Elsevier

Atmospheric Environment

Volume 57, September 2012, Pages 80-90
Atmospheric Environment

High resolution mapping of total deposition of acidifying pollutants

https://doi.org/10.1016/j.atmosenv.2012.04.037Get rights and content

Abstract

A framework has been developed to estimate dry and wet deposition over Southern Belgium for a variety of acidifying substances on a 5 × 5 km2 grid. Concentrations of different compounds in the atmosphere or in the precipitation are provided by the measurement networks (both stations and gauges) and are interpolated over Southern Belgium. Dry deposition velocities are calculated using local meteorology and land use information, following the approach described in Zhang et al., 2001, Zhang et al., 2003. Local precipitation is provided by merged radar-gauge observations. This is the first high resolution framework for Southern Belgium computing both time- and space-dependent deposition, using a modified kriging interpolation method (for SO2 and NO2), as well as radar-based precipitation. Estimated dry and wet depositions are compared with long range transport (LRT) model results, based on the European emission inventories. Although a good agreement is observed between our results and LRT model results on the annual totals averaged over Southern Belgium, the extent of agreement for the spatial variability of the annual deposition differs significantly from one pollutant to another. This new framework provides consistent high resolution maps for several pollutants, while improving the mapping of dry and wet deposition in Southern Belgium, in order to assess critical loads exceedances.

Highlights

► A framework is developed to map high resolution acid deposition in Southern Belgium. ► Results for 2008–2009 are compared with results of long range transport (LRT) models. ► There is a good agreement between the annual totals averaged over Southern Belgium. ► The framework provides consistent deposition patterns for several pollutants. ► The framework provides higher resolution deposition maps than LRT models.

Introduction

Dry and wet deposition processes are fundamental removal mechanisms for tropospheric substances, both gaseous and in the form of aerosols (Ruijrok et al., 1995; Wesely and Hicks, 2000; Petroff et al., 2008). The deposition of these substances and their penetration in the soil and the vegetation can lead to a variety of perturbations of the ecosystem, e.g. acidification, eutrophication, destruction of the crops (Bobbink et al., 2010; Sutton et al., 2011; Bell, 1986). The understanding of the wet and dry deposition processes is therefore essential to assess these environmental impacts and eventually prevent them.

Our main concern here are the atmospheric substances involved in the evaluation of the “total potential acid deposition”, as defined in the context of the UNECE LRTAP convention (UNECE Convention on Long-Range Transboundary Air Pollution, 2004). These substances include gases, e.g. nitrogen dioxide NO2, sulfur dioxide SO2, ammonia NH3, nitric acid vapor HNO3 (and in a lesser extent, nitrogen monoxide NO) and aerosols, e.g. ammonium NH4+, nitrate NO3 and sulfate SO42−. Deposition of base cations is also needed in order to assess the critical loads, but will not be addressed in the present study.

In Belgium, deposition is currently estimated using emission-based long range transport (LRT) eulerian models, such as EMEP (Simpson et al., 2003) or BelEUROS (cf. for example, Deutsch, 2008). Despite the advantages of such models, this approach suffers from one systematical flaw: the value of the critical loads exceedances is strongly dependent on the spatial scale of the grid on which both the deposition and these loads are mapped (Bak, 2001; Smith et al., 1995; Spranger et al., 2001). Such models calculate deposition patterns across Europe, while inferential (high resolution) models are used to compute deposition patterns across a country or region. No detailed inferential model exists for Southern Belgium, and the purpose of the present study is to fill this gap.

The purpose of the present study is firstly to construct a framework that can provide high resolution wet and dry deposition estimates over Southern Belgium for various pollutants (on a 5 × 5 km2 grid), secondly to present the resulting maps using 2008 and 2009 data, and finally to evaluate the maps by comparing the results with LRT model outputs. The framework will make use of pollutants concentrations in the air and in the precipitation, measured in the different networks and on merged radar-gauge rain observations (Goudenhoofdt and Delobbe, 2009). Time- and space-dependent dry deposition velocities are computed using the models described in Zhang et al., 2001, Zhang et al., 2003, with local meteorological data from the ALADIN model run at the Royal Meteorological Institute of Belgium (www.kmi.be), as well as assimilated ALADIN fields (Brasseur et al., 2004).

In the following sections, our methodology is detailed in Section 2, results are detailed in Section 3 and major conclusions are summarized in Section 4.

Section snippets

Measurement networks

A number of pollutants of interest have been measured in regional networks in Belgium (Fig. 1). SO2 is measured in the regional telemetric networks, covering the whole country and yielding concentrations every hour (i.e. the mean of two half-hourly values). In 2008, 70 stations have measured SO2 concentrations in Belgium. Considering the area of Belgium (30,528 km2), the telemetric network is quite dense. The NOx ≡ NO + NO2 are measured in the telemetric networks, like the SO2, on a hourly

Results

Deposition of the different pollutants is computed for the years 2008 and 2009 on a 5 × 5 km2 grid. The time step for the computation varies from pollutant to pollutant, ranging from one hour for SO2 and NOx to one week for precipitation ions, as explained in Section 2.1.

Conclusions

We have developed a framework for the computation of wet and dry deposition over Southern Belgium. Improvements on the previous framework both for inferential modeling and wet deposition estimation include computation of time- and space-dependent deposition velocities for gases and aerosols, on the basis of local meteorology (ALADIN) and land use (COSW), incorporation of a modified kriging interpolation method (RIO), and high resolution radar-based precipitation data.

This framework produces

Acknowledgments

We would like to thank the two anonymous referees for their constructive comments and suggestions. T. de Vos acknowledges O. Brasseur for his kind advice concerning meteorological data treatment, D. Andrieux for his numerous insightful comments, the IRM for providing the precipitation data and the ISSeP for the land use data.

References (41)

  • M.L. Wesely

    Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models

    Atmospheric Environment

    (1989)
  • M.L. Wesely et al.

    A review on current status of knowledge on dry deposition

    Atmospheric Environment

    (2000)
  • L. Zhang et al.

    A size-segregated particle dry deposition scheme for an atmospheric aerosol module

    Atmospheric Environment

    (2001)
  • L. Zhang et al.

    Modelling gaseous dry deposition in AURAMS: a unified regional air-quality modelling system

    Atmospheric Environment

    (2002)
  • J. Bak

    Uncertainties in large scale assessments of critical loads exceedances

    Water, Air and Soil Pollution Focus

    (2001)
  • J.N.B. Bell

    Effects of acid deposition on crops and forests

    Cellular and Molecular Life Sciences

    (1986)
  • R. Bobbink et al.

    Review and revision of empirical critical loads

  • O. Brasseur et al.

    Traitement et modélisation des données en matière de pollution atmosphérique

    (2004)
  • G.P. Cressman

    An operational objective analysis system

    Monthly Weather Review

    (1969)
  • E. Cunningham

    On the velocity of steady fall of spherical particles through fluid medium

    Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character

    (1910)
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