FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385694264> ?p ?o ?g. }

• W4385694264 endingPage "116866" @default.
• W4385694264 startingPage "116866" @default.
• W4385694264 abstract "Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction." @default.
• W4385694264 created "2023-08-10" @default.
• W4385694264 creator A5017246564 @default.
• W4385694264 creator A5055763417 @default.
• W4385694264 creator A5057422251 @default.
• W4385694264 creator A5085254387 @default.
• W4385694264 creator A5087953918 @default.
• W4385694264 date "2023-11-01" @default.
• W4385694264 modified "2023-10-17" @default.
• W4385694264 title "Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method" @default.
• W4385694264 cites W1529987495 @default.
• W4385694264 cites W1562322033 @default.
• W4385694264 cites W1964706288 @default.
• W4385694264 cites W1966119361 @default.
• W4385694264 cites W1995001525 @default.
• W4385694264 cites W2009091183 @default.
• W4385694264 cites W2049880621 @default.
• W4385694264 cites W2052648234 @default.
• W4385694264 cites W2071778288 @default.
• W4385694264 cites W2088791809 @default.
• W4385694264 cites W2148169128 @default.
• W4385694264 cites W2207584501 @default.
• W4385694264 cites W2340599229 @default.
• W4385694264 cites W2593577374 @default.
• W4385694264 cites W2804216629 @default.
• W4385694264 cites W2805155961 @default.
• W4385694264 cites W2886375368 @default.
• W4385694264 cites W2898892341 @default.
• W4385694264 cites W2905821642 @default.
• W4385694264 cites W2910247400 @default.
• W4385694264 cites W2910678430 @default.
• W4385694264 cites W2920688722 @default.
• W4385694264 cites W2930499469 @default.
• W4385694264 cites W2946497275 @default.
• W4385694264 cites W2969708871 @default.
• W4385694264 cites W2971216588 @default.
• W4385694264 cites W2972699721 @default.
• W4385694264 cites W2972998160 @default.
• W4385694264 cites W2986199940 @default.
• W4385694264 cites W2987619620 @default.
• W4385694264 cites W2990989151 @default.
• W4385694264 cites W2995678734 @default.
• W4385694264 cites W3005959450 @default.
• W4385694264 cites W3006842164 @default.
• W4385694264 cites W3006936644 @default.
• W4385694264 cites W3012037987 @default.
• W4385694264 cites W3013877406 @default.
• W4385694264 cites W3016719170 @default.
• W4385694264 cites W3030898280 @default.
• W4385694264 cites W3032512766 @default.
• W4385694264 cites W3036820233 @default.
• W4385694264 cites W3038405344 @default.
• W4385694264 cites W3044909247 @default.
• W4385694264 cites W3083760974 @default.
• W4385694264 cites W3083895593 @default.
• W4385694264 cites W3092501609 @default.
• W4385694264 cites W3096846826 @default.
• W4385694264 cites W3097445852 @default.
• W4385694264 cites W3122993262 @default.
• W4385694264 cites W3124266898 @default.
• W4385694264 cites W3125466270 @default.
• W4385694264 cites W3126720980 @default.
• W4385694264 cites W3128791930 @default.
• W4385694264 cites W3131607873 @default.
• W4385694264 cites W3137141913 @default.
• W4385694264 cites W3141849089 @default.
• W4385694264 cites W3164027654 @default.
• W4385694264 cites W3176297190 @default.
• W4385694264 cites W3185051919 @default.
• W4385694264 cites W3195698865 @default.
• W4385694264 cites W3200182853 @default.
• W4385694264 cites W3208003173 @default.
• W4385694264 cites W4220874831 @default.
• W4385694264 cites W4293243124 @default.
• W4385694264 cites W4316039248 @default.
• W4385694264 doi "https://doi.org/10.1016/j.envres.2023.116866" @default.
• W4385694264 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37567384" @default.
• W4385694264 hasPublicationYear "2023" @default.
• W4385694264 type Work @default.
• W4385694264 citedByCount "0" @default.
• W4385694264 crossrefType "journal-article" @default.
• W4385694264 hasAuthorship W4385694264A5017246564 @default.
• W4385694264 hasAuthorship W4385694264A5055763417 @default.
• W4385694264 hasAuthorship W4385694264A5057422251 @default.
• W4385694264 hasAuthorship W4385694264A5085254387 @default.
• W4385694264 hasAuthorship W4385694264A5087953918 @default.
• W4385694264 hasConcept C100970517 @default.
• W4385694264 hasConcept C105795698 @default.
• W4385694264 hasConcept C111368507 @default.
• W4385694264 hasConcept C127313418 @default.
• W4385694264 hasConcept C127413603 @default.
• W4385694264 hasConcept C146978453 @default.
• W4385694264 hasConcept C153294291 @default.
• W4385694264 hasConcept C154945302 @default.
• W4385694264 hasConcept C158709400 @default.
• W4385694264 hasConcept C166957645 @default.
• W4385694264 hasConcept C173727882 @default.
• W4385694264 hasConcept C18903297 @default.

• next