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Air pollution modelling for Chennai city using GIS as a tool
Model design parameters
The following parameters have been chosen for the mobile emission model.
The parameters are:
- Develop estimates of the production of
automobile exhaust pollutants in space and time
A more accurate, verifiable, estimate of the pollutants may prove more useful in predicting the impact of motor vehicles.
- Comprehensive representation of vehicle technologies
Differences in vehicle technologies / characteristics have been shown to significantly affect vehicle emission rates. The list of desired vehicle characteristics are model year, engine size, weight (or mass), emission control type(s), fuel delivery type, transmission type, cross-sectional area, and number of cylinders.
- Separate and quantify high-emitting vehicle emissions
A small percentage of the fleet disproportionally contributes to total mobile source emissions. By separating this small high-emitting portion of the operating fleet, it will be easier to predict the impacts of control strategies that may target high emitters.
- Separate start, hot-stabilized, and enrichment emission quantities and locations
By separating estimates into specific emission modes, mode-specific impact strategies can be more efficiently evaluated. Further, emission rates for each mode are predicted using different variables. Engine starts are primarily influenced by vehicle characteristics and engine temperature. Hot stabilized and enrichment emissions are primarily influenced by vehicle characteristics and operating condition.
- Include Speed related factors
The relation between speed and emission levels has been well established various.
- Include emission rates from the statistical approach
Emission rates from the statistical approach need to be included because the research indicates that modal parameters better characterize accurate emission rate estimation. Because the modal emission rates models are available, they can be immediately integrated into the model framework. The approach also produces separate start and running exhaust emission estimates, addressing one of the previously defined model design parameters.
- Include activity measures from travel demand forecasting models
Travel demand forecasting models are the primary predictive tools for regional level vehicle activity. Despite their well-documented problems, they have characteristics that make them very attractive for a spatially-resolved model. First of all, they have a defined structure and connectivity that translates into a spatial form (zones, links, and nodes). Second, they develop estimates using socioeconomic information, allowing the model to be indirectly affected by social and economic changes.
- Use of Geographic\ Information Systems
Using GIS is important because it is designed to handle the spatial data management and modeling functions key to the research goals. Without GIS, complex spatial analysis and manipulation algorithms would have to be re-created. Its widespread use and popularity among planning agencies is significant enough to warrant its use.
Model approach
The conceptual design of the proposed research model
The following sections describe the five major tiers of the model design.
Spatial environment
The objective of the spatial environment tier is to unify input data under a common zonal and lineal structure. The size and scope of the zones and lines depend on the users and their specific needs.
Zonal data
The zonal module defines the polygon structure used to represent data and emission estimates for engine starts. It is the role of the zonal module to combine the polygons of various input data (i.e. socioeconomic, land use, TAZ) into a single polygon dataset.
Lineal data
The road module defines the lineal data used for predicting running exhaust emissions.
Conflation
Conflation is the blending of two line databases. Conflating the abstract travel demand forecasting network and a spatially accurate comprehensive road database is needed to improve the spatial accuracy of the travel model results.
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