2002

The People's Republic of China is experiencing severe air pollution with very serious public health and economic consequences. Over the past decade, the Chinese government has sought to utilize bureaucratic, political, legal and educational vehicles to address these problems. This paper examines the ways in which those policy measures have been communicated to, understood by, and acted upon by the citizenry, drawing in important part on household and epidemiological surveys conducted in Anhui. Our study suggests that the central government's message has yet to be absorbed to the degree intended and then considers both why this has been the case and how the effectiveness of policy mechanisms might be enhanced.

One of the common challenges for life cycle impact assessment and risk assessment is the need to estimate the population exposures associated with emissions. The concept of intake fraction (a unitless term representing the fraction of material or its precursor released from a source that is eventually inhaled or ingested) can be used when limited site data are available or the number of sources to model is large. Although studies have estimated intake fractions for some pollutant‐source combinations, there is a need to quickly and accurately estimate intake fractions for sources and settings not previously evaluated. It would be expected that limited source or site information could be used to yield intake fraction estimates with reasonable accuracy. To test this theory, we developed regression models to predict intake fractions previously estimated for primary fine particles (PM_2.5) and secondary sulfate and nitrate particles from power plants and mobile sources in the United States. Our regression models were able to predict pollutant‐specific intake fractions with R² between 0.53 and 0.86 and equations that reflected expected relationships (e.g., intake fraction increased with population density, stack height influenced the intake fraction of primary but not secondary particles). Further analysis would be needed to generalize beyond this case study and construct models applicable across source categories and settings, but our analysis demonstrates that inclusion of a limited number of parameters can significantly reduce the uncertainty in population‐average exposure estimates.

Recent economic expansion and population growth in developing countries have had a big impact on the development of large cities like Delhi, India. Accompanied by Delhi's rapid spatial growth over the last 25 years, urban sprawl has contributed to increased travel. The vehicle fleet projected at current growth rates will result in more than 13 million vehicles in Delhi in 2020. Planning and managing such a rapidly growing transport sector will be a challenge. Choices made now will have effects lasting well into the middle of the century. With such rapid transport growth rates, automobile emissions have become the fastest increasing source of urban air pollution. In India, most urban areas, including Delhi, already have major air pollution problems that could be greatly exacerbated if growth of the transport sector is managed unwisely. The transport plans designed to meet such large increases in travel demand will have to emphasize the movement of people, not vehicles, for a sustainable transportation system. Therefore, a mathematical model was developed to estimate the optimal transportation mix to meet this projected passenger-km demand while satisfying environmental goals, reducing congestion levels, and improving system and fuel efficiencies by exploiting a variety of policy options at the minimum overall cost or maximum welfare from transport. The results suggest that buses will continue to satisfy most passenger transport in the coming decades, so planning done in accordance with improving bus operations is crucial.