Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 3.089 IF 3.089
  • IF 5-year<br/> value: 3.700 IF 5-year
    3.700
  • CiteScore<br/> value: 3.59 CiteScore
    3.59
  • SNIP value: 1.273 SNIP 1.273
  • SJR value: 2.026 SJR 2.026
  • IPP value: 3.082 IPP 3.082
  • h5-index value: 45 h5-index 45
Atmos. Meas. Tech., 10, 2093-2104, 2017
https://doi.org/10.5194/amt-10-2093-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
08 Jun 2017
Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter
Siwei Li1, Everette Joseph2, Qilong Min2, Bangsheng Yin2, Ricardo Sakai1, and Megan K. Payne1 1NOAA Center for Atmospheric Sciences, Howard University, Washington, DC 20001, USA
2Atmospheric Sciences Research Center, State University of New York at Albany, Albany, NY 12203, USA
Abstract. 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.

Citation: Li, S., Joseph, E., Min, Q., Yin, B., Sakai, R., and Payne, M. K.: Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter, Atmos. Meas. Tech., 10, 2093-2104, https://doi.org/10.5194/amt-10-2093-2017, 2017.
Publications Copernicus
Download
Short summary
Monitoring fine aerosol concentration is important because of the adverse impacts of high fine-particle concentration on human health. However, monitoring fine aerosols is difficult during cloudy and nighttime periods. In this study, an empirical model using measurements from ceilometers was developed to measure fine aerosol mass concentration even under cloudy or nighttime conditions. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring.
Monitoring fine aerosol concentration is important because of the adverse impacts of high...
Share