A STUDY ON TRANSPORT RELATED AIR POLLUTION

Abstract

In a rapidly urbanising country like India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a rate of more than 7% per annum. During the period of 50 years from 1951 to 2001, the number of vehicles has increased from 3 lakhs to 430 lakhs, i.e. an increase of almost 143 times. This has lead to overcrowded roads and a polluted environment. The transportation sector is the main contributor to the ever-increasing air pollutant concentration in most of our major metropolitan cities. According to a study conducted by RITES, cars and two-wheelers contribute to 11% and 78% of the total transport related air pollution and vehicles are responsible for around 64% of the total air pollution load. This alarming increase in the pollution in our cities has been primarily responsible for most of the respiratory problems in our metropolitan cities. Some of the measures that can reduce pollution are being practised and their efficiency in controlling or reducing the pollution problem is being —investigated foz5r future–enforce–ment. Transportation and environmental engineers are jointly responsible for the studies related to air pollution like, assessment of air quality, calibration of models available elsewhere and for developing predictive models for prediction of air quality. For studying the implications of any transportation project, for planning and for enforcement, predictive models could prove to be quite a handy tool as they assist the transportation engineer in quantifying the environmental impacts. Many standard prediction models have been developed abroad and the most popular among them are the CALINE models and the HIWAY models. However, the suitability of these models for Indian conditions iv must be thoroughly investigated before they are applied for prediction of pollutant concentration in India. In the present study, an effort has been made to study the air quality in eight selected locations in Delhi in terms of the concentrations of carbon monoxide, hydrocarbons, nitrogen oxides, sulphur dioxide and suspended particulate matter. These pollutants have also been related to the various traffic parameters using linear regression analysis to achieve a better understanding of the observed data. Results of linear regression analysis show that while carbon monoxide and hydrocarbons are related to the number of petrol driven vehicles, nitrogen oxides and sulphur dioxide are related to the number of diesel driven vehicles and suspended particulate matter is related to the total traffic volume. The application of CALINE4 for prediction of CO concentrations shows that CALINE4 generally under-predicts in most cases. This means that predicted values will generally be less than the observed values and therefore the modelled values can be safely adopted for decision-making purposes. The maximum deviation from the observed values is 26.8% for the standard case and 24.28% for the worst-case wind angle condition. Calibration of the CALINE4 model has been carried out by developing a linear regression equation between predicted values and the observed values. Validation of the software has been done by comparing the concentrations of carbon monoxide observed at km 174.0 on NH-58 with the corrected values obtained after applying the predicted values in the calibration equation. The validation results are encouraging with a mean variation of 7.06 % from the observed values. Some strategies for control of air pollution are also suggested and presented.