1.2 Aim and Objectives
2.2.4 Non-degradation
2.3.2.4 Modelling
The use of models in AQM allows the calculation and prediction of air quality levels to be carried out and effectively links monitoring data with the inventories of emissions in an area (Elsom, 1992;
Middleton, 1997). Modelling techniques can be used to evaluate the suitability of emission reduction measures prior to implementation, using criteria such as reductions in ambient concentrations and deposition level and cost-effectiveness, and to demonstrate compliance with ambient air quality standards, where dispersion modelling augments the single-point concentrations of monitored data (Fedra, 2002; Scorgie and Malan, 2002). Modelling establishes individual source contributions to ambient pollutant levels at specified receptor points, which facilitates the ranking of sources and identification of reduction opportunities, and aids the investigation of complaints by determining the responsible source or identifying the non-liability of a particular source for the impact (Scorgie and Malan, 2002).
Three significant model classes are distinguishable: empirical or statistical based models, deterministic models using mathematical equations, and a hybrid of the two approaches (Committee on Air Quality Management in the United States, 2004). Empirical or statistical based models use observable relationships between pollutant concentrations and emission rates and do not account for physical and chemical transformations in the atmosphere. Deterministic models describe the physics and chemistry
of atmospheric processes influencing pollutant emissions, formation, transport and removal. Hybrid models, therefore, use empirical or statistical relationships as a foundation combined with physically and chemically based algorithms (Committee on Air Quality Management in the United States, 2004).
Some commonly used models include the empirical rollback model, receptor-based model, and emissions-based model (Committee on Air Quality Management in the United States, 2004). The empirical rolIback model is the simplest AQM model, alIowing pollutant concentration and emission rates to be linearly related. The proportional relationship implies that emission controls that decrease emissions by a given percentage wilI see a requisite, and proportional, reduction in concentrations.
Limitations include applicability to primary pollutants or secondary pollutants with simple transformation mechanisms, and polIutants that mix uniformly in an airshed as spatial and temporal variations are not accounted for (Committee on Air Quality Management in the United States, 2004).
Receptor models use more sophisticated statistical techniques, exclude chemical and physical processes, and are seen as providing a more accurate representation of pollutant concentrations than rollback models. They are commonly used to estimate the relative contributions of various emission sources to measured ambient concentrations of particulate matter composition at monitoring sites (Committee on Air Quality Management in the United States, 2004). Successful use is noted for primary particulate matter, with a combination of receptor models providing effective strategies, but limited in application to secondary particulate matter.
Emissions-based models solve equations related to a mathematical representation of relevant physical and chemical processes, in time and space to determine the relationship between pollutant emissions and concentrations (Committee on Air Quality Management in the United States, 2004). Emissions- based models vary in complexity, from simple box models, simulating the evolution of concentrations in an idealised air parcel that is welI mixed, to dispersion models, where each individual plume parcel of air is simulated as it advects and mixes with ambient air. Examples of the latter are the United States Environmental Protection Agency's Industrial Source Complex 3 and CaIPUFF. State-of-the- science three-dimensional chemical transport models, recreate distributions of gases and particles, winds and turbulence, and chemical reactions, with exhaustive requirements in terms of inputs, requiring additional programs to collect and resolve data, time and resources (Committee on Air Quality Management in the United States, 2004). The predictive ability of emissions-based models is a strong advantage as various changes in emissions can be used to examine the air quality responses in pollutant concentrations.
Each model that is available has inherent strengths and weaknesses, and varies in complexity and applicability; therefore, for valuable inputs for AQM, they should be understood and quantified.
Technical, as well as interpretative, experience is necessary to acknowledge the outcomes of the modelling exercise and the extent of its utility (Burger and Scorgie, 2005). Figure 2.6 further
illustrates that while technical experience increases linearly for the use of increasingly complex models, even simple models require a high level of interpretation to understand the limitations of estimates provided.
Technical Experience...-- ...--
-
/ ; 'Interpretive Experience ~
Simple MODELS
...--
. /
Complex
Figure 2.6. The relationship of modelling experience against modelling complexity (Source:
Burger and Scorgie, 2005)
This concept can be extended to address all AQMS tools, as the fundamentals of technical, or scientific, expertise address the selection of appropriate tools, the skills to utilise tools represented as specialised training, and the cross application of results across tools (Burger and Scorgie, 2005).
Interpretative, or management, experience requires an understanding of the reasons and significance of parameters and information needed for various tools, comprehension of produced results, ratings of significance, and the clear communication of information (Burger and Scorgie, 2005). The selection of an appropriate model is dependent on the characteristics of the terrain and the pollutants to be modelled, the availability of human resources and meteorological data, as well as the objectives of the exercise (Scorgie and Malan, 2002).
Modelling as an analysis tool, must be included in an operational framework and cannot be used in isolation of the regulatory and management setting, as model choice and use is influenced by these factors (Fedra, 2002). Examples include the regulatory standards that determine the level of compliance used, and the use of air quality forecasts to support public information. The prohibitive cost of initiating modelling can be offset by the subsidisation by national and provincial government for training, as well as the provision of nationally relevant models. Screening models can also be useful in mediating the costs of embarking on expensive, complex monitoring and modelling, in the event of low-level pollution (Burger and Scorgie, 2005). Additionally, models provide estimates of concentrations and include a degree of uncertainty and inaccuracy, which must be acknowledged, and are liable to over- or under-predict by a value of two (Elsom, 1992). Modelling estimates that will be incorporated into policy require careful consideration of outputs to mitigate against model limitations
being carried over into legislation (Cocks et ai., 1998). Uncertainty analysis and peer review are mechanisms by which to quantify and reduce error, as wel1 as the use of more than one model and inter-comparison of results to ensure optimum policy outputs.
Simulation model1ing is seen as key to future AQM as a result of the tool's prediction and forecasting capabilities (Longhurst et al., 1996). Use of model1ing is increasing, as models are developed to be more user-friendly and accessible to a variety of stakeholders, in addition to the benefits of employing simulation modelling becoming evident. Multi-pollutant AQM, where strategies have evolved to be able to mitigate pollutants in combination, is now a possibility as models that are able to process multiple pol1utants have been developed, such as the OMEGA-2 (Optimisation Model for Environmental Integrated Assessment) model (Committee on Air Quality Management in the United States, 2004; Reis et ai., 2005).
2.3.2.5 Emission Control Measures
The emIssIon control measures discussed are an example of employing the emissions standards strategy as a component of a significant strategy; this section focuses on the actual control measures implemented rather than the strategic components. The AQM management tools discussed previously represent the situational assessment of air quality, evaluation and goals to work toward; emission control measures allow for changes to the emissions profile of an area to be made. The control measures can be directed at stationary or mobile sources, old or new installations, and can be technological changes or social and behavioural changes (Committee on Air Quality Management in the United States, 2004; Schueneman, 1977; Shah et ai., 1997). The pollutant to be control1ed, the costs and benefits arising from the control action, and the effectiveness and viability of the measure also influence the choice of measure (Shah et ai., 1997). The choice of control measures is strongly motivated by the local conditions experienced, such that areas with extremely poor air quality and large exceedances of standards should be more willing and enjoy more public and political support for the implementation of control measures than areas where air quality is not highly valued and relatively moderate air pollution experienced.
Consideration should be taken of the enforcement of the control measures selected, specifically the availability of the skills and financial resources within the enforcement agency to ensure compliance.
Technical knowledge is necessary to implement and enforce a large percentage of control measures, and as such, regulation can make use of general policy statements indicating desired emissions without explicitly stating technologies to be used for their achievement (Schueneman, 1977). This condition places the onus on the emitter, rather than the regulator, and can be further emphasised by requesting emitters to conduct compliance monitoring with independent verification, or carried out by independent bodies. A mechanism for the evaluation of a control measure is needed to ascertain
progress and attribute changes in air quality to actions taken, and this is especially relevant for communities affected by emissions, and should influence the selection of measures (Schueneman, 1977). Controls to be prescribed include limitations on the amount or nature of stack emissions, equipment specifications or requirements, prohibitions on equipment use or practices, limitations on fuel or raw material composition, location restrictions for certain sources, and the requirement for adoption of additional controls during poor dispersion episodes (Schueneman, 1977).
Emission standards are a popular and well-documented form of control measure in AQM, with the earliest forms of air pollution control making use of visible emission standards. The latter example refers to a subjective standard, where visual appearance or odour is used, and a common form is the regulation of the opacity of a stack plume using a Ringelmann chart for comparison (Boubel et ai., 1994). Objective standards use physical or chemical measurement, and are further divided to apply to a pollutant uniformly over conditions or to apply to pollutant emissions from specific equipment or processes. The 'best practicable means' are often used as the emission standard based on demonstrated performance for emitter categories; the 'best available control technology' (BACT) is a variant excluding the consideration of economic constraints on control technology. 'Lowest achievable emission rate' is more stringent than BACT and states that the lowest emission rate demonstrated in any circumstance must be implemented, and is individually applied to emitters, generally in areas exceeding the ambient standards (Boubel et ai., 1994). Implementing the 'best available technology not entailing excessive cost' (BA TNEEC), takes into consideration economic constraints facing emitters.
Fugitive and secondary sources may present difficulties in attaining emiSSIOn standards and are challenging to regulate effectively; fugitive emissions are non-point sources such as dust from outdoor storage piles and unpaved roads, and secondary sources are different in character from major sources and necessary for the operation of the activity (Boubel et ai., 1994). Indirect sources require consideration as well, for determining emission standards and planning compliance, and are represented by areas such as parking lots at shopping centres and sport stadia, and the intersection of major highways and interchanges. Emissions trading and applying the principle of rollback, using linear relationships between emissions and ambient concentrations, are means of achieving standards.
New source controls are a distinct category of control measures, as they are specifically developed for sources of which the design and construction, and operation, can be influenced (Boubel et ai., 1994;
Schueneman, 1977). In addition, new sources enter a setting where regulations are in place and a number of emitters are functioning. Demonstration of compliance with standards, without prior datasets, presents a challenge, as well as, effectively, having to attain stricter degrees of control to 'compensate' for older sources. The principle underlying implementing stricter new source controls is that older, 'dirtier' sources will leave the market as they deteriorate, leaving cleaner, efficient
industries and improved air quality. Two methods can be used, firstly to develop prototypes of desired source design and operation and legislate these as required, or to allow operators to develop a unique programme of control and assume responsibility for alterations or re-design if compliance is not achieved. The latter means encourages innovation and introduces new technology into the market; an example is promulgation of stringent standards for transport-related pollutants by the USA in 1970 resulting in the development of the catalytic converter and three-way catalyst (Gerard and Lave, 2005).