Introduction Physics authors titles recent submissions

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1. Introduction

Fine particular matter PM 2.5 , airborne particles less than 2.5 m  in the aerodynamic diameter has been reported to be associated with many adverse health effects including cardiovascular and respiratory morbidity and mortality Habre et al., 2014; Madrigano et al., 2013. Previous studies have indicated that the severe PM 2.5 pollution resulted in more than 1.2 million premature deaths in 2010 Lim et al., 2012 and 1.6 million deaths in 2014 Rohde and Muller 2015. It is thus an urgent need to obtain accurate spatiotemporal distributions of ground-level PM 2.5 concentration for the environmental health concerns. With a large spatiotemporal coverage, the satellite-derived aerosol optical depth AOD has been widely used to expand PM 2.5 estimation beyond those only provided by ground monitoring stations Di et al., 2016; Li et al., 2016; van Donkelaar et al., 2016. Many AOD products have been adopted in the estimation of ground PM 2.5 , including those retrieved from the Moderate Resolution Imaging Spectroradiometer MODIS Li et al., 2017b, Multi-Angle Implementation of Atmospheric Correction MAIAC Xiao et al., 2017, Multiangle Imaging SpectroRadiometer MISR You et al., 2015, Geostationary Operational Environmental Satellite AerosolSmoke Product GASP Paciorek et al., 2008, and Visible Infrared Imaging Radiometer Suite VIIRS Wu et al., 2016. In addition, many models are developed to establish the relationship between AOD and PM 2.5 AOD-PM 2.5 relationship, such as multiple linear regression MLR Gupta and Christopher 2009b, geographically weighted regression GWR Hu et al., 2013, back-propagation neural network BPNN Gupta and Christopher 2009a, linear mixed effects LME model Lee et al., 2011, and so on. These models have played an irreplaceable role in modeling the relationship between satellite-derived AOD and 4 ground-observed PM 2.5 AOD-PM modeling. The AOD products are retrieved from satellite top-of-atmosphere TOA reflectance by the atmospheric radiative transfer model e.g., 6S, MODTRAN Kaufman et al., 1997a; Levy et al., 2007. Hence, the previous process of satellite-based PM 2.5 estimation is to firstly retrieve AOD from satellite TOA reflectance for AOD-PM 2.5 studies, the AOD products are usually provided by others, and subsequently estimate ground-level PM 2.5 from satellite-derived AOD. Previous studies have reported that a great uncertainty exists in the retrieval of AOD, especially in urban area Munchak et al., 2013. The retrieval error of AOD will inevitably be accumulated in the estimation of PM 2.5 . To avoid the intermediate errorprocess, whether can we estimate ground-level PM 2.5 directly from satellite reflectance? To estimate ground PM 2.5 from satellite reflectance is actually a process to model the relationship between them denoted as Ref-PM modeling using statistical approaches. Previous studies Radosavljevic et al., 2007; Ristovski et al., 2012 adopted neural networks to learn a functional relationship between MODIS observations and ground AOD, and the results show that neural-network-based AOD retrievals were more accurate than the physically based products. In addition, neural network models were developed to estimate ground-level PM 2.5 using satellite-derived AOD, and outperform the conventional models Li et al., 2017b; Wu et al., 2012. We can find that the physical process of AOD retrieval can be simulated by neural networks, and the same to AOD-PM 2.5 relationship. Therefore, previous studies provide sufficient evidences for the potential to estimate ground PM 2.5 from satellite reflectance, and neural network models are a preferred choice. According to our previous study Li et al., 2017a, deep learning, which is the further development of neural network, 5 may be a better tool for Ref-PM modeling. It is thus promising to estimate ground PM 2.5 directly from satellite reflectance. In this study, we propose a deep learning-based solution to estimate ground-level PM 2.5 using satellite reflectance rather than AOD products. The Ref-PM modeling is validated with the data from Wuhan Metropolitan Area Fig. 1 in 2016. We also establish the AOD-PM modeling using 3-km MODIS AOD products for comparison. The proposed solution for air quality assessment will significantly promote the application of satellite remote sensing in environmental monitoring.

2. Study region and data