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

Abstract

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.

Retrieval of aerosol profiles combining sunphotometer and ceilometer measurements in GRASP code

“…Ceilometers only measure at one wavelength and are less accurate than classic lidars, but they are cheaper and more operative than multiwavelength lidar systems and they also can work continuously unattended. In fact, ceilometers have been previously used to obtain aerosol properties as PM2.5 (Li et al, 2017), PM10 (Münkel et al, 2007), aerosol backscatter coefficients (Heese et al, 2010;Wiegner and Geiss, 2012;Wiegner et al, 2014;Madonna et al, 2015) or aerosol hygroscopic growth (Haeffelin et al, 2016). Moreover, there are some programs nowadays as E-PROFILE, a program of EUMETNET (EUropean METeorological services NETwork), and the COST Action ES1303 TOPROF (TOwards operational ground based PROFiling with ceilometers, doppler lidars and microwave radiometers for improving weather forecasts) dealing with the harmonization and better characterization of ceilometer measurements and products;…”

Analysis of influential factors for the relationship between PM<sub>2.5</sub> and AOD in Beijing

“…Recent studies have proposed methods to estimate the surface PM 2.5 based on the AOD observations from satellites (van Donkelaar et al, 2006(van Donkelaar et al, , 2010(van Donkelaar et al, , 2013Drury et al, 2008;Wang et al, 2010b;Xin et al, 2016). Although PM 2.5 from AOD has no high temporal resolution and is not available when it is cloudy or very pollutant, these methods provide the spatial distribution of PM 2.5 globally or regionally (Paciorek et al, 2008;Li et al, 2017;Wang et al, 2017).…”

Herramientas para controlar los delitos contra la fauna y flora en Colombia

“…La teledetección implica la recopilación e interpretación de datos obtenidos sobre la superficie de la tierra a distancia, gracias a la información satelital, es una herramienta tan potente que permite calcular, por ejemplo, la cantidad de aerosoles suspendidos en la atmósfera en una columna dada con base en la medición de la extinción de la luz (van Donkelaar et al, 2010). Como ventajas, esta herramienta obtiene información en amplias áreas, y como limitaciones se tiene la disponibilidad de información en la noche y en condiciones nubladas, aunque se avanza en modelos matemáticos que permitan identificar características en el ambiente bajo estas condiciones (Li et al, 2017).…”