Environmental Engineering Reference
In-Depth Information
1.E+03
1.E+02
HFCs
Fuel and CO
2
Traditional air pollutants and black carbon
1.E+01
1.E+00
1.E-01
1.E-02
1.E-03
Notes:
Box-plots represent the 5th and 95th percentile results from uncertainty analysis; whiskers extend to
lower and upper bounds; emissions do not represent comparative climate impacts.
Figure 2.3
Summary of estimated ranges in global emissions from maritime
shipping
Representing spatial (and temporal) activity of commercial shipping is fundamentally
similar to modeling any mobile source: the location and intensity of the
eet activity must
be depicted. Two global ship-reporting datasets, ICOADS and the Automated Mutual-assis-
tance Vessel Rescue system (AMVER), have previously been used as proxies of ship tra
fl
c
to geographically resolve the global emissions inventories (Corbett and Fischbeck, 1997;
Corbett et al., 1999; Endresen et al., 2003; Wang, 2006; Wang and Corbett, 2005). Recently,
a global shipping network has been developed (see Figure 2.4); this network has been used
to combine the best of the bottom-up and top-down approaches (Wang, 2006; Wang and
Corbett, 2005; Corbett et al., 2006a; 2006b; Wang et al., 2006; Corbett et al., 2007).
Freight energy and emissions trends
3
The multimodal and multicargo freight context must be considered when forecasting
oceangoing environmental trends. This is because all freight modes respond to common
drivers of change (e.g. economic growth, population demographics, energy prices), and
cross-mode in
uences need to be included (e.g. metropolitan road congestion around one
port diverting some cargoes to other ports). This applies whether one is considering air
fl
