Emissions from Forest Fires: Methods of Estimation and National Results - The Greenhouse Gas Balance of Italy - page 86

6.4 Fire Emissions Modelling

According to Debano et al. ( 1998 ), quantitative predictions of fire effect mod-
els can be distinguished based on the fuel consumption modelling approach.
Empirical models (McRae 1980 ; Brown et al. 1991 ; Prichard et al. 2005 ) use sta-
tistical relationships derived from measured woody fuel consumption data (Hollis
et al. 2010 ) while physical models fully describe heat transfer processes (Albini
1976a ). Semi-physical models result in the combination of these two approaches
(Albini et al. 1995 ; Albini and Reinhardt 1995 , 1997 ).
Up to now, among a conspicuous number of fuel consumption models,
CONSUME (Fuel Consumption model, Ottmar et al. 1993 , 2006 ) and FOFEM
(First Order Fire Effect Model, Reinhardt et al. 1997 ) are those mostly used.
CONSUME falls into the empirical model category, predicting fuel consumption
by combustion phase, heat release and pollutant emissions (Prichard et al. 2005 ).
FOFEM employs the physical model of heat transfer BURNUP (Woody fuel con-
sumption model, Albini et al. 1995 ; Albini and Reinhardt 1995 ) to predict woody
and litter fuel consumption and heat release.
At regional scale, Urbanski et al. ( 2011 ) presented the 2003-2008 Wildland
Fire Emission Inventory (WFEI) in the contiguous United States. The prod-
uct combines observation from satellite data, fuel loading maps, fuel consump-
tion models (both COMSUME and FOFEM), and an EF database. Air pollutant
emissions associated with forest burning in Texas were estimated by Dennis et al.
( 2002 ), based on survey and field data on area burned and land covered, and
FOFEM for fuel consumption and emission factors.
At local scale, several case studies have been performed to determine the
impact of fire events on air quality and identify the source of air pollution. Clinton
et al. ( 2006 ) implemented FOFEM algorithms to quantify the source and composi-
tion of smoke and emissions from wildland fires that affected Southern California,
USA, in October 2003. French et al. ( 2011 ) applied the two models to compare
the different methodologies of FOFEM and CONSUME to estimate carbon loss
from terrestrial biosphere resulting from wildland fires in Canada. Bacciu et al.
( 2012 ) applied FOFEM in Mediterranean areas, estimating type and amount of
Mediterranean vegetation fire emissions from Sardinian fires (2005-2009), and
compared 2005 emission estimates with the Italian Emission Inventory (NEI-
PROV, De Lauretis et al. 2009 ) (Table 6.1 ).

Table 6.1 Average pollutant and GHG mass (Gg) by category from Sardinian fires (2005-2009)

YEAR PM 10 PM 2.5 CH 4 CO CO 2 NO X SO 2 TOTA L

0.78 0.66 0.32 5.97 200.97 0.32 0.12 209.15

Mean

2005 0.41 0.34 0.16 2.98 0.18 0.07 4.14

0.62 0.62 0.34 3 - 0.09 0.03 4.74

NEI-PROV

The total emissions estimated for the 2005 fire season were compared with the 2005 fire emis-
sions from NEI-PROV (modified from Bacciu et al. 2012 ). (CO 2 emissions from fires not present
in the NEI-PROV report)