Inverse Modeling of Aerosol Dynamics Using Adjoints: Theoretical and Numerical Considerations

Sandu, Adrian; Liao, Wenyuan; Carmichael, G. R.; Henze, D. K.; Seinfeld, John H.

doi:10.1080/02786820500182289

Cited by 37 publications

(30 citation statements)

References 21 publications

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“…Henze et al (2004) and Sandu et al (2005b) used the adjoint method for inverse modeling of aerosol distributions in box model simulations. Hakami et al (2005) used the adjoint of STEM for inverse modeling of black carbon aerosol, treated as an inert tracer.…”

Section: Adjoint Modelingmentioning

confidence: 99%

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem

2009

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Abstract. Influences of specific sources of inorganic PM 2.5 on peak and ambient aerosol concentrations in the US are evaluated using a combination of inverse modeling and sensitivity analysis. First, sulfate and nitrate aerosol measurements from the IMPROVE network are assimilated using the four-dimensional variational (4D-Var) method into the GEOS-Chem chemical transport model in order to constrain emissions estimates in four separate month-long inversions (one per season). Of the precursor emissions, these observations primarily constrain ammonia (NH 3 ). While the net result is a decrease in estimated US NH 3 emissions relative to the original inventory, there is considerable variability in adjustments made to NH 3 emissions in different locations, seasons and source sectors, such as focused decreases in the midwest during July, broad decreases throughout the US in January, increases in eastern coastal areas in April, and an effective redistribution of emissions from natural to anthropogenic sources. Implementing these constrained emissions, the adjoint model is applied to quantify the influences of emissions on representative PM 2.5 air quality metrics within the US. The resulting sensitivity maps display a wide range of spatial, sectoral and seasonal variability in the susceptibility of the air quality metrics to absolute emissions changes and the effectiveness of incremental emissions controls of specific source sectors. NH 3 emissions near sources of sulfur oxides (SO x ) are estimated to most influence peak inorganic PM 2.5 levels in the East; thus, the most effective controls of NH 3 emissions are often disjoint from locations of peak NH 3 emissions. Controls of emissions from industrial sectors of SO x and NO x are estimated to be more effective than surface

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Section: Adjoint Modelingmentioning

confidence: 99%

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem

2009

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“…Knowing the concentration of nucleated particles and the time interval in which they formed gives the nucleation rate. This is different from other methods that are based on fitting the nucleation rate using an aerosol dynamics model (Lehtinen et al, 2004;Sandu et al, 2005) or on correcting the appearance rate for coagulation (O'Dowd et al, 1999;Kulmala et al, 2001;Kerminen and Kulmala, 2002).…”

Section: Introductionmentioning

confidence: 99%

An inverse modeling procedure to determine particle growth and nucleation rates from measured aerosol size distributions

Verheggen

Mozurkewich

2006

Atmos. Chem. Phys.

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Abstract. Classical nucleation theory is unable to explain the ubiquity of nucleation events observed in the atmosphere. This shows a need for an empirical determination of the nucleation rate. Here we present a novel inverse modeling procedure to determine particle nucleation and growth rates based on consecutive measurements of the aerosol size distribution. The particle growth rate is determined by regression analysis of the measured change in the aerosol size distribution over time, taking into account the effects of processes such as coagulation, deposition and/or dilution. This allows the growth rate to be determined with a higher timeresolution than can be deduced from inspecting contour plots ("banana-plots"). Knowing the growth rate as a function of time enables the evaluation of the time of nucleation of measured particles of a certain size. The nucleation rate is then obtained by integrating the particle losses from time of measurement to time of nucleation. The regression analysis can also be used to determine or verify the optimum value of other parameters of interest, such as the wall loss or coagulation rate constants. As an example, the method is applied to smog chamber measurements. This program offers a powerful interpretive tool to study empirical aerosol population dynamics in general, and nucleation and growth in particular.

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“…The study of Hakami et al (2005) deals only with inert carbonaceous aerosols, and the work of Dubovik et al (2004), though global in scale, does not include full chemistry or aerosol thermodynamics. Detailed adjoint modeling of aerosols began with the theoretical investigations of Henze et al (2004) and Sandu et al (2005b). However, these are preliminary studies performed on idealized box model systems.…”

Section: Introductionmentioning

confidence: 99%

Development of the adjoint of GEOS-Chem

2007

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Abstract. We present the adjoint of the global chemical transport model GEOS-Chem, focusing on the chemical and thermodynamic relationships between sulfate -ammonium -nitrate aerosols and their gas-phase precursors. The adjoint model is constructed from a combination of manually and automatically derived discrete adjoint algorithms and numerical solutions to continuous adjoint equations. Explicit inclusion of the processes that govern secondary formation of inorganic aerosol is shown to afford efficient calculation of model sensitivities such as the dependence of sulfate and nitrate aerosol concentrations on emissions of SO x , NO x , and NH 3 . The accuracy of the adjoint model is extensively verified by comparing adjoint to finite difference sensitivities, which are shown to agree within acceptable tolerances. We explore the robustness of these results, noting how discontinuities in the advection routine hinder, but do not entirely preclude, the use of such comparisons for validation of the adjoint model. The potential for inverse modeling using the adjoint of GEOS-Chem is assessed in a data assimilation framework using simulated observations, demonstrating the feasibility of exploiting gas-and aerosol-phase measurements for optimizing emission inventories of aerosol precursors.

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Inverse Modeling of Aerosol Dynamics Using Adjoints: Theoretical and Numerical Considerations

Cited by 37 publications

References 21 publications

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem

An inverse modeling procedure to determine particle growth and nucleation rates from measured aerosol size distributions

Development of the adjoint of GEOS-Chem

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Inverse Modeling of Aerosol Dynamics Using Adjoints: Theoretical and Numerical Considerations

Cited by 37 publications

References 21 publications

Inverse modeling and mapping US air quality influences of inorganic PM&lt;sub&gt;2.5&lt;/sub&gt; precursor emissions using the adjoint of GEOS-Chem

Inverse modeling and mapping US air quality influences of inorganic PM&lt;sub&gt;2.5&lt;/sub&gt; precursor emissions using the adjoint of GEOS-Chem

An inverse modeling procedure to determine particle growth and nucleation rates from measured aerosol size distributions

Development of the adjoint of GEOS-Chem

Contact Info

Product

Resources

About

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem

Inverse modeling and mapping US air quality influences of inorganic PM<sub>2.5</sub> precursor emissions using the adjoint of GEOS-Chem