Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data
Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study ma...
Ausführliche Beschreibung
Autor*in: |
Wang, Shasha [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020transfer abstract |
---|
Übergeordnetes Werk: |
Enthalten in: SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota - Wang, Meimei ELSEVIER, 2018, an international journal for scientific research into the environment and its relationship with man, Amsterdam [u.a.] |
---|---|
Übergeordnetes Werk: |
volume:734 ; year:2020 ; day:10 ; month:09 ; pages:0 |
Links: |
---|
DOI / URN: |
10.1016/j.scitotenv.2020.139457 |
---|
Katalog-ID: |
ELV050602055 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV050602055 | ||
003 | DE-627 | ||
005 | 20230626030840.0 | ||
007 | cr uuu---uuuuu | ||
008 | 200625s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.scitotenv.2020.139457 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica |
035 | |a (DE-627)ELV050602055 | ||
035 | |a (ELSEVIER)S0048-9697(20)32974-0 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 630 |a 640 |a 610 |q VZ |
100 | 1 | |a Wang, Shasha |e verfasserin |4 aut | |
245 | 1 | 0 | |a Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
264 | 1 | |c 2020transfer abstract | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. | ||
520 | |a Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. | ||
700 | 1 | |a Hu, Deyong |4 oth | |
700 | 1 | |a Yu, Chen |4 oth | |
700 | 1 | |a Chen, Shanshan |4 oth | |
700 | 1 | |a Di, Yufei |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Wang, Meimei ELSEVIER |t SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |d 2018 |d an international journal for scientific research into the environment and its relationship with man |g Amsterdam [u.a.] |w (DE-627)ELV001360035 |
773 | 1 | 8 | |g volume:734 |g year:2020 |g day:10 |g month:09 |g pages:0 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.scitotenv.2020.139457 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a SSG-OLC-PHA | ||
951 | |a AR | ||
952 | |d 734 |j 2020 |b 10 |c 0910 |h 0 |
author_variant |
s w sw |
---|---|
matchkey_str |
wangshashahudeyongyuchenchenshanshandiyu:2020----:apnciatmsreatrpgncetlxihnetrmtoadu |
hierarchy_sort_str |
2020transfer abstract |
publishDate |
2020 |
allfields |
10.1016/j.scitotenv.2020.139457 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica (DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Wang, Shasha verfasserin aut Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Hu, Deyong oth Yu, Chen oth Chen, Shanshan oth Di, Yufei oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:734 year:2020 day:10 month:09 pages:0 https://doi.org/10.1016/j.scitotenv.2020.139457 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 734 2020 10 0910 0 |
spelling |
10.1016/j.scitotenv.2020.139457 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica (DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Wang, Shasha verfasserin aut Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Hu, Deyong oth Yu, Chen oth Chen, Shanshan oth Di, Yufei oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:734 year:2020 day:10 month:09 pages:0 https://doi.org/10.1016/j.scitotenv.2020.139457 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 734 2020 10 0910 0 |
allfields_unstemmed |
10.1016/j.scitotenv.2020.139457 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica (DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Wang, Shasha verfasserin aut Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Hu, Deyong oth Yu, Chen oth Chen, Shanshan oth Di, Yufei oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:734 year:2020 day:10 month:09 pages:0 https://doi.org/10.1016/j.scitotenv.2020.139457 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 734 2020 10 0910 0 |
allfieldsGer |
10.1016/j.scitotenv.2020.139457 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica (DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Wang, Shasha verfasserin aut Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Hu, Deyong oth Yu, Chen oth Chen, Shanshan oth Di, Yufei oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:734 year:2020 day:10 month:09 pages:0 https://doi.org/10.1016/j.scitotenv.2020.139457 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 734 2020 10 0910 0 |
allfieldsSound |
10.1016/j.scitotenv.2020.139457 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica (DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 DE-627 ger DE-627 rakwb eng 630 640 610 VZ Wang, Shasha verfasserin aut Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. Hu, Deyong oth Yu, Chen oth Chen, Shanshan oth Di, Yufei oth Enthalten in Elsevier Science Wang, Meimei ELSEVIER SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota 2018 an international journal for scientific research into the environment and its relationship with man Amsterdam [u.a.] (DE-627)ELV001360035 volume:734 year:2020 day:10 month:09 pages:0 https://doi.org/10.1016/j.scitotenv.2020.139457 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 734 2020 10 0910 0 |
language |
English |
source |
Enthalten in SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota Amsterdam [u.a.] volume:734 year:2020 day:10 month:09 pages:0 |
sourceStr |
Enthalten in SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota Amsterdam [u.a.] volume:734 year:2020 day:10 month:09 pages:0 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
dewey-raw |
630 |
isfreeaccess_bool |
false |
container_title |
SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |
authorswithroles_txt_mv |
Wang, Shasha @@aut@@ Hu, Deyong @@oth@@ Yu, Chen @@oth@@ Chen, Shanshan @@oth@@ Di, Yufei @@oth@@ |
publishDateDaySort_date |
2020-01-10T00:00:00Z |
hierarchy_top_id |
ELV001360035 |
dewey-sort |
3630 |
id |
ELV050602055 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV050602055</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626030840.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200625s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.scitotenv.2020.139457</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV050602055</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0048-9697(20)32974-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Shasha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Deyong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Chen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Shanshan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Di, Yufei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Wang, Meimei ELSEVIER</subfield><subfield code="t">SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal for scientific research into the environment and its relationship with man</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV001360035</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:734</subfield><subfield code="g">year:2020</subfield><subfield code="g">day:10</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.scitotenv.2020.139457</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">734</subfield><subfield code="j">2020</subfield><subfield code="b">10</subfield><subfield code="c">0910</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
author |
Wang, Shasha |
spellingShingle |
Wang, Shasha ddc 630 Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
authorStr |
Wang, Shasha |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV001360035 |
format |
electronic Article |
dewey-ones |
630 - Agriculture & related technologies 640 - Home & family management 610 - Medicine & health |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
630 640 610 VZ Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
topic |
ddc 630 |
topic_unstemmed |
ddc 630 |
topic_browse |
ddc 630 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
d h dh c y cy s c sc y d yd |
hierarchy_parent_title |
SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |
hierarchy_parent_id |
ELV001360035 |
dewey-tens |
630 - Agriculture 640 - Home & family management 610 - Medicine & health |
hierarchy_top_title |
SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV001360035 |
title |
Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
ctrlnum |
(DE-627)ELV050602055 (ELSEVIER)S0048-9697(20)32974-0 |
title_full |
Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
author_sort |
Wang, Shasha |
journal |
SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |
journalStr |
SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
zzz |
container_start_page |
0 |
author_browse |
Wang, Shasha |
container_volume |
734 |
class |
630 640 610 VZ |
format_se |
Elektronische Aufsätze |
author-letter |
Wang, Shasha |
doi_str_mv |
10.1016/j.scitotenv.2020.139457 |
dewey-full |
630 640 610 |
title_sort |
mapping china's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
title_auth |
Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
abstract |
Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. |
abstractGer |
Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. |
abstract_unstemmed |
Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
title_short |
Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data |
url |
https://doi.org/10.1016/j.scitotenv.2020.139457 |
remote_bool |
true |
author2 |
Hu, Deyong Yu, Chen Chen, Shanshan Di, Yufei |
author2Str |
Hu, Deyong Yu, Chen Chen, Shanshan Di, Yufei |
ppnlink |
ELV001360035 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth |
doi_str |
10.1016/j.scitotenv.2020.139457 |
up_date |
2024-07-06T17:59:02.351Z |
_version_ |
1803853488300490752 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV050602055</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626030840.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">200625s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.scitotenv.2020.139457</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001169.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV050602055</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0048-9697(20)32974-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Shasha</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000–2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2–5 W·m−2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0–2 W·m−2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Deyong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Chen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Shanshan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Di, Yufei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Wang, Meimei ELSEVIER</subfield><subfield code="t">SPG-56 from Sweet potato Zhongshu-1 delayed growth of tumor xenografts in nude mice by modulating gut microbiota</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal for scientific research into the environment and its relationship with man</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV001360035</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:734</subfield><subfield code="g">year:2020</subfield><subfield code="g">day:10</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.scitotenv.2020.139457</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">734</subfield><subfield code="j">2020</subfield><subfield code="b">10</subfield><subfield code="c">0910</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
score |
7.4001713 |