Using daily satellite observations to estimate emissions of short‐lived air pollutants on a mesoscopic scale

Abstract

[1] Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Using satellite observations for emission estimates has important advantages over bottom-up emission inventories: they are spatially consistent, have high temporal resolution, and enable updates shortly after the satellite data become available. We present a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short-lived atmospheric constituents on a mesoscopic scale ($25 Â 25 km 2 ). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NO x emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO 2 column retrievals of the OMI and GOME-2 satellite instruments. Closed loop tests show that the algorithm is capable of reproducing new emission scenarios. Applied with real satellite data, the algorithm is able to detect emerging sources (e.g., new power plants), and improves emission information for areas where proxy data are not or badly known (e.g., shipping emissions). Chemical transport model runs with the daily updated emission estimates provide better spatial and temporal agreement between observed and simulated concentrations, facilitating improved air quality forecasts.Citation: Mijling, B., and R. J. van der A (2012), Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale,

The version 3 OMI NO<sub>2</sub> standard product

“…Satellite NO 2 data have been used as a proxy for (1) NO x emissions (van der A et al, 2017;Beirle et al, 2011;Boersma et al, 2015;Castellanos and Boersma, 2012;Curier et al, 2014;Ding et al, 2015;Duncan et al, 2014, de Foy et al, 2014Ghude et al, 2013;Jaeglé et al, 2004;Konovalov et al, 2006Konovalov et al, , 2010Lamsal et al, 2011;Liu et al, 2016;Lu et al, 2015;Lu and Streets, 2012;Martin et al, 2006;McLinden et al, 2016;Mijling and Van Der A, 2012;Richter et al, 2004Richter et al, , 2005Russell et al, 2012;Stavrakou et al, 2008;Streets et al, 2013;Vinken et al, 2014;Zhang et al, 2007;Zhou et al, 2012); (2) groundlevel NO 2 (Lamsal et al, 2008) and NO 2 deposition (Nowlan et al, 2014); and (3) emissions of co-emitted gases, including other pollutants, like particulate matter, and greenhouse gases, such as CO 2 (Berezin et al, 2013;Konovalov et al, 2016;Reuter et al, 2014).…”

Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

“…, λ a i,t−2 , λ a i,t−1 , λ p i,t , where the ensemble means of λ p i,t are all 1. After multiple iterations, the smooth operator can give comparatively good estimation for λ f i,t since anthropogenic emissions are stable at a certain timescale (Mijling et al, 2012). It is a compromise between prescribed prior emissions and letting the system propagate all observation information from one step to the next without any guidance (Peters et al, 2007), for the case M = 4.…”

“…Variational DA algorithms have also been applied to constrain emissions of air pollution, such as black carbon, organic carbon, dust, NH 3 , SO x and NO x (Hakami et al, 2005;Elbern et al, 2007;Henze et al, 2007Henze et al, , 2009Yumimoto et al, 2007Yumimoto et al, , 2008Dubovik et al, 2008;Wang et al, 2012;Guerrette and Henze, 2015). These studies have indicated that DA can efficiently reduce the uncertainty in the emission inventories and lead to improvements in the forecasting of air quality (Mijling and van der A, 2012).…”

Inverse Modeling of Nitrogen Oxides Emissions from the 2010 Russian Wildfires by Using Satellite Measurements of Nitrogen Dioxide

“…Instead, we follow the inverse modeling approach that has earlier been successfully used (e.g., [15][16][17][18]) to estimate anthropogenic NOx emissions. Within this approach, the relationship between NO2 columns and NOx emissions can be simulated by a chemistry transport model, and the NOx emissions (or related emission parameters) as well as the NOx lifetime can be estimated by fitting simulated NO2 columns to satellite data.…”