ANNEX 1: REPORT FROM RESEARCH FELLOW Yanxu Zhang, Harvard University
30 September 2014
Report
Result highlights
The air quality over China during 2015-2050 is simulated with a state-of-the-science atmospheric
chemistry and transport model (GEOS-Chem). Compared with the reference scenario, the
national mean of ozone and PM2.5 concentrations in 2050 are predicted to reduce by 2.4 ppbv
and 1.5 μg/m3, respectively, if adopting a high renewable energy pathway as defined in the
CNREC CGE model. The total avoided death during 2015-2050 is calculated as 1,750,000
(492,000-2,920,000 as 95% confidence interval), with a reduction in associated economic loss of
2.9 (0.83-4.9 as 95% confidence interval) trillion RMB. More than 80% of the avoided death and
reduction in associated economic loss is predicted to happen during 2030-2050 with only 20%
during 2015-2030. More than 87% of this avoided death is contributed by the decrease of PM2.5
concentrations, while ozone contributes the remaining 13%.
Chapter 1. Air quality modeling
1.1 Atmospheric transport and chemistry model: GEOS-Chem
1.1.1 General introduction
The Chinese air quality is simulated in the global chemical transport model, GEOSChem*. Simulations are performed at 1/2° latitude by 2/3° longitude horizontal resolution over
China region embedded in a 4° latitude by 5° longitude global simulation†. The model is driven
by the meteorological data from the Goddard Earth Observing System (GEOS, version 5) of the
NASA Global Modeling Assimilation Office (GMAO). The model contains 47 vertical layers up
to 0.01 hPa. GEOS-Chem uses the same advection algorithm with the GEOS general circulation
model‡. Convective transport in GEOS-Chem is computed from the convective mass fluxes in the
meteorological archive. Boundary layer mixing in GEOS-Chemis calculated by a non-local
scheme§. The wet deposition by rain is considered for both water-soluble aerosols and gases, and
the scavenging by snow and cold/mixed precipitation is also considered for aerosol. Dry
deposition is calculated based on the resistance-in-series scheme for all the species with
gravitational settling for dust and coarse sea salt**.
1.1.2
Model chemistry
GEOS-Chem includes a detailed chemistry for 156 gas phase and aerosol phase species
and 479 chemical reactions. The simulation contains a gas phase HOx–NOx–VOC-ozone-BrOx
chemistry, which considers the production and loss of ozone through reacting with HOx, NOx,
VOC and BrOx. GEOS-Chem also includes a detailed sulfate-nitrate-ammonium-carbonaceousdust-seasalt aerosol chemistry, which is coupled to gas phase chemistry. GEOS-Chem considers
the thermodynamics of inorganic aerosols and the in-cloud sulfate formation based on cloud
water pH. Besides the directed emitted primary organic aerosol (POA), the formation
*
version 9-02, http://acmg.seas.harvard.edu/geos/.
The global model provides initial and boundary conditions for the China domain.
‡ http://gmao.gsfc.nasa.gov/GEOS/.
§ The non-local scheme takes into account the large eddy transport under unstable boundary layer
condition, which is not well represented by a “local” scheme.
** The resistance-in-series scheme considers the aerodynamic, boundary resistance and canopy surface
resistances during dry deposition process.
†
of secondary organic aerosol (SOA) by reversible partitioning* of semi-volatile products† of VOC
oxidation is also included in GEOS-Chem model for the oxidation products of terpene, isoprene
and aromatic hydrocarbons. GEOS-Chem also considers the formation of SOA from irreversible
aerosol uptake of glyoxal and methylglyoxal. On top of the anthropogenic‡ fraction of aerosol,
GEOS-Chem simulates the dust and the sea salt aerosol in different size bins. Aerosols interact
with gas-phase chemistry in GEOS-Chem through the effect of aerosol extinction on photolysis
rates, heterogeneous chemistry, and gas-aerosol partitioning of semi-volatile compounds.
1.1.3
Emission inventory processing
We take the Chinese national total emissions of SO2, NOx and NMVOC from CNREC
CGE model. This emission inventory includes two scenarios (reference and high renewable
energy) for the year 2015, 2020, 2030, 2040 and 2050, as summarized in Table 1. We further
interpolate the national total for each scenario in each year to the GEOS-Chem model grid based
on the Representative Concentration Pathways 2.6 (RCP) emission inventory§ as developed by
van Vuuren et al. (2007). The original RCP emission inventory has a spatial resolution of 1/2°
latitude by 1/2° longitude horizontal resolution and was regridded into the GEOS-Chem grid by
Holmes et al. (2012). The RCP emission inventory specifies emissions of anthropogenic ozone
and aerosols precursors** (NOx, CO, CH4, VOCs, BC, OC, NH3, SO2), as well as the long-lived
greenhouse gases (CO2, N2O, HFCs, PFCs and SF6). These include emissions from surface
transport, shipping, aviation, energy production and distribution, industrial combustion,
residential and commercial fuel use, solvent use, waste management and disposal, biomass
burning (grass and forest files), agriculture (e.g. fertilizer NOx and NH3), and agricultural waste
burning.
Table 1. Projected Chinese emissions for GEOS-Chem (unit: million tons / year)
Year
SO2
NOx
NMVOC
a
b
REF
RE
REF
RE
REF
RE
2015
18.7
17.9
28.5
28.1
9.38
8.45
2020
17.4
15.0
41.0
39.6
9.36
7.81
2030
16.4
12.2
15.3
12.1
8.62
5.52
2040
13.1
8.15
13.1
9.06
7.11
3.86
2050
9.87
4.39
10.7
5.91
6.50
2.74
a
b
Reference scenario; High renewable energy scenario.
1.2 Modeled pollutant concentrations
We run the GEOS-Chem model with present-day meteorological data (reference year
2004) and future emissions as described above for the year 2015, 2020, 2030, 2040 and 2050. As
an example, Figure 1-5 show the spatial distribution of modeled annual mean concentrations of
*
Compounds absorbed by particles can be fully released to the gaseous phase when temperature changes.
This is opposite to the assumption of “irreversible uptake” mentioned later in the paragraph.
† Compounds with medium volatility. A significant portion of these compounds can be found in both the
gaseous and particulate phases depending on the environmental temperature.
‡ Man-made or contributed by human activities.
§ This emission inventory serves as input for climate and atmospheric chemistry modeling as part of the
preparatory phase for the development of new scenarios for the Intergovernmental Panel on Climate
Change (IPCC)'s Fifth Assessment Report and beyond.
** The compounds or elements that contribute to the production of another pollutant.
ozone, PM2.5, SO2 and NOx at ground level over China in 2050. The provincial mean of the
concentrations of ozone and PM2.5 in these years are tabulated in Table 2-3.
50oN
50oN
50oN
45oN
45oN
45oN
o
40 N
40 N
40oN
35oN
35oN
35oN
o
o
o
30 N
30 N
30oN
25oN
25oN
25oN
o
o
20 N
20oN
20 N
80oE
0
90oE
15
100oE
110oE
30
120oE
130oE
80oE
45
60
0 ppbv
90oE
15
100oE
110oE
30
120oE
45
130oE
80oE
60
ppbv
0.00
90oE
1.50
100oE
110oE
3.00
120oE
130oE
4.50
6.00
ppbv
Figure 1. Predicted ground ozone concentrations (ppbv) in 2050: left) reference
scenario; middle) high renewable energy scenario; right) the difference between these
two scenarios.
50oN
50oN
50oN
45oN
45oN
45oN
o
o
40 N
40 N
40oN
35oN
35oN
35oN
o
30 N
30 N
30oN
25oN
25oN
25oN
o
o
20oN
20oN
20 N
80oE
0
90oE
25
100oE
110oE
50
120oE
75
80oE
130oE
0ug/m3
100
90oE
25
100oE
110oE
50
120oE
130oE
75
80oE
100
ug/m3
0.00
90oE
2.50
100oE
110oE
5.00
120oE
130oE
7.50
10.00
ug/m3
Figure 2. Same as Figure 1, but for PM2.5 (μg m-3).
50oN
o
45 N
o
50oN
50oN
45oN
45oN
o
40 N
40 N
40oN
35oN
35oN
35oN
o
30 N
30 N
30oN
25oN
25oN
25oN
o
o
20oN
o
20 N
20 N
80oE
0
90oE
6
100oE
110oE
12
120oE
130oE
18
80oE
25
0 ug/m3
90oE
6
100oE
110oE
12
120oE
80oE
130oE
18
25
0.00ug/m3
Figure 3. Same as Figure 1, but for non-dust PM2.5 (μg m-3)
50oN
50oN
45 N
45oN
40oN
40oN
o
35 N
35oN
30oN
30oN
o
25oN
o
25 N
20oN
20oN
80oE
0.00
90oE
0.08
100oE
0.15
110oE
120oE
0.23
130oE
0.30
80oE
0.00
ppbv
90oE
0.08
100oE
0.15
110oE
120oE
0.23
130oE
0.30
ppbv
90oE
2.50
100oE
5.00
110oE
120oE
7.50
130oE
10.00
ug/m3
Figure 4. Same as Figure 1, but for SO2(ppbv).
50oN
50oN
50oN
o
o
45 N
45 N
45oN
40oN
40oN
40oN
o
o
35 N
35 N
35oN
30oN
30oN
30oN
o
25 N
25 N
20oN
20oN
20oN
80oE
0.00
25oN
o
90oE
2.50
100oE
110oE
5.00
120oE
130oE
7.50
10.00
80oE
ppbv
0.00
90oE
100oE
2.50
110oE
5.00
120oE
7.50
130oE
10.00
80oE
0.00ppbv
90oE
100oE
1.00
2.00
110oE
120oE
3.00
130oE
4.00
Figure 5. Same as Figure 1, but for NOx(ppbv).
Table 2. Modeled provincial mean concentrations of ozone (ppbv)
Province
Xinjiang
Heilongjiang
Jilin
Hebei
Neimonggu
Beijing
Tianjin
Liaoning
Ningxia
Shandong
Shaanxi
Shanxi
Qinghai
Gansu
Henan
Jiangsu
Xizang
Shanghai
Anhui
Chongqing
Hubei
Zhejiang
Sichuan
Jiangxi
Guizhou
Hunan
Fujian
Yunnan
2015
RE
REF
53.7
53.7
39.8
39.9
46.5
46.7
47.9
48.0
49.6
49.7
48.1
48.3
49.1
49.3
49.7
50.0
60.1
60.3
48.4
48.6
56.4
56.6
51.1
51.3
50.8
51.0
56.7
56.9
45.4
45.5
48.8
49.1
47.8
47.9
46.7
46.9
46.3
46.5
49.5
49.6
47.2
47.4
44.8
44.9
48.2
48.4
41.0
41.1
41.6
41.8
41.3
41.4
38.5
38.7
33.1
33.3
2020
RE
REF
54.5
54.6
40.6
40.8
47.0
47.2
44.8
44.9
50.1
50.3
45.6
45.6
45.4
45.4
48.4
48.6
61.4
61.7
43.3
43.3
57.0
57.3
49.0
49.1
52.5
52.8
58.1
58.4
42.1
42.1
45.6
45.6
49.9
50.2
45.5
45.5
43.6
43.6
49.9
50.1
47.3
47.4
44.0
44.0
49.5
49.7
41.0
41.1
42.5
42.7
42.3
42.5
38.5
38.6
34.9
35.1
2030
RE
REF
51.0
51.4
35.0
35.9
40.4
41.8
46.9
47.8
45.7
46.5
46.6
47.6
47.9
49.1
45.0
46.6
53.3
54.8
48.3
49.6
50.1
51.7
49.2
50.2
45.3
46.4
50.9
52.1
45.1
46.1
46.4
48.0
43.1
44.2
41.7
43.4
44.2
45.6
43.3
45.1
41.7
43.4
39.4
41.1
41.6
43.2
35.5
37.1
36.0
37.6
35.0
36.7
32.7
34.3
28.2
29.5
2040
RE
REF
50.7
51.3
33.8
35.1
38.5
40.6
45.9
47.5
44.8
46.0
45.6
47.2
46.7
48.6
43.2
45.6
51.6
53.9
47.0
49.2
48.3
50.8
48.2
49.8
44.7
46.4
49.7
51.5
44.1
45.9
44.6
47.2
43.5
45.3
39.4
42.3
42.5
45.0
41.3
44.3
39.9
42.7
37.1
40.0
40.2
42.9
33.5
36.2
34.6
37.3
33.2
36.0
30.8
33.2
28.0
30.0
2050
RE
REF
49.9
50.6
32.3
34.0
35.8
38.9
44.2
46.6
43.7
45.2
43.9
46.3
44.7
47.6
40.3
44.0
49.3
52.5
44.4
48.0
45.6
49.4
46.4
48.9
43.1
45.3
47.9
50.3
42.0
45.1
41.4
45.8
42.4
45.1
35.9
40.6
39.6
43.8
38.0
42.7
36.7
41.2
33.6
38.4
37.4
41.4
30.6
34.8
32.1
36.3
30.4
34.6
27.9
31.7
26.8
29.9
ppbv
Guangxi
Taiwan
Hong Kong
Macau
Guangdong
Hainan
National
35.7
31.3
33.4
33.1
33.9
23.2
47.6
35.8
31.4
33.4
33.1
33.9
23.2
47.8
36.7
31.7
31.9
31.6
33.4
23.3
48.2
36.8
31.8
31.7
31.4
33.4
23.3
48.3
31.1
26.8
31.2
31.1
30.6
22.9
43.4
32.4
27.7
32.2
32.1
31.7
22.9
44.5
30.4
25.7
30.1
30.2
29.6
23.4
42.6
32.5
27.0
31.9
31.8
31.4
23.4
44.2
28.8
24.1
28.3
28.6
27.9
23.8
41.0
32.1
26.0
31.1
31.2
30.7
23.8
43.4
Table 3. Modeled provincial mean concentrations of PM2.5 (μg/m3)
Province
Xinjiang
Heilongjiang
Jilin
Hebei
Neimonggu
Beijing
Tianjin
Liaoning
Ningxia
Shandong
Shaanxi
Shanxi
Qinghai
Gansu
Henan
Jiangsu
Xizang
Shanghai
Anhui
Chongqing
Hubei
Zhejiang
Sichuan
Jiangxi
Guizhou
Hunan
Fujian
Yunnan
Guangxi
Taiwan
Hong Kong
2015
RE
29.7
11.9
18.4
40.6
48.6
39.1
38.5
27.6
56.2
41.1
47.5
49.8
21.8
62.8
50.0
34.8
10.0
23.4
38.5
37.7
41.1
20.7
20.8
25.2
24.7
30.2
15.5
9.7
17.9
5.5
11.6
REF
29.8
12.0
18.5
40.8
48.7
39.3
38.7
27.7
56.3
41.3
47.8
50.0
21.9
62.9
50.3
34.9
10.0
23.4
38.6
38.0
41.3
20.8
21.0
25.3
24.8
30.4
15.5
9.8
18.0
5.5
11.7
2020
RE
30.5
12.6
19.7
42.5
49.4
41.0
40.7
29.5
58.3
43.5
50.1
51.9
22.5
64.4
52.3
36.8
10.8
24.5
40.3
40.5
43.4
21.6
22.7
26.5
27.0
32.2
16.2
10.9
19.5
5.7
12.3
REF
30.6
12.7
19.9
42.8
49.6
41.2
41.0
29.8
58.6
43.8
50.5
52.2
22.6
64.5
52.6
37.0
10.9
24.6
40.5
40.8
43.7
21.7
23.0
26.6
27.3
32.4
16.2
11.0
19.7
5.7
12.4
2030
RE
28.3
8.9
13.6
32.8
46.0
31.9
30.0
20.9
50.5
30.5
38.6
40.8
20.6
59.4
38.6
26.1
9.0
17.8
29.8
26.4
31.4
16.0
14.7
20.2
16.4
22.4
12.3
6.9
12.4
4.6
9.1
REF
28.5
9.3
14.3
34.5
46.4
33.4
31.9
22.2
51.5
33.4
40.5
42.8
20.8
60.0
41.9
28.9
9.3
19.6
32.7
29.1
34.3
17.6
16.0
22.1
18.3
24.6
13.4
7.5
13.8
4.8
10.0
2040
RE
28.3
8.1
12.3
30.1
45.5
29.6
27.1
19.0
49.4
26.1
35.9
37.7
20.6
58.9
33.1
22.0
9.2
15.2
25.0
22.6
27.0
13.7
13.0
17.0
13.8
19.0
10.5
6.2
10.4
4.1
7.6
REF
28.7
8.7
13.3
32.3
46.1
31.5
29.6
20.7
50.7
29.9
38.6
40.4
21.0
59.8
37.7
25.8
9.7
17.6
29.1
26.2
31.0
15.8
14.7
20.0
16.4
22.3
12.3
7.0
12.5
4.6
9.1
2050
RE
28.0
7.5
11.3
28.0
45.1
27.8
24.8
17.3
48.3
22.5
33.6
35.1
20.5
58.3
28.5
18.5
9.2
13.0
20.9
19.2
23.0
11.6
11.4
14.1
11.5
15.8
8.8
5.5
8.6
3.7
6.2
REF
28.6
8.1
12.5
30.7
45.8
30.2
27.8
19.4
50.0
27.2
36.8
38.5
20.9
59.4
34.3
23.2
10.0
16.0
26.2
23.7
28.2
14.4
13.5
18.0
14.7
20.0
11.2
6.6
11.2
4.3
8.2
Macau
Guangdong
Hainan
National
10.8
13.9
1.6
28.7
10.8
13.9
1.6
28.8
11.4
14.8
1.6
29.9
11.5
14.8
1.6
30.0
8.2
10.5
1.4
25.0
9.0
11.6
1.4
25.8
6.9
8.7
1.3
23.9
8.2
10.5
1.3
25.1
5.6
7.1
1.2
22.9
7.4
9.4
1.3
24.4
The modeled ozone concentrations are the highest over northwest China where the
elevation is highest and more influenced by stratosphere sources* (Figure 1 left). The average
concentrations can achieve levels of 40-50 ppbv in provinces such as Xinjiang, Ningxia, Gansu
and Xizang (Table 2). The ozone concentrations are generally lower over more populous east
China, with levels generally lower than 40 ppbv (Table 2). Compared with reference scenario
(REF), the high renewable energy scenario (RE) causes 2-3 ppbv lower national mean ozone
concentrations in 2050 (Table 1). The spatial pattern of the difference between these two
scenarios resembles that of anthropogenic emissions, with higher decreased ozone concentrations
up to 6 ppbv over southeast China. The decrease in ozone concentrations in northwest China is
quite small (< 2 ppbv) despite of the high natural background.
The spatial distribution of PM2.5 concentrations is quite different from that of ozone and
is highest over west Inner Mongolia and south Xinjiang, where the dust emissions are the highest
(Figure 2). The annual mean PM2.5 concentrations over these regions can be higher than 100
μg/m3, which is even higher than those measured in urban regions. The dust fraction is not
influenced by the anthropogenic emissions, and the spatial pattern of reduced PM2.5
concentrations between the REF and RE scenarios is determined by the non-dust fraction of
PM2.5 concentrations as shown in Figure 3. Overall, high renewable energy reduces PM2.5 the
most over Henan (5.9 μg/m3), Anhui (5.3 μg/m3) and Hunan (4.2 μg/m3) provinces, and 1.5 μg/m3
for the national mean.
Unlike ozone and PM2.5, SO2 and NOx have much smaller influence from natural
sources. The spatial patterns of these pollutants as well as the difference between scenarios more
follow those of their corresponding anthropogenic emissions (Figure 4 and 5). Overall, high
renewable energy scenario reduces the national mean NOx concentrations for 0.68 ppbv.
Interestingly, the lower ozone concentrations under the RE scenario prolongs the lifetime of SO2
in the atmosphere, which compensates the effect of anthropogenic emission reduction for SO2. As
a result, the RE scenario is only 0.0008 ppbv lower than the REF scenario for the national mean
concentrations for SO2.
We evaluate our model results by comparing with previous studies. We focus on the
comparison of results at present-day (i.e. 2015 for the REF scenario) because of the similar
assumptions for emissions with other studies. As tabulated in Table 2 and 3, the national mean of
modeled ozone and PM2.5 concentrations for the REF scenario in 2015 are 47.8 ppbv and 28.8
μg/m3. This is close to the result of Wang et al. (2013) for ozone (45.7±10.2 ppbv), which was
also calculated by the GEOS-Chem model. Silva et al. (2013) have conducted a multi-model
study (the Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP) for the
global air quality, which contains 14 different models developed by groups all over the world.
They found the ozone and PM2.5 concentrations range 34.1-82.6 ppbv and 18.5-25.6 μg/m3 over
East Asia, respectively. Indeed, not only our model results fall in these calculated ranges, but also
close to the ensemble means (59.8 ppbv and 22 μg/m3 for ozone and PM2.5, respectively). The
correspondence of our model results with previous studies at present-day has lent us the
*
This is known as the stratosphere ozone layer (20-30 km above ground), where large amount of ozone
(~10 ppmv) is produced by absorbing the incident solar UV radiation.
confidence for our predictions of pollutant concentrations in the future, even though a direct
comparison with other studies is unachievable because of the varied assumptions for future
pollutant emissions in different model studies.
Chapter 2. Risk assessment
2.1 Mortality calculation
With the calculated annual mean concentrations for various pollutants under the REF and
RE scenarios, we calculate the associated avoided death (ΔMort) by applying the following heath
impact function (Anenberg et al. 2010):
ΔMort= y0(1-e-βΔC)Pop
where y0 is the baseline mortality rate, β is the concentration-response factor, ΔC is the
concentration difference of pollutants between RE and REF scenarios, and Pop is the exposed
population. β is derived from relative risks* (RR) estimated in long-term epidemiological studies
assuming log-linear relationships between pollutant concentrations and RR (Silva et al., 2013).
As no certain association has been built up for environmental level NOx and mortality by
epidemiology studies, we exclude NOx in our further health impact analysis. We also exclude
SO2 because of the much smaller health effects than ozone and PM2.5. We adopt a concentrationresponse factor of 0.52% (0.27%-0.77% as 95% confidence interval) increase in mortality per 10
ppbv increase of ozone (Bell et al., 2004). For PM2.5, we differentiate the associated death risk
into long-term and short-term following Huang and Zhang (2013). Although reliable health
impact studies have been conducted over the developed countries, such as the Harvard Six Cities
adult cohort study and American Cancer Society study (Dockery et al., 1993; Pope et al., 1995),
they are not directly applicable for Chinese population because of the much higher PM2.5
concentrations than in the developed countries. Instead, we adopt the mean of concentrationresponse factor for the short-term effects by four studies over China: 0.35% (0.05%-0.65% as 95%
confidence interval) per 10 μg/m3 increase (Huang et al., 2012; Kan et al., 2007; unpublished data
from Peking University and South China Institute of Science). We use a value of 2.96% (0.76%5.04% as 95% confidence interval) per 10 μg/m3 increase for the long-term effect based on a meta
study by Kan and Chen (2002). The population and mortality data of each province in China in
2012 is obtained from the National Bureau of Statistics of China†.
The economic loss associated with the death caused by air pollution is assessed by value
statistical life (VSL), which is defined as the marginal cost of death prevention in a certain class
of circumstances. We adopted a VSL of 1.68 million RMB following Huang and Zhang (2013).
2.2 Risk and economical loss assessment
Figure 6 illustrates the national total avoided death by adopting the high renewable
energy pathway over 2015-2050. The avoided death is quite small (4,000-5,000 per year) during
2015-2020 because of the smaller difference in the anthropogenic emissions between the RE and
REF scenarios. The avoided death number becomes more significant since 2020, and is over
50,000 per year in 2030 and achieves a level of more than 90,000 per year in 2050 (Figure 6). The
total avoided death during 2015-2050 is calculated as 1,750,000, with a reduction in associated
economic loss of 2.9 trillion RMB. More than 87% of this avoided death is contributed by the
decrease of PM2.5 concentrations, while ozone contributes the remaining 13%. This is because of
*
Relative risk is the ratio of the probability of an event occurring (for example, developing a disease, being
injured) in an exposed group to the probability of the event occurring in a comparison, non-exposed group.
† http://www.stats.gov.cn/
the larger concentration-response factor for PM2.5 than ozone, as well as its larger sensitivity to
anthropogenic emission reductions.
Table 4 tabulates the avoid death in each province during the period of 2015-2050
associated with exposure to ozone and PM2.5. The avoided death count during 2015-2050 is the
largest over Shandong (199,000), Henan (196,000), Jiangsu (160,000), Anhui (124,000), Hunan
(110,000) and Hubei (108,000), because of their large exposed population and significant air
quality improvement. On the other hand, Hainan (227), Macau (244) and Xizang (936) have the
smallest avoided death count because of the small populations.
100000
90000
Avoided death per year
80000
70000
PM2.5
Ozone
60000
50000
40000
30000
20000
10000
0
2015
2020
2030
2040
2050
Figure 6. Avoided death per year by adopting high renewable energy pathway over China during
2015-2050.
Table 4. Avoided death per year associated with exposure to ozone and PM2.5 during 2015-2050
by adopting high renewable energy policies
Ozone
PM2.5
Province
2015 2020 2030 2040
2050 2015 2020 2030
2040
2050
Xinjiang
3
7
18
27
31
14
31
72
120
153
Heilongjiang
13
21
89
129
171
48
91
261
349
412
Jilin
15
18
99
150
215
57
96
334
446
533
Hebei
42
8
220
367
566
263 410
2482
3195
4001
Neimonggu
8
11
47
68
90
28
50
146
204
260
Beijing
7
2
39
63
96
43
68
376
481
600
Tianjin
6
1
32
53
82
33
52
332
423
523
Liaoning
38
27
228
355
531
157 262
1205
1567
1874
Ningxia
3
5
25
37
52
17
27
104
142
173
Shandong
77
-5
399
681
1127
400 589
5600
7453
9236
Shaanxi
21
26
171
264
393
150 221
1268
1714
2087
Shanxi
Qinghai
Gansu
Henan
Jiangsu
Xizang
Shanghai
Anhui
Chongqing
Hubei
Zhejiang
Sichuan
Jiangxi
Guizhou
Hunan
Fujian
Yunnan
Guangxi
Taiwan
Hong Kong
Macau
Guangdong
Hainan
National
16
2
12
39
53
1
10
27
16
25
21
48
15
16
28
12
21
15
8
1
0
14
0
633
7
4
19
-1
5
2
4
-4
17
20
2
70
6
17
29
6
33
18
10
-3
0
-3
0
378
102
17
84
270
400
9
102
259
177
275
264
462
197
178
335
147
190
173
73
24
1
255
0
5361
166
26
125
480
677
15
169
454
287
453
443
742
323
290
537
234
309
276
109
40
2
408
0
8759
258
34
171
830
1131
22
279
774
454
721
726
1116
508
457
826
355
472
429
153
63
3
630
-1
13765
133 191
4
9
48
87
425 597
190 284
2
5
16
29
176 241
155 209
208 280
40
68
274 417
61
76
124 178
184 243
18
37
70
119
82
117
1
15
6
8
0
0
73
96
1
1
3501 5203
1253
19
267
5419
4377
12
659
3287
1595
2814
1474
2197
1452
1280
2776
687
558
1186
141
128
5
1610
7
45383
1665
32
383
7639
6007
23
905
4717
2159
4045
2071
2961
2263
1744
4052
1067
789
1709
216
212
9
2511
10
63285
2092
41
477
9667
7476
37
1128
6040
2668
5165
2637
3621
2976
2145
5142
1413
1004
2137
295
282
12
3230
12
79547
2.3 Cost analysis for precursor emissions
Because of the non-linear nature of the atmospheric chemistry reaction system, the
response of pollutant concentrations is often not proportional to the magnitude of emission
reductions. The sensitivity* of pollutant concentrations to their precursor emissions is largely
dependent on the chemistry regime the state of atmosphere belongs to. The concentrations of
pollutants could increase even if emissions are reduced if certain conditions are met (e.g.
Madronich 2014). Therefore, the sensitivities of pollutant concentrations to the emissions of their
precursors for different years need to be calculated separately. We alternatively decrease the
emissions of varied compounds (i.e. SO2, NOx and VOC) under the RE scenario for 10%, and the
sensitivity can be calculated by dividing the change of ozone and PM2.5 concentrations by the
corresponding change of emissions. Although the health risks associated with direct NOx and
SO2 exposure are excluded in this study as noted in section 2.1, the NOx and SO2 are important
precursors of ozone and PM2.5. The SO2 emissions contribute to the sulfate aerosol, which is an
important component of PM2.5 in China. Similarly, NOx contributes to both the nitrate aerosol
(another important component of PM2.5) and ozone. Therefore, the SO2 and NOx emissions are
still included in our cost analysis. Table 5 lists the reduction of economic loss associated with per
unit pollutant emission reductions in 2015, 2020, 2030, 2040 and 2050. The economic benefits
are 4,800-6,900, 3,300-36,000, and 2,000-3,400 RMB per ton emission reductions for SO2, NOx
*
Defined as the ratio of the change of concentrations to the change of emissions.
and VOC, respectively. The sensitivity to NOx emission has larger variability than those of SO2
and VOC largely because NOx emissions are important for the ozone and nitrate chemistry.
Table 5. Economic benefits of unit pollutant emission reductions (RMB/ton)
2015
2020
2030
2040
2050
SO2
5027
6425
4835
5816
6905
NOx
6501
3298
28739
32395
35892
VOC
3155
3443
2459
2354
2084
2.4 Uncertainty analysis and future directions
The uncertainty associated with our assessment is mainly from the concentrationresponse factors of ozone and PM2.5. Varying the concentration-response factors causes large
uncertainty for out estimate for the total avoided death: 1,750,000 (492,000-2,920,000 as 95%
confidence intervals) and the reduction in associated economic loss: 2.9 (0.83-4.9) trillion RMB
during 2015-2050. To reduce this uncertainty, long-term cohort study for the association between
air pollution and its health effect for Chinese population under relatively higher exposure
concentrations is in urge need.
Another source of uncertainty relies on the spatial allocation of the emission inventory,
because it influences the spatial distribution of their concentrations. We assume the spatial
distribution of anthropogenic emissionis the same with the RCP 2.6 emission inventory, which is
not necessarily the case because the RCP inventory bears different assumptions for different
emission sources (van Vuuren et al., 2007). Furthermore, we also assume the same spatial pattern
for emissions under the REF and RE scenarios. This misses the changes of spatial distribution of
emissions driven by the enforcement of renewable energy related policies. Developing future
inventories with higher spatial resolution is thus identified as another priority for future study.
Lastly, we conduct all the GEOS-Chem simulations under the present-day climate and
meteorological conditions without considering the climate change factors. According to Wang et
al. (2013), the ozone sensitivity to domestic emissions is slightly larger over east China but lower
over west China under future climate. This implies more stringent emission reduction is required
over the eastern China to meet a given ozone air quality target if considering the compensating
effect of climate change. Indeed, the climate change factors, as well as the feedback between air
pollution and climate change, are also needed to take into consideration in the future studies.
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