Climate Change, Air Quality and Urban Health in China
Xuan Yang, Hui Liu, Jess Dow, Haoyang Guo, Feng Jia, Arthur Frank, Charles Nathan Haas, and
Longjian Liu for Drexel-SARI LCHCT Research Group
Background
As the largest developing country in the world,
China is undergoing rapid expansion of
urbanization and industrialization. Outdoor air
pollution has become one of the top environmental
concerns in China. Apart from the emission of air
pollutants, changes in climate may also affect air
quality.
Conclusion
Figure 1: API Trends and All-cause mortality
 Significant variation in API across
120 cities of China was observed.
This variation was affected by
meteorological indicators.
 Heat index and precipitation
were significantly and negatively
associated with air pollution in
2012.
Objective
The study aimed to evaluate the impacts of the
changes in climate and social environmental factors
on air quality, and associations between air quality
index and health outcome using data from multicities and regions across the mainland China in
years of 2012 to 2013.
Methods
We applied panel data analysis, with adjustment for
time-varying related factors and location-specific
factors, to estimate associations between weekly
Air Pollution Index (API) and meteorological factors
for 120 cities in China. Multivariate linear regression
was used to examine associations between annual
mortality rates and API across 8 economic regions,
adjusting for meteorological and city-level
demographic factors.
(a)
(b)
Table 1. Statistics of variables measured for 120 cities in China
2012
Variables
N
Mean
SD
Daily Data
a
65.05
26.41
43680
API
Heat Index
70619
46.30
26.41
Precipitation (0.1 mm)
70598
21.98
82.96
Sunshine Hours (hrs)
69639
6.09
4.12
Pressure (kPa)
70628
92.38
10.42
Annual Data
Mortality (‰)
112
6.53
2.44
Waste water (10,000 cu.m)
120 12358.35 16729.16
120 3322.96 3446.62
GDP (100 million Yuan) b
GDP per capita(Yuan)
Population (10,000)
N
SD
Panel data analysis showed that in 2012, API was
negatively associated with heat index (estimated by
temperature and relative humidity) and precipitation.
This association remained significant after adjusting
for sunshine hours and pressure. The association
between API and Heat Index was positive but not
significant in 2013. However, precipitation still had
negative significant effect on API.
After adjusting for climate predictors and city-level
demographic factors, we can see that as A P I
increases by 10 points, the average all-cause
mortality rate would increase by 0.56‰ in 2012.
Association between API and mortality in 2013 is
positive but not significant.
Table 2. Regression coefficients of panel data analysis (2012)
OLS Models
1
2
3
4
Heat Index
-0.31 ***
-0.24 ***
-0.24 ***
-0.24 ***
Precipitation
-
-0.11
***
-0.11
***
-0.11
22793
75.99
41.81
Sunshinehours
-
-
0.04
0.18
70437
70169
70431
70439
47.66
21.05
6.46
92.37
25.40
85.54
4.04
10.38
Pressure
Location Effect
Time Effect
R-Square
0.13
0.17
0.17
0.25
0.17
107
120
120 56197.63 29178.59
120
564.95
425.16
2013
Mean
5.84
3611.14
1.42
3804.03
120 58405.30 28971.20
120
568.98
430.18
a
b
The annual average value of API in 2013 was
significantly higher than that in 2012 (75.99 vs.
65.05, p<0.0001). Southern coastal region of China
had the lowest annual average API (46.00±18.93
in 2012 and 49.94± 21.49 in 2013).
(c)
Figure 1 (a) shows a general higher API in 2013 than 2012. In both years, API decreased from Jan to July, and then increased from
July to December. (b) and (c) show the annual average API and all-cause mortality rate in China, 2013.
API: Air Pollution Index.
Results
 API is positively associated with
all-cause mortality.
GDP: Gross Domestic Product.
***
***
Table 3. Regression coefficients of panel data analysis (2013)
OLS Models
1
2
3
4
Heat Index
-0.56
-0.48 ***
-0.41 ***
-0.42 ***
Precipitation
-0.13 ***
-0.18 ***
-0.18 ***
Sunshinehours
-2.88 ***
-2.64 ***
Pressure
0.61 ***
Location Effect
Time Effect
R-Square
0.14
0.16
0.20
0.21
1
-0.07
*
Two-way Fixed Effect Models
2
3
4
-0.14 ***
-0.14 ***
-0.17
***
-0.07
***
-0.08
-
0.17
**
0.13
-
-
-
-3.36
Y
Y
Y
Y
Y
Y
Y
Y
0.50
0.52
0.51
0.51
-
-0.07
-
1
0.14
-
*
Two-way Fixed Effect Models
2
3
4
0.08
0.08
0.05
-0.05 ***
-0.07 ***
-0.08
-1.00 ***
-1.01
-3.31
Y
Y
Y
Y
Y
Y
Y
Y
0.77
0.78
0.78
0.78
***
***
***
***
***
* for p-value < 0.1, ** for p-value < 0.05, and *** for p-value < 0.01.
Table 1 shows variables measured for 120 cities in China.
Table 4. Correlation coefficients between mortality and related predictors
Moratlity(‰)
a
API
Heat Index
Precipitation (0.1 mm)
Sunshine Hours (hrs)
Pressure (kPa)
b
GDP (100 million Yuan)
GDP per capita(Yuan)
waste water (10,000 cu.m)
N
2012
Rate
Prob.
112
46
46
46
46
112
112
112
0.08
-0.09
0.18
-0.09
0.19
0.11
-0.03
0.25
0.39
0.53
0.23
0.56
0.20
0.24
0.78
0.01
N
2013
Rate
Prob.
83
44
44
44
44
83
83
-0.07
-0.05
0.00
0.04
0.19
0.08
0.12
0.52
0.74
0.99
0.81
0.23
0.48
0.28
a
API: Air Pollution Index.
GDP: Gross Domestic Product.
b
Table 4 shows the relationships of API, meteorological
factors, economic factor, waste water discharge with allcause mortality. Results show that all-cause mortality rates
were positively correlated with API, precipitation, pressure,
GDP, and waster water discharge, and negatively correlated
with Heat Index, sunshine hours and GDP per capita.
Panel data analysis of the association between API and
meteorological predictors.
Discussion
 Two-way fixed effects model can
control for potential confounding
factors. In this study, we control
for time-varying effects using
weekly fixed effects and locationspecific effects using city fixed
effects.
 API is an indicator of air pollution
based on the level of pollutants:
PM10, SO2, NO2, CO, and ozone.
Data
for
these
individual
pollutants is unavailable.
 Mortality
was
decreasing,
however API was higher in 2013
than 2012. There might be other
confounding factors exist.
 Short of data source for 2013.
Table 5. Associations between Mortality, API, and Heat Index
Mortality(‰)
2012a
95% CI
P-value
API
Heat Index
Precipitation (0.1 mm)
Sunshine Hours (hrs)
Pressure (kPa)
0.056
-0.055
0.015
-0.180
0.125
0.030 0.006 0.106
0.109 -0.122 0.013
0.512 -0.032 0.063
0.349 -0.565 0.205
0.006 0.039 0.212
2013b
P-value
0.001
-0.008
-0.018
-0.244
0.048
0.903
0.815
0.481
0.269
0.240
Acknowledgements
95% CI
-0.015
-0.080
-0.069
-0.687
-0.034
0.017
0.063
0.033
0.198
0.130
Note. a: Adjusting for GDP per capita and waster water discharge per people
Note. b: Adjusting for GDP per capita
Table 5 shows that after adjusting for climate predictors and related
demographic factors, in 2012, API is significantly associated with allcause mortality. A 10-point increase in API leads to a change in the
mortality rate by 0.56‰. The results for 2013 show a non-significant,
however, positive association between API and all-cause mortality.
The study is supported by a joint research
grant from Drexel University and Shanghai
Advance Research Institute (SARI) of the
Chinese Academy of Science. *Drexel
research group for the study (Drexel-SARI
Co-Research and Education on Low Carbon
and Healthy City Technology, LCHCT):
Longjian Liu, MD, PhD (PI), Charles Nathan
Haas, PhD (Co-PI), and research members:
Seth Welles, PhD, ScD, Arthur Frank, MD,
Shannon Marquez, PhD, Jin Wen, PhD,
Peter Decarlo, PhD, and Mimi Sheller, PhD.
Current research and training students:
Xuan Yang, Hui Liu, Jess Dow, Haoyang
Guo, and Feng Jia.