Emissions from Forest Fires: Methods of Estimation and National Results - The Greenhouse Gas Balance of Italy - page 83

Fire emissions (FE) interfere with local, regional and global phenomena in the bio-
sphere (Forster et al. 2001 ; Spichtinger et al. 2001 ) and influence climate (Randerson
et al. 2006 ; Urbanski et al. 2011 ). Hydrocarbons and nitrogen oxides can lead to the
formation of ozone in smoke plumes, acting as short-lived climate forcer (Urbanski
et al. 2011 ; Naeher et al. 2007 ). In addition, FE (especially particulates) contributes
to air quality degradation and pollution (e.g. Miranda et al. 1994 ; Schollnberger et al.
2002 ; Simmonds et al. 2005 ; Hodzic et al. 2007 ), sources of significant health and
environmental problems. Recent studies have highlighted that fire can be a source of
extremely toxic products (e.g. mercury, Friedli et al. 2009a ; dioxins EFSA 2012 ).
The demand for quantification of FE, and the definition of emission inventory,
is then increasing due to the need of (1) identification of the source of air pollution
affecting human health, (2) accounting for GHG emissions by governments and com-
panies (Bell and Adams 2009 ), and (3) providing key inputs to air quality and climate
change issue analysis and modeling (Granier et al. 2011 ; Urbanski et al. 2011 ).
This chapter reviews the contemporary state of the research concerning atmos-
pheric emissions from forest fires. An overview of algorithms and models used to
estimate FE, along with the analysis of input uncertainties, is firstly illustrated.
Then, a description of emission factors for various GHG gases is proposed.
Finally, the analysis of historical trends, temporal and spatial patterns, and future
scenarios of FE is presented.

6.2 Quantification of Forest Fire Emissions
and Uncertainty Sources

Calculating FE for a specific fuel type (i) requires, according to Seiler and Crutzen
( 1980 ), the quantification of the area burned ( BA , ha), fuel loading ( F l , t ha − 1 ), the
proportion of biomass consumed (also known as combustion completeness, CC, %),
and the species-specific emission factor (EF). FE is commonly formulated as:

FE I = BA · F L · CC · EF L

(6.1)

The Seiler and Crutzen ( 1980 ) approach has been applied, after changes, refinements
and integrations, to FE studies from global to local scale and for a variety of aims
and purposes (e.g. Battye and Battye 2002 ; French et al. 2004 ; Narayan et al. 2007 ;
Wiedinmyer and Neff 2007 ; Shultz et al. 2008 ; Wiedinmyer et al. 2010 ; Thonicke
et al. 2010 ; Carvalho et al. 2011 ). A common element of these studies and applica-
tions is the analysis of uncertainties in the factors of the above mentioned Eq. 6.1 . In
particular, BA , EF , and F l have been pointed out by several Authors as the factors lim-
iting the accuracy of long-term FE data sets (Peterson 1987 ; Peterson and Sandberg
1988 ; Battye and Battye 2002 ; Schultz et al. 2008 ). To represent their variability then
becomes the challenge in FE inventories development (Langmann et al. 2009 ).
Large systematic errors in BA assessment may exist in relation to the report-
ing system (Peterson 1987 ). Barbosa et al. ( 1999 ), Korontzi et al. ( 2004 ), Al-saadi
et al. ( 2008 ) analyzed the burnt area uncertainty and its effects of FE using different
spatial datasets. New advances in remote sensing have made datasets of active fires