NO<sub><i>x</i></sub> lifetimes and emissions of cities and power plants in polluted background estimated by satellite observations

Abstract

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.

High-resolution airborne imaging DOAS measurements of NO<sub>2</sub> above Bucharest during AROMAT

“…A typical lifetime of NO x is 3.8 h as shown in Liu et al (2016), where lifetimes of urban NO x were estimated from satellite data. For simplicity, the lifetime of NO x , τ , is set to 3.8 h.…”

“…10 Estimation of the urban NO x emission rate Several studies have investigated the NO x emission rate of point sources and urban areas Shaiganfar et al, 2011;Beirle et al, 2011;Liu et al, 2016). Here we adapt the method presented in Ibrahim et al (2010) and Shaiganfar et al (2011), where urban emissions are estimated from an encircled area, by integrating along the route of a circle S. This method is based on Gauss's divergence theorem, describing the relation between the flux of a vector field through a closed surface (measured) and the divergence of the vector field inside the enclosed volume (emissions inside that volume).…”

NO<sub><i>x</i></sub> emission trends over Chinese cities estimated from OMI observations during 2005 to 2015

“…OMI NO 2 observations sorted according to wind direction from wind fields developed by the European Center for Medium-range Weather Forecasting (ECMWF) have been fitted by Beirle et al (2011), who have used the exponentially modified Gaussian function, which allows for a simultaneous fit of the NO x lifetime and emissions for megacities without further input from CTMs. In the previous work, we advanced this method for estimating NO x emissions from sources located in a polluted background (Liu et al, 2016a). An alternative approach to quantifying urban NO x emissions, proposed by Valin et al (2013), involves rotating satellite observations according to wind directions such that all observations are aligned in one direction (from upwind to downwind), thus increasing the number of observations.…”

Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

“…Tropospheric column densities of important trace gases, such as NO 2 , SO 2 , CO, and HCHO, derived from satellite instruments generate an abundance of useful information on the emission sources of these gases (e.g., Duncan et al, 2010;Boeke et al, 2011;Lin, 2012;Pechony et al, 2013;Stavrakou et al, 2015;Wang et al, 2015;Liu et al, 2016), despite the biases intrinsic to satellite retrievals (Boersma et al, 2008;Lin et al, 2014). Many studies have compared model-simulated column densities with satellite-derived columns to validate the accuracy of bottom-up emissions (e.g., van Noije et al, 2006;Kim et al, 2009;Sheel et al, 2010;Itahashi et al, 2014;Han et al, 2015) and have attributed discrepancies between modeled and satellite-based column densities to errors in the magnitudes and/or spatial distributions of the emission inventories used in their models.…”