Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Filetype[PDF-3.50 MB]


Details Supporting Files You May Also Like

Details:

  • Journal Title:
    Atmospheric Measurement Techniques
  • Personal Author:
  • NOAA Program & Office:
    NCAS (Center for Atmospheric Science at Howard University)
  • Description:
    Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ∼ 56, ∼ 34 and ∼ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ∼ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ∼ 66 and ∼ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.
  • Keywords:
  • Source:
    Atmospheric Measurement Techniques, 10(6), 2093-2104
  • DOI:
  • Document Type:
    Journal Article
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26