Source emission inventory scaling

A source model incorporates measured or estimated values for an emission rate factor and the dispersion factor. Whenever either of these enter the receptor model as observables, we call it a hybrid model. The three applications considered here are emission inventory scaling, micro-inventories, and dispersion modeling of specific sources within a source type. 

Emission Inventory scaling, proposed by (24), uses the relative emission rates of two source types subject to approximately the same dispersion factor (e.g., residential heating by woodstoves and natural gas) to approximate the source contribution from the source type not included in the chemical mass balance (e.g., natural gas combustion). The ratio of the emission rates is multiplied by the contribution of the source type which was included in the balance. 

The construction of a mass balance model follows the general outline of this chapter. First, one defines the spatial and temporal scales to be considered and establishes the environmental compartments or control volumes. Second, the source emissions are identified and quantified. Third, the mathematical expressions for advective and diffusive transport processes are written. And last, chemical transformation processes are quantified. This model-building process is illustrated in Figure 27.4. In this example we simply equate the change in chemical inventory (total mass in the system) with the difference between chemical inputs and outputs to the system. The inputs could include numerous point and nonpoint sources or could be a single estimate of total chemical load to the system. The outputs include all of the loss mechanisms transport... 

Breivik, K., Alcock, R., Li, Y.-F., Bailey, R.E., Fiedler, H., Pacyna, J.M., 2004. Primary sources of selected POPs Regional and global scale emission inventories. Environ. Pollut. 128, 3-16. 

A U. S. national biogenic sulfur emissions inventory with county spatial and monthly temporal scales has been developed using temperature dependent emission algorithms and available biomass, land use and climatic data. Emissions of dimethyl sulfide (DMS), carbonyl sulfide (COS), hydrogen sulfide (H2S), carbon disulfide (CS2), and dimethyl disulfide (DMDS) were estimated for natural sources which include water and soil surfaces, deciduous and coniferous leaf biomass, and agricultural crops. The best estimate of 16100 MT of sulfur per year was predicted with emission algorithms developed from emission rate data reported by Lamb et al. (1) and is a factor of 22 lower than an upper bound estimate based on data reported by Adams et al. 

In contrast to studies of ambient contaminant concentrations in urban areas, comparatively little work has determined rates of contaminant emissions, with the exception of PCBs (e.g. Offenberg et ah, 2005 Totten et al, 2006 Gasic et ah, 2009). This information is of critical importance to risk assessment studies and the design of effective policies and programmes aimed at emissions reductions. There are numerous examples of national scale emissions inventories of single-chemical or point-source. These do not account for many of the important non-point sources of contaminants in urban areas, nor are they comparable due to differences in categorization and research priorities in different jurisdictions (Seika et al., 1996). 

The term primary pollutants describes those pollutants which are emitted directly to the atmosphere from sources such as road traffic, power plants, and industry. Their emissions are described in emissions inventories which are compiled on spatial scales relating to the nature of the pollutant itself. Thus, for pollutants having a local impact on health, emissions inventories are likely to be compiled on a spatially disaggregated grid basis within a city, whereas for a globally acting pollutant, the location of emission is... 

Fig. 5 European NH3 emissions from nonagricultural sources according to the EMEP inventories (webdab system 12 May 2012). Note that the scale in this figure is different from the one that is used in Fig. 4...

This basic picture of organic aerosol was relatively well developed by the end of the 1990s. Chemical transport models were fed by inventories for POA emissions from a wide array of sources, and those emissions were treated in a variety of microphysics modules as effectively non-volatile and often chemically inert particles [19, 20]. SOA models evolved from relatively primitive treatments that simply converted a fixed fraction of VOC emissions into equally non-volatile secondary material (for example 12% of monoterpene emissions) to more sophisticated two-product representations that treated the equilibrium partitioning of surrogate species based on smog-chamber experiments [21-23]. Even today some global-scale models represent SOA as a fixed non-volatile fraction of VOC emissions [24, 25]. 

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