Phosphorus removal in denitrifying woodchip bioreactors varies by wood type and water chemistry.

Denitrifying woodchip bioreactors are a practical nitrogen (N) mitigation technology but evaluating the potential for bioreactor phosphorus (P) removal is highly relevant given that (1) agricultural runoff often contains N and P, (2) very low P concentrations cause eutrophication, and (3) there are few options for removing dissolved P once it is in runoff. A series of batch tests evaluated P removal by woodchips that naturally contained a range of metals known to sorb P and then three design and environmental factors (water matrix, particle size, initial dissolved reactive phosphorus (DRP) concentration). Woodchips with the highest aluminum and iron content provided the most dissolved P removal (13±2.

Characterizing the Performance of Denitrifying Bioreactors during Simulated Subsurface Drainage Events.

The need to mitigate nitrate export from corn and soybean fields with subsurface (tile) drainage systems, a major environmental issue in the midwestern United States, has made the efficacy of field-edge, subsurface bioreactors an active subject of research. This study of three such bioreactors located on the University of Illinois South Farms during their first 6 mo of operation (July-Dec. 2012) focused on the interactions of seasonal temperature changes and hydraulic retention times (HRTs), which were subject to experimental manipulation.

Spatial variation in the bacterial and denitrifying bacterial community in a biofilter treating subsurface agricultural drainage.

Denitrifying biofilters can remove agricultural nitrates from subsurface drainage, reducing nitrate pollution that contributes to coastal hypoxic zones. The performance and reliability of natural and engineered systems dependent upon microbially mediated processes, such as the denitrifying biofilters, can be affected by the spatial structure of their microbial communities. Furthermore, our understanding of the relationship between microbial community composition and function is influenced by the spatial distribution of samples.