Abstract

Abstract. One of the main issues arising from the comparison of ground-based and satellite measurements is the difference in spatial representativeness, which for locations with inhomogeneous spatial distribution of pollutants may lead to significant differences between the two data sets. In order to investigate the spatial variability of tropospheric NO2 within a sub-satellite pixel, a campaign which lasted for about 6 months was held in the greater area of Thessaloniki, Greece. Three multi-axial differential optical absorption spectroscopy (MAX-DOAS) systems performed measurements of tropospheric NO2 columns at different sites representative of urban, suburban and rural conditions. The direct comparison of these ground-based measurements with corresponding products from the Ozone Monitoring Instrument onboard NASA's Aura satellite (OMI/Aura) showed good agreement over the rural and suburban areas, while the comparison with the Global Ozone Monitoring Experiment-2 (GOME-2) onboard EUMETSAT's Meteorological Operational satellites' (MetOp-A and MetOp-B) observations is good only over the rural area. GOME-2A and GOME-2B sensors show an average underestimation of tropospheric NO2 over the urban area of about 10.51 ± 8.32 × 1015 and 10.21 ± 8.87 × 1015 molecules cm−2, respectively. The mean difference between ground-based and OMI observations is significantly lower (6.60 ± 5.71 × 1015 molecules cm−2). The differences found in the comparisons of MAX-DOAS data with the different satellite sensors can be attributed to the higher spatial resolution of OMI, as well as the different overpass times and NO2 retrieval algorithms of the satellites. OMI data were adjusted using factors calculated by an air quality modeling tool, consisting of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the Comprehensive Air Quality Model with Extensions (CAMx) multiscale photochemical transport model. This approach resulted in significant improvement of the comparisons over the urban monitoring site. The average difference of OMI observations from MAX-DOAS measurements was reduced to −1.68 ± 5.01 × 1015 molecules cm−2.

Highlights

  • Nitrogen oxides (NOx = nitrogen monoxide (NO) + NO2) are among the most important trace components of the atmosphere playing a key role in the tropospheric photochemistry (e.g., Seinfeld and Pandis, 1998)

  • Some positive excursions are observed at suburban conditions (SCs) because this site is likely affected by NO2 transported from urban areas or by local pollution sources depending on weather conditions

  • Negative values of the vertical column densities (VCDs) obtained from measurements at higher elevation angles, such those used in this study (15 and 30◦ as discussed in Sect. 2.2), indicate that the NO2 absorption of the Fraunhofer reference spectrum is underestimated and cannot be assumed negligible compared to the analyzed spectra

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Summary

Introduction

T. Drosoglou et al.: Comparisons of ground-based tropospheric NO2 MAX-DOAS measurements eas are characterized by inhomogeneous spatial and temporal patterns in NOx concentrations. Tropospheric NO2 column measurements derived from satellite sensors (OMI/Aura, hereafter OMI; GOME-2/MetOp-A, hereafter GOME-2A; and GOME2/MetOp-B, hereafter GOME-2B) are compared with data from MAX-DOAS systems that were deployed in three different sites within the greater area of Thessaloniki, Greece, for a period of a few months. Air quality at these locations is representative of urban, suburban and rural conditions. In order to minimize the effect of smoothing of NO2 gradients in the satellite data and improve their comparison with the ground-based data, adjustment factors (AFs) were calculated by an air quality modeling tool, consisting of Weather Research and Forecasting (WRF) meteorological model and the Comprehensive Air Quality Model with Extensions (CAMx), and were applied to OMI data

The Phaethon system
Ground-based measurements
Satellite observations
Air quality simulations
NO2 tropospheric columns in the greater area of Thessaloniki
Reconstruction of OMI observations
Summary and conclusions
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