A dynamical ammonia emission parameterization for use in air pollution models

Gyldenkærne, Steen

doi:10.1029/2004jd005459

Cited by 105 publications

(187 citation statements)

References 46 publications

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“…Reconciling the discrepancy then requires us to better understand the bottom-up emissions from the underlying processes. Previous studies have shown that NH 3 emissions are highly sensitive to the magnitude and timing of fertilizer application as well as variations of meteorology (Søgaard et al, 2002;Gyldenkaerne et al, 2005;Paulot et al, 2014), but these factors are neither sufficiently represented nor well evaluated in the Chinese NH 3 emission estimates. Here we construct an improved bottom-up Chinese NH 3 emission inventory from fertilizer application and livestock waste, with the objective to better estimate fertilizer application practices and emission factors.…”

Section: Improving Bottom-up Estimates Of Agricultural Nh 3 Emissionsmentioning

confidence: 99%

“…where T i and W i are 2 m (meter) air temperature in • C and 10 m wind speed in m s −1 for month i, respectively (Søgaard et al, 2002;Gyldenkaerne et al, 2005). We use the gridded (0.5 • × 0.5 • ) soil pH data from the University of Wisconsin Nelson Institute Center for Sustainability and the Global Environment (SAGE, 2015), and the soil CEC data (0.5 • × 0.5 • ) from the ISRIC-World Soil Information (ISRIC WSI, 2015) of the World Data Center for Soils.…”

Section: Emission Factor From Fertilizer Applicationmentioning

confidence: 99%

“…We use the crop-planting dates of Sacks et al (2010). Following Paulot et al (2014), a Gaussian distribution function (Gyldenkaerne et al, 2005) is applied to account for uncertainties and interannual variations of application dates:…”

Section: Fertilizer Application Magnitudementioning

confidence: 99%

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Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

et al. 2018

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Abstract. Current estimates of agricultural ammonia (NH 3 ) emissions in China differ by more than a factor of 2, hindering our understanding of their environmental consequences. Here we apply both bottom-up statistical and top-down inversion methods to quantify NH 3 emissions from agriculture in China for the year 2008. We first assimilate satellite observations of NH 3 column concentration from the Tropospheric Emission Spectrometer (TES) using the GEOS-Chem adjoint model to optimize Chinese anthropogenic NH 3 emissions at the 1/2 • × 2/3 • horizontal resolution for MarchOctober 2008. Optimized emissions show a strong summer peak, with emissions about 50 % higher in summer than spring and fall, which is underestimated in current bottom-up NH 3 emission estimates. To reconcile the latter with the topdown results, we revisit the processes of agricultural NH 3 emissions and develop an improved bottom-up inventory of Chinese NH 3 emissions from fertilizer application and livestock waste at the 1/2 • × 2/3 • resolution. Our bottom-up emission inventory includes more detailed information on crop-specific fertilizer application practices and better accounts for meteorological modulation of NH 3 emission factors in China. We find that annual anthropogenic NH 3 emissions are 11.7 Tg for 2008, with 5.05 Tg from fertilizer application and 5.31 Tg from livestock waste. The two sources together account for 88 % of total anthropogenic NH 3 emissions in China. Our bottom-up emission estimates also show a distinct seasonality peaking in summer, consistent with topdown results from the satellite-based inversion. Further evaluations using surface network measurements show that the model driven by our bottom-up emissions reproduces the observed spatial and seasonal variations of NH 3 gas concentrations and ammonium (NH + 4 ) wet deposition fluxes over China well, providing additional credibility to the improvements we have made to our agricultural NH 3 emission inventory.

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Section: Improving Bottom-up Estimates Of Agricultural Nh 3 Emissionsmentioning

confidence: 99%

Section: Emission Factor From Fertilizer Applicationmentioning

confidence: 99%

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Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

et al. 2018

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“…Here the main sources are livestock, manure management and application of fertilizer (Reis et al, 2009;Skjøth et al, 2011). Nearly all ammonia emissions are due to volatilization of ammonia from wet surfaces (Elzing and Monteny, 1997;Gyldenkaerne et al, 2005). Volatilization of ammonia is a physical process that is highly temperature dependent (Gyldenkaerne et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

The effect of climate and climate change on ammonia emissions in Europe

Skjøth

Geels

2013

Atmos. Chem. Phys.

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Abstract. We present here a dynamical method for modelling temporal and geographical variations in ammonia emissions in regional-scale chemistry transport models (CTMs) and chemistry climate models (CCMs). The method is based on the meteorology in the models and gridded inventories. We use the dynamical method to investigate the spatiotemporal variability of ammonia emissions across part of Europe and study how these emissions are related to geographical and year-to-year variations in atmospheric temperature alone. For simplicity we focus on the emission from a storage facility related to a standard Danish pig stable with 1000 animals and display how emissions from this source would vary geographically throughout central and northern Europe and from year to year. In view of future climate changes, we also evaluate the potential future changes in emission by including temperature projections from an ensemble of climate models. The results point towards four overall issues. (1) Emissions can easily vary by 20 % for different geographical locations within a country due to overall variations in climate. The largest uncertainties are seen for large countries such as the UK, Germany and France. (2) Annual variations in overall climate can at specific locations cause uncertainties in the range of 20 %. (3) Climate change may increase emissions by 0-40 % in central to northern Europe. (4) Gradients in existing emission inventories that are seen between neighbour countries (e.g. between the UK and France) can be reduced by using a dynamical methodology for calculating emissions. Acting together these four factors can cause substantial uncertainties in emission. Emissions are generally considered among the largest uncertainties in the model calculations made with CTM and CCM models. Efforts to reduce uncertainties are therefore highly relevant.It is therefore recommended that both CCMs and CTMs implement a dynamical methodology for simulating ammonia emissions in a similar way as for biogenic volatile organic compound (BVOCs) -a method that has been used for more than a decade in CTMs. Finally, the climate penalty on ammonia emissions should be taken into account at the policy level such as the NEC and IPPC directives.

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“…Although the Eulerian approach is powerful and widely used for elucidating the chemical and physical mechanism in the atmosphere, the Lagrangian approach demonstrates key advantages in presenting sub-grid scale process, minimizing numerical diffusion, artificial dilution and computing resources. The Lagrangian approach has been widely adopted in various models in atmospheric ammonia modeling such as the FRAME model (Singles et al, 1998;Kryza et al, 2011;Zhang et al, 2011), the TREND model (Asman and van Jaarsveld, 1992;Asman, 2001), the ACDEP model (Hertel et al, 1995Gyldenkoerne et al, 2005;de Leeuw et al, 2003;Skjøth et al, 2002Skjøth et al, , 2004Skjøth et al, , 2011, The TERN model (ApSimon et al, 1994), and the NAME model (Redington and Derwent, 2002). Most existing Lagrangian models for atmospheric ammonia modeling are either box-based models or use a simplified dry chemical scheme.…”

Section: Wen Et Al: Modeling Atmospheric Ammonia and Ammoniummentioning

confidence: 99%

Modeling atmospheric ammonia and ammonium using a stochastic Lagrangian air quality model (STILT-Chem v0.7)

et al. 2013

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Abstract.A new chemistry module that simulates atmospheric ammonia (NH 3 ) and ammonium (NH + 4 ) was incorporated into a backward-in-time stochastic Lagrangian air quality model (STILT-Chem) that was originally developed to simulate the concentrations of a variety of gas-phase species at receptors. STILT-Chem simulates the transport of air parcels backward in time using ensembles of fictitious particles with stochastic motions, while accounting for emissions, deposition and chemical transformation forward in time along trajectories identified by the backward-in-time simulations. The incorporation of the new chemistry module allows the model to simulate not only gaseous species, but also multi-phase species involving NH 3 and NH were compared with observations, which show broad agreement between simulated concentrations and observations. Since the model is based on back trajectories, the influence of each major process such as emission, deposition and chemical conversion on the concentration of a modeled species at a receptor can be determined for every upstream location at each time step. This makes it possible to quantitatively investigate the upstream processes affecting receptor concentrations. The modeled results suggest that the concentrations of NH 3 at those sites were significantly and frequently affected by Ohio, Iowa, Minnesota, Michigan, Wisconsin, southwestern Ontario and nearby areas. NH 3 is mainly contributed by emission sources whereas particulate NH + 4 is mainly contributed by the gas-to-aerosol chemical conversion of NH 3 . Dry deposition is the largest removal process for both NH 3 and particulate NH + 4 . This study revealed the contrast between agricultural versus forest sites. Not only were emissions of NH 3 higher, but removal mechanisms (especially chemical loss for NH 3 and dry deposition for NH + 4 ) were less efficient for agricultural sites. This combination explains the significantly higher concentrations of NH 3 and particulate NH + 4 observed at agricultural sites.

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A dynamical ammonia emission parameterization for use in air pollution models

Cited by 105 publications

References 46 publications

Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates

The effect of climate and climate change on ammonia emissions in Europe

Modeling atmospheric ammonia and ammonium using a stochastic Lagrangian air quality model (STILT-Chem v0.7)

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