Synergistic use of OMI NO2 tropospheric columns and LOTOS–EUROS to evaluate the NOx emission trends across Europe

https://doi.org/10.1016/j.rse.2014.03.032Get rights and content

Highlights

  • NOx emission trends across Europe were estimated based on OMI NO2.

  • Increasing bias between model and observations was used to estimate trends.

  • Derived emission trend and national emission reporting compare within a factor 2.

Abstract

In this study trends in the tropospheric NO2 concentrations during 2005–2010 across Europe were derived from the synergistic use of OMI NO2 tropospheric columns and the chemistry transport model LOTOS–EUROS and were compared to reported NOx emissions. The chemistry transport model captures a large fraction of the variability in NO2 columns at a synoptic timescale, although a seasonal signal in the bias between the modelled and retrieved column data remains. Using a simulation with constant emissions in time, trends were derived on the basis of the systematically changing bias between the modelled and retrieved columns. Significant negative trends of 5–6% a 1 were found in highly industrialized areas across Western Europe. Strongest decreases in NO2 concentrations are observed over a region with many power plants in Northern Spain (10–20% a 1) and over the Po Valley (11% a 1). A source apportionment simulation was performed to evaluate the sensitivity of the Ozone Monitoring Instrument (OMI) to NOx emission sources across Europe, identifying the importance of changes in the energy sector in Northern Spain. A comparative study on in-situ NO2 measurements shows that annual reductions at the surface are of 2–3% a 1. This trend increases from rural polluted sites to remote areas (~ 4.5% a 1). The observed variability with station type may be explained by the increase in primary NO2 emissions combined with the representativeness of the measurement sites. Comparing country average trends in NO2 columns with national NOx emission totals shows that these are generally within a factor 2 of each other. A better agreement was found for western European countries than for eastern European countries. The method described here is a promising methodology to complement and evaluate trends in NO2 columns and indirectly emission strengths. A strong advantage is the fact that the methodology using satellite data is in principle consistent throughout the entire domain.

Introduction

Nitrogen oxides (NOx = NO + NO2) play a key role in tropospheric chemistry regulating the level of ozone and therefore maintaining the oxidizing capacity in the troposphere (Crutzen, 1979). Nitrogen oxides affect the global climate indirectly by perturbing the levels of the greenhouse gases, ozone and methane (Solomon et al., 1999). Exposure to ozone leads to adverse health effects for humans (Sunyer et al., 1997) and causes vegetation stress and reduced crop yields (Van Dingenen et al., 2009). There is growing evidence that exposure to nitrogen dioxide itself also leads to adverse health impacts (Chen et al., 2012, Latza et al., 2009). Furthermore, nitrogen oxides are the precursors for (ammonium) nitrate, which is an important component of particulate matter (Schaap, Müller, & Ten Brink, 2002). Finally, nitrogen oxides and its products contribute to acidification and eutrophication of soils and surface waters (Bobbink, Hornung, & Roelofs, 1998). Hence, in the last decades continuous efforts have been made on a national and an international level to design effective abatement strategies for NOx emissions in Europe (Amann et al., 2011).

Establishing (long term) trends in pollutant emissions and concentrations is a key part of evaluating the impact of policies. Traditionally, concentrations of air pollutants are monitored using in-situ measurement networks (Tørseth et al., 2012), whereas emissions are estimated on annual basis within the convention for long range transport and air pollution (CLRTAP). Establishing trends based on monitoring networks is hampered by different equipment used at individual sites or countries, replacement of instruments, etc. (Cooper et al., 2012, Sicard et al., 2009). Moreover, large areas in (south eastern) Europe are not covered by these networks. Similarly, the approaches and quality of emission reporting are variable among European countries (Pouliot et al., 2012). Recently, a general consensus has been reached that satellite remote sensing is a viable means to provide a measurement-based characterization of NO2 on a regional to a global scale (Castellanos and Boersma, 2012, Hilboll et al., 2013, Martin et al., 2003, Richter et al., 2005, van der A et al., 2006). Moreover, recent studies present first attempts to estimate emission strengths and trends following a top-down approach (Konovalov et al., 2008, Martin et al., 2003, Mijling et al., 2013, Stavrakou et al., 2008, Zhou et al., 2012). Although satellite data are only available during cloud free and under daylight conditions, they are in principle derived using a consistent approach over large regions. Hence, trend analyses based on satellite data may provide a valuable independent source of information to compliment traditional monitoring strategies (Bovensmann et al., 1999, Levelt et al., 2006).

The NOx lifetime in the boundary layer is short and varies from several hours at lower latitudes to 1–2 days at higher latitudes (Beirle, Platt, Wenig, & Wagner, 2003). The short lifetime of NOx combined with the heterogeneous distribution of its sources and sinks implies that NOx tropospheric concentrations are highly variable in both space and time. The short life time also implies that the atmospheric levels of nitrogen dioxide respond fast to emission changes, albeit masked by the considerably large impact of varying synoptic meteorological conditions. Hence, the significance of the derived trends in previous studies was often impaired by high variability in the satellite column on a synoptic time scale. This caveat has been overcome by means of a spectral analysis and assessing the low frequency variability (Castellanos & Boersma, 2012) or by means of the development of a statistical model accounting for the synoptic variability (Zhou et al., 2012). Alternatively, one could use a chemistry transport model to explain the impact of meteorological variability. The advantage of using a CTM above a statistical approach has been shown for particulate matter (Manders, Schaap, & Hoogerbrugge, 2009). The use of a CTM is further supported by a recent evaluation of NO2 tropospheric columns from the LOTOS–EUROS model using MAX-DOAS observations (Vlemmix, Eskes, Piters, Kelder, & Levelt, 2011). The evaluation showed that the LOTOS–EUROS model driven a high resolution emission database was able to accurately explain the observed variability as function of wind direction, wind speed, mixing layer height and relative humidity. These results suggest that LOTOS–EUROS may enable to better model the variability due to meteorological changes. Therefore, we will explore the use of the LOTOS–EUROS, to explain the daily variability due to meteorology to assess trends in NO2 tropospheric columns measured by OMI over Europe for the period of 2005–2010.

Section 2 provides background information concerning the LOTOS–EUROS chemistry transport model and the OMI NO2 data used in this study. In Section 3, the results of a source apportionment study carried out to investigate the OMI sensitivity to emission source sectors are presented. In Section 4, the methodology and results of the trend analysis are discussed. For comparison purposes also the trends in the NO2 surface concentrations from in-situ measurements were evaluated applying the same methodology. Results are compared to reported emission changes for countries in Europe. Conclusions and possibilities for future work are presented in Section 5.

Section snippets

LOTOS–EUROS

LOTOS–EUROS (Schaap et al., 2008) is a 3D chemistry transport model which simulates the air pollution in the lower troposphere over Europe. Currently, the model is used for operational air quality forecasts over Europe, as contribution to the Monitoring Atmospheric Composition and Climate (MACC) project, and over the Netherlands, published on the website of the National Institute for public health and environment (RIVM). In this section, we describe the LOTOS–EUROS model (version 1.7.3) in the

NO2 column sensitivity to source categories

In 2005, the anthropogenic NOx emissions over Europe were dominated by combustion processes in road transport with a 40% share, followed by power plants, industry, off-road transport and the residential sector (Pouliot et al., 2012). These numbers are for the EU27 and the importance of the sectors varies regionally. In the last decades mitigation strategies over Europe have principally targeted combustion processes by power generation, road transport and industries (Rafaj, Amann, Siri, &

OMI NO2

In this section the methodology to derive trends in tropospheric NO2 from both OMI and in-situ measurements is presented and the results are subsequently discussed. For the trend analysis the measurements are used in synergy with the LOTOS–EUROS model. In this study a multi-year simulation is performed for 2005 to 2010 using a single and thus constant a-priori anthropogenic NOx emission database. We use the model to estimate the variability due to synoptic variability in weather systems, which

Conclusions and outlook

In this study trends in the tropospheric NO2 concentrations during 2005–2010 across Europe were successfully derived from synergistic use of OMI NO2 tropospheric columns and the chemistry transport model LOTOS–EUROS. The chemistry transport model captured a large fraction of the variability in NO2 columns at a synoptic timescale, although a seasonal signal in the bias between the modelled and retrieved column data remains. Using a simulation with constant emissions in time, trends were derived

Acknowledgements

We acknowledge the free use of tropospheric NO2 column data from the OMI sensor from www.temis.nl. This study was funded by the EU FP7 project EnerGEO (grant number 226364) and ESA project GLOBEMISSION (grant number AO/1-6721/11/I-NB).

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