A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations

Abstract

Abstract. In this proof-of-concept paper, we apply a bulk-mass-modeling method using observations from the NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument for retrieving particulate matter (PM) concentration over the contiguous United States (CONUS) over a 2-year period (2008–2009). Different from previous approaches that rely on empirical relationships between aerosol optical depth (AOD) and PM2.5 (PM with particle diameters less than 2.5 µm), for the first time, we derive PM2.5 concentrations, during both daytime and nighttime, from near-surface CALIOP aerosol extinction retrievals using bulk mass extinction coefficients and model-based hygroscopicity. Preliminary results from this 2-year study conducted over the CONUS show a good agreement (r2∼0.48; mean bias of −3.3 µg m−3) between the averaged nighttime CALIOP-derived PM2.5 and ground-based PM2.5 (with a lower r2 of ∼0.21 for daytime; mean bias of −0.4 µg m−3), suggesting that PM concentrations can be obtained from active-based spaceborne observations with reasonable accuracy. Results from sensitivity studies suggest that accurate aerosol typing is needed for applying CALIOP measurements for PM2.5 studies. Lastly, the e-folding correlation length for surface PM2.5 is found to be around 600 km for the entire CONUS (∼300 km for western CONUS and ∼700 km for eastern CONUS), indicating that CALIOP observations, although sparse in spatial coverage, may still be applicable for PM2.5 studies.

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

“…Compared with the existing methods [15,[20][21][22][23][24][25][26]30,31], although just having a modest improvement (4% for the empirical method) for the test correlation between the simulated GAC and PM 2.5 , this proposed method provides a more flexible framework to fuse the influence of multiple scaling, shift, and other potential factors on the conversion that involves complex atmospheric and chemical processes of aerosols; this method is convenient to train and use in support of the AD tool, and makes the conversion easily adjusted or improved using the data of additional influential factors if available. The polynomial conversion from proxy GAC to PM 2.5 was conducted to employ the stable RSE loss function to obtain a slightly better correlation than linear conversion (0.58 vs. 0.56).…”

“…Furthermore, Li et al [23] also conducted particle correction of AOD for PM 2.5 and Zeng et al [25] also used visibility for vertical correction. For available vertical profile information in conversion, cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) can provide the extinction profile data for conversion [31] with limited spatiotemporal coverage [17] and coarse spatial resolution of 2 • (latitude) × 5 • (longitude) [27]. However, for the purpose of applications, a formula of conversion from satellite AOD of MODIS to ground extinction coefficient is more practical than using LIDAR sensors to obtain the vertical profile that is not always available.…”

Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance

“…In spite of this limitation, Toth et al (2019) still found that CALIOP has some representative skill to estimate PM2.5 within a few hundred kilometers of an observation over the United States (US).…”

“…Most satellite studies about PM2.5 estimate are based on aerosol optical depth (AOD) product (Shin et al, 2019 and references therein). These studies are mainly based on three groups of approaches: statistical methods including machine learning (e.g., Lee et al, 2011;Hu et al, 2017;He and Huang, 2018;Wei et al, 2019a;Xue et al, 2019), chemical transport models (Geng et al, 2015;Di et al, 2016;van Donkelaar et al, 2016), and vertical correction models (Zhang and Li, 2015;Gong et al, 2017;Li et al, 2018;Toth et al, 2019). The performance of each approach is affected by the study area and period, as well as the spatial and temporal resolutions of data (Shin et al, 2019).…”

Temporal and Spatial Autocorrelation as Determinants of Regional AOD-PM2.5 Model Performance in the Middle East

“…In another study of MODIS AOD over eastern North America, Sullivan et al (2015) found that spatial coherence r for AOD dropped below 0.3 at ∼750 km on average, and that the range of the semivariogram for the summer was almost twice that of the winter (∼2200 km vs. ∼1300 km, respectively) [23]. Toth et al (2019) studied decay in spatial correlation for PM 2.5 in the contiguous U.S. (CONUS) and found the e-folding length in correlation (distance or time for correlation to reduce below 1/e, about 0.37) to be ∼600 km; regional analysis by 10 km bin averages found the e-folding length to be ∼700 km in the eastern CONUS and ∼300 km in the western CONUS [24]. In their temporal autocorrelation analysis in the Southeastern U.S., Kaku et al (2018) found that the e-folding time was 3 days for ground-monitored PM 2.5 and only 1 day for ground-monitored AERONET AOD.…”