Abstract. We present a new method to quantify NOx emissions and corresponding atmospheric lifetimes from OMI NO2 observations together with ECMWF wind fields without further model input for sources located in a polluted background. NO2 patterns under calm wind conditions are used as proxy for the spatial patterns of NOx emissions, and the effective atmospheric NOx lifetime is determined from the change of spatial patterns measured at larger wind speeds. Emissions are subsequently derived from the NO2 mass above the background, integrated around the source of interest. Lifetimes and emissions are estimated for 17 power plants and 53 cities located in non-mountainous regions across China and the USA. The derived lifetimes for the ozone season (May–September) are 3.8 ± 1.0 h (mean ± standard deviation) with a range of 1.8 to 7.5 h. The derived NOx emissions show generally good agreement with bottom-up inventories for power plants and cities. Regional inventory shows better agreement with top-down estimates for Chinese cities compared to global inventory, most likely due to different downscaling approaches adopted in the two inventories.

Power plant exhausts can be identified and quantified from satellite observations.

Abstract. Air mass factor (AMF) calculation is the largest source of uncertainty in NO2 and HCHO satellite retrievals in situations with enhanced trace gas concentrations in the lower troposphere. Structural uncertainty arises when different retrieval methodologies are applied within the scientific community to the same satellite observations. Here, we address the issue of AMF structural uncertainty via a detailed comparison of AMF calculation methods that are structurally different between seven retrieval groups for measurements from the Ozone Monitoring Instrument (OMI). We estimate the escalation of structural uncertainty in every sub-step of the AMF calculation process. This goes beyond the algorithm uncertainty estimates provided in state-of-the-art retrievals, which address the theoretical propagation of uncertainties for one particular retrieval algorithm only. We find that top-of-atmosphere reflectances simulated by four radiative transfer models (RTMs) (DAK, McArtim, SCIATRAN and VLIDORT) agree within 1.5 %. We find that different retrieval groups agree well in the calculations of altitude resolved AMFs from different RTMs (to within 3 %), and in the tropospheric AMFs (to within 6 %) as long as identical ancillary data (surface albedo, terrain height, cloud parameters and trace gas profile) and cloud and aerosol correction procedures are being used. Structural uncertainty increases sharply when retrieval groups use their preference for ancillary data, cloud and aerosol correction. On average, we estimate the AMF structural uncertainty to be 42 % over polluted regions and 31 % over unpolluted regions, mostly driven by substantial differences in the a priori trace gas profiles, surface albedo and cloud parameters. Sensitivity studies for one particular algorithm indicate that different cloud correction approaches result in substantial AMF differences in polluted conditions (5 to 40 % depending on cloud fraction and cloud pressure, and 11 % on average) even for low cloud fractions (<  0.2) and the choice of aerosol correction introduces an average uncertainty of 50 % for situations with high pollution and high aerosol loading. Our work shows that structural uncertainty in AMF calculations is significant and that it is mainly caused by the assumptions and choices made to represent the state of the atmosphere. In order to decide which approach and which ancillary data are best for AMF calculations, we call for well-designed validation exercises focusing on polluted conditions in which AMF structural uncertainty has the highest impact on NO2 and HCHO retrievals.

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O 4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing directions are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky images taken from the ground and backscatter profiles from a lidar.

Abstract.We present an analysis of SO 2 column densities derived from GOME-2 satellite measurements for the Kılauea volcano (Hawai'i) for [2007][2008][2009][2010][2011][2012]. During a period of enhanced degassing activity in March-November 2008, monthly mean SO 2 emission rates and effective SO 2 lifetimes are determined simultaneously from the observed downwind plume evolution and meteorological wind fields, without further model input. Kılauea is particularly suited for quantitative investigations from satellite observations owing to the absence of interfering sources, the clearly defined downwind plumes caused by steady trade winds, and generally low cloud fractions. For March-November 2008, the effective SO 2 lifetime is 1-2 days, and Kılauea SO 2 emission rates are 9-21 kt day −1 , which is about 3 times higher than initially reported from ground-based monitoring systems.