- ข้อความ
- ประวัติศาสตร์
concerns about climate change are t
concerns about climate change are the motiva-
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
specified levels of GHG reductions through the
use of biofuels and the lack of published infor-
mation about how the emerging ethanol indus-
try is currently performing in relation to these
mandates provide justification for the objectives
of the current study. Our goal is to quantify the
NEY and GHG emissions of corn-ethanol systems
on the basis of an integrated understanding of
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
plied generally to all corn-ethanol at a national
or global level and are not specific to a particular
corn-ethanol biorefinery facility and associated
corn supply. Instead, our focus is on direct-effect
life cycle GHG emissions and the degree of vari-
ation due to differences in the efficiencies of crop
production, ethanol conversion, and coproduct
utilization of recently built ethanol biorefiner-
ies and related advanced systems. This informa-
tion is captured with LCA software called the
Biofuel Energy Systems Simulator (available at
www.bess.unl.edu).
LCA of Corn-Ethanol Systems
Direct-effect life cycle energy and GHG as-
sessment of corn-ethanol considers the energy
used for feedstock production and harvesting,
including fossil fuels (primarily diesel) for field
operations and electricity for grain drying and
irrigation (Liska and Cassman 2008). Energy ex-
pended in crop production also includes upstream
costs for the production of fertilizer, pesticides,
and seed; depreciable cost of manufacturing farm
machinery; and the energy required in the pro-
duction of fossil fuels and electricity. Energy used
in the conversion of corn to ethanol includes
transportation of grain to the biorefinery, grain
milling, starch liquefaction and hydrolysis, fer-
mentation to biofuel, and coproduct processing
and transport. Energy used for the construction
of the biorefinery itself is also included in the
assessment and is prorated over the life of the
facility.
Most previous LCA studies evaluated the ef-
ficiency of the entire U.S. corn-ethanol industry,
which requires the use of aggregate data on av-
erage crop and biorefinery performance parame-
ters (Farrell et al. 2006). These studies rely on
U.S. Corn Belt averages for corn yields, hus-
bandry practices, and crop production input rates
based on weighted state averages and average
biorefinery efficiency based on both wet and dry
mill types. Such estimates do not capture the
variability among individual biorefineries, and
they utilize data on crop production and ethanol
plant energy requirements that are obsolete com-
pared to plants built within the past 3 years,
which account for the majority of current ethanol
production.
There are also different methods for determin-
ing coproduct energy credits. The approach used
most widely is the displacement method, which
assumes that coproducts from corn-ethanol pro-
duction substitute for other products that require
energy in their production. For corn-ethanol, dis-
tillers grains coproducts are the unfermentable
components in corn grain, including protein, oil,
and lignocellulosic seed coat material (Klopfen-
stein et al. 2008). As such, distillers grains
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
specified levels of GHG reductions through the
use of biofuels and the lack of published infor-
mation about how the emerging ethanol indus-
try is currently performing in relation to these
mandates provide justification for the objectives
of the current study. Our goal is to quantify the
NEY and GHG emissions of corn-ethanol systems
on the basis of an integrated understanding of
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
plied generally to all corn-ethanol at a national
or global level and are not specific to a particular
corn-ethanol biorefinery facility and associated
corn supply. Instead, our focus is on direct-effect
life cycle GHG emissions and the degree of vari-
ation due to differences in the efficiencies of crop
production, ethanol conversion, and coproduct
utilization of recently built ethanol biorefiner-
ies and related advanced systems. This informa-
tion is captured with LCA software called the
Biofuel Energy Systems Simulator (available at
www.bess.unl.edu).
LCA of Corn-Ethanol Systems
Direct-effect life cycle energy and GHG as-
sessment of corn-ethanol considers the energy
used for feedstock production and harvesting,
including fossil fuels (primarily diesel) for field
operations and electricity for grain drying and
irrigation (Liska and Cassman 2008). Energy ex-
pended in crop production also includes upstream
costs for the production of fertilizer, pesticides,
and seed; depreciable cost of manufacturing farm
machinery; and the energy required in the pro-
duction of fossil fuels and electricity. Energy used
in the conversion of corn to ethanol includes
transportation of grain to the biorefinery, grain
milling, starch liquefaction and hydrolysis, fer-
mentation to biofuel, and coproduct processing
and transport. Energy used for the construction
of the biorefinery itself is also included in the
assessment and is prorated over the life of the
facility.
Most previous LCA studies evaluated the ef-
ficiency of the entire U.S. corn-ethanol industry,
which requires the use of aggregate data on av-
erage crop and biorefinery performance parame-
ters (Farrell et al. 2006). These studies rely on
U.S. Corn Belt averages for corn yields, hus-
bandry practices, and crop production input rates
based on weighted state averages and average
biorefinery efficiency based on both wet and dry
mill types. Such estimates do not capture the
variability among individual biorefineries, and
they utilize data on crop production and ethanol
plant energy requirements that are obsolete com-
pared to plants built within the past 3 years,
which account for the majority of current ethanol
production.
There are also different methods for determin-
ing coproduct energy credits. The approach used
most widely is the displacement method, which
assumes that coproducts from corn-ethanol pro-
duction substitute for other products that require
energy in their production. For corn-ethanol, dis-
tillers grains coproducts are the unfermentable
components in corn grain, including protein, oil,
and lignocellulosic seed coat material (Klopfen-
stein et al. 2008). As such, distillers grains
0/5000
concerns about climate change are the motiva-tion for establishment of an emissions tradingmarket in the Europe Union and the Chicago Cli-mate Exchange in the United States (Ellermanand Buchner 2007). In addition, cap-and-tradesystems for GHG reduction will be implementedin seven northeastern states under the RegionalGreenhouse Gas Initiative (www.rggi.org) andin a five-state Western Climate Initiative, witha national program looming (Kintisch 2007).Given these trends, standard metrics and life cy-cle assessment (LCA) methods using updatedindustry data are needed to provide accurateestimates of the GHG emissions from biofu-els to (1) comply with national renewable fuelstandards and state-level LCFSs, (2) participatein emerging markets that allow monetizationof GHG mitigation (McElroy 2007; Liska andCassman 2008), and (3) reduce negative envi-ronmental impacts of biofuels at regional, na-tional, and international levels (Lewandowskiand Faaij 2006; Roundtable on Sustainable Bio-fuels, http://cgse.epfl.ch/page65660.html).The recent legislative mandates to achievespecified levels of GHG reductions through theuse of biofuels and the lack of published infor-mation about how the emerging ethanol indus-try is currently performing in relation to thesemandates provide justification for the objectivesof the current study. Our goal is to quantify theNEY and GHG emissions of corn-ethanol systemson the basis of an integrated understanding ofhow current systems are operating with regard tocrop and soil management, ethanol biorefining,and coproduct utilization by livestock. Emissionsfrom the indirect effects of land use change thatoccur in response to commodity price increasesattributable to expanded biofuel production (e.g.,Searchinger et al. 2008) are not considered inour study, because such indirect effects are ap-plied generally to all corn-ethanol at a nationalor global level and are not specific to a particularcorn-ethanol biorefinery facility and associatedcorn supply. Instead, our focus is on direct-effectlife cycle GHG emissions and the degree of vari-ation due to differences in the efficiencies of cropproduction, ethanol conversion, and coproductutilization of recently built ethanol biorefiner-ies and related advanced systems. This informa-tion is captured with LCA software called theBiofuel Energy Systems Simulator (available atwww.bess.unl.edu).LCA of Corn-Ethanol SystemsDirect-effect life cycle energy and GHG as-sessment of corn-ethanol considers the energyused for feedstock production and harvesting,including fossil fuels (primarily diesel) for fieldoperations and electricity for grain drying andirrigation (Liska and Cassman 2008). Energy ex-pended in crop production also includes upstreamcosts for the production of fertilizer, pesticides,and seed; depreciable cost of manufacturing farmmachinery; and the energy required in the pro-duction of fossil fuels and electricity. Energy usedin the conversion of corn to ethanol includestransportation of grain to the biorefinery, grainmilling, starch liquefaction and hydrolysis, fer-mentation to biofuel, and coproduct processingand transport. Energy used for the constructionof the biorefinery itself is also included in theassessment and is prorated over the life of thefacility.Most previous LCA studies evaluated the ef-ficiency of the entire U.S. corn-ethanol industry,which requires the use of aggregate data on av-erage crop and biorefinery performance parame-ters (Farrell et al. 2006). These studies rely onU.S. Corn Belt averages for corn yields, hus-bandry practices, and crop production input ratesbased on weighted state averages and averagebiorefinery efficiency based on both wet and drymill types. Such estimates do not capture thevariability among individual biorefineries, andthey utilize data on crop production and ethanolplant energy requirements that are obsolete com-pared to plants built within the past 3 years,which account for the majority of current ethanolproduction.There are also different methods for determin-ing coproduct energy credits. The approach usedmost widely is the displacement method, whichassumes that coproducts from corn-ethanol pro-duction substitute for other products that requireenergy in their production. For corn-ethanol, dis-tillers grains coproducts are the unfermentablecomponents in corn grain, including protein, oil,and lignocellulosic seed coat material (Klopfen-stein et al. 2008). As such, distillers grains
การแปล กรุณารอสักครู่..
concerns about climate change are the motiva-
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
specified levels of GHG reductions through the
use of biofuels and the lack of published infor-
mation about how the emerging ethanol indus-
try is currently performing in relation to these
mandates provide justification for the objectives
of the current study. Our goal is to quantify the
NEY and GHG emissions of corn-ethanol systems
on the basis of an integrated understanding of
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
plied generally to all corn-ethanol at a national
or global level and are not specific to a particular
corn-ethanol biorefinery facility and associated
corn supply. Instead, our focus is on direct-effect
life cycle GHG emissions and the degree of vari-
ation due to differences in the efficiencies of crop
production, ethanol conversion, and coproduct
utilization of recently built ethanol biorefiner-
ies and related advanced systems. This informa-
tion is captured with LCA software called the
Biofuel Energy Systems Simulator (available at
www.bess.unl.edu).
LCA of Corn-Ethanol Systems
Direct-effect life cycle energy and GHG as-
sessment of corn-ethanol considers the energy
used for feedstock production and harvesting,
including fossil fuels (primarily diesel) for field
operations and electricity for grain drying and
irrigation (Liska and Cassman 2008). Energy ex-
pended in crop production also includes upstream
costs for the production of fertilizer, pesticides,
and seed; depreciable cost of manufacturing farm
machinery; and the energy required in the pro-
duction of fossil fuels and electricity. Energy used
in the conversion of corn to ethanol includes
transportation of grain to the biorefinery, grain
milling, starch liquefaction and hydrolysis, fer-
mentation to biofuel, and coproduct processing
and transport. Energy used for the construction
of the biorefinery itself is also included in the
assessment and is prorated over the life of the
facility.
Most previous LCA studies evaluated the ef-
ficiency of the entire U.S. corn-ethanol industry,
which requires the use of aggregate data on av-
erage crop and biorefinery performance parame-
ters (Farrell et al. 2006). These studies rely on
U.S. Corn Belt averages for corn yields, hus-
bandry practices, and crop production input rates
based on weighted state averages and average
biorefinery efficiency based on both wet and dry
mill types. Such estimates do not capture the
variability among individual biorefineries, and
they utilize data on crop production and ethanol
plant energy requirements that are obsolete com-
pared to plants built within the past 3 years,
which account for the majority of current ethanol
production.
There are also different methods for determin-
ing coproduct energy credits. The approach used
most widely is the displacement method, which
assumes that coproducts from corn-ethanol pro-
duction substitute for other products that require
energy in their production. For corn-ethanol, dis-
tillers grains coproducts are the unfermentable
components in corn grain, including protein, oil,
and lignocellulosic seed coat material (Klopfen-
stein et al. 2008). As such, distillers grains
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
specified levels of GHG reductions through the
use of biofuels and the lack of published infor-
mation about how the emerging ethanol indus-
try is currently performing in relation to these
mandates provide justification for the objectives
of the current study. Our goal is to quantify the
NEY and GHG emissions of corn-ethanol systems
on the basis of an integrated understanding of
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
plied generally to all corn-ethanol at a national
or global level and are not specific to a particular
corn-ethanol biorefinery facility and associated
corn supply. Instead, our focus is on direct-effect
life cycle GHG emissions and the degree of vari-
ation due to differences in the efficiencies of crop
production, ethanol conversion, and coproduct
utilization of recently built ethanol biorefiner-
ies and related advanced systems. This informa-
tion is captured with LCA software called the
Biofuel Energy Systems Simulator (available at
www.bess.unl.edu).
LCA of Corn-Ethanol Systems
Direct-effect life cycle energy and GHG as-
sessment of corn-ethanol considers the energy
used for feedstock production and harvesting,
including fossil fuels (primarily diesel) for field
operations and electricity for grain drying and
irrigation (Liska and Cassman 2008). Energy ex-
pended in crop production also includes upstream
costs for the production of fertilizer, pesticides,
and seed; depreciable cost of manufacturing farm
machinery; and the energy required in the pro-
duction of fossil fuels and electricity. Energy used
in the conversion of corn to ethanol includes
transportation of grain to the biorefinery, grain
milling, starch liquefaction and hydrolysis, fer-
mentation to biofuel, and coproduct processing
and transport. Energy used for the construction
of the biorefinery itself is also included in the
assessment and is prorated over the life of the
facility.
Most previous LCA studies evaluated the ef-
ficiency of the entire U.S. corn-ethanol industry,
which requires the use of aggregate data on av-
erage crop and biorefinery performance parame-
ters (Farrell et al. 2006). These studies rely on
U.S. Corn Belt averages for corn yields, hus-
bandry practices, and crop production input rates
based on weighted state averages and average
biorefinery efficiency based on both wet and dry
mill types. Such estimates do not capture the
variability among individual biorefineries, and
they utilize data on crop production and ethanol
plant energy requirements that are obsolete com-
pared to plants built within the past 3 years,
which account for the majority of current ethanol
production.
There are also different methods for determin-
ing coproduct energy credits. The approach used
most widely is the displacement method, which
assumes that coproducts from corn-ethanol pro-
duction substitute for other products that require
energy in their production. For corn-ethanol, dis-
tillers grains coproducts are the unfermentable
components in corn grain, including protein, oil,
and lignocellulosic seed coat material (Klopfen-
stein et al. 2008). As such, distillers grains
การแปล กรุณารอสักครู่..
concerns about climate change are the motiva-
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
ระบุระดับของก๊าซเรือนกระจกลดลงผ่าน
ใช้เชื้อเพลิงชีวภาพและการตีพิมพ์สำหรับข้อมูลเกี่ยวกับวิธีใหม่ -
ลองสินธุ - เอทานอลในปัจจุบันคือการแสดงในความสัมพันธ์เหล่านี้
เอกสารให้เหตุผลสำหรับวัตถุประสงค์
ของปัจจุบันการศึกษา เป้าหมายของเราคือปริมาณการปล่อยก๊าซเรือนกระจกของระบบ
เนย์ และเอทานอลข้าวโพด
บนพื้นฐานของความเข้าใจรวมของ
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
tion for establishment of an emissions trading
market in the Europe Union and the Chicago Cli-
mate Exchange in the United States (Ellerman
and Buchner 2007). In addition, cap-and-trade
systems for GHG reduction will be implemented
in seven northeastern states under the Regional
Greenhouse Gas Initiative (www.rggi.org) and
in a five-state Western Climate Initiative, with
a national program looming (Kintisch 2007).
Given these trends, standard metrics and life cy-
cle assessment (LCA) methods using updated
industry data are needed to provide accurate
estimates of the GHG emissions from biofu-
els to (1) comply with national renewable fuel
standards and state-level LCFSs, (2) participate
in emerging markets that allow monetization
of GHG mitigation (McElroy 2007; Liska and
Cassman 2008), and (3) reduce negative envi-
ronmental impacts of biofuels at regional, na-
tional, and international levels (Lewandowski
and Faaij 2006; Roundtable on Sustainable Bio-
fuels, http://cgse.epfl.ch/page65660.html).
The recent legislative mandates to achieve
ระบุระดับของก๊าซเรือนกระจกลดลงผ่าน
ใช้เชื้อเพลิงชีวภาพและการตีพิมพ์สำหรับข้อมูลเกี่ยวกับวิธีใหม่ -
ลองสินธุ - เอทานอลในปัจจุบันคือการแสดงในความสัมพันธ์เหล่านี้
เอกสารให้เหตุผลสำหรับวัตถุประสงค์
ของปัจจุบันการศึกษา เป้าหมายของเราคือปริมาณการปล่อยก๊าซเรือนกระจกของระบบ
เนย์ และเอทานอลข้าวโพด
บนพื้นฐานของความเข้าใจรวมของ
how current systems are operating with regard to
crop and soil management, ethanol biorefining,
and coproduct utilization by livestock. Emissions
from the indirect effects of land use change that
occur in response to commodity price increases
attributable to expanded biofuel production (e.g.,
Searchinger et al. 2008) are not considered in
our study, because such indirect effects are ap-
การแปล กรุณารอสักครู่..
ภาษาอื่น ๆ
การสนับสนุนเครื่องมือแปลภาษา: กรีก, กันนาดา, กาลิเชียน, คลิงออน, คอร์สิกา, คาซัค, คาตาลัน, คินยารวันดา, คีร์กิซ, คุชราต, จอร์เจีย, จีน, จีนดั้งเดิม, ชวา, ชิเชวา, ซามัว, ซีบัวโน, ซุนดา, ซูลู, ญี่ปุ่น, ดัตช์, ตรวจหาภาษา, ตุรกี, ทมิฬ, ทาจิก, ทาทาร์, นอร์เวย์, บอสเนีย, บัลแกเรีย, บาสก์, ปัญจาป, ฝรั่งเศส, พาชตู, ฟริเชียน, ฟินแลนด์, ฟิลิปปินส์, ภาษาอินโดนีเซี, มองโกเลีย, มัลทีส, มาซีโดเนีย, มาราฐี, มาลากาซี, มาลายาลัม, มาเลย์, ม้ง, ยิดดิช, ยูเครน, รัสเซีย, ละติน, ลักเซมเบิร์ก, ลัตเวีย, ลาว, ลิทัวเนีย, สวาฮิลี, สวีเดน, สิงหล, สินธี, สเปน, สโลวัก, สโลวีเนีย, อังกฤษ, อัมฮาริก, อาร์เซอร์ไบจัน, อาร์เมเนีย, อาหรับ, อิกโบ, อิตาลี, อุยกูร์, อุสเบกิสถาน, อูรดู, ฮังการี, ฮัวซา, ฮาวาย, ฮินดี, ฮีบรู, เกลิกสกอต, เกาหลี, เขมร, เคิร์ด, เช็ก, เซอร์เบียน, เซโซโท, เดนมาร์ก, เตลูกู, เติร์กเมน, เนปาล, เบงกอล, เบลารุส, เปอร์เซีย, เมารี, เมียนมา (พม่า), เยอรมัน, เวลส์, เวียดนาม, เอสเปอแรนโต, เอสโทเนีย, เฮติครีโอล, แอฟริกา, แอลเบเนีย, โคซา, โครเอเชีย, โชนา, โซมาลี, โปรตุเกส, โปแลนด์, โยรูบา, โรมาเนีย, โอเดีย (โอริยา), ไทย, ไอซ์แลนด์, ไอร์แลนด์, การแปลภาษา.
- ว้าว คุณอยู่ที่ไหน
- แมงมุม
- Necklaces and rings are accessories.
- ไกลมากๆ
- กระชาย
- แปลเป็นภาษาไทย
- Can you buy a box of cornflakes?
- Information,
- He didn't pay any atten0tion to you
- ถ้าวันหนึ่งฉันสามารถมีพลังอันมหาศาลสามาร
- Merci pour une expérience formidable.
- โปรเจค
- โดนตัวอะไรต่อยไม่รู้อ่ะ เจ็บ ไม่ใช่ผึ้ง
- A little while agoBeen working..
- อากาศตอนนี้เป็นไงบ้าง
- On her instagram Minji's mom said that M
- Cooling down the engine of a car
- I am having my lunch now. time here in t
- คุณจะโอนเงินมายังไง
- คุณจะช่วยฉันใช่ไหม
- 我在中国人中说稍。
- in the winter season the summit of Doi I
- ฉันอาจจะรักคุณมากเกินไป
- ตกใจ
