Inverse modelling for mercury over Europe

Roustan, Yelva; Bocquet, Marc

doi:10.5194/acp-6-3085-2006

Cited by 29 publications

(21 citation statements)

References 22 publications

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“…A technique referred to as data assimilation (DA hereafter) was introduced to couple models and observations, and to improve the accuracy of input data of model forecasts, such as initial conditions or boundary conditions (Talagrand, 1997;Roustan and Bocquet, 2006). In meteorology, DA has been employed to improve forecasts for more than three decades (Lorenc, 1986;Kalnay, 2003;Lahoz et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

et al. 2014

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Abstract. This paper presents a new application of assimilating lidar signals to aerosol forecasting. It aims at investigating the impact of a ground-based lidar network on the analysis and short-term forecasts of aerosols through a case study in the Mediterranean basin. To do so, we employ a data assimilation (DA) algorithm based on the optimal interpolation method developed in the POLAIR3D chemistry transport model (CTM) of the POLYPHEMUS air quality modelling platform. We assimilate hourly averaged normalised range-corrected lidar signals (PR 2 ) retrieved from a 72 h period of intensive and continuous measurements gases Research InfraStructure (ACTRIS) network and an additional system in Corsica deployed in the framework of the pre-ChArMEx (Chemistry-Aerosol Mediterranean Experiment)/TRAQA (TRAnsport à longue distance et Qualité de l'Air) campaign. This lidar campaign was dedicated to demonstrating the potential operationality of a research network like EARLINET and the potential usefulness of assimilation of lidar signals to aerosol forecasts. Particles with an aerodynamic diameter lower than 2.5 µm (PM 2.5 ) and those with an aerodynamic diameter higher than 2.5 µm but lower than 10 µm (PM 10−2.5 ) are analysed separately using the lidar observations at each DA step. First, we study the spatial and temporal influences of the assimilation of lidar signals on aerosol forecasting. We conduct sensitivity studies on algorithmic parameters, e.g. the horizontal correlation length (L h ) used in the background error covariance matrix (50 km, 100 km or 200 km), the altitudes at which DA is performed (0.75-3.5 km, 1.0-3.5 km or 1.5-3.5 km a.g.l.) and the assimilation period length (12 h or 24 h). We find that DA with L h = 100 km and assimilation from 1.0 to 3.5 km a.g.l. during a 12 h assimilation period length leads to the best scores for PM 10 and PM 2.5 during the forecast period with reference to available measurements from surface networks. Secondly, the aerosol simulation results without and with lidar DA using the optimal parameters (L h = 100 km, an assimilation altitude range from 1.0 to 3.5 km a.g.l. and a 12 h DA period) are evaluated using the level 2.0 (cloud-screened and quality-assured) aerosol optical depth (AOD) data from AERONET, and mass concentration measurements (PM 10 or PM 2.5 ) from the French air quality (BDQA) network and the EMEP-Spain/Portugal network. The results show that the simulation with DA leads to better scores than the one without DA for PM 2.5 , PM 10 and AOD. Additionally, the comparison of model results to evaluation data indicates that the temporal impact of assimilating lidar signals is longer than 36 h after the assimilation period.

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Section: Introductionmentioning

confidence: 99%

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

et al. 2014

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“…Details on the emissions of the anthropogenic species aside from mercury may be found in Zhang et al (this issue). 25 Biomass burning emissions are derived from the FINN fire emissions product (Wiedinmyer et al, 2006(Wiedinmyer et al, , 2011. We take the burned biomass derived by FINN and multiply it by a vegetation-type specific emission factor to derive biomass burning emissions of mercury as GEM.…”

Section: Gem-mach-hg Is the Mercury Version Of The Eccc's Current Opementioning

confidence: 99%

“…Pan et al (2007) used 4DVar to optimize mercury emissions in China. Roustan and Bocquet (2006) Considering significant uncertainties in biomass burning emissions of mercury, we constrain the FINN-derived biomass burning Hg emissions in North America by optimizing the vegetation-specific emission factors used to construct the emissions fields used in the model in order to make the modelled Hg concentrations better match the GEM observations. To achieve this, we use an inverse model to find the maximum a posteriori solution (MAP, Rodgers, 2000).…”

Section: Inverse Modelmentioning

confidence: 99%

How important is biomass burning in Canada to mercury contamination?

Fraser¹,

Dastoor²,

Ryjkov³

2017

Preprint

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Abstract.Wildfire frequency has increased in past four decades in Canada, and is expected to increase in future as a result of climate change (Wotton et. al. 2010). Mercury (Hg) emissions from biomass burning are known to be significant; however, the impact of biomass burning on air concentration and deposition fluxes in Canada has not been previously quantified. We use 10 estimates of burned biomass from FINN (Fire Inventory from NCAR) and vegetation-specific Emission Factors (EFs) of mercury to investigate the spatiotemporal variability of Hg emissions in Canada. We use Environment and Climate Change Canada's GEM-MACH-Hg (Global Environmental Multi-scale, Modelling Air quality and Chemistry model, mercury version) to quantify the impact of biomass burning in Canada on spatiotemporal variability of air concentrations and deposition fluxes of mercury in Canada. We use North American gaseous elemental mercury (GEM) observations (2010)(2011)(2012)(2013)(2014)(2015), 15 and an inversion technique to optimize the emission factors for GEM for five vegetation types represented in North American fires to constrain the biomass burning impacts of mercury. We use three biomass burning Hg emissions scenarios in Canada to conduct three sets of model simulations for 2010-2015: two scenarios where Hg is emitted only as GEM using literature or optimized EFs, and a third scenario where Hg is emitted as GEM using literature EFs and particle bound mercury (PBM) emitted using the average GEM/PBM ratio from lab measurements. The three biomass burning emission scenarios represent the range of 20 possible values for the impacts of Hg emissions from biomass burning in Canada on Hg concentration and deposition.We find total biomass burning Hg emissions to be highly variable from year to year, and estimate average 2010-2015 total atmospheric biomass burning emissions of Hg in Canada to be between 6-14 t during the biomass burning season (i.e., from May to September), which is 3 -7 times the mercury emission from anthropogenic sources in Canada for this period. On average, 65% of the emissions occur in the provinces west of Ontario. We find that while emissions from biomass burning have a small 25 impact on surface air concentrations of GEM averaged over individual provinces/territories, the impact at individual sites can be as high as 95% during burning events. We estimate average annual mercury deposition from biomass burning in Canada to be between 0.3 -2.8 t, compared to 0.14 t of mercury deposition from anthropogenic sources during the biomass burning season in Canada. Compared to the biomass burning emissions, the relative impact of fires on mercury deposition is shifted eastward, with on average 54% percent of the deposition occurring in provinces west of Ontario. While the relative contribution of Canadian 30 biomass burning to the total mercury deposition over each province/territory is no more than 9% between 2010-2015, the local contribution in some locations (including areas downwind of biomass burning) can be as high as...

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“…Roustan and Bocquet (2006) used inverse modeling for optimizing boundary conditions for gaseous elemental mercury (GEM) dispersion modeling. They applied the adjoint techniques using the POLAIR3D CTM with the Petersen et al (1995) mercury (Hg) chemistry model and available GEM observations at four EMEP stations.…”

Section: Inverse Modelingmentioning

confidence: 99%

“…The use of data assimilation in atmospheric chemistry is more recent, because numerical deterministic models of atmospheric chemistry have been used routinely for air quality forecasting only since the mid 1990s; previously, most air quality forecasts were conducted with statistical approaches (Zhang et al, 2012a). Data assimilation has also been used in air quality since the 1990s for re-analysis to produce air pollutant concentration maps (e.g., Elbern and Schmidt, 2001), inverse modeling to improve (or identify errors in) emission rates (e.g., Elbern et al, 2007;Vira and Sofiev, 2012;Yumimoto et al, 2012), boundary conditions (e.g., Roustan and Bocquet, 2006) and model parameters (e.g., Barbu et al, 2009;Bocquet, 2012). Regarding air quality re-analyses, the 2008/50 European Union (EU) Air Quality Directive (AQD) suggests the use of modeling in combination with fixed measurements "to provide adequate information on the spatial distribution of the ambient air quality" (Borrego et al, 2015;OJEU, 2008).…”

Section: Introductionmentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

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Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

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Inverse modelling for mercury over Europe

Cited by 29 publications

References 22 publications

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

How important is biomass burning in Canada to mercury contamination?

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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