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Modeling of Urban Airborne Pollution


Although clean air is the basic requirement for human health, most people who travel in the huge modern metropolitans have to suffer the horrific air quality every day. There are abundant examples of serious regional atmospheric pollution events historically, such as London smog, Indonesian fires, and Beijing sandstorms. These atmospheric pollutants are either natural or human-induced, or both. This paper focuses on direct human-made air pollution in a “Sim City”. The urban air system is a highly complex adaptive system which results from the interactions between the natural environment and concentrated human activities. Air quality is influenced widely over time by multiple pollution sources, complex weather situations, geographical features, and city layouts. Modeling is a great tool to analyze a complex adaptive system and a well-designed model can create the better understanding and management of such a system. Numerous mathematical models have been created by environmental scientists; however this paper presents a simple qualitative model for management purposes rather than research purposes.

An initial urban airborne pollution model was built on 3-D version of NetLogo. NetLogo is a programmable modeling environment which is particularly well suited for qualitative modeling of a complex spatio-temporal system. This model simulates a typical grid city and natural environment. Additionally, three major types of pollution sources, namely point, line and area sources, were drawn in this study area and represent industry sites, automobiles and burning areas respectively. These pollution sources emit different major pollutants which disperse over land over time. The dispersal of airborne pollution under different weather situations and its influence to urban air quality were computer-generated.

Beyond this simple model, three typical trials were explored for different purposes and both spatial analysis and temporal analysis are involved in this spatio-temporal model. Firstly, it was found that the changes of urban air quality in most high latitude cities appear to show a clear seasonal trend owing to the extreme seasons. Seasonal wind direction is only considered in this model. Secondly, an air quality monitoring system is widely used in many cities and the sample locations and the number of monitoring sites are significant in the system. The outcomes of exploring with this model include the ability to identify the sample number and locations of air quality monitoring sites in this area for the purpose of air quality monitoring. Thirdly, one of biggest advantages of modeling is to review history and predict the future. The air quality of some historical industrial cities has undergone remarkable changes and the primary pollutants have been altered during the past half century. There are two key trends of urban air pollution: one is that air quality continues to improve owing to effective environmental polices; and the other is the primary sources of air pollution in most big cities has changed due to the rapid increase in the number of motor vehicles. The simulation of these trends will be presented in this report.

Discussion and Conclusion

This model simulates the dispersal pattern of air pollution in an urban area and displays the effect of air pollutants on the land. The spatio-temporal model in 3-D NetLogo allows us the ability to explore the air pollution interaction of the complex adaptive atmosphere system in both spatial and temporal dimensions. The uses of this model are wide. For instance, a civil engineer could recommend suitable locations for the industry areas and residential areas by exploring with this model when considering a particular local climate. A real estate company may estimate the value of land by looking at the results of this model. An environmental expert can develop air quality management and assessment in an urban area.

As with all others models, this model is a limited simulation. It is impossible and invaluable to try to describe everything in a model. Firstly, this is not quantitative model; there are no any real environmental data involved. Therefore the results of this model can only obtain some simple general idea of urban air pollution. However, with the benefits of Information Technology, we have abundant digital environmental data to create high accurate quantitative environmental models. Secondly, although the varieties of air pollutants are simulated in this model, their different behaviors and the interactions amongst them and between them with atmosphere are not considered. The chemical and photochemical interactions are very important in studying urban air pollution. Also there are many other pollutants and pollution sources that are not considered such as dust and sandstorms from nearby deserts. Thirdly, it is always a big challenge to model the affects of meteorology and complex weather conditions. Only two parameters of wind are introduced in this model. In fact, the sunlight? atmosphere pressure and atmosphere temperature are other very significant factors in the dispersal of air pollutants. Fourthly, the urban structure is another important factor of urban air pollution. For example, the “hot island” phenomenon, which results from bad urban structure, will worsen air quality. Finally, this model does not consider the influence of topography which is another important factor in terms of air pollution. The main reason is the weakness of NetLogo which is not suitable to simulate complex geographical features.

The recent integration of 3 G (Geographical Information System (GIS), Remote Sensing (RS), and Global Position Systems (GPS)) could be beneficial to building a better spatio-temporal urban air pollution model. Firstly, GIS can combine many databases such as long term environmental data, meteorological data and topographical data to structure a comprehensive quantitative model and the model could be able to auto-adjust by continued updating of databases. Also GIS offers more complex spatial analysis methods. Secondly, multiple spectral RS data can be applied in air pollution monitoring. For example, a number of video cameras with both visible and infrared spectrums can identify urban air quality quickly. Thirdly, mobile air quality monitors with GPS is a flexible method to gather data to adjust an urban air pollution model.

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