File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data

TitleEstimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data
Authors
KeywordsAnthropogenic heat
China
Cubist
Points-of-interest
Remote sensing
Issue Date2020
Citation
Environmental Pollution, 2020, v. 266, article no. 115183 How to Cite?
AbstractRapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top–down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m−2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m−2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.
Persistent Identifierhttp://hdl.handle.net/10722/335851
ISSN
2023 Impact Factor: 7.6
2023 SCImago Journal Rankings: 2.132
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Qian-
dc.contributor.authorYang, Xuchao-
dc.contributor.authorOuyang, Zutao-
dc.contributor.authorZhao, Naizhuo-
dc.contributor.authorJiang, Qutu-
dc.contributor.authorYe, Tingting-
dc.contributor.authorQi, Jun-
dc.contributor.authorYue, Wenze-
dc.date.accessioned2023-12-28T08:49:13Z-
dc.date.available2023-12-28T08:49:13Z-
dc.date.issued2020-
dc.identifier.citationEnvironmental Pollution, 2020, v. 266, article no. 115183-
dc.identifier.issn0269-7491-
dc.identifier.urihttp://hdl.handle.net/10722/335851-
dc.description.abstractRapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top–down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m−2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m−2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.-
dc.languageeng-
dc.relation.ispartofEnvironmental Pollution-
dc.subjectAnthropogenic heat-
dc.subjectChina-
dc.subjectCubist-
dc.subjectPoints-of-interest-
dc.subjectRemote sensing-
dc.titleEstimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.envpol.2020.115183-
dc.identifier.pmid32673933-
dc.identifier.scopuseid_2-s2.0-85087787260-
dc.identifier.volume266-
dc.identifier.spagearticle no. 115183-
dc.identifier.epagearticle no. 115183-
dc.identifier.eissn1873-6424-
dc.identifier.isiWOS:000572960600010-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats