Global inventory of nitrogen oxide emissions constrained by space‐based observations of NO2 columns

Randall V Martin,Paul I Palmer,Kelly Chance,Daniel J Jacob,Thomas P Kurosu,Mathew J Evans

doi:10.1029/2003jd003453

Abstract

We use tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument to derive top‐down constraints on emissions of nitrogen oxides (NOx ≡ NO + NO2), and combine these with a priori information from a bottom‐up emission inventory (with error weighting) to achieve an optimized a posteriori estimate of the global distribution of surface NOx emissions. Our GOME NO2 retrieval improves on previous work by accounting for scattering and absorption of radiation by aerosols; the effect on the air mass factor (AMF) ranges from +10 to −40% depending on the region. Our AMF also includes local information on relative vertical profiles (shape factors) of NO2 from a global 3‐D chemical transport model (GEOS‐CHEM); assumption of a globally uniform shape factor, as in most previous retrievals, would introduce regional biases of up to 40% over industrial regions and a factor of 2 over remote regions. We derive a top‐down NOx emission inventory from the GOME data by using the local GEOS‐CHEM relationship between NO2 columns and NOx emissions. The resulting NOx emissions for industrial regions are aseasonal, despite large seasonal variation in NO2 columns, providing confidence in the method. Top‐down errors in monthly NOx emissions are comparable with bottom‐up errors over source regions. Annual global a posteriori errors are half of a priori errors. Our global a posteriori estimate for annual land surface NOx emissions (37.7 Tg N yr−1) agrees closely with the GEIA‐based a priori (36.4) and with the EDGAR 3.0 bottom‐up inventory (36.6), but there are significant regional differences. A posteriori NOx emissions are higher by 50–100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25–35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NOx emissions are appreciably higher in the western United States, the Sahel, and southern Europe.

Highlights

[1] We use tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument to derive top-down constraints on emissions of nitrogen oxides (NOx NO + NO2), and combine these with a priori information from a bottomup emission inventory to achieve an optimized a posteriori estimate of the global distribution of surface NOx emissions
We show here that ‘‘top-down’’ information derived from space-based observations of NO2 columns can reduce significantly the uncertainties in NOx emissions when combined with a bottom-up inventory
Our previous air mass factor (AMF) calculation described by Martin et al [2002b] improved on earlier work by applying accurate surface reflectivities obtained from GOME measurements at appropriate wavelengths [Koelemeijer et al, 2003], accounting for scattering by clouds, and resolving the spatial and temporal variability in the vertical profile of NO2

Summary

Introduction

[2] Nitrogen oxide radicals (NOx NO + NO2) originating from combustion, lightning, and soils largely control tropospheric ozone production [Kasibhatla et al, 1991; Penner et al, 1991; Murphy et al, 1993; Jacob et al, 1996]. [13] Martin et al [2002b] showed that the total error in the retrieval of tropospheric NO2 columns over continental source regions is largely determined by the AMF calculation. They derived through propagation of errors an overall error of 53% on the AMF calculation including contributions from errors in surface reflectivity (28%), the NO2 profile (15%),. Sensitivity of the Air Mass Factor to the NO2 Shape Factor [14] Most GOME NO2 retrievals have assumed a globally uniform shape factor [Leue et al, 2001; Velders et al, 2001; Lauer et al, 2002; Richter and Burrows, 2002], which leads to bias in the retrieval [Martin et al, 2002b] and presents difficulty when comparing retrieved and modeled columns, as the model may have in general a different shape factor than assumed in the retrieval [Palmer et al, 2001]. We assume a lognormal distribution of errors on Ea and Et, so that maximum likelihood yields ln

Biomass burning

South Africa

Findings

Conclusions