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GIS applications in air pollution modeling

GIS applications in air pollution modeling

Niraj Sharma
E-mail:[email protected]

Kirti Bhandari,Prasad Rao, Anuradha Shukla
Scientists, Central Road Research Institute, Mathura Road, New Delhi.

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.

 

 

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 –

  • 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
 

 

GIS applications in air pollution modeling

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.

Acknowledgement
The authors are grateful to Director, CRRI for kindly permitting to publish the paper and presenting the same in the conference.

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