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Air pollution modeling is the term used to describe using mathematical theory to understand or predict the way pollutants behave in the atmosphere. Modeling can be used to run scenarios, test theories, and understand environmental impact under different emission rates, weather and development scenarios. There are lots of different methods and techniques, but the goal is always the same – make an assessment of pollutant impact over a given area using an existing set of data. To do this we make some assumptions, we use some rules, and we add some data most commonly meteorological and terrain data. The model is only as good as the data we feed it.
Air pollution models are the only method that quantifies the deterministic relationship between emissions and concentrations/depositions, including the consequences of past and future scenarios and the determination of the effectiveness of abatement strategies. This makes air pollution models indispensable in regulatory, research, and forensic applications.
Air quality modeling provides useful support to decision-making processes incorporating environmental policies and management processes. They generate information that can be used in the decision-making process. The main objectives of models are: to integrate observations, to predict the response of the system to future changes, and to make provision for future development without compromising quality.
Modeling can be used to run scenarios, test theories, and understand environmental impact under different emission rates, weather and development scenarios. There are lots of different methods and techniques, but the goal is always the same – make an assessment of pollutant impact over a given area using an existing set of data. Modeling provides the ability to assess the current and likely future air quality to enable ‘informed’ policy decisions to be made.
ADVANTAGES OF MODELLING
Air pollution modeling has a few advantages.
Firstly, you can assess a completely hypothetical situation before it occurs. For example, an industrial operation may propose a new facility. In their design, they might specify a stack several meters tall, emitting pollutants from a known process. Using a model the emissions can be roughly quantified. These emissions can be fed into a model, which can predict the emissions spatially around the source. This analysis might show that concentrations are too high based on a given operating mode, or might show that under certain wind conditions, the concentrations in a residential area are unacceptably high.The power of this is that the problem can be solved before it even exists. Often, industrial emitters need to demonstrate their environmental impact. The severity of their impact dictates whether their emissions are allowed, or need further permitting. Similarly, a model can be used to predict alternative situations. For instance, the facility may decide that it wants to upgrade its infrastructure, change fuel, or install better, more effective scrubbers. The emissions given these changes can then be quantified. These are called scenarios.
DISADVANTAGES OF MODELLING
A well-known quote states that ‘all models are wrong but some are interesting’. Models are only as good as the data that you put into them, and they don’t always reflect reality with total accuracy. They are however interested in that you can answer questions and test scenarios that may be either impossible to test, like the impact of a new road, or factory. They can also be used to assess questions that are too expensive to test with data collection – like air quality at a regional or global scale. Models rely on accurate inputs. Meteorological data needs to be treated with particular care, as wind speed and direction play a pivotal role in dispersion.
TYPES OF MODELS
There are several different types of air quality models, all used for differing purposes. The most common models are broadly known as Atmospheric Dispersion Models (ADM). These models use mathematical assumptions about the way that the atmosphere behaves, to assess the impact of emissions. Typically, these types of models are used to model emissions from a fixed point, like a stack of an industrial facility.
One of the most widely-used and respected ADMs is CALPUFF. CALPUFF uses 3 major inputs – a meteorological model, a dispersion model, and a post-processing package, and is a preferred USEPA model. CALPUFF is complex and is therefore very powerful when used correctly. It can be used at a wide range of scales – from a few square kilometers to several hundred. It can deal with complex terrain and can handle complex atmospheric chemistry processes. CALPUFF outputs its data in a range of ways – perhaps most usefully as a ‘grid’ that can easily be visualized in Geographic Information Systems (GIS) packages, or proprietary software.
A second widely used model is AERMOD. AERMOD is also USEPA approved but differs slightly in that it assumes a steady-state – i.e., continuous emissions and environmental factors, whereas CALPUFF is a non-steady state and can be tuned for changes in environmental conditions, like the weather. Generally, CALPUFF is used for smaller-scale assessments (less than 10KM2) and AERMOD for larger, more regional projects.
Air pollution modeling helps to assess the problem and can help find necessary actions required for controlling pollution in the environment. There is no model as such which can directly control the pollution or air quality problem but it can help to assess what exactly the problem is and can make it easy to find the remedies or actions to be taken for controlling pollution or improving air quality.