Soot emissions from ground vehicles, aircraft, combustion of wood and coal, and burning of agricultural waste have profound effects on both the environment and public health. Black carbon, the principal component of soot, has been identified as the second most important climate-warming agent after carbon dioxide. It impacts global climate change through multiple mechanisms, including direct absorption of radiation in the atmosphere, lowering snow/ice pack albedo, and changing cloud reflectivity and stability. There is also a strong correlation of atmospheric black carbon concentration with cardiovascular mortality and cardiopulmonary hospital admissions, and the polycyclic aromatic hydrocarbons (PAH) commonly absorbed on the carbon particles are known human carcinogens. These adverse health effects have driven strict environmental standards on both soot particle mass and number emissions.

Accordingly, understanding the formation and oxidation of soot particles, and developing engineering models that allow combustion system design to minimize soot emissions, are critical needs. Current engineering soot models range from empirical two–equation models describing formation and oxidation to more complex models using detailed kinetics to predict PAH formation followed by more heuristic models for particle inception, growth, and oxidation. All of these models fail to be predictive design tools except under thermodynamic conditions and for fuels with which they have been specifically designed (i.e. tuned) and validated.

In view of the broad range of combustion technologies that produce soot emissions and their documented impacts on both health and climate, development of a clear fundamental understanding of the PAH formation, particle inception and growth, and subsequent oxidation processes is a priority of the IEA Combustion Technology Collaboration Program (TCP). This task links directly to numerous other subtasks within the Combustion TCP, including Low Temperature Combustion, Low Emission Gas Turbine Combustion, Gas Engines, and Solid Fuel Combustion.

There is also significant potential for cross-linkage and collaborations with several other TCPs, including the TCP on Advanced Motor Fuels, the Bioenergy Technology Collaboration Programme, the TCP on Clean Coal Centre, the Climate Technology Initiative TCP, and the TCP on Greenhouse Gas R&D.


Overall task scope and objectives:

The objective of this task is the development of a predictive soot modelling capability that is applicable across the range of soot production sources. Hence we seek to understand and quantitatively describe how soot precursor formation, particle inception, and particle growth are impacted by pressure, temperature and fuel type. We anticipate the need to accommodate pressures ranging from 1–200 bar, temperatures from soot inception to over 2000K, and fuel types ranging from wood pyrolysis gases to mineral-source hydrocarbons. Our model aspirations also include subsequent atmospheric reactions and transport mechanisms that affect the ultimate impact of soot emissions on both climate and health.

To address this broad range of applications, our modelling approach must be based on a scientifically-sound understanding of the various sub-processes involved. Hence detailed kinetic mechanisms must be developed for various fuel classes to understand and characterize how the composition of soot precursor molecules changes with fuel type and thermodynamic conditions. Detailed studies of the soot inception process are also required to unravel the complex physicochemical process of particle nucleation, understand how the topology of the soot particle varies with fuel type and conditions, and determine how variations within these steps might impact oxidative processes. These studies will also be essential to the development of a predictive capability for particle size and concentration – properties which will couple closely with subsequent atmospheric processes. Lastly, we anticipate the need for a significant effort dedicated to the development of engineering models. This last step will be essential if the full impact of this task is to be realized.