Research Background and Methodology
Catalogue
Emission inventory ........................................................................................................................................ 1
Health impact assessment ............................................................................................................................ 6
References .................................................................................................................................................... 8
Emission inventory
The first step in assessing the health impacts from coal-fired power plant emissions is obtaining
information on how much is emitted and where the emissions take place. For the purposes of this
project, a database of over 2000 coal-fired power plants. As Chinese government and companies, unlike
their counterparts in e.g. Europe and the U.S. do not report plant-level emission data, the emission data
for the power plants had to be estimated based on national total emissions, reported fleet-level
emission rates of large utilities, available plant-specific information, and national regulation on power
plant emissions. The resulting estimates are robust on the national and company level, to the extent
that reported emission data is accurate, but there are additional uncertainties involved in estimating the
emissions from individual power plants.
Emissions from operating power plants were estimated for year 2011. The operating data of the power
plants is obtained from the China Electricity Council (CEC) yearbook 2012. The publication has data on
installed capacity, operating hours, and thermal efficiency. This data is also used to establish sizedependent average values for those power plants for which data is missing.
Capacity, more
Thermal efficiency
Operating
than (MW)
0
20
50
100
300
500
1,000
2,000
(LHV net)
28.4%
30.2%
31.4%
33.3%
35.2%
36.8%
38.2%
39.4%
hours (h/a)
3761
3793
4302
5055
4644
5322
5537
5928
Table 1. Average operating parameters used for operating power plants lacking data.
Plant type
Subcritical
Supercritical
Ultrasupercritical
Under construction,
steam condition unknown
Planned, steam condition
unknown
Thermal
efficiency
(LHV net)
39.0%
42.0%
44.0%
41.4%
41.8%
Table 2. Thermal efficiencies assumed and estimated for new power plants in the Platts database and the WRI Global Coal Risk
Assessment report.
The locations of the power plants were mapped by Greenpeace, up to district or county level, and when
possible, exact coordinates where used. Information on the ownership of the power plants was
obtained from Platts World Electric Power Plants database.
Information on pollution controls installed at the power plants is from Ministry of Environmental
Protection, which maintains a list of all power plants with FGD and de-NOx equipment installed. This
data also has the year of operation for the power plants, which helps establish the emission limit values
applying to each unit at the power plant. However, the power plant listings in the CEC and MEP data do
not completely match each other, and average penetration rates for each province and power plant size
class were applied to those power plants that could not be matched between the two databases.
Power plant efficiency was based on steam conditions (subcritical, supercritical or ultrasupercritical)
reported in the WEPP database. All power plants commissioned after 2011, and those still in the
pipeline, were assumed to have both FGD and de-NOx equipment installed, and to meet the new 2011
emission standards. This is a conservative assumption, given that the existing power plant fleet still does
not meet the old 2003 standard.
Data on coal quality, namely flue gas volume and mercury content, comes from USGS World Coal Quality
Inventory. First, average values of all thermal coal samples were calculated from the database for each
province. Second, the average values for traded coal were estimated by taking average of values for
each province weighted by their coal exports. Lastly, the average values for coal burned in each province
were estimated by calculating the average of the values for the province’s domestic coal and traded
coal weighted by the percent of coal that the province imports. Flue gas volume per energy input
(Nm3/GJ) was calculated by first converting the energy content given in the database from Higher
Heating Value (HHV) to Lower Heating Value (LHV), using an empirical formula provided by World Coal
Institute (2007):
LHV = HHV - 0.212H- 0.0245M- 0.0008O,
where LHV and HHV are given in MJ/kg; M is percent moisture, H is percent hydrogen and O is percent
oxygen (from ultimate analysis on net as received basis). Flue gas volume per kg of fuel is calculated on
the basis of the empirical formula in European Standard EN 12952-12.
Province
Anhui
Beijing
Chongqing
Fujian
Gansu
Guangdong
Guangxi
Guizhou
Hebei
Heilongjiang
Henan
Hubei
Hunan
Inner Mongolia
Jiangsu
Jiangxi
Jilin
Liaoning
Ningxia
Qinghai
Shaanxi
Shandong
Shanxi
Sichuan
Xinjiang
1
Flue gas volume1
Mercury content
3
(Nm /GJ)
(mg/GJ)
344.9
7.8
356.6
7.4
349.5
4.9
359.4
3.8
348.5
2.2
354.5
3.5
354.4
5.1
347.8
9.1
350.9
4.5
345.1
2.9
347.6
7.9
354.5
3.9
353.2
5.2
345.8
10.5
353.6
4.6
352.1
7.9
346.3
4.2
349.9
6.1
348.2
11.8
348.6
2.8
342.4
7.5
350.3
4.4
347.3
6.4
348.4
4.0
347.1
1.4
On dry, normal temperature and pressure and 6% O 2 basis, in line with the Chinese emission standards.
Yunnan
345.6
5.7
Table 3. Average properties estimated for the coal burned in each province.
Based on these data, air pollution emissions for each power plant were first calculated assuming that all
power plants meet the national emission standards applying to them. After this, the emission rates were
adjusted so that the total modeled emissions from all power plants and from each company's power
plants match the reported total. Total emissions of acid gases and particulate matter from the power
sector were taken from China Environment Statistical Yearbook 2012 (National Bureau of Statistics
2013). Information on the emissions of large power companies is compiled from the companies' CSR
reports. It was also ensured that the total power plant emissions make up a reasonable share of the
reported total emissions of each province.
Power plant
commissioning date
2004 or later
before 2004
before 1997
2012 or later
new power plants in key
regions
Pollutant stack emission
limits (mg/Nm3)
SO2
NOx
TSP
400
450
400
650
1200
1100
100
100
50
100
50
50
200
30
20
Table 4. Stack emission concentration limits applying to operating power plants in 2011, and to new power plants. 2
Power plant
commissioning date
2004 or later
before 2004
before 1997
mg/Nm3
SO2
TSP
1200
100
1200
100
1200
200
Table 5. Exceptions to general emission limits appying to power plants burning domestic low-sulphur coal in Chongqing,
Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi and Inner Mongolia.
TSP emissions were converted to primary PM10 and PM2.5 emissions using the emission factors in U.S.
EPA AP-42:
TSP to PM10
TSP to PM2.5
0.675
0.300
Table 6. Ratios between different size ranges of particulate matter.
Company
China Power Investment
Huadian
Datang International Power
2
Emission rate (g/kWh)
SO2 NOx
TSP
2.36
2.4
0.38
3.23
3.0
1.33
0.3
0.3
0.12
GB 13223-2011 Emission standard of air pollution for thermal power plants
Datang total
Huaneng Power International
Huaneng total
Guodian
China Resources Power
Guangdong Yudean
Shenhua
1.92
0.57
N/R
2.14
0.56
0.44
0.21
3.17
1.55
N/R
N/R
1.35
1.36
0.87
0.29
N/R
N/R
N/R
0.38
0.07
0.1
Table 7. Air pollution emission rates as reported in key power companies' CSR reports for 2011 (N/R=not reported).
Mercury emissions were estimated based on the average mercury content of coal as shown above, and
removal rates associated with different pollution controls according to Wu et al (2009). Estimated
mercury emissions were 20% lower than in the earlier estimate for 2005 by Streets et al (2008), which is
in line with the increased penetration of FGD equipment and increased coal consumption in the power
sector.
ESP
ESP+FGD
coal washing
29.4%
69.0%
30.0%
Table 8. Mercury removal rates of different technologies according to Wu et al. (2009).
Stack parameters (stack height and diameter, flue gas temperature and velocity) are required for
estimating how high the flue gases rise initially, which influences their dispersion. Actual stack
parameters were compiled for a few power plants in Beijing and Shanghai, but for the vast majority, this
information was not readily available. Zhou et al (2006) argue that Chinese power plants are built to
very similar engineering standards and most power plants will conform with the guideline values.
Furthermore, their results show that the total health impacts are not particularly sensitive to varying the
assumed stack parameters within feasible range. Recommended values for the stack parameters were
taken from Lan et al (2011), except for flue gas temperature, typical European values from Pregger &
Friedrich (2009) were used.
Stack height (m)
Capacity, up
to (MWe)
25
50
200
300
800
1,200
8,000
New
Flue gas
Existing
power
temperature Exit velocity
Diameter
power plant plant
(°C)
(m/s)
(m)
80
80
140
14
4
100
100
140
23
4
120
150
140
20
4
150
180
140
20
4
180
240
110
30
7
210
240
100
30
7
240
240
100
30
7
Table 9. Stack parameters used for modeling when plant-specific data is not available (most modeled sources).
Selection of the directly modeled sources was done by first dividing the modeling domain into a 0.5x0.5°
grid, and selecting the largest source within each grid cell that contained at least 1200MW of coal-fired
capacity. Additional sources were selected to maximize the share of total emission inventory that is
modeled directly, to maximize spatial coverage and to maximize coverage of key regions (Beijing-TianjinHebei, Yangtze River and Pearl River deltas). The directly modeled sources cover 50% of coal-fired
capacity, 43% of estimated SO2 emissions in 2011, and 41% , 40% and 48% of NOx, TSP and mercury
emissions, respectively.
Health impact assessment
The health impacts resulting from the exposure to PM2.5 were estimated using concentration-response
functions adapted from the WHO Global Burden of Disease 2010 project (Lim et al 2012). The study is
the most up-to-date and authoritative look into preliminary deaths caused by PM2.5 in China and
globally, and developed a new risk model with emphasis on applicability at high average concentrations.
The risk functions in the model level off at high concentrations, taking into account the findings showing
that risk for the same concentration increase is higher at low concentrations. Total mortality is
evaluated as a sum of four cause-specific mortality risks: stroke, lung cancer, Ischemic Heart Disease
(IHD), and Chronic Obstructive Pulmonary Disease (COPD). These four causes are responsible for 45% of
total deaths in China. The cause-specific approach provides better transferability from one country to
another than earlier approaches that used all-cause mortality as the indicator, and provides a
breakdown of the causes of the preliminary deaths attributed to PM2.5 from coal-fired power plants.
If the concave risk functions from Global Burden of Disease 2010 were used directly to attribute impacts
on different sectors, the sum total of impacts attributed to all sectors would be smaller than the actual
total impacts. For this reason, based on a recommendation from the report authors (Burnett&Cohen
2013), average impacts for a 10 µg/m3 increase over the observed concentration range were used for
attribution. The average risk ratio 𝑅𝑅𝑎𝑣𝑔 was calculated for each mortality risk as
𝑐𝑎𝑣𝑔
𝑅𝑅𝑎𝑣𝑔 =
𝑅𝑅(𝑐)
𝑐=15µg/m3 RR(c − 10µg/m3 )
𝑐𝑎𝑣𝑔 −15µg/m3
∑
,
where 𝑅𝑅(𝑐) is the ratio of mortality risk at concentration 𝑐 to the risk at a counterfactual no-harm
concentration, and 𝑐𝑎𝑣𝑔 is the population-weighted average PM2.5 concentration, taken to be 60 µg/m3
(the average concentration estimated for China for Global Burden of Disease 2010 by Brauer et al (2012)
was 55 µg/m3). The summation is started from 15 µg/m3, because this represents the no-harm
concentration in the risk model (5 µg/m3) plus the concentration increase for which 𝑅𝑅𝑎𝑣𝑔 is calculated
(10 µg/m3).
Non-fatal health impacts were evaluated by using concentration-response functions recommended by
Kan et al (2005) for health impact assessment in China, when available. The response functions were
applied conservatively, using the factor for PM10 health effects for exposure to PM2.5. The Kan et al
functions were complemented with functions for infant mortality, lost working days and sickness days
from literature, following WHO recommendations. Recent epidemiological evidence on the link between
PM2.5 and risk of low birth weight in babies was used from a new nine-country study. While overall
mortality is estimated on the basis of all-cause mortality, cause-specific factors are used to complement
the analysis and provide a breakdown of causes of death.
Application of these response functions requires data on the age structure of the Chinese population,
and on baseline incidence of the different health conditions. These were obtained from official statistics,
with the exception that World Bank data on low birth weight, and data from academic studies done in
China on asthma, were used.
Health impact
Stroke mortality
Lung cancer mortality
COPD mortality
Ischemic heart disease
mortality
Concentration-response function
Increase per
Pollutant Age group 10µg/m3
Reference
12.2%
(3.2%-14.8%)
PM2.5
305.6% (1.7%-7.4%)
PM2.5
30Lim et al 2012;
4.1% (1.9%-5.7%)
PM2.5
30Burnett&Cohen 2013
5.5% (3.9%-9.0%)
PM2.5
30Woodruff et al 1997
4% (2%–7%) (in Hurley et al 2005)
Infant mortality
PM10
1-12
months
Low birth weight
PM2.5
newborns
Asthma, children
Asthma, adults
Chronic Bronchitis
Respiratory Hospital
Admission
PM10
PM10
PM10
0-15
16all
6.95%
0.4% (0.0%–0.8%)
4.6% (1.5%–7.7%)
PM10
all
1.3% (0.1%–2.5%)
PM10
all
0.95% (0.6%–1.3%)
PM10
all
0.34% (0.19%–0.49%)
PM10
all
0.39% (0.14%–0.64%)
Sick leave days
PM2.5
15-64
4.6% (3.9%–5.3%)
Restricted activity days
PM2.5
18-64
4.8% (4.2%–5.3%)
Cardiovascular Hospital
Admission
Outpatient Visits (internal
medicine)
Outpatient Visits (pediatrics)
10% (3%–18%)
Dadvand et al 2013
Kan et al 2005
Ostro 1987 (in Hurley
et al 2005)
Table 10. Concentration-response relationships used to estimate health impacts of particulate matter exposure.
Health impact
Baseline
incidence or
prevalence
Unit
Reference
Stroke mortality
0.14% deaths per year
Ministry of Health 2011
Lung cancer mortality
COPD mortality
Ischemic heart disease
mortality
Infant mortality
0.04% deaths per year
0.06% deaths per year
Ministry of Health 2011
Ministry of Health 2011
0.08% deaths per year
Ministry of Health 2011
1.21%
Low birth weight
Asthma, children
Asthma, adults
Chronic Bronchitis
Respiratory Hospital Admission
Cardiovascular Hospital
Admission
Outpatient Visits (internal
medicine)
Outpatient Visits (pediatrics)
Sick leave days
Restricted activity days
2.34%
1.97%
1.42%
0.69%
1.02%
1.37%
cases per year
cases
cases
cases
cases per year
National Bureau of Statistics
2012
World Bank 2012
Chen 2003
To et al 2012
Ministry of Health 2011
Ministry of Health 2011
cases per year
Ministry of Health 2011
cases per year
Ministry of Health 2011
31%
deaths per year
13% cases per year
2.34 workdays per year
39.96 workdays per year
Ministry of Health 2011
Ministry of Health 2011
Ministry of Health 2011
Table 11. Baseline incidence of health conditions included in health impact assessment. For asthma and chronic bronchitis, the
epidemiological relationship applies to prevalence, not annual incidence of new cases.
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