Contributions of mobile source emissions to watershed N budgets along an urban to rural gradient

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Excess nitrogen (N) is a serious water-quality problem in most of the estuaries in the US, especially those downstream of developed coastal basins. While the major sources of this N, atmospheric deposition, wastewater, fertilizer, and other agricultural sources are well known, their relative importance as N sources to particular estuaries is not. One trouble is that the deposition monitoring network used by most researchers in the US was designed to avoid influences from nearby sources such as suburban/urban areas. This is significant because a major source of emissions especially in urban areas are mobile sources such as motor vehicles and construction equipment and a portion of their emissions are deposited nearby. This work will allow more accurate estimates of nitrogen fluxes from urban catchments by including deposition from mobile source emissions. As part of this research, N budgets will be constructed for sub-watersheds along a land-use gradient from the urban core to the rural/suburban fringe of Baltimore that take into account the contribution of mobile sources to atmospheric N inputs. As part of this research, students will be involved in collecting samples from collectors placed throughout the study area at schools associated with the Baltimore Ecosystem Study Schoolyard LTER. Results of this study will have implications for the management of coastal systems' especially those near major metropolitan areas. This proposal is an important first step in quantifying the contribution of a significant and previously overlooked source of N to our coastal systems. Because of this gap in our understanding it is difficult to design an integrated plan to manage N inputs to estuaries. This is the first project to consider mobile source deposition in watershed N budgets and to relate these estimates to the two methods widely used by researchers and managers to estimate deposition (NADP/CASTNet) and emissions (EPA's National Mobile Inventory Model).