Dry deposition of gases may involve absorption by liquid surfaces or adsorption to solid surfaces. Dry deposition of particles involves sticking to surfaces by diffusion, interception, and impaction. Very large particles are also removed by gravitational settling. Fog deposition is a special case of particle deposition in which fog droplets containing dissolved gases and particles are removed by settling or impaction on surfaces.
Emission and dry deposition may be coupled through surface processes. In the simplest such case, deposited gases and particles may be temporarily stocked at the surface and then re-emitted. There may also be biogeochemical, transport, and other processes that take place within the surface reservoir, causing the re-emitted species to be different from that deposited or to be re-emitted in a new location. Accounting for these processes requires that the atmospheric model be coupled to a model for the surface reservoir that tracks the material deposited, its transformations and transport, and the eventual emission. Coupled models for the atmosphere and surface reservoirs are called Earth system models, global biogeochemical models, or multimedia models, with the preferred terminology depending on their level of detail. Earth system models couple atmosphere and surface reservoirs in a global 3-D dynamical framework; global biogeochemical models generally have simpler (or absent) representations of transport; and multimedia models are often regional in scale and empirically based. Coupled models must still use a surface boundary condition for their atmospheric component, and the computations of emission and dry deposition follow the same approaches as atmosphere-only models.
In this chapter we first discuss the different processes emitting material to the atmosphere and their representation in models (Section 9.2). We then discuss the representation of dry deposition as a one-way uptake by the surface (Section 9.3). Finally, we discuss two-way exchange in which the surface provides both a source and a sink (Section 9.4).
9.2 Emission
Emissions in atmospheric models are usually provided by bottom-up emission inventories that calculate emissions from knowledge of the underlying processes. In these inventories, the emission flux Ei of species i is computed in general form as:
Ei = A × Fi × Si
(9.1)
where A is the activity rate for the process driving the emission, Fi is an emission factor for species i that measures the amount of emission per unit of activity, and Si is a scaling factor to account for local meteorological variables, surface properties, and other effects not included in the specifications of A and Fi. For example, emission of SO2 from coal combustion may be computed as the product of an annual coal combustion rate (A), the amount of SO2 emitted per unit mass of coal burned (Fi), and a seasonal scaling factor to account for changing power demand (Si). Emission of ammonia from livestock manure may be computed as the product of the number of heads of livestock (A), a mean rate of ammonia emitted per head (Fi), and a temperature-dependent scaling factor (Si). Scaling factors are often calculated within the atmospheric model at individual time steps to yield time-dependent emissions consistent with the local model environment.
The bottom-up approach provides a consistent framework to quantify emissions guided by our best knowledge of the driving processes. A given process may emit many different species, but the activity rate A is common to all. Information on A is obtained from socioeconomic, ecological, or other geographical databases. Emission factors Fi for the different species emitted by a given activity are typically estimated from field or laboratory experiments. Scaling factors Si adjust the emissions to account for information that is not resolved in the activity rate databases, or for conditions in which emission factors differ from the base case Fi.
Bottom-up emission inventories give the total emission of a species as the sum of contributions from different activities. This enables atmospheric chemistry models to determine the contributions of different source types to atmospheric concentrations and to make future projections. For example, a bottom-up inventory for NOx emissions with sector information for power plants and vehicles can be used to separate the contributions of these two source types to ozone pollution. Projections of future activity rates from a socioeconomic model can be used through the bottom-up approach to project future emissions and from there future atmospheric concentrations.
A defining feature of a bottom-up emission inventory is that it is not directly constrained by observed atmospheric concentrations. As a result, an atmospheric model simulation driven by bottom-up emissions may simulate atmospheric concentrations that disagree with observations. Analysis of this disagreement may point to errors in the bottom-up emission estimates and the need to improve these estimates. We refer to atmospheric observations as providing top-down constraints on emissions.
Top-down constraints from atmospheric observations can be used to optimize emissions in two different ways. The first is to use observed surface air concentrations as boundary conditions for the atmospheric model. This completely ignores bottom-up information on emissions, and is often done in the case of long-lived gases for which atmospheric concentrations are known better than emissions. The emissions can then be diagnosed from the atmospheric model implicitly by mass balance (i.e., to balance the loss computed by the model). The second, more general way to use top-down information from atmospheric observations is to apply correction factors to the bottom-up emissions in order to match the observations. This can be done by statistical optimization using various inverse modeling methods (see Chapter 11). Top-down correction factors applied to the bottom-up inventories improve by design the simulation of observed atmospheric concentrations, but can be difficult to interpret in terms of the underlying processes because they are statistical fits with no intrinsic physical meaning. Ultimately, the best use of top-down constraints is to guide improvements in the bottom-up inventories.
We describe here standard methods to produce bottom-up emission inventories for different processes. Table 9.1 gives global emission estimates for selected species, with contributing processes broadly classified as terrestrial biogenic, open fires, oceanic, anthropogenic, volcanic, lightning, and mechanical. This classification follows standard practice in the atmospheric chemistry literature, but there are ambiguities and inconsistencies that need to be recognized. For example, terrestrial biogenic emissions associated with agriculture are classified as anthropogenic, but those affected by inadvertent human influence (such as nitrogen deposition) are generally not. Open fires are generally not classified as anthropogenic, although most are set by humans. Oceanic emissions are often biogenic, but are separated from terrestrial biogenic emissions because they are derived by different bottom-up methods. The following subsections cover terrestrial biogenic, open fire, volcanic, anthropogenic, and mechanical emissions in order. Oceanic emissions are generally computed as a two-way exchange process (Section 9.4). Lightning is coupled to deep convection and was covered in Section 8.10.
Table 9.1 Global emissions to the atmosphere (Tg a–1)
Species Terrestrial biogenic Open fires Ocean biogenic Anthropogenic Volcanic Lightning Mechanical Total
NOx (as N) 11 7 – 32 – 5 – 55
CO 80 460 20 610 – – – 1170
Methane 190 50 – 290 – – – 530
Isoprene 520 – – – – – – 520
SO2 (as S) – 1 – 57 10 – – 68
Ammonia 3 6 8 45 – – – 62
Black carbon (as C) – 11 – 7 – – – 18
Dust – – – – – – 1500 1500
Sea salt – – – – – – 5000 5000
Typical estimates for circa 2015. Dash indicates a zero or negligible source.
9.2.1 Terrestrial Biogenic Emissions
Biological organisms emit a wide range of volatile compounds through growth, metabolism, and decay. Photosynthesis and respiration are dominant processes. Photosynthesis converts CO2 to molecular oxygen and releases volatile organic by-products. Respiration is either aerobic, in which molecular oxygen is converted to CO2, or anaerobic, i
n which another oxidant such as nitrate or sulfate is used to oxidize organic carbon. Biogeochemical carbon models provide estimates of photosynthesis and respiration rates, as well as related quantities such as net primary productivity (NPP). They also differentiate between autotrophic respiration by green plants and heterotrophic respiration by decomposers. Box 9.1 gives a summary of the major processes and rates.
Box 9.1 Terrestrial Carbon Cycle
Biogeochemical models of the terrestrial carbon cycle describe the flow of carbon as it is captured from the atmosphere by photosynthesis, transferred through different ecosystem pools, and eventually respired back to the atmosphere. The rate of photosynthesis by green plants is called the gross primary productivity (GPP). Some of the carbon fixed by green plants is respired by the plants themselves; this is called autotrophic respiration. The rest is transferred to other ecosystem pools through litter fall and plant mortality. The amount of carbon that is fixed by green plants and not autotrophically respired is the NPP. It represents the net source of carbon to the ecosystem from green plants. Most of that carbon is eventually consumed by decomposers (bacteria and other biota) through heterotrophic respiration. The net amount of carbon delivered to the ecosystem by green plants (NPP) and not consumed by decomposers is called the net ecosystem productivity (NEP). It represents the net accumulation of carbon in the undisturbed ecosystem. Disturbances such as fires, erosion, and harvest provide an additional sink for that carbon. The net accumulation of carbon after all these disturbances have been taken into account is called the net biome production (NBP).
Box 9.1 Figure 1 gives current global estimates of these different carbon fluxes. Half of the carbon fixed by green plants (GPP) is transferred to other ecosystem pools (NPP) while the rest is autotrophically respired. Eighty percent of the transferred carbon is respired by decomposers and the remaining 20% accumulates in the undisturbed ecosystem (NEP). Fires, erosion, and harvest balance most of the NEP. The residual NBP is 1.4 Pg C a–1, just 1% of the GPP. The NBP represents the global build-up of terrestrial carbon, so that the terrestrial biosphere is not in steady state. This is of fundamental importance for our understanding of anthropogenic perturbation to the carbon cycle because it balances a significant part of the current fossil fuel source of CO2 (6.4 Pg C a–1).
Box 9.1 Figure 1 Global flows in the terrestrial carbon cycle.
Carbon fluxes are often used as activity rates in (9.1) to estimate the terrestrial biogenic emissions of other species with emission factors. Deriving emission factors for individual species requires field or laboratory measurements that must then be extrapolated to produce regional and global estimates. Meteorological variables such as light, temperature, and soil moisture often have a large effect on emissions and are applied in the model as local scaling factors.
We present here three basic algorithms to compute the terrestrial biogenic emissions of methane, nonmethane volatile organic compounds (NMVOCs), and NOx in atmospheric models. Emissions of other species generally follow algorithms of similar structure.
Methane. The main natural source of methane is wetlands, where bacteria reduce organic carbon to methane under anaerobic conditions. Some of that methane is oxidized as it rises to the surface and encounters aerobic waters, while the rest escapes to the atmosphere. A simple formulation (Kaplan, 2002) expresses the methane emission rate [E, gCH4 m–2 d–1] in a given model grid square as a function of wetland fractional extent [W, m2 m–2], heterotrophic carbon respiration [R, gC m–2 d–1], and an emission factor [F, gCH4 gC–1] dependent on temperature (T) and the depth of the water table (D):
E = W × R × F(T, D)
(9.2)
This formulation can be applied in models using gridded wetland and water table data available from satellites (Bloom et al., 2010). More advanced formulations derive methane emissions from a full biogeochemical model (Figure 9.1; Riley et al., 2011) or account for seasonal variation in the pool of organic carbon reducible to methane (Bloom et al., 2012).
Figure 9.1 Annual emission of methane from wetlands
(Riley et al., 2011).
Nonmethane volatile organic compounds. Terrestrial plants are the largest global source of NMVOCs (Guenther et al., 2006). Major species emitted by plants include isoprene, terpenes, sesquiterpenes, alkenes, carbonyls, and alcohols. They may be emitted as by-products of photosynthesis, as responses to injury, and from metabolism and decay. Emission fluxes depend on plant type, life stage (phenology), and foliage density; on radiative and meteorological variables within the canopy; and on external perturbations such as cutting, air pollution, and insect infestation. A standard measure of foliage density is the leaf area index (LAI; m2 leaf per m2 of land surface, counting only one side of the leaf). NMVOC flux measurements can be made at the leaf or plant level using chamber devices, at the canopy level from towers extending above the canopy top, and at the landscape level from aircraft. Flux measurements from towers and aircraft are generally made by eddy correlation, i.e., where w′ and C′ are the turbulent (residual) components from fast collocated measurements of vertical wind velocity and atmospheric concentrations. The measured fluxes, including their environmental dependences, are then extrapolated to produce regional and global emission inventories. The extrapolation is done with varying detail depending on the information available. It generally resolves different plant functional types (PFTs). A simple PFT classification might resolve only deciduous trees, evergreen trees, shrubs, and grasses. A more elaborate classification might resolve deciduous trees into tropical and temperate, broadleaf and fineleaf, etc.
The most advanced bottom-up emission inventories have been developed for isoprene (CH2=C(CH3)–CH=CH2), which is the dominant NMVOC emitted by vegetation globally and accounts alone for about half of the global NMVOC source (Guenther et al., 2006). Isoprene is produced in the chloroplasts of plants and is released to the atmosphere through leaf stomata. Emission only takes place in daytime when the stomata are open. Canopy emission fluxes depend on plant species, foliage density, leaf age, temperature, photosynthetically active radiation (PAR), and water stress. This is commonly represented in bottom-up emission models by multiplying base emissions Eo tabulated for each PFT under standard conditions with an ensemble of scaling factors describing the sensitivity to local environmental variables. In the MEGAN emission model (Guenther et al., 2012), the canopy emission flux E for a given PFT is given as
E = Eo × CCE × Λ × γPAR × γT × γAGE × γSM
(9.3)
where Eo is the base emission per unit area of Earth surface under standard conditions, Λ is the LAI, and the dimensionless scaling factors γ describe the sensitivity to above-canopy radiation (PAR), surface air temperature (T), leaf age distribution (AGE), and soil moisture (SM). The coefficient CCE enforces E = Eo under standard conditions, which for MEGAN are defined as T = 303 K, PAR = 1000 μmol photons m–2 s–1, Λ = 5 m2 m–2, a leaf age distribution of 80% mature, 10% growing, and 10% senescing, and a volumetric soil moisture of 0.3 m3 m–3. The total isoprene emission flux for a given model gridsquare is obtained by summing the contributions from all PFTs in that gridsquare. Bottom-up emission inventories for other species generally follow the same kind of algorithms as for isoprene, but with less sophistication.
Figure 9.2 shows the MEGAN base emissions Eo under standard conditions for Europe and for Central/North America. Values are high for tropical forests, the southeastern USA, and boreal forests, reflecting PFTs with strong potential for isoprene emission. Values are low in the US Midwest where crops are poor isoprene emitters. Figure 9.3 shows as examples of scaling factors the dependences of isoprene emission on air temperature (γT) and LAI (Λ × γPAR), taken from Guenther et al. (2006). Emission depends both on the instantaneous temperature and on the temperature for the past ten days. The dependence on LAI would be linear were it not for canopy light extinction measured by γPAR. Because of this extinction, there is a saturation effect limited by the penetration of light in the canopy.
&n
bsp; Figure 9.2 Base isoprene emission Eo [μg m–2 h–1] under standard conditions in (a) Central/North America and (b) Europe.
From A. Guenther and C. Wiedinmeyer, NCAR, personal communication.
Figure 9.3 Scaling factors γT (a) and ΛγPAR (b) in the MEGAN bottom-up isoprene emission inventory (9.3). Here, T24 is the air temperature for the past 1–10 days (assumed constant), and the calculation of γPAR is for two solar zenith angles (20° and 40°) and three leaf angular distributions (clumped, horizontal, and mixed). The figure shows how isoprene emission saturates as LAI exceeds 2 and light penetration inside the canopy becomes the limiting factor.
Adapted from Guenther et al. (2006).
Combining base emissions and scaling factors through (9.3) yields the global mean distributions of isoprene emission in Figure 9.4. Emissions are highest in tropical forests because of elevated temperature, LAI, and PAR. Emissions at northern mid-latitudes show strong seasonality driven by phenology and temperature.
Figure 9.4 Global distribution of isoprene emission in January and July.
From Guenther et al. (2012).
Nitrogen oxides. Nitrogen is essential to life and has an active biogeochemical cycle in terrestrial ecosystems. Specialized bacteria present in all ecosystems convert atmospheric nitrogen (N2) to ammonia, a process called biofixation, and the resulting fixed nitrogen then cycles through the ecosystem. Fixed nitrogen can also be directly delivered to the ecosystem by fertilizer application or by deposition of atmospheric ammonia and nitrate. Biological processes that cycle nitrogen within the ecosystem include assimilation (conversion of inorganic nitrogen to biological material), mineralization (conversion of organic nitrogen to inorganic forms), nitrification (aerobic microbial oxidation of ammonium to nitrite and on to nitrate), and denitrification (anaerobic microbial reduction of nitrate to N2). Volatile N2O and NO are generated as by-products of nitrification and denitrification.
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