Modeling and Simulation – Evaluating Scenarios
There are many complex processes in the natural world which cannot be understood in terms of logical algorithms. There are a couple of methods by which such processes can be understood. One of the methods is through data collection from the environment. The power of this method is borne out through data-driven programming. Machine Learning, Artificial Intelligence, and Deep Learning are technology monikers for using data to learn about a complex or natural process or mechanism. The final goal of such data-driven systems is to mimic naturally occurring phenomenon using hardware and software.
The other method used to understand complex and natural systems is to model them using complex mathematical models and juxtaposing such models in a way wherein input to a model could be the output of another model. Air pollution modeling is an example wherein weather modeling output is an input to the air pollution modeling system. As we start constructing a model the nascent and early outputs may be far from the actual complex system. However, as we understand the missing components and start simulating or mimicking them in the model, the results tend to get better over time.
The other side of the modeling coin is simulation. Once we have a model that we are relatively confident and sure of, interesting simulations can be run by varying the model inputs to predict outcomes of the complex system. Another example could be the impact on energy consumption in a yet to be constructed building based on the construction type. This type of energy modeling can help building owners to lower operational costs of a building.
The Urban Development and Environmental areas have not taken advantage of the modeling technology especially in India wherein the benefits of understanding ill effects of growing urban phenomenon like traffic density, construction, industrialization etc. have not been given their due. For e.g. the pollution control departments still spend a lot of money in setting up pollution monitoring stations rather than on modeling and simulation which would most certainly benefit them in the long run.
Air pollution modeling is a numerical tool used to describe the causal relationship between emissions, meteorology, atmospheric concentrations, deposition, and other factors. Air pollution measurements give important, quantitative information about ambient concentrations and deposition, but they can only describe air quality at specific locations and times, without giving clear guidance on the identification of the causes of the air quality problem. Air pollution modeling, instead, can give a more complete deterministic description of the air quality problem, including an analysis of factors and causes (emission sources, meteorological processes, and physical and chemical changes), and some guidance on the implementation of mitigation measures.
Modeling and Simulation allow us to evaluate scenarios at much lower costs as opposed to building out the different scenarios. For e.g. an urban city administrator could simulate the effect on air pollution if a new bypass road were to be located in a certain section of the city. The air pollution model output could be used as an input to determine population exposure statistics for the city population which in turn could provide input to the health department.
Governments and companies need to spend more resources on technologies like Machine Learning, Modeling and Simulation so that we can better understand non-trivial natural processes. As mentioned earlier, the immediate results may not provide accurate results but slowly and surely the model will get better thus providing a powerful tool for assessments at relatively low costs.
Please contact us at Valluri Technology Accelerators to understand Air Pollution Modeling and its uses.
– Sashi Narrain
- Air Pollution Modeling – An Overview by Aaron Daly and Paolo Zannetti, The EnviroComp Institute, Fremont, CA (USA)