Estimating ground-level PM2.5using aerosol optical depth determined from satellite remote sensing

Author(s):  
Aaron van Donkelaar ◽  
Randall V. Martin ◽  
Rokjin J. Park
2019 ◽  
Vol 16 (9) ◽  
pp. 1343-1347 ◽  
Author(s):  
Yibo Sun ◽  
Qiaolin Zeng ◽  
Bing Geng ◽  
Xinwen Lin ◽  
Bilige Sude ◽  
...  

Author(s):  
Charles Marseille ◽  
Martin Aubé ◽  
Africa Barreto Velasco ◽  
Alexandre Simoneau

The aerosol optical depth is an important indicator of aerosol particle properties and associated radiative impacts. AOD determination is therefore very important to achieve relevant climate modeling. Most remote sensing techniques to retrieve aerosol optical depth are applicable to daytime given the high level of light available. The night represents half of the time but in such conditions only a few remote sensing techniques are available. Among these techniques, the most reliable are moon photometers and star photometers. In this paper, we attempt to fill gaps in the aerosol detection performed with the aforementioned techniques using night sky brightness measurements during moonless nights with the novel CoSQM: a portable, low cost and open-source multispectral photometer. In this paper, we present an innovative method for estimating the aerosol optical depth by using an empirical relationship between the zenith night sky brightness measured at night with the CoSQM and the aerosol optical depth retrieved at daytime from the AErosol Robotic NETwork. Such a method is especially suited to light-polluted regions with light pollution sources located within a few kilometers of the observation site. A coherent day-to-night aerosol optical depth and Ångström Exponent evolution in a set of 354 days and nights from August 2019 to February 2021 was verified at the location of Santa Cruz de Tenerife on the island of Tenerife, Spain. The preliminary uncertainty of this technique was evaluated using the variance under stable day-to-night conditions, set at 0.02 for aerosol optical depth and 0.75 for Ångström Exponent. These results indicate the set of CoSQM and the proposed methodology appear to be a promising tool to add new information on the aerosol optical properties at night, which could be of key importance to improve climate predictions.


2015 ◽  
Vol 15 (12) ◽  
pp. 17251-17281 ◽  
Author(s):  
J. Xu ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
J. Kim ◽  
M. Choi ◽  
...  

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in East China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean Geostationary Ocean Color Imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5/AOD ratio exhibits high consistency with ground-based measurements (MFB = −0.52–8.0 %). We evaluate the satellite-derived PM2.5 vs. the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in-situ observations in both annual averages (r = 0.81, N = 494) and monthly averages (MFB = 13.1 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical speciation of GOCI-derived PM2.5 reveals that secondary inorganics (SO42−, NO3−, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating are responsible for an increase in the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over East China for 2013 is 53.8 μg m−3, threatening the health and life expectancy of its 600 million residents.


2016 ◽  
Vol 168 ◽  
pp. 169-179 ◽  
Author(s):  
Wei You ◽  
Zengliang Zang ◽  
Lifeng Zhang ◽  
Mei Zhang ◽  
Xiaobin Pan ◽  
...  

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