FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2762691029> ?p ?o ?g. }

• W2762691029 endingPage "300" @default.
• W2762691029 startingPage "289" @default.
• W2762691029 abstract "Determining the relationship between columnar aerosol optical depth (τext) and surface particulate matter concentrations (PM2.5) is desired to estimate surface aerosol concentrations over broad spatial and temporal scales using satellite remote sensing. However, remote sensing studies incur challenges when surface aerosol pollution (i.e. PM2.5) is not correlated with columnar conditions (i.e., τext). PM2.5 data fusion models that rely on satellite data and statistical relationships of τext and PM2.5 may not be able to capture the physical conditions impacting the relationships that cause columnar and surface aerosols to not be correlated in the western U.S. Therefore, an extensive examination of the atmospheric conditions is required to improve surface estimates of PM2.5 that rely on columnar aerosol measurements. This investigation uses datasets from both routine monitoring networks and models of meteorological variables and aerosol physical parameters to understand the atmospheric conditions under which surface aerosol pollution can be explained by column measurements in California and Nevada during 2013. A novel quadrant method, that utilizes statistical analysis, was developed to investigate the relationship between τext and PM2.5. The results from this investigation show that τext and PM2.5 had a positive association (τext and PM2.5 increase together) when local sources of pollution or wildfires dominated aerosol pollution in the presence of a deep and well-mixed planetary boundary layer (PBL). Moreover, τext and PM2.5 had no association (where the variables are not related) when stable conditions, long-range transport, or entrainment of air from above the PBL were observed. It was found that seasonal categorization of the relationship between τext and PM2.5, an approach commonly used in statistical models to estimate surface concentrations with satellite remote sensing, may not be enough to account for the atmospheric conditions that drive the relationships between τext and PM2.5. For all stations, winter showed the maximum average PM2.5 concentrations (14.1 μg m−3, σ = 11.6 μg m−3) meanwhile, τext reached minimum values (0.06 μg m−3, σ = 0.04) during the same season. Conversely, spring presented the minimum average PM2.5 concentrations (9.4 μg m−3, σ = 6.9 μg m−3) and the average values of τext during spring had the second highest values (0.11, σ = 0.06) averaged for all stations." @default.
• W2762691029 created "2017-10-20" @default.
• W2762691029 creator A5011861864 @default.
• W2762691029 creator A5021492042 @default.
• W2762691029 creator A5028340594 @default.
• W2762691029 creator A5045009966 @default.
• W2762691029 creator A5072957861 @default.
• W2762691029 creator A5084266905 @default.
• W2762691029 date "2017-12-01" @default.
• W2762691029 modified "2023-10-15" @default.
• W2762691029 title "Toward understanding atmospheric physics impacting the relationship between columnar aerosol optical depth and near-surface PM mass concentrations in Nevada and California, U.S.A., during 2013" @default.
• W2762691029 cites W1492748946 @default.
• W2762691029 cites W1579933083 @default.
• W2762691029 cites W1967433218 @default.
• W2762691029 cites W1967433657 @default.
• W2762691029 cites W1972585310 @default.
• W2762691029 cites W1979268012 @default.
• W2762691029 cites W1986515151 @default.
• W2762691029 cites W1991420765 @default.
• W2762691029 cites W1995123924 @default.
• W2762691029 cites W1997527250 @default.
• W2762691029 cites W2018630616 @default.
• W2762691029 cites W2031528200 @default.
• W2762691029 cites W2050250373 @default.
• W2762691029 cites W2066387723 @default.
• W2762691029 cites W2069977802 @default.
• W2762691029 cites W2075924373 @default.
• W2762691029 cites W2083540000 @default.
• W2762691029 cites W2086810878 @default.
• W2762691029 cites W2089433206 @default.
• W2762691029 cites W2103826029 @default.
• W2762691029 cites W2105667384 @default.
• W2762691029 cites W2108162680 @default.
• W2762691029 cites W2111689862 @default.
• W2762691029 cites W2139437201 @default.
• W2762691029 cites W2140282454 @default.
• W2762691029 cites W2142217564 @default.
• W2762691029 cites W2145203860 @default.
• W2762691029 cites W2150751323 @default.
• W2762691029 cites W2158989459 @default.
• W2762691029 cites W2161669929 @default.
• W2762691029 cites W2171171809 @default.
• W2762691029 cites W2175111766 @default.
• W2762691029 cites W2337698548 @default.
• W2762691029 cites W2404713802 @default.
• W2762691029 cites W2416278194 @default.
• W2762691029 cites W2510748614 @default.
• W2762691029 cites W3091458773 @default.
• W2762691029 doi "https://doi.org/10.1016/j.atmosenv.2017.10.023" @default.
• W2762691029 hasPublicationYear "2017" @default.
• W2762691029 type Work @default.
• W2762691029 sameAs 2762691029 @default.
• W2762691029 citedByCount "14" @default.
• W2762691029 countsByYear W27626910292018 @default.
• W2762691029 countsByYear W27626910292019 @default.
• W2762691029 countsByYear W27626910292020 @default.
• W2762691029 countsByYear W27626910292021 @default.
• W2762691029 countsByYear W27626910292022 @default.
• W2762691029 countsByYear W27626910292023 @default.
• W2762691029 crossrefType "journal-article" @default.
• W2762691029 hasAuthorship W2762691029A5011861864 @default.
• W2762691029 hasAuthorship W2762691029A5021492042 @default.
• W2762691029 hasAuthorship W2762691029A5028340594 @default.
• W2762691029 hasAuthorship W2762691029A5045009966 @default.
• W2762691029 hasAuthorship W2762691029A5072957861 @default.
• W2762691029 hasAuthorship W2762691029A5084266905 @default.
• W2762691029 hasBestOaLocation W27626910291 @default.
• W2762691029 hasConcept C121332964 @default.
• W2762691029 hasConcept C126857682 @default.
• W2762691029 hasConcept C127313418 @default.
• W2762691029 hasConcept C1276947 @default.
• W2762691029 hasConcept C135343436 @default.
• W2762691029 hasConcept C139992725 @default.
• W2762691029 hasConcept C147789679 @default.
• W2762691029 hasConcept C153294291 @default.
• W2762691029 hasConcept C178790620 @default.
• W2762691029 hasConcept C185592680 @default.
• W2762691029 hasConcept C18903297 @default.
• W2762691029 hasConcept C19269812 @default.
• W2762691029 hasConcept C196558001 @default.
• W2762691029 hasConcept C205649164 @default.
• W2762691029 hasConcept C24245907 @default.
• W2762691029 hasConcept C24890656 @default.
• W2762691029 hasConcept C2779345167 @default.
• W2762691029 hasConcept C39432304 @default.
• W2762691029 hasConcept C521259446 @default.
• W2762691029 hasConcept C559116025 @default.
• W2762691029 hasConcept C62649853 @default.
• W2762691029 hasConcept C86338904 @default.
• W2762691029 hasConcept C86803240 @default.
• W2762691029 hasConcept C91586092 @default.
• W2762691029 hasConceptScore W2762691029C121332964 @default.
• W2762691029 hasConceptScore W2762691029C126857682 @default.
• W2762691029 hasConceptScore W2762691029C127313418 @default.
• W2762691029 hasConceptScore W2762691029C1276947 @default.
• W2762691029 hasConceptScore W2762691029C135343436 @default.
• W2762691029 hasConceptScore W2762691029C139992725 @default.
• W2762691029 hasConceptScore W2762691029C147789679 @default.

• next