ELECTRONIC SUPPLEMENTARY MATERIAL
Table of Contents
S1. Methods ................................................................................................................................................... 2
S1.1 Annual river flow (Q)........................................................................................................................ 2
S1.2 River flow distances (per grid cell and to sea) .................................................................................. 2
S1.3 Heat release for different power plants ............................................................................................. 3
S1.4 Heat exchange coefficients AT for equilibrium temperature model .................................................. 4
S1.5 Ambient river temperature ................................................................................................................ 4
S1.6 Climate zones .................................................................................................................................... 7
S1.7 Effect factor ....................................................................................................................................... 8
S2. Results...................................................................................................................................................... 8
S2.1 Residence time analysis ..................................................................................................................... 8
S2.2 River temperature July-August ........................................................................................................ 10
S2.3 Impact assessment .......................................................................................................................... 10
S3 Array of freshwater ecosystem damage................................................................................................. 12
Endpoint characterization factors ........................................................................................................ 12
S4 Water footprint / LCA analysis ............................................................................................................... 14
Goal and Scope........................................................................................................................................ 14
Inventory ............................................................................................................................................. 14
Impact assessment ............................................................................................................................... 17
References ................................................................................................................................................... 22
S1
S1. Methods
S1.1 Annual river flow (Q)
Figure S1: Annual river flow (Q) per grid cell (0.5 arc minute resolution) published by Fekete et al. (2002).
S1.2 River flow distances (per grid cell and to sea)
The average distance in a grid cell is estimated as the half distance of flow length within a grid
cell. The average flow length per grid cell is roughly 100 km as obtained from comparison of
flow accumulation results and river length data from Vörösmarty and colleagues (2000), as
presented in Table S1. The average travel distance is therefore 50 km.
Table S1: River length (Vorosmarty et al. 2000) versus cell length based on flow accumulation procedure in
ArcGIS (ESRI 2007).
River
Mississippi
St Lawrence
Colorado
Columbia
Number of cells from
source to mouth
43
26
18
17
S2
River length [km]
4185
3175
1808
1791
Figure S2: Flow distance to sea (or inland sink) for each grid cell.
S1.3 Heat release for different power plants
Table S2: Heat release in terms of energy per unit power produced. Also shown is the heat release rate of the whole
power plant (500 and 50 MW). The data represents gas and coal power plants analyzed in this paper and a nuclear
power plant (NPP) described in Verones et al. (2010). The heat flux is required for calculating dT in the release grid
cell.
MJ/kWh
Heat release
(per energy unit)
K·m3 /kWh
K·m3/(s ·MW)
Heat flux
500 MW
Heat flux
50 MW
K·m3/s
Natural gas (CCGT)
2.53
0.60
0.17
83.6
8.4
Coal (USC-PC)
4.17
0.99
0.28
137.9
13.8
NPP (Swiss case)
8.85
2.1
0.58
291.7
29.2
(Verones et al. 2010)
S3
S1.4 Heat exchange coefficients AT for equilibrium temperature model
Table S3: Heat exchange coefficients [W/m2 °C] in different countries.
Country
Winter
Summer
Switzerland (Kuhn 1997)
17
34
South Germany (Maniak 2005)
15
35
North Germany (Maniak 2005)
25
50
Nebraska USA (Gu 1998)
Arkansas USA (Wu et al. 2001)
30
27
37
S1.5 Ambient river temperature
River temperature is estimated based on two approaches: extrapolation from river measurements
point using kriging, and air/water temperature regression. Both approaches have uncertainties that
are considered. Kriging directly results in a variance of the estimate for each grid cell (stdwater2)
as shown in Figure S5. The air temperature model is based on the 1907 measurement points and
respective air temperature data that yields a standard deviation (stdair) of 2.93°C for the entire US
(Figure S3).
S4
Water Temperature (July-Sept) (°C)
35
y = 0.9193x + 0.5509
R² = 0.6807
30
25
20
15
10
5
Air Temperature in July (°C)
0
0
5
10
15
20
25
30
35
Figure S3: Regression analysis of the measured river temperatures (average of July, August and September) and the
air temperatures in July. The root mean square error of the regression model is 2.93 and the standard error 2.63.
The final temperature estimate for the peak temperature season (Twater,peak season) for each grid cell
is based on the combination of kriging and air temperature regression results, as described in the
manuscript and illustrated in Figure S4. The standard deviation of Tmedian (denoted by stdaggregated)
is calculated based on the uncertainty information of the two approaches for each grid cell, using
the weight of the measurement based (wwater) and the weight of the air temperature-based (wair)
approach:
𝑠𝑡𝑑𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑
= √𝑤𝑎𝑖𝑟 2 ∗ 𝑠𝑡𝑑𝑎𝑖𝑟 2 + 𝑤𝑤𝑎𝑡𝑒𝑟 2 ∗ 𝑠𝑡𝑑𝑤𝑎𝑡𝑒𝑟 2
Equation S1
The weights are calculated as follows:
wair 
Tmedian  Twater
Tair  Twater
; wwater  1  wair
Equation S2
S5
20°C
25°C
23.9°C
Figure S4: Red curve represents the distribution of the combination of estimates based on the air temperature
regression (steep blue curve) and the kriging results (flat blue curve). The table shows the arithmetic mean and the
standard deviation (std) used to calculate the distribution functions and the weight of each approach. The combined
temperature estimate (Twater,peak season ) in this example has a mean of 23.9°C and a standard deviation of 3.21°C.
Figure S5: Variance of water temperature estimate based on kriging of measurements; overlaid by locations of water
temperature stations (green dots on red grid cells).
S6
Figure S6: The average air temperature in July.
S1.6 Climate zones
Figure S7: Water temperature measurement stations plotted on climatic zones (green are temperate
zones and orange-red are subtropical zones). Based on FAO (2007).
S7
S1.7 Effect factor
We calculate the temperature tolerance interval (µTTI,r ) for each climate zone r:
TTI ,r  a ,r  Ti  b,r
Equation S3
Ti is the ambient water temperature in grid cell i
Table S4: Parameters for species sensitivity distribution in different climate zones.
Climate
Tropical
Temperate
Combined (all
species)
Verones et al. (2010 )
µa,r
µb,r
-0.760
-0.885
-0.836
28.28
23.80
27.46
-0.866
27.05
S2. Results
S2.1 Residence time analysis
Table S5: Residence time analysis for each grid cell of potential release to the river mouth.
residence time until Frequency of
sea (days)
grid cells
0
1088
5
1412
10
542
15
585
20
254
30
137
50
29
80
12
More
0
S8
S2.2 Heat dissipation
100%
90%
1-Heatriver,AVG,i
80%
70%
60%
50%
40%
30%
20%
10%
0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Base case (5 m): 1-Heatriver,AVG,i
Figure S8: Relevance of river depth: Scatter plot between average heat loss (%) in the river before
reaching sea (1-Heatriver,AVG,i) based on depth of 5 m (x-axis) vs. 12.5 m (red triangles) and 2.5 m (green
circles) for each grid cell.
1600
1400
Number
of grid cells
1200
1000
800
depth 2m
600
depth 5m
400
depth 12.5m
200
0
Figure S9: Histogram showing frequency distribution of grid cells based on the energy left as a % of the released
heat present on average during the residence time in the river from point of release to reaching the sea; assessed for
different river depths.
S9
S2.2 River temperature July-August
Figure S10: Modeled average river temperature for the months July-September.
S2.3 Impact assessment
Table S6: Relevance of power plant size: Frequency distribution of the ratio of thermal impacts between a
50 MW and a 500 MW coal power plant.
Ratio of thermal impacts per kWh:
50 MW plant impact/500 MW plant impact
0.10
0.26
0.41
0.57
0.73
0.89
1.04
1.20
1.51
1.99
2.93
4.03
6.07
9.06
>9.07
S10
Number of grid cells
1
23
156
309
546
733
944
18
30
65
47
19
10
32
141
Table S7: CFs for thermal impacts
Percentile
1%
2.30%
15.90%
50%
84.10%
97.70%
99%
min
max
Thermal Impact of 500 MW coal plant
(PDF m3 yr)
Thermal Impact of 50 MW coal plant
(PDF m3 yr)
3.8E-06
5.1E-06
2.5E-05
6.3E-05
4.0E-04
6.9E-04
7.8E-04
2.02E-07
1.24E-03
3.5E-06
5.5E-06
2.7E-05
6.6E-05
3.9E-04
6.9E-04
7.8E-04
2.01E-07
1.24E-03
Figure S11: CF (PDF m3 yr / kWh coal power) as a function of distance to sea: Linear regression (red
dashed line) and an estimate plus minus one order of magnitude (black lines) with a constant CF assumed
after 700 km distance to sea. The calculated average CF of coal power (3.0E-04 PDF m3 yr / kWh coal) is
in the range of the linear regression at a distance to sea between 1000 and 2000 km.
S11
S3 Array of freshwater ecosystem damage
In LCA, typically 3 areas of protection (AoP) are considered: human health, ecosystem quality
and resources. Some methods try to aggregate these three AoPs into a single-score indicator but
this is highly debated. Based on ISO 14044, each impact category needs to be reported
independently in order to provide the complete picture. However, within an AoP it is more useful
to summarize all impacts on ecosystem quality as the addressed issue is the same and units of
damages can be aggregated with a few assumptions in order to assess relative relevance of one
impact category. In this case we are interested in the relevance of thermal emissions compared to
other established impacts on freshwater ecosystem which are ecotoxicity, eutrophication and
freshwater consumption.
We employ the state-of-the-art impact assessment methods ReCiPe (Goedkoop et al. 2009) for
eutrophication, USEtox (Rosenbaum et al. 2008) for toxicity calculation (freshwater ecotoxicity)
and the method from Pfister et al. (2009) for water consumption. The latter is spatially explicit on
watershed level and can be used to address geographical differences.
Endpoint characterization factors
All characterization factors are developed for a specific midpoint and can be translated to a
common endpoint regarding ecosystem quality measured as PDF m2 yr or PDF m3 yr. In our
project we transform all impacts to PDF m3 yr in order to best represent aquatic ecosystem
damages.
USEtox measures the impact in “CTU” which is equivalent to “PAF m3 yr” (Rosenbaum et al.
2008). Regarding the conversion of PDF to PAF there are two main approaches published: While
in the Ecoindicator99 report (Goedkoop and Spriensma 2001) the equation PDF = 10 PAF is
promoted; in the ReCiPe report PDF is set equal to PAF based on fish studies. Consequently,
CTU can be considered equal to 0.1 PDF m3 yr or 1.0 PDF m3 yr. As an average estimate, we
chose CTU to be equivalent to 0.3 PDF m3 yr (geometric mean).
The conversion from eutrophication impacts measured as kg phosphorus (P) equivalents are
transformed to endpoints by the factor 56.2 PDF m3 yr / kg P, based on analysis of the ReCiPe
endpoint scheme.
S12
Water consumption impacts are already available as endpoint (PDF m2 yr) and need to be
transposed from area to volumes of impacts. There are different approaches for such conversion.
ReCiPe weights area and volume based on total global species density in the respective
ecosystems, resulting that 1 PDF m2 yr equals 28 PDF m3 yr in terms of impact. Considering
ecosystems from an equal-quality-per-area perspective, average water depth might be used to
determine a conversion between m3 and m2: Average water depth of all freshwater ecosystem on
earth is ~50 m, based on the global freshwater volume of ~90,000 km3 (USGS 2010) and an
inland water surface area of 1.8 million km2 (van Velthuizen et al. 2007). The resulting
conversion of 1 PDF m2 yr equaling 50 PDF m3 yr for the average water volume is biased by
lakes, and therefore we can also transform water volumes to land area by applying an average
river depth. Based on US conditions, the depth estimate equation from Schulze and colleagues
(2005) and the flow data from Fekete et al. (2002), we estimate an average river depth of ~2m.
Combining river and lakes we estimated ~5m depth to be representative. However, these water
depth reflections neglect the third dimension, as strata and volumes are much more relevant in
aquatic than terrestrial ecosystems, with biologically active zones much larger than in soils. As a
reference, USEtox used average freshwater depth of 2.5m for modeling fate and exposure. From
a global perspective, also total freshwater vs. total land area can be analyzed to come up with a
conversion factor: ~90,000 km3 (USGS 2010) related to ~130 million km2 (van Velthuizen et al.
2007) result a conversion of 1 PDF m2 yr per 0.7 PDF m3 yr. Taking the five estimates into a
geometric mean results (an average conversion factor of 1 PDF m2 yr per 6.25 PDF m3 yr.
S13
S4 Water footprint / LCA analysis
Goal and Scope
Life cycle assessment of latest-build US coal and gas power plants. We analyze the life cycle of
electricity production as e.g. suggested by scope 3 carbon footprints (including infrastructure and
supply chain related impacts).
Functional unit. The functional unit is 1 kWh of electricity at power plant. The case study is
based on literature data on latest-build gas and coal power plants of ~500 MW capacity. Generic
technologies for coal and natural gas power production are compared, with two types of cooling
technologies (closed-loop, open-loop).
Geographic scope. The geographic scope is the United States (continuous states). Differences
between East and West coast are distinguished for the coal supply chain (based on ecoinvent
data). Climatic differences are analyzed for the impact assessment employing two archetypes:
humid and arid region.
Temporal scope. Current state-of-the art technology is represented by datasets for the 20002010–a reference year of 2005 is selected.
Impacts addressed. We analyze impacts concerning aquatic ecosystems along the supply chain.
These include:
- Aquatic eutrophication
- Freshwater Ecotoxicity
- Heat release impacts
- Freshwater consumption
- Freshwater Eutrophication (only for discussion)
Inventory
Inventory of coal and gas power plants have been updated based on ecoinvent 2.2 data (ecoinvent
Centre 2010) and results from the NEEDS project (Bauer 2008) that assess state-of-the art power
plant technology for the year 2000-2010. The gas power plant is based on combined cycle while
the coal is a Ultra Supercritical Pulverized Coal power plant
Coal power plant. For coal state-of-the art power production Ultra Supercritical Pulverized Coal
(USC-PC) is assumed.
S14
There is no state-of-the art power production for coal in ecoinvent v2.2 (ecoinvent Centre 2010).
We therefore employ the data set from the EU commissioned “NEEDS” project
(http://www.needs-project.org), for 2005 (as a baseline) in the format of ecoinvent v2.2. They
provide a dataset for 350 MW and 600 MW power plants, based on ecoinvent v1.3 (Bauer 2008).
We linearly scale between 350 MW and 600 MW power plant for modeling 500 MW. The coal
supply was changed from European to a US dataset. The remaining supply chain was not
changed, as according to our analysis it did not significantly contribute to the impacts considered.
Cooling water systems are adjusted too. Net thermal efficiency is assumed to be 45%. The coal
power plant has a flue-gas desulfurization unit.
Natural gas power plant. For natural gas state-of-the art power production, a combined cycle
power plant (CCGT), best technology LCI is provided in Ecoinvent v2.2. The dataset refers to a
400MW plant built in 2001 in Germany.
The supply chain has been changed from European to a US dataset for the process called “natural
gas, at consumer” from Ecoinvent v2.2. The remaining supply chain is not changed, as according
to preliminary analysis it does not significantly contribute to the impacts considered. Cooling
water systems are adjusted too. Net thermal efficiency is assumed to be 57.5%
Cooling systems. Cooling technologies are very relevant in the context of water consumption,
and in Ecoinvent, the cooling processes are integral part of the power plant operation. Also
emissions of heat and antifouling chemicals are important and different for once-through and
closed loop cooling systems.
We included a separate module for cooling water systems to account for the differences in water
consumption and heat release to water bodies. The data on water consumption for different
cooling systems for coal and natural gas power production is derived from Stillwell et al.
(Stillwell et al. 2009) and described in Table S8.
S15
Table S8: Freshwater consumption per unit of power production (m3/ MWh) for conventional coal and gas power
plants with different cooling systems. Based on Stillwell et al. (Stillwell et al. 2009)
Cooling technology
once through
cooling tower
Natural gas (CCGT) 0.38
0.68
Coal (USC-PC)
1.8
1.1
Heat releases are adapted from Ecoinvent. For the cooling process we calculate emissions to
water, based on the energy balance and deduct the emission of heat to water from the emission to
air. For once through cooling, we assume 95% of the heat going into the water while 5% are
dissipated in the air. Based on Verones et al. (2010) and Stillwell et al. (2009), we assume that
heat emission to water from cooling tower are 0.01% of those from once-through systems, as
presented in Table S9. Coal has lower efficiency and therefore higher cooling water demand per
unit electricity production.
Table S9: Freshwater heat release per unit of power production (MJ kWh-1) for conventional coal and gas power
plants with different cooling systems. Based on Verones et al. (2010), Yang and Dziegieleweski (2007) and Stillwell
et al. (2009).
Cooling technology
once through
cooling tower*
Natural gas (CCGT)
2.53
0.00025
Coal (USC-PC)
3.90
0.00039
*Cooling tower has assumed heat emission through sewer system and disposal of blow-down
waste stream
Chemical use to prevent bio-fouling in closed loop cooling systems is retrieved from Ecoinvent
and did not show to be significant in current impact assessment methods. However, this is mainly
a mismatch of emissions and characterization factors available, which is corrected by adjusting
the characterization factor (CF) as described below. Application of amines or alternative
antifouling agents is not included in the assessment.
S16
US coal supply. The coal supply is adjusted based on Eastern and Western coal supply. For
Eastern coal and Western coal supply, Appalachian and Wyoming coal characteristics are used,
respectively. The coal energy content (LHV) in the NEEDS dataset is 26.6MJ/kg, while the
eastern US coal (Appalachian) typically has a LHV of 27.5 MJ/kg and the Western coal has a
LHV of 20.2 MJ/kg. Accordingly we adjusted the amount of coal to be mined.
The mining activities are also very different in the two regions and do not match ecoinvent data.
While in Wyoming mainly surface mining occurs (Detweiler and Yu 1998), the Appalachian coal
is sourced 60-80% from underground mining (McIlmoil and Hansen 2009). Accordingly, we
adjusted the mining dataset in Ecoinvent: 5% and 70% are set “underground mine, hard coal” for
Western and Eastern coal supply respectively while the remaining 95% and 30% are modeled by
the process “open cast mine, hard coal”. These Ecoinvent datasets are compiled from literature
and considered “global” processes, as data is very scarce. The differences are not further explored
in this project as local surveys would be required.
Furthermore, the ash content is very different for the two coals. US average ash disposal is: 0.064
kg / kg of coal, (ecoinvent V2.2) while in the NEEDS dataset it was set 0.068 kg / kg of coal
burnt. Based on the ash content of the coal from different origins as reported by Dones et al.
(2007), the ash disposal is accordingly adjusted to 0.085 kg / kg of coal for the East and 0.028 kg
/ kg of coal for the West.
Impact assessment
The ecological impacts are aggregated as presented above. We shortly describe the impact
assessment methods used (focus on relevant points) as well as the main emissions and pathways
in terms of impact in the case study.
Aquatic eutrophication. In the ReCiPe method, PDF are derived from analysis of P
concentration and macrofauna species richness in Dutch freshwater ecosystems (more than 100
species included). Source: ReCiPe 2008 (Goedkoop et al. 2009).
Freshwater ecotoxicity is a combination of many different substances having impacts on
ecosystem. In USEtox, the species number used to derive the PDF ranges between 1 and 450
(personal communication with Ronnie Juraske, USEtox co-author) and the main distinction is
made between organics and inorganics. In ReCiPe, more than 50% of chemicals are tested on 2
S17
or 3 species only, while the maximum species tested for one chemical is 246. 44% of the species
tested are vertebrates and invertebrates (each), 10% are fungi and 2% bacteria. Sources: USEtox
(Rosenbaum et al. 2008); ReCiPe 2008 (Goedkoop et al. 2009); van Zelm et al. (2009).
Water consumption. As water consumption is not an emission having impacts due to
concentrations, effects are addressed differently. Water consumption affects water resource and
therefore has similarities with impacts by land use. Water resources are not only affecting surface
and ground water bodies but are also crucial for wetlands which are relevant for terrestrial
ecosystems. Groundwater ecosystems are implicitly included, which are considered to be a
relevant ecosystem itself, beyond pure ecosystem service provide. Impacts are addressed based
on water limitation of the vegetation growth. This climatic parameter is used as a proxy to
determine water dependence of the overall ecosystem. The share of the growth limited by water
is used to estimate the PDF. Therefore PDF is not based on damages to species but to ecosystem
productivity, although the method relates productivity to species richness. To align water impacts
with land use impacts, the inverse of the precipitation is used to quantify the area deprived of the
ecosystem with the underlying assumption that all water equally contributes to ecosystem quality,
whether evaporated on site or contributing to flow. Source: Pfister et al. (2009).
Water consumption impacts are primarily caused by evaporation of water in cooling towers or
increased evaporation due to elevated temperature of water in the natural water system after
once-through cooling.
Thermal emissions. For thermal emissions, the results are derived on analysis of 50 freshwater
species (fish, mollusks and other invertebrates) for (sub-)tropical and temperate regions. Sources:
Verones et al. (2010), De Vries et al. (2008).
Direct heat emissions to water are by far the most important contributor for power production.
Indirect heat release from landfill and through waste water treatment plant are considered of
minor relevance but included (by the assumption of heat transfer form waste water treatment and
landfilling of blow-down).
S18
Table S10: Comparison of midpoint water impacts between gas and coal based power generation per kWh.
US NGCC Electricity
Once-through
cooling
US Coal Electricity
Cooling
tower
Once-through
cooling
Cooling
tower
Freshwater eutrophication (kg P eq./ kWh) × 104
0.0383
0.0383
3.96
3.96
Freshwater ecotoxicity (PAF m3 yr/ kWh) × 104
61.3
61.3
2230
2230
Water consumption (PDF m2 yr/ kWh) × 104
1.17
2.11
1.95
3.47
Heat releases (PDF m3 day/ kWh) × 104
327
0.0647
540
0.113
Table S11: Comparison of endpoint water impacts between gas and coal based power generation per kWh.
US NGCC Electricity
Once-through
cooling
US Coal Electricity
Cooling Once-through
tower
cooling
(PDF m3 yr/kWh) × 104
Cooling
tower
Freshwater eutrophication
2.15
2.15
223
223
Freshwater ecotoxicity
18.4
18.4
670
670
Water consumption
7.34
13.2
12.2
21.7
Heat releases
0.896
0.00018
1.48
0.00031
Freshwater Acidification is mainly caused by precipitation of the airborne emissions ammonia
(NH3), sulfur dioxide (SO2), sulfur trioxide (SO3) sulfuric acid (H2SO4) and nitrogen oxides
(NOx). There is no recommended method for aquatic acidification so far (JRC 2011). Two recent
methods exists but have not yet been tested widely: (1) The recent version Q2.21 of the
“IMPACT 2002+” method (Humbert et al. 2012) provides global average CFs based on a
conversion factor of 8.82E-03 PDF·m2 ·y/kg SO2 that is applied to the midpoint CF by Guinee et
al. (2002). (2) A spatially explicit model by Roy et al. (2014), which provides high resolution as
well as country and continental average CFs for NH3, SO2 and NOx. We apply the conversion
S19
factors of CFs for SO3 and H2SO4 based on the CF for SO2 as reported by Guinee et al (2002) in
order to have CF’s for all relevant emissions as reported in table S12.
Table S12: CF (PDF m2 yr / kg substance emission) for freshwater acidification based on Roy et al. (2014) for the
US, European and global average and the IMPACT2002+ method vQ2.21 (Humbert et al. 2012)
IMPACT 2002+
Roy et al. (2014)
Representative for Europe
US average
Global average
9.64
European
average
4.24
Ammonia
0.014
Sulfur dioxide
Sulphur trioxide
Sulfuric acid
0.011
0.0085
0.0069
4.79
3.83
3.11
4.52
3.62
2.94
1.58
1.26
1.03
Nitrogen oxides
0.0044
1.56
1.37
1.85
0.80
Due to the very high difference in the CF values of IMPACT 2002+ and Roy et al. (2014), which
are not explained or discussed in either publication, and the limited experience with these
methods, we only account for this impact category in the discussion of the results. The results are
presented in Table S13, indicating a factor 422 and 450 higher impacts for coal and gas,
respectively, based on Roy et al. (2014) compared to IMPACT 2002+.
Table S13: midpoint and endpoint aquatic acidification impacts per kWh electricity for gas and coal power
generation based on the IMPACT2002+ method vQ2.21 (Humbert et al. 2012) and the Us values from Roy et al.
(2014).
Coal
Gas
Original units
[PDF m2 yr/kWh]
Roy et al.
IMPACT
2014
2002+
3.8E-03
9.1E-06
1.7E-02
3.8E-05
Endpoint units
[PDF m3 yr/kWh]
Roy et al.
IMPACT
2014
2002+
2.5E-02
5.9E-05
1.1E-01
2.4E-04
Normalized value
according to Table 1 [-]
Roy et al.
IMPACT
2014
2002+
0.372373
0.000881
1.639516
0.003643
Based on the CF of Roy et al. (2014), 97% of impacts in NGCC are due to SO2 emissions; mainly
during natural gas production (sourcing of natural gas due to combustion of unpurified “sour gas”
in gas turbines) and therefore in the supply chain. In coal power, 54% of freshwater acidification
effects are caused by SO2 emissions and 37% by NOx emissions; mainly form operation of coal
power plant. Since the state-of-the art coal power plant studied has flue-gas desulfurization, the
S20
overall emissions are ~4 times lower than for gas power plant (for both methods). However this is
not the case for average coal power installed in the US as indicated by the datasets of the “U.S.
Life Cycle Inventory Database” (NREL 2012), which have ~10 times higher acidifying emissions
for lignite and bituminous coal and even ~20 times higher for anthracite coal compared to our
results. For gas, our acidification results 41% lower than results based on NREL (2012), which
can be mainly explained by the high efficiency of the state-of-the-art power plant.
However, the acidifying emissions of natural gas production are questionable, since LCI analysis
by the US National Energy Technology Laboratory (NETL 2010), which assumes no sour gas
combustion, does not show high SOx emissions for natural gas power (~500 times lower than our
results). The assumption of no sour gas combustion leads to underestimation of SOx emissions,
since it is acknowledged that sour gas is directly combusted in many places in the US, even if
detailed data is not available and often self-reported (Moore et al 2014). Therefore, natural gas
acidification impacts are highly uncertain not only because of characterization models as
explained above, but also due to the uncertain estimates of SOx emissions in natural gas
production. This topic needs further research, which is beyond the scope of this paper.
Uncertainties of impact assessment methods. The uncertainties of the results are very high.
Some major uncertainties are coming from the toxicity assessment, where discrepancies between
the two-of-the-art impact assessment methods ReCiPe and USEtox are alarming. Further
uncertainty is caused by the conversion from PDF m2 yr to PDF m3 yr, where we estimate a kvalue of 8.5 according to Slob (1994) (the 95% interval is determined by µ/k and µ*k). The
transformation of USEtox midpoint CTU to endpoint is assumed to have a k-value of 3.2
Another uncertainty factor is the time horizon: Most toxicity impacts are coming from long-term
emissions of landfills, which are by ecoinvent definition (Doka and Hischier 2005) emissions that
occur after 100 years or later. Some impact assessment schemes tend to exclude these effects due
to uncertainty of occurrence.
The third major uncertainty is coming from the different approaches to model impacts on
ecosystem resulting in the same units. This issue needs to be further addressed in future research.
S21
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