Elsevier

Atmospheric Environment

Volume 98, December 2014, Pages 134-145
Atmospheric Environment

Inter-comparison between HERMESv2.0 and TNO-MACC-II emission data using the CALIOPE air quality system (Spain)

https://doi.org/10.1016/j.atmosenv.2014.08.067Get rights and content

Highlights

  • The performance of two emission datasets was evaluated by means of air quality.

  • The datasets are based on a bottom-up and downscaling approach, respectively.

  • NO2, SO2, O3 and PM10 were modelled over Spain using both emission datasets.

  • Model performance improves in urban areas when using the bottom-up dataset.

  • Results with the downscaled emissions show consistence at background stations.

Abstract

This work examines and compares the performance of two emission datasets on modelling air quality concentrations for Spain: (i) the High-Elective Resolution Modelling Emissions System (HERMESv2.0) and (ii) the TNO-MACC-II emission inventory. For this purpose, the air quality system CALIOPE-AQFS (WRF-ARW/CMAQ/BSC-DREAM8b) was run over Spain for February and June 2009 using the two emission datasets (4 km × 4 km and 1 h). Nitrogen dioxide (NO2), sulphur dioxide (SO2), Ozone (O3) and particular matter (PM10) modelled concentrations were compared with measurements at different type of air quality stations (i.e. rural background, urban, suburban industrial). A preliminary emission comparison showed significant discrepancies between the two datasets, highlighting an overestimation of industrial emissions in urban areas when using TNO-MACC-II. However, simulations showed similar performances of both emission datasets in terms of air quality. Modelled NO2 concentrations were similar between both datasets at the background stations, although TNO-MACC-II presented lower underestimations due to differences in industrial, other mobile sources and residential emissions. At Madrid urban stations NO2 was significantly underestimated in both cases despite the fact that HERMESv2.0 estimates traffic emissions using a more local information and detailed methodology. This NO2 underestimation problem was not found in Barcelona due to the influence of international shipping emissions located in the coastline. An inadequate characterization of some TNO-MACC-II's point sources led to high SO2 biases at industrial stations, especially in northwest Spain where large facilities are grouped. In general, surface O3 was overestimated regardless of the emission dataset used, depicting the problematic of CMAQ on overestimating low ozone at night. On the other hand, modelled PM10 concentrations were less underestimated in urban areas when applying HERMESv2.0 due to the inclusion of road dust resuspension, whereas the underestimation at suburban industrial stations indicated deficiencies in fugitive emission sources characterization (agricultural operations, windblown dust emissions).

Introduction

According to the European regulations (EC, 2008), local to regional air quality modelling systems are useful tools to assess the dynamics of air pollutants, to forecast the air quality, to develop emission abatement plans and alert the population when health-related issues occur (EEA, 2011). Emission datasets play a key role in modelling air quality as they provide crucial model input, next to e.g. meteorological fields and boundary conditions, and can be one of the main sources of uncertainty in the modelling results (e.g. Menut and Bessagnet, 2010).

For global and regional applications, gridded emission inventories (e.g. EMEP; Mareckova et al., 2012) based on top-down approaches (i.e. based on aggregated activity data and emission factors) and downscaling methodologies applied to national reported emission inventories are generally used as model input for the assessment of air quality. However, in regard to high-resolution air quality modelling, the use of local information combined with bottom-up approaches (i.e. based on specific activity data and emission factors) is preferable to more accurately characterise the local emission sources and obtain more realistic results (e.g. Kannari et al., 2007). Unfortunately, the development of (local) high resolution bottom-up emissions requires a huge investment of time and resources, as well as having access to local and detailed information, which may not always be available to the model developer. Moreover, it cannot be automatically assumed that a bottom-up emission dataset is better than a top-down or a downscaled one. More complex models have the potential to provide more accurate predictions, but they also require more detailed input data that may contain simple assumptions and therefore offset the potential accuracy gains.

Comparisons between air quality model simulations using multiple emission datasets and observational data may help to validate emission estimates, confirm distribution patterns and identify gaps in emission datasets (Lamarque et al., 2010, Denier van der Gon et al., 2011). Timmermans et al. (2013) compared the simulated average concentrations of PM and NO2 over the Paris region using, on the one hand, the TNO-MACC-I downscaled emission inventory and, on the other hand, the EU FP7 MEGAPOLI bottom-up emission inventory, which included refined local emission data over the megacity of Paris. Results showed that modelled concentrations were more consistent with observational data when using the local bottom-up inventory. In the same direction, Amnuaylojaroen et al. (2014) applied different anthropogenic emission inventories (RETRO, INTEX-B, MACCity, SEAC4RS) in the WRF-Chem to examine the differences in predicted CO and O3 surface mixing ratios for Southeast Asia. The simulations showed that none of the emission datasets were better than the others and any of them could be used for air quality simulations.

The main goal of the present paper is to assess and contrast the performance of two emission datasets on modelling air quality concentrations for Spain: (i) the High-Elective Resolution Modelling Emissions System (HERMESv2.0) (Guevara et al., 2013), a high resolution emission model developed in the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC – CNS) that estimates atmospheric emissions for Spain using mainly bottom-up approaches and with a temporal and spatial resolution of 1 h and up to 1 km2 and (ii) the TNO-MACC-II emission inventory (Pouliot et al., 2012, Kuenen et al., 2014), a consistent high-resolution European emission inventory setup applying a downscaling methodology to the national official reported emissions to EMEP and that is widely used for the scientific community, as for example in the EC JRC/US EPA AQMEII model inter-comparison (Solazzo et al., 2012). For this purpose, the air quality system CALIOPE-AQFS (http://www.bsc.es/caliope) was run over Spain for February and June 2009 using the two emission datasets. The concentration results obtained running the four simulations (one for each emission input data and period of time) were evaluated against observational data. The analysed pollutants are nitrogen dioxide (NO2); sulphur dioxide (SO2); ozone (O3) and particular matter with a diameter less than 10 μm (PM10). The analysis focusses on three types of stations so multiple environments are covered in the study: (i) rural (background) EMEP stations, (ii) urban (background and traffic) stations located in Barcelona and Madrid greater areas and (iii) suburban (industrial) stations located near large point sources.

Section 2 describes the model setup and the observational dataset used. Section 3 performs an emission comparison and analyses the modelled concentrations against available observational data. Finally, Section 4 summarizes and discusses the results.

Section snippets

Methodology

The CALIOPE-AQFS system (WRF-ARW/CMAQ/BSC-DREAM8b) is a state-of-the-art modelling framework implemented in the MareNostrum3 supercomputer and that integrates the Weather Research and Forecasting – Advanced Research Weather meteorological model (WRF-ARW) (Skamarock and Klemp, 2008), the Community Multiscale Air Quality Modeling System (CMAQ) (Byun and Schere, 2006) and the mineral Dust REgional Atmospheric Model (BSC-DREAM8b) (Basart et al., 2012). The system works with a temporal resolution of

Emissions

The total annual emissions for Spain 2009 estimated by HERMESv2.0 and TNO-MACC-II are summarized in Table 1. Significant discrepancies between the two emission datasets (HERMESv2.0 – TNO-MACC-II) are detected for CO (542.1 kt·year−1, +28%), NOx (−171.2 kt·year−1, −20%) and SOx, (160.4 kt·year−1, −61%). For CO, differences are mainly due to the road transport (SNAP07) and agricultural sectors (SNAP10) for which HERMESv2.0 reports higher emissions. For NOx, total differences come mostly from the

Conclusions

This work analyses the impact of two emission datasets, the HERMESv2.0 emission model and the TNO-MACC-II emission inventory, on modelling air quality concentrations within the air quality system CALIOPE-AQFS for Spain. In order to perform this task, the concentration results driven by each one of the emission datasets have been analysed and contrasted against available observational data for February and June 2009.

A preliminary emission comparison showed high discrepancies between the two

Acknowledgements

The authors wish to thank S. Basart for providing the BSC-DREAM8b outputs, as well as the Severo Ochoa Program awarded by the Spanish Government (SEV-2011-00067), the Beatriu Pinós programme for the post-doctoral grant held by M.T. Pay (2011 BP-A 00427) and the EU FP7 projects MACC (grant agreement no.: 218793) and MACC-II (grant agreement no.: 283576) for financial support. Authors also want to thank the two anonymous reviewers whose comments helped to improve this paper substantially. All

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