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Research Publications: UM-SG-RS-2013-01
Title:
Ecological engineering practices for the reduction of excess nitrogen in human-influenced landscapes: a guide for watershed managers.
Year:
2013Authors:
Passeport, E; Vidon, P; Forshay, KJ; Harris, L; Kaushal, SS; Kellogg, DQ; Lazar, J; Mayer, P; Stander, EKSource:
Environmental Management 51 ( 2 ) : 392 - 413DOI:
10.1007/s00267-012-9970-yAbstract:
Excess nitrogen (N) in freshwater systems, estuaries, and coastal areas has well-documented deleterious effects on ecosystems. Ecological engineering practices (EEPs) may be effective at decreasing nonpoint source N leaching to surface and groundwater. However, few studies have synthesized current knowledge about the functioning principles, performance, and cost of common EEPs used to mitigate N pollution at the watershed scale. Our review describes seven EEPs known to decrease N to help watershed managers select the most effective techniques from among the following approaches: advanced-treatment septic systems, low-impact development (LID) structures, permeable reactive barriers, treatment wetlands, riparian buffers, artificial lakes and reservoirs, and stream restoration. Our results show a broad range of N-removal effectiveness but suggest that all techniques could be optimized for N removal by promoting and sustaining conditions conducive to biological transformations (e.g., denitrification). Generally, N-removal efficiency is particularly affected by hydraulic residence time, organic carbon availability, and establishment of anaerobic conditions. There remains a critical need for systematic empirical studies documenting N-removal efficiency among EEPs and potential environmental and economic tradeoffs associated with the widespread use of these techniques. Under current trajectories of N inputs, land use, and climate change, ecological engineering alone may be insufficient to manage N in many watersheds, suggesting that N-pollution source prevention remains a critical need. Improved understanding of N-removal effectiveness and modeling efforts will be critical in building decision support tools to help guide the selection and application of best EEPs for N management.
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