Emissions from Natural Sources and Biomass Burning

Im Dokument Emission Inventories and Projections (Seite 117-120)

Chapter 3 Emission Inventories and Projections

3.3. Emissions from Natural Sources and Biomass Burning

Though air quality is a measure of the anthropogenic perturbation of the ‗‗natural‘‘

atmospheric state, it has to be considered in the wider context of the interactions with natural

emissions and biomass burning [Monks et al., 2009]. The term ―natural‖ in this context should not be equated with ―free of human influence,‖ in light of the significant impacts of all kinds of human activities on vast areas of the global land surface [Haberl, 2007]. Natural emissions and biomass burning are significant contributors to the emission load of the atmosphere. Natural emissions include CH4 from termites, mineral dust from deserts, NOx emissions from lightning, sea salt, dimethyl sulphide from oceans, SO2 from volcanic activities and VOC emissions from vegetation. Biomass burning results from both natural and anthropogenic fires. The uncertainties associated with natural and biomass burning emissions are substantial.

3.3.1 Natural emissions

Natural sources of atmospheric gases and particles include living and dead organisms, soil, lightning, oceans, and volcanoes. Natural emissions occur in the absence of people, but human activities can substantially alter these emissions. Methods have been developed for estimating global emissions of trace gases and particles from all major natural sources, including, for example, plant foliage VOC [Guenther et al., 2006], mineral dust [Mahowald et al., 2006], volcanic SO2 [Andres and Kasgnoc, 1998], lightning NOx [Price et al., 1997], soil NOx [Lee et al., 1997], wetlands methane [Fung et al., 1991], and wildfires [van der Werf et al., 2003]. The resolutions of these models range from hourly and 1 km × 1 km for plant foliage VOC to monthly and 1° × 1° for wetlands methane.

The uncertainties associated with natural emissions are substantial and are highly dependent on the spatial and temporal scales considered. For example, annual global isoprene emissions are known to within a factor of two, but the uncertainty associated with isoprene emission at a particular hour and location can exceed a factor of five [Guenther et al., 2006]. In addition, uncertainties vary greatly for the various compounds emitted from vegetation foliage and wildfires. For example, the uncertainties associated with emissions of sesquiterpenes from foliage and NH3 from wildfires are much higher than those associated with isoprene from foliage and CO2 from fires.

A high resolution (1 km × 1 km) biogenic VOC and NOx emission model (MEGAN) and driving variables are available at http://cdp.ucar.edu. A high resolution (10 km ×10 km) inventory of NOx, SO2, NH3, PM, NMVOC, CH4, CO and dimethyl sulphide emissions from natural sources in Europe was completed in 2007 [NATAIR, http://natair.ier.uni-stuttgart.de, see Friedrich, 2009, and references therein]. Emission sources included as ―natural‖ comprise vegetation (especially forests and forest and agricultural soils), primary biological aerosol particles, wild animals, humans, anoxic soil processes in wetlands, macro- and micro-seepages from geothermal and non-geothermal sources, wind-blown dust and Saharan dust, volcanoes, and lightning. Emissions from pets, biomass burning, and forest fires are also dealt with, even though they are often considered to be anthropogenic activities. The methodology developed to estimate emissions from these sources will be used to update the EMEP/CORINAIR Guidebook.

Uncertainty assessments of natural emission sources have focused on comparisons of available input databases, e.g., Guenther et al. [2006], Ito and Penner [2004] and Hoelzemann et al.

[2004]. The uncertainties associated with emission factors and emission algorithms are more difficult to quantify for natural sources. Comparisons of different emission estimates for any of these sources tend to agree within about a factor of two on annual global scales. However, the models are generally based on at least some of the same emission factor data and so are not independent estimates. Global satellite observations are beginning to provide a valuable tool for assessing emissions of, among many others: foliar isoprene [Shim et al., 2005], wildfires [Pfister et al., 2005], lightning [Boersma et al., 2005], methane [Frankenberg et al., 2005], and mineral dust [Mahowald et al., 2003]. These observations are valuable both for providing some confidence in natural emission estimates and for indicating regions and seasonalities of major discrepancies.

As estimates of present-day natural emissions have improved, research efforts have focused more on how these emissions will respond to climate and landcover change. Natural emissions of

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mineral dust, wetland methane, foliar VOC, and wildfires are all very sensitive to changes in

landcover (e.g., vegetation type and density) and soil moisture [e.g., Flannigan et al., 2005; Guenther et al., 2006; Mahowald et al., 2006]. Foliar VOC emissions are also sensitive to ambient temperature and solar radiation. Emissions can vary by a factor of two or more on time scales of years to decades.

An improved understanding of the processes controlling these variations is required for accurate predictions of future natural emissions.

Table 3.3 presents a summary of some recent estimates of the relative magnitudes of

emissions from the major natural emission sources. Biomass burning is included in this table and also expanded on in section 3.3.10. Each one of these species/source categories is the subject of ongoing scientific research, and we present selected results to give an idea of the relative magnitudes of natural source contributions. This table is not exhaustive in either species or sources.

Table 3.3. Some estimates of global annual emissions from natural sources Biogenic Desert/Soil

Biogenic VOC: 500-750 Tg isoprene from Guenther et al. [2006].

Biomass burning and soil NOx: from Lee [1997]. Biomass inventory includes deforestation, savannah burning, agricultural waste burning, and biofuel combustion, yielding an estimate of 8 Tg N yr-1. This estimate includes sources beyond the tropics. Further refinement of the AERONOX soils emission model resulted in an estimate of 7 Tg N yr-1. Biomass burning emissions of NMVOC and SO2 from Lamarque et al. [2010].

Dust: PM2.5 and PM10 data from Mahowald et al. [2006], based on modelling and satellite observations of aerosol optical depth.

Lightning NOx:

*5 Tg N (± 3 Tg) [from Schumann and Huntrieser, 2007].

*5 Tg N from Lee [1997].

* 12.2 Tg N from Price [1997].

Biogenic CH4 from Fung et al. [1991]:

*Wetlands, bogs, swamps, and tundra: 200-400 Tg CH4.

*Termites: 10-200 Tg CH4.

*Tropical biomass burning: 50-100 Tg CH4. Geogenic CH4 from Etiope and Klusman [2010].

Volcanic: 13.4 Tg SO2 [from Andres and Kasgnoc, 1998].

3.3.2. Biomass burning

In the last few decades biomass burning has been recognized as an important source of atmospheric trace gases and particulate matter, which may exert a significant influence on

atmospheric chemistry, particularly in the tropics, and in the longer term on climate [Hao and Liu, 1994; Schultz et al., 2008; Seiler and Crutzen, 1980]. Types of vegetation cover subjected to fires

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include forestland, wooded land, grassland, and agricultural land. Recent studies aimed at investigating the spatial and temporal distributions of vegetation burning using satellite data to identify burn area have started to evaluate the interannual variability of wildfire emissions on global and regional scales, [e.g., Duncan et al., 2003; Schultz et al., 2008; van der Werf et al., 2004; van der Werf et al., 2006]. The results showed that wildfires have a large interannual variability [Duncan et al., 2003], and therefore the resulting emissions are very variable in time and space. Globally,

biomass burning contributes at present about 50% of the total direct CO and BC emissions [Pétron et al., 2004] and about 15% of surface NOx emissions [IPCC, 2001].

An illustration of the interannual variability of biomass burning NOx emissions is shown in Figure 3.6, which is obtained by combining the RETRO biomass burning dataset [Schultz et al., 2008]

with the GFED dataset [van der Werf et al., 2003]. The types of biomass burning considered included forest fires, savannah burning and grassland fires by main regions in the period 1970-2005.

Figure 3.6. NOx emissions from biomass burning (forest fires, savannah burning, and grassland fires) by main world regions in the period 1970-2005. For the definition of world regions, REF = Central and Eastern Europe + Russia; OECD90 = OECD member states as of 1990; MAF = Middle East + Africa; LAM = Latin America and Caribbean; ASIA =Asia excluding the Middle East. See IPCC publications for countries included in each world region.

From Figure 3.6 it is clear that the Middle East and Africa region, MAF, constitutes the major contributor to global biomass burning NOx emissions. The extensive savannah burning in this region contributes about 50% of global emissions from biomass burning. The contribution from MAF is rather constant over the period 1970-2005. The region displaying a high inter-annual variability is ASIA, mainly due to cropland burning, although the absolute value of the contribution is smaller than that of MAF, with a peak in 1997-1998 that coincided with the important forest fires in Indonesia. In this regard, Chang and Song [2010], based on a comparison of new high spatial resolution global burned area products, L3JRC (1 km resolution) with MDC45A1 (500 m resolution) over the domain of tropical Asia running from India to Indonesia, indicated that these satellite datasets led to better classifications of vegetation cover type and more representative burned areas, which are still nevertheless subject to high uncertainties, when compared, for example, to GFED products.

According to Ito and Penner [2004] and detailed by Schultz et al. [2008], there are three main types of uncertainty that limit the potential accuracy of any global long-term biomass burning

emission dataset. First, for burned areas, data on accurate long-term monitoring of fire scars have become available only recently, generally for periods after 1995, and thoroughly performed in only a few regions. Although these satellite fire products have provided key information on the spatial and temporal patterns of fire occurrence, their quantitative use is still limited because of remote sensor limitations, satellite orbital drift, cloud and smoke presence, and especially limited statistics of ground calibrations, and because retrieval procedures may vary for different ecosystems and observing conditions [e.g., Giglio and Kendall, 2004]. It should be noted that fires in the mid-latitude regions

0

1970 1975 1980 1985 1990 1995 2000 2005

REF OECD90 MAF LAM ASIA

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are well monitored with sophisticated networks, while those in the tropics are still scattered and scarce, due to limited resources at the local level for records and accessibility. This was revealed when tropical Asia experienced some of the most severe wildland fire events under extreme climatic conditions [Chang and Song, 2010; Streets et al., 2003]. Second, regarding the fuel consumption, fire severity and hence the amount of fuel actually combusted in a fire, depends on the fuel characteristics (load and density, moisture, vegetation type, organic content and moisture of the soil) and the rate of spreading, which is determined largely by wind speed, fraction of fuel consumed by the fire, fuel bulk density, topography, etc. These factors are highly variable even within a single fire and they are poorly documented on larger scales. A few studies have tried taking these factors into account on a case-by-case basis, but very few data are able to support general parameterizations for global-scale modelling of fire emissions. Finally, for emission factors, the amounts of individual chemically active trace species and aerosols released from a fire depend on the fuel type and fire characteristics, and are often poorly determined. In practice, the emission factors compiled by Andreae and Merlet [2001]

have become a reference dataset used by many modellers. Results from different studies nevertheless stress that more complete combustion, as in flaming fires, would lead to a larger fraction of highly oxidized species (e.g., CO2, NOx), while smouldering fires release more material in reduced form (e.g., CO, NH3 and NMVOC species), which indicates that emission factors may vary with season, and that fire characteristics can be very different from one fire to another even within the same geographical location.

Given the high uncertainty of biomass burning emissions and the variability in time and space, recent studies [Chang and Song, 2010; Lamarque et al., 2010; Mieville et al., 2010; Schultz et al., 2008] have investigated past and present trends of emissions from biomass burning in order to improve inputs to global and regional climate and atmospheric chemistry models, e.g., those related to the RCP scenarios for supporting IPCC AR5. A compilation of these studies for the past and present trends in combination with future projections can provide information on the capacity of emission models to represent the contribution of biomass burning to global emissions. Figure 3.7 illustrates the trends of CO emissions from global biomass burning according to several different models and scenarios. The result of a comparison of RCP scenario projections shows that AIM seems to better capture the global biomass burning trends, but is also the only model to indicate an increase of CO from this emission source, ranging from a contribution of 45% to global direct emission of CO in 2000 to more than 70% in 2100.

An analysis of the AIM dataset of future projections vs. vegetation type is shown in Figure 3.8, which indicates that the future increase results from intensification of savannah burning, especially in MAF, and in forest fires in ASIA and LAM. Figure 3.9 displays the evolution of the spatial distribution of forest fires based on AIM (RCP 6.0) outputs.

Im Dokument Emission Inventories and Projections (Seite 117-120)