Formalizing and solving the PM10 control problem

Abstract Atmospheric Particulate Matter (PM10) control is at the moment a great challenge for air quality management, due to the strong non linearities that affect formation and accumulation of this pollutant. This work presents the formalization and application of a two-objective methodology to select effective particulate matter control strategies on a mesoscale domain. The two considered objectives are emission reduction costs and the PM10 exposure index. The decision variables are the precursor emission reductions due to ablation technologies. The nonlinear relationships linking air quality objective and precursor emissions are described by neuro-fuzzy models, identified through the processing of simulations of the TCAM deterministic multiphase modeling system, performed in the framework of the CityDelta-CAFE Project (EU 6th Framework Program). The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by high emissions and frequent and persistent secondary pollution episodes.

[1]  Markus Amann,et al.  Integrated assessment of European air pollution emission control strategies , 1998, Environ. Model. Softw..

[2]  Ling L. Lim,et al.  Integrated decision support system for urban air quality assessment , 2005, Environ. Model. Softw..

[3]  Claudio Carnevale,et al.  Selecting effective ozone exposure control policies solving a two-objective problem , 2007 .

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

[5]  M. Giugliano,et al.  Primary and secondary carbonaceous species in PM2.5 samples in Milan (Italy) , 2007 .

[6]  Ina De Vlieger,et al.  Costs and benefits of an enhanced reduction policy of particulate matter exhaust emissions from road traffic in Flanders , 2006 .

[7]  A. Clappier,et al.  A Metamodeling Approach for Integrated Assessment of Air Quality Policies , 2004 .

[8]  Shih-Ping Han.  A globally convergent method for nonlinear programming , 1975 .

[9]  Peter Wåhlin,et al.  A European aerosol phenomenology—1: physical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe , 2004 .

[10]  Giovanna Finzi,et al.  GAMES, a comprehensive gas aerosol modelling evaluation system , 2006, Environ. Model. Softw..

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

[12]  Stefan Reis,et al.  Innovative approaches in integrated assessment modelling of European air pollution control strategies - Implications of dealing with multi-pollutant multi-effect problems , 2005, Environ. Model. Softw..

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

[14]  John H. Seinfeld,et al.  Fundamentals of Air Pollution Engineering , 1988 .

[15]  Marialuisa Volta,et al.  Multi-objective analysis of ground-level ozone concentration control. , 2004, Journal of environmental management.

[16]  Marialuisa Volta.  Neuro-fuzzy models for AIR quality planing: The case study of ozone in Northern Italy , 2003, 2003 European Control Conference (ECC).

[17]  R. Friedrich,et al.  Tropospheric Ozone Abatement , 2000 .

[18]  Prakash Karamchandani,et al.  The development and application of a simplified ozone modeling system (SOMS) , 1994 .

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..