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Air pollution modelling for Chennai city using GIS as a tool
The following section will briefly cover the past use of GIS in transportation and air quality analysis and the issue of spatial data quality.
Earlier applications of GIS in mobile emission modeling
Emission inventories
Models have been developed to estimate hourly estimates of emissions, which utilises GIS in developing mobile source estimates for input into photochemical models. The main function of the GIS in such model was the spatial aggregation of travel demand forecasting model features into a grid. Spatially defined vehicle mixes by trip purpose, temporal factors, hourly temperatures, trip volumes, trip speeds, and modal percentages are used as inputs.
Zonal estimates were allocated to traffic analysis zone centroids that were re-allocated to grid cells. Link estimates were allocated to nodes and re-allocated to cells. The use of points to represent these features did not take full advantage of the spatial structure provided by the original input data. Traffic Analysis Zones (TAZ) falling along grid cell boundaries should have their portions divided.
This strategy would limit grid cell sizes to those significantly larger than TAZs, which can be quite large (30-40 square km) for some metropolitan areas. Also, no mention is made of strategies for identifying the confidence ranges of the estimates.
The model supports the use of GIS, but did not take full advantage of the research value of GIS. Further, the model did not have the flexibility to answer the diverse impact or mitigation questions that arise from estimating emissions.
GIS for transportation planning and air quality analysis
Researchers used GIS as a preprocessor and postprocessor to mobile emission modeling. Although they relied on existing models to estimate emissions, they showed how GIS could be valuable in the management of emission related data. They made the connection between the needs of transportation planners and decision-makers and the spatial tools and features of GIS.
Microscale analysis
Researchers at Utah State University used GIS in developing microscale analyses of a small group of intersections. They linked a GIS with CALINE3 and CAL3QHC to predict pollutant concentration levels. The value of GIS (outside of spatial data storage and data visualization) was its ability to compare concentration results to other non-related data. The contribution is significant to this research because it provides a foundation for the argument that a GIS approach is not restricted to developing emission inventories, but can be easily expanded to a number of other related issues.
Influencing decision-makers
Othofer developed an interesting approach to predicting location specific emission production estimates for changing control strategies. Instead of developing estimates using detailed location-specific emission producing activities and emission rates, they disaggregated large zonal estimates using emission-producing activities. The advantage of this approach is its simplicity and its straightforward recognition that the data needed to predict emissions at smaller levels does not exist or the relationships are undefined. The disadvantage is that the ability to predict changes among the disaggregated levels is a function only of the change of the overall larger units. Thus, the true effects of activity changes on emissions cannot be measured. The project produced high-quality graphics that indicated locational variation in emission-producing activities. The project was successful because elected officials could ‘see’ areas that have potentially high emissions and therefore had evidence for developing actions for those specific areas. Although, the modeling capability of the project is limited, its ability to influence action through spatial communication is a noteworthy contribution to the use of GIS in this arena.
Lacunae in air pollution modelling for Chennai city
Chennai City is the fourth largest metropolis in India. The Chennai metropolitan area covers an extent of 1172 Sq.km of which the corporation area, which is identified as the city extends over 172 Sq.km. As per 2001 census the population of Chennai City is 42.16 lakhs. The vehicle population during 1999–2000 is around 11.15 lakhs.
The ambient air quality of Chennai has deteriorated with an increase in the number of vehicles and industrial pollution. A recent study by the State Pollution Control Board (PCB) found that the levels of suspended particular matter (SPM) ranged from 274 to a mind-boggling 1,470 micrograms/cubic meter (mg/m3) at several areas, which was much higher than the WHO prescribed limit of 200 mg/m3. The level of carbon monoxide ranged from 12 to 70 parts per million (ppm) as against the permitted 35-ppm. The study also showed that emission from nearly 50 percent of the vehicles in the city exceeded the permitted levels and the pollution load in the atmosphere increased by 3.5 percent annually.
The entire city has got only 6 ambient air quality monitoring stations. With this limited number of stations, to represent the air quality in chennai city spatially is a difficult task. Hence a co-ordinated methodology to map the air quality in Chennai City using GIS is explained in the following paragraphs.
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