MODELING OF URBAN TRAFFIC EMISSIONS

Abstract

Urban road transport emissions are constantly increasing worldwide. In terms of pollution, Delhi had been ranked fourth among the 41 most polluted cities of the world (Goyal, 2007). It is observed from various studies that road transport is a major source of air pollution in megacity Delhi. In India, vehicular population and air pollution emissions are increasing in other rapidly growing cities also. To study urban road transport emissions, their health impacts, and future trends, the present thesis focus on development and application of VAPI model for this purpose. Considering CO and NO,, as indicator pollutants largely emitted from fossil fuel driven vehicles, the model results have been validated in Chapter 5 by comparing CO and NO,, emission estimates with their ambient air concentrations. To understand the health status of megacity Delhi with respect to air pollution, this thesis evaluates the health risks in various districts of megacity Delhi in terms of mortality and morbidity (e.g., Total Mortality, Cardiovascular Mortality, Respiratory Mortality and Hospital Admission COPD) due to air pollution (Chapter 2). Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP) model has been used to evaluate health risks. New Delhi district shows least number of mortality and morbidity cases among all districts while North-West Delhi district has highest number of cases from 2002 onwards. It is inferred from this study that there is urgent need to reduce urban air pollution in Delhi especially in the North-West Delhi district. To appraise better measures to regulate the traffic emissions, proper quantification of vehicular emissions is a primary need. There are numerous models available to estimate vehicular emissions. But most of these models are based on the USA and European road transportation conditions. A new model, namely Vehicular Air Pollution Inventory (VAPI) model, has been developed. and applied in this thesis that estimates road transportation emissions in Indian conditions (Chapter 3). The VAPI model can estimate emissions of 11 types of pollutants from 23 Indian vehicle categories. Impact of altitude on emissions of various pollutants (e.g., CO, HC, 1-3 Butadiene, Formaldehyde, Acetaldehyde) from various vehicle categories have been assessed through VAPI model (Chapter 4). Delhi, Dehradun and Mussoorie have been taken as study areas because of their distinct geographical and climatic conditions. Findings reveal that ambient temperature, humidity and altitude influence vehicular emissions of CO, NO,,, 1-3 Butadiene, Formaldehyde and Acetaldehyde. Altitude dominates over other climatic factors (temperature, humidity) for influencing emissions of most of the pollutants from different vehicle categories while ambient temperature is second to altitude. Thus, this study signifies that consideration of specific parameters of topography (altitude) and meteorology (ambient temperature) are necessary to avoid errors in vehicular emission estimations at a given location. It is inferred from the study that the government should apply proper measures to reduce emissions from vehicles (especially personal vehicles) in high altitudinal areas where the environmental conditions (e.g., less oxygen concentration) are not suitable for higher emissions. Comprehensive exhaust emission inventories of eleven categories of pollutants from different vehicles for megacity Delhi for the period 1991 to 2010 have been developed by VAPI model (Chapter 5). Estimations show that emissions of most of the pollutants from private vehicles (e.g., two wheelers, cars) are increasing from 1991 to 2010 while moderate decline is observed in 2001. In case of commercial vehicles (e.g., three wheelers, taxis, buses, LCVs, HCVs) emission trends of most of the pollutants are not similar because of temporal changes in policies and technology. It is observed from the study that among all vehicle categories the two wheelers dominate in emissions of CO, HC, Acetaldehyde and Total PAHs, cars emit more CO2, 1-3 Butadiene, Benzene, Formaldehyde, and Total Aldehyde and HCVs are responsible for higher emissions of NOx and PM. These results can be used to design appropriate policy measures to reduce emissions of specific air pollutants. Exhaust emission inventory of 11 pollutants is further extended for the year 2011 to 2020 in Chapter 6 based on two scenarios, (i) Business as Usual (BAU) and (ii) Best Estimates Scenario (BES). BAU scenario is the extension of previous exhaust emission inventory while BES scenario is developed according to future transportation policies (e.g., metro rail, shift of two wheeler two-stroke to four-stroke, etc). Significant differences have been observed between BAU and BES scenarios in terms of emissions. Delhi metro rail emerge as a significant measure to reduce emissions. Emission inventory of non-exhaust pollutants (e.g., VOCs, PM10, PM2.5) have also been developed for the period 1991 to 2010 (Chapter 7). Finally, uncertainty in model results and sensitivity to input parameters have been estimated by commercially available Oracle Crystal Ball 11.1.1.0.00 software in Chapter 8. It is observed that emissions of various pollutants from three wheelers, cars, buses and taxies have highest uncertainty. Furthermore, emission estimates are much sensitive to VKT and vehicle population in comparison to other input parameters. Thesis ends with conclusions and recommendations discussed in Chapter 9.