures relative humidity (RH) and temperature with
sensory accuracy of 1.6%, 0.3 8C, respectively. SWAT probe
measures globe temperature with sensor accuracy of 0.3 8C. The
test system is shown in Fig. 1.
Three points were measured in a room along the diagonal. The
field investigator measured thermal comfort variables (ambient air
temperature, relative humidity, velocity and globe temperature) at
the 0.1, 0.6 and 1.1 m heights while each respondent filled in the
questionnaire. Operative temperature (T0) was calculated as the
average of air temperature and mean radiant temperature.
2.3. Measurement of indoor climate
Swema 3000, multi-purpose test system for professional
measurements in indoor climate, was utilized in this study.
The multi-purpose Swema 3000 is ideal in a broad range of
applications, including indoor climate, thermal comfort, air-
Fig. 1. Swema 3000 test system.
J. Han et al. / Energy and Buildings 41 (2009) 139–145
141
2.4. Calculation of thermal comfort indices
Following the same approach as that used by previous studies
such as ASHRAE research project RP-884 [18], the environmental
and comfort indices were calculated with the Fountain model
of thermal sensation [19] and a thermal comfort index calculator
available on online (http://atmos.es.mq.edu.au/rdedear/pmv/),
using data from the survey questionnaire responses and thermal
variable measurements obtained by this field study were used
as input data to the model and calculator tool. Chair insulation was
not considered in the study for its minor influence in the winter.
3. Results and discussion
3.1. Outdoor and indoor climate environments
During the investigation period, the mean daily maximum
temperature for both the urban and rural areas was in the range
of 4.35–26.65 8C. Meanwhile, the mean daily maximum relative
humidity was in the range of 30.8–84.8%. Outdoor climatic
variables (air temperature, relative humidity and air velocity)
are measured by climatic station.
Table 2 provides statistical summaries of the indoor measurements and comfort indices for the residences of the urban area and
the rural areas in the 2006 winter season samples. Mean air and
radiant temperature (averaged across the three heights of 0.1, 0.6,
and 1.1 m) generally fell between 7 and 17 8C in urban residences
and 6 and 11 8C in rural residences. Mean RH values were in the
range of 30.77–76.78% urban residences and 68.04–85.57% in rural
residences. The mean air velocity (average over the three heights)
was 0.05 m/s in both places. Operative temperatures fell within the
6–17 8C range. The predicted mean vote (PMV) fell within 2.23 to
0.96 in urban residences and 1.78 to 0.74 in rural residences. The
PMV value is increased by 0.17 and 0.46 for each degree increase of
the operative temperature in the urban and rural residences, which
were obtained from a linear regression. Mean predicted percentage
of dissatisfied (PPD) fell in the range of 5–86.5% in urban residences
and 5–66.2% in rural residences.
Table 2
Statistical summary of indoor climatic and comfort indices
Place
Urban area
Rural area
Mean air temperature (8C)
Mean, standard deviation
Minimum, maximum
12.1, 2.04
7.67, 16.42
8.66, 1.22
6.19, 11.22
Mean radiant temperature (8C)
Mean, standard deviation
Minimum, maximum
11.94, 2.12
7.2, 16.83
8.48, 1.25
5.93, 11.45
Mean operative temperature (8C)
Mean, standard deviation
Minimum, maximum
12, 2.08
7.43, 16.63
8.57, 1.23
6.06, 11.24
Mean relative humidity (%)
Mean, standard deviation
Minimum, maximum
62.59, 9.81
30.77, 76.78
78.79, 4.34
68.04, 85.57
Mean air velocity area (m/s)
Mean, standard deviation
Minimum, maximum
0.06, 0.04
0.02, 0.23
0.05, 0.04
0, 0.17
Predicted mean vote (PMV)
Mean, standard deviation
Minimum, maximum
0.62, 0.81
2.23, 0.96
0.41, 0.82
1.78, 0.74
Predicted percent of dissatisfied
(PPD) (%)
Mean, standard deviation
Minimum, maximum
24.85, 22.41
5.2, 86.1
21.74, 18.1
5, 66.2
Fig. 2. Cumulative frequency distribution of the indoor and ambient temperature in
the rural area.
3.2. Cumulative frequency distribution of the indoor and ambient
temperature
Figs. 2 and 3 give the cumulative frequency distribution of the
indoor and ambient temperature in the rural and urban areas,
respectively. A comparative analysis of the cumulative frequency
distribution of the indoor and ambient temperature in both areas
was gained from the two figures. As Fig. 2 shows in rural
residences, the indoor temperature of the 50th percentile value of
the distribution is close to 8.86 8C, while for the ambient
temperature it is 7.86 8C for a difference of one centigrade
between the indoor and ambient temperatures. As Fig. 3 shows
in urban residences, the indoor temperature of the 50th percentile
value of the distribution is close to 11.17 8C, while for the ambient
temperature it is 10.83 8C for approximately no difference in
indoor and ambient temperatures at the 50th percentile value of
the distribution.
Fig. 3. Cumulative frequency distribution of the indoor and ambient temperature in
the urban area.
J. Han et al. / Energy and Buildings 41 (2009) 139–145
142
Fig. 4. Frequency of thermal sensation vote.
3.3. Thermal comfort and the questionnaire
Fig. 6. MTSV and PMV of the rural residence.
3.3.1. Thermal sensation—urban and rural residence
The frequency distribution of thermal sensation votes of the
urban and rural area is given in Fig. 4. The mean thermal sensation
votes of urban and rural area, range from 1 (slightly cool) to 0
(neutral), are 77.3 and 80%, respectively; most of thermal sensation
votes were equal to zero. Mean thermal sensation votes (MTSV)
obtained directly from questionnaires and PMV calculated by
Fanger’ PMV model [20] both the urban and rural areas have been
plotted against operative temperature in Figs. 5 and 6, respectively.
Fig. 5 shows the mean ASHRAE thermal sensation votes and PMV
for each half-degree operative temperature bin in the urban
residence. The regression line of MTSV fitted to the bin means was
highly significant (Prob < 0.0001, R2 = 0.82) and a standard error
on the regression coefficient was 0.23. The fitted MTSV and PMV
equations are:
MTSV ¼ 0:21T 0 2:93
PMV ¼ 0:17T 0 2:42
(1)
ðProb < 0:0001; R2 ¼ 0:89Þ
(2)
when the operative temperature under 12.8 8C. The linear slope of
MTSV is higher than that of PMV. Fig. 6 shows the mean ASHRAE
thermal sensation votes and PMV for each half-degree operative
temperature bin in the rural residence. The regression line of MTSV
fitted to the bin means was highly significant (Prob < 0.0001,
R2 = 0.86) and standard error on the regression coefficient was 0.22.
The fitted MTSV and PMV equations are:
MTSV ¼ 0:22T 0 2:53
PMV ¼ 0:46T 0 4:16
(3)
ðProb < 0:0001; R2 ¼ 0:94Þ
(4)
The figure indicates the linear slope of PMV is higher than that
of MTSV. MTSV is higher than PMV when the operative
temperature exceeded 6.8 8C. Whereas, MTSV is lower than PMV
when the operative temperature over 6.8 8C.
The regression relationships indicate that MTSV does not agree
with PMV. MTSV is higher than PMV when the operative
temperature exceeded 12.8 8C. Whereas, MTSV is lower than PMV
3.3.2. Thermal preference—urban and rural residence
Thermal preference is assessed directly according to the
answers of the question: ‘‘at the present time, would you prefer
to want warmer, no change or want cooler?’’ The frequency
distribution of thermal preference of the urban and rural residence
is given in Fig. 7. Among the urban occupants 67.9 and 32.1% voted
Fig. 5. MTSV and PMV of the urban residence.
Fig. 7. Frequency of thermal preference.
J. Han et al. / Energy and Buildings 41 (2009) 139–145
143
Fig. 8. Comparison of thermal acceptability between rural and urban residence.
for ‘‘want warmer’’ and ‘‘no change’’, respectively. Importantly, no
respondent voted for ‘‘want cooler’’. Among the rural occupants 62
and 36% voted for ‘‘want warmer’’ and ‘‘no change’’. It shows that
the percentage of the rural occupants voting ‘‘no change’’ is a little
more than the percentage of the urban occupants.
3.3.3. Thermal acceptability—urban and rural residences
Thermal acceptability is a quite controversial aspect of
thermal comfort because it can be defined with reference to
different scales. Four acceptability ratings based on different
scales are compared by Fato et al. [7]. Thermal acceptability for
this paper is obtained directly from the occupants who
answered ‘‘acceptable’’ to the questionnaire when asked
whether their thermal conditions were acceptable or not. The
percentage of actual unacceptable votes for each half-degree
operative temperature bin was plotted as a function of the
operative temperature. Fig. 8 shows the comparative results of
thermal acceptability between the rural and urban residences.
Results indicate that the percentage of unacceptable votes of
urban residences is higher than the rural residence at the same
operative temperature. The percentage of acceptable votes of
rural occupants is higher than the urban occupants at the same
operative temperature, which indicates the cold tolerance of the
rural occupants is higher than the urban occupants. In addition,
Fig. 10. Comparison of thermal sensation vote between the rural and urban
residence.
the frequency distribution of thermal acceptability of the urban
and rural residence is given in Fig. 9. Urban occupant vote is 68
and 32% for ‘‘acceptable’’ and ‘‘unacceptable’’, respectively. Rural
occupants vote is 78 and 22% for ‘‘acceptable’’ and ‘‘unacceptable’’, respectively.
3.4. Thermal sensation vote—urban residence vs. rural residences
MTSV for half-degree operative temperature bin in the urban
and rural residence was obtained directly from the questionnaires
according to ASHRAE seven-point scale. Fig. 10 compares mean
thermal sensation vote between the urban and rural area. The
figure indicates that the mean MTSV of the rural area is
approximately 0.4 higher than that of the urban area at the same
operative temperature. The neutrality is derived by solving Eqs. (1)
and (3) for a mean sensation of zero and the neutral operative
temperature for the urban and rural residences are 14.0 and
11.5 8C, respectively. The mean MTSV of the rural area is higher
than that of the urban area at the same operative temperature, and
the neutral operative temperature for rural residences is lower
than that of urban residences. One possible reason is that the
occupants in the rural area have stronger ability to tolerate cold
than those in the urban area because of their physiological
acclimatization or past thermal history. The other reason that
expectations of the rural area occupants is lower than that of the
urban area occupants considering their economic level. Statistical
data from questionnaires shows that the occupants’ incomes of the
rural area are far below that of the urban area, which leads to the
following conclusion: occupants of the rural area have relative low
expectation for their thermal comfort. This agrees with Fanger’s
study, who has introduced an expectancy factor in non-airconditioned buildings in warm climates [16].
3.5. Relationship between MTSV and PMV
Fig. 9. Frequency of thermal acceptability.
MTSV for half-degree operative temperature bin in the urban
and rural residences was obtained directly from the questionnaires. PMV for half-degree operative temperature bin in the urban
and rural residence was calculated by Fanger’s PMV model. The
relationship between MTSV and PMV is given in Fig. 11. The linear
regression line of MTSV and PMV was significant (Prob < 0.0001,
R2 = 0.75) and standard error on the regression coefficient was
J. Han et al. / Energy and Buildings 41 (2009) 139–145
144
Fig. 11. Relationship between MTSV and PMV.
0.32. The fitted equation was:
MTSV ¼ 0:78PMV 0:11
ð3 PMV 0Þ
(5)
An interesting phenomenon is observed from this equation. If
PMV equals to 1, the MTSV is 0.89. MTSV is higher than PMV,
which implies that Fanger’s PMV heat balance model with six key
parameters does not describe exactly occupants’ thermal sensation
under natural ventilated environment. Thermal adaptability such
as context factors, expectations, behavioral adjustments and so on
should be considered at the same time.
3.6. Personal environmental control and indoor air quality (IAQ)
The availability and appropriate use of controls in a building
allows occupants to modify the internal environment. In naturally
ventilated buildings, control over indoor temperature and
ventilation can be obtained by applying typical strategies/controls
such as opening windows, doors, ventilators, etc. However, for
extreme conditions in winter, hibachis, air-conditioners or electric
furnaces may be used to improve indoor thermal environment.
The data of the study show that 94% of rural residence occupants
make use of hibachi, and the rest use electric furnaces. However,
only 18% occupants in the urban area use hibachis, while others
use air-conditioners and electric furnaces. The different percent of
improving thermal environmental methods indicates that the
electric energy consumed by the urban area is much more than
that by the rural area. Indoor air quality problems between the
rural and urban residences are quite different. The indoor air
pollution source in rural areas is fuel burning but indoor air
pollution sources in urban areas include outdoor pollution from
industrial sources and traffic and building and decoration
materials. The main contaminants in urban areas are volatile
organic compounds (VOCs), formaldehyde, benzene, etc. But the
main pollutant in rural areas is particulate matter (PM), carbon
dioxide (CO2), carbonic oxide (CO), sulfur dioxide (SO2) and so on,
Wang et al. [21]. In the rural area, carbon dioxide (CO2)
concentrations of all rooms were lower than the national standard,
but PM and SO2 concentrations in many rooms are higher than
the standard because of the pollutant source of charcoal basins.
3.7. Possible improvements of the housing thermal environment
Because of increased use of air-conditioning consuming plenty
of electric energy and creating a serious peak electricity load
problem, people are seeking passive techniques to improve indoor
thermal environment and occupants’ thermal comfort. Santamouris et al. [22] have summarized the recent progress on passing
cooling technique in the residences of low-income households. As
a rule, passive cooling techniques include: (1) improving urban
microclimate techniques, (2) solar and heat protection techniques
and (3) heat dissipation techniques. Improving urban microclimate techniques may involve increasing green spaces in urban
environments, planting roof and wall for an ecologic way to
improve the indoor thermal environment, and using reflective or
cooling materials. Solar and heat protection may involve switchable glazing technology, using reflective coatings on the roofs, solar
control and shading of building surface, thermal insulation, etc.
Heat dissipation techniques include ground cooling system,
natural ventilation techniques, hybrid ventilation systems, night
ventilation, wind tower, using oscillating or ceiling fans, etc. In
China, the commonly used passive techniques in the summer are
increasing green area in the urban, planting roof and wall, thermal
insulation, natural ventilation, oscillating or ceiling fans, etc. In
winter, the commonly used passive techniques are wall insulations, using carbon basins and electric heaters, etc. In addition, airconditioners for cooling and heating are far more popular in the
urban area than in the rural area.
3.8. Comparisons with previous thermal comfort studies under
natural ventilated environment
In this study, the neutral operative temperature for the urban
residences is 14.0 8C in winter under natural ventilated
environment, which is 6.8 8C lower than that of the city of
Ilam in western Iran [6] and 6.7 8C lower than that of Bari in
southern Italy [7] in natural ventilated buildings during the
winter season. The difference may be contributed to clothing
insulation. The mean clothing insulation in this study is 2.0 clo,
which is higher than that of Iran (1.5 clo) and Italy (0.88 clo).
Thermal experience may be another reason. The indoor mean
operative temperature in this study is 12 8C, which is lower than
that of Iran (21.9 8C) and Italy (28 8C). It indicates that people
who used to experience lower temperature environment will
have lower neutral temperature, which is in accordance with
thermal adaptive theory. The linear regression coefficient of
MTSV in half-degree operative temperature is 0.22, which is
similar to that of Italy (0.28).
4. Conclusions
This study investigates thermal environment and comfort of
residences between the urban and rural area in Hunan province,
which is located in central southern China. A total of 103 occupants
from 53 residences from the urban area of Changsha and the rural
area of Yuping in Hunan province of China provided thermal
perception data in the cold winter in 2006.
Occupant thermal sensation responses in houses of the urban
area are different from those in residences of the rural area. Mean
thermal sensation vote of the rural area is 0.4 higher than that of
the urban area at the same operative temperature. Moreover, the
percentage of acceptable votes of rural occupants is higher than the
urban occupants. The difference is attributed to the occupants of
the rural area having relative lower expectation than the occupants
of the urban area. The other possible reason is that the occupants of
the rural area have stronger ability to tolerate cold than the
occupants of the urban area because of their physiological
acclimatization or past thermal history. Low expectation and
ability to tolerate cold temperatures by the rural subjects may
explain the observed differences. The mean PMV calculated by
J. Han et al. / Energy and Buildings 41 (2009) 139–145
Fanger’s model is higher than the MTSV obtained from the
questionnaire data. The difference is attributed to the different
combination of the objective and subjective conditions that have
not been considered in Fanger’s model.
The comparative results of this field survey can be helpful to
recommend the sustainable thermal standards for buildings of the
rural area in the future for central southern China.
Acknowledgements
The authors would like to thank Ms. Ping Wang, Wen Lin and
Mr. Peng Zhang for assisting field experiments. The work of this
paper is financially supported by the Natural Science Foundation of
China (No. 50478055) and the 11th Five Year National Science and
Technology Support Key Project of China (Nos. 2006BAJ02A05,
2006BAJ04B04)
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