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4.1. Introduction *

In order to complement the satellite-based estimation of emission factors (EFs) of NOx, fire emission rates (FERs) of NOx are also converted for tropical and subtropical biomes and regions. The translation into EFs of NOx is performed by applying a conversion factor of 0.41 kg MJ-1 (see Sect. 3.3.3) as suggested by Vermote et al. (2009), following the findings of Wooster et al. (2005).

4.2. Conversion into emission factors of NO

*

The summarized correlation coefficients as obtained from the linear regression between tropospheric NO2 vertical columns (TVC NO2) and FRP are shown in Fig. 4.1. Here, the correlation coefficients in the tropical/subtropical and boreal regions are based on the retrieval of TVC NO2 as described in Sects. 2.2.1 and 3.2.1, respectively. The global map is quite revealing in several ways. First, a very strong relationship between the two time series is found for the major biomass burning regions. Hence, the seasonal variation of TVC NO2 is explained by the seasonal cycle of fire radiative power (FRP) to a large degree. Second, the empirical relationship forms the basis of the approach to estimate FERs of NOx as it is a reasonable tool to separate between different NOx sources (see Sect. 2.3).

The fits of the linear regression for the different types of open biomass burning are summarized in Fig. 4.2. The calculated FERs of NOx and their possible uncertainties are discussed in detail in the previous Sects. 2.3.5 and 3.4.

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Fig. 4.1: Correlation coefficients (r) of the temporal relationship between TVC NO2 and FRP

based on monthly averages from 2007-2012 on a 1° x 1 grid. FERs of NOx are derived for the

tropical and subtropical regions Africa north of Equator (ANE), Africa south of Equator (ASE), central South America (CSA), northern Australia (NAU), and Southeast Asia (SEA) (see Table 2.1). The boreal regions Eurasia (EUR) and North America (NAM) are defined in Table 3.1.

The EFs of NOx are derived for tropical forest (evergreen broadleaf forest), savanna and grassland (open shrublands, woody savannas, and savannas), crop residue (croplands), and boreal forest (evergreen needleleaf forest, deciduous needleleaf forest, mixed forest, and woody savannas). Here, the land cover types listed in brackets are the labels from the UMD classification scheme, which are used for the estimation of FERs in the earlier Sects. 2.3.4 and 3.3.2.

Figure 4.3 provides the arithmetic means of EFs as obtained from satellite data as well as arithmetic means of EFs reported by Andreae and Merlet (2001) and Akagi et al. (2011). While the error bars denote one standard deviation of the arithmetic mean in the two latter studies, they indicate the minimum and maximum values obtained for the different regions as highlighted in Fig. 4.2. The satellite-based EFs of NOx are estimated at 1.83, 1.48, 2.96, and 0.72 g kg-1 for tropical forest, savanna and grassland, crop residue, and boreal forest, respectively.

107 Fig. 4.2: Best fitting least-squares regression lines for evergreen broadleaf forest (light green), open shrublands (beige), woody savannas (orange), savannas (yellow), croplands (red), and boreal forest (dark green) as defined in Table 2.2 and 3.1.

From the data in Fig. 4.3 it is apparent that the arithmetic means of satellite-based EFs for tropical forest, crop residue, and boreal forest fall within one standard deviation of means reported by Andreae and Merlet (2001) and/or Akagi et al. (2011). However, the EFs for savanna and grassland obtained in this study are lower by a factor of 2.5 when compared to the other two values.

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Fig. 4.3: Emission factors (EFs) of NOx (g kg-1) for tropical forest (light green), savanna and

grassland (ochre), crop residue (red), and boreal forest (dark green). Satellite-based EFs are represented by squares, reported values from Andreae and Merlet (2001) are indicated by a cross, and the asterisk denotes EFs from Akagi et al. (2011).

A possible explanation for this might be the seasonal cycle in NOx emissions rates (Mebust and Cohen 2013). It is possible that the fuel for laboratory-controlled combustion experiments has been mainly taken during that part of the fire season, where the highest emission rates are expected. In contrast, the satellite-derived EFs are based on the entire fire season, and thus, represent rather average values. Another reason could be the difference of the overpass times of GOME-2 on board MetOp-A (09:30 LT) and MODIS on board Terra (10:30 LT). Tropospheric NO2 amounts and thus, production rates of NOx from fire (Pf) could be underestimated for the actual Terra

overpass due to the diurnal fire cycle (e.g. Giglio, 2007) with its sharp increase in the morning, in particular in African woody savannas and savannas. As a consequence, slope values of the best fitting least-squares regression lines could be too low for the morning measurements (GOME-2 vs. MODIS Terra).

One of the issues that emerge from these findings is that recent fire emission inventories might have overestimated NOx emissions from savanna and grassland fires by a factor of 2.5.

109 Note that the results obtained in this study might be affected by errors in the satellite- based retrieval of TVC NO2 and FRP as well as in the few assumptions made. A detailed discussion on the possible errors can be found in Sects. 2.3.5 and 3.4.

4.3. Summary and conclusions *

The EFs of NOx are estimated at 1.83, 1.48, 2.96, and 0.72 g kg-1 for tropical forest, savanna and grassland, crop residue, and boreal forest, respectively. The EFs presented here fall within one standard deviation of the mean EFs as reported by Andreae and Merlet (2001) and/or Akagi et al. (2011). While there is overall agreement between the EFs and the comparable values presented in the literature for tropical forest, crop residue, and boreal forest, substantial differences for savanna and grassland are found.

Consequently, recent fire emission inventories could have overestimated NOx emissions from savanna and grassland fires by a factor of 2.5. As savanna and grassland is one of the major land cover types burned on Earth, this would have a significant effect on the estimation of open biomass burning emissions of NOx on the global scale.

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Chapter 5