A community nitrogen footprint analysis of Baltimore City, Maryland

Abstract

Abstract The nitrogen footprint tool (NFT) provides a novel way for communities to understand the environmental impacts of their collective activities and consumption. Reactive nitrogen (Nr; all N species except N2) is created by the Haber–Bosch process for food production and as a by-product of fossil fuel combustion and two natural processes, biological nitrogen fixation and lightning. While it is a vital input for food production, too much Nr has a negative effect on the environment. Calculating the amount of Nr released to the environment as a result of an entity’s resource consumption is the first step in reducing those Nr losses. The nitrogen (N) footprint method has previously taken this approach at the personal and institution scale. In this study, the approach is extended, for the first time, to the spatial patterns of the community nitrogen footprint within a large city, through the integration of diverse geographic information to calculate the N footprint distribution within the City of Baltimore, Maryland, USA. The total N footprint of Baltimore City was ∼19 000 MT N or 30 kg N per capita in 2016, dominated by the food production sector (73%), followed by the energy and transportation sectors (15% combined). There was geographic variability among census block groups’ per capita N footprint within Baltimore City; driven primarily by economic and development factors. Several management scenarios were assessed to better understand what actions may reduce the Baltimore N footprint at the city and community scale over time. The study explored the effect and efficacy of reducing meat consumption based on differences in city consumption patterns, increasing the use of renewable energy sources, and reducing electricity consumption on the city’s total N footprint. The model for the Baltimore City N footprint calculation can be applied to other communities in the United States at the spatial grain of the census block group or any country with this level of data to provide an indicator of nitrogen sustainability.