Matches in SemOpenAlex for { <https://semopenalex.org/work/W3049270178> ?p ?o ?g. }
- W3049270178 endingPage "11036" @default.
- W3049270178 startingPage "11025" @default.
- W3049270178 abstract "Black carbon (BC), the strongest light-absorbing particle, is believed to play substantial roles in regional air quality and global climate change. In this study, taking advantage of the high quality of moderate resolution imaging spectroradiometer products, we developed a new algorithm to estimate the BC columnar concentrations over China by simulating the BC and non-BC aerosol mixing states in detail. The results show that our new algorithm produces a reliable estimation of BC aerosols, in which BC columnar concentrations and their related parameters (aerosol absorption and BC surface concentration) show reasonable agreements and low biases compared with ground-based measurements. The uncertainties of BC retrievals are mainly associated with the surface and aerosol assumptions used in the algorithm, ranging from −14 to 44% at higher aerosol optical depth (AOD > 0.5). The proposed algorithm can improve the capability of space-borne aerosol remote sensing by successfully distinguishing BC from other aerosols. The acquired BC columnar concentrations enable the spatial pattern of serious BC aerosol pollution over East China to be characterized, showing that it exhibits higher levels in winter. These nationwide results are beneficial for estimating BC emissions, proposing mitigation strategies for air pollution, and potentially reducing the uncertainties of climate change studies." @default.
- W3049270178 created "2020-08-21" @default.
- W3049270178 creator A5013128812 @default.
- W3049270178 creator A5023844035 @default.
- W3049270178 creator A5042529535 @default.
- W3049270178 creator A5061624232 @default.
- W3049270178 creator A5088654499 @default.
- W3049270178 date "2020-08-13" @default.
- W3049270178 modified "2023-10-14" @default.
- W3049270178 title "Estimating the Columnar Concentrations of Black Carbon Aerosols in China Using MODIS Products" @default.
- W3049270178 cites W1559399347 @default.
- W3049270178 cites W1606767976 @default.
- W3049270178 cites W1693050202 @default.
- W3049270178 cites W1907369419 @default.
- W3049270178 cites W1970631746 @default.
- W3049270178 cites W1971641092 @default.
- W3049270178 cites W1977518971 @default.
- W3049270178 cites W1977702720 @default.
- W3049270178 cites W1977992740 @default.
- W3049270178 cites W1983146165 @default.
- W3049270178 cites W1987337512 @default.
- W3049270178 cites W2000516706 @default.
- W3049270178 cites W2004691206 @default.
- W3049270178 cites W2007791627 @default.
- W3049270178 cites W2012090324 @default.
- W3049270178 cites W2020994040 @default.
- W3049270178 cites W2032398531 @default.
- W3049270178 cites W2032481287 @default.
- W3049270178 cites W2036020873 @default.
- W3049270178 cites W2037490486 @default.
- W3049270178 cites W2041829466 @default.
- W3049270178 cites W2051583395 @default.
- W3049270178 cites W2054036271 @default.
- W3049270178 cites W2054799169 @default.
- W3049270178 cites W2072464080 @default.
- W3049270178 cites W2084089284 @default.
- W3049270178 cites W2085435232 @default.
- W3049270178 cites W2089433206 @default.
- W3049270178 cites W2093512959 @default.
- W3049270178 cites W2095598361 @default.
- W3049270178 cites W2099493323 @default.
- W3049270178 cites W2101443406 @default.
- W3049270178 cites W2103977502 @default.
- W3049270178 cites W2107045929 @default.
- W3049270178 cites W2109252580 @default.
- W3049270178 cites W2112335693 @default.
- W3049270178 cites W2119713599 @default.
- W3049270178 cites W2121757798 @default.
- W3049270178 cites W2125763679 @default.
- W3049270178 cites W2131140592 @default.
- W3049270178 cites W2132002340 @default.
- W3049270178 cites W2136495481 @default.
- W3049270178 cites W2144742083 @default.
- W3049270178 cites W2150587437 @default.
- W3049270178 cites W2162214076 @default.
- W3049270178 cites W2164990341 @default.
- W3049270178 cites W2167929905 @default.
- W3049270178 cites W2324091296 @default.
- W3049270178 cites W2337267684 @default.
- W3049270178 cites W2340031338 @default.
- W3049270178 cites W2417623769 @default.
- W3049270178 cites W2465847861 @default.
- W3049270178 cites W2472277974 @default.
- W3049270178 cites W2508987909 @default.
- W3049270178 cites W2751473031 @default.
- W3049270178 cites W2783794519 @default.
- W3049270178 cites W2793324643 @default.
- W3049270178 cites W2804267160 @default.
- W3049270178 cites W2806385655 @default.
- W3049270178 cites W2888110008 @default.
- W3049270178 cites W2891119716 @default.
- W3049270178 cites W2898407312 @default.
- W3049270178 cites W2900908395 @default.
- W3049270178 cites W2931269009 @default.
- W3049270178 cites W2938639417 @default.
- W3049270178 cites W2959270227 @default.
- W3049270178 cites W2981043630 @default.
- W3049270178 cites W3023485003 @default.
- W3049270178 cites W621873917 @default.
- W3049270178 doi "https://doi.org/10.1021/acs.est.0c00816" @default.
- W3049270178 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32790296" @default.
- W3049270178 hasPublicationYear "2020" @default.
- W3049270178 type Work @default.
- W3049270178 sameAs 3049270178 @default.
- W3049270178 citedByCount "12" @default.
- W3049270178 countsByYear W30492701782021 @default.
- W3049270178 countsByYear W30492701782022 @default.
- W3049270178 countsByYear W30492701782023 @default.
- W3049270178 crossrefType "journal-article" @default.
- W3049270178 hasAuthorship W3049270178A5013128812 @default.
- W3049270178 hasAuthorship W3049270178A5023844035 @default.
- W3049270178 hasAuthorship W3049270178A5042529535 @default.
- W3049270178 hasAuthorship W3049270178A5061624232 @default.
- W3049270178 hasAuthorship W3049270178A5088654499 @default.
- W3049270178 hasConcept C108597893 @default.
- W3049270178 hasConcept C111368507 @default.
- W3049270178 hasConcept C120665830 @default.
- W3049270178 hasConcept C121332964 @default.