A comparison of reanalysis techniques: applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale.

To fulfill the requirements of the 2008/50 Directive, which allows member states and regional authorities to use a combination of measurement and modeling to monitor air pollution concentration, a key approach to be properly developed and tested is the data assimilation one. In this paper, with a focus on regional domains, a comparison between optimal interpolation and Ensemble Kalman Filter is shown, to stress pros and drawbacks of the two techniques. These approaches can be used to implement a more accurate monitoring of the long-term pollution trends on a geographical domain, through an optimal combination of all the available sources of data. The two approaches are formalized and applied for a regional domain located in Northern Italy, where the PM10 level which is often higher than EU standard limits is measured.

[1]  P. Butelli,et al.  Major chemical components of PM2.5 in Milan (Italy) , 2005 .

[2]  G. Evensen.  Inverse methods and data assimilation in nonlinear ocean models , 1994 .

[3]  Philippe Thunis,et al.  Analysis of model responses to emission-reduction scenarios within the CityDelta project , 2007 .

[4]  Bruce Denby,et al.  Comparison of two data assimilation methods for assessing PM10 exceedances on the European scale , 2008 .

[5]  A. Jaecker-Voirol,et al.  Heteromolecular nucleation in the sulfuric acid-water system , 1989 .

[6]  A. Nenes,et al.  ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K + –Ca 2+ –Mg 2+ –NH 4 + –Na + –SO 4 2− –NO 3 − –Cl − –H 2 O aerosols , 2007 .

[7]  J. Seinfeld,et al.  Second-generation inorganic aerosol model , 1991 .

[8]  Arnold Heemink,et al.  A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe , 2009 .

[9]  Claudio Carnevale,et al.  Multi-criteria analysis for PM10 planning , 2009 .

[10]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[11]  Claudio Carnevale,et al.  A multi-objective nonlinear optimization approach to designing effective air quality control policies , 2008, Autom..

[12]  Claudio Carnevale,et al.  A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy. , 2010, The Science of the total environment.

[13]  D P Chock,et al.  Comparison of stiff chemistry solvers for air quality modeling. , 1994, Environmental science & technology.

[14]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[15]  M. Sofiev,et al.  On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations , 2012 .

[16]  Guus J. M. Velders,et al.  Uncertainty assessment of local NO2 concentrations derived from error-in-variable external drift kriging and its relationship to the 2010 air quality standard , 2006 .

[17]  Adrian Sandu,et al.  Continuous versus Discrete Advection Adjoints in Chemical Data Assimilation with CMAQ , 2011 .

[18]  C. Rosenzweig,et al.  A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations , 2009 .

[19]  William P. L. Carter.  Environmental chamber studies for development of an updated photochemical mechanism for VOC reactivity assessment , 1997 .

[20]  Claudio Carnevale,et al.  Design and validation of a multiphase 3D model to simulate tropospheric pollution. , 2008 .

[21]  Vikas Singh,et al.  A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations , 2011, Environ. Model. Softw..

[22]  O. Talagrand,et al.  4D-variational data assimilation with an adjoint air quality model for emission analysis , 2000, Environ. Model. Softw..

[23]  R. Vautard,et al.  CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010 , 2007 .

[24]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[25]  R. J. Yamartino,et al.  The CALGRID mesoscale photochemical grid model—I. Model formulation , 1992 .

[26]  Nils van Velzen,et al.  A problem-solving environment for data assimilation in air quality modelling , 2010, Environ. Model. Softw..

[27]  P. Thunis,et al.  The impact of thermodynamic module in the CTM performances , 2012 .

[28]  C Carnevale,et al.  MODIS and OMI satellite observations supporting air quality monitoring. , 2009, Radiation protection dosimetry.

[29]  Philippe Thunis,et al.  Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project , 2007 .

[30]  P. Messina,et al.  Impact of assimilated observations on improving tropospheric ozone simulations , 2011 .

[31]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[32]  A. Nenes,et al.  ISORROPIA: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols , 1998 .

[33]  Isabelle Herlin,et al.  Assimilation of OMI NO2 retrievals into a regional chemistry-transport model for improving air quality forecasts over Europe , 2011 .

[34]  Claudio Carnevale,et al.  POEM-PM: an emission model for secondary pollution control scenarios , 2006, Environ. Model. Softw..