Vehicle Activity Patterns and Emissions in Pune, India

Abstract

India continues to experience substantial economic growth, with increased travel demand for both passengers and goods. As a result, roadway congestion is increasing in many cities and putting significant pressure on India's transportation infrastructure. One key concern is air pollution related to the increased transportation activity. To understand the transportation impacts on air quality, it is necessary to develop and apply appropriate modeling tools to the area of concern. Recently, a new on-road mobile source emissions model designed for use in developing countries has been developed: the international vehicle emissions (IVE) model. The IVE model was developed jointly by researchers at the International Sustainable Systems Research Center and the University of California at Riverside. This paper describes a research program that applied the IVE modeling approach to the city of Pune, India. As part of the approach, an extensive vehicle activity–fleet composition study took place in Pune; it consisted of videotaping and measuring traffic patterns, tracking representative vehicles with Global Positioning System data loggers, monitoring start–stop patterns in instrumented vehicles, and conducting surveys in the field. In this paper, the methodology and results of the study are described in detail, followed by mobile-source emissions predictions based on the calibrated IVE model. This overall effort was designed to support estimates of the air pollution impacts of on-road transportation in Pune that can now be used extensively in the development of air quality and transportation management plans for the region. It is also hoped that the collected data can be extrapolated to other Indian cities to support further environmental improvement efforts.

Assessment of motor vehicle use characteristics in three Indian cities

“…In contrast, in high-income countries such as the USA, the UK, France, Australia, and Japan, national authorities carry out field surveys of drivers for estimating fuel efficiency and annual mileage (Schipper, 2008). In low and middle-income countries, there have been very few attempts to estimate vehicular characteristics (Barth et al, 2007;Huo et al, 2009). These studies reported counts of vehicle types on the road and parking lots, and used only visual inspection.…”

Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): Emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

“…The IVE model is specifically 130 designed to calculate emissions from motor vehicles in developing countries for local conditions and has been used extensively in several countries worldwide, including Nepal (Shrestha et al, 2013), India (Barth et al, 2007), China (Guo et al, 2007;Wang et al, 2008), and Iran (Shahbazi et al, 2016). Emissions were estimated by the product of the base emission factors, the correction factors, and the distance travelled or the total starts for each of the vehicle categories.…”

“…Since we lack survey data for trucks and cars in Kathmandu, we used the data from Pune, India for these two types of vehicles (Barth et al, 2007). The travel distances were obtained from the vehicle registration number and vehicle kilometers traveled (VKT).…”

Benchmarking vehicle and passenger travel characteristics in Delhi for on-road emissions analysis

“…This is a result of high growth rate of vehicles sold in the city. For another setting in IndiaPune (2011 population-5 million), Barth et al (2007) reported an average age of 4.6 years for passenger cars, for year 2003. The age distribution of vehicular fleet in Indian settings is in complete contrast with those observed in Europe or the United States.…”

“…The vehicular characteristics were developed for the city of Pune using video camera recording of traffic, to determine the type of vehicles on road (Barth et al, 2007). The study team also conducted parking lot surveys (visual inspection only) for odometer readings, engine size, and model year and on-board GPS recording to determine vehicle speeds.…”