## 1 Introduction
In recent years, the tropospheric NO\({}_{2}\) retrieval algorithm has seen continuous improvements in these three aspects. In terms of DOAS fitting, [PERSON] et al. (2015) improved the spectral fitting of NO\({}_{2}\) from OMI in the 405-465 nm spectral window by updating the wavelength calibration, improving the OMI slit function, and accounting for the weak absorbers of the O\({}_{2}\)-O\({}_{2}\) collision complex and liquid water [6]. Most OMI SCD retrieval algorithms from the QA4 ECV ([[http://www.qa4](http://www.qa4) ecv.eu/]([http://www.qa4](http://www.qa4) ecv.eu/)) consortium's institutes are adopting the optical depth fitting method in the spectral range of 405-465 nm and including the intensity offset correction [28]. The newly updated DOAS retrieval algorithm for the GOME-2 instrument applies linear intensity offset correction to counteract the additional contribution to the scattering intensity [29]. One of the earliest STS methods is the reference sector method (RSM), which assumes that the contribution of the troposphere in the clean Pacific region can be neglected, so the total column in this region can be regarded as the proxy of the global stratosphere NO\({}_{2}\) column [23]. However, this simple method can causes systematic artifacts in the stratosphere. Thereafter, a modified reference sector method (MRSM) was proposed by [PERSON] et al. (2013), which defines \"unpolluted\" pixels based on the a priori knowledge of the tropospheric contribution derived from a global chemical transport model (CTM) [26; 30]. There is another method that directly uses the stratospheric NO\({}_{2}\) concentrations provided by CTMs for STS. However, the results of these two methods greatly depend on the CTMoutput [27; 31]. Recently, stratospheric NO\({}_{2}\) column estimation was improved by the STRatospheric Estimation Algorithm from the Mainz (STREAM) method, which is a flexible and robust algorithm and requires no auxiliary data from chemical transport models. It directly determined the stratospheric NO\({}_{2}\) concentration based on the total column measurements by assigning a high weighting factor for mid-altitude cloudy pixels and a low weighting factor for potentially polluted pixels. It has been successfully applied to estimate the stratospheric NO\({}_{2}\) from GOME, SCIAMACHY, GOME-2, and OMI instruments [32]. Tropospheric AMF is the largest uncertainty source of tropospheric NO\({}_{2}\) retrievals, especially for polluted regions [33]. It is calculated by a radiative transfer model (RTM) and is affected by many factors, e.g., surface pressure, surface albedo, viewing geometry, and a priori profiles. Previous studies have put forward many improvements in AMF calculation to consider these factors more realistically. For instance, [PERSON] et al. (2014, 2015) adjusted the surface pressure information from GOES CTM by the GMTED2010 surface elevation dataset [34; 35]. Besides, the higher-resolution a priori NO\({}_{2}\) profiles from TM5-MP CTM have been used in several studies to derive AMFs [28; 29; 36]. Inspired by the development of sensors and algorithms and to fulfill the EMI's potential application in air quality monitoring, in this study, a total and tropospheric NO\({}_{2}\) retrieval algorithm based on EMI measurements is presented, which also includes the three key steps introduced above. A brief description of the EMI instrument and data used in this study are given in Section 2. The NO\({}_{2}\) retrieval algorithm for EMI is presented in Section 3, and some preliminary results and comparisons with OMI and TROPOMI NO\({}_{2}\) products are presented in Section 4. Section 5 gives a summary and the path forward for this work.