Dry Deposition of Reactive Nitrogen From Satellite Observations of Ammonia and Nitrogen Dioxide Over North America

Kharol, Shailesh Kumar; Shephard, Mark W.; McLinden, Chris A.; Zhang, Leiming; Sioris, Christopher E.; O’Brien, Jason; Vet, Robert; Cady‐Pereira, Karen; Hare, E. W.; Siemons, Jacob; Krotkov, Nickolay A.

doi:10.1002/2017gl075832

Cited by 76 publications

(97 citation statements)

References 59 publications

(80 reference statements)

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“…There may also be differences in that our model has more of the NH x deposition coming down as NH + 4 rather than as NH 3 . Our fire + bidi NH x deposition values (Table 4) are well in line with reported NH 3 deposition in Kharol et al (2017), who report satellite-derived NH 3 deposition of about 2.1 to 7.0 × 10 −5 moles m −2 day −1 in Alberta, and are at the low end of NH 3 deposition values reported within Behera et al (2013).…”

Section: Effect On Depositionsupporting

confidence: 75%

Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada

et al. 2018

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Abstract. Atmospheric ammonia (NH 3 ) is a short-lived pollutant that plays an important role in aerosol chemistry and nitrogen deposition. Dominant NH 3 emissions are from agriculture and forest fires, both of which are increasing globally. Even remote regions with relatively low ambient NH 3 concentrations, such as northern Alberta and Saskatchewan in northern Canada, may be of interest because of industrial oil sands emissions and a sensitive ecological system. A previous attempt to model NH 3 in the region showed a substantial negative bias compared to satellite and aircraft observations. Known missing sources of NH 3 in the model were re-emission of NH 3 from plants and soils (bidirectional flux) and forest fire emissions, but the relative impact of these sources on NH 3 concentrations was unknown. Here we have used a research version of the high-resolution air quality forecasting model, GEM-MACH, to quantify the relative impacts of semi-natural (bidirectional flux of NH 3 and forest fire emissions) and direct anthropogenic (oil sand operations, combustion of fossil fuels, and agriculture) sources on ammonia volume mixing ratios, both at the surface and aloft, with a focus on the Athabasca Oil Sands region during a measurement-intensive campaign in the summer of 2013. The addition of fires and bidirectional flux to GEM-MACH has improved the model bias, slope, and correlation coefficients relative to ground, aircraft, and satellite NH 3 measurements significantly.By running the GEM-MACH-Bidi model in three configurations and calculating their differences, we find that averaged over Alberta and Saskatchewan during this time period an average of 23.1 % of surface NH 3 came from direct anthropogenic sources, 56.6 % (or 1.24 ppbv) from bidirectional flux (re-emission from plants and soils), and 20.3 % (or 0.42 ppbv) from forest fires. In the NH 3 total column, an average of 19.5 % came from direct anthropogenic sources, 50.0 % from bidirectional flux, and 30.5 % from forest fires. The addition of bidirectional flux and fire emissions caused the overall average net deposition of NH x across the domain to be increased by 24.5 %. Note that forest fires are very episodic and their contributions will vary significantly for different time periods and regions.This study is the first use of the bidirectional flux scheme in GEM-MACH, which could be generalized for other volatile or semi-volatile species. It is also the first time CrIS (Cross-track Infrared Sounder) satellite observations of NH 3 have been used for model evaluation, and the first use of fire emissions in GEM-MACH at 2.5 km resolution.

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Section: Effect On Depositionsupporting

confidence: 75%

Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada

et al. 2018

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“…Also analogous to other surface fluxes, F O 3 , [O 3 ], and hence v d , can be directly measured by the eddy covariance (EC) method (e.g. Fares et al, 2014;Gerosa et al, 2005;Lamaud et al, 2002;Munger et al, 1996;Rannik et al, 2012) with random uncertainty of about 20 % (Keronen et al, 2003;Muller et al, 2010). Apart from EC, F O 3 and v d can also be estimated from the vertical profile of O 3 by exploiting flux-gradient relationship (Foken, 2006) (termed the gradient method, GM) (e.g.…”

Section: Introductionmentioning

confidence: 99%

Importance of dry deposition parameterization choice in global simulations of surface ozone

et al. 2019

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Dry deposition is a major sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale and how parameterization choice can impact surface ozone simulations. Here, we present the results of the first global, multidecadal modelling and evaluation of ozone dry deposition velocity (v d ) using multiple ozone dry deposition parameterizations. We model ozone dry deposition velocities over 1982-2011 using four ozone dry deposition parameterizations that are representative of current approaches in global ozone dry deposition modelling. We use consistent assimilated meteorology, land cover, and satellite-derived leaf area index (LAI) across all four, such that the differences in simulated v d are entirely due to differences in deposition model structures or assumptions about how land types are treated in each. In addition, we use the surface ozone sensitivity to v d predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated v d values from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean v d among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforests in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime v d in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime v d is estimated to be 1 % yr −1 and leads to up to 3 ppbv of surface ozone changes over 1982-2011. The interannual coefficient of variation (CV) of July daytime mean v d in NH is found to be 5 %-15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations is more sensitive to LAI, while in others it is more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results...

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“…Due to their spatial and temporal coverage, satellite remote sensing instruments can capture some of this natural variability by estimating emissions and emission factors over various seasons, burning conditions, fuel types, and moisture content. Previous studies using satellite CO, NH 3 , and NO 2 data have assessed the relationship between these species (e.g., Coheur et al, 2009;Krol et al, 2013;Luo et al, 2015;Paulot et al, 2017;Pechony et al, 2013;R'Honi et al, 2013;Yurganov et al, 2011) and have also estimated emissions and emission factors (e.g., Mebust and Cohen, 2014;Mebust et al, 2011;Schreier et al, 2014Schreier et al, , 2015Tanimoto et al, 2015;Whitburn et al, , 2016b. Emission factors have also been estimated using groundbased Fourier transform infrared (FTIR) spectrometers (e.g., Lutsch et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area

et al. 2019

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In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH 3 , and NO 2 . CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH 3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO 2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH 3 and NO x . The satellite-derived emission rates were ∼ 50-300 kt d −1 for CO, ∼ 1-7 kt d −1 for NH 3 , and ∼ 0.5-2 kt d −1 for NO x (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R ∼ 0.4-0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH 3 /CO, NO x /CO, and NO x /NH 3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH 3 /CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NO x /CO satellite-measured emission ratios of ∼ 0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼ 0.003, which may have been affected by the short lifetime of NO x . Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼ 1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼ 30 kt of NH 3 and ∼ 7 kt of NO x (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

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Dry Deposition of Reactive Nitrogen From Satellite Observations of Ammonia and Nitrogen Dioxide Over North America

Cited by 76 publications

References 59 publications

Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada

Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada

Importance of dry deposition parameterization choice in global simulations of surface ozone

Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area

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