A new approach to design source-receptor relationships for air quality modelling

Air quality models are often used to simulate how emission scenarios influence the concentration of primary as well as secondary pollutants in the atmosphere. In some cases, it is necessary to replace these air quality models with source-receptor relationships, to mimic in a faster way the link between emissions and concentrations. Source-receptor relationships are therefore also used in Integrated Assessment Models, when scenario responses need to be known in very short time. The objective of this work is to present a novel approach to design a source-receptor relationship for air quality modeling. Overall the proposed approach is shown to significantly reduce the number of simulations required for the training step and to bring flexibility in terms of emission source definition. A regional domain application is also presented, to test the performances of the proposed approach. A novel approach to design source-receptor relationships for air quality is proposed.It needs a small number of simulations to be implemented.It also brings flexibility in terms of application in Integrated Assessment Models.A case study on a regional domain is presented.

[1]  Qi Ying,et al.  Source contributions to the regional distribution of secondary particulate matter in California , 2006 .

[2]  Bart Degraeuwe,et al.  Quantification of non-linearities as a function of time averaging in regional air quality modeling applications , 2015 .

[3]  A. Kirkevåg,et al.  Transboundary acidification, eutrophication and ground level ozone in Europe in 2011 , 2013 .

[4]  A. M. Dunker,et al.  Efficient calculation of sensitivity coefficients for complex atmospheric models , 1981 .

[5]  Adrian Sandu,et al.  Adjoint sensitivity analysis of ozone nonattainment over the continental United States. , 2006, Environmental science & technology.

[6]  Claudio Carnevale,et al.  An integrated assessment tool to define effective air quality policies at regional scale , 2012, Environ. Model. Softw..

[7]  P. Thunis,et al.  Indicators to support the dynamic evaluation of air quality models , 2014 .

[8]  Greg Yarwood,et al.  Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model , 2008 .

[9]  Julio Lumbreras,et al.  Advancements in the design and validation of an air pollution integrated assessment model for Spain , 2014, Environ. Model. Softw..

[10]  Brian Eder,et al.  Incorporating principal component analysis into air quality model evaluation , 2014 .

[11]  Petra Seibert,et al.  Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode , 2004 .

[12]  Greg Yarwood,et al.  Comparison of source apportionment and source sensitivity of ozone in a three-dimensional air quality model. , 2002, Environmental science & technology.

[13]  Adrian Sandu,et al.  Adjoint sensitivity analysis of regional air quality models , 2005 .

[14]  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.

[15]  M. Contaldi,et al.  Technical and Non-Technical Measures for air pollution emission reduction: The integrated assessment of the regional Air Quality Management Plans through the Italian national model , 2009 .

[16]  Jens Borken-Kleefeld,et al.  Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications , 2011, Environ. Model. Softw..

[17]  G. Righini,et al.  Assessment of the AMS-MINNI system capabilities to simulate air quality over Italy for the calendar year 2005 , 2014 .

[18]  M. MatJafri,et al.  Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia , 2013 .