Comparisons of Ground-Based Tropospheric NO2 MAX-DOAS Measurements to Satellite Observations with the Aid of an Air Quality Model Over the Thessaloniki Area, Greece
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Atmos. Chem. Phys., 17, 5829–5849, 2017 www.atmos-chem-phys.net/17/5829/2017/ doi:10.5194/acp-17-5829-2017 © Author(s) 2017. CC Attribution 3.0 License. Comparisons of ground-based tropospheric NO2 MAX-DOAS measurements to satellite observations with the aid of an air quality model over the Thessaloniki area, Greece Theano Drosoglou1, Alkiviadis F. Bais1, Irene Zyrichidou1, Natalia Kouremeti1,2, Anastasia Poupkou1, Natalia Liora1, Christos Giannaros1, Maria Elissavet Koukouli1, Dimitris Balis1, and Dimitrios Melas1 1Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece 2Physikalisch-Meteorologisches Observatorium Davos, Dorfstrasse 33, 7260 Davos Dorf, Switzerland Correspondence to: Theano Drosoglou ([email protected]) Received: 11 July 2016 – Discussion started: 5 September 2016 Revised: 6 April 2017 – Accepted: 8 April 2017 – Published: 11 May 2017 Abstract. One of the main issues arising from the com- times and NO2 retrieval algorithms of the satellites. OMI parison of ground-based and satellite measurements is the data were adjusted using factors calculated by an air quality difference in spatial representativeness, which for locations modeling tool, consisting of the Weather Research and with inhomogeneous spatial distribution of pollutants may Forecasting (WRF) mesoscale meteorological model and the lead to significant differences between the two data sets. In Comprehensive Air Quality Model with Extensions (CAMx) order to investigate the spatial variability of tropospheric multiscale photochemical transport model. This approach NO2 within a sub-satellite pixel, a campaign which lasted for resulted in significant improvement of the comparisons over about 6 months was held in the greater area of Thessaloniki, the urban monitoring site. The average difference of OMI Greece. Three multi-axial differential optical absorption observations from MAX-DOAS measurements was reduced spectroscopy (MAX-DOAS) systems performed mea- to −1.68 ± 5.01 × 1015 molecules cm−2. surements 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 Instru- 1 Introduction ment onboard NASA’s Aura satellite (OMI/Aura) showed good agreement over the rural and suburban areas, while the Nitrogen oxides (NOx D NO C NO2) are among the most im- comparison with the Global Ozone Monitoring Experiment- portant trace components of the atmosphere playing a key 2 (GOME-2) onboard EUMETSAT’s Meteorological Oper- role in the tropospheric photochemistry (e.g., Seinfeld and ational satellites’ (MetOp-A and MetOp-B) observations is Pandis, 1998). They affect the oxidation capacity and the ra- good only over the rural area. GOME-2A and GOME-2B diative forcing in the lower atmospheric layers by control- sensors show an average underestimation of tropospheric ling the ozone formation, contributing to nitric acid (HNO3) 15 NO2 over the urban area of about 10.51 ± 8.32 × 10 and nitrate radical formation and affecting the hydroxyl lev- and 10.21 ± 8.87 × 1015 molecules cm−2, respec- els (Solomon et al., 1999; Finlayson-Pitts and Pitts, 1999). tively. The mean difference between ground- The main emission sources of nitrogen oxides are fossil-fuel based and OMI observations is significantly lower combustion, biomass burning, microbiological processes in (6.60 ± 5.71 × 1015 molecules cm−2). The differences the soil, lightning and aircrafts (Lee et al., 1997; Jaeglé et found in the comparisons of MAX-DOAS data with the al., 2005). The most important anthropogenic source are the different satellite sensors can be attributed to the higher high-temperature combustion processes occurring in vehicle spatial resolution of OMI, as well as the different overpass engines and industrial and power plants (EEA, 2013). Hence, urban areas, with heavy road traffic, as well as industrial ar- Published by Copernicus Publications on behalf of the European Geosciences Union. 5830 T. Drosoglou et al.: Comparisons of ground-based tropospheric NO2 MAX-DOAS measurements eas are characterized by inhomogeneous spatial and temporal et al., 2008; Chen et al., 2009; Herman et al., 2009; Irie et patterns in NOx concentrations. al., 2012; Li et al., 2013; Ma et al., 2013; Hendrick et al., Nitrogen dioxide (NO2) is mainly produced by the oxi- 2014). Several studies have shown that satellite sensors un- dation of nitrogen monoxide (NO) and only a small propor- derestimate the tropospheric NO2 levels over regions charac- tion of NOx is emitted directly as NO2 (Hewitt and Jackson, terized by inhomogeneous pollution loadings such as urban 2009). However, in the case of diesel vehicles, the fraction and industrial locations, which is known also as the gradient of directly emitted NO2 to the total NOx emissions is much smoothing effect (Chen et al., 2009; Ma et al., 2013). Irie et higher, resulting in a significant increase of NO2 emissions al. (2012) have shown a clear bias of less than 10 % between and more frequent breaching of NO2 air quality limits in ur- space-borne and ground-based observations over the Tokyo ban locations in recent years (Grice et al., 2009; Keuken et area in Japan, which is characterized by significant spatial al., 2012). variations in NO2 concentrations. In Celarier et al. (2008), Thessaloniki is the second largest city of Greece and, with the correlation coefficient of the comparison of OMI-derived a population of more than 1 million inhabitants, hosts about tropospheric NO2 with different multi-axial (MAX)-DOAS 10 % of the total population of the country (Resident Popula- instruments in Cabauw, the Netherlands, were found to be tion Census, 2011). The main air pollution sources in Thes- about ∼ 0.6. Kramer et al. (2008) estimated similar correla- saloniki are road transport, domestic heating and industrial tion coefficients between OMI and concurrent MAX-DOAS facilities, while the air quality of the city is affected by the observations over Leicester, UK, while they found significant local topographic and meteorological characteristics and re- underestimation of OMI in late autumn and winter months gional pollution transport (Poupkou et al., 2011). NO2 con- when comparing with weighted near-surface concentrations. centrations in the city have tended to stabilize during the last Ma et al. (2013) have shown a systematic underestimation of decades with the highest NO2 levels observed at the traf- about 26–38 % in tropospheric NO2 over Beijing by OMI, fic hotspots in the center of the city (Moussiopoulos et al., depending on the satellite retrieval algorithm and time pe- 2008). According to Zyrichidou et al. (2009), the mean value riod. of tropospheric NO2 column until 2008 over Thessaloniki In this study, tropospheric NO2 column measurements was 3.9 ± 3.8 × 1015 and 4.2 ± 3.8 × 1015 molecules cm−2, derived from satellite sensors (OMI/Aura, hereafter OMI; as observed by the Global Ozone Monitoring Experiment-2 GOME-2/MetOp-A, hereafter GOME-2A; and GOME- (GOME-2) and Ozone Monitoring Instrument (OMI) satel- 2/MetOp-B, hereafter GOME-2B) are compared with data lite instruments, respectively. from MAX-DOAS systems that were deployed in three dif- Well-established methods are used worldwide for monitor- ferent sites within the greater area of Thessaloniki, Greece, ing NO2 concentrations based on both in situ measurements for a period of a few months. Air quality at these locations from local air quality networks and remote sensing from is representative of urban, suburban and rural conditions. In ground-based instruments and satellite sensors (e.g., Or- order to minimize the effect of smoothing of NO2 gradients dóñez et al., 2006; Blond et al., 2007; Brinksma et al., in the satellite data and improve their comparison with the 2008). Space-borne measurements provide information on ground-based data, adjustment factors (AFs) were calculated NO2 concentrations at a larger scale and over areas such as by an air quality modeling tool, consisting of Weather Re- oceans and deserts, where ground-based systems cannot be search and Forecasting (WRF) meteorological model and the easily deployed. On the other hand, the spatial and tempo- Comprehensive Air Quality Model with Extensions (CAMx), ral resolution of the satellite data are, respectively, limited by and were applied to OMI data. the satellite footprint size and its overpass time. Thus, well- established, extended and relatively dense ground-based net- works in areas where significant spatial and temporal varia- 2 Instrumentation and data tions in NO2 loading are observed can improve the validation of the satellite data sets. 2.1 The Phaethon system Remote sensing of NO2 concentrations is based mainly on the differential optical absorption spectroscopy (DOAS) Phaethon is a miniature ground-based MAX-DOAS system analysis (Platt, 1994; Platt and Stutz, 2008) of radiance data, that performs fast, spectrally resolved measurements in the measured either from space or from the ground. The first wavelength range of 300–450 nm which are used for the re- studies applying the DOAS method to zenith ground-based trieval of total and tropospheric columns of atmospheric trace measurements for the retrieval of tropospheric and strato- gases. The prototype system (Phaethon #1) was developed spheric NO2 date back to the 1970s (Brewer et al., 1973; in 2006 at the Laboratory of Atmospheric Physics (LAP) Noxon, 1975). In the last few decades, the DOAS analy- in Thessaloniki, Greece (Kouremeti et al., 2008, 2013). sis has been widely applied in ground-based systems for the Phaethon has been recently upgraded to improve its perfor- monitoring of air quality species like NO2 in the atmosphere mance and two new clone systems (Phaethon #2 and #3) and is considered as a reference technique for the validation have been assembled. The system comprises a cooled minia- of satellite observations (e.g., Celarier et al., 2008; Kramer ture charge-coupled device (CCD) spectrograph (AvaSpec- Atmos. Chem. Phys., 17, 5829–5849, 2017 www.atmos-chem-phys.net/17/5829/2017/ T.