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Exploration of PM networks and data over the US: AQS and VIEWS

Exploration of PM networks and data over the US: AQS and VIEWS. Presented at CAPITA group seminar, 2011-04-21 Li Du and Ratish Menon CAPITA, St Louis. Conceptual framework of aerosol characterization.

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Exploration of PM networks and data over the US: AQS and VIEWS

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  1. Exploration of PM networks and data over the US: AQS and VIEWS Presented at CAPITA group seminar, 2011-04-21 Li Duand Ratish Menon CAPITA, St Louis

  2. Conceptual framework of aerosol characterization http://datafedwiki.wustl.edu/index.php/2011-01-12:_The_conceptual_framework_of_aerosol_characterization

  3. http://datafedwiki.wustl.edu/index.php/Workspace,_analyses#Datasets_.26_networkshttp://datafedwiki.wustl.edu/index.php/Workspace,_analyses#Datasets_.26_networks Coverage of datasets 6 day EPA FRM PM10 3 day 1 day EPA FRM PM25 6 day 3 day 1 day VIEWS aerosol chemistry Continuous monitoring Data count per station Station count time series

  4. Datafed browser user interface

  5. Data processing scheme Aggregates data from all the stations in the selected spatial coverage Raw data Select spatial and temporal coverage Moving window smoothing operator for spatially and temporally aggregated data Spatially aggregated data is aggregated over time range

  6. Distribution function 95th percentile Big Bend National Park, TX ug/m3 84th percentile average 50th percentile 16th percentile

  7. Sub-region division of contiguous US

  8. Time trend of PM in two different subregions PM25 PM10

  9. 30 Episodicity identification E 0 5 50 38 E=95th Percentile/50th Percentile E 0

  10. http://datafedwiki.wustl.edu/index.php/2011-04-06:_ConUS_regional_analyses_using_AQS_H,_AQS_D_http://datafedwiki.wustl.edu/index.php/2011-04-06:_ConUS_regional_analyses_using_AQS_H,_AQS_D_ and_VIEWS#Analyses_Results

  11. Assessment of spatial and temporal variation Spatial variation coefficient of variation average average Temporal variation average average coefficient of variation

  12. Spatial and temporal variation – Industrial Midwest http://datafedwiki.wustl.edu/index.php/2011-04-10:_Methodology_of_assessing_spatial_and_temporal_variations Spatial variation Temporal variation Dust Dust Sulfate Sulfate

  13. AQS_H data network assessment classified by types of instrument Beta attenuation TEOM Nephelometer FDMS Data count per station Station count time series

  14. PM25 Correlation: Continuous monitoring and EPA FRM Industrial Midwest Daily average of hourly PM25 EPA FRM PM25

  15. Industrial Midwest seasonal correlations Spring Summer Fall Winter

  16. Industrial Midwest (winter season) Rural Suburban Urban

  17. Industrial Midwest Winter season Rural Beta Attenuation TEOM TEOM Suburban Beta Attenuation FDMS Beta Attenuation TEOM Urban FDMS

  18. PM25 Correlation: Continuous monitoring and EPA FRM Northwest Daily average of hourly PM25 Overall EPA FRM PM25

  19. Northwest seasonal correlations Spring Summer Winter Fall

  20. Northwest (spring season) Urban Rural Suburban

  21. Northwest spring season Rural Beta Attenuation TEOM Nephelometer Suburban Beta Attenuation TEOM Nephelometer Urban TEOM Beta Attenuation Nephelometer

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