Derivation of PM10 mass concentration from advanced satellite retrieval products based on a semi-empirical physical approach
Introduction
PM10 (particulate matter with an aerodynamic diameter less than 10 μm), the inhalable particles, acting as a transmission vector for viruses (Schuit et al., 2020; Yan et al., 2018), has adverse health effects (Dobaradaran et al., 2016; Jeong, 2013; Khaniabadi et al., 2017; Nourmoradi et al., 2015; Wang et al., 2009). Hence, monitoring of PM10 is of great significance. However, due to the weak extinction of coarse particles to sunlight, remote sensing of PM10 from satellites is more challenging than PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm). Generally, the PM10 is estimated using ground-based measurements. In-situ monitoring can provide accurate day and night PM10 measurements. In China, in-situ stations are mainly located in eastern cities. It has limited spatial coverage and spatial representation. Moreover, the selection of the appropriate monitoring locations is also critical. Commonly, the PM10 is monitored based on the tapered element oscillating microbalance (TEOM) technique or with beta attenuation monitors. Instrument calibration and manual operation at different stations may probably reduce the comparability of data. Satellite remote sensing technique is a useful tool, which can complement the lack of in-situ PM10 measurements, especially over ocean, the regions between land and ocean, and the Northwestern China. In addition, high-resolution remote sensing results have large spatial coverage and spatial representation, which can provide scientific basis for site selection on the ground. Of course, it also depends on the development and improvement of satellite sensors and inversion approaches. Moreover, the missing data during the period without in-situ stations can be reconstructed based on satellite historical observation.
In these regards, the satellite observations with large spatial coverage can represent an extension to existing ground networks. Currently, there are mainly three methods of determining PM10 from satellite remote sensing data. One is the mathematical statistical method, which is of high accuracy (Lee et al., 2010; Li et al., 2005; Liu et al., 2005; Seo et al., 2015; Wu et al., 2016; Guo et al., 2017), but somewhat lacking of clear physical mechanism. The second one is the chemical transport model method, which cannot operate without an accurate emission inventory or metrological field (Boys et al., 2014; Geng et al., 2015; Van Donkelaar et al., 2014; Van Donkelaar et al., 2010). The third one is the semi-empirical physical approach. For semi-empirical physical approach, the relationship between each variable and PM is clearly expressed through reasonable physical assumptions (Lin et al., 2015; Yan et al., 2017; Zhang and Li, 2015). Easy and fast operation is the premier advantage of this method. There is an obvious nonlinear relationship between PM and satellite-derived aerosol optical depth (AOD), hence the aerosol mass extinction efficiency and the effective radius are two important factors characterizing this nonlinear relationship (Koelemeijer et al., 2006; Van Donkelaar et al., 2006; Wang et al., 2010). However, to determine above two aerosol properties is usually difficult in practice. In these regards, the Volume-to-Extinction Ratio (VE10) that adequately characterize the nonlinear relationship between extinction and volume can be used as an alternative to mass extinction efficiency and effective radius in capturing relationship between PM and AOD.
Many sensors currently can offer AOD products of high accuracy. However, few fine mode fraction (FMF) products are available from satellite remote sensing (Jethva et al., 2010; Kleidman et al., 2005; Levy et al., 2010; Levy et al., 2007). This is due to the down looking satellite measurements of surface reflected radiance are not very sensitive to particle size. The GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm derives an extended set of aerosol parameters benefiting from multi-angular polarimetry POLDER/PARASOL measurements (Dubovik et al., 2011, Dubovik et al., 2014). A solid quality of retrieved aerosol characteristics can be obtained by PARASOL/GRASP products (Chen et al., 2020). In this study, we use the POLDER/GRASP aerosol products including AOD, fine mode AOD, and the real part of the refractive index data at 490 nm, which are public available at GRASP-Open website (https://www.grasp-open.com/). Previous study by Wei et al. (2020a) indicated a high quality of POLDER/GRASP aerosol products (e.g. AOD, FMF, fine mode AOD, etc.) over China. In this study, we firstly design the sensitivity tests to investigate the impact of cutting diameter on VE10. Then, the VE10 model is established and tested based on data from Aerosol Robotic Network (AERONET). The extinction-volume conversion, vertical correction, humidity correction and weighting are applied based on the semi-empirical physical approach. The PM10 remote sensing method is developed and the results are compared with the in-situ measurements over China from Jan. to Oct. in 2013. Finally, we discuss the error sources of derived PM10 and provide both the theoretical and expected error estimations of the method.
Section snippets
Principles of PM10 remote sensing method
A multi-parameter remote sensing formula to calculate dry PM2.5 mass concentration near surface was established by Zhang and Li (2015). In order to obtain the near-surface fine particle extinction from fine mode AOD, the aerosol vertical variability in the real atmosphere as a uniform mixture vertical distribution in the planetary boundary layer is assumed. Then, a relationship between fine particle extinction and its volume is developed to convert fine particle extinction to its volume. The
Statistical study based on ground-based observations
The VE10 model is studied with consideration of different aerosol types using AERONET (Holben et al., 1998) observation around the world. In this study, datasets collected over approximately 10 years at seven AERONET sites are used. The Lev.1.5 AOD at 500 nm, FMF derived from spectral AOD using spectral decomposition approach (O'Neill et al., 2003, O'Neill et al., 2001), and PVSD (Particle Volume Size Distribution) data inverted from sky-radiance by Dubovik and King (2000) are used in this
Estimation of PM10 based on POLDER/GRASP aerosol products
In order to demonstrate the developed PM10 remote sensing method, we use the advanced POLDER/GRASP products over China. The aerosol products from multi-angular polarimetry POLDER/PARASOL are expected to be reliable because the polarization signal received by the sensor mainly comes from the radiation contribution of fine-mode aerosol (Deuzé et al., 2001; Tanré et al., 2011). The latest PARASOL aerosol products generated by the GRASP algorithm (Dubovik et al., 2011, Dubovik et al., 2014)
Discussion
In addition to the theoretical errors of two semi-empirical models, the inter-comparison of columnar volume-to-extinction ratio, and particle effective density between previous studies and this study is discussed in this section. Moreover, a sensitivity test is conducted to discuss how sensitive is this approach when distinct elevated layers with large AOD values are present above the PBL. Meanwhile, the influences of inputs' uncertainties are also discussed.
Conclusions
The improved remote sensing method of near-surface PM2.5 by using semi-empirical physical models has been proposed and demonstrated. The columnar volume-to-extinction ratio, which can realize the conversion of optical extinction to volume concentration, was found to be well described by a quadratic polynomial function of FMF. In addition, the particle effective density can be obtained from the real part of the complex refractive index. Both semi-empirical physical models of columnar
Funding
This work was supported by the National Outstanding Youth Foundation of China [Grant number 41925019]; the National Key R&D Program of China [Grant number 2016YFE0201400]; the National Natural Science Foundation of China [Grant number 41671367]; O. Dubovik and C. Chen recognize support from the CaPPA Project (Chemical and Physical Properties of the Atmosphere) that is funded by the French National Research Agency under contract “ANR-11-LABX-0005-01funded by the French National Research Agency
Disclosures
The authors declare that there is no conflict of interest regarding the publication of this paper.
Data availability
The ERA5 data sets are publicly available from ECMWF website at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 (last access: 20 May 2019). The authors would like to acknowledge the use of POLDER data “POLDER/PARASOL Level-1 data originally provided by CNES (http://www.icare.univ-lille1.fr/) and AERIS/ICARE Data and Services Center. The POLDER/GRASP results are generated by Laboratoire d'Optique Atmosphérique and Cloudflight Austria GmbH using GRASP software (//www.grasp-open.com
Declaration of Competing Interest
None.
Acknowledgements
Thanks to the AERONET team for providing the data for this study. Thanks to Brent Holben, the AERONET Project Scientist, Physical Scientist at NASA, the PI of Solar Village site, Ascension Island site, Lanai site, GSFC site and Mongu site. Thanks to Paulo Artaxo, the professor of the University of São Paulo, the PI of Cuiaba Miranda site. We also acknowledge the use of ECMWF data and POLDER/GRASP data.
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