2005

More than 220 Tg N are processed annually through the global agriculture/animal/human food chain. It is suggested that aerobic denitrification, reduction of nitrite formed in the first stage of nitrification, is an important source not only of global N₂O but also of NO_x. A simple top‐down method indicates a globally averaged yield of 2% for N₂O emitted as a consequence of human disturbances to the global nitrogen cycle. This yield can account not only for the contemporary budget of atmospheric N₂O but also for trends observed over the past 1000 years. The associated microbial source of NO_x is estimated assuming a NO_x/N₂O ratio of 3, consistent with results from a variety of laboratory and field studies. This source is significant, particularly for large developing countries such as China and India for which its contribution is comparable to that from fossil fuel.

Cities in developing countries like India are facing some of the same concerns that North American cities are: congestion and urban growth. However, there is a sense of urgency in cities like Delhi, India in that this growth is far more rapid as both urbanization and motorization are ongoing processes that have not yet peaked. In this paper, we examine land use change and its relationship with transportation infrastructure and other planning related variables in a spatial context. We estimate land use change models at two different scales from separate data. Cellular automation and Markov models were used to understand change at the regional scale and discrete choice models to predict change at the local level. The results suggest that land use in the Delhi metropolitan area is rapidly intensifying while losing variety. These changes are affected by industrial, commercial and infrastructure location and planners and policy-makers need to better understand the implications of location decisions. We also examine these results in the context of a policy framework for data-based planning that links land use and transportation models for Delhi.

Data on travel behavior in developing countries like India is minimal. This is especially true for the relatively poor residents of urban India. They are dependent on fewer options for transportation and have little choice in terms of employment location given their dependence on walking or bicycles. This is significant in cities like Chennai because employment is highly concentrated in the center of the city. In this study, the results of a survey of 70 households in Chennai were analyzed to estimate statistical models of travel behavior with respect to mode choice and trip frequency. The households were located in two different parts of the city: one group of households lived close to the city center (in a settlement called Srinivasapuram) and the other at the periphery (in a location called Kannagi Nagar). We analyze the differences in travel behavior due to differences in accessibility to employment and services between the two settlement locations. The results indicate that differences in accessibility appear to strongly affect travel behavior. Residents in the centrally located settlement were more likely to use non-motorized modes for travel (walk or bicycle) than the peripherally located residents. It is vital therefore that, policy makers in India consider location of employment in the planning of new housing for low-income households.