Gis Sem 7

Integrated Remote Sensing and factor analytic GIS model for evaluating groundwater pollution potential Background In order to fulfill growing needs, pollutants are being increasingly added to the groundwater system through various human activities and natural processes. Applications of fertilizers and pesticides to enhance crop production have become a common practice. In case fertilizer application exceeds the plant uptake, the residual joins the water table. This increases nitrate concentration in groundwater. Similarly, excess applications of pesticides that are complex organic chemicals may have adverse health effects. Long-term use of saline irrigation water combined with poor management and adverse climatic conditions for example, low rainfall and high evaporation, leads to accumulation of salts in the root zone. Poor agricultural practice results in a loss of crop yield and deterioration of soil structure. Poorly designed and improperly managed waste disposal sites contribute significant amount of leachate. This leachate may affect the water quality. Improperly designed and maintained septic tank becomes a threat to groundwater quality. Disposal of waste through wells adds pollutant to the groundwater and also accelerate their movement towards a production well. In and around major pollutant is industrial waste that includes heavy metals, toxic compounds and radioactive material. Another significant source of metal contaminants are tailing produced at mining sites. An indiscriminate groundwater development in coastal aquifers leads to an excessive saltwater intrusion. The impact of the intrusion is further aggravated by an excessive upcoming of the freshwater-saltwater interface below a pumping well, caused by faulty well design or operation (too long a pumping spell and/or too little a rest period between the two successive pumping spells). In India, even during ancient time, well-defined legislation was in existent to control the water pollution. The Athervaveda provides panel action for polluting water. "}k u vkiLrUos {jUrq ;ks u% nqjfiz;s ra fun~/ke: A ifo=s.k I`fFkfo eksr iqukfe AA " AvFkZosn dk.M 12 & 'yksd 30 A Which means, let us have the life-sustaining water through good conduct and proper means (adequate resources), " O goddess Earth give punishment to those who are responsible for polluting water". In recent years, land and water sectors were put to stresses in order to meet the demand of the growing population. Groundwater is more dependable source of water as compared to surface water (Viverkar 1999). Gradually, quality of water in addition to quantity is gaining importance in the selection of suitable sites for the groundwater development. Groundwater Pollution Potential (GWPP) of any given geographical location depends upon a wide range of above surface, surface and subsurface environmental parameters Brown 1972, Jackson1986). Evaluation of GPP involves decision-making keeping in view multiple interwoven criteria (ESCAP 1996). Generally, data required for GWPP is either not available or not sufficient. Collection, storage, and processing of required data is difficult, costly, and time consuming. As a result GWPP studies are generally based on questionable data. Remotely sensed data has proven capability in providing many above surface, surface and sub surface characteristics of a land unit. Synergistic use of remote sensing and ancillary data can be made for the development of the database required for GWPP (Daniel.et.al.1994, ESCAP 1996) GIS can be used to store, process and retrieve the developed database. GPP. Groundwater and its pollution system is a complex system (Biawas 1971, Hamil et. al. 1996). In this study an attempt has been made to represent the GWP system by Factor Analytic Model (FAM). Study area & data used Present study has been carried in a part (Fig. 1) of Hardwar district of Uttaranchal State, India. Synergistic use of, (1) SOI topographic sheet at 1: 250,000 scale, (2) Landsat TM FCC at 1: 50,000 scale for April, June, August and November months of 1988 and 1998 along with field survey have been made to asses the GPP. The area under investigation Ratmau watershed,

covering about 500 km Sq. is bounded between latitudes 290 55' N to 300 10' N and longitude 770 55' to 780 05' E. The area is drained by Ratmau stream system. In general area is slopping towards south. Summer season begins from the end of April continues up to middle of June. The temperature variation during summer months is between 130 C to 450C. During winter it is (-) 20 C to 340 C. Hills are generally covered with Sal, Shisham, Khair and Bambo. Ground elevation in the area varies between 810 m to 280 m above mean sea level. Ground slope varies between 25m/Km to 0.3m/Km.. The present study aims to study the response of Ratmau watershed by formulating rainfall-runoff relationship. Synergistic use of remote sensing techniques and conventional techniques been utilized to extract the required data. The soil of the study area is generally coarse grained and aquifers are water table type. The area receives about 1000 mm annual rainfall; vegetation density is highly variable in spatial and temporal domain. It varies from 0 % to 95 %. Ground elevation varies from 215 m to 400 m; land slope varies from 0.6 % to 6 %, depth of groundwater table varies from less than 1 m to more than 20 m. The database for the study was generated through the integrated used of remote sensing and conventional techniques. Capabilities of GIS have been used to analyze and process the database and evaluation of GPP. Various themes were integrated using the concepts of linear mixing of influential parameters. The Factor Analytic Model (FAM) The FAM involves decomposing (Satty 1988, Mandoza 1997) the complex groundwater pollution system in to a number of simpler components forming a cascade. At each cascade level decision have been taken in simpler manner. The decision process moves from one cascade to another to arrive at the final decision. FAM has been used to develop a decision support system for weighting a particular land characteristic keeping in view its GPP. The FAM finally ranks a land unit in to a predefined GPP Class, based on its attributes. FAM was developed and calibrated using historical database consisting of 200 observed records. The FAM is mathematically sound but pair wise comparison is highly subjective. In order to get optimal results with minimum subjectivity the FAM was further modified to accommodate multi criteria. The decision cascade process starts from the lowest cascade level and progressively moves upwards until final decision is made. At each level pair wise comparisons have been made between factors at that level. These comparisons lead to priority vectors that are propagated up the cascade to arrive at a final priority vector. Decision cascading has been carried out in the following steps (Table 1). 1. The decision making process has been decomposed in to a set of cascades. At the top level is the goal of the analysis. The elements of the lower level include the attribute such as objectives perhaps even more redefined attributes follows at the next lower level - until the last level. 2. In the second phase, pair wise comparisons of the attributes or elements at a particular cascade level relative to their contribution or significance to the elements of the next higher cascade level is made. This phase constitutes much of the evaluation (qualitative) or assessment (quantitative) of the decision making process. Specifically the input matrix of pair wise comparisons express the relative of influence of an element over the others. 3. In the third phase, the pair wise input matrix is decomposed spectrally. Spectral decomposition provides an estimate of the relative influence weight (RIW) of the elements at a particular cascade.

4. Groundwater Pollution Potential (GPP) of a land unit was determined by linear mixing the above surface, surface, and subsurface parameters influencing the GPP. Linear mixing modeling is a branch of statistical science (Wang 1990, Maselli et.al. 1996, Bryant 1996, Kant.and Badrinath 1998). It is a method of analyzing a set of observations (obtained from a given sample) from their inter correlation to determine whether the variations can be accounted adequately by a number of basic categories smaller than that which the investigation was started (Fruchter, 1967). Let us consider a multivariate system consisting of 'p' responses described by the observable random variables X1, X2, X3.. Xp.

The observable random vectors have mean x and co variance 'S'. The Linear Mixing Model (LMM) postulates that 'X' is linearly dependent upon few unobservable variables F1, F2, ..... Fm called linear composite (LC) and additional source of variations e1, e2, ..... ep called specific factors. Hence LMM in matrix form may be written as : X - x =L F + e

Where, (X-x) is a vector having p elements containing deviations of observed variable X and its mean value x, L is matrix of LC loading having prows and m column, C is vector of Composite having m rows and e is error vector having p elements. From the above equation it is evident that "p" deviations (X1 - x1) .... (xp - xp ) are expressed in terms of (p + m) random variables, c1, c2, .... cm, e1 .... ep. With so many unobservable quantities a direct solution of LMM from the observations on x1, x2 .... xp is difficult. However, with the help of following assumptions about the random vectors c' and e', the model reduces to simple and easy form. These assumptions are (1) Original variables are linearly related. (2) Common composite 'c' and unique factors 'e' have mean zero and standard deviation unity. (3) Common factor 'c' and unique factor 'e' is independent. The LMM proceeds by imposing conditions that allow one to uniquely estimate the loading and the specific variance matrix. The loading matrix is then rotated, where the rotation is determined by some, 'ease of interpretation', method. Once the loading and the specific variance matrix are obtained composites are identified and estimated values for the composites themselves (called composites scores) are frequently constructed. (Johnsons, and Wichern, 1988). In LMM method composites are determined so as to account for maximum variance of all the observed variables. The residual terms (i.e. specific factors e1) are assumed to be small in this method. (Joreskog, Klovan and Reyment ; 1976). Min [{(X-x) - LF}T { (X-x)-LF}]-1

Subject to Σfi = 1, fi > 0 Developed FAM was calibrated using historical database consisting of 200 memory records. The database was subjected to Factor Analysis. Weights or membership function each cascade level for different land characteristics has been developed. The importance of a particular land characteristic was decided on the basis of a linguistic measure of importance. A comparison was made between various themes, land elements on a common scale and a confusion matrix representing their relative importance was developed. The confusion matrix was decomposed spectrally in to components. The first component accounts about 90 % variation in the data. The vector corresponding to this component represents the weights to different land characteristic that were considered influencing the decision. For data mixing RIW at different cascade level is shown in the Table 1. These weights were used to map the GPP of the study area. GWPP map is shown in Fig. 2 Conclusions For over all developments of a region reliable estimate of groundwater quality and quantity is of paramount importance. Generally sufficient data required for groundwater pollution potential mapping are not available for Indian watersheds. Satellite data can be analyzed to generate database required for GWPP studies. Generated database can be put to FAM for extracting the most influential composite and subsequently the variable loading. Using the proposed FAM the study area was classified in to different classes in terms of their potential to pollute the groundwater. The model efficiency was tested by carrying out field surveys and found to above 80 percent. The model can be used for evaluating the GPP in any area after calibration. The added

advantage of the proposed approach is that it compresses the data up to 70% that helps in efficient analysis and prediction. Table.1 Variable Loading (RIW) Goal

Level Level I Feature

Level II RIW Feature

Level III RIW Feature Agriculture Water Body Barren Land (0.65) Land use (0.44) Thin forest Thick forest Settlement Low Slope Land Slope (0.26) Mild Slope Milder Slope Distance from Paleo Less than 50m (0.18) Surface Channel More than 50m Up to 50m Distance from Flood Plain 0.04 More than 50m Sand Sandy Soil 0.06 loamLoamy sandClay GWPP Less than 0.5 km Distance from Urban 0.02 0.5km - 1.0m areas More than 1.0 km Sand and Boulder Sand Boulder and 0.24 Aquifer Media (0.5) Clay Sub Surface Sand and Clay Permeability in Vertical High (0.5) Direction Low < 5m (0.11) Groundwater Depth (0.60) 5-15m > 15m Ground High Water Rainfall Recharge (0.32) Medium Low SAR Value Low Water Quality (0.08) SAR Value High

RIW 0.40 0.25 0.18 0.10 0.05 0.02 0.72 0.21 0.07 0.90 0.10 0.90 0.10 0.56 0.27 0.13 0.04 0.65 0.28 0.07 0.63 0.28 0.09 0.90 0.04 0.73 0.19 0.08 0.65 0.28 0.07 0.75 0.25

Environmental Modelling using G.I.S. - Modelling Migration of Pollutants using G.I.S. Abstract Kanchipuram District, one of the 29 districts of TamilNadu is taken as the study area in this project. Since our selected concept of prediction can be applied for large regions, this district is quite enough to suit it. Kanchipuram district measuring 4,43,210 hectares, forms northern parts of

TamilNadu. It falls with in the geographical co-ordinates latitude 12°14’00” – 13°02’00” and longitude 79° 31’30”-80°15’30”.Ground water resources underlying the ground surface in the district of Kanchipuram may be vulnerable to contamination. Contamination of these ground water resources could be prevented by development of accurate vulnerability models and appropriate recommendations for decreasing the potential impact of ground water contaminants being used/applied on the land surface. This study represents a portion of ongoing research, utilizing readily available data, to develop such a vulnerability model. More specifically, the purpose of this study is to test a vulnerability index equation, using cell-based Geographic Information System (GIS) analysis. The vulnerability index equation incorporates soil characteristics, hydrogeologic factors, depth to ground water, and extent of irrigation. The vulnerability index equation is calibrated to a subset of ground water analytical results. The spatial distribution of ground water vulnerability is then verified by measuring the spatial correlation of the vulnerability indices versus the detections of contaminants in ground water. Spatial representations of these results are produced to assist land use planners/ agriculturists in planning and regulating land use in the district without harming the quality of ground water resources. The Use of Remote Sensing and GIS to Estimate Air Quality Index (AQI) Over Peninsular Malaysia

Abstract: The recent August 2005 haze episode was not a new experience for Malaysia as this phenomenon has been occurring almost every year. History revealed that the worst haze episode took place during May-November 1997. On the 23rd September 1997, the Sarawak capital, Kuching was declared in the state of emergency as its Air pollution Index (API) reached 839. This was the highest API ever been recorded in Malaysia. This paper reports result of a study in order to compute API using satellite-based method. Seven dates of NOAA-14 AVHRR satellite recorded data were used, representing seven days during the September 1997 thick haze episode in Malaysia. Five locations of air pollution station were selected where major pollutants have been measured conventionally. Haze information was extracted from the satellite data using ‘sky-light’ model. Relationship between the satellite recorded reflectance and the corresponding pollutant measurement was determined using regression analysis. Finally, accuracy of the result was assessed using RMSE technique. The result proven that satellite-based method using spaceborne remote sensing data was capable of computing API spatially and continuously. 1 Introduction Haze is said to be a partially opaque condition of the atmosphere caused by very tiny suspended solid or liquid particles in the air (Morris, 1975). Haze (originating from open burning or forest fire) usually contains large amount of particulate matter (e.g., organic matter, graphitic carbon). This particulate matter is hazardous to health, especially associated with lung and eye deceases. Besides that it is capable of reducing visibility, increasing the atmospheric greenhouse effects and affecting the tropospheric chemistry. Coventionally, PM 10 can be measured from ground instruments such as air sampler, sun photometer and optical particle counter, however these instruments is impractical if measurement are to be made over relatively large areas or for continuous monitoring. The haze episode which occured during mid-May to November 1997 is considered the worst since 1980 ( five similar haze episodes had occured in April 1983, August 1990, June 1991, October 1991 and August 1994). On 19th September 1997 Malaysian government had declared that Kuching (capital of Sarawak) was in the state of emergency when the PM10 API (Air Pollution Index) exceeded 650 (hazardous level). By 23rd September 1997 the condition worsened as Kuching’s PM10 API reached 839, the highest ever been recorded by the country. This paper reports results of a study to determine PM 10 from NOAA-14 AVHRR satellite data. Their concentration and spatial distribution will be quantified based on updated measurement

system, AQI. This current study is an extension of previous work by Ahmad and Hashim (1997, 2000, 2002), and mazlan et. al (2004) that produced models to quantify haze in API.

Figure. 1. Raw NOAA AVHRR data dated 22 September 1997. Location of the selected air pollution stations are damarcated as letter A,B,C,D and E designated for Kuala Lumpur, Prai, Pasir Gudang, Bukit Rambai, and Bukit Kuang respectively. Combination of band 1, 2 and 4 are used to visually differentiate between haze (orange), low clouds (yellow) and high clouds (white).

2 Materials This study involved the usage of three types of data namely; ground-truth data, satellite data and ancillary data. 2.1 Ground-truth data Conventional measurements of haze were complementarily used throughout performing data processing for extraction of PM 10 information. PM 10 measurements in micrograms per meter cube (?gm-3) from 1st to 30th September 1997 were carried out by ASMA (Alam Sekitar Malaysia Sdn. Bhd.) to represent the actual haze intensity over the study area. For the purpose of this study, the measurement was later converted to AQI.

Table 1. Air Quality Index (AQI) for Particulate Matter up to 10 micrometers in diameter (PM 10)

2.2 Satellite data Seven sets of NOAA-14 AVHRR data dated 22, 23, 25, 2, 28, 29 and 30 September 1997 acquired from SEAFDEC (Southeast Asia Fishery Development Centre) receiving station were used. NOAA-14 AVHRR was suitable for haze study as it offers high spectral and temporal resolution with a minimum cost. Some useful characteristics of NOAA-14 AVHRR satellite are shown in Table 2.

Table 2. NOAA-14 AVHRR sensor and spectral characteristics (Source: Kidwell et al., 1995) 2.3 Ancillary data Meteorological information over study area, including visibility (Figure 2), air temperature, pressure, relative humidity, wind, etc were obtained from MMS (Malaysian Meteorological Service).

Fig.ure2. Reducing visibility of Petronas Twin Towers resulted from the appearance of haze

3 Method Three modules incorporated in this study are (1) Derivation of haze model, (2) Regression analysis, and (3) Accuracy Assessment. 3.1 Derivation of haze model Prior to further data processing, post launch calibration of visible Band 1 NOAA-14 AVHRR was earlier implemented in order to compensate data degradation due to extreme temperature change before and after launching of AVHRR sensor to space (Rao et al., 1996). Clouds and haze were successfully differentiated using thresholding technique (Baum et al., 1997). This to ensure both were not being misinterpreted between each other. Model used in this study is based on Siegenthaler and Baumgartner (1996), which make use of skylight to indicate the existence of haze. Skylight is an indirect radiation, which occurs when radiation from the sun being scattered by elements within the haze layer. It is not a direct radiation, which is dominated by pixels on the earth surface. Figure 3 shows electromagnetic radiation path propagating from the sun towards the NOAA-14 AVHRR satellite penetrating through a haze layer. Path number 1, 3 and 4 are skylight caused by direct radiation, whereas path 2 is indirect radiation.

Figure 3. Model used in this study is based on the skylight parameter (Source : Modified after Siegenthaler and Baumgartner, 1996)

This model can be described by: σ-R=L–V (1) where, σ : reflectance recorded by satellite sensor,

R L V

: reflectance from known object from earth surface , : skylight, and : lost radiation caused by scattering and absorption.

3.2 Regression analysis Calibration pixels of NOAA-14 AVHRR data were sampled within a radius of 2.5 km from each of the air pollution stations. The relationship between PM 10 AQI and satellite-recorded reflectance of band 1 AVHRR, were analysed using linear regression. 3.3 Accuracy Assessment In order to verify the accuracy of the regression model, RMSE (Root-mean-squared Error) was implemented to the AQI values obtained by the model.

4 Results The scatter plot for PM 10 versus satellite reflectance of band 1 NOAA AVHRR with its linear regression trend is shown in Figure 4 where the coefficient of determination, R2 is 0.5563. The linear regression model can be expressed as: PM10_Concentration (AQI) = (5.174 x Satellite_Reflectance) – 77.877

(3)

Figure 4. PM 10 in AQI versus satellite reflectance in percentage. Linear regression trend is shown in black line

The RMSE varies accordingly for all the five PM 10 ground stations ranging from 7 to 62 and with the average of 33 (Table 3). It is believed that the relatively high RMSE was due to limited number of air pollution stations used. Future study will consider of using more air pollution stations as well as other value-added ancillary data in order gain better and reliable accuracy.

Table 3. Average RMSE for respective haze components at Penang and Johor Bahru The spatial distribution of PM 10 can be shown in a colourful map (Figure 5) consisting of regions in green (good), yellow (moderate), orange (unhealthy for sensitive groups), red (unhealthy), purple (very unhealthy) and maroon (hazardous). Cautionary Statements for every region are given in detail in Table 1.

Figure 5. PM 10 concentration in AQI for 22nd September 1997. The PM 10 level in most area was good and moderate.

5 Discussion and Suggestion Integration of remote sensing and GIS (global positioning system) technology has been widely used in the field of atmospheric science related to air pollution. Our current and future studies on this field will focus on such integration. Besides that, the usage of 36 air pollution stations (Fig. 6) will increase the accuracy of the result (previously only five stations were used).

Figure 6. Air pollutions stations used in current study

The use of GIS interpolation to map haze based on iso-lines seems to be successful. Some of the outcomes of using GIS to determine spatial distribution of PM 10 have been obtained as shown in Figure 7, 8 and 9.

Figure 7. Iso-lines of PM 10 for 10 and 11 August 2005.

Figure 8. Iso-lines of PM 10 for 12 and 13 August 2005.

Figure 9. Iso-lines of PM 10 for 14 and 15 August 2005.

6 Conclusion

The study shows that remote sensing technique is capable of determining PM 10 concentration spatially and continuously with minimum cost and time. These are useful in order to provide haze early warnings, so that necessary measures could be taken effectively by both government authorised party as well as public. The result can further be improved by using integration of both remote sensing and GIS technology. Current progress of using this integration technique has show a promising outcomes.

Geospatial Applications in Air Pollution Modeling - A case study of Tuticorin district in Tamil Nadu Introduction Tuticorin is one of the numerous districts of Tamil Nadu in India. The district sprawls over an area of 4621 square kilometres. Density of population of 315 persons per square kilometre is lower than average density of 428 persons per square kilometre for Tamil Nadu.

Surface of SPM

The district has 1051 females for every 1000 males. Sixty three percent of the population is literate. Fifty nine percent live in rural areas while seventy percent depend on agriculture. The district has a coastline of 135 kilometres and produces 30 percent of the total salt produced in India.

Surface of SO2

The port of Tuticorin - a ISO 9002 certified port - facilitates Indian exports to about 20 countries. The port city and its hinterland constitute the hub of numerous large metallurgical, fertilizer, chemical and power plants of the district. Many believe these industries, together with establishments, which constitute backward and forward linkages to them, are polluting the environment of the district.

Surface of NOx

The air quality data collected from 108 stations together with associated information on them constituted the major inputs. Some stations were located in the vicinity of major market places, bus stands and industries. Most of the stations were located within the premises of residential buildings, school buildings, and the buildings of government departments. The sampling points were located 3 to 5 metres above the ground.

Pollution via SPM, SO2, NOx

The Study Inputs for the study include a scanned map, a database on air quality data collected from 108 stations and a 4-band multispectral image. Spatial data editor is used to create a vector of district boundary from georeferenced-scanned map. The vector is then used for creating a region of district boundary. Database is imported as a vector of 108 data collection stations. Database on air quality data on SPM, SO2, and NOx is used for generating surfaces depicting spatial spread of pollution in the district.

Hexagon grid

Class / training raster

Raster extraction process via district boundary region is used for creating the extracts of surfaces of pollutants. Relief Shading, contrast enhancement, pseudo coloring, processes are applied on surfaces for comprehension of qualitative and quantitative spread of pollution. Threshold contour lines are extracted from surfaces. Extracts of contour lines are exclusively combined to distinguish more polluted areas from less polluted ones. Resultant output indicates that 68 percent of the district is reasonably polluted. Profile views along the road from Kovilpatti to Thiruchendur are created for three surfaces.

Air quality standards

For developing models for monitoring / keeping SPM within sustainable limits, a multispectral image is imported, georeferenced and classified via a supervised classification process, wherein ground truth information is used for creating the training set. The resultant class raster is edited to incorporate tag values representing levels of pollution manifested by the class raster of land cover. A surface raster of sustainability is created with data articulated within limits of air quality standards.

SPM pollution via model

The properties of hexagon grid cells - created via polygon grid process - are extracted from surface raster of sustainable SPM pollution (created via surface modeling process), prevailing air quality raster (created via supervised classification of multispectral image) and actual SPM pollution surface raster. A table with a computed field (with implied one-to-one attachment with polygon ID) for air quality records of hexagon cells is created. A theme map of records of computed field displays the spatial spread of air quality achieved via management formula for keeping the air quality standards. Following table indicates the changes statistically.

Comments Geospatial applications for modeling air pollution include the following steps: importing, georeferencing, spatial data editing, surface modeling, relief shading, pseudo color editing, creating and using region, extracting and merging vectors, supervised classification, polygon grid sampling, extracting grid cell properties, and theme mapping. The inputs for study came from Professor N.C. Chandrasekar and Mr. T. Jayasekar of VOCC, Tuticorin and Professor Victor Rajamanickam, of Tamil University, Thanjavur. The study - for reasons of paucity of funds - is not exhaustive. Yet it vindicates the potential benefits of geospatial modeling of air pollution in rural, urban, and regional planning for activities of dwelling, working and amenities. Note The contents of this feature is protected by the Indian Copyright Law. No part of this feature may be reproduced for use in any other form, by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without prior permission from Physical Planning Consultants (india) Limited, 57/D, Beltola Road, Calcutta 700025, India.

GIS applications in air pollution modeling Status of Vehicular Pollution in India Motor vehicles have been closely identified with increasing air pollution levels in urban centers of the world (Mage et al, 1996; Mayer 1999) . Besides substantial CO2 emissions, significant quantities of CO, HC, NOx, SPM and other air toxins are emitted from these motor vehicles in the atmosphere, causing serious environmental and health impacts. Like many other parts of the world, air pollution from motor vehicles is one of the most serious and rapidly growing problem in urban centers of India (UNEP/WHO, 1992; CSE, 1996; CRRI, 1998). The problem of air pollution has assumed serious proportions in some of the major metropolitan cities of India and vehicular emissions have been identified as one of the major contributors in the deteriorating air quality in these urban centers (CPCB, 1999). Although, recently, improvement in air quality with reference to the criteria pollutants (viz. NOx, SO2, CO and HC) have been reported for some of the cities, the air pollution situation in most of the cities is still far from satisfactory (CPCB, 2000). The problem has further been compounded by the concentration of large number of vehicles and comparatively high motor vehicles to population ratios in these cities (CRRI, 1998). In India, the number of motor vehicles has grown from 0.3 million in 1951 to approximately 50 million in 2000, of which, two wheelers (mainly driven by two stroke engines) accounts for 70% of the total vehicular population. Two wheelers, combined with cars (four wheelers, excluding taxis) (personal mode of transportation) account for approximately four fifth of the total vehicular population. The problem has been further compounded by steady increase in urban population (from approximately 17% to 28% during 1951-2001; Sharma; 2001 and larger concentration of vehicles in these urban cities specially in four major metros namely, Delhi, Mumbai, Chennai and Kolkatta which account for more than 15% of the total vehicular population of the whole country, whereas, more than 40 other metropolitan cities (with human population more than 1million) accounted for 35% of the vehicular population of the country. Further, 25% of the total energy (of which 98% comes from oil) is consumed by road sector only. Vehicles in major metropolitan cities are estimated to account for 70% of CO, 50% of HC, 30-40% of NOx, 30%of SPM and 10% of

SO2 of the total pollution load of these cities, of which two third is contributed by two wheelers alone. These high level of pollutants are mainly responsible for respiratory and other air pollution related ailments including lung cancer, asthma etc., which is significantly higher than the national average (CSE, 2001; CPCB, 2002) Vehicular Pollution Modeling in India In air pollution problems, the air quality models are used to predict concentrations of one or more species in space and time as related to the dependent variables. They form one of the most important components of an urban air quality management plan (Elsom, 1994, Longhurst et al., 2000). Modelling provides the ability to asses the current and future air quality in order to enable “informed” policy decisions to be made. Thus, air quality models play an important role in providing information for better and more efficient air quality management planning. An effective air quality management system must be able to provide the authorities with information about the current and likely future trends, throughout the area enabling them to make necessary assessments regarding the extent and type of the air pollution control management strategies to be followed. The air quality models can be classified as point, area or line source models depending upon the source of pollutants, which it models. Line source models are used to simulate the dispersion of vehicular pollutants near highways or roads where vehicles continuously emit pollutants. Several models have been suggested to predict pollutant concentration near highways or roads treating them as line sources. Vehicular pollution modelling, in general, refers to carrying out air pollution prediction estimates due to vehicular activity in a region. In urban environment it has to be taken into consideration along with other types of sources viz. area and/or point sources (FIG. 1). Whereas, the highway dispersion models are generally used for analyzing the output of an existing or proposed highways/ roads at a distance of tens to hundreds of meters downwind. In this region, the effect of vehicular pollution and vehicular activity is considered to be primary consideration for air quality prediction analysis. At present, most of the widely used highway dispersion models are Gaussian based (Briggs et al., 2000; Baratt, 2000).

Fig 1. Area of Concern for Modellers

In India various Gaussian based line source models like CALINE 3 and 4, GM and HIWAY 2 are routinely used to predict the impact of vehicular pollution along the roads/highways. Most of these predictions or estimations are carried out as part of Environmental Impact Assessment (EIA) studies. The EIA notification of May 4, 1994 of Ministry of Environment and Forests, Government of India (MoEF, 1994) had made it mandatory for all new and existing highway/roads projects, part of EIA requirements, prediction estimates of vehicular pollutants along the highways/roads are routinely carried out by using various Gaussian based dispersion models. Based on the

modeling exercise, Environmental Management Plan (EMP) is suggested so that the predicted air pollution level does not exceed the National Ambient Air Quality Standards (NAAQS). Although Central Pollution Control Board (CPCB), Delhi under the Ministry of Environment and Forests had issued necessary guidelines for air quality modeling (CPCB, 1998), but unfortunately they do not contain any reference/guidelines, with respect to line source models, resulting in use of different type of line source models. The experience so far has shown that the values of various input parameters to these models are often adopted from other countries without understanding their applicability in Indian context, resulting in inaccurate and unreliable predictions. Moreover, many times these models are used for predicting air pollution levels along the roads under the urban environmental conditions. Interpretation based on that modeling exercise should be drawn very carefully when as these Gaussian based dispersion models have been found to perform poorly under these conditions (Namdeo and Colls, 1996; Micallef and Colls, 1999; Briggs et al., 2000). 1 Inadequacies of vehicular pollution modelling Various line source models (viz. CALINE 4, GM, HIWAY 2, etc.) generally require various input parameters pertaining to meteorology, traffic, road geometry land use pattern. Besides the basic Gaussian dispersion approach, each dispersion model differs with respect to the treatment of modified wind and turbulence due to vehicle wakes (thus dispersion parameters ?y and ?z) near the roads. Different models also take care of cases of oblique and parallel winds and treatment of hot exhaust plumes from vehicles in different ways. Adequacies, limitations, reliability and associated uncertainties of these dispersion models have already been discussed by various researchers (Hanna,1988 ;Scorer, 1998 etc.). Various Gaussian based dispersion models, initially developed in West (particularly in USA) are extensively used in India without properly calibrating them for Indian climatic and traffic conditions. Moreover, various input parameters, used in these models are not accurately known, leading to incorrect or sometimes even unreliable predictions. Greatest inaccuracy in vehicular pollution modeling exercise in India occurs due to the considerations for improper emission factors used for different categories of vehicles. Emission factors expressed in terms of grams of pollutants per unit of distance traveled (in km) depend, on several factors including type of fuel, engine type, driving cycle, age of the vehicle, speed of vehicle, driving mode etc. Uncertainties and unreliability associated with the emission factors have already been discussed in detail and reported by various researchers (Kulhwein and Friedrich, 2000 and Vlieger et al., 2000). Unfortunately in India, no serious efforts have been made to accurately determine the emission factors for different categories of in-use vehicles as a function of vehicle speed, engine technology, fuel quality and age of the vehicles. Various researchers had used emission factors, which were obtained from limited experimental data on chassis dynamometer under laboratory conditions or directly adopting emission factors which are applicable to European vehicles. While use of emission factors obtained from old generation vehicles grossly over predicts the emissions from the new generation Euro I, Euro II compliance vehicles presently plying on Indian roads, the use of emission factors developed for European vehicles to that of Indian vehicles grossly under predicts the emissions from these Indian vehicles. The problem is further compounded, as vehicles with a wide range of engine technology with the different quality of fuels are being used in these vehicles (CPCB; 2000a, 2000b). In India, vehicles as old as belonging to 1970’s and as new as Euro II and Euro III compliant vehicles can be found to be plying on the roads. The quality of fuel supplied in whole country is also not same. While, better quality fuel, comparable to Europe and other developed countries is being supplied in Delhi and few other major metros, the quality of fuel being supplied in other parts of the country is still poor. Thus, with different combination of vehicles (age wise and technology wise), with a wide range of fuel quality, finding reliable emission factor for different categories of vehicle, under Indian driving and road conditions with limited emission testing facilities is a task, which requires immediate attention. Further, with recent emphases on replacing old technology vehicles with the latest ones, and improvement in fuel quality for whole country, the existing facilities need to be upgraded keeping in tune with the latest developments that are taking place in the other parts of the world. Recently, CPCB (CPCB, 2000a; Sengupta, 2000) has suggested a set of emission factors for different

categories of vehicles on the basis of year of manufacture and engine technology. However, it is still a long way before more reliable emission factors that reflect Indian traffic conditions are worked out 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 

 

  

Traffic parameters: Traffic volume (hourly and peak), traffic composition (two wheelers, three wheelers, cars, buses, goods vehicle etc.), type of the fuel used by each category of vehicles, fuel quality, average speed of the vehicles. Meteorological parameters: Wind speed, Wind direction, stability class, mixing height Emission parameters: Expressed in grams /distance traveled. It is different for different categories of vehicles and is a function of type of the vehicle, fuel used, average speed of the vehicle and engine condition etc. Road geometry: Road width, median width, length and orientation of the road, number and length of each links. Type of the terrain: Urban or rural, flat or hilly Background concentration of pollutants



Receptor location

3. Integration of GIS with CALINE-4 results 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). Table 1. Predicted Eight Hour Averages of CO Conc (PPM) (Worst Case)

Fig. 4 Observed Traffic Pattern on NH-2

Fig 5. Observed CO Values at six Locations

Fig 6. CO Pollution Profiles at Ballabhgarh

Conclusions In the present study integrated modelling approach involving GIS has been used for pollution mapping of vehicular pollutants along the highway. Further GIS can also be used to highlight the impact of various inputs viz. traffic (traffic volume, composition, age etc.) in terms of emission factors and meteorological parameters. While GIS does not improve implicitly, the ability to forecast travel or improve the accuracy of spatial data, nor does it improve the accuracy and predictive capabilities of various integrated models but by using GIS these data, as well as a variety of other types and resolutions of spatial data, required for emission modeling can be brought together into an integrated modelling environment.

Air pollution modelling for Chennai city using GIS as a tool Introduction If the London fog disaster in the early fifties had not killed thousands of people over a short period, few would have bothered about acute pollution disasters. The air that man breathes is polluted by industrial and automobile emission bringing into the atmosphere Suspended particular matter (SPM), Oxides of Sulfur and nitrogen, Carbon monoxide, photochemical oxidants and Hydrocarbons. These pollutants, individually and collectively, have teratogenic, carcinogenic or mutagenic effects and can also cause respiratory ailments, the physiological barriers being ineffective against them. In the 1980s, the polluted air in the cities could be traced to the chimneys of factories. But by the 1990s it was more than apparent that the major contributor to the haze and the poisons in the air was not factories but automobile – cars, buses, trucks, three-wheelers and two-wheelers. For over a decade now, there is no dispute over the fact that more than half the pollution load in our cities is due to automobile exhaust. In the light of these concerns, there is clearly a need for improved information on levels of trafficrelated air pollution. This information is required for a wide range of purposes: to help investigate the relationship involved as inputs to health risk assessment, to assist in establishing and

monitoring air quality standards, and to help evaluate and compare transport policies and plans. For all these purposes information is required not only on temporal trends in air pollution but also on geographic variations. GIS based vehicular pollution models are needed to identify pollution hot spots, to define at risk groups, to show changes in spatial patterns of pollution resulting from policy or other interventions and to provide improved estimates of exposure for epidemiological studies. This paper presents a methodology to develop a GIS based vehicular pollution model using a coordinated approach, taking into consideration of all the parameters influencing vehicle emissions, unlike other conventional approaches which have used GIS as a preprocessing/post processing tool. Geographic Information Systems A geographic information system (GIS) is a computer-based information system that enables capture, modeling, manipulation, retrieval, analysis and presentation of geographically referenced data. The rise of GIS technology and its use in a wide range of disciplines provides transportation and air quality modelers with a powerful tool for developing new analysis capability. The organization of data by location allows data from a variety of sources to be easily combined in a uniform framework. Another important feature of GIS is its ability to bridge the technical gap between the need of analysts and decision-makers for easy understanding of the information. The user friendliness of GIS is a feature that has made GIS one of the most used platforms for planning all over the world. The ability of GIS to answer technical questions also makes GIS an excellent tool. Literature on GIS data structures, applications, and vendor products are substantial. 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 locationspecific 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 highquality 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 coordinated methodology to map the air quality in Chennai City using GIS is explained in the following paragraphs. 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. Fleet characteristics An improved capability to identify the emission significant components of the operating fleet is important to emission rate accuracy. Spatially variant emission estimates are needed, requiring spatially resolved sub-fleet characterization. Therefore, there is a need for identifying procedures that can accurately predict spatially resolved vehicle characteristics for urban areas. Vehicle characteristics The first vehicle characteristic module has two major tasks: determine individual vehicle location parameters and emission-specific characteristics. Vehicle geocoding Address Geocoding is a process whereby standard address fields of road name, road type, and ZIP code are used to identify corresponding lines in a road database. Decoding Raw registration data can usually provide a few important vehicle characteristics (VIN, make, model, model year, and number of cylinders), but more information can be developed from the vehicle identification number (VIN). These files should represent a comprehensive description of the region’s fleet characteristics. These files can be further processed to develop the emissionrate specific fleet distributions. High emitting vehicles A high emitting vehicle is one that has malfunctioning or tampered with emission control systems causing higher than normal emissions. It is expected that a small percentage of high emitting vehicles account for a large percentage of total emissions. High emitter determination is an important model design parameter and therefore it is appropriate to characterize these vehicles differently. Technology grouping Once vehicles are identified as high or normal emitters, they are characterized into technology groups. Technology grouping is the process of combining vehicles according to the emission standards of the make. Vehicle activity The emission-important vehicle activity estimates provided by the regional travel models are: the number and location of peak hour (or daily) trip origins, road segment volumes, and road segment average speeds. Temporal travel behavior and modal (idle, cruise, acceleration, and deceleration) operations. This 

Engine Start Activity

 

Intra-zonal Running Exhaust Activity Modal Activity

Road grade The impacts of road grade on emissions are included in the model design. Road grade affects vehicle emissions by impacting the load on the engine. Gravity exerts a force on a vehicle that must be counteracted to maintain a constant speed. Road grade is not included in mandated emission models because tests on the actual effects have not been completed and because metropolitan areas do not maintain spatially defined road grade estimates. Including vehicle activity impacts resulting from road grade provides an important step in emission model development.

Conceptual design of the proposed research model Temporal variability The temporal variability also plays a major role in. Although average speed cannot be predicted to determine LOS F during off-peak hours, volume-to-capacity ratios provide sufficient information for selection of appropriate speed and acceleration profiles.

Facility emissions Facilities are divided into zones and lines corresponding to the previously mentioned emission modes of engine starts and running exhaust (respectively). Facility estimates are used to allocate emission production to those vector spatial data structures currently used by transportation planners. By tying emission production estimates to facilities, tasks regarding research, reporting, validation, or control strategy development are made easier. Engine start zonal facility estimates Zonal facilities include the zonal representations of land use, and Census blocks. The model design allows for other zonal designations to be included, but only the three mentioned have been required. The zones have been included in the definition of facilities because planners to aggregate socioeconomic information use them. While running exhaust emissions occur within zones, they are better tied to modal activity that occurs on the road. Engine starts, however, occur at trip origins, generally characterized with point or zonal information. Management of data dictionary The directory structure may include the following data Directories               

zone: stores all zonal data and coverages road: stores all lineal data and coverages grid: stores all vector grid data emest: stores all emission estimate data tech: stores all technology group data grade: stores all road grade related data raster: stores all raster data raw: place to store backup copies of data and programs aml: stores all AML code code: stores all C code templates: stores a number of INFO file templates spac: stores all speed / acceleration profiles modmat: stores all modal matrices lookup: stores all ASCII lookup files temp: stores temporary files used during program runs

Contribution of Geographic Information Systems in the proposed Mobile emission Model Geographic information systems can be advantageously used in the proposed model for the following activities Spatial data organization Data in the model can be organized based on their spatial character. Structuring the multiple layers of data in this manner provides data connectivity that would be difficult without GIS and topology.

Framework of the GIS based Mobile Emission Model Spatial data joining Data sets of different characteristics can be merged together to form a single entity. Specifically, GIS allows improved spatial resolution of the travel demand forecasting model network by conflating to a spatially accurate road database. This capability of GIS also allows linkages to occur between the various area sources of information (land use, Population, etc.). Spatial query GIS provides the ability to search data by locational parameters. Specifically, the technique can be used to predict the on-road fleet distribution required for the identification of the fleet registered within a certain distance from the individual road segments. Spatial aggregation GIS provides the ability to aggregate irregular polygon data and line data into regular userdefined grid cells. This capability makes GIS vital for efficiently developing mobile emission inventories, regardless of the modeling approach used. Spatial data visualization The map-making and graphic display capabilities found in most GISs are extremely useful in communicating model results to individuals from various technical backgrounds. Given the importance of mobile emissions in determining transportation improvements, this feature has significant value. Conclusion This paper has made an attempt towards an integrated mobile emission modeling. Further work in this direction is on the way and further improvements and changes in the proposed methodology will be incorporated. There is always a scope for further refinement of the methodology with appropriate changes and additions

Mapping Air Quality Analysis in GIS Abstract: Pollution is an important issue being monitored and regulated in industrial and developing cities. Thus, air quality is one of the major environmental issues being monitored in the Emirate of

Dubai. This paper discusses the analytical model used to identify the probable causes of the pollutants and the potential affected areas using GIS. The model has been developed based on the types of pollutants, features found in the surrounding areas, including wind speed and direction. The Environment Department of Dubai Municipality captures the data in real-time through air quality monitoring stations that has been in place. This data is read by GIS servers and undergoes the modelling procedure discussed in this paper to analyse the source of the air pollutants and its dispersion to the surrounding areas.

Visualization of Air Pollution Patterns Using GIS: Case of Lahore City Abstract Atmospheric pollution particularly in urban area has a strong impact upon daily life. Lahore is the second largest city of Pakistan. Its economic growth and rising energy consumption are causing the increase in air pollution. The main sources of the air pollution are motor vehicles and industrial activities. SO2, NO2, CO2, CO, O3 and Particulate Matter (Pm) are investigated as the pollution indicators. This research is an endeavor towards the development of an Air Pollution Information System, which can support the dissemination of the information about different pollutants and can facilitate the identification of the most polluted geographical areas using Geographic Information System (GIS) tools and techniques.