GIS applications in air pollution modeling Another source of inaccuracy in these models pertain to non- availability of onsite meteorological data. Although use of on-site meteorological data about wind speed, direction, atmospheric stability conditions and mixing height is recommended, but most often modelers in India rely on nearest Indian Meteorological Department (IMD) data, which does not reflect actual field conditions and add to inaccurate prediction estimates. Different aspects of traffic engineering and related researches are mainly carried out at CRRI, IIT’s and at various other educational Institutes. However, traffic related data is available for few cities only and that too is quite old (CRRI, 1992; Tiwari, 2001). Moreover, since last few years, a lot of changes have taken place in terms of modal split, traffic volume, traffic composition and averaged speed of the vehicles. Any air pollution prediction estimates (modelling) based upon old statistics, will not truly represent the actual air pollution situation and likely effects on it by various traffic management and transportation policy measures. Pollution Mapping using Geographic Infoprmation System A geographic information system (GIS) is a computer-based tool for mapping and analyzing geographic phenomenon that exist and events that occur on Earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other information systems and make it valuable to a wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies. Map making and geographic analysis are not new but a GIS performs these tasks faster and with more sophistication than do traditional manual methods. A GIS can be made up of a variety of software and hardware tools. The important factor is the level of integration of these tools to provide a smoothly operating, fully functional geographic data processing environment. In general, a GIS provides facilities for data capture, data management, data manipulation and analysis, and the presentation of results in both graphic and report form, with a particular emphasis upon preserving and utilizing inherent characteristics of spatial data. The ability to incorporate spatial data, manage it, analyze it, and answer spatial questions is the distinctive characteristic of geographic information systems. Recently, several efforts have been made for mapping traffic related pollution and determining pollution patterns in urban areas using GIS. While, some of the early pioneers of GIS in late 60’s and early 70’s were transportation scientists and both early and more recent application of GIS have been to select transportation routes, which minimize the route’s impact on the environment (Alexander and Waters, 2000) as part of the Comprehensive Environmental Impact assessment (CEIA) process (Li et al.,; 1999). But, it was in late 80’s, that the first widespread use of GIS in transportation research (GIS-T) actually took place (Thill, 2000). However, the application of GIS in transportation related air quality modeling and management was started only in early 90’s (USEPA, 1998). Bruckman et al., 1992; Souleyerette et al., 1992). Medina et al. (1994) presented the framework for air quality analysis model that integrated CADD, GIS, transportation and air quality models linking traffic information within GIS framework for use in vehicle emission and air pollution dispersion models (Fig 2). Hallmark and O’Neil (1996) described the development of a model that combined the micro scale air quality model applicable for intersection (CAL3QHC) with GIS. Andersons et al. (1996) described the use of GIS as a tool to illustrate the spatial patterns of emission and to visualize the impact, congestion has on emissions. The model consisted of an integrated urban model that interfaced with emission rate model (MOBILE 5C). The integrated model allowed the impact of transportation and land use policy changes to be simulated in terms of their air quality impact. Briggs et al. (1997) described the application of GIS as a tool, Combined with least square regression analysis for mapping traffic related air pollution to generate predictive models of pollution surfaces, based on monitored pollution data and exogenous information.  Fig 2. The GIS Structure for Vehicular Pollution Modelling (Gualtieri and Tartaglia, 1998) In another related study, Briggs et al.,( 2000) have discussed about a wide range of line source dispersion models which can be used for the mapping purpose and concluded that, in general, the performance of line source models (Including that of Gaussian based highway dispersion models) has not always been good under urban conditions. Instead, they suggested a GIS based regression-mapping technique to model spatial patterns of traffic related air pollution for assessing exposure as part of epidemiological studies. Clarmunt et al. (2000) described a new framework for real time integration analysis and visualisation of urban traffic data within GIS system. The framework is based on proactive interaction between the spatial – temporal database and visualisation level and between the visualisation and end- user levels. Ziliskopoulous and Waller (2000) developed an internet based GIS that brings together spatio –temporal data, models and users in a single efficient framework, to be used for a wide range of transportation applications. Jensen et al.(2001) and Kousa et al. (2002) have described development of mathematical models for determining the human exposures to various air pollutants. In these models, GIS framework enabled the temporal and spatial mapping of traffic emissions, air quality levels along with population exposure to ambient air pollutants. Namdeo et al. (2002) has described the developed and application of TEMMS (Traffic Emission Modeling and Mapping Suite), which is a software package that facilitates the integration of transport, emission and dispersion models. TEMMS is designed to support urban local authorities in forecasting and managing urban air quality .In the software, ROADFAC model allows link –based emission from a vehicle fleet to be calculated, while mobile source emission estimates based on SATURN transport model are used as input to dispersion model (ADMS – Urban or Airviro). These models have been integrated, via a database exchanger with the MapInfo geographic information system. The MapInfo geographic information system and a custom built Window - based graphical user interface (GUI) allows modeling and mapping of link based vehicle flow and emissions and grid based air quality. The uses of recent techniques like ANN and GIS in air pollution related research are at nascent stage in India. Although. GIS has been used quite extensively in transportation related research, but only few studies have been carried in air pollution related research with the use of GIS. Sikdar (2001) applied GIS for air pollution profiling for Delhi city, from observed short term (hourly) air pollution data and demonstrated its usefulness in transport development and traffic management planning. Application of GIS in air Quality Modelling: A Case Study A case study of National Highway (NH2) corridor between Delhi and Agra was undertaken to predict the concentration of vehicular pollutants. The total length of the highway is about 198 km starting from Delhi via Faridabad, Ballabgarh, Hodal, Mathura and Farah ending at Agra (Fig 3). Various air pollutants viz. CO, HC, NOx, SO2, SPM were measured at the six sampling locations along the highway. Meteorological parameters (wind speed, wind direction, temperature, humidity) were also measured on site. Mixing height data pertaining to the sampling period was collected from the IMD. Traffic characteristics data (traffic volume, composition, speed etc,) were also measured at the six sampling sites. CALINE-4 highway dispersion model (CL-4; Coe et al., 1998) has been used to predict the level of vehicular pollutants along the highway. In the present study, the modelling exercise has been carried out for CO only as the levels of CO are considered to be the indicators of vehicular pollution.  Fig 3. Base Map of the Study Corridor 1. CALINE-4 description CALINE–4 (Benson, 1992) is a fourth generation line source air quality model developed by the California Department of Transportation that predicts CO impacts near roadways. Its main objective is to assist planners to protect public health from adverse effects of excessive CO exposure. The model is based on the Gaussuian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over roadways. For given source strength, meteorology, and site geometry and site characteristics the model can reliably predict (1-hour and 8-hours) pollutant concentrations for receptors located within 150 meters of the roadway. The model can also predict the worst-case scenario (combination of wind speed, direction and stability class) which produces the maximum pollutant concentrations at the pre-identified receptor points along the highway. 2. Input requirement for CALINE – 4 CALINE-4 highway dispersion model requires the following data as input -
NH-2 is a four lane divided carriageway which caters to the traffic between Delhi and Kolkata and other cities on NH-2 as well as the predominant tourist traffic between Agra and Delhi. The whole stretch of the corridor was mapped using toposheets of 1:50,000 scale on GIS. Fig. 3 shows the survey locations for pollution measurements. The pollution profiles for the study corridor have been developed. The study corridor has been divided into six major stretches, each having a relatively homogenous traffic density (Fig 4) through its length. The diurnal pattern of the observed CO values at the six sampling sites is shown in Fig 5. CALINE-4 has been used to predict CO concentrations (worst case) along different lengths from the median (centre of the road) (Table 1). A separate pollution profile has also been developed for all these stretches in TransCAD, a GIS based software specifically created for transportation problems. The 8 hr (0-8 hrs, 8-16 hrs, 16-24 hrs) CO prediction data was attached to the respective receptor points and DEMs (digital elevation maps) were made to show the 3-dimensional profile of pollution concentrations along the highway for all the six component stretches of the highway. Figure 6 shows the pollution profiles developed for Ballabhgarh . It is evident that the maximum concentration occurs at the centre of the road and gradually reduces with distance from the centre and at about 90 to 100 meters distance, the concentration reaches the background level (impact zone). 
 Fig. 4 Observed Traffic Pattern on NH-2  Fig 5. Observed CO Values at six Locations  Fig 6. CO Pollution Profiles at Ballabhgarh |