Please login first
Inverse Modeling of Nitrogen Oxides Emissions from the 2010 Russian Wildfires by Using Satellite Measurements of Nitrogen Dioxide
1 , * 1 , 2
1  Institute of Applied Physics, Russian Academy of Sciences, 46 Ulyanov Str., Nizhniy Novgorod, 603950, Russia
2  Institute for Physics of Microstructures, Russian Academy of Sciences, Academicheskaya Str., 7, Afonino, Nizhny Novgorod region, 603087, Russia


Nitrogen oxides (NOx) play a major role in the atmospheric oxidation processes leading to ozone and secondary organic aerosol formation in the lower atmosphere. Wildfires are one of the important sources of NOx emissions; however, knowledge of NOx emissions from fires is currently insufficient and available estimates provided by emission inventories have mostly not been validated against atmospheric measurements. Recent studies indicated that useful observational constraints to biomass burning (BB) NOx emissions are provided by satellites measurements of nitrogen dioxide (NO2), but available BB NOx emission estimates inferred from such measurements involve quantitative assumptions regarding the atmospheric NOx lifetime. In this study, we investigated NOx emissions from the extreme wildfires that occurred in the European part of Russia in summer 2010. To this end, we analyzed tropospheric NO2 retrievals from measurements performed by the OMI satellite instrument in the framework of an original inverse modeling method. A quantitative relationship between BB NOx emissions and tropospheric NO2 columns was simulated using the mesoscale CHIMERE chemistry transport model. Our analysis indicated that such a relationship depends strongly on BB emissions of volatile organic compounds and that a dependence of the effective NOx lifetime on the NOx fluxes can be essentially nonlinear. Our estimates of the total NOx emissions in the study region are found to be at least 40% larger compared to the respective data from the GFASv1.0 and GFED4.1s global fire emission inventories.

Keywords: nitrogen oxides; wildfires; satellite measurements; chemistry transport model; inverse modeling; biomass burning emissions