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In this thesis, I present the conceptual combination of the tunnel study with remote sensing. In other words, I present a technique for measuring fleet-averaged modal emissions from a large aggregate population of vehicles (as in a tunnel study), which employs relatively simple and rugged spectroscopic techniques (as in remote sensing). In combining these two ideas, even more flexibility in measurement location and traffic conditions is achieved, ensuring as random and representative a sampling of the vehicle population as is possible. In that sense, this approach could be referred to as "A Tunnel-less Tunnel Study," since it combines the inherent fleet-averaging ability of the tunnel study with the mobility of remote sensing. Specifically, this method combines both OP-FTIR and OP-UV spectroscopy and uses some local meteorological data collection to filter the data for appropriate weather conditions.
Traffic emission is a major source of urban air pollution. Vehicle is a significant contributor of carbon monoxide (CO), nitrogen oxides (NOx), black carbon (BC) and ultrafine particle (UFP) on-road and near-roadways. People living and working near roadways are facing health risks due to the exposure of elevated pollutant concentration. In order to develop cost-efficient strategies to mitigate near-road air pollution for protecting public health and promoting sustainable growth, it is imperative to characterize on-road traffic emission and its impacts on near-road air quality. On-road chasing method was used in this study to investigate on-road vehicle emissions. This is the first application of this method in China. The method was developed and improved during a four-year field campaign and is an efficient approach to characterize emissions of a large number of on-road vehicles. Fleet average and individual emission factors (EF) are derived and reported. The large sample size significantly expands the database of real world Chinese vehicle emissions. Some of the results are reported for the first time in China: BC and UFP number EF of vehicles by type; EF of buses linked with emission standard; EF distribution based on large sample size; contribution of "heavy emitter" to entire onroad emission; spatial variance of UFP number concentration at on-road, roadside and ambient environments. My work suggests that diesel trucks are a major source of summertime BC in Beijing. Furthermore, "heavy emitter" accounts for a significant portion of BC emissions in Beijing and Chongqing. I also observed a clear downward trend of BC EF of diesel trucks in Beijing from 2008 to 2010, and of buses with more tighten emission standard. These observations indicate the effectiveness of traffic emission control measures (i.e. improvement of fuel quality in Beijing and enforcement of stringent emission standards) on BC EF reduction of diesel vehicles. However, comparison works between BC and NOx EF did not show NOx EF improvement as that of BC. The results and conclusions in this dissertation provide support for policy makers to evaluate and modify current traffic related air pollution control measures and propose future work.